Search Results (694)

Open burning of agricultural residues remains a significant source of air pollution and greenhouse gas emissions in Southeast Asia. This study evaluated a community-scale smokeless vertical charcoal kiln for biochar production from corncob residues under practical operating conditions. Carbonization at 415–435 °C for 150–180 min produced biochar yields of 23.3–28.3%, with the 150 min treatment giving the highest yield. The biochar exhibited high carbon content (71–71.5%), low volatile matter (15.7–16.7%), fixed carbon content of 51–54%, and moderately alkaline pH (8.97–9.08). Atomic H/C and O/C ratios (approximately 0.55 and 0.27) indicated moderate aromaticity and stability consistent with IBI Class 1 criteria, while SEM revealed a macropore-dominated porous structure. Theoretical carbon sequestration potential was estimated at 0.69–0.74 tCO₂-eq per tonne of dry feedstock. These findings demonstrate the technical feasibility of community-scale smokeless biochar production and provide physicochemical characterization of the resulting biochar, suggesting potential relevance for carbon storage and soil amendment; however, agronomic performance and emissions require direct evaluation, and results are specific to corncob feedstock. Overall, this work contributes to sustainable agricultural waste management by demonstrating a low-cost, community-accessible pathway that simultaneously supports climate change mitigation, air quality improvement, and socio-economic accessibility for smallholder farming systems in Southeast Asia. Full article

Straw incorporation is an important agronomic practice for sustainable cropland management, improving soil structure and enhancing soil organic carbon (SOC) sequestration. In this study, a systematic review was conducted using the Web of Science (WoS) database, and a random-effects meta-analysis was performed on 63 field experiments in mainland China, including 846 treatment–control comparisons, to evaluate the effects of straw application rate and duration on soil aggregates, aggregate-associated carbon, and SOC. Across all studies, pooled effect sizes indicated that straw incorporation significantly increased Mean Weight Diameter (MWD) (22%), macroaggregate content (17%), and SOC (25%), while enhancing aggregate-associated carbon in macroaggregates (35%), microaggregates (30%), and silt–clay fractions (22%). It also reduced silt–clay fraction content by 17%, indicating improved aggregate redistribution. Interaction analysis suggested that short-term incorporation (<5 years) at 5–8 t ha⁻¹ and long-term incorporation (≥5 years) at 10–15 t ha⁻¹ were more favorable for macroaggregate formation and carbon sequestration. Precipitation negatively affected microaggregate carbon, highlighting environmental regulation of carbon stability. Overall, straw incorporation strengthens soil structural stability, promotes SOC accumulation, and provides evidence-based guidance for sustainable residue management and climate-smart agriculture in mainland China. Full article

A sustainable method of improving soil fertility and developing climate-resilient cropping systems is vermicomposting agricultural waste. This study hypothesized that vermicompost derived from mixed organic agricultural wastes would significantly improve soil fertility, crop productivity, and drought resilience compared to single-substrate treatments and the unamended control. The effects of vermicompost generated from mixed organic wastes using Eisenia fetida on soil quality, crop performance, and drought resilience of lettuce (Lactuca sativa, Eden variety) were evaluated in this study using a randomized complete block design. Crop performance indicators included germination, growth characteristics, biomass, SPAD chlorophyll content, and yield, while soil physicochemical properties, including pH, organic carbon, total nitrogen, available phosphorus, exchangeable potassium, electrical conductivity (EC), and cation exchange capacity (CEC), were assessed both before and after amendment application. The effects of drought stress were evaluated using leaf surface temperature, wilting score, recovery time, and survival rate. The results demonstrated that vermicompost application significantly improved soil fertility and crop performance relative to the control treatment (p < 0.05). The best-performing treatment (T2) increased soil organic carbon by approximately 22–28%, total nitrogen by 18–24%, available phosphorus by 20–27%, and exchangeable potassium by 16–21% compared with the control. Fresh biomass increased by approximately 14–17%, marketable yield improved by 16–24%, and SPAD chlorophyll values increased by nearly 20%, indicating enhanced photosynthetic efficiency and nutrient uptake. T2 showed the most resilience under drought stress, with ~94.9% survival rate, reduced wilting severity, shortened recovery time and sustained stable leaf temperature (~27.8 °C), whereas low-performing treatments and the control recorded survival rates of only ~70–78%. Mixed organic waste substrates consistently outperformed single-substrate treatments, demonstrating synergistic effects on nutrient cycling, microbial activity, soil structural quality, and drought tolerance. These findings provide quantitative evidence that vermicomposting can simultaneously enhance soil fertility, crop productivity, and drought resilience, highlighting its strong potential as a scalable climate-smart strategy for sustainable agriculture, circular bioeconomy development, and organic waste valorization in smallholder farming systems. Full article

