Search Results (440)

Sustainability of animal production requires reducing reliance on soybean meal by identifying viable alternative protein sources. Within the framework of the Italian Agritech National Research Center, seven Italian research groups collaborated to evaluate unconventional feed ingredients and their effects on animal performance and product quality. Alternative legume seeds (peas, chickpeas, faba bean, and lupins) can partially or completely replace soybean meal without impairing productivity, while enhancing product health value and shelf-life through bioactive compounds. Microalgae (Chlorella, Spirulina) improved carotenoid content, antioxidant activity, fatty acid profile, and cholesterol levels in poultry products, with limited effects in pigs. Insects supported optimal growth in fish at 25–30% inclusion, whereas maximum recommended levels are 15% in broilers and 24% in laying hens to sustain growth, egg production, and quality. Camelina by-products are suitable for poultry diets at up to 5–10%, beyond which performance declines. Whole-plant soybean silage, tef (Eragrostis tef), and triticale–lupin intercropping represent promising protein-rich resources for ruminants, provided diets maintain balanced protein-to-energy ratios, adequate fibre characteristics, and appropriate harvest timing under drought-prone conditions. Collectively, these findings highlight the potential of diverse protein sources to improve the sustainability of livestock systems while preserving productivity and enhancing the nutritional quality of animal-derived foods. Full article

Early seedling survival is a key determinant of reforestation success under increasingly variable climatic conditions. Fertilizers used to mitigate nutrient limitations are believed to mitigate early establishment stress, but their effectiveness under heterogeneous field conditions remains uncertain. This study specifically tests whether an amino-acid-based nitrogen fertilizer can provide a more efficient and ecologically sustainable Nitrogen source compared with conventional mineral formulations. Using a dataset of 6238 seedlings from seven operational Austrian reforestation sites, we quantify amendment performance and examine interactions with relief, soil depth, water availability, and management practices. We apply CatBoost to identify influential predictors of mortality and summarize results across repeated evaluations. Further, for the reported settings, we can reliably predict tree seedling mortality for three out of four seedlings, with an average model accuracy of 76.4% and an AUC of 0.82 across sites. The arginine-based fertilizer increased survival probabilities by up to 15% on moist, deep soils but showed no consistent benefit under shallow or drought-prone conditions. The results highlight the potential of amino-acid-based N supply as a more ecologically aligned alternative and support operational decisions on when and where fertilizers may improve oak establishment under changing climatic conditions. Full article

To address severe seasonal drought affecting over 60% of China’s olive-growing regions, this study evaluates whether arbuscular mycorrhizal fungi (AMF) can enhance drought tolerance in elite olive cultivars (Arbequina and Koroneiki) under simulated arid conditions. A controlled pot experiment inoculated seedlings with two AMF strains (Rhizophagus intraradices [AMF1], Funneliformis mosseae [AMF2]) under full irrigation or a 32-day water deficit. Biomass, root colonization, photosynthesis, PSII efficiency, osmolytes, antioxidants, and lipid peroxidation were measured. Data were analyzed via two-way ANOVA, Pearson’s correlation, and principal component analysis (PCA). Under optimal hydration, both AMF strains colonized >60% of roots, significantly increasing Arbequina biomass by 25–35% (p < 0.05) and Koroneiki biomass. Drought reversed benefits in Arbequina but triggered resilience: AMF1 restored photosynthesis (18%), Fv/Fm (37%), and water potential (18%) (p < 0.05) while reducing lipid peroxidation (79%) (p < 0.01). In Koroneiki, AMF2 restored Ψw to 47% of pre-irrigation levels and increased root volume (137%), PSII efficiency (43%), osmolytes (100%), and carotenoids (28%) (p < 0.01). PCA ranked Arbequina–drought–AMF1 as the most resilient combination. Pairing AMF strains with specific cultivars offers a scalable, chemical-free strategy to stabilize olive productivity in southwest China’s aridifying climate, advancing climate-smart agriculture for drought-prone regions. Full article

