Ian Malcolm Small

Estimating the botanical composition in mixed pastures is challenging due to resource‐intensive sampling methods overlooking grassland spatial variability. We aimed to (I) classify bahiagrass ( Paspalum notatum Flügge) and rhizoma peanut ( Arachis glabrata Benth.) in grass–legume pastures using aerial multispectral imagery and machine learning, (II) estimate the proportion of grass and legumes on an area basis, and (III) estimate the botanical composition on a dry matter basis to guide grazing management. Unmanned aerial vehicle‐based images were taken in the early and late warm seasons of 2023 and 2024. Field campaigns were conducted for ground‐truth species labeling ( n = 554) across nine paddocks under three treatments (fertilized grass, unfertilized grass, and grass–legume mixed) using a high‐precision positioning system. Ninety‐six points were harvested and manually sorted for ground truth botanical composition estimation on a dry matter basis. The classification dataset was split into training and testing using three different approaches to assess spatial generalization performance. A random forest (RF) was employed as the base model, and a convolutional neural network (CNN) was developed using fivefold cross‐validation on the training set. Our CNN model outperformed the RF model, achieving an average cross‐validated overall accuracy of 93% and 88% for grass–legume classification on the grid‐based and repeated spatial hold‐out testing set. Combining land cover classification, compressed height, and vegetation indices showed promising potential to predict total biomass ( R ² = 0.85) and grass biomass ( R ² = 0.83) in bahiagrass–rhizoma peanut pastures using stepwise multiple linear regression. This approach also enabled the estimation of legume proportion by difference.

Carinata (Brassica carinata) is an oilseed crop gaining global attention as a sustainable biofuel feedstock. Sclerotinia stem rot (SSR), caused by Sclerotinia sclerotiorum, has been identified as a major disease of concern for carinata in the southeastern United States. This study determined the optimal growth stage for a single fungicide application to control SSR and evaluated the relationship between SSR severity and seed yield among multiple varieties. A field trial was conducted over four seasons at the University of Florida Institute of Food and Agricultural Sciences North Florida Research and Education Center in Quincy. Treatments were arranged in a randomized complete block split-plot design with variety as the whole plot and the fungicide (Proline ^® ) application timing as the subplot. A disease severity index (DSI) was calculated from SSR assessments at several growth stages. The final DSI in non-treated plots increased from an average of 13.2 (± 10.1 SD) in 2018 and 12.5 (± 11.1 SD) in 2019 to an average of 49.2 (± 16.5 SD) in 2020 that remained at 40.4 (±10.9 SD) in 2021. The correlation of yield with DSI was significant (P ≤ 0.01) for varieties AGR044-321E (r = -0.49), Avanza 641 (r = -0.59), DH-040.342 (r = -0.28), and M-01 (r = -0.62). Early to full flowering was the optimal growth stage for a fungicide application to manage SSR on carinata, but seasonal differences in disease affected the magnitude of control. Effective management using well-timed fungicide applications can support the sustainability of carinata as a biofuel crop.

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. [ ] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

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 7-8 fungicide app...

Athelia rolfsii, causal agent of ‘southern blight’ disease, is a soil-borne fungal pathogen with a wide host range of more than 500 species. This study's objectives were to: (i) quantify the effect...

Brassica carinata is a non-food industrial oilseed crop that can be grown in the winter in the southeast US without impacting food, feed, or fiber crops. Carinata is a low carbon advanced renewable fuel feedstock and a good source of animal protein. Carinata research in the SE US through a public-private partnership has developed a comprehensive body of knowledge regarding carinata agronomics, life cycle analysis, best management practices and economics. This article aims to help growers and others interested in carinata to understand its biology, agronomy, and production aspects.

This publication discusses land preparation and planting, fertilization, crop residue management, irrigation, disease and nematode management, variety selection, weed control, insect management, and harvesting and storage for soybean production in Florida. Written by D. L. Wright, J. A. Ferrell, S. Sanjel, and I. Small, and published by the UF/IFAS Agronomy Department, revised July 2022.

Diverse field characteristics, weather patterns, and management practices can result in variable microclimates. The objective of this study was to relate in-field microclimate conditions with peanut diseases and yield and determine the effect of irrigation and fungicides within these environments. Irrigation did not have a major impact on disease and yield. Stem rot (Athelia rolfsii) and early (Passalora arachidicola) and late (Nothopassalora personata) leaf spot were most affected by changes in environmental patterns across seasons. Average nontreated stem rot was 12.9% in 2017, which dropped considerably in 2018 (0.2%) but emerged again in 2019 (3.2%). Stem rot incidence varied across the field, and the response to fungicides depended on management zone. Leaf spot defoliation in nontreated plots was severe in 2019, reaching an average of 73% at 126 days after planting, but only reached 15% in 2017 and 35% in 2019 at the same stage. A low-input fungicide schedule was able to reduce foliar disease in all zones and seasons but the microclimatic conditions in the low-lying area favored leaf spot in 2017 and 2018, although not in the dryer 2019 season. Seasonal differences in disease and plant growth affected the level of protection against average yield loss using a standard low-input program, which was not as great in 2017 (527 kg/ha) as in 2018 (2,235 kg/ha) or 2019 (1,763 kg/ha). Disease prediction models built on dynamic environmental factors in the context of multiple pathogens and natural field conditions could be developed to improve within-season management decisions for more efficient fungicide inputs.