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Title: Minimum temperature, rainfall and agronomic management impacts on corn grain aflatoxin contamination

Author
item DAMIANIDIS, D - Auburn University
item ORTIZ, B - Auburn University
item BOWEN, K - Auburn University
item Windham, Gary
item HOOGENBOOM, G - University Of Florida
item HAGAN, A - Auburn University
item KNAPPENBERGER, T - Auburn University
item Abbas, Hamed
item Scully, Brian
item MOURTZINIS, S - University Of Wisconsin

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/2/2018
Publication Date: 8/10/2018
Citation: Damianidis, D., Ortiz, B.V., Bowen, K.L., Windham, G.L., Hoogenboom, G., Hagan, A., Knappenberger, T., Abbas, H.K., Scully, B.T., Mourtzinis, S. 2018. Minimum temperature, rainfall and agronomic management impacts on corn grain aflatoxin contamination. Agronomy Journal. 110:1697-1708. https://doi.org/10.2134/agronj2017.11.0628.
DOI: https://doi.org/10.2134/agronj2017.11.0628

Interpretive Summary: Contamination of corn prior to harvest is a major problem for producers world-wide. Crop management practices, environmental conditions, corn genotypes, and the interactions of these variables influence the levels of aflatoxin found in corn kernels. Limiting crop stress during critical growth stages can improve yields and suppress the amount of aflatoxin found in kernels at harvest. Field studies were conducted at two locations in Alabama from 2010 to 2014 to evaluate the effects of planting dates, plant densities, and environmental factors on preharvest aflatoxin contamination. Planting in mid-April resulted in lower levels of aflatoxin at harvest compared to corn planted in mid-March. No differences in aflatoxin contamination was observed regardless of the planting densities (44,480 to 74,130 plants per hectare) used in our studies. Minimum ambient air temperatures and rainfall models accounted for 50 to 70% of the aflatoxin data variability. Statistical models were created to describe the relationship of average daily temperatures and rainfall from silking to harvest on aflatoxin accumulation. Additional studies are needed to better understand the effect of weather variables on aflatoxin accumulation so crop management practices can be employed to limit the amount of this toxin in preharvest corn.

Technical Abstract: Contamination of corn (Zea mays L.) with aflatoxin is a worldwide issue and can make corn unmarketable. Management, environment, genotype and their interaction may have an influence on toxin accumulation. Alleviating crop stress during critical growth stages should improve yields and lessen contamination. A study was conducted at Fairhope (2010 – 2014) and Prattville (2013 – 2014) Alabama to evaluate the effect of planting dates, plant densities and during the season climatic conditions on preharvest aflatoxin contamination. The experiment had a split-split-plot design, was replicated six times, with inoculation assigned to the main plots, planting date to the subplots, and planting density to the sub-subplots. Results showed that mid-April planting resulted in significant (p – value < 0.05) or relative reduction in aflatoxin contamination compared to mid-March planting. Plant densities (44,480, 54,360, 64,250, and 74,130 plants ha-1) did not have an influence in toxin accumulation. Multiple linear regression indicated that minimum air temperature and rainfall models could explain from approximately 50 up to 76% of the observed aflatoxin variability. The influence of average daily minimum temperature on aflatoxin contamination changes direction (positive/negative) through the season starting 14 days before mid-silk (negative) and extending to physiological maturity (positive). A positive and negative linear relationship was found between cumulative rainfall and aflatoxin (e.g., the 14- days period before and after mid-silk) and between aflatoxin and yield (e.g., Fairhope 2011), respectively. In conclusion, a better understanding of the influence of weather variables on corn contamination may lead to better crop management and development of more accurate prediction systems.