Recent innovations in task-agnostic imagery featurization have lowered the computational costs of using machine learning to predict ground conditions from satellite imagery. These methods hold particular promise for the development of imagery-based monitoring systems in low-income regions, where data and computational resources can be limited. However, these relatively simple prediction pipelines have not been evaluated in developing-country contexts over time, limiting our understanding of their performance in practice. Here, we compute task-agnostic random convolutional features from satellite imagery and use linear ridge regression models to predict maize yields over space and time in Zambia, a country prone to severe droughts and crop failure. Leveraging Landsat and Sentinel 2 satellite constellations, in combination with district-level yield data, our model explains 83% of the out-of-sample maize yield variation from 2016 to 2021, slightly outperforming a model trained on Normalized Difference Vegetation Index (NDVI) features, a common remote sensing approach used by practitioners to monitor crop health. Our approach maintains an $R^2$ score of 0.74 when predicting temporal variation alone, while the performance of the NDVI-based approach drops to an $R^2$ of 0.39. Our findings imply that this task-agnostic featurization can be used to predict spatial and temporal variation in agricultural outcomes, even in contexts with limited ground truth data. More broadly, these results point to imagery-based monitoring as a promising tool for assisting agricultural planning and food security, even in contexts where computationally expensive methodologies remain out of reach. Full article

Climate change is expected to increase the frequency of extreme soil moisture events, such as winter waterlogging followed by spring drought, particularly in temperate regions of Europe, North America and Northeast China. While N₂O emissions from paddy soils under waterlogging and subsequent drainage have been widely studied, knowledge of upland arable soils under wheat cultivation remains limited. We hypothesized that: (1) in upland soils, combined waterlogging and drought reduces N₂O emissions compared to continuous waterlogging, and (2) plant presence mitigates soil nitrate accumulation and N₂O emissions across different moisture regimes. A greenhouse experiment was conducted using intact upland soil cores with and without wheat under four moisture treatments: control (60% water-holding capacity, WHC), drought (30% WHC), waterlogging, and waterlogging followed by drought. Daily and cumulative N₂O fluxes, soil mineral nitrogen (NH₄⁺-002DN and NO₃⁻-N), and total nitrogen uptake by wheat shoots were measured. Prolonged waterlogging resulted in the highest cumulative N₂O emissions, whereas the transition from waterlogging to drought triggered a sharp but transient N₂O peak, particularly in soils without plants. Wheat presence consistently reduced N₂O emissions, likely through nitrate uptake, which limited substrate availability for incomplete denitrification. Moisture regimes strongly affected nitrate dynamics, with drought promoting nitrate accumulation and waterlogging enhancing nitrate loss. These findings highlight the vulnerability of upland soils in regions prone to seasonal moisture extremes. Effective management of soil moisture and nitrogen, including the promotion of plant growth, is essential to mitigate N₂O emissions and improve nitrogen use efficiency under future climate scenarios. Full article

Drought is a critical hazard to agricultural productivity in semi-arid regions such as the Antalya Agricultural Basin of Türkiye. This study assessed agricultural drought from 2001 to 2023 using multiple remote sensing-based indices processed in Google Earth Engine (GEE). Vegetation indicators (Normalized Difference Vegetation Index, Normalized Difference Water Index, Normalized Difference Drought Index, Vegetation Condition Index, Temperature Condition Index, and Vegetation Health Index) were derived from MODIS datasets, while the Precipitation Condition Index was calculated from CHIRPS precipitation data. Composite indicators included the Scaled Drought Composite Index, integrating vegetation, temperature, and precipitation factors, and the Soil Moisture Condition Index derived from reanalysis soil moisture data. Results revealed recurrent moderate drought with strong seasonal and interannual variability, with 2008 identified as the driest year and 2009 and 2012 as wet years. Summer was the most drought-prone season, with precipitation averaging 5.5 mm, PCI 1.1, SDCI 15.6, and SMCI 38.4, while winter exhibited recharge conditions (precipitation 197 mm, PCI 40.9, SDCI 57.3, SMCI 89.6). Interannual extremes were detected in 2008 (severe drought) and wetter conditions in 2009 and 2012. Vegetation stress was also notable in 2016 and 2018. The integration of multi-source datasets ensured consistency and robustness across indices. Overall, the findings improve understanding of agricultural drought dynamics and provide practical insights for irrigation modernization, efficient water allocation, and drought-resilient planning in line with Türkiye’s National Water Efficiency Strategy (2023–2033). Full article

