Climate change, through changes in temperature, precipitation, and frequency of extreme events, has influenced agricultural production and food security over the past several decades. In order to assess climate and weather-related risks to fruit and grape production in Serbia, changes in bioclimatic indices and frequency of the occurrence of unfavourable weather events are spatially analysed for the past two decades (1998–2017) and the standard climatological period 1961–1990. Between the two periods, the Winkler and Huglin indices changed into a warmer category in most of the viticultural regions of Serbia. The average change shift was about 200 m towards higher elevations. Regarding the frequency of spring frost, high summer temperatures and water deficit, the most vulnerable regions in terms of fruit and grape production are found alongside large rivers (Danube, Sava, Great and South Morava), as well as in the northern part of the country. Regions below 300 m are under increased risk of high summer temperatures, as the number and duration of occurrences increased significantly over the studied periods. The high-resolution spatial analysis presented here gives an assessment of the climate change influence on the fruit and grapes production. The presented approach may be used in regional impact assessments and national planning of adaptation measures, and it may help increase resilience of agricultural production to climate change.

The objective of this study is to assess the possibility of using unmanned aerial vehicle (UAV) multispectral imagery for rapid monitoring, water stress detection and yield prediction under different sowing periods and irrigation treatments of common bean (Phaseolus vulgaris, L). The study used a two-factorial split-plot design, divided into subplots. There were three sowing periods (plots; I—mid April, II—end of May/beginning of June, III—third decade of June/beginning of July) and three levels of irrigation (subplots; full irrigation (F)—providing 100% of crop evapotranspiration (ETc), deficit irrigation (R)—providing 80% of ETc, and deficit irrigation (S) providing—60% of ETc). Canopy cover (CC), leaf area index (LAI), transpiration (T) and soil moisture (Sm) were monitored in all treatments during the growth period. A multispectral camera was mounted on a drone on seven occasions during two years of research which provided raw multispectral images. The NDVI (Normalized Difference Vegetation Index), MCARI1 (Modified Chlorophyll Absorption in Reflectance Index), NDRE (Normalized Difference Red Edge), GNDVI (Green Normalized Difference Vegetation Index) and Optimized Soil Adjusted Vegetation Index (OSAVI) were computed from the images. The results indicated that NDVI, MCARI1 and GNDVI derived from the UAV are sensitive to water stress in S treatments, while mild water stress among the R treatments could not be detected. The NDVI and MCARI1 of the II-S treatment predicted yields better (r² = 0.65, y = 4.01 tha⁻¹; r² = 0.70, y = 4.28 tha⁻¹) than of III-S (r² = 0.012, y = 3.54 tha⁻¹; r² = 0.020, y = 3.7 tha⁻¹). The use of NDVI and MCARI will be able to predict common bean yields under deficit irrigation conditions. However, remote sensing methods did not reveal pest invasion, so good yield predictions require observations in the field. Generally, a low-flying UAV proved to be useful for monitoring crop status and predicting yield and water stress in different irrigation regimes and sowing period.

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.

The application of crop growth simulation and water management models will become increasingly important in the future. They can be used to predict yield reductions due to water scarcity and allocate water to ensure profitable crop production. The objective of this research was to calibrate the AquaCrop model for common bean (Faseolus vulgaris L.) grown in temperate climates and to test whether the model can be used for different irrigation strategies to achieve high yield productivity. The model was calibrated using data obtained from two years of experimental research in the Serbian territory of the Syrmia region. There were three sowing periods/plots: I—mid April, II—end of May/beginning of June, and III—third decade of June/beginning of July; and three levels of irrigation/subplots: full irrigation (F) providing 100% of crop evapotranspiration (ETc), mild deficit irrigation (R) at 80% of ETc, and moderate deficit irrigation (S) at 60% of ETc. The results show that the AquaCrop model accurately predicts common bean yield, biomass, canopy cover, and water requirements. The statistical indices of the calibrated dataset, coefficient of determination (R²), normalized root mean square error (NRMSE), mean bias error (MBE), and Willmott agreement index (d) for yield and biomass were: 0.91, 0.99; 6.9%, 11.4%; −0.046, 1.186 and 0.9, 0.89, respectively. When testing three irrigation strategies, the model accurately predicted irrigation requirements for the full and two deficit irrigation strategies, with only 29 mm, 32 mm, and 34 mm more water than was applied for the Fs, Rs, and Ss irrigation strategy, respectively. The AquaCrop model performed well in predicting irrigated yield and can be used to estimate the yield of common bean for different sowing periods and irrigation strategies.

Optimizing irrigation and sowing schedules is critical for enhancing crop performance and resource efficiency, especially in water-limited environments. However, the balancing the trade-offs between crop yield, energy use, and environmental impacts remains a complex challenge. This study investigates the eco-efficiency of common bean (Phaseolus vulgaris L.) cultivation in Vojvodina region (Serbia) under three irrigation regimes (100%, 80%, and 60% of crop evapotranspiration—ET_c) and three sowing periods (mid-April, late May/early June, and late June/early July). A combined energy analysis and cradle-to-farm gate Life Cycle Assessment (LCA) was employed to assess sustainability trade-offs. Results show that early sowing with full irrigation achieved the highest crop yields, energy use efficiency, and net energy gain while minimizing specific energy input. However, this strategy also incurred the greatest environmental burden due to elevated water and fertilizer inputs. In contrast, late sowing and deficit irrigation reduced environmental impacts at the expense of productivity and energy performance. The most balanced outcome—combining acceptable yield with lower environmental pressure—was observed under early sowing (mid-April) and moderate deficit irrigation (60% of ET_c). Importantly, the study reveals discrepancies between energy and environmental assessments; energy analysis favors high-yield, high-input systems, whereas LCA emphasizes environmental burdens per unit area, often favoring low-input strategies. These findings underscore the need for integrated, site-specific management approaches that optimize both agronomic performance and environmental sustainability, particularly under growing climate and resource constraints.