Search Results (26)

The need for systems that forecast and respond proactively to meteorological disasters is growing amid climate variability. Although the early warning system in South Korea includes countermeasure information, it remains limited in terms of data recency, granularity, and regional adaptability. Additionally, its closed architecture hinders interoperability with external systems. This study aims to redesign the countermeasure function as an independent cloud-based platform grounded in the common standard terminology framework in South Korea. A multi-dimensional data model was developed using attributes such as crop type, cultivation characteristics, growth stage, disaster type, and risk level. The platform incorporates user-specific customization features and history tracking capabilities, and it is structured using a microservices architecture to ensure modularity and scalability. The proposed system enables real-time management and dissemination of localized countermeasure suggestions tailored to various user types, including central and local governments and farmers. This study offers a practical model for enhancing the precision and applicability of agrometeorological response information. It is expected to serve as a scalable reference platform for future integration with external agricultural information systems. Full article

This paper presents a new model for low-cost solar-powered Automatic Weather Stations based on the ESP-32 microcontroller, modern sensors, and intelligent forecasts for Brasilia. The proposed system relies on compact, multifunctional sensors and features an open-source firmware project and open-circuit board design. It includes a BME688, AS7331, VEML7700, AS3935 for thermo-hygro-barometry (plus air quality), ultraviolet irradiance, luximetry, and fulminology, besides having a rainfall gauge and an anemometer. Powered by photovoltaic panels and batteries, it operates uninterruptedly under variable weather conditions, with data collected being sent via WiFi to a Web API that adapts the MZDN-HF (Meteorological Zone Delimited Neural Network–Hourly Forecaster) model compilation for Brasilia to produce accurate 24 h multivariate forecasts, which were evaluated through MAE, RMSE, and R² metrics. Installed at the University of Brasilia, it demonstrates robust hardware performance and strong correlation with INMET’s A001 data, suitable for climate monitoring, precision agriculture, and environmental research. Full article

Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence (AI) approaches have shown promising potential for soil moisture retrieval at the local scale but have rarely demonstrated substantial products for spatially continuous grids. This paper presents the retrieval of daily soil moisture (SM) over a 500 m grid for croplands in South Korea using random forest (RF) and automated machine learning (AutoML) models, leveraging satellite images and meteorological data. In a blind test conducted for the years 2013–2019, the AutoML-based SM model demonstrated optimal performance, achieving a root mean square error of 2.713% and a correlation coefficient of 0.940. Furthermore, the performance of the AutoML model remained consistent across all the years and months, as well as under extreme weather conditions, indicating its reliability and stability. Comparing the soil moisture data derived from our AutoML model with the reanalysis data from sources such as the European Space Agency Climate Change Initiative (ESA CCI), GLDAS, the Local Data Assimilation and Prediction System (LDAPS), and ERA5 for the South Korea region reveals that our AutoML model provides a much better representation. These experiments confirm the feasibility of AutoML-based SM retrieval, particularly for local agrometeorological applications in regions with heterogeneous land surfaces like South Korea. Full article

The study explores the feasibility of adapting the EOStat crop monitoring system, originally designed for monitoring crop growth conditions in Poland, to fulfill the requirements of a similar system in Ukraine. The system utilizes satellite data and agrometeorological information provided by the Copernicus program, which offers these resources free of charge. To predict crop yields, the system uses several factors, such as vegetation condition indices obtained from Sentinel-3 Ocean and Land Color Instrument (OLCI) optical and Sea and Land Surface Temperature Radiometer (SLSTR). It also incorporates climate information, including air temperature, total precipitation, surface radiation, and soil moisture. To identify the best predictors for each administrative unit, the study utilizes a recursive feature elimination method and employs the Extreme Gradient Boosting regressor, a machine learning algorithm, to forecast crop yields. The analysis indicates a noticeable decrease in crop losses in 2022 in certain regions of Ukraine, compared to the previous year (2021) and the 5-year average (2017–2021), specifically for winter crops and maize. Considering the reduction in yield, it is estimated that the decline in production of winter crops in 2022 was up to 20%, while for maize, it was up to 50% compared to the decline in production. Full article

