CITATION: Goni, A. A., Oladimeji Y. U., Mani, J. R., Isah, A. S., (2023)DO CLUSTER FARMING PRACTICES IMPROVE PROFITABILITY AND PRODUCTIVITY INDICES OF SMALLHOLDER RICE FARMERS? EVIDENCE FROM BORNO STATE, NIGERIA, Agricultural Socio-Economics Journal, 23(2), 221-230 DOI:
DO CLUSTER FARMING PRACTICES IMPROVE PROFITABILITY AND PRODUCTIVITY INDICES OF
SMALLHOLDER RICE FARMERS? EVIDENCE FROM BORNO STATE, NIGERIA
Abdulsalam Alkali Goni
1*, Yusuf Usman Oladimeji
1, Jamila Rabe Mani
2, Abdulazeez Shero Isah
31Department of Agricultural Economics, Institute for Agricultural Research, Ahmadu Bello University, Nigeria 2National Agricultural Extension and Research Liaison Services, Ahmadu Bello University, Nigeria
3Department of Agronomy, Institute for Agricultural Research, Ahmadu Bello University, Nigeria
*corresponding author: [email protected]
Abstract The study examined the comparative analysis of technical efficiency of cluster and non-cluster rice farming in Borno state, Nigeria. Primary data were collected through structured questionnaire administered to 232 farmers comprising of 93 clustering and 139 non-clustering rice farmers in Borno State, Nigeria. Data were subjected to analytical techniques that included descriptive statistics, gross margin, t-test and stochastic frontier production function (SFPF). Cluster rice farming enterprise per hectare was more profitable by producing a gross margin (GM) of ₦196,020.62/ha thus returning N1.72 on every N1.00 invested as compared to non-cluster farming which produced a GM of ₦99,619.32/ha and thus had a return of N0.96 on every N1.00 invested. The SFPF revealed an average technical efficiency (TE) of 0.76 for cluster farming was higher than 0.58 for non-cluster farmers. Hence, cluster rice farming was more technically efficient compared to non-cluster rice farming. The determining factors of TE in cluster farming include seed (-0.49), fertilizer (0.242), agro-chemicals (0.341) and labour (0.747) compared to non-cluster which included fertilizer (0.207), agro-chemicals (-0.291) and labour (0.668). Inefficiency variables were insignificant in cluster farming while household members active in farming (0.811), years of farming experience (-0.226), and amount of credit utilized (0.5e-4) were statistically significant in non-cluster farming. Insecurity, pest infestation and shortage of water were critical production constraints faced by cluster farmers compared to non-cluster farmers faced with constraints such as shortage of water, insecurity and flooding. Non-clustering farmers should adopt production cluster farming to boost their profit, increase their efficiency and take advantage of the enormous services attributed to working in groups.
Keywords: rice, cluster, profitability, stochastic production function
http://dx.doi.org/10.21776/ub.agrise.2023.023.2.11 Received 12 September 2022 Accepted 20 December 2022 Available online 30 April 2023
INTRODUCTION
The Nigerian agricultural sector remains the mainstay of the economy contributing about 22.35%
of the country’s Gross Domestic Product (GDP) in the first quarter of 2021, showing an increase of 1%
point from the same quarter of 2020. Crop sub-
percentage of the Nigerian population (NBS 2021).
Rice, in particular, has been a key focal commodity, as consumption is estimated to be rising at 50,000 metric tons (MT) annually and is expected to reach 36 million MT by 2050. Thus far, local rice production has not been able to match the demand leading to deficit. This has resulted to substantial yearly imports which stand at about one million MT (FMARD 2011; Hussaini et al., 2021). This is because, majority of the farming population, about 70%, are small scale farmers operating on farmland of less than 2 ha and operate on low profit border and low capital input and with attendance risk aversion. Ultimately, these result to low output, low market inefficiency and low value addition (Apata et al., 2018).
