DETERMINANTS OF AGRICULTURAL RISK MANAGEMENT BEHAVIOUR OF CROP FARMERS IN NIGERIA BY FAOSIYAT OYEYEMI OLAJIDE B.Agric (Ilorin), M.Sc. (Agric. Ext & Rural Devt) (Ibadan) Y R MATRIC NO. 125113 RA B A THESIS IN THE DEPARTMENT OF AGRICULTURAL EX TLENISION AND RURAL DEVELOPMENT N A D SUBMITTED TO THAE IB FACULTY OF AGRICULTUFRE AND FORESTRY IN PARTIAL FULFILMENT OF THE RE QOUIREMENTS FOR THE AWARD OF THE DEGREE OF TDOYCTOR OF PHILOSOPHY SI R OF THE E IV UNIVERSITY OF IBADAN, IBADAN N U APRIL 2014 1 DEDICATION This work is dedicated to Allah (SWT), the One; .....Who causes to grow for mankind the crops, the olives, the date palms and every kind of fruit. ….Who Alone knows the unseen (such as the fluctuations in weather pattern). And to the love and sacrifices of my inestimable father- Alhaji Abdulfattah Abidoye Olajide. AR Y LIB R N AD A F I B O ITY ER S V U N I 2 ABSTRACT Agricultural risks constitute a fundamental challenge in Nigeria, leading to low productivity among farmers. Farmers risk management behaviour determines the extent to which they overcome risk types. Information on crop farmers‟ risk management behaviour in Nigeria is however scanty. Therefore determinants of agricultural risk management behaviour of crop farmers in Nigeria were investigated. Multistage sampling technique was used. Of the agro-ecological zones, Coastal, Rainforest and Guinea savannah were randomly selected. Thereafter, 10% of the states in thRe zYones (Lagos, Osun and Niger) and 10% of the Local Governments Areas (LGAs) in theA states were selected. Two communities were selected from each of the LGAs and 15% Rof crop farmers were chosen in the selected communities to give 323 crop farmers. IntIerBview schedule was used to collect data on respondents‟ risks types, risk exposure lev elLs and risk management strategies. Indices were used to categorise farmers on their risk Ntypes (production, marketing, financial and social) and risk behaviour (superior, active, Adi-function, mono-function and part-time risk managers). Data were analysed using descDriptive statistics, chi-square, Pearson Product Moment Correlation, ANOVA and multinBomAial logistic regression at p= 0.05. Most (90.0%) respondents were males, marrieId (89.7%), and had at least primary school education (62.3%) with farm sizes of less thFan 5 hectares (72.3%). Age and years of farming experience were 53.2±10.5 and 28.3 ±O12.1 years respectively. Majority (94.2%) identified inadequate cash-flow, pests andY diseases (91.3%), ill-health of farmer/farm employee (89.0%) and volatility in outpTut price (85.5%) as types of agricultural risks. Respondents were more vulnerable to SprodIuction (9.85) and financial (9.84) risks. Majority (81.3%) were moderately or highlyR exposed to agricultural risks. Risk management strategies highly utilised were reduEcing leverage (2.94), maintaining good relations with contracting partners (2.73), use oIf Vfertilizers (2.65) and use of improved seedlings (2.57), while 73.9% of the farmers tNhat had crop insurance coverage affirmed that it was effective in managing risks. Use oUf risk management strategies was low for 47.1%, with marketing strategies being the least (1.17) utilised. Superior agricultural risk managers accounted for 14.2%; active (26.8%); di-function (33.2%); mono-function (21.9%) and part-timers (3.9%), with the coastal zone having the highest percentage of superior (19.0%) and active (43.1%) risk managers. There were significant relationships between level of risk management and each of sex, marital status, educational level and farm size. While the Guinea savannah zone had the highest level (259.58) of agricultural risk exposure, the coastal zone had the highest level (75.89) of 3 agricultural risk management. Significant predictors of agricultural risk management behaviour were farm size, organization membership and risk exposure level for mono- function and active managers. Di-function and superior managers were significantly predicted by farm size and risk exposure level respectively. Crop farmers in the zones encountered more of production and financial risks and lacked adequate risk management strategies. Their low level of insurance coverage indicated that factors other than awareness determined participation in insurance. Crop farmers should utilise more risk management strategies in order to reduce their risk exposure levels. RY Keywords: Agricultural risks, Crop farmers‟, risk management, AgricuAltural risk management behaviour BR Word count: 490 LI DA N BA OF I SI TY R IV E UN 4 ACKNOWLEDGEMENT All praises and adorations belong to Allah the Bestowal of knowledge for making this academic pursuit a success. May He shower His blessings on the best of mankind Muhammad (SAW) and all his followers till the day of judgement. My deep appreciation goes to my supervisor Dr L. A. Akinbile for his tutoring, patience and encouragement during the course of this research. Indeed he has shown me that he is not just my teacher but my mentor. May Allah in His infinite mercy, guide, guard and grant him, His blessings. I appreciate my Head of Department, Prof A. A. LadeleR foYr his fatherly role towards the completion of this work. Special thanks to other members of my supervisory committee; Dr K. K. Salman and Dr O. B. Oyesola for their Aadvice and commitment. I also acknowledge the interest and support of other lecturerBs in Rthe department: Prof A. E. Adekoya, Drs; J. O. Oladeji, O. T. Yekinni and B. R. Ola jiLde Ifor their constructive comments. To my darling husband of inestimable value- MuhaAmmNad Adebiyi (Ifemi), I cannot have wished for anything more than the bliss he gives mDe. Thank you for your support in all manners. May Allah grant him bliss in both lives. TAo my children; Labeebah, Abdallah and Habeebullah, thank you for your support (inc luIdBing turning my laptop into your toy). May you all be blessed. To my wonderful mothers- AlhajaF Sinot Olajide and Alhaja Fawziyah Adedamola; thank you for your prayers, affection a nOd time (most especially the nannies I turned you to). Special thanks to my sibYlings- Abdulwaheed and Baseerah; Faheed and Lateefah; Abdulmajeed and Baseerah; IKTehinde and Risqat; Abdulhakeem and Halimah; Moshood and Eniola; Ibrahim and AiSsha. And the Abdulfattah Gbadamosi family, the Ojularis, the Shofunlayos; theyE havRe all been very supportive and I am glad to say we finally realised our dream. Here II Vcannot but remember one of my mentors; the late Dr Olukade for his words of encouragNement. He was one of those special people that believed in me and encouraged me to pursuUe my Ph.D. I wish him Allah‟s bliss in his grave. Dr Afusat Jagun Hassan, I will always appreciate you, as you laid the first step in getting this degree. Special thanks too to my friends (Dr Sidiqat Aderinoye-Abdulwahab; Mahrufah Yusuf-Oshoala; Kawthar Alabi Adeniyi; Simiat Uthman; Aisha Hassan; Saidat Shonoiki; Hasanah Ajani; Bola Osunwole; Victor Chibuzor Umunakwe and Felix Ighodo) for their support. May we always bring happiness to one another. 5 Thanks also to Mahruf Aderemi, Sheyi Olukayode, Mayowa Omotosho, Tunde Adedamola and Daud Balogun for their moral support during my fieldwork and post data analysis. Y AR LIB R AN AD IB O F ITY RS VE U N I 6 CERTIFICATION I certify that this research work was carried out under my supervision by Miss Faosiyat Oyeyemi Olajide in the Department of Agricultural Extension and Rural Development, University of Ibadan, Nigeria. _____________________ _________________________________ Y Date Dr L. A. Akinbile, B.Sc, M.Sc, Ph.D (IbadanA) NRigeria LIB R DA N BA OF I TY SI VE R UN I 7 TABLE OF CONTENTS Content Pages Title page 1 Dedication 2 Abstract 3 Acknowledgement 5 Certification RY7 Table of contents A 8 List of tables B R 12 List of figures LI 14 N Chapter One: INTRODUCTION A 1.1 Background to the study AD 15 1.2 Statement of the problem IB 18 1.3 Objectives of the study F 20 1.4 Hypotheses of the study O 20 1.5 Significance of the study Y 21 1.6 Delimitation of theS stuId Ty 21 1.7 Conceptual deRfinition of terms 21 E Chapter TwIo:V LITERATURE REVIEW 2.1 InNtroduction 23 2.2 UConcept of risk 23 2.3 Categorisation of agricultural risks 24 2.4 Agricultural risk management 29 2.5 Crop farming and risk management 31 2.5.1 Production risk management strategies 31 2.5.2 Marketing risk management strategies 32 8 2.5.3 Financial risk management strategies 34 2.5.4 Human/ Personal risk management strategies 36 2.6 Risk perception and level of risk exposure 37 2.7 Attitude towards agricultural risks 40 2.7.1 Types of attitude towards agricultural risks 41 2.8 Agricultural insurance 43 2.9 The Nigerian Agricultural Insurance Corporation (NAIC) RY46 2.10 Review of literature on agricultural risk management A 47 BR CHAPTER THREE: THEORETICAL AND CONCEPTUAL FRLAMIEWORK 3.1 Theoretical framework N 50 3.11 Social cognitive theory A 50 3.12. The theory of planned behaviour A D 51 3.13 The pest belief model IB 52 3.14 Perceived attributes theory F 53 3.2 Conceptual framework O 53 ITY CHAPTER FOUR: MESTHODOLOGY 4.1 Study AreEa R 56 4.2 PopulIaVtion of the study 58 4.3 U SaNmpling procedure and sample size 58 4.4 Research design 60 4.5 Data collection procedure 60 4.6 Validity and reliability of instruments 60 4.7 Measurement of variables 60 4.7.1 Dependent variable 60 9 4.7.2` Independent variables 61 4.7.2.1 Socioeconomic characteristics of respondents 61 4.7.2.2 Farmers‟ perceived types of agricultural risks 62 4.7.2.3 Farmers‟ perceived level of agricultural risk exposure 62 4.7.2.4 Attitude towards agricultural risks 62 4.7.2.5 Effectiveness of agricultural insurance in managing risks 62 4.8 Analysis of objectives and hypotheses of the study RY63 4.9 Data analysis RA 66 B CHAPTER FIVE: RESULTS AND DISCUSSION LI 5.1 Socioeconomic characteristics of crop farmers N 67 5.1.2 Credit and information sources of crop farmers A 88 5.2 Farmers perceived types of agricultural risks AD 90 5.3 Farmers perceived level of agricultural riIsBk exposure 93 5.3.1 Likelihood of occurrence of agricultFural risks 93 5.3.2 Impact of risk O 98 5.3.3 Level of agricultural risk eYxposure 102 5.3.4. Agricultural risk e TSxpoIsure based on crop enterprise 108 5.4 Attitude towarRds agricultural risks 110 5.5 FarmerVs‟ uEse of agricultural risk management strategies basedI on risk sources 113 5.6 UENffectiveness of agricultural insurance in managing risks 118 5.6.1: Adoption and effectiveness of agricultural insurance 118 5.6.2: Level of satisfaction with NAIC processes 121 5.6.3: Inhibitors and motivators for agricultural insurance 123 5.7 Farmers level of risk management 126 5.7.1 Level of risk management 126 10 5.7.3 Farmers risk management behaviour 128 5.8 Determinants of agricultural risk management behaviour of crop farmers 131 5.9 Testing of hypotheses 135 5.9.1 Relationship between crop farmers‟ socioeconomic variables and their level of risk management 135 5.9.1.1 Variables measured at nominal level RY135 5.9.1.2 Variables measured at interval level A 137 5.9.2. Relationship between crop farmers‟ risk exposure BR level and their level of risk management LI 139 5.9.3. Difference in crop farmers‟ level of agricultural`` N risk exposure across the three agro- ecological zone A 141 5.9.4. Relationship between crop farmers‟ attitude towAardDs agricultural risks and their level of risk managementI B 143 5.9.5. Difference in crop farmers‟ attitude toFwa rds agricultural risks across the three agro- ecol oOgical zones 145 5.9.6 Difference in crop farmeTrs‟ Ylevel of risk management across the three agSro- Iecological zones 147 CHAPTER SIX: SURMMARY, CONCLUSION AND RECOMMENDATIONS 6.1 Summary E 149 6.1.1 MajorI fVindings 149 6.2 UCNonclusion 151 6.3 Recommendations 152 References 154 Appendix – Questionnaire for crop farmers 168 11 LIST OF TABLES Table 1: Sampling procedure and sample size 59 Table 2a: Analysis of objectives 64 Table 2b: Analysis of hypotheses 65 Table 3: Analysis of data 66 Table 4: Socioeconomic characteristics of crop farmers 75 Table 5: Credit and sources of information of crop farmers RY89 Table 6: Farmers‟ distribution on sources and types of agricultural risks A 92 Table 7: Means of respondents‟ likelihood of occurrence BR agricultural risks LI 96 Table 8: Ranking of risk sources in terms of likelihood N of occurrence A 97 Table 9: Means of respondents‟ impact of agriculturaAl risDks 100 Table 10: Ranking of risk sources in terms of i mIpBact of agricultural risks F 101 Table 11: Ranking of respondents‟ ag riOcultural risk exposure levels 105 Table 12: Ranking of risk categToriYes in terms of agricultural risk exposure levelsS I 106 Table 13: Ranking of Rrespondents‟ agricultural risk exposure levelVs baEsed on crop enterprise 109 Table 14: DisItribution of respondents based on attitude towards U N risks related statements 111 Table 15: Distribution of respondents based on attitude towards agricultural risks 112 Table 16: Farmers use of agricultural risk management strategies 116 12 Table 17: Ranking of risk categories in terms of use of risk management strategies 117 Table 18: Effectiveness of agricultural insurance in managing risks 120 Table 19: Level of satisfaction with NAIC processes 122 Table 20: Inhibitors and motivators for agricultural insurance 125 Table 21: Farmers level of risk management 127 Table 22: Agricultural risk managerial levels RY (Risk management behaviour) A 130 Table 23: Parameter estimates of the multinomial logit regression B R 133 Table 24: Marginal estimates of the multinomial logit regression LI 134 Table 25: Chi square test of relationship between crop farmers‟ sNocio economic variables and their level of risk management A 136 Table 26: PPMC analysis of relationship between croAp faDrmers‟ socioeconomic variables and their level of risk managemIBent 138 Table 27: PPMC analysis of relationship beFtwe en risk exposure level and crop farmers‟ level of ri sOk management 140 Table 28: Analysis of VarianceT (AYNOVA) Test for difference in agricultural riskS exIposure levels across zones 142 Table 29: Post hoc teRsts using Scheffe 142 Table 30: PPMVC aEnalysis of relationship between attitude towards agIricultural risks and farmers level of risk management 144 TableU 31:N Analysis of Variance (ANOVA) Test for difference in attitude towards agricultural risks across zones 146 Table 32: Post hoc tests using Scheffe 146 Table 33: Analysis of Variance (ANOVA) Test for difference in agricultural risk management across zones 148 Table 34: Post hoc tests using Scheffes 148 13 LIST OF FIGURES Figure 1: Risk management process 29 Figure 2: Social cognitive theory 50 Figure 3: The Pest belief model 52 Figure 4: Conceptual framework for the study 55 Figure 5: Agro-ecological zones in Nigeria 57 Figure 6: Sex Y69 Figure 7: Marital status R 70 Figure 8: Religion A 71 Figure 9: Organization membership B R 77 Figure 10: Level of participation I 78 Figure 11: Off farm occupation L 80 Figure 12: Farm ownership structure AN 81 Figure 13: Labour availability D 83 Figure 14: Major source of labour A 84 Figure 15: Major marketing channel IB 86 Figure 16: Market accessibility F 87 Figure 17: Risk exposure level OY 107 SI T VE R I UN 14 CHAPTER ONE INTRODUCTION 1.1 1.1 Background to the study Agriculture serves as the foundation of the economy in many developing countries, as it is the prime source of income for most families and businesses. In Nigeria in spite of the dominant role of the petroleum sector, agriculture contributes a high share of the GDP and also serves as the largest employer of labour (Alegieuno, 2010). Although, agriculture dominates major economic policies in many countries, Morales et al (2008) observeRd thYat it is also considered as one of the most vulnerable sectors of any economy. This is because agricultural production takes place in an environment characterised by highR levAels of risks due to changing biophysical, economic, political and institutional condIitBions (Chong 2005; Ibitayo, 2006), and these conditions are often beyond the control oLf agricultural producers (Mishra & Uematsus, 2011). Any farm production decision plaNn is typically associated with several potential outcomes. This means that due to compleAxities of physical and economic systems, the outcomes of farmers‟ production decisions Dand actions are uncertain. As a result of this, many possible outcomes are usually associAated with a single action or production plan. According to Olson (2004), agricultural r isIkBs stem from five basic sources: 1. Production risks: these refer to variatFions in crop yields/livestock production due to weather conditions (such as excess ivOe rainfall and drought), diseases and pests, seed/breed quality and inefficient produYction techniques. For instance, with regards to weather conditions, Africa is one oIfT the most vulnerable continents to climate variability because of multiple stresses anSd low adaptive capacity. This variability can distort crop calendar and change the diRstribution of animal diseases and parasites, thereby threatening food production andE security (Anuforom, 2009). 2. MarketingI V(Price) risks: these are related to the variations in commodity prices and quantiNties that can be marketed as a result of increases in supply, changed demand or loss ofU marketing power due to small size of farm sellers relative to buyers. For example, price fluctuations due to oversupply (glut) of farm products (such as crops) and marketing difficulties often lead to financial losses, or even bankruptcy on the part of farmers. Marketing risks also include fluctuation in input costs, inefficient storage and fluctuation in transporting costs of farm produce. 3. Financial risks: these relate to farmers‟ access to funds and their ability to pay bills when due. Financial risks also include variations in interest rates of borrowed funds and inability 15 of farmers to pay back borrowed funds (default risk) due to a shortage in liquidity. For example, unexpected changes may occur in access to credit or other sources of income and this affects the financial viability of the farm in terms of cash-flow. 4. Legal and environmental risks: these relate to changes in government regulations on environment and farming practices and the possibility that lawsuits may be initiated against the farmer/farm by other businesses or individuals. For example, changes in government policies on food safety and environmental practices such as regulations on use of pesticides and herbicides may impact on farmers‟ production decisions. ChangeYs in government regulations on tax provisions and payment also create legal risks for faRrmers. 5. Human Resources (Social) risks: these refer to the possibility that RfamAily or farm labourers/employees may not be available to provide labour or manageBment as a result of breach in contracts, disability, accident, sickness or death. Social riskIs also include loss in yield due to theft and contracting risks. Occurrence of war or con fLlict around farming area may also distort farming operations due to insecurity. N With this diversity in risks sources, farmers need to Amanage their risks effectively in order to withstand adverse outcome and to avoid thAreaDtening the existence of an enterprise as the base for income generation (Hardaker et Bal, 1997). Risk management is therefore an essential aspect of the farming busineFss (ISalimonu & Falusi, 2009). According to Organisation for Economic, Cooperation and Development (OECD, 2009), risk management refers to the system o f Omeasures/strategies by individuals and organizations that contribute to reducing, conYtrolling and regulating risks. These strategies start with decisions on the farm/houseIhTold, on the set of outputs to be produced, the allocation of farm resources, the use oRf othSer inputs and techniques as well as the diversification of activities on and off-farmE. As observed by Mojarradi et al (2008), risk management strategies attempt to aIdVdress risk problems prior to the occurrence of the potential harming event (ex-ante). TNhese strategies can either be formal or informal (Cervantes-Godoy et al, 2013). UEx-ante informal strategies are arrangements that involve individuals/households or groups such as communities or villages, while ex-ante formal strategies are market-based activities and publicly provided mechanisms (World Bank, 2001). Ex-ante informal strategies include: use of improved and resistant seedlings, avoidance of highly risky crops, pests control, irrigation, using farmers‟ cooperative, sequential marketing, diversification of income sources and enterprise diversification. Diversification of income sources occurs when a farmer does not rely entirely on income derived from farming only. This implies that the farmer (or his/her spouse or other family members) has non-farm income source(s). 16 Diversification of enterprises refers to the production of two or more crops or livestock enterprises simultaneously by a farmer. Sequential marketing involves gradual release of a commodity into the market for sale, instead of dumping the entire quantity at once to depress market price, while cooperative marketing agreement is a way of sharing market risks with others and increasing marketing power to source more favourable prices. Ex-ante formal strategies include: forward contracts, commodity exchange/future market, and agricultural insurance. Forward contracting is a mechanism through which farmers agree with a buyer to deliver a proposed quantity of a commodity at an agreed pYrice. The principal benefits of forward contracts are helping farmers reduce price Rrisk and stabilising their income (Kingwell, 2000; Liddle, 2004). Commodity exchange is aAn exchange for buying and selling commodities for future delivery. Commodity exchanRges are markets where raw or primary products are exchanged. A commodity excIhBange where future contracts are traded is also referred to as future market. Future mark etLs/ commodity exchange markets are standardised in terms of contract terms and are trNaded in organised exchanges under rules and regulations (Larson et al, 1998). AnD exAample in Nigeria is the Abuja Securities and Commodity Exchange (ASCE). The ASCE is primarily involved with the trading of commodities such as maize, sorgIhBum A and millet. The Abuja Securities & Commodity Exchange (ASCE) was originaFlly incorporated as a Stock Exchange on June 17, 1998, but it was converted to a commoOdity exchange on August 8, 2001. The conversion was due to the need for an alternative ins titutional arrangement that would manage the effect of price fluctuations in the maTrkeYting of agricultural produce after the abolishment of commodity Boards in 1986. TIhe ASCE was therefore established to reduce the inherent risks in agricultural marketRing.S However, the ASCE is yet to achieve the purpose for which it was created, as a resulEt of the dominance of the stock market and lack of proper understanding of how the comImVodity market works (This Day Live, 2012). In order to improve the operations of the exNchange, Commodities Brokers Association of Nigeria (CBAN) was inaugurated in Jan 2U014. CBAN is a crop of trained professionals who are to drive the operations of commodity exchanges in Nigeria. The Abuja Securities and Commodity Exchange has also concluded plans to set up a market information system for 12 commodity markets in the country. According to Abdurrahim (2014), the market information systems for the 12 major markets would be replicated in the 36 states in Nigeria so as to enable people get information about commodities prices in Nigeria, thus reducing farmers‟ level of exposure to marketing risks. 17 Farmers may also manage risks through insurance, which according to Olubiyo et al, (2009) is one of the best strategies to address farm risks. Njavro et al, (2007) also asserted that insurance is probably the best known risk pooling tool. In Nigeria, agricultural insurance has been implemented by the Federal Government through the Nigerian Agricultural Insurance Corporation (NAIC). The corporation was established to protect Nigerian farmers from the effects of natural hazards by introducing measures that ensure a prompt payment of appropriate indemnity (compensation) sufficient to keep the farmer in business after suffering a loss. Its primary mandate is to provide insurance cover to all categories of farmers namYely: small, medium and large scale holders, either in groups or as individuals. The scheRme was also subsidised by 50% by the Federal Government (NAIC, 2010). Kailiang aAnd Wenjun (2007) opined that financial subsidy is necessary and crucial in implementRing agricultural insurance. IB As agricultural risk management is an essential task for f aLrmers; one of the most fundamental and complex decision that a farmer has to make iNs the choice of a portfolio of risk management strategies which would provide the bestA income safety net for him/her. Farmers may therefore implement diverse risk managemDent strategies in the context of their production plans, the available portfolio of finanIcBial, Aphysical and human capital. 1.2 Statement of the problem F Food insecurity is a funda OYme ntal challenge in Nigeria (Abu, 2012); despite the fact that the Gross Domestic ProdIuTct (GDP) growth of the country is largely driven by agriculture with the crop sub-sector Scontributing about 85% of the agricultural GDP (Eluhaiwe, 2008; Federal Ministry of ARgriculture and Water Resources, 2008). This crisis has further been intensified by higEh level of subsistence farming in Nigeria (Olawepo, 2010; IFAD, 2012; Haliru, 2012)I, Vincreasing population (Afolayan et al, 2010) and low agricultural productivity levels (ANjayeoba, 2010). UThe low agricultural productivity level is a function of the multitude of risks farmers face (such as production, marketing, financial and social risks) and the extent to which they are able to manage risks (Rao and Bockel, 2008). Haile (2009) thus asserted that food insecurity is partly due to lack of appropriate risk management capacities in Nigeria. Accordingly, part of the key features of the new agricultural policy in Nigeria is the reduction of risks and uncertainties in agriculture. Therefore, investments in farm risks management are important channels in raising the nation‟s food security level. 18 An integral part of this investment is an assessment of farmers‟ perceptions and preferences with regards to agricultural risks (especially those pertaining to crop production) in the country. Moreover, as crop farmers‟ ability to gauge and manage risks adequately determine their success or otherwise, farmers need to utilise risk management strategies which according to Le and Cheong (2009) will enhance their ability to sustain their businesses. One notable risk management strategy is agricultural insurance (Sadati et al, 2010). However, as valuable as it is, Olson (2004) observed that few farmers usually utilise agricultural insurance schemes while Abdulmalik et al (2013) affirmed that there is aY low level of participation in insurance activities in Nigeria. Hence, investigating the perRceptions of farmers‟ on the effectiveness of crop insurance in managing farm rRisksA cannot be overemphasised. Given the importance risks play in investment and behaviourIalB decisions of crop farmers; Kahan (2008) observed that farmers need to understa nLd risks and have risk management skills to better anticipate problems and reduce cNonsequences. OECD (2009), also posited that studies investigating sources of risks, pAerceived risk exposure and risk attitude will contribute to the efficient allocation oAf agDricultural resources. In spite of the fundamental role of farmers‟ risk managemenIt Bbehaviour on agricultural productivity and food security, Lien et al (2003) observed thFat l ittle work has been done in practice to examine how farmers perceive risks and risk maOnagement. Understanding the determinants of farmers‟ production behaviour (such as their risk management behaviour) is therefore of primary concern (Mendola, 2007). Y Furthermore, as farmeIrTs confront different situations (for example differences in agro- ecological zones or vegeStations) their experience and preferences toward risks may have a major effect on EdecRision-making in each given situation. For instance, in the case of production riIskV such as weather, coastal communities are more prone to floods, while those in the savannah are more prone to drought. Thus, with the highly diversified agro-ecological naturUe ofN Nigeria, it is essential to analyse farmers‟ risk perceptions based on their localities and how these variations influence their risk management behaviour. In line with the foregoing, the study sought to provide answers to the following questions: 1. What are the types of agricultural risks perceived by crop farmers? 2. How do crop farmers perceive their level of risk exposure? 3. What attitude do crop farmers have towards agricultural risks? 4. What are the risk management strategies utilised by crop farmers? 19 5. How do crop farmers perceive effectiveness of agricultural insurance in managing risks? 6. At which level of risk management do crop farmers operate? 7. Which factors determine the risk management behaviour of farmers? 1.3 Objectives of the study The general objective of the study was to identify the determinants of agricultural risk management behaviour of crop farmers in Nigeria. The specific objectives were to: Y 1. Identify the types of agricultural risks perceived by crop farmers in the stuAdy aRrea. 2. Determine crop farmers‟ perception of their level of risk exposure. 3. Describe the attitude of crop farmers towards agricultural risks. R 4. Describe the risk management strategies utilised by crop farmers IinB the study area. 5. Examine crop farmers‟ perception of the effectiveness of aLgricultural insurance in managing risks. N 6. Determine crop farmers‟ level of agricultural risk maAnagement. 7. Identify the determinants of risk managementA behDaviour of crop farmers. B 1.4 Hypotheses of the study I The hypotheses of the study stated in null foFrm are as follows: 1. There is no significant relation sOhip between selected socioeconomic characteristics of crop farmers and their lTeveYl of risk management. 2. There is no significaInt relationship between crop farmers‟ perceived level of risk exposure and tRheirS level of risk management. 3. There is nEo significant difference in crop farmers‟ perceived level of risk exposure acrossI Vthe three agro-ecological zones. 4. TNhere is no significant relationship between crop farmers‟ attitude towards Uagricultural risks and their level of risk management. 5. There is no significant difference in crop farmers‟ attitude towards agricultural risks across the three agro-ecological zones. 6. There is no significant difference in crop farmers‟ level of risk management across the three agro-ecological zones. 20 1.5 Significance of the study Given the variety of risks inherent in agricultural production, crop farmers‟ livelihood can only be guaranteed when effective and efficient strategies are formulated against possible losses and failures in production. Understanding agricultural risk is therefore a starting point to help farmers make good managerial decisions in situations of risks. According to Haile (2008), risk identification and assessment are ways of improving early warning systems and crises prevention. An assessment of crop farmers‟ risk exposure level would help in building the policy framework on risk management. Y Efforts to understand the risk perceptions and preferences of farmers as weRll as the determinants of their risk management behaviour are also necessary in ordeAr to impact vigilance and establish level of awareness on available strategies. MoreBoveRr, researches on risks sources and strategies can be helpful in saving cost and time in eLxteInsion activities. Furthermore, strengthening effective risk management capab ilities can help deal with the growing food crises in the country. For instance, knoNwing how farmers perceive agricultural insurance will provide policy makers anDd inAdustry operators the necessary information on what to focus on in order to improve Athe adoption of agricultural insurance in Nigeria. B I 1.6 Delimitation of the study F This study focuses only on th e Oproduction, marketing, financial and social sources of risks, because the legal/environTmeYntal sources of risks are not well developed in Nigeria. I 1.7 Theoretical aRnd cSonceptual definition of terms Risk: the potentiaEl deviation between the expected and the real outcomes resulting from an economic decIiVsion (Székely & Pálinkás, 2009). It may also be defined as the chance of a bad outcome;N the variability of outcomes; and the uncertainty of outcomes in farm activities. AttituUde towards risk: a chosen response to uncertainty that matters and it is influenced by perception (Hillson and Murray-Webster, 2005). Attitude to risk also refers to farmers‟ state of mind on those uncertainties that can affect their production. Risk exposure level: the product of the severity (magnitude of loss) and the likelihood of occurrence of identified risks (PMI, 2004). Risk Perceptions: these refer to perceived sources/types of risks as well as the risk exposure level of farmers. 