Search Results (18,255)

In arid regions with limited water resources, improving cropland water-use efficiency (WUEc) is crucial for maintaining crop production. This study aims to investigate how changes in meteorological and vegetation factors affect WUEc in drylands and to identify its primary drivers, which are essential for understanding how cropland ecosystems respond to complex environmental changes. Using remote sensing data, we analyzed the spatiotemporal patterns of WUEc in Xinjiang from 2002 to 2022 by applying STL decomposition, Sen’s slope combined with the Mann–Kendall test, and an XGBoost–SHAP model, quantifying its key controlling factors. The results indicate that from 2002 to 2022, WUEc in Xinjiang showed an overall declining trend. Prior to 2007, WUEc increased at 0.05 gC·m⁻¹·m⁻²·a⁻¹, after which it fluctuated downward at −0.01 gC·m⁻¹·m⁻²·a⁻¹. Intra-annual peaks consistently occurred in May and during September–October. Spatially, WUEc exhibited significant heterogeneity, increasing from south to north, with 53.26% of the region showing declines. Temperature (T) and leaf area index (LAI) emerged as the primary meteorological and vegetation drivers, respectively, influencing WUEc change in 45.7% and 17.6% of the area. Both variables were negatively correlated with WUEc, with negative correlations covering 60% of the region for T and 83% for LAI. These findings provide scientific guidance for optimizing crop structure and water-resource management strategies in arid regions. Full article

Eucalyptus has become a major plantation crop in southern China, with a carbon sequestration capacity significantly higher than that of other species. However, its long-term carbon sequestration capacity and regional-scale potential remain highly uncertain due to commonly applied short-rotation management practices. The InTEC (Integrated Terrestrial Ecosystem Carbon) model is a process-based biogeochemical model that simulates carbon dynamics in terrestrial ecosystems by integrating physiological processes, environmental drivers, and management practices. In this study, the InTEC model was enhanced with an optimized eucalyptus module (InTEC_euc) and a data assimilation module (InTEC_DA), and driven by multiple remote sensing products (Net Primary Productivity (NPP) and carbon density) to simulate the carbon budgets of eucalyptus plantations from 2003 to 2023. The results indicated notable improvements in the performance of the InTEC_euc model when driven by different datasets: carbon density simulation showed improvements in R² (0.07–0.56), reductions in MAE (5.99–28.51 Mg C ha⁻¹), reductions in RMSE (8.1–31.85 Mg C ha⁻¹), and improvements in rRMSE (12.37–51.82%), excluding NPP_Lin. The carbon density-driven InTEC_euc model outperformed the NPP-driven model, with improvements in R² (0.28), MAE (−8.15 Mg C ha⁻¹), RMSE (−9.43 Mg C ha⁻¹), and rRMSE (−15.34%). When the InTEC_DA model was employed, R² values for carbon density improved by 0–0.03 (excluding ACD_Yan), with MAE reductions between 0.17 and 7.22 Mg C ha⁻¹, RMSE reductions between 0.33 and 12.94 Mg C ha⁻¹ and rRMSE improvements ranging from 0.51 to 20.22%. The carbon density-driven InTEC_DA model enabled the production of high-resolution and accurate carbon budget estimates for eucalyptus plantations from 2003 to 2023, with average NPP, Net Ecosystem Productivity (NEP), and Net Biome Productivity (NBP) values of 17.80, 10.09, and 9.32 Mg C ha⁻¹ yr⁻¹, respectively, offering scientific insights and technical support for the management of eucalyptus plantations in alignment with carbon neutrality targets. Full article

In the current era, Indian agriculture faces a significant demand for increased food production, which has led to the integration of advanced technologies to enhance efficiency and productivity. Drones have emerged as transformative tools for enhancing precision agriculture, reducing costs, and improving sustainability. This study provides a comprehensive review of drone adoption in Indian agriculture by examining its effects on precision farming, crop monitoring, and pesticide application. This research evaluates technological advancements, regulatory frameworks, infrastructure, farmers’ perceptions, and the financial accessibility of drone technology in the Indian agricultural context. Key findings indicate that, while drone adoption enhances efficiency and sustainability, challenges such as high costs, lack of training, and regulatory barriers hinder widespread implementation. This paper also explores the growing market for agricultural drones in India, highlighting key industry players and projected market growth. Furthermore, it addresses regional differences in adoption rates and emphasizes the increasing social acceptance of drones among Indian farmers. To bridge the gap between potential and practice, the study proposes several policy and institutional recommendations, including government-led financial incentives, training programs, and public–private partnerships to facilitate drone integration. Moreover, this review article also highlights technological advancements, such as AI and IoT, in agriculture. Finally, open issues and future research directions for drones are discussed. Full article

