Artificial neural network (ANN) in forecasting of poverty line and economic-energetic efficiencies into the maize-based agroecosystems

ABSTRACT

In Mexico, corn is cultivated in small agroecosystems by rural farmers on communal lands. These farmers are economically vulnerable, and low yields from their plots affect both their economic and food security. A Feed-Forward Back Propagation Artificial Neural Network (ANN) aids in estimating the variables with the greatest impact on the agroecosystem related to Economic Efficiency (EC ha⁻¹), Energy Efficiency (ENE ha⁻¹), and the ‘Poverty Coverage Line’ (PCOVER). With an R = 0.86, the ANN has identified that the variables with the most significant impact on EC ha⁻¹ are the ‘cultivated area’, the ‘total energy consumed per hectare’, and the ‘presentation of products in the market’. For ENE ha⁻¹ and PCOVER, the key variables are the ‘cultivated area’, the ‘planting rate’, and the ‘total energy consumed per hectare’. The ANN demonstrates its utility by predicting that the cultivated acreage and the total energy invested by the farmer in their activities are the primary factors contributing to the poverty line in the agricultural sector of a rural community in Mexico. The variables feeding into this ANN encompass the fundamental energy and economic investments in other crops, making it adaptable and replicable in various agricultural contexts.

KEYWORDS:

• Agroecosystem
• artificial neural network
• artificial intelligence
• economic- energetic efficiencies

Acknowledgments

We would like to thank the Instituto Tecnológico Superior de Zongolica for their invaluable support to carry out this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).