Research article Special Issues

Productivity differences and food security: a metafrontier analysis of rain-fed maize farmers in MasAgro in Mexico

  • Received: 30 November 2016 Accepted: 13 April 2017 Published: 18 April 2017
  • Rain-fed maize production in Mexico includes approximately 6 million hectares which variation in productivity represents huge challenges to meeting the sustainable intensification goals of the Sustainable Modernization of Traditional Agriculture (MasAgro) program. We use the information available from farmers participating in this program to investigate the differences in productivity and the effects of the promoted practices and technologies in seven defined rain-fed maize regions. We do this by applying metafrontier analysis to measure the technical efficiency and the technology gap. The results show a range of technical efficiency from 70 to 100%, which indicates the gains that can be achieved through improved management of the current inputs and practices of farmers in the program, and a range of the environment–technology gap between 32 and 82%, which indicates the limitations of the production environment which would require innovations in technologies and policies particularly adapted for the dry, the tropical and the more traditional regions. Furthermore, the results show that the use of hybrid seed and selling into maize markets have the largest impact in increasing maize yields in all regions. The difference between the MasAgro farmers and the average farmers in each region suggest that scaling the project will contribute to increasing maize production and Mexico’s food self-sufficiency.

    Citation: M. Laura Donnet, Iraís Dámaris López Becerril, J. Roy Black, Jon Hellin. Productivity differences and food security: a metafrontier analysis of rain-fed maize farmers in MasAgro in Mexico[J]. AIMS Agriculture and Food, 2017, 2(2): 129-148. doi: 10.3934/agrfood.2017.2.129

    Related Papers:

  • Rain-fed maize production in Mexico includes approximately 6 million hectares which variation in productivity represents huge challenges to meeting the sustainable intensification goals of the Sustainable Modernization of Traditional Agriculture (MasAgro) program. We use the information available from farmers participating in this program to investigate the differences in productivity and the effects of the promoted practices and technologies in seven defined rain-fed maize regions. We do this by applying metafrontier analysis to measure the technical efficiency and the technology gap. The results show a range of technical efficiency from 70 to 100%, which indicates the gains that can be achieved through improved management of the current inputs and practices of farmers in the program, and a range of the environment–technology gap between 32 and 82%, which indicates the limitations of the production environment which would require innovations in technologies and policies particularly adapted for the dry, the tropical and the more traditional regions. Furthermore, the results show that the use of hybrid seed and selling into maize markets have the largest impact in increasing maize yields in all regions. The difference between the MasAgro farmers and the average farmers in each region suggest that scaling the project will contribute to increasing maize production and Mexico’s food self-sufficiency.


