Prediction of bean production and yields, with artificial neural network models and climate data
DOI:
https://doi.org/10.18633/biotecnia.v24i2.1664Keywords:
artificial intelligence, Zacatecas, temperature, rainfall, rainfed crops, Phaseolus vulgaris L.Abstract
The state of Zacatecas ranks first in the production of rainfed beans in Mexico. Due to the economic and food security repercussions, it is important to predict yields, production and harvested area, as well as to know the climatological variables that have the greatest effect on bean cultivation. The objectives of the present work were 1) to develop ANN models for the prediction of the harvested area, yields and production of rainfed beans in the state of Zacatecas, using data on maximum and minimum air temperature, precipitation and evaporation during the period 1988-2019. 2) to determine the input variables that have the greatest influence on bean production and yield through sensitivity analysis. Due to the limited availability of climatic data, the Climatol library of the R statistical package was used to fill in missing data. The results show that the RNA models capture the influence of climate on bean production, with an overall efficiency of 0.89 for Rto and 0.86 for SC. The production was estimated using the outputs, Rto and SC, from RNA models and an R2 =0.80 was obtained. According to the sensitivity analysis, Evaporation of the cycle is the most important variable in predicting yield, while precipitation in August (Pp_Ago) and minimum temperature (Tmin) had a greater influence on production.
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