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.

Three super-sweet corn hybrids (Zea mays L. convar. saccharata) with different ripening characteristics were grown in three water supplies (regularly irrigated, deficit-irrigated, and unirrigated) to examine the spectral reflectance at leaf and canopy levels, leaf area index (LAI) during the stage of development and their relationship with the yield and nutritional quality. Under unirrigated conditions, the plant’s height, diameter, and weight of ears per plants, as well as total carotenoid content of kernels, decreased, SPAD values of leaves, normalized vegetation differential index (NDVI) and LAI were low. The difference in SPAD and LAI was higher between the hybrids during the silking than the tasseling period under water deficiency. Under unirrigated dry conditions, no correlation was found between SPAD, NDVI, and yield. Using deficit irrigation, SPAD measured during tasseling correlated closely with the individual husked ear and the final yields as well as the sugar and carotene contents of kernels. Under this condition, NDVI did not correlate with the yield, but LAI was correlated with the yield and its carotene content. Under deficit irrigated conditions, the 46 to 49 range of SPAD measured during tasseling predicted 23.5 to 26.7 t ha⁻¹ depending on the hybrid, but this range predicted a yield lower by 6.4 to 10.1% during silking.

The knowledge of the evapotranspiration of natural ecosystems and plant populations is of fundamental importance in several branches of science, research and practical uses. Nevertheless, the harmonisation of the large number of methods and user needs often causes problems. The objective of the analyses was to explore the output range and sensitivity of models of different physical approaches under local conditions. We performed descriptive statistical and sensitivity analysis of 10 commonly used estimation models - one of them with two variants. Correlation between modelled and measured evapotranspiration data series was assessed. The magnitude of the model outputs, their variability and responses to the changes of selected atmospheric parameters were evaluated. Priestley-Taylor, Penman-Monteith-FAO-56, Shuttleworth-Wallace (parameterized with alternative radiation balance), Szász and Makkink proved to be the most sensitive methods. As regards the systematic error, Makkink and Shuttleworth-Wallace showed the best agreement with pan evaporation, while Shuttleworth-Wallace, Blaney-Criddle and Makkink models were found to be the closest to the Penman-Monteith-FAO-56 method as a reference value.

The study presents a systematic review of published scientific articles investigating the effects of interventions aiming at aflatoxin reduction at the feed production and animal feeding phases of the milk value chain in order to identify the recent scientific trends and summarize the main findings available in the literature. The review strategy was designed based on the guidance of the systematic review and knowledge synthesis methodology that is applicable in the field of food safety. The Web of Science and EBSCOhost online databases were searched with predefined algorithms. After title and abstract relevance screening and relevance confirmation with full-text screening, 67 studies remained for data extraction, which were included in the review. The most important identified groups of interventions based on their mode of action and place in the technological process are as follows: low-moisture production using preservatives, acidity regulators, adsorbents and various microbiological additives. The results of the listed publications are summarized and compared for all the identified intervention groups. The paper aimed to help feed producers, farmers and relevant stakeholders to get an overview of the most suitable aflatoxin mitigation options, which is extremely important in the near future as climate change will likely be accompanied by elevated mycotoxin levels.

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.