Search Results (15)

Climate variability poses major challenges to agriculture worldwide amid an increasing world population and growing food demand. This study evaluates the impact of climate variability on rice production in Liberia. Rice yields and production data (1990–2023) were attained from the Food and Agriculture Organization Statistics (FAOSTAT), while temperature and precipitation were sourced from ERA5 Agrometeorological Indicators and the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS). Trends and relationships were analyzed using Mann–Kendall, Sen’s slope tests, and Spearman’s rank correlation. Multiple linear regression estimates climate variables’ impact on rice productivity. The results show that mean, minimum, and maximum temperatures increased by 0.57 °C, 0.55 °C, and 0.55 °C, respectively, with precipitation variability at 180.31 mm. Climate variables showed diverse correlations with rice production. Regression results revealed a significant negative impact of minimum temperature (p-value = 0.015) on production and a positive effect of precipitation on yields (p-value = 0.036). Farmers in Liberia recognized climate impacts and adopted adaptation strategies, but resilience is hindered by limited credit access, low technology adoption, reliance on traditional practices, and inadequate extension services. Overall, the findings highlight the sensitivity of rice production in Liberia to climate variability and underscore the need for guided adaptation and institutional support to augment farmer resilience. Full article

The use of cover crops is one of the most effective practices for maintaining, or even improving, the carbon balance of agricultural soils, while offering various ecosystem benefits. However, replacing bare soil with cover crops can increase transpiration and potentially reduce the water available for subsequent cash crops. The study takes place in southwestern France where it is essential to strike a balance between carbon storage and water availability, and where agroecological practices are encouraged and water resources are limited and expected to diminish with climate change. In this study, estimates of cover crop biomass production, as well as of the components of the water and carbon cycles, are carried out using a hybrid approach, AgriCarbon-EO, combining modeling, remote sensing, and assimilation, with quantification of target variables and their uncertainties at decametric resolution. The SAFYE-CO₂ agrometeorological model used in AgriCarbon-EO is calibrated to represent cover crops development, and simulated variables are compared with CO₂ fluxes and evapotranspiration measured by eddy covariance (for NEE, R² = 0.57, RMSE = 0.97 gC·m⁻²; for ETR, R² = 0.42, RMSE = 0.87 mm), as well as to an extensive above-ground biomass dataset (R² = 0.71, RMSE = 93.3 g·m⁻²). Knowing the local performance of the approach, a large-scale, decametric-resolution modeling exercise was carried out to simulate winter cover crops in southwestern France, over five contrasting fallow periods. The significant variability in cover crop phenology and above-ground biomass was characterized, and estimates of the amount of humified carbon added to the soil by cover crops were quantified at the pixel level. With amounts ranging from 40 to 130 gC·m⁻² for most of the considered pixels, these new SOC values show clear trends as a function of cumulative evapotranspiration. However, the impact of cover crops on soil water content appears to be minimal due to spring precipitation. Full article

This study analyzes the evolution of hydrometeorological risks and severe weather events in Catalonia through an extensive review of 21,312 news reports aired by Televisió de Catalunya (TVC) between 1984 and 2019, 10,686 (50.1%) of which focused on events within Catalonia. The reports are categorized by the type of phenomenon, geographic location, and reported impact, enabling the identification of temporal trends. The results indicate a general increase in the frequency of news coverage of hydrometeorological and severe weather events—particularly floods and heavy rainfall—both in Catalonia and the broader Mediterranean region. This rise is attributed not only to a potential increase in such events, but also to the expansion and evolution of media coverage over time. In the Catalan context, the most frequently reported hazards are snowfalls and cold waves (3203 reports), followed by rainfall and flooding (3065), agrometeorological risks (2589), and wind or sea storms (1456). The study highlights that rainfall and flooding pose the most significant risks in Catalonia, as they account for the majority of the reports involving serious impacts—1273 cases of material damage and 150 involving fatalities. The normalized data reveal a growing proportion of reports on violent weather and floods, and a relative decline in snow-related events. Full article

A farm-specific early warning system has been developed to mitigate agricultural damage caused by climate change. This system utilizes weather data at the farm level to predict crop growth, forecast weather disaster risks, and provide risk alerts to farmers and local governments. For effective implementation, local governments must lead operating early warning services that reflect regional agricultural characteristics and farmers’ needs, while the central government provides foundational data. The system connects data from each region to the cloud, enabling the establishment of a nationwide integrated service operation framework that includes the central government, metropolitan cities, municipalities, and farmers. Full article

