Journal of Agrometeorology

Weather-based forecasting models for false smut disease of rice (Oryza sativa L.) in West Bengal

2025-03-14T00:48:38+00:00

Innovative trend analysis of monsoon season rainfall of Kerala state

2025-05-04T18:00:00+00:00

Trend analysis of hydrometeorological parameters and groundwater depth in Peshawar district, Pakistan

2025-04-25T00:50:35+00:00

Land suitability classifications in response to ecological requirements of Panax notoginseng in Vietnam

2025-05-12T14:39:05+00:00

Adaptive Neuro-Fuzzy inference system (ANFIS) based models for estimation of reference evapotranspiration (ET0)

2025-05-29T22:45:36+00:00

Trend analysis of extreme climate indices for Coimbatore using non-parametric method

2025-06-04T17:37:06+00:00

Effect of meteorological variables on evaporation duct height (EDH): A case study in Basra, Iraq

2025-05-25T15:10:36+00:00

Relation between the cloud cover and photosynthetically active radiation (PAR) in Baghdad, Iraq

2025-07-02T15:54:26+00:00

Application of artificial intelligence and statistical recurrent models in predicting rainfall: A case study of Ludhiana, Punjab

2025-06-03T17:54:05+00:00

Microclimatic conditions under different shade trees and their effect on tea leaf growth rate

2025-02-16T11:17:09+00:00

Micrometeorological variations within the tea canopy influence the tea yield to a considerable extent. An experiment was conducted at the Experimental Garden of Assam Agricultural University Jorhat, Assam during 2022 - 2024 to examine the effects of five shade tree species viz., Sao koroi (Albiziachinensis), Xil koroi (Albizia odoratissima), Neem (Azadirachta indica), Amla (Phyllanthus emblica), and Areca nut (Areca catechu) on micrometeorological parameters such as air temperature (AT), canopy temperature (CT), relative humidity (RH), photosynthetically active radiation (PAR), soil moisture (SM), soil temperature (ST), and rainfall (RF) affecting leaf growth and yield. The highest green leaf growth rate (GLGR) occurred during the monsoon season (41.7 ± 12.1 kgha^-1day^-1), with the highest GLGR (41.8 ± 13.1 kgha^-1day^-1) achieved under Neem shade. GLGR has a significant positive correlation with most of the parameters except rainfall which showed no significant influence on GLGR. Regression analysis revealed that rainfall negatively impacted GLGR. This study highlights the role of shade trees in mitigating stress and optimizing growth conditions, providing insights into sustainable tea cultivation practices.

Evaluation of water use efficiency and yield on shallot (Allium cepa L.) cultivation under conventional irrigation and sensor-based drip irrigation

2025-05-06T19:31:32+00:00

Water shortage is a critical problem in unirrigated agricultural land in hilly regions, especially during the dry season. Inefficient irrigation practices and a lack of attention to crop water needs exacerbate the water shortage. A pot experiment aimed to evaluate conventional irrigation (CI) and sensor-based drip irrigation (SDI) approach on shallot cultivation in terms of total irrigation, water percolation, yield, and water use efficiency. Results revealed that the total amount of irrigation water in the CI was significantly higher than in the SDI at each growth phase, resulting in higher water percolation throughout the shallot's growth phases in the CI. The irrigation water use efficiency (IWUE) value increased significantly by 87.7% in the SDI compared to the CI, but resulted in a 26.7% yield reduction. This study provides information indicating that CI tends to use excessive amounts of irrigation water, so that it requires innovative water management to be more efficient, leading to an increased yield by using the SDI approach. Irrigation practices considering optimal soil water content at each plant growth phase are essential to improve water use efficiency and prevent excessive water percolation.

