INTRODUCTION

The agricultural sector of Cameroon is the backbone of the economy. Just like many other Sub-Saharan African countries, agriculture supplies bulk of the population as food, raw material for agro-industries and export markets. As a primary industry that provides employment for almost 70 percent of the population. Agriculture contributes to about 30% of the country’s Gross Domestic Product (GDP). In addition, agriculture contributes enormously to the country’s foreign export earnings (Emmanuel Tambi, 1998). The major food crops in Cameroon are yam, cassava, plantains, oil palm, maize, groundnuts, potatoes, millet, sorghum, rice, and beans.

Rice is the most cultivated crop in the world. According to Dr. Jacques Diouf, the Director-General of the UN Food and Agriculture Organization (FAO) rice is the staple food for over half of the world’s population. Rice cultivation is the principal activity and source of income for millions of households around the globe. Several countries of the world depend on rice as a source of foreign exchange earn-ings and government revenue (United Nation international year of rice, 2004).

Cameroon represents about 0.3 percent of rice production in Africa in 2008 (Piebiep Goufo, 2008). During the’80s, rice was regarded by many as a foreign crop and was consumed only during special events or festive periods (Christmas day, New Year day, Birthday, Easter day) of the year (Djomo et al., 2012). However, today rice has gradually become a staple food for the rural and urban people of Cameroon.

Rice production in Cameroon is upland, lowland, and irrigated rice. The lowlands and upland rice are mostly grown indigenously by small scale farmers who rely mainly on family labor while irrigated rice is grown on an intensive scale with the use of machineries. The cultivation of rice is carried out in major areas like Maroua and Kouseri in the Far North Region and Ndop in the North-West Region. The consumption of rice has increased faster than other food crops and according to projection; this trend is likely to continue (Djomo et al., 2012). The population growth in Cameroon has increased almost fourfold since independence in 1960 with an annual growth rate of 2.5% (Piebiep Goufo, 2008). Despite the expansion of population growth, the trend of food production as a whole has not increased as anticipated (Mulua and Utomakili, 1998).

Cameroon has the proper climatic requirements for rice production, good soil, availability of rural manpower at a relatively low cost, a high demand for rice from neighboring countries like Nigeria, Equatorial Guinea and Gabon, (Piebiep Goufo, 2008:Eshankeh Chindon, 2008). Therefore with such resources, Cameroon has the potential to produce rice that will entirely meet its rice needs.

However, the production of rice in Cameroon is estimated at 80,000 to 200,000 metric tons annually instead of 500,000 tons required to meet the nation's demand (MINADER).

Report from the Ministry of Agriculture and Rural Development (MINADER), showed that the demand and supply equation of rice has been difficult to balance, forcing the authorities to resort in undesirable imports to feed the growing population. This implies that the import levels of rice are incredibly high, causing the government not only to lose huge sums of money due to reliance on importation of rice, but also hinder the growth of local rice farmers( FAOSTAT).

In an effort to boost rice production, the government created three development companies namely; Societe d’Expansion et de Modernisation de Rizculture de Yagoua (SEMRY) in 1954, the Upper Noun Valley Development Authority (UNVDA) in 1974 and the Societe de Development de la Rizculturedans la plaine de Mbo (SODERIM) in 1978. The main objective of these companies was to boast rice production in the country. However, SODERIM was shut down ten years later due to mismanagement and poor control (Mulua and Utomakili, 1998). SEMRY and UNVDA operated as rice research and development institutes to support the production of rice by providing inputs, access to credit, access to processing machines, access to extension services and a market to farmers produce. In addition, the government created the Cameroon Rural Financial Corporation (CARFIC), an agricultural bank to help prospective rice farmers who own hectares of land but did not have the necessary funds to operate the farms.

In the wake of 2014, the state launched the second generation agriculture. It aims at increasing agricultural production (rice) by modernizing the production tools and also the opening of ten professional agricultural high schools in the 10 regions of country to offer agricultural enterprise-like training to young Cameroonian aged 12-22.

The Government has also carried out new strategies within family holdings to increase their productivity of rice. These programs are the Competitivity of Agropastral Families (ACEFA) and the Common Agricultural Program for Africa (PACA). The objectives of these programs are: To provide advisory support to family holdings through producers’ organizations thus leading to better control of the production and management; and to provide financial support to group or individual investment projects owned by families and led by producers’ organizations on strategic enterprises such as rice production (MINADER).