Accurate monitoring of inundation in rice paddies is essential for optimizing water use efficiency and mitigating methane emissions; yet, detecting water beneath dense rice canopies remains a major challenge. This study proposed a reliable classification approach applied to the Winter–Spring 2025 season in An Giang province, Vietnam, by integrating multi-temporal ALOS-2 PALSAR-2 (L-band) and Sentinel-1 (C-band) SAR data with in situ field surveys. Time-series Sentinel-1 observations were used to estimate rice phenology (rice age), while multi-polarization backscatter from ALOS-2 PALSAR-2 was analyzed to discriminate inundated from non-inundated conditions across different growth stages. Results demonstrated that L-band signals, particularly in VV polarization, penetrated dense vegetation effectively, enabling classification of inundated vs. non-inundated fields with an overall accuracy of 81% and a Kappa coefficient of 0.77. The resulting multi-date inundation maps revealed distinct flooding regimes consistent with local field survey observations. These findings demonstrated the potential of L-band VV SAR data for characterizing sub-canopy inundation conditions under rice canopies. Crucially, the approach provides essential data for greenhouse gas inventories and supports the verification of low-emission water management practices, such as Alternate Wetting and Drying (AWD). Overall, the study demonstrated the value of multi-frequency SAR integration for advancing agricultural monitoring and climate-smart management in rice-growing regions. Full article

Recent advances in artificial intelligence (AI), machine learning (ML), deep learning (DL), and remote sensing technologies have transformed agricultural monitoring, precision farming, and climate-resilient decision-making. However, the widespread adoption of AI-driven agricultural systems remains constrained by the black-box nature of advanced predictive models, particularly deep neural networks. Explainable Artificial Intelligence (XAI) has emerged as a critical solution for improving transparency, interpretability, accountability, and trust in AI-based agricultural remote sensing systems. This review provides a comprehensive synthesis of the recent developments in XAI applications within smart agriculture, with emphasis on interpretable remote sensing analytics and sustainable decision-making. The review discusses the evolution of AI in agriculture, major remote sensing platforms, explainability frameworks, and the integration of XAI with satellite imagery, unmanned aerial vehicles (UAVs), Internet of Things (IoT), and geospatial big data. Key agricultural applications, including crop classification, yield prediction, disease detection, soil property assessment, irrigation management, carbon monitoring, and climate adaptation, are critically evaluated. Furthermore, the review compares intrinsic and post hoc explainability methods such as attention mechanisms, saliency maps, and counterfactual explanations. The interpretation of model outputs and reported results from recent studies is discussed to demonstrate how XAI improves model reliability and stakeholder confidence. Challenges related to data heterogeneity, scalability, uncertainty, ethics, fairness, and computational complexity are also analyzed. Finally, future perspectives are presented regarding hybrid explainable frameworks, physics-informed AI, edge computing, digital twins, and trustworthy autonomous agricultural systems. The review emphasizes the central role of XAI in enabling transparent and sustainable agricultural intelligence under rapidly changing climatic and environmental conditions. Full article

Although climate-smart agricultural practices are increasingly promoted, comparative environmental and economic assessments across multiple practices and crops remain limited. This study evaluates five climate-smart agricultural practices in Dutch potato and onion production, including soil management, biodiversity enhancement, sustainable irrigation systems, crop protection, and green energy use. It compares them with conventional production systems using integrated Life Cycle Assessment and Life Cycle Costing. Specifically, Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) methodologies were applied to assess the environmental and economic sustainability of the studied systems, respectively. Among the evaluated practices, soil management exhibited the best overall environmental performance for both crops, achieving reductions of up to 42% and 66% in greenhouse gas emissions for potatoes and onions, respectively, compared with the baseline under the modelled conditions. Biodiversity measures significantly reduced freshwater eutrophication and ecotoxicity-related impacts, particularly in potato cultivation, while crop protection practices mainly improved pesticide-related toxicity categories. Similarly, soil management and biodiversity demonstrated the best economic performance, with profits increasing to approximately €3318/ha and €3121/ha for potatoes and €3898/ha and €3694/ha for onions, respectively, compared with baseline profits of €2879/ha and €3526/ha. The results suggest that the implementation of CSA practices can improve both the environmental and economic sustainability of intensive vegetable production systems under the modelled Dutch conditions, although the effectiveness of each practice depends strongly on crop-specific environmental hotspots and management assumptions. The findings provide evidence to support farmers and policymakers in selecting cost-effective climate-smart practices while identifying priorities for future field validation and uncertainty assessment. Full article

The unique production life cycles of perennial crops make them vulnerable to predicted future climate changes. This paper describes how a new framework specific to perennial crops was developed and integrated into an existing spatio-temporal agricultural land sequencer (STALS) to generate real-world land use insights for a case study region, the Murrumbidgee Irrigation Area, Australia. Model outputs illuminated the role of perennials in a water-constrained future and highlighted the benefit of the operational tactic of deficit irrigation in maintaining the feasibility of perennial crops in the mid-to-long-range planning horizon. Furthermore, diversity of life cycle in land use was shown to maintain economically viable agriculture in the study region. The future of perennial crops as a proportion of land use area in a climate-smart landscape may need to be reevaluated. Full article

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