Peanut Yield Loss in the Presence of Defoliation Caused by Late or Early Leaf Spot.

Late and early leaf spot, respectively caused by Nothopassalora personata and Passalora arachidicola, are damaging diseases of peanut (Arachis hypogaea) capable of defoliating canopies and reducing yield. Although one of these diseases may be more predominant in a given area, both are important on a global scale. To assist informed management decisions and quantify relationships between end-of-season defoliation and yield loss, meta-analyses were conducted over 140 datasets meeting established criteria. Slopes of proportion yield loss with increasing defoliation were estimated separately for Virginia and runner market type cultivars. Yield loss for Virginia types was described by an exponential function over the range of defoliation levels, with a loss increase of 1.2 to 2.2% relative to current loss levels per additional percent defoliation. Results for runner market type cultivars showed yield loss to linearly increase 2.2 to 2.8% per 10% increase in defoliation for levels up to approximately 95% defoliation, after which the rate of yield loss was exponential. Defoliation thresholds to prevent economic yield loss for Virginia and runner types were estimated at 40 and 50%, respectively. Although numerous factors remain important in mitigating overall yield losses, the integration of these findings should aid recommendations about digging under varying defoliation intensities and peanut maturities to assist in minimizing yield losses.

Biocontrol Strains Differentially Shift the Genetic Structure of Indigenous Soil Populations of Aspergillus flavus.

Biocontrol using non-aflatoxigenic strains of Aspergillus flavus has the greatest potential to mitigate aflatoxin contamination in agricultural produce. However, factors that influence the efficacy of biocontrol agents in reducing aflatoxin accumulation under field conditions are not well-understood. Shifts in the genetic structure of indigenous soil populations of A. flavus following application of biocontrol products Afla-Guard and AF36 were investigated to determine how these changes can influence the efficacy of biocontrol strains in reducing aflatoxin contamination. Soil samples were collected from maize fields in Alabama, Georgia, and North Carolina in 2012 and 2013 to determine changes in the population genetic structure of A. flavus in the soil following application of the biocontrol strains. A. flavus L was the most dominant species of Aspergillus section Flavi with a frequency ranging from 61 to 100%, followed by Aspergillus parasiticus that had a frequency of <35%. The frequency of A. flavus L increased, while that of A. parasiticus decreased after application of biocontrol strains. A total of 112 multilocus haplotypes (MLHs) were inferred from 1,282 isolates of A. flavus L using multilocus sequence typing of the trpC, mfs, and AF17 loci. A. flavus individuals belonging to the Afla-Guard MLH in the IB lineage were the most dominant before and after application of biocontrol strains, while individuals of the AF36 MLH in the IC lineage were either recovered in very low frequencies or not recovered at harvest. There were no significant (P > 0.05) differences in the frequency of individuals with MAT1-1 and MAT1-2 for clone-corrected MLH data, an indication of a recombining population resulting from sexual reproduction. Population mean mutation rates were not different across temporal and spatial scales indicating that mutation alone is not a driving force in observed multilocus sequence diversity. Clustering based on principal component analysis identified two distinct evolutionary lineages (IB and IC) across all three states. Additionally, patristic distance analysis revealed phylogenetic incongruency among single locus phylogenies which suggests ongoing genetic exchange and recombination. Levels of aflatoxin accumulation were very low except in North Carolina in 2012, where aflatoxin levels were significantly (P < 0.05) lower in grain from treated compared to untreated plots. Phylogenetic analysis showed that Afla-Guard was more effective than AF36 in shifting the indigenous soil populations of A. flavus toward the non-toxigenic or low aflatoxin producing IB lineage. These results suggest that Afla-Guard, which matches the genetic and ecological structure of indigenous soil populations of A. flavus in Alabama, Georgia, and North Carolina, is likely to be more effective in reducing aflatoxin accumulation and will also persist longer in the soil than AF36 in the southeastern United States.

Epidemics and Yield Losses due to Corynespora cassiicola on Cotton.

Target spot, caused by Corynespora cassiicola, has recently emerged as a problematic foliar disease of cotton. This pathogen causes premature defoliation during boll set and maturation that can subsequently impact yield, and on certain cotton cultivars loss can be substantial. This study sought to better understand target spot epidemics and disease-incited yield losses on cotton. In order to establish a range of disease, varying numbers of fungicide applications were made to each of two cotton cultivars in each of four site-years. Target spot intensity was rated over several dates beginning in late July or early August and continuing into September. Yield of seed plus lint (seed cotton) was recorded at harvest. When analyzed across cultivars, a second or third fungicide application increased yield compared with no treatment. Lack of significant yield response with a single fungicide application may have been due to timing of that application which preceded disease onset. The cultivar PhytoGen 499 WRF had consistently greater defoliation than any of the three Deltapine cultivars grown in each site-year. However, yields of both cultivars responded similarly to the fungicide regimes. Yield loss models based on late August defoliation were only predictive at site-years where conditions favored target spot development, i.e., abundant rain and moderate temperatures. Epidemic development fit the Gompertz growth model better than it did a logistic model. Knowledge of the underlying mathematical character of the epidemiology of target spot will prove useful for development of a predictive model for the disease.