Marcia Helena Galina Dompieri

O estudo propõe um modelo baseado em agentes integrando a Teoria do Comportamento Planejado (TCP) para simular o impacto de políticas públicas na diversificação agrícola no município de Santana de São Francisco (SE). Implementado no NetLogo, o modelo simulou cenários com variações climáticas, preços de mercado e incentivos financeiros. Os resultados indicaram que incentivos econômicos isolados não aumentaram significativamente a diversificação, enquanto choques climáticos extremos (alta variabilidade de precipitação e umidade) tiveram maior influência nas decisões dos produtores. Entrevistas revelaram barreiras como falta de recursos financeiros e humanos, reduzindo a eficácia das políticas. Conclui-se que estratégias eficazes devem combinar incentivos, suporte técnico e adaptação climática, além de envolver os produtores no desenho das políticas.

The objective of this work was to evaluate the impact of extreme and exceptional droughts, classified by Agência Nacional de Águas e Saneamento Básico (ANA), on agricultural production diversity, measured by the Shannon index, from 2014 to 2023. Data from the drought monitor of ANA and from Instituto Brasileiro de Geografia e Estatística were used to calculate diversity indices at the municipal level in Brazil. The unconditional quantile regression was applied to evaluate the effects of droughts on diversified systems, considering the production value of temporary and permanent crops, as well as livestock inventory. The obtained results reveal that less diversified municipalities are more vulnerable, particularly in the Northeastern region. Permanent crops show a higher resilience, while temporary crops sustain losses even in diversified systems. Livestock exhibits adaptive responses, with an increased diversity in some cases. Although diversification reduces vulnerabilities, it requires complementary policies for annual crops and targeted support to the Northeast.

This study aimed to manipulate databases from the Landsat-8 project to select orbital scenes with minimal cloud coverage aligned with the growth periods of cotton, corn, and soybean crops in the Cerrado biome. Employing an Extract, Transform, Load (ETL) approach, the research focused on extracting and transforming large datasets by integrating georeferenced information from IBGE and data from the Agricultural Risk Zoning (ZARC). The planting windows for the 2014 to 2019 harvests were identified with a 20% or lower climate risk, considering variables such as soil and climate conditions. The results indicated that the availability of images with low cloud coverage is inversely related to the planting windows, especially during critical periods. To streamline the processing, algorithms were developed in R and Python, and a script for automatic image downloading was implemented. Despite data standardization challenges, applying big data techniques proved essential for the analysis. This study enhances efficiency in agricultural planning and emphasizes the importance of further exploring remote sensing technologies in response to current food security and sustainability demands.

Este artigo propõe um algoritmo para análise de agrupamentos de mapas temáticos com classes categóricas ordinais para suporte ao zoneamento territorial. O método proposto combina o Mapa Auto-Organizável com técnicas de segmentação de grafos para clusterização dos dados. A abordagem foi avaliada com dados sintéticos e aplicada no zoneamento ambiental da bacia do Alto Taquari, MS/MT. Os resultados mostraram a capacidade do algoritmo separar os dados em grupos diferenciáveis unimodais.

Brazilian agricultural production presents high spatial diversity, challenging the conception of public policies. This article proposes an approach for grouping Brazilian municipalities according to their agricultural production. We combine a feature extraction using autoencoders and clustering based on k-means and Self-Organizing Maps. We used panel data from IBGE’s annual estimates of the production value of permanent and temporary crops, animal products, aquaculture, plant extractivism, forestry, planted areas, and herd population between 1999 and 2018. We analyzed different structures of simple stacked and incomplete autoencoders, varying the number of layers and neurons in each, and evaluated the asymmetric exponential linear loss function to handle the sparse data. We applied the Isomap, Kernel PCA, Truncated SVD, and MDS dimensionality reduction methods for comparative purposes. Results showed that the autoencoders could extract characteristics from the transformed raw data to allow the clustering of municipalities to reveal regional and evenintra-regional patterns. The autoencoders improved comparative performance as the intrinsic dimensionality increased.

Brazilian agricultural production presents a high degree of spatial diversity, which challenges designing territorial public policies to promote sustainable development. This article proposes a new approach to cluster Brazilian municipalities according to their agricultural production. It combines a feature extraction mechanism using Deep Learning based on Autoencoders and clustering based on k-means and Self-Organizing Maps. We clustered the panel data from IBGE’s annual estimates of Brazilian agricultural production between 1999 and 2018. The results show that in comparison with the ground truth adopted, the autoencoder model combined with the Self-Organizing Maps and the k-means algorithm presented a better result than clustering the raw data using k-means. It demonstrated the ability of simple stacked autoencoders to reduce the dimensionality and create a new space of features in their latent layer where the data can be analyzed and clustered.

In Brazil, agribusiness has a considerable role in the country's GDP. Because of this, the State needs territorial planning to minimize the impacts on natural resources, especially in the Pantanal and Amazon biomes, where agribusiness has expanded. The lower the agricultural diversification, the lower the pattern of land use homogeneity, generally associated with agribusiness, especially when it occupies large areas with more technological productive units. This paper investigates the relationship between spatial diversification patterns and the dynamics of native vegetation in Brazil. We propose a feature engineering and clustering approach for 5570 Brazilian municipalities between 1999 and 2018. It was based on the unsupervised artificial neural network Self-Organizing Map (SOM) to divide the municipalities into homogeneous groups of agricultural products diversity trends. The results were compared with the change in vegetation area using data from the national land use-mapping project called Mapbiomas. The analysis allowed the identification of three different regimes of modification in native vegetation, particularly related to municipalities in Brazil's Midwest and North regions, indicating substantial changes in the Cerrado and Amazon biomes.

O objetivo do trabalho foi a análise dos dados da produção agropecuária municipal (arroz, cana-de-açúcar, peixes, bovinos e ovinos) na região do Baixo São Francisco, o mapeamento do uso e ocupação do solo, assim como a comparação desses resultados com dados de contaminação das águas em pisciculturas, córregos afluentes do rio São Francisco e canais de abastecimento, localizados na principal área produtora de peixes da região. A classificação das imagens orbitais (sensor MSI, satélite Sentinel-2) utilizou técnicas da estatística multivariada (ACP e K-means), sendo que os dois primeiros componentes permitiram uma melhor separação das classes, além de proporcionar menor tempo de processamento. A classe equivalente à pastagem degradada ou em processo de degradação foi a mais representativa (28%). Quase 20% da área foi contabilizada como solos expostos somados a poucos aglomerados urbanos; e 15% da água superficial continental classificada apresentou alta ocorrência de sedimentos em suspensão, indicando forte assoreamento dos corpos hídricos. Por fim, foram apresentados resultados da análise das águas em diferentes corpos hídricos, quanto à ocorrência de piretróides (deltametrina e cipermetrina).