In recent years, the importance and severity of weather-related disasters have escalated, attributed to rising temperatures and the occurrence of extreme weather events due to global warming. The focus of disaster management has shifted from crisis management (e.g., repairing and recovering from damage caused by natural disasters) to risk management (e.g., prediction and preparation) while concentrating on early warning, thanks to the development of media and communication conditions. The Rural Development Administration (Korea) has developed the “early warning service for weather risk management in the agricultural sector” that detects weather risks for crops from high-resolution weather information in advance and provides customized information to respond to possible disaster risks in advance in response to the increasing number of extreme weather events. The core technology of this service is damage prediction technology that determines the overall agricultural weather risk level by quantifying the current growth stage of cultivated crops and the probability of possible weather disasters according to the weather conditions of the farm. Agrometeorological disasters are damages caused by weather conditions that can affect crops and can be predicted by estimating the probability of damage that may occur from the interaction between hazardous weather and crop characteristics. This review introduces the classification of possible weather risks by their occurrence mechanisms, based on the developmental stage of crops and prediction techniques that have been developed or applied to date. The accumulated crop growth and weather risk information is expected to be utilized as support material for farming decision-making, which helps farmers proactively respond to crop damage due to extreme weather events by providing highly reliable disaster forecasts through the advancement of prediction technology. Full article

Climate plays a major role in the spatiotemporal distribution of most agricultural systems, and the economic losses related to climate and weather extremes have escalated significantly in the last decades. South America is one of the most productive agricultural areas of the globe. In recent years, remote sensing data and geographic information systems have been used to improve geo-environmental hazard assessment. However, food security is still highly dependent on small farmer practices that are frequently the most vulnerable to climate extremes. This work reviews climate and weather extremes’ impacts on crop production for South American countries, focusing on the projected ones considering different climate scenarios and countries. A positive trend in the productivity of maize, mainly related to agricultural improvements, was recently observed in Colombia, Ecuador, and Uruguay by up to 200%, as well as in the case of soybean in Bolivia and Uruguay by about 125%. Despite the generalized adverse impacts of climate extremes, results from agrometeorological models generally indicate an increase in crop production in southern regions of Chile (and highlands) and Brazil mainly related to increased temperature. Positive impacts in response to CO₂ fertilization are also foreseen in Peru and Brazil (southeast, south, and Minas Gerais); in particular, in Brazil, increases in productivity can be raised by about 40%. The use of double-cropping systems, although with very good results in recent years, may also be at risk in a few decades, mainly due to forecasted precipitation decrease, delay in rainy season onset, and temperature increase. The development of timely early warning systems is imperative to produce technically accurate alerts and the interpretation of the risk assessment based on the link between producers and consumers. Promoting climate index insurance is crucial to build resilient food production, but its implementation should rely on regional or international support systems. Moreover, the implementation of adaptation and mitigation also requires climate-resilient technologies that involve an interdisciplinary approach. Full article

The evaluation of weather forecast accuracy is of major interest in decision making in almost every sector of the economy and in civil protection. To this, a detailed assessment of Bologna Limited-Area Model (BOLAM) seven days fine grid 3 h predictions is made for precipitation, air temperature, relative humidity, and wind speed over a large lowland agricultural area of a Mediterranean-type climate, characterized by hot summers and rainy moderate winters (plain of Arta, NW Greece). Timeseries that cover a four-year period (2016–2019) from seven agro-meteorological stations located at the study area are used to run a range of contingency and accuracy measures as well as Taylor diagrams, and the results are thoroughly discussed. The overall results showed that the model failed to comply with the precipitation regime throughout the study area, while the results were mediocre for wind speed. Considering relative humidity, the results revealed acceptable performance and good correlation between the model output and the observed values, for the early days of forecast. Only in air temperature, the forecasts exhibited very good performance. Discussion is made on the ability of the model to predict major rainfall events and to estimate water budget components as rainfall and reference evapotranspiration. The need for skilled weather forecasts from improved versions of the examined model that may incorporate post-processing techniques to improve predictions or from other forecasting services is underlined. Full article

