Estudos sobre fatores abióticos têm ganhado destaque nas pesquisas sobre o Amarelecimento Fatal (AF) da palma-de-óleo (Elaeis guineensis Jacq.). A aplicação de técnicas geoestatísticas, especialmente a krigagem ordinária, amplia a análise desses fatores, proporcionando uma visão mais holística do ambiente de estudo. O presente trabalho objetivou analisar a influência da Resistência à Penetração do Solo (RPS) sobre a incidência do AF da palma-de-óleo, utilizando técnicas geoestatísticas. O estudo foi realizado na Fazenda Nogami, em Igarapé-Açu (Pará), em uma plantação de 6,3 hectares com 10,24% de plantas afetadas. Após aferir a RPS com um penetrômetro de solos, os dados foram submetidos a testes descritivos e de normalidade, seguidos pela construção do semivariograma e pela krigagem ordinária, gerando o mapa temático da distribuição espacial da RPS. A partir do inventário fitossanitário georreferenciado e do mapa, determinou-se a RPS para cada planta, constituindo dois grupos: Plantas Com AF e Plantas Sem AF. Dada a não normalidade dos dados (teste de Shapiro-Wilk), utilizou-se o teste de Mann-Whitney, revelando maior prevalência de AF em áreas com maior RPS.

Soil bioanalysis and vegetation indices such as NDVI and EVI help to monitor soil fertility and the vigor of oil palm. This contributes to optimized and sustainable crop management in the Amazon. However, the integration of these data to elucidate spatial patterns and optimize soil management remains limited. The objective of this study was to evaluate the relationship between Soil Bioanalysis (BioAS) and vegetation spectral indices (NDVI and EVI) within a conventional oil palm (Elaeis guineensis Jacq.) plantation in northeastern Pará, Brazil. The following attributes were analyzed: Arylsulfatase, β-glucosidase, SQI Biological, SQI Chemical, SQI FertBio, and cycling, storing, and supplying nutrients functions. Geostatistical analysis revealed strong spatial dependence for most of the attributes, allowing interpolation by ordinary kriging to generate predictive maps. Principal Component Analysis (PCA) indicated an inverse relationship between the spectral indices and the soil’s biological attributes, suggesting that areas with higher vegetative vigor may be associated with changes in nutrient dynamics and other soil-related factors, such as physical properties and water availability, under the studied conditions. In addition, orthogonality was observed between NDVI, EVI, and some soil attributes, such as F3 - Supplying Nutrients, SQI Chemical, SQI FertBio, and arylsulfatase, indicating a lack of direct correlation under the studied conditions. These results highlight the importance of integrating soil bioanalysis, remote sensing, and geostatistical techniques to improve the understanding and monitoring of soil conditions and oil palm productivity.

Black pepper (Piper nigrum L.) is a crop of significant economic importance in the Amazon, especially in the state of Pará, where intensive production systems predominate. Understanding the spatial variability of soil attributes and their relationship with plant vigor is essential to optimize agricultural practices and input use. Geotechnology-based approaches enable the generation of more precise management zones, contributing to efficient resource use and increased profitability. This study aimed to delimit potential management zones in black pepper crops based on the spatial analysis of soil bulk density (BD) integrated with the NDVI (Normalized Difference Vegetation Index), evaluated using the Bivariate Moran’s Index. The research was conducted in a production area in the municipality of Baião, Pará, Brazil, using soil samples to determine bulk density and UAV images for NDVI calculation. Data were interpolated by kriging and analyzed to identify spatial associations between soil compaction and NDVI. Soil bulk density ranged from 1.14 to 1.80 Mg m⁻³, while NDVI values ranged from 0.07 to 0.91, revealing a clear inverse spatial relationship between soil compaction and vegetative vigor. The integration of BD and NDVI allowed the delineation of site-specific management zones, supporting more efficient decision-making in precision agriculture.

The Amazon Deforestation Arc remains a critical region for environmental governance, where land-use strategies must consider distinct legal and institutional frameworks across the Amazon and Cerrado biomes. This study applies the Potential Productivity Model (M3P), a theoretical radiation-based framework, to estimate the upper physiological limits of sugarcane (Saccharum officinarum L.) productivity on degraded pastures within the Arc of Deforestation. The model integrates satellite-derived solar radiation with climatic variables to quantify potential productivity under optimal biophysical conditions, providing an objective benchmark for planning-oriented bioenergy assessments. Estimated potential yields range from 153 to 178 t·ha⁻¹·yr⁻¹, consistent with global reference values reported for sugarcane in high-radiation environments and relevant for informing public policies such as Brazil’s Agroecological Zoning of Sugarcane. The results demonstrate that agroclimatic potential alone is insufficient to guide land-use decisions. While degraded pastures associated with the Cerrado biome may accommodate sugarcane cultivation as part of productive land recovery strategies, areas belonging to the Amazon biome require priority actions focused on ecological restoration through agroforestry and integrated crop–livestock–forest systems. Overall, the M3P model offers a scalable and scientifically grounded decision-support framework for strategic planning in environmentally sensitive tropical regions.

Land use and land cover (LULC) changes are major drivers of environmental transformation in sensitive regions, such as the Marajó Archipelago in the Brazilian Amazon. This study assessed accumulated anthropization of LULC in the Immediate Geographic Region of Soure–Salvaterra, Eastern Amazon, between the reference years 1985 and 2023, using MapBiomas data and spatial statistical techniques. Bivariate Local Moran’s I (LISA) was applied to evaluate intertemporal spatial associations between areas classified as natural in 1985 and anthropized in 2023. In this approach, High–Low indicates natural areas associated with low anthropization in 2023, whereas High–High indicates areas where natural cover in 1985 was spatially associated with higher anthropization in 2023. The results indicated a strong predominance of High–Low, with values above 94% in all municipalities and up to 99.86% in Santa Cruz do Arari. In contrast, High–High had localized concentrations in Salvaterra (3.53%), Cachoeira do Arari (1.28%), and Soure (1.16%), especially in coastal zones and inland sectors. Low–Low, associated with lower anthropogenic pressure or possible signs of natural regeneration, was extremely low (≤0.0004%). These findings indicate that LISA is useful for identifying local LULC patterns and supporting environmental assessment and territorial planning in tropical regions.