Nigerian Journal of Agriculture, Food and Environnmt.e 7(1):34-41 Published March, 20011 Egbewole et al., 201 1 TECHNICAL EFFICIENCY OF LUMBER RECOVERY FROM HIGH FOREST TREE SPECIES IN SELECTED SAWMILLS OF SOUTHWESTERN NIGERIA Egbewole1, Z. T., Ogunsanwo2, O. Y. and Omole2, A. O. ABSTRACT 1 Nasarawa State University, Keffi. Department orfe Fsotry Wildlife and Fisheries Shabu-Lafia CampuLsa,f i a. 2Department of Forest Resource Management, Univye orsf iItbadan. Nigeria. E-mail: tundeegbe@yahoo.comΩ ,: 08053620713 This study investigates the technical performanfcfieci ency (TPE) of twenty seven sawmills purposellye cted and grouped into three classes :small<5003/fdtay, medium 501-10003/fdt ay and large scale >10013/fdt ay based on their production capacities. 243 logs obtained from 20 species ooof dw sourced from Southwestern Nigeria were exam iin e2d7 selected sawmills using the variables such as wood specloiegs ,s izes, shapes and sawkerf, which have dirmepcta ct on waste generated from sawn logs and log conversion enffcicyie. Whereas 135 structured questionnaire was utsoe dobtain information relating to experience of headrig roapteors, age of machine, number of machine useds afowri ng operation, hands involved in operation and effective duratoiofn o peration while a model multiple factor equat iownas adopted to determine the over all performance efficiency oef athssessed sawmills. The average TPE in the smc allel sawmills was 53.41%, medium scale sawmills with 58.79% had tighhee hst performance efficiency while large scalwe msaills with 41.94% had the least TPE. The average lumber recoveryR ()% wLas 53.69%, large-sized diameter logs had thgehe hsit %LR of 56.48 and small sized logs had 51.77%. Ondo State ha dh itghheest mean %LR of 56.15 followed by Lagos (5%3.)0 a9nd lastly by Oyo State (51.71%). Variations in log diameters csleas had significant influence on %LR, slab voluamnde dust volume at (p<0.05). Significant correlation also exist betewne log diameter class and LR (0.853**). Noticeavbaleri ation in %LR was observed among the various wood species, Ceibaa npdernat had the highest mean lumber recovery %LRe v aolfu 58.21% while Nauclea diderichii had the least (47.89%).r Faoppreciable reduction of wood-wastes generate sda iwnmills, greater use should be made of large sized logs, routine maainntecen of machines especially the saw blades is snaercye, seffort should be made towards inclusion of wood mizer headrig thant dhles smaller sized los with minimal wastages oabnsdo lete headrig need to be replaced promptly while cognate expeceri esnhould be a major determinant in the appointm oefn theadrig operators. To avoid genetic erosion, choice of isepse wcas left to saw millers’ discretion. Keywords: LRF: Lumber recovery factor, Headrig: Bda snaw machine, TPE: Technical performance efficyie, n Wc WG: wood waste generate d INTRODUCTION Sawmilling has been defined severally, as a sysatnedm as an industry (Bennett, 1974) and as a pr o(cLeuscsas, 1982). As a process it involves converting log ilnutmo ber using different methods such as live sagw (sinawing around the log), slash cutting, and cant sawinigli tfya c(Okigbo, 1964). Factors influencing lumber yield and value haven b iedentified as the factor influencing the timbecr orvery from logs during conversion in the mills. These inclu ldoeg: shape (sweep, tape crooked and straight)s, ilzoegs (girth and the length). Kinds of conversion and proces sminagchine, machine maintenance culture, availab oilift y machine parts and experience of the operators (joB,a d1e990). According to Zobel and Talbert (199 l1o)g, straightness improves both yield and quality ofb teimr. In order to reduce the volume of wood waisnt etsh e log conversion process and sustain the sawmills anrde scpoornding profit margin, there should be an inivteen s research focus on efficient conversion of log s ot oa stem down the percentage of waste in Nigerwiam silals (Badejo, 1990). Technical Performance Efficiency of a sawmill (TP isE )the state of being competent in