Food Security and Safety Focus Area, Faculty of Natural and Agricultural Sciences, North-West University, Private Bag X2046, Mmabatho 2735, South Africa.
Department of Animal Health, Faculty of Natural and Agricultural Sciences, North-West University, Private Bag X2046, Mmabatho 2735, South Africa.
Toxins (Basel). 2022 Aug 22;14(8):574. doi: 10.3390/toxins14080574.
The dangers of population-level mycotoxin exposure have been well documented. Climate-sensitive aflatoxins (AFs) are important food hazards. The continual effects of climate change are projected to impact primary agricultural systems, and consequently food security. This will be due to a reduction in yield with a negative influence on food safety. The African climate and subsistence farming techniques favour the growth of AF-producing fungal genera particularly in maize, which is a food staple commonly associated with mycotoxin contamination. Predictive models are useful tools in the management of mycotoxin risk. Mycotoxin climate risk predictive models have been successfully developed in Australia, the USA, and Europe, but are still in their infancy in Africa. This review aims to investigate whether AFs' occurrence in African maize can be effectively mitigated in the face of increasing climate change and food insecurity using climate risk predictive studies. A systematic search is conducted using Google Scholar. The complexities associated with the development of these prediction models vary from statistical tools such as simple regression equations to complex systems such as artificial intelligence models. Africa's inability to simulate a climate mycotoxin risk model in the past has been attributed to insufficient climate or AF contamination data. Recently, however, advancement in technologies including artificial intelligence modelling has bridged this gap, as climate risk scenarios can now be correctly predicted from missing and unbalanced data.
人群水平霉菌毒素暴露的危害已有充分记录。气候敏感型黄曲霉毒素（AFs）是重要的食物危害。气候变化的持续影响预计将影响主要农业系统，从而影响粮食安全。这将是由于产量下降对食品安全产生负面影响。非洲的气候和自给农业技术有利于产生 AF 的真菌属的生长，特别是在玉米中，玉米是一种常见的与霉菌毒素污染相关的主食。预测模型是管理霉菌毒素风险的有用工具。霉菌毒素气候风险预测模型已在澳大利亚、美国和欧洲成功开发，但在非洲仍处于起步阶段。本综述旨在探讨在气候变化和粮食不安全日益加剧的情况下，利用气候风险预测研究，是否可以有效减轻非洲玉米中 AF 的发生。使用 Google Scholar 进行系统搜索。开发这些预测模型的复杂性从简单回归方程等统计工具到人工智能模型等复杂系统各不相同。过去，非洲无法模拟气候霉菌毒素风险模型，这归因于气候或 AF 污染数据不足。然而，最近，包括人工智能建模在内的技术进步弥补了这一差距，因为现在可以从缺失和不平衡的数据中正确预测气候风险情景。