21 Risk management: the system of measures/strategies by farmers aimed at reducing, controlling and regulating risks (OECD, 2009). Such measures include diversification of farm enterprise, pest control practices, forward pricing of inputs, cooperative marketing, crop insurance, maintaining adequate records, securing back up labour and maintaining good human relations with labourers and contacting partners. Level of risk management: the extent of utilization or application of management practices or tools that reduce farm risks. Risk management behaviour: farmers extent of utilization of risk management strateYgies based on the risk source. It is the farmers‟ level of risk management and is reflRected in behavioural types. These behavioural types are superior risk managers, active risAk managers, di-function risk managers, mono-function risk managers and part-time riskB maRnagers. LI AN D BA F I O ITY RS IV E UN 22 CHAPTER TWO LITERATURE REVIEW 2.1 Introduction This section deals with the following subsections: 1. Concept of risk: definition of risks 2. Categorisation of agricultural risks: categories of agricultural risks according to different authors Y 3. Agricultural risk management: definition of risk management and metRhods of managing risks A 4. Crop farming and risk management: examples of risk management sRtrategies (based on the four sources of risks measured in the study) available to crIopB farmers. 5. Risk perception and risk exposure: concept of risk perce ptLion and risk exposure, methods of measuring perception, factors affecting riskN perception ,calculating risk exposure level A 6. Attitude towards agricultural risks: definitionA, mDethods of measuring risk attitude and types of risk attitude 7. Agricultural insurance: definition, ty pIeBs and advantages of index based over traditional insurance F 8. The Nigerian Agricultural IOnsurance Corporation (NAIC): establishment and operations of the corporatiYon 9. Review of literature IonT agricultural risk management studies: review on attitude to risks and effects oSf socioeconomic variables on attitude to risks, variables affecting risk perceptionRs and use of risk management strategies. E 2.2 CNoncIep Vt of risk URisk is the uncertainty of decision-makers with regards to future events, which is reflected in incomplete information and can result in economic losses or deviations from a- priori fixed target values (Mehr & Hedges, 1963). It may also be defined as the potential deviation between the expected and the real outcomes resulting from an economic decision (Székely & Pálinkás, 2009). According to PMI (2004), risk is an uncertain event or condition that could have a positive or negative effect on one or more objectives. In relation to agricultural production therefore, risk refers to the uncertainty with regards to the farming 23 environment, which can cause deviations in farm‟s profitability. Thus agricultural risk is connected with unpredictable circumstances which determine the final output, value and cost of any agricultural production process (Cervantes-Godoy et al, 2013). For instance, when aggregate crop yield changes sharply, farm prices can fluctuate substantially and farmers may realise returns that differ greatly from their expectations. This indicates that risks may lead to both positive and negative outcomes; however, a negative outcome has greater importance and is more considered by most decision makers because a negative outcome may result in serious adverse consequences thereby threatening the existence of an economic entity. Y Risk and uncertainty are sometimes interchanged and while some scholarsR such as Knight (1921) made a distinction between the two (risk as known probabilitiAes of future events and uncertainty as unknown probabilities of future event), others suBch aRs Moschini and Hennessy (2001) observed that this distinction is not very operative sLincIe there is widespread acceptance of probabilities as subjective beliefs. As observed by O ECD (2009), there is no risk without some uncertainty and most uncertainties typicallAy imNply some levels of risks. D 2.3 Categorisation of agricultural risks BA Agriculture constitutes one of the mFost iImportant sectors of the Nigerian economy as its contributions in terms of employmOent generation, gross domestic product (GDP) and foreign exchange cannot be underest imated. Although Nigerian agriculture is characterised by a highly diversified agro-ecToloYgical condition which makes the production of a wide range of agricultural products posIsible, smallholder farmers constitute the bulk of agricultural producers in the couRntryS. They are therefore an important group that requires attention. Increasing their pEroductivity and incomes can thus make a substantial contribution to food security (ZhoIuV, 2010; Apata et al, 2011). These small holders farmers usually operate under constrainNed conditions and these conditions may often be intensified by the diversity of risks inherUent in agriculture. Nigerian farmers like farmers in other countries face a variety of risks; however the dominance of the crop sector in the nation‟s agricultural production suggests that the key risks will largely be characteristic of crop farming. As highlighted by Wenner (2010), farmers are confronted with an array of risks that affect their financial returns and overall welfare. These agricultural risks have been categorised in several ways by authors. For example, Baquet et al (1997); White (2002), categorised agricultural risks into five basic sources; 24 (1) Production risk: this is one of the typical features of agriculture (World Bank, 2005). It occurs as a result of the uncertain natural growth processes of crops and livestock. Agricultural producers cannot usually predict with certainty the quantity and quality of output their production process will yield, due to external factors such as weather, disease, pests, genetics, machinery efficiency, and the quality of inputs. For instance, unpredictable weather can expose farm households to significant production uncertainties and this can result in food insecurity. Production risks are often industry or enterprise specific. For example, while changes in soil fertility may affect a crop farmer, a poYultry farmer would not be concerned with this risk. The diversity in agro-ecological coRnditions of Nigeria also shapes the prominence of certain risks in certain parts of the Acountry, for example production risks such as floods and drought. The impact of BclimRate change will also likely lead to increased severe weather conditions. As obserLveId by Medugu (2009) Nigeria is one of the countries expected to be most affected b y the impacts of climate change through sea level rise along her coast line, intensAifieNd desertification, erosion and flooding disasters and general land degradation. DAlso as global warming increases, agricultural adaptation to climate change will only be meaningful, if irrigated agriculture gains prominence. However as agricultural pBrodAuction in Nigeria is still predominantly rain-fed; it will particularly be vulnerable t o Ithe impacts of climate change. The change in weather conditions may also influenceF the occurrence of pests and diseases, which is feared by most farmers as one of t hOe major risks with very huge potential production loss. Nigeria witnessed significTant Yflooding lately in several parts of the country leading to substantial economic lossI. (2) Market or Price risk:S this refers to uncertainty about the prices farmers will receive for commodities or thRe prices they must pay for inputs due to the high volatility in the prices of agricuIltVural E commodities. As observed by Miller, Dobbins, Pritchett, Boehlje and EhmkNe (2004), price uncertainty has always been a major consideration in farming, while faUrm commodity prices have fluctuated dramatically in recent years due to technological change which has made more of the inputs involved in production to be purchased. The nature of price risk varies significantly from commodity to commodity and it also depends on the consumers‟ ability to substitute products and on the extent of market integration- which is dependent on infrastructure and the types of markets available. Market risk also occurs when delivering farm produce to markets as farmers face huge losses when they are unable to deliver perishable farm products to the right market at the right time. This is a significant source of risk in many developing countries, as a result of 25 lack of infrastructures and well developed institutions. Price risks depend on the consumers‟ ability to substitute products and on the extent of market integration. Market integration is dependent on infrastructure and the types of markets available. According to Luke (2011), farmers are exposed to unpredictable competitive markets for inputs and outputs, while the high transportation and marketing costs in developing countries usually isolate local rural markets from national and international markets. (3) Financial risk: this occurs as a result of the method in which capital is acquired and financed and how farmers organize their businesses and acquire production assetYs. It reflects the farmer‟s ability to pay financial obligations. Financial risk has thrRee basic components: the cost and availability of debt capital; the ability to meet cashA flow needs in a timely manner and the ability to maintain and grow equity (Miller,R 2008). When a farmer borrows money, the farmer creates an obligation to repay dIeBbt and because the debt has to be repaid within a certain period, financial resources arLe thereby diverted from farming activity. Cash flows are also important because of onNgoing farm obligations, such as cash input costs, debt repayment and family livDingA expense. As many agricultural production cycles extend over long periods, farmAers must predict expenses they will only be able to recover after marketing their farImB products. Other aspects of financial risks are; increasing interest rates, the prospeFct of loans being called by lenders, and restricted credit availability. (4) Institutional/ Legal/Environmenta l Orisks: these risks are generated by unforeseen changes in regulations that affect faTrmeYrs‟ activities. Changes in government regulations and legal policies affect agriculSturaIl production and they can have significant impact on farmers‟ profitability. ExamRples of such government policies include; tax laws, regulations for chemical use,E restrictions in conservation practices or land use. For instance, governmeInVt‟s decision to limit imports of a certain crop will affect the crop‟s price. There is alsNo growing concern globally over the impact of agriculture on the environment as wUell as the production of genetically modified organisms (GMO). (5) Social/Personal/ Human Resources risks: these refer to factors such as problems with human health or personal relationships that can affect the farm business. Accidents, illness, death, and divorce are examples of personal crisis that can threaten farm viability. Social risks may also be in the form of contracting risk; which refers to the reliability of contracting partners (Harwood et al, 1999). Social risks can also involve assets and this includes theft, fire, or loss or damage to equipment/ buildings/livestock. 26 According to Holzmann and Jorgensen (2001), agricultural risks can be classified in to six and these are: natural, health, social, economic, political and environmental. A summarised classification of agricultural risks has also been made by other authors. For example, Lehner (2002) classified agricultural risks into two: (1) Internal risks; these are risks which can be influenced by farmers. The sources of internal risks are located primarily within the farm and they can often be managed through internal measures, such as improved hygiene or financial management. Examples are equipment and financial risks. Y (2) External risks; these risks are beyond farmers‟ influence. They are derived frAom Ra farm‟s environment so the farmer has little (if any) control over them. ExamplesR are market and political risks. IB Based on the classification made by Huirne et al (2000); Szé kLely and Palinkas (2009), agricultural risks are classified as either business risks (which Nincludes production, market, personal and institutional risks) or financial risks (issueAs related to financing business operations). Lagerkvist (2005) also categorised agriculturDal risks into three categories: (1) Economic risks; these are risks related to exIpBosu Are to an uncertain economic outcome of the farm business. (2) Social and personal risks; these Oare rFelated to the social and personal context of the farmer and concern the retroactio ns to the farm business operation from that context. (3) Environmental risks; thesTe rYefer to the dependence of agricultural production on the natural environment andI their impact on the natural environment. As explained Rby SBoehlje (2002), an alternative taxonomy is to categorise risk as tactical/operationaEl risk and strategic risk. Tactical risks are the traditional risks faced by farm and agribusiIneVss firms and they can be categorised as business risk and financial risk. Business Nrisk is the inherent uncertainty in the financial performance of the firm independent of theU way it is financed; while financial risk is the added variability of net returns to owner‟s equity that results from the financial obligation associated with debt financing. Strategic risks focus on the sensitivity of the strategic direction and the ultimate value of a company to uncertainties in the business climate such as: political, government policy, macro-economic, social and natural contingencies. They also include industry dynamics encompassing input markets, product markets, competitive and technological uncertainties. As a result of availability of information to measure tactical risks as well as the availability of accepted 27 tools and techniques to transfer these risks to others (such as insurance), tactical risks are often easier to manage than strategic risks. According to Hardaker et al, (2004), three major types of risk in farming can be identified; yield, price and transaction risks, while Ellis (1988), identified four types of risks: natural hazards (weather, pests and diseases), market fluctuations (of output prices), social uncertainty (due to differences over control of resources) and state actions and wars. Nmadu et al (2012) described agricultural risks as exogenously-caused or endogenously-induced. Exogenous risks arise from extreme weather conditions or thRreaYts of disease and pest outbreaks and are independent of farmers‟ production decisions eA.g. drought. Endogenous risk is incurred solely by farmers‟ production decisions e.g. a change in the quality of seedling used for production. BR Risks can also be differentiated on their level of occurrence. SLysItemic risks are those risks that affect and are common to all farm households (such as price and weather risks). These risks occur when there is a high degree of correlation amNong individuals in the same region or country. Risks that are specific to a particularD farAmer such as local pest or disease infections are called idiosyncratic risks. IdiosyncratiAc risks are independent or uncorrelated with any other risks. Holzmann and Jorgensen (I2B001) summarised the systemic characteristic of risks as micro (idiosyncratic) meso (affeFctin g a whole community) and macro (affecting a whole region or country). Risks can also be categorised based on their level of occurrence and magnitude of impact. NormalY risk s O are those with high frequency of occurrence, but low damage. Catastrophic risks aIrTe events associated with low probability of occurrence (rare) leading to major and usuaSlly irreversible losses with potentially adverse impact (severity) on farm production. ForR example, arable farmers can be exposed to extreme weather events, such as excessiveE rainfall and drought, which may result in potential damage to crops resulting in hIeVavy losses for farms. In between these two are medium risks and these are risks assoNciated with a medium level of occurrence and medium impact. According to Ali and KapoUor (2008) the types of risks influence the ability and means used to manage and cope with the risks. In general, the types of risks faced by farmers depend on the type of farming systems, climate, policies and the institutional environment (Hazell & Norton, 1986). Boehlje et al (2005) concluded that the total risks farmers face is much more complex and pervasive than is often perceived. 28 2.4 Agricultural risk management Although, risk may sometimes be inevitable, it is often manageable (Agriculture Outlook, 2000). Risk management involves choosing among alternatives for reducing risks that threaten the economic success of a farm business (Harwood et al, 1999). According to Székely and Palinkas, (2009), it is the range of strategies and instruments applied, to avoid or minimise losses and to utilise opportunities. Kostov and Lingard (2003) defined it as the process of simplifying the decision problem aimed at restructuring it in such a way that the risk is excluded. Risk management is therefore an essential tool for farmers to anticiYpate, avoid and react to shocks. For an individual farmer, risk management involves finRding the preferred combination of activities that will reduce the effects of risks on his/heAr farm. The focus of risk management should be on risk that matters and this requirBes aRn evaluation of tradeoffs between changes in risk, expected returns and entrepreneur iaLl fIreedom. Hardaker et al (1997) characterised the process by wNhich farmers arrive at risk management decisions and practices. The risk managemenAt process starts with the farmers acquiring knowledge of their own context. Then theD risks are identified, analysed and assessed. After assessment, if action is deemed wBortAhwhile, the farmer then selects the most suitable option/strategy for avoiding, preventi nIg or managing the risks. The process is then continuously monitored. OF SI TY ER IV UN Figure 1: Risk management process: Hardaker et al (1997) Managers have a variety of mechanisms for managing risk and each method depends upon the nature of the risk involved. According to Miller et al (2004), four general methods for managing risk are: avoidance, reduction, assumption/retention, and transfer. 29 1. Avoidance: one strategy farmers can employ is to avoid specific risks by organizing the business so that certain types of risk are absent. Due to the financial conditions under which most farmers live, they often avoid activities that involve more risk but which frequently could bring more income gains. This inability to manage risk and accumulate and retain wealth can lead to a poverty trap (World Bank, 2001). For example, a farmer may decide not to select a particular agricultural enterprise due to its level of risk. 2. Reduction: this refers to the process of lowering the risks associated with the business venture. Farmers may reduce risk by diversifying across different agricultural enterpriYses. 3. Assumption/retention; this is the process of retaining or accepting risks with tAhe oRbjective that assuming this increased risk is to maintain, control and/or eRnhance overall profitability. Assumption may occur simply because the risks cannot bBe transferred. Risks can be borne by maintaining liquid assets so as to build the opeLratIions capacity to bear risk. N 4. Transfer/ Shift: this occurs when one party lowers thAeir risk by shifting that risk to someone else. It is often in exchange for a fee and thDe more risk that is shifted, the higher the cost. Examples are crop insurance and forwBardA contracts. Luke (2011) asserted that risk manage mIent strategies can be classified into two broad categories; ex-ante and ex-post risk OmanFagement strategies. Farmers implement ex-ante strategies because of lack of mechan isms to cope with risks ex-post. Some of the strategies that are usually used include: TirrigYation, crop insurance, growing resistant varieties, forward contracting, income and enIterprise diversification as well as increasing the political participation of farmeRrs inS decisions which affect their welfare and their future. Ex-post risk strategies are copEing strategies once livelihoods are threatened. Ex-post strategies include: sale of produIcVtive assets such as livestock, re-deploying labour, using up food reserves on farm andN drawing down on other savings and asset liquidation. Risk management may also be broadUly classified as either on-farm measures or risk sharing strategies (European Commission, 2005). Strategies relating to on-farm measures include; selection of products benefitting from public intervention, diversification of enterprise and vertical integration. Risk sharing measures include; marketing and production contracts, off farm diversification and insurance. Hoogerveen et al (2005) also made a distinction between prevention and mitigation risk management strategies; while prevention strategies aim at reducing the probability of the risk occurring, mitigation strategies help to reduce the impact of a future risky event. According to Holzmann and Jogersen (2001), risk management strategies can be 30 based on arrangements made at different institutional levels: farm household or community arrangements, market based mechanisms and government policies. In making risk management decisions, farmers consider and respond to a combination of external and internal factors, such as market access, the resources available to the farm household, attitude toward risk and perceptions of risk management strategies. 2.5 Crop farming and risk management Y The risk management strategies utilise by crop farmers differ since risks Rand the willingness/ability to bear risks differ from farm to farm. Hence crop farmers mAay utilise a variety of risk management tools simultaneously. Some risk management straRtegies (based on the four sources of risks considered in this study) are discussed belowL. IB 2.5.1 Production risk management strategies These strategies help farmers in reducing large lossesA in Nyields as a result of uncertain natural growth processes of crops that can be caused bDy fluctuations in weather, quality or quantity of input use. A  Diversification of enterprise; this refers to tIheB production of two or more crop enterprise simultaneously by a farmer (Alimi & AFyanwale, 2005). Farmers‟ ability to mitigate risk by diversifying may to a certain eOxtent allow farmers to adopt riskier high-return crops (Lanjouw & Lanjouw, 2001). YYield variability on the farm can be reduced by combining different production procIeTsses through diversification. It may include different crops, combinations of cropsS and livestock, different end points in the same production process or different variatRions in the same crop. Diversification entails that a favourable gain in one farm eVnteErprise help cope with a loss in another farm enterprise. Depending on the farm‟s siItuation, however, the costs of diversifying may outweigh the benefits, as diversNifying often requires specialized equipment; a broader range of managerial exUpertise and labour. Moreover the advantages of diversification may often be limited by resources, climatic conditions and market outlets.  Flood Control; This can be through channelization or by having adequate drainage. This helps to reduce yield risks as a result of excessive rainfall  Supplemental irrigation due to abnormal weather such as drought is another means to protect against variation in yield. This is especially important for crop farmers. 31  Cultural practices; this can be used to reduce yield risk. Such practices include minimum soil tillage, crop rotation and shifting cultivation. These are practices that help to improve soil fertility.  Excess machine capacity; this improves the rate at which farmers plant and harvest crops. By having such resources, the farmer can avoid delays at either planting or harvest that may reduce yield losses  Other production management strategies include; use of improved and resistant seedlings/breeds, buying seedlings/birds/fingerlings from reputable sources, RfertiYlizer application, consulting people with crop/poultry/aquaculture knowledge, peAsts control, use of new/well maintained machinery/equipment and avoidance oRf highly risky crops/using crops benefitting from public intervention for example caIsBsava and cocoa. 2.5.2 Marketing risk management strategies L These strategies aim at minimising farmers‟ income by sNhifting marketing risks either by locking in prices, guaranteeing an outlet for farm produActs or by spreading risks across market types and time. AD  Contracting; This is a relationship or co-or dIinBation between farmers and buyers (e.g. agribusiness firms) where the chaFracteristics of the product, such as price, quantity, quality, are set by the parties invo lvOed before the time of delivery (Cervantes-Godoy et al 2013). Contracting can reduceY risk by guaranteeing prices, market outlets, or other terms of exchange in advanceI. TThere are two types of contracts; production contracts and marketing contracts. SProduction contracts are contracts that prescribe production processes to be usRed and/or specify who provides inputs. These contracts typically give the contractorE (the buyer of the commodity) considerable control over the production process (PIeVrry, 1997). They usually specify in detail the production inputs, the quality and quantNity of a particular commodity that is to be supplied by the contractor. Firms usually enUter into production contracts with farmers to ensure timeliness and quality of commodity deliveries, and to gain control over the methods used in the production process. Production contracting is also ideal when there is a high variability in supply. Contract production is common in the poultry and livestock industries. A major advantage for the farmer is that a favourable price/market is guaranteed for the output. However the disadvantage is that the farmer loses the opportunity of benefiting from upside price potential, since the sale of the product is fixed by conditions of the contract. 32 Likewise the farmer has a risk of losing his/her only sale outlet when the contract is terminated (EC, 2008). The loss of flexibility and profit opportunities in the market place is however offset by the cost of receiving a predictable cash flow. Marketing contracts; these are either verbal or written agreements between a buyer and a farmer that set a price and/or an outlet for a commodity before harvest or before the commodity is ready to be marketed (Perry, 1997). They are also referred to as forward contracts. The major difference between marketing and production contracts is that in marketing contracts, ownership of the commodity is generally retained by the farmer while the commodiYty is produced, management decisions (such as varieties/breeds, or input use and timRing) are typically taken by the farmer. Forward contracting reduce price risk by allowAing farmers to agree and be sure of the price they will sell their agricultural commoRdities in future before they are ready for disposal IB  Vertical integration; a vertically integrated firm retains Now n Lership or control of a commodity across two or more phases of production aAnd/or marketing. This decreases risk associated with the quantity and quality of inputs - backward integration or outputs - forward integration. Vertical integration also diveArsifDies profit sources across two or more production processes. IB  Sequential marketing; this involves grFadu al release of the commodity for sale into the market instead of dumping the en tiOre quantity at once to depress market price. Sequential marketing (spreading of saleYs) is possible if the agricultural product is either non-perishable or an effective aTnd economic storage facility exists (Alimi & Ayanwale, 2005).  Storage: this is a waSy ofI avoiding seasonally low prices. Storage is effective when the products are not pRerishable and there is a realistic expectation of a market price increase. However, sVtoreEd commodities may deteriorate and may also be stolen.  CoopNeratIive marketing; this is a way of sharing market risks with others and increasing mUarket power to source more favourable prices. Farmers can join a farmers‟ cooperative to achieve this.  Direct sales: selling directly to final consumers can enhance profitability and reduce risks. Examples are farmers selling their farm products along roadsides or in markets.  Forward contracts; a future contract is an agreement priced and entered on an exchange to trade at a specified future time a commodity or other asset with specified attributes (or in the case of cash settlement, an equivalent amount of money). 33  Future markets/ Commodity exchange markets are standardised in terms of contract terms and are traded in organised exchanges under rules and regulations (Larson et al, 1998). They are usually for specific standardized products. In developing countries, access to futures market is low. An example in Nigeria is the Abuja Securities and Commodity Exchange (ASCE). The ASCE is primarily involved with the trading of commodities such as maize sorghum and millet. A commodity option gives the holder the right, without obligation, to buy or sell a futures contract at a specific price within a specified period of time, regardless of the market price of the future. Y  Other marketing risk management strategies include; forward price of inputs, uRsing and sharing marketing information with others and keeping adequate records oRf faArm produce. 2.5.3 Financial risk management strategies IB These strategies help farmers in reducing large losses or b aLnkruptcy as a result of fluctuations in prices or income. They help to enhance the viabilNity of the farm enterprise  Diversification of income sources; diversification is an eAffective way of reducing income variability. This occurs when a farmer does noAt reDly entirely on income derived from farming only. This implies that the farmer I(oBr his/her spouse or other family members) has non-farm income source(s). Earning of f-farm income is another strategy that farmers may use to mitigate the effects ofO agrFicultural risk on farm family household income. Diversification can ensure sYuffi cient cash flow for meeting production costs, debt commitments, and family Texpenses. In fact, it may provide a more reliable stream of income than farm retSurnIs, although it can also increase the probability of stopping the farm enterprise. R  Liquidity; thisE involves the farmer‟s ability to generate cash quickly and efficiently in order to mIeVet his or her financial obligations. Liquidity can be enhanced by holding cash, storedN commodities, or other assets that can be converted to cash on short notice without inUcurring a major loss. Farmers may also hold liquid credit reserves by securing access to additional capital from lenders through an open line of credit.  Reduced leverage; leveraging refers to the farmer‟s use of debt to finance farm operations. That is, the farmer makes use of the use of borrowed funds to help finance the farm business. Increasing the degree of leverage increases the likelihood that in a year of low farm returns the producer will be unable to meet his or her financial obligations, and this heightens the potential for bankruptcy. According to Harwood et al (1999), highly 34 leveraged farmers operate in an environment of greater financial risk than those who choose a low leveraged farm structure. The optimal amount of leverage depends on several factors, including farm profitability, the cost of credit, tolerance for risk, and the degree of uncertainty in income.  Controlling family expenditure; in most subsistence farming households, household expenses usually interacts with farm income, hence farmers may reduce financial risk by controlling household expenditure.  Membership of cooperatives; Farmers can also increase their access to credit by jRoiniYng a cooperative. A  Marketing Cooperatives; joining a marketing cooperative provides theR opportunity to benefit from volume sales or purchases. These benefits may bLe iInB form of enhanced prices or reduced costs.  Crop insurance; the use of insurance involves the exchAangNe of a fixed, relatively small payment (premium) for protection from uncertainD, but potentially huge losses. The benefits of crop insurance are that it ensures a relAiable level of cash flow and allows more flexibility in the farmers‟ marketing pla nsI.B Crop insurance helps farmers to survive disasters and it can also serve as coFllateral for operating loans, thereby enhancing farmers‟ access to credit. As observed by Hardaker et al (1997), the idea behind insurance is that of risk pooling, whichY inv o Olves combining the risks faced by a large number of individuals who contribute through premium payments to a common fund that is used to cover the losses incuSrreId T by any individual in the pool. Hence, insurance is more attractive to risk-averse farmers. Farmers should consider some critical factors when deciding whetEher Ror not to buy crop insurance. Such factors include: how much loss can the farmer Vwithstand without insurance; what are the trade-offs between insurance costs and pNotenItial losses; what are the major sources of crop risk in the farmer‟s area and how oUften can the farmer have a disastrous or below average yield in a year.  