Search Results (144)

The agricultural sector faces increasing vulnerability to climate change, necessitating effective adaptation measures to maintain productivity and enhance system resilience. Despite this critical need, limited studies explore the factors influencing farmers’ adaptive responses within specific climatic zones. This study aimed to identify adaptation measures essential for agricultural sustainability in the three purposively selected Grama Niladari divisions (GNDs) known for their diverse crop varieties in the Maho Agrarian Zone, a region characterised by the Maha (Northeast Monsoon) and Yala (Southwest Monsoon) agricultural seasons. A mixed-methods descriptive research design, integrating quantitative surveys and qualitative focus group data, was employed. The findings reveal a highly experienced farming community: 34.9% of farmers have over 30 years of farming experience. A total of 96.7% of farmers reported noticing changes, including a shift in seasons (over 80%) and unpredictable rainfall patterns (53%). A vast majority (62.8%) of farmers lack access to agricultural insurance, leaving them financially exposed to crop losses. Furthermore, while younger and middle-aged groups demonstrated the highest awareness of climate-smart agriculture (CSA), there is a strong, consistent perception across all age groups that government and associated institutions are not providing sufficient support for adaptation efforts. The results offer actionable recommendations for empowering local planning authorities, optimising climate communication strategies, and prioritising the development of practical CSA training modules, ultimately synthesising local knowledge with expert insight to support global resilience-building initiatives. Full article

Reliable estimation of rice yield is essential for food security planning, climate-resilient agriculture, and informed policy decisions. This review synthesizes recent research on the integration of remote sensing and crop simulation models for rice yield estimation. The analysis shows that optical and Synthetic Aperture Radar (SAR) data are the most commonly used remote sensing sources, with SAR proving especially valuable in monsoon-affected regions due to its ability to provide consistent observations under cloud cover. Among crop simulation models, DSSAT, APSIM, ORYZA, and WOFOST are most frequently applied, either independently or in combination with satellite-derived information. Across the reviewed studies, integrated approaches, particularly those using data assimilation and hybrid modeling, consistently achieve higher accuracy and better spatial representation of yield compared to standalone remote sensing or crop model methods. Despite these advances, limitations related to data availability, model calibration, scale mismatches, and climate-induced uncertainty remain significant. Based on the reviewed evidence, future efforts should focus on developing practical hybrid frameworks, improving multi-sensor data fusion, and designing scalable systems suited to data-limited regions. Overall, integrating remote sensing with crop simulation models offers a robust pathway for improving rice yield forecasting and supporting climate-adaptive agricultural management. Full article

Accurate and timely mapping of paddy rice is essential for agricultural management, food security, and climate-resilient policy. However, high-precision mapping remains challenging in subtropical monsoon regions due to persistent cloud cover, long revisit intervals, and striping noise, which compromise satellite data quality and availability. To address these limitations, a rice mapping framework suitable for different geographical environments was developed based on a random forest (RF) by combining time-series harmonic analysis (HANTS) with Sentinel-1 and Sentinel-2 multi-source data. To address these limitations, a rice mapping classification algorithm for different geographical environments was developed by combining Harmonic Analysis of Time Series (HANTS) with Sentinel-1/2 multi-source data. The research obtained annual maps of single-season and double-season rice in the research area from 2019 to 2024, with a spatial resolution of 10 m. The results indicated that the Sentinel-1, Sentinel-2, GEE, and HANTS algorithm can effectively support the yearly mapping of single- and double-season paddy rice in Anhui Province, China. The resultant paddy rice map has a high accuracy with overall accuracies exceeding 92% and Kappa coefficients above 0.84. HANTS effectively captures key phenological features of paddy rice, and it can especially enhance the discrimination between single- and double-season rice; compared to existing rice mapping products, the proposed approach reduces classification errors by an average of 3.92% in six major rice-producing cities, each with cultivation areas exceeding 1 million hectares; spatial correlation analysis indicates substantial heterogeneity in rice cultivation patterns across northern, central, and southern Anhui, associated with both biophysical and anthropogenic factors. These results indicate that integrating phenological data with machine learning can enhance the accuracy of long-term, high-resolution crop monitoring, and annual rice maps will offer valuable support for food security assessment, water resource management, and policy planning. Full article

