Accurate wheat yield estimation at the farm scale is crucial for food security, market strategies, trade planning, and storage decisions. However, predicting crop production using remote sensing at farm scale presents significant challenges. This research aimed to develop a field-scale wheat yield prediction model using multi-temporal vegetation indices derived from Sentinel-2 MSI imagery for the rabi seasons of 2018–19 and 2019–20 from Badsu village in Alwar district, Rajasthan. Vegetation indices derived from cloud-free Sentinel-2 images spanning the crop growth cycle were processed to generate multiple vegetation indices, grouped into greenness, chlorophyll content, and dryness indicators. Spearman’s rank correlation (ρ) assessed relationships between indices and wheat yield across various phenological stages and their combinations. Linear and multiple linear regression (MLR) models were developed using the most significant indices. Findings indicate that Wide Dynamic Range Vegetation Index (WDRVI), Normalized Green-Red Difference Index (NGRDI), and Normalized Difference Water Index-2 (NDWI2), representing greenness, chlorophyll, and water stress, respectively, exhibited strong correlations with yield, except during harvesting and crown root initiation. The best-performing model achieved an RMSE of 0.47 tons/ha and an R² of 0.74, demonstrating the effectiveness of remote sensing indices for precise wheat yield estimation at the field level in diverse agricultural Conditions.

Efficient irrigation scheduling is critical for sustaining wheat (Triticum aestivum L.) productivity under water-limited and thermally stressed environments. A two-year field study (Rabi 2022–23 and 2023–24) was conducted at Lovely Professional University, Punjab, to evaluate the impact irrigation scheduling on agrometeorological indices, heat use efficiency (HUE), dry matter accumulation, and yield performance of wheat. The experiment was laid out in a randomized block design with ten irrigation treatments, including soil moisture depletion- and plant stress index (PSI) based schedules, alongside rainfed and recommended irrigation regimes. Results revealed that irrigation at 50% depletion of field capacity significantly enhanced phenological duration, leaf area index, crop growth rate, and relative water content compared with sub-optimal and rainfed treatments. The highest grain (5.99 t ha⁻¹) and straw yields (7.58 t ha⁻¹) were recorded under 50% FC depletion, followed closely by 0.50 PSI and 30% FC depletion. Heat and heliothermal use efficiencies were also superior in these treatments, underscoring the importance of maintaining adequate soil moisture during critical growth stages. The findings demonstrate that thermal indices can serve as reliable predictors of wheat growth and yield, while precise irrigation scheduling is essential for enhancing resource use efficiency and mitigating climate-induced yield losses.

Climate change is altering global temperature and precipitation patterns, endangering food security, water resources, and socio-economic stability worldwide (Nicholson, 2017;Tierney et al., 2015).These changes drive more frequent and severe extreme weather events, such as erratic rainfall and prolonged droughts, which undermine agriculture and livelihoods, particularly in regions with limited adaptive capacity (Funk et al., 2015;Ongoma et al., 2018).Africa is especially vulnerable due to its reliance on rain-fed farming and inadequate infrastructure, facing heightened risks due to their semi-arid climate, rising temperatures, and unpredictable rainfall (Gebrechorkos et al., 2019;Rowhani et al., 2011).Agriculture, water, and food security are highly vulnerable to shifts in rainfall and temperature.Increasing droughts and extreme rains disrupt livelihoods, highlighting the need for long-term climate studies to guide adaptation.Dependence on rain-fed farming, socioeconomic constraints, and limited research make this zone critical for resilience strategies.In Tanzania, where agriculture employs over 70% of the population, declining rainfall and intensifying extremes exacerbate food insecurity, particularly in these semi-arid regions (Craparo et al., 2015;Rowhani et al., 2011).Various studies have been reported on trend analysis of rainfall and temperature across the globe using parametric and nonparametric approaches including innovative trend analysis (Khan et al., 2024;Yewale and Jadhav, 2025;Abdulfattah et al., 2025).While climate research in East Africa has grown, central Tanzania remains underexplored, particularly in assessing long-term climate trends critical for agriculture and water management (Nicholson, 2017;Yang et al., 2014).By focusing on these regions, this study addresses this gap, offering robust insights into climate trends.The findings aim to inform agricultural policy, enhance water resource management, and strengthen disaster risk reduction, fostering resilience in one of Tanzania's most climate-sensitive regions (

