Journal of Agrometeorology

Remote sensing based yield estimation of wheat crop at farm scale: A case study of Badsu village of Alwar district, Rajasthan

SUDESH SINGH CHOUDHARY — 2025-12-01

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.

Impact of shade net and polyethene sheet on microclimate, growth and productivity of French bean (Phaseolus vulgaris L.) in Punjab, India

KOMAL RANI — 2025-12-01

Modification of microclimate is a major factor that can affect growth and productivity of French bean. A study was conducted at Ludhiana, Punjab, India during winter (2021-22) and spring season (2022) to find out the effect of microclimatic modifications using polythene sheet and shade net on production of French bean. Four treatments were formulated i.e. Control, whole season covered, covered during vegetative stage and covered during reproductive stage of two varieties (FBP-1 and Kentucky wonder) of French bean. Structures on different treatments were installed after emergence of the crop. The crop took 60-70 days to attain physiological maturity during winter season while the crop matured in 60-65 days during spring season. Higher green pod yield (167.0 q ha^-1) was obtained for whole season cover conditions as compared to open conditions (130.7 q ha^-1) during winter season. Pod yield was recorded less during spring season, yield under cover condition (94.8 q ha^-1) was higher as compared to open condition (58.3 q ha^-1). Among the both varieties FBP-1 performed better than Kentucky wonder during both the seasons. Under covered condition higher chlorophyll content along with higher vegetative & reproductive growth and earliness of crop has been observed. During spring season due to rise in temperature less yield has been obtained.

Crop vulnerability and climate adaptation to moisture stress in the semi-arid zones of Senegal

SUJATHA PEETHANI — 2025-12-01

Abiotic stressors have a significant impact on crop productivity, with moisture stress being especially important. This study investigates the consequent shifts in sorghum yields in Senegal, using NASA Power and CHIRPS data from 1990 to 2024. Matam, Mbane, Gamadji Sarre, and Yang-Yang were identified as hotspots by the Rainfall Anomaly Index (RAI) with low rainfall, exhibiting only 12–15% rainy days. Precipitation was categorized into Above-Normal (AN) or Below-Normal (BN) using the Rainfall Anomaly Index (RAI; AN if RAI ≥ 0, BN if RAI < 0). Sorghum yields were notably lower during BN years. APSIM model was used to assess the impact of fertilizer doses (40 kg ha^-1 and 60 kg ha^-1) and sowing dates on yield variations. The results indicate minimal yield fluctuation with increased fertilizer within recommended limits and highlight that reliable rainfall forecasts (80% or greater accuracy) can significantly influence farm-level decision-making. These findings emphasize the crucial role of rainfall variability in agricultural planning and climate adaptation strategies.

Climatic trends and its impact on rice production in different agroclimatic zones of West Bengal, India

ROHIT PRAMANICK — 2025-12-01

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.

Change in the productive potential of rainfed maize under climate change scenarios in the Lerma–Toluca Sub-basin, Mexico

J. E. REYES-ANDRADE — 2025-12-01

Climate change represents a challenge for agricultural production in Mexico, so this research determined the productive potential in the Lerma-Toluca Basin under historical (1991-2020) and future (2050 and 2070) scenarios with RCP 4.5 and 8.5. For the scenarios, temperature and precipitation images were generated using the spline interpolation method of the ANUSPLIN package. The productive potential was estimated based on the agroecological requirements of the crop, classifying the territory as high, medium and unsuitable. The results show that, in the current scenario, medium potential predominates (51.8%) over high potential (17.3%). For the future scenarios with RCP 4.5 in 2050, the medium potential increases 0.69% and the high potential 0.43%; in 2070, the medium potential decreases 0.68% and the high potential increases 1.79%. With RCP 8.5 in 2050, the medium potential decreases by 1.4%, and the high potential increases by 2.16%; in 2070, the medium decreases by 14.96%, and the high potential increases by 13.15%, compared to the historical scenario. Climate change will raise the temperature and reduce precipitation, increasing productive potential; however, extreme weather events can affect production, so adaptation strategies are required to face climate risks and guarantee food security.

Heat use efficiency and yield optimization in wheat as influenced by irrigation scheduling

GURLEEN KAUR — 2025-12-01

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.