According to the IITA (2009, 2019), farmers’
low productivity is associated with poor seed value chain, poor seed supply coordination, lacks of hybrid seeds, insecticide and herbicide, organic and inorganic manures, low capital formation in research activities and ineffective marketing systems. Thus, farmers must be supported with incentives such as credit and inputs to increase food production and make available local raw materials for agro-allied products and, increase self-sufficiency and bridge supply-demand deficit that gulfed Nigeria US$10 million yearly food import (Mgbenka and Mbah , 2016).
In order to achieve high productivity, food self- sufficiency and for exports, new tools are needed to enhance the capacity of Nigerian agricultural sector to meet domestic and global challenges. Among various tools, cluster farming has emerged as a potential tool for socioeconomic growth and development in many developed country and of recent, Nigerian agricultural development (FAO, 2019a, b and c). Cluster farming consist of group of farm producers coming together to cultivate farmland, working together in farm activities, sell in bulk so as to reduce cost of transportation and other transaction costs (Montiflor et al., 2018). In cluster farming, many small-holder farmers usually referred to as satellites are attached to a hub farm to form a cluster and emerge as entrepreneurial, and revenues and the production costs are shared among members of the groups (Karki et al., 2021).
Worldwide, cluster farming have obtain due attention of policy makers in the last two decades, because it has been considered as potential driver of competitive agriculture (Balcha et al., 2014). The farming technique boosts local farm products which
results in better productivity and output which in turn reduce import and accelerate food sufficiency in Africa (African hervesters, 2020). The device has also become a viable factors for the agriculture in the 21st century to address globalization, high-value production, distribution and packaging innovation, and more efficient production (Karina et al., 2021).
Cluster farming have shown to increase profits of smallholder farmers through bulk purchase of inputs and output sales, as well as easy access to agricultural information and new agricultural techniques and innovations which is an hedge compared to non-cluster farming (Montiflor et al., 2018). The absence of extension services, inadequate and bottlenecks in accessing credit, poor infrastructure, and access to markets among others are many hindrances non-cluster farmers faced that can be overcome by providing extension agents, input providers and market access through cluster farming (Oakeshott, 2016). Therefore, cluster farming is identified as one of the farming techniques required to enhance productivity and efficiency of rice production which is necessary because of rising level and high demand for imported rice in Nigeria. It is pertinent to emphasize that cluster farming is expected to improve productivity and increase profitability by working together to enjoy opportunity of economic of scale in inputs procurement which include fertilizer and agrochemical, obtain latest information and services, share long time experience in labour and farm land and working together to respond to economic and social demand and other farm goals.
In Borno State, agriculture is majorly subsistence and mainly rainfall driven with less than 5 percent of land under cultivation equipped with irrigation facilities (FAO, 2019a and b). Agriculture is also providing the bulk of employment, income, food, and clothing for the rapidly growing population, and contributes about 65% of the State’s GDP majorly from cash crops and food crops:
cereals, root and tuber crops including rice (FAO, 2019c). The subsistence nature of production does not allow farmers to produce much thus leaves them with very little to sell and thus contributes very little to their net income, food security and likelihood of falling under a vicious poverty cycle. The study assess the economies of cluster and non-cluster farming by comparing their profitability as well as efficiency of production using gross margin analysis and the stochastic frontier approach in order to ascertain the most viable production system.
RESEARCH METHODS Study Area
The research was accomplished in Borno State Nigeria, which is located between Latitude 11° 30’
North and Longitudes 13° 00’ East and occupies a landmass of 70,898 square kilometers (Ahlers, 2013). The landscape is largely plain low land with vast sandy soil, short grasses and thorny shrubs. Its main rivers are Alaw, Baga and Gada Bul. The climate of the area is made up of two seasons dry and hot, with minimum temperature of 15°C, and the maximum temperature of 45°C. The annual precipitation fluctuates from 500 to 700 mm (Nigerian Metrological Agency, 2008). The rainy season is usually from May to September with low relative humidity and short wet seasons. This climatic conditions are suitable for grow rice. The crop is suited for regions with an assured supply of water (about 4 – 5 months of rainfall or 1000 – 1300 mm), high humidity (60 – 80% relative humidity) and a prolonged sunshine with an average temperature range of 21 - 42oC on a soil with high water retention capacity preferably clay or loamy clay soil (WARDA, 2020). The State has a projected population of 6.6 million people by 2022 at 3.4%
annual growth rate and farming is the major occupation in the state (NPC, 2006).