Agrivoltaics (APV) offers a promising dual land-use solution for food and energy production, yet empirical data regarding its impact on leguminous crops in tropical monsoon climates remain limited. This study evaluated the microclimate, growth, and yield of soybean (Glycine max) under an APV system compared to an open-field control during the wet and dry seasons in Bogor, Indonesia. The APV structure reduced incident solar radiation by approximately 35%, significantly lowering soil temperatures and maintaining higher soil moisture across both seasons. In the wet season, the APV treatment significantly increased grain yield (3528.8 vs. 1708.3 kg ha⁻¹, +106%) relative to the open field by mitigating excessive heat and radiative loads, which enhanced pod retention. In the dry season, APV maintained a yield advantage (2025.6 vs. 1724.4 kg ha⁻¹, +17%), driven by improved water conservation and a higher harvest index. Notably, shading did not delay phenological development or hinder vegetative growth in either season. These findings demonstrate that APV systems can contribute to sustainably higher yields and stability in tropical environments by buffering against season-specific environmental stresses, suggesting a viable pathway for sustainable agricultural intensification in equatorial regions. Full article

Machine learning (ML) has emerged as a practical approach to forecasting crop yields in climate-vulnerable, rainfed agricultural systems where production uncertainty is strongly influenced by monsoon variability. In India’s semi-arid and sub-humid regions, reliable yield forecasts are critical for agribusiness planning and managing climate risks. This study presents a standardized evaluation of three widely used ML forecasting models—Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR)—for rainfed cereal yields in eight Indian administrative divisions from 2000 to 2025. The study applied a unified methodological framework that included data cleaning, z-score normalization, domain-informed feature selection, strict chronological train–test splitting, and five-fold cross-validation. The dataset integrates agroclimatic and soil variables, including temperature, precipitation, relative humidity, wind speed, and soil pH, comprising approximately 1250 division-year observations. Model performance was assessed on an independent, temporally held-out test set using root mean square error (RMSE), mean absolute error (MAE), and R². The results show that RF provides the most robust predictive performance under realistic forecasting conditions. It achieved the lowest RMSE (0.268 t/ha) and the highest R² (0.271), outperforming LR and SVR. Although the explained variance is modest, it reflects strict temporal validation and the inherent uncertainty of rainfed systems. Feature importance analysis highlights temperature and precipitation as dominant yield drivers. Overall, this study establishes a conservative and reproducible baseline for operational machine learning (ML)-based yield forecasting in precision agribusiness. Full article

Drought remains one of the most damaging natural hazards to agricultural production and is projected to continue posing substantial risks to food security in the future, particularly in major rice-growing regions. Based on the RCP4.5 and RCP8.5 scenarios under CMIP5, this study used a process-based crop growth model to simulate the growth of rice in China in different future periods (short-term (2031–2050), medium-term (2051–2070), and long-term (2071–2090)). We fitted rice vulnerability curves to evaluate the rice drought risk quantitatively according to the simulated water stress (WS) and yield. The results showed that the drought hazard in major rice-growing areas in China (MRAC) were low in the middle and high in the north and south. The areas without rice yield loss will decline in the future, while the areas with a high yield loss will increase, especially in southwestern China and the middle and lower Yangtze Plain (MLYP). Owing to the markedly increased evaporative demand and the reduced moisture transport caused by a weakening East Asian summer monsoon, northeastern China will be a high-risk area in the future, with the expected yield loss rates in scenarios RCP4.5 and RCP8.5 being 39.75% and 45.5%, respectively. In addition, under the RCP8.5 scenario, the yield loss rate of different return periods in south China will exceed 80%. A significant gap between rice supply and demand affected by drought is expected in the short-term future. The gaps will be 67,770 kt and 78,110 kt under the RCP4.5-SSP2 and RCP8.5-SSP3 scenarios, respectively. The methodology developed in this paper can support the quantitative assessment of drought loss risk in different scenarios using crop growth models. In the context of the future expansion of Chinese grain demand, this study can serve as a reference to improve the capacity for regional drought risk prevention and ensure regional food security. Full article

