UAV-based NDVI estimation of sugarbeet yield and quality under varied nitrogen and water rates

Abstract

The accuracy of the traditional soil and plant-based techniques for assessing sugarbeet demand for nitrogen (N) and yield prediction is generally low. Refining N and irrigation water management is a key to maximizing return for sugarbeet (Beta vulgaris L.) growers from agronomic, economic, and environmental perspective. The use of Normalized Difference Vegetative Index (NDVI) in combination with the unmanned aerial vehicle (UAV)-based data collection for in-season estimation of sugarbeet root yield and sugar concentration has potential for precision N management. Sugarbeet field trials were conducted in Idaho in 2019 and 2020 to assess (1) effects of water and N fertilizer rates on yield and estimated recoverable sugar (ERS) and (2) feasibility of predicting root yield and ERS using UAV NDVI. At the lowest N rate, application of water at 100% level resulted in greater yield, compared to 50%, in both years. At higher N rates, 50% level produced higher yields. At each N level, application of water at 100% level resulted in lower ERS, compared to 50%. The UAV NDVI was strongly correlated with root yield and ERS. The relationship between UAV NDVI and root yield and ERS was stronger in July (60 days after planting) compared to June (40 days after planting). Estimating the yield and ERS potential in late June/early July and topdressing the crop before the end of July may help to improve N use efficiency while optimizing sugarbeet production.

Description
Keywords
Beta-vulgaris l., Drip irrigation, root yield, soil, fertilizer, performance, prediction, management, biomass, potato
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