performancteh eo ra bility to produce a desired effect with a minimum effcoorts, t, expenditure of time and waste. For exampffliec,i eency of a machine is determined by the ratio of work doonuet p(ut) to the energy that supplied (input) the kw (oErncarta Dictionary, 2009). Williston, (1981) defined effeicnicy in terms of sufficient volume and appropridaitaem eter classes of log supply to the mill to keep it opienrga tat the planned rate. As such, there shoulda nb ea dequate inventory of raw materials to keep the mill openrag tiat maximum efficiency over time. The factorst tchoantribute to changes in efficiency include technological ivnantoion, installed capacity and capacity utiliza,t iocnhange in scale of output, capacity investment per worker eantdrepreneurial skills (Kaise, 1971). Badejo anndil uOde, (1987), observed a variation in percentage LR einir tahppraisal of small size sawmilling operation sN iigeria. They attributed the variation to the variationsth ien years of experience of the headrig operatoinrsd,s kof headrig NJAFE VOL. 7 No.1, 2011 34 UNIVERSITY OF IBADAN LIBRARY Nigerian Journal of Agriculture, Food and Environnmt.e 7(1):34-41 Published March, 20011 Egbewole et al., 201 1 used, sawkerf, log sizes, and log form but repo rptedrcentage LR without quantifying the effect ofe steh identified factors as they affect overall percenet aTgPE of sawmills in the study area. Fuwape, (1) 9re8p5orted mean percentage LR of 56%, time efficiency as widolerking time ranging between 12.79-76.43%, effveec ti working time ranging between 23.6-87.21% on sepea nraotes without considering the cumulative effeocf ttsh ese factors on percentage TPE of the sawmills, thouagchk n owledged their individual influences on penrtacege LR. Hence the need for an approach that can bring htoegr estome identified quantifiable factors which coomnmly affect the percentage TPE of sawmills in Nigeriao.w Hever, much attention had been on investigatineg L tuhmber Recovery Factor (LRF) which Dobie, (1972) and Bjoa daend Giwa, (1985) defined as the ratio of theu vmoel of sawn wood output to that of the volume input ofs l opgrocessed in the mill, regardless of the type psr ocfessing equipment used and the species of wood involveudm. bLer recovery factor cannot solely determine TPf Ea o sawmill but depends on the entire mill operatiosnyaslt em. It is therefore an indication of the eeffnicciy of how a sawmill is being run. When the ratio is high, thoelu vme of wood waste generated is low. This study therefore examines the technical perafonrcme efficiency (TPE) of 27 selected sawmills gu s2in43 logs obtained from 20 species of wood sourced from hSwoeust tern Nigeria based on the variables such asd woo species, log sizes, shapes, saw geometry, expce roiefn headrig operators, age of machine, numfb emra ochine used for sawing operation, hands involved in opioenra at nd effective duration of operation which h advireect impact on waste generated from sawn logs and lu mrebceorvery factor and consequently on the technical performance efficiency (TPE) of any given sawm iTllh. e study aimed at providing information thaut lcdo bring about a more efficient log-processing techniquet wthoauld enhance optimum income to sawmill ownerso uthgh reduction in wood waste generation. MATERIALS AND METHODS Study area The three states of Lagos, Oyo and Ondo statehse i ns otuth western Nigeria were purposely selectesde db aon preponderance use of various types of headrig mneasc hoif the CD4, CD5 and CD6 categories. These mneasc hi are mostly horizontal band saw and the only vaornia taipart from the maintenance status of the ma cwhhiniceh depends on its maintenance status is the sizeg o tfh leoy can saw. South-Western Nigeria lies betwloenegni tude 20 121E and 60E and between latitude0 6211N and 80 371N (Agboola, 1979) with a total land area of 77,8m182 k and a projected population of 135,031,160 millionn 2 i007 (FAOSTAT, 2007). The predominant vegetatinio n Southwestern Nigeria ranges from Coastal belt onf gmroave swamp forest especially Ogun