Rising greenhouse gas emissions have intensified global warming and climate change, leading to higher temperatures and irregular rainfall that directly affect agricultural productivity-the sector most vulnerable to climate impacts (Trinh, 2018).Thailand is among the most climate-vulnerable nations (Germanwatch, 2021).Over the past four decades , temperatures have risen substantially, with the northern, northeastern, central, and eastern regions experiencing more rapid warming than the southern region, although there have been no substantial differences in rainfall changes.Current climate variability threatens Thailand's rain-fed agriculture, especially major crops such as rubber, rice, maize, sugarcane, and cassava, with each requiring specific water and temperature conditions that may shift under changing climates (Sdoodee, 2013).Research studies on climate change and rubber production have been reported from different countries, including Nigeria (Mesike and Esekhade, 2014), India (Raj and Dey, 2004), Indonesia (Prasada et al., 2021), and China (Shao-jun et al., 2020).However, published research in Thailand remains limited, with most studies concentrating on the southern region.For example, Thaiburi et al., (2021), using 1989-2019 panel data, reported that both rainfall and temperature had negatively affected yields.Makkaew and Sdoodee (2015) reported that increased rainfall days in Songkhla province had reduced rubber tapping opportunities.Thaiburi (2022) analyzed data from 2010 to 2020 across five southern provinces of Thailand, further confirming that temperature, rainfall, and their variability substantially influenced rubber production.Limsakul and Paengkaew (2014) demonstrated that rubber yield in southern coastal provinces was closely linked to ENSO, with higher yields during El Nio and lower yields during La Nia, reflecting the importance of rainfall intensity and frequency.The present study investigated the influence of climatic factors on rubber yields across the three key rubber-cultivation regions (southern, eastern, northeastern) of Thailand.Given the growing disruptions to rainfall patterns caused by climate change, the analysis focused on rainfall variability, with temperature included as a control variable, under the hypothesis that rainfall exerts a stronger influence on rubber yields than temperature.The study's findings should provide valuable insights for policymakers in formulating strategies to support farmers and improve water management practices, thereby enhancing the resilience of Thailand's rubber sector to climate variability.

Changing climate has become one of the major perils for agriculture throughout the world. Rice is one of the most important food grains in India. In this study, the effect of climatic factors on rice production of different agroclimatic zones and districts of West Bengal, India, has been assessed for the period of 1996-2019. Mann-Kendall test was used to figure out trend, Sen’s slope estimator to figure out the degree of change, Pettitt’s test to detect change point in the data, and finally, multiple linear regression to understand the relationship between climatic factors and rice yield. The results reveal that, in general, the rainfall is decreasing. Area is decreasing, and yield is increasing significantly in most cases. Production is increasing, but not significantly everywhere. In West Bengal and specifically in the Vindhyan Alluvial Zone, climatic factors have a significant impact on yield. Bankura is the only district in West Bengal without a significant increase in yield due to a lack of irrigation facilities and other non-environmental factors.

This paper reviews how the Internet of Things (IoT) is transforming agriculture into a data-driven, technology-enabled sector. IoT applications in farming include soil and weather monitoring, precision irrigation, nutrient management, crop health surveillance, and post-harvest supply chain traceability. By integrating field-deployed sensors, drones, wireless networks, and cloud-based analytics, farmers can continuously track soil moisture, nutrient content, crop growth, and microclimate conditions. These insights enable real-time decision-making that improves resource-use efficiency, reduces input waste, and minimizes environmental impacts. IoT-based automation also allows remote control of pumps, fertigation systems, and spraying equipment, further enhancing labor productivity and operational sustainability. Despite these benefits, adoption remains constrained by high initial costs, limited rural connectivity, device interoperability issues, and data security concerns. Future research and policy efforts must focus on developing affordable, interoperable solutions, strengthening rural digital infrastructure, and integrating IoT with emerging technologies such as artificial intelligence and machine learning to achieve scalable, climate-resilient agriculture.

Baghdad, one of Iraq’s most crowded cities, faces severe air pollution caused by rapid population growth, dense traffic, and limited green spaces. Monitoring at five sites in Al-Rusafa during 2024–2025 showed that pollutant levels, especially PM₁₀, PM₂.₅, and TSP, exceed national and global limits. The most polluted areas lacked vegetation and had heavy traffic, while greener zones showed lower concentrations. Seasonal variations were evident: winter had the highest pollution, summer the lowest but with greater plant stress. Ascorbic acid and the Air Pollution Tolerance Index (APTI) proved reliable indicators of plant resistance. Overall, the study confirms plants’ role as effective bio monitors and stresses the need for pollution control in Baghdad’s urban areas.