Drought stress severely constrains yield and quality stability in longan (Dimocarpus longan Lour.), an important medicine and food homology fruit in China. Melatonin (MT) shows potential for alleviating abiotic stress, but its mechanisms in drought-stressed longan remain unclear. Here, we investigated two cultivars (Shixia and Chuliang) under drought and exogenous MT treatments (CW, well-watered control; CM, exogenous MT application under well-watered control; DW, drought stress; DM, exogenous MT application under drought stress), revealing the following findings: (i) Drought treatment significantly reduced endogenous MT levels in both studied cultivars, and the reduction was reversed by exogenous foliar MT application. Specifically, under drought conditions, exogenous MT treatment increased endogenous MT content by 272.7% in Shixia and 53.6% in Chuliang, respectively. (ii) Drought and exogenous MT treatments modulated the activities of plant defense enzymes (superoxide dismutase, SOD; peroxidase, POD; phenylalanine ammonia lyase, PAL; and catalase, CAT) and the levels of related metabolites (malondialdehyde, MDA; proline, Pro). Across both cultivars, drought stress increased the activities of SOD, POD, and PAL, as well as the Pro content. Exogenous MT treatment, however, reduced the activities of SOD, POD, and PAL while increasing CAT activity and MDA content to some extent in both cultivars. Notably, the Pro content was significantly reduced in Shixia but significantly increased in Chuliang following exogenous MT application under drought stress. (iii) Drought and exogenous MT treatments regulated gene expression in longan cultivars. Relative to CW, 848, 3356, and 2447 differentially expressed genes (DEGs) were detected in CM, DW, and DM in Shixia, respectively. Relative to CW, 1349, 5260, and 5116 DEGs were identified in CM, DW, and DM in Chuliang. A gene ontology analysis indicated significant enrichment for abiotic stress defense and hormone-responsive processes. The KEGG pathway analysis showed significant enrichment in protein processing in the endoplasmic reticulum (ko04141), amino sugar and nucleotide sugar metabolism (ko00520), ascorbate and aldarate metabolism (ko00053), plant–pathogen interaction (ko04626), and starch and sucrose metabolism (ko00500). These findings provide physiological and transcriptomic insights into MT-regulated drought responses in longan, highlighting its potential for improving productivity in drought-prone regions. Full article

Yazd province in central Iran is highly prone to dust and sand storms, causing significant environmental, economic, and health impacts. This study investigates the spatiotemporal dynamics of dust storms in Yazd over 2003–2022 using ground-based meteorological station records and satellite-derived aerosol optical depth (AOD) data from MODIS (MYD08_D3 v6.1) at monthly, seasonal, and annual scales. Analysis of ten synoptic stations data revealed an increasing trend of ~0.5 dusty days/year, with the highest frequency in spring and winter, particularly from March to May. MODIS AOD data confirmed these patterns and showed a rising annual aerosol load, peaking in May. Spatial analysis indicated that central and northern regions are most affected, consistent across datasets. The increasing frequency and intensity of dust storms are driven by natural and anthropogenic factors, including regional drought, desertification, drying wetlands, land use changes, and transboundary dust transport (from Iraq, Syria, Saudi Arabia). These findings underscore the value of integrating in situ and remote sensing observations to monitor dust events. To mitigate impacts, policymakers should prioritize long-term environmental monitoring and interventions addressing both natural and human factors influencing dust emissions. This study provides actionable insights for decision-makers to enhance environmental resilience and protect public health in arid regions. Full article

Leaf water content (LWC) and chlorophyll content (CHL) are pivotal physiological indicators for assessing citrus growth and stress responses. However, conventional measurement techniques—such as fresh-to-dry weight ratio and spectrophotometry—are destructive, time-consuming, and limited in spatial and temporal resolution, making them unsuitable for large-scale monitoring. To achieve efficient large-scale monitoring, this study proposes a synergistic inversion framework integrating UAV multispectral remote sensing with intelligent optimization algorithms. Field experiments during the 2024 growing season (April–October) in western Hubei collected 263 ground measurements paired with multispectral images. Sensitive spectral bands and vegetation indices for LWC and CHL were identified through Pearson correlation analysis. Five modeling approaches—Partial Least Squares Regression (PLS); Extreme Learning Machine (ELM); and ELM optimized by Particle Swarm Optimization (PSO-ELM), Artificial Hummingbird Algorithm (AHA-ELM), and Grey Wolf Optimizer (GWO-ELM)—were evaluated. Results demonstrated that (1) VI-based models outperformed raw spectral band models; (2) the PSO-ELM synergistic inversion model using sensitive VIs achieved optimal accuracy (validation R²: 0.790 for LWC, 0.672 for CHL), surpassing PLS by 15.16% (LWC) and 53.78% (CHL), and standard ELM by 20.80% (LWC) and 25.84% (CHL), respectively; and (3) AHA-ELM and GWO-ELM also showed significant enhancements. This research provides a robust technical foundation for precision management of citrus orchards in drought-prone regions. Full article