Data Collection, Sampling Procedure and Sample Size
The study was accomplished with aid of primary data collected through questionnaire administration with assistance of trained enumerators under the supervision of the researcher.
A three-stage involving purposive and random sampling technique was employed to generate data for this study. Foremost, the purposive selection of three (3) LGAs: Jere, Konduga and Mafa was carried out. These were LGAs where rice is predominantly grown and had a combination of both clustering and non-clustering farmers. In the second stage, the villages with the high intensity of rice production and had concentration of cluster and non- cluster groups and lesser prone to security challenges were listed, thereafter, four (4) villages in each LGA were selected randomly. The third stage was achieved by listing the name of cluster and non- cluster farmers in each village and used a Slovin’s formula adopted by Sani and Oladimeji. (2017) to obtain number of respondents with Slovin’s assumptions of 95% confidence interval, 5%
expected margins of error, and applying the finite population correction factor. The formulae is stated as;
……….(1) Where: n is the number of rice farmers, N = total population of the rice farmers and e = precision level at 5%
Using the above formulae, 42% of the rice farmers were selected randomly and proportionally spread across the sampled villages from a population of 554 (rice farmers) as obtained from Borno State Agricultural Development Program, BOSADP (2020). Thus resulting in a sample size of 232 farmers. This were segregated to 93 cluster and 139 non cluster farmers.
Analytical Technique
The tools of data analysis include descriptive statistics (mean, frequency and percentage), and inferential statistics mainly stochastic frontier production function (SFPF). Gross margin (GM) analysis was used to estimate the cost and return in cluster and non-cluster farming. GM was used for this study because it is perceived that the farmer has negligible fixed assets therefore their fixed cost were also negligible (Olukosi and Erhabor, 2005).
GM = GI – TVC ……….……….(2) Where: GM = Gross Margin (N/ha); GI = Gross Income (N/ha); TVC = Total Variable Cost (N/ha) comprises cost of seed, cost of fertilizer, cost of agrochemicals, and cost of labour. T-test was used to compare significant level between gross margins of both systems. Also, different functional forms of the SFPF was used to seek the best fit production function to achieve efficiency. The Cobb-Douglas form is very easy to estimate in the estimation of SFPF because it provides transformation which is linear in the logarithms of the inputs, hence. The Cobb-Douglas SFPF was stated as;
ln Y1 = α0 + β1lnX1 + β2lnX2 + β3lnX3 + β4lnX4 + V1
– U1 ………(3)
ln = The natural logarithm; Y = Yield (kg/ha) of clustering and non-clustering farm; α0 = intercept (constant term); β1-β4= Regression coefficients to be estimated.
X1= Rice seedlings (kg/ha); X2 = Amount of labour utilized (Man-day/ha); X3 = Fertilizer (kg/ha); X4 = Agro-insecticides and herbicides (litres/ha); V1 = Disturbance term not under the control of the rice farmers and U1 = Disturbance term under the control of the rice farmers (technical inefficiency)
The determinant of the technical inefficiency (U1) was modelled in terms of the socio-economic and institutional variables that was assumed to influence
The model to identify determinants of inefficiency are expressed as;
Ui = δ0+ δ1lnZ1 + δ2lnZ2 + δ3lnZ3 + δ4lnZ4 + δ5lnZ5 + δ6lnZ6 + δ7lnZ7 + δ8lnZ8 …………(4)
U1 = Technical inefficiency; δ0 = constant; δi = Regression coefficients to be estimated, Z1 = age of the rice farmer (years); Z2 = family members active in rice farming (number of persons); Z3 = farming experience (years); Z4 = formal education (years); Z5
= membership of cooperative society (years); Z6 = amount of credit obtained (N); Z7 = extension contact (numbers); Z8 = access to market.
Economic efficiency (EE) was estimated as a product of technical efficiency (TE) and allocative efficiency (AE). Thus:
EE = TE × AE ……….… (5)
The estimation of the parameters of the SPFF and the inefficiency model was simultaneously obtained and the maximum likelihood estimation (MLE) method using the program FRONTIER Software (Coelli, 1996). The TE of an individual farm is defined as the ratio of the observed output (Yi) to the corresponding frontier output (Y*), given the available rice technology, conditional on the level of inputs used by the farm, hence the TE of the farm is expressed as follows;
Technical efficiency = Y*/Yi* = ƒ(Xi, β) exp(Vi – Ui)/ƒ(Xi,β) exp(Vi) = exp(-Ui). …………(6) RESULTS AND DISCUSSION Profitability of Rice Production
The average amount of fertilizer used was 2.9 L/ha with a prevailing selling price of ₦1800/L for
of ₦173/kg. The standard agronomic requirement of fertilizer required for rice production per ha is about 250-350kg/ha of NPK (granula) 20:10:10 applied at sowing while for NPK (Liquid gel) 9:24:3 is about 1.5-2L/ha (WARDA, 2020). This showed a shortfall of about 131-231kg/ha (52.4-60%) of fertilizer by the non-cluster farmers which will greatly affect their yield while cluster farmers used slightly above the required quantity of fertilizer. Cluster farmers had access to NPK (liquid gel) which was provided by their respective cooperative societies. Liquid fertilizer is easy to use with lesser quantity required which results in higher yield. It also has the advantage of direct application to foliar where nutrient is absorbed directly through the leaves and are more readily available for plants to use. It also mixes well with other fertilizers and agro-chemicals and can be delivered directly through an irrigation system.
Quantity of agro-chemicals used was 8.17 L/ha with an average market price of ₦2000/L for cluster farming while that for non-cluster farming was 4.32 L/ha with an average market price of ₦2115/L. The standard agronomic requirement for agrochemicals is about 4-6L/ha of herbicide and pesticide combined at pre and post emergence stages (WARDA, 2020). The mean compensation rate of
₦1,370.44 per man-day was derived from analysis, giving the mean labour man-day and asking price to be about 45 man-day/ha at ₦61,669.82/ha for cluster farming while the mean compensation rate for non- cluster farming was ₦1,528.1 per man-day, giving the mean labour man-day and asking price to be 22 man-day/ha at ₦33,617.27/ha respectively.
Irrigation cost were identical (₦12,583 and ₦12,732 /ha) for cluster and non-cluster farmers respectively while other cost (transportation and rent) incurred in rice production were ₦14,242/ha for cluster farming compared to ₦11,062/ha for non-cluster farming.
Table 1. Average Cost and Return Per Hectare In Cluster and Non-Cluster Rice Production.
Variables
Cluster Non-cluster
Quantity Total Value
% cost Quantity Total Value
% cost
‘000 ‘000
Variable cost
seed (kg/ha) 72.58 3.629 3.19 84.47 16.809 16.17
Fertilizer (liter and kg/ha) 2.9 5.220 4.59 119.24 20.628 19.84
Agrochemicals(L/ha) 8.17 16.34 14.37 4.32 9.136 8.79
Labour (man-days/ha)
land clearing 4.03 5.529 4.86 1.03 1.597 1.54
Ploughing/ridging 1.82 7.918 6.97 1.18 7.448 7.16
Planting 1.25 0.625 0.55 1.15 0.575 0.55
Fertilizer application 1.4 1.492 1.31 0.45 0.489 0.47
Agrochemicals application 2.33 4.447 3.91 0.57 0.837 0.81
Weeding 7.72 9.696 8.53 3.05 4.639 4.45
Harvesting 10.06 17.162 15.1 5.08 10.784 10.37
Threshing 15.13 14.797 13.02 7.64 6.868 6.61
Bagging 0 0 0 0.51 0.377 0.36
Irrigation 1 12.583 11.07 1 12.732 12.24
Transportation 1 8.534 7.51 1 6.415 6.17
Farm rent 1 5.708 5.02 1 4.647 4.47
TVC(N/ha) 113.683 100 103.986 100
GI(N/ha) 309.703 203.605
GM(N/ha) 196.02 99.619
ROI 1.72 0.96
T-value
TVC 1.97*
GM 1.97*
Source: Field survey 2021, $1 = ₦409
The TVC incurred totaled N113,683.17/ha for clustering rice farmers and N103,986.12/ha for non- clustering rice farmers. Result also showed that clustered farmers had a slightly higher TVC as compared to non-clustered farmers by 8.53%. The t- test revealed a statistical significant difference between TVC of cluster and non-cluster rice farming at 10% level of probability. This difference might be as a result of paying more attention in the production process and deploying of more labour by clustering rice farmers in order to realize a higher yield. Results also revealed that average gross income (AGI) were
₦307,703.79 and ₦203,605.44 /ha for cluster and non-cluster rice farmers, respectively. This difference of about 33.83% in their gross income (GI) is attributed to their difference in yields which is likely to result from the type of seed used and the employment of modern farming techniques. This corroborates the finding of Tanko, (2019), who stated that improved seeds have shown to enhance crop performance, increase output, and use less fertilizer input.
