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.

Soybean (Glycine max L. Merrill) is a globally important grain legume that occupies a unique position between pulses and oilseeds due to its dual role as a protein-and oil-rich crop.Achieving high and stable productivity in soybean depends largely on effective crop management practices that regulate growth, physiology and yield formation.Crop growth is governed by the total photosynthate produced by source organs and the efficiency with which assimilates are partitioned to reproductive sinks (Mohanty et al., 2017).Consequently, appropriate fertilizer management plays a critical role in enhancing growth and productivity by improving photosynthetic efficiency and assimilate allocation.Numerous studies have demonstrated that fertilizer application significantly influences plant physiological functioning and yield enhancement in major commercial crops such as maize, wheat and rice (Scharf et al., 2002;Jiang et al., 2004;Stewart et al., 2005).In soybean, optimized nutrient management plays a critical role in enhancing physiological efficiency by improving nutrient availability, leaf area development, chlorophyll synthesis and enzymatic activity, thereby sustaining higher photosynthetic rates and improved plant water relations.In addition, the successful development and adoption of superior varieties are fundamental to achieving sustained increases in crop yield and grain quality (Lv et al., 2020).Varietal differences in nutrient uptake efficiency, stress tolerance and physiological adaptability further influence crop responses to fertilizer regimes under varying environmental conditions.Hence, integrating appropriate fertilizer management with suitable varietal selection is essential to exploit the physiological potential of soybean for enhanced productivity.The field experiment was conducted during the kharif season of 2024-25 at the Experimental Farm of the Department of Meteorology, College of Agriculture, Vasantrao Naik Marathwada Krishi Vidyapeeth (VNMKV), Parbhani.The experiment was laid out in a split-plot design with three replications.Fertilizer management levels constituted the main-plot treatments, comprising 100% recommended dose of fertilizer (RDF), 75% RDF, 50% RDF and vermicompost at 5 t ha, while three soybean varieties (MAUS 725, MAUS 612 and MAUS 158) were assigned to the subplots.Sowing was carried out at a spacing of 45 cm 5 cm using a seed rate of 70 kg ha during the 26 th Standard Meteorological Week (SMW) of 2024 (1 st July 2024).Grain and straw yields were recorded in kg ha, while key gas exchange parameters, including photosynthetic rate (mol m s) and stomatal conductance (mmol m s), were measured periodically at six distinct phenological stages using a portable infrared gas analyser (IRGA).All collected data were subjected to analysis of variance (ANOVA) appropriate for a split-plot design.Treatment means were compared using the least significant difference (LSD) test at the 5% level of significance. Effect on grain and straw yieldThe effect of fertilizer management treatments and soybean varieties on grain and straw yield of soybean, (Table 1) was statistically significant.Among the fertilizer management treatments, application of 100% RDF recorded the highest grain

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.

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.

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.

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.

The FAO56-Penman-Monteith (FAO56-PM) method is widely used to calculate crop water requirements (Sharma & Changade, 2025).It also serves as a standard benchmark for evaluating alternative ETo methods in data-scarce regions (Ghebretnsae et al., 2025).However, its requirement for comprehensive climate datasets is a significant constraint in countries like Eritrea, where meteorological stations often lack complete records.For such datalimited contexts, researchers are strongly recommended to use the FAO56-PM method with estimated missing variables or the Hargreaves-Samani equation as reliable alternatives (Allen et al., 1998).Research has shown that the relative importance of climatic variables for ETo estimation is region-specific, influenced by the local climate regime, geographic location, season, and other factors.Wind speed was found to have less impact on the accuracy of ET o estimates particularly in humid climates of China (Gong et al., 2006), semiarid climates of Tunisia (Jabloun & Sahli, 2013), and semi-arid part of Manitoba (Ndulue & Ranjan, 2021).Contrarily, wind speed was a major source of error in less humid climate and windy areas of Cte d'Ivoire (Koudahe et al., 2018).Solar radiation and relative humidity were more important than wind speed for an accurate PM-ET o calculation (Gong et al., 2006).Ndulue and Ranjan (2021) works showed that the effect of solar radiation on ETo estimates is higher than that of wind speed and relative humidity in tropical sub-humid of Brazil.Solar radiation worked poorly for humid conditions but yielded quite good results for semiarid conditions of Cte d'Ivoire (Koudahe et al., 2018).Gong et al. (2006) concluded that the accuracy rank of these three climate variables in terms of FAO56-PM ETo estimates differ from region to region.Thus, identifying a proper ETo estimation model in climate data-limiting agricultural area is crucial.The southern Central Highlands (CHLs) of Eritrea have a favorable climate and significant agricultural potential.To support the shift from traditional to commercial farming, the region is prioritizing efficient water use.Accurate estimation of reference evapotranspiration (ETo) is crucial for this, but is hindered by scarce climate data.This study addresses this gap by evaluating the performance of the FAO-56 Penman-Monteith and Hargreaves-Samani ETo methods under Eritrea's data-scarce conditions.The FAO56-PM method was evaluated using limited data from the Halhale manual meteorological station in Eritrea.The station (15.060N, 38.50 W, 1917 m AMSL), located in the country's southern CHLs, complies with FAO agrometeorological recommendations.Over 90% of the area's rainfall occurs in summer (June to August), coinciding with the northernmost movement of the Intertropical Convergence Zone.Data collection and processing followed Allen et al. (1998).Mean monthly values for maximum and minimum temperature, relative humidity, wind speed at 2m, and sunshine hours were used.After quality control for missing data and outliers, mean monthly reference evapotranspiration (ETo) was determined.According to Allen et al. (1998), ETo calculated from mean monthly data is very similar to the average of daily ETo values.