Análisis de varianza en modalidad hídrica para la producción de maíz grano en México: caso de Jalisco, Sinaloa y Nayarit

Karina  Pérez-Robles; Placido Salomón  Álvarez-López; Elizabeth  Trujillo-Ubaldo

doi:10.17268/agroind.sci.2022.03.09

Authors

Karina Pérez-Robles Cátedra CONACYT/Universidad Autónoma de Nayarit; Unidad Académica de Agricultura.
Placido Salomón Álvarez-López Universidad Autónoma de Guadalajara; Unidad Académica de ciencias Sociales Económico y Administrativas.
Elizabeth Trujillo-Ubaldo Cátedra CONACYT/Universidad Autónoma de Nayarit; Unidad Académica de Economía.

DOI:

https://doi.org/10.17268/agroind.sci.2022.03.09

Keywords:

Production performance, regional development, irrigation, variance analysis, Zea mays

Abstract

The cultivation and production of grain maize constitute an important opportunity to promote the socio-economic and agricultural development of the communities of Mexico. According to the National Agricultural Planning 2017-2030 related to corn grain proposed by the federal government through the Secretariat of Agriculture and Rural Development (SADER); pointed as a producing region with production potential to the states of Sinaloa, Nayarit, and Jalisco. Grouped by the territorial structure that produces corn grain that predominates in this region. Same that contributed 35% of the national production of this grain in 2021. In order to find significant differences in the yield of corn in water modalities (irrigation and temporal), the analysis of variances for the proposed case of the states that make up the Western Territorial Region is addressed. With a temporality from 2008 to 2018. According to the analysis of variance of a ten-year cohort, it is concluded that in the Western Territory Region it was observed that the average yield of irrigated grain maize production is 1.6 times greater than that of temporary. In the case of Sinaloa, it corresponds to 5.7, in Jalisco to 1.3, and in Nayarit to 1.6 times greater than the irrigation than the storm. This corresponds to and justifies a policy that supports the optimal use of water resources, through irrigation channels or hunting that includes benefits for small producers and not only in agroextractivism.

References

Alonso, J. C., & Montenegro, S. (2015). A Monte Carlo Study to compare 8 normality tests for least-squares residuals following a first order autoregressive process. Estudios Gerenciales, 31(136), 253–265.

Anderson, M. J. (2006). Distance-Based Tests for Homogeneity of Multivariate Dispersions. Biometrics, 62(1), 245–253.

Castañeda, Y., González, A., Chauvet, M., & Ávila, J. F. (2014). Industria semillera de maíz en Jalisco: Actores sociales en conflicto. Sociológica (México), 29(83), 241-279.

Delgado, D. C., & Rodríguez, J. A. L. (2019). La producción de maíz en Sinaloa, México, y sus implicaciones para el medio ambiente/The Production of Corn in Sinaloa, Mexico and its Implications for the Environment. Letras Verdes. Revista Latinoamericana de Estudios Socioambientales, 25, 100-118.

DOF. (2021). Presidencia de la República: Ley de Desarrollo Rural Sustentable. Junio 03, 2021.

Enciso, A. L. (2022). México, primer importador de maíz en el mundo: CNA. La Jornada, Ed.

FAOSTAT. (2022) The Food and Agriculture Organization Corporate Statistical Database. Crops.

Finch, H. (2005). Comparison of the performance of nonparametric and parametric MANOVA test statistics when assumptions are violated. Methodology, 1(1), 27–38.

Guzzon, F., Arandia Rios, L. W., Caviedes Cepeda, G. M., Céspedes Polo, M., Chavez Cabrera, A., Muriel Figueroa, J., ..., & Pixley, K. V. (2021). Conservation and use of Latin American maize diversity: Pillar of nutrition security and cultural heritage of humanity. Agronomy, 11(1), 172.

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1999). Análisis multivariante (Hair).pdf (p. 814). p. 814.

INEGI. (2017). Instituto Nacional de Estadística y Geografía: Encuesta Nacional Agropecuaria 2017.

Isaza, L., Acevedo, E., & Hernández, F. (2015). Comparación de Pruebas de Normalidad. XXI Simposio Internacional de Estadísticas 2015, 77 (Zimmerman 2011), 8–11.

Jaramillo, J. G., Peña, B., Valeriano, J., Díaz, R., & Espinosa, A. (2018). Caracterización de productores de maíz de temporal en Tierra Blanca, Veracruz. Revista Mexicana de Ciencias Agrícolas, 9, 911-923.

Juárez-Hernández, S., & Sheinbaum Pardo, C. (2020). Assessing the potential of alternative farming practices for sustainable energy and water use and GHG mitigation in conventional maize systems. Environment, Development and Sustainability, 22(8), 8029-8059.

Kruskal, W. H. (1952). A Nonparametric test for the Several Sample Problem. The Annals of Mathematical Statistics, 23(4), 525–540.

Lemeshko, B. Y., & Lemeshko, S. B. (2008). Power and robustness of criteria used to verify the homogeneity of means. Measurement Techniques, 51(9), 950–959.

Lilliefors, H. W. (1967). On the Kolmogorov-Smirnov Test for Normality with Mean and Variance. Journal of American Statistics, 62(318), 399–402.

Montesillo-Cedillo, J. L. (2017). Rendimiento por hectárea de sorgo grano y de fríjol en México: riego vs temporal. Economía Informa, 403, 91-101.

Montesillo-Cedillo, J. L. (2016). Rendimiento por hectárea del maíz grano en México: distritos de riego vs temporal. Economía Informa, 398, 60-74.

Montilla, J. M. (2010). Relevancia de los tests estadísticos t y f en comparación de medias para muestras. ACADEMIA. ISSN 1690-3226. Volumen IX (18). Pág. 4-14.

Pedrosa, I., Juarros-Basterretxea, J., Robles-Fernández, A., Basteiro, J., & García-Cueto, E. (2015). Pruebas de bondad de ajuste en distribuciones simétricas, ¿qué estadístico utilizar? Universitas Psychologica, 14(1), 245–254.

Pérez, E. F. P., & Solís, D. V. (2019). El dilema de los campesinos frente a los imperativos del mercado neoliberal en Los Altos de Chiapas, México. Estudios Rurales, 9(18), 1.

Santillán-Fernández, A., Salinas-Moreno, Y., Valdez-Lazalde, J. R., Bautista-Ortega, J., & Pereira-Lorenzo, S. (2021a). Spatial delimitation of genetic diversity of native maize and its relationship with ethnic groups in Mexico. Agronomy, 11(4), 672.

Santillán-Fernández, A., Salinas-Moreno, Y., Valdez-Lazalde, J. R., Carmona-Arellano, M. A., Vera-López, J. E., Pereira-Lorenzo, S. (2021b). Relationship between Maize Seed Productivity in Mexico between 1983 and 2018 with the Adoption of Genetically Modified Maize and the Resilience of Local Races. Agriculture, 11, 737

Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación – SAGARPA. (2017). Planeación Agrícola Nacional 2017-2030. México.

Servicio de Información Agroalimentaria y Pesquera - SIAP; (2021). Avance de siembras y cosechas, riego. SADER; México.

Valencia Romero, R., Sánchez Bárcenas, H., & Robles Ortiz, D. (2019). Soberanía Alimentaria de granos básicos en México: un enfoque de cointegración de Johansen a partir del TLCAN. Análisis económico, 34(87), 223-248.

Zimmerman, D. W. (1998). Invalidation of Parametric and Nonparametric Statistical Tests by Concurrent Violation of Two Assumptions. The Journal of Experimental Education, 67(1), 55–68.