From the above figure (Fig. 1), Cameroon imports approximately 450,000 tons of rice each year while approximately less than 200,000 tons of rice is locally produced. According to the National Statistics Institute (INS), Cameroon imported approximately 728,443 tons of rice in 2016 for 193.7 billion francs CFA. Importation of rice in Cameroon increased sharply in 2002 after world prices of rice decreased steeply. Therefore, despite the resource potential, availability of internal and external markets and the role played by the government (SEMRY, UNVDA, CARFIC, and The Second Generation Agriculture), the production of rice has not increase to the level of meeting the nation’s demand. Under these circumstances, it is important to explore the potentials of rice farms and to analyze the technical efficiency of rice farmers in Cameroons’ North-West Region.

The research objective is to analyze the determinants of rice production and the technical efficiency of rice farmers in the North-West Region of Cameroon.

Multiple studies into farm efficiency have also been conducted within and outside of Cameroon. In Cameroon, a study was conducted in the west region to analyze the technical efficiency of small rice farmers (Djomo et al., 2012;Mahmudul Hasan et al., 2016). Findings from the Authors revealed that farm size and labor are fundamental to rice production.

In Ghana, a translog production function was used to analyze rice production; the results revealed that farm labor, land and fertilizers affects rice output significantly (Monica Addison et al., 2016). In Ethiopia, analysis carried out on rice farmers in Fogera district showed that agrochemicals helped in increasing rice productivity (Tedesse et al., 2016;Thomas Masterson. 2016),

Previous literatures have highlighted socio-economic characteristics as key factors that affect technical inefficiency of farmers. Extension services play a critical role in providing information and services needed by households to their livelihood and wellbeing. Therefore, Households who are able to receive assistance from extension service stand the chance of being technically efficient (Tadesse et al., 2016;Jimjel et al., 2014). However, other studies found out that some farmers are technically inefficient when they have access to extension services (A.A Girei et al., 2016).

The rural households in Cameroon are poor and at time lack the basic farm inputs to carry out farm production. Therefore, access to credit will offer an opportunity for households to purchase quality farm inputs (high yielding seeds, Fertilizer Machineries) to be able to increase their output. According Djomo et al., 2012, access to credit increases the technical efficiency of rice farmers in the West Region of Cameroon.

Farm union refers to the coming together of households who are engage in farming activities. It comprises the coming together of both female and male farmers. Member of farm union help each other towards farm activities. They provide labor assistance, share advice about farming techniques, and at time provide financial assistance to other members. Such assistance has a positive influence on farmers’ productivity. Farm associations have a positive impact on technical efficiency (Louis et al., 2017). Thus, household members of a farm union stand the chance of being technically efficient.

The paper proceeds as follows: Chapter 2 outline the method; Chapter 3 present results and discussion (Summary Statistics and Estimation Results); and Chapter 4 presents Conclusion and recommendations.

METHODOLOGY

All production processes represent a transformation of inputs (labor, capital, and raw material) into outputs. From a general point, any increase in farm/firm output will result from either an increased quantity of inputs with no change in output per unit of input, or increased productivity of inputs with no change in the quantity of input or finally a combination of changes in inputs and productivity (Martin and Ernest, 2017). This situation makes the concept of efficiency a central issue in production economics. A firm can be described as being economically efficient when it achieve both technical and allocative efficiency. Technical efficiency is a measure of a firms’ success in producing a maximum output from a given set of inputs (G.E Battase and T.J, Coelli. 1957). Technical efficiency can be modeled as either output-oriented or input-oriented.

The production function is appropriate and widely used to analyze technical efficiency (Coellie et al., 2005). An understanding of the production frontier represents the maximum output attainable from each input level. Farm holdings or firms that operate on the frontier are considered to be technically efficient. While those that operate beneath the frontier are considered not technically efficient. Efficiency cannot be easily measured since precise measurements rest on the assumption of an efficient isoquant. Several approaches to efficiency measurement have been developed. Among these, the parametric approach also known as stochastic frontier analysis (SFA) and the non-parametric approach (Data Envelopment Analysis).

The non-parametric approach has the power of accommodating multiple outputs and inputs in technical efficiency analysis. However, it fails to take into consideration the possible impact of random shocks like measurement error and other types of noise in the data (Martin and Ernest, 2017). Also, the DEA is not appropriate in an agricultural application of technical efficiency. This is because agricultural output is likely to be affected by climatic conditions, natural disaster which could contribute to the presence of noise in the data. On the other hand, the stochastic frontier analysis (SFA) does not accommodate multiple inputs and outputs and it is more likely to be influenced by mis-specification issues. However, the fact that SFA incorporates stochastic components into the model increased its applicability in the analysis of technical efficiency of agricultural productions. Thus for this study, a parametric analysis (SFA) was used (Aigner et al., 1976).

Study area and data

In the study, farm level, cross-sectional data were used to carry out the estimations. The study used primary data that was collected through survey questionnaires from randomly selected farm household from Bamunka district in the north-west region of Cameroon. The period of the survey was from December 2018 to January 2019. During this period, 144 household farmers were interviewed face to face in the above district. Households in this district are mostly specialized in producing rice for home consumption and for business purpose as well. The questionnaires were structured based on the conditions of the rice farmers including socio-economic questions and farm inputs available to the farmer. Socio-economic variables such as Farm union (yes=1, no=0), credit access (yes=1, no=0), and extension services (yes=1, no=0) were used. While Farm inputs such as the quantity of seed, fertilizers, herbicides, labor, and land size were also included in the questionnaire. Previous studies (Djomo et al., 2012;Monica Addison, 2016) have used these variables in their works.

Methodological framework

This study is based on the production theory that relates farm inputs to farm outputs. Such is the foundation for computing efficiency of production. Several approaches have been applied to estimate production efficiency. However, in this study, the stochastic production frontier was used (Coellie et al., 2005:Mueesen and Julien, 1977). The stochastic frontier model is given by equation (1)

The above equation is an exponential function. To estimate the parameters, it employs a Cobb Douglas form of stochastic frontier production (Binam et al., 2004;Mazyimayi et al 2012;Nelson et al., 2015).

where ln denotes natural logarithm, Y_i refers to the output of rice, X_ij is the vector of the farm inputs, β refer to the vector of parameters to be estimated and e_i refers to the composite error term. The error term in the above equation (2) has two components.

Where V_i and U_i are independent of each other, V_i denotes the random error not under the control of the farmers, assumed to be independently and identically distributed as N(0,σ²v)and U_i is a one sided efficiency component that capture the technical inefficiency of the farmer and are assumed to be distributed as a truncations at zero of the N(0,σ²v). In this study, a one step estimation approached proposed (Hung-Jen and Peter, 2002) will be used. This approach takes in to consideration exogenous variables that affect technical efficiency. The author furthered argued that the existence of a correlation between the exogenous variables and the input variables are inevitable, thus leading to bias results.

The variance parameters sigma square(σ²) and gamma (γ) are estimated from the stochastic frontier model. They are expressed as

The parameters must lie between zero and one. When γ =0, it means that the deviation from the frontier is influence by random factors, while when γ=1, it means the deviation is due to technical inefficiency.

The technical efficiency (TE) of an individual farm is specified in terms of (y i *), conditioned on the level of inputs used by the farm. It is mathematically expressed as

Empirical Model Specification

Stochastic Production Frontier

Different functional forms including the Cobb-Douglas, translog, linear, and quadratic functions were explored. However, the Cobb-Douglas and the translog are the most commonly used in carrying out empirical studies on production. Results of pre-estimation test indicate that Cobb- Douglas production function is the most appropriate functional form for rice production in the study area. The following Cobb-Douglas production is specified as

Where the subscript i represents the i-th farm; ln refers to natural logarithm; rice output is the yield of rice measured in bags (one bag is equivalent to 100 kilogram); farm size refers to the land area or room that was use to cultivate rice; seed is the quantity of rice in kilograms that was planted; fert refers to the quantity of fertilizer in terms of kilogram that was applied on the farm; herb is the quantity of herbicides in terms of liter that was applied on the cultivated area labor refers to the number of people (laborers) employed during rice cultivation. v_i and u_i are as defined before. The technical inefficiency model is specified as follow,

Where u_i refers to the inefficiency term; δʹ^s are the unknown parameters to be estimated; farm union refers to a situation in which the farmer belongs to an association of farmers; credit access is to find out if the farmer had access to credit or not;

The hypothesis test was done to compare if a restricted model Ordinary least square (OLS) and an unrestricted model the stochastic frontier (Cobb Douglas production functions) is appropriate for this study. The null hypothesis H₀: restricted model OLS is the adequate

Model variables and Hypothesis

The study used rice output as a dependent variable to stochastic frontier production function. The explanatory variables of the model are as follows. Farm size, seed (kilogram), fertilizer (kilogram), labor (amount of laborer employed on a rice field) and herbicide (liters). The agricultural inputs are expected to affect rice output positively.