water-side , area Igbokoda, Ilaje and Ijaw in Ondo State and Epe ainr eLaagos State through tropical rainforest tharte aspd across southern part of Ogun, Oyo, Ondo and Ekiti stanteds caovered by savanna in the northern parts of Oysou,n and Ekiti States. The area which has 85 constitutered sfto reserves with a forest area cover of 842,4e9c9ta hres is endowed with natural forest resources and mineerpaol sdits with extensive fertile soils (FORMECU, 1)9.9 8 Log dimension and volume estimation A total of 243 logs of varying sizes and shapes swoausrced from 20 timber species that are usuaolulyg hst after in the study area were selected for the study (eT a1b).l Twenty seven sawmills out of 641 in the s tuadreya were purposively selected in each of the selected s tbaatessed on observed preponderance of horizontal sbawnds in operation and scale of output. Rate of log convoenr saind different form of waste generated in thec epsros of log conversion were also examined. One hundred andty thfivire (135) copies of structured questionnairer ew e administered in the 27 selected sawmills while s5p orendents comprising 1 sawmill manager, 2 heaodpreigr ators, 1 mill technician and 1 timber contractor weree sceteld per sawmill, the questionnaire were usedob ttoa in information relating to the experience of headoripge rator, age of machines, numbers of machine uins ed operation, machine maintenance culture in prac tnicuem, ber of people involved in log conversion oaptieorns, effective duration of operation (hrs) and totale i dtilme (hrs) using stop watch while saw geomektreyr f(, gullet depth, saw pitch, hook angle and cutting angle)e w meerasured using veneer caliper and compass (Fu 1w9a8p5e). Determination of percentage Lumber recovered The percentage lumber recovered is the ratio ouf mvoel of lumber recovered from each processed lotgh atto of log volume expressed in percentage (Lucas 1982t,e W 1h9i83, Egbewole t al, 2006). This is expressed as: LR = x ……………………………………………………………………………..1 where T = thickness of lumber (inch), W = width of lubmer (inch), L = lumber length (ft), The percentage lumber recovered was estimated thus: %LR = ∑ VL = (VL1 + VL2 + VL3 +---+ VLn) ∑ VT ( VT1 + VT2 + VT3 +-----+ VT243) NJAFE VOL. 7 No.1, 2011 35 UNIVERSITY OF IBADAN LIBRARY Nigerian Journal of Agriculture, Food and Environnmt.e 7(1):34-41 Published March, 20011 Egbewole et al., 201 1 %LR = x 100 % --------------------------------------------------------------------2 Where, %LR = percentage Lumber recove red ∑ VL = (VL1 + VL2 + VL3 +-----+ VLn) = total volume of lumber recovered 3()m ∑ VT = (VT1 + VT2 + VT3 +-----+ VT243) = total volume of all logs processed 3 )( m Determination of effective duration of log conversion While determination of effective duration of logn cvoersion in selected sawmills was by measuringt imthe taken in conversion of logs and the down time in eachm saillw using stop watch as follows; i time taken in converting each of the 9s l o=g effective working time(X hrs) ii down time = idle working time ( Y hrs) iii reasons for idle working time were noted Data analysis Correlation coefficient ( r ) was used to inveasteig the degree of association and the directiorne loaft ionship between the measured variable, while multiplea lrin re gression analysis was carried out to deteer mthien effects of the factors using the relationship describeldo wb e. The coefficient of determination (2 R) and standard error (SE) of estimation, mean square error (MSE) wasos dael termined to know the proportion of variatioxnp laeined by the regression equation. Y = a + b1X1 + b1X2 + b3X3 + b4X4 +……+ bnXn + ℮……………..