Regional differences in rainfed white corn production in Mexico
DOI:
https://doi.org/10.29312/remexca.v15i1.3170Keywords:
agricultural policy, Cobb-Douglas function, white corn productionAbstract
This research aims to identify the functional form that best represents the production of rainfed white corn in Mexico. Two variants of the constant elasticity of substitution function and the Cobb-Douglas function were tested using a cross-sectional sample of 10 924 corn farmers obtained from the ‘questionnaire to collect the 2008 baseline: SAGARPA programs’. The analysis was performed at national and regional levels using fourteen factors of production. The results show that the Cobb-Douglas model provides better fits and estimators consistent with theoretical principles. Similarly, it is shown that the use and effect of each production factor on the yield of rainfed white corn is different between the regions of the country, so it was shown that public policy actions should be differentiated based on the needs and particularities of each region. An analysis of the effect of each input at the regional level and a discussion of the possible effects of some support programs for the agricultural sector focused on a particular input are presented.
Downloads
References
Aldama, J. M.; Plata, F. S.; Bordi, I. V. y Guevara, M. R. 2015. Estrategias para la producción de maíz frente a los impactos del cambio climático. Rev. Cienc. Soc. 21(4):538-547.
Arroyo, G. 1987. Regiones agrícolas de México: modernización agrícola, heterogeneidad estructural y autosuficiencia alimentaria. In: lecturas de análisis regional en México y América Latina. Universidad Autónoma Chapingo. 481-509 pp. https://www.gob.mx/cms/uploads/attachment/file/568112/Informe-final-U022-PF.pdf.
CTEEA-UAA. 2009. Comité Técnico Estatal de Aguascalientes y Universidad Autónoma de Aguascalientes. Levantamiento de la línea base para la evaluación de los programas de SGARPA. SAGARPA-Gobierno del Estado de Aguascalientes. FAO-México. 51 p.
CONEVAL. 2015. Consejo Nacional de Evaluación de la Política de Desarrollo Social. Diagnóstico de la capacidad productiva de los hogares rurales y pérdidas post cosecha. Ciudad de México. https://www.coneval.org.mx/Evaluacion/ECNCH/ Documents/Integral-productores-30072015.pdf. 35-44 pp.
Constantin, P. D.; Martin, D. L. and Rivera, E. B. B. 2009. Cobb douglas, translog stochastic production function and data envelopment analysis in total factor productivity in Brazilian agribusiness. J. Operations and Supply Chain Management. 2(2):20-33. http://dx.doi.org/10.12660/joscmv2n2p20-33.
Cortés, C. H. 2009. El enfoque territorial del desarrollo rural y las políticas públicas territoriales. Revista Electrónica del Centro de Estudios en Administración Pública. UNAM. http://investigacion.politicas.unam.mx/encrucijadaCEAP/arts-n3-09-12-2009/art-ineditos3-2-hernandez-cortes.pdf. 1-10 pp.
Donnet, M. L.; López-Becerril, I. D.; Black, J. R. and Hellin, J. 2017. Productivity differences and food security: a metafrontier analysis of rain fed maize farmers in MasAgro in Mexico. AIMS Agriculture and Food. 2(2):129-148. https://doi.org/ 10.3934/agrfood.2017.2.129.
FIRA. 2016. Fideicomisos Instituidos en Relación con la Agricultura. Panorama Agroalimentario Maíz Ciudad de México, Banco de México. https://www.gob.mx/cms/uploads/attachment/file/200637/panorama-agroalimenta rio-ma-z-2016.pdf. 14-22 pp.
Flores, M. L. y Gutiérrez-Sánchez, J. R. 2000. Investigación fisiotécnica de maíz de temporal en la región alta del norte de México. Rev. Fitotec. Mex. 23(2):195-209.
Galarza, F. B. y Díaz, J. G. 2015. Productividad total de los factores en la agricultura peruana: estimación y determinantes, economía. Lima, Pontificia Universidad Católica del Perú. 38(76):77-16.
González-Estrada, A. y Alferes-Varela, M. 2010. Competitividad y ventajas comparativas de la producción de maíz en México. Rev. Mex. Cienc. Agríc. 1(3):81-396.
