The estimation of crop evapotranspiration (ETc) is crucial for irrigation water management, especially in arid regions. This can be particularly relevant in the Po Valley (Italy), where arable lands suffer from drought damages on an annual basis, causing drastic crop yield losses. This study presents a novel approach for vegetation-based estimation of crop evapotranspiration (ETc) for maize. Three years of high-resolution multispectral satellite (Sentinel-2)-based Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Leaf Area Index (LAI) time series data were used to derive crop coefficients of maize in nine plots at the Acqua Campus experimental farm of Irrigation Consortium for the Emilia Romagna Canal (CER), Italy. Since certain vegetation indices (VIs) (such as NDVI) have an exponential nature compared to the other indices, both linear and power regression models were evaluated to estimate the crop coefficient (K_c). In the context of linear regression, the correlations between Food and Agriculture Organization (FAO)-based K_c and NDWI, NDRE, NDVI, and LAI-based K_c were 0.833, 0.870, 0.886, and 0.771, respectively. Strong correlation values in the case of power regression (NDWI: 0.876, NDRE: 0.872, NDVI: 0.888, LAI: 0.746) indicated an alternative approach to provide crop coefficients for the vegetation period. The VI-based ETc values were calculated using reference evapotranspiration (ET₀) and VI-based K_c. The weather station data of CER were used to calculate ET₀ based on Penman-Monteith estimation. Out of the Vis, NDWI and NDVI-based ETc performed the best both in the cases of linear (NDWI RMSE: 0.43 ± 0.12; NDVI RMSE: 0.43 ± 0.095) and power (NDWI RMSE: 0.44 ± 0.116; NDVI RMSE: 0.44 ± 0.103) approaches. The findings affirm the efficacy of the developed methodology in accurately assessing the evapotranspiration rate. Consequently, it offers a more refined temporal estimation of water requirements for maize cultivation in the region.

Due to the intensification of the poultry sector, poultry manure is being produced in increasing quantities, and its on-site management is becoming a critical problem. Animal health problems can be solved by stricter the veterinary and environmental standards. The off-site coupled industrial chicken manure recycling technology (Hosoya compost tea) fundamentally affects the agricultural value of new organic-based products. Due to the limited information available on manure recycling technology-related microbiological changes, this was examined in this study. A pot experiment with a pepper test plant was set up, using two different soils (Arenosol, slightly humous Arenosol) and two different doses (irrigation once a week with 40 mL of compost tea: dose 1, D1; irrigation twice a week with 40 mL of compost tea: dose 2, D2) of compost tea. Compost tea raw materials, compost tea, and compost tea treated soils were tested. The products (granulated manure, compost tea) and their effects were characterized by the following parameters: aerobic bacterial count (log CFU/g), fluorescein diacetate activity (3′,6′-diacetylfluorescein, FDA, µg Fl/g soil), glucosidase enzyme activity (GlA; PNP/µmol/g), and identification of microorganisms in compost tea with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). Furthermore, we aimed to investigate how the microbiological indicators tested, and the effect of compost tea on the tested plant, could be interpreted. Based on our results, the microbiological characteristics of the treated soils showed an increase in enzyme activity, in the case of FDA an increase +0.26 μg Fl/g soil at D1, while the GlA increased +1.28 PNP/µmol/g with slightly humous Arenosol soil and increased +2.44 PNP/µmol/g at D1; and the aerobic bacterial count increased +0.15 log CFU/g at D2, +0.35 log CFU/g with slightly humous Arenosol and +0.85 log CFU/g at W8. MALDI-TOF MS results showed that the dominant bacterial genera analyzed were Bacillus sp., Lysinibacillus sp., and Pseudomonas sp. Overall, the microbial inducers we investigated could be a good alternative for evaluating the effects of compost solutions in soil–plant systems. In both soil types, the total chlorophyll content of compost tea-treated pepper (Capsicum annuum L.) had increased as a result of compost tea. D1 is recommended for Arenosol and, D2 for slightly humous Arenosol soil.

The management and recycling of organic manure, which is generated in increasing quantities in fastgrowing industries such as broiler chicken production, has become extremely significant. The European Union also encourages the application of organic fertilizers through the European Green Deal, which one of the aims to minimize chemical fertilizer use and increase the use of organic fertilizers. In the present study, the treatment and application of broiler chicken manure was investigated from an environmental standpoint, using the life cycle assessment methodology (ISO 14040:2006) and including three impact categories (global warming potential (GWP), acidification potential (AP), eutrophication potential (EP)). In the first scenario, the environmental impact of the production of a composted and pelleted poultry litter (CPPL) product from a so-called Hosoya composting plant was evaluated. The environmental impact of producing CPPL in the amount necessary to supply a 100 ha area with nutrients was analysed, compared to the environmental impact of producing NPK fertilizer combinations (AN, CAN, urea, TSP, MAP, KCl) with the same active ingredient content. In the second scenario, the environmental impact on maize production was assessed when nutrient replenishment was carried out with CPPL and NPK fertilizer combinations. Based on the results of the first scenario, the environmental impact of producing the CPPL is similar for GWP and higher for AP and EP than for the NPK fertilizer combinations. But need to considering that the same amount of NPK active substance requires a much larger amount of broiler chicken manure to be processed than the amount of chemical fertilizer to be produced. In contrast, in the second scenario a much lower environmental impact was already observed for GWP, AP and EP when the nutrient supplementation was done with CPPL in maize production. Overall, the results indicate that CPPL can be viable alternative to chemical fertilizers, not just from an environmental standpoint but from financially due to continuously rising fertilizer prices.

