Aflatoxins (AFs) are harmful secondary metabolites produced by various moulds, among which Aspergillus flavus is the major AF-producer fungus. These mycotoxins have carcinogenic or acute toxigenic effects on both humans and food producing animals and, therefore, the health risks and also the potential economic damages mounted by them have led to legal restrictions, and several countries have set maximum allowable limits for AF contaminations in food and feed. While colonization of food and feed and AF production by A. flavus are highly supported by the climatic conditions in tropical and subtropical geographic regions, countries in the temperate climate zones are also increasingly exposed to AF-derived health risks due to climate change. In the present study, we have reviewed the available mathematical models as risk assessment tools to predict the possibility of A. flavus infection and levels of AF contaminations in maize in a changing climatic environment. After highlighting the benefits and possible future improvements of these models, we summarize the current agricultural practices used to prevent or, at least, mitigate the deleterious consequences of AF contaminations

The toxins produced by Aspergillus flavus can significantly inhibit the use of maize. As a result of climate change, toxin production is a problem not only in tropical and subtropical areas but in an increasing number of European countries, including Hungary. The effect of meteorological factors and irrigation on mould colonization and aflatoxin B1 (AFB1) mycotoxin production by A. flavus were investigated in natural conditions, as well as the inoculation with a toxigenic isolate in a complex field experiment for three years. As a result of irrigation, the occurrence of fungi increased, and toxin production decreased. The mould count of fungi and toxin accumulation showed differences during the examined growing seasons. The highest AFB1 content was found in 2021. The main environmental factors in predicting mould count were temperature (T_avg, T_max ≥ 30 °C, T_max ≥ 32 °C, T_max ≥ 35 °C) and atmospheric drought (RH_min ≤ 40%). Toxin production was determined by extremely high daily maximum temperatures (T_max ≥ 35 °C). At natural contamination, the effect of T_max ≥ 35 °C on AFB1 was maximal (r = 0.560–0.569) in the R4 stage. In the case of artificial inoculation, correlations with environmental factors were stronger (r = 0.665–0.834) during the R2–R6 stages.

Maize is an important crop used as food, feed, and industrial raw material. Therefore, it is critical to maximize maize yield on available land by using optimal inputs and adapting to challenges posed by climate change. The Leaf Area Index (LAI) is a key parameter that provides significant assistance in forecasting maize yields. This study focuses on modeling the Leaf Area Index for maize. Specifically, it compiles and systematizes the main findings of papers published over the past approximately 10–15 years. Our results are organized and presented based on the five most commonly used models: CERES-Maize, AquaCrop, WOFOST, APSIM, and RZWQM2. The limitations of these models’ applicability are also discussed. We present the limitations of these models and compare their minimum climate input requirements. Additionally, we evaluate the performance of the models across different climate zones, explore how the integration of remote sensing data sources can enhance model estimation accuracy, and examine the potential for spatial scalability in maize LAI modeling.

Maize production is increasingly challenged by climate change, which affects plant physiology, fungal colonization, and mycotoxin contamination. Aspergillus flavus, a saprophytic fungus, thrives in warm, dry conditions, leading to aflatoxin B1 (AFB1) accumulation, and posing significant food safety risks. Macro- and micro-climatic factors, including temperature, humidity, and precipitation, influence kernel development, leaf wetness duration, and mycotoxin biosynthesis. Nitrogen availability and irrigation play crucial roles in modulating plant responses to these stressors, affecting chlorophyll content, yield parameters, and fungal interactions. To investigate these interactions, a Completely Randomized Design (CRD) was employed from 2020 to 2022 to assess physiological changes in SY Orpheus maize hybrid under varying climatic conditions. Rising temperatures and declining relative humidity (RH) significantly reduced kernel number per ear length from 25.60 ± 0.34 in 2020 to 17.89 ± 0.39 in 2022 (p < 0.05), impacting yield. The AFB1 levels peaked in 2021 (156.88 ± 59.02 µg/kg), coinciding with lower humidity and increased fungal stress. Water availability improved kernel numbers and reduced AFB1 accumulation (p < 0.05) but did not significantly affect the total fungal load (p > 0.05). Nitrogen supplementation enhanced plant vigor, suppressed AFB1 biosynthesis, and influenced spectral indices. Potential confounding factors such as soil variability and microbial interactions may require further investigations.

Interannual variability in climatic conditions represents a major source of uncertainty in cool-climate viticulture, highlighting the need for cultivar-specific assessments of climate–quality relationships. A multi-year on-farm experiment with six monitoring sites has been conducted in vineyards representative of the Tokaj wine region to monitor and assess vintage effect. This study, as the first part of a broader research project evaluating must components, quantifies relationships between climatic indices and key yield- and sugar-related traits (berry weight, total soluble solids, and total dry extract) in Vitis vinifera L. cv. Furmint grown in the Tokaj wine region over three contrasting vintages. Thermal, radiative, and water-availability variables were calculated for discrete phenological phases and statistically analyzed to identify climatic predictors of berry growth and must composition. Berry weight exhibited pronounced vintage sensitivity, showing consistent associations with precipitation-related variables during early developmental stages. In contrast, total soluble solids and total dry extract displayed weaker and less consistent responses to interannual climatic variability. Several widely used heat-accumulation indices showed limited explanatory power, indicating a moderate climatic sensitivity of sugar-related traits in this cultivar. Overall, the results suggest that early-season climatic conditions exert a stronger influence on berry growth than late-season thermal extremes, while compositional parameters related to sugar accumulation remain comparatively stable. These findings highlight the need to incorporate cultivar-specific response functions into statistical models that assess projected climate-change effects on grape quality.