We applied a robust framework for agricultural drought identification in the State of Espírito Santo, Brazil, by employing the Vegetation Condition Index (VCI) based on data obtained through the Enhanced Vegetation Index (EVI). By doing so, we analyzed the interrelationships between the VCI and anomalies in the Land Surface Temperature (LST), along with connections between the VCI and data considering water deficits in vulnerable areas. When it came to image processing, we focused on the use of analytics and GIS algorithms, while the Scott–Knott method elucidated the statistical analyses. Consequently, we identified drought areas followed by periods susceptible to their occurrence, indicating 2016 as the driest year. The North macroregion presented the lowest average values regarding VCI values in the most vulnerable periods, followed by the Central one. We also call attention to the highest LST averages observed in 2015 and 2016, as strong El Niño events marked the same timeframe periods. The methodological approach was efficient for the identification, analysis, and characterization of agricultural drought occurrences, enabling mitigation actions, as well as the management of the exploitation and protection of water resources. Moreover, further research should be conducted by incorporating other indices to enhance the understanding of agricultural drought and its effects on vegetation.

Utilizing path analysis, we examined the interconnectedness among six meteorological variables. Among these, three pertain to energy conditions—air temperature, net solar radiation, and reference evapotranspiration (ET0)—while the others are associated with hydrological conditions: precipitation, relative humidity, and water deficiency. These variables were assessed across five distinct temporal delay levels to understand their influences on the normalized difference vegetation Index (NDVI) and enhanced vegetation index (EVI) within grassland areas situated in the state of Espírito Santo, southeastern Brazil. The images underwent processing using analytical algorithms and a geographic information system (GIS). The direct and indirect impacts of these variables on the NDVI and EVI exhibited remarkable similarity across varying temporal delays and geographic regions. Meteorological variables explained over 50% of the observed variation in both indices, occasionally even reaching levels of 70%. Temperature and relative humidity primarily exerted direct effects on the indices. Conversely, precipitation exhibited indirect effects on the indices, often in conjunction with other hydrological variables. ET0 demonstrated a direct effect on the vegetation indices, particularly after a delay of 32 days. Solar radiation and water deficiency displayed direct effects up to the 32-day mark, implying that vegetation responds more promptly to these variables. The proposed methodology enabled a consistent and stable assessment of the direct and indirect effects of meteorological variables on vegetation indices.

This study evaluated the genetic diversity of 33 Coffea canephora genotypes through morphophysiological and molecular analyses, aiming to identify promising genotypes for pre-breeding purposes in the southern region of Espírito Santo, Brazil. Cutting-propagated seedlings were evaluated 120 days after planting, considering height, stem and crown diameter, number of leaves, fresh and dry shoot and root weight, chlorophyll content, and root characteristics. Molecular analysis was performed on 32 genotypes; one was excluded due to absent DNA, and 18 ISSR markers were used. Morphological data were analyzed by ANOVA, Scott–Knott’s mean test, principal component analysis, and cluster analysis. The results revealed significant diversity among genotypes. The first two principal components explained 75.5% of the total variability. Genotypes 2, 3, 4, 5, 6, 10, 32, and 33 stood out as those that produced the most vigorous seedlings. Molecular analysis also revealed genetic diversity among genotypes, with the formation of 16 groups, while the morphophysiological analysis revealed four groups. The Mantel test demonstrated a small but significant positive difference (r = 0.228; p = 0.018) between the genetic and morphophysiological distances of the genotypes. This diversity indicates that the genotypes evaluated are promising for use in C. canephora breeding programs.