Version 1
: Received: 25 October 2023 / Approved: 25 October 2023 / Online: 26 October 2023 (04:58:59 CEST)
How to cite:
Duoduaa, A.T.; Manu, A.; Lawler, T.J.; Avornyo, V.K.; Adu-Gyamfi, R.; Mohammed, A. Spatial Analysis of UAS-generated data to Evaluate Nitrogen Fertilizer Placement Alternatives, Predict and Map End-of Season Rice Yield. Preprints2023, 2023101665. https://doi.org/10.20944/preprints202310.1665.v1
Duoduaa, A.T.; Manu, A.; Lawler, T.J.; Avornyo, V.K.; Adu-Gyamfi, R.; Mohammed, A. Spatial Analysis of UAS-generated data to Evaluate Nitrogen Fertilizer Placement Alternatives, Predict and Map End-of Season Rice Yield. Preprints 2023, 2023101665. https://doi.org/10.20944/preprints202310.1665.v1
Duoduaa, A.T.; Manu, A.; Lawler, T.J.; Avornyo, V.K.; Adu-Gyamfi, R.; Mohammed, A. Spatial Analysis of UAS-generated data to Evaluate Nitrogen Fertilizer Placement Alternatives, Predict and Map End-of Season Rice Yield. Preprints2023, 2023101665. https://doi.org/10.20944/preprints202310.1665.v1
APA Style
Duoduaa, A.T., Manu, A., Lawler, T.J., Avornyo, V.K., Adu-Gyamfi, R., & Mohammed, A. (2023). Spatial Analysis of UAS-generated data to Evaluate Nitrogen Fertilizer Placement Alternatives, Predict and Map End-of Season Rice Yield. Preprints. https://doi.org/10.20944/preprints202310.1665.v1
Chicago/Turabian Style
Duoduaa, A.T., Raphael Adu-Gyamfi and Amisu Mohammed. 2023 "Spatial Analysis of UAS-generated data to Evaluate Nitrogen Fertilizer Placement Alternatives, Predict and Map End-of Season Rice Yield" Preprints. https://doi.org/10.20944/preprints202310.1665.v1
Abstract
TThe following three objectives were tested in this study: (1) investigate the utility of low-altitude remote sensing using UAS technology to compare the effects of different N application systems in rice production; (2) use spatial extrapolation to scale up plot-level generated to farmer field rice yield data based on crop spectral signatures, and (3) predict and map out rice productivity as a function of N placement systems. Images were captured on a UAV platform at midseason of the rice crop. Orthomosaics were developed for selected fields in rice-producing zones. Grain yields were assessed from low, medium, and high crop health plots delineated based on NDVI values. On the plot scale, UDP outyielded non-UDP by 0.84%. Individual plot yield data were scaled up to the farmer field level through Jenks natural breaks classification and es-tablishing an empirical relationship between OSAVI and plot yields. Assessment of the scaled-up field levelfield-level data also confirmed the superiority of UDP N man-agement over the non-UDP systems in promoting rice yields. Scaling up plot scale da-ta to whole field levels also facilitated generating and mapping expected yield maps for individual farmer fields in the three zones studied. This study has established a tangible simple but tangible protocol protocol for predicting and mapping rice yields in small-scale farmer fields using UAS data.
Keywords
Unmanned Aerial Systems (UAS); Urea Deep Placement (UDP); Linear Regression; Plot Scale; Field Scale; Crop Health; NDVI; OSAVI; Jenks Natural Breaks Classification
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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