Statistical model for predicting corn grain yield
DOI:
https://doi.org/10.29312/remexca.v12i3.2482Keywords:
Zea mays L., nitrogen, population densityAbstract
The growth of the world population leads to the demand for food, and these must be obtained through the efficient use of resources, this could be achieved by planning and prioritizing the factors that involved in production processes. Simulation models are a tool with which it can visualize scenarios and quantify the inputs to use. In this work, with data on maximum maize yields (RG) from 1943 to 2017 obtained from global field experiments and predominantly data from the United States of America (80%), a statistical model was generated to estimate grain yield in maize (RGE) and to support the decision-making of those involved in the grain maize production process. The most important variables to express the model were: population density (DP), potassium dose (K), irrigation sheet (LR), nitrogen dose (N) and phosphorus dose (P) and were used to generate the model with the stepwise multiple regression method and expressed as: RGE= 3.158205 + 0.693319 (DP) - 0.022246 (K) + 0.005990 (LR)+ 0.010687 (N) + 0.013794 (P), had an R2= 0.73 and a standard error of 0.964 Mg ha-1. DP was the variable that explained in greater proportion the value of RGE, with the analysis of RG data the increase in the planting rate over time was observed to achieve a higher DP and increase the RG, which generated the demand for inputs.
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