Pa ge 1 Pa ge 28 American Journal of Multidisciplinary Research and Innovation (AJMRI) Sources of Technical Inefficiency for Smallholder Field Pea Producers in Ethiopia Gadisa Muleta1* Volume 3 Issue 4, Year 2024 ISSN: 2158-8155 (Online), 2832-4854 (Print) DOI: https://doi.org/10.54536/ajmri.v3i4.2761 https://journals.e-palli.com/home/index.php/ajmri Article Information ABSTRACT Received: April 15, 2024 Accepted: June 10, 2024 Published: June 24, 2024 This study intended to measure the technical efficiency and sources of inefficiency for field pea producers in Ethiopia. The study used primary data collected from 207 smallholder farmers. A multi-stage sampling technique was followed to select sample households. A stochastic frontier was the model used, and a single-step estimation approach was followed. The result indicated that the mean technical efficiency is 49.23 %, and the average productivity of the crop is 0.96 tons/hectare. This indicates that the technical efficiency could be increased by 51.77 % through proper utilization of production variables, i.e. agrochemicals, oxen power, plot size, and seed rate. The inefficiency model result also confirmed that the technical inefficiency of field pea production was negatively affected by access to off-farm income, access to credit services, extension contacts, adoption of new technologies, access to training, and participation in contract farming of the crop. Therefore, this study recommends agricultural policies favoring vocational training to farmers on the new agricultural technology packages, availing access to off-farm incomes, facilitating credit services to farmers, and establishing legal frameworks that encourage contract farming. Keywords Technical Efficiency, Field Pea, Stochastic Frontier Model, Ethiopia INTRODUCTION Field pea (Pisum sativum L.) is among the most important cool season legumes mainly cultivated for food and feed across the globe (Muoni et al., 2019). It is a nutritionally rich crop with high protein, vitamins, minerals, and carbohydrates that are affordable for resource-poor consumers. This enables the crop to be called ‘poor man’s meat’. Furthermore, it is naturally rich in iron, zinc, and magnesium, and contributes much to alleviating hidden hunger (Amarakoon et al., 2012; Kandel et al., 2016; Rezene et al., 2015). Pulses in general, and field peas in particular, are very important crops in terms of assuring sustainable agriculture by being used as crop rotation and intercropping that will contribute much in atmospheric nitrogen fixing and providing essential nutrients back to the soil (Powers and Thavarajah, 2019). It is the most efficient Nitrogen-fixing crop, and it obtains 80% of its Nitrogen requirement from fixation. As a result, the crop is considered environmentally friendly and economically feasible (Blaine et al., 2021). This legume- based nitrogen use efficiency offers a sustainable and cost-effective alternative. Pulses also foster other benefits like maintaining soil health, improving biodiversity, enhancing soil organic carbon, improving the water retention capacity of the soil, and reducing greenhouse gas emissions (Foyer et al., 2016; Peoples et al., 2019; Stagnari et al., 2017). Globally, legume crops are reported to fix about 21 metric tons of nitrogen, out of which 5 to 7 metric tons are returned to the soil by pulse crops, which saves 8 to 12 billion USD (Christine et al., 2019). Reports also confirmed that Field peas have a positive effect in improving soil organic carbon through their ability of nitrogen fixation (Stagnari et al., 2017). They also have a positive effect in improving cereal productivity when used as crop rotation. In Australia, wheat production increased by 30% as a result of crop rotation compared to monocropping. In Denmark, the nitrogen uptake of different crops increased by 23 to 59% as a result of crop rotation (Foyer et al., 2016; Peoples et al., 2019). It is also reported to have ecological and economic importance, minimize the negative impacts of cereal-based mono- cropping, and amend soil fertility through crop rotation (Fikere et al., 2014; Muoni et al., 2019). Canada, Russia, France, China, India, Ukraine, USA, Australia, Germany, and Ethiopia were the top 10 countries in terms of production. The average cultivated land has decreased from 7,646,813 to 7,159,958 hectares, while the