Precipitation estimation is a challenging task, especially in regions where its spatial distribution is irregular and highly variable. This study evaluated the spatial distribution of annual rainfall in a semiarid Brazilian basin under different regimes and its impact on land use and land cover dynamics. Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) records and observed data from 40 weather stations over a time series of 55 years were used, in addition to the Standardized Precipitation Index. Spatiotemporal analysis was carried out based on geostatistics. Remote sensing images were also interpreted for different rainfall regimes using the Normalized Difference Vegetation Index and Modified Normalized Difference Water Index. The Gaussian semivariogram model best represented the rainfall spatial structure, showing strong spatial dependence. Results indicated that rainfall amount in the basin significantly increases with elevation, as expected. There is high variation in the dynamics of water storage that can threaten water security in the region. Our findings point out that the application of geostatistics for mapping both the annual precipitation and the Standardized Precipitation Index provides a powerful framework to support hydrological analysis, as well as land use and land cover management in semiarid regions.

Water scarcity and changing rainfall distribution have caused uncertainties in relation to agricultural production in semiarid areas. In this context, water reuse for irrigation is a promising alternative, although requiring irrigation and agricultural management. Production of forage plants is strategic for semiarid areas due to their high tolerance to stresses and use as animal fodder. The objective of this work was to evaluate the combined performance of treated wastewater irrigation and mulching on forage sorghum and on soil attributes in Northeast Brazil. Sorghum was cropped in November 2018, three months before the beginning of the hydrologic year, and cultivated over three cycles until April 2019. The experiment was designed with five irrigation depths (60%, 80%, 100%, 120%, and 140% of crop evapotranspiration), and with four soil cover conditions (0% (witness), soil covered with native vegetation, with coconut coir, and with macerated moringa seeds), adopting four replications. Irrigation with treated wastewater promoted linear increases in forage sorghum yield at irrigation depths of up to 140% of crop evapotranspiration. Mulch promoted a 24% increase in productivity in relation to the area where conservation was not practiced, being able to control salinity while also contributing to the higher incorporation of organic matter. Irrigation with treated wastewater had no negative agronomic impacts on soil, once natural rainfall events typical of the Brazilian semiarid region allowed effective salt leaching from shallow sandy soils.

Precision agriculture requires accurate methods for classifying crops and soil cover in agricultural production areas. The study aims to evaluate three machine learning-based classifiers to identify intercropped forage cactus cultivation in irrigated areas using Unmanned Aerial Vehicles (UAV). It conducted a comparative analysis between multispectral and visible Red-Green-Blue (RGB) sampling, followed by the efficiency analysis of Gaussian Mixture Model (GMM), K-Nearest Neighbors (KNN), and Random Forest (RF) algorithms. The classification targets included exposed soil, mulching soil cover, developed and undeveloped forage cactus, moringa, and gliricidia in the Brazilian semiarid. The results indicated that the KNN and RF algorithms outperformed other methods, showing no significant differences according to the kappa index for both Multispectral and RGB sample spaces. In contrast, the GMM showed lower performance, with kappa index values of 0.82 and 0.78, compared to RF 0.86 and 0.82, and KNN 0.86 and 0.82. The KNN and RF algorithms performed well, with individual accuracy rates above 85% for both sample spaces. Overall, the KNN algorithm demonstrated superiority for the RGB sample space, whereas the RF algorithm excelled for the multispectral sample space. Even with the better performance of multispectral images, machine learning algorithms applied to RGB samples produced promising results for crop classification.

RESUMO A região semiárida do nordeste brasileiro é marcada por elevadas incertezas quanto à ocorrência de chuvas intensas, bem como por anos sequenciados de secas. Esses fenômenos naturais, somados à reduzida utilização de práticas conservacionistas, podem gerar degradação dos solos. Nesse contexto, o presente estudo objetivou avaliar a importância do uso de cobertura morta e espécies de oleaginosas não convencionais perenes (Moringa oleifera Lam. e Azadirachta indica A. Juss.) no controle da erosão do solo em parcelas de erosão, em condições de chuvas naturais. O estudo realizou-se durante os anos de 2016 e 2017 na bacia representativa do Mimoso, localizada no município de Pesqueira (PE), Brasil. Na área de estudo, instalaram-se três parcelas de erosão, cada uma com área igual a 40 m2 (2 × 20 m), que foram intensamente monitoradas no tocante à intensidade de precipitação, à umidade e à temperatura do solo, ao escoamento superficial e à produção de sedimentos. Para cada parcela experimental, adotaram-se os seguintes tratamentos: oleaginosas; oleaginosas + cobertura morta; solo descoberto. A cobertura do solo com oleaginosas e cobertura morta resultou em maior retenção de umidade do solo e menores valores de escoamento superficial e perdas de solo. O desenvolvimento da estrutura das oleaginosas diminuiu o impacto direto das gotas de chuva no solo, reduzindo as perdas de solo quando comparadas ao descoberto.

Water stress on crops can severely disrupt crop growth and reduce yields, requiring the accurate and prompt diagnosis of crop water stress, especially in semiarid regions. Infrared thermal imaging cameras are effective tools to monitor the spatial distribution of canopy temperature (Tc), which is the basis of the daily water stress index (DWSI) calculation. This research aimed to evaluate the variability of plant water stress under different soil cover conditions through geostatistical techniques, using detailed thermographic images of Neem canopies in the Brazilian northeastern semiarid region. Two experimental plots were established with Neem cropped under mulch and bare soil conditions. Thermal images of the leaves were taken with a portable thermographic camera and processed using Python language and the OpenCV database. The application of the geostatistical technique enabled stress indicator mapping at the leaf scale, with the spherical and exponential models providing the best fit for both soil cover conditions. The results showed that the highest levels of water stress were observed during the months with the highest air temperatures and no rainfall, especially at the apex of the leaf and close to the central veins, due to a negative water balance. Even under extreme drought conditions, mulching reduced Neem physiological water stress, leading to lower plant water stress, associated with a higher soil moisture content and a negative skewness of temperature distribution. Regarding the mapping of the stress index, the sequential Gaussian simulation method reduced the temperature uncertainty and the variation on the leaf surface. Our findings highlight that mapping the Water Stress Index offers a robust framework to precisely detect stress for agricultural management, as well as soil cover management in semiarid regions. These findings underscore the impact of meteorological and planting conditions on leaf temperature and baseline water stress, which can be valuable for regional water resource managers in diagnosing crop water status more accurately.

Maize crop under rainfed conditions is widely grown in the Brazilian Northeast on community farms for human and animal consumption. The application of mulch could be adopted to enhance rainwater harvesting and soil moisture levels, although such practice is rarely applied in rainfed cultivation. This study aimed to evaluate the spatial variability of maize productivity, based on soil physical attributes and plant growth and yield, under a rainfed regime in the presence and absence of mulch in the Brazilian semi-arid region. The study was conducted from May to September 2016 in Pesqueira - PE (locality of the Semiarid of Pernambuco State, Northeast, Brazil), during which the development and production of maize in experimental plots were evaluated, and maize was grown using the traditional cropping system with and without mulching. Two grids with 40 points in a 4 × 10 m mesh were established, with spacing of 1 m for soil sampling and maize biometric evaluation. For statistical analysis, 30 samples were randomly selected from each plot. Maize cultivated in the presence of mulch showed higher development, with symmetrical distribution for all variables except for soil moisture and stem diameter. Sand content, soil moisture, height of the first cob, number of leaves, leaf length, plant height, and cob weight presented moderate to strong spatial scale dependence for the two cropping conditions, with higher spatial dependence consistently observed for the development parameters in the covered area.