Nitrogen (N) management poses unique challenges in organic vegetable production, particularly in sandy soils with low organic matter content and poor water and nutrient retention. Monitoring crop N status provides opportunities to tailor nutrient management approaches to site- and season-specific conditions, potentially improving yields and N use efficiency. Monitoring other aspects of crop quality such as NO 3 -N content can further guide management decisions and may promote adherence to restrictions and consumer preferences. Frequent measurements of plant N status and other quality attributes, however, can be time-consuming, labor-intensive, and expensive. Remote sensing technologies have been established as promising tools for modeling crop N status, NO 3 -N content, and other quality attributes in an array of agronomic and horticultural commodities, although research on celery has been limited. This experiment employed a hyperspectral imaging approach, where leaf reflectance was collected from celery tissue in two different field trials and across two production seasons. One experiment evaluated impacts of N application timing and fertilizer selection, and the other examined effects of crop rotation with a leguminous cover crop and compost application on organic celery yield, quality, and N status. Spectra collected at two crop stages of celery were used to model soluble solids, total N, NO 3 -N, and dry matter contents (external validation R 2 = 0.84, 0.76, 0.73, 0.65, respectively). The models provide initial characterization of leaf reflectance in organic celery, while highlighting potential for robust generalizability in modeling quality attributes and improving water and nutrient use efficiency under various management practices and environmental conditions.
Ray et al. (Sun,) studied this question.