Journal of Agrometeorology

Assessment of Hydrological Drought and Vegetation Cover Trend in the Iraqi Marshlands Using Remote Sensing and GIS

FADHAA TURKI DAKHIL — 2026-06-04

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.

Influence of Mulching on Soil Hydrothermal Regime and Yield of Tomato in the Upper Brahmaputra Valley Zone of Assam

ARMEENA SULTANA — 2026-06-04

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 14^th 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).

Integrated Drought Diagnostics in Telangana (1981–2023): Trend Analysis, Multi-Index Assessment, Quadrant Framework, and Interpretable Machine Learning

VELUSAMY GUHAN — 2026-06-04

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.

Structural Attributes of Urban Trees Controlling Carbon Dioxide Sequestration: An Applied Study in Al-Umma Park – Baghdad

AL-ZAHRAA A. MOHSEN — 2026-06-04

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.

Hybrid Machine Learning Approach to Model Cedar Forest Cover Changes in Morocco’s Middle Atlas

ANASS LEGDOU — 2026-06-04

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.

Assessing the Impact of Climate Change on Soil Moisture and Temperature Regimes in Northern Algeria

NADIA AYACHE — 2026-06-04

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.

Machine Learning-Driven Detection of Corn Leaf Diseases for Smart Agriculture

K. THIRUMALA LAKSHMI — 2026-06-04

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.

Simulating Wheat (Triticum aestivum L.) Yield Under Different Sowing Dates and Nitrogen Management Using CERES-Wheat Model in Tropical Highlands of Ethiopia

YARED TESFAYE — 2026-06-04

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.

Future Precipitation Dynamics and Their Implications for Agricultural Water Security in Iraq: A PlaSim Assessment

DOHA SOROR — 2026-06-04

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.

Interactive Effects of IoT-Based Microclimatic Regulation and Nitrogen Supply on Growth, Yield, and Water Productivity of Hydroponically Grown Bokchoy Crop

VIKAS SHARMA — 2026-06-04

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.

Performance Evaluation of Regional Heatwave Prediction Using Statistical and Deep Learning Models

SULOCHANA DEVI — 2026-06-04

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.

Simulating the Winter Wheat Production of Egypt Using WOFOST-PCSE Crop Model

MARWA SAMY MOHAMED — 2026-06-04

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 (H_ind) 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 H_ind (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.

Spatio-Temporal Variation and Seasonal Rainfall Trends Across Agro-Climatic Zones of Himachal Pradesh (1981–2024) and Their Implications for Maize and Wheat Yields

RAM MANOHAR — 2026-06-04

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.

Evaluation of Agrometeorological Indices and Yield Response of Greengram Varieties to Varied Sowing Windows

T. JYOTHI — 2026-06-04

Fusion of Multispectral and Microwave Sensor Data for Monitoring Land Surface Temperature in Agro Meteorological Studies

MAHESH PALAKURU — 2026-06-04

Identification of Agro-Climatic Twins Based on Climatic and Agri-Ecological Similarities for the Sharing of Agricultural Technologies: Insights from Indian Punjab

PRABHJYOT KAUR — 2026-06-04

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.