Search Results (28)

Climate change is progressively altering the thermal environment of European agriculture, with direct consequences for high-value perennial crops such as olive (Olea europaea L.) and grapevine (Vitis vinifera L.). Although the Growing Degree Days (GDD) index is widely applied to characterize crop thermal requirements, no systematic evidence exists on the actual GDD values accumulated at the locations where these crops are currently grown across Europe. This study introduces a “reverse agroclimatology” approach that anchors GDD calculations exclusively to olive grove and vineyard areas identified in the Corine Land Cover (CLC) dataset for five reference years (1990, 2000, 2006, 2012, and 2018), using ERA5-Land reanalysis daily temperature data as the climatological input. For each CLC reference year, GDD was computed for olive cultivation (T_base = 7 °C, January–May) and viticulture (T_base = 10 °C, April–October) exclusively over registered cultivation pixels, and per-country means were subjected to linear regression trend analysis (p < 0.05). For olive cultivation across 11 Mediterranean countries, statistically significant positive GDD trends were detected in 7 countries, with long-term (1985–2023) country means ranging from 476.2 GDD in France to 1214.3 in Cyprus, indicating that we can revise the known GDD thresholds. The first appearance of olive cultivation in Slovenia’s 2012 CLC dataset, with a median of 546.5 GDD, provides land use-mapped evidence of a spatial displacement of cultivation boundaries. For vineyard cultivation across 22 European countries, significant positive trends were identified in 18 countries, with warming rates reaching 19.25 GDD yr⁻¹ in Turkey, 15.83 GDD yr⁻¹ in Albania, and 14.89 GDD yr⁻¹ in Bosnia and Herzegovina. Mediterranean and Balkan vineyards already exceed the classical 2000 GDD threshold of viticultural suitability across all reference years. In contrast, central and northern European registered vineyards operate below it, though their warmest sites are increasingly approaching or crossing it in the most recent periods. The cultivation-anchored GDD framework, built on openly available data and a fully reproducible R-based pipeline, provides a practical and updatable tool for monitoring the evolving thermal conditions of European olive and wine production under ongoing climate change. Full article

Pinus radiata D. Don is planted in South Central Chile on a wide range of sites using genetically improved genotypes for timber production. As drought events are expected to increase with ongoing climatic change, the variability in gas exchange, which could impact growth and water use, needs to be evaluated. In this study, we assessed the genotypic variability of leaf-level light-saturated photosynthesis (A_sat), stomatal conductance (g_s), transpiration (E), intrinsic water use efficiency (iWUE), and Chlorophyll a fluorescence (OJIP-test parameters) among 30 P. radiata genotypes (i.e., full-sib families) from third-cycle parents at age 6 years on three sites in Central Chile. We also evaluated tree height (HT), diameter at breast height (DBH), and stem index volume (VOL). Families were ranked for HT as top-15 and bottom-15. In the OJIP-test parameters we observed differences at the family level for the maximum quantum yield of primary PSII photochemistry (Fv/Fm), the probability that a photon trapped by the PSII reaction center enters the electron transport chain (ψ_Eo), and the potential for energy conservation from photons captured by PSII to the reduction in intersystem electron acceptors (PI_ABS). Fv/Fm, PI_ABS, and ψ_Eo ranged from 0.82 to 0.87, 45 to 95, and 0.57 to 0.64, respectively. Differences among families for growth and not for leaf-level physiology were detected. DBT, H, and VOL were higher in the top-15 families (12.6 cm, 8.4 m, and 0.10 m³, respectively) whereas A_sat, g_s, E, and iWUE were similar in both the top-15 and bottom-15 families (4.0 μmol m⁻² s⁻¹, 0.023 mol m⁻² s⁻¹, 0.36 mmol m⁻² s⁻¹, and 185 μmol mol m⁻² s⁻¹, respectively). However, no family by site interaction was detected for growth and leaf-level physiology. The results of this study suggest that highly improved genotypes of P. radiata have uniformity in leaf-level physiological rates, which could imply uniform water use at the stand-level. The family variation found in PI_ABS suggests that this parameter could be incorporated to select genotypes tolerant to environmentally stressful conditions. Full article