Integration of CERES-rice crop simulation model and MODIS LAI (MOD15A2) for rice yield estimation

2025-04-15T17:28:12+00:00

In this study assimilation of MODIS LAI (MOD15A2) into DSSAT-CERES-rice crop simulation model was used to develop advance yield estimates of rice crop during pre-harvest stage (F3) in Palakkad district of Kerala during Mundakan (September- January) season 2022-23 and 2023-24. The free parameters identified as inputs for the DSSAT-CERES-rice crop simulation model were adjusted and optimized sequentially during assimilation process until a minimum value of cost function is reached. This helped to minimize the deviation between MODIS- LAI and model generated LAI and the yield predicted by the model consequently is taken as the predicted yield. The average predicted yield during 2022-23 and 2023-24 was 5590 kgha^-1 and 5124 kgha^-1 respectively. The yield prediction by simulation model integrated with remote sensing products had higher accuracy than using simulation model alone during both the years with number of panchayats having the BIAS above ± 10 per cent reduced from 20 to 12 and 23 to 11 during 2022-23 and 2023-24 respectively. The findings clearly show that incorporating satellite data into crop simulation models can produce more accurate rice production forecasts than crop simulation techniques used alone.

Evaluating the use of extended range forecasts in DSSAT for predicting rice yield: A case study of Madhya Pradesh, India

2025-03-07T10:51:08+00:00

This study evaluates the potential of Extended Range Forecasts (ERFs) in improving rainfed rice yield simulations during three kharif seasons (2019–2021) using the DSSAT v4.8 model for Madhya Pradesh. Three weather datasets were evaluated: (1) observed weather, (2) observed + ERF + climatological normal and (3) observed + climatological normal. The ERF generated as weekly interval during the crop season with a total of 19 initial conditions (IC) were used for ERF dataset. The yields simulated using hybrid datasets (2 & 3) were related with those obtained with the observed weather data (1). Results indicated that integrating ERFs during the reproductive and ripening phases improves yield simulations, with the most notable improvements observed in 2021. However, benefits varied across seasons and growth phases. The findings highlight the potential of ERFs to enhance seasonal yield forecasts when applied strategically, particularly by bridging observed data and climatological normal during key crop phases.

Sensitivity analysis of AquaCrop model for input parameters in simulating growth and yield of pearl millet (Pennisetum Glaucum L.) in semi-arid region of Nigeria

2025-06-02T10:16:18+00:00

The need for a localized crop model that will aid in evaluating various strategies for efficient water management, especially in the semi-arid Lake Chad region does not need to be overemphasized. Therefore, as a step to simplify the calibration of the AquaCrop model, this study assessed the sensitivity of the model’s output variables to pearl millet crop input parameters under water stress conditions of Maiduguri, Northeastern Nigeria. The analysis was carried out using the Local Sensitivity Analysis (LSA) technique under a 50 % deficit irrigation scenario. The result revealed that the effects of the input parameters on canopy cover (CC) and biomass yield (BMY) simulations were time-dependent. Overall, a significant number of the model’s inputs were found to be non-influential; these parameters could be set within their predetermined range in order to simplify the model. Whereas, the influential parameters should be given higher consideration during calibration, data collection, and future model development. The results of this study could also be validated using more advanced methods like the Global Sensitivity Analysis (GSA) technique, on different crop varieties that have longer phenological stages and under severe water and fertility stresses.

Assessment of agricultural suitability through remote sensing: A Google Earth Engine and GIS-based approach for integrated urban planning

2025-06-26T21:13:19+00:00

This study utilizes remote sensing (Sentinel-2 images via Google Earth Engine) to analyze maize growth in the El Meniaa region, Algeria, and assess agricultural land suitability. Using vegetation indices (NDVI, EVI, NDPI), growth cycles were characterized, showing a cyclical NDVI evolution (0.51 at the start, peaking at 0.71, and dropping to 0.06-0.09 at season end). A multi-criteria approach (AHP method) revealed that the topographic criterion (weight 0.413, notably aspect) is the most influential for agricultural suitability, followed by climatic data (weight 0.327, including temperature) and vegetation indices (weight 0.216, including NDVI). This research demonstrates the effectiveness of integrating remote sensing and multi-criteria analysis to accurately model crop phenology and map areas of high agricultural suitability, offering a transferable methodological framework for arid regions of Algeria.