Inefficiency Determinants

The explanatory variables in the inefficiency model include the socio-economic factors that affect farmers’ efficiency. These variables include farm union, extension service and credit access,

Farm union is expected to have a positive effect on technical efficiency. It is believed that farmers who belong to farm union/association share resourceful knowledge on farming techniques (Luis et al., 2017). At times, the group can assist its members with labor assistance on their farms. As such, farmers who belong to such union are expected to enhance farm productivity.

Credit access is the ease at which a farmer can get credit from either a formal or an informal financial institution. Farmers who have access to credit are able to buy quality farm inputs (high yielding seeds, fertilizers, herbicides) to enhance their farm productivity (Djomo et al., 2012). Thus, farmers who have access to credit are expected to have a positive influence of farm technical efficiency.

Extension services help farmers with technical advice on agricultural production, and also supplies them with the necessary inputs and services to boost their agricultural outputs. Therefore, rice farmers who have access to extension services stand the chance of being technically efficient. Djomo et al., 2012:Tadesse et al., 2016;Jimjel et al., 2014 and Mango et al., 2015 found out that farmers who had access to extension services were more technically efficient than those who did not have access.

RESULTS AND CONSIDERATION

Summary Statistics

The farm inputs and socio-economic characteristics of the rice farmers are provided in Table 1. According to the survey, on averagely 19.18 bags of rice was produced. The highest quantity of rice produce was 289 bags, while the lowest quantity produce was 2 bags. The mean cultivated area available for rice cultivation was 20.54 rooms, which ranged from a minimum of 4 rooms to a maximum of 350 rooms. The average amount of seed used by rice farmers was 45.89 kilograms while the minimum and maximum quantity of seeds was 5 and 700 kilograms respectively. Averagely, 13.46 kilograms of fertilizer was applied on the cultivated area. The highest quantity of fertilizer applied was 115 kilograms, while the minimum quantity was 2 kilograms. The mean quantity of herbicides applied was 5.36 liters, with a minimum quantity of 2 liters and a maximum quantity of 52 liters. Averagely, 7 laborers were employed on the rice fields.

For the categorical variables, Majority of the farmers (102 respondents) had access to credit, giving a percentage of 70.8%. The percentage of respondents who participated in farm union was 40 percent. Regarding extension services, 56.25 received extension services

Hypothesis test and Model selection

The alternative hypothesis of an unrestricted model (Stochastic frontier model) is accepted against a restricted OLS model. Table 2

Estimates of Stochastic Frontier Analysis

The maximum likelihood estimates of a Cobb Douglas stochastic production frontier model are presented in Table 3. The log likelihood ratio test (based on log likelihood values for restricted and an unrestricted models) lead to the acceptance of a stochastic frontier analysis. From the estimate results in Table 3, the coefficient of farm size is positive and highly significant at 1 percent level. This implies that farmers with relatively large plots of farmland tend to have higher output of rice. This could be because larger plots allow farmers to diversify their activities, for instance by growing rice and keeping a small livestock. This should increase their income and enable them to increase their productivity. This result is in line with a similar study on tomato production in Cameroon and Nigeria (Thomas Masterson, 2007;Jimjel et al., 2014). Also, farmers with large farm size are able to use more capital in order to substitute labor (Yu Sheng, Jiping Ding, and jikun Huang, 2019)

The positive and significant coefficient of herbicide indicates that the use of herbicides helps reduce the growth of weed and thus improve rice production. As such, farmers should be mindful of the method in which they apply herbicides on their rice field. According to Tooba Maher, (2019) Herbicides should be used in combination with other method of weed control such as manual weeding. For effective weed management, herbicides should be applied in specific stages of rice cultivation; during land preparation, in the nursery, and during early crop growth. The positive relationship is in line with theoretical expectation. Leonard Gianessi. (2013) confirmed that the use of herbicides on farms increases the productivity of crops.

The coefficient of the fertilizers is positive and statistically significant at 1 percent. This is not surprising; since the use of fertilizer tends to increase productivity. This finding is consistent with those of Djomo et al. (2012). The coefficient of labor participant is positive related to rice output. This result indicates that more rice output can be obtained by using more human labor. Human labor is mostly needed in clearing the fields, planting, weeding, and harvesting. This result agrees with the findings of Djomo et al. (2012) and Nelson et al. (2015).

