ECONOMICS OF WEED CONTROL PRACTICES ON RICE FARMS IN OBAFEMI-OWODE AREA OF OGUN STATE, NIGERIA

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1 ARPN Journal of Agrcultural and Bologcal Scence Asan Research Publshng Network (ARPN). All rghts reserved. ECONOMICS OF WEED CONTROL PRACTICES ON RICE FARMS IN OBAFEMI-OWODE AREA OF OGUN STATE, NIGERIA Ologbon O. A. C. 1 and Yusuf S. A. 2 1 Department of Agrc. Economcs and Farm Management, Olabs Onabanjo Unversty, Yewa Campus, Ayetoro, Ogun State, Ngera 2 Department of Agrcultural Economcs, Unversty of Ibadan, Ibadan, Oyo State, Ngera E-Mal: chrslogem@yahoo.com ABSTRACT The economc analyss of weed control practces on rce farms n Obafem-Owode area of Ogun State, Ngera was examned. The study was based on prmary data collected from 88 respondents. Multstage samplng procedures were used to randomly select the communtes that were ntervewed. Ten (10) major weeds were found to be predomnant on the rce farms vsted, whch were Gunea grass, Broom weed, Stubborn grass, Carpet grass, Centro, Trdax, Amaranth, Pg weed, Goat Weed and Water leaf among others. Weed control was done by manual and chemcal applcaton methods. Average weed cost estmate n the study area was N80, per hectare, Farmers producton effcency was postvely nfluenced by land sze, hred labour, quantty of fertlzer used, cost herbcdes and number of farm land cultvated. Only the frequency of manual weedng sgnfcantly ncreases the neffcency level on rce farm. It was recommended that local rce farmers should be encouraged by supplyng requred technology nputs that may mprove on ther level of producton, whle further educaton should be gven to farmers on fertlzer applcaton to avod ts excess applcaton. Keywords: weeds control practces, producton effcency, rce farm, herbcdes, cost estmates. INTRODUCTION Precson farmng or ste-specfc farmng s a recent agrcultural technology born out of the avalablty of new technologes (e.g. Dfferental Global Postonng System, DGPS), and socally due to greater level of farmer and government concerns about the reducton of potental agrochemcal contamnants (fertlzers or herbcdes). Precson farmng conssts of applyng just the rght amount of crop nputs at the rght locaton and s based on crop and ste specfc requrements. Precson weed management s recommended only f the weed populaton wthn a feld s dstrbuted n patches. Such agrcultural management has drawn attenton to the need for methods to descrbe and analyze the spatal dstrbuton of weeds, and develop weed maps for a ste-specfc herbcde treatment. Several technques have been used to map weeds (González-Andújar et al., 2001). Methods to estmate weed denstes and to evaluate weed speces wth real tme detectors or remote sensng have been reported (Wartenberg and Dammer, 2001). Weeds restrct crop producton and so mpose opportunty costs on producers and consumers from producton. Such costs could translate nto hgher gran prces to consumers. Alternatvely, wdespread weed control practces ncreases producton and ths may generate lower prce under the compettve market condton. Weed detecton and dentfcaton have been reported by several authors usng remote sensng technques for surveyng weed nfestatons on range and forest lands (Evertt et al., 1996). There has been a consderable research effort over the last few years to dentfy weed threshold levels n non-organc crops (e.g., Wlson, et al., 1993; Welsh, et al., 1999; Wartenberg and Dammer, 2001) however, very few studes have amed to do the same for organc crops. Ths study had estmated the weed control expendture of rce farmers and estmated the effect of the ncluson of weed control nput varables on the techncal effcency of rce farms n Obafem- Owode area of Ogun State, Ngera. Conceptual framework n estmatng the yeld loss effect of weed nfestaton The mpact of weeds