Estimating evapotranspiration is crucial for irrigation and agricultural applications. Although the FAO-56 Penman–Monteith method is highly accurate under conditions of abundant data, its extensive requirements limit its practical application. In the Mediterranean region, most empirical formulas used to estimate evapotranspiration are temperature-based and require calibration to be effective. The current study aims to introduce a novel formula to determine reference evapotranspiration using temperature, relative humidity, and extraterrestrial radiation daily in the Mediterranean region and evaluate its performance. Multi-linear regression was applied to agrometeorological data from the California Irrigation Management Information System (CIMIS) to develop the ETo formula. The formula was then validated using data from 252 stations in four countries (Greece, Spain, Portugal, and Cyprus) over the growing period of six years (2018–2023). The GAIA formula consistently outperformed formulas with the same or fewer variables, including Berti and Ahooghalaandari, across all metrics. The largest differences were observed in RMSE and the index of agreement. There is a strong correlation between GAIA and the FAO-56 Penman–Monteith formula (coefficient of determination = 0.88). While the GAIA formula shows a high coefficient of determination, its performance is somewhat lower than that of Copais and Valiatzas, particularly in terms of Pearson correlation and the coefficient of determination. A key difference is that Copais and Valiatzas rely on incoming solar radiation, whereas GAIA uses extraterrestrial radiation. Relative humidity was found to be the most influential variable, accounting for over 71% of the variance in ETo. Effective evapotranspiration (ETo) calculation methodologies, especially in areas with limited agrometeorological data, can significantly enhance irrigation system efficiency and promote sustainable water management. The GAIA formula offers a cost-effective method for estimating reference evapotranspiration (ETo) during the growing season with enhanced accuracy, eliminating the need for expensive equipment. However, its primary limitation is that it is validated in the Mediterranean region and within a specific geographical latitude range. Full article

This study aims to address the challenges of climate change, which has led to extreme temperature events and reduced rainfall, using Internet of Things (IoT) technologies. Specifically, we monitored the effects of drought on maize crops in the Republic of Croatia. Our research involved analyzing an extensive dataset of 139,965 points of weather data collected during the summer of 2022 in different areas with 18 commercial sensor nodes using the Long-Range Wide Area Network (LoRaWAN) protocol. The measured parameters include temperature, humidity, solar irradiation, and air pressure. Newly developed maize-specific predictive models were created, taking into account the impact of urbanization on the agrometeorological parameters. We also categorized the data into urban, suburban, and rural segments to fill gaps in the existing literature. Our approach involved using 19 different regression models to analyze the data, resulting in four regional models per parameter and four general models that apply to all areas. This comprehensive analysis allowed us to select the most effective models for each area, improving the accuracy of our predictions of agrometeorological parameters and helping to optimize maize yields as weather patterns change. Our research contributes to the integration of machine learning and AI into the Internet of Things for agriculture and provides innovative solutions for predictive analytics in crop production. By focusing on solar irradiation in addition to traditional weather parameters and accounting for geographical differences, our models provide a tool to address the pressing issue of agricultural sustainability in the face of impending climate change. In addition, our results have practical implications for resource management and efficiency improvement in the agricultural sector. Full article

Frost is one of the most frequent, intense, and influential agrometeorological disasters that occurs frequently in Northeast China. The study of the spatiotemporal changes of ground frost is of great significance for farmers and policymakers in Northeast China, as it can inform decisions related to crop selection, planting schedules, and the development of regional climate adaptation plans. In this study, the spatiotemporal changes of frost indices (last spring frost (LSF), first fall frost (FFF), and frost-free period (FFP)) in Northeast China were analyzed from 1961 to 2020. Then, we investigated the mutation characteristics of the frost indices and their correlation with geographical factors. The results revealed that (1) the LSF, FFF, and FFP in Northeast China were concentrated at 120–140 DOY, 260–280 DOY, and 110–170 days, respectively. The spatial distribution of frost indices exhibited significant spatial heterogeneity. (2) The LSF, FFF, and FFP showed significant trends of advancement, delay, and extension, with trends of −1.94 days/10 a, 1.72 days/10 a, and 4.21 days/10 a, respectively. (3) More than 80% of the LSF, FFF, and FFP of the sites showed trends of advancement, delay, and extension, with greater variability in the central part of Heilongjiang Province. (4) The FFF and FFP experienced an abrupt change in the late 1990s. (5) The correlation between latitude and LSF, FFF, and FFP was the strongest, with correlation coefficients of 0.77, −0.79, and −0.78, respectively. This study provides a comprehensive understanding of the changing characteristics of ground frost indices that impact agricultural production in Northeast China against the backdrop of climate change. The findings hold significant scientific value in guiding the adaptation of agricultural production layouts in Northeast China to the evolving climatic conditions. Full article

