Application of the Aquacrop model for a corn crop (Zea mays L.)

Authors

  • José Alexander Ramos Cantillo Ing. Agr. Universidad de los Llanos
  • Eduardo Becerra Vélez Ing. Agr. Universidad de los Llanos
  • Julián Fernando Cárdenas Hernández Ing. Agr. MSc. Docente Universidad de los Llanos
  • Rodrigo Jiménez Pizarro Ing. Quim. PhD. Universidad Nacional de Colombia

DOI:

https://doi.org/10.22579/22484817.730

Keywords:

physiology, cereals, simulation models

Abstract

In agricultural production, a large number of descriptive models are used that seek to predict the productivity, development and behavior of crops under different agronomic management scenarios, edaphic factors, environmental conditions and genotypic characteristics. Among these models, one of the most widely used and widely distributed is AquaCrop, which is descriptive and simulates the biomass and potential crop yield of a crop in response to water availability. This work was carried out with the purpose of measuring growth and understanding the behavior of the different organs of BM709 hybrid commercial corn, in relation to its accumulation of dry matter under the conditions of the plain. This evaluation consisted of 10 samples of biometric measurements in fixed plants, and which were monitored by the Eddy covariance tower, to receive information on the climatic conditions of the area. For the simulations of the corn behavior, version 5.0 of the AquaCrop was used, which was adapted to the climatic conditions and soils of the area for greater reliability in the estimation of the study variables: total dry matter of the plant, foliar area and growth rates The analysis of crop development in the AquaCrop software presented a representativeness between the simulated and the real over 90%, generating in this way a pattern of reliability in the decision-making process related to maximizing the yields of the corn crop. Total dry matter production is the result of efficiency when the crop intercepts and uses the available solar radiation during the growth phase, which under of the plain conditions is good due to environmental and climatic factors, including CO2, radiation, temperature and precipitation, it should be noted that at 40°C, when water conditions, radiation, agronomic management are adequate, corn could maintain or increase its productivity due to the high concentration of CO2 in the environment and its ability to reach a crop growth rate efficient, with an index of critical leaf area.

References

Andrade, F., Sadras, V. Bases para el manejo del maíz, el girasol y la soja (No. F01 INTA 16999). INTA, Buenos Aires, Argentina. EEA Balcarce. 2000.

Andrade, F., Cirilo, A., Uhart, S., Otegui M. Ecofisiología del cultivo de Maíz. Estación experimental Agropecuaria Balcarce, Buenos Aires: Editorial La Barrosa. 1996.

Bello, C, Patiño, J., Almanza, E, Monroy, J, Steduto, P, Mejías, P. Uso del modelo Aquacrop para estimar rendimientos para el cultivo del maíz en los departamentos de Córdoba, Meta, Tolima y Valle del Cauca. Colombia: FAO. 2013.

Béziat, P, Ceschia, E., Dedieu, G. Carbon balance of a three crop succession over two cropland sites in South West France. Agricultural and Forest Meteorology, 149: 1628-1645. 2009.

Cifuentes, J. G. Determinación de niveles económicos óptimos de uso de agua en la producción de maíz (Zea mays L.) usando el simulador de rendimientos desarrollado por la FAO AquaCrop Model. Zamorano, Honduras. 2010.

Corbin, K, Scoot D, Erandathie L, Schuh A. Assessing the impact of crops on regional CO2 fluxes and atmospheric concentrations. Tellus B, 62 (5): 521-532. 2010. Disponible En: http://www.tellusb.net/index.php/tellusb/article/view/16602

Departamento Administrativo Nacional de Estadística (DANE). Maíz tecnificado en Colombia. Bogotá D.C. 2005.

Departamento Nacional de Planeación (DPN). Política para el desarrollo integral de la Orinoquia: Altillanura - Fase 1. (Documento CONPES 3797), Bogotá D.C.: 2014.

D’Andrea K.E., Otegui M.E., Cirilo A.G., Eyhérabide G. Genotypic Variability in Morphological and Physiological Traits among Maize Inbred Lines – Nitrogen Responses. Crop Science, 46: 1266-1276. 2006.

Etter, A., Sarmiento, A., Romero, M.H., Land use changes (1970-2020) and the carbon emissions in the Colombian Llanos. En: Ecosystem Function in Savvanas. Measurement and modeling at landscape to global scales. CRC Press, p. 383-402. 2010.

FENALCE. Índice Cerealistas primer semestre 2011. Bogotá D.C: Federación Nacional de Cultivadores de Cereales y Leguminosas. 2011.

Fernández, M. Efectos del cambio climático en el rendimiento de tres cultivos mediante el uso del modelo Aquacrop. FONADE, IDEAM y BID, Bogotá. 2013.