………………....3 Where, X1---Xn = independent variables (factors consid)e r ed Analysis of time, human and material efficiency Time efficiency is an important component of thee ro-avll efficiency of a sawmill, other componentsc luinde efficiency of labor, managerial and conversiofnic eiefncy of machines. The time efficiency was determined by measuring tothtael sawing time ( tT) known as effective sawing period (Te) and the down time, known as the idle timei) (fTor each log converted. The equation develope dF ubwyape (1985) and used by Egbewoelet al (2005) was adopted to calculate performance efnficcyi of the mills. The equation is expressed as: Ỵ = x {﴾ + ﴾ + ﴾ + -----+ ﴾ x 100 ------------------------------------4 Where:Ỵ = Performance efficiency of the mill (%) n = number of factors considered (5) X1 = duration of log conversion operation of a chno saewmill (0-9hr) X2 = numbers of machine involved in log convoenrs i X3 = numbers of hands actively involved in lcoogn version X4 = level of experience of hands actively invodl vine log conversion (5---10yrs) X5 = volume of log input/ lumber recovered 3 )( m a = actual, e = expected, 0hrs =o zeeffriciency, 9hrs = 100% time efficiency The assumption of equation 4 are that a typicalm siall whas the following i 1 headrig machine, 1 rip saw machines,a w1 doctoring machine ii Average of 2 operators for the headrig minaec,h 1 operator for the rip saw machine, 1 saw doctors machine operator. iii the headrig machine has a capacity to ceortn avbout 6503ft (18.39m3) of log within 9hrs of uninterrupted operation iv the logs to be processed are readily abvlaei la (about 18.39m3) in required quantity and log geometry (shapes and dimension) v that lumber recovery factor (LRF) rangeosm fr (0--1). vi that the model is flexible enough to accommteod oather quantifiable factors (a X--- Xn) The limitations of equation 4 for a typical sawm airlle i if the actual duration of log conversionp eration of a chosen sawmill is zero 1(a =X 0 hrs), then the performance efficiency will be zero (Y = 0 %) ii that any efficiency rating depends on ‘aacl tduuration of log conversion operation (0-9hr)’ iii that a sawmill with zero efficiency (Y = %0 ) at a given time can be put back to produce at its installed capacity (highest efficiencleyv el) if the limiting factors are controlled. RESULTS AND DISCUSSION Out of the 243 sampled logs, 29 representing a(b1o1u.9t %) of total logs were of large diameter cl(a>s5s5 .01cm), 115 (47.3%) medium diameter class (40.01--55cm) a9n9d (40.7%) were chosen from small diameter clloagss (≤ 40cm). The results of the study indicated thagt ela driameter logs had the highest mean LR of 54. 4c8lo%s,ely NJAFE VOL. 7 No.1, 2011 36 UNIVERSITY OF IBADAN LIBRARY Nigerian Journal of Agriculture, Food and Environnmt.e 7(1):34-41 Published March, 20011 Egbewole et al., 201 1 followed by the medium sized logs with 54.18% anadst lly by small sized logs with 51.77%. Analysis of Variance (ANOVA) indicated that the influence oof g ldiameter classes is significant on the % LR o, dw woaste due to slab and wood saw dust a≤t 0(p.05). The results of 2-way interactions among l othge-diameter-classes and log shape was significant on the % LR while 2-wnatye riactions on log-diameter-class and years of reiexnpcee of headrig operators was not significant on the % L R ( p≤ 0.05). However, The results of 3-way interactions among the log-diameter-class, log shape and theel olef vexperience of headrig operators was signnifti coan the % LR at p≤ 0.05. There was no significant difference in dthiaem eter classes of the logs being processed both within and between the selected sawmills. The tr eosfu Dl uncan Multiple Range Test (DMRT) showed tthhaetr e was no significant difference in the 53.74% LR oinbetad in the large diameter class logs and thatht eo f5 4.18% from medium diameter class logs but the two cla sasres significantly different from the 51.77% lumr be recovered from small diameter class. The resu clto orrfelation analysis indicated that there wasr oan sgt positive significant correlation between log diameter clanssd total log input (0.904**), between log diame ctelarss and lumber recovery (0.853**), between log diametelars sc and slab volume (0.878**) and also betweegn lo diameter class and volume of waste due to sawt (d0u.4s38**) between log shape and slab volume (70*.