Arsenic (As) contamination has significantly increased in recent decades due to anthropogenic activities. This is a serious challenge for human health, environmental quality, and crop productivity. Biochar (BC) is an important practice used globally to remediate polluted soils. Likewise, melatonin (MT) has also shown tremendous results in mitigating metal toxicity and improving crop productivity. Nevertheless, the mechanism of combined BC and MT in alleviating As toxicity in rice (Oryza sativa L.) remains unexplored. In this study, we investigated how As affected rice and how the combined BC and MT facilitated As tolerance. The study comprised a control, As stress (100 mg kg⁻¹), As stress (100 mg kg⁻¹) + BC (2%), As stress (100 mg kg⁻¹) + MT (100 µM) and As stress (100 mg kg⁻¹) + BC (2%) + MT (100 µM). Arsenic significantly decreased rice growth and yield by increasing electrolyte leakage (EL), malondialdehyde (MDA), and hydrogen peroxide (H₂O₂). Co-applying BC and MT substantially enhanced rice growth and yield by increasing chlorophyll synthesis (48.12–92.42%) leaf water contents (40%), antioxidant activities (ascorbate peroxide: 56.43%, catalase: 55.14%, peroxidase: 57.77% and superoxide dismutase: 57.52%), proline synthesis (41.35%), MT synthesis (91.53%), and phytochelatins synthesis (125%) nutrient accumulation in rice seedlings and soil nutrient availability. The increased rice yield with BC + MT was also linked with reduced H₂O₂ production, As accumulation, soil As availability, and an increase in OsAPx6, OsCAT, OsPOD, OsSOD OsASMT1, and OsASMT2 and a decrease in expression of OsABCC1. Biochar + MT enhanced residual OM- and Fe, ((Fe₂As) and Mn (Mn₃(AsO₄)₂) bound forms of As leading to a substantial increase in rice growth and yield. Thus, the combination of BC and MT is an eco-friendly approach to mitigate As toxicity and improve rice productivity. Full article

Seeds of Cucurbitaceae crops represent a promising yet underexplored source of bioactive compounds with potential applications beyond nutrition, particularly in the cosmetics industry. This review examines the seeds of Citrullus lanatus (watermelon), Cucumis melo (melon), and Cucurbita pepo (pumpkin), focusing on their biochemical composition and evaluating their functional value in natural cosmetic development. Although these fruits are widely consumed, industrial processing generates substantial seed by-products that are often discarded. These seeds are rich in polyunsaturated fatty acids, proteins, carbohydrates, and phytochemicals, positioning them as sustainable raw materials for value-added applications. The incorporation of seed-derived extracts into cosmetic formulations offers multiple skin and hair benefits, including antioxidant activity, hydration, and support in managing conditions such as hyperpigmentation, acne, and psoriasis. They also contribute to hair care by improving oil balance, reducing frizz, and enhancing strand nourishment. However, challenges such as environmental instability and low dermal permeability of seed oils have prompted interest in nanoencapsulation technologies to improve delivery, stability, and efficacy. This review summarizes current scientific findings and highlights the potential of Cucurbitaceae seeds as innovative and sustainable ingredients for cosmetic and personal care applications. Full article

In arid regions, water scarcity and soil potassium destruction are major constraints on the sustainable development of the jujube industry. In this regard, the use of crop models can compensate for time-consuming and costly field trials to screen for better irrigation regimes, but their predictive accuracy is often compromised by parameter uncertainty. To address this issue, we utilized data from a three-year (2022–2024) field trial (with irrigation at 50%, 75%, and 100% of evapotranspiration and potassium applications of 120, 180, and 240 kg/ha) to simulate the growth process of jujube trees in arid regions using the WOFOST model. In this study, parameter sensitivity analyses were conducted to determine that photosynthetic capacity maximization (A_max), the potassium nutrition index (K_status), the water stress factor (SWF), the water–potassium photosynthetic coefficient of synergy (α), and potassium partitioning weight coefficients (β_i) were the important parameters affecting the simulated growth process of the crop. Path analysis using segmented structural equations also showed that water stress factor (SWF) and potassium nutrition index (K_status) indirectly controlled yield by significantly affecting photosynthesis (path coefficients: 0.72 and 0.75, respectively). The ability of the crop model to simulate the growth process and yield of jujube trees was improved by the introduction of water and potassium parameters (R² = 0.94–0.96, NRMSE = 4.1–12.2%). The subsequent multi-objective optimization of yield and crop water productivity of dates under different combinations of water and potassium treatments under a bi-objective optimization model based on the NSGA-II algorithm showed that the optimal strategy was irrigation at 80% ET_c combined with 300 kg/ha of potassium application. This management model ensures yield and maximizes crop water use efficiency (CWP), thus providing a scientific and efficient irrigation and fertilization regime for jujube trees in arid zones. Full article