Enhanced hybrid CEEMDAN-GMDH regression model for forewarning sucking pests in cotton crops of Coimbatore, Tamil Nadu

N. NARANAMMAL — 2025-12-01

Effective pest management relies on early and accurate forecasting, yet current models struggle to capture regional specific complex relationship between weather conditions and pest incidence. This study addresses this gap by developing a robust crop pest forecasting model using the Group Method of Data Handling (GMDH) regression. We employed three decomposition techniques like Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to break down nonlinear data into Intrinsic Mode Functions (IMFs). These IMFs were then predicted using GMDH regression, incorporating weather variables as independent factors. The ensemble forecasts were constructed by aggregating the predicted IMFs. The study utilized pest incidence data from 2015 to 2023 for aphid, jassid, thrips, and whitefly pests. Findings indicated that the CEEMDAN-GMDH model outperformed others for forecasting the incidence of aphid, thrips, and whitefly pests, with improvements of 16.3%, 4.3%, and 13.6% over the univariate GMDH model, respectively. For jassid, the EEMD-GMDH model provided the best forecasts, despite CEEMDAN’s superior decomposition capabilities. The study concludes that integrating decomposition methods, with GMDH regression provides a more reliable tool for predicting pest incidences in cotton crops, thereby aiding in better pest management strategies.

Machine learning approaches for clear-sky Land Surface Albedo (LSA) retrieval using OCM-3 data over diverse Indian landscapes

ALIYA M. KURESHI — 2025-12-01

This study presents reliable methods for estimating clear-sky land surface albedo (LSA) using machine learning (ML) and satellite data, aiming to improve climate models and environmental monitoring. Top-of-atmosphere (TOA) radiance data from the Ocean Colour Monitor-3 (OCM-3) sensor aboard the Earth Observing Satellite (EOS-06) satellite containing 13 spectral bands were used, supported by 2.4 million synthetic simulations generated via the 6S (Second Simulation of a Satellite Signal in the Solar Spectrum) Radiative Transfer Model (RTM). The simulations spanned diverse land covers, atmospheric states, sun and viewing geometries covering wavelengths from 0.4 to 2.5 µm. Three ML models namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression (MLR) were tested. Models were trained on 70% of the simulated data and tested on 30%. Validation with actual OCM-3 data included additional aerosol and water vapor information from MODIS. LSA estimations were compared to the MODIS standard product (MCD43A3). Among the three models, RF achieved the best performance, with the lowest RMSE (0.00036) and strong agreement across various land types with MODIS data. The results confer the potential of ML models, especially RF, combined with radiative simulations, and can be used for operational estimation of LSA for OCM-3 data.

Machine learning modeling of reference evapotranspiration in Central Luzon, Philippines

LEA S. CAGUIAT — 2025-12-01

Reference evapotranspiration (ET_o) is crucial for calculating irrigation requirements. Instruments that directly measure ET_o are still costly and limited while the empirical models are data intensive. Meteorological data of Central Luzon, Philippines (1985-2019) were used to estimate ET_o using the FAO Penman-Monteith method. The performances of machine learning algorithms in estimating ET_o were analyzed using ground-based weather data. Optimal models were determined using decision thresholds (RMSE<0.39 mm day^-1, R²>0.75, MSE<0.15 mm day^-1, MAE<0.30 mm day^-1). The models were further assessed using principal component analysis for finding relevant variables (σ²=0.95) and the Wilcoxon test for comparing two samples (α=0.05). Results show that optimal model required only two or three weather variables depending on the station. In general, the algorithms can be ranked as follows: Gaussian progress regression, Neural network, Support vector machines, Ensemble of trees, Regression trees, and Linear regression. The study reveals that machine learning can accurately predict ET_o using ground-based weather data, and it can be a good alternative to data-intensive empirical models.

Development of a decision support system for real-time forewarning of pests and diseases of different crops, for usability in Agro-Advisory Services

THARRANUM. A. MEHNAJ — 2025-12-01

Forewarning pests and diseases in real-time is one of the key components in Agromet Advisory Bulletin (AAB) of India Meteorological Department (IMD). In order to facilitate it, a comprehensive knowledge databank on weather-based pests and diseases of crops were collected to develop a decision support system (DSS) comprising of algorithms on thumb rules of pests and diseases prediction of major crops of kharif and rabi seasons. The algorithm was validated with the real-time observation on pests and diseases of five crops (rice, sorghum, chickpea cotton and maize) during rabi 2021-22 and kharif 2022 seasons, grown over 12 District Agromet Units (DAMU) locations across the country. The DSS upon validation, yielded prediction of pest diseases with correctness varying between 33 to 100 percent across the crops and locations. The forecast accuracy was more reliable during rabi season in comparison to kharif season crops/pests/diseases. For effective operationalization of weather based heuristic models and thumb rules, these have to be tested and validated in all the agroclimatic zones of the country.

Assessment of the impact of dust pollution on chlorophyll, carotenoids, and ascorbic acid in the vegetation leaves of some areas in Baghdad – Al-Rusafa, Iraq

HUDA HADI JASSIM — 2025-12-01

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.

