The field experiments were conducted during 2019-20 and 2021-22 to study the effect of soil hydrothermal regimes under different mulching materials on growth and yield of the tomato. It was planted on four dates (25-October, 14-November, 3-December and 8-January) with the three mulch treatments, i.e., non-mulch (M0), rice straw (M1), and black polythene (M2). The results revealed that the crop planted on 14th November had highest yield under all mulch treatments. The yield under black polythene (392.6 q ha-1) increased by 47.8% than the non-mulch (265.7 q ha-1) treatment. Based on the findings from the first crop season, a mid-term correction was implemented in the 2021–22 field experiment by planting tomatoes at the optimum time (14 November) and applying four mulch treatments, including the three used in the previous year along with an additional transparent polythene mulch (M3). Across seasons, mulching significantly altered soil temperature and moisture regimes during the critical growth period, thereby influencing the growth and yield of the crop. The highest and statistically at par yields were recorded under M2 (370.6 q ha-1) and M3 (374.6 q ha-1), which decreased significantly under M1 (336.1 q ha-1) and M0 (249.3 q ha-1). Polythene mulches considerably increased the weekly mean night soil temperature (by up to 1.84°C in 2019–20 and 5.1°C in 2021–22), which alleviated the adverse effects of prolonged exposure to sub-optimal (<10°C) night temperatures and contributed to higher yields of tomato under black and transparent polythene mulches (M2 and M3).
The Iraqi Marshlands are among the most important wetland ecosystems in the Middle East, due to their significant environmental, economic, and social role. However, in recent decades, they have faced increasing challenges resulting from climate change and human activities, which have directly impacted their water resources. This study aims to assess the hydrological drought and vegetation cover trend in the Iraqi Marshlands using remote sensing data, focusing on the Land Surface Water Index (LSWI) and the Normalized Difference Vegetation (NDVI) extracted from Sentinel 2 and Landsat satellite images for the period 1984–2024. The results showed a significant decline in water surface and vegetation cover area over the studied period, with severe droughts recorded in the last two decades. This reflects the interaction between climate change and human-induced pressures, such as dam construction and reduced water supplies. These results highlight the seriousness of the ongoing hydrological drought and its impact on the marsh ecosystem, and underscore the need to adopt integrated water resource management strategies and develop long-term environmental monitoring programs using remote sensing techniques.
With the dramatically increasing global urbanization, cities have evolved as a leading Greenhouse gases (GHG) emissions contributor. In this regard, urban parks are important carbon sinks as trees absorb Carbon Dioxide (CO2) from the atmosphere through photosynthesis that helps in achieving climate sustainability. The current study aimed to evaluate the sequestered CO2 by urban trees in a park in Baghdad called Al- Umma Park. The estimate was based on field measurements of each tree for six dominant species of 236 individual: Acacia spp., Ficus religiosa, Ficus benghalensis, Albizia lebbeck, Phoenix dactylifera, and Syzygium cumini. CO2 sequestration were estimated using allometric equations. Results showed that Phoenix dactylifera had the highest average annual CO2 sequestration (38.39 ± 9.87 kg/year per tree). While Acacia spp. obtained the least (1.73 ± 0.47 kg/year per tree). The total annual sequestration for all trees was 4047.2 kg/year. Multiple linear regression analysis demonstrated that tree structural attributes strongly explain variations in annual CO2 sequestration (R² = 0.97, p < 0.001). Tree diameter emerged as the primary predictor of sequestration potential, while tree height contributed a secondary but significant effect. Overall, results highlight the relevance of urban parks to improving carbon balance locally and contribute to urban sustainability.
Winter wheat is a critical strategic crop for Egypt's food security. The accurate simulation of its production is therefore fundamental for future planning and evidence-based policy. This study applies the World Food Studies (WOFOST) crop simulation model to estimate winter wheat yield across Lower Egypt (Nile Delta). Following sensitivity analyses to determine the optimal model configuration, WOFOST was calibrated using data from 14 growing seasons (2000–2014) and validated for the period 2015–2020. Model performance, evaluated using relative bias (rbias) and root mean square error (RMSE), showed reasonable agreement with observed yields (RMSE: 0.33–0.39; rbias: 0.4–5.8%). A strong negative correlation was identified between yield and air temperature in the Nile Delta, with Pearson correlation coefficients exceeding -0.75 for maximum temperature (Tmax) and -0.85 for both mean (Tmean) and minimum temperature (Tmin). Sensitivity experiments imposing temperature perturbations (±0.5°C and ±1.0°C) revealed that a 1.0°C increase in mean daily temperature reduced the total weight of storage organs (TWSO), total above-ground biomass (TAGP), and straw yield (STR) by 5.8–11.4%, 6.22–12.12%, and 6.54–12.76%, respectively, while the harvest index (Hind) increased marginally (0.43–0.81%). Conversely, a 1.0°C cooling increased TWSO, TAGP, and STR by 6.1–11.7%, 6.4–12.92%, and 6.68–14.04%, respectively, accompanied by a slight decrease in Hind (0.27–1.11%). The retrieved results demonstrate the efficacy of the WOFOST model in simulating winter wheat production under variable climatic conditions, supporting its potential application for future climate scenario assessments.
