Polymer-coated controlled-release fertilizers’ (CRFs) unique nutrient release mechanism has the potential to mitigate the leaching of mobile soil nutrients, such as nitrate-nitrogen (NO₃-N). The study aimed to evaluate the capacity of a polymer-coated CRFs to maintain maize (Zea mays L.) crop growth/health indicators and production goals, while reducing NO₃-N leaching risks compared to conventional (CONV) fertilizers in North Florida. Four CRF rates (168, 224, 280, 336 kg N ha⁻¹) were assessed against a no nitrogen (N) application and the current University of Florida Institute for Food and Agricultural Sciences (UF/IFAS) recommended CONV (269 kg N ha⁻¹) fertilizer rate. All CRF treatments, even the lowest CRF rate (168 kg N ha⁻¹), produced yields, leaf tissue N concentrations, plant heights, aboveground biomasses (AGB), and leaf area index (LAI) significantly (p < 0.05) greater than or similar to the CONV fertilizer treatment. Additionally, in 2022, the CONV fertilizer treatment resulted in increases in late-season movement of soil NO₃-N into highly leachable areas of the soil profile (60–120 cm), while none of the CRF treatments did. However, back-to-back leaching rainfall (>76.2 mm over three days) events in the 2023 growing season masked any trends as NO₃-N was likely completely flushed from the system. The results of this two-year study suggest that polymer-coated CRFs can achieve desirable crop growth, crop health, and production goals, while also having the potential to reduce the late-season leaching potential of NO₃-N; however, more research is needed to fully capture and quantify the movement of NO₃-N through the soil profile. Correlation and Principal Component Analysis (PCA) revealed that CRF performance was significantly influenced by environmental factors such as rainfall and temperature. In 2022, temperature-driven nitrogen release aligned with crop uptake, supporting higher yields and minimizing NO₃-N movement. In 2023, however, rainfall-driven variability led to an increase in NO₃-N leaching and masked the benefits of CRF treatments. These analyses provided critical insights into the relationships between environmental factors and CRF performance, emphasizing the importance of adaptive fertilizer management under varying climatic conditions.

Greenhouse ventilation involves removing air from inside the greenhouse and replacing it with outside air. The ventilation may be natural (caused by wind and temperature forces) or mechanical (accomplished by using fans). The purposes of ventilation are to control high temperatures during the summer caused by the influx of solar radiation, to maintain relative humidity at acceptable levels during winter, to provide uniform air flow throughout the greenhouse, and to maintain acceptable levels of gas concentration in the greenhouse. Ventilation systems for greenhouses must be considered for three climatic conditions that typically occur during the year—winter, summer, and spring–fall. This 4-page fact sheet is a minor revision written by J. A. Watson, C. Gómez, D. E. Buffington, R. A. Bucklin, R. W. Henley, and D. B. McConnell, and published by the Department of Agricultural and Biological Engineering, November 2019. AE-10/AE030: Greenhouse Ventilation (ufl.edu)

Citrus greening disease (HLB) and citrus canker cause financial losses in Florida citrus groves via smaller fruits, blemishes, premature fruit drop, and/or eventual tree death. Management of these two diseases requires early detection and distinction from other leaf defects and infections. Automated leaf inspection with hyperspectral imagery (HSI) is tested in this study. Citrus leaves bearing visible symptoms of HLB, canker, scab, melanose, greasy spot, zinc deficiency, and a control class were collected, and images were taken with a line-scan HSI camera. YOLOv8 was trained to classify multispectral images from this image dataset, created by selecting bands with a novel variance-based method. The ‘small’ network using an intensity-based band combination yielded an overall weighted F1 score of 0.8959, classifying HLB and canker with F1 scores of 0.788 and 0.941, respectively. The network size appeared to exert greater influence on performance than the HSI bands selected. These findings suggest that YOLOv8 relies more heavily on intensity differences than on the texture properties of citrus leaves and is less sensitive to the choice of wavelengths than traditional machine vision classifiers.

This study investigated consumer attitudes and intentions toward adopting artificial intelligence (AI) in food traceability systems, an emerging technology aimed at enhancing food safety and transparency. Data collected from an online survey of 1013 United States consumers, the study applies structural equation modeling (SEM) to examine the factors influencing consumer acceptance. Results showed that attitudes and perceived behavioral control are positively associated with decisions to accept AI-assisted food traceability, with trust in science, food safety concerns, fear of food technology, and risk information-seeking behavior also important factors. The model explained 80.2 % of the variance in intention and 18.0 % in attitude. Perceived behavioral control had a stronger impact on intention than attitude, suggesting that consumers who feel they have control over their food choices are more likely to support AI-enhanced traceability. Additionally, trust in scientific institutions emerged as a key predictor of acceptance, underscoring the importance of transparent and science-backed communication strategies. The study also highlights how concerns about foodborne illnesses and proactive risk information-seeking behaviors are positively associated with attitudes toward AI-assisted traceability. In contrast, food technology neophobia is negatively associated with acceptance, indicating the need for targeted educational campaigns to reduce skepticism. These findings provide valuable insights for policymakers, food industry stakeholders, and science communicators in designing effective strategies to enhance consumer confidence in AI-driven food safety initiatives. By addressing consumer concerns and fostering trust, AI-assisted traceability can be more successfully integrated into food systems, ultimately reducing foodborne illness risks and improving public health.

Historical repository of UF/IFAS Extension numbered publications made available on the EDIS website. UF/IFAS Extension is a federal, state, and county partnership dedicated to developing knowledge in agriculture, human and natural resources, and the life sciences and to making that knowledge accessible to sustain and enhance the quality of human life.

JAFSCD: Journal of Agriculture, Food Systems, and Community Development. Publication of the Thomas A. Lyson Center for Civic Agriculture and Food Systems