upon a producton system can be demonstrated usng the basc concept of producton functon. The quantty of rce output s determned by the quantty of fxed and varable nputs nto the producton process, represented algebracally by producton functon. Y = f (V, F) (1) Y s yeld, V and F are varable and fxed nputs n rce producton, respectvely. The varable and fxed producton nputs nclude such factors as rce varety, sol type, sol fertlty, ranfall, temperature, among others. Weed nfestaton affects the parameters of ths relatonshp and reduce output for any gven level of nput. The yeld loss assocated wth weeds can be expressed as a reducton n output resources (excludng expendture on weed control) to neutralze the effects of weeds, or any combnaton of consequent output and revenue adjustments between the extremes. Introducng nput varables specfcally for weed control extends the producton functon framework as follows: Y = f (V, H, F) (2) H s weed control nput such as a herbcde n rce producton. Increasng the weed control nput varable wll reduce losses and result n a hgher level of outputs V and F. The above framework avods comparson of the benefts of a weed control technology to a hypothetcal and usually unattanable weed-free scenaro. Weed losses (L) are defned as the losses resultng from yeld reducton due to resdual weeds after control, n addton to prce 503

2 ARPN Journal of Agrcultural and Bologcal Scence Asan Research Publshng Network (ARPN). All rghts reserved. penaltes for contamnated gran. Weed control expendture (E) s defned as the costs ncurred on herbcdes applcaton, mechancal weedng and tllage. Ths mples a drect fnancal mpact of weeds ether as a result of ncome reducton from lower output, or the prce effect from ncreasng producton cost. Conceptually, weed loss effect s computed as the addton of yeld loss effect (YL) and prce penalty (PP) of weed nfestaton on a farm. Specfcally, the yeld loss effect of weed nfestaton on rce farms s determned as: n YL AjkY0 j = 1 = D (3) jk YL = Yeld Loss due to weed nfestaton (Tonnes/Ha) A = Area of farm plot nfested by weed (Ha) Y 0 = Estmated weed-free yeld (Tonnes/Ha) D = Yeld loss coeffcent (a constant) j = 1 n (number of speces of weed endemc on the farm plots of the th rce farmer). The subscrpts j and k stand for weed type (or speces) and weed densty, respectvely. j = 1 for grass and 2 for broad-leaves weeds, respectvely (whch were the focused weed types n ths study). K and D are predetermned quanttes n lteratures (e.g., Jones et al., 2000). D s a proportonal factor bounded by zero; an ncrease n ts value for any crop represents greater yeld damage due to hgher weed densty (Jones et al., 2000). The revenue loss n rce crop s a functon of the yeld loss due to weed nfestatons and the commodty (rce) market prce. The loss n revenue due to weed nfestaton s computed as: RL = m YL = 1 p. (4) RL = Revenue Loss (Ngera Nara /Ha) P = Unt commodty marlet prce (Ngera Nara/Kg of paddy rce) m = Number of rce plots of the th farmer nfested by weed YL s as prevously defned The prce penalty arses as a result of rce gran contamnaton on the feld. Gran contamnaton refers to a reducton n the quantty and qualty of gran as a result of drect contact of grans wth chemcal substances durng weed treatment, and gran deformty as a result of stunted growth caused by heavy weed nfestaton. The prce penalty arsng from gran contamnaton s computed as: PP = N f pc( T. Pr + Gc ) (5) PP = Total Prce Penalty n the study area (Ngera Nara) N f = Number of farm plots affected by severe weed nfestaton n the 2008 croppng season n the study area Pc = Percentage area of farm penalzed for weed contamnaton (%Ha) T = Total estmated tonnage of rce gran contamnated n the referenced croppng season (Tonne) P r = Average prce reducton, computed as the dfference between the askng