Search Results (687)

Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale. Full article

With increasing constraints on extensive farming—including soil degradation, salinisation and more frequent climatic anomalies—the development of ‘smart’ agriculture requires the integration of affordable, non-invasive methods for monitoring the physiological state of plants. A key indicator for assessing productivity and the early detection of stress is the rate of photosynthetic CO₂ assimilation (A); however, widely available commercial gas analysers are characterised by high cost, technical complexity and considerable weight, which limits their use in large-scale field studies. Here, a new handheld system for measuring assimilation was developed and tested, based on the accumulative principle of recording changes in CO₂ concentration using simple infrared sensors and without maintaining a constant air flow around the leaf. A comparison was carried out between a prototype of the developed system and a commercial gas analyser when measuring leaf assimilation under irrigation and simulated drought conditions. The results demonstrated the consistency of the readings from the two systems. The developed system is characterised by its compact size, low cost, and the absence of moving parts and consumables. The proposed system has the potential to be effective for large-scale screening tasks and rapid diagnosis of stress-induced changes; it represents a promising, affordable tool for addressing applied tasks in precision agriculture, environmental monitoring and physiological research. Full article

Farmer Producer Organizations (FPOs) have gained increasing attention as institutional mechanisms for improving the resilience of smallholder farming systems under changing climatic conditions. This study examines the role of FPOs in promoting the adoption of Climate-Smart Agriculture (CSA) practices, improved irrigation strategies, and sustainable land management in the arid region of Pali district, Rajasthan, India. A comparative assessment was conducted between FPO-associated member and non-member farmers to evaluate differences in climate change perception, adoption behaviour, and adaptive capacity. The study employed a mixed-methods research design using primary data collected from 408 farm households through structured interviews, focus group discussions, and key informant consultations. Descriptive statistics, mean comparison tests and regression analysis were used to examine adoption patterns and identify the major factors influencing farmers’ responses to climate risks. The findings indicate that delayed rainfall, rising temperatures, and increasing drought frequency are widely perceived by farmers as major threats to agricultural production. FPO membership was associated with higher levels of climate-risk awareness and greater reported adoption of CSA practices; however, these findings should be interpreted as associations rather than causal effects. Farmers linked with FPOs reported stronger uptake of improved and stress-tolerant crop varieties, crop diversification, mixed farming systems, agroforestry, soil moisture conservation, rainwater harvesting, improved irrigation methods, and integrated pest management practices. Education, farm size, access to extension services, market linkages, and climate information were also found to significantly influence adoption decisions. The study highlights the important contribution of FPOs in reducing transaction costs, improving access to inputs, technical knowledge, credit and markets, and encouraging collective responses to climate stress. Strengthening FPO governance, expanding extension support, and targeting vulnerable farmer groups can substantially enhance climate resilience and support sustainable agricultural transitions in arid regions. The findings demonstrate that farmer organizations can serve as effective intermediary institutions linking household-level adaptation strategies with broader goals of irrigation efficiency, land management, and rural sustainability. Full article

Soil hydraulic functioning plays an important role in soil water management under increasingly variable climatic conditions. Total water storage alone, however, does not necessarily reflect the stability of retained water after drainage. This study evaluated the effects of waste paper cellulose and biochar, applied individually and in combination, on soil hydraulic behaviour across contrasting soil types. Water-holding capacity (WHC), maximum capillary water capacity (WMCC), water retention capacity after 24 h drainage (WRCC24), soil texture, and organic matter were determined in 64 soil and soil-related samples. Retention efficiency (RE = WRCC24/WMCC) was used as an indicator of water retention stability. WHC was strongly associated with soil organic matter, whereas RE was primarily related to soil texture and likely reflected differences in pore-system characteristics. Cellulose markedly increased WHC, particularly in soils with initially low hydraulic performance, but changes in WHC were not directly related to changes in RE, indicating partly independent hydraulic responses. Combined cellulose–biochar treatments showed complementary effects: cellulose primarily enhanced total water storage, while biochar improved retention stability. The results demonstrate that total water storage and retention stability may respond differently to soil amendments and should therefore be evaluated together when assessing amendment performance. The findings also highlight the potential of combined cellulose–biochar amendments for improving water retention stability under water-limited conditions. Full article