In Niger (a fully Sahelian country), the use of climate information is one of the early warning strategies (EWSs) for reducing socio-economic vulnerabilities in farmer communities. It helps farmers to better anticipate risks and choose timely alternative options that can allow them to generate more profit. This study assesses the impacts of the use of climate information and services that benefit end-users. Individual surveys and focus groups were conducted with a sample of 368 people in eight communes in Southwestern Niger. The survey was conducted within the framework of the ANADIA project implemented by the National Meteorological Direction (NMD) of Niger. The survey aims to identify different types of climate services received by communities and evaluates the major benefits gained from their use. Mostly, the communities received climate (73.6%) and weather (99%) information on rainfall, temperature, dust, wind, clouds, and air humidity. Few producers in the area (10%) received information on seasonal forecasts of the agrometeorological characteristics of the rainy season. The information is not widely disseminated in the villages during the roving seminars conducted by the NMD. For most people, this information is highly relevant to their needs because of its practical advice for options to be deployed to mitigate disasters for agriculture, livestock, health, water resources, and food security. In those communities, 82% of farmers have (at least once) changed their routine practices as a result of the advice and awareness received according to the climate information. The information received enables farmers (64.4%) to adjust their investments according to the profile of the upcoming rainfall season. The use of climate information and related advice led to an increase of about 64 bunches (equivalent to 10 bags of 100 kg) in annual millet production, representing an income increase of about 73,000 FCFA from an average farmland of 3 ha per farmer. In addition, the use of climate information helps to reduce the risks of floods and droughts, which often cause massive losses to crop production, animal and human life, infrastructure, materials, and goods. It has also enabled communities to effectively manage seeds and animal foods and to plan social events, departures and returns to rural exodus. These analyses confirm that the use of climate information serves as an EWS that contributes to increasing the resilience of local populations in the Sahel. Full article

Climate change is often linked with record-breaking heavy or poor rainfall events, unprecedented storms, extreme day and night time temperatures, etc. It may have a marked impact on climate-sensitive sectors and associated livelihoods. Block-level weather forecasting is a new-fangled dimension of agrometeorological services (AAS) in the country and is getting popularized as a climate-smart farming strategy. Studies on the economic impact of these microlevel advisories are uncommon. Agromet advisory services (AAS) play a critical role as an early warning service and preparedness among the maize farmers in the Parambikulam–Aliyar Basin, as this area still needs to widen and deepen its AWS network to reach the village level. In this article, the responses of the maize farmers of Parambikulam–Aliyar Basin on AAS were analyzed. AAS were provided to early and late Rabi farmers during the year 2020–2022. An automatic weather station was installed at the farmers’ field to understand the real-time weather. Forecast data from the India Meteorological Department (IMD) were used to provide agromet advisory services. Therefore, the present study deserves special focus. Social media and other ICT tools were used for AAS dissemination purposes. A crop simulation model (CSM), DSSAT4.7cereal maize, was used for assessing maize yield in the present scenario and under the elevated GHGs scenario under climate change. Our findings suggest that the AAS significantly supported the farmers in sustaining production. The AAS were helpful for the farmers during the dry spells in the late samba (2021–2022) to provide critical irrigation and during heavy rainfall events at the events of harvest during early and late Rabi (2021–22). Published research articles on the verification of weather forecasts from South India are scanty. This article also tries to understand the reliability of forecasts. Findings from the verification suggest that rainfall represented a fairly good forecast for the season, though erratic, with an accuracy score or HI score of 0.77 and an HK score of 0.60, and the probability of detection (PoD) of hits was found to be 0.91. Verification shows that the forecasted relative humidity observed showed a fairly good correlation, with an R² value of 0.52. These findings suggest that enhancing model forecast accuracy can enhance the reliability and utility of AAS as a climate-smart adaptation option. This study recommends that AAS can act as a valuable input to alleviate the impacts of hydrometeorological disasters on maize crop production in the basin. There is a huge demand for quality weather forecasts with respect to accuracy, resolution, and lead time, which is increasing across the country. Externally funded research studies such as ours are an added advantage to bridge the gap in AAS dissemination to a great extent. Full article

This present study aims to determine the performance of using the Weather Research and Forecasting (WRF) Model, coupled with the urban canopy models (UCMs), in simulating the 2 m air temperature and 2 m relative humidity in Metro Manila. The simulation was performed during a high heat event on 22–29 April 2018, which coincided with the dry season in the Philippines. The four urban canopy model options that were used in this study include, the bulk (no urban), SLUCM, BEP, and BEM. The results of the simulations were compared with the hourly observations from three weather stations over Metro Manila from the National Oceanic and Atmospheric Administration Integrated Surface Dataset (ISD) and one agrometeorological station in Naic, Cavite. After model validation, the urban heat island (UHI) was then characterized to determine the spatial-temporal variations in the cities of Metro Manila. Statistical results show that the WRF simulation for 2 m air temperature agrees with measurements with an RMSE of <3.0 °C, mean bias error of <2.0 °C, and index of agreement of >0.80. WRF simulation for relative humidity still presents a challenge where simulation errors are higher than the acceptable range. The addition of UCMs does not necessarily improve the simulation for 2 m air temperature, while the use of BEP improved the 2 m relative humidity simulation. The results suggest the importance of using actual urban morphology values in WRF to accurately simulate near-surface variables. On the other hand, WRF simulation shows the presence of urban heat islands, notably in the northwest and central area of Metro Manila during daytime, extending throughout Metro Manila during nighttime. Lower air temperature was consistently observed in areas near Laguna Lake, while higher air temperature due to stagnant winds was observed in the northwest area of Metro Manila. High heat index was also observed throughout Metro Manila from daytime until nighttime, especially in areas near bodies of water like Manila Bay and Laguna Lake due to high humidity. Full article

