Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/22/2018
Publication Date: 10/25/2018
Citation: Thorp, K.R., Thompson, A.L., Harders, S.J., French, A.N., Ward, R.W. 2018. High-throughput phenotyping of crop water use efficiency via multispectral drone imagery and a daily soil water balance model. Remote Sensing. 10(11):1682. https://doi.org/10.3390/rs10111682.
Interpretive Summary: Improving the efficiency of water use for agricultural crop production requires informational tools for quantifying how much water is used by the crop. In particular, tools for quantifying water use have rarely been applied to breeding plots with aim to develop drought tolerant varieties. In this study, we developed an approach to rapidly quantify crop water use for a large number of field plots in a cotton breeding experiment. The approach provided new and improved data that permitted selection of varieties that were more drought tolerant and used water more efficiently. The approach is mainly useful for plant breeders and researchers aiming to use informational technologies to quantify plant traits. To the extent that the methodology is widely adopted and used in plant breeding efforts to develop drought-tolerant varieties, the research will benefit producers and the society as a whole.
Technical Abstract: Improvement of crop water use efficiency (CWUE), defined as crop yield per volume of water used, is an important goal for both crop management and breeding. While many technologies have been developed for measuring crop water use in crop management studies, rarely have these techniques been applied at the scale of breeding plots. The objective was to develop a high-throughput methodology for quantifying water use in a cotton breeding trial at Maricopa, Arizona in 2016 and 2017, using evapotranspiration (ET) measurements from a co-located irrigation management trial to evaluate the approach. Approximately weekly overflights with an unmanned aerial system provided multispectral imagery from which plot-level fractional vegetation cover (fc) was computed. The fc data were used to drive a daily ET-based soil water balance model for seasonal crop water use quantification. A mixed model statistical analysis demonstrated that differences in ET and CWUE could be discriminated among eight cotton varieties (p < 0.05), which were sown at two planting dates and managed with four irrigation levels. The results permitted breeders to identify cotton varieties with more favorable water use characteristics and higher CWUE, indicating that the methodology could become a useful tool for breeding selection.