prce for uncontamnated gran and the actual prce due to contamnaton (Ngera Nara) G c = Average gran cleanng costs per farm (cost ncurred n wadng off the effect of gran contamnaton, n Ngera Nara) Consequently, Weed Loss Effect = YL + PP (6) YL and PP are as prevously defned. Effcency analyss of weed control technology n rcebased farmng systems Effcency s the maxmzaton of output nput rato. There are three components of effcency namely; techncal, allocatve and economc effcency. Techncal effcency (TE) s the measure of effectveness n whch maxmum output s obtaned from a gven combnaton of nputs.e., the ablty to operate on the Producton Fronter). Techncal effcency assumes the essental nature of output of goods and servces to reman unchanged and focus on reducng the cost of nput for producton. Allocatve effcency (AE) refers to the stuaton resources are gven n proft maxmzng sense so that the margnal value products of resources are equal to ther unt prces. Economc effcency (EE) combnes techncal and allocatve effcences. Perfect techncal and allocatve effcency mples that the frm s maxmzng proft and mnmzng cost for a gven level of output.e., operatng on the expanson path (Ojo, 2003). Conceptually, the th rce farm technology s represented by a stochastc producton fronter as follows: Y = f ( X β ) + ε (7) Y denotes output of the th rce farm; and X s a vector of actual nput quanttes used by the th farm, ncludng weed control herbcdes and manual labour; β s a vector of parameters to be estmated and ε s the composte error term (Agner et al., 1977, Meeusen and Vanden Broeck, 1977), defned as: ε = V U (8) V s assumed to be ndependently and dentcally 2 dstrbuted N(0, σ V ) random errors, and U U I s s a non negatve random varables, assocated wth techncal neffcency n rce producton, assumed to be ndependently and dentcally dstrbuted and truncatons (at zero) of the normal dstrbuton wth mean µ and 504

3 ARPN Journal of Agrcultural and Bologcal Scence Asan Research Publshng Network (ARPN). All rghts reserved. 2 varance, σ U (/N(µ,σµ 2 /). The maxmum lkelhood estmaton of equaton (7) provdes estmators for β and varance parameters, σ = σv + u andy 2 /u 2 = σu. Subtractng V from both sdes of equaton (7) yelds. _ Y = Y V = f(x β) µ (9) Y s the observed output of the th farm, adjusted for the stochastc nose captured by V. Equaton (9) s the bass for dervng the techncally effcent nput vector and for analytcally dervng the dual cost fronter of the producton functon represented by equaton (7). For a gven level of output Y, the techncally effcent nput vector for the th t farm, X s derved by smultaneously solvng equaton (9) and the nput ratos X 1 /X =K ( >1), K s the rato of observed nputs X 1 and X. Assumng that the producton functon n equaton (7) s self-dual (e.g. Cobb-Douglas), the dual cost fronter can be derved algebracally and wrtten n a general form as follows: C = h( W, Y, α) (10) C s the mnmum cost of the th farm assocated wth output, Y. W s a vector of nput prces for the th farm and α s a vector of parameters. The economcally effcent nput vector for the th e farm X s derved by applyng shepherd s lemma and substtutng the frm s nput prces and output level nto the resultng system of nput demand equaton: δc = X e K (W,, Y, ф); K= 1, 2, m nputs (11) Ф s a vector of parameters. The observed, techncally effcent and economcally effcent costs of producton of the th t farm are equal to W X, W 1 X 1 and, W X e. The convectonal ways of measurement of costs are compute techncal (TE) and Economc (EE) effcency ndces for the th frm as follows: TE = W X t W X (12) EE = W X e W X (13) Followng Farrell (1957), the Allocatve effcency (AE) ndex can be derved from Equatons (6) and (7) as follows: AE 1 = EE = W X e W X (14) Thus the total cost or economc neffcency of the th t frm (W X - W X e ) can be