Digestate is commonly used as a liquid organic fertilizer, as it contains nutrients that are important for plant growth and thus help reduce usage of mineral fertilizers. Since the digestate application leads to the release of greenhouse gases (GHGs) into the atmosphere, it is necessary to find a suitable application method and fertilizer rate with minimal gas emissions while providing sufficient nutrients to crops. The aim of this study was to investigate the relationship between selected GHGs and ammonia (NH₃) release into the atmosphere and different rates of digestate applied, i.e., 0, 10, 20, 30, and 40 m³ ha⁻¹. Two digestate incorporation methods were used, i.e., a disc application unit (D) and strip-till (S). The fluxes, i.e., methane (CH₄), ammonia, and carbon dioxide (CO₂), were monitored using the wind tunnel method. Crop growth and potential nutrient utilization by silage maize were assessed through stand condition monitoring by the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) using remote sensing. Under the given conditions, the digestate rates and the compared application methods had significant effects on the level of fluxes. The rate of digestate was confirmed to affect the yield of silage maize. The yield increased by more than 8% when using the disc applicator. Based on our results, it is advisable to apply digestate by strip-till technology at rates of approximately 20 m³ ha⁻¹.

This study was conducted to understand the long-term influence of biostimulator NeOsol in combination with different manure types on soil’s physical properties and crop status. NeOsol is a soil biostimulator that should stimulate the biological reactions of the soil profile and improve the soil’s physical and chemical properties. A six-year experiment was conducted with eight treatments: NPK, cattle manure, pig manure, poultry manure, and the same four treatments with the NeOsol added on top. The in situ sampling of soil properties provided data on unit draft (UD), bulk density (BD), and saturated hydraulic conductivity (SHC). Furthermore, remotely sensed data were analyzed to describe crop status via three selected vegetation indices (VI), and crop yields were assessed last. The variants treated with NeOsol demonstrated decreases in UD over time; BD, SHC, and VI did not significantly change. The impact on yield was significant and increased over time. When comparing the variants with manure application to those without one, the cattle manure led to significantly higher SHC; the pig manure led to significantly lower UD and BD but significantly higher SHC and yield; and the poultry manure led to significantly lower UD and BD but higher yield.

This study aims to optimize the estimation of reference evapotranspiration (ETo) in data-scarce regions by integrating ERA5-Land reanalysis data with machine learning (ML) models. Daily meteorological data from 33 stations across Turkey’s diverse climate zones (1981–2010) were utilized to train and validate three ML models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM). The methodology involved rigorous quality control of ground-based observations, spatial correlation of ERA5-Land grids to station locations, and performance evaluation under various data-limited scenarios. Results indicate that while ERA5-Land provides highly accurate solar radiation (R_s) and temperature (T) data, variables like wind speed (U₂) and relative humidity (RH) exhibit systematic biases. Among the used models, XGBoost demonstrated superior performance (R² = 0.95, RMSE = 0.43 mm day⁻¹, and MAE = 0.30 mm day⁻¹) and computational efficiency. This study provides a robust, regionally calibrated framework that corrects reanalysis biases using ML, offering a reliable alternative for ETo estimation in areas where local measurements are insufficient for sustainable water management.

The application of digestate as a fertilizer offers a promising alternative to synthetic inputs on permanent grasslands, with benefits for productivity and environmental performance. This four-year study evaluated the impact of two digestate application methods—disc injection (I) and band spreading (S)—combined with four dose variants (0, 20, 40, and 80 m³·ha⁻¹), including split-dose strategies. Emissions of ammonia (NH₃), carbon dioxide (CO₂), and methane (CH₄) were measured using wind tunnel systems immediately after application. Vegetation status was assessed via Sentinel-2-derived Normalized Difference Vegetation Index, Normalized Difference Water Index, and Modified Soil Adjusted Vegetation Index, and agronomic performance through dry matter yield (DMY), net energy for lactation (NEL), and relative feed value (RFV). NH₃ and CO₂ emissions increased proportionally with digestate dose, while CH₄ responses suggested a threshold effect, but considering solely the disc injection, CH₄ flux did not increase markedly with higher application rates. Disc injection resulted in significantly lower emissions of the monitored fluxes than band spreading. The split-dose I_40+40 variant achieved the highest DMY (3.57 t·ha⁻¹) and improved forage quality, as indicated by higher NEL values. The control variant (C, no fertilization) had the lowest yield and NEL. These results confirm that subsurface digestate incorporation in split doses can reduce emissions while supporting yield and forage quality. Based on the findings, disc injection at 40+40 m³·ha⁻¹ is recommended as an effective option for reducing emissions and maintaining productivity in managed grasslands.