Combining satellite and meteorological insights for yellow stem borer risk prediction in rice cultivation

2025-02-16T10:38:30+00:00

Yellow stem borer (YSB) is a major pest responsible for substantial rice yield losses which can be significantly reduced through accurate forecasting, enabling timely interventions. This study aimed to develop a forewarning model for YSB using weather parameters and remotely sensed vegetation indices based on 19 years (2000–2018) of data from Raipur, Chhattisgarh. Weather variables and satellite derived vegetation indices were used as predictors, with pest population as the response variable. The model developed for the 39^th Standard Meteorological Week (SMW) indicated that lag-time period of four week i.e., advance prediction of peak YSB population by 35^th SMW achieved with high coefficient of determination (R² = 0.77), low root mean square error (RMSE = 0.34) and low mean absolute percentage error (MAPE = 15%). Key predictors included the interaction of land surface wetness index and enhanced vegetation index, evening relative humidity and maximum temperature. A risk zoning map generated using the model indicated that most of Raipur falls under a low pest risk zone. Overall, this study highlights the potential of integrating satellite-based variables into pest forewarning systems, providing a foundation for more accurate agromet-advisory services in India.

Analysing hydrological balance of Pindar-Nandakini River Basin, Kumaon Himalaya using SWAT model

2025-04-04T23:39:18+00:00

Water availability from the central Himalayan River Basins is threatened by warming-mediated glacier melt and declining precipitation. This study employed the Soil and Water Assessment Tool (SWAT) model to analyze the water balance of the Pindar-Nandakini River Basin (PRB). The model was calibrated and validated using high-resolution data, achieving strong performance (R²: 0.85, NSE: 0.71). Runoff and evapotranspiration (ET) account for approximately 29.7% and 28.9% respectively. Lateral soil flow is a major contributor (23.7% of precipitation), significantly influencing streamflow and groundwater levels. Snowmelt contributes around 10.3%, while deep groundwater flow is minimal. The model considerably simulated seasonal runoff patterns, particularly peak flows during the monsoon. Sediment loading, at 563.1 t ha^-1 annually, is a significant concern. The study also underscores the critical role of ET and runoff in the hydrological processes of the basins, revealing potential challenges during high-flow events and climate-driven forest greening trends. These findings emphasize the importance of the SWAT model in understanding the complex hydrological processes within the Himalayan glacier basins, highlighting the basin's vulnerability to climate change impacts, particularly glacier retreat.

AgroMetLLM: An evapotranspiration and agro-advisory system using localized large language models in resource-constrained edge

2025-06-30T00:52:00+00:00

We introduce AgroMetLLM, an on-device agrometeorological advisory system that combines five validated evapotranspiration (ET) models with various quantized Large Language Models (LLMs) on a Raspberry Pi 4B. The users specify a location, 3-7-day horizon by using Gradio interface and LLM; Open-Meteo APIs then supply daily inputs T_max, T_min, T_mean, RH_mean precipitation, Food and Agriculture Organization (FAO) reference evapotranspiration (ET₀), and Rs for multiple Indian sites. Computed ET ranges (mm day⁻¹) across locations were: FAO ET₀ 2.84-6.21; Hargreaves-Samani 6.28-13.74; Turc 0.17-0.21; Priestley-Taylor 5.64-9.06; Makkink 2.73-4.38. A few-shot prompting strategy, based on curated examples for 3-, 5-, and 7-day forecasts, is used to guide the Qwen LLM under Ollama to produce structured, five-point advisories in 1-2 s. One-way ANOVA (F = 3.30-6.71, p = 0.016-0.0002) and Kruskal-Wallis tests (χ² = 9.61-15.48, p < 0.05 except Turc p = 0.088) confirm significant ET differences among models and LLM sizes. All outputs and metadata persist in SQLite, and Matplotlib renders comparative bar charts in the dashboard. These results demonstrate that compact, quantized LLMs can reliably deliver actionable irrigation guidance-matching cloud-based accuracy-while operating offline, with minimal latency and energy use, thus empowering resource-constrained smallholder farmers.

Food consumption and relative growth rate of Cnaphalocrocis medinalis (Guenee) on rice under elevated temperature and carbon dioxide conditions

2025-04-23T00:12:50+00:00

The present studies were conducted at Punjab Agricultural University, Ludhiana during 2019-22. The impact of variable minimum:maximum temperature for 10:14 h, CO₂ and RH on food consumption and relative growth rate (RGR) of Cnaphalocrocis medinalis was analysed. The food consumption and RGR of C. medinalis larvae were significantly influenced with change in temperature, CO₂ and RH conditions. Food consumption and RGR increased with increase in temperature, CO₂and RH. The increase in temperature (22:32°C to 26:35°C), CO₂concentration (400 to 450 ppm) and RH (75 to 85 %) was found to increase the food consumption (0.0210 to 0.0450 g larva^-1) and RGR (0.0200 to 0.0770 mg mg^-1day^-1).

