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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Publications at this Location » Publication #373203

Research Project: Towards Resilient Agricultural Systems to Enhance Water Availability, Quality, and Other Ecosystem Services under Changing Climate and Land Use

Location: Agroclimate and Natural Resources Research

Title: Assessment of heat unit availability and potential lint yield of cotton in Oklahoma

Author
item MASASI, BLESSING - OKLAHOMA STATE UNIVERSITY
item TAGHVAEIAN, SALEH - OKLAHOMA STATE UNIVERSITY
item Gowda, Prasanna
item Moriasi, Daniel
item Starks, Patrick - Pat

Submitted to: Applied Engineering in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/20/2020
Publication Date: 12/1/2020
Citation: Masasi, B., Taghvaeian, S., Gowda, P.H., Moriasi, D.N., Starks, P.J. 2020. