Profitability of Small-Scale Yam Farms in Kogi State, Nigeria

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INTRODUCTION

Yam (Dioscorea spp.) is a tropical crop grown on about 5 million hectares of land in about 47 countries in tropical and sub-tropical regions (Acquaah, 2005; IITA, 2009). It is a primary agricultural commodity and its cultivation stretches from the humid rainforest in the south to the Northern Guinea Savannah of Nigeria (Ezeburo, 2012). Nigeria is the largest producer of yam accounting for 36 million MT or about 71% of global output (IITA, 2002; Acquaah, 2005; Epharaim et al., 2010). The yam sub-sector is

capable of earning the country US$18.6 billion in foreign exchange annually (Nwosu, 2008). Yam is a major food item for millions of people in West Africa and contributes significantly to rural food security (Ile et al., 2006). Yam also provides income for semi-subsistence and commercial producers and a source of export income for nations and accounts for about 32% of farm income earned from crops (Chukwu and Ikwelle, 2000). There is a direct relationship between

Journal of Applied Agricultural Research 2014, 6(2): 95-105 ISSN 2006-7496 © Agricultural Research Council of Nigeria, 2014

PROFITABILITY OF SMALL-SCALE YAM FARMS IN KOGI STATE, NIGERIA

*Simpa, J. O.1 and J. N. Nmadu2

1Department. of Agricultural Bio-Environmental Technology, The Federal Polytechnic, Nasarawa, Nigeria

2Department of Agricultural Economics and Extension Technology, Federal University of Technology, Minna, Nigeria

*jamessimpa@yahoo.com _______________________________________________________________________

ABSTRACT The study examined the parameters that influence the profitability of small-scale yam farms in Kogi State, Nigeria. A multi-stage sampling technique was used to select 180 representative farms from six villages in three Local Government Areas of the State. Structured questionnaire was used to collect the data. Descriptive statistics and multiple regression models were used to analyze the primary data collected. The gross margin per hectare, return per naira invested per hectare and production profitability per hectare were ₦463,039.88, 2.02 and 202.44%, respectively. The multiple regression estimates revealed that the model was of a good fit and age, years of farming experience, household size, and extension contacts of the farmer and farm size were the important and significant socio-economic parameters influencing the gross margin of the farms. Age had negative relationship with the farmers’ income while other significant variables had positive effects. The results also showed that inadequate capital to finance farm production, lack of appropriate storage structure, lack of access road and high cost of labour were some of the constraints facing the farmers. The study concludes that yam production is profitable and age, farm size, household size, extension contacts and farm size have effects on income from small-scale yam farms. It is recommended that farm sizes should be expanded by provision of credit and farm inputs at subsidized rate and extension services to be improved to provide farmers with new technologies in order to increase margin. Social amenities and infrastructural facilities should be provided in farm centres to encourage young and educated men and women to take up yam production. Keywords: Small-scale yam farms, profitability, gross margin ________________________________________________________________________

yam production and economic development of yam producing regions in Nigeria (Migap and Audu, 2012). Yam has the potential for increased commercial exploitation in Nigeria. However, there are some limiting factors (Sullivan et al., 2008). The main impediment to expansion of yam production as a commercial industry has been relatively high labour requirement (Opio, 1990). Yam farmers face problems of deteriorating soil fertility, prevalence of diseases and pests, competitive weeds and need for new varieties (Aseidu and Alieu, 2010). The declining role of yam in the diet of urban poor due to its high price as a result of low yield and low returns to labour which discouraged the subsistence farmers from producing excess for market had resulted in a range of health problems (Sullivan et al., 2008). Profitability is the financial reward the farmer gets from yam production. It is the primary goal of all business outfits. The basis of farmers’ decision for producing a crop in excess of their household requirement by allocating the productive, but scarce resources in the production depends on the relative financial profitability of the individual enterprise (Carlos, 2001; Don, 2009). Profit is a function of farm type, size, location and commodity produced as well as yield, output price, operation costs which include fertilizer, herbicides, and insecticide (Blank, 2002; Jolejole et al., 2009). Farm profitability is the key to a crop production enterprise as crop producers would only embrace new methods if they are profitable. Government policies and decisions affect farmers’ profit (Acquaah, 2005). Studies have been carried out on profitability of yam production using gross margin analysis in many parts of Nigeria and it was confirmed that yam production is profitable; these include Abubakar et al. (2005); Sanusi and Salimonu (2006); Ojo (2007); Izekor and Olumese (2010); Ephraim et al. (2010). The objectives of this study were to examine the

socio-economic characteristics of the small-scale farmers; determine the profitability of small-scale yam farm enterprises; examine the socio-economic factors influencing profitability of small-scale yam farms in the study area and examine the constraints facing small-scale yam farmers in the study area.

