Quantifying the effects of soil texture and weather on cotton development and yield using UAV imagery

Authors: FENG, AIJING - University Of Missouri; ZHOU, JIANFENG - University Of Missouri; Vories, Earl; Sudduth, Kenneth - Ken

Submitted to: Precision Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/21/2022
Publication Date: 2/11/2022
Citation: Feng, A., Zhou, J., Vories, E.D., Sudduth, K.A. 2022. Quantifying the effects of soil texture and weather on cotton development and yield using UAV imagery. Precision Agriculture. 23:1248–1275. https://doi.org/10.1007/s11119-022-09883-6.
DOI: https://doi.org/10.1007/s11119-022-09883-6

Interpretive Summary: Crop development is affected by soil texture, plant available water, and weather conditions. Understanding their interactions is key for improving management to achieve optimal production. Soil apparent electrical conductivity can provide detailed information about the spatial variation of soil texture within fields, which is relatively stable over time. Unmanned aerial vehicle (UAV) technology can provide information about the spatial and temporal variation of plant properties within fields. A field study was conducted by ARS scientists and university colleagues from Portageville and Columbia, Missouri, in 2018 and 2019 with the goal to quantify the effects of soil and weather conditions on cotton development and production. Results showed that the cotton growth varied under different soil textures. Soil clay content in shallower layers affected crop development in earlier growth stages (June and July) while clay content in deeper layers affected the later-season growth stages (August and September). The study indicated that the integration of soil and weather information was able to predict crop growth and yield. This approach may be useful to researchers and to farmers who are interested in obtaining high resolution information regarding crop status.

Technical Abstract: Crop development and production are partly determined by soil conditions, plant available water, and weather conditions. Quantification of their interactions is the key for optimizing field management, such as precision irrigation and fertilization, to achieve optimal production. The goal of this study was to quantify the effects of soil and weather conditions on cotton development and production using temporal areal imagery data and soil apparent electrical conductivity (ECa) of the field. Soil texture, i.e., sand and clay content, calculated using ECa based on a model from a previous study, was used to estimate the soil characteristics, including field capacity, wilting point and total available water. A water stress coefficient was calculated using soil texture and weather data. Unmanned aerial vehicle (UAV)-based multispectral imaging systems were used to acquire imagery data at three growth stages of cotton in 2018 and 2019, respectively. Image features of canopy size and several vegetation indices (VIs) were extracted from the derived orthomosaic images. Pearson correlation, analysis of variance (ANOVA) and a machine learning method (XGBoost) were used to quantify the relationships between crop response (variables extracted from UAV images) and environments (soil texture and weather conditions). Results showed that the cotton NDVI varied monthly under different soil textures in both 2018 and 2019. Soil clay content in shallower layers (0-0.4 m) affected crop development in earlier growth stages (June and July) while clay content in deeper layers (0.4-0.7 m) affected the later-season growth stages (August and September). It was also found that soil clay content at 0.4-0.7 m had a higher impact on crop development when water inputs were not sufficient, while features related to crop water stress had a higher contribution to the prediction of crop growth when water stress was less. The study indicates that the integration of soil and weather information was able to predict crop growth and yield.