外文摘要：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.
外文关键词：artificial intelligence;artificial neural network;Agroecosystem;economic- energetic efficiencies
作者：Vasquez, Ruben Purroy;Lasserre, Alberto A Aguilar;Palacios, Ramiro Meza;Lambert, Gregorio Fernandez
作者单位：Tecnol Nacl Mexico
期刊名称：ARCHIVES OF AGRONOMY AND SOIL SCIENCE
期刊影响因子：0.0
出版年份：2024
出版刊次：70（1）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台