METHODOLOGY Kogi State is located between latitude 6o30 N and 8o30’N and longitude 5o51 E and 8o00’E, in the Guinea forest-savanna ecological zone of Nigeria. The population of the State is 3,314,043 (NPC, 2006). The State has a tropical climate with rainy and the dry seasons. The rainy season lasts from March to October while dry season falls between November and February. The annual rainfall ranges from 1016 mm to 1524 mm (KADP, 1995; KO-SEED, 2004). The study used a multi-stage sampling technique for selection of the respondents. The study area was divided into three blocks. At the first stage, one Local Government Area each was randomly selected from the blocks. At the second stage, two villages each were selected randomly from the selected Local Government Areas. At third stage, yam farmers were randomly selected from the selected villages for response using structured questionnaires. A total of 10% of the yam farmers in each village was selected, and sample size of 180 yam farmers was used for the study. A pilot test was carried out. The data used was for 2011/2012 cropping season, but collected in 2013. The data were analysed using various profitability indices as presented in equations (1) - (6) and Cobb-Douglas function equation was used for the analysis of the influence of socio-economic characteristics of the farmers on gross margin of their farms (7). Straight line

Simpa and Nmadu, 2014

depreciation method was used for depreciation of fixed items. Gross Margin Analysis: GM = GFI – TVC …1 Gross Margin Percentage:

Gross margin (%) = …2 Farm earning = GM + value of products consumed at home … 3

Return per Naira Invested (R/N): R/N = …4 Return per Gross Farm Income:

GM/GFI = …5 Production profitability Index (PPI):

PI = …6 where; GM = gross margin (₦/ha) GFI = gross farm income TVC = total variable cost TC = total cost (Manteuffel, 1984; Olukosi and Erhabor, 2005; Reddy et al., 2008; Izekor and Olumese, 2010; Aldona, 2012). Cobb Douglas functional form is specified as: InY = In β0 + β1InX1 + β2InX2 + β3InX3 + β4InX4 + β5InX5 + β6InX6 + β7InX7 + β8InX8 + β9InX9 + β10InX10 + β11InX11 + β12InX12 + β13InX13 + β14InX14 +e …7 where: Y = Gross margin (₦/hectare)

X1 = gender (dummy: male = 1, female = 0) X2 = age of the farmer (years) X3 = marital status (dummy: married = 1, otherwise = 0) X4 = farming experience (years) X5 = educational level (No. of years spent in school) X6 = household size (No. of people in a farm household) X7 = membership of farmers’ association (dummy: Yes = 1, No = 0) X8 = farm size (hectare) X9 = source of finance (dummy: borrowed = 1, otherwise = 0) X10 = source of labour (dummy: hired = 1, otherwise = 0) X11 = source of planting materials (dummy, reserved = 1, otherwise = 0) X12 = Distance farm to market (km) X13= extension contacts (Nos) X14= distance from settlement to farm (km). B0 = intercept, B1 – B14 = regression parameters to be estimated and e = error terms. Statistics such as the explanatory power of the model (R2), the significance of the estimated coefficient, the magnitude of the estimated coefficient were used to describe result of the regression model. Finally, a 5-point Likert type scale was used to elicit data on constraints facing yam farmers in the study area. The scores were weighed and the weighted average found. The critical mean of 3.0 was used to accept or reject an item as a constraint of yam production in the study area. The constraint that scored equal to or more than critical means of 3.0 was accepted as constraint to yam production or otherwise, rejected.