The second part of table 3 present results from the inefficiency model. It should be noted that the signs of the coefficient in the inefficiency model are interpreted in the opposite way and as such, a negative sign means that the variable increase efficiency and positive sign means it decreases efficiency. The estimated coefficient of credit access has a negative significant on the technical inefficiency of rice farmers in this region. The implication of the result is that technical inefficiency effect of rice productivity decreases with access to credit. This means that access to credit by rice farmers contributes to achieving a higher level of technical efficiency (Amadou, 2007).

The estimated sigma square is significantly different from zero. This indicates goodness of fit and the correctness of the specified distribution assumption of the composite error term. The gamma value (0.837), which is significant, suggests that about 83 percent of the variation in farmers is attributed to inefficiency while the remaining 17 percent is due to measurement error and random shock.

From table 4 the mean efficiency level of the farmer is 83 percent. This means that on average, a typical rice farmer produce 84 percent of maximum output. In other words about 16 percent of potential output is lost due to technical inefficiency. From interviews conducted with farmers, the technical inefficiency could be attributed to lack of modern equipments and lack of technical knowledge. The lack of modern farm equipment was a major hindrance to the farmers to increase rice production. Also, the inefficiency level of the farmer could be attributed to their level of education. From interviews with farmers, most of the farmers were illiterates. Most farmers were primary school dropouts and had little knowledge about modern farming techniques.

According to results on table 5 each of the variables has a negative effect on technical inefficiency. Therefore, increasing access to credit, increasing access to extension services and participating in farm union reduces the level of technical inefficiency.

CONCLUSION AND RECOMMENDATION

The objective of this study was to estimate and analyze the technical efficiency of some few rice farmers in the North-West region of Cameroon. The results from Maximum Likelihood estimates of Cobb-Douglas Stochastic Production Frontier model showed that farm output is influenced by farm size, fertilizer, labor and herbicides. Therefore, to increase rice output, farmers can increase the use of these farm inputs. The results from the analysis showed that the mean technical efficiency of the rice farmers in the study area is 0.843, implying that there are existing opportunities to increase the technical efficiency if resources are optimally used. More so, social economic factor like credit access was found to have a significant influence on technical efficiency

The study was carried out on a regional scale (144 household in North-West region of Cameroon).

To improve the technical efficiency of rice farming in the study area, the policy implications of these findings are that technical efficiency of rice farmers could be increased by 16 percent through having access to credit. This result proves that government efforts (SEMRY, UNVDA, CARFIC, and The Second Generation Agriculture) of providing credit to farmers have improved their technical efficiency towards rice production. However, rice output has not yet met the nation demand. Policies related to concessional credits to farmers seem to be fulfilling their objectives, since positive effects are found. Nevertheless, government policies should focus on ways to make credit accessible to farmers especially those farmers in the rural areas who are engaged in rice production. Government financial institutions like Cameroon Development Bank (CDB), International Bank of Cameroon for Savings and Credit (BICEC), Commercial banks, and Credit Union should extend credit to rice farmers in rural areas. Monetary instruments such as savings deposit rate and loan rates should be managed within reasonable ranges in other to instigate an increase in the flow of credit to the agricultural sector. To sum it up, mandatory credit allocation to the agricultural sector, in general, may also be implemented so that the sector is not crowded out of credit.

적 요

농업은 고용, 식량 및 생계를 목적으로 하는 카메룬 경제의 중심이다. 그러나 카메룬 농업은 낮은 생산성, 비 효율성 및 수입에 의존하여 전염병에 시달리고 있다. 이러한 이유로 본 연구는 카메룬 북서부 지역의 쌀 농가의 기술적 효율성을 조 사했다. 구조화된 설문지와 인터뷰를 사용하여 144명의 농부 로부터 데이터를 수집했다. 기술 통계 및 확률적 프런티어 분 석을 사용하여 데이터를 분석하였다. 확률적 프런티어 분석의 결과는 분산 매개 변수 (시그마 제곱 및 감마)가 통계적으로 유의하다는 것을 보여준다. 농장 규모, 비료, 노동 및 제초제 의 계수는 긍정적이고 중요했다. 평균 기술 효율성 수준이 84%로 나타낸 것은 가용 자원을 효율적으로 활용한다면 쌀 농민의 기술 효율성을 16%로 증가할 수 있다. 비효율성 모델 은 신용 액세스가 기술적 비효율 성과 부정적인 관련이 있는 중요한 요소임을 보여준다. 이 결과는 농민에게 생산성을 향 상시키기 위해 신용을 제공하려는 정부의 노력 (SEMRY, UNVDA, ARFIC및 2세대 농업)과 일치한다.