This study analyzes the evolution of hydric and thermal regimes in the main soils of the Algiers region, including the Sahel and the Mitidja Plain, using climatic data from several representative stations. The results show significant spatial and temporal variability linked to recent climatic fluctuations. Most stations exhibit a xeric moisture regime typical of Mediterranean climates, with alternating wet and dry periods. Since the 2000s, the Mitidja Plain has shown a gradual trend toward wetter conditions, marked by more wet days and fewer dry days. Temperature data indicate an increase of about 0.8 °C since 1984, from 17.3 °C to 18.1 °C on average, with all stations classified within the thermic regime (mean annual temperature > 8 °C). These changes illustrate the effects of regional climate change—greater rainfall variability and general warming—which affect soil classification and pedogenesis through changes in water availability, leaching, and oxidation. The study underscores the importance of sustainable land management to mitigate soil degradation and erosion.
IoT-powered climate intelligence is redefining protected hydroponics by delivering dynamic, real-time orchestration of temperature, humidity, and nutrient environments where even subtle micro-variations can decisively shape plant performance, yield potential, and system efficiency. This experiment, conducted in the Hi-Tech polyhouse at Lovely Professional University, Punjab, assessed the combined influence of IoT-regulated microclimate and nitrogen supply on hydroponic bokchoy (Brassica rapa var. chinensis). The experiment was conducted in a Split-Plot Design (SPD) with three replications, where three microclimatic regimes [MCC₁ (16–18°C; 80–85% RH), MCC₂ (21–23°C; 70–75% RH), and MCC₃ (26–28°C; 60–65% RH)] were assigned to the main plots, and three nitrogen levels [N₁ (100 ppm), N₂ (150 ppm), and N₃ (200 ppm)] were allocated to the sub-plots, resulting in nine treatment combinations. The IoT-based monitoring system demonstrated high precision (R² > 0.90), ensuring reliable environmental control. Growth, yield, and water productivity were significantly influenced by both factors and their interaction. A moderate regime of 21–23°C and 70–75% RH combined with 150 ppm nitrogen consistently delivered superior outcomes, including earlier maturity, enhanced vegetative growth, higher yield (370 g plant⁻¹), and improved water productivity (118 g L⁻¹). In contrast, suboptimal combinations reduced productivity by nearly half. The findings emphasize that synchronized climate automation and balanced nitrogen management are essential for maximizing efficiency and sustainability in smart hydroponic systems.
This study aims to provide an integrated diagnostic of drought risk by investigating historical rainfall and temperature variability from 1981–2023 across six drought‑prone districts of Telangana using statistical indices, trend diagnostics, and machine learning approaches. Monthly and annual datasets from IMD gridded archives were processed to compute mean, standard deviation, and coefficient of variation (CV) for rainfall, minimum, and maximum temperatures. Multiple drought indicators, including the Z Score Index (ZSI), Deciles Index (DI), Percent of Normal Index (PNI), Standardized Precipitation Index (SPI), China Z Index (CZI), and Rainfall Anomaly Index (RAI), were applied to capture severity, duration, and spatial extent of droughts. Trend analysis using the Mann–Kendall test and Sen’s slope revealed statistically significant increases in maximum temperatures (+0.03 to +0.06 °C per year), while rainfall showed high variability (CV ranging from 22% in Khammam to 38% in Rangareddy) but no consistent long‑term trend. A quadrant‑based climate stress framework was developed by integrating rainfall magnitude, variability, extremes, and peak maximum temperature, classifying districts into Climate Stable, Rainfall Unpredictable, Dry Stable, and High Risk Climate Stress Zones. To enhance predictive capacity, machine learning models (Random Forest, Gradient Boosting, SVM, and Neural Networks) were trained on rainfall and temperature predictors, with SHAP analysis providing interpretability by identifying key drivers such as rainfall CV, Tmax slope, SPI, and ZSI. Model performance was robust, with Gradient Boosting achieving 89.1% accuracy and Random Forest 87.2%, confirming ensemble methods as the most reliable classifiers. Results confirm that all districts experienced mild to extreme drought years, with SPI identifying 6–9 severe drought years per district and Rangareddy and Mahbubnagar showing the highest risk. The integrated framework, combining statistical indices, visualization, and interpretable machine learning, provides a replicable methodology for semi‑arid regions and offers actionable insights for policymakers to strengthen agricultural resilience, water resource management, and climate adaptation strategies.