The global climate change, driven by natural processes and increasing human activities, is especially significant for Georgia. The region is experiencing increases in temperature, desertification, redistribution of precipitation, and a rise in the frequency and severity of extreme weather events. Georgia’s complex topography and its proximity to the Black and Caspian seas make it essential to employ high-resolution regional climate models to evaluate future climate change risks. In this study, we examine the results of a high-resolution simulation of mean and extreme precipitation and temperature using the Abdus Salam International Centre for Theoretical Physics Regional Climate Model version 4.7.1 for the period 1985–2008, providing an initial evaluation of the model’s performance for the territory of Georgia. The model domain (1524 km; 2388 km) encompasses the entirety of Georgia’s territory and surrounding regions. The simulation, conducted at a 12 km horizontal grid spacing using ERA5 data as boundary conditions, indicates that the least discrepancy between observed and modeled average annual temperatures and precipitation, falling within a −1 to 1 °C and −200 to 200 mm range, respectively, was observed at most stations of eastern Georgia. The largest disparities between the model and observed average annual precipitation totals were noted along the Black Sea coast, in the Kolkheti Lowland, and in some high mountain stations in western Georgia. The most significant differences in average annual temperatures between the model and observations were observed in Ambrolauri, Mt. Sabueti, and Dedoplistskaro. For Georgia territory, such a long run with such a high resolution using ERA5 as boundary conditions was conducted for the first time. Overall, the modeling results are quite satisfactory, providing a solid basis for the successful utilization of the regional climate model RegCM4.7.1 with the selected parameterization for modeling monthly mean and extreme temperatures and precipitation in Georgia. Full article

Remote sensing technology is widely used to obtain evapotranspiration (ET_a), but whether it can distinguish the differences in farmland energy balance components and ET_a under different irrigation methods has not been studied. We used Landsat 8 data as the primary dataset to drive the METRIC model and inverted the surface parameters and ET_a of the Shiyang River Basin from 2014 to 2018. After improving the METRIC model using T_a obtained by the regression method instead of interpolation to calculate the net radiation flux (R_n), R² was improved from 0.45 to 0.53, and the RMSE was reduced from 61 W/m² to 51 W/m². The ET_a estimation results on satellite overpass days performed well, with R² equal to 0.93 and RMSE equal to 0.48 mm when compared with the Eddy covariance method (EC) observations. Subsequently, the different growth stages and daily average ET_a estimates of maize were compared with three observations (water balance, WB; Bowen ratio and energy balance method, BREB; and EC). The daily estimates of ET_a correlate well with the observations of BREB (R²_BI = 0.82, R²_DI = 0.92; RMSE_BI = 0.46 mm/day, RMSE_DI = 0.32 mm/day) and EC (R²_BI = 0.85, R²_DI = 0.92; RMSE_BI = 0.45 mm/day, RMSE_DI = 0.34 mm/day), and the estimation for drip irrigation was found to be better than for border irrigation. The total accuracy of the ET_a estimation on the five-year overpass day of maize farmland reached R² = 0.93 and RMSE = 0.48 mm. With sufficient remote sensing data, the 4-year average ET_a of maize was 31 mm lower for DI than for BI, and the mean value of ET_a obtained from the three observation methods was 40 mm. The METRIC model can be used to distinguish ET_a differences between the two irrigation methods in maize farmlands. Full article

The Scholander-type pressure chamber to measure midday stem water potential (MSWP) has been widely used to schedule irrigation in commercial vineyards. However, the limited number of sites that can be evaluated using the pressure chamber makes it difficult to evaluate the spatial variability of vineyard water status. As an alternative, several authors have suggested using the crop water stress index (CWSI) based on low-cost thermal infrared (TIR) sensors to estimate the MSWP. Therefore, this study aimed to develop a low-cost wireless infrared sensor network (WISN) to monitor the spatial variability of MSWPs in a drip-irrigated Cabernet Sauvignon vineyard under two levels of water stress. For this study, the MLX90614 sensor was used to measure canopy temperature (Tc), and thus compute the CWSI. The results indicated that good performance of the MLX90614 infrared thermometers was observed under laboratory and vineyard conditions with root mean square error (RMSE) and mean absolute error (MAE) values being less than 1.0 °C. Finally, a good nonlinear correlation between the MSWP and CWSI (R² = 0.72) was observed, allowing the development of intra-vineyard spatial variability maps of MSWP using the low-cost wireless infrared sensor network. Full article

