Optimizing dryland crop management to regional climate. Part I: U.S. southern high plains cotton production

Authors: Mauget, Steven; Marek, Gary; ADHIKARI, PRADIP - Idaho State Department Of Agriculture; Leiker, Gary; Mahan, James; Payton, Paxton; ALE, SRINIVASULU - Texas Agrilife Extension

Submitted to: Frontiers in Sustainable Food Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/13/2019
Publication Date: 1/23/2020
Citation: Mauget, S.A., Marek, G.W., Adhikari, P., Leiker, G.R., Mahan, J.R., Payton, P.R., Ale, S. 2020. Optimizing dryland crop management to regional climate. Part I: U.S. southern high plains cotton production. Frontiers in Sustainable Food Systems. 3:120. https://doi.org/10.3389/fsufs.2019.00120.
DOI: https://doi.org/10.3389/fsufs.2019.00120

Interpretive Summary: West Texas is home to the largest continuous cotton production area in the U.S. Historically, about half of farmers have relied on irrigation water from the Ogallala aquifer, while the other half have relied on summer rainfall. But as aquifer levels drop in the future more farmers will need to know which farming practices are best at maximizing yield and profits without irrigation. To test 32 possible cotton farming practices ARS scientists in Lubbock used a cotton crop model driven by weather inputs from 21 west Texas weather stations during 2005-2016. Those practices were defined by all the possible combinations of 4 planting dates, 2 fertilizer application levels, and 4 planting densities. The practice that produced the highest lint yields planted on the earliest planting date (May 15), at the lowest plant density (~ 1 plant per foot of row), and applied no fertilizer. Thus these crop simulations recommend planting on or before May 15 at a low plant density as part of management practices that maximize the yields and profits of west Texas cotton farmers.

Technical Abstract: Over semi-arid agricultural regions such as the U.S. Southern High Plains (SHP) producers of dryland crops need to know which management practices increase yields and decrease production risk. Here, an in-silico modelling approach is used to explore management options (MO) that increase dryland cotton yields and estimate those practice’s yield risk effects under current SHP climate conditions. To simulate current dryland yield variability, dense distributions of lint yield outcomes were generated using a crop model driven by weather inputs from 21 SHP weather stations during 2005-2016. Management effects were explored by repeating simulations over 32 MOs defined by 4 planting dates, 4 planting densities, and applying or not applying nitrogen. Both earlier planting date and decreased plant density increased median simulated yields, with earlier planting having the greatest positive yield effects. The MO that produced the highest median lint yields planted on the earliest planting date (May 15), at the lowest density (3 plants m-1), and applied no nitrogen. Recent SHP field studies generally confirm the earlier planting date effect, but suggest insignificant yield effects for different seeding rates. Even so, negligible yield effects and lower input costs favor lower seeding densities from a profit standpoint. These crop simulations demonstrate an modelling-based method for climate-related agricultural risk management, and suggest mid-May planting dates and low plant densities as part of management practices that increase yields and profits in dryland SHP cotton production.