Jaramillo-Albuja, J. G.; Peña-Olvera, J. H.; Díaz-Ruiz, R. y Espinosa-Calderón, A. 2008. Caracterización de productores de maíz de temporal en Tierra Blanca, Veracruz. Rev. Mex. Cienc. Agríc. 9(5):911-923, https://doi.org/10.29312/remexca.v9i5.1501.
Kagin, J.; Taylor, J. E. and Yúnez-Naude, A. 2016. Inverse productivity or inverse efficiency? evidence from Mexico. J. Development Studies. 52(3):396-411. https://doi.org/10.1080/00220388.2015.1041515.
Muyanga, M. and Jayne, T. S. 2019. Revisiting the farm size productivity relationship based on a relatively wide range of farm sizes: evidence from kenya. Am. J. Agric. Econ. 101(4):1140-1163. https://doi.org/10.1093/ajae/aaz003.
Pavelescu, F. M. 2011. Some aspects of the translog production function estimation. Bucarest, The Institute of National Economy. Romanian J. Econ. 32(1):131-150.
Rangel-Fajardo, M. A.; Tucuch-Haas, J.; Bastos-Barbudo, D. C.; Villalobos-González, A.; Nava-García, J. R. y Burgos-Díaz, J. A. 2018. Rendimiento de grano bajo régimen de temporal de materiales híbridos y avanzados de maíz en Yucatán. Ed. Univesidad Juárez Autónoma de Tabasco e Instituto Nacional de Ivestigaciones Forestales Agrícolas y Pecuarias (INIFAP). 80-93 pp.
Reséndiz-Ramírez, Z.; López-Santillán, J. A.; Briones-Encinia, F.; Mendoza-Castillo, M. C. y Varela-Fuentes, S. 2014. Situación actual de los sistemas de producción de grano de maíz en Tamaulipas, México. Investigación y Ciencia. 22(62):69-75.
SAGARPA. 2009. Secretaría de Agricultura, Ganadería. Desarrollo Rural y Pesca. Base de datos: programas de la SAGARPA. Ciudad de México, México.
StataCorp. 2019. [Stata/IC], 16.0. College Station, Stata Corp.
Yúnez, N. A. 2014. Old foods and new consumers in Mexico under economic reforms. Afr. J. Agric. Res. Econ. 9(1):33-53. http://dx.doi.org/10.22004/ag.econ.176464.
Yúnez, N. A.; Juárez-Torres, M. and Barceinas-Paredes, F. 2006. Productive efficiency in agriculture: corn production in Mexico. AgEcon Search No. 1004-2016-78433. 1-13 pp. https://ageconsearch.umn.edu/record/25754/files/pp060326.pdf.
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Revista Mexicana de Ciencias Agrícolas
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The authors who publish in Revista Mexicana de Ciencias Agrícolas accept the following conditions:
In accordance with copyright laws, Revista Mexicana de Ciencias Agrícolas recognizes and respects the authors’ moral right and ownership of property rights which will be transferred to the journal for dissemination in open access. Invariably, all the authors have to sign a letter of transfer of property rights and of originality of the article to Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP) [National Institute of Forestry, Agricultural and Livestock Research]. The author(s) must pay a fee for the reception of articles before proceeding to editorial review.
All the texts published by Revista Mexicana de Ciencias Agrícolas —with no exception— are distributed under a Creative Commons License Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), which allows third parties to use the publication as long as the work’s authorship and its first publication in this journal are mentioned.
The author(s) can enter into independent and additional contractual agreements for the nonexclusive distribution of the version of the article published in Revista Mexicana de Ciencias Agrícolas (for example include it into an institutional repository or publish it in a book) as long as it is clearly and explicitly indicated that the work was published for the first time in Revista Mexicana de Ciencias Agrícolas.
For all the above, the authors shall send the Letter-transfer of Property Rights for the first publication duly filled in and signed by the author(s). This form must be sent as a PDF file to: revista_atm@yahoo.com.mx; cienciasagricola@inifap.gob.mx; remexca2017@gmail.
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 International license.