average yield decreased from 14,323,297.26 to 14,166,029.75 tons between 1994 and 2022 (FAOSTAT, 2024). In Ethiopia, field pea is the second most important pulse crop in terms of total production following the Faba bean, and the third crop in terms of area coverage following the Faba bean and Haricot bean. The average cultivated area and the total production for field pea were 220,194.82 ha and 3,803,35.89 tons respectively while that of fababean was 520,551.70 ha and 10,916,09.34 tons respectively. The national average productivity of the crop is estimated to be 1.73 tons per hectare during the 2022 main season (ESS, 2022). Despite all the contributions of the crop, the advancements made to improve the production and productivity of field peas lagged behind other crops (Amarakoon et al., 2012). Even though the national and regional 1 Ethiopian Institute of Agricultural Research, National Agricultural Biotechnology Research Center, Ethiopia * Corresponding author’s e-mail: gadisamuleta@gmail.com Pa ge 29 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 3(4) 28-35, 2024 research institutions of the country released more than 30 improved varieties of field pea, the average national productivity of the crop is yet about 1.7t/ha (ESS, 2022; Kindie et al., 2019). This result is very low compared to the potentials of the improved varieties which is 4.17 t/ ha reported by Mogiso, (2017), and that of the world which is 1.98 tons/hectare (FAOSTAT, 2024) This yield reduction is the result of biotic and abiotic factors. Scholars reported that diseases like Ascochyta blight and Powdery mildew, Aphids, Lodging, Pod shattering, and the use of low-yielding local varieties were among the major yield-limiting constraints (Rezene et al., 2015). Additionally, the technical efficiency of the farm households also matters as the yield reported from the research field and the farm household plots show big differences. Therefore, this research aimed to assess the level of the technical efficiency of smallholder field pea producers and their sources of technical inefficiency in Ethiopia. MATERIALS AND METHODS Data This study used primary data collected from households randomly selected from the two giant Field Pea producing, Ethiopia’s Oromia and Amhara regions. These two regions account for more than 75% of the national field pea production. A multi-stage random sampling procedure was followed. The two regions were purposively selected based on their high potential in field pea production. One zone from the Amhara region and four zones from the Oromia region were randomly selected out of the potential fababean-producing zones of the two regions. In the third stage, ten districts from the Oromia and three districts from the Amhara region were randomly selected. Then a total of twenty peasant associations were randomly selected from thirteen districts. Finally, a total of 207 sample households, (71 from Amhara and 136 from Oromia region) were randomly selected and interviewed for the data collection. Variables Based on the literature reviewed, the production variables and the inefficiency variables listed in Table 1 were selected to be included in this study. Table 1: Lists of variables and their expected effect on the profit efficiency of fababean producers Var. code Description and measurements Expected effect yi Field pea output per plot measured in kg pi Production Variables p1 Plot size allocated for field pea measured in hectares + p2 The adult labor force used per plot measured in an hour + p3 Oxen hours used in farming activities are measured in hour + p4 Quantity of fertilizer used per plot measured in kg + p5 Quantity of seed used per plot measured in kg + p6 Quantity of agrochemicals used per plot measured in kg + xi Inefficiency Variables x1 Age of the household head - x2 Education of the household head + x3 Membership in improved seed producers’ group + x4 Experience in Field Pea production + x5 Access to extension services + x6 Market distance - x7 Access to credit service + x8 Sex of the household head + x9 Participation in contract farming + x10 Livestock owned + x11 Access to off-farm income +/- x12 Total land owned + x13 Adoption of Field Pea improved seed + x14 Access to training in pulse production + x14 Family size + x14 Plot distance from the residence - Source: Authors’ compiled based on reviewed literature