André Medeiros de Andrade

Agricultural pests have caused significant economic and environmental impacts on major commercially essential crops. The cicada Quesada gigas (Hemiptera: Cicadidae), commonly known as the coffee cicada, is a significant pest in coffee cultivation across the Americas. Using the MaxEnt algorithm, we predicted the potential global distribution of Q. gigas under current and future climate change scenarios (SSP2-4.5 and SSP5-8.5) for the years 2050, 2070, and 2090. The model exhibited excellent performance (AUC = 0.903, CBI = 0.868, TSS = 0.881), highlighting isothermality and precipitation of the wettest month as the main predictors, with relative contributions of 38.2% and 36.3%, respectively. However, the permutation importance analysis indicated that the coldest month's mean temperature was the most influential variable on the species' potential distribution, contributing to 56.7%. Currently, Q. gigas predominantly occupies tropical and subtropical regions of the Americas, with high climatic suitability in major coffee-producing areas, such as southeastern Brazil. Future projections indicate a progressive reduction in highly suitable areas, particularly under the SSP2-4.5 scenario, with estimated decreases of 3.55% by 2050, 4.28% by 2070, and 2.67% by 2090. Under the SSP5-8.5 scenario, projections suggest greater stability in climatically suitable areas for the pest, with estimated reductions of 0.97% by 2050, 3.57% by 2070, and 2.83% by 2090. Regarding phytosanitary risks, the model identified potentially favorable climatic conditions in Africa, Asia, and Oceania. However, there are no confirmed records of the pest in these continents to date. Climate change may partially restrict the potential habitat of Q. gigas, but its persistence in major coffee-producing regions underscores the need for preventive monitoring strategies. This study offers valuable insights for the adaptive management of pests and the development of biosecurity policies in response to future climate scenarios.

The objective of this work was to analyze agricultural drought using both precipitation data and the Vegetation Condition Index (VCI) based on remote sensing, to evaluate the effect of these conditions on maize yields in the semiarid region of Jequitinhonha Valley, Brazil, from 2001 to 2021. Pearson’s correlation assessed the relationships between precipitation, VCI anomalies, and corn yields. Remote sensing methods can be used to assess agricultural drought at the sub-regional scale in the Jequitinhonha Valley. The methods proposed by the present study allow for the identification of risk areas susceptible to agricultural drought. For future research, it is recommended that yield data be categorized in terms of water source, distinguishing between irrigated and rainfed crops.

Soybeans are key in generating foreign currency for the world economy. Geotechnologies, through vegetation indices (VIs) generated by orbital images or remotely piloted aircraft, are essential tools for assessing the impact of climate on productivity and the ecoclimatic suitability of crops. This study aimed to correlate the growth indices from the CLIMEX model, previously validated, with VIs derived from orbital remote sensing and ecological niche modeling for soybean cultivation in six irrigated pivots located in the northwest of Minas Gerais, Brazil. The maximum normalized difference vegetation index (NDVImax) and the maximum soil-adjusted vegetation index (SAVImax) were extracted from Landsat-8 OLI/TIRS sensor images for the 2016 to 2019 harvests during the R1 to R3 phenological stages. The maximum NDVI values varied across the study regions and crops, ranging from 0.27 to 0.95. Similarly, SAVI values exhibited variability, with the maximum SAVI ranging from 0.13 to 0.85. The growth index (GI_w), derived from the CLIMEX model, ranged from 0.88 to 1. The statistical analysis confirmed a significant correlation (p < 0.05) between NDVImax and GI_w only for the 2018/19 harvest, with a Pearson correlation coefficient of r = 0.86, classified as very strong. Across all harvests, NDVI consistently outperformed SAVI in correlation strength with GI_w. Using geotechnologies through remote sensing shows promise for correlating spectral indices and climate suitability models. However, when using a valid model, all crops did not correlate. Still, our study has the potential to be improved by investigating new hypotheses, such as using drone images with better resolution (spatial, spectral, temporal, and radiometric) and adjusting the response of soybean vegetation indices and the phenological stage. Our results correlating the CLIMEX model of growth indices with vegetation indices have the potential for monitoring soybean cultivation and analyzing the performance of varieties but require a more in-depth view to adapt the methodology.

Several remote sensing indices have been used to monitor droughts, mainly in semi-arid regions with limited coverage by meteorological stations. The objective of this study was to estimate and monitor agricultural drought conditions in the Jequitinhonha Valley region, located in the Brazilian biomes of the Cerrado and Atlantic Forest, from 2001 to 2021, using vegetation indices and the meteorological drought index from remote sensing data. Linear regression was applied to analyze drought trends and Pearson’s correlation coefficient was applied to evaluate the relationship between vegetation indices and climatic conditions in agricultural areas using the Standardized Precipitation Index. The results revealed divergences in the occurrences of regional droughts, predominantly covering mild to moderate drought conditions. Analysis spatial of drought trends revealed a decreasing pattern, indicating an increase in drought in the Middle and Low Jequitinhonha sub-regions. On the other hand, a reduction in drought was observed in the High Jequitinhonha region. Notably, the Vegetation Condition Index demonstrated the most robust correlation with the Standardized Precipitation Index, with R values ​​greater than 0.5 in all subregions of the study area. This index showed a strong association with precipitation, proving its suitability for monitoring agricultural drought in heterogeneous areas and with different climatic attributes. The use of remote sensing technology made it possible to detect regional variations in the spatio-temporal patterns of drought in the Jequitinhonha Valley. This vision helps in the implementation of personalized strategies and public policies, taking into account the particularities of each area, in order to mitigate the negative impacts of drought on agricultural activities in the region.

The region of the Maritime Antarctic suffers significantly from climate change, resulting in regional warming and consequently affecting coverage. This study characterized three surface zones of Collins Glacier and three other zones in ice-free areas on the Fildes Peninsula, which has an area of 29.6 km². We used TerraSAR-X satellite images from 2014 to 2016 and analyzed the influence of meteorological and environmental conditions on these surface zones. We used five images from the TerraSAR-X satellite, three of these data were obtained during the same period of fieldwork. The classification considered three classes on the glacier (higher moisture, transition in moisture, and lower moisture) and three on the ice-free areas (wet snow, transition wet snow-bare ice, and bare ice), using Maximum Likelihood and ISODATA methods. In low-altitude glaciers with maritime influence, such as Collins, monitoring variations in surface zones is essential because the continuous increase in exposed ice may indicate potential retreat. The TerraSAR-X images enabled the classification of land features, highlighting the potential for continuous monitoring in the Maritime Antarctic, regardless of weather conditions and solar illumination.

This work aimed to characterize the variation in the thermal regime of the active layer in a permafrost area on Fildes Peninsula, Antarctica, and relate this variability with meteorological data between 2014 and 2016. The monitoring site was installed to continuously monitor the temperature and moisture of the active layer, radiation flow on the surface, and air temperature. We used data collected to generate the indexes freezing degree-days, thawing degree-days, frost number, n-factor, apparent thermal diffusivity, and active layer thickness. The temperature of the active layer is not homogeneous, varying with depth and position in the transect, with the greatest variations in soil with better drainage and lower moisture content. Among the evaluated factors, air and soil surface temperature are the ones that most influence the thermal gradient of the active layer. We identified that near the surface there is a greater influence of albedo and cloudiness and at -35 cm depth there is a greater influence of net radiation and soil moisture. The average depth of the active layer in 2014 was -44.3 cm and in 2015 -47.7 cm and the frost number index indicates that there was a predominance of continuous permafrost in the transect during the monitoring.