Cowpeas are prone to abiotic (heat and drought) and biotic (pathogens and insect pests) stresses, with the former representing the predominant challenge, causing poor growth and reduced yield globally under changing climatic conditions. Cowpea can synthesize phytochemicals to respond to these stresses; however, there is limited information on the impact of weather on phytochemical biosynthesis in the cowpea phyllosphere. Phytochemical profiles were determined via chromatographic and spectrophotometric analyses of leaf samples from six cowpea varieties grown during 2020–2021. A total of 10 fatty acid methyl esters (FAMEs) and 62 diverse metabolites were identified across varieties and seasons, with higher levels in 2020 under elevated temperatures and rainfall. The Queen Anne (QA) variety exhibited the maximum concentration of elaidic oleic acid (cis + trans), behenate, lignocerate, methyl laurate, and methyl palmitate (with the highest concentration at 258.415 µg/mL), and the Whippoorwill Steele’s Black (WP) variety predominantly exhibited diverse phytochemicals with high peak areas during 2020, including phenolic acids, phytohormones, alkaloids, flavonoids, and amino acids. While higher overall increases were observed in 2020, some compounds and varieties peaked in 2021, including FAMEs in the Colossus (CL) variety and other phytochemicals in QA. Flavonoid, flavone, and flavonol biosynthesis; phenylalanine metabolism; and tyrosine metabolism were significantly affected, leading to the accumulation of metabolites. Understanding plant–climate interactions will help farmers with variety selection and planting decisions. This study suggests that further research on the temperature mechanism for the biosynthetic pathways of these metabolites in the screened cowpea varieties is required. Full article

Satellite Precipitation Products (SPPs) play a crucial role in hydrological modeling, particularly in data-scarce and climate-sensitive basins such as the Magat River Basin (MRB), Philippines—one of Southeast Asia’s most typhoon-prone and infrastructure-critical watersheds. This study presents the first full-cycle evaluation of nine widely used multi-source precipitation products (2000–2024), integrating raw validation against rain gauge observations, bias correction using quantile mapping, and post-correction re-ranking through an Entropy Weight Method–TOPSIS multi-criteria decision analysis (MCDA). Before correction, SM2RAIN-ASCAT demonstrated the strongest statistical performance, while CHIRPS and ClimGridPh-RR exhibited robust detection skills and spatial consistency. Following bias correction, substantial improvements were observed across all products, with CHIRPS markedly reducing systematic errors and ClimGridPh-RR showing enhanced correlation and volume reliability. Biases were decreased significantly, highlighting the effectiveness of quantile mapping in improving both seasonal and annual precipitation estimates. Beyond conventional validation, this framework explicitly aligns SPP evaluation with four critical hydrological applications: flood detection, drought monitoring, sediment yield modeling, and water balance estimation. The analysis revealed that SM2RAIN-ASCAT is most suitable for monitoring seasonal drought and dry periods, CHIRPS excels in detecting high-intensity and erosive rainfall events, and ClimGridPh-RR offers the most consistent long-term volume-based estimates. By integrating validation, correction, and application-specific ranking, this study provides a replicable blueprint for operational SPP assessment in monsoon-dominated, data-limited basins. The findings underscore the importance of tailoring product selection to hydrological purposes, supporting improved flood early warning, drought preparedness, sediment management, and water resources governance under intensifying climatic extremes. Full article

Agricultural drought, increasingly intensified by climate change, poses a significant threat to food security and water resources in semi-arid regions, including Türkiye’s Konya Closed Basin. This study evaluates six satellite-derived indices—Vegetation Health Index (VHI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Precipitation Condition Index (PCI), Evapotranspiration Condition Index (ETCI), and Soil Moisture Condition Index (SMCI)—to monitor agricultural drought (2001–2024) and proposes a drought vulnerability map using a novel Drought Vulnerability Index (DVI). Integrating Moderate Resolution Imaging Spectroradiometer (MODIS), Climate Hazards Center InfraRed Precipitation with Station (CHIRPS), and Land Data Assimilation System (FLDAS) datasets, the DVI combines these indices with weighted contributions (VHI: 0.27, ETCI: 0.25, SMCI: 0.22, PCI: 0.26) to spatially classify vulnerability. The results highlight severe drought episodes in 2001, 2007, 2008, 2014, 2016, and 2020, with extreme vulnerability concentrated in the southern and central basin, driven by prolonged vegetation stress and soil moisture deficits. The DVI reveals that 38% of the agricultural area in the basin is classified as moderately vulnerable, while 29% is critically vulnerable—comprising 22% under high vulnerability and 7% under extreme vulnerability. The proposed drought vulnerability map offers an actionable framework to support targeted water management strategies and policy interventions in drought-prone agricultural systems. Full article