Evapotranspiration (ET) is an essential part of energy flow between the surface of the earth and the atmosphere, simultaneously involving the water, carbon, and energy cycles. It is mainly determined by climate, land use, and land cover changes. Additionally, there is still a need for quantitative characterization of the impacts of climate factors and human activities on ET and regional water resource efficiency in arid and semiarid regions. Based on Landsat-8 remote sensing imagery and land use data, the crop planting structures in the Liangzhou District of the middle reaches of the Shiyang River Basin were identified using a multiband and multi-temporal approach in this study. Subsequently, the ET of major cash crops was inverted using the three-temperature model. This research quantitatively describes the responses of wheat and corn to the climate and human activities over a two-year period. Furthermore, the impact of crop planting structures and climatic factors on ET was elucidated. The results indicate that a combination of multi-temporal green and shortwave infrared 1 bands is the optimal spectral combination to extract the planting structures. Compared to 2019, the wheat area decreased by 23.27% in 2020, while the corn area increased by 5.96%. Both crops exhibited significant spatial heterogeneity in ET during the growing season. The typical daily range of ET for wheat was 0.4–7.2 mm/day, and for corn, it was 1.5–4.0 mm/day. Among the climatic factors, temperature showed the highest correlation with ET (R = 0.80, p ≤ 0.05). Our research findings provide valuable insights for the fine identification of crop planting structures and a better understanding of the response of ET to climatic factors and planting structures. Full article

Brazil is one of the largest suppliers of commodities in the world, partly due to the agricultural expansion in the Brazilian savannas (also known as Cerrado) that began in the 1970s. However, as areas with better soil and climate for agriculture become scarce, farmers have been advancing to the ecotone between the savanna and xeric shrubland, where precipitation is less reliable for rainfed agriculture. The expected increase in temperature will lead to extended drought periods, with negative consequences for surface and groundwater resources. This study explores the hazards associated with making land-use decisions based on current climatology in regions where projected increases in temperature and reductions in water availability are anticipated to pose significant challenges to rainfed agriculture in the Brazilian Cerrado biome. We modeled future farmland expansion and how that matches with future climate change predictions (2016–2046). According to our estimates, at least 129 thousand km² of cropland and 418 thousand km² of pastures will be added in places with projected higher annual temperatures ranging from 26–30 °C. This is equivalent to ~60% of the current agricultural areas, and a novel agro-climatology will emerge for the Cerrado biome. Therefore, we discuss the agro-environmental policies that are pushing and pulling farmland expansion in the Cerrado. For instance, payments for environmental services could support the conservation of native vegetation on private land in regions with the highest temperature increases and deforestation risks. Moreover, in areas with expected reduced water yields, such as in the western Cerrado, the protection of riparian vegetation and strict regulation of water use could mitigate future risks to agriculture. Full article

Land use and land cover change (LULCC) is the result of both natural and socio-economic determinants. The aim of this study was to model the determinant factors of land cover changes in Raya Valley, Southern Tigray, Ethiopia. Multistage sampling was used to collect data from 246 households sampled from lowlands (47), midlands (104), highlands (93), and sub-alpine (2) agro-climatological zone. Descriptive statistics and logit regression model were used to analyze the field survey data. Agricultural land expansion, fuelwood extraction, deforestation, overgrazing and expansion of infrastructure were the proximate causes of LULCC in the study area. Agricultural land expansion (p = 0.084) and wood extraction for fuel and charcoal production (p = 0.01) were the prominent causes for LULCC. Persistent drought (p = 0.001), rapid population growth (p = 0.027), and climate variability (p = 0.013) were the underlying driving factors of LULCC. The determinants of LULCC need to be considered and mitigated to draw robust land use policy for sustainable land management by the smallholder farmers. This study provides important results for designing and implementing scientific land management strategies by policy makers and land managers. Full article

In the subtropical climate of Taiwan, the cool season (January–June) is most productive for rice cultivation. However, the cool season also sees a large variability and weather impact on the crop. To assess the effect of winter monsoon variability and the warming climate, a common ORYZA(v3) model was used to derive the potential growth and yield of the japonica rice variety in different agro-climatological areas of Taiwan. The simulation was constructed for three planting dates (15 January, 30 January, and 14 February) in three time periods (1986–2005, 2006–2025, and 2026–2045) under a high-emission (RCP8.5) scenario, using a dynamically downscaled regional climate simulation data set (CORDEX). The result indicates that increased temperature during the early season significantly shortens the rice vegetative phase in all planting dates. Compared to the 1986 condition, rice maturation is projected to be 6–9 days and 7–11 days earlier by 2045 for the central-west and the north-east regions, respectively. In the future, decreased duration of crop growth will lead to a lowered yield, while increased CO₂ can enhance rice yield by 8.5–18%. Rice yield is projected to decline by 3.3-to-10% during 2026–2045, offsetting the fertilizing effect of increasing CO₂. Meanwhile, yield variability will increase in the future, due to more exposure to extremely low- and high-yield conditions. As such, a large yield reduction resulting from the increased variability (down to 34%) can offset the increased mean yield. Full article