Average gross margin (AGM) were
₦196,020.62 and ₦99,619.32 /ha for cluster and non-cluster rice farmers respectively. The t-test also indicated a statistical significant difference at 10%
level of probability between the GM of the two farming techniques favoring cluster system. The estimated results of GM analysis showed that clustering have demonstrated to be more profitable.
This corroborates the findings of Ahmad, Xu, Yu and Wang, (2017); Satyarini and Pangarso, (2021) in their studies.
Technical efficiency estimates
The Maximum Likelihood Estimate (MLE) and inefficiency estimates of the Cobb-Douglass frontier in Table 2 revealed that the generalized log likelihood ratios (LLR) for cluster and non-cluster rice farmers were -47.059 and -183.381,
respectively which showed that inefficiency exist in the estimated result. The LLR value of cluster and non-cluster signifies the value that maximizes the joint densities in the estimated model showed that the Cobb-Douglas function used in this estimation is an adequate representation of the data. The gamma (γ) values are estimated to be 0.86 for cluster and 0.93 for non-cluster rice farmers, statistically significant at 1% level of probability and in line with the postulate that true γ-value should be greater than zero. The sigma squared (σ2) values for cluster (1.79) and non-cluster (8.03) rice farmers were statistically significant at 10 and 1 % level of probability, respectively which indicates a good fit and correctness of the specified distributional assumptions of the composite error terms.
Efficiency Variables
Table 2 revealed that a percentage increase in coefficient of seed used for cluster farming will increase the output of rice by 49.0% while that for non-cluster farming will result in 0.0081% increase in output and insignificant. This might be due to seed being readily availability and over utilization of seed as it skewed away from the standard agronomic seed rate of 50-60kg/ha. This agrees with the findings of Sani and Oladimeji (2017) and Abdurrahman, Timothy, Muhammad and Siewa, (2015), who observed that hybrid seed is a key variable in production, and the amount used influences to a large magnitude the output obtained. Similarly, a percentage increase in the coefficient in amount of fertilizer used will increase rice output by 24.2% for cluster and 20.74% for non-cluster rice farmers respectively which confirmed that fertilizer is one of the most important land supplementing input because it enhances the quality of land by raising yields per hectare. This result is in tandem with the findings of Sani and Oladimeji (2017) who reported the coefficient of fertilizer for sorghum farmers in
will increase yield. The findings of Oseghale et al.
(2017); Uphoff and Dazzo, (2016), indicated that fertilizer if applied in the right quantity will enhances soil fertility, further translates to growth and increases yield enormously with a substantial ability to increase profit.
In the same vein, a percentage increase in the coefficient of agro-chemicals use will enhance the
This implies that output is elastic to changes in the amount of agro-chemicals used. Omari, (2014);
Kughur (2012); Narcoda, Vasquez, Ladoe and Mora-Garcia, (2019) also reported that agrochemicals are used to improve crop productivity, control pest, and treat or control diseases.