*4)2 while there was no significant correlation betweloegn shape and lumber recovery (0.029) and also ebeentw log shape and waste due to dust volume (0.074) . The prediction equation used for lumber recoverRy )( Lis therefore stated thus: LR = 0.015 + 0.0053(Ls) – 0.002(LL) + 0.496(Tv) .–0 02(Dcl) + 0.0011 (Ex) - 0.003(Kf) +℮ …………..…19 Where : LR = lumber recovered (variable predict ethde), predictors are Dc = diameter class, Ls = hloagp se, LL = log length, Ex = experience of the headrig opersa (toyrs), Kf = saw kerf (mm), Tv= total volume,2 R= (0.897) coefficient of determination, SE = 0.1217. (Tab)l e 3 The implication of this is that at any given loiga mdeter class of known log shape, log length, reiexpnece of headrig operator, known saw kerf , the averageb elurm recovery can be estimated. The results of sresgiorne analysis however showed that the effects of sawf kweilrl only contribute about 7.8 lumber recoverfyfi ceiency and this effect was not significant on the wood twe agseneration at ≤p0.05. This trend is in agreement with Fuwape, (1985); Lucas (1983); Sanwo, (1982); Egbl e weto al, (2006) and Williston (1981). The mean technical performance efficiency (TPE) for log conversiont hine 9 selected small scale≤ (1 0,000m3 log/year) sawmills was 53.41%. It was observed that small scale salsw mini lOndo State had the highest mean TPE of 58 .07% followed by Lagos State with 55.97% and lastlyt hbey small scale sawmills in Oyo State with 46.17F%or. the medium scale sawmills, the mean TPE for log coniovne rsin the 9 selected medium scale (10,001—20,030 0m log/year) sawmills was 58.79%. It was observed mtheadt ium scale sawmills in Lagos State had the hsitg mhe an technical TPE of 63.47% followed by Ondo State w 5i6th.81% and lastly by the medium scale sawmillOs yino State with 56.10% (TPE). While, the trend wase dreiffnt for the large scale sawmills,. The resultw sehdo that, the mean technical performance efficiency (%TPE) fogr cloonversion in the 9 selected large sca≥le 2(0 ,000m3 log/year) sawmills was 41.54%. It was also obse rtvheadt large scale sawmills in Ondo State had thgeh ehsit mean TPE of 47.25% followed by Oyo State with 43.42% aLnadgos State with 35.15%. The implication of thseu rlets of the mean TPE for log conversion in the eachh eo fs telected sawmill is that the sawmill will no tp teorformance better than its present TPE unless there are imepmroevnts on their operational procedures (Table 4). In term of time efficiency, the percentage effeec tiwvorking time (EWT)for log conversion operatio nras nged between 46.67-70% with a minimum of 46.67% obse rivne tdhe small scale sawmill in Oyo State followeyd b 53.33% in the Large scale sawmills in Lagos Stahteil ew the maximum effective working time of 70% was observed in the large scale sawmills in Ondo S tTahtee. result of percentage idle working time (IWTa) sw a reverse of the trend above with a range ranged ebeent w30-53.33% with a minimum of 307% observed ien th medium scale sawmill in Ondo State followed by 311%.1 in the medium scale sawmills in Lagos State ew thhile maximum idle working time of 53.33% was observe dth ien small scale sawmills in Oyo State. (Table 4) However, the results of analysis of variance intdeidc athat there was significant difference in theE T, Ptime efficiency, machine efficiency, man efficiency ienr mt s of years of log conversion operations bothh iwn itand between sawmill scales and selected states at .(0P5<). 0The results are in conformity with Fuwape9, 8(51) who obtained a result of idle working time ranged beetnw e12.79-76.43%, effective working time rangingw beeetn 23.6-87.21 and mean 56% LR in His evaluation ofs 1a8w mills for log conversion efficiency in Ondo Set.a t (Table 5) CONCLUSIONS The findings have revealed that the kind of wopoedc sies process, the type of headrig machine (bawnd) ussed to process it and the kind of operator who mannedh tehaed rig machine have direct and significant im poanc tthe conversion efficiency obtained during log procegs sain d consequently the volume of wood waste geende.r aIft NJAFE VOL. 7 No.1, 2011 37 UNIVERSITY OF IBADAN LIBRARY Nigerian Journal of