Location: Sugarbeet and Potato Research
Project Number: 3060-21650-001-010-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 1, 2019
End Date: Feb 28, 2023
Objective:
Objectives for the life of the project will be to: (1) Determine the yield potential and composition of new short season elite cowpea breeding lines in multiple Texas environments (genotype x environment analysis); (2) Advance a breeding pipeline of new second cycle cowpea lines; (3) Evaluate the ability of unmanned aerial systems ability as high throughput phenotyping tools to estimate biomass, growth and plant populations of cowpea and other pulses, towards predicting yields in breeding pipelines; and (4) Develop near infrared spectroscopy to estimate and predict composition, including protein fiber and minerals, of cowpeas and other pulses.
Approach:
To accomplish project goals, a genetics-by-environmental interaction study will be conducted for multiple environments to evaluate new varieties for suitability across planting dates and crop rotations. Specifically, biomass, grain yield, and grain composition will be evaluated in replicated plantings following fallow, maize, sorghum and wheat double crops in various planting dates. We will make crosses in the greenhouse and advance new lines into testing by the third year. We will apply expertise obtained in phenotyping maize by unmanned aerial systems (UAS, i.e. drones) to the cowpea trials and to other pulses. UAS surveys throughout the growing season will permit the estimation of biomass, growth rates and plant populations in maize. We have found that features extracted from UAS can help predict grain yield with accuracy suitable for breeding. UAS flights will be conducted 10 times throughout the growing season, the images will be orthomosaicked, georectified, and 3D point clouds will be developed. Information from various plots will be extracted and evaluated for repeatability and significant varietal differences in statistical analysis. It is likely that other phenotypes of interest including N fixation, disease, and abiotic stress can also be estimated by UAS and we will make the data publicly available. Finally, we will scan grain samples harvested in the field using Fourier-Transformed near infrared spectroscopy (FT-NIRS). After submitting a sub-set for wet chemistry analysis, calibrations for predicting protein and other compositional traits will be developed and applied to the additional samples. Overall, this project will advance future breeding of cowpea, catalyze continued breeding of cowpea at Texas A&M, and most importantly will be used to support the release of new and future elite cowpea varieties that will better meet producer’s needs.