RESULTS AND DISCUSSION The socio-economic characteristics of the yam farmers in the study area (Table 1) are consistent with the findings of Ojo et al. (2009); Olorusanya et al. (2009); Musa et al. (2011); Ohajianya (2010); Simonyan and

Profitability of Small-Scale Yam Farms

Obiakor (2012). The main source of labour depicts that 62.2% of the respondents use family labour. The family labour is more readily available, disciplined, reliable and convenient and it could reduce production cost and increase profit (Fasasi, 2006). About 80% of the respondents were not members of any farmers’ association. This might imply that the farmers have limited access to credit, new innovations and inputs and marketing outlets (Simpa, 2011). Majority (94.4%) of the respondents accessed credit from limited informal sector for farm operations. And this translates into ineffectiveness, small scope and low output

and invariably low income. About 99.4% of the respondents had farm sizes of between 0.1–2 hectares indicating that the farmers are smallholders, which might be due to shifting cultivation under traditional hoe-cutlass method and they could be efficient within the framework of state of technology (Simpa, 2014). About 75% of the respondents travelled a distance of more than 10 km from the farm to the market to sell their outputs while most of the farmers (55.5%) covered distance of more than 15 km daily to their farms. These long distances reduce farmers’ efficiency and profit. About 52% of the farmers got their planting materials from their reserve of previous year’s harvest;

Table 1: Socio-economic characteristics of the yam farmers in the study area Variable Frequency Percentage Mean Minimum Maximum Age 21 – 30 21 11.7 31 – 40 20 11.1 41 – 50 56 31.1 51 – 60 54 30.1 Above 60 29 16.1 Total 180 100 49.1 21 75 Gender Male 150 83.3 Female 30 16.7 Total 180 100 Marital status Single 20 11.1 Married 127 70.6 Widow 20 11.1 Others 8 4.9 Total 180 100 Household size (Nos) 1 – 5 32 17.8 6 – 10 110 61.1 11 – 15 38 21.1 Total 180 100 8.2 1 18 Educational level (yrs) No formal education 86 47.8 Primary 54 30 Secondary 34 18.9 Tertiary 6 3.3 Total 180 100 9.69 0 19 Years of farming experience 1 – 10 19 10.6 11 – 20 36 20 21 – 30 58 32.2 31 – 40 39 21.7 40 and above 28 15.6 Total 180 100 29 5 60 Main source of labour Family labour 112 62.2 Hired labour 68 37.8 Total 180 100

which in most cases account for a large share of cost of production. This reduced farmers’ quest for finance. Table 2 shows the cost and return analysis of the study as per hectare. The total variable cost was ₦201,478.19/ha, which constitute 98.88% of the total cost of ₦203,750.20/ha. This finding conforms to Ojo et al. (2009). The planting materials made up of 70.86% (₦144,387.44/ha) of the total cost and this agrees with Musa et al. (2011). The total fixed cost per hectare was ₦2,272.01/ha and this was 1.12% of the total cost. The fixed cost constitutes a small portion of the total cost,

thus the appropriateness of the use of gross margin analysis. This is supported by the findings of Olorunsanya et al. (2009), but contrary to Musa et al. (2011). The gross farm income (GFI) and gross margin (GM) were ₦616,227.08/ha and ₦412,476.88/ha, respectively. The gross margin percentage was 66.94%. The value of home consumed yam and the actual farming earning were ₦50,563 and ₦463,039.88/ha, respectively. The value of home consumed yam showed that the farmers consumed a lot of yam. The return per naira invested per hectare was ₦2.02. The return per gross margin was 0.67 and the production profitability index

Membership of farmers association Member of association 36 20 Not a member 144 80 Total 180 100 Extension contacts 0 – 1 165 91.7 2 – 3 10 5.5 3 and above 5 2.8 Total 180 100 2.27 0 7 Main source of farm finance Personal savings 117 65 Relatives/friend 31 Cooperative 22 12.2 Commercial banks 10 5.6 Total 180 100 Farm size (ha) 0.1 – 1 135 75 1.1 – 2 38 21.1 2.1 – 3 6 3.3 3.1 – 3.5 1 0.6 Total 180 100 0.78 0.1 3.2 Source of planting materials Reserved 116 52 Purchase 54 30 Gift 10 18 Total 180 100 Distance from settlement to farm (km) 1 – 5 26 14.5 6 – 10 25 13.8 11 – 15 80 44.5 16 and above 49 27.2 Total 180 100 5.7 1 56 Distance from farm to market 1 – 10 45 25 11 – 20 60 33.3 21 – 30 48 26.7 31 – 40 21 11.7 41 and above 6 3.3 Total 180 100 5.2 1 55 Source: Field Survey (2013)