Monitoring financial ratios; ratios such as debt-to-asset, debt-to-equity, and asset turnover are important in monitoring overall financial performance. Trend monitoring also helps farmers to be able to predict future costs and prices  Maintaining adequate records of financial transactions; information on farm transaction is critical in evaluating past performance and in planning for future accomplishments. This information is usually provided through farm records. 35 Other financial risk management strategies include: adjusting timing of capital expenses/keeping fixed cost low; making credit arrangement before production starts; maintaining credit reserves; controlling production costs; sharing information on risk management and leasing/renting farm equipment rather than buying. 2.5.4 Human/ personal risk management strategies These strategies aim at minimizing the impact of social risks on crop farmers  Developing good human relations with employees and contracting partners so as to improve motivation and reliability; Human resources are both a source of risk anYd an important part of risk management, because at the core of dealing with evAery Rrisk lies people such as farm employees, customers and labourers. R  Buying personal insurance for employees as well as for the farmer. IB  Securing emergency/backup labour in case of labour problems/sh oLrtages.  Securing labour contracts and fixing labour price before pArodNuction starts.  Having backup equipment in case of emergencies D  Using cultural practices to reduce theft suchB as Ausing scarecrow to scare off birds and using native medicine against thieves. I  Improving farm security by fencingO farmF or using guards. Management of risk is aYn i mportant activity for farmers worldwide and farmers manage risks through a contIinTuous adaptive process, whereby decisions are made based on perceptions of the exterSnal environment, resources and the farmers‟ own attitudes and preferences (IFAD, R2000). Different farmers confront different situations, hence their experience and prEeferences toward risk have a major effect on decision-making (Nguyen et al, 2005). TIhVis means that in considering farm risks, the agro-ecological context, the productioNn systems, the household types, farmers‟ goals, attitude towards risks, risk sources as wUell as the level of risk exposure are crucial. Therefore the management task facing farmers is to choose a combination of strategies that best suits the unique conditions of their particular farm and personal circumstances. 36 2.6 Risk perception and risk exposure Farmers‟ risk-management decisions are usually influenced by their personal experience and subjective perceptions of a particular risk. According to Zinn (2009), from a realist perspective, risk is seen as a real threat. Farmers risk perceptions can be measured directly or indirectly. In the direct method, risk perception can be measured with a questionnaire. With the aid of a Likert scale, farmers can quantify their subjective expected probability of a risk and the magnitude of loss if the risk occurs. However the direct method only estimates probability and outcomes in relYative terms. In the indirect method, the measures of central tendency and variation aAre inRdirectly derived from probability distribution functions (Smidts, 1990). Difficulties inR risk perception elicitations may however occur in catastrophe situations due to lack of dBata. When a farmer moves from events with considerable historical and scientific data Lto Ithose where there is greater uncertainty and ambiguity, accessing risk perception mNay n ot be so easy. Moreover Boehlje (2002) affirmed that risk characteristics influence hAow the risk is perceived. Hence, different types of risk generate different reactions. AcDcording to Breukers et al (2009), a number of risk characteristics affect risk perceptioBns Aand these factors include: (1) Controllability; if risk management strategi eIs are readily available, the risk is likely to be perceived as a threat and vice versa. F (2) Familiarity; farmers may not have aO higher perception of new risks than familiar risks due to lack of experience on new rYisks. New risks may thus be underestimated if farmers are not aware of them. AcIcoTrding to Kunreuther (2002), decision-makers estimate the likelihood of an eventS by the ease with which they can imagine or recall past instances of the event and in cRases where the information on an event is conspicuous, many people will tend toV ovEerestimate the probability of the event occurring. For instance, the farmer‟s subjectiveI probability of flood or drought occurring characteristically increases if any of the twNo events has just recently occurred. Garvin (2001) corroborated this fact by stating thUat personal experience and memory influence the way people perceive risks. Familiarity can also be likened to the availability heuristic- which relates to the ease with which an instance is brought to mind. People tend to think that events are more probable if they can recall an incident of its occurrence. (3) Scale of impact; Risk perception may be higher for risks that have immediate consequences, long-term impacts or affect a large area, than for risks which consequences may not be immediate. 37 (4) Personal damage; if people are personally affected by the consequences of the event related to the risk, they will perceive a higher risk than when the consequences are incurred by others. (5) Visibility; events that are difficult to imagine are often attached a lower probability of occurrence. Visibility can be likened with vividness- which refers to how concrete or imaginable an event is. Thus, Ogurtsov (2008) asserted that farmers are affected more strongly by vivid information than by pallid, abstract, or statistical information. (6) Socio-demographics; risk perception and decision-making vary considerably amYong farms as a result of differences in socio-demographic circumstances. For Rinstance education can affect priorities of farmers, and thus their attitudes. It mayR alsAo influence the level of understanding of a risk, which also affects risk perception Women may also perceive risk differently from men. Likewise, age may also IhBave influence on perceptions. L (7) Farm characteristics; the technical farm structure (such Nas farm size, organisational structure of the farm and the presence of off-farm acDtivitAies) determines the magnitude of possible consequences of the risk. Moreover, the financial position of a farm may also affect risk preference. For example, excess oIf Breso Aurces leads to relaxation of controls and reduced fears of failure leading to highF le vels of risk taking. It may also affect farmers access to informational and edOucational resources related to agricultural risks. Psychological characteristics; for example as a result of bad past experiences farmers may be stimulated to take risk TredYucing measures. The managerial capabilities of the farmer may also risk perception aInd risk-management decisions. (8) Location of the fRarmSer/farm; geographic location partly determines the activities and market circumEstances of agricultural producers. (9) External IsVources of information; these can significantly influence farmers‟ decisions. FarmNers are more likely to be influenced by expert opinion on topics which they lack kUnowledge on than on topics they believe they understand. They may also be selective in the evidence they will accept (Siegrist & Cvetovich, 2004). (10) Farmers‟ social network; farmers who have frequent social contacts with other farmers in their area are liable to experience some degree of dependency and are likely to account for the other's interests when taking decisions. (11) Absence/presence of safety measures; people adjust the riskiness of their behaviour/ attitude towards risks in the presence of safety measures. 38 As risk may be socially constructed, individual and group responses to risk may vary and may be influenced by societal culture. Risk perception is a decision maker‟s assessment of the risk inherent in a particular situation. It therefore reflects the decision maker‟s interpretation of the likelihood of exposure to the risk Hence, risk perception is a subjective statement of risk by decision-makers and it is a mental interpretation of risk, as the chance of a loss occurring and the magnitude of the loss. This means how often is a potentially harmful event going to occur and what are the consequences when it does occur. Y Therefore, in measuring risks, farmers are usually more concerneAd wRith the probability of occurrence of adverse consequences and the ability of these cRonsequences to disrupt business significantly. This can also be likened to their level of rBisk exposure level, since level of risk exposure is the product of likelihood of iden tiLfiedI risks to occur and consequence of the identified risk (Zinn, 2009). Likelihood reNfers to the probability of the risk occurring and according to Briggeman et al (2004) it Ais the chance that a potential or exposure event will occur. For example the likelihood Dthat drought could occur during the production period. Consequence refers to the seveBrityA or potential loss expected. For instance the impact of drought on crop yields, such as tIhe level at which yield is reduced. A recent development in risk exposure level is risk sFcore-carding. According to Boehlje (2002) r isOk score-carding identifies the potential sources of risk for a particular business, to assesYs the severity of those risks, and to aggregate these scores into an overall risk assessmeInTt that can then be compared to a standard which discriminates acceptable from unaccepStable risks. For each of the risk types identified under each risk category, the probabiRlity of occurrence and the magnitude of the potential consequences are evaluated on aV scaEle. A pair of numbers can then assess each risk for a specific business. For example, if aI crop farmer records a pair of numbers (such as 1, 3) for drought, this would indicate Na ranking of 1 on the probability of occurrence scale and 3 on the potential conseUquence/ severity scale. This score coordinates can then be used to create a graphical synopsis of risk exposures of different types of risks. Risk perception is important in understanding farmers‟ managerial decisions and behaviour. Another factor that plays an important role in understanding farmer‟s behaviour apart from risk perception is attitude towards risks. 39 2.7 Attitude towards agricultural risks Given that agriculture is a risky business, an important factor in explaining farmers‟ risk management behaviour is their attitude towards risk. An attitude may be defined as a learned disposition to behave in a consistently favourable or unfavourable way with respect to a given object (Schiffman & Kanuk, 2000). Attitude to risk is a chosen response to uncertainty that matters and it is influenced by perception (Hillson & Murray-Webster, 2005). Risk attitude deals with a decision-maker‟s interpretation of a risk and how much the decision maker dislikes the outcomes resulting from the risk. According to Dillon and HardYaker (1993) attitude to risks refers to the extent to which a decision-maker is willing to Rface risk (risk preference) or seeks to avoid risk (risk aversion). Risk attitude therefore Areflects the extent to which a decision maker generally or consistently dislikes or lIikBes t Rhe risk content. Farmers‟ attitude to risk is important in understanding their be hLaviours as individuals‟ preferences influence a wide variety of risk-taking behaviour. NFor instance, Wencong et al (2006) asserted that a decision maker‟s risk preference (atAtitude towards risks) affects the type of agricultural activities and corresponding scales Dthat he/she will select. They added that given a fixed amount of productive resourceAs such as capital and arable land, the combination of production activities with the h iIghBest level of expected income/risk would be selected if the decision maker was a riskF taker. Attitude towards risks is also a unique reflection of a person‟s personality a nOd it is influenced by socioeconomic factors and life experiences (Bard & Barry, 2000). As observed by AjzenI T(20Y02), attitude is one of the considerations that guide human behaviour. Thus, attitudeS to risks influences how a farmer perceives risks and manages his business. AscertainingR the attitude of farmers toward risk is therefore an important first step in understanding Etheir behaviour and coping strategies they normally adopt to mitigate the effects of risIk Vthey constantly face within the environment they operate (Dadzie & Acquah, 2012). FoNr example in the context of agricultural risk management, the more risk averse a farmeUr is, the more aggressive the farmer is in managing or minimizing his or her exposure to risk and hence, the higher the level of risk management (Hardaker et al 1997). Anderson, Dillon and Hardaker (1977) and Barry (1984), observed that attitude towards risks have been studied using different theories (such as safety first, prospect theory and expected utility theory) and elicitation techniques (such as experimental methods, direct elicitation of utility functions and observed economic behaviour). According to Gomez-limon et al (2002), the direct estimation of the utility function method involves direct interaction 40 with the decision maker, who expresses his or her preferences among various alternatives. Regression techniques can then be used to obtain the utility function of decision makers.. Experimental methods (often regarded as a variant of the direct elicitation of utility functions method) uses real bets instead of hypothetical gains and losses. The observed economic behaviour method is based on the difference between the observed behaviour and that predicted by the empirical models. The direct estimation of utility functions through preferences among various alternatives can be found in the works of Hamal and Anderson (1982), Feinerman and Finkelshtain (1996) while experimental methods using real bets Yis in Binswanger and Sillers (1983). R All these methods have often been criticised. Some of the criticisms iAn the direct estimation method include: interviewer bias, the selection of probabilitiesB, reRluctance to play lottery games, lack of reality of the scenarios in place and insufficie nLt eIxperience on the part of the decision maker in the evaluation of hypothetical situations. The observed economic behaviour method, difficulties such as the influence of other noNn-monetary objectives in the decision-making process (e.g. leisure) and constraints (fiDnanAcial limitations, lack of technical information, etc.) that influence attitudes to risAk arise (Dadzie & Acquah, 2012). Furthermore, the experimental method often prIovBes to be difficult to implement in practice, since the financial costs involved in a rFeal situation with many producers is too high. Moreover Bard and Barry (2000); Gomez-Limon et al (2002) have also observed that elicitation techniques are often toYo c o Ostly and time consuming in terms of implementation. Bard and Barry (2000) therIeTfore concluded that since true risk attitudes are not always apparent, attitude to riskSs should usually be measured indirectly and this can be through attitudinal scale. Attitudinal scale defines a scale of statements that reflect the respondent‟s attitude towards aEn uRnderlying variable and establishes a score that reflects a quantitative measurementI oVf the attitude. Lagerkvist (2005) used this approach to examine farmers risk attitudes Nthrough their responses to sources of risks, while Bard and Barry (2000), also used a likertU scale to assess risk attitudes by obtaining farmers‟ opinions towards risk management tools. 2.7.1 Types of attitude towards agricultural risks Sauer (2011) asserted that as a result of differences in chosen adjustment decisions on farm level, risk attitudes vary across farmers. Farmers‟ attitudes towards risks can be classified in to three types; risk averse, risk preferring (seekers) and risk neutral. 41 Risk averse farmers are characterised as cautious individuals who have preferences for less risky sources of income. Such a farmer is willing to sacrifice some level of expected return so as to reduce the probability of a loss. Such a farmer would always want to avoid risks and would diversify among a variety of production activities taking account of their risk features (Qasim, 2012). According to Winsen et al (2011) a risk averse person will not accept whatever risk no matter the increase in return. Risk averse farmers usually have low risk bearing ability and are also called risk avoiders. Ellis (2000) described a risk-averse person as one who prefers a situation in which a given income is certain to a situation yieldingY the same expected value for income but which involves uncertainty. As a risk aversRe farmer would take managerial decisions to reduce risks (or variation in income ratheRr thAan decisions to maximise income); this prompts the farmer to utilise as many risk manBagement strategies he is able to. LI Risk-seeking/preferring farmer takes the challenge of havNing greater income volatility in exchange for anticipated higher returns (Qasim, 2012). Such farmers are willing to take the risk of doing better than expected while being awareD of Athe possibility of doing less-well than expected. Risk seekers are more adventurous anAd they are usually more concerned with the potentials of a substantial gain than a loss. TIheBy also have a greater risk bearing ability, as they are willing to take huge risks in order tFo maximise profits on investment. A risk-neutral farmer is indiffeOrent between certain and uncertain outcomes with the same expected value of incomeY. They usually ignore the risk features when making decis\ions. Risk neutral indiIviTduals lie between the other two groups. They have acceptable levels of risk bearing abilSity and their focus is usually not that of highest outcome or largest losses. Also, their primRary concern is to achieve a substantial outcome over time. UnderwVooEd and Ingram (2010) however identified four different groups of risk attitude profiIles: managers, maximisers, conservators and pragmatists. Maximisers seek for risks, lettNing the possible gain outweigh the possible negative consequences of any given risk whileU, conservators avoid any risks no matter the possible profits. Risk managers carefully select between risk as to maximise profit and at the same time minimise losses, while pragmatist are indifferent of the risk and instead behave in such a way to leave the most options open. Furthermore, the authors also identified the four different risk environments in which producers operate. Boom times are characterised by little risk and high profit while, recession times are defined by high risk low profit. Uncertain times are characterised by times in which risks and profits are uncertain, and moderate times are defined as times when 42 both risk and profits are moderate. They concluded that in any given situation risk attitude should be adapted to the prevailing risk environment. 2.8 Agricultural insurance Agricultural insurance is the stabilisation of income, employment, price and supplies of agricultural products by means of regular and deliberate savings and accumulation of funds in small instalments by many in favourable time periods to defend some or few oYf the participants in bad time periods (Arene, 2005). It is a confident supporting tool for Rfinancial resources of agricultural producers/ investors and it is an effective tool for risk mAanagement in agriculture (Sadati et al, 2010). Agricultural insurance schemes are a potenRtial tool to cope with income losses trough indemnity payments and therefore stabilize inIcBome and economic performance of farms (Wondimagegn et al, 2011). Insurance is fre quLently used to cover the financial consequences of many risks (Pritchet et al, 1996). It caNn also serve as a security for losses resulting from natural disasters. The fundamental pArinciple of insurance is to pay a premium for someone else to take the risk so as to reAducDe the risk exposures due to price and yield variability. As observed by Skees (2003), anB insurance contract requires no collateral or repayment history and the basic requirement i s Iex ante financing of the risk via a premium. He further stressed that among the poorestF of the poor the inability to pay premiums of any form may also preclude any form of in sOurance. According to Miller, DTobbYins et al (2004), the number of alternative crop insurance programmes has expanded Irapidly in recent years, while Skees (2003) affirmed that traditional approachesR to Sagricultural insurance are often problematic due to the correlation between crop risksE as well as the hidden and asymmetric information problems, which create ample opportIuVnity for abuse. FoNr an insurance programme to be successful, the insurer must have adequate informUation about the nature of the risks being insured. However this is extremely difficult for farm level yield insurance as farmers will always know more about their potential crop yields than any insurer, hence the insurer may not be able to properly classify risk, thus making the insurance unsustainable. These asymmetric information leads to adverse selection in which farmers who know that they have been favourably classified buy the insurance, while those who have not been favourably classified do not buy. Therefore, insurers need to acquire better information to properly classify and assign premium rates. 43 Furthermore crop insurers must also be able to monitor farmers‟ behaviour, as there are cases where insured farmers may change their behaviour in a way that increases their risk exposure levels. This is also known as moral hazard. For example an insured farmer may negligently become careless in their use of risk management tools (e.g reducing fertilizer or using low quality seedlings). Therefore, it is important that insurers are able to access the cause of loss and the impact of the loss without relying on information provided by the insured farmers. However this is not usually the case with multiple-peril crop insurance, as it is usually difficult to identify if a loss has occurred due to some covered risk events or duYe to poor management. It is also not easy to measure the magnitude of loss withouAt reRlying on information provided by the farmer. Index based insurance products have been developed to mitigate the traditional problems associated with multiple-peril cropB inRsurance. These products are an alternative form of insurance that make payments basLedI on either area yields or some objective weather event such as temperature or rainfall, ra ther than on measures of farm yields. Index insurance is a different approach to inNsuring crop yields and the precondition for index insurance to work best for the DindiAvidual farmer is correlated risk. Skees (2004) itemised the relative advantages and chAallenges associated with index insurance as against traditional multiple-peril crop insuranIceB. These advantages include; 1. No moral hazard: Moral hazard cannoFt oc cur under index based insurance because the indemnity does not depend on the inOdividual producer‟s realised yield. 2. No adverse selection: Index iYnsurance is based on widely available information, so there are no informational asyImTmetries to be exploited, hence there is no adverse selection under index based insSurance. 3. Low administrativRe costs: Unlike farm-level multiple-peril crop insurance policies which require underEwriting and inspections of individual farms, index insurance products indemnitiIeVs are paid based on the realised value of the underlying index as measured by goverNnment agencies or other third parties. 4. SUtandardized and transparent structure: The terms of the contracts are usually relatively easy for purchasers to understand since the index insurance policies are sold in various denominations as simple certificates with a structure that is uniform across underlying indexes. 5. Availability and negotiability: Since they are standardized and transparent, index insurance policies can easily be traded in secondary markets. 44 6. Reinsurance function: Index insurance can be used to transfer the risk of widespread correlated agricultural production losses. Thus, it can be used as a mechanism to reinsure insurance company portfolios of farm-level insurance policies. There challenges that must be addressed if index insurance markets are to be successful include; 1. Basis Risk: The occurrence of basis risk depends on the extent to which the insured‟s losses are positively correlated with the index. Without sufficient correlation, “basis risk” becomes too severe, and index insurance is not an effective risk management Ytool. Careful design of index insurance policy parameters (coverage period, Rtrigger, measurement site, etc.) can help reduce basis risk. A 2. Security and dissemination of measurements: The viability of index insRurance depends critically on the underlying index being objectively and accurately ImBeasured. The index measurements must then be made widely available in a timely m anLner. 3. Precise actuarial modeling: There is need for sufficient hisNtorical data on the index and actuarial models that use these data to predict the likeDlihoAod of various index measures. 4. Education: Index insurance policies are typically Amuch simpler but significantly different than traditional farm level insurance policieBs, hence some level of education may be needed to help potential users assess wh etIher or not index insurance instruments can provide them with effective risk maOnagFement. This can be through training and educative materials. 5. Marketing: A marketing pTlanY must be developed that addresses how, when, and where index insurance policies aIre to be sold. 6. Reinsurance: In RmoSst transition economies, insurance companies do not have the financial resouErces to offer index insurance without adequate and affordable reinsurance. EffectiveI aVrrangements must therefore be made between local insurers‟ international reinsuNrers, national governments, and possibly international development organizations. InU conclusion, Teweldemedhin and Kafidii (2009) observed that the decision to buy insurance against risk in agriculture should be an economic one which requires the consideration of two critical factors. (1) The amount of loss that a farmer can withstand without insurance. (2) The trade-offs between insurance costs and potential losses. An enabling environment is a prerequisite for effective and efficient insurance markets in developing countries like Nigeria. This includes the availability of insurance 45 companies and the range of products available to farmers. These components are largely missing in developing countries. For example, Olubiyo et al (2009) observed that, private insurance companies in Nigeria do not have agricultural insurance schemes; hence this limits the participation of farmers in insurance schemes in the country. The Nigerian Agricultural Insurance Corporation (NAIC) was the only insurance company available for farmers in the country until 2012, when the monopoly of NAIC on agricultural insurance was disbanded. 2.9 The Nigerian Agricultural Insurance Corporation (NAIC) RY NAIC was established in 1984 with the mandate of providing insuranceA cover to all categories of farmers, namely – small, medium and large scale holders, eitherR in groups or as individuals (NAIC, 2010). According to Kwatri (2007), NAIC was estIaBblished because the general insurance companies were not interested in agricultural insu raLnce due to the high rate of natural disasters associated with the agricultural industry.ThNe scheme at inception began with the underwriting of two crops items: rice and maize anAd two livestock items: cattle and poultry. It gradually progressed into covering majAoritDy of the crops and livestock items obtainable in the country including export crops sBuch as cocoa, Tea/coffee, cotton and rubber. The corporation has since inceptioFn, isIsued out almost a million policies with the volume of risk amounting to about N100 billion thereby earning the corporation a premium sum of about N2 billion. The corpo rOation has also settled claims worth about N500m to various farmers and cooperative gYroups (NAIC, 2010). The Corporation‟s standard procedure is that claims vouchers mustI bTe processed within 24 hours and such claims settled within a maximum of one weeRk. InS order to make the purchase of insurance more attractive to farmers in the country, thEe Federal government subsidised the premiums by 50% (i.e farmers pay only 50% of tVhe premium, while the state and federal government pay the 50% balance). AccordinNg toI EC (2008), subsidised insurance programmes have not lead to the development of a pUrivate market for crop insurance; hence there is a need for government to facilitate the creation and sharing of information and databases so as to overcome the problem of setting up viable insurance mechanisms. A worthwhile venture in this regard by the Nigerian government was the disbandment of NAIC from the monopoly of exclusivity of agricultural insurance so as to stimulate competition in the agricultural sector. Although, NAIC has the exclusive right to insure all subsidised agricultural risks, opportunities abound for other insurance companies in the areas of commercial unsubsidised agricultural risks. 46 2.10 Review of literature on agricultural risk management studies The economic analysis of risk management requires some quantification of farmers‟ preferences (attitude to risks) and perceptions with respect to risk; as well as the strategies/activities implemented by farming households to manage risks. With regards to farmers‟ attitude to risks, several studies have concluded that farmers are risk averse in nature. Such studies include; Torkamani and Haji-Rahimi (2001); Salimonu (2007); Ayinde (2008); Ajijola et al (2011). Several factors influencing farmers‟ preferences on risks have also been identified. For example, Mehta (2012) observed that that nRon-Yfarm income helps farmers to take more risk. Also, while Yesuf and Bluffstone (20A07); Ayinde (2008); Ding et al (2010) observed that farmers having more income are usRually more risk seeking than others, Cohen and Einav (2007); Onyemauwa et al (2013B); Ihli et al (2013) concluded that farm income and risk aversion are positively correla tLed.I Ghadim and Pannell (1999); Yesuf and Bluffstone (2007); Nielsen et al (2013) also Nconcluded that age positively correlates with farmers‟ risk aversion level. Also, accordinAg to Hoag, Keske and Goldbach (2011), women show a slightly higher aversion to risk tDhan men, while Kisaka,-Iwayo et al (2005) observed that risk aversion is higher amoBng Afarmers having more dependants. With regards to the effect of education on risk av erIsion, Mishra and Goodwin (2005); Wissink (2013) concluded that higher education inFcreases the willingness to take risks (lesser risk aversion). O In relation to farmers‟ pYerceptions on sources of agricultural risks, Lucas and Pabuayon (2011) asserted thIaTt age has negative effects on farmers risk perception. Also, according to Adeola (201S2), older farmers are likely to perceive the environmental hazards of pesticides than youngR due to accumulated knowledge and experience of farming systems. Egondi et al (V201E3) found out that married people in one of their study area had a higher perception ofI health risks, while individuals with at least primary level education perceived higher leNvels of air pollution than those without primary level education. In terms of the effectU of credit on farmers perception Lucas and Pabuayon, (2011) affirmed that availability of credit is positively related with farmers‟ perception of risk, while Synder (2004) observed that lower income leads to a greater perception of risk. However, Patrick et al (1985) concluded that farmers‟ perceptions varied across geographic areas and by farm type, while Wilson et al (1993) observed that risk perceptions were highly complex and individualistic in nature. 