Agro-meteorological disasters are a significant cause of crop yield reduction. Northeast China is a major base for commodity grain production and is also highly sensitive to climate change. Early frost is one of the most significant meteorological disasters in Northeast China. The typical weather system serves as the primary meteorological cause of the occurrence of early frost. The Northeast Cold Vortex is a cyclonic system of certain intensity located in Northeast China, which has the potential to induce severe weather conditions such as extreme low temperatures and intense convection. Despite extensive research on the first occurrence of frost in Northeast China, the evolutionary characteristics under the combined influence of climate change and the Northeast Cold Vortex remain unclear. This limitation hinders the development of effective monitoring and early warning systems for early frost, as well as the formulation of disaster prevention and mitigation plans for crop production. Therefore, this study aims to objectively document the occurrence of early frost in maize crops in Northeast China from 1961 to 2021 under the influence of the Northeast Cold Vortex. It seeks to unveil the climatic characteristics and evolutionary patterns of early frost events in maize crops within this region, considering the impact of the Northeast Cold Vortex. Additionally, it endeavors to analyze the factors contributing to varying degrees of early frost caused by the Northeast Cold Vortex. The results showed that the occurrence of both early frost and frost influenced by the Northeast Cold Vortex exhibited a declining trend. Furthermore, there was also a decreasing proportion of initial frost attributed to the Northeast Cold Vortex, with a decline rate of 2% per decade, indicating a diminishing dominance of initial frost caused by this weather system. The onset date for the early frost under the influence of the Northeast Cold Vortex progressively advanced from southeast to northwest, occurring 4 days earlier than during the period from 1961 to 1990 between 1991 and 2021. While early frost displayed an increasing spatial distribution from southeast to northwest, it is noteworthy that the majority concentration of the Northeast Cold Vortex was observed in central regions, highlighting its predominant role in causing early frost in Northeast China. 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

Solar radiation received at the Earth’s surface provides the energy driving all micro-meteorological phenomena. Local solar radiation measurements are used to estimate energy mediated processes such as evapotranspiration (ET); this information is important in managing natural resources. However, the technical requirements to reliably measure solar radiation limits more extensive adoption of data-driven management. High-quality radiation sensors are expensive, delicate, and require skill to maintain. In contrast, low-cost sensors are widely available, but may lack long-term reliability and intra-sensor repeatability. As weather stations measure solar radiation and other parameters simultaneously, machine learning can be used to integrate various types of environmental data, identify periods of erroneous measurements, and estimate corrected values. We demonstrate two case studies in which we use neural networks (NN) to augment direct radiation measurements with data from co-located sensors, and generate radiation estimates with comparable accuracy to the data typically available from agro-meteorology networks. NN models that incorporated radiometer data reproduced measured radiation with an R² of 0.9–0.98, and RMSE less than 100 Wm⁻², while models using only weather parameters obtained R² less than 0.75 and RMSE greater than 140 Wm⁻². These cases show that a simple NN implementation can complement standard procedures for estimating solar radiation, create opportunities to measure radiation at low-cost, and foster adoption of data-driven management. Full article

In view of climate change and the active extension of soybean cultivation in Russia, the identification of yield-limiting factors has become a relevant task. The objective of this study was to identify the climatic factors associated with the variation in soybean productivity under the contrasting eco-geographical conditions of the Krasnodar (KR) and Primorye (PR) territories of Russia. An analysis of 424 soybean varieties from the global collection of the N.I. Vavilov Institute (VIR) at experimental stations in KR and PR in 1987–2005 showed that the soybean yields were higher and time to maturity was longer in KR than in PR, while the 1000 seed weight, on average, was irrelevant to the place of cultivation. The agrometeorological regression models of the observations in 1972–2017 of varieties accepted as the standards showed that the yield in PR was positively related to the sum of the temperatures above 10 °C and negatively related to precipitation in October, while in KR it was positively related to the hydrothermal coefficient. The stability of the soybean yield and of the time to maturity were higher in PR than in KR. Under the conditions of increasing temperatures and the absence of reliable trends for precipitation, a lack of moisture becomes a significant disadvantage for soybean in KR, while in PR conditions are improving. Full article

Today, good food criteria also include healthy capacity. So, the wine on our table should not only have good organoleptic qualities, but should be characterized by a high healthy potential. For the first time, extensive research was conducted on commercial red wine grape varieties cultivated in different Bulgarian regions in two consecutive years. Antioxidants, including trans-resveratrol, quercetin, and total phenolic content and antioxidant potential in wine grapes and wines were determined by HPLC, UV, and NIR methods. The results obtained showed similar concentration levels compared to the same varieties, produced in other countries. Trans-resveratrol showed the greatest contribution to the radical scavenging capacity. The factor with largest impact on the content of the tested substances was definitely the variety. Among agro-meteorological condition, temperature amplitude, rain fall, and UV irradiation before ripening had strong influences. Maintaining the balance between the level of synthesized and degraded and captured antioxidants during the wine making process was crucial to preserving the antioxidant properties of the final wine product. NIR spectroscopy showed very good accuracy of determination of trans-resveratrol, quercetin, total phenolic content, and the antioxidant activity of tested grape varieties and red wines. It could be a promising technique in the quantification of their antioxidant parameters. Full article