Hybrid SARIMA–Bi-LSTM model for monthly rainfall forecasting in the agroclimatic zones of Chhattisgarh

2025-04-30T19:59:50+00:00

This study proposes a hybrid Seasonal Autoregressive Integrated Moving Average (SARIMA)–Bidirectional Long Short-Term Memory (Bi-LSTM) model for monthly rainfall forecasting in the agroclimatic zones of Chhattisgarh, India. Accurate rainfall prediction is critical for agricultural planning and water resource management, especially under increasing climate variability. The analysis utilizes 120 years (1901–2020) of monthly rainfall data, preprocessed for time series modeling. SARIMA serves as a statistical baseline, effectively capturing linear and seasonal trends, while Bi-LSTM, a deep learning model, is adept at learning long-term and non-linear dependencies. The hybrid SARIMA–Bi-LSTM model leverages the strengths of both approaches to improve forecasting accuracy. Model performance was evaluated using standard metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Results show that Bi-LSTM outperforms SARIMA, and the hybrid model delivers the best generalization across agroclimatic zones. In the Chhattisgarh Plains, the hybrid model achieved the lowest validation RMSE (41.70 mm), MAE (25.93 mm), and the highest R² (0.906). The study highlights SARIMA’s limitations in capturing non-linearities and Bi-LSTM’s tendency to overfit, both addressed in the hybrid approach. This work demonstrates the effectiveness of hybrid models in enhancing rainfall forecasting and informs climate-resilient agricultural practices.

Strategic targeting and tailoring of Agromet Advisory Services for Kharif sorghum in India

2024-12-19T00:47:53+00:00

Timely availability of reliable, location- and crop-specific weather information is critical for prioritizing actions and minimizing agricultural losses. To tailor and target advisories specific to crop and location, a nested approach was adopted. This approach was applied to Kharif sorghum in which four production zones (Primary, Secondary, Tertiary and others) and four efficiency zones (most efficient, efficient, not efficient, and inefficient) were delineated for two recent decades (2001–2010) and (2011–2020) across 168 sorghum-growing districts. Nesting these zones revealed significant transitions over time, with more than 30 districts showing a notable decline in the most efficient cropping zone (MECZ) and efficient cropping zone (ECZ), underscoring the need for focused attention and intervention. By prioritizing transition zones and tailoring advisories using integrated decision support tools and feedback mechanisms, this approach aims to build resilience and minimize losses due to climate variability and extreme weather events in the miller growing regions.

Climate change and its effects on maize yield in Nepal: An empirical analysis using the ARDL model

2025-01-24T03:20:25+00:00

This study analyzes the impact of climate change on maize yield in Nepal’s Gulmi (hilly) and Rupandehi (Terai) districts using climatic data from 1981 to 2023 on rainfall, relative humidity, maximum temperature, and minimum temperature applying the Autoregressive Distributed Lag (ARDL) model. The findings obtained ARDL model shows that rainfall positively influences yield in both regions. Relative humidity has a positive long-term effect in Gulmi but a negative impact in Rupandehi. Maximum temperature increases yield in Gulmi but significantly reduces it in Rupandehi, indicating regional sensitivity. Minimum temperature negatively affects Gulmi yields but has a negligible positive effect in Rupandehi. The ARDL models demonstrate strong explanatory power, with adjusted R² values of 0.86 (Gulmi) and 0.80 (Rupandehi), confirming a significant long-term relationship between climate variables and yield. Error correction terms suggest that 28% (Gulmi) and 30% (Rupandehi) of short-term yield deviations adjust back to long-run equilibrium annually. These results highlight the importance of localized climate adaptation strategies in agriculture.