47 To quantify farmers‟ perceptions on risk sources, Briggerman et al (2004) designed a score card (involving likelihood, potential and exposure) to access the risks faced by an agricultural firm, while Zinn (2009) affirmed that risk exposure is a function of likelihood of occurrence of risks and consequence of the risks. Shadbolt et al (2010) diverted from other previous studies on risks by considering the negative and positive sides of risks. Their study considered farmers perceptions of risks and likelihood of the events occurring. Farmers have also ranked their perceived sources of risks based on level of importance. Production and price risks appear to be very important to farmers as seen inY the works of Meuwissen et al (2001); Lien et al (2003); Le and Cheong (2009); FakayoRde et al (2012). Family health; access to market; output and input price variability were hAowever the most important types of risks to farmers as found in Njavro et al (2005). TBhe Rresearch carried out by Mac Nicol et al (2007), identified sources of risk that commeLrciIal sugarcane farmers in the province of KwaZulu-Natal (KZN) South Africa, perceive to pose the greatest threat to the viability of their businesses. According to the study, the mosNt important risk sources were found to be the threat posed by land reform, minimum wDageA legislation and the variability of the sugar price. In USDA (1997), farmers‟ degree ofA concern was greatest regarding changes in government laws and regulations, decreasesI Bin crop yields or livestock production and uncertainty regarding commodity prices. F Although production and pric eO risks have been considered very important, Le and Cheong (2009) observed that while production risk management strategies were perceived as being effective, price risk TmaYnagement strategies had lower levels for perceived effectiveness. However, aSccoIrding to Meuwissen et al (2001), farmers‟ perceptions do not necessarily mean actuRal usage of the strategies. Farmers risk perceptions may also change over time. Gray Eet al (2009) identified possible reasons for change in farmers risk perceptions. IFVor example, they suggested that increased importance of accidents and health problemsN may be related to the farm ownership structure and increased awareness of laws relateUd to health and safety, while reduction in perceived risks related to rainfall variability could be due to farmers using strategies to manage rainfall risks. Concerning risk management decisions, Velandia et al (2009) examined factors influencing producers risk management adoption decisions while taking into considerations the possibility of simultaneous utilization of multiple reducing instruments and the potential correlations among these adoption decisions. Also, Hucks et al (2011) analyzed awareness and knowledge of risk management techniques and found out that significant relationship 48 exists between education and risk management. Breukers et al (2009) explained that higher level of education influences the level of understanding of a risk and this may indicate a higher knowledge of risk management tools to combat the risk. Nadhomi et al (2013), concluded that age of household head was negatively related with adoption of soil and water conservation practice (a risk management tool used to mitigate risk of erosion) while Hucks et al (2011) observed that larger farmers had greater risk management knowledge than those with smaller farms. Wondimagegn et al (2011) accessed the patterns, trend and determinants of crop diversification at farm level. TYhey found out that access to market information and irrigation intensity significanRtly and positively affect crop diversification. Livestock ownership was significant bAut however negative suggesting that household with larger number of livestock arIe Bless R likely to grow more crops. L Juma et al (2009) studied the effects of production risk Non f arm technology adoption among small holder farmers and they found out that yieldA variability and the risk of crop failures affect technology adoption decisions in lowD-income, rain-fed agriculture. The direction and magnitude of effects depend on theB farAm technology under consideration. They concluded that although productivity gains are nIecessary, they are not sufficient conditions to attract farmers to adopt new technologies aFnd agricultural innovations; what matters more is the implication of risks. O Psychological factors maYy also have greater influence on farmers‟ use of risk management strategies than sIoTcioeconomic variables (Gomez-limon et al, 2002). The effect of psychological variableSs on risk management were also highlighted by Ajieh (2010) who identified lack of truRst in settlement as part of the constraints influencing adoption of agricultural insVuraEnce in Nigeria. INn reIlation to the use of insurance as a risk management tool, Ogurtsov (2008) addreUssed the impact of farmer‟s personal risk characteristics (risk perception and risk attitude) on catastrophe insurance purchase. The results showed that farm and farmer‟s personal characteristics had a significant impact on actual (catastrophe) insurance purchase. Also, insurance subsidies were one of the main reasons to purchase insurance coverage as seen in some of the previous agricultural studies on crop insurance such as Mishra and Goodwin, (2003); Sherrick et al (2004); Babcock and Hart, (2005). 49 CHAPTER THREE THEORETICAL AND CONCEPTUAL FRAMEWORK 3.1 Theoretical framework The theoretical approach used to guide this study was drawn from the following theories: Social Cognitive Theory, Theory of Planned Behaviour, Pest Belief Model and Perceived Attributes Theory. These theoretical components provide insights into the factors that influence crop farmers‟ assessments of agricultural risks as well as their use ofY risk management strategies. AR 3.1.1 Social cognitive theory BR The Social Cognitive Theory of Bandura (1986) describLesI individuals as self organising, proactive, self reflecting and self regulating. IndivNidua ls are conceptualised as being governed by a triadic reciprocal interaction which ocAcurs between human behaviour, environmental factors and personal factors (such as Dcognitive, affective and biological events). People learn by observing others with theA environment, behaviour and personal factors all influencing development. F I B Y O T RS I E NI V U Figure 2: Social Cognitive theory, Bandura (1986) 50 The social cognitive theory helps to understand the interactions that exist between farmers‟ behavior, environment and personal factors. Therefore, in understanding farmers‟ behavior, one must take into account both the individual (the farmer‟s life history of learning and experiences) and the environment (those stimuli that the person is aware of and responding to). The characteristics of the farmers as well as his/her environment are thus important in the study of risk perceptions and risk management behavior. For instance, farmers risk management behavior influences and is influenced by their environment (sucYh as differences in agro-ecological zones, availability of necessary inputs and actions oRf fellow farmers with regards to risks,) and farmers characteristics (such as educatAional level, knowledge of risk management tools and risk perceptions). BR LI 3.1.2 Theory of planned behaviour N The theory of planned behaviour propounded Dby AAjzen in 1985 predicts human behaviour. The theory states that individual performAance of a given behaviour is primarily determined by the person's attitude towards the IbeBhaviour, the influence of the person's social environment (subjective norms) and the Fper son‟s perceived behavioural control over the opportunities, resources, and skills neceOssary to perform the behaviour. Attitude towards the behavYiou r refer to the degree to which performance of a specific behaviour is positively or InTegatively valued and it indicates that an individual has a favourable or unfavourabSle attitude towards the behaviour. Subjective norms indicate the perceptions on whethRer people are expected to perform the recommended behaviour by their friends, family anEd the society, while perceived behavioural control refers to an individual‟s perceived easIeV or difficulty of performing a particular behaviour. AN behavioural intention is formed from attitude towards the behaviour, subjective normU, and perceived behavioural control. The more favourable the attitude towards behaviour and subjective norm, and the greater the perceived behavioural control, the stronger the person‟s intention to perform the behaviour in question would be. In relation to this study, farmers‟ attitude to risk is partly explained by the degree to which they positively or negatively value risk, the perceptions of their friends, family and the society on risks and their ability to carry out the skills/tasks necessary to achieve their aims. This theory is thus crucial in understanding farmers‟ attitude to risk. 51 3.13 Pest belief model The pest belief model is a framework predicting the relationships between beliefs and pest management decisions. According to Heong and Escalada (1999), pest management behaviour is determined by four components: (1) Perceived susceptibility: this refers to the subjective risk of getting pest attacks if no precautions are taken. . (2) Perceived severity: this refers to the severity of the pest attack. (3) Perceived benefits: this refers to the degree to which a certain action reducesY the perceived susceptibility or severity of the pest attack and AR (4) Perceived barriers: the perceived negative aspects of a particular actioRn. B LI DA N A F I B O Figure 3: The Pest BeliefY Mo del, Heong and Escalada (1999) In the context of thiIs Tstudy, farmers risk management behaviour can therefore be governed by these four Scomponents: the likelihood of occurrence of agricultural risks (perceived susceptibilRity); the severity or economic impact of the risks (perceived severity); the efficacy of theE risk management strategy (perceived benefits) and the perceived negative aspect of a riIskV management strategy such as its cost (perceived barriers). Farmers perceived level of sNusceptibility and severity will lead to belief in their level of risk exposure, while perceUived benefits and barriers will lead to their belief in the effectiveness of using risk management strategies. The pest belief model is brought in to this study so as to be able to understand the link between farmers‟ beliefs/ risk perceptions and their risk management behaviour. 52 3.1.4 Perceived attributes theory of innovation The Perceived attributes theory of Rogers (1995), explained the five attributes upon which an innovation is judged: that it has an advantage over other innovations or the present circumstance (relative advantage), that it is compatible with the circumstances into which it will be adopted (compatibility), that that it is not too complex to learn or use (complexity), that it can be tried out (trialability) and that results are visible or can be observed (observability). For example, farmers may judge agricultural insurance on the basis of its compatibility with their own values or on the level of complexity such as documenYtary requirement, or based on its advantage of allowing a farmer to be able to substitute Ra certain small expense for the possibility of a large uncertain loss. RA In relation to this study, farmers‟ use of risk management strategiBes can therefore be influenced by five attributes: the advantages the strategy h aLs I(relative advantage) compatibility of the strategy with farmers‟ values (compatibNility); comprehension of the strategy (complexity) triability of the strategy as well as the vAisibility of the positive results of using the strategy (observability). The perceived attributeD theory is brought in to this study so as to be able to understand farmers‟ adoption of riBsk mAanagement tools. I 3.2 Conceptual framework F The determinants oYf a gr Oicultural risk management behaviour of crop farmers in Nigeria are conceptualisedI Tbased on the roles played by the independent and intervening variables in explaining theS dependent variable (Figure 4). Farmers‟ socioReconomic characteristics (such as age, sex, educational level, farming experience, farm eEnterprise and farm size) often influence their perceived sources of risks and attitude towaIrVds agricultural risks. The above variables together with the farmers‟ agro-ecologicaNl zone may also determine farmers‟ perception of their level of risk exposure. FarmUers perceived level of risk exposure and their socioeconomic characteristics affect their participation in agricultural insurance scheme and this ultimately determines their perception of the effectiveness of agricultural insurance in managing risks. All these variables are expected to influence crop farmers‟ level of agricultural risk management behaviour. For example, higher levels of educational attainment or larger farm sizes are usually associated with a high risk attitude which in turn stimulates a lower level of risk management. Likewise a higher perceived level of risk exposure is expected to stimulate a farmer‟s interest in risk 53 management strategies (such as crop insurance) and this increases the farmer‟s level of risk management. Furthermore, government policies on risk management, farmers‟ access to risk reducing technologies, lack of infrastructural facilities, poverty level and outcome history are also variables which can affect farmers‟ level of risk exposure, their perception of the effectiveness of agricultural insurance as well as their risk management behaviour. For example, lack of good roads may affect farmers‟ access to markets for farm products thus increasing their exposure to agricultural risks. According to Cervantes-Godoy et al (20Y13), institutional and political settings in developing countries are frequently less develoRped thus contributing to a greater incidence of market imperfections in key areas such aAs credit and insurance and this in turn lowers farmers‟ access to risk management toolsB andR strategies. N LI DA BA F I O SI TY R VE UN I 54 Y R INDEPENDENT VARIABLES INTERVENING DEAPENDENT VARIABLE VARIABLES R Agricultural risk So cioeconomic B management behaviour characteristics I Attitude of Farmers’ L  Superior risk  Sex to agric risks managers Perceived level of  R isk seeking risk exposure  Age N  Active risk  Government managers  Risk averse policies  Educational  Likelihood  Access to risk level Farmers’ perception of A reducing  Di-function risk  Impact effectiveness of agric technologies managers  Farm D i n g i n s u r a n c e  Lack of experience  Risk exposure Perceived sources of  Not effectiv BAe infrastructural  Mono-function risk  Low effectiveness facilities managers  Farm risks  Moderately effective  Poverty level enterprise  Production F V erIy effective  Outcome  Part-time risk  Marketing history  Farm size managers  Financial  Personal O Farmers’ participation Y in agric insurance Agro-ecological zone  Awar eness  Coastal  Rain forest  Level of  Guinea Savanah ITS partic ipation R Figure 4: Conceptual FrEamework for Determinants of Agricultural Risk Management Behaviour of Crop Farmers in Nigeria IV UN 55 CHAPTER FOUR METHODOLOGY 4.1 Study area o o This study was carried out in Nigeria. The country lies between latitudes 4 and 14 o o north of the equator and longitudes 3 and 15 east of Greenwich and it is bordered on the west by the Republic of Benin, on the north by Niger, on the east by Chad and Cameroon and on the south by the Atlantic Ocean. The Federal Republic of Nigeria has a total areYa of 2 2 923,770 km (Land area: 910,770 km2; Water area: 13,000 km ) and is occupied bRy about 140 million people (NPC, 2006). Nigeria is blessed with mineral, physical, bioAlogical and energy resources (renewable and non renewable) such as: forests, crude oil, nRatural gas, solid minerals, as well as marine and aquatic resources. IB The major industries are agriculture, oil (upstream and dow Lnstream), iron and steel processing, plastics, textiles, and pharmaceuticals. Out of allA theN major industries, agriculture serves as the largest employer of labour; while dominating the country‟s real sector by 41% (CBN, 2004). Although, agricultural landholdings areD generally small and scattered, the primary policy of agriculture in Nigeria is to makBe thAe country self-sufficient in its food and fibre requirement. Major agricultural enterFpris eIs found in the country include: crop farming, poultry production and livestock. The country is divided in toY ni n Oe major agro-ecological zones (Oyenuga, 1967): (i) mangrove forest and coastal IvTegetation, (ii) freshwater swamp communities, (iii) rainforest zone, (iv)derived savannaS, (v) southern guinea savanna zone, (vi) northern guinea savanna zone, (vii) jos plateauR, (viii) sudan savanna, and (ix) sahel savanna. VE UN I 56 RY RA LI B AN BA D I O F ITY ER S IV 57 UN 4.2 Population of the study This study focussed on crop farmers who had at least five years farming experience, as it is expected that this category of farmers will have experience in agricultural risk management. 4.3 Sampling procedure and sample size Multistage random sampling procedure was used for the study. Thirty-five percent of the nine agro-ecological zones in the country (mangrove forest and coastal vegetaYtion, freshwater swamp forest, rainforest, derived savannah, southern guinea savannah, Rnorthern guinea savannah, jos plateau, sudan savannah and sahel savannah) were randomAly selected. This gave; mangrove forest and coastal zone, rainforest zone and southern gRuinea savannah zone. Ten percent of the states in each of the zones ( coastal- rainforest-I aBnd southern guinea savannah- were then randomly sampled to give Lagos (coastal), Os uLn (rainforest) and Niger (southern guinea savanna). Thereafter, 10% of the local goverNnments in the selected states were randomly chosen to give: Badagry and Ojo (Lagos);A Boripe, Osogbo and Ede south (Osun); Bosso, Shiroro and Paikoro (Niger). Two coAmmDunities were randomly selected from each of the local government to give: Topo and ABjara (Badagry); Ajangbadi and Igbede (Ojo) in Lagos. In Osun state, Egbeda and Aagba (B oIripe); Ajenisua and Owode (Osogbo); Sekona and Loogun (Ede south) were randomlyF selected. In Niger state Maikunkele and Beji (Bosso); Kuta and Shiroro (Shiroro) ; OKafinkoro and Adunnu (Kafinkoro) were randomly sampled. Fifteen percent of the cYrop farmers (registered with the Agricultural Development Project) in each of the selecteIdT community were randomly selected to arrive at a total of 323 farmers. Out of the threeS hundred and twenty three questionnaires administered, a response rate of 96% was achieRved with three hundred and ten (310) questionnaires retrieved. VE I UN 58 Table 1: Table showing sampling procedure and sample size 35% of 9 agro- 10% of 10% of 2 communities in Farmers 15% of ecological zones states local each local population in farmers in governme government area sampled zones nt areas in communities State Badagry Topo Mangrove and Ajara coastal zone Lagos Y 406 61 Ojo Ajangbadi R Igbede RA Boripe Egbeda Aagba B I Rainforest zone Osun Osogbo LAjenisua Owode AN734 1 1 0 Sekona Ede south Loogun D IB A Bosso FM aikunkele Southern O Beji guinea Niger Y 1013 152 savannah zone Kuta IT Shiroro Shiroro S Kaffinkoro Paikoro ER Adunnu 323 IV UN 59 4.4 Research design This study was designed to generate basic knowledge and identify variables associated with agricultural risks in terms of farmers‟ sources of risks, level of exposure, attitude towards risks, risk responses and risk management behavior using the survey method. 4.5 Data collection procedure Five focus group discussions- FGDs (One FGD in Lagos, two FGDs in Osun and two FGDs in Niger states) were conducted to generate a deeper understanding of farmers‟ Yrisks perceptions and responses. An interview schedule was also developed to gather infoRrmation on farmers‟ socioeconomic characteristics and information on agricultural risk mAanagement. Publications such as journals and books as well as web content also providedR complementary data. Trained enumerators were employed for data collection. IB 4.6 Validity and reliability of instrument L Face and construct validity of the instrument was cAondNucted by experts in the fields of Agricultural Extension, Economics and AccountiDng. The overall reliability of the instrument was determined through split-half methodA and a reliability coefficient of 0.86 was obtained. An internal reliability of 0.77 and 0.B81 were obtained for the agricultural risk management scale and attitude towards agriFcul tuIral risk scale. 4.7 Measurement of variables O 4.7.1 Dependent variable Y The dependent variaIbTle of this study is farmers‟ agricultural risk management behaviour. This refers toS farmers‟ level of risk management and is reflected in behavioural types. In measurinEg faRrmers‟ level of agricultural risk management, strategies were generated based on the pVroduction, marketing, financial and social sources of risks. Respondents were asked to Ntick I(from a list) the risk management strategies they utilise. A total of 42 strategies from Uthe four sources of agricultural risks (production- 11 strategies; marketing- 8 strategies; financial- 13 strategies and social- 8 strategies) were presented to respondents. The frequency of utilization of these strategies was scored as follows: Utilise all the time = 3, Utilise sometimes/when need be = 2, Utilise rarely = 1, Never utilise = 0. Based on the scores obtained by respondents, the minimum score obtained was zero, while the maximum was 126. Using the mean, respondents were grouped into high and low categories of risk management. Respondents‟ raw scores in each of the four categories were also used to group them into five groups of risk management behaviour: 60 1. Superior risk managers; these are farmers who utilize at least fifty percent of the risk management strategies under each of the four categories of risk management. 2. Active risk managers; these refer to farmers who utilize at least fifty percent of the risk management strategies under three of the four categories of risk management 3. Di-function managers; these are farmers who utilize at least fifty percent of the risk management strategies under two of the four categories of risk management 4. Mono-function risk managers; these refer to farmers who utilize at least fifty percent of the risk management strategies under one of the four categories of risk management. YThis set of risk managers can be production, marketing, financial or social risk mRanagers depending on their area of core competence. A 5. Part-time risk managers; these are farmers who utilize less than fifty perRcent of the risk management strategies under each of the four categories of risk manaIgBement. 4.7.2` Independent variables L 4.7.2.1 Socioeconomic characteristics of respondents: N 1. Age: actual age in years. DA2. Sex: male or female. 3. Marital Status: single; married; divorced or wBidoAwed. 4. Religion: Islam; Christianity; Traditional. I 5. Educational Level: respondents weOre aFsked to tick their last completed level of education from the following list: No forma l education; Primary school; Secondary school; N.C.E, O.N.D; H.N.D, B.Sc; PostgradYuate; 6. Credit sources: FrienIdTs/family; Cooperatives; Private moneylenders; Commercial Banks; NACRDBR; MSicrofinance banks; Others (specify). Respondents also ranked their credit source in order of importance. 7. FarmingI eVxpe Erience; in actual years. 8. MemNbership of organization: respondents were asked to indicate whether they belong to anUy organization, the number of such organizations, the positions they hold in the organization and their level of participation in their organizations activities. 9. Involvement in off farm occupation; respondents were asked to indicate if farming was their only occupation or not 10. Farm Enterprise: major crop farmed; other crops cultivated; other agricultural enterprises. 11. Farm ownership: Sole proprietorship; Company; Partnership. 12. Labour sourcing: Friends/Family; Partnerships/Cooperatives; Labourers. 61 13. Labour availability: Always available; Sometimes available; Rarely available; Never available. 14. Level of Production: farm area cropped. 15. Marketing channel: This refers to the outlet farmers‟ use in disposing their farm produce. Traders/ Middlemen; Processing industry; Directly to individuals/ household (Consumers) 16. Market accessibility: Highly accessible; Moderately Accessible; Not Accessible. 17. Source(s) of Information: other farmers/friends/relatives; Extension/DevelopmYent agents; Print media; Electronic Media; Professionals. Respondents also AranRked the source(s) in order of importance. 4.7.2.2 Farmers’ perceived types of agricultural risks: A list of agriBcultRural risks types based on the four categories of risk sources was administered and resIpondents were asked to tick those applicable to them. L 4.7.2.3 Perceived level of risk exposure: N Level of risk exposure = Likelihood of occurrence × DImpAact of risk Likelihood of occurrence; 1= Never, 2= Unlikely,A 3= Possible, 4= Likely, 5= Very likely Impact of risk; perceived average economic IloBss from agricultural risks; 1= 0%-20%, 2= 21%-40%, 3= 41%- 60F%, 4= 61%- 80%, 5= 81%-100% of produce 4.7.2.4 Attitude towards agricult uOral risks: respondents‟ attitude towards risk was measured using a 5 point TattiYtudinal scale (Strongly agree, Agree, Undecided, Disagree and Strongly disagree). IPositive questions attracted a score of 5 to 1, while negative questions were from S1 to 5. Each respondent total score was computed. Highest score was 105, loweEst scRore was 21. Based on the mean (x), respondents were grouped into two categorieIs V(Risk seeking- adventurer and risk averse- avoider). The lower the scores, the more Nrisk averse the farmer is. 4.7.2U.5 Effectiveness of agricultural insurance in managing risks This was done using six criteria namely; farmers‟ level of participation in agricultural insurance schemes, farmers‟ level of satisfaction, efficiency of crop insurance, inhibiting factors and motivating factors 1. Farmers’ level of participation in agricultural insurance schemes; Respondents were asked to indicate if they were aware of NAIC agricultural insurance scheme, if they have ever purchased agricultural insurance, the frequency and premium paid. 62 2. Farmers level of satisfaction; Respondents (who purchased agricultural insurance) indicated their level of satisfaction with NAIC procedures. 3. Effectiveness of crop insurance: Respondents who purchase crop insurance were asked to indicate how efficient it is in managing agricultural risks. Insignificant; Low significant; Moderately significant; Very significant. 4. Inhibiting factors; respondents ticked from a list, the factors that inhibit them from patronizing NAIC. 5. Motivating factors; respondents ticked from a list factors that can motivate them/suYstain interest to purchase agricultural insurance. R A 4.8 Analysis of objectives and hypotheses of the study BR An analysis of objectives and hypotheses of the study was carried oLut aIs shown in Table 2. The data requirement and analytical tools are also indicated. N DA IB A O F ITY ER S IV UN 63 RY Table 2a: Analysis of objectives A Objectives Meaning Data Requirement BRA priori- Analytical I expected tool signs 1. Identify the types of agricultural To find out the types of agricultural Risks faced Frequency, risks as perceived by crop farmers in risks faced by crop farmers Ranking of risks L Percentage the study area Mean 2. Determine crop farmers‟ To find out the level of vulnerability Likelihood of ocAcurreNnce of identified +ve Mean perception of their level of risk of crop farmers to agricultural risks risks· exposure. Impact of Didentified risks Level of risk exposure Ranking of risks 3. Describe the attitude of crop To find out the disposition of crop RespAonses to attitudinal statements -ve Mean farmers towards agricultural risks. farmers towards agricultural risks B 4. Describe the risk management To find out the risk management IRisk management strategies Mean strategies utilised by crop farmers in strategies that crop farmers u the study area. Fse used 5.Find out crop farmers‟ perception To find out farmers view about the Level of efficiency of crop insurance +ve Mean of the effectiveness of crop insurance e fficiency of crop ins uOrance in managing risks 6. Determine crop farmers‟ level of To find out farmYers level of Extent of utilization of agricultural -ve Mean agricultural risk management. agricultuIraTl risk management risk management strategies 7 Analyze factors that determine the To find out the factors that influence Farmers personal characteristics Multinomial risk management behaviour of crop Rthe Srisk management behavior of Level of risk exposure Logit farmers. E farmers Attitude to risk regression Farmers risk management behaviour IV 64 UN RY Table 2b: Analysis of hypotheses A Hypothesis Meaning Data Requirement BRA priori- Analytical I expected tool signs 1 Test of relationship between To find out the extent to which the Socioeconomic variables Chi-square selected socioeconomic farmers socioeconomic variables Level of risk management L PPMC characteristics of crop farmers and influence their level of risk their level of risk management. management N 2 Test of relationship between crop To find out the extent to which the Level of risk exAposure +ve PPMC farmers‟ perceived level of risk farmers level of risk exposure Level of risk management exposure and their level of risk influence their level of risk D management. management 3.Test of difference in crop farmers‟ To find out the difference that exists ·LeveAl of risk exposure ANOVA perceived level of risk exposure in crop farmers‟ level of risk across zones across the three agro-ecological exposure across the agro-ecologicaIl B zones. zones in the study area 4. Test of relationship between crop To find out the extent to which Attitudinal scores -ve PPMC farmers‟ attitude towards agricultural farmers‟ attitude towards F risks and their level of risk agricultural risks influence their management. level of risk managem eOnt 5. Test of difference in crop farmers‟ To find out the difference that exists Attitudinal scores ANOVA attitude towards agricultural risks in crop faIrmers‟Y attitude towards across the three agro- across the three agro- ecological agricultural risks across the agro- ecological zones zones. ecological Tzones 6. Test of difference in crop farmers‟ To fSind out the difference that level of risk management ANOVA level of risk management across the exists in crop farmers‟ level of across the three agro- three agro-ecological zones. ERagricultural risk management across ecological zones the agro-ecological zones level IV 65 UN 4.9 Data analysis Descriptive statistics used include; frequencies, percentages, means, column charts and pie charts. Inferential statistics was applied as follows: Table 3: Analysis of data Hypothesis Statistical tools used 1 PPMC (variables at interval level) Chi-square (variables at nominal level) 2 PPMC Y 3 ANOVA R 4 PPMC A 5 ANOVA BR 6 ANOVA I L Multinomial logistic regression was used to analyAse Nfactors that determine crop farmers‟ agricultural risk management behaviour. ThDe part-time group was used as the reference category. Hypothesised variables in the mAodel were; age, marital status, formal education, farming experience, farm size, m aIjBor source of information, membership of organisation, attitude to risks, risk exposureF level and agro- ecological zone. O ITY S VE R NI U 66 CHAPTER FIVE RESULTS AND DISCUSSION This chapter presents the results, interpretation and discussion of the data collected. The findings of the study are reported under nine sections: 1. Socioeconomic characteristics of crop farmers 2. Farmers perceived types of agricultural risks 3. Farmers perceived level of risk exposure Y 4. Attitude towards agricultural risks R 5. Farmers‟ use of agricultural risk management strategies A 6. Effectiveness of agricultural insurance in managing risks R 7. Farmers‟ level of risk management IB 8. Determinants of agricultural risk management behaviour of cro pL farmers 9. Testing of Hypotheses N 5.1: Socioeconomic characteristics of crop farmers A This section presents the socioeconomic chaDracteristics of crop farmers. The characteristics are sex, marital status, religion,I aBge, A educational level, farming experience, farm size, major crops cultivated, secondaryF fa rm enterprises, organizational membership, off farm occupation, farm ownership, laboOur availability, labour sourcing, marketing channel and market accessibility. Sex: Figure 6 shows that 90.I7T% wYere males, while 9.3% were females. The distribution of respondents indicates thaSt 94.8%, 93.1% and 87.3% of the respondents in coastal, rainforest and guinea savannah Rzones respectively were males. This implies that crop farming is a male dominated occupaEtion in Nigeria. According to Hassan and Nhemachena (2008), males and females differ Vbecause of the differences in access to assets and decision making process. For instance, Nas Imales usually exert more influence over the decision making process than femalUes (Hoag, Keske and Goldbach, 2011), majority of the respondents should display authority and control in taking major decisions on farming practices, thus stimulating a higher level of risk management. Also because males are usually more active and agile than females, respondents‟ ability to utilize risk management tools should be higher implying a higher level of agricultural risk management. However, because women show a slightly higher aversion to risk than men (Hoag, Keske and Goldbach, 2011), respondents‟ level of risk aversion may be lower than what it could have been if a higher proportion of the crop farmers were females. 