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The objective of this study was to evaluate the structural characteristics, yield and water use efficiency of forage cactus under daytime and nighttime irrigation in a Brazilian semiarid region. The experiment followed a completely randomized design in a 3 × 2 factorial scheme, with ten replications: three clones of forage cactus ("IPA Sertânia"—IPA, "Miúda"—MIU, "Orelha de Elefante Mexicana"—OEM) and two irrigation schedules (daytime and nighttime). Irrigation was applied once a week using a graduated cylinder. The structural characteristics (i.e., plant height and width, total number of cladode—TNC; cladode number per emergence order—CN1, CN2 and CN3; cladode area—CA; cladode area index—CAI), productive characteristics (fresh mass production per plant—FM and dry mass—DM) and water use efficiency (WUEFM and WUEDM) were obtained from the plant harvests. Our results showed that the irrigation schedules did not lead to significant differences in most of the response variables (p > 0.05), except for the TNC (13.2 und), CN2 (7.4 und) and CAI (1.58 m2 m−2) of MIU. It was observed that OEM presented the highest yield, WUEFM and WUEDM (p < 0.05). Adopting the OEM clone, regardless of the irrigation schedule, is the strategy that achieves the best production. [ABSTRACT FROM AUTHOR]

The objective of this study was to correlate the biophysical parameters of forage cactus with visible vegetation indices obtained by unmanned aerial vehicles (UAVs) and predict them with machine learning in different agricultural systems. Four experimental units were conducted. Units I and II had different plant spacings (0.10, 0.20, 0.30, 0.40, and 0.50 m) with East–West and North–South planting directions, respectively. Unit III had row spacings (1.00, 1.25, 1.50, and 1.75 m), and IV had cutting frequencies (6, 9, 12 + 6, and 18 months) with the clones "Orelha de Elefante Mexicana", "Miúda", and "IPA Sertânia". Plant height and width, cladode area index, fresh and dry matter yield (FM and DM), dry matter content, and fifteen vegetation indices of the visible range were analyzed. The RGBVI and ExGR indices stood out for presenting greater correlations with FM and DM. The prediction analysis using the Random Forest algorithm, highlighting DM, which presented a mean absolute error of 1.39, 0.99, and 1.72 Mg ha−1 in experimental units I and II, III, and IV, respectively. The results showed potential in the application of machine learning with RGB images for predictive analysis of the biophysical parameters of forage cactus. [ABSTRACT FROM AUTHOR]

In addition to being an important instrument in the search for increasingly greater productivity, agricultural production with adequate use of irrigation systems significantly minimizes the impact on water resources. To meet high productivity and yield, as well as industrial quality, a series of studies on sugarcane cultivation are necessary. Despite being able to adapt to drought, sugarcane is still a crop highly dependent on irrigation to guarantee the best quality standards. Our study aimed to analyze the agronomic performance and technological attributes of two sugarcane cultivars, evaluating the vegetative and productive pattern, as well as the industrial quality of the cultivars RB92579 and SP80–1816, which were cultivated under split-irrigation management in the Sugarcane Research Unit of IF Goiano—Campus Ceres, located in the state of Goiás in the Central-West region of Brazil. A self-propelled sprinkler irrigation system (IrrigaBrasil) was used, duly equipped with Twin 120 Komet sprinklers (Fremon, USA). The cultivars were propagated vegetatively and planted in 0.25 m deep furrows with 1.5 m between rows. The experiment was conducted in a completely randomized design (CRD), with a bifactorial split-plot scheme (5 × 2), with four replications, where the experimental plots were subjected to one of the following five split-irrigation management systems: 00 mm + 00 mm; 20 mm + 40 mm; 30 mm + 30 mm; 40 mm + 20 mm; or 60 mm + 00 mm. At 60 and 150 days after planting (DAP), the following respective irrigation management systems were applied: 00 mm + 00 mm and 20 mm + 40 mm. Biometric and technological attributes, such as plant height (PH) and stem diameter (SD), were evaluated in this case at 30-day intervals, starting at 180 DAP and ending at 420 DAP. Measurements of soluble solids content (°Brix), apparent sucrose content (POL), fiber content (Fiber), juice purity (PZA), broth POL (BP), reducing sugars (RS), and total recoverable sugars (TRS) were made by sampling stems at harvest at 420 DAP. RB92579 showed total recoverable sugar contents 11.89% and 8.86% higher than those recorded for SP80–1816 under split-irrigation with 40 mm + 20 mm and 60 mm + 00 mm, respectively. Shoot productivity of RB92579 reached 187.15 t ha−1 under split-irrigation with 60 mm + 00 mm, which was 42.16% higher than the shoot productivity observed for SP80–1816. Both cultivars showed higher qualitative and quantitative indices in treatments that applied higher volumes of water in the initial phase of the culture, coinciding with the dry season. Sugarcane cultivar RB92579 showed a better adaptation to the prevailing conditions in the study than the SP80–1816 cultivar. [ABSTRACT FROM AUTHOR]

