Pre-Harvest Modelling and Mitigation of Aflatoxins in Maize in a Changing Climatic Environment—A Review
Abstract
:1. Introduction
2. Environmental and Ecological Background
2.1. Pathogen Occurrence
2.2. The Role of Ecophysiological Factors in Aflatoxin Contamination
2.2.1. Laboratory (In Vitro) Findings
2.2.2. Effect of Climatic Conditions in Field Environment
2.3. Effects of Climate Change on Crop Production-Yields
2.4. Effects of Climate Change on Crop Production—A Risk Factor in Food Safety
3. Risk Assessment and Modelling
3.1. Role and Significance of Risk Assessment and Modelling
3.2. Modelling
3.2.1. Types of Models
3.2.2. In Vitro vs. In Vivo Fungal Growth Modelling
3.2.3. Aflatoxin Prediction in Different Crops
3.2.4. AF Models in Maize
3.3. Opportunities and Limitations of Model Usage
4. Agronomy—Preventive and Corrective Steps
4.1. Resistance Breeding
4.2. Biocontrol
4.3. Good Agricultural Practice (GAP)
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Substrate | Parameter | Growth | Aflatoxin Production | ||||
---|---|---|---|---|---|---|---|
min | opt | max | min | opt | max | ||
Whe at 1 | Temperature | 15 | 35 | >42.5 | 15 | 25 | 42.5 |
aw | 0.80 | 0.95 | >0.95 | 0.85 | 0.93 | 0.95 | |
Sorghum 1 | Temperature | 15 | 37 | ns | 15 | 37 | ns |
aw | <0.91 | 0.97 | ns | 0.94 | 0.97 | ns | |
Rice 1 | Temperature | 20 | 33 | 42 | <20 | 35 | 37 |
aw | 0.80 | 0.90 | 0.99 | 0.85 | 0.96 | 0.99 | |
Malt extract-sucrose 1 | Temperature | 12–15 | 37 | 42 | 12 | 31 | 37 |
aw | 0.8–0.85 | 30–37 | ns | 0.85 | 0.99 | 0.99 | |
Maize-based medium 2 | Temperature | 20 | 30 | ns | ns | 30 | ns |
aw | 0.90 | 0.99 | ns | ns | 0.98 | ns | |
Other in vitro medium 3 | Temperature | ns | 35 | ns | ns | 33 | ns |
aw | ns | 0.95 | ns | ns | 0.99 | ns |
Year | Authors | Name | Characteristics |
---|---|---|---|
2003 | Dowd, P. F. [116] | Mycotoxin predictor | Not published thoroughly. |
2008 | Chauhan et al. [83] | - | Semi-mechanistic daily model based on aridity index. |
2008 | Battilani et al. [10] | - | Empirical, decadal, aridity-based model. |
2013 | Battilani et al. [17] | AFLA-maize | Mechanistic model evaluated in Italy. |
2015 | Chauhan et al. [115] | - | Semi-mechanistic, aridity-based model evaluated in Kenya. |
2018 | Damianidis et al. [117] | - | Semi-mechanistic, weekly, aridity-based. |
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Dövényi-Nagy, T.; Rácz, C.; Molnár, K.; Bakó, K.; Szláma, Z.; Jóźwiak, Á.; Farkas, Z.; Pócsi, I.; Dobos, A.C. Pre-Harvest Modelling and Mitigation of Aflatoxins in Maize in a Changing Climatic Environment—A Review. Toxins 2020, 12, 768. https://doi.org/10.3390/toxins12120768
Dövényi-Nagy T, Rácz C, Molnár K, Bakó K, Szláma Z, Jóźwiak Á, Farkas Z, Pócsi I, Dobos AC. Pre-Harvest Modelling and Mitigation of Aflatoxins in Maize in a Changing Climatic Environment—A Review. Toxins. 2020; 12(12):768. https://doi.org/10.3390/toxins12120768
Chicago/Turabian StyleDövényi-Nagy, Tamás, Csaba Rácz, Krisztina Molnár, Károly Bakó, Zsombor Szláma, Ákos Jóźwiak, Zsuzsa Farkas, István Pócsi, and Attila Csaba Dobos. 2020. "Pre-Harvest Modelling and Mitigation of Aflatoxins in Maize in a Changing Climatic Environment—A Review" Toxins 12, no. 12: 768. https://doi.org/10.3390/toxins12120768