67 Previous studies such as; Otitolaye et al (2009), Raufu (2010), Odoemenem and Adebisi (2011), Ogunniyi et al (2011) agreed on male dominance in agriculture in Nigeria. Marital status: As indicated in Figure 7, married respondents‟ constituted 89.7% of the total respondents in the study area. With more dependants to feed, crop farmers in the study area are expected to be risk averse as Kisaka,-Iwayo et al (2005) observed that risk aversion is higher among farmers having more dependants. This higher level of risk aversion may be as a result of family commitments and responsibilities in marriage. The high level of risk aversion is expected to influence level of risk management positively. Marital status can Yalso influence farmers‟ perception of agricultural risk because with more dependants to cRater for, married respondents usually have a higher perception of agricultural risks. EAgondi et al (2013) found out that married people in one of their study area had a hBigheRr perception of health risks. Married respondents dependants/children may also servLe aIs a reliable source of labour, especially since most of the respondents rely on labour fro m friends/family (Figure 14). Sourcing of labour from friends/family is a form of rNisk management due to the accessibility and good interpersonal relationship whichD usAually exist between friends and family. A Religion: Figure 8 shows that more than t wIoB thirds (73.8%) of the crop farmers were Muslims, 25.2% were Christians, while 1.0 % were traditionalists. Religion has the potential to affect world views; hence it ma yO hav Fe effect on respondents‟ attitude towards risk. Moreover, Nwankwo et al (20Y09) asserted that influences from religion cannot be underestimated in the adopItTion decision. For instance, religious considerations may determine the adoption oSr utilisation of some risk management strategies. For example, if farmers believe that loss in yield due to unfavourable weather conditions is from God, they may be unwilling Eto uRtilise strategies to reduce the risk. Furthermore, ethical considerations may also affeIcVt the use of crop insurance by farmers. N U 68 AR Y LIB R N A BA D OF I Y FiguSre 6:I Se Tx distribution of respondents R IV E 69 UN RY BR A N LI AD A F I B Y O SI T FiEgureR 7: Distribution of respondents according to marital status IV 70 UN Y AR LIB R AN AD F I B O ITY FiEgureR S 8: Distribution of respondents according to religion IV 71 UN Age: Age distribution of the respondents as presented in Table 4 reveals that about 2.3% of the respondents were less than thirty years. Also 35.5% were between 31 to 50 years, while majority (60.2%) were above 50 years. The mean age was 53.2 ± 10.5 years and this implies that crop farmers in the study area are gradually moving beyond their active and productive age. This confirms the observation of Omotayo (2010) that one of the major problems of agricultural development in Nigeria is the ageing farm population, while Eluhaiwe (2008) asserted that the agricultural labour force in Nigeria is fast declining. Mohiuddin et al (2009) also observed that young farmers are more agile and can work more than old farmers duYe to the high level of physical energy needed in agricultural production. MajorityR of the respondents are also expected to be risk averse as Ghadim and Pannell (1999) aAffirmed that age is positively correlated with risk aversion. This high level of risk aversioRn is expected to influence respondents‟ level of risk management positively. Moreover, aIgBe is associated with more experience, hence majority of the farmers are expected to hav e La high perception of the occurrence and impact of agricultural risks. According to BonabNana-Wabbi (2002) in Adeola (2012), older farmers are likely to perceive the enviroDnmAental hazards of pesticides than young due to accumulated knowledge and experiencAe of farming system. However, Lucas and Pabuayon (2011) observed that age has nIeBgative effects on farmers risk perception, while, Nadhomi et al (2013), concluded thFat age of household head was negatively related with adoption of soil and water conOservation practice (a risk management tool used to mitigate risk of erosion). Educational Level: Table 4I aTlsoY reveals a disparity in the educational level of respondents across the zones as 81.1%S, 63.7% and 54.0% of the respondents in coastal, rainforest and guinea savannah zoneRs respectively affirmed that they had one form of formal education or the other. ConsidEering the three zones, farmers in the coastal zone appear to have a higher level of formIaVl education when compared with those from the other two zones. This high value maNy be as a result of the coastal nature of the zone. Adelekan (2009) asserted that coastUal towns are often the most developed of Africa‟s urban centres. Thus by implication, they may have a high concentration of educational facilities. This high literacy level is expected to have a positive influence on crop farmers‟ knowledge of risk management tools as they are able to understand how to reduce or avoid risks. In a study by Egondi et al (2013), individuals with at least primary level education perceived higher levels of air pollution than those without primary level education. Education also increases the ability to source information from a variety of channels like print media. According to Thomas et al 72 (1990), education assists people receive, decode and understand information; hence they are able to make better decisions. Breukers et al (2009) also observed that higher level of education influences the level of understanding of a risk and this may indicate a higher knowledge of risk management tools to combat the risk. Crop farmers in the guinea savannah zone recorded the lowest rate of literacy levels when compared with the other two zones. Low level of formal education inhibits communication flow between information sources and farmers (Olujide & Oladele, 2011). It also limits farmers‟ ability to work efficiently as their capacities in adopting new production technologies that may help to reduce risks is limYited. However, Wissink (2013) found out that higher education increases the willingnessR to take risks, while Mishra and Goodwin (2005); Acquah and Dadzie (2012); RosRlan Aet al (2012) asserted that higher education leads to less risk aversion. Lower levels of risk aversion or high level of willingness to take risks may impact negatively on the useI oBf risk management tools. L Farming experience: Concerning crop farmers farming experieNnce, 7.1% of the respondents had between five to ten years experience, while 53.9% haDd bAetween 11 to 30 years experience (Table 4). More than one third of crop farmers in the Athree zones (coastal – 36.2%, rainforest- 41.1%, guinea savannah- 38.6%) had more th aIn Bthirty years farming experience. The mean farming experience of the respondents was 28.3 ± 12.1 years. As observed by Oladele (2008), experience contributes to f aOrmer Fs‟ ability to improve on their farm activities. Farmers with higher farming expYerience being seasoned farmers are usually exposed to a variety of risky situations onI fTarms. They therefore have a higher level of understanding of risks and ways of reducinSg their risk exposure level. The high mean value should influence farmers‟ level of risk Rmanagement positively. Farm size: TableE 4 indicates that in terms of farm size, 55.2 % of the respondents did not have more thIanV 2 hectares. About thirty six percent of the crop farmers had between 2.1 and 5 hectares, Nwhile less than ten percent (9.3 %) had more than 5 hectares. The general mean was 3.4 hUectares. This shows that majority of crop farmers in the study area operate at a subsistence level. The subsistence level of operation may indicate a low income status among the respondents and this is likely to have a positive influence on risk management. According to Ding et al (2010), income is positively correlated with farmers‟ attitude towards risks, while Wissink (2013) and Flaten et al (2005) concluded that larger farm size increases the willingness to take risk (lower risk aversion). Breukers et al (2009) also acknowledged that excess of resources leads to relaxation of controls in farming operations thus leading to a high 73 risk seeking behaviour. Hence, farmers with lesser income are usually more risk averse than wealthier ones (Dadzie & Acquah, 2012). This study confirms the observation of Adesoji et al (2006); Eyo and Asuquo, (2011); Saka et al (2011) that most farmers in the country operate on a small scale level. Furthermore, apart from the lower level of risk aversion usually associated with larger farm sizes, Teklewold and Kohlin (2010) opined that increased transaction costs of implementing risk management strategy for larger farm sizes may deter a farmer from utilising a risk management strategy. Majority of the respondents are also expected to have a greater perception of agricultural risks as Synder (2004) observedY that lower income (which may be inferred from lower farm sizes), leads to a greater pAerceRption of risk. Major crops cultivated: More than one third (36.5%) of the crop farmIerBs cu Rltivated cereals as their major crop as shown in Table 4. Legumes were planted as a mLajor crop by only 5.5% of the respondents and this was only in the guinea savannah zone. Twenty seven percent of the crop farmers, had root and tuber as the major crop theyN cultivate and 20% of them cultivate fruit and vegetables. Thirteen percent plant casDh crAops such as cocoa as their major crop, and more than 90% of this group were in the raAinforest zone. This implies that majority of cash crop farmers in the country are in the raIiBnforest zone. Thus, farmers in the zone are expected to have a higher risk seeking attitFude and a lower level of risk management due to the networth of the type of crops pred omOinant in the zone Secondary farm enterprises: TaYble 4 shows that 12.6 % of the crop farmers rear cattle, while 19.7% and 33.9% rear IshTeep and goats respectively. Across the zones, no respondent in the coastal zone kept cattSle, 1.7% of the respondents‟ reared sheep, while 5.2% kept goats. . However, 52.2 % andR 72.6% of respondents in rainforest and guinea savannah respectively kept either sheep Eor goat or both. Thirty six percent of the respondents kept poultry, while only 2.3 % IofV them operated a fish farm as their secondary farm activity. Crop farmers‟ involvemNent in livestock production is a form of on farm diversification and this is an impoUrtant tool in agricultural risk management. The high level of on farm diversification should influence farmers‟ level of risk management positively. 74 Table 4: Socioeconomic Characteristics of Crop Farmers Variable Description Coastal zone Rainforest Guinea Total (N=58) (N=102) Savanah (N=310) (N=150) Freq % Freq % Freq % Freq % Age 20-30 0 0 4 3. 9 3 2 7 2. 3 31-40 3 5.2 11 10.8 21 14 35 11.3 41-50 15 25.9 15 14.7 45 30 75 24.2 51-60 28 48.3 27 26.5 64 42.7 119 38.3 60-70 10 17.2 35 34.3 15 10 RY60 19.4 Above 70 2 3.4 10 9.8 2 1.3 14 4.5 Mean 53.1 55.3 51.2 A 53.2 (10.5) Formal Educational Level No formal education 1 1 18 .9 3 7 36.3 6 9 BRI 46 11 7 37 .7 Primary 27 46.6 40 39.2 61 40.7 128 41.3 Secondary 17 29.3 21 20.6 L19 12.7 57 18.4 NCE/OND 1 1.7 2 2 1 0.6 4 1.3 HND/B.Sc 2 3.5 2 2 N 0 0 4 1.3 Farming experience A 5 - 10 years 0 8. 6 1 0 D9. 8 A 7 4. 7 1 7 5. 5 11 - 20 years 5 25.9 25 24.5 36 24 66 21.3 21 - 30 years 5 29.3 IB25 24.5 49 32.7 79 25.5 31 - 40 years 18 34.5 19 18.6 47 31.3 84 27.1 Above 40 years 30 1.7 F 23 22.5 11 7.3 64 20.6 Mean 25.9 30.3 27.9 28.3 Farm size O Y 0.1 - 2 ha 55 94.8 60 58.8 56 37.3 171 55.2 T 2.1 - 5 ha I 3 5.2 34 33.3 73 48.7 110 35.5 Above 5 ha S 0 0.0 8 7.9 21 14.0 29 9.3 Mean 1.5 3.3 5.4 3.4 Major crop cultivatRed; E Cereals 20 34.4 35 34.3 59 39.4 113 36.5 LeIguVmes 0 0.0 0 0.0 17 11.3 17 5.5 Roots and Tubers 16 27.7 25 24.5 45 30.0 86 27.7 N Cash crops 4 6.9 35 34.2 0 0.0 39 12.6 U Fruits and vegetables 18 31.0 7 12.0 29 19.3 63 20.3 Secondary Farm enterprises* Plus Cattle 0 0 7 15.7 32 21.3 39 12.6 Plus Sheep 1 1.7 10 9.9 50 33.3 61 19.7 Plus Goat 3 5.2 43 42.3 59 39.3 105 33.9 Plus Poultry 34 6.9 35 34.3 44 29.3 113 36.5 Plus Fishery 3 5.2 3 2.9 1 0.7 7 2.3 Source: Field Survey, 2011. *Multiple responses 75 Membership of organization: Figure 9 shows that in terms of membership of organizations, less than one- sixth of crop farmers in the three agro-ecological zones did not belong to any organization. More than half of the respondents in the three zones (coastal – 51.7%, rainforest- 64.7% and guinea savannah- 64.7%) belonged to only one organization. In coastal zone, only 32.8% affirmed that they were members of two or more organizations, while in rainforest and Guinea savanah zones, 19.6% and 20.6% respectively belonged to more than one organization. Moreover, one fifth (20.6%) of the respondents as shown in Figure 10 affirmed that they were either leaders or executives in their organizations. This showsY that majority of crop farmers in Nigeria are inclined towards social networks and this is Rlikely to have positive effects on their level of risk management. Membership of farmers Aassociations creates an avenue for farmers to reduce their risks (Shehu et al, 2010). AByeR and Oji (2007) also observed that membership of a solidarity group enhances farmeLrs‟I access to credit and other production inputs such as fertilizer, chemicals and improved seeds. They are also able to share information with one another thus improving theirN level of risk management. According to Tekleword and Kohlin (2010), membership ofA organization is a form of social capital, which also acts as a forum for sharing experAiencDe and exchanging information about market behaviour. IB O F ITY ER S IV N U 76 Y AR LIB R DA N IB A OF SI TY FiEgureR 9: Respondents’ membership of organisation IV 77 UN RY BR A N LI DAA F I B O SI TY Figure 10E: ReRspondents’ level of participation in organizational activities IV 78 UN Off farm occupation: According to Babatunde (2008), farmers get income from both on- farm and off-farm activities. As revealed in Figure 11, majority of the respondents (67.1) indicated that farming was their only occupation. The presence of other sources of income enhances the risk bearing ability of farmers (Ayinde, 2008) and this reduces their level of risk management. Also according to Adenegan et al (2013), off farm labour results in farm inefficiency. Hence, since majority of the respondents are full time farmers, they should have a higher level of agricultural risk management. In a related study by Teweldemedhin and Kafidii (2009), 71.4% of the commercial farmers had off farm income. Y Farm ownership: Majority (97.7%) of the crop farmers affirmed that in terms Rof farm ownership, their farms were being operated under a sole proprietorship or was faAmily owned (Fig 12). Only 2.3% indicated that their farms operated under a partneBrshRip arrangement. None of the farms sampled was being operated under a company nLamIe. This implies that some unique benefits such as loan acquisition from formal institu tions achieved by farms operated as a legal entity would not be enjoyed by most of tAhe Nrespondents. Farm ownership type may affect the supply or availability of labour, asD most corporate entities will usually have a constant supply of labour. The predominanAce of family/sole proprietorship owned farms among the respondents may indicate th aIt Blabour may not be readily available at all time. F O ITYS VE R I UN 79 RY RA LIB AN AD F I B O ITYS FigureR 11: Respondents’ off-farm occupation E NI V 80 U AR Y BR N LI A AD F I B O TY SI ERFigure 12: Farm ownership structure IV 81 UN Labour availability: Access to farm labour is an important element in agricultural production as inability to hire labour when necessary will limit farm yields. Figure 13 shows that 64.2 % affirm that farm labour was readily available. Thirty percent of the respondents observed that farm labour was sometimes available when it is needed, while only about 7.1 % affirmed that it is either rarely available or not available when it is needed. This means that more than one-third (36.8%) of the respondents may sometimes, rarely or never have farm labour when needed. This may be an indication of the farmers‟ involvement in off farm occupation (32.9% in figure 11), as observed by Mishra and Goodwin (2004) involvemeYnt in off farm occupation usually decreases farm efficiency. Lack of labour affeActs Rfarming activities negatively. For instance, shortage of labour (a type of social risk) leads to increasing cost of labour and this further exposes farmers to financial risksB. R Labour sourcing: Majority of the respondents (76.5) use family anLd fIriends as their major source of farm labour, while only 16.5 % hire labourers (Figure 14) . Heavy reliance on hired labour increases total production costs (Okwoche et al, 20A12)N. The reliance on family and friend for farm labour suggests that there will be a harmDonious working relationship (which usually coexists between family and friends) and thisA improves level of risk management. Ojo (2005) also found that labour in agricultura l IpBroduction is usually dominated by family labour. F O ITYS VE R I UN 82 Y AR LIB R DA N IB A OF ITY ER S Figure 13: Labour availability among respondents IV 83 UN Y RA R B N LI A AD F I B O TY SI R Figure 14: Major source of labour E NI V 84 U Marketing channel: As revealed in Figure 15, most of the respondents (82.3%) use middlemen as their major marketing channel. Less than 6% use processing industry, while about 12.2% affirmed that their major marketing outlet is through direct sales to consumers or individuals. Heavy reliance on middlemen may erode a larger percentage of farmers‟ profit thus reducing cash-flow. It also lowers the bargaining power of farmers and increases the probability of reduced sales due to relationship strain between farmers and middlemen and this increases farmers‟ level of marketing risk exposure. As observed by Nwankwo et al (2009), marketing problems and activities of organized middlemen may limit farmYers‟ income. Farmers choice of marketing channel is often a function of the delay whicRh occurs between when farm produce are sold and when payment are made through thAe marketing channel (Ogunleye and Oladeji, 2007). This delay is an indication of risk andR the shorter the delay, the lower the risk and the higher the chance that farmers wLill IpBrefer the particular channel over others. Market accessibility: Figure 16 shows that market was highlyN accessible to 69.1 % of the respondents. Thirty percent observed that it was moderaDtelyA accessible, while 0.6% affirmed that it was rarely accessible. This finding may indicaAte that respondents utilise marketing risk management strategies that ensure that farm prIoBducts are sold. Supply of farm products to several markets is a risk mitigating strategy (Okereke, 2012). Moreover, good market access also stimulates farmers to improve on tOheir F risk management capacities so as to increase their yield (Wissink, 2013). ITY ER S V I UN 85 RY BR A N LI A AD F I B O ITY RS E Figure 15: Major marketing channel V 86 UN I Y RA R B N LI A AD F I B O TY SI ER Figure 16: Market accessibility V 87 UN I Section 5.1.2: Credit and information sources of crop farmers Credit sources: As indicated in Table 5, personal savings, cooperatives and friends/family were ranked as the first, second and third most important sources of credit by the respondents. Only 4.8% of the respondents affirmed that they sourced credit from Nigerian Agricultural Cooperative and Rural Development Bank (now Bank of Agriculture). This implies that the use of insurance as a risk management tool may be unpopular among the respondents as bank of agriculture makes the purchase of insurance a necessary criterion for obtaining loans. Sources such as microfinance banks, commercial banks and private mYoney lenders were not also popular among the respondents. Infact less than 7% of the cropR farmers obtained loan from each of the three sources of credit. According to GanRa eAt al (2009), problems encountered by farmers during loan acquisition such as the needB for guarantors and collaterals by bank of agriculture as well as the interest charges by cLomImercial banks affect their use. Also, Udoh (2005) observed that formal credit sources have scared many crop farmers due to hindrances surrounding their usage. This stuNdy confirms the findings of Ayinde (2008); Adebayo and Adeola (2008); OkwochDe eAt al (2012) that informal credit sources are more popular among farmers. AvailabiAlity of credit is positively related with farmers‟ perception of risk (Lucas and PabuaIyBon, 2011). Every activity in agricultural production is influenced by the size and app lication of funds (Okereke, 2012). As such, access to credit enhances the liquidity of fFarmers and this enables them afford risk reducing strategies such as improved seedlings . O Sources of Information: AIccTordYing to Nwankwo et al (2009), farmers seek information from various sources in Sorder to reduce risk and uncertainty. As shown in Table 5, crop farmers in the study aRrea ranked friends/family as their most important source of information on agricultural risEk management. This was followed by extension agents, electronic media and print meIdiVa respectively. This means that the basic source of information on agricultural risk manNagement is through friends/family members. The implication is that for any agricuUltural risk management programme to be successful in the study area much emphasis should be placed on dissemination of information through fellow crop farmers. The high ranking of friends/family corroborates the findings of Nwankwo et al (2009) that relevant and reliable information from close relatives is regarded as more authentic than information from outside sources. Banmeke and Omoregbee (2009) also observed that friends and family was an important source of information in their study. 88 Table 5: Credit and sources of information of crop farmers Variable Coastal Rainforest Guinea Savanah Total Freq Mean Rank Freq Mean R Freq Mean R Freq Mean R Credit source* Savings 58 2.50 2nd 101 2.54 1st 147 2.53 2nd 306 2.53 1st Friends/family 49 1.87 3rd 91 2.23 3rd 131 1.97 3rd 271 2.02 3rd Cooperatives 34 2.74 1st 43 2.28 2nd 129 2.55 1st 206 2.52 2nd NACRDB 2 1.50 4th 3 1.67 4th 10 1.90 4th 15 1.69 4th Microfinance banks 3 1.33 5th 4 1.25 6th 6 1.83 5th 13 1.47 5th Commercial Banks 1 1.00 7th 1 1.00 7th 3 1.33 7th 5 1.11 7th Y Moneylenders 3 1.00 6th 4 1.25 5th 14 1.64 6th 21 1.30 6th R Sources of A information * Family/ friends R50 2.53 1st 93 2.20 1st 136 2.21 1st 279 2.31 1st Extension agents 34 2.20 2nd 60 2.19 2nd 101 2.01 2nd 195 I 2B.13 2nd Print media 25 1.08 4th 16 1.88 4th 33 1.97 4th L74 1.64 4th Electronics 37 1.68 3rd 54 2.09 3rd 86 2.04 3rdN 176 1.94 3rd Source: Field Survey, 2011. *Multiple responses DA IB A O F Y T RS I E NI V U 89 Section 5.2: Farmers perceived types of agricultural risks Production risks: Table 6 reveals that in the coastal zone, almost all the respondents (96.6%) indicated that flood was a risk to them. The incidence of pests and diseases was also indicated by majority (93.1) of the crop farmers. However only one quarter (25.9%) affirmed that drought was a source of risk. The coastal nature of the zone may be responsible for the high ranking of flood in the zone, as Adeoti et al (2010) observed that flooding is a key problem in the coastal areas. Majority of the respondents in the rainforest zone also indicated flood (86.3%) and pests and diseases (90.2%) as major risks in the zone. In the guinea savaYnnah zone, almost all respondents (96.0%) also indicated that drought was a type Rof risk experienced and this may be linked to the vegetation of the zone. According toA Etuonovbe (2011), Nigeria‟s climate is characterised by strong latitudinal zoneBs wRhich becomes progressively drier as one moves northwards from the coast. HoweveLr thIe use of low quality seedling by almost half of the respondents (49.0%) indicates that m uch still needs to be done on farmers‟ awareness and access to high quality seedlings. AN Marketing risks; across the zones, majority of the samDpled crop farmers (82.8% in coastal; 89.2% in rainforest and 84.7% in guinea savannah)A indicated that volatility in output price was a type of risk. As observed by Boehlje aInBd Brent (2007), output price volatility has increased in recent years. Many respondeFnts also rated volatility in input price (84.5% in coastal; 86.3% in rainforest and 80. 7%O in guinea savannah) and market failure (82.8% in coastal; 87.5 % in rainforest and 77.3% in guinea savannah) as types of risks. Market failure occurs when farmers are noItT abYle to dispose their products (Salimonu & Falusi, 2009). Market failure as well aSs volatility in input and output prices significantly affects farm income. According tRo Druilhe and Barreiro-Hurlé (2012), failures in agricultural input markets are commEon in developing countries and are a major constraint to productivity growth, whilIe VSharma and Kumar (2001) affirmed that price instability has macroeconomic implicatioNns. Furthermore, more than half of the respondents (62.9%) acknowledged that loss of baUrgaining power (in selling output) was also a type of risk they encounter. This implies that much of farm income may be eroded due to lack of adequate negotiation skills, although farmers may also deliberately agree to customers price due to lack of adequate post-harvest facilities. Small scale farmers require improved bargaining power to enhance their productivity (Okwoche et al, 2012). Financial risks: Inadequate cash flow was a type of risks to most of the respondents (94.2%) as shown in Table 6. More than three-quarters (88.4%) of the crop farmers also affirmed that 90 access to credit was a concern in terms of risk. However less than half of the respondents (49.7%) acknowledged that changes in interest rate was a type of risk and this implies that majority of the crop farmers do not patronize credit sources that are interest based. Social risks: More than three-quarters of the sampled respondents indicated that variability in labour costs (84.5% in coastal; 91.2% in rainforest and 79.3% in guinea savannah); lack of labour (84.5% in coastal; 89.2% in rainforest and 64.7% in guinea savannah) and ill-health of farmer/farm employee (86.2% in coastal; 93.1% in rainforest and 87.3% in guinea savannah were types of risks they face. According to Ulimwengu (2009), health impediments aYffect farmers‟ agricultural efficiency negatively. This may be through loss of labour, prRoductive adults‟ knowledge, and assets to cope with illness (World Bank, 2007). ConAcerning the incidence of fire outbreaks, a larger percentage of farmers (90.0%) in thBe gRuinea savannah zone rated it as a type of risk and this may be due to the dry nature of LtheI zone. Furthermore, using the ranking in Table 6, the major tyNpes of risks in the study area were; inadequate cash flow (94.2%), occurrence of pests aAnd diseases (91.3%), sickness/ill health of farmer and labourers (89.0%), lack of access tDo credit (88.4%), volatility in output price (85.8%) and variability in labour costs (84.2B%).A F I Y O SI T ER IV N U 91 Table 6: Farmers’ distribution on sources and types of agricultural risks COASTAL RAINFOREST GUINEA SAV. TOTAL RANK Freq % Freq % Freq % Freq % Production risks Drought 15 25.9 83 81.4 144 96.0 242 78.1 12th Excessive rainfall/flood 56 96.6 88 86.3 116 77.3 260 83.9 7th Pests and Diseases 54 93.1 92 90.2 137 91.3 283 91.3 2nd Shortfall in production 40 69.0 87 85.3 132 88.0 259 83.5 8th Limited knowledge about usage of chemicals 37 63.8 85 83.3 129 86.0 251 81.0 10th Rainfall fluctuations 33 56.9 74 72.5 112 74.7 219 70.6 15th Low quality seedlings 27 46.6 52 51.0 73 48.7 1R52 Y49.0 27thMarketing risks Volatility in inputs costs 49 84.5 88 86.3 121 80.7 A258 83.2 9th Volatility in output price 48 82.8 91 89.2 127 84R.9 266 85.8 5th Market failure 41 70.7 73 71.6 104 69.3 218 70.3 16th Inaccessibility to markets 35 60.3 57 55.9 8L7 IB58.0 179 57.7 24thConsumer Preference 33 56.9 58 56.9 90 60.0 181 58.4 23rdLoss of bargaining power 35 60.3 64 62.7N 96 64.0 195 62.9 22ndInefficient storage/Perishability 42 72.4 74 72.6 101 71.3 217 70.0 17th Avialability of transport facilities 39 67.2 68 6A6.7 99 66.0 206 66.5 19th Variability in transport costs 37 63.8 70 D68.6 103 68.7 210 67.7 18th Financial risks A Access to credit 53 91.4 89 87.3 132 88.0 274 88.4 4th Inadequate cash flow 56 96.I6B97 95.1 139 92.7 292 94.2 1st Default risk 37 F6 3.8 61 59.8 104 69.3 202 65.2 21thChanges in interest rate O26 44.8 57 55.9 71 47.3 154 49.7 27thSocial risks Lack of labour 49 84.5 91 89.2 97 64.7 237 76.5 13th Variability in labour costs ITY 49 84.5 93 91.2 119 79.3 261 84.2 6thDamage to equipment 43 74.1 73 71.6 107 71.3 223 71.9 14th Sickness/ill health of farmer/laSbourer 50 86.2 95 93.1 131 87.3 276 89.0 3rdWar/Conflict R 37 63.8 66 64.7 102 68.0 205 66.1 20thTheft 33 56.9 57 55.9 84 56.0 174 56.1 25th Fire ourbreaks E 39 67.2 72 70.6 135 90.0 246 79.4 11th Contracting riskIV 36 62.1 55 53.9 74 49.3 165 53.2 26thSource: FNield Survey, 2011 U 92 Section 5.3: Farmers perceived level of risk exposure Farmers perceived level of risk exposure is a function of the probability of occurrence of risks and the ability of the risks to disrupt business significantly. According to Zinn (2009), level of risk exposure is the product of likelihood of identified risks to occur and consequence (impact) of the identified risks. Likelihood refers to the probability of the risk occurring, while consequence/impact refers to the severity or potential loss expected. 5.3.1 Likelihood of occurrence of agricultural risks: Y Likelihood of occurrence of agricultural risks refers to the probability Rof risks occurring. It is the chance that a potential or exposure event will occur. Table 8 inAdicates that among the farmers, financial risks were ranked first in terms of likelihood oRf occurrence of agricultural risks. As shown in Table 7, respondents ranked access to cIrBedit and inadequate cash flow as their most critical financial risks. Lack of credit is a ke yL problem in agricultural production (Ogunniyi et al 2011) and it often leads to inadeNquate cash flow/shortage in working capital. As observed in Table 7, respondents in theA guinea savannah zone were the most prone to the two financial risks. During the FoDcus Group Discussion (FGD), crop farmers in that zone stated that; BA “access to credit is a major pFrob leIm of agricultural production, which occurs every time and affectsO their level of production adversely‖. Across the zones, crop farmers Yin t he rainforest zone(13.79) were the least vulnerable to financial risks. This may be aI rTeflection of the type of crops grown by farmers as the zone had the highest number of casSh crop farmers as shown in Table 4. As observed Rin Table 8, production risks were perceived to be the second most important source oEf agricultural risks in terms of likelihood of occurrence by the respondents. This may be IdVue to the fact that most agricultural production risks are dependent on nature or biologicaNl processes. Respondents in the three zones ranked issues related to rainfall (flood and fUluctuations in rainfall pattern) as well as occurrence of pests and diseases as their major production risks. The mean value in Table 7 shows that the degree of likelihood for the occurrence of flood was greater in the coastal zone than in the other two zones. This means that crop farmers in the coastal zone are the most prone to floods. According to Adeoti et al (2010), one of the key environmental problems of coastal areas is flooding. During the FGD, farmers in the zone also explained that flood usually occurs every year. 93 Flood usually occurs every year; the time is just what we cannot predict perfectly as sometimes it comes earlier than expected. The likelihood of occurrence of pests and diseases was also ranked high and this corroborates the findings of Okuneye (2002) that there is a high incidence of pests and diseases in the country. Apart from incidence of pests and diseases, farmers in the guinea savannah zone had higher rates of occurrence of drought. According to Obioha (2007), drought induced desertification is regarded as the most pressing environmental problem in the dry-land part of Nigeria. Generally, in terms of likelihood of production risks across the zones, crop farYmers in the rainforest zone were the least vulnerable (89.34), while those in the guinea sRavannah zone were the most vulnerable (91.31). This implies that farmers in the guinea savAannah zone face more agricultural production risks than the other two zones. BR Respondents ranked marketing risks as their third sourceL ofI agricultural risks in terms of likelihood of occurrence as shown in Table 8. The keyN ma rketing risks as shown in Table 7 were fluctuations in inputs costs and output prices, aAnd perishability of farm produce. According to Odoemenem and Adebisi (2011) availaDbility of major farm inputs at the appropriate time and affordable price is a problemA in the country, while Ikpi and Mordi (2006) also observed that inadequacies in the sIuBpply and delivery of farm inputs as well as poor post-harvest facilities (leading to iFncreased post harvest losses) hinder significant breakthrough in agricultural productio nO. Respondents however had lower levels of likelihood means for lack of transport faYcilities, and harvests not meeting customers‟ standard (consumer preference). As exIpTlained during the FGD, crop farmers observed thus: about consumSer preference, it does happen but not on all our harvests. When somRe of the harvests do not meet our buyers‘ expectations, they comVplaEin and try to bring the price down. Another time they have a dIomineering power on price is when there is market failure or when Nyour products are highly perishable. At that time you don‘t have option, U you are willing to push your harvests as soon as possible so as to prevent loss. Across the zones, the order of vulnerability to marketing risks was from coastal (28.03) to rainforest zone (28.05) and it was highest in guinea savannah zone (28.35). This order indicates that as we move towards the savannah part of the country agricultural marketing risks increase. 