Ensuring food security in Pakistan, particularly for rice production, is a critical challenge due to increasing population demands and the growing impact of climate change variability. Accurate estimation of rice crop yields is essential for optimizing resource allocation, managing supply chains, and forecasting economic growth while minimizing agricultural losses. This study utilizes a Deep Neural Network (DNN) to predict rice yields in Pakistan by analyzing the effects of maximum temperature and precipitation trends under high-emission scenarios (SSP5-8.5) derived from CMIP6 climate models. Historical (1980–2014) and future (2015–2100) climate projections were evaluated using key variables, including precipitation, meteorological conditions, cultivated area, and crop yields. Results from CMIP6 SSP5-8.5 indicate a significant rise in maximum temperatures and increased precipitation variability, exacerbating risks to rice crop yields. DNN demonstrated superior accuracy in forecasting these trends, achieving high R-squared values and low error metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings reveal that Pakistan, particularly Eastern South Asia, is highly vulnerable to climate extremes, with severe implications for rice production and agricultural sustainability. These results highlight the urgent need for policymakers to adopt climate adaptation strategies, including advanced predictive modeling and resilient agricultural practices, to safeguard rice production and ensure long-term food security in Pakistan’s monsoon-dependent regions. This study aligns with Sustainable Development Goal 2 (Zero Hunger) by contributing to food security and sustainable agricultural development, and with Sustainable Development Goal 13 (Climate Action) by addressing climate change impacts on agriculture and promoting resilience in rice production systems. Full article

Rainfall deficits and erratic dry spells pose major challenges in rainfed ecosystem. In-situ moisture conservation practices (MCP) like ridge–furrow methods, improve soil moisture but are inadequate during 2–3 week dry spells at critical crop stages (flowering and maturity), leading to yield loss. Supplemental irrigation (SI) using an ex-situ rainwater harvesting (RWH) pond can mitigate these effects, but optimizing the pond design is challenging due to limited runoff and storage losses. This study aims to design RWH pond for small farm holders with a 1.0 ha area and evaluate its efficient use for SI during intermittent dry spells and critical crop stages. The design volume was estimated using the SCS-CN method based on daily rainfall data (1974–2010) for the northeast monsoon. A pond with a capacity of 487.5 m³, constructed for a 1 ha micro-watershed, was used to observe the runoff for design validation. The harvested runoff can be used as SI for a cultivable area of 0.4 ha, based on the watershed-to-cultivable area ratio. Statistical analysis of observed and estimated runoff data from 2011 to 2023 revealed a strong correlation (r = 0.87), confirming the pond design. Harvested rainwater, applied through micro-irrigation (rain gun) at a depth of 50 mm during moisture stress periods, significantly improved cotton productivity. The combined use of harvested rainwater and MCP increased yield in the range of 3.8 to 25.3%, improved rainwater use efficiency (1.52 to 3.13 kg ha⁻¹ mm⁻¹), and had a higher benefit-cost ratio (1.15 to 2.43) over a 13-year period. This study concludes that integrating in-situ MCP with ex-situ RWH with micro-irrigation significantly improves rainfed crop productivity in vertisols. Full article

This study analyzes winds over the Red Sea (17 N, 39.5 E) and consequences for the northeast African climate in early summer (May–July). As the Indian SW monsoon commences, NNW winds > 6 m/s are channeled over the Red Sea between 2000 m highlands, forming a low-level jet. Although sea surface temperatures of 30C instill evaporation of 8 mm/day and surface humidity of 20 g/kg, the air mass above the marine layer is dry and dusty (6 g/kg, 100 µg/m³). Land–sea temperature gradients drive afternoon sea breezes and orographic rainfall (~4 mm/day) that accumulate soil moisture in support of short-cycle crops such as teff. Statistical analyses of satellite and reanalysis datasets are employed to reveal the mesoscale structure and temporal response of NE African climate to marine winds via air chemistry data alongside the meteorological elements. The annual cycle of dewpoint temperature often declines from 12C to 4C during the Indian SW monsoon onset, followed by dusty NNW winds over the Red Sea. Consequences of a 14 m/s wind surge in June 2015 are documented via analysis of satellite and meteorological products. Moist convection was stunted, according to Cloudsat reflectivity, creating a dry-east/moist-west gradient over NE Africa (13–14.5 N, 38.5–40 E). Diurnal cycles are studied via hourly data and reveal little change for advected dust and moisture but large amplitude for local heat fluxes. Inter-annual fluctuations of early summer rainfall depend on airflows from the Red Sea in response to regional gradients in air pressure and temperature and the SW monsoon over the Arabian Sea. Lag correlation suggests that stronger NNW winds herald the onset of Pacific El Nino. Full article

