Year 2026, Volume 11, Issue 1
| Year : | 2026 |
| Volume : | 11 |
| Issue : | 1 |
| Authors : | Milena Monteiro FEITOSA, José de Jesus Sousa LEMOS |
| Title : | HYBRID MODEL OF ARTIFICIAL NEURAL NETWORKS AND PRINCIPAL COMPONENT DECOMPOSITION FOR PREDICTING GREENHOUSE GAS EMISSIONS IN THE BRAZILIAN REGION OF MATOPIBA |
| Abstract : | Greenhouse gas (GHG) emissions in agricultural production represent a global environmental challenge, requiring an understanding of the factors that influence them in order to develop sustainable practices. The general objective is to investigate the factors that influence GHG emissions and reductions in agricultural production in the MATOPIBA region, from 2006 to 2017. The methodology employed involved factor analysis with decomposition into principal components and the use of Artificial Neural Networks (ANNs) to assess interactions between variables. The data was obtained from the Agricultural Census, MapBiomas, SEEG and NOAA, considering indicators such as vegetation cover, cattle numbers, pesticide use, climate variability and industrial GDP. The results showed that 70.3% of the municipalities showed an increase in GHG emissions, with the Economic-Industrial and Agricultural Practices Effect being the main factor associated with the growth in emissions, while the Livestock Intensification Effect was the main factor associated with their reduction. It is concluded that sustainable agricultural practices, combined with efficient livestock management and the maintenance of vegetation cover, are fundamental to minimizing GHG emissions in the MATOPIBA region. |
| Keywords : | Greenhouse Gas Emissions (GHG); Agricultural Production; MATOPIBA; Artificial Neural Networks (ANN); Sustainability. |
ISSN 2490-3434 (Printed)
ISSN 2490-3442 (Online)
ISSN 2490-3442 (Online)
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