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Can Leaf Area Index and Plant Height Measurement Improve Sensor-based Nitrogen Recommendations and Yield Prediction for Corn?

出版	ProQuest Dissertations & Theses, 2023
ISBN	9798380376723
URL	http://books.google.com.hk/books?id=1jpt0AEACAAJ&hl=&source=gbs_api

註釋Nitrogen (N) management remains a significant challenge for corn growers due to the unpredictability and influence of weather conditions, soil properties, and soil biological activity on N transformations in the soil. Innovative technology is needed to assist farmers in making accurate in-season N recommendations to improve N use efficiency (NUE) and reduce the environmental impacts of N losses. Sensor-based aerial imagery can be collected using unmanned aerial vehicles (UAVs) to assist with N management decisions and help improve NUE. However, there are limitations associated with vegetative indices from aerial imagery in guiding N decisions because the indices can reach a "saturation point" once the corn canopy closes. We hypothesized that adding leaf area index (LAI) data and plant height measurements could improve our understanding of how plant biomass is related to the vegetative indices for predicting corn N response by adding in a third dimension to the analysis. Corn N rate trials were established in Delaware, Maryland, and Pennsylvania (0, 30, 60, 90, 120, and 150% of university-based N rates; DE and MD did not have a 0 N rate) and four replicates in a randomized complete block design. In-season UAV-multispectral imagery, LAI, and plant height measurements were obtained at the V6 and R2 corn growth stages. Plant height was also derived from UAV imagery using structure from motion (UAV-SFM) using Pix4D photogrammetry software. Drone-derived vegetative indices, UAV-SFM, and LAI were used to predict the sidedress N rates and grain yields, which were compared to yield data at harvest.