Application of the Aquacrop model for a corn crop (Zea mays L.) (Revista Sistemas de Producción Agroecológicos, 2019)
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.
Keywords: physiology; cereals; simulation models