94 Social risks were ranked fourth in terms of likelihood of occurrence of agricultural risks as revealed in Table 8. As shown in Table 7, ill health, low access to labour and increasing labour costs were the key social risks among farmers. The mean age of the farmers‟ (53.2 years) may be responsible for the high occurrence of ill health since farming is physically demanding. The increasing rate of youth migration to urban centres for off farm occupation has also affected labour availability, thereby increasing wage rate. According to crop farmers in the rainforest zone: ―the choice of farming as a job is no longer attractive to our youths, Y we the aged ones are left to farm. How much work can we do at this R age? Imagine i am above 60 years can you compare me with a 2R0 oAr 30 year old man? Of course the energy is almost gone‖. B In terms of labour costs, crop farmers in the coastal zone observed th uLs: I ―we usually source labour from republic of Benin, andN their price is usually very high‖. A The likelihood of occurrence of social risks was AhighDest in the rainforest zone (24.16). Respondents in the guinea savannah zone howeIvBer had higher rate for the occurrence of fire outbreaks. This may be due to the naturFe o f vegetation in the zone as Afolayan (1978) observed that annual burning (fire outbreaks) occurs in most tropical savannah grasslands. Generally across the zones, in tOerms of likelihood of occurrence of agricultural risks, the rainforest zone (89.34) wIaTs thYe least vulnerable, while the guinea savannah zone (91.31) had the highest level of vuSlnerability. ER IV UN 95 Table 7: Means of respondents’ likelihood of occurrence of agricultural Risks COASTAL RAINFOREST GUINEA SAV TOTAL Production risks Drought 2.5 8 3.1 0 4.0 1 3.2 3 Excessive rainfall/flood 4.51 3.80 3.53 3.95 Pests and Diseases 4.34 4.15 4.20 4.23 Shortfall in productionn e.g. Reduction in soil fertility 3.05 3.03 3.17 3.08 Limited knowledge about usage of chemicals/fertilizers 2.76 2.93 3.11 2.93 Rainfall fluctuations 3.10 3.14 3.02 3.09 Low quality seedlings 3.05 3.19 3.23 Y 3.16 Production Total 23.39 23.34 24.R27 23.67 Price/marketing risks A Volatility in inputs costs 3.6 9 3.8 0 R 3.8 7 3.7 9 Volatility in output price 3.60 3.62 3.73 3.65 Market failure 3.07 3.15 IB 3.21 3.14 Inaccessibility to markets 2.98 2. 81L 2.90 2.90 Consumer Preference 2.88 N2.77 2.56 2.74 Loss of bargaining power 3.06 3.15 3.19 3.13 Inefficient storage/Perishability 3.44 A 3.32 3.38 3.38 Avialability of transport facilities 2.18 D 2.39 2.44 2.34 Variability in transport costs 3.A13 3.04 3.07 3.08 Marketing Total IB28.03 28.05 28.35 28.14 Financial risks Access to credit F 4.3 0 4.0 3 4.4 0 4.2 4 Inadequate cash flow O 4.14 3.98 4.27 4.13 Default risk 3.20 3.01 3.48 3.23 Changes in interest rate Y 2.86 2.77 2.72 2.78 Financial Total IT 14.50 13.79 14.87 14.39 Social/human risks Lack of labour S 3.6 2 3.7 1 3.2 1 3.5 1 Variability in labour costRs 3.41 3.47 3.19 3.36 Damage to equipmenEt 3.00 3.08 3.06 3.05 Sickness/ill health of farmer/labourer 3.44 3.52 3.32 3.43 War/Conflict IV 2.59 2.52 2.63 2.58 Theft N 2.86 2.77 2.81 2.81 Fire oUurbreaks 1.98 2.43 3.15 2.52 Contracting risk 2.70 2.66 2.45 2.60 Social Total 23.60 24.16 23.82 23.86 Total 89.52 89.34 91.31 90.06 Source: Field Survey, 2011. 96 Table 8: Ranking of risk sources in terms of likelihood of occurrence Production Marketing Financial Social Likelihood Means 23.67 28.14 14.39 23.86 Risk number 7 9 4 8 Standard scores(Mean) 3.38 3.13 3.59 2.98 Rank th2nd 3rd 1st 4 Y AR LIB R N AD A F I B Y O TSI R VE UN I 97 5.3.2 Impact of risk Impact of agricultural risks refers to the consequences that result from an event/risk. It indicates the severity, potential loss or perceived average economic loss that could arise from the risk. Respondents ranked production risks as having the most severe impact on their production as revealed in Table 10. Generally across the zones, Table 9 shows that the major production risks with severe impacts were flooding (4.02), drought (3.83) as well as pests and diseases (3.58). Flooding is a major issue for farmers as Olorunfemi (2011) observed that it is the most widespread of all environmental hazards and is capable of causing huge anYnual losses. For instance, the 2012 flood which occurred in several parts of Nigeria affectRed about 7.7 million people and destroyed several thousands of farmland. According toR croAp farmers in the coastal zone during the FGD: B ―flood impact is usually total loss, however what we do is to mLovIe upland in the rainy season, because flooding does not usually ocNcur upland. During dry periods, we can then farm in the lowlands but Asometimes flood comes earlier than we expect for example in 2009‖. D Concerning drought, the impact was greater in guineAa savannah zone than the other two zones as the zone lies in the sub Saharan pIaBrt of the country. As observed by Ajayi and Olufayo (2007) the magnitude oFf drought increases towards the northern part of the country. In terms of seve riOty of impact of production risks, as seen in Table 9, guinea savannah zone Y(20.13) was the most vulnerable, followed by rainforest zone (19.62) and thIeTn coastal zone (19.45). Marketing riskRs wSere ranked second by respondents in terms of impact as shown in Table 10. The keyE marketing risks with severe impacts as revealed in Table 9 include: market failure (3.38), inaccessibility to markets (3.15), volatility in output prices (2.99) and input costs (2.8N4) Ias V well as perishability of farm produce. In relation to post harvest loss, Atser (2010U) observed that post-harvest loss could be as much as one third of farmers‟ production. Considering the impact of marketing risks, the crop farmers in the guinea savannah (25.54) were the most vulnerable. Social/human risks were ranked third in terms of impact of agricultural risks on production as seen in Table 10. The major social risks in Table 9 were; fire outbreaks, ill- health of farmer/labourer, variability in labour costs and lack of labour. The impact of fire outbreaks was greater in the guinea savannah zone. As observed by Obioha (2007), drought 98 related degradation such as forest fire has had more and far reaching negative impact on the environment compared with other agents of land degradation. According to respondents in the zone; ―when fire breaks out on farmland, the impact is much, depending on the size of your farmland that it affects. We however do fire tracing to reduce its effect‖. Labour is also a major resource in agricultural production, hence its shortage reduces production. It becomes more important in developing countries like Nigeria where mechanization is only common in large commercial farms. During the FGD, crop farmRersY ( in the coastal zone) commented on the issue of labour availability and contracting rAisks (which often leads to shortage of labour): R ―when our labourers default, we become short of labour. Even IwBhen you go beyond your limit to make them comfortable while doing your work, tLhey can still default and this ultimately affects our production and income negativNely‖. Okuneye (2002) also observed that the result of young men Aleaving farming is that it reduces labour availability, productivity and production thereby Dincreasing costs of food production. The rainforest zone (22.03) was the most vulnerabBle tAo social risks. In terms of financial risks which were Iranked fourth as revealed in Table 10, crop farmers observed that lack of access Oto cFredit (3.42) and inadequate cash flow (3.15) had more significant impacts than deYfaul t risk (2.05) or changes in interest (1.87). Low credit results in low efficiency, thereby affecting utilization of resources at farmers‟ disposal (Ibrahim et al, 2009). TabSle 9I s Thows that the order of vulnerability to impact of financial risks was: rainforest (10.15R) to coastal (10.62) to guinea savannah (10.72). Crop farmers in the guinea savanah zoEne observed during FGD; “acceIssV to credit is a major issue hindering agricultural production. I have joined a nuNmber of cooperatives so as to have access to credit‖. UGenerally across the zones, in terms of impact of agricultural risks on respondents, the guinea savannah zone (78.02) was the most vulnerable followed by the coastal (77.09) and lastly the rainforest zone (76.47). 99 Table 9: Means of respondents’ impact of agricultural risks COASTAL RAINFOREST GUINEA SAV TOTAL Production risks Drought 3.71 3.79 3.93 3.83 Excessive rainfall/flood 4.08 3.98 4.00 4.02 Pests and Diseases 3.66 3.57 3.52 3.58 Shortfall in productionn e.g. Reduction in soil fertility 1.87 1.80 1.96 1.88 Limited knowledge about usage of chemicals/fertilizers 1.98 2.06 2.23 2.10 Rainfall fluctuations 1.96 2.12 2.14 2.07 Low quality seedlings 2.19 2.30 2.35 Y 2.28 Production Total 19.45 19.62 20.13 19.76 Price/marketing risks R Volatility in input costs 2.85 2.79 RA2.88 2.84Volatility in output price 2.99 2.92 3.06 2.99 Market failure 3.37 3.35B 3.43 3.38 Inaccessibility to markets 3.13 3.1I8 3.15 3.15 Consumer Preference 2.75 L2.60 2.78 2.71 Loss of bargaining power 2.43 2.53 2.61 2.53 Inefficient storage/Perishability 2.88 N 2.65 2.73 2.76 Avialability of transport facilities D2.19A 2.25 2.33 2.26Variability in transport costs 2.49 2.40 2.56 2.48 Marketing Total A25.08 24.67 25.54 25.09 Financial risks Access to credit IB 3.41 3.29 3.57 3.42 Inadequate cash flow F 3.17 3.01 3.28 3.15Default risk O 2.05 1.99 2.11 2.05Changes in interest rate 1.99 1.86 1.76 1.87Financial Total Y 10.62 10.15 10.72 10.50Social/human risks Lack of labour IT 3.02 3.11 2.51 2.92Variability in labour costs S 2.70 2.89 2.56 2.73Damage to equipment R 2.51 2.26 2.44 2.41Sickness/ill health of farmer/labourer 3.14 3.27 3.04 3.16 War/Conflict VE 2.76 2.69 2.70 2.72Theft I 2.46 2.25 2.35 2.35Fire ourbreNaks 3.20 3.39 3.81 3.47Contracting risk 2.15 2.17 2.22 2.18 Social Total 21.94 22.03 21.63 21.94 TotalU 77.09 76.47 78.02 77.29 Source: Field Survey, 2011. 100 Table 10: Ranking of risk sources in terms of impact of agricultural risks Production Marketing Financial Social Impact Means 19.76 25.09 10.50 21.94 Risk number 7 9 4 8 Standard scores (Mean) 2.82 2.79 2.63 2.75 Rank 1st 2nd 4th 3rd RY A R LI B AN AD F I B O ITY S VE R I UN 101 5.3.3 Level of agricultural risk exposure Farmers‟ level of risk exposure refers to the likelihood of identified risks to occur and the perceived impact (magnitude of loss) of identified risks. As seen in Table 12, respondents ranked production risks as the most important source of agricultural risks. Luke (2011) observed that production risk is very serious with respect to farming operations, while Difalco and Chavas (2009) asserted that production risk is a typical feature of agriculture. Across the zones, Table 11 reveals that farmers in the coastal zone observed that flood was their most serious production risk. The incidences of flooding and drought have Ybeen heightened as a result of climate change. According to Ede (2011), the effect ofR climate change already evident in Nigeria include the problem of flooding in the coastaAl areas and desert encroachment in the northern part of the country. Drought wIasB th Re major risk to farmers in the guinea savannah zone. Accordingly, AERC (2009) ob sLerved that drought is the single largest risk in agriculture. Frequent occurrence of drought is therefore a great hindrance to increased agricultural production. This findinAg cNorroborates that of Mshelia (2011) that farmers in the country experience more Dcrop losses as a result of weather changing conditions (such as flood, drought and raiAnfall fluctuations). Crop farmers in the rainforest zone ranked incidence of pests and d isIeBases as their key production risk. Ismaila et al (2010) asserted that incidence of pests anFd diseases are major factors affecting agricultural production in the country. Vegetab leO farmers as reported in Martin (1996) also ranked diseases/pests as their most important production risk. Generally across the zones the order of vulnerability to production rIisTks Ywas from rainforest (67.18) to coastal (67.77) and it was highest in Guinea savannaSh (71.86). Financial risksR were ranked second as seen in Table 12. Indeed liquidity is the life wire of any farm bEusiness as every activity in agricultural production is influenced by the size and applicatiIonV of funds (Okereke, 2012). Several authors have also highlighted liquidity as a major prNoblem of farmers (Adebayo & Adeola, 2008; Eluhaiwe, 2008; Odoemenem & AdebUisi, 2011; Okwoche et al, 2012). As shown in Table 11, respondents were more vulnerable to lack of access to credit and shortage of working capital than default risk and changes in interest rate. This may be attributed to the fact that majority of crop farmers according to Ayinde (2008); Gana et al (2009); Eyo and Asuquo (2011) do not patronize formal credit sources. Moreover, Combe (1997) asserted that one of the main problems for farmers in developing countries is their lack of access to finance, which acts as an obstacle for investment needed to improve the quality and quantity of their production as well as 102 improving their standard of living. Crop farmers in the rainforest zone (36.38) were the least exposed to financial risks. Their higher rate of off-farm diversification and cultivation of cash crops may be responsible for this. As observed by Luke, Job and Benard (2011), non-farm investment reduces household exposure to risk because of the imperfect correlation between non-farm income and farm income, while Oseni and Winters (2009) asserted that non-farm participation helped relax liquidity constraints of farmers. The guinea savannah zone (41.85) had the highest level of financial risk exposure and this may be due to their lower levels of education as Ibrahim et al (2009) affirmed that education enhances farmers‟ access to cYredit agencies. As part of an initiative to improve access to capital in agriculture, the AgovRernment recently launched the Nigeria Incentive Based Risk Sharing System (NIRSAL). The fund which effectively came in to operation on March 15, 2012 has the objectiveR of engendering an increase in formal credit inflows into agriculture, thereby increasiLng IcBapacity of banks to lend, refocusing lending on integrated value chains and establishing a differentiated guarantee mechanism to share credit-related risks in the value chain (CBNN, 2012). The anticipated net impact of NIRSAL is an improvement in the pricing, maDnageAment and undertaking of risks in formal lending to agric-related enterprises A Marketing risks were perceived to be thIeB third most important source of agricultural risks by the respondents as revealed in TabFle 12. Respondents key marketing risks as shown in Table 11 were: fluctuations in outp uOt prices (10.91) and input costs (10.76), market failure (10.61) as well as perishability of Yfarm produce (9.31). In a study by Martin (1996), changes in product prices and input IcTosts were the most important market risks to the cropping, vegetables and flowers fSarmers. According to Ikpi and Mordi (2006), inadequacies in the supply and delivery oRf farm inputs as well are part of the problem militating against self-sufficiency in fooEd production in Nigeria; while Odoemenem and Adebisi (2011) also observed thatI pVoor post-harvest facilities could be as high as twenty percent of farm produce. RespondeNnts however affirmed that lack of transport facilities, loss of bargaining power and consuUmer preference were not much of a problem to them. In terms of level of risk exposure to marketing risks, crop farmers in the guinea savannah zone (80.94) were more vulnerable than the other two zones. This variation across the zone may be an indication of the variation in farmers‟ ages, as older farmers may be more experienced in terms of marketing issues than younger farmers. Concerning respondents‟ exposure to social risks; sickness/ ill health of farming household or contracting partners such as labourers (10.84), and low access to labour (10.25) 103 and variability in labour costs (9.17) were the major social risks as shown in Table 11. Crop farmers in the rainforest zone were the most vulnerable to occurrence of ill health/ lack of labour and they also had the highest level of risk exposure to social risks. As observed in Table 4, farmers in the zone were the oldest across the zones. The problem of aging farm population has also been identified by Adesoji and Farinde (2006); Okoedo-Okojie and Aphunu (2008); Gana et al (2010) amongst others. Farmers‟ ages would probably be responsible for the higher risk exposure to labour scarcity. Labour cost is usually a function of the demand for labour. Since majority of the farmers in the country are small scaYle in nature and dependent on manual labour, Takeshima et al (2013), affirmed thatR manual farming activities cost is on the rise. Furthermore, in terms of exposure to sRociaAl risks, crop farmers in the guinea savannah zone (64.93) were slightly more vulnerable than their counterparts in coastal zone (64.81), while their key social risk LwIasB incidence of fire outbreaks. This is expected due to the drier nature of the zone. The ranking in Table 11 indicates that the major agriNcultural risk exposure levels include; flood (15.88), occurrence of pests and diseaseDs (1A5.16), lack of access to capital (14.51), inadequate cash flow (13.02), drought (12.36A) and volatility in output prices (10.91). Taking into consideration production ,I Bmarketing, finance and social agricultural risks, crop farmers in the guinea savannah Fzone (259.58) were the most vulnerable followed by coastal zone (251.40) and lastly thOe rainforest zone (247.93). Thus in terms of priority, crop farmers in the guinea savannah z one require more risk management tools. This becomes more important as the zone IalTso Yhad the largest mean for farm size among the three zones studied. The higher risk eSxposure level recorded for the guinea savannah zone (which is in the northern part of RNigeria) may be attributed to the findings of Environmental Rights Action et al (2012E) that the northern part of Nigeria is severely threatened environmentally as a result of vaIriVable and unpredictable rainfall, seasonal fires and overgrazing amongst others. INn terms of risk exposure levels, using the mean of 252.87 in Table 11 and a standard deviaUtion of 74.83, Figure 17 shows that 18.7% of the respondents were at a low level of risk exposure. Half of the respondents (50.3%) were at a moderate level of risk exposure, while 31.0% were at a high level of risk exposure. 104 Table 11: Ranking of respondents’ agricultural risk exposure levels COASTAL RAINFOREST GUINEA SAV TOTAL RANK Production risks Drought 9.57 11.75 15.76 12.36 5th Excessive rainfall/flood 18.40 15.12 14.12 15.88 1st Pests and Diseases 15.88 14.82 14.78 15.16 2nd Shortfall in production 5.70 5.45 6.21 5.79 25th Limited knowledge about usage of chemicals etc 5.46 6.04 6.94 6.15 24th Rainfall fluctuations 6.08 6.66 6.46 22nd 23rd Low quality seedlings 6.68 7.34 7.59 7.20 19th Production Total 67.77 67.18 71.86 68.94 Price/marketing risks Volatility in inputs costs 10.52 10.60 11.15 10.76 8th Y Volatility in output price 10.76 10.57 11.41 10.91 6Rth Market failure 10.35 10.55 11.01 10.61 9th Inaccessibility to markets 9.33 8.94 9.14 9.14A13th Consumer Preference 7.92 7.20 7.12 R7.41 17th Loss of bargaining power 7.44 7.97 8.33 7.91 15th Inefficient storage/Perishability 9.91 8.80 9.23 B 9.31 11th Avialability of transport facilities 4.77 5.38 5.69 I 5.29 27th Variability in transport costs 7.79 7.30 7 .8L6 7.64 16th Marketing Total 78.79 77.31 80.94 79.01 Financial risks N Access to credit 14.66 13.26 A 15.71 14.51 3rd Inadequate cash flow 13.12 11.D98 14.01 13.02 4thDefault risk 6.56 5.99 7.34 6.62 21st Changes in interest rate 5.69 A5.15 4.79 5.20 28thFinancial Total 40.03 36.38 41.85 39.35 Social/human risks B Labour availability 1F0.93 I 11.54 8.06 10.25 10thVariability in labour costs 9.21 10.03 8.17 9.17 12thDamage to equipment O7.53 6.96 7.47 7.34 18thSickness/ill health of farmer/labourer 10.80 11.51 10.09 10.84 7th War/Conflict Y 7.15 6.78 7.10 7.02 20thTheft T 7.04 6.23 6.60 6.60 22ndFire ourbreaks I 6.34 8.24 12.00 8.68 14thContracting risk S 5.81 5.77 5.44 5.67 26thSocial Total 64.81 67.06 64.93 65.57Total R 251.40 247.93 259.58 252.87 Source: Field SurvEey, 2011. IVN U 105 Table 12: Ranking of risk categories in terms of agricultural risk exposure levels Production Marketing Financial Social Risk exposure Scores 68.94 79.01 39.35 65.57 Risk number 7 9 4 8 Standard scores (Mean) 9.85 8.78 9.84 8.20 Rank 1st 3rd 2nd 4th Y AR R IB L AN AD F I B O ITY S ER NI V U 106 RY RA LI B N AD A IBF Y O T SI ER Figure 17: Risk exposure level of respondents IV 107 UN 5.3.4 Agricultural risk exposure based on crop enterprise Table 13 indicates that fruits and vegetable farmers were more vulnerable to production risks than other crop farmers. These farmers were also particularly more prone to flood and pests/diseases than others. As observed by Pena and Hughese (2007), vegetables are highly sensitive to environmental extremes like floods, while Bempah et al (2011) asserted that pesticides are widely used in fruit and vegetables because of their susceptibility to insect and diseases attack. According to Akinmusire (2011), part of the serious challenges affecting the existence of fruits and vegetables is pest attacks. Legume crop farmers werYe the least vulnerable to production risks. They however recorded higher rates of AshoRrtfall in production and limited knowledge on usage of chemicals. R The major marketing risks among the crop farmers were marketB failure, as well as volatility in output and input prices. Concerning perishability, fruitsL anId vegetable farmers also recorded a higher level of risk exposure and this may be Nrela ted to the observation of Aworh (undated) that postharvest losses of fruits and veAgetables are extremely high in Nigeria. D In terms of financial risks, roots and tuber fAarmers were more prone to such risks. This finding corroborates the result of Onubu ogIuB and Onyeneke (2012) that low production capital is the major constraint of root and tFuber crop farmers. The low level of occurrence of inadequate cash flow among cash cro pO farmers may be an indication of the high net worth of cash crops and this is likely to Yimprove the liquidity of cash crop farmers. According to Debela (2009), perennial cashI Tcrops can help relax the liquidity constraints of households. Concerning socialS risks, the major social risks for cereals; roots and tubers, fruits and vegetable and cashE croRp farmers were; lack of labour, variability in labour costs and ill health. However theI mVajor social risks for legume farmers were fire outbreaks and lack of labour. The highN ranking of fire outbreaks was because all the legume crop farmers were in the guineUa savannah zone. 108 Table 13: Ranking of respondents’ agricultural risk exposure levels based on crop enterprise Cereals Roots & T Fruits & veg Legumes Cash crops Production risks Drought 14.24 3rd 10.05 7th 14.66 5rd 8.78 9th 7.08 17th Excessive rainfall/flood 20.17 1st 9.91 8th 23.40 1st 10.15 5th 16.63 1st Pests and Diseases 15.25 2nd 13.26 4th 19.85 2nd 13.22 2nd 11.26 5th Shortfall in production 5.97 26th 6.01 24th 5.45 26th 6.92 17th 4.71 23rd Limited knowledge about usage of chemicals etc 5.44 27th 7.89 16th 5.05 28th 6.70 20th 6.31 19th Rainfall fluctuations 6.02 25th 6.30 23rd 7.65 19th 4.98 26th 6.08 20th Low quality seedlings 7.31 18th 7.54 20th 7.35 20th 5.73 23rd 6.62 18th 74.39 60.96 83.41 56.48 58.69 Price/marketing risks Y Volatility in inputs costs 8.50 11th 11.05 5th 13.41 6th 9.98 7th 8.64 13th Volatility in output price 12.14 5th 10.12 6th 12.68 8th 8.06 A12Rth 10.04 7thMarket failure 10.09 8th 9.40 11th 12.67 9th 8.66 10th 9.27 10th Inaccessibility to markets 8.21 14th 9.91 9th 8.91 14th 7.70 13th 8.85 12th Consumer Preference 6.23 23rd 9.69 10th 6.85 23rd 6R.41 22nd 8.05 14th Loss of bargaining power 7.71 15th 8.78 14th 8.60 15thB5.62 24th 9.02 11th Inefficient storage/Perishability 7.76 9th 9.71 19th 10.80 10tIh 7.51 16th 9.56 8th Avialability of transport facilities 4.78 28th 5.07 26th 5.06 L27th 6.90 19th 4.37 26th Variability in transport costs 7.67 16th 7.45 21st 7.N31 21st 9.75 8th 9.38 9th73.10 81.17 86.29 70.59 77.16 Financial risks Access to credit 12.75 4th 19.76 1sDt A13.62 4th 12.60 3rd 12.56 3rdInadequate cash flow 12.10 6th 16.25 2nd 13.28 7th 13.31 1st 10.96 6th Default risk 7.08 19th 6.86 22nd 8.58 16th 4.63 27th 3.25 28th Changes in interest rate 6.59 20th 3.8B8 AI 28th 6.47 24th 3.20 28th 4.53 25th38.52 46.75 41.96 33.74 31.28Social/human risks Labour availability 11.36 7tFh 9.16 13th 10.00 11th 10.13 6th 13.78 2ndVariability in labour costs 8.63 10th 8.59 15th 9.28 13th 8.40 11th 12.44 4th Damage to equipment 6.34O22nd 7.78 18th 5.87 25th 6.91 18th 7.45 21st Sickness/ill health of farmer/labourer Y8.3 4 12th 13.36 3rd 13.50 5th 6.67 21st 7.78 15thWar/Conflict T 6.10 24th 7.17 17th 7.81 18th 7.68 14th 7.09 16thTheft I 7.46 17th 5.69 25th 7.92 17th 7.53 15th 4.58 24thFire ourbreaks 8.21 13th 9.32 12th 9.61 12th 10.63 4th 8.27 22nd Contracting risk S 6.46 21st 4.12 27th 7.29 22nd 5.48 25th 4.01 27thR 62.89 65.18 71.29 63.43 65.39Total E 248.90 254.06 282.95 224.24 232.52 Source: Field Survey, 2011 IV UN 109 5.4 Attitude towards agricultural risks Table 14 reveals that crop farmers in the rainforest zone (53.40) were more risk seeking than the coastal zone (50.45) and guinea savannah zone (48.01). Table 15 shows that 18.7% of the respondents had scores between 21 and 42 while majority (67.4) had scores ranging from 42 to 62. Also 13.9% of the respondents had above 63. The mean value was 50.6. More than three quarter (84.2%) of the respondents had scores below the mean value (indicating a favourable attitude) and this implies that this category of respondents is risk averse. This result corroborates previous studies by Torkamani and Haji-Rahimi (2001); Binici et al (20Y03); Olarinde and Manyong (2007); Salimonu (2007); Ayinde (2008); Ajijola et al (20R11) that majority of farmers are risk averse. However 15.8% of the respondents were Arisk seekers (indicating an unfavourable attitude). Risk seekers have a greater risk beariBng Rability and they primarily focus on higher outcome potentials (Salimonu, 2007). In a relaLtedI study by Dadzie and Acquah (2012), 10.0% of the food crop farmers studied were risk seNeke rs. This variation across the zones in farmers‟ attitude tAowards agricultural risks may be connected with the farmers involvement in off farm occupDation as Ayinde (2008) posited that the presence of other sources of income enhances the risAk bearing ability of farmers, while Sarap and Vashist (1994), Kisaka-Iwayo et al (2005 );I BDing et.al (2010) asserted that income is positively correlated with risk bearing abilityF. Kouame and Komenan (2012) also asserted that more wealth is correlated with a lower dOegree of risk aversion. In terms of age however, crop farmers in the rainforest zone were thYe o ldest. If the observation of Nielsen et al (2013), that age is positively correlated with farImTers‟ level of risk aversion were to have been positive in this case; respondents in the rainSforest zone should have had the highest risk aversion level rather than the lowest. The findRing of this study therefore shows that the presence of other sources of income has a higher eEffect on risk aversion than age. IV UN 110 Table 14: Distribution of respondents based on attitude towards risks related statements Coastal Rainforest Guinea Savanah Total 1 I regard myself as the kind of person who is willing to take a few more risks than others. 3.50 3.67 3.38 3.52 2 I am generally cautious about accepting new risk management ideas 3.07 2.92 3.13 3.04 3. I must be willing to take a number of risks for my farm activities to be profitable 2.86 3.05 2.64 2.85 4 I am more concerned about large loss in my farm operation than missing a substantial gain. 2.48 2.39 2.33 2.40 5 I am ready to adopt a new risk management idea, once i hear it is Y beneficial 1.87 1.95 AR1.99 1.946 Profit is reduced when farm risks are managed 1.84 2.01 1.78 1.88 7 I encourage other farmers to adopt new and beneficial R technologies that will reduce farm risks 1.66 1.57 1.53 1.59 8 I don‟t adopt risk management tools until I see them working for IB people around me 3.40 L3.75 3.39 3.51 9 I am capable of influencing major decisions on my farm 2.00N 2.73 1.91 2.21 10 I believe only in traditional methods of managing farm risks A2.95 2.76 2.43 2.71 11 I am less willing to take risks than my friends do AD 2.99 3.51 3.04 3.18 12 With respect to my farming operations, i like to take riskBs 2.08 2.44 1.70 2.07 13 I am concerned about a substantial gain than a largeIloss in my farm activities F 2.50 2.81 2.32 2.54 14 I am always one of the last set of farmer s tOo try a new idea 3.96 3.82 3.84 3.8715 I am reluctant in taking risks when it comes to my farming activities TY 1.87 1.73 1.70 1.77 16 Using risk management straStegiIes help to reduce farm risks 1.72 1.76 1.81 1.76 17 With respect to mEy farRming operations, i do not like to take risks 1.95 1.88 1.80 1.8818 Farm loss isI rVeduced when risks are managed 1.94 1.99 1.78 1.9019 Using Nrisk management strategies is a waste of time 1.79 2.01 1.68 1.83 20 IUmust be reluctant to take a number of risks for my farmactivities to be profitable 2.08 2.43 1.96 2.16 21 With respect to the conduct of my farm operations, I like to play it safe 1.94 2.22 1.87 2.01 50.45 53.40 48.01 50.62 Source: Field Survey, 2011. 