Agriculture, Food and Environnmt.e 7(1):34-41 Published March, 20011 Egbewole et al., 201 1 this volume of generated wood waste can be drallsyt irceaduced at any particular time, sawn board lavbalei for use in the housing and construction industry wnicllr ei ased. It will be possible to reduce the exotef netx ploitation pressure on the forest reserve areas, invariabelrye ftohre, the existing forest Reserve Area may bdee m toa last longer. However, the findings have also shown tahleu ev of conversion efficiency in a sawmill as amnp oi rtant forest management practice in any country and haalsvoe provided valuable information that could enchea n efficiency in log conversion process in sawmillrso tuhgh reduction of wood waste and dust volume. eS tinhce logs used were obtained from species of well-known tirm tbr ees commonly sought after by saw millers du eth teoir high utility values, coupled with the classificant io f such logs into diameter classes, the resuf lttsh iso study would no doubt have wide range of applicability,r mesoo as various classes of sawmills were consid. ered RECOMMENDATIONS In order to reduce the voulume of wood wastes ein ltohg conversion process to engender sustainabreles t fo management and profitable production of the sawsm, tihlle following recommendations are made (i) It is suggested that obsolete headrigse bpela rced promptly (where and when necessary). Ceo genxapterience should be a major determinant in the appointme nhte oafdrig operators in sawmills, where feasible,y olongl s of large diameter sizes≥ 4(5cm) with good and fairly cylindrical bole shoublde processed. The sawmillers should be given technical advice on new conversion procesgsu larerly especially on small diameter logs. Maintnecnea culture should be encouraged in sawmills as expde inct ea well managed sawmill. Finally, it is recommended that at regular inte,r vthael performance efficiency of any sawmill shobueld assessed by adopting the equation used in this study sinhcee e tquation is flexible enough to accommodateq aunayn tifiable factor in the process of estimating sawmill effnicciey instead of equating lumber recovery factor cwhh iis the ratio of lumber output to log input, this is necaersys to ensure high level of performance in sawmngil lindustry. Adoptions of these approaches will undoubtedly lrt etosu high level of performance in sawmilling indtruys. 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Illinosis: Waveland Press Inc. pp6 -34707 Table 1: Mean values of some assessed paramet ewroso odn species processed across surveyed sawmills S/ Sources of Trade No of logs Total Slab Bark Dust Lumber Lumber % lumber N variation name volume volume volume volume recovery recovery recovery (m3) (m3) (m3) (m3) volume factor (LRF) (m3) 1 Zanthoxylum Ata 8 .688 .139 .042 .091 .384 .559 55.92 leprieurii 2 Mitragyna Abura 12 .624 .125 .045 .093 .317 .508 50.77 ciliate 3 Phyllanthus Ashasha 17 .640 .133 .048 .101 .332 .519 51.86 discoideus 4 Pterygota Oporopor 13 .660 .134 .044 .099 .362 .549 54.85 macrocarpa o 5 Blighia Ishin 8 .340 .068 .019 .042 .191 .561 56.08 sapida 6 Ceiba Araba 22 .974 .186 .068 .135 .567 .582 58.21 pentandra 7 Milicia Iroko 10 .598 .120 .047 .094 .293 .489 48.94 exceslsa 8 Funtumia Ire 8 .635 .132 .044 .099 .350 .551 55.05 elastica 9 Anthostemma Odogbo 11 .666 .134 .048 .111 .365 .548 54.79 aubryanum 10 Cola Igi-obi 6 .568 .120 .052 .105 .314 .553 55.31 accuminata 11 Khaya Mahogany 16 .537 .113 .050 .111 .280 .522 52.20 ivorensis 12 Drypetes Osunsunr 6 .603 .124 .043 .102 .332 .551 55.07 gilgiana o 13 Ficus Ipin 13 .610 .119 .047 .106 .316 .518 51.85 exaspirata 14 Mansonia Mansonia 21 .323 .065 .024 .049 .180 .557 55.71 altissima 15 Harungana Uturu 3 .671 .146 .041 .091 .383 .571 57.09 madagascari ensis. 