Livestock farming is a livelihood activity and is critically important for the food and nutritional security of the majority of the population in West African countries, including Senegal. Nevertheless, livestock farming operates far below the optimum production potential, mainly due to demographical, biophysical, economic, environmental, and sociopolitical challenges. To address these issues, we conducted this review with an overall objective of characterizing different livestock farming systems and to identify challenges and opportunities to improve livestock production in West Africa through the broader perspectives from the case of Senegal. Pastoral, agropastoral, and off-land systems are the three major livestock production systems in this region, which are unique in terms of agroclimatology and degree of intensification and integration. The major challenges identified in livestock farming systems are lack of pasture and quality feed, scarcity of water resources, climate change, undeveloped breeding and management of livestock, poor marketing and trade, and socioeconomic constraints. Moreover, we contribute to the literature on crop-livestock farming in Senegal and West Africa by proposing plausible interventions to improve the productivity of the farming system to improve food and nutritional security. Concentrated efforts must be taken in co-designing effective management interventions for sustainable intensification of livestock sector in the region, considering site-specific approaches. Full article

Due to the proliferation of precision agriculture, the obstacle of estimating evapotranspiration (ET) and its components from shadow pixels acquired from remote sensing technology should not be neglected. To accurately detect shaded soil and leaf pixels and quantify the implications of shadow pixels on ET inversion, a two-year field-scale observation was carried out in the growing season for a pinot noir vineyard. Based on high-resolution remote sensing sensors covering visible light, thermal infrared, and multispectral light, the supervised classification was applied to detect shadow pixels. Then, we innovatively combined the normalized difference vegetation index with the three-temperature model to quantify the proportion of plant transpiration (T) and soil evaporation (E) in the vineyard ecosystem. Finally, evaluated with the eddy covariance system, we clarified the implications of the shadow pixels on the ET estimation and the spatiotemporal patterns of ET in a vineyard system by considering where shadow pixels were presented. Results indicated that the shadow detection process significantly improved reliable assessment of ET and its components. (1) The shaded soil pixels misled the land cover classification, with the mean canopy cover ignoring shadows 1.68–1.70 times more often than that of shaded area removal; the estimation accuracy of ET can be improved by 4.59–6.82% after considering the effect of shaded soil pixels; and the accuracy can be improved by 0.28–0.89% after multispectral correction. (2) There was a 2 °C canopy temperature discrepancy between sunlit leaves and shaded leaves, meaning that the estimation accuracy of T can be improved by 1.38–7.16% after considering the effect of shaded canopy pixels. (3) Simultaneously, the characteristics showed that there was heterogeneity of ET in the vineyard spatially and that E and T fluxes accounted for 238.05 and 208.79 W·m⁻², respectively; the diurnal variation represented a single-peak curve, with a mean of 0.26 mm/h. Our findings provide a better understanding of the influences of shadow pixels on ET estimation using remote sensing techniques. Full article

The Guazuma crinita Mart. is a dominant species of great economic importance for the inhabitants of the Peruvian Amazon, standing out for its rapid growth and being harvested at an early age. Understanding its vertical growth is a challenge that researchers have continued to study using different hypsometric modeling techniques. Currently, machine learning techniques, especially artificial neural networks, have revolutionized modeling for forest management, obtaining more accurate predictions; it is because we understand that it is of the utmost importance to adapt, evaluate and apply these methods in this species for large areas. The objective of this study was to build and evaluate the efficiency of the use of a deep neural network for the prediction of the total height of Guazuma crinita Mart. from a large-scale continuous forest inventory. To do this, we explore different configurations of the hidden layer hyperparameters and define the variables according to the function HT = f(x) where HT is the total height as the output variable and x is the input variable(s). Under this criterion, we established three HT relationships: based on the diameter at breast height (DBH), (i) HT = f(DBH); based on DBH and Age, (ii) HT = f(DBH, Age) and based on DBH, Age and Agroclimatic variables, (iii) HT = f(DBH, Age, Agroclimatology), respectively. In total, 24 different configuration models were established for each function, concluding that the deep artificial neural network technique presents a satisfactory performance for the predictions of the total height of Guazuma crinita Mart. for modeling large areas, being the function based on DBH, Age and agroclimatic variables, with a performance validation of RMSE = 0.70, MAE = 0.50, bias% = −0.09 and VAR = 0.49, showed better accuracy than the others. Full article