Hack H., Bleiholder H., Buhr L., Meier U., Schnock-Fricke E., Weber E., Witzenberger A. Einheitliche Codierung der ph¨anologischen Entwicklungsstadien mono-und dikotyler Pflanzen Erweiterte BBCH-Skala, Allgemeine. Nachrichtenblatt des Deutschen Pflanzenschutzdienstes, 44: 265-270. 1992.

Hsiao, T, Heng, L, Steduto, P, Raes D, Fereres E. AquaCrop-The FAO crop model for predicting yield response to water: III. Model parameterization and testing for maize. Agron. J. 101: 448-459. 2009.

Kiniry J.R., Bonhomme R. Predicting maize phenology. In: Predicting crop phenology. Ed.T. Hodges. CRC Press. Boca Raton, Ann. Arbor. Boston, p 115-131. 1991

Montgomery, E.G. Correlation studies of com. Nebraska Agricultural Station Annual Report, Lincoln, v. 24, p.108-159. 1911.

Nelson, G. C., Rosegrant, M. W., Koo, J., Robertson, R., Sulser, T., Zhu, T., Lee, D. Cambio climático: el impacto en la agricultura y los costos de adaptación. IFPRI. 2009.

Ojeda, W, Flores, H, Sifuentes, E, Mejia, E., Flores, H. Simulación del rendimiento de maíz (Zea mays L.) en el norte de Sinaloa usando el modelo Aquacrop. AGROCIENCIA, 47(4), 347-359. 2013.

Quiroz, A., D. Marín. Evaluación de la asociación maízquinchoncho, con siembra escalonada y dos niveles de fertilización. I. Fenología y crecimiento. Agron. Trop. 50: 99-122. 2000.

Raes, D., Steduto, P, Hsiao, T, Fereres, E. Aquacrop-The FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description. Journal of Agronomy, p 438-447. 2009.

Rincón, Á., Ligarreto, G., Sanjuanelo, D. Crecimiento del maíz y los pastos (Brachiaria sp.) establecidos en monocultivo y asociados en suelos ácidos del piedemonte llanero colombiano. Agron. Colomb, 25(2), 264-272. 2007.

Santos, M., Segura, M., Ñústez, C. Análisis de crecimiento y relación fuente-demanda de cuatro variedades de papa (Solanum tuberosum L.) en el municipio de Zipaquirá (Cundinamarca, Colombia). Revista Facultad Nacional de Agronomía, Medellín, 63 (1): 5253-5266. 2010.

Saunders, M., Kansiimet, F., Jones, M. Agricultural encroachment: implications for carbon sequestration in tropical African wetlands. Global Change Biology, 18: 1312-1321. 2012.

Salinger, M.; Desjardins, R.; Jones, B.; Sivakumar, M.; Strommen, N.; Veerasamy, S.; Lianhai, W. Climate variability, agriculture and forestry: an update. World Meteorological Organization. WMO-841. Geneva-Switzerland. 51 p. 1997.

Steduto, P., Hsiao T, Raes D, Fereres E. AquaCrop-The FAO crop model for predicting yield response to water: I. Concepts and underlying principles. Agron. J.101: 426-437. 2009.

Paliwal R. El maíz en los trópicos: mejoramiento y producción; morfología del maíz tropical (en línea). Roma IT. FAO. 2001. Recuperado 18 Noviembre 2015. Disponible En: http://www.fao.org/docrep/003/x7650s/x7650s00.htm

Tollenaar, M., Deen W, Echarte L, Liu W. Effect of crowding stress on dry matter accumulation and harvest index in maize. Agronomy Journal, 98 (4): 930-937. 2006.

Toyer, A.F.; Brown, W.L. Selection for early flowering in corn: seven late synthetics. Crop Science, 16 (6): 767-773. 1976.

Torres, J, Moreno, G, Barón, F. Variabilidad del crecimiento y rendimiento del cultivo de maíz para choclo (zea mays l.) como respuesta a diferencias en las propiedades químicas del suelo en la sabana de Bogotá, Colombia. Rev. Fac. Nal. Agr. Medellín, 65 (2): 6579-6583. 2012.

Ulloa, A. Controlando la naturaleza: ambientalismo transnacional y negociaciones locales en torno al cambio climático en territorios indígenas en Colombia. Iberoamericana, 13 (49), 2013.

Downloads

Published

2019-12-15

Issue

Section

Artículos originales

How to Cite

Application of the Aquacrop model for a corn crop (Zea mays L.). (2019). Revista Sistemas De Producción Agroecológicos, 10(2), 19-49. https://doi.org/10.22579/22484817.730

Similar Articles

You may also start an advanced similarity search for this article.