Table 1 cont’d.: Socio-economic characteristics of the yam farmers in the study area

202.44%. All these indices reveal that yam production is profitable in the study area and this is consistent with studies conducted by Musa et al. (2011) which revealed that yam production was profitable in their study area. This implies that the government should provide an enabling environment for yam farmers to produce for consumption and export, and thus guarantee food security and foreign exchange. Individual investors and agricultural corporate bodies can invest in yam production and explore comparative advantage enjoyed by the country as world leading producer and the demand of the existing market in the country. Table 3 presents the results of ordinary least square (OLS) regression model. It indicates that R2 squared was 0.690 which suggests that the explanatory variables in the model specification were important and they

explained 69% of the variation in the dependent variable (gross margin). This shows that the model is of good fit and has a good predictive ability. The higher the value of R2, the better the goodness of fit of the specified model. The F-ratio was 26.11 and significant at 1% level, which implies that the independent variables included in the model adequately explain the variation in the dependent variable (gross margin). The result revealed that age, years of farming experience, household size, farm size and extension contacts with coefficients of -6.809, 11.173, 20.397, 17.469 and 37.188, respectively are important and all significant at 1% level of probability. It was also revealed that years of farming experience, household size, farm size and extension contacts had positive relationship with gross margin while age had negative effects according to a priori

Table 2: Costs and returns analysis of yam production in Kogi State, Nigeria

Items Cost/return per hectare Percentage

Variable cost Labour 40,294.43 19.78 Planting materials 144,387.44 70.86 Staking materials 5,912.13 2.90 Herbicides 954.57 0.47 Fertilizers 3,852.25 1.89 Transportation 5,490.01 2.69 Baskets 587.36 0.29 Total variable cost (TVC) 201478.19 98.88 Fixed costs: Depreciation: Cutlass 465.83 0.23 Hoes 798.96 0.39 Axes 176.15 0.09 Knapsack sprayer 831.07 0.41 Total fixed cost (TFC) 2,272.01 1.12 Total cost (TC) 203,750.20 Profitability Indies Gross farm income (GFI) 616,227.08 Home consumption 50,563.88 Gross margin (GM) 412,476.88 GM percentage 66.94 Farm earning 463,039.88 Return per Naira invested 2.02 Return per GFI 0.67 Production profitability index (PPI) 202.44

Source: Field Survey (2013)

expectation. This means that increase in years of farming experience, household size, farm size and extension contacts would result in an increase in gross margin equal to the value of coefficients of these significant variables individually. The positive influence

of years of experience on gross margin could be due to improvement in resources-use efficiency as years of experience increased. This conforms to Olorunsaya et al. (2009). If farm size expands with appropriate technology gross margin increases ceteris

Table 3: Estimates of regression parameters on the determinants of profitability of yam farmers in Kogi State Variable Parameter Coefficient Standard error t-ratio Constant β0 124.267 130.211 0.954 Gender (X1) β1 -69.819 39.949 -1.749 Age (X2) Β2 -6.809 1.364 4.989*** Marital status (X3) β3 -8.8864 34.025 -0.261 Farming experience (X4) β4 11.173 1.613 6.923*** Educational level (X5 ) Β5 -0.407 11.681 -0.035 Household size (X6 ) β6 20.397 4.467 4.566*** Membership of farmers’ association (X7 )

Β7 32.274 29.964 1.077

Farm size (X8) Β8 17.469 3.340 5.229*** Source of finance (X9) β9 -31.320 32.982 -0.952 Source of labour (X10) β10 -38.604 40.777 -0.947 Source of planting materials (X11)

β11 1.446 1.881 0.077

Distance farm to market (X12)

β12 -3.071 2.740 -1.121

Extension contacts (X13) β13 37.188 10.070 3.693*** Distance from settlement to farm (X14)

β14 2.083 5.693 0.368

R-square (R2) 0.690 Adjusted R2 0.664 F-value 26.11*** Source: Field Survey (2013) ***P<0.01

