Fertilization analysis in the corn agroecosystem in the Papaloapan basin

Authors

  • Roberto de Jesús López Escudero Campus Veracruz-Colegio de Postgraduados. Carretera Xalapa-Veracruz km 88.5, Tepetates, Manlio Fabio Altamirano, Veracruz, México. CP. 91690. Tel. 229 2010770, ext. 6430 https://orcid.org/0000-0001-9457-4045
  • Gustavo López Romero Campus Veracruz-Colegio de Postgraduados. Carretera Xalapa-Veracruz km 88.5, Tepetates, Manlio Fabio Altamirano, Veracruz, México. CP. 91690. Tel. 229 2010770, ext. 6430 https://orcid.org/0000-0003-1831-0368
  • Verónica Lango Reynoso Campus Veracruz-Colegio de Postgraduados. Carretera Xalapa-Veracruz km 88.5, Tepetates, Manlio Fabio Altamirano, Veracruz, México. CP. 91690. Tel. 229 2010770, ext. 6430 https://orcid.org/0000-0003-0330-7860
  • Héctor Daniel Inurreta Aguirre Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias. Carretera Toluca-Zitácuaro km 4.5. Vialidad Adolfo López Mateos, Col. Los Ángeles, Zinacantepec, Estado de México. CP. 51350 https://orcid.org/0000-0002-7537-5400

DOI:

https://doi.org/10.29312/remexca.v14i8.3378

Keywords:

corn grain, fertilization, study, SWAT

Abstract

Corn is the main crop of the basic basket in Mexico and one of the products of greatest commercial and economic importance nationwide. Among the main factors of agronomic management that determine its level of productivity is fertilization. Currently, as a result of the armed conflicts between Russia and Ukraine, there is a shortage and, consequently, a rise in the price of fertilizers. Therefore, this work studied the fertilization factor in the Papaloapan basin to analyze the effect of chemical nutrition on the corn crop based on its productivity in t ha-1 of grain. Three fertilization scenarios were simulated with the use of the SWAT model, and the results of the factors were mapped by statistically analyzing the difference in means of the treatments through an Anova, which yielded a value of P= 2e-16, showing a significant difference in the effects of the factors analyzed on the yield of the crop. In conclusion, the fertilization factor was higher than the non-fertilization factor, with a difference between means of 7.8 t ha-1 of grain, which indicates that the absence of fertilization work of the crop negatively impacts the productivity of corn grain. Nonetheless, the efficiency between half and full doses of nitrogen fertilization in terms of yield was found to be similar, with the half-dose factor being more efficient than the previous one.

Downloads

Download data is not yet available.

References

Akhavan, S.; Abedi, K. L.; Mousavi, S. F.; Afyuni, M.; Eslamianand, S. S. and Abba, P. K. C. 2010. Applications of SWAT model to investigate nitrate leaching in Hamadan-Bahar Watershed, Iran. Ecosystems and Environment. 139:675-688. https://doi.org/10.1016/j.agee.2010.10.015. DOI: https://doi.org/10.1016/j.agee.2010.10.015

ASERCA. 2018. Agencia de servicios a la comercialización y desarrollo de mercados agropecuarios. Maíz grano cultivo representativo de México. Agencia de servicios a la comercialización y desarrollo de mercados agropecuarios. https://www.gob.mx/aserca/articulos/maiz-grano-cultivo-representativo-de- mexico?idiom=es.

Bada-Carbajal, L. M.; Osorio-Antonia, J. y Ramírez-Hernández, Z. 2021. Evolución de la producción del maíz en Veracruz, México. Investigación Administrativa. 50(128):12807. https://doi.org/10.35426/iav50n128.07.

Bourges, H. 2013. El maíz: su importancia en la alimentación de la población mexicana. In: Álvarez-Buylla, E. R.; Piñeyro-Nelson, A. Ed. El maíz en peligro ante los transgénicos: un análisis integral del caso de México. Universidad Nacional Autónoma de México (UNAM)-UCCS, UV. 231-248 pp. https://www.jstor.org/ stable/43695257.

