Abstract
Determining the optimal nitrogen (N) rate in corn remains a critical issue, mainly due to unaccounted spatial (e.g., soil properties) and temporal (e.g., weather) variability. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors may provide opportunities to improve N management by the timely informing of spatially variable, in-season N applications. Here, we developed a practical decision support system (DSS) to translate spatial field characteristics and normalized difference red edge (NDRE) values into an in-season N application recommendation. On-farm strip-trials were established at three sites over two years to compare farmer’s traditional N management to a split-application N management guided by our UAV sensor-based DSS. The proposed systems increased nitrogen use efficiency 18.3 ± 6.1 kg grain kg N−1 by reducing N rates by 31 ± 6.3 kg N ha−1 with no yield differences compared to the farmers’ traditional management. We identify five avenues for further improvement of the proposed DSS: definition of the initial base N rate, estimation of inputs for sensor algorithms, management zone delineation, high-resolution image normalization approach, and the threshold for triggering N application. Two virtual reference (VR) methods were compared with the high N (HN) reference strip method for normalizing high-resolution sensor data. The VR methods resulted in significantly lower sufficiency index values than those generated by the HN reference, resulting in N fertilization recommendations that were 31.4 ± 10.3 kg ha−1 higher than the HN reference N fertilization recommendation. The use of small HN reference blocks in contrasting management zones may be more appropriate to translate field-scale, high-resolution imagery into in-season N recommendations. In view of a growing interest in using UAVs in commercial fields and the need to improve crop NUE, further work is needed to refine approaches for translating imagery into in-season N recommendations.
Highlights
Nitrogen (N) fertilizer management in cereal crop production remains a critical issue
The Unmanned aerial vehicles (UAVs)-sensor-based N management increased N use efficiency (NUE) as measured by partial factor productivity of N (PFPN) by 18.3% ± 6.1%, optimizing N rates compared to the traditional farmer management across years and sites
Alternative image sources such as satellite- or airplane-acquired imagery may provide a rapid solution to scale the decision support system (DSS) using a commercial platform and needs further investigation. We expect that such an integrated system would allow the proposed DSS to be adapted for practical use by farmers. This is the first paper that successfully translated N stress detected by UAV-based multispectral images into a more informed dynamic N recommendation system in commercial fields, considering soil and crop spatial variability
Summary
Nitrogen (N) fertilizer management in cereal crop production remains a critical issue. Only one-third to one-half of the N fertilizer input is recovered in the harvested product [4,5,6,7], suggesting both low N use efficiency (NUE; the ratio of N recovered in harvested products relative to N inputs) and high potential N losses to the environment, resulting in negative impacts [7,8,9]. Low NUE has been attributed to several factors, including: (1) poor synchrony between N fertilizer applications and crop N demand and (2) unaccounted-for spatial and temporal variability in soil-available N and crop N need [4,12,13]. Improvements in NUE may be achieved by applying a portion of the N fertilizer in-season, thereby allowing N availability to coincide more closely with crop N demand [14]. 25% to 42% of N uptake may occur after R1 [15,16,17,18], further demonstrating the value of mid- and late-season N applications
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