A copula-based joint return period approach to characterising extreme rainfall in West Java

A. NABILA — 2025-12-01

Climate change presents recurring challenges in understanding extreme weather events, particularly the persistence of dry and wet periods. West Java is among the region’s most vulnerable to such rainfall variability. This study analyses the relationship between consecutive dry days (CDD) and consecutive wet days (CWD). It estimates joint return periods (JRP) using a copula-based approach to assess the spatial characteristics of climate extremes in West Java. Marginal distributions were fitted for each indicator, followed by copula modelling using the Inference Function for Margins method and model selection based on the Akaike’s information criterion (AIC). The inverse Gaussian (ING) distribution was most suitable for CDD, while the generalised extreme value (GEV) distribution best represented CWD. We found that the Gaussian and Frank copulas best captured the overall dependence structure between CDD and CWD. JRP analysis showed that simultaneous extremes (AND scheme) were significantly rarer than single-variable extremes (OR scheme). These findings provide valuable input for identifying high-risk areas and developing more locally adaptive climate risk mitigation strategies.

Bayesian Tweedie Compound Poisson Gamma (TCPG) modeling for statistical downscaling of rainfall in West Java, Indonesia

SITI ROHMAH ROHIMAH — 2025-12-01

Global climate models (GCM) are effective in representing climate processes at the global scale; however, they often exhibit biases and limited accuracy at the local scale. This limitation is particularly critical in monsoon-dominated regions such as West Java, where statistical downscaling (SD) provides an appropriate approach. This research aims to predict monthly rainfall in West Java using the Bayesian Tweedie Compound Poisson Gamma (TCPG) model with combined scenarios of bias correction and dummy variables. Bias correction used empirical quantile mapping (EQM) with CHIRPS data. Monthly rainfall as the response variable was modelled using a Bayesian TCPG regression, with parameter estimation performed through Bayesian Markov chain Monte Carlo (MCMC) using the Metropolis Hastings algorithm. The best model scenario was achieved using dummy variables without bias correction, with CNRM-ESM2-1 identified as the most effective Decadal Climate Prediction Project (DCPP) model. These findings enhance rainfall prediction accuracy in tropical monsoon regions and support agricultural and water resource planning in West Java.

A study on vertical profiling of air pollutants and meteorological variables in Visakhapatnam, an Indian coastal urban environment

PRIYANKA PRIYADARSHINI NYAYAPATHI — 2025-12-01

Air pollution in coastal urban environments is a complex interplay of emission sources and meteorological conditions, often inadequately captured by traditional horizontal monitoring. This study investigates the vertical distribution of major air pollutants PM_2.5, PM₁₀, SO₂, NO₂, NO and CO across five high-rise multi-storey buildings in Rushikonda, Visakhapatnam, during summer and winter seasons. Over 30 days of continuous monitoring with a distinct vertical gradient, where noticeable variations were observed, particularly for particulate matter, with PM_2.5 and PM₁₀concentrations decreasing by up to 10.2% and 15.4%, respectively, from ground to elevated levels. However, statistical data analysis and 3-D visualization of the relationship between the pollutants and the meteorological parameters revealed critical thresholds for temperature, relative humidity (RH), and height influencing pollutant stratification. 3D surface visualizations further emphasized RH's role in enhancing particulate concentrations via hygroscopic growth and suppressing vertical dispersion, besides the long-range transport of air mass could also contribute to the high concentration values of particulate matter. The findings highlight the utility of vertical monitoring using existing urban infrastructure and underscore its relevance in refining air quality management in coastal cities.

Applications of Internet of Things (IoT) in agriculture: A review

PENKI RAMU — 2025-12-01

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.

Determining optimum weather parameters for higher yield of kharif maize in Punjab

PRABHJYOT KAUR — 2025-12-01

Comparative evaluation of evapotranspiration models with lysimeter data in Ranchi

JOSNA MURMU — 2025-12-01

Comparative analysis of weather-driven models for sorghum yield prediction in Bundelkhand

RITU SINGH — 2025-12-01

Effect of climate variability on rubber production in Thailand

SIRIKLAO SANGKHAPHAN — 2025-12-01

Rice yield prediction in Dakshina Kannada district using ensemble machine learning

A. SHAILESH RAO — 2025-12-01

Water balance assessment in Malang, Sumenep, and Sumbawa Regencies of Indonesia

Y. A. FATA — 2025-12-01

Effect of net house, mulching and weather parameters on whitefly (Bemisia tabaci Genn.) of okra (Abelmoschus esculentus L. Moench) in Nepal

JANAK PANT — 2025-12-01

Relationship in colony dynamics of honey bee (Apis cerana F.) to weather: Insights for sustainable beekeeping practices

SHIVANI SHARMA — 2025-12-01

Trend analysis of rainfall and temperature in metropolitan cities of India using Mann-Kendall test

M. MURUGAN — 2025-12-01

Trend analysis of temperature and precipitation in central Tanzania using regression and non-parametric approaches

SHABAN JUMA ALLY — 2025-12-01

Trend analysis of monthly and seasonal temperature in different districts of Hyderabad-Karnataka region

SHANTAPPA DUTTARGANVI — 2025-12-01