The present-day issues related to the cotton-growing industry, namely yield estimation, pest effect, and growth phase diagnostics, call for integrated, scalable monitoring solutions. This write-up reveals Cotton Multitask Learning (CMTL), a transformer-driven multitask framework that launches three major agronomic tasks from UAV pictures at one go: boll detection, pest damage segmentation, and phenological stage classification. CMTL does not change separate pipelines, but rather merges these goals using a Cross-Level Multi-Granular Encoder (CLMGE) and a Multitask Self-Distilled Attention Fusion (MSDAF) module that both allow mutual learning across tasks and still keep their specific features. The biologically guided Stage Consistency Loss is the part of the architecture of the network that enables the system to carry out growth stage transitions that occur in reality. We executed CMTL on a tri-source UAV dataset that fused over 2100 labeled images from public and private collections, representing a variety of crop stages and conditions. The model showed its virtues state-of-the-art baselines in all the tasks: setting 0.913 mAP for boll detection, 0.832 IoU for pest segmentation, and 0.936 accuracy for growth stage classification. Additionally, it runs at the fastest speed of performance on edge devices such as NVIDIA Jetson Xavier NX (Manufactured in Shanghai, China), which makes it ideal for deployment. These outcomes evoke CMTL’s promise as a single and productive instrument of aerial crop intelligence in precision cotton agriculture. Full article

Nitrous oxide (${\mathrm{N}}_2\mathrm{O}$) emissions from agriculture are rising due to increased fertilizer use and intensive farming, posing a major challenge for climate mitigation. This study introduces a novel reinforcement learning (RL) framework to optimize farm management strategies that balance crop productivity with environmental impact, particularly ${\mathrm{N}}_2\mathrm{O}$ emissions. By modeling agricultural decision-making as a partially observable Markov decision process (POMDP), the framework accounts for uncertainties in environmental conditions and observational data. The approach integrates deep Q-learning with recurrent neural networks (RNNs) to train adaptive agents within a simulated farming environment. A Probabilistic Deep Learning (PDL) model was developed to estimate ${\mathrm{N}}_2\mathrm{O}$ emissions, achieving a high Prediction Interval Coverage Probability (PICP) of 0.937 within a 95% confidence interval on the available dataset. While the PDL model’s generalizability is currently constrained by the limited observational data, the RL framework itself is designed for broad applicability, capable of extending to diverse agricultural practices and environmental conditions. Results demonstrate that RL agents reduce ${\mathrm{N}}_2\mathrm{O}$ emissions without compromising yields, even under climatic variability. The framework’s flexibility allows for future integration of expanded datasets or alternative emission models, ensuring scalability as more field data becomes available. This work highlights the potential of artificial intelligence to advance climate-smart agriculture by simultaneously addressing productivity and sustainability goals in dynamic real-world settings. Full article

The increasing demand for food necessitates that agri-food systems adopt innovative techniques to enhance food production while optimizing the use of limited resources, such as water. In agriculture, hydrogels are being increasingly used to enhance water retention and reduce irrigation requirements. However, most of these materials are based on synthetic polymers that are not biodegradable. This raises serious environmental and health concerns, highlighting the urgent need for sustainable, biodegradable alternatives. Biomass-derived from agro-industrial waste presents a substantial potential for producing hydrogels, which can effectively function as water collectors and suppliers for crops. This review article provides a comprehensive overview of recent advancements in the application of agro-industrial waste for the formulation of hydrogels. Additionally, it offers a critical analysis of the development of hydrogels utilizing natural and compostable materials. Agro-industrial and food waste, which are rich in hemicellulose and cellulose, have been utilized to enhance the mechanical properties and water absorption capacity of hydrogels. These biomaterials hold significant potential for the development of effective hydrogels in agricultural applications; they can be either hybrid or natural materials that exhibit efficacy in enhancing seed germination, improving water retention capabilities, and facilitating the controlled release of fertilizers. Natural hydrogels derived from agro-industrial waste present a sustainable technological alternative that is environmentally benign. Full article