Water scarcity, combined with low soil fertility, constitutes one of the main limiting factors in crop productivity in semi-arid regions. However, nutritional supplementation techniques with nitrogen (N) and molybdenum (Mo) can lead to positive enhancements in the production of these crops. The objective was to evaluate the effect of increasing doses of N in the presence and/or absence of Mo on the activity of antioxidant enzymes and the productive increase of forage sorghum subjected to water deficit in the semi-arid region. The experiment was conducted in the field using a randomized block design, with four replications, in a 5 × 2 × 3 factorial scheme, comprising five doses of N (urea): 0, 50, 100, 150, and 300 kg ha−1, two doses of Mo (sodium molybdate): 0 and 160 g ha−1, and three production cycles. The highest yields of green mass (GM) (47.98 Mg ha−1), dry mass (DM) (19.66 Mg ha−1), water use efficiency (WUE) (5.57 kg/m³), and N use efficiency (NUE) (0.26 kg2/g) occurred at the highest N dosage (300 kg ha−1 N). The highest contents and extraction of total N, Mo, chloride (Cl), and potassium (K) were found in regrowth 2 and at the highest N dose (300 kg ha−1). The interaction of N and Mo resulted in higher catalase (CAT) enzyme activity. The meteorological conditions during the cycles strongly influenced the nutrient contents and extraction. The results of the study provide support for producers to use N and Mo fertilization strategies to improve crop productivity, even under water deficit conditions. [ABSTRACT FROM AUTHOR]

Brackish water can promote physicochemical changes in the soil. Aiming to mitigate the effect of excess salts in the soil, the use of organic matter promotes restructuring. The aim was to evaluate the productive and nutritional characteristics of cactus pear under different brackish water depths (ID) and levels of organic matter (OM). A factorial arrangement of 4 × 4 × 4 with four replications was utilized. Plots consisted of ID (0, 12, 20, and 28% reference evapotranspiration—ETo), and subplots were composed of OM levels (0, 15, 30, and 45 t/ha) and days after planting (DAP; 180, 270, 360, and 450 days). The growth, yield, and chemical composition of cactus pear were affected by ID and OM and/or by their interaction. The regular and increasing application of ID from 192 to 456 mm/year and a rainfall of 110 mm/year in cactus pear crops in biosaline systems improves the growth, freshness, dry matter yields, accumulation capacity per unit area, and chemical composition of cactus pear. The increase in OM up to the range from 30 to 45 Mg/ha linearly increases the agronomic performance of cactus pear. Biosaline systems with cactus pear should be adopted with the combined use of regular supplementary ID and OM, measuring at 304 mm/year and 45 Mg/ha, respectively. [ABSTRACT FROM AUTHOR]