The Atlas cedar forests in the Moroccan Middle Atlas, particularly the Sidi M'Guild region, are undergoing rapid degradation due to increasing climatic stress and anthropogenic pressure. This study introduces a hybrid modelling approach integrating random forest (RF), cellular automata (CA) and Markov chains to simulate forest cover dynamics from 1990 to 2032. The model integrates remote sensing data from Landsat 4, 8 and Sentinel-2, bioclimatic variables (temperature, seasonality, rainfall of the driest quarter) and indicators of human influence (density of occupancy, proximity to forest edges). The results project a 91% decline in Cedrus atlantica and a 74% decline in juniper, contrasted with a 1,290% expansion of holm oak, indicating a major ecological shift to drought-tolerant hardwoods. The RF–AdaBoost classifier achieved 98% accuracy, and the RF–CA–Markov framework demonstrated strong predictive power (Kappa = 0.72). These results offer a solid tool to anticipate forest transitions and guide adaptive forest management strategies, aligned with Morocco's national reforestation efforts.
Rainfall changes across Iraq were explored under two Shared Socioeconomic Pathways (SSPs): SSP1–2.6 (low-emission) and SSP2-4.5 (medium-emission) using the Planet Simulator (PlaSim) model. Bias correction of precipitation dynamics was performed against ERA5 reanalysis data (1995–2024) for near-future (2026–2050), mid future (2051–2075), and far future (2076–2100) scenarios. Under SSP1-2.6, precipitation shows a U-shaped trajectory with a 24% decrease (near-future) and gradual recovery to 9% below baseline by 2100 along with increased interannual variability. Under SSP2-4.5, significant deficits of 16–21% endure through each interval with negligible variation. These divergent trajectories have significant consequences for rainfed winter wheat production in northern Iraq, which relies on winter-spring precipitation (DJF-MAM). SSP1-2.6 Increased variability under threatens the predictability of crop phenology, while SSP2-4.5 Fundamental reshaping of these agricultural systems would be essential to avoid chronic deficits in food availability. The differences highlight the extent to which global emission pathways will seal Iraq’s fate when it comes to water security and agricultural viability.
Rising temperatures are increasing the frequency and impact of heat extreme events in India. So, this is increasing the need for early warning and climate-risk management. Heat risks and behaviors vary across regions, so national model can miss local temperature dynamics. We present a season-aware, region-wise framework to forecast daily maximum temperature and identify heatwave days in IMD defined Central India region. In Central India's severe pre-monsoon heat is recurrent and closely linked to crop-weather stress, irrigation demand, and outdoor labor exposure. Heatwaves are first categorized at grid-cell level using operational threshold-and -departure rules. Then heatwaves are aggregated to a region-day label using a spatial coverage threshold and minimum 2-day duration. In this paper, we benchmark conventional statistical time-series models against recurrent sequence models under same splits and evaluation window using national meteorological temperature data. Performance is evaluated with temperature-error metrics and event-based measures for heatwave-day detection. Results show that recurrent models give the strongest overall skill, with the LSTM model delivering the promising improvements. Seasonal statistical modeling improves over non-seasonal baselines by capturing the seasonal cycle. This framework offers a compact regional benchmark and practical guidance for region specific early warning systems. This work supports agrometeorological advisories for farm operations, water planning and heat-stress risk management during extreme heat.
In this study, the authors attempted to predict corn disease using machine learning (ML) algorithms. They attempted to predict the crop disease into four categories, such as healthy (class 1), Grey Leaf Spot (class 2), Common Rust (class 3), and Northern Leaf Blight (class 4), using bagging, boosting, random forest and ensemble algorithms. The entire database is split into a 70:30 ratio for training and testing the classifiers, respectively, and a 5-fold cross-validation has been done to evaluate the performance of the classifier. They used a handcrafted feature extraction method to extract the features from the leaf image, such as color, texture, vegetation indices, and morphological features and fed them into the machine learning algorithms for further classification. The ensemble learning technique combines different ML supervised algorithms and predicts the result by majority voting. The usage of the ensemble technique may overcome the different types of errors and focus on different data patterns as multiple ML techniques are used. The overall accuracy of Bagging, boosting, random forest, and ensemble algorithms is 84.6%, 86.9%, 89.6%, and 91.9%, respectively. Compared to the other methods, the ensemble algorithm exhibits more accuracy. The class-wise healthy, Grey Leaf Spot, Common Rust, and Northern Leaf Blight accuracy is 99.1%, 97.5%, 98.3%, and 98.3%, respectively, for the ensemble model. Though the ensemble techniques combine 3 different types of ML algorithms for prediction, the average time taken to predict the disease is about 6.89 ms. Thus, the authors suggest that the ensemble algorithm predicts crop disease better than individual ML techniques.