The sustainable utilization of saline water resources represents an effective strategy for alleviating water scarcity in arid regions. However, the mechanisms by which prolonged saline water irrigation influences soil salinization and dryland crop growth are not yet fully understood. This study examined the effects of six irrigation water salinity levels (CK: 0.87 g·L⁻¹, S1: 2 g·L⁻¹, S2: 4 g·L⁻¹, S3: 6 g·L⁻¹, S4: 8 g·L⁻¹, S5: 10 g·L⁻¹) on soil salinization dynamics and jujube growth during a three-year field experiment (2020–2022). The results showed that soil salinity within the 0–1 m profile significantly increased with rising irrigation water salinity and prolonged irrigation duration, with the 0–0.4 m layer accounting for 50.27–74.95% of the total salt accumulation. A distinct unimodal salt distribution was observed in the 0.3–0.6 m soil zone, with the salinity peak shifting downward from 0.4 to 0.5 m over time. Meanwhile, soil pH and sodium adsorption ratio (SAR) increased steadily over the study period. The dominant hydrochemical type shifted from SO₄²⁻-Ca²⁺·Mg²⁺ to Cl⁻-Na⁺·Mg²⁺. Crop performance exhibited a nonlinear response to irrigation salinity levels. Low salinity (2 g·L⁻¹) significantly enhanced plant height, stem diameter, leaf area index (LAI), vitamin C content, and yield, with improvements of up to 12.11%, 3.96%, 16.67%, 16.24%, and 16.52% in the early years. However, prolonged exposure to saline irrigation led to significant declines in both plant growth and water productivity (WP) by 2022. Under high-salinity conditions (S5), yield decreased by 16.75%, while WP declined by more than 30%. To comprehensively evaluate the trade-off between economic effects and soil environment, the entropy weight TOPSIS method was employed to identify S1 as the optimal irrigation treatment for the 2020–2021 period and control (CK) as the optimal treatment for 2022. Through fitting analysis, the optimal irrigation water salinity levels over 3 years were determined to be 2.75 g·L⁻¹, 2.49 g·L⁻¹, and 0.87 g·L⁻¹, respectively. These findings suggest that short-term irrigation of jujube trees with saline water at concentrations ≤ 3 g·L⁻¹ is agronomically feasible. Full article

Interest in the wine sector focusing on no- or low-alcohol wines is growing. De-alcoholation, typically a post-fermentation process, faces restrictions in some countries and is often quite costly. Using raw materials like low-sugar grapes suitable for this purpose seems logical, yet the literature currently lacks contributions in this area. In this review paper, we outline an ideal ripening process where the goal of producing “low-sugar grapes” can be achieved through various methodologies applied at (i) the whole-canopy level (minimal pruning, hedge mechanical pruning with or without hand finishing, cane pruning combined with high bud load and no cluster thinning, applications of exogenous hormones, late irrigation, and double cropping); (ii) the canopy microclimate level, involving changes in the leaf area-to-fruit ratios (netting, apical or basal leaf removal, late shoot trimming, use of antitranspirants); and (iii) through new technologies (high-yield plots from vigor maps and the adoption of agrivoltaics). However, the efforts in this survey extend beyond merely achieving the production of low-sugar grapes in the vineyard, which is indeed primary but not exhaustive. Therefore, we also explore solutions for obtaining low-sugar grapes while simultaneously enhancing features such as lower acidity, increased phenolics, and aroma potential, which might boost consumer appreciation. The review emphasizes that (i) grapes intended for low-alcohol wine production should not be viewed as a low-quality sector but rather as an alternative endeavour, where the concept of grape quality remains firmly intact and (ii) viticulture for low sugar concentration is a primary strategy, rather than merely a support to dealcoholization techniques. Full article

The widespread use of pesticides in modern agriculture has significantly enhanced food production by managing pests and diseases; however, their degradation in soil can lead to unintended consequences for soil fertility and nutrient cycling. This review explores the mechanisms of pesticide degradation, both abiotic and biotic, and the soil factors influencing these processes. It critically examines how degradation products impact soil microbial communities, organic matter decomposition, and key nutrient cycles, including nitrogen, phosphorus, potassium, and micronutrients. This review highlights emerging evidence linking pesticide residues with altered enzymatic activity, disrupted microbial populations, and reduced nutrient bioavailability, potentially compromising soil structure, water retention, and long-term productivity. Additionally, it discusses the broader environmental and agricultural implications, including decreased crop yields, biodiversity loss, and groundwater contamination. Sustainable management strategies such as bioremediation, the use of biochar, eco-friendly pesticides, and integrated pest management (IPM) are evaluated for mitigating these adverse effects. Finally, this review outlines future research directions emphasizing long-term studies, biotechnology innovations, and predictive modeling to support resilient agroecosystems. Understanding the intricate relationship between pesticide degradation and soil health is crucial to ensuring sustainable agriculture and food security. Full article