Brazil, despite possessing the largest renewable freshwater reserves in the world (8.65 trillion m3 annually), faces growing challenges in water management due to increasing demand. Agriculture, responsible for 68.4% of water consumption, is one of the main drivers of this demand, especially in the São Francisco River Basin, where irrigation accounts for 81% of total water withdrawals. Water bodies play a crucial role in sustaining ecosystems and supporting life, particularly along the East-West axis of Alagoas, a water-rich region in the ENEB. This study aimed to map and quantify the spatiotemporal variations of water bodies in the ENEB region and assess the impacts of human activities using MODIS satellite data, applying hydrological indices such as NDWI, MNDWI, and AWEI. Between 2003 and 2022, significant variations in the extent of water bodies were observed, with reductions of up to 100 km2 during dry periods and expansions of up to 300 km2 during wet seasons compared to dry periods. AWEI and MNDWI proved to be the most effective indices for detecting water bodies with MODIS data, providing accurate insights into water dynamics. Additionally, the MapBiomas Rios dataset, despite being resampled from a 30 m to a 500 m resolution, offered the most accurate representation of water bodies due to its methodology for data acquisition. Changes in albedo and surface temperature were also detected, highlighting the influence of climate change on the region's water resources. These findings are crucial for guiding the sustainable management of water resources, not only in Alagoas but also in other regions of Brazil and similar semi-arid areas around the world. The study demonstrates the hydrological variability in the state of Alagoas, indicating the need for adaptive strategies to mitigate the impacts of climate change and anthropogenic pressures, supporting the need for informed decision-making in water resource management at both local and national levels. [ABSTRACT FROM AUTHOR]

The fast and effective monitoring of agrochemical residues is essential for assuring food safety, since many agricultural products are sprayed with pesticides and commercialised without waiting for the pre-harvest interval. In this study, we investigated the use of spectral reflectance combined with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to evaluate the discrimination of fungicide-contaminated lettuces, considering three maximum residue limits (MRLs) [3.5, 5, and 7 mg carbon disulphide (CS2) kg−1]. The non-systemic Mancozeb fungicide (dithiocarbamate) was adopted in this research. Spectral reflectance (Vis/NIR) was measured by a hand-held spectrometer connected to a clip probe with an integrating sphere. The lettuce spectra were pre-treated (centring, standard normal variate, and first derivative) before data processing. Our findings suggest that PCA recognised inherent similarities in the fungicide-contaminated lettuce spectra, categorising them into two distinct groups. The PLS-DA models for all MRLs resulted in high accuracy levels, with correct discriminations ranging from 94.5 to 100% for the external validation dataset. Overall, our study demonstrates that spectroscopy combined with discriminating methods is a promising tool for non-destructive and fast discrimination of fungicide-contaminated lettuces. This methodology can be used in industrial food processing, enabling large-scale individual analysis and real-time decision making. [ABSTRACT FROM AUTHOR]

The proposal for a method of identifying the occurrence of desertification that has a strong association with in situ data leads to more assertive results when analysing the contribution of climate and social and economic factors to advancing the process. This study aimed to develop a methodology called the RisDes_Index to evaluate the evolution of the desertification process based on satellite data. The concept of the RisDes_Index method was based on the reflectance variables of the R, B and G bands, albedo and LAI of the Landsat 5/TM and Landsat 8/OLI satellites. Principal component analysis was used to assess the biophysical basis of the RisDes_Index by associating the results with micrometeorological data, physical and chemical properties, and vegetation cover data collected from five experimental sites in the semi-arid region of Brazil. These sites included one from a seasonally dry forest (i.e., the Caatinga), an agricultural cactus plantation, an area undergoing desertification, and two irrigated sugarcane crops (wetlands), one with and one without straw cover. The RisDes_Index was applied to all pixels of the images from 5 December 1991, 14 November 2001, 20 November 2009 and 6 October 2016 of an important desertification nucleus (DN) in the semi-arid region of Brazil, i.e., the DN of Cabrobó. The proposed RisDes_Index was able to identify areas with significant processes of desertification, which mainly occur in areas of sandy, acidic, bare soils with a high β value (Bowen ratio) and high soil temperature. The results of the RisDes_Index showed that in 5 December 1991, desertified areas comprised 38% of the total area of the DN of Cabrobó, expanding to 51% in 2016. Application of the RisDes_Index confirmed the advance of desertification in the DN of Cabrobó. This was due to a consequent increase in the water deficit and intensified deforestation to increase the areas of livestock farming. The RisDes_Index proved to be a robust method, as its estimation based on simple satellite products exhibited a strong association with biophysical variables of areas with different land uses and degradation levels. Thus, it is suggested that the RisDes_Index be applied in various regions of the world, with the idea of directing action to meet the advance of desertification. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]