Composting represents an efficient technology that enables the effective utilization of by-products and waste materials. Moreover, it proves to be highly suitable for processing raw materials and converting them into fertilizers that would not be recommended for direct application without undergoing pre-processing. This is particularly crucial in the case of poultry manure, which possesses potentially hazardous properties and necessitates pre-treatment. One increasingly prevalent form of compost is known as compost tea, which involves the immersion of compost in water. In this experiment, compost tea or compost solution were created using a product called composted and pelletized poultry litter (CPPL). Four compost:water ratio (CWR) (1/2.5, 1/5, 1/10, 1/20) were applied, along with three different extraction durations (24, 48, and 72 hours) and three distinct extraction temperatures (20 °C, 35 °C, and 50 °C). Since the 1/10 and 1/20 ratios were found to be the best for subsequent applicability and spreadability, their content parameters were measured further. After elimination of the experiment, the most important nutrients (nitrogen content (nitrate and ammonium), phosphorus and potassium) were determined. The results showed that the nutrient content was highest for all four parameters at the extraction temperature of 35 °C. For example, while at 20 and 50°C the NO3 – content ranged from 263 to 768 mg/l and from 210 to 534 mg/l, at 35 °C it ranged from 498.33 to 2636.67 mg/l, irrespective of the mixing ratio and extraction time. If the extraction temperature is not taken into account, the nutrient content increased with the increase of the extraction time, so that the highest values were measured at 72 hours extraction time obviously. The data measured in the present experiment will serve as a basis for subsequent experiments with different indicator plants, investigating the effect of compost when applied as a solution.

Climate variability threatens smallholder maize production in semi-arid Southern Africa, necessitating accurate irrigation management. We developed an Earth Observation–machine learning framework integrating Sentinel-1 SAR, TU Wien retrievals, and meteorological data to generate daily 10 m resolution root-zone soil moisture estimates (0–100 cm) for South Africa’s Vhembe District (2017–2022). Five algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and Multivariate Adaptive Regression Splines (MARS)—were calibrated using ~50,000 observations from two monitoring stations across six depths and five growing seasons. RF and XGBoost achieved highest accuracy (R² = 0.96–0.97, RMSE < 0.025 cm³/cm³), detecting critical irrigation thresholds (management allowable depletion = 0.23 cm³/cm³, field capacity = 0.35 cm³/cm³) with operational precision (nRMSE < 0.05). Depth-stratified validation revealed strong SAR surface correlations (r = 0.84–0.85 at 10 cm) declining systematically with depth (r < 0.2 below 40 cm), confirming ML models integrate satellite observations at shallow layers with meteorological gap-filling at depth. District mapping showed 79–94% of maize areas required irrigation during dry years (2017–2019, 2021–2022) versus 32% in wet 2020–2021. The framework provides a transferable pathway for precision irrigation in smallholder systems, pending vegetation-corrected retrievals and expanded validation.

Mycotoxin contamination of cereal crops poses escalating food safety risks across the Central and Balkan European (CBE) corridor under climate change, yet no PRISMA 2020-compliant synthesis of remote sensing (RS) and machine learning (ML) evidence for this region exists. We conducted an AI-augmented systematic review applying a four-stage automated pipeline—PICO domain scoring, SBERT semantic deduplication, and Thompson-sampling reinforcement learning—to 36,038 corpus records (2010–2025), yielding 156 included studies (inter-rater κ = 0.81 (95% CI: 0.74–0.88)). Logistic growth modelling identified a 56-fold corpus expansion with inflection at t₀ = 2024.8 (R² = 0.981). Satellite multispectral imaging dominated the literature (91.7% of studies); random forest and gradient boosting models achieved R² = 0.74–0.80 for aflatoxin B1 and deoxynivalenol prediction in CBE maize and wheat when integrating vegetation indices, land surface temperature, and precipitation covariates. Deep learning surpassed classical ML in annual study count from 2021, reaching ~60% relative share by 2025, though the performance advantage narrows at field scale relative to laboratory hyperspectral benchmarks (98–99% accuracy). A five-percentage-point CBE–global performance gap is largely consistent with differences in sample size and multi-toxin design scope rather than algorithmic access. The country × mycotoxin gap matrix identifies zero eligible studies for four CBE nations and for T-2/HT-2 toxins across the Balkan states. Climate-driven satellite mycotoxin prediction emerges as the field’s active research frontier.