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ARS Home » Southeast Area » Mississippi State, Mississippi » Crop Science Research Laboratory » Corn Host Plant Resistance Research » Research » Publications at this Location » Publication #346519

Research Project: Genetic Improvement of Maize with Enhanced Resistance to Aflatoxin and Insects

Location: Corn Host Plant Resistance Research

Title: Evaluating a generic drought index as a predictive tool for aflatoxin contamination of corn: from plot to regional level

Author
item Damianidis, Damianos - Auburn University
item Ortiz, B - Auburn University
item Windham, Gary
item Bowen, K - Auburn University
item Hoogenboom, G - Institute For Sustainable Agriculture
item Scully, Brian
item Hagan, A - Auburn University
item Knappenberger, T - Auburn University
item Woli, P - Texas A&M University
item Williams, William

Submitted to: Crop Protection Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/23/2018
Publication Date: 8/3/2018
Citation: Damianidis, D., Ortiz, B.V., Windham, G.L., Bowen, K.L., Hoogenboom, G., Scully, B.T., Hagan, A., Knappenberger, T., Woli, P., Williams, W.P. 2018. Evaluating a generic drought index as a predictive tool for aflatoxin contamination of corn: from plot to regional level. Crop Protection Journal. 113:64-74. https://doi.org/10.1016/j.cropro.2018.07.013.
DOI: https://doi.org/10.1016/j.cropro.2018.07.013

Interpretive Summary: Infection of corn grain by the fungus Aspergillus flavus and production of aflatoxin by this fungus can be harmful to both humans and animals that consume the infected grain. Being able to predict aflatoxin contamination would be beneficial to growers, but this is difficult to do because of the complex interactions of the fungus with environmental conditions. The goal of this study was to determine whether a drought index could be used to predict risk for aflatoxin contamination of corn during the growing season. Aflatoxin risk assessment was calculated using field data collected at Mississippi State, MS, and in growers' fields in south Georgia. Data on aflatoxin contamination was collect over 13 growing seasons in Mississippi, and over 27 years in Georgia. The Agricultural Reference Index for Drought (ARID), a generic drought index for calculating drought on a daily basis, was evaluated as an aflatoxin risk prediction tool. Midsilk (when 50% of corn plants in a plot had silks exposed) was used to split growing seasons in two time periods which were then divided into individual weeks prior to or after midsilk. Weekly ARID values were calculated for all periods to evaluate growing season alterations in aflatoxin risks. Statistical regression models were used to predict aflatoxin risk as a function of the weekly ARID values. Our results revealed that ARID may be used as a predictive tool to assess aflatoxin risk. We also found that soil type and hybrid susceptibility to aflatoxin contamination were statistically significant independent factors, and that there are critical periods during the growing season when drought conditions have the most effect on aflatoxin contamination. These findings can be used to minimize risk due to aflatoxin contamination by adapting site-specific management strategies such as irrigation schedule, selection of adapted hybrids, and selecting optimum harvest dates.

Technical Abstract: Corn (Zea mays L.) kernel infection by Aspergillus flavus and subsequent aflatoxin accumulation in grain can have a deleterious effect on both humans and animals that consume contaminated grain. Predicting the aflatoxin risk is challenging due to complex interactions of biotic and abiotic stress factors that govern and exacerbate the phenomenon. The goal of this study was to determine whether a drought index could be used to predict the risk for pre-harvest aflatoxin contamination in corn. Risk assessment was approached at: 1) field (plot) level with data obtained from an in-field controlled experiment (Mississippi study), and 2) state level, where corn fields were sampled at a county level (Georgia study). The data used for this study consisted of historical records on aflatoxin contamination collected over thirteen growing seasons from 2000 to 2011, 2013 and 2014 at Mississippi State, Mississippi, and from random corn fields in fifty-three counties across Georgia between 1977 and 2004. A controlled experiment was conducted at Mississippi with two soil types (a Leeper silty clay loam and a Myatt loam), and three commercial hybrids characterized by different susceptibility levels to aflatoxin contamination. The Agricultural Reference Index for Drought (ARID), a generic drought index for calculating drought on daily basis was evaluated as an aflatoxin risk prediction tool. Mid-silk day was selected to split each growing season into two time periods, which were further divided into positive and negative weeks representing weeks after and before mid-silk, respectively. Weekly ARID factors were calculated for all periods to evaluate the in-season alterations in aflatoxin risk. In both studies, multiple logistic regression models were used to predict aflatoxin risk as a function of the weekly ARID values. In Mississippi, risk level changes were additionally tested according to soil type and corn hybrid aflatoxin susceptibility. The United States Food and Drug Administration restricts corn grain consumption by humans and young animals if the contamination level is above 20 µg/kg; thus, this threshold (20 mg/kg) was selected to develop a binary dependent variable for the logistic model from the raw aflatoxin data. The results revealed that ARID might be used as a predictive tool to assess aflatoxin risk, soil type and hybrid susceptibility to aflatoxin contamination were statistically significant independent factors, and that there are critical week windows during the growing season when changes in drought conditions affect the likelihood for aflatoxin contamination. These findings can be used to minimize risk by adapting site-specific management strategies such as triggering irrigation during critical risk weeks, selecting the most appropriate hybrid for a given site/location based on soil type, and determining optimum harvest date.