The drought in Serbia in the summer of 2017 heavily affected agricultural production, decreasing yields of maize, sunflower, soybean, and sugar beet. Monitoring moisture levels in crops can provide timely information about potential risk within a growing season, thus helping to create an early warning system for various stakeholders. The purpose of this study was to quantify the level of moisture stress in crops during summer and the consequences that it can have on yields. For that, maize and sunflower yield data provided by an agricultural company were used at specific parcels in the Backa region of Vojvodina province (Serbia) for 2017, 2018, 2019, and 2020. The crop moisture level was estimated at each parcel by calculating the normalized difference moisture index (NDMI) from Sentinel-2 data during the summer months (June–July–August). Based on the average NDMI value in July, the new crop moisture stress (CMS) index was introduced. The results showed that the CMS values at a specific parcel could be used for within-season estimation of maize and sunflower yield and the assessment of drought effects. The CMS index was tested for the current growing season of 2022 as an early warning system for yield reduction, demonstrating the potential to be included in a platform for digital agriculture, such as AgroSens, which is operational in Serbia.

The study exploits the air temperature and precipitation data from ERA5‐Land reanalysis and E‐OBS gridded observations that are freely available from the Copernicus Climate Change Service. The objectives of the study are to analyze the distribution of Köppen climate zones and to detect the changes in the presence and coverage of the specific climate types in Southeastern Europe. The results are shown separately for the following reference periods: 1961–1990, 1971–2000, 1981–2010, and 1991–2020. In the period 1961–1990, the most dominant climate type in Southeastern Europe was fully humid temperate climate with warm summer (Cfb), while fully humid continental climate with warm summer (Dfb) was also highly present there, together with fully humid temperate climate with hot summer (Cfa). In the period 1991–2020, the shift of Köppen climate zones appeared in such a way that the area with Dfb continental climate, that is often called snow or cold climate, is significantly reduced and this type is replaced with Cfb temperate climate. At the same time, Cfa climate type with hot summer is spread across wider area, mainly instead of Cfb with warm summer, now reaching almost the same percentage of coverage as Cfb type.

• Quantification of starch in ambient air by laser spectroscopy bioaerosol monitoring. • Insights into atmospheric processes via high temporal resolution data. • Identification of sources of airborne particles. • Notable amounts of airborne starch are related to cereal grain storage facilities.

In this study we used meteorological parameters and predictive modelling interpreted by model explanation to develop stress metrics that indicate the presence of drought and heat stress at the specific environment. We started from the extreme temperature and precipitation indices, modified some of them and introduced additional drought indices relevant to the analysis. Based on maize’s sensitivity to stress, the growing season was divided into four stages. The features were calculated throughout the growing season and split in two groups, one for the drought and the other for heat stress. Generated meteorological features were combined with soil features and fed to random forest regression model for the yield prediction. Model explanation gave us the contribution of features to yield decrease, from which we estimated total amount of stress at the environments, which represents new environmental index. Using this index we ranked the environments according to the level of stress. More than 2400 hybrids were tested across the environments where they were grown and based on the yield stability they were marked as either tolerant or susceptible to heat, drought or combined heat and drought stress. Presented methodology and results were produced within the Syngenta Crop Challenge 2019.

In this paper, we consider an environmental interface as a complex system, in which difference equations for calculating the environmental interface temperature and deeper soil layer temperature are represented by the coupled maps. First equation has its background in the energy balance equation while the second one in the prognostic equation for deeper soil layer temperature, commonly used in land surface parametrization schemes. Nonlinear dynamical consideration of this coupled system includes: (i) examination of period one fixed point and (ii) bifurcation analysis. Focusing part of analysis is calculation of the Lyapunov exponent for a specific range of values of system parameters and discussion about domain of stability for this coupled system. Finally, we calculate Kolmogorov complexity of time series generated from the coupled system.

Although corn production is affected by several harmful insects, its most important pest in the southeastern region of Europe is the European corn borer (ECB), Ostrinia nubilalis Hübner, 1796 (Lepidoptera: Pyralidae). Chemical control of O. nubilalis remains the main strategy in conventional corn production. The key to successfully achieving a high efficiency of insecticides is determining the appropriate moment of application, including the exact time in the insect’s life cycle when it is most vulnerable. In this study, monitoring data on the flight dynamics of ECB adults from a seven-year period (2014–2020) were exploited for the development of a predictive model of adult numbers within the growing season. ECB monitoring was performed by using light traps at 15 different locations in the Vojvodina region (Serbia) during the specified time period. First, the calendar for Vojvodina was created based on the analytics of the collected monitoring data. Additionally, the calendar was converted to the probability of ECB occurrence during the growing season, specifying the time interval between the appearance of each generation of the pest. Second, using machine learning techniques, a phenological model was designed that included daily values of relevant meteorological features, such as cumulative degree-days, relative humidity, and precipitation. The calendar had a lower prediction error when compared to the phenological model, and it was tested as a supporting management tool for the ECB in 2021, with a root-mean-square error of the number of adults of 46.67. Such an approach could significantly reduce both the consumption of insecticides and the number of chemical treatments, respectively. Above all, this approach has broad potential in IPM and organic farming, and it is fully compatible with biological control methods.