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.

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.

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 ETo 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.

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.

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.

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 39th Standard Meteorological Week (SMW) indicated that lag-time period of four week i.e., advance prediction of peak YSB population by 35th 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.

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 26th to 30th and 49th to 2nd 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.

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.