Food and feed production must be increased or maintained in order to meet the demands of the earth’s population. Under this scenario, the question that arises is how to address the demand for agricultural products given that the pressures on land use have already increased. In addition, it is obvious that climate change will have a serious negative impact and threaten the productivity and sustainability of food production systems. Therefore, understanding and predicting the outcome of crop production, while considering adaptation and sustainability, is essential. The need for information on decision making at all levels, from crop management to adaptation strategies, is constantly increasing and methods for providing such information are urgently needed in a relatively short period of time. Thus arises the need to use effective data, such as satellite and meteorological data, but also operational tools, to assess crop yields over local, regional, national, and global scales. In this work, three modeling approaches built on a fusion of satellite-derived vegetation indices, agro-meteorological indicators, and crop phenology are tested and evaluated in terms of data intensiveness for the prediction of wheat yields in large scale applications. The obtained results indicated that medium input data intensity methods are effective tools for yield assessments. The methods, namely, a semi-empirical regression model, a machine learning regression model, and a process-based model, provided high to moderate accuracies by fully relying on freely available datasets as sources of input data. The findings are comparable with those reported in the literature for detailed field experiments, thereby introducing a promising framework that can support operational platforms for dynamic yield forecasting, operating at the administrative or regional unit scale. Full article

The Integrated Agrometeorological Prediction System (IAPS) was a two-year project for the development of the long term forecast (LRF) for agricultural producers. Using LRF in decision-making, to reduce the risks and seize the opportunities, represents short-term adaptation to climate change. High-resolution ensemble forecasts (51 forecasts) were made for a period of 7 months and were initiated on the first day of each month. For the initial testing of the capacity of LRF to provide useful information for producers, 2017 was chosen as the test year as it had a very hot summer and severe drought, which caused significant impacts on agricultural production. LRF was very useful in predicting the variables which bear the memory of the longer period, such are growing degree days for the prediction of dates of the phenophases’ occurrences and the soil moisture of deeper soil layers as an indicator for the drought. Other project activities included field observations, communication with producers, web portal development, etc. Our results showed that the selected priority forecasting products were also identified by the producers as being the highest weather-related risks, the operational forecast implementation with the products designed for the use in agricultural production is proven to be urgent and necessary for decision-making, and required investments are affordable. The total cost of the full upgrade of agrometeorological climate services to meet current needs (including monitoring, seamless forecasting system development and the development of tools for information dissemination) was found to be about three orders of magnitude lower than the assessed losses in agricultural production in the two extreme years over the past decade. Full article

Reference evapotranspiration (ETo) is a key agrometeorological index for rational irrigation management. The standard method for ETo estimation, proposed by the FAO, is based on a complicated Penman–Monteith equation and requires many meteorological inputs, making it difficult for practical use by farmers. At present, there are many alternative simplified approaches for ETo estimation; most of them are directed at cutting the number of required meteorological inputs for calculation. Among them, special attention should be paid to the various temperature-based methods of ETo assessment. One of the temperature-based models for ETo computation was realized in the free mobile app ETo Calculator (Ukraine). The app gives Ukrainian farmers an opportunity to assess ETo values on a daily or monthly scale using mean air temperature, obtained through free online meteorological forecasts and archive services, as the only input. The objective of the study was to test the app’s accuracy compared to FAO-based calculations in five key regions of Ukraine, each representing a particular climatic zone of the country. It was established that the app provides relatively good accuracy of ETo estimation even in raw (not adjusted to wind speed and relative air humidity) runs. The results of the statistical comparison with the FAO-calculated values on the daily scale are as follows: R² within 0.82–0.87, RMSE within 0.74–0.81 mm, MAE within 0.60–0.70 mm, MAPE within 18.07–25.50%, depending on the region. The results of the statistical comparison with the FAO-calculated values on the monthly scale are: R² within 0.88–0.95, RMSE within 0.50–0.72 mm, MAE within 0.33–0.59 mm, MAPE within 8.96–24.08% depending on the region. The ETo Calculator (Ukraine) is a good alternative to the complicated Penman–Monteith method and could be recommended for Ukrainian farmers to be used for irrigation management. Full article