decomposed nto ts t) t techncal (W X- W X and Allocatve (W X - W X e ) components and t can also be measured n a non parametrc approach. Under ths approach, data envelopment analyss (DEA) (Charnes et al., 1978) s used to derve techncal, scale, allocatve and economc effcency measures. METHODOLOGY The study area The area of study was Obafem-Owode Local Government Areas (LGAs) of Ogun State, Ngera. The State comprses of four (4) dvsons, namely Egba, Ijebu, Remo, and Yewa, out of Obafem-Owode LGA forms major rce-growng area of the State, belongng to Egba. Ogun State s n the South-western zone of Ngera. It s bounded n the West by the Republc of Benn, n the East by Ondo State, n the North by Oyo State and n the South by Lagos State and the Atlantc Ocean. The average ranfall n Ogun State ranges between 1250mm and 1800mm wth a slght bmodal ranfall dstrbuton whch peaks n June and October, whch largely supports the producton of rce and other arable crops. Average temperature and average relatve humdty range from c and 80-90%, respectvely. Data, source and method of collecton Prmary data were collected for ths study usng structured questonnares and focus ntervew methods through a stratfed random samplng technque. Responses to the researcher s questons depended largely on memory recall of the farmers, snce nformaton was sought on the rce croppng actvtes of the 2008 croppng season. A sngle ran-fed croppng regme (extendng between May and October 2008) was covered n ths study. Relevant nformaton from journal materals and statstcal publcatons were also accessed. Data relatng to soco-economc characterstcs, types of weed encountered n the 2008 croppng season, detals of weed control nputs and cost, crop producton nput and output estmates were obtaned from the rce farmers among other varables. Samplng technque Obafem-Owode was purposvely selected from among the 20 LGAs n Ogun State due to the predomnant concentraton of rce farmers n the area. Smple random selecton was consequently carred out to select at least 60% of the major rce-growng communtes wthn LGA to make up the samplng frame for ths study. Ths resulted n the selecton of Obafem, Ogunmakn, Oba, Ofada, Mosunmore, Marako, Aywere, Owode to represent the LGA. Fnally, a total of 95 respondents were ntervewed from the LGA out of whch 7 dd not gve complete nformaton, resultng n the 88 questonnares used for ths study. Estmatng weed control expendture on rce farms The control cost of weed was determned from the survey, whch ncludes the average manual weedng labour costs, the herbcde costs (pre-emergent and postemergent herbcde) and treatment (or applcaton) costs on rce farms cultvated n the 2008 croppng season. The costs were determned as follows, followng (ABARE, 1999): CC = MWC + HC 505

4 ARPN Journal of Agrcultural and Bologcal Scence Asan Research Publshng Network (ARPN). All rghts reserved. CC = Total control cost (Ngera Nara/ Ha) MWC = Manual weedng cost (Ngera Nara / Ha) HC = Herbcde costs (Ngera Nara / Ha) Herbcde costs were computed as: HC = NAM (P e + P 0 + Tr) HC = Herbcde cost (Ngera Nara / ha) N = Number of tmes farm were treated wth herbcdes A = Farm sze (Ha) P e = Total cost of farm pre-emergence herbcde (Ngera Nara)/ annum P 0 = Total cost of farm post-emergence herbcde (Ngera Nara / annum) Tr = Treatment / applcaton cost per tme (Ngera Nara / manday) M = Total quantty of labour utlzed on herbcde applcaton (manday) Although the need for control weeds that germnate pror to crop plantng s mportant to tllage costs, there are other reasons for tllage. Consequently, t s assumed that 75% of all tllage costs are attrbutable to weed control (ABARE, 1999). Stochastc fronter producton model The stochastc fronter producton model was specfed to estmate techncal effcency and ts determnants n weed controlled rce producton