Agriculture production in Nigerien rural areas mainly depends on weather variability. Weather forecasts produced by national or international bodies have very limited dissemination in rural areas and even if broadcast by local radio, they remain generic and limited to short-term information. According to several experiences in West Africa, weather and climate services (WCSs) have great potential to support farmers’ decision making. The challenge is to reach local communities with tailored information about the future weather to support strategic and tactical crop management decisions. WCSs, in West Africa, are mainly based on short-range weather forecasts and seasonal climate forecasts, while medium-range weather forecasts, even if potentially very useful for crop management, are rarely produced. This paper presents the results of a pilot initiative in Niger to reach farming communities with 10-day forecasts from the National Oceanic and Atmospheric Administration—Global Forecast System (NOAA-GFS) produced by the National Centers for Environmental Prediction (NCEP). After the implementation of the download and treatment chain, the Niger National Meteorological Directorate can provide 10-day agrometeorological forecasts to the agricultural extension services in eight rural municipalities. Exploiting the users’ evaluation of the forecasts, an analysis of usability and overall performance of the service is described. The results demonstrate that, even in rural and remote areas, agrometeorological forecasts are valued as powerful and useful information for decision-making processes. The service can be implemented at low cost with effective technologies making it affordable and sustainable even in developing countries. Nonetheless, the service’s effectiveness depends on several aspects mainly related to the way information is communicated to the public. Full article

The crop coefficient (Kc) is widely used for operational estimation of actual evapotranspiration (ETa) and crop water requirements. The standard method for obtaining Kc is via a lookup table from FAO-56 (Food and Agriculture Organization of the United Nations Irrigation and Drainage Paper No. 56), which broadly treats Kc as a function of four crop-growing stages. However, the distinctive physiological characteristics of overwintering crops, such as winter wheat (Triticum aestivum L.), which is extensively planted in the North China Plain (NCP), are not addressed in this method. In this study, we propose a stage-wise method that accounts for Kc variations for winter wheat at each critical phenological stage, thereby estimating Kc at finer temporal scales. Compared with the conventional FAO method, the proposed stage-wise method successfully captures the bimodal pattern in Kc time series for winter wheat, which is shown at both ten-day and phenological time scales. In addition, the accuracies of the proposed stage-wise Kc method and the FAO method were evaluated using micro-meteorological measurements of ETa collected at the Gucheng agrometeorological experimental station in the NCP. Using a leave-one-out strategy, the evaluation revealed that the stage-wise method significantly outperformed the FAO method at both daily and critical phenological time scales, with root-mean-square errors in ETa for the stage-wise method and the FAO method being 0.07 mm·day-1 and 0.16 mm·day-1, respectively, at the daily time scale, and 0.01 mm·day-1 and 0.27 mm·day-1 at the critical phenological time scale. Generally, the FAO method underestimates ETa during the initial stage and overestimates ETa during both the development and mid-season stages. It is shown that the proposed stage-wise method is important for the water-stressed NCP where precision irrigation is highly desirable, especially during the critical phenological stages. Results from this study provide insight into accurate estimation of water requirements for winter wheat at phenological time scales. Full article

Extension agrometeorology is applied in agrometeorological extension work to advice and serve farmers. In agrometeorology, response farming has been developed decades ago. Climate change complicates response farming, but does not alter it. This paper reports on new operationalization of that response farming in new educational commitments in agroclimatology. It is explained how “Science Field Shops” are an example in Indonesia. This was based on a thorough analysis of what climate change means for farmers in Asia. For Africa, we report on eying the training of agrometeorological extension trainers (“product intermediaries”) in West Africa, based on a thorough analysis of what climate change means for farmers in Africa. We also compare experience with reaching farmers in South Africa and farmer communities in Zambia, as new forms of supporting response farming, all under conditions of a changing climate. The paper, for the first time, connects results from four different programs the senior author is taking part in. There is first and foremost the need for training material to make it possible for the product intermediaries to participate in training extension intermediaries. This should, particularly, bring new knowledge to farmers. With what is presently available and with new approaches, climate extension should be developed and tested with farmers in ways that improve farmer preparedness and decision making. Full article