ASHS 2025 Conference

Nitrogen (N) management is a major challenge in organic vegetable production, aiming to supply sufficient N for optimal yield and quality while minimizing N losses. These challenges are exacerbated by uncertain mineralization patterns of different organic fertilizer products and season-specific impacts on soil N availability. Monitoring plant tissue N dynamics throughout the production season can provide meaningful information regarding fertility management, but sampling plant tissue can be labor-intensive and costly, and lab analysis may be time-consuming. Considering that N from organic nutrient sources is generally not immediately available for crop uptake, the need for reliable tools to rapidly monitor plant N status is paramount in improving N use efficiency, particularly in organic systems. In this study, a hyperspectral imaging approach was explored. Celery samples were collected from two research trials repeated in two production seasons at midseason (approximately 70 days after transplanting; DAT) and final harvests (approximately 110 DAT). One experiment focused on integrated nutrient management practices, comparing celery grown following a sunn hemp cover crop to a weedy fallow (whole plots), and evaluating impacts of composts (subplots) including an unamended control, yard waste compost, vermicompost, and a mixed compost treatment. The other experiment compared ratios of preplant N:in-season N application from 0-100% preplant N (whole plots) under two organic preplant fertilizers (subplots) contrasting in composition and anticipated N mineralization. At each harvest, six plants from each experimental unit were weighed and allocated into representative portions for crop quality analyses on fresh and dry bases. A spectroradiometer with a leaf clip reflectance probe was used to collect leaf reflectance spectra (350-2500 nm) from approximately eight leaves from each experimental unit. Spectra were normalized to a mean of zero and a standard deviation of one across wavelengths. To ensure balanced representation in training and validation, the data were stratified by harvest timing, trial, and year, and randomly split following 80% calibration and 20% external validation distribution. The final model used 19 latent components, explained 76.4% of the variation within external validation data, and had a root mean square error of 0.36. The model can be categorized as providing “approximate quantitative predictions,” and total N content from dried aboveground biomass in the original dataset spans from 1.0 - 4.3 g N/100 g DW. Building robust models using hyperspectral data to predict crop N status under diverse production practices and environmental conditions is an area deserving of continued research in organic vegetable production.