Table 4: Production constraints faced by yam farmers in Kogi State, Nigeria Constraints Total

respondent Weighted

score Weight

means (X) Remarks

Inadequate capital to finance farm production 180 846 4.7 ** Lack of appropriate storage structure 180 840 4.6 ** Lack of access road to convey seeds and output 180 801 4.5 ** High cost of labour 180 722 4.0 ** Bulkiness of yam making transportation difficult 180 702 3.9 ** High cost of seed yam 180 675 3.8 ** Lack of seed of improved variety for planting 180 673 3.7 ** Lack of standard in grading for grading yam 180 650 3.6 ** Low price for yam at harvest period 180 665 3.6 ** Decreasing soil fertility 180 653 3.6 ** Difficultly in mechanization of operations 180 589 3.3 ** High storage pests infestation and disease infection 180 302 1.7 * Inadequate staking materials 180 187 1.0 * Pest attack on the field 180 159 0.9 * Unavailability of yam sett 180 167 0.9 * Lack of market 180 132 0.7 * Lack of labour to carry out operations at the right time 180 101 0.5 * Total (X) 49.0 Critical mean 3.0 ≥3.0 = **

<3.0 = * Source: Field Survey (2013) Critical mean =3.0, **mean accepted as a constraint, *mean not accepted as a constraint

paribus. Increase in extension contacts would result in access to new technologies and information and invariably increase in gross margin. Increase in household size could result in an increase in gross margin if the members of the household are available to supply farm labour. The farmer would not need to obtain credit to hire labour, otherwise, the increase in household size would affect output and income negatively. This justifies the finding of Ohiajianya (2011). The negative relationship between age and gross margin implies that as age increases the farmer becomes less productive as par a priori expectation. Therefore, as yam farmers are aging, young people should be encouraged to take up yam production by making available to them farm inputs at subsidized rate and provision of social amenities in rural areas. All these imply that the government policies should be formulated to encourage the experienced farmers to remain in yam production. Factors such as access to credit, mechanization of farm operations and access to farmland should be enhanced to expand farm size. Improved extension services would result in increased yam output; therefore, more extension agents should be sent to the field to educate the farmers on modern yam production technology. The various constraints faced by the farmers and their weighted score are presented in Table 4. The most critical constraints are inadequate capital to finance farm production, lack of an appropriate storage structure, lack of access road, high cost of labour, bulkiness of yam and difficulty in mechanization of operations. All these reduce output and increase cost of production (Komolafe, 2004; Odinwa et al., 2011; Simpa, 2011). Therefore, there is the need to provide farmers with access to credit which has an impact on the important production inputs such as farm size, planting materials, and even land. Lack of good access roads results in high cost of

transportation of outputs and inputs and thus reduces the income of the farmers and middlemen. Feeder roads should be constructed and maintained to assist the farmers. Storage facilities should be provided to store yam for off-season and help in price stabilization which would enhance farmers’ income and encourage expansion of farm size. Research work should be intensified on yam operations mechanization to reduce drudgery and high labour cost. High cost of planting material with a weighted mean score of 3.8 was also one of the major problems of yam production in the study area. This finding agreed with the result in Table 2 of this study which puts the cost of planting material at 70.86% of the total cost. These findings agreed with Migap and Audu (2012). Lack of standard in grading of yam, low price for yam at harvest and decreasing soil fertility had the same weighted mean score of 3.6 and they were accepted as constraints (Simpa, 2011). Government should provide farmers with fertilizer at the right time and in an adequate quantity and at an affordable price directly without third party or middlemen to solve the challenges of decreasing soil fertility.

CONCLUSIONS AND RECOMMENDATIONS

The farmers are active, well experienced, but poorly educated with poor extension contacts and earned low income. The poor education and extension contacts could have affected adoption of new technologies by the farmers and these translated into small holdings and low income. However, yam production is still profitable within the available technology with gross margin percentage of 66%, return per naira invested per hectare of 2.02 and production profitability index of 202.44%. Age, farm size, extension contacts, farming experience and household size influence farmer’s

margin. The small-scale farms’ gross margin should be enhanced by providing the farmers credit and farm inputs at subsidized rate, improving farmers’ educational level and increasing extension contacts. All these would lead to expansion of farm size and adoption of new technologies. Experienced yam farmers should be encouraged to remain on the farm while young educated men and women are motivated to take up yam production to solve the problem of ageing yam farmers by provision of social amenities and infrastructural facilities in farm centres.

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Profitability of Small-Scale Yam Farms in Kogi State, Nigeria

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