n the study area. Accordng to Tzouvelekas et al. (2001), the producton technology of the farmers assumes to be specfed by Cobb-Douglas Fronter producton functon (Wadud et al., 2000) that s defned by: LnY 1 = Lnβ0 + β 1 Lnx 1 + β 2 Lnx 2 + β 3 Lnx 3 + β 4 Lnx 4 + β 5 Lnx 5 + β 6 Lnx 6 + β 7 Lnx 7 + V-U Y 1 = Rce output (Kg) X 1 = Hectares of land cultvated to rce X 2 = Quantty of seed planted (Kg) X 3 = Hred labour (number) X 4 = Household labour (number) X 5 = Quantty of fertlzer (Kg) X 6 = Cost of weed control (both manual weedng and herbcde) (N) X 7 = Number of farmland (number) V = Non-negatve truncaton at zero or half normal dstrbuton wth N (U, δ U 2 ) U = Techncal neffcency effect whch are assumed to be ndependent. The Ineffcency model n the weed-controlled rce producton system s gven as: U = θ + n 0 θ 1 = 1 Z + e U = Measure of techncal neffcency n weed controlled rce farms Z = varables hypotheszed to explan techncal neffcency n the weed controlled rce producton systems, ncludng: Z 1 = Age of farmers (year) Z 2 = Years of formal educaton (year) Z 3 = Year of experence n rce farmng (year) Z 4 = Varety of rce cropped (1= Improved; 0 = Local) Z 5 = Gender of farmer (1= male, 0 = female) Z 6 = Weed control technology (1 = use of herbcde, 0 = manual weedng) Z 7 = Frequency of weed control (numbers). RESULTS AND DISCUSSIONS Common weeds of rce farm n the study area Ten weeds were dentfed on rce farm plots n Obafem-Owode Local Government Area. The major weeds assocated wth rce n the study area are manly grasses. The farmers preferred the use of selectve herbcdes whch they reported to have found more relevant to ther case n the area. Manual weedng was also used as a follow-up to herbcde for total eradcaton of weeds especally weed nfestaton was severe. The dentfed weeds are lsted below accordng to ther ncreasng order of control dffculty, as reported by the rce farmers thus: Gunea grass (Pancum maxmum); Broom weed (Sda acuta; Stubborn grass (Eleucne ndca); Carpet grass (Anonopus compressus); Centro (centrocema pubenscen); Trdax (Trdax procumbens); Amaranth (Amarathus spnosus); Pg weed (Boeshata dffusa); Goat Weed (Agerantum conyzodes) and Water leaf (talnum trangulare). Weed control expendture Table-1. Estmates of weed control expendture. Cost tem Weed control cost (Ngera nara per hectare ) Manual weedng 51, Herbcde 20, Tllage 8, Total cost of weed eradcaton 80, Mean farm sze = 4.37 Ha The average amount spent on rce farm n the study area on manual weedng was N51, / ha whle 506

5 ARPN Journal of Agrcultural and Bologcal Scence Asan Research Publshng Network (ARPN). All rghts reserved. the average cost of weed control usng herbcde (both preemergence and post-emergence) was N20, / ha. The average amount spent on tllage actvtes (manly ploughng and rdgng) was N8, / ha. Ths resulted n total weed control expendture of N80, / hectare. Techncal effcency estmates As presented n Table-2, land, hred labour, and fertlzer are statstcally sgnfcant at 1% probablty level, whle herbcde and number of farm land cultvated to rce are statstcally sgnfcant at 5% and 10% level of sgnfcance, respectvely. Other hypotheszed varables have no sgnfcant effect on rce output. The coeffcents of all the varables are postve except for hred labour and organc fertlzer whch have negatve coeffcents. Ths mples a reducton effect on rce output wth an ncreasng usage of hred labour and organc fertlzer. Ths s lkely to be as a result of the subsstence level of rce producton n the area, even though many households are nvolved n rce producton. The general Lkelhood Rato Test for rce producton was Table-2. Stochastc producton fronter for rce producton n the study area. Varable Coeffcent t-value Constant Land 7.405* Hred labour * Household labour Seed Fertlzer ** Herbcde 2.433** Number of farmland 