Minimum Temperature, Rainfall, and Agronomic Management Impacts on Corn Grain Aflatoxin Contamination

다국어 초록 (Multilingual Abstract)

Evaluation of the influence of planting date on corn aflatoxin.
Evaluation on how in‐season weather conditions influence corn aflatoxin.
Evaluation of the influence of planting density on corn aflatoxin.
Identification of the growing season periods when the aflatoxin risk is highest.
Evaluation of the relationship between yield and corn aflatoxin.

Aflatoxins are a group of toxins produced by fungi found on corn (Zea mays L.). Aflatoxin contamination can make it unmarketable. Fortunately, management practices that reduce stress during critical growth stages lessen contamination. A study was conducted at Fairhope, AL (2010–2014), and Prattville, AL (2013–2014), to evaluate the effect of planting dates, plant densities, and in‐season weather conditions on preharvest aflatoxin contamination. The experiment had a split‐split‐plot design replicated six times, with inoculation method assigned to the main plots, planting date to the subplots, and planting density to the sub‐subplots. Results showed that delaying planting from mid‐March to mid‐April reduced aflatoxin levels and increasing the planting density from 44,480 to 74,130 plants ha−1 did not impact toxin accumulation. Multiple linear regression indicated that minimum air temperature and rainfall models could explain from 50 to 76% of the observed aflatoxin variability. However, the effect of both variables on aflatoxin contamination levels changed during the period pre‐silking (14 d prior) to physiological maturity. Minimum temperature alone had the strongest positive influence on aflatoxin over the 2 wk after mid‐silk. A reduction in rainfall during 2 wk prior mid‐silk and from Day 43 after mid‐silk to physiological maturity resulted on high aflatoxin contamination levels. 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.