Comparative Technical Efficiency of Maize Growers in the Northern Region of Cote D’ivoire: Stochastic Meta Frontier Analysis
DOI:
https://doi.org/10.54536/ajmri.v5i6.7491Keywords:
Stochastic Meta-Frontier, Technical Efficiency, Technological Gap, Varietal InnovationAbstract
This study investigates maize production efficiency in northern Côte d’Ivoire, a savannah region where maize farming is closely integrated with cotton-based production systems. The analysis is motivated by the national policy objective of achieving cereal self-sufficiency in a context of increasing agronomic and technological constraints. A key gap in the literature is that few studies account for technological heterogeneity between farmers using traditional maize varieties and those adopting improved hybrid seeds, while also clearly distinguishing technical inefficiency from technology-driven productivity differences. The empirical analysis is based on a cross-sectional sample of 313 maize-producing farms drawn from cooperative-based cotton and maize farmers. Data were collected through a survey conducted under the COMPACI (Competitive African Cotton Initiative) project in collaboration with Cotton Made in Africa (CmiA), which promotes the dissemination of improved agricultural technologies, particularly hybrid maize seeds. Methodologically, the study applies a Stochastic Meta-Frontier Analysis (SMFA) production function framework, which enables the estimation of group-specific production frontiers and a common meta-frontier, while decomposing productivity differentials into technical efficiency and technology gap components. The results reveal a substantial technology gap of approximately 63% in favor of improved maize systems, with hybrid seed adopters achieving significantly higher productivity levels. Market orientation and greater land allocation to cash and alternative food crops are found to reduce technical efficiency. Overall, the findings highlight the central role of technological upgrading in enhancing maize productivity and strengthening food security in the study area.
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Adjanohoun, A., Noumavo, P. A., Sikirou, R., Allagbe, M., Gotoechan-Hodonou, H., Dossa, K. K., Yehouenou, B., Kakai, R. G., & Baba-Moussa, L. (2012). Effets des rhizobactéries PGPR sur le rendement et les teneurs en macroéléments du maïs sur sol ferralitique non dégradé au Sud-Bénin. International Journal of Biological and Chemical Sciences, 6(1), 279–288.
Aigner, D., & Chu, S. (1968). On estimating the industry production function. The American Economic Review, 58(4), 826–839.
Aigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37.
Alvarez, A., & Arias, C. (2004). Technical efficiency and farm size: a conditional analysis. Agricultural Economics, 30(3), 241–250.
Audibert, M., Mathonnat, J., & Henry, M. (2003). Malaria and property accumulation in rice production systems in the savannah zone of Côte d’Ivoire. Tropical Medicine & International Health.
Battese, G. E., & Corra, G. S. (1977). Estimation of a production frontier model: with application to the pastoral zone of eastern Australia. Australian Journal of Agricultural Economics, 21(3), 169–179.
Battese, G. E., Rao, D. S. P., & O’Donnell, C. J. (2004). A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. Journal of Productivity Analysis, 21(1), 91–103.
Beke, T. E. (2010). Innovation, gap technologique et efficacité technique : Le cas de la riziculture en Côte d’Ivoire. CEDRES, 304–315.
Berndt, E. R., & Christensen, L. R. (1973). The translog function and the substitution of equipment, structures, and labor in U.S. manufacturing 1929–68. Journal of Econometrics, 1(1), 81–113.
Boqiang, L., & Zhao, H. (2016). Technological progress and energy rebound effect in China’s textile industry: evidence and policy implications. Renewable and Sustainable Energy Reviews, 60, 173–181.
Boris, J. L., et al. (2020). Analysis of irrigation infrastructure efficiency and agricultural productivity in the Philippines using a stochastic meta-frontier analysis. Journal of Agricultural Economics, 71(3), 674–689.
Callivoire. (2014). Développement de variétés hybrides de maïs blanc et jaune adaptés aux conditions agro-climatiques ivoiriennes (Rapport technique).
Charcosset, A., & Gallais, A. (2009). Application of quantitative genetics for maize breeding. Comptes Rendus Biologies, 332(11), 1022–1029.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), 429–444.
Christensen, L. R., Jorgenson, D. W., & Lau, L. J. (1971). Conjugate duality and the transcendental logarithmic production function. Econometrica, 39(4), 255–256.
Cobb, C. W., & Douglas, P. H. (1928). A theory of production. American Economic Review, 18(1), 139–165.
Coelli, T., & Fleming, E. (2004). Diversification economies and specialisation efficiencies in a mixed food and coffee smallholder farming system in Papua New Guinea. Agricultural Economics, 31(2–3), 229–239.
CORAF. (2018a). Impact de l’adoption des variétés améliorées de maïs sur l’amélioration du bien-être et la réduction de la pauvreté des maïsiculteurs de la zone UEMOA.
CORAF. (2018b). La filière maïs en Afrique de l’Ouest et du Centre: Contraintes et perspectives de développement.
Debreu, G. (1951). The coefficient of resource utilization. Econometrica, 19(3), 273–292.
Ding, L., Rangaraju, P., & Poursaee, A. (2019). Application of generalized regression neural network method for corrosion modeling of steel embedded in soil. Soils and Foundations, 59.
Djimasra, N. (2012). Déterminants de la performance des exportations de coton : cas des pays africains. Revue d’Économie Théorique et Appliquée, 2(1).
Farah, S. B., & Amara, N. (2023). Towards a comparative analysis of olive farmers’ technical efficiency. Journal of Competitiveness, 15(3).
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A, 120(3), 253–290.
Food and Agriculture Organization. (2015). The state of food and agriculture 2015: Social protection and agriculture.
Food and Agriculture Organization. (2016). The state of food and agriculture 2016: Climate change, agriculture and food security.