Pa ge 30 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 3(4) 28-35, 2024 Analytical Framework In the production process, farmers aim to maximize their profit by maximizing their production and minimizing their costs of production. Though some farmers produce efficiently compared to others, not all farmers become successful in their farming practices. The deviation from the optimal production is the failure to optimize their production, which may result from the inefficiency of the farmers and/or the random shocks. Therefore, a farm household can deviate from the optimal production frontier as the result of both the farmer’s inefficiency and possible random fluctuations that are out of the control of the farmers. The stochastic Frontier model is widely used in the field of agricultural economics for econometric modeling of production and assessing technical efficiency. Technical efficiency can be modeled as either output-oriented or input-oriented (S. C. Kumbhakar et al., 2015). Following Aigner et al., (1977) and Meeusen and van Den Broeck, (1977), the functional form of the output-oriented stochastic production frontier model that we used for this specific research can be expressed as: lnyi = lnyi *- ui, ui ≥ 0 (1) Where: yi * is the frontier output level, yi is the observed output level, and ui ≥ 0 is the production inefficiency that reducing farm output. lnyi * = f(xi ; β) + vi (2) Where: f is the functional form to be used, xi is a vector of input variables, β is a vector of coefficients to be estimated, and vi is a zero mean random error. For the simplicity, equation 2 can be re-written as: lnyi = f(xi ; β) + εi (3) Where: εi is the composite error term, while yi, xi, β and f are as defined earlier. εi = vi - ui (4) From equation 1, the term u_i is the log difference between the maximum output and the actual output that can be expressed as: ui = lnyi * - lnyi (5) This implies that ui x 100 is the percentage by which the actual output can be increased if the production is fully efficient, or the percentage of the output lost due to the technical inefficiency of the farmer. By rearranging equation 1, ui can also be expressed as the ratio of the actual output to the frontier output. e(-ui ) = yi/yi * = TEi (6) Production efficiency varies across companies, and over time (Battese and Coelli, 1992). Therefore, it is logical to ask about what factors are responsible for the variations in efficiency. Battese and Coelli further extended the stochastic frontier model and suggested that the determinants of inefficiency can be expressed as a linear function of a set of explanatory variables that reflect the characteristics inherent to a company (Battese and Coelli, 1995). The extended form of Battese and Coelli allows the estimation of efficiency, and factors affecting the inefficiency in a single step. This will overcome the estimation bias of the widely used two-step approach (Battese and Coelli, 1995; S. Kumbhakar et al., 1991; S. C. Kumbhakar et al., 2015; Wang and Schmidt, 2002). The transcendental logarithmic function is the functional form used in this research. This functional form captures the effects of the interactions of the input variables that play a significant role in the process of maximum likelihood estimation. The functional form of the transcendental logarithmic function can be expressed as follows: (7) Where: yi is the total quantity of output, xi is the quantity of input used, vi is the two-sided error term, and ui is the one-sided error term or the technical inefficiency effects The linear functional form of the variables affecting the technical inefficiency can be expressed as: (8) Where: δk is a vector of parameters (regression coefficient) to be estimated, zi is a vector of inefficiency variables (institutional, demographic, and socio-economic variables), and εi is the random error of the model. Consent to Participate The sample households that participated in the primary data collection were asked for their consent to give the required data, and the researchers also assured them that the information they give will be kept confidential, and only used for the intended purpose. Based on this, all the participants gave verbal consent to the researcher and participated in the interview for data collection. RESULTS AND DISCUSSIONS Descriptive Results The descriptive result of the