Location: Plant Stress and Germplasm Development Research
Project Number: 3096-21000-022-024-R
Project Type: Reimbursable Cooperative Agreement
Start Date: Mar 1, 2019
End Date: Dec 30, 2019
Objective:
Study the effects of management decisions on the efficient use of water in cotton production in Australia and the U.S. with a specific focus on low input and rain-grown production areas. This work will develop and incorporate management decision tools for growers using remote and ground-based sensing technologies and new algorithms for crop management.
Approach:
1. Implementation of improved canopy temperature-based irrigation scheduling for long interval irrigation. We have established, with previous CI funding, a stress unit accumulation algorithm for both full and deficit irrigation conditions. We will continue tests of our improved method in production settings. Calibration of the method in terms of temperature and time thresholds for full and deficit irrigation systems will be continued along with the timing and concentration of PGR application.
2. Implementation of a rain-grown planting matrix in Texas will allow us to begin to investigate the interactions among; planting dates, varieties, planting geometries and chemical inputs on the performance of rained cotton. Collaborations with Australian research partners will involve efforts to compare rain-grown approaches on the two continents through data analysis and crop modeling.
3. Aerial imaging of crops has become increasingly important as a tool for crop management. As analytic power and technology continues to develop and the capacity for deployment becomes better understood by producers and researchers, aerial imaging will be a commonly used on-farm and research tool. Currently, the cost of equipment, software, and personnel capable of producing meaningful output with contemporary technology can be prohibitive. While the ability to generate useful output with expensive technology is documented, the degree of relevant output that can be generated with minimal aerial imaging and inexpensive processing technology is unknown. Open source imaging software coupled with off-the-shelf drone hardware is neither cost prohibitive or difficult to obtain. We have evaluated a combination of cheap, consumer-grade hardware and open-source software for its utility in estimating cotton yield in a rain-grown production setting.