Table 2. Results of MLE of stochastic frontier production function for cluster and non-cluster rice production
Variables Cluster farming Non-cluster farming
Coefficient T-value Coefficient T-value
Production function
Constant 6.127*** 7.6564 5.3283*** 5.9719
Seed (kg/ha) -0.49*** -3.0969 -8.14E-02 -0.5159
Fertilizer (kg / L per ha) 0.242* 1.7816 0.2074* 1.9549
Agro-chemicals (L) 0.341** 2.4975 -0.2914* -1.9425
Labour (Man-day) 0.747*** 11.2038 0.6682*** 5.6351
Return to Scale (RTS) 0.84 0.503
Inefficiency model
Constant 0.0982 8.84E-02 -5.33E-02 -0.0444
Age (years) -3.20E-03 -0.233 -9.42E-02 -1.3799
Active farming household -0.1309 -0.9725 0.8106*** 2.8824
farming experience -8.93E-02 -0.1144 -0.226*** -2.5984
formal education 1.09E-02 0.3109 -0.4762 -1.3048
cooperative (years) 1.9266 1.91E-01 8.31E-02 0.0832
credit accessed (N) 3.00E-05 0.845 0.5E-4*** 2.9688
No of extension contact -0.2972 -1.27 0.6761 0.8833
Distance to market (km) -6.17E-02 -1.41 0.2131 -1.0815
Diagnostic statistics
Sigma-squared 0.6772* 1.79* 6.8002*** 8.0309
Gamma 0.8646*** 9.7393 0.9767*** 138.15
Log likelihood function -47.059 -183.381
LR test 25.824 144.85
no of observations 93 139
Mean efficiency 0.7678 0.5891
Source: Field Survey, 2021. ***; **; * =p<0.01; p<0.05; p<0.10, respectively Similarly, a percentage increase in the
coefficient of labour will boost rice output by 4.7 and 6.7% for cluster and non-cluster rice farming respectively. Rice farming is labour intensive in nature and therefore increasing the quantity of labour employed will likely affect output positively.
This showed that labour is an important variable for both farming techniques in rice production. Sani and Oladimeji (2017) reported the importance of labour in crop production, among small scale farmers in many developing countries where mechanization is not common while human power plays a crucial role in agricultural production, it has variously been attributed to the practice of split-plot cropping on
small scattered land holdings and lack of affordable equipment.
The average technical efficiencies (ATE) were 0.77 and 0.59 for cluster and non-cluster rice farmers respectively, that is, average of 77% and 59% of potential output were obtained from a given mixture of production inputs. This shows that, in the short run, there are minimal scopes of (23% and 41%) to increasing their efficiency, by adopting the technologies and techniques used by the most efficient cluster and non-cluster rice farmers respectively. Mohammed (2012), also reported the ATE of farmers to be 0.726 with a scope to increase
efficiency by 28% on order to achieve the efficiency level of the best practicing farmer
The input elasticities generated from the SFPF were used to compute the return to scale (RTS). The result showed an RTS of 0.84 and 0.503 for cluster and non-cluster rice farming respectively. This implies that rice production under both farming techniques exhibited a decreasing RTS. However, efforts could still be made by farmer under both farming techniques to increase their present scope of rice production. This corroborates the findings of Goni et al. (2013) and Oseghale, (2017) on rice production in Jere; low land rice farmers in Niger and Ogun whom reported an RTS of 0.86 and 0.93 respectively. Thus stating that improvements can still be archived by employing more input resources.
Inefficiency Variables
The result of the inefficiency model is also depicted in Table 2 and it revealed that, a unit increase in the coefficient for active household members in farming will increase rice output by -1.3 and 8.1% for cluster and non-cluster rice farming respectively. This implies that increasing the number of household members in farming will increase technical inefficiency or decrease TE of farmers. However, household members’ active in farming play a vital role in the supply of family labour as more household members participation in farming increase the number of cheap labour and hence increase in TE as well as output, as reported by Usman, (2011). This implies that TE of non- cluster rice farmers can be increase without taking into consideration of increasing the household members active in farming.
