|dc.description.abstract||Aflatoxins are potent carcinogens and contaminated corn grains if consumed can have a deleterious effect on both humans and animals. Pre-harvest aflatoxin contamination in corn (Zea mays L.) is a continuing concern in the Southeast United States particularly in seasons with above normal temperatures, and lower than normal precipitation; conditions that promote in-field drought. If predicting aflatoxin accumulation in grain is feasible, then contamination concerns could be minimized.
Three studies are included in this dissertation: In the first study a research was conducted to determine whether a drought index could be used to predict the risk for pre-harvest aflatoxin contamination in corn, as well as to determine risk differences in-season and among sites. Our hypothesis is that aflatoxin risk changes as the drought conditions within the growing season change. Two datasets were considered: 1) data collected from a controlled experimental site at Starkville, MS over 13 years (2000 – 2011, and 2013 – 2014), on two soil types (a silty clay loam and a loam), with three commercial hybrids with different susceptibility levels to aflatoxin contamination, and 2) data from random corn fields collected from 1977 - 2004 across fifty three Georgia counties. The Agricultural Reference Index for Drought (ARID), a generic drought index calculated on daily basis, was evaluated as an aflatoxin risk prediction tool. ARID factors were calculated for weekly windows before and after silking to evaluate the in-season changes in aflatoxin risk. Multiple logistic regression models were used to predict aflatoxin risk as a function of the derived weekly ARID values and risk level changes were tested according to soil type and corn hybrid susceptibility. If grain contamination with aflatoxins exceeds 20 ppb, then the United States Food and Drug Administration restricts corn contamination by humans and young animals. Therefore, this threshold (20 ppb) was selected to transform the raw aflatoxin data into a binary dependent variable for the logistic model.
Results from the first study revealed: 1) aflatoxin risk might be assessed by ARID, 2) soil type and hybrid susceptibility to aflatoxin contamination were statistically significant, and 3) ARID based risk changed during the growing season. These findings could be used to minimize aflatoxin risk by adapting site-specific management strategies such as: 1) triggering irrigation during critical risk weeks, 2) altering planting dates and/or select hybrids with suitable relative maturities to reduce plant exposure to drought stress during critical growth windows, 3) based on soil type, selecting the most appropriate hybrid for a given site/location, 4) separating a field into management zones, i.e. to segregate harvest if needed, and 5) determining best harvest timing.
Weather fluctuations have an impact on the extent of aflatoxin contamination, in part by stressing the crop, and thus predisposing the host plant to A. flavus infection and subsequent aflatoxin contamination. Planting dates and plant densities that alleviate crop stress during critical growth stage windows are expected to reduce mycotoxin contamination.
The objectives of the second study were to: 1) assess the effect of agronomic practices (planting date and plant density) on preharvest aflatoxin contamination in rainfed corn grown in the Coastal Plains of South and Central Alabama, 2) identify weather variables that influence aflatoxin contamination in corn, 3) determine the relative weight those variables have on corn contamination, and d) determine time windows during the growing season when weather variables are associated to corn aflatoxin contamination.
Field experiments were conducted at Fairhope, AL, and Prattville, AL, for five and two years, respectively. The experimental design was a split–split plot design, with inoculation, planting date, and plant density assigned to main plots, sub-plots, and sub-subplots. Five time windows were considered: 1) a 2 week window before mid-silk, 2) a 2-week window after mid-silk, 3) the second 2-week window after mid-silk, 4) the third 2-week window after mid-silk, and 5) a variable in length window from the end of the third 2-week window to corn physiological maturity. For each of those windows average daily minimum temperature and cumulative rainfall were calculated. Multiple regression analysis with stepwise selection was used to study the influence of weather parameters on aflatoxin contamination for the five time windows defined. Six models were developed; three from pooled Fairhope data (2010 – 2014) and three from pooled data over Fairhope (2010 – 2014) and Prattville (2013 – 2014). The response variable in each of the models was corn aflatoxin contamination; explanatory variables tested were: 1) both derived cumulative rainfall and derived average daily minimum temperature variables for the five windows defined earlier (overall model x 2), 2) derived cumulative rainfall variables only (rainfall model x 2), 3) derived average daily minimum temperature variables only (minimum temperature model x 2).
The results from the second study showed: that mid-April planting date resulted in significant (p – value < 0.05) or relative reduction in aflatoxin contamination. Plant densities tested did not influence toxin accumulation. A significant negative linear relationship was found between aflatoxin and yield for 2011 in Fairhope. The overall model developed from Fairhope data only and from Fairhope and Prattville data combined had an R2 equal to 87 and 76%, respectively. Rainfall models alone could explain more than 50% of the observed variability. The relative weight of derived weather variables that influence corn contamination for the window around silking was determined. Daily minimum temperatures for the first and third 2 week windows following silking had the largest impact on aflatoxin contamination with partial R2 equal to 40 and 27% (Overall Model – Fairhope). The effect direction (positive/negative) of average daily minimum temperature on aflatoxin contamination, as indicated e.g.; by the minimum temperature models, is changing through the windows considered herein. A better understanding on the influence of weather variables on the contamination process may improve pre and post-harvest management practices, assist farmers in decision making, and improve efficiency and accuracy of monitoring and prediction.
Planting dates and plant densities have an influence on corn yield and when they interact with weather conditions that can impose plant stresses yield losses for dryland corn can be significant. Optimum planting dates and optimum plant densities are location specific and their determination is needed for sound management. However, this information is usually obtained through large scale experiments that are time consuming and expensive or through modeling approaches which require data that are not always readily available. Environmental stresses result in 13C discrimination (Δ), and questions arise whether yield differences as affected by planting dates and plant densities can be reflected on 13C discrimination values from corn grains harvested within and at the end of the season.
The objectives of the third study were: 1) to explore if Δ is a suitable tool to explain corn yield differences resulting from planting date and plant density practices under the environmental conditions of Coastal Plains in Alabama, and 2) explore if Δ observations from corn grains sampled within season can account for attained yield differences. Field experiments were conducted at Fairhope and Prattville, as stated earlier. Corn grain was harvested at milk (R3) and at harvest maturity and analyzed for δ 13C.
The results of the third study showed that the relationship between yield and 13C discrimination in corn grains harvested at milk (R3) and at harvest maturity was not consistent between years x locations and within year x location. Δ values of grain samples reflected yield differences between mid-March and mid-April planted corn in Prattville (for both grain harvest times) and in Fairhope (for grain harvested at R3 only) in 2013 and 2014, respectively. 13C discrimination in corn grain was significantly influenced by plant density only for samples harvested at milk (R3) and harvest maturity in Fairhope in 2013 and 2014, respectively. In Fairhope, lower plant densities tend to have higher Δ and lower yield per unit area compared to higher corn densities. The inconsistencies in the relationship between Δ and corn yield indicate that factors not measured in this study can influence 13C discrimination in corn grain. Therefore, more research is needed to elucidate the effect of different factors under field conditions before Δ can be used as a tool to assess corn attained yield differences.||en_US