Capetillo, B. A.; López, C. C. J.; Zetina, L. R.; Chávez, R.; Matilde, H. C.; Cadena, Z. M. y López, L. J. A. 2021. Modelo conceptual de fertilización nitrogenada para maíz (Zea mays L.) en Veracruz, Ciudad de México. https://doi.org/10.5377/ribcc. v7i14.12606.

Cayuela, L. 2010. Modelos lineales: regresión, Anova y Ancova. Eco Lab. Centro andaluz de medio ambiente. Universidad de Granada. Notas de clase. 1-57 p. https://tauniversity.org/sites/default/files/modelos-lineales-regrsion-anova-y-ancova.pdf.

CEPAL. 2022. Comisión Económica para América Latina y el Caribe. Efectos económicos y financieros en América Latina y el Caribe del conflicto entre la Federación de Rusia y Ucrania. Reflexiones de Alicia Bárcena, Secretaría Ejecutiva de la Comisión Económica para América Latina y el Caribe. Argentina. 10 p. https://repositorio.cepal.org/handle/11362/47831.

Dobermann, A. R. 2005. Nitrogen use efficiency-state of the art. Agronomy, Faculty Publications. 316 p. https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article= 1319&context=agronomyfacpub.

FIRA. 2016. Fideicomisos Instituidos en Relación con la Agricultura. Dirección de Investigación y Evaluación Económica y Sectorial. Panorama Agroalimentario. México, DF. https://www.gob.mx/fira/documentos/panorama- agroalimentario.

Garbanzo-León, G.; Alvarado-Hernández, A.; Vargas-Rojas, J. C.; Cabalceta-Aguilar, G and Vega-Villalobos, E. V. 2021. Nitrogen and potassium fertilization in corn in an Alfisol of Guanacaste, Costa Rica. Fertilización con nitrógeno y potasio en maíz en un alfisol de Guanacaste, Costa Rica. Agronomía Mesoamericana. 32(1):137-148. Doi:10.15517/am.v32i1.39822.

Garg, K. K.; Bharati, L.; Gaur, A.; George, B.; Acharya, S.; Jella, K. and Narasimhan, B. 2011. Spatial mapping of agricultural water productivity using the SWAT model in Upper Bhima Catchment, India. Irrigation and Drainage. https://doi.org/10. 1002/ird.618. DOI: https://doi.org/10.1002/ird.618

INEGI. 2015. Instituto Nacional de Geografía y Estadística. Guía para la interpretación de la Cartografía. Edafología WRB escala 1:250 000 serie III. México, DF. 60 p. https://www.inegi.org.mx/contenidos/productos/prod-serv/contenidos/espanol/ bvinegi/productos/nueva-estruc/702825076221.pdf.

Lalinde, J. D. H.; Castro, F. E.; Rodríguez, J. E.; Rangel, J. G. C.; Sierra, C. A. T.; Torrado, M. K. A. y Pirela, V. J. B. 2018. Sobre el uso adecuado del coeficiente de correlación de Pearson: definición, propiedades y suposiciones. Archivos Venezolanos de Farmacología y Terapéutica. 37(5):587-595.

Murillo, L. M. y López, R. E. 2005. Organización social y producción en la cuenca del Río Papaloapan. In: Vargas, S. y Mollard, E. Ed. Problemas socioambientales y experiencias organizativas en las cuencas de México. Instituto Mexicano de Tecnología del Agua (IMTA). 245-275. http://187.174.234.55/handle/20. 500.12013/2113.

Neitsch, S. L.; Arnold, J. G.; Kiniry, J. R. and Williams, J. R. 2011. Soil and water assessment Tool. Theoretical documentation version 2009. Backland Research Center. Texas. USA. 618 p. https://oaktrust.library.tamu.edu/bitstream/handle/ 1969.1/128050/TR406-SoilandWaterAssessmenToolTheoreticalDocumentation. pdf?sequence=1.