The objective of this study is to determine how easterly jets and associated convections interact over tropical North Africa during the Jul–Sep season, using reanalysis and satellite datasets for 1990–2024. Four indices are formed to describe mid- and upper-level zonal winds, and moist convection over the Sahel and India. Time-space regression identifies the large-scale features modulating the easterly jets. Cumulative departures are analyzed and ranked to form composites in east wind/convective phases and weak wind/subsident phases. The upper-level tropical easterly jet accelerates over the Arabian Sea during and after Pacific La Nina and the cool-west Indian Ocean dipole, and shows four year cycling aligned with thermocline oscillations. The mid-level Africa easterly jet strengthens during Atlantic Nino conditions that enhance the Sahel’s convection in the Jul–Sep season. Both jets accelerate when convection spreads west of India, whereas brief spells of decoupling suppress North African crop yields. The case of 15–20 August 2018 is analyzed, when a surge of Indian monsoon convection and tropical easterly jet penetrated the Sahel, leading to widespread uplift and rainfall. Full article

Groundwater is essential for irrigated agriculture, yet its use remains unsustainable in many regions worldwide. In countries like Pakistan, the situation is particularly pressing. The irrigated agriculture of Pakistan heavily relies on groundwater resources owing to limited canal-water availability. The groundwater quality in the region ranges from good to poor, with the lower-quality water adversely affecting soil structure and plant health, leading to reduced agricultural productivity. The delineation of quality zones with respect to irrigation parameters is thus crucial for optimizing its sustainable use and management. Therefore, this research study was carried out in the Lower Chenab Canal (LCC) irrigation system to assess the spatial distribution of groundwater quality. The geostatistical analysis was conducted using Gamma Design Software (GS+) and the Kriging interpolation method was applied within a Geographic Information System (GIS) framework to generate groundwater-quality maps. Semivariogram models were evaluated for major irrigation parameters such as electrical conductivity (EC), residual sodium carbonate (RSC), and sodium adsorption ratio (SAR) to identify the best fit for various Ordinary Kriging models. The spherical semivariogram model was the best fit for EC, while the exponential model best suited SAR and RSC. Overlay analysis was performed to produce combined water-quality maps. During the pre-monsoon season, 17.83% of the LCC area demonstrated good irrigation quality, while 42.84% showed marginal quality, and 39.33% was deemed unsuitable for irrigation. In the post-monsoon season, 17.30% of the area had good irrigation quality, 44.53% exhibited marginal quality, and 38.17% was unsuitable for irrigation. The study revealed that Electrical Conductivity (EC) was the primary factor affecting water quality, contributing to 71% of marginal and unsuitable conditions. In comparison, the Sodium Adsorption Ratio (SAR) accounted for 38% and Residual Sodium Carbonate (RSC) contributed 45%. Therefore, it is recommended that groundwater in unsuitable zones be subjected to artificial recharge methods and salt-tolerated crops to enhance its suitability for agricultural applications. Full article

Sowing date significantly affects plant growth, development, and yield, holding a crucial role in soybean cultivation. This study was conducted in the southern coastal region of Korea under recent climate change conditions to investigate the effects of five different sowing dates on climatic characteristics, growth, and yield. Compared to historical data, the southern coastal region has experienced a consistent increase in average temperature during the soybean cultivation period, along with frequent abnormal summer climate events such as concentrated heavy rainfall and monsoons. These climate changes prolonged the vegetative growth period in earlier sowings, leading to an increased risk of lodging at maturity due to vigorous vegetative growth. Furthermore, earlier sowing delayed flowering and exposed plants to longer post-flowering photoperiods, consequently reducing the number of pods. Therefore, in the southern coastal region of Korea, it is crucial to re-evaluate conventional sowing practices and establish region-specific optimal dates, with careful consideration given to postponing the soybean sowing date to late June in order to enhance yield stability and improve the feasibility of double-cropping systems by shortening the growing period. Full article