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    [1] Eakin H, Bausch JC, Sweeney S (2014) Agrarian Winners of Neoliberal Reform: The 'Maize Boom' of Sinaloa, Mexico. J Agrar Chang 14: 26-51. doi: 10.1111/joac.12005
    [2] Sweeney S, Steigerwald D, Davenport F, et al. (2013) Mexican Maize Production: Evolving Organizational and Spatial Structures since 1980. Appl Geogr 39: 78-92. doi: 10.1016/j.apgeog.2012.12.005
    [3] Camacho-Villa TC, Almekinders C, Hellin J, et al. (2016) The evolution of the MasAgro hubs: responsiveness and serendipity as drivers of agricultural innovation in a dynamic and heterogeneous context. J Agric Educ Ext 22: 455-470. doi: 10.1080/1389224X.2016.1227091
    [4] Appendini K (2014) Reconstructing the Maize Market in Rural Mexico. J Agrar Chang 14: 1-25. doi: 10.1111/joac.12013
    [5] Ali M, Byerlee D (1991) Economic efficiency of small farmers in a changing world: a survey of recent evidence. J Int Dev 3: 1-27. doi: 10.1002/jid.4010030102
    [6] Fan S, Brzeska J, Keyzer M, et al. (2013) From Subsistence to Profit Transforming Smallholder Farms. International Food Policy Research Institute Washington, DC.
    [7] O'Donnell CJ, Rao DSP, Battese GE (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empir Econ 34: 231-255. doi: 10.1007/s00181-007-0119-4
    [8] Yunez-Naude A, Juarez-Torres M, Barceinas-Paredes F (2006) Productive Efficiency in Agriculture: Corn Production in Mexico. 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia.
    [9] Kagin J, Taylor E, Yúnez-Naude A (2015) Inverse productivity or inverse efficiency?: Evidence from Mexico. J Dev Stud 52: 396-411.
    [10] Villano R, Bravo-Ureta B, Solís D, et al. (2015) Modern Rice Technologies and Productivity in the Philippines: Disentangling Technology from Managerial Gaps. J Agric Econ 66: 129-154. doi: 10.1111/1477-9552.12081
    [11] Villano R, Fleming P, Fleming E (2008) Measuring Regional Productivity Differences in the Australian Wool Industry: A Metafrontier Approach. AARES 52nd Annual Conference.
    [12] Kramol P, Villano R, Kristiansen P, et al. (2015) Productivity differences between organic and other vegetable farming systems in northern Thailand. Renew Agric Food Syst 30: 154-169. doi: 10.1017/S1742170513000288
    [13] Turrent A, Wise T, Garvey E (2012) Achieving Mexico's Maize Potential. GDAE Working Paper 12-03.
    [14] DOF, Diario Oficial de la Federacion. Programa Sectorial de Desarrollo Agropecuario, Pesquero y Alimentario 2013­2018. 2013, Available from: https://www.gob.mx/cms/uploads/attachment/file/82434/DOF_-_Diario_Oficial_de_la_Federaci_n.pdf
    [15] FAO, IFAD, WFP (2015) The State of Food Insecurity in the World 2015. Meeting the 2015 international hunger targets: taking stock of uneven progress. Rome, FAO.
    [16] Hartkamp AD, White JW, Rodríguez Aguilar A, et al. (2000) Maize Production Environments Revisited: A GIS-based Approach. CIMMYT, Mexico D.F.
    [17] Fischer R, Byerlee D, Edmeades G (2014) Crop yields and global food security: will yield increase continue to feed the world? Canberra: Australian Centre for International Agricultural Research.
    [18] SIAP, Sistema de Información Agrícola y Pecuaria. Anuario Estadístico de la Producción Agrícola 2012. 2013, Available from: http://infosiap.siap.gob.mx/aagricola_siap/icultivo/index.jsp
    [19] SIAP, Sistema de Información Agrícola y Pecuaria. Anuario Estadístico de la Producción Agrícola 2012 - 2015. 2016, Available from: http://infosiap.siap.gob.mx/aagricola_siap/icultivo/index.jsp
    [20] INEGI, Instituto Nacional de Estadística y Geografía. Continuo de Elevaciones Mexicano 2.0. Marco Geoestadístico Nacional MGM. 2014, Available from: http://www.inegi.org.mx/geo/contenidos/datosrelieve/continental/continuoelevaciones.aspx
    [21] SMN, Servicio Meteorológico Nacional (2015) Resúmenes Mensuales de Temperaturas y Lluvia.
    [22] Aigner D, Lovell C, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econom 6:21-37. doi: 10.1016/0304-4076(77)90052-5
    [23] Battese GE (1992) Frontier production functions and technical efficiency: a survey of empirical applications in agricultural economics. Agric Econ 7: 185-208. doi: 10.1016/0169-5150(92)90049-5
    [24] Greene WH (2008) The Econometric Approach to Efficiency Analysis. In: The Measurement of Productive Efficiency and Productivity Change. Oxford University Press.
    [25] Namonje-Kapembwa T, Black R, Jayne TS (2015) Does Late Delivery of Subsidized Fertilizer Affect Smallholder Maize Productivity and Production? 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205288, Agricultural and Applied Economics Association; Western Agricultural Economics Association.
    [26] Dadzie S, Dasmani I (2010) Gender difference and farm level efficiency: Meta-frontier production function approach. J Dev Agric Econ 2: 441-451.
    [27] Hayami Y, Ruttan V (1970) Agricultural productivity differences among countries. Am Econ Rev 60: 895-911.
    [28] Mehrabi Boshrabadi H, Villano R, Fleming E (2008) Technical efficiency and environmental - technological gaps in wheat production in Kerman Province of Iran: A meta-frontier analysis. Agric Econ 38: 67-76.
    [29] Mariano M, Villano R, Fleming E (2011) Technical efficiency of rice farms in different agroclimatic zones in the Philippines: An application of a stochastic metafrontier model. Asian Econ J 25: 245-269. doi: 10.1111/j.1467-8381.2011.02060.x
    [30] Bravo-Ureta B, Greene W, Solis D (2012) Technical Efficiency Analysis Correcting for the Biases from Observed and Unobserved Variables: An Application to a Natural Resource Management Project. Empir Econ 43: 55-72. doi: 10.1007/s00181-011-0491-y
    [31] Kunbhaker S, Wang H, Horncastle A (2015) A Practitioner's Guide to Stochastic Frontier Analysis Using Stata, Cambridge University Press.
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