The presence of micro and nanoplastics in the food chain constitutes an emergent multifactorial food safety and physiological stress problem, which must be approached with a strategic perspective since it affects public health when consuming products that have this pollutant, such as fish and crustaceans, fruits, and vegetables. In this review, the authors present the results by scientists from different disciplines who are dedicated to discovering their chemical constitution and origin, the contents of these microparticles in edible plants, the contamination of water-irrigated soils, the mechanisms that concentrate microplastics in these soils, methods to determine them, contamination of freshwater sources of cities, and the negative effect of nano and microplastics on various food products and their detrimental impact on the environment. Recent findings of plant uptake mechanisms complement this, but more research is needed. Full article

Climate change is having many effects in the agricultural sector, which are being studied worldwide. Undoubtedly, warmer winters and earlier springs produce changes in frost regimes and severity that will affect the sustainability of agricultural production in the area. The Mediterranean region and the Iberian Peninsula (IP) are among the areas where the greatest impact of climate change is expected. Daily data from 68 weather stations of the IP belonging to the European Climate Assessment and Dataset (1975–2018) were used to conduct a spatiotemporal study of the frost regime. The variables calculated include the probability of three frost types according to their severity, frost day, mean absolute minimum yearly temperature, first frost day, last frost day, and frost-free period. These variables were integrated into a geographic information system, which allowed the graphical visualization of their patterns using of geostatistical interpolation techniques (kriging). Changes in frost variables were investigated using the Mann–Kendall test and Sen’s slope estimator. A general reduction in the number of frosts per year is observed (values between −0.04- and −0.8-day frosts per year), as well as an increase in the mean absolute minimum temperature (values between 0.04 and 0.10 °C per year), with very high significant trends throughout the territory. The reduction in the number of frosts is more pronounced at a higher elevation. Frost dates vary greatly due to the orographic characteristics of the IP. The generalized trend is of a significant delay of the autumn frosts (values between 0.4 and 1.06 days/year), as well as early spring frosts (between −0.429 and −1.29 days/year), and as a consequence a longer frost-free period, all changes were much stronger than those found in other regions of the world. These effects of climate change must be mitigated by modifying species, varieties, and cultivation techniques to guarantee sustainable agriculture. Full article

Regional climate projections are widely used in impact studies such as adaptations in agronomy. The big challenge of the climate modeling community is to serve valuable instructions regarding the reliability of these simulations to encourage agronomists to use this kind of information properly. The study validates 15 high-resolution ensembles from the Coordinated Regional Climate Downscaling Experiment-European Domain (EURO-CORDEX) for maximum temperature, minimum temperature, and precipitation to fulfill this task. Three evaluation metrics are calculated (mean absolute error, root mean square error, and correlation) for the means and the 5th and 95th percentiles. The analyses are elaborated for annual and monthly means and the vegetation periods of maize and winter wheat. Only arable lands are considered to exclude the effects of the topography. Furthermore, an ensemble selection is applied based on the evaluation metrics to reduce the data use. The five models with the best performance in the case of winter wheat are CNRM-CM5-CLMcom-CCLM4-8-17_v1, MOHC-HadGEM2-ES-IPSL-WRF381P_v1, MOHC-HadGEM2-ES-KNMI-RACMO22E_v2, MOHC-HadGEM2-ES-CLMcom-CCLM4-8-17_v1, and MPI-M-MPI-ESM-LR-KNMI-RACMO22E_v1. In the case of the vegetation period of maize, the models with the best skills are MPI-M-MPI-ESM-LR-KNMI-RACMO22E_v1, CNRM-CM5-IPSL-WRF381P_v2, MPI-M-MPI-ESM-LR-SMHI-RCA4_v1a, MOHC-HadGEM2-ES-IPSL-WRF381P_v1, and MOHC-HadGEM2-ES-KNMI-RACMO22E_v2. Quantifying the errors in climate simulations against observations and elaborating a selection procedure, we developed a consistent ensemble of high time and space resolution climate projections for agricultural use in Romania. Full article