111 Table 15: Distribution of respondents based on attitude towards agricultural risks Score Freq % Std dev Std Er Mean Risk Seeking 50.6 Risk Averse < 50.6 1-21 0 0 22-42 58 18.7 43-63 209 67.4 6.07 0.41 50.6 49 (15.8) 261 (84.2) 64-84 27 8.7 85-105 16 5.2 RY BR A LI DA N A IB O F TY I ER S V UN I 112 Section 5.5 Farmers’ use of agricultural risk management strategies Generally in terms of use of agricultural risk management strategies, as seen in Table 17, production strategies ranked first among those adopted. This implies that majority of the respondents utilise production strategies more than other strategies and this can be due to the fact that production risks were ranked most important in terms of risk exposure. From the means indicated in Table 16, strategies that majority of the respondents adopted were those related to seeds (use of improved seedlings-2.60 and buying seeds from reputable sources- 2.52); soil enhancement (use of fertilizers-2.65 and soil conservation practices-2.31) andY pest control measures (2.41). However, use of irrigation facilities (1.69), cultivatinRg crops benefitting from public intervention (1.31) and use of flood control measurAes had low adoption rates (1.59). The low use of irrigation facilities amongst crop faBrmeRrs confirms the observation of Mshelia (2012) that ninety percent of crop productionL inI Nigeria is based on rain-fed agriculture. Crop farmers in the rainforest zone had lower adoption rates in terms of production risk management strategies than the other two zoneNs. However, despite the fact that the coastal zone was less vulnerable to production rDisksA than the guinea savannah zone, they adopted more production risk management strAategies than the guinea savannah zone. This may be as a result of their higher level of IedBucation as Gana et al (2009) found out that education affects the technical competence Fof farmers. Table 17 shows that the seconOd category in terms of use of strategies was financial risk management strategies. Financial Strategies with higher adoption rates as shown in Table 16 include: minimizing leverIagTe (Y2.94), increasing liquidity (2.57), use of cooperatives (2.56) and controlling family eSxpenditure (2.40). In relation to farm liquidity, Ahsan and Roth (2010) in their studyR found out that increasing liquidity was one of the most important strategies among fEarmers. However, maintenance of adequate farm records (1.36), monitoring financial ratiIoVs (0.74) and use of crop insurance (0.14) were the least adopted financial strategiesN. According to Mshelia (2012), part of the challenges of agricultural insurance in NigerUia is the low level of awareness among farmers. Overall, farmers in the rainforest zone had better financial risk management skills than the other zones. This may be connected with their higher rate of off farm diversification as this might have enhanced their liquidity, thus making it easier for them to procure items needed to reduce risks. In terms of social risk management strategies, Table 16 reveals that major social strategies that respondents utilise are: maintaining good relations with labourers/employees/contracting partners (2.73), securing labour before production (2.27) 113 and use of new/well maintained farm equipment (2.15). However, improving farm security (1.32), securing backup labour (1.28) and use of personal insurance (0.00) were the least utilised strategies. In fact none of the crop farmers held a personal insurance policy. Crop farmers in the coastal zone had the highest adoption level, followed by guinea savannah and lastly rainforest zone. Table 17 also shows that marketing strategies were the least utilised by respondents and this may be due to the fact that marketing risk management strategies according to Le and Cheong (2011) are often beyond the control of farmers due to their complexity, reRliabYility and availability. For instance, the use of futures/commodity exchange market (which is based on availability) had the lowest adoption rate (0.02), although a future exchange mAarket exists in Abuja. This confirms the findings of Cervantes-Godoy et al (2013) BthaRt in developing countries, futures market is not widely accessible and it is mostLly Iused in commercial agriculture. Production contract and cooperative marketing (whic h may be a bit complex depending on the terms between the contracting partners) also Nhad very low adoption rates- 0.17 and 0.52 respectively. As observed by Dadzie andD AcAquah (2012), contract sales and hedging as strategies are not common with food cropA farmers in their study. More than 80% of the pipfruit farmers in the study of Martin ( 1I99B6) did not also use forward contracting and futures markets as a risk management tool. F Vertical integration had a m eaOn score of 1.24. In relation to this, Fakayode et al (2012) observed that further processing of farm produce by farmers (which is a form of vertical integration) is low TandY attributable to lack of funds to purchase appropriate equipment or lack of techSnicaIl knowhow and technologies. Concerning thRe use of farm records, respondents had a mean score (1.56), with the guinea savannVah Ezone having the lowest adoption rate as shown in Table 16. This low adoption rateI in the zone conforms with the findings of Ampaire and Rothschild (2010) that lack of reNcord keeping is attributable to low levels of education among farmers. Majority of the reUspondents however affirmed that they regularly use and share market information (2.48) with other farmers. According to Ahsan and Roth (2010) sharing of experience among farmers was one the most important strategies in their study. Generally in terms of marketing risk management, farmers in the coastal zone performed best followed by their counterparts in the guinea savannah zone and lastly farmers in the rainforest zone. The ranking in Table 16 indicates that strategies with high utilization rate include; reducing leverage/outside equity (2.94), having good human relations with 114 labourers/employees/contracting partners (2.73), use of fertilizer to improve fertility (2.65), use of improved seedlings (2.60), increase in liquidity (2.57) and membership of cooperatives (2.56). Y AR LIB R N DA IB A F Y O IT ER S V I UN 115 Table 16: Farmers use of agricultural risk management strategies Coastal Rainforest Guinea Savanah Total Production strategies Use of improved seedlings 2.72 2.46 2.62 2.60 Buying seedlings from reputable source 2.71 2.35 2.49 2.52 Diversification of farm enterprise 1.90 1.93 1.95 1.93 Use of fertilizer to improve fertility 2.81 2.51 2.64 2.65 Use of irrigation techniques 1.79 1.30 1.97 1.69 Flood control (e.g chanelization) 1.86 1.35 1.56 1.59 Cultivating crops benefitting from public inervention (e.g cassava) 1.22 1.36 1.34 1.31 Consulting people with crop knowledge 2.48 2.02 1.90 2.13 Using soil conservation techniques 2.55 2.16 2.22 2.31 Pest Control Practices 2.62 2.24 2.36Y 2.41Timely farm activities 2.67 2.22 2.34 2.41 25.33 21.90 23.39 23.54 Marketing strategies R Production contract 0.22 0.17 0.11 0.17 Marketing contract 1.56 1.22 A1.37 1.38 Cooperative marketing 0.62 0.41 R 0.54 0.52 Using sequential sales 1.40 1.41 1.69 1.50 Ensuring direct sales to wholesaler and processors 2.43 2.1I8B 2.25 2.29 Future/commodity exchange market 0.07 L0.00 0.00 0.02Vertical integration of farm produce 1.13 1.23 1.36 1.24 Using/sharing market information with other farmers 2.63 2.35 2.47 2.48 Keeping adequate records of farm produce 1A.70 N 1.54 1.44 1.56Forward pricing of inputs 0.56 0.52 0.59 0.5612.32 11.03 11.82 11.73 Financial strategies D Crop insurance 0.05 0.28 0.09 0.14 increase liquidity e.g. maintaining credit reserves BA 2.55 2.61 2.54 2.57having off farm employment I 1.91 2.02 1.77 1.90Making credit arrangement before production 2.14 1.98 1.99 2.04keeping fixed costs low F 1.80 1.96 1.82 1.86Sharing information on financial risk managOement 2.02 2.06 2.15 2.08Controlling family expenditure 2.36 2.43 2.41 2.40Monitoring financial ratios 0.85 0.71 0.67 0.74using lowest possible production costsY 2.02 2.09 1.95 2.02Membership of cooperatives 2.62 2.49 2.56 2.56 keeping adequate records of finaIncial transactions 1.54 1.36 1.19 1.36Reducing leverage ( outside equityT) 2.95 2.92 2.96 2.94 Leasing/renting expensive faSrm equipment 2.20 2.24 2.12 2.1925.01 25.15 24.22 24.79 Social strategies Securing labour contactsR before production 2.30 2.34 2.17 2.27 Securing backupV/emEergency labour 1.40 1.28 1.17 1.28Having good hIuman relations with labourers/employees/contracting2 .p8a7rtners 2.59 2.73 2.73ImprovingN farm security e.g. fencing and use of guards 1.44 1.20 1.32 1.32Use new/well maintained equipment/machinery 2.26 2.01 2.18 2.15havinUg backup machinery/equipment 1.47 1.14 1.29 1.30using traditional practices like scarecrow and native medcine 1.50 1.53 1.55 1.53Personal insurance 0.00 0.00 0.00 0.00 13.24 12.09 12.41 12.58 Total Scores 75.89 70.17 71.84 72.64 Source: Field Survey, 2011. 116 Table 17: Ranking of risk categories in terms of use of risk management strategies Risk sources Production Marketing Financial Social RMS scores 23.54 11.73 24.79 12.58 RMS number 11 10 13 8 Standard scores (mean) 2.14 1.17 1.91 1.57 Rank 1st 4th 2nd 3rd Source: Field Survey, 2011 Y R RA LI B AN AD IB F O ITY ER S IV UN 117 Section 5.6: Effectiveness of agricultural insurance in managing risks 5.6.1: Adoption and effectiveness of agricultural insurance As seen in Table 18, majority (57.1%) of the respondents were not aware of agricultural insurance. Tolongbose et al (1995) also found that 58.3% of the crop farmers sampled in their study were not aware of agricultural insurance. This shows that much has to be done in ensuring that farmers are aware of market instruments such as insurance that can help reduce agricultural risks. Many of the respondents were hearing about it for the first time at the time of interview and it had to be clearly explained before they could understaRnd.Y The coastal zone recorded the highest level of awareness (53.4%), while the rainforeAst zone had the lowest level (32.4%). R When asked about their source of information, almost half (48.1%IB) of the respondents who were aware of agricultural insurance explained that they lear nLt about it through their friends or from family members. However 16.5% affirmed thatN they were told by extension agents, while 21.1% said they learnt about it either thrAough NACRDB (now Bank of Agriculture) or other formal sources of credit. RespondeDnts that heard through the electronic or print media were 14.3%. This indicates that fBrienAd/ family member is a very strong and effective means of passing information on agri cuIltural risk management. However, only 17.3% of thosOe awFare of agricultural insurance (7.4% of the total respondents) had ever purchased it. The food crop farmers in the study of Dadzie and Acquah (2012) also neglectedT thYe use of crop insurance to deal with risk in their farming business, however their negleIct was mainly due to lack of awareness of crop insurance. Also in Teweldemedhin anRd KSafidii (2009), 95.2% and 98.2% of the commercial and communal farmers in their stuEdy had no insurance cover for their livestock. The rainforest zone however had the highIesVt level of 33.3% for those farmers who had ever purchased insurance. This implies that a higher percentage of crop farmers purchase agricultural insurance in the rainfoUrestN zone than the other two zones. Almost half (43.5%) of those who insured their farms purchased crop insurance regularly. The low adoption rate despite awareness corroborates the findings of Tologbonse et al (1995); Ajijola et al (2011) who found that out of 51.7% and 10.0% respectively of farmers who were aware of agricultural insurance, none purchased it. According to Abdulmalik et al (2013), farmers‟ participation in insurance activities is low despite the existence of NAIC. This low rate of adoption indicates that there are strong factors preventing those aware from adopting it. Therefore, awareness is not a major determining factor in adoption of agricultural insurance even though it is a prerequisite. 118 The mean premium paid by respondents who had adopted agricultural insurance was ₦8,750, ₦11,000 and ₦14,200 in the coastal, rainforest and guinea savannah zones respectively. The difference in means may be a reflection of the difference in average farm sizes across the zones. Concerning efficiency of agricultural insurance in managing agricultural risks, Table 18 shows that 26.1 % of the respondents observed crop insurance had insignificant effect. More than one fifth (21.7%) of the crop farmers affirmed that crop insurance had significant effect on risk management, while majority (52.2%) observed that the effect was either moderately or very significant. Across the zones, respondents inY the coastal zone had a higher perception of the efficiency of crop insurance in mRanaging agricultural risks than the other two zones. RA LI B AN AD F I B O ITY ER S NI V U 119 Table 18: Effectiveness of agricultural insurance in managing risks COASTAL RAINFOREST GUINEA SAV TOTAL FREQ % FREQ % FREQ % FREQ % Awareness of Agric Insurance Yes 31 53.4 33 32.4 69 46 133 42.9 No 27 46.6 69 67.6 81 54 177 57.1 Source of awareness Family/friends 15 48.4 18 54.5 31 45.5 64 48.1 Extension/development agents 6 19.4 5 15.2 11 16.4 22 16.5 NARCDB/Other formal credit sources 3 9.6 6 18.2 19 27.3 28 21.1 Print media 5 16.1 1 3.0 0 0 6 4.5 Radio 2 6.5 3 9.1 8 10.8 RY13 9.8 Ever purchased Agric Ins (N=310)* Yes 2 3.5 11 10.8 10 6A.7 23 7.4 No 56 96.5 91 89.2 140BR93.3 287 92.6 Ever purchased Agric Ins (N=133)** I Yes 2 6.5 11 33.3 L10 14.5 23 17.3 No 29 93.5 22 6N6.7 59 85.5 110 82.7 Frequency of purchasing Agric Insurance Frequently 1 50.0 4DA36.5 5 50 10 43.5Sometimes 1 60.0 5 45.4 3 30 9 39.1 Rarely 0 0.0 A2 18.1 2 20 4 17.4 Premium B Average premium paid 8750 I 11000 14200 11316 Minimum premium paid 400F0 5500 5250 4917Maximun Premium paid O13000 13000 18500 14833 Efficiency of Agric. Insurance (N=23)* ** Not effective TY 0 0.0 3 27.2 3 30.0 6 26.1Low effectiveness 0 0.0 2 18.2 3 30.0 5 21.7Moderately effective I 2 100.0 4 36.4 4 40.0 10 43.5 Very effective S 0 0.0 2 18.2 0 0.0 2 8.7Mean 3.0 2.5 2.2 2.6 Source: Field Survey,R 2011. * N= 310: ToItaVl p Eopulation of respondents **N=133N: Population of respondents who are aware of agricultural insurance. ***NU= 23: Population of respondents who adopted agricultural insurance 120 5.6.2: Level of satisfaction with NAIC processes In terms of satisfaction with NAIC processes, Table 19 shows that respondents were more satisfied with the amount of premium paid and settlement of claims period than the documentary requirements, information delivery processes and accessibility. The lower means recorded for information delivery and accessibility may be due to the zoning of the NAIC offices, in which there is only one office in each state of the federation. This zoning structure is likely to affect respondents‟ access to crop insurance due to increased traveYlling time and transportation costs. In order to boost the capacity of subsistence farmeArs, tRhere is a need to increase their access to insurance (Haliru, 2012). LIB R AN D A IB O F SI TY R IV E UN 121 Table 19: Level of satisfaction with NAIC processes COASTAL RAINFOREST GUINEA SAV TOTAL Mean Mean Mean Mean Documentary requirements 2.7 2.3 2.5 2.5 Accessibility 2.5 1.8 3.0 2.4 Premium paid 3.0 3.5 2.7 3.1 Settlement of claims 3.0 3.2 3.5 3.2 Information Delivery 2.2 2.4 2.5 2.4 Source: Field Survey, 2011 RY A R LI B AN AD F I B O SI TY R VE UN I 122 5.6.3: Inhibitors and motivators for agricultural insurance When crop farmers who were aware of agricultural insurance but had not purchased were asked about the major factor inhibiting them from purchasing an insurance policy as seen in Table 20, majority of them (70.2%) indicated that agricultural insurance was somehow complicated. Sixty-five percent claimed it was not accessible, while 63.2% observed that the premium was high. Ajieh (2010) also concluded in his study that unpaid claims, bureaucracy and high premium were part of the major constraints hindering participation of poultry farmers in agricultural insurance. Thirty nine percent ofY the respondents associated their non-patronage to religious reasons. These religious reasoRns were the belief that loss was from God and the non-compliance of insurance procRedurAe with their ethical beliefs. Part of the government initiative in making insuraBnce process more compatible with investors‟ ethical beliefs is the incorporation of T aLkafIful in to mainstream insurance. N According to Maysami and Kwon (1999) takaffuAl insurance is a type of joint guarantee insurance mechanism based on the law of lDarge numbers in which a group of members pool their financial resources togetBherA against certain loss exposures. The conceptual nature of Takaful entails mutual hIelp/solidarity, mutual responsibility, mutual cooperation as well as mutual protection. TFakaful is an alternative to conventional insurance and its products are not entirely new toO the insurance industry in Nigeria, having been in the market for close to a decade (Jankara, 2011). He further explained takaful as an ethical financing and cooperative riIskT prYotection method which invigorates human capital, human solidarity and emphasisesS dignity, community self help and economic self development. As the potential of TakafRul insurance is vast, Daniel (2012) observed that the National Insurance Commission has Eentered into a collaboratory agreement with GIZ (a German agency for sustainable dIeVvelopment) to conduct a diagnostic study on Takaful insurance business in Nigeria. TNakafful can as well be incorporated into agricultural insurance policy so as to cater for faUrmers who are excluded due to ethical reasons. Furthermore, 7.0% of the respondents indicated that loss was low; while 64.9% affirmed that insurance offices were not easily accessible. In a bid to stimulate competition in the agricultural insurance sector, the National Insurance Commission (NAICOM), recently disbanded the monopoly of Nigerian Agricultural Insurance Commission (NAIC) from the exclusivity of agricultural insurance. Although, NAIC has the exclusive right to insure all 123 subsidised agricultural risks, opportunities abound for other insurance companies in the areas of commercial unsubsidised agricultural risks. Table 20 shows the motivating factors that respondents believed can either sustain or improve their interest in agricultural insurance. A higher percentage identified local availability (88.4) and higher propensity in getting claims (87.1%) as their possible key motivating factors. Seventy nine percent of the respondents said they would be stimulated to purchase an agricultural insurance policy if there were low bureaucratic procedures, while 61.0% affirmed that that the pedigree of the insurance company issuing the policy will aYffect their decision. Concerning propensity to get claims and insurance company involved,R Mshelia (2012), asserted that low level of trust among farmers is one of the major chAallenges of agricultural insurance in the country, while Cole et al (2013) indicated tIhaBt u Rncertainty about insurance (and whether the provider was trusted to pay out) was a sigLnificant determinant of the low take-up rate. N In relation to ethical considerations 28.7% of the respoAndents would be motivated if insurance processes are compatible with their ethical beliDefs. A F I B O Y T RS I VE NI U 124 Table 20: Inhibitors and motivators for agricultural insurance COASTAL RAINFOREST GUINEA SAV TOTAL FREQ % FREQ % FREQ % FREQ % Inhibitors* n=33 n= 22 n=59 n=114 Complicated procedure 22 66.7 18 81.8 40 67.8 80 70.2 Loss is from God 5 15.2 4 18.2 17 28.8 26 22.8 Ethical beliefs 2 6.1 4 18.2 15 25.4 21 18.4 Loss is Low 3 9.1 1 4.5 4 6.8 8 7.0 Accessibility 26 78.8 13 59.1 35 59.3 74 64.9 high premium 21 63.6 12 54.5 39 66R.1 Y72 63.2 Motivators* n=58 n=102 n=150 n=310 More Awareness 22 37.9 68 66.7 118 A78.7 208 67.1 Local availability 41 70.7 93 91.2 14R0 93.3 274 88.4 Hloiwgh perremiPurmob ability of receiving 44 75.9 81 79.4LIB118 78.7 243 78.4claims 50 86.2 84 82 .4 136 90.7 270 87.1Less bureaucracy 49 84.5 83 81.4 115 76.7 247 79.7 If required by lender of loans 44 75.9 77 N75.5 81 54.0 202 65.2 Ethical Compatibility 7 12.1 A33 32.4 49 32.7 89 28.7 Risk exposure level 43 74.1 D68 66.7 85 56.7 196 63.2 Insurance company issuing the policy 34 58.6A 77 75.5 78 52.0 189 61.0 Source: Field Survey, 2011 B *Multiple responses OF I Y T I ER S V UN I 125 Section 5.7 Farmers’ level of risk management Level of risk management refers to farmers‟ level of ability to manage risks. It is a function of the number of strategies utilised as well as the frequency of utilisation of the strategies. 5.7.1 Level of risk management As indicated in Table 21, less than one percent (0.7%) of the respondents had a risk management score of not more than 25 out of a maximum score of 142.Thirty percentY had between 26 and 50, while majority (55.5%) had between 51 and 75. Twelve percRent had scores ranging from 75 to 100 . Less than two percent (1.5%) however had scoreAs above 100 and three-fifth of this category were crop farmers from the coastal zone. RThe Table also indicates that more than half of the respondents (52.9) were in theI Bhigh level of risk management category. More than sixty percent (67.2%) of crop fa rmLers in the coastal zone were in this category. Fifty-three percent of those in the guineaN savannah zone were also in the same category, while only 40.2% of crop farmers in Athe rainforest zone were in this category. This result implies that farmers in the coastAal zDone are better risk managers than the other two zones. F I B O TY RS I VE NI U 126 Table 21: Farmers level of risk management COASTAL RAINFOREST GUINEA SAV. TOTAL Freq % Freq % Freq % Freq % Scores 0 - 25 1 1.7 0 0 1 0.7 2 0.7 26-50 4 6.9 45 44.1 44 29.3 93 30.0 51-75 37 63.8 47 46.1 88 58.7 172 55.5 75-100 13 22.4 9 8.8 16 10.6 38 12.3 Above 100 3 5.2 1 1 1 0.7 5 1.5 High level (≥72.6) 39 67.2 41 40.2 84 56.0 16Y4 52.9 Low level (< 72.6) 19 32.8 61 59.8 66 44.0 Source: Field Survey, 2011 RA R146 47.1 B N LI A AD F I B O ITY ER S IV N U 127 5.7.2 Farmers’ risk management behaviour As seen in Table 22; 19.0%, 10.2% and 15.3% of the crop farmers in the coastal, rainforest and guinea savannah zones were superior managers having at least 50% agricultural risk management mark in all four categories studied. Furthermore, about 15%, 29% and 43% in the rainforest, guinea savannah and coastal zones respectively were active managers, having at least 50 percent scores in three categories of risk management. Table 19 also shows the breakdown of farmers in the active categories. Active risk managers in the coastal (60.0%) and rainforest zones (46.7%) fall in the production/finance/mRarkeYting category, while 58.1% of those in the guinea savannah zone were active in the production/finance/social category. This implies that while those active manaAgers in the coastal and rainforest zones were not good social managers, those in thBe gRuinea savannah zone were not good marketing managers. LI Di-function managers also accounted for 33.2% of the crNop farmers sampled. This set of farmers had 50 percent scores in only two categories oAf agricultural risk management. About 72% and 77% of di-function managers in the coaDstal and guinea savannah majored in the production/social risk management category. BIn tAhe rainforest zone, about 83% of the di-function managers were active in the producItion/finance categories. This indicates that majority of di-function managers in the coaFsta l and guinea savannah zones lack financial risk management skills, unlike those in the Orainforest zone who are active in financial skills. Also, mono function manaYgers account for 21.9% of the respondents. This category of farmers had 50 percent scoresI Tin only one category of agricultural risk management. Majority (92.7%) of the respondenSts in this category were production managers, while 4.4% and 2.9% were marketing and Rfinance managers respectively. None of the respondents were social mono functionV mEanagers. This indicates that mono function managers in the study area specialize in Ithe production category. UONnly 4% of the farmers were part-time agricultural risk manager. These farmers are those who have less than 50% scores in all the categories of risk management. The guinea savannah zone had the highest percentage of part-timers with 5.4% of their crop farmers belonging to the category. In summary it can be deduced from Table 19, that in terms of risk management behaviour, crop farmers in the coastal zone are better than the ones in the guinea savannah zone, while farmers in the rainforest zone recorded the lowest level. The extent to which they 128 are exposed to devastating risk from flood may be responsible for this variation. Furthermore by using standard scores as seen in Table 17, respondents had more skills in production risk management than financial skills, while social skills were better than marketing skills. Y RA R LI B AN AD F I B Y O IT ER S IV UN 129 Table 22: Agricultural risk managerial levels (Risk management behaviour) MANAGERIAL LEVELS COASTAL RAINFOREST GUINEA SAV TOTAL Freq % Freq % Freq % Freq % Superior risk managers 11 19.0 10 10.2 23 15.3 44 14.2 Active risk managers 25 43.1 15 14.5 43 28.7 83 26.8 Di-function risk managers 18 31.0 41 40.2 44 29.3 103 33.2 Monofunction risk managers 3 5.2 33 32.2 32 21.3 68 21.9 Part-time risk managers 1 1.7 3 2.9 8 5.4 12 3.9 Active managers Y Production/marketing/finance 15 60.0 7 46.7 3 R7.0 25 8.1 Production/marketing/social 7 28.0 4 26.7 15 34.9 26 8.4 Production/finance/social 3 12.0 2 13.3 25 A58.1 30 9.7 Marketing/finance/social 0 0.0 2 13.3 Di-function managers LIB R0 0.0 2 0.6 Production/finance 4 22.2 34 82.9 7 15.9 45 14.5 Production/marketing 1 5.6 5 1 2.2 3 6.8 9 2.9 Production/social 13 72.2 2 N4.9 34 77.3 49 15.8 Monofunction managers A Production 3 100.0 D31 93.9 29 90.6 63 20.3 Marketing 0 0.0A 0 0.0 3 9.4 3 1.0 Finance 0 IB0.0 2 6.1 0 0.0 2 0.6Social 0 0.0 0 0.0 0 0.0 0 0.0Source: Field Survey, 2011 F Note: Superior and Part-time risk ma Y nOagers do not have sub categories, since the two levels are made up of farmers having either less than or more than 50% in all the categories of risk management measured in theI sTtudy. RS VE NI U 130 5.8: Determinants of agricultural risk management behaviour of crop farmers Determinants of agricultural risk management behaviour were analyzed with relevant variables using multinomial logit regression. Hypothesized variables were; age, formal education, farming experience, farm size, organization membership, attitude to agricultural risks, risk exposure level, coastal agro-ecological zone, rainforest agro-ecological zone and guinea savanah agro-ecological zone. The variable measuring coastal agro-ecological zone was however dropped by the model due to co-linearity. The part-time group of the dependent variable (Part time managers) was used as the reference/base category. The chi-square vYalue of 140.84 and the significance level (p=0.0000) indicates that the explanatory variablRes in the model are significant in explaining the risk management behaviour of cropR farAmers in the study area. The parameter and marginal estimates are presented inI BTables 23 and 24 respectively. L Result of the analysis in Table 23 indicates that faNrm size (r=0.015, p=0.05), organization membership (r=0.079, p=0.01) and risk exposuAre levels (r=0.066, p=0.01) were significant variables in determining crop farmers thDat are classified as mono-function managers relative to the reference group (part-timerAs). Being a Mono-function crop farmer was positively influenced (determined) by org aIniBzation membership. This implies that being a member of an organization improves theF probability of a crop farmer being classified as a mono-function manager relative to b eOing a part-timer. Table 24 shows that membership of organization tends to increase theY probability of being a mono-function manager by 80.1%. According to Shehu et al (20I1T0), membership of organization creates an avenue for farmers to reduce their risks and tShis helps to improve their level of risk management. Farm size and risk exposure levels Rwere negative predictors of mono-function managers and this implies that larger farm sEizes or higher risk exposure levels lower the probability of a crop farmer being classifIieVd as a mono-function manager relative to part-timer. Larger farm size is an indicationN of more wealth and as observed by Kouame and Komenan (2012), more wealth is assocUiated with a risk seeking attitude thereby lowering farmers‟ level of risk management. Unit increments in farm size and risk exposure level reduce the probability of being a mono- function manager by 37.1% and 37.4% respectively. One possible explanation for risk exposure level may be that when the level of risk exposure becomes unbearable or overwhelming, a farmer may decide not to do anything and this ultimately reduces his/her level of risk management. 131 Farm size (r=0.003, p=0.05) was also a significant and negative predictor of being a di-function risk manager. The marginal effect in Table 24 indicates that the probability of being classified as a di-function manager relative to the part-timer decreases by 16.7% for every unit increment in farm size. Furthermore, farm size (r=0.026, p=0.05), organization membership (r=0.034, p=0.05), attitude to agricultural risks (r=0.059, p=0.01) and risk exposure level (r=0.086, p=0.01) were significant predictors of active risk managers. While farm size was negatively associated with the group; organization membership, attitude to agricultural risks andY risk exposure level were positive predictors of the group. For every unit increment in faRrm size, the probability of being an active manager (relative to part-timer) decreases bRy 18A.8% , while for every unit increment in organization membership, the probability Bof being an active manager (relative to part-timer) increases by 22.7%. Having highe r LriskI exposure level also increase the odds of being classified as an active risk manager by 32.8%. According to Paul and Routray (2001) household ability to manage risk is determNined by their risk exposure level. Active farmers are therefore stimulated to increaseD theAir level of risk management due to a rise in their risk exposure level. Attitude toA agricultural risks was also positively associated with the active group. An explanatIioBn for this may be because high level of education is usually associated with a higFh le vel of risk seeking attitude and according to Ibrahim, Afolami et al (2011), higher level of education is believed to be associated with access to information on improvYed te Ochnologies and productivity; thereby improving the farmers level of risk managemeTnt. Being Superior riSsk mIanagers was also positively influenced by their risk exposure level (r=0.09, p=0.01)R and for a unit increase in risk exposure level; the probability of being a superior manager Eis increased by 19.4%. As observed by Ali and Kapoor (2008) farmers‟ response to rIisVk is often consistent with their perceptions of that risk. Hence, a high level of risk expoNsure stimulates the superior risk managers to utilise more risk management strateUgies so as to reduce risk exposure level. 132 Table 23: Parameter estimates of the multinomial logit regression for determinants of risk management behaviour of crop farmers Variable Monofunction Difunction Active Superior Age 0.1249 0.5982 0.2181 0.3072 (0.7913) (0.1926) (0.6416) (0.5230) Formal Education -0.169 0.4125 0.5181 0.3397 (0.8054) (0.534) (0.6219) (0.6317) Farming Experience 0.7276 -0.3671 -0.2258 -0.3038 (0.8747) (0.4122) (0.6219) (0.5197) Farm size -0.1915 -0.2096 -0.1602 -R0.8Y049(0.015)** (0.0032)** (0.0263)** (0.2066) Organization Membership 1.4323 0.9291 1.7556 1.1712 (0.0793)* (0.8977) (0.0341)** A(0.1600) Attitude to agric risks 0.8914 0.9984 0.12B15 R 0.9537(0.1485) (0.1036) (0.05I95)* (0.1552) Risk exposure level -0.1925 0.1817 L0.1744 0.1756(0.0663)* (0.8546) (0.0865)* (0.0920)* Rainforest zone 1.6136 0.1325 N -1.5457 -1.215 (0.2399) (0.9915A) (0.2240) (0.3498) Guinea savanah zone 1.2883 0.1D974 -0.6086 -0.7727(0.3429) (A0.8709) (0.6187) (0.5375) B **Significant at 0.05 I *Significant at 0.1 F The multinomial logit regression coef fiOcients are above, while the r values are in brackets SI TY VE R NI U 133 Table 24: Marginal estimates of the multinomial logit regression for determinants of risk management behaviour of crop farmers Variable Parttimer Monofunction Difunction Active Superior Age -0.106 -0.3612 0.9336 -0.3823 -0.8400 (0.3984) (0.2042) (0.0253) (0.3119) (0.78070 Formal Education -0.9644 -0.7609 0.3314 0.5081 0.1787 (0.5941) (0.0737) (0.6184) (0.4044) (0.9706) Farming Experience 0.7088 0.4805 -0.4951 0.4150 -0.978Y0 (0.5668) (0.0627) (0.1655) (0.8990) (0R.7133)Farm size 0.4913 -03714 -0.1665 0.1883 0.1357 (0.0201) (0.6381) (0.1871) (0.86R33)A(0.0401)Organisation Membership -0.267 0.8009 -0.3211 0.2267 0.4103 (0.1931) (0.2323) (0.0002) (0B.0215) (0.5657) Attitude to agric risks -0.2949 -0.1719 -0.211 9LI0.5663 -0.7831(0.0734) (0.6819) (0.N9743) (0.3541) (0.8732)Risk exposure level -0.1524 -0.3740 -0.1326 0.3281 0.1938 (0.5690) (0.0000) A(0.1610) (0.0001) (0.0024) Rainforest zone 0.1033 0.3007 D 0.1436 -0.3196 -0.1340(0.7663) (0.0002) (0.1363) (0.0002) (0.0456) Guinea savanah zone 0.2651 0I.B198 A5 0.8124 -0.1606 -0.1195 -0.9937 (0.0301) (0.4287) (0.0565) (0.0694) Marginal effects are above, while the r valuFes are in brackets. O SI TY ER NI V U 134 Section 5.9: Testing of Hypotheses Six hypotheses were tested in the study. Results of the hypotheses testing are as presented below: 5.9.1 Relationship between crop farmers’ socioeconomic variables and their level of agricultural risk management. 