Similarly, a percentage increase in the coefficient for years of farming experience were - 0.0089 and -2.3% for cluster and non-cluster rice farming respectively. This implies that as the years of farming experience increases, technical inefficiency decreases. This also means that farmers with more years of experience tend to be more technically efficient as compared to those with fewer years of experience. The inverse relationship of the
coefficient in line with a priori expectation and attests to the fact that more years of farming practices tends towards perfectness. Tanko (2019), observed that, farmers with more years of experience in rice farming reduces inefficiency in the use of resources and boosts rice production in Kano state, Nigeria.
Furthermore, a unit increase in the coefficient for amount of credit accessed were 3.0e-5 and 0.5e- 4% for cluster and non-cluster rice farming respectively. The positive sign of the coefficient indicates that farmers who had less access to credit were less efficient to those having more access to credit. The amount of credit accessed enhances the capacity of the farmers to acquire production inputs on time to enhance productivity (Abdullahi, 2016).
Also the timely access to credit is very important in production because untimely access may leads to diversification of the funds for consumption hence, the expected impact of such funds might not be felt on the enterprise. This conforms to the findings of Chekene and Chancellor (2015), who reported the significance for accessing credit in the right amount and time by adopters of improved rice variety in Borno state in order to enhance their production capacities.
Technical Efficiency (TE) Distribution of Rice Farmers
TE distribution estimates for cluster and non- cluster rice farmers are presented in Table 3. The results showed that the farmers with the best and least practices had TE of 0.94 and 0.15 for cluster rice farmers while TE for non-cluster rice farmers were 0.90 and 0.13 respectively. The result also showed that on the average, cluster and non-cluster rice farmers obtained 76% and 58% possible output from a specified combination of productive resources. Muhammed (2012), reported the TE of sorghum farmers with a range from 0.16 to 0.92 with a mean TE of 0.73 implying 0.27 deficit to achieve TE frontier which could be attained through better use of inputs such as land, seed and fertilizer in the short term given the prevailing state of technology.
Table 3. Technical efficiency (TE) distribution of rice farmers
Efficiency Range Technical efficiencies
Cluster Non-cluster
0.01-0.20 1 1.08 11 7.91
0.21-0.40 6 6.45 12 8.63
0.41-0.60 5 5.38 36 25.9
0.61-0.80 32 34.41 63 45.32
0.81-1.00 49 52.69 17 12.23
Total 93 100 139 100
Min 0.156 0.13
Max 0.942 0.901
Mean 0.768 0.589
T-value between cluster and
Non-cluster rice farmers 1.79*
Source: Field Survey, 2021
Constraint Faced by Farmers In Rice Production
The constraints faced by rice farmers presented in Table 4 were rated according to their severity as stated by the farmers of both production techniques. Insecurity, shortage of water and flooding are some of their greatest production constrains. Yau et al. (2021),
reported that insurgency has affected the nation economically, politically and socially and in particular, the agriculture sectors in Borno State where farmers in the state continue to experience the menace of the lingering Boko Haram insurgency were many have been killed and more were displaced, making them abandon their farms.
Table 4. Constraints faced in rice production by cluster and non-cluster rice farmers
Variable Cluster Non-cluster
F % Rank F % Rank
Insecurity 69 36 1st 44 21 2nd
Pest infestation 41 21 2nd 19 9 5th
Shortage of water 26 13 3rd 57 27 1st
Inadequate processing
machines and infrastructure 25 13 4th 37 18 4th
Financial constraint 18 9 5th 11 5 6th
Flooding 15 8 6th 42 20 3rd
Total 194 100 210 100
Source: field Survey, 2021 (**) Multiple responses allowed CONCLUSION
Based on the findings of this study, cluster rice farming enterprise per hectare was more profitable and efficient as compared to their non-clustering counterparts by returning N1.72 on every N1.00 invested with a mean efficiency of 76%. Farmers are encourage to operate production cluster in order to enjoin opportunities therein.
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