Neitsch, S. L.; Arnold, J. G.; Kiniry, J. R. and Williams, J. R. 2009. Soil and water Assessment Tool. Theoretical documentation. Backland research center. Texas, USA. 494 p. https://swat.tamu.edu/mdia/99192/swat2009-theory.pdf.

Paz-Pellat, F. 2018. Base de datos de perfiles de suelos en México. Elementos para Políticas Públicas. 2:210-235. https://www.elementospolipub.org/ojs/index.php/ epp/article/view/16.

R Core Team. 2021. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/.

Sánchez-Hernández, M. A.; Jiménez-Maya, J. B.; Morales-Terán, G.; Acevedo-Gómez, R.; Antonio-Estrada, C. y Villanueva-Verduzco, C. 2019. Rendimiento de grano en maíces adaptados a condiciones de la baja cuenca del Papaloapan. Tropical and Subtropical Agroecosystems. 22(1):519-529.

Saxton, K. E.; Rawls, W. J.; Romberger, J. S. and Papendick, R. I. 1986. Estimating generalized soil water characteristics from texture. Trans. ASAE. 50:1031-1035. https://doi.org/10.2136/sssaj1986.03615995005000040039x. DOI: https://doi.org/10.2136/sssaj1986.03615995005000040039x

Saynes-Santillán, V.; Turrent-Fernández, A. y Etchevers, J. D. 2019. Agricultura sustentable: el papel de la fertilización inteligente con nitrógeno y fósforo en México. Elementos para políticas públicas. 3(3):275-290. https://elementospolipub.org/ojs/index.php/epp/article/view/35.

SIAP. Servicio de Información Agroalimentaria y Pesquera. 2022. Resumen de producción anual de maíz en México. https://nube.siap.gob.mx/cierreagricola/.

Sharpley, A. N. and Williams, J. R. 1990. EPIC-erosion/productivity impact calculator. USDA. Agricultural research service. Technical Bulletin No. 1768. Washington, DC. EUA. 235 pp. https://agris.fao.org/agris-search/search.do?recordID=US9551549.

Sierra, M. M.; Palafox, C. A.; Rodríguez, M. F.; Espinosa, C. A.; Gómez, M. N.; Caballero, H. F. y Vázquez, C. G. 2008. H-520, híbrido trilineal de maíz para el trópico húmedo de México. Agricultura Técnica en México. 34(1):119-122. https://www.scielo.org. mx/scielo.php?script=sci-arttext&pid=S05682517200800010001.

Tinoco, C. A. 2019. Tecnología de producción de maíz de temporal en el sur de Veracruz. Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias (INIFAP). Campo Experimental Cotaxtla. 17 p. https://www.gob.mx/cms/uploads/ attachment/file/723016/Maiz-temporal-tehuantepec.pdf.

Ureta, C. 2020. Maize yield in Mexico under climate change. Agricultural systems. Elsevier. 177(12):102697-XX pp. Doi: 10.1016/j.agsy.2019.102697.

Vigouroux, Y. 2008. Population structure and genetic diversity of new world maize races assessed by DNA microsatellites. American Journal of Botany. 95(10):1240-1253. Doi: 10.3732/ajb.0800097. DOI: https://doi.org/10.3732/ajb.0800097

Downloads

Published

2023-12-12

How to Cite

López Escudero, Roberto de Jesús, Gustavo López Romero, Verónica Lango Reynoso, and Héctor Daniel Inurreta Aguirre. 2023. “Fertilization Analysis in the Corn Agroecosystem in the Papaloapan Basin”. Revista Mexicana De Ciencias Agrícolas 14 (8). México, ME:e3378. https://doi.org/10.29312/remexca.v14i8.3378.

Issue

Vol. 14 No. 8 (2023)

Section

Articles

License

Copyright (c) 2023 Revista Mexicana de Ciencias Agrícolas

Creative Commons License

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.