Food and Agriculture Organization, International Fund for Agricultural Development, World Health Organization, World Food Programme, & UNICEF. (2018). The state of food security and nutrition in the world 2018.
FAOSTAT. (2007). Rice trends in Sub-Saharan Africa.
FAOSTAT. (2016). Base de données statistiques sur l’alimentation et l’agriculture.
Gniza, D. I., & Loa, R. L. B. (2023). Socioeconomic determinants of decisions and degree of crop diversification among smallholder rice producers in côte d’ivoire. American Journal of Multidisciplinary Research and Innovation , 2(1), 65-73.
Greene, W. H. (2008). Econometric analysis (6th ed.). Pearson.
Gutierrez, R. G., Carter, S., & Drukker, D. M. (2001). On boundary-value likelihood-ratio tests. Stata Technical Bulletin, 10(60).
Hayami, Y. (1969). Sources of agricultural productivity gap among selected countries. American Journal of Agricultural Economics, 51(3), 564–575.
Hayami, Y., & Ruttan, V. W. (1970). Agricultural productivity differences among countries. The American Economic Review, 60(5), 895–911.
Hayami, Y., & Ruttan, V. W. (1971). Agricultural development: An international perspective. Johns Hopkins University Press.
Helfand, S. M., & Levine, E. S. (2004). Farm size and the determinants of productive efficiency in the Brazilian Center-West. Agricultural Economics, 31(2–3), 241–249.
Jatin, T., et al. (2018). Intra-sectoral efficiency assessment of Indian banking sector based on DEA meta-frontier approach. Journal of Banking & Finance, 94, 125–141.
Jianxu, D., et al. (2018). Analysis of technical efficiency of major agricultural producing countries in Asia using meta-frontier approach. Agricultural Systems, 162, 85–93.
Koopmans, T. C. (1951). Analysis of production as an efficient combination of activities. In Activity analysis of production and allocation. Wiley.
Lau, L. J., & Yotopoulos, P. A. (1989). The meta-production function approach to technological change in world agriculture. Journal of Development Economics, 31(2), 241–269.
Leibenstein, H. (1966). Allocative efficiency vs. X-efficiency. The American Economic Review, 56(3), 392–415.
Leibenstein, H. (1978). On the basic proposition of X-efficiency theory. The American Economic Review, 68(2), 328–332.
Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estadistica, 4, 209–242.
Mamam, T. S., Gauthier, B., Afio, Z., & Aliou, S. (2016). Évaluation du niveau d’efficacité technique des systèmes de production à base de maïs au Bénin. European Scientific Journal, 12(27).
Meeusen, W., & Van den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18(2), 435–444.
Minader. (2014). Stratégie nationale de développement des cultures vivrières autre que le riz.
Minagri. (2012). Plan d’investissement détaillé pour la mise en œuvre du programme national d’investissement agricole 2010–2015.
Missihoun, A., Agbangla, C., Adoukonou-Sagbadja, H., Ahanhanzo, C., & Vodouhe, R. (2012). Gestion traditionnelle et statut des ressources génétiques du sorgho au Nord-Ouest du Bénin. International Journal of Biological and Chemical Sciences, 6, 1003–1018.
Mundlak, Y., & Hellinghausen, R. (1982). The intercountry agricultural production function: another view. American Journal of Agricultural Economics, 64(4), 664–672.
Ngom, C. A. B., Sarr, F., & Fall, A. A. (2016). Mesure de l’efficacité technique des riziculteurs du bassin du fleuve Sénégal. Économie rurale, 91–105.
Nguyen, T. P. T. (2018). Comparison of efficiency and technology across banking systems. Benchmarking: An International Journal, 25(9), 3809–3830.
Nuama, E. (2006). Mesure de l’efficacité technique des agricultrices de cultures vivrières en Côte d’Ivoire. Économie rurale, 39–53.
Nuama, E. (2010). L’efficacité technique des riziculteurs ivoiriens : la vulgarisation en question. Économie rurale, 36–47.
Nuss, E. T., & Tanumihardjo, S. A. (2011). Quality protein maize for Africa. Advances in Nutrition, 2(3), 217–224.
Nyemeck, B., Tonyè, J., Wandji, N., Nyambi, G., & Akoa, M. (2004). Factors affecting technical efficiency among smallholder farmers. Food Policy, 24, 531–545.
O’Donnell, C. J., Rao, D. S. P., & Battese, G. E. (2008). Metafrontier frameworks for the study of firm-level efficiencies. Empirical Economics, 34(2), 231–255.
Programme alimentaire mondial. (2019). Projet de plan stratégique de pays – Côte d’Ivoire (2019–2023).
Rongead. (2014). Diagnostic de la filière maïs en Côte d’Ivoire.
Ruttan, V., Binswanger, H., Hayami, Y., Wade, W., & Weber, A. (1978). Factor productivity and growth: a historical interpretation. In Induced innovation. Johns Hopkins University Press.
Shephard, R. W. (1953). Cost and production functions. Princeton University Press.
Shephard, R. W. (1970). Theory of cost and production functions. Princeton University Press.
Tanumihardjo, S. A. (2011). Vitamin A: biomarkers of nutrition for development. The American Journal of Clinical Nutrition, 94(2), 658–665.
Wal, S. M., Asseffa, T., & Belachew, S. K. (2023). The Relationship between Ethnic Enclave Settlements with Development Spectrum in Gambella City Ethiopia. American Journal of Multidisciplinary Research and Innovation , 2(1), 83-98.
Yéo, Y. A. (2011). Analyse de la compétitivité du maïs local en Côte d’Ivoire.
Zhu, X., & Demeter, R. M. (2012). Technical efficiency and productivity differentials of dairy farms. Agricultural Economics Review, 13(1), 66–92.
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