Journal of Agrometeorology

India Would Warm by 3°C or Higher by the End of this Century: How to Cope with It?

2025-12-11T00:42:33+00:00

Climate change has emerged as one of the most crucial challenges of the twenty-first century, with far-reaching consequences for ecosystems, economies, and societies worldwide. In tropical countries such as India, the agricultural activities of working labour which involves higher levels of physical exertion had been badly affected by the summer time heat stress in recent years. For example, February 2025 was India’s hottest in 125 years, with many states breaching 40°C. The unusual warming at several locations in India is expected to increase faster in coming decades and could become vulnerable to physiological acclimatization among the city dwellers as well as farmers in rural areas carrying out activities in outdoor and indoor work places. Other extreme weather events like floods and droughts are also becoming more frequent and intense, disrupting communities and the infrastructure they rely on. Worsening storms and floods have continued to inundate entire cities; crippling droughts parch farmland; and intensifying climate risks threaten water supplies. India seems to be on track for a 3°C rise in temperature or higher over the pre-industrial average by 2100. In addition to preventing further climate change through appropriate mitigation measures such as phasing out use of fossil fuels, and renewable energy and bioenergy generation, it is of fundamental interest to analyse the existing impacts and implement appropriate adaptation measures and strengthen our early warning systems. Managing water sustainably together with building more efficient irrigation systems and better drainage, restoration of forest ecosystems in degraded areas and improve land and forest management through nature-based solutions for increasing food production, seems fundamental to climate resilience in India. A clear and consistent Sector-Specific Policies and Regulations to support NDC Implementation in India with a tailored intervention for high emission sectors is needed.

Performance of the FAO-56 Penman-Monteith Method with Limited Meteorological Data in Eritrea: A Case Study of Halhale

2025-10-21T14:00:50+00:00

Climate Change Impact on Pigeon Pea (Cajanus cajan) Yield in Maharashtra and Karnataka: A Panel Regression Approach

2025-07-26T15:59:06+00:00

Evaluating Tomato productivity using hydrogels in a greenhouse environment in Zimbabwe

2025-07-10T01:44:18+00:00

Reduced Frequency of Soil Moisture Measurements for Cost-effective Irrigation Management in Arid Regions

2025-12-05T05:58:15+00:00

Land Use Land Cover Changes and its Association with Land Surface Temperature over Palakkad District, Kerala

2025-10-06T21:01:08+00:00

Applications of Machine Learning in Agrometeorological Forecasting and Modeling: A Short Review from the Journal of Agrometeorology

2025-12-30T22:05:42+00:00

Photosynthetic Rate, Stomatal Conductance and Yield of Soybean under Optimized Fertilizer Management and Varietal Selection

2025-11-05T04:30:59+00:00

Artificial Intelligence in Agriculture: Techniques and Outcomes

2025-08-12T18:43:28+00:00

Artificial Intelligence (AI) is emerging as a transformative driver of modern agriculture by enabling intelligent, data-driven solutions across crop production, soil and water management, climate forecasting, pest and disease detection, livestock monitoring, and supply chain optimization. The review article systematically addresses and provides answers to the five-research scope of purpose. This review establishes the relevance of AI to current agricultural needs by synthesizing how these technologies align with the demands of precision, sustainability, and resilience. The article highlights the agricultural parameters such as yield, soil health, water resources, and livestock well-being that are being effectively monitored and managed through AI applications. It examines key techniques including machine learning, deep learning, computer vision, and robotics, which underpin advancements in predictive analytics, automation, and decision support. The review evaluates measurable outcomes, including yield improvements, reduced chemical and water use, enhanced energy efficiency, and optimized post-harvest processes. Finally, the study identifies major challenges such as data heterogeneity, affordability barriers, digital literacy gaps, and ethical concerns, while also discussing future prospects for broader and equitable adoption. This review provides actionable insights for researchers, practitioners, education, extension and policymakers, contributing to the development of sustainable and resilient agricultural practices through AI by aligning its findings with this scope of purposes.