A comparison of evapotranspiration between seasonally frozen and permafrost soils has important theoretical value for studying land surface processes and ecological environmental evolution on the Tibetan Plateau. In this work, the actual (ET_a) and reference (ET₀) evapotranspiration and crop coefficient (K_c) were calculated via eddy covariance data and meteorological gradient data from sites in the Naqu Prefecture and Tanggula Mountains. The variations, differences, and factors influencing the ET_a and ET₀ were analysed. The results revealed that at the two sites in 2008, the annual total ET_a values were 493.53 and 585.17 mm, which accounted for 83.58% and 144.39% of the total annual rainfall, respectively. The ET_a at the Naqu site was affected mainly by the Tibetan Plateau monsoon (TPM), whereas the ET_a at the Tanggula site was strongly affected by both the TPM and the freezing–thawing processes of the permafrost. The annual total ET₀ values at the two sites were 819.95 and 673.15 mm, respectively. The monthly total ET₀ at the Naqu site was greater than that at the Tanggula site. The ET_a and ET₀ values at the two sites were low in winter–spring, high in summer–autumn, and concentrated from May to October. When snow was present, the ET_a values at the Naqu site were relatively high, and the ET₀ values at both sites were very small and even negative at the Naqu site. The ET_a and ET₀ values at the two sites were significantly positively correlated with the net radiation (R_n), surface temperature (T₀), air temperature (T_a), water vapour pressure (e) and soil water content (smc), and negatively correlated with the wind speed (ws). The correlation between the ET_a and the T₀ at the Naqu site was the most significant, and the coefficient of partial correlation was 0.812; meanwhile, the correlation between the ET_a and the smc at the Tanggula site was the most significant, and the coefficient of partial correlation was 0.791. The R_n at the Naqu and Tanggula sites both had greater impacts on the ET₀. Full article

Laterite is the predominant zonal soil in China’s southernmost tropical rainforest and monsoon forest regions, where typhoons are the primary source of precipitation. These storms pose significant risks of land and soil degradation due to heavy rainfall. In recent years, a substantial area of sloping land has been converted to agricultural use in these regions, predominantly for the cultivation of crops grown in laterite soil. These activities contribute to soil erosion, exacerbate environmental challenges, and hinder the pursuit of sustainable development. There is a paucity of research reports on the processes and mechanisms of runoff and sediment on sugarcane-cropped slopes in regions with laterite soil under heavy rainfall conditions. In this study, four different heavy rainfall scenarios of 75, 100, 125, and 150 mm/h were designed to assess the impact on sugarcane growth at four key stages and to measure the resulting effects on initial runoff time, surface runoff, and sediment yield from laterite soil slopes under controlled laboratory conditions. The results showed that the Horton model explained much of the variation in infiltration rate on the sugarcane-cropped laterite slopes. The cumulative sediment yield on the sugarcane-cropped laterite slopes followed a second-degree polynomial function. The initial runoff time, infiltration intensity, runoff intensity, and sediment yield were all linearly related to the leaf area index (LAI) and rainfall intensity on the sugarcane-cropped slope surface. The leaf area index exerted a greater influence on the initial runoff time and infiltration intensity than rainfall intensity. However, rainfall intensity exerted a greater influence on the runoff intensity and sediment yield than the leaf area index. Compared with the bare sloping land, the average sediment yield was reduced by 12.2, 33.1, 58.2, and 64.9% with the sugarcane growth stages of seedling, tillering, elongation, and maturity, respectively. Full article

Heavy rainfall significantly impacts agriculture by damaging crops and causing substantial economic losses. The Paravanar River Basin, a coastal river basin in India, experiences heavy rainfall during the monsoon season. This study analyzed both ground-level rainfall measurements and farmers’ experiences to understand the effects of heavy rainfall on agriculture. Rainfall data from nine rain gauge locations were analyzed across three cropping seasons: Kharif 1 (June to August), Kharif 2 (September to November), and Rabi (December to May). To determine the frequency of heavy rainfall events, a detailed analysis was conducted based on the standards set by the India Meteorological Department (IMD). Villages near stations showing increasing rainfall trends and a higher frequency of heavy rainfall events were classified as vulnerable. The primary crops cultivated in these vulnerable areas were identified through a questionnaire survey with local farmers. A detailed analysis of these crops was conducted to determine the cropping season most affected by heavy rainfall events. The impacts of heavy rainfall on the primary crops were assessed using the Delphi technique, a score-based crop risk assessment method. These impacts were categorized into eight distinct types. Among them, yield reduction, waterlogging, crop damage, soil erosion, and crop failure emerged as the most significant challenges in the study area. Additional impacts included nutrient loss, disrupted microbial activity, and disease outbreaks. Based on this evaluation, risks were classified into five categories: low risk, moderate risk, high risk, very high risk, and extreme risk. This categorization offers a framework for understanding potential consequences and making informed decisions. To address these challenges, the study recommended mitigation measures such as crop management, soil management, and drainage management. Farmers were also encouraged to conduct a cause-and-effect analysis. This bottom-up approach raised awareness among farmers and provided practical solutions to reduce crop losses and mitigate the effects of heavy rainfall. Full article