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.

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.

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.

Effects of Grass-Based Crop Rotation, Nematicide, and Irrigation on the Nematode Community in Cotton.

Plant-parasitic and free-living nematodes - bacterivores, fungivores, omnivores, predators - comprise the nematode community. Nematicide application and crop rotation are important tools to manage plant-parasitic nematodes, but effects on free-living nematodes and nematode ecological indices need further study. The nematicide fluopyram was recently introduced in cotton (Gossypium hirsutum) production and its effects on the nematode community need assessment. This research was conducted in 2017 and 2018 at a long-term field site in Quincy, FL where perennial grass/sod-based (bahiagrass, Paspalum notatum) and conventional cotton rotations were established in 2000. The objective of this research was to evaluate the effects of fluopyram nematicide, crop rotation phase, and irrigation on free-living nematodes and nematode ecological indices based on three soil sampling dates each season. We did not observe consistent effects of crop rotation phase on free-living nematodes or nematode ecological indices. Only omnivores were consistently negatively impacted by fluopyram. Nematode ecological indices reflected this negative effect by exhibiting a degraded/ stressed environmental condition relative to untreated plots. Free-living nematodes were not negatively impacted by nematicide when sod-based rotation was used.

Predictive Modeling of Brown Rust of Sugarcane Based on Temperature and Relative Humidity in Florida.

Logistic regression models were developed from 5 years (2014 to 2018) of disease severity and weather data in an attempt to predict brown rust of sugarcane at the Everglades Research and Education Center in Belle Glade, Florida. Disease severity (percentage area of the top visible dewlap leaf covered by rust) was visually assessed in the field every 2 weeks for two varieties susceptible to brown rust. A total of 250 variables were derived from weather data for 10- to 40-day periods before each brown rust assessment day. A subset of these variables were then evaluated as potential predictors of severity of brown rust based on their individual correlation or their biological meaningfulness. Analyses of correlation and stepwise logistic regression allowed us to identify afternoon humid thermal ratio (AHTR), temperature-based duration variables, and their interaction terms as the most significant variables associated with brown rust epidemics of sugarcane in Florida. The nine best predictive models were identified based on model accuracy, sensitivity, specificity, and estimates of the prediction error. The prediction accuracy of these models ranged from 73 to 85%. Single-variable model BR2 (based on AHTR) classified 89% of the epidemic and 81% of the nonepidemic status of the disease. More than 83% of the epidemics and 81% of the nonepidemic status of sugarcane brown rust was correctly classified via multiple-variable models. These models can be used as components of a rust disease warning system to assist in the management of brown rust epidemics of sugarcane in south Florida.

Sixty-One Years Following Registration, Phorate Applied In-Furrow at Planting Suppresses Development of Late Leaf Spot on Peanut.

Late and early leaf spot are caused by Nothopassalora personata and Passalora arachidicola, respectively, and are damaging diseases of peanut (Arachis hypogaea L.) capable of defoliation and yield loss. Management of these diseases is most effective through the integration of tactics that reduce starting inoculum and prevent infection. The insecticide phorate was first registered in 1959 and has been used in peanut production for decades in-furrow at planting to suppress thrips. Phorate further provides significant suppression of Tomato spotted wilt virus infection beyond suppression of its thrips vector alone by activating defense-related responses in the peanut plant. From six experiments conducted from 2017 to 2019 in Blackville, SC, Reddick, FL, and Quincy, FL, significantly less leaf spot defoliation was exhibited on peanuts treated with phorate in-furrow at planting (26%) compared with nontreated checks (48%). In-season fungicides were excluded from five of the experiments, whereas the 2018 Quincy, FL, experiment included eight applications on a 15-day interval. Across individual experiments, significant suppression of defoliation caused by late leaf spot was observed from 64 to 147 days after planting. Although more variable within location-years, pod yield following phorate treatment was overall significantly greater than for nontreated peanut (2,330 compared with 2,030 kg/ha; P = 0.0794). The consistent defoliation suppression potential was estimated to confer an average potential net economic yield savings of $90 to $120 per hectare under analogous leaf spot defoliation. To our knowledge, these are the first data in the 61 years since its registration demonstrating significant suppression of leaf spot on peanut following application of phorate in-furrow at planting. Results support phorate use in peanut as an effective and economical tactic to incorporate to manage late and early leaf spot infections and development of fungicide resistance.

Nematicide influence on cotton yield and plant-parasitic nematodes in conventional and sod-based crop rotation.