5.9.1.1 Variables measured at nominal level The result of the chi-square test in Table 25 shows that there were signifYicant relationships between level of agricultural risk management and sex ( =23.932)R, marital status ( =33.042) and educational level ( = 43.71) of crop farmers. In teArms of sex, males are usually more active and with a higher level of physical energy BthatR may be needed to implement risk management strategies on farm. Furthermore, withL hoIusehold dependents, farmers are stimulated to reduce their risk exposure levels by utilisin g risk management tools. In relation to educational level, knowledge of agricultural riskN management tools is often influenced by the literacy level which is needed to improDve cAommunication flows. Senadjki et al (2012) also observed a significant relationship bAetween educational level and farmers‟ level of risk management. The contingency coIeBfficient values in Table 25 shows that the strength of the relationships were 27.8% Ffor sex; 32.6% for marital status and 35.2% for educational level. This implies that e dOucational level has greater influence on crop farmers‟ use of risk management tools thanY marital status and sex. IT ER S NI V U 135 Table 25: Chi square test of relationship between crop farmers’ socioeconomic variables and their level of risk management Variable 2ᵡ df p value Contingency coefficient Sex 23.932 2 0.001* 0.278 Marital status 33.042 6 0.007* 0.326 Educational level 43.71 8 0.000* R0.35Y2 Major source of information 8.614 6 0.196 A0.164 Membership of organisation 11.177 6 0.083 BRI 0.187* Significant at 0.05 L DA N A B OF I ITY RS VE UN I 136 5.9.1.2 Variables measured at interval level Table 26 indicates that farm size (r= -0.100) had a significant relationship with level of agricultural risk management. This shows that farm size influences the way crop farmers manage their farms against risks. The negative correlation suggests that farmers with smaller farm sizes manage better than the older ones. However, age (r= -0.143) and farming experience (r= -0.177) had no significant relationship with level of agricultural risk management. Farmers‟ age was negatively correlated with level of agricultural risk management, thus indicating that younger farmers utilise more risk management tools Ythan older farmers. This implies that younger crop farmers in the country are more receRptive of productive ideas than the older ones. In a related study, Cole and Kirwan A(2009) also observed that risk management was decreasing as farmers‟ age increasesB. TRI here was also a negative correlation between farming experience and level of agricuLltural risk management. This implies that younger farmers having fewer years of farmNing experience manage their farms better than older farmers having several years of farmiAng experience. D IB A OF SI TY R IV E UN 137 Table 26: PPMC analysis of relationship between crop farmers’ socioeconomic variables and their level of risk management Variable r value p value Age -0.056 0.329 Farming experience 0.088 0.120 Y Farm size -0.100 0.049* R * Significant at 0.05 A R LI B DA N A F I B O SI TY ER NI V U 138 5.9.2. Relationship between crop farmers’ risk exposure level and their level of risk management Table 27 shows that a positive correlation (r= 0.207) exists between crop farmers‟ risk exposure level and their level of risk management. This implies that as farmers risk exposure level increases so do their level of risk management improves. This could be adduced to the fact that the decision to utilise more risk management strategies may often be influenced by the level of risk exposure. According to Ali and Kapoor (2008) farmers‟ responses to risk is often consistent with their perceptions of that risk, while Yesuf and Randy (2008) affirYmed that farm household base their investment and production decisions partly on thAe pRerceived risk of failure. This result also corroborates the findings of Paul and RoutRray (2010) that households „ability to manage risk is determined by the severity and freIqBuency of risks they face (i.e risk exposure level). L AN AD F I B O ITY S VE R I UN 139 Table 27: PPMC analysis of relationship between Risk exposure level and crop farmers’ level of risk management Variable r value p value Risk exposure level vs level of risk management 0.235 0.000* Y * Significant at 0.05 R RA LI B N AD A IB O F SI TY R IV E UN 140 5.9.3 Difference in crop farmers’ level of agricultural risk exposure across the three agro-ecological zones. The result as presented in Table 28 shows that a significant difference (F= 40.72) exists in farmers‟ level of agricultural risks exposure across the three ecological zones. When a Scheffe adjustment was made for the number of comparisons as shown in Table 29, significant differences exist between two pairs of zones in the comparison; Guinea savannah and Coastal were significantly different with mean difference of 8.18, as well as Guinea savannah and rainforest with mean difference of 11.65. However no significant differYence exists in risk exposure between coastal and rainforest zones with mean difference Rof 3.47. This implies that farmers in the coastal and rainforest zones do not differ signifiAcantly from each other in terms of agricultural risk exposure levels. This variation mayB beR connected with respondents‟ ages as Lucas and Pabuayon (2011) asserted that age LhasI negative effects on farmers risk perception. Moreover, the extent to which crop faNrme rs perceive the impact of marketing and financial risks as well as the extent of percepAtion of occurrence of production and financial risks may also be responsible for this variatDion. A F I B O SI TY ER NI V U 141 Table 28: Analysis of Variance Test (ANOVA) for difference in agricultural risk exposure levels across zones Variable Fvalue p value Risk exposure level 40.72 0.000* * Significant at 0.05 RY Table 29: Post hoc tests using Scheffe RA Zone pairs Mean difference LIBp value Coastal & Rainforest 3.47 AN 0.146 Coastal & Guinea savanah A8.D18 0.000* Rainforest & Guinea savanah IB 11.65 0.000* * Significant at 0.05 F O SI TY R E NI V U 142 5.9.4. Relationship between crop farmers’ attitude to agricultural risks and their level of risk management Table 30 shows that significant relationship (r= -0.142) exists between crop farmers‟ attitude to agricultural risks and their level of agricultural risk management. This finding implies that farmers‟ attitude to risk correlates with their level of risk management. According to Ajzen (2002), attitude is one of the considerations that guide human behaviour. The negative correlation indicates that farmers that are risk averse are better risk managers than farmers who are risk seeking. This indicates that farmers that are risk averse tend toY use more risk management strategies than risk seekers. The tendency to be risk averse leRads to a higher usage of a variety of risk management tools in a bid to reduce level of Aexposure to agricultural risks. For instance, Jordan and Grove (2008) concluded that BriskR aversion has a positive influence on the use of both cash forward contracting and LheIdging through future contracts/options, while Mohammed and Ortmann (2005) affiNrmed that insurance is more attractive to risk averse farmers. The result of this studAy corroborates the findings of Harington and Niehaus (1999); Bard and Barry (2000) aDnd Anton (2008) that farmers‟ level of risk management is influenced by their attitude towAards risks. F I B O ITY ER S NI V U 143 Table 30: PPMC analysis of relationship between attitude towards agricultural risks and farmers level of risk management Variable r value p value Attitude towards risk vs level of risk management -0.137 0.016* * Significant at 0.05 AR Y LIB R N DA IB A F O SI TY ER NI V U 144 5.9.5. Hypothesis 5: Difference in crop farmers’ attitude towards agricultural risks across the three agro- ecological zones. The result as presented in Table 31 shows that a significant difference (F= 16.98) exists in farmers‟ attitude towards agricultural risks in the three zones. The result of the Scheffe adjustment made for the number of comparisons in Table 32 shows that significant differences exist between each pair of zones in the comparison, that is, Rainforest and Coastal had mean difference of 2.95; Rainforest and Guinea savannah had mean difference of 5.40; Coastal and Guinea savannah had mean difference of 2.44 and all the differencesY are significant. AR According to Bard and Barry (2000), attitude to risk is often a uniquRe reflection of a person‟s personality and it is influenced by life experiences. Such life eBxperiences include farmers‟ exposure to agricultural risks. As observed from Tables 11L anId 14, farmers in the Guinea savannah zone had the highest level of risk exposure. TNhey were also more averse to risks than the others. Hence, the variation in risk exposureA level across the zones has been reflected in respondents‟ attitude towards agricultural riskDs. Furthermore, attitude to risk may also be inAfluenced by socioeconomic factors. For instance, crop farmers‟ involvement in off fa rmIB occupation (Figure 11) may be connected with the variation in respondents‟ attitudeFs towards risks. According to Ayinde (2008), the presence of other sources of incom e Oenhances the risk bearing ability of farmers. Hoag, Keske and Goldbach (2011) also oYbserved that women show a slightly higher aversion to risk than men. Thus, the higher pIerTcentage of women in the Guinea savannah zone (compared to other zones) as indicatedS on Figure 6 may also be responsible for the higher level of risk aversion in the zone. RThe variation in respondents‟ ages across the zones as shown in Table 4 is also reflecteVd inE respondents‟ attitude towards risks. I UN 145 Table 31: Analysis of Variance Test (ANOVA) for difference in attitude towards agricultural risks across zones Variable F value p value Attitude towards risk 16.98 0.000* * Significant at 0.05 Y Table 32: Post hoc tests using Scheffe RA R Zone pairs Mean difference p valuIeB Coastal & Rainforest 2.95 N0 .0 L33* Coastal & Guinea savanah 2.44 A 0.046*D Rainforest & Guinea savanah 5.4 A 0.000* * Significant at 0.05 F I B O SI TY R IV E UN 146 5.9.6 Difference in crop farmers’ level of risk management in the three agro- ecological zones. As seen in Table 33, a significant difference exists in farmers‟ level of risk management (F= 6.75) in the three zones. The Scheffe adjustment made as shown in Table 34 indicates that there exist significant difference in the level of risk management in coastal /rainforest with mean difference of 5.72 and coastal/ Guinea savannah with mean difference of 4.05. However, there was no significant difference in level of risk management between rainforest and Guinea savannah with mean difference of 1.67. Y This implies that in terms of level of agricultural risk management, cropA faRrmers in the coastal zone differ significantly from those from other two zones anRd this may be connected with their higher level of formal education, which improvesB farmers‟ ability to source information from a variety of information channels. AccoLrdinIg to Breukers et al (2009), higher level of education influences the level of unNder standing of a risk. The knowledge of risk management tools to combat risk enhanAces farmers‟ ability in adopting new production technologies that may help to reduce riskDs. The variation in level of risk management mAay also be connected with respondents‟ membership of organizations. As shown on FIiBgure 9, the variation among crop farmers belonging to two or more organizations acrFoss the zones aligns with the variation in level of risk management across the zones. M emObership of farmers associations creates an avenue for farmers to reduce their risks (ShehYu et al, 2010). Tekleword and Kohlin (2010) also observed that membership of organizatIioTn is a form of social capital, which acts as a forum for sharing experience and exchanginSg information about market behaviour and this can help improve their level of risk manRagement. VE NI U 147 Table 33: Analysis of Variance Test (ANOVA) for difference in agricultural risk management across zones Variable F value p value Risk management level 6.75 0.001* * Significant at 0.05 Table 34: Post hoc tests using Scheffes Y AR Zone pairs Mean difference p vaBlueR Coastal & Rainforest 5.72 L0.0I02* Coastal & Guinea savanah 4.05 AN 0.008* Rainforest & Guinea savanah 1.51 D 0.731 * Significant at 0.05 BA OF I ITY S VE R NI U 148 CHAPTER SIX SUMMARY, CONCLUSION AND RECOMMENDATIONS 6.1 SUMMARY Food insecurity is one of the top developmental challenges in Nigeria and it is partly due to lack of appropriate agricultural risk management capacities in the country. This is because farmers are confronted with several risks that have the potential to reduce output and farmers‟ productivity. This study therefore identified the determinants of agriculturalY risk management behaviour among crop farmers in Nigeria. R The study focussed on crop farmers who had at least five years farmingA experience. Focus Group Discussions were conducted to generate a deeper understandRing of farmers‟ risks perceptions and responses, while an interview schedule was alsoI dBeveloped to gather information on farmers‟ socioeconomic characteristics and inform atLion on agricultural risk management. Through random sampling, 323 questionnaires weNre administered in three agro- ecological zones in the country and 310 questionnaires wereA received. Data were subjected to descriptive and inferential analysis. Crop farmers weAre Dcategorised on the basis of their risk management abilities. B 6.11 Major findings F I The study found that majori tyO of the crop farmers in the study area were males (91.7%) and 89.7% were marriedY. The mean age was 53.2 years, while majority (38.3%) of the crop farmers fell betweenI Tage bracket 51 and 60 years. More than one-third (37.7%) of the crop farmers had no formal education, although farmers in the coastal zone were more educated than those inR theS other zones studied. The mean farming experience was 28.3 years, while the modal cElass was 31 and 40 years. Farm sizes were on the average of 3.4 hectares with the guiIneVa savannah zone having larger farm sizes. Minority (32.9%) had off farm occupatioNn and for most of the crop farmers (97.7%), the farm ownership structure was sole proprUietorship. Majority (84.8%) belonged to one or more organizations, while friends/family (90.0%) was the major source of information on agricultural risk management. Friends/family was also the major source of labour for majority of the respondents (76.5%). The study also established that the major types of risks in the study area were; inadequate cash flow (94.2%), occurrence of pests and diseases (91.3%), sickness/ill health of farmer and labourer (89.0%), lack of access to credit (88.4%), volatility in output price (85.8%) and variability in labour costs (84.2%). In terms of risk exposure level, respondents‟ 149 most important sources of agricultural risks were production (9.85) followed by financial (9.84). Marketing risks (8.78) were perceived to be third source of agricultural risks, while social risks (8.20) were ranked last. Agricultural risks with high exposure levels were; flood (15.88), occurrence of pests and diseases (15.16), lack of access to capital (14.51), inadequate cash flow (13.02), drought (12.36) and volatility in output prices (10.91). The means for risk exposure levels across the zones were: coastal (251.40); rainforest (247.93) and guinea savannah (259.58). The general mean value for risk exposure level was 252.87. While less than one-fifth (18.7%) of the respondents were at a low level of risk exposure, majority Ywere at a moderate level (50.3%) or high level (31.0%) of risk exposure. R The mean value for respondents‟ attitude towards agricultural riskRs wAas 50.6 and more than three quarter (84.2%) of the respondents were risk-averse, withB the rainforest zone being more risk seeking than the other zones. Furthermore, in LterIms of utilization of agricultural risk management strategies, respondents had the highes t scores under production strategies, while marketing strategies had the lowest utilizatioNn rate. Strategies with high utilization rate include; reducing leverage/outside equity D(2.9A4), having good human relations with labourers/employees/contracting partners (2.73A), use of fertilizer to improve fertility (2.65), use of improved seedlings (2.60), incrIeBase in liquidity (2.57) and membership of cooperatives (2.56). The means for agricultu ral risk management levels across the zones were: coastal (75.89); rainforest (70 .1O7) a Fnd guinea savannah (71.84). The general mean value for crop farmers‟ level of aYgricultural risk management was 72.6. Almost half of the respondents (47.1%) of the reIsTpondents were in the low level category. Forty-one percentS of the respondents were either superior or active agricultural risk managers, one third wRere di-function managers, while one quarter were either mono-function managers or part-Etimers. Moreover, the coastal zone had the highest number of active and superior riskI Vmanagers, while the rainforest zone dominated the di-function and mono- function Nrisk managers‟ categories. The guinea savannah zone had the highest number of part-tUime risk managers. Majority (57.1%) of the respondents were not aware of agricultural insurance and less than one tenth (7.4%) of those aware adopted crop insurance. Although respondents who adopted perceive crop insurance as having significant effect on risk management, they complained that they were not satisfied with documentary requirements, accessibility and information delivery process of NAIC. Barriers preventing respondents from purchasing crop insurance include: complicated procedures (70.2%), high premium (63.2%) and accessibility 150 (64.9%). while strong motivators to improve interest in crop insurance were; local availability of insurance company (88.4%), high propensity in getting claims, (87.1%), less bureaucracy (79.7%) and lower premium (78.4%). A multinomial logit regression analysis showed that for the mono-function managers, having larger farm size, and a higher risk exposure level tend to make the crop farmer a part- timer, while membership of organization tend to make the farmer a mono-function manager. The di-function managers were negatively predicted by farm size. At the active level, being a member of an organization, having higher risk exposure levels and a high risk attitude tenYd to make the farmer an active manager, whereas having larger farm sizes tend to make thRe farmer a part-timer. The superior managers were also positively influenced by risk exRposAure level. Chi-square analysis revealed that sex ( =23.932), marital statIuBs ( =33.042) and educational level ( = 43.71), had significant relationship with Lcrop farmers‟ level of agricultural risk management. Pearson Product Moment CorrNelation also established that farm size (r= -0.100), risk exposure level (r= 0.207) and DfarmAers‟ attitude towards agricultural risks (r= -0.142) had significant relationships with fAarmers level of risk management. There was a significant difference in attitude towards agricultural risks (F= 16.98) across the zones. There were also significant differences in crop fIarBmers‟ level of risk exposure (F= 40.72) and level of agricultural risk management (F= 6F.75) across the zones, although farmers in coastal and rainforest zones did not differ s igOnificantly from each other in terms of level of risk exposure while those in the guinYea savannah and rainforests did not differ significantly in terms of level of risk manIaTgement. Major predictors of agricultural risk management behaviour were: marital Sstatus, formal education, major source of information, ecological zone and risk expoEsurRe level. V 6.2 CNONICLUSION TUhe following conclusions are reached on the basis of the findings of the study:  The study area is under a threat due to the high level of agricultural risk exposure especially production and financial risks. Moreover, the significant difference in levels of risk exposure across the study area indicates the disparity in the occurrence and impact of agricultural risks and that risk exposure is a function of the farmers‟ local environment.  Farmers will be willing to utilize more risk management strategies if the risk management strategies are made accessible and affordable, as majority of the crop farmers in the study 151 area are risk averse. The higher risk attitude of crop farmers in the rainforest zone may imply that more energy may be expended in making them utilize risk management strategies.  There is suboptimal use of agricultural risk management strategies among respondents as marketing strategies had low utilization rate and almost half of the respondents had low level of agricultural risk management despite the fact that majority are highly exposed to risks. The low use of strategies and the high percentage of di-function and mono-function managers in the rainforest zone further corroborate the high risk attitude of the farmeYrs in the zone. R  There is a low level of use of crop insurance as majority of the respondeRnts Aare unaware of crop insurance. Moreover, since majority of those aware do not utilise crop insurance, awareness may not be the major determining factor for crop insurancIe.B  There is the need to enhance farmers‟ network as membership o fL organization positively influenced the risk management behaviour of farmers. N DA 6.3 RECOMMENDATIONS A 1. The Federal Government needs to create a cIoBnducive environment for farmers to operate so as to reduce crop farmers‟ level Fof risk exposure especially the production and financial risks. Emphasis should aOlso be placed on the level of vulnerability across the zones. Measures to reducTe rYisk exposure level of farmers include; development of pest/disease tolerant seeId varieties, improved subsidy packages on key inputs for production, improvingS farmers access to affordable credit, disaster prevention (such as flood control) EandR investment in irrigation infrastructures. 2. The MiniIstVries of Agriculture should empower farmers to take their own risk management decisiNons by sensitizing and training them on available risk management strategies esUpecially marketing strategies. Initiatives to improve farmers‟ financial literacy should also be encouraged. Likewise, risk reducing technologies should be made accessible and affordable to crop farmers so as to improve adoption. 3. The Nigerian Agricultural Insurance Corporation needs to strengthen their awareness campaigns so that farmers can be aware of the benefits of agricultural insurance and encouraged to adopt it. The insurance offices should be made available in farmers‟ communities instead of the use of zonal offices in each state of the federation as is 152 presently practiced. There should also be lesser bureaucracy, while claims period is shortened. The introduction of takafful (an alternative to conventional insurance, which entails ethical financing and cooperative risk protection) in general insurance can also be extended in to agricultural insurance so as to serve those excluded due to ethical considerations. 4. In order to facilitate the sharing of information on risks, crop farmers should be encouraged to join more than one farmers‟ organizations so as to improve farmers‟ level of risk management. Y R BR A I L AN D IB A OF ITY S VE R I N U 153 REFERENCES Abdulmalik, R.O., Oyinbo, O. & Sami, R. A. 2013. Determinants of crop farmers‟ participation in agricultural insurance in federal capital territory, Abuja, Nigeria. Greener Journal of Agricultural sciences, 2(3):021-026. 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In Social Theories of Risk and Uncertainty, Blackwell Publishing Ltd., pp. 1-17. 167 APPENDIX QUESTIONNAIRE UNIVERSITY OF IBADAN FACULTY OF AGRICULTURE AND FORESTRY DEPARTMENT OF AGRICULTURAL EXTENSION AND RURALDEVELOPMENT TITLE: DETERMINANTS OF AGRICULTURAL RISK MANAGEMENT BEHAVIOUR OF CROP FARMERS IN NIGERIA NOTE: This questionnaire is intended to cover the above topic. It is purely for scienYtific research. All information supplied would be treated as “STRICTLY CONFIDENRTIAL”. Please tick () as appropriate or fill in the gaps where necessary. A SECTION A: SOCIOECONOMIC CHARACTERISTICS R State:__________________________; Town/ village: ________L___I_B________ 1. Sex: Male ( ) Female ( ) 2. Marital Status: Single ( ), Married ( ), Divorced ( ), WiNdowed ( ) 3. Age: ________________ (in years) A 4. Religion: Islam ( ), Christianity ( ), TraditionAal ( D ), Others ( specify) 5. Educational background: No formal EducIaBtion ( ), Primary School ( ), Secondary School ( ), OND/NCE ( ), HND/FB.S C ( ), Postgraduate ( ) Others(specify) ________________________ 6. Please tick and rank your source( s)O of credit. Credit source ITiTck tYhose applicable Rank those ticked 1- Most important Friends/family Cooperatives S Private money lendRers Commercial Ebanks MicrofinaVnce banks NNAICRDB UOthers (specify) 7. Are you a member of any organization? Yes ( ), No ( ). 8 If yes, how many organizations______________ What are the names of the organisations? ________________________________ 9 Do you hold any position in the organizations? Yes ( ), No ( ). 10 If yes, what positions? Leaders ( ), Executive members ( ), Ordinary members ( ). 168 11 How well do you participate in organizational activities? Adequately ( ), Moderately ( ), Rarely ( ) 12 How long have you been in to crop farming ________________ (in years)? 13 What is the major crop you cultivate?____________________ 14 What other crops do you grow on your farm________________________ 15 What other types of agricultural enterprises are you in to? Cattle ( ), Sheep ( ); Goat( ), Poultry ( ); Fishery ( ), Snailery ( ). Others (specify) _______________________________ Y 16 What is the ownership structure of your farm; Sole proprietorship ( ), CompaRny ( ), Partnership ( ). Others (specify) ________________________ A 17 From which sources do you earn income? R Income source Tick those applicable IB Off farm: L Wages/salary Pensions AN Investments Farm income D Others (specify) A IB F 18 What is the size of your farmed l anOd? ___________ (in hectares) 19 From which sources do you obtain labour for farm activities? Friends/Family ( ), Partnerships/CooperativesT ( Y), Hired labourers ( ). Others (specify) 20 How available is farm Ilabour when it is needed. Always available ( ), Sometimes available ( ), RarRely Savailable ( ), Never available ( ). 21 Which categoEry (ies) of people do you sell your farm produce to? Middlemen ( ); ProcessiIngV industry ( ); Directly to individuals/ household (Consumers) ( ); Others (specNify). _______________________________ 22 HUow easy is it to source markets for your farm produce? Highly accessible ( ), Moderately Accessible ( ), Not Accessible ( ). 169 SECTION B: FARMERS’ AGRICULTURAL RISK FACTORS 23 Please indicate your perceived types of risks as well as your perceived level of risk exposure. Sources Tick relevant Likelihood of occurrence Perceived Average economic sources of 1=Never loss from risks risks 2= Unlikely (1= 0%-20%, 2= 21%-40%, 3=Possible 3= 41%- 60%, 4=Likely, 5=Very likely 4= 61%- 80%, 5= 81%-100% of produce) Production types of risks Drought Y Excessive rainfall/ flood Pests and Diseases AR Shortfall in production e.g. Reduction in soil fertility R Limited knowledge about usage of chemicals/ fertilizers B Rainfall fluctuations I Low quality seedlings L Marketing types of risks N Volatility in inputs costs Volatility in output price Market failure A Inaccessibility to markets D Consumer Preference A Loss of bargaining power Inefficient storage/ Perishability IB Availability of transport facilities Variability in transport costs Financial types of risks F Access to credit Inadequate cash flow O Default risk Changes in Interest rate Y Social types of risks Labour availability SI T Variability in labour costs Damage to equipment Sickness/Ill health of farmer/ labourer War/Conflict ER Theft V Fire outbreaks I Contracting risk UN 170 24 The questions in the table below relate to agricultural risk management, please indicate your responses to them. Questions Utilise Level of utilization Yes Utilise all the Utilise Utilise rarely No time sometimes Production risk management strategies 1 Use of Improved seedlings 2 Buying seedlings from reputable source Y 3 Diversification of farm enterprise R 4 Use of fertilizer to improve fertility A 5 Use of Irrigation techniques R 6 Flood control (e.g channelization) 7 Cultivating crops benefitting from public intervention. B E.g cassava LI 8 Consulting people with crop knowledge 9 Using soil conservation techniques e.g. crop rotation, minimum tillage N 10 Pest Control Practices A 11 Timely farm activities A D Marketing risk strategies 12 Production contracts B 13 Marketing contracts I 14 Cooperative marketing F 15 Using sequential sales 16 Ensuring direct sales to wholesalers/ processors O 17 Future/commodity exchange markets 18 Vertical integration of farm produce ITY 19 Using/ sharing market informatioSn with other farmers 20 Keeping adequate records of farm produce 21 Forward price of inputs R Financial risk StrateEgies 22 Crop Insurance 23 Increasing liqIuiVdity e.g. maintaining credit reserves 24 Having off farm employment 25 MUakingN credit arrangement before production 26 Keeping fixed costs low 27 Sharing information on risk management 28 Controlling family expenditure 29 Monitoring financial ratios 30 Using lowest possible production costs 31 Membership of cooperatives 32 Keeping adequate records of financial transactions 33 Reducing leverage (outside equity) 34 Leasing/ renting expensive farm equipment 171 Social risk management strategies 35 Securing labour contracts before production 36 Securing backup/emergency labour 37 Having good human relations with labourers/employees/contracting partners 38 Improving farm security e.g. fencing and use of guards 39 Use new/ well maintained equipment/ machinery 40 Having backup machinery/equipment 41 Using traditional practices like scarecrow and native medicine 42 Personal insurance Y 25 From which sources do you obtain information on risk management strategies RA R Sources Tick those relevant Rank those ticked 1- Most important;I 2B- Next important etc Other farmers/Friends and relatives Self L Extension/Development agents Television N Print media A Radio Others (specify) D IB A O F SI TY VE R UN I 172 26 The following questions pertain to your attitude towards agricultural risks, kindly indicate your responses. SA A U D SD 1 I regard myself as the kind of person who is willing to take a few more risks than others. 2 I am generally cautious about accepting new risk management ideas 3. I must be willing to take a number of risks for my farm activities to be profitable 4 I am more concerned about large loss in my farm operation than missing a substantial gain. 5 I am ready to adopt a new risk management idea, once i hear it is beneficial 6 Profit is reduced when farm risks are managed Y 7 I encourage other farmers to adopt new and beneficial technologies that will reduce farm A R risks 8 I don‟t adopt risk management tools until I see them working for people around me R 9 I am capable of influencing major decisions on my farm 10 I believe only in traditional methods of managing farm risks IB 11 I am less willing to take risks than my friends do L 12 With respect to my farming operations, i like to take risks 13 I am concerned about a substantial gain than a large loss in my farm activitAies N 14 I am always one of the last set of farmers to try a new idea 15 I am reluctant in taking risks when it comes to my farming activities D 16 Using risk management strategies help to reduce farm risks A 17 With respect to my farming operations, i do not like to takIe Brisks 18 Farm loss is reduced when risks are managed 19 Using risk management strategies is a waste of timFe 20 I must be reluctant to take a number of risks fOor my farm activities to be profitable 21 With respect to the conduct of my farmY operations, I like to play it safe SI T ER IV UN 173 SECTION C: EFFECTIVENESS OF AGRICULTURAL INSURANCE IN MANAGING RISKS 28 Are you aware of the Nigerian Agricultural insurance Corporation (NAIC) agricultural insurance scheme? Yes ( ), No ( ) 29 If yes, how did you hear about it? Family/Friends ( ), Extension/Development agents ( ), NARCDB/ Other formal credit sources ( ), Print media ( ), Radio ( ), Others (specify) __________________________ 30 Have you ever bought their agricultural insurance? Yes ( ), No ( ). Y 31 If Yes, how often do you buy it? Frequently ( ), Sometimes ( ), Rarely ( R ). 32 What is your perception of the effectiveness of agricultural insurance? Very eAffective ( ), Moderately effective ( ), Low effectiveness ( ), Not effective ( ). R 33 What is the average premium you have paid for crop insurance? ____I__B_______________ 34 Please indicate your level of satisfaction with NAIC activities L Services Excellent Very Good GNood Bad Very bad Documentary Requirements A Accessibility D Premium paid A Prompt Settlement of claims B Information Delivery F I 35 What factors prevent you from buOying agricultural insurance? Inhibiting factors Y Tick those Rank those ticked. E.g. IT applicable 1- Most important factor, 2- next S important It is complicated loss is an act of GRod InsuraInce p Erocedures are against my ethical vValues loNss is too low UAccessibility High premium Others ( specify ) 174 36 What factors will motivate you to purchase agricultural insurance? Motivators Tick those Rank those ticked. E.g. applicable 1-Most important factor 2-next important Subsidy of premium Higher Probability of receiving claims Lesser bureaucracy Insurance company issuing the policy Level of risk exposure Y If required by lender of loans R Compatibility of insurance procedures A with my ethical values R More awareness B Local availability I Others (specify) L N Thank you. A Olajide, F. O D A F I B Y O ITS VE R I UN 175