In large-scale rice farming systems, the design of efficient cropping schedules is essential for improving labor management and operational efficiency. However, climate change, including rising temperatures and increased frequency of extreme weather events, has altered crop growth dynamics, making it difficult to achieve optimal management using conventional experience-based scheduling. In addition, the need to distribute operations across numerous fields and optimize labor allocation has increased the complexity of schedule design. In this study, we propose a decision-support method for designing rice cropping schedules using a heading date prediction model and climatological temperature data. The method adjusts transplanting dates based on predicted heading and maturity dates and determines operation periods through both forward and backward scheduling. A case study conducted on a large-scale farming system in Chiba Prefecture demonstrated that the proposed method effectively dispersed the distribution of heading and maturity dates, leading to improved temporal distribution of operations. The standard deviation of heading dates decreased from 11.7 to 8.7 days, indicating a reduction in peak labor demand. The novelty of this study lies in extending a heading date prediction model from growth prediction to practical applications in cropping schedule design and visualization. This approach enables a transition from experience-based planning to data-driven decision-making and contributes to labor distribution in large-scale farming under climate change conditions. Full article

Nano-/microplastics (NMPs) accumulation in agricultural soils has become a growing environmental concern due to its negative impacts on soil health, crop productivity, and food safety. Biochar has gained considerable attention as a sustainable soil amendment capable of improving soil quality and mitigating emerging pollutants. This review examines the role of biochar and modified biochar in reducing the mobility, bioavailability, and plant uptake of NMPs through adsorption, aggregation, and immobilization mechanisms. In addition, biochar improves soil fertility by enhancing nutrient retention, water holding capacity, soil structure, and microbial activity, while also contributing to climate change mitigation through carbon sequestration. However, certain biochars may negatively affect saline–alkaline soils because of their high pH and salinity. Generally, biochar application offers multiple environmental benefits, including soil restoration, pollutant mitigation, and enhanced agricultural sustainability. This review synthesizes recent advances in understanding the mechanisms by which biochar influences NMPs behavior in soil–plant systems and highlights current knowledge gaps and future research directions needed to support its effective application in sustainable agriculture. Full article

Drought is a slowly evolving, multi-driver hydro-meteorological hazard whose accurate early prediction is a cornerstone of climate-smart agriculture and water-resource planning. Existing data-driven drought forecasting frameworks suffer from three persistent limitations: (i) most models concatenate heterogeneous climate variables into a single flat feature vector, implicitly assuming a single dominant driver such as precipitation, even though atmospheric moisture demand, radiation and wind-mediated evapotranspiration co-determine drought onset; (ii) wavelet preprocessing is typically applied to the full series, introducing future-information leakage that violates the operational causality requirement of forecasting; and (iii) most architectures predict a single horizon and provide no causal attribution explaining when, where and which climatic variables initiated the event. This study proposes WaveDroughtNet, a multi-modal, multi-horizon deep-learning framework that addresses these limitations through five integrated components: (a) a strictly causal Daubechies-4 wavelet decomposition computed in a rolling fashion; (b) six modality-specific encoders with stochastic modality dropout (p = 0.15); (c) cross-modal multi-head attention with four heads; (d) a four-layer temporal convolutional network (TCN) backbone with dilation factors yielding a 240-step receptive field; and (e) a post hoc DroughtOriginTracer that combines temporal attention, modal-attribution and inter-district propagation scans. The Standardised Precipitation Evapotranspiration Index (SPEI), used as the supervisory target, is computed following the canonical Vicente-Serrano formulation. water balance $D=P-\mathrm{P}\mathrm{E}\mathrm{T}$ (Hargreaves PET) at a 4-week (≈1-month) timescale, fitted with a three-parameter log-logistic distribution via L-moments, validated by Kolmogorov–Smirnov goodness-of-fit testing ($\alpha =0.05$) per district, and standardised through the inverse-normal cumulative distribution function. Trained on 18,304 weekly district records from NASA POWER reanalysis (2014–2025) covering all 32 districts of Tamil Nadu, India, WaveDroughtNet uses only 256,869 parameters and produces, in a single forward pass, four forecasts (1 week, 1 month, 3 months, 1 year). On the held-out 2024 test partition ($N=1728$), the model attains weighted $F1=0.9221$ and $R^2=0.8512$ at the 1-week horizon, and weighted $F1=0.8498$ and $R^2=0.6812$ at the 1-year horizon. Diebold–Mariano tests confirm that WaveDroughtNet significantly outperforms naive persistence, seasonal naive, LSTM, ConvLSTM and a vanilla Transformer at the 3-month and 1-year horizons (p < 0.001). The DroughtOriginTracer successfully back-projects 15 Coimbatore events to causal origins 29–41 weeks prior to onset. We explicitly acknowledge three limitations that constrain operational deployment in its current form—zero severe events in the 2024 test partition ($F{1}_{severe}$ = 0.000), static inter-district modelling, and absence of vegetation-index supervision—and propose concrete mitigation pathways in the Discussion. Full article

