Peanut disease epidemiology under dynamic microclimate conditions and management practices in North Florida

Sensor-Based Quantification of Peanut Disease Defoliation Using an Unmanned Aircraft System and Multispectral Imagery

Early leaf spot (Passalora arachidicola) and late leaf spot (Nothopassalora personata) are two of the most economically important foliar fungal diseases of peanut, often requiring seven to eight fungicide applications to protect against defoliation and yield loss. Rust (Puccinia arachidis) may also cause significant defoliation depending on season and location. Sensor technologies are increasingly being utilized to objectively monitor plant disease epidemics for research and supporting integrated management decisions. This study aimed to develop an algorithm to quantify peanut disease defoliation using multispectral imagery captured by an unmanned aircraft system. The algorithm combined the Green Normalized Difference Vegetation Index and the Modified Soil-Adjusted Vegetation Index and included calibration to site-specific peak canopy growth. Beta regression was used to train a model for percent net defoliation with observed visual estimations of the variety 'GA-06G' (0 to 95%) as the target and imagery as the predictor (train: pseudo-R2 = 0.71, test k-fold cross-validation: R2 = 0.84 and RMSE = 4.0%). The model performed well on new data from two field trials not included in model training that compared 25 (R2 = 0.79, RMSE = 3.7%) and seven (R2 = 0.87, RMSE = 9.4%) fungicide programs. This objective method of assessing mid-to-late season disease severity can be used to assist growers with harvest decisions and researchers with reproducible assessment of field experiments. This model will be integrated into future work with proximal ground sensors for pathogen identification and early season disease detection.[Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Environmental Factors Influencing Stem Rot Development in Peanut: Predictors and Action Thresholds for Disease Management

Peanuts grown in tropical, subtropical, and temperate regions are susceptible to stem rot, which is a soilborne disease caused by Athelia rolfsii. Due to the lack of reliable environmental-based scheduling recommendations, stem rot control relies heavily on fungicides that are applied at predetermined intervals. We conducted inoculated field experiments for six site-years in North Florida to examine the relationship between germination of A. rolfsii sclerotia: the inoculum, stem rot symptom development in the peanut crop, and environmental factors such as soil temperature (ST), soil moisture, relative humidity (RH), precipitation, evapotranspiration, and solar radiation. Window-pane analysis with hourly and daily environmental data for 5- to 28-day periods before each disease assessment were evaluated to select model predictors using correlation analysis, regularized regression, and exhaustive feature selection. Our results indicated that within-canopy ST (at 0.05 m belowground) and RH (at 0.15 m aboveground) were the most important environmental variables that influenced the progress of mycelial activity in susceptible peanut crops. Decision tree analysis resulted in an easy-to-interpret one-variable model (adjusted R2 = 0.51, Akaike information criterion [AIC] = 324, root average square error [RASE] = 14.21) or two-variable model (adjusted R2 = 0.61, AIC = 306, RASE = 10.95) that provided an action threshold for various disease scenarios based on number of hours of canopy RH above 90% and ST between 25 and 35°C in a 14-day window. Coupling an existing preseason risk index for stem rot, such as Peanut Rx, with the environmentally based predictors identified in this study would be a logical next step to optimize stem rot management. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Use of image analysis to assess radial growth of Passalora arachidicola and Nothopassalora personata on solid media

Despite significant research on early and late leaf spot diseases of peanut, in vitro study of the respective causal agents, Passalora arachidicola and Nothopassalora personata, has been limited due to cultural challenges that make growth of these fungi difficult to quantify with traditional methods. Studies were conducted to evaluate the practicality of image analysis to assess radial growth and tissue volume by correlating these assessments to dry mass. Image analysis was also used to estimate radial growth rates for these fungi over time. Tissue area and volume were significantly correlated to dry mass for P. arachidicola in two separate experiments, and for N. personata when medium had been removed from tissues prior to dry mass assessments. Tissue area densities were the same for P. arachidicola and Pseudocercospora smilacicola, evaluated as a nonstromatal cercosporoid comparison, whereas tissue volume densities were greater for P. archidicola and N. personata than P. smilacicola. A quadratic relationship was observed between radial growth and incubation time for all isolates evaluated. Growth rates of P. arachidicola isolates were 2 to 4 times faster than N. personata during the first week of incubation and slowed over time. Growth rates of NP18R, a phenotype variant of N. personata, increased after neighboring colonies met and was nearly 2.5 times faster than the fastest rates observed for P. arachidicola. These experiments demonstrate that when fungal tissues are observable, image analysis is a useful assessment tool for P. arachidicola and N. personata. Care should be taken to monitor fungal phenotypic changes in these species because phenotype degeneration can affect growth rates.

Application of Hydrothermal Time Models to Predict Sclerotial Germination of Athelia rolfsii

Athelia rolfsii, causal agent of "southern blight" disease, is a soilborne fungal pathogen with a wide host range of more than 500 species. This study's objectives were to (i) quantify the effects of two environmental factors, temperature and soil moisture, on germination of A. rolfsii inoculum (sclerotia), which is a critical event for the onset of disease epidemics and (ii) predict the timing of sclerotial germination by applying population-based threshold-type hydrothermal time (HTT) models. We conducted in vitro germination experiments with three isolates of A. rolfsii isolated from peanuts, which were tested at five temperatures (T), ranging from 17 to 40°C, four matric potentials (Ψm) between -0.12 and -1.57 MPa, and two soil types (fine sand and loamy fine sand), using a factorial design. When Ψm was maintained between -0.12 and -0.53 MPa, T from 22 to 34°C was found to be conducive to sclerotial germination (>50%). The HTT models were fitted for a range of T (22 to 34°C) and Ψm (-0.12 to -1.57 MPa) that accounted for 84% or more of variation in the timing of sclerotial germination. The estimated base T ranged between 0 and 4.5°C and the estimated base Ψm between -2.96 and -1.52 MPa. The results suggest that the HTT modeling approach is a suitable means of predicting the timing of A. rolfsii sclerotial germination. This HTT methodology can potentially be tested to fine-tune fungicide application timing and in-season A. rolfsii management strategies. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.