Cassava (Manihot esculenta Crantz) is essential for global food security, especially in tropical regions. As an important genetic resource, its genetics plays a key role in crop breeding, enabling the development of more productive and pest- and disease-resistant varieties. Scientometrics, which quantitatively analyzes the production and impact of scientific research, is crucial for understanding trends in cassava genetics. This study aimed to apply bibliometric methods to conduct a scientific mapping analysis based on yearly publication trends, paper classification, author productivity, journal impact factor, keywords occurrences, and omic approaches to investigate the application of genetics to the species from 1960 to 2022. From the quantitative data analyzed, 3246 articles were retrieved from the Web of Science platform, of which 654 met the inclusion criteria. A significant increase in scientific production was observed from 1993, peaking in 2018. The first article focused on genetics was published in 1969. Among the most relevant journals, Euphytica stood out with 36 articles, followed by Genetics and Molecular Research (n = 30) and Frontiers in Plant Science (n = 25). Brazil leads in the number of papers on cassava genetics (n = 143), followed by China (n = 110) and the United States (n = 75). The analysis of major methodologies (n = 185) reveals a diversified panorama during the study period. Morpho-agronomic descriptors persisted from 1978 to 2022; however, microsatellite markers were the most widely used, with 102 records. Genomics was addressed in 87 articles, and transcriptomics in 65. By clarifying the current landscape, this study supports cassava conservation and breeding, assists in public policy formulation, and guides future research in the field. Full article

Excessive nitrogen (N) fertilizer usage in agriculture has prompted the exploration of sustainable strategies to enhance nitrogen use efficiency (NUE) while maintaining crop yield and quality. Processed tomatoes (Solanum lycopersicum L.) were grown for two years (2023 and 2024) following a two-way factorial randomized complete block (RCBD) design, considering three biostimulants and three N regimes as two factors, to assess their morphophysiological, biochemical, anatomical and yield performances. Nitrogen application significantly influenced biomass accumulation, the leaf area index (LAI), nitrogen uptake and yield with notable comparable values between reduced and optimal nitrogen dose, indicating improved nitrogen use efficiency. Biostimulants showed limited effects alone but enhanced plant performance under reduced nitrogen conditions, particularly improving chlorophyll content, crop growth, N uptake, yield and anatomical adaptations. Moreover, compared to 2024, biostimulant application enhanced tomato growth more evidently in 2023 due to environmental variations, likely due to the occurrence of stress conditions. Importantly, biostimulants, together with N regimes, i.e., optimal and reduced doses, showed improved anatomical traits, especially regarding leaf thickness and thickness between the two epidermises, indicating adaptive responses that may support sustained productivity under N-limited conditions. Among the biostimulants used, the processed tomatoes responded better to protein hydrolysate and endophytic N-fixing bacteria than to seaweed extract. These findings suggest that although biostimulants alone were not affected, integrating them with reduced N fertilization provides a viable strategy for optimizing tomato production, conserving resources and minimizing the environmental impact without compromising yield or quality. Full article

Nitrogen (N) and potassium (K) are essential macronutrients for plants whose functions and interactions profoundly influence plant physiological metabolism, environmental adaptation, and agricultural production efficiency. This review summarizes research advances in plant N and K nutrition and their interaction mechanisms, elucidating the key physiological functions of N and K individually and their respective absorption and transport mechanisms involving transporters such as NRTs and HAKs/KUPs. The review discusses the types of nutrient interactions (synergism and antagonism), with a primary focus on the physiological basis of N–K interactions and their interplay in root absorption and transport (e.g., K⁺-NO₃⁻ co-transport; NH₄⁺ inhibition of K⁺ uptake), photosynthesis (jointly optimizing CO₂ conductance, mesophyll conductance, and N allocation within photosynthetic machinery to enhance photosynthetic N use efficiency, PNUE), as well as sensing, signaling, co-regulation, and metabolism. This review emphasizes that N–K balance is crucial for improving crop yield and quality, enhancing fertilizer use efficiency (NUE/KUE), and reducing environmental pollution. Consequently, developing effective N–K management strategies based on these interaction mechanisms and implementing Balanced Fertilization Techniques (BFT) to optimize N–K ratios and application strategies in agricultural production represent vital pathways for ensuring food security, addressing resource constraints, and advancing green, low-carbon agriculture, including through coordinated management of greenhouse gas emissions. Full article