1. Technical Efficiency and Its Determinants in Mali’s Rice Production.
  M. KANE Abdoulah Mamary, Dr. Bino Teme, Dr. Abdoulaye Hamadoun
Institut d'Economie Rurale (IER) Rue Mohamed V, BP:258 Bamako- Mali; University of Bamako (UB)/ISFRA, Mali.
Introduction
Résultats
The Mali is the largest producer and consumer of cereal, mainly rice in West African. Despite severely limited and dwindling resources on a per capita basis, Mali has maintained sufficient growth in rice production since Rice, maize and millet/sorghum are the main cereal crops in Mali, together accounted for more than 95 percent of cereal production in Mali. From 1989 to 2009, rice acreage has increased, while those of cotton (the first industrial culture and economic) have decreased. While some argue that the trend growth performance can be maintained in the future (Brown, 1995), others are optimistic about the prospects for rice production in Mali (B.E.Bravo-Ureta, D Solis, Lopez, Maripani and Thiam 2007, KONE and ALL 2004, Audibert 1997, contribute to a better understanding of the grain economy, especially that of the rice sector in Mali Recently, productivity and efficiency in Mali’s agricultural production have attracted considerable attention (Jamison and Moock 1984, Kalirajan 1986, Aly et al. 1987, Battese and Coelli 1992, Battese and Coelli 1995, Audibert 1997, Bravo-ureta and A thiam 2007). However, earlier studies have basically neglected identification of factors influencing technical inefficiency in malian farming. Filling these gaps in the literature is useful and desirable to those interested in Mali’s rice policy-makers. In particular, analyzing efficiency determinants is perhaps more important than merely presenting a set of efficiency indices.
The use of general agricultural census data and economic survey of Mali, have evaluated the production function frontier for growing rice. Technical efficiency and its determinant are analyzed.
The dependent variable is rice output for the various regions for the period covered by the study.
A production function of the rice in Mali, however the independents variables are as follows:
Variable area, variable labor, variable seed and variable fertilizer. The variable seed, all the independents variables have a positive correlation with a the Rice productivity but no significant for Area and every significant for the Variables Labor and Fertilizer . The estimated results indicates that the Area coefficient of correlation is implying A positive but no significant impact of the variables Area on Rice production in Mali. In another word one (1) percentage increase in the variable Area, will increase rice productivity by 1.94, holding other variables constant, so a positive correlation between variable area and rice productivity in Mali. In Fact Mali dispose a vast Area. Table 3 presents estimation results for rice production, with parameter estimates for the frontier equation (3) reported in the top half of the table. Also the partial coefficient of the labor variable is positive and very significant it slope is implying that an increase on the labor will increase the rice productivity in Mali. So a positive correlation with rice productivity. Mali is a country which has an abundant labor force, 80% of the active population involved in agriculture.it is commonly know that the labor is a key of the productivity in agriculture in particularly on the rice production .
Furthermore, the coefficient of correlation of the variables seed is ambiguous and negative, it means the increase on one (1) percent of seed in Mali will decrease the rice productivity de 8,12 ,holding other variables constant ,the variable seed is inversely related to the rice productivity in Mali. The Seed subsidies stood at 60%, resulting in 54 million FCFA being used to subsidize seed for rainfed rice, especially the NERICA 4 variety. (Mali’s Statistic 2008). The National Seed Policy defines all institutional, legal and financial provisions in relation to production and use of seed. The strategies to take into consideration in the framework of implementation of the National Seed Policy are constructed around many axes (Rice national Strategy). The elasticities of labor rose over time in all regions. In contrast, the elasticities of fertilizer declined uniformly. The elasticities of other inputs increased also. Apart from possible data problems, Mali’s peculiar agricultural policies, in the authors’ view, is a major factor leading to this seemingly irrational producer behavior.
Table 3: Parameter estimates of the frontier production and efficiency functions.
Photo1: Data restitution on rice (Nerica4) with farmers, Soninkegni. Photo2: Women rice (Nerica4) production, Sibi.
Without the seed, all the inputs such as fertilizer, Area and labor have a positive and significant impact on rice
production in Mali. As causes, we have a bad system supply of seed and the poor quality of seed.
Matériel et méthodes
Stochastic frontier production functions have been used extensively to analyze technical efficiency in the past two decades. The original models of Aigner, Lovell and Schmidt (1977) and Meeusen and van den Broeck (1977) have been altered and extended in a number of ways. One development has been to express inefficiency as an explicit function of firm-specific variables. The model can be estimated by a two-stage technique where the stochastic frontier is obtained first and the predicted efficiencies are then regressed upon firm-specific variables. Including dummy variables, the appropriate models are estimated using the computer program FRONTIER 4.1 (Coelli 1994)4. Instead of directly estimating v2 and u2, FRONTIER 4.1 seeks estimates of 2 = v2+ u2 and  = u2/2 via its maximum likelihood procedure (Coelli 1992, 1994). If the null hypothesis  = 0 is accepted, this would indicate that u2 is zero and thus the term Uit should be removed from the model, leaving a specification with parameters that can be consistently estimated by ordinary least squares (Coelli 1994). The model is specified as:
(1) Yit = Xitb + (Vit - Uit) i = 1,..., N; t = 1,...,T,
The technical efficiency index can be calculated as:
(2) EFFit = E(Yit*|Uit, Xit)/ E(Yit*|Uit = 0, Xit),
where Yit* is the production of the ith firm in year t, which equals Yit when the dependent variable is in original units or exp(Yit) when the dependent variable is in logarithms. With four conventional inputs, the translog production frontier can be written as:
(3) LnYit = + LnA + (LnA)2 + (LnAL) + (LnA*lnS) + (LnAF)
+ (LnL + (LnL)2 + (LnL)(LnS) + (LnL*LnF) + (LnS)
+ (LnS) 2 + (LnS*LnF) + (LnF) + (LnF) 2 + Tit + (Vit - Uit)
The rice output Y is measured in kg per ha (where 1 ha =15 mu and 1 kg = 2 jin) and. The input variables, area (A), labor (L), seed (S) and fertilizer (F), are all expressed on a kg per ha basis.
Conclusions
The marginal productivity of labor in rice production is quite low and should not be relied on as an important source for future yield growth. While further increases of the fertilizer input can be justified for rice, but the scopes for output growth may be limited. Also, the impacts of chemical fertilizer on environment should be carefully considered (Audibert 1997). Mali can longer repeat its past rice production growth, which has been driven primarily by increased chemical fertilizer usage. With Frontier Product Service (FPS), without the seed, all the inputs such as fertilizer, Area and labor have a positive and significant impact on rice production in Mali. As causes, we have a bad system supply of seed and the poor quality of seed. The solution is to revise this system of input supply by developing a good policy to procurement and distribution of inputs.
Remerciements
Contact
I want to thank the Institute of Rural Economic (IER), and the University of Bamako/ISFRA, Mali.
Thanks are given to the Office of the irrigated perimeter of Baguineda (OPIB)/Mali for various information and assistance.
Especially I would like to provide thanks to AFRICA RICE for opportunities to all young researchers working in this field (Rice production and food security in Africa) and farmers for collaboration.
The Statistical and Planning Center (CPS), Ministry of Agriculture, Mali has provided assistance in data collection.
Nom : Abdoulah Mamary KANE
Courriel :
Organisation : IER (Institut d’Economie Rurale), Mali
Tél. : (00223) / /
Thème : Technical Efficiency and Its Determinants in Mali’s Rice Production.