16 Nauclea Opepe 18 .627 .128 .042 .090 .300 .479 47.89 diderichii 17 Distemonant Ayan- 11 .654 .129 .045 .091 .323 .495 49.45 hus iroko bentamianus 18 Erythrophleu Obo 16 .629 .122 .045 .094 .343 .545 54.55 m suaveolens 19 Anoigeissus Ayin 10 .666 .140 .048 .083 .345 .518 51.83 leiocarpus 20 Triplochyton Obeshe 17 .651 .136 .047 .089 .328 .504 50.40 scleroxylon Total 243 .634 .128 .046 .096 .340 .5369 53.69 Field survey 2008, * highest %LR NJAFE VOL. 7 No.1, 2011 39 UNIVERSITY OF IBADAN LIBRARY Nigerian Journal of Agriculture, Food and Environnmt.e 7(1):34-41 Published March, 20011 Egbewole et al., 201 1 Table 2: Mean values and Duncan’s mean separaatiloune sv of some assessed parameters S/N Sources of No of Total Slab Bark Dust Lumber Lumber % lumber variation logs volume volume volume volume recovery recovery recovery (cm3) (cm3) (cm3) (cm3) volume factor (LRF) (cm3) 1 Diameter class (cm) i Small ≤ 40 81 .378a .091a .029a .066a .196a .518b 51.77b cm ii Medium 81 .635 b .126b .046b .098b .344b .542a 54.18a 40.01-55cm iii Large 81 .983c .194c .070c .147a .555c .565a 56.48a ≥55.01cm 2 Work experience (years) i ≤5yrs 27 .629ab .126ab .045a .096a .330ab .524a 52.44a ii 6-10yrs 126 .63b1 .128b .046a .097a .341b .540a 54.03a iii 11yrs-above 90 .66a3 .134a .046a .093a .353a .533a 53.27a 3 Saw ker f I ≤2mm 27 .663a .133a .045a .094a .363a .548a 54.80a ii 2.01-2.50mm 135 .63a 4 .130a .049a .095a .339a .535a 53.57a iii 2.51mm- 81 .624a .124a .046a .098a .333a .533a 53.27a above 4 Log shape straight 137 .641b .128b .046a .095a .352a .549a 54.87a Tapered 42 .63a4 .129b .045a .096a .331b .522b 52.15b Crooked 64 .62a3 .124a .047a .100a .334a .536a 53.59a Mean with the same letters are not significantflyfe drei nt at (p<0.05), Field survey 2008 Table 3: Regression analysis for log form, expnecreie of headrig operators and lumber recovery (LR) S/n Independent Unstandardized Standardized t Sig. Regres Coefficient of Variables coefficient coefficient sion determination (x1..x5) B Std. Beta ANO (R 2) Error VA (Constant) 0.0146 .048 .306 .760 1 Log shape(1x) .0053 .003 0.038 1.794 .074 ns 2 Length(x2) -0.002 .012 -0.004 -0.174 .8 n6s 2 3 Total volume 0.496 .027 0.953 18.331 .000** (x3) 4 Diameter class -0.002 .010 -0.007 -0.130 .8 9ns7 (x4) 5 Experience 0.0011 .004 0.006 0.251 .8 n0s2 (x5) 6 Kerf (x6) -0.003 .004 -0.012 -0.522 .6 0ns2 .00** .897 Note: **highly significant at 1% probability leve Pl,<0.05, * = significant, ns = not signifnict.a Y (LR) = Dependent variable (Lumber recovery ), x1…x6 = Predictors, NJAFE VOL. 7 No.1, 2011 40 UNIVERSITY OF IBADAN LIBRARY Nigerian Journal of Agriculture, Food and Environnmt.e 7(1):34-41 Published March, 20011 Egbewole et al., 201 1 Table 4: showing log input, waste generated aned l uthmber recovery in selected sawmills on produnc tsicoale and State basis State Sawmill No of Log Total Effective Idle Lumber LRF Scale logs Conversion volume Working Working recovery method (m3) Time (%) Time (%) volume (m3) Lagos Small 27 Sawing around, .542 57.78 42.22 .281 .517 State T&T Medium 27 Sawing around, .580 68.89 31.11 .312 .540 T&T Large 27 Sawing around, .720 53.33 46.67 .362 .503 T&T Oyo state Small 27 Sawing around,.5 25 46.67 53.33 .279 .530 T&T Medium 27 Sawing around, .554 66.67 33.33 .278 .501 T&T Large 27 Sawing around, . 660 57.77 42.22 .356 .541 T&T Ondo Small 27 Sawing around, .530 61.11 38.89 .279 .526 State T&T Medium 27 Sawing around, .613 70.00 30.00 .325 .532 T&T Large 27 Sawing around, .720 60.00 40.00 .362 .503 T&T Source: Field survey 2008 Table 5: Technical Performance Efficiency (TPE) lofogr conversion in selected sawmills on production scale aond S tate basis State scale Time Machine Men Experience LRF TPE Efficiency Efficiency Efficiency (Year) (%) (%) (%) (%) Small Lagos 5.2/9 5/4 7/8 7.7/7 .537 55.97 Oyo 4.2/9 4/4 6/8 9.7/7 .513 46.19 Ondo 5.5/9 5/4 9/8 5.7/7 .543 58.07 Mean 4.97/9 4.7/4 7.3/8 7.7/7 .531 53.41 Medium Lagos 6.2/9 7/8 11/16 11.3/7 .533 63.47 Oyo 6.0/9 6/8 10/16 10.4/7 .504 56.10 Ondo 6.3/9 7/8 12/16 7.7/7 .553 56.81 Mean 6.2/9 6.7/8 11/16 9.8/7 .530 58.79 Large Lagos 4.8/9 10/16 15/32 6.0/7 .560 35.15 Oyo 5.2/9 9/16 18/32 8.7/7 .549 43.42 Ondo 5.4/9 8.3/16 13/32 7.6/7 .537 47.25 Mean 5.13/9 9.1/16 15.3/32 7.43/7 .549 41.94 Field survey 2008 NJAFE VOL. 7 No.1, 2011 41 UNIVERSITY OF IBADAN LIBRARY