production inputs and output variables is presented in Table 2. The quantity of field peas is used as a dependent variable. The mean plot size under the field pea in the study area was 0.45 hectares, indicating the smallholder farming system. This plot includes both the owned land and rented-in land. Man-equivalent labor used in the model was measured in hours, and it includes both family and hired. The fertilizer used is the amount of inorganic fertilizer measured in kilograms. Oxen hour is the total time it takes a pair of oxen to complete land preparation or plowing. Agrochemicals include the amounts of pesticides, herbicides, fungicides, and other chemicals used per the field pea plot. In recent years, different diseases have emerged, and fababean and field pea farmers in Ethiopia are spraying different agrochemicals. Field pea production in the study area is dominated by male-headed households (89%). In terms of age, 61% of the respondents were aged between 30 to 50 years, with an average age of 48 years. The majority of respondents (90 %) had an experience of 10 years and above in field pea production, with an average experience of 26 years. The Pa ge 31 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 3(4) 28-35, 2024 household size for more than 94 % of the respondents ranged from 1 to 8 with an average of 6 members. The average land holding in the study areas is about 1 ha (See Table 3). Table 2: Descriptive results Variable Mean Std. Dev. Min Max Field pea yield (kg) 429.25 489.99 5 2500 Labor used (hr.) 83.89 53.28 12 333 Fertilizer used (kg) 16.66 27.34 0.1 120 Seed used (kg) 59.76 42.26 10 260 Oxen power (hr.) 30.03 23.55 5 120 Agrochemicals (kg) 0.489 0.664 0.1 2.75 Plot size (ha) 0.446 0.375 0.1 2.50 Livestock (TLU) 7.990 4.330 0.1 22.1 Plot distance (minutes) 24.79 41.89 0 400 Market distance (minutes) 82.65 53.81 0 180 Age of the head (yr.) 48.25 12.21 25 79 Experience in field pea 25.94 11.71 2 55 Family size (AE) 5.750 2.020 1 15 Education of the head (yr.) 5.950 3.380 0 14 Land owned (ha) 1.52 1.270 0 6 Sex of the head 0.89 0.309 0 1 Access to off-farm income 0.76 0.429 0 1 Seed production (no/yes) 0.44 0.498 0 1 Training on pulse (no/yes) 0.67 0.473 0 1 Access to credit services 0.46 0.500 0 1 Access to extension contacts 0.88 0.321 0 1 Field pea adoption (no/yes) 0.65 0.479 0 1 Contract farm (no/yes) 0.15 0.362 0 1 Source: Authors’ survey result The field pea yield per hectare in the study area is very low compared to both the national average productivity of the crop and the yield potentials of the varieties that the majority of the sample households are using. The national average productivity of the crop is 1.73 tons per hectare (ESS, 2022), while the average productivity of the crop in the study area is 0.96 tons/hectare. The result of the focus group discussion showed that the yield reduction was mainly due to diseases and pests that have been devastatingly affecting the crop in recent years. Econometric Results This study used the stochastic frontier model to assess the technical efficiency of field pea producers in the study area. Labor, fertilizer, seed, plot size, agrochemicals, and oxen power were the first-order variables (production variables) included in the model. Besides, the squared values of all the production variables and their interactions were also included. Finally, sex, age, education, livestock holding, experience in field pea production, access to off-farm income, plot distance, distance from the main market, access to extension services, access to credit, participation in contract farming, adoption of field pea technologies, access to training in pulse production, land ownership and participation in field pea improved seed production were the variables included as the variables affecting the inefficiency of field pea producers. After testing for heteroscedasticity and multicollinearity, all the production variables, the squared values of all the production variables, and their interactions, along with the inefficiency variables, were fitted in the model. The assumption behind this is that multiple relationships may exist among the variables and the linear and quadratic associations may also co-exist among the variables and their interactions with the dependent variables. The result depicted that seed, agrochemicals, and oxen power were the production