Comprehension of spatio-temporal groundwater recharge (GWR) under climate change is imperative to enhance water resources availability and management. The main aim of this study is to examine climate change’s effects on spatio-temporal GWR. This study was done by ensembling five climate models and the physically-based WetSpass-M model to estimate GWR during baseline (1986 to 2015), mid-term (2031 to 2060), and long-term (2071 to 2100) periods for the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. In comparison to the Identification of unit Hydrographs and Component flows from Rainfall, Evaporation, and Streamflow (IHACRES)’s baseflow and direct runoff with corresponding WetSpass-M model outputs, the statistical indices showed good performance in simulating water balance components. Projected future temperature and rainfall will likely increase dramatically compared to the baseline period for RCP4.5 and RCP8.5. In comparison to the baseline period, the annual GWR had been projected to increase by 4.28 mm for RCP4.5 for the mid-term (MidT4.5), 15.27 mm for the long-term (LongT4.5), 2.38 mm for the mid-term (MidT8.5), and 13.11 mm for the long-term for RCP8.5 (LongT8.5), respectively. The seasonal GWR findings showed an increasing pattern during winter and spring, whereas it declined in autumn and summer. The mean monthly GWR for MidT4.5, LongT4.5, MidT8.5, and LongT8.5 will increase by 0.34, 1.26, 0.18, and 1.07 mm, respectively. The watershed’s downstream areas were receiving the lowest amount of GWR, and prone to drought. Therefore, this study advocates and recommends that stakeholders participate intensively in developing and implementing climate change resilience initiatives and water resources management strategies to offset the detrimental effects in the downstream areas. Full article

Drought stress is a major constraint to cassava productivity, especially in drought-prone regions. Although cassava is considered drought-tolerant, prolonged or severe water scarcity significantly reduces tuber yield, carbon assimilation capacity and overall plant growth. The development, selection and deployment of cassava genotypes with enhanced drought tolerance and water use efficiency (WUE) will help to achieve food security. The ability of cassava genotypes to maintain productivity under drought stress is enhanced by drought-responsive genes that regulate stress-related proteins and metabolites, contributing to stomatal closure, osmotic adjustment, antioxidant defense, and efficient carbon assimilation. Therefore, this comprehensive review aimed to document: (i) the effects of drought stress on cassava’s physiological, biochemical and agronomic traits, and (ii) the mitigation strategies and breeding technologies that can improve cassava yield production, drought tolerance and WUE. The key traits discussed include stomatal regulation, chlorophyll degradation, source–sink imbalance, root system architecture and carbon allocation dynamics. In addition, the review presents advances in genomic, proteomic and metabolomic tools, and emphasizes the role of early bulking genotypes, drought tolerance indices, and multi-trait selection in developing cassava cultivars with enhanced drought tolerance, drought escape and drought avoidance mechanism. Therefore, the integration of these strategies will accelerate the development, selection and deployment of improved cassava varieties, which contribute to sustainable productivity and global food security under climate change. Full article

The integration of Artificial Intelligence (AI) into remote sensing data classification has revolutionized agriculture and environmental monitoring. AI is one of the main technologies used in smart farming that enhances and optimizes the sustainability of agricultural production. The use of AI in agriculture can involve land use mapping and crop detection, crop yield monitoring, flood-prone area detection, pest disease monitoring, droughts prediction, soil content analysis and soil production capacity detection, and for monitoring the evolution of forests and vegetation. This review examines recent advancements in AI-driven classification techniques for various applications regarding agriculture and environmental monitoring to answer the following research questions: (1) What are the main problems that can be solved through incorporating AI-driven classification techniques into the field of smart agriculture and environmental monitoring? (2) What are the main methods and strategies used in this technology? (3) What type of data can be used in this regard? For this study, a systematic literature review approach was adopted, analyzing publications from Scopus and WoS (Web of Science) between 1 January 2020 and 31 December 2024. By synthesizing recent developments, this review provides valuable insights for researchers, highlighting the current trends, challenges and future research directions, in the context of achieving the Sustainable Development Goals. Full article