Ecological Shifts Under Climate Change: Understanding Pest Responses and Agricultural Vulnerability

2025-11-24T04:57:55+00:00

Climate change profoundly affects agricultural insect pests by altering their biology, distribution, and interactions within agroecosystems, threatening global food security. Rising temperatures, elevated atmospheric CO₂, and shifting precipitation patterns accelerate pest development, expand geographic ranges, and increase voltinism, intensifying crop damage. These shifts disrupt traditional pest management frameworks, as phenological mismatches among pests, host plants, and natural enemies weaken biological control. Moreover, abiotic stresses compromise the performance of biocontrol agents, such as entomopathogenic fungi, necessitating climate-specific strain selection. Adaptive integrated pest management (IPM) strategies that incorporate real-time monitoring, predictive modeling, precision agriculture technologies, and emerging tools such as CRISPR and sterile insect techniques are essential for climate-resilient agriculture. Sustainable approaches that leverage natural products and minimize reliance on chemical pesticides further support ecosystem health. This review synthesizes current knowledge on climate-driven pest dynamics, range expansions, and tritrophic disruptions based on literature searched in Web of Science, Scopus, PubMed, and Google Scholar from January 2000 to November 2025 using Boolean strings. This review proposes a comprehensive climate-adaptive IPM framework to safeguard agricultural productivity amid ongoing environmental change.

Analysis of Temporal and Spatial Variations in Extreme Precipitation over Kerala

2025-09-30T03:51:51+00:00

Kerala, an ecologically sensitive state in southwestern India, is increasingly vulnerable to rainfall-induced disasters such as floods and landslides. This study analysed 124 years (1901–2024) of high-resolution daily rainfall data from the India Meteorological Department (IMD) to examine spatial and temporal trends across Kerala. The analysis assessed changes in rainy days and the frequency of heavy (HRF), very heavy (VHRF), and extremely heavy rainfall (EHRF) events, along with shifts in the onset of the southwest monsoon (SWM) and northeast monsoon (NEM) and rainfall irregularity using the Precipitation Concentration Index (PCI). Results revealed strong spatial heterogeneity: northern Kerala receives higher SWM rainfall (~3000 mm), while southern regions experience more intense rainfall during the NEM and winter seasons. Breakpoint analysis indicated a recent change in NEM rainfall around 2020, with a steep increase in slope from -0.527 to 23.048. High PCI values (11–21) in northern and central-western regions reflect strong rainfall concentration and elevated flood risks. Rainy days and EHRF events increased during the SWM and summer, while declines during the NEM and winter could affect water availability and winter cropping. Long-term projections suggest the SWM may advance toward May and the NEM extend into late October. These changing rainfall dynamics hold significant implications for agriculture, water management, and climate adaptation planning, emphasizing the need for location-specific strategies.

Assessment of Agricultural and Meteorological Drought in Southern Iraq’s Wetlands using Vegetation Condition and Drought Indices

2025-09-23T04:53:40+00:00

The southern Iraq’s wetlands face many challenges that affect their environment and the livelihoods of local communities, including the problem of drought. This study aims to evaluate both meteorological and agricultural drought conditions within the marshland regions over a four-decade span (1984, 1994, 2004, 2014, and 2024). Satellite imagery from the Landsat multispectral scanner (MSS), Thematic mapper (TM), Enhanced thematic mapper (ETM) was employed to derive the vegetation condition index (VCI) to assess agricultural drought and climatic data were used to derive reconnaissance drought index (RDI) to assess meteorological drought. The result showed that the severity of the meteorological droughts increased over the period. In 1984, most of areas were under no drought condition while in 2024, most of the areas were under moderate to severe drought condition. The study also revealed that there was mild meteorological drought in 1994, but the agricultural drought conditions was severe and extreme. Overall, it suggests that the climate change and water scarcity have exacerbated agricultural drought condition in the region.

Assessment of land surface temperature and urban heat island using remote sensing in the Kurdistan region, Iraq

2025-09-16T22:23:33+00:00

Urban heat island (UHI) is a prevalent environmental hazard in modern cities, with higher surface and air temperatures than adjacent rural regions. The current study assessed the spatiotemporal distribution of land surface temperature (LST) in Iraq's Kurdistan region and the existence of urban heat islands during the daytime and at nighttime. The land surface temperature (LST) was composited from 2001 to 2024 using the historical Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra satellite 8. The average LSTs of the rural and arid regions were contrasted with the average LSTs of the urban and suburban areas in three governorates of the study area, namely Erbil, Sulimaniyah, and Duhoke. Daytime and nighttime LST were also compared. The results revealed that the highest values of LST occurred in the urban region of the southern parts of the study area, where the mean value was 32.2 ⁰C during the daytime. During the summer, Erbil had a higher temperature of 49.5 ⁰C, while Sulimaniyah had the lowest (0.98 ⁰C). According to annual data, almost 80% of the study region had an NLST score of 0.6 or 0.7. The biggest difference in LST mean value between urban and suburban regions was recorded in the summer daytime in Erbil city, with a value of 5.1 ⁰C, while the smallest variances were reported in the fall season for all governorates in the study area, reaching 0.01 ⁰C at night in Sulimaniyah city.