Unmanned aerial vehicle (UAV)-based remote sensing combined with artificial intelligence (AI) has emerged as a key enabler of climate-smart agriculture (CSA). However, the extent to which these technologies operationalize CSA’s three pillars, productivity, adaptation, and mitigation, remains unevenly assessed. This study presents a PRISMA-guided systematic review of 59 peer-reviewed studies examining UAV–AI applications in agricultural systems. The synthesis categorizes platform configurations, sensor modalities, analytical architectures, geographic distribution, and data integration strategies, and evaluates their alignment with CSA objectives. Results indicate that productivity-oriented applications, including yield estimation, biomass mapping, and nutrient assessment, are the most mature, while adaptation-focused stress detection is also well established. In contrast, mitigation-oriented applications, such as carbon quantification and greenhouse gas monitoring, remain comparatively underrepresented. The analysis further reveals a growing convergence toward multimodal sensing and cross-scale data integration linking UAV observations with satellite and environmental datasets. However, substantial variability in validation approaches and dataset representativeness limits generalizability and scalability. Advancing UAV–AI contributions to CSA therefore requires methodological standardization, interoperable data governance, and strengthened institutional capacity. Collectively, the findings position UAV–AI systems as emerging components of climate-smart agricultural intelligence infrastructure rather than isolated monitoring tools. Full article

Drought stress is the most pervasive abiotic constraint on global crop productivity, with projected intensification under climate change threatening the yields of staple crops including wheat, rice, maize, and legumes. Conventional breeding approaches have delivered limited gains against drought tolerance, constrained by the polygenic and multifactorial nature of stress adaptation, the complexity of genotype-by-environment interactions, and the inadequacy of field-based phenotyping under variable stress conditions. Omics technologies, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics, have substantially advanced the molecular dissection of drought tolerance by enabling high-resolution characterization of stress-responsive genes, regulatory networks, adaptive proteins, and metabolic reprogramming pathways. Specific traits targeted include root system architecture and depth, osmotic adjustment capacity through proline and glycine betaine accumulation, antioxidant defense mechanisms, ABA-mediated stomatal regulation, LEA protein accumulation, epigenetic stress memory, and yield stability under water deficit. This review systematically examines omics-based strategies for drought stress mitigation across major crops, highlighting individual omics contributions, multi-omics integration frameworks, computational tools including machine learning and AI-driven predictive modelling, and translational breeding applications. Case studies in wheat, rice, maize, and legumes illustrate how omics-driven approaches accelerate precision breeding for drought resilience through marker-assisted selection, genomic selection, and CRISPR-based gene editing. Challenges including data integration complexity, high implementation costs, limited cross-species transferability, and the need for field-scale validation of microbiome-based strategies are critically addressed. Future perspectives encompassing single-cell and spatial omics, AI-driven predictive breeding, digital agriculture integration, and international data governance frameworks are discussed. By aligning with climate-smart agriculture principles, multi-omics approaches provide a robust and transformative foundation for developing drought-resilient crop cultivars suitable for water-limited production systems worldwide. Full article

Tomato, cucumber, and sweet pepper represent the backbone of greenhouse vegetable cultivation. Over recent decades, developments in agronomic practices have been central to improving yield, resource-use efficiency, resilience to abiotic stresses, and product quality. This review synthesizes dispersed evidence on water and nutrient management, cultivar improvement, grafting, canopy management, biological inputs, and postharvest-oriented agronomy, while highlighting that the three crops exhibit markedly different responses to these practices. These responses are primarily driven by crop-specific differences in source–sink balance, root-zone regulation, canopy architecture, reproductive stability, and postharvest metabolic regulation. Tomato typically demonstrates substantial improvements in yield and water use efficiency under optimized fertigation strategies, with canopy management additionally promoting source–sink balance and stress resilience. Cucumber, by contrast, is particularly sensitive to water deficits, salinity, and nutrient imbalances, necessitating stricter control of irrigation and fertilization to maintain stable root-zone water flux and transpiration dynamics. Sweet pepper often exhibits greater physiological complexity, as yield stability is strongly influenced by microclimate-sensitive metabolic and ionic balance, frequently associated with trade-offs in quality, including firmness, color development, and nutritional composition. The success of grafting, microbial inoculants, and biostimulants further varies considerably among crops, reinforcing the need for crop-specific strategies rather than generalized approaches. Postharvest-oriented agronomy, involving the regulation of nutrient supply, harvest timing, and canopy structure, is becoming increasingly important for prolonging shelf life and improving quality in line with market demands. Sustainability-oriented practices, including nutrient recycling and water-saving strategies, additionally contribute to reducing environmental burdens while maintaining profitability. By identifying species-specific physiological constraints and agronomic priorities, this review highlights that crop-customized and physiologically integrated management strategies are essential for improving productivity, resilience, and quality in protected cultivation. Full article