Evaluation of empirical methods for estimating reference evapotranspiration in Central High Lands and Arid Western Lowlands of Eritrea

2025-07-02T17:17:50+00:00

FAO Penman-Monteith (FAO56-PM) method remains difficult to implement across Eritrea due to severe shortages of standardized meteorological data. This study evaluated the accuracy of five alternative empirical methods by comparing them with the FAO56-PM model using established performance metrics (R², RRMSE, NSE, %MBE, and MAPE). Cumulative Performance Index (CPI) was used to determine the overall performances of five alternative ET_o methods. The study identified the modified Hargreaves-Samani (CPI=3.6), Romanenko (CPI=3), and Schendel (CPI=2.6) methods as the most viable simplified alternatives for the data-scarce Central Highlands. However, no method proved optimal for the Arid Western Lowlands. Hargreaves-Samani and Blaney-Criddle methods performed poorly, with combined CPI values of 1.7 and 1.4, respectively. The findings suggest that the modified Hargreaves-Samani and Romanenko methods can effectively replace the FAO56-PM model for estimating crop water requirements in both irrigated and rainfed agricultural systems across all crop types in the Central Highlands. However, the study underscores the critical need for rigorous local calibration and validation of the Hargreaves-Samani, Blaney-Criddle, and Schendel methods to enhance their accuracy.

Trend analysis of air surface temperature using Mann-Kendall test and Sen’s slope estimator in Tunisia

2025-05-04T18:12:00+00:00

This study examines long-term (2003–2021) air surface temperature (AST) trends on monthly, seasonal, and annual patterns based on Atmospheric Infrared Sounder (AIRS) data from seven Tunisian sites using the MAKESENS model. Monthly AST patterns reveal a single peak, with January showing the lowest (280.29° K) and July the highest temperatures (312.40° K). Over 19 years, central stations exhibited stronger warming trends than desert and coastal regions. Tozeur showed the highest annual warming trend at 0.070° K year^-1, while EL-Borma recorded a slight cooling trend of -0.009° K year^-1. The Mann-Kendall test on AST data reveals significant monthly, seasonal, and annual trends. Positive trends were observed in January, February, April, May, November, and December, with negative trends in March, August, and October. Seasonally, warming trends was significant in winter and spring, with cooling trends in summer and autumn. Annual trends were predominantly positive across most stations. Warming was slower in summer and autumn along the Mediterranean coast but accelerated in winter and spring, particularly in continental areas. Regions south of 34° N warmed faster than northern and eastern regions, reinforcing the Mediterranean coastline as a climate change hotspot.

Rainfall analysis for crop planning for paddy grown in North Bank Plain Zone of Assam

2025-06-24T20:19:41+00:00

This study was undertaken to analyse the rainfall data for crop planning in three districts (Lakhimpur, Biswanath, and Sonitpur) of the North Bank Plain Zone (NBPZ) of Assam using long-term rainfall data (1991-2020). During the study period, the mean annual rainfall of 3209, 1811, and 1828 mm was observed in Lakhimpur, Biswanath, and Sonitpur, respectively. A non-significant decreasing trend of annual rainfall was observed in Lakhimpur (2.75 mm year^-1) and Sonitpur (8.62 mm year^-1), while an increasing trend was observed in Biswanath (8.98 mm year^-1). In all districts, regardless of probability levels, the maximum and minimum expected rainfall was found between the 26^th to 30^th and 49^th to 2^nd SMW, respectively. The expected weekly rainfall during the monsoon season was lower in Biswanath and Sonitpur at all probability levels compared to the Lakhimpur district. Based on the understanding of existing patterns, variability of rainfall, probability of occurrence of rainfall in a period, observed rainfall trends, etc. the contingency crop planning for Sali, Ahu and Boro rice grown in the zone were suggested for the concern districts.

Agrometeorological advisory services in Bangladesh for sustainable agriculture development: An overview

2025-03-25T17:50:37+00:00

Agromet Advisory Services (AAS) is a program run by the Agro-meteorological Information Systems Development Project (AMISDP) under Department of Agricultural Extension (DAE), Ministry of Agriculture, Bangladesh to address the issues related to climate change and variability impact on food security and sustainable agricultural output and other issues. By maximizing the benefits of favorable weather and reducing the negative effects of unfavorable weather, AASs provide farmers with a unique type of input in the form of advisories that can significantly improve agricultural productivity. This might significantly alter Bangladesh's situation with regard to food security and the reduction of poverty. AMISDP, DAE, provides agro-meteorological services that are a step toward supporting weather-based crop and livestock management plans and operations aimed at improving crop production in a sustainable way. The current article provides an overview of the project and discusses the various actions and initiatives that fall under these services, as well as the ways in which farmers and the environment may benefit from the use of weather and climate information.