Spatiotemporal Analysis of Drought Characteristics in Nineveh, Iraq using the Standardized Precipitation Evapotranspiration Index (SPEI)

2025-08-16T00:19:46+00:00

Combining station observations with bias-corrected gridded climate data is crucial for reliable drought assessment in data-sparse regions. This study investigates the spatiotemporal characteristics of drought in Nineveh, Iraq, using the Standardized Precipitation Evapotranspiration Index at three- and six-month timescales (SPEI03 and SPEI06). Monthly station observations (1992-2013) were used to bias-correct TerraClimate data (2001-2023), which were then utilized to extend the record and compute SPEIs based on precipitation and potential evapotranspiration (PET). Drought frequency, duration, severity, and intensity were quantified, and trends were assessed using the Mann–Kendall test and Sen’s slope estimator. Results show notable interannual variability and a clear shift toward more frequent, severe, and persistent droughts in recent decades. The northern and northeastern areas emerged as drought hotspots, with Tel-Afar station experiencing the longest and most severe events. Comparisons between 2001–2011 and 2012–2023 reveal a marked intensification and expansion of severe and extreme drought zones. Trend analysis confirms widespread declines in moisture availability, especially for SPEI06, indicating increased exposure to prolonged water deficits. These findings highlight substantial spatial heterogeneity and emphasize the need for localized drought adaptation, improved water resource management, and early-warning systems to mitigate escalating risks to agriculture and livelihoods under a changing climate.

Impact of Climate Variability on Maize Yield in Semi-Arid Region of Tamil Nadu, India

2025-10-11T16:00:45+00:00

Climate variability poses serious challenges to productivity and food safety in rain-fed semi-arid areas. A study on the impact of Tmax, Tmin, and precipitation on the yield of maize was performed in Ariyalur and Perambalur districts, Tamil Nadu, using historical data from 1985 to 2020 and future projection data from 2021 to 2100 under the Shared Socioeconomic Pathways-SSP2-4.5 climate change scenario. Climate extremes analysis shows the results that there is an increase in warm nights (TN90P), warm days (TX90p), heavy rainfall events (R10mm, R20mm), and shorter dry spells (CDD), reflecting more heat and extreme rainfall in both districts. Temperature is increasing considerably; Max and Min temperatures are projected to rise by 1.5 to 2°C by 2100. Patterns of precipitation are changing, with more frequent moderate rainfall events of 10-20 mm and fewer dry spells. From Ariyalur, in conditions of a rise in minimum temperature by 1°C, there has been a reduction of up to 38.2% in maize yield, and it explained 20-25% of variability in yield. Perambalur experiences a 21.7% yield reduction per 1°C with less intensity. The model from Ariyalur outperforms the one from Perambalur, adjusted R² being 0.967 and 0.511, respectively, which suggests that local sites have different sensitivities to climate. The findings from the present research signify the urgent need for adaptive strategies, including heat-tolerant varieties of maize, efficient irrigation, and integrated pest management, which could help mitigate climate risks.

Climatological Understanding of Heat and Cold Wave Variability in Eastern Uttar Pradesh

2025-10-26T19:24:26+00:00

This study examines the trends and impacts of heat waves (HWs) and cold waves (CWs) in Eastern Uttar Pradesh, India, from 1961 to 2020, utilizing gridded daily maximum and minimum temperature data from the Indian Meteorological Department. This study analyzes the decadal totals of days, maximum continuous duration days, and mean maximum and minimum temperatures of HWs and CWs across nine meteorological stations. The findings reveal a significant increase in HW occurrences, particularly in stations like Fatehpur and Varanasi, while a decline in CW events is noted across the region. The Excess Heat Factor (EHF) index indicates a rising trend in heat stress events, and this study suggests that the intensity of HWs is increasing due to changes in temperature variability rather than mean warming alone.