Climate change is emerging not only as a threat to global food production but also as a major driver of declining nutritional quality in food crops. Throughout this review, terms such as nutrient decline, imbalance, and nutritional quality changes are used to describe relative changes in the nutritional attributes of edible crop tissues, as reported in the source studies. Elevated atmospheric CO₂, altered rainfall patterns, shifts in solar radiation, and rising temperatures influence soil processes, plant metabolism, and genotype × environment interactions that determine nutrient composition and density. Evidence from controlled experiments, free-air CO₂ enrichment (FACE) studies, field trials, and meta-analyses suggests a recurrent tendency toward reduced concentrations of essential macronutrients and micronutrients, including protein, iron, zinc, and selected B-vitamins in a range of cereals, legumes, and horticultural crops, while responses remain context-dependent and are not universally observed across all nutrients, cultivars, or production systems. These reductions raise serious concerns for populations already experiencing widespread micronutrient deficiencies. This review synthesizes the current knowledge on the extent and mechanisms of climate-driven nutrient decline across major crops, highlighting variability among species, cultivars, and production environments. We also evaluate the potential health consequences, particularly heightened risks of anemia, impaired immunity, developmental challenges, and other deficiency-related disorders. Regions such as South Asia, Southeast Asia, and Sub-Saharan Africa are identified as highly vulnerable due to their strong dependence on nutrient-poor staples and existing burdens of hidden hunger. Furthermore, we assess key mitigation and adaptation pathways, including agronomic innovations, climate-smart agricultural practices, biofortification, advanced breeding strategies, and the emerging use of microbial and cyanobacterial biostimulants to enhance nutritional resilience in cropping systems. Finally, this review provides an integrated synthesis of climate-induced nutrient decline, its health implications for vulnerable populations, and priority actions needed to protect global food and nutrition security in the face of accelerating climate change. Full article

Climate change, soil degradation, and the disruption of global nutrient cycles are placing unprecedented pressure on agricultural systems and global food security. These challenges are increasingly recognized as central concerns for planetary health, as agriculture simultaneously depends upon and alters critical Earth system processes. Microbe-based agricultural inputs (including biofertilizers, biostimulants, and biocontrol agents) have been widely promoted as climate-smart solutions capable of enhancing productivity, resilience, and environmental sustainability. However, despite rapid scientific and commercial advances, their performance in the field remains highly variable and strongly context-dependent. This review critically examines the evidence base underpinning climate-smart microbial solutions, with a particular focus on their capacity to confer climate resilience across diverse crops, soils, and climatic conditions. We synthesize current knowledge on the functional roles of beneficial microorganisms, including extremophilic and stress-adapted taxa, while highlighting key biological, technological, ecological, and socio-economic constraints that limit predictability and scalability. Special attention is given to evidence gaps related to long-term field performance, ecosystem-level impacts, and the trade-offs associated with widespread microbial deployment. We further assess recent innovations such as synthetic microbial consortia, microbiome engineering, advanced formulations, and data-driven decision tools. Then we highlight how these new technologies may address context dependency but still need validation under real-world conditions. Finally, we discuss policy, regulatory, and capacity-building considerations required to responsibly integrate microbial solutions into climate-smart agriculture frameworks. Overall, this review argues that microbial inoculants should be viewed not as universal inputs but as context-specific tools whose successful deployment depends on robust evidence, ecological sensitivity, and system-level integration. Advancing microbial solutions for agriculture will therefore require aligning technological innovation with broader planetary health objectives, ensuring that efforts to enhance agricultural productivity also support long-term ecosystem stability and resilience. Full article