1.919*** Dagnoss statstcs sgma square Gamma Log of lkelhood functon LR test Fgures n parenthess are the t-values * 1% sgnfcant level; ** 5% sgnfcant level; *** 10% sgnfcant level Sources of neffcency n rce producton systems Estmates of techncal neffcency of rce producton are presented n Table-3. Only the frequency of manual weedng sgnfcantly ncreases the neffcency level on rce farm, showng that cost of manual weedng has much negatve effect on the overall output level of rce. Age, gender, educatonal status of the farmers, varety of rce planted and weed control technology do not sgnfcantly nfluence the level of techncal neffcency, aganst apror expectaton. Ths mght be due to level / rate of rce producton n the country generally. For example, Sanzdur (2006) observed that farmers wth no educaton ncurred sgnfcantly hgher losses and recorded sgnfcantly low level of proft when compared wth those wth hgher level of educaton. Table-3. Determnants of techncal neffcency. Ineffcency varables Coeffcent t-value Constant Age Gender Educaton Years of experence Varety of rce Weed control technology Frequency of manual weedng * Fgures n parenthess are the t-values * 1% sgnfcant level; ** 5% sgnfcant level; *** 10% sgnfcant level Techncal effcency estmates of rce farms The frequency dstrbuton of the techncal effcency estmates s presented on Table-4. Table-4. Techncal effcency of rce farmers. Class nterval Frequency Percent dstrbuton and above Total Mnmum = 61 Mean = 78 Maxmum = 99 33% of the rce farmers are effcent at 75-79%, followed by the 70-74% (29.5% of farmers). The tables showed that majorty of the farmers n Obafem-Owode Local Government Area are effcent from 70% and above, approachng the technology fronter. 507

6 ARPN Journal of Agrcultural and Bologcal Scence Asan Research Publshng Network (ARPN). All rghts reserved. CONCLUSIONS AND RECOMMENDATIONS Rce farmers n the study area dsplay hgh level of techncal effcency, even though they operate at subsstence level of producton. Educatonal level of the farmers does not necessarly contrbute to ther producton effcency as reported n lterature. It was recommended that the local rce farmers should be motvated to use more of mproved technologes that may ncrease ther level of producton as well as the cost ncurred on tllage and manual weedng. Furthermore, the farmers should be more educated on the approprate use of fertlzer as excess of t was found to be detrmental to rce producton output. REFERENCES Australan Bureau of Agrcultural and Resource Economcs Australan gran ndustry: performance by GRDC agro ecologcal Zones; Project 1608; AGPS, Canberra. Evertt J.H., Escobar D.E., Alanz M.A., Davs M.R and Rcherson J.V Usng spatal nformaton technologes to map Chnese tamarsk (Tamarx chnenss) nfestatons. Weed Scence. 44: González-Andújar J.L., Martínez-Cob A.L., López- Granados F and García-Torres L Spatal dstrbuton and mappng of Orobanche crenata nfestaton n contnuous Vca faba croppng for sx years. Weed Scence. 49(6): Jones R.E and Medd R.W Economc thresholds and the case for longer term approaches to populaton management of weeds. Weed Technology. 14(2): Ojo S.O Productvty and Techncal Effcency of Poultry egg producton n Ngera. Internatonal Journal of Poultry Scence. 2(6): Sanzdur C. R Techncal Effcency of Dry Season Cereals. Journal of Amercan Socety. 35: Tzouvelekas V., Cramer G.L and Wales E.J Techncal Effcency of Alternatve Farmng Systems: the Case of Greek organc and Convectonal olve-growng farms. Food Polcy. 26: Wadud A. and Whte B Farm Household Effcency n Bangladesh: A Comparson of Stochastc Fronter and DEA Method. Appled Economcs. 32: Wartenberg G. and Dammer K.H Ste-specfc real tme applcaton of herbcde n practce. In: Proceedngs of the 3 rd European Conference on Precson Agrculture; Grener and Blackmore, Montpeller, France. pp