Optimizing Wet Season Planting Time for Rice Varieties in Tropical Lowlands Based on Thermal Time and Radiation Use Efficiency

2025-11-15T04:23:04+00:00

Rice production stability is essential to maintain Indonesia’s food security, yet it is increasingly affected by climate variability. This study quantified thermal time as growing degree days (GDD) and radiation use efficiency (RUE) to evaluate rice performance across wet-season planting windows and to identify a suitable planting period for tropical lowland ecosystems. The field investigation was carried out in Sidoarjo, East Java, Indonesia, during the 2023–2024 wet season using three representative varieties: Pandan Wangi, Inpari 32, and Intani 602. Rice was transplanted at three planting periods representing early (November), mid (January), and late (March) wet season planting. The experiment applied a randomized block design with two factors with combine anlyzed. Data were analyzed using analysis of variance and regression. The results indicated that planting time significantly affected all yield components. The hybrid Intani 602 achieved the highest panicle number, grain weight, and grain yield (7.56 to 9.54 t ha⁻¹), demonstrating superior adaptability and physiological performance. Regression analysis showed a significant negative relationship between GDD and grain yield and a positive relationship between RUE and grain yield. The findings emphasize the importance of matching variety selection with planting time to enhance productivity and resilience under tropical climates. Developing suitable agroclimatic-based planting calendars is recommended to support sustainable rice production systems.

Validation of a Simple MODIS Land Surface Temperature-Based Model for Potential Evapotranspiration (PET) using Long-Term Global Dataset

2025-12-04T04:27:42+00:00

Accurate and usable potential evapotranspiration (PET) estimation is important for managing water resources around the world, planning agriculture, and adapting to climate change. Complex energy balance models yield valuable insights; yet practical applications necessitate straightforward, resilient, and simply implementable long-term monitoring methodologies. This study confirms a more straightforward empirical model that estimates monthly PET only utilizing MODIS land surface temperature (LST) data of 25 years (2000–2024), addressing a deficiency in the comprehension of simple model transferability across global climatic regimes. The LST products (MOD11A1/MYD11A1) processed in Google Earth Engine to confirm the accuracy of PET predictions against the FAO-56 Penman–Monteith (FAO-PM) technique, which was based on data from 58 ground-based meteorological stations in 5 Major Köppen–Geiger climate zones. The model was very accurate (R² = 0.76, RMSE = 30.02 mm/month); however, it was completely unique in different areas because of environmental controls. The model worked well in the Continental and Mediterranean climate zones (R² = 0.93, NSE = 0.88), but it had trouble in the Tropical Wet (R² = 0.39, NSE = -6.15) and Polar (R² = 0.64, NSE = -2.64) regions because of the moisture in the air and the complicated way energy is divided. The initial comprehensive analysis of basic LST-based model constraints sets essential standards for operational implementation and underscores the necessity for climate-zone-specific parameterization in this global, long-term validation. The results enhance the comprehension of environmental influences on remote sensing-derived PET estimation and inform water resource management in a dynamic climate.

Prototype Framework for Agricultural Drought Monitoring in Northern Thailand Using Satellite-Based Evaporative Stress Index

2026-01-14T03:55:43+00:00

Agricultural drought threatens crop production in Northern Thailand, where complex terrain and limited meteorological stations restrict effective ground-based monitoring. This study developed a prototype framework for agricultural drought monitoring using the satellite-based Evaporative Stress Index (ESI). Bias-corrected reference evapotranspiration (ETo) from TerraClimate was combined with satellite-derived actual evapotranspiration (ETa) from SSEBop to calculate 10-day ESI values during 2012–2023. A classification system based on consecutive ESI patterns was developed to generate action-oriented maps for emergency response. Temporal analysis revealed persistent agricultural drought from late 2021 through early 2022. Spatial analysis identified significant heterogeneity across the region, revealing localized stress areas that regional averages failed to detect. The consecutive-period classification prioritized areas under constant stress requiring emergency intervention over those experiencing only temporary fluctuations. Overall, the proposed prototype framework provides decision-support capabilities that can be integrated with exposure and resistance factors to guide resource allocation in regions with sparse ground-based monitoring infrastructure.