Plant-parasitic nematodes (Rotylenchulus reniformis (reniform, RN), Helicotylenchus dihystera (spiral), and Mesocriconema ornatum (ring)) and yield were investigated in cotton phases of conventional (peanut-cotton-cotton) and sod-based (bahiagrass-bahiagrass-peanut-cotton) rotations with or without irrigation and fluopyram nematicide at a long-term research site, established in 2000, in Quincy, Florida, USA. Objectives were to determine impacts of nematicide application on cotton yield and evaluate effects of nematicide on plant-parasitic nematodes in these rotations in 2017 and 2018. Reniform nematode population densities were greater in conventional cotton than sod-based cotton. Ring and spiral nematode population densities were greater in sod-based cotton than conventional cotton. Plots receiving nematicide had increased RN population densities in preplant 2018 soil samples and spiral nematode population densities in preplant 2017, harvest 2017, preplant 2018, and harvest 2018 soil samples compared to untreated plots. Cotton seed yield in conventional rotation was increased by 18% following nematicide application in 2017 but decreased by 10% in sod-based rotation in 2018, relative to the untreated control. Sod-based rotation had greater cotton yield than conventional rotation in 2017 and 2018. Nematicide application did not improve cotton yield in sod-based rotation and was inconsistent in conventional rotation.Plant-parasitic nematodes (Rotylenchulus reniformis (reniform, RN), Helicotylenchus dihystera (spiral), and Mesocriconema ornatum (ring)) and yield were investigated in cotton phases of conventional (peanut­cotton­cotton) and sod-based (bahiagrass­bahiagrass­peanut­cotton) rotations with or without irrigation and fluopyram nematicide at a long-term research site, established in 2000, in Quincy, Florida, USA. Objectives were to determine impacts of nematicide application on cotton yield and evaluate effects of nematicide on plant-parasitic nematodes in these rotations in 2017 and 2018. Reniform nematode population densities were greater in conventional cotton than sod-based cotton. Ring and spiral nematode population densities were greater in sod-based cotton than conventional cotton. Plots receiving nematicide had increased RN population densities in preplant 2018 soil samples and spiral nematode population densities in preplant 2017, harvest 2017, preplant 2018, and harvest 2018 soil samples compared to untreated plots. Cotton seed yield in conventional rotation was increased by 18% following nematicide application in 2017 but decreased by 10% in sod-based rotation in 2018, relative to the untreated control. Sod-based rotation had greater cotton yield than conventional rotation in 2017 and 2018. Nematicide application did not improve cotton yield in sod-based rotation and was inconsistent in conventional rotation.

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.

Weather-Based Predictive Modeling of Orange Rust of Sugarcane in Florida.

Epidemics of sugarcane orange rust (caused by Puccinia kuehnii) in Florida are largely influenced by prevailing weather conditions. In this study, we attempted to model the relationship between weather conditions and rust epidemics as a first step toward development of a decision aid for disease management. For this purpose, rust severity data were collected from 2014 through 2016 at the Everglades Research and Education Center, Belle Glade, Florida, by recording percentage of rust-affected area of the top visible dewlap leaf every 2 weeks from three orange rust susceptible cultivars. Hourly weather data for 10- to 40-day periods prior to each orange rust assessment were evaluated as potential predictors of rust severity under field conditions. Correlation and stepwise regression analyses resulted in the identification of nighttime (8 PM to 8 AM) accumulation of hours with average temperature 20 to 22°C as a key predictor explaining orange rust severity. The five best regression models for a 30-day period prior to disease assessment explained 65.3 to 76.2% of variation of orange rust severity. Prediction accuracy of these models was tested using a case control approach with disease observations collected in 2017 and 2018. Based on receiver operator characteristic curve analysis of these two seasons of test data, a single-variable model with the nighttime temperature predictor mentioned above gave the highest prediction accuracy of disease severity. These models have potential for use in quantitative risk assessment of sugarcane rust epidemics.

Comparison of Progress of Brown Rust and Orange Rust and Conditions Conducive for Severe Epidemic Development During the Sugarcane Crop Season in Florida.