Global climate change poses escalating ecological challenges, with agriculture contributing approximately 30% of anthropogenic greenhouse gas emissions, primarily from nitrous oxide (N₂O) and methane (CH₄). The farmland carbon-nitrogen cycle represents a key nexus for coordinating pollution control and carbon mitigation. This study applies bibliometric methods, including co-occurrence analysis, clustering, and burst detection, to 1286 publications retrieved from the Web of Science Core Collection (1990–2025) and CiteSpace 6.2.R4. Results indicate that China (444 papers, centrality 0.42), the United States (211 papers), and Germany (151 papers) are leading contributors, with major institutions forming a multi-centered international collaboration network. Keyword analysis identified 11 core clusters (modularity Q = 0.82, silhouette S = 0.91), with nitrous oxide emerging as the central theme (frequency 670). The field has evolved through three stages: fundamental emission mechanism studies (1990–2005), agricultural management practices (2006–2015), and integrated mitigation strategies with microbial mechanism exploration (2016–2025). Current frontiers emphasize microbial-mediated carbon-nitrogen cycling and yield-scaled emission assessments bridging theory and practice. Future research should prioritize cross-scale coupling analysis, multi-objective management frameworks, smart agricultural technologies, and policy integration. This study provides a systematic bibliometric mapping of the evolution of synergistic carbon-nitrogen research in agricultural systems, offering a quantitative overview of development trends and research gaps. Full article

Evapotranspiration (ET) is a fundamental process within the water cycle and the agricultural water balance, optimizing resource allocation, maintaining soil health, and enhancing ecosystem resilience to climate change. Because ET represents a primary consumptive use of irrigation on agricultural lands, enhancing water-use efficiency and sustainable water management requires accurate estimation of evapotranspiration to support long-term sustainability and productivity. This study offers an effective means to visualize spatial and temporal patterns of reference evapotranspiration (ETo) across various vegetation management practices. This study examined the impacts of agroforestry buffers (ABs), grass buffers (GBs), biofuel crops in an agroforestry watershed (BCa), and biofuel crops in a grass buffer watershed (BCg) on ETo, compared to a corn (Zea mays L.)–soybean (Glycine max L.) rotation (RC) for claypan soil in Northern Missouri, USA. The experimental watersheds were located at the Greenley Memorial Research Center, Missouri, USA. Campbell Scientific sensors and Photosynthetically Active Radiation (PAR) smart sensors were installed to measure net radiation, anemometers, humidity, and air temperature. All instruments were mounted on masts at a height of 2 m above ground level in crop, tree, grass, and biofuel areas. Measured meteorological data were recorded hourly from April to October during 2017 and 2018. Daily ETo predictions were calculated using the Penman–Monteith model. These ETo predictions were displayed across the landscape using Python-based GIS for selected dates (each Saturday) for the watersheds. The methodology was implemented using the software programs of Python 2.7.10 and ArcGIS 10.3.1. The results indicated that ETo increased by 11%, 17%, 18%, and 25% in 2017, and by 7%, 9%, 14%, and 20% in 2018 for AB, BCa, BCg, and GB, respectively, compared to RC management. This process may improve soil water recharge in perennial management systems. Accurate estimation of ET in agricultural regions is critical for understanding water balance, hydrological and ecosystem processes, and climate variability. Given that agriculture constitutes the majority of global water consumption, precise ET estimation is particularly significant for sustainable water management, especially in regions experiencing water scarcity. These outcomes may support effective planning and management of agricultural water resources by enabling optimized irrigation and agricultural production. Full article

Climate change continues to threaten global food security. Climate-smart agriculture (CSA) offers a solution to addressing this challenge in urban agriculture (UA). This paper addresses a gap in the empirical literature on decision-making about the adoption of CSA practices by examining the determinants of CSA adoption among small-scale urban crop (SSUC) farmers in eThekwini (ETH) Municipality, South Africa. Grounded in a utility theory framework, the paper draws on 412 respondents (Cochran-estimated) from a multi-stage sample design across four wards, providing reasonable coverage of SSUC farmers in ETH Municipality. While the sample size is statistically representative of SSUC farmers in ETH Municipality, it is a single metropolitan case rather than universal. The results show strong complementarities among these CSA practices, for example, between OM and CD (r ≈ 0.70, p < 0.001) and M and CD (r ≈ 0.61, p < 0.001). The multivariate probit (MVP) model predicts that the socio-economic and institutional factors age, gender, marital and employment status, education, credit access, extension contact, land tenure, and location (distance from home to farm plots) (p < 0.05) were significant determinants of adopting CSA practices by SSUC farmers. The findings contribute to the global literature on the UA–CSA nexus, demonstrating that socio-economic and institutional factors shape the adoption of bundled CSA practices. While the findings underscore the need for integrated, custom, and UA context-specific policy and extension interventions to strengthen urban food system resilience, UA farmers, practitioners, researchers, and policymakers should apply these insights elsewhere with caution. Full article