Timely crop yield forecasts at a national level are substantial to support food policies, to assess agricultural production, and to subsidize regions affected by food shortage. This study presents an operational crop yield forecasting system for Poland that employs freely available satellite and agro-meteorological products provided by the Copernicus programme. The crop yield predictors consist of: (1) Vegetation condition indicators provided daily by Sentinel-3 OLCI (optical) and SLSTR (thermal) imagery, (2) a backward extension of Sentinel-3 data (before 2018) derived from cross-calibrated MODIS data, and (3) air temperature, total precipitation, surface radiation, and soil moisture derived from ERA-5 climate reanalysis generated by the European Centre for Medium-Range Weather Forecasts. The crop yield forecasting algorithm is based on thermal time (growing degree days derived from ERA-5 data) to better follow the crop development stage. The recursive feature elimination is used to derive an optimal set of predictors for each administrative unit, which are ultimately employed by the Extreme Gradient Boosting regressor to forecast yields using official yield statistics as a reference. According to intensive leave-one-year-out cross validation for the 2000–2019 period, the relative RMSE for voivodships (NUTS-2) are: 8% for winter wheat, and 13% for winter rapeseed and maize. Respectively, for municipalities (LAU) it equals 14% for winter wheat, 19% for winter rapeseed, and 27% for maize. The system is designed to be easily applicable in other regions and to be easily adaptable to cloud computing environments such as Data and Information Access Services (DIAS) or Amazon AWS, where data sets from the Copernicus programme are directly accessible. Full article

In the era of global climate change, extreme weather events frequently occur. Many kinds of agro-meteorological disasters that are closely related to environmental conditions (such as sunshine hours, temperature, precipitation, etc.) are witnessed all over the word. However, which factor dominates winter wheat production in the middle and lower reaches of the Yangtze River remains unresolved. Quantifying the key limiting meteorological factor could deepen our understanding of the impact of climate change on crops and then help us to formulate disaster prevention and mitigation measures. However, the relative role of precipitation, sunshine hours and maximum daily temperature in limiting winter wheat yield in the middle and lower reaches of the Yangtze River is not clear and difficult to decouple. In this study, we used statistical methods to quantify the effect of precipitation, maximum temperature and sunshine hours extremes on winter wheat (Triticum aestivum L.) yield based on long time-series, county-level yield data and a daily meteorological dataset. According to the winter wheat growing season period (October of the sowing year to May of the following year), anomaly values of cumulative precipitation, average sunshine hours and average daily maximum temperature are calculated. With the range of −3 σ to 3 σ of anomaly and an interval of 0.5 σ (σ is the corresponding standard deviation of cumulative precipitation, mean maximum temperature and mean sunshine hours, respectively), the corresponding weighted yield loss ratio (WYLR) represents the impact of this kind of climate condition on yield. The results show that excessive rainfall is the key limiting meteorological factor that can reduce winter wheat yield to −18.4% in the middle and lower reaches of the Yangtze River, while it is only −0.24% in extreme dry conditions. Moreover, yield loss under extreme temperature and sunshine hours are negligible (−0.66% for extremely long sunshine hours and −8.29% for extreme cold). More detailed analysis results show that the impact of excessive rainfall on winter wheat yield varies regionally, as it causes severe yield reductions in the Huai River basin and the middle to southern part with low elevation and rainy areas of the study area, while for drier areas in the Hubei province, there is even an increase in yield. Our results disclosed with observational evidence that excessive precipitation is the key meteorological limiting factor leading to the reduction in winter wheat yield in the middle and lower reaches of the Yangtze River. The knowledge of the possible impact of climate change on winter wheat yield in the study area allows policy-makers, agronomists and economists to better forecast a plan that differs from the past. In addition, our results emphasized the need for better understanding and further process-based model simulation of the excessive rainfall impact on crop yield. Full article