variables that positively and significantly affected the field pea yield at a 1 % significance level (See Table 3). The interaction effect of the production variables denotes the complementarities of the production inputs in agricultural production. The negative complement suggests that a joint increase of the variables decreases the production efficiency, while the positive complementarity suggests a joint increase of the variables increases the production efficiency. The result of this study disclosed Pa ge 32 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 3(4) 28-35, 2024 that the squared values and the interaction of some variables were found to be the most important variables. The squared value of labor and the squared value of the plot size positively and significantly affected field pea production efficiency at 5 %. Table 3: Maximum likelihood estimation results Variables Coef. St. Err. z P>z Labor 0.408 0.459 0.890 0.374 Fertilizer 0.142 0.140 1.020 0.310 Seed 3.007 *** 0.712 4.220 0.000 Plot size 0.697 1.018 0.680 0.494 Chemical 0.912 *** 0.272 3.350 0.001 Oxen 0.664 *** 0.249 2.660 0.008 0.5 labor2 0.816 ** 0.330 2.470 0.013 0.5 fertilizer2 -0.091 0.057 -1.600 0.109 0.5 Seed2 -0.159 0.355 -0.450 0.654 0.5 Plotsize2 0.822 ** 0.346 2.380 0.017 0.5 Oxen2 -0.407 0.440 -0.920 0.356 0.5 Chemical2 -0.091 0.169 -0.540 0.590 Labor x Fertilizer -0.064 0.039 -1.630 0.103 Labor x Seed -0.865 *** 0.248 -3.480 0.000 Labor x Plot size -0.118 0.228 -0.520 0.604 Labor x Oxen -0.049 0.283 -0.170 0.863 Labor x Chemical 0.281 ** 0.114 2.460 0.014 Fertilizer x Seed 0.075 0.055 1.360 0.174 Fertilizer x Plot size -0.019 0.050 -0.370 0.709 Fertilizer x Oxen -0.025 0.073 -0.340 0.736 Fertilizer x Chemical -0.001 0.026 -0.020 0.983 Seed x Plot size 0.578 ** 0.233 2.470 0.013 Seed x Oxen 0.299 0.362 0.830 0.409 Seed x Chemical -0.846 *** 0.156 -5.410 0.000 Plot size x Oxen -0.365 0.300 -1.220 0.223 Plot size x Chemical 0.050 0.128 0.390 0.698 Oxen x Chemical 0.472 *** 0.166 2.840 0.005 _cons -0.456 2.061 -0.220 0.825 Sex of the head -0.004 0.309 -0.010 0.990 Off-farm -0.742 ** 0.319 -2.320 0.020 Seed production -0.238 0.252 -0.950 0.344 Livestock owned 0.001 0.024 0.060 0.953 Training on pulse -0.512 * 0.267 -1.920 0.055 Age of the head 0.001 0.015 0.080 0.937 Family size 0.071 0.061 1.180 0.239 Land owned 0.071 0.090 0.790 0.431 Plot distance 0.001 0.002 0.650 0.517 Market distance 0.000 0.002 0.130 0.900 Access to credit -0.628 * 0.330 -1.910 0.057 Access to extension -0.684** 0.334 -2.050 0.040 Education of the head -0.005 0.031 -0.160 0.873 Adoption of field pea -0.428 * 0.250 -1.710 0.086 Experience in field pea -0.006 0.015 -0.360 0.718 Pa ge 33 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 3(4) 28-35, 2024 Contract farming -1.077 * 0.624 -1.730 0.084 _cons 2.233 *** 0.836 2.670 0.008 U_sigma _cons -0.536 0.512 -1.050 0.295 V_sigma _cons -1.895*** 0.512 -3.700 0.000 Sigma _u 0.765 *** 0.196 3.910 0.000 Sigma _v 0.388 *** 0.099 3.910 0.000 Lambda 1.973 *** 0.261 7.570 0.000 Log-likelihood = -201.6479, No. of obs. = 207, Wald chi2(27) = 266.36, and Prob > chi2 = 0.0000 Source: Authors’ survey result The complementary effects of some variables were also found to be important variables in this study. The interaction of labor with seed and seed with the chemical was found to have a negative effect on the field pea efficiency, while the interaction of labor with chemical, seed with plot size, and oxen with the chemical was found to have a positive effect on field pea production efficiency. The inefficiency variables also fitted in the model along with the production variables and their interactions in a single-step estimation, and the result revealed that access to off-farm income extension, credit, training on pulse production, adoption of field pea technologies, and participation in field pea contract farming were the important variables that affected technical efficiency in field pea production. Access to off-farm income had a negative effect, and a similar result was reported by (Adnan et al., 2021). Access to credit also showed a negative effect, and this result is similar to the result reported by (Mujuru et al., 2022; Wongnaa et al., 2019). Similarly, access to extension services and adoption of new technologies were negatively related to the inefficiency, and this is in line with the finding reported by (Wongnaa et al., 2019). Estimation of the Technical Efficiency Scores The technical efficiency score was estimated and presented in Table 4. The average technical efficiency score is 49.23% with a standard deviation of 21.90%, and the minimum and maximum efficiency scores are 5.01% and 99.13%, respectively. This result indicates that field pea producers in the study area are producing far below the frontier, and the production is 51.77% short of the potential. This implies that field pea producers in the study would increase their output by 51.77% through efficient utilization of their resources and solving all the sources of their technical inefficiencies. Table 4: Summary of profit efficiency scores Efficiency score range Frequency Percent (%) < 0.1 006 2.90 0.1 – 0.2 023 11.11 0.2 – 0.3 017 8.21 0.3 – 0.4 026 12.56 0.4 – 0.5 030 14.49 0.5 – 0.6 028 13.53 0.6 – 0.7 038 18.36 0.7 – 0.8 026 12.56 0.8 – 0.9 012 5.80 > 0.9 001 0.48 Total 207 100 Mean 0.4923 Minimum 0.0590 Std. dev 0.2190 Maximum 0.9913 Source: Authors’ survey result The result of this research (Table 2) indicated that field pea producers in the study area are harvesting an average yield of 0.96 tons/hectare. However, the technical efficiency score presented in Table 4 indicates 51.77% technical inefficiency. This implies that solving all the sources of technical inefficiencies will boost the productivity of field peas to 1.95 tons/hectare. Pa ge 34 https://journals.e-palli.com/home/index.php/ajmri Am. J. Multidis. Res. Innov. 3(4) 28-35, 2024 CONCLUSIONS This study analyzed the level of technical efficiency and its determinant factors for field pea producers in Ethiopia. This crop is very important and is part of most dishes in Ethiopia. The result of this research revealed that Field pea producers in the study area are inefficient, and producing far below the potential. The average technical efficiency of field pea producers is 49.23 %, and its average productivity is 0.96 tons/hectare. Devastatingly increasing diseases and pests were reported to be responsible for the significant yield reduction in field pea production in the study area. The national average productivity of the crop is 1.76 tons/hectare. This indicates that farmers are producing the crop at 51.77 % grain loss. The amount of seed used, oxen power/plowing chemicals sprayed, access to off-farm income, extension, credit, training and adoption of new field pea technologies were among the most important variables that affected the technical efficiency of field pea producers in the study area. The amount of seed used, sprayed agrochemicals, and oxen power for plowing were the production variables that positively affected the technical efficiency of field pea producers. The households in the study area are using an amount that is below the optimum level of these inputs. Along with the first-order variables, their squared values were included, and the squared values of labor and plot size were positively and significantly related to the technical efficiency of field pea producers. Moreover, the result confirmed that the complementarities among the inputs were also very important. The interaction of labor with chemicals, seed with plot size, and oxen with chemicals were among the variables positively and significantly affected the technical efficiency of field pea producers. Conversely, the interaction of seed with chemicals, and seed with labor were the variables that negatively related to the technical efficiency. The finding of this research confirmed that access to off-farm income, access to credit services, access to extension contacts and training, adoption of improved technologies, and participation in contract farming were the responsible factors for the technical inefficiency of field pea producers in the study area. Households with access to these variables are technically more efficient compared to households with no access to these variables. Based on the findings of this research, giving vocational training to farmers, delivering technologies along with their packages to make farmers adopt new technologies, and facilitating ways in which smallholders get access to off-farm incomes and credit services will contribute much to opening the prospects for farmers to become more technically efficient in their farming business. 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