Brown rust (caused by Puccinia melanocephala) and orange rust (caused by P. kuehnii) are two major diseases of sugarcane in Florida. To better understand the epidemiology of these two rusts, disease severity and weather variables were monitored for two seasons in cultivars CL90-4725 (susceptible to brown rust and resistant to orange rust) and CL85-1040 (susceptible to orange rust and resistant to brown rust). Brown rust was most severe during mid-May to mid-July, whereas orange rust severity peaked during two periods: mid-May to early August and then November to December. Overall, disease severity was higher for orange rust than for brown rust. Maximum disease severity was correlated with the number of hours at night with an average temperature of 20 to 22.2°C for brown rust one season and orange rust both seasons. Slightly higher correlation was obtained when relative humidity above 90% was included in the number of hours at night with an average temperature of 20 to 22.2°C for brown rust but not orange rust, suggesting that leaf wetness is not a limiting factor for either disease in Florida. Epidemics of brown rust began at lower night temperatures (16.7 to 22.2°C) in one season, but epidemics of orange rust lasted longer under higher temperatures. The correlation of rust severity on recently emerged leaves with conducive temperatures recorded in 10-, 20-, or 30-day windows starting 7 days before disease assessment suggested that earlier inoculum production is needed to create severe epidemics that result in yield loss.

Metalaxyl Resistance in Phytophthora infestans: Assessing Role of RPA190 Gene and Diversity Within Clonal Lineages.

Prior work has shown that the inheritance of resistance to metalaxyl, an oomycete-specific fungicide, is complex and may involve multiple genes. Recent research indicated that a single nucleotide polymorphism (SNP) in the gene encoding RPA190, the largest subunit of RNA polymerase I, confers resistance to metalaxyl (or mefenoxam) in some isolates of the potato late blight pathogen Phytophthora infestans. Using both DNA sequencing and high resolution melt assays for distinguishing RPA190 alleles, we show here that the SNP is absent from certain resistant isolates of P. infestans from North America, Europe, and Mexico. The SNP is present in some members of the US-23 and US-24 clonal lineages, but these tend to be fairly sensitive to the fungicide based on artificial media and field test data. Diversity in the level of sensitivity, RPA190 genotype, and RPA190 copy number was observed in these lineages but were uncorrelated. Controlled laboratory crosses demonstrated that RPA190 did not cosegregate with metalaxyl resistance from a Mexican and British isolate. We conclude that while metalaxyl may be used to control many contemporary strains of P. infestans, an assay based on RPA190 will not be sufficient to diagnose the sensitivity levels of isolates.

Evaluation of the BlightPro Decision Support System for Management of Potato Late Blight Using Computer Simulation and Field Validation.

The objective of this study was to evaluate the utility of the BlightPro decision support system (DSS) for late blight management using computer simulation and field tests. Three fungicide schedules were evaluated: (i) calendar-based (weekly) applications, (ii) applications according to the DSS, or (iii) no fungicide. Simulation experiments utilized 14 years of weather data from 59 locations in potato-producing states. In situations with unfavorable weather for late blight, the DSS recommended fewer fungicide applications with no loss of disease suppression; and, in situations of very favorable weather for late blight, the DSS recommended more fungicide applications but with improved disease suppression. Field evaluation was conducted in 2010, 2011, 2012, and 2013. All experiments involved at least two cultivars with different levels of resistance. DSS-guided and weekly scheduled fungicide treatments were successful at protecting against late blight in all field experiments. As expected, DSS-guided schedules were influenced by prevailing weather (observed and forecast) and host resistance and resulted in schedules that maintained or improved disease suppression and average fungicide use efficiency relative to calendar-based applications. The DSS provides an interactive system that helps users maximize the efficiency of their crop protection strategy by enabling well-informed decisions.

Acquired Resistance to Mefenoxam in Sensitive Isolates of Phytophthora infestans.

The systemic fungicide mefenoxam has been important in the control of late blight disease caused by Phytophthora infestans. This phenylamide fungicide has a negative effect on the synthesis of ribosomal RNA; however, the genetic basis for inherited field resistance is still not completely clear. We recently observed that a sensitive isolate became tolerant after a single passage on mefenoxam-containing medium. Further analyses revealed that all sensitive isolates tested (in three diverse genotypes) acquired this resistance equally quickly. In contrast, isolates that were "resistant" to mefenoxam in the initial assessment (stably resistant) did not increase in resistance upon further exposure. However, there appeared to be a cost associated with acquired resistance in the initially sensitive isolates, in that isolates with acquired resistance grew more slowly on mefenoxam-free medium than did the same isolates that had never been exposed to mefenoxam. The acquired resistance of the sensitive isolates declined slightly with subsequent culturing on medium free of mefenoxam. To investigate the mechanism of acquired resistance, we employed strand-specific RNA sequencing. Many differentially expressed genes were genotype specific, but one set of genes was differentially expressed in all genotypes. Among these were several genes (a phospholipase "Pi-PLD-like-3," two ATP-binding cassette superfamily [ABC] transporters, and a mannitol dehydrogenase) that were up-regulated and whose function might contribute to a resistance phenotype.