Climate twin regions are spatially separated, distant geographic areas that share very similar climate conditions. The climatic parameters, such as temperature patterns, rainfall levels, and seasonal cycles, are statistically comparable in these regions. Owing to these shared climatic parameters, such regions often exhibit analogous agroecosystems, biotic communities, and socio-economic practices. Subsequently, these regions provide a basis for the potential transferability and adaptive application of crop species, land-use practices, and livelihood strategies under similar environmental conditions. The purpose of studying these climate twins is to help scientists and planners understand how a region’s climate might change in the future. For example, in scenarios where a region is projected to experience increased temperatures and reduced precipitation, the researchers can examine its present-day climate analogue to construe potential environmental responses and agricultural impacts. In simple terms, climate twin regions act like real-world previews of future climate conditions, helping societies prepare for climate change more effectively. The climate of a region is the outcome of several factors, such as latitude, altitude, distance from water bodies, soil type, etc., and its agri-eco-resources determine its agricultural productivity. With this hypothesis, we analysed to identify agro-climatic twins of Punjab having similar climate and agri-eco-resources and to understand whether they too are as highly agri-productive.
Understanding long-term precipitation variability and its agricultural implications is essential for climate-sensitive mountain regions. This study investigates spatial and seasonal precipitation trends across the agro-climatic zones of Himachal Pradesh using long-term gridded rainfall data from the NASA POWER database (1981–2024). Monthly precipitation was aggregated into annual and seasonal series (winter, pre-monsoon, monsoon, and post-monsoon). Trends were detected using the Modified Mann–Kendall test and Sen’s slope estimator, while spatial patterns were represented using Inverse Distance Weighting interpolation. The results indicate a consistent increasing trend of precipitation across the state, with monsoon rainfall contributing the largest share of the observed increase. The winter precipitation also showed rising trend in higher elevation regions influenced by Western Disturbances. To evaluate agricultural implications, maize (kharif) and wheat (rabi) yields were analysed in relation to seasonal rainfall using Pearson correlation. The results reveal that maize productivity is more responsive to monsoon rainfall, whereas wheat yield shows stronger dependence on winter precipitation. These findings highlight spatially differentiated climate sensitivity of crops and provide insights for climate-responsive agricultural planning in the western Himalayan region.
This study assesses climate change impacts on wheat production in the tropical highlands of Ethiopia using the DSSAT-CERES-Wheat model. Field experiments were conducted across three locations (Bore, Kulumsa, Sinana) during 2023–2024, evaluating two wheat cultivars (Shaki, Boru) under nitrogen rates (0, 46, 69, 92 kg ha⁻¹) and sowing dates (early, normal, late). The model was calibrated and validated using phenological, growth, and yield data, showing strong agreement between simulated and observed values. For Shaki, grain yield calibration yielded an RMSE of 130 kg ha⁻¹, NRMSE of 3.4%, and a d-index of 0.91; for Boru, an RMSE of 140 kg ha⁻¹, NRMSE of 3.6%, and a d-index of 0.90. Biomass RMSE was 190 kg ha⁻¹ (Shaki) and 200 kg ha⁻¹ (Boru), with d-index >0.91. Anthesis date RMSE was 1.7–1.9 days (d-index >0.92), while LAI simulations had RMSE of 0.14–0.17. Validation results confirmed robust model performance for critical variables: anthesis date (RMSE: 1.9–2.0 days), LAI (RMSE: 0.15–0.18), biomass (RMSE: 187–197 kg ha⁻¹), and grain yield (RMSE: 127-137 kg ha⁻¹) for Shaki and Boru, respectively. Key findings indicate early planting with 92 kg N ha⁻¹ maximises yields, mitigating climate-driven losses. Confirms DSSAT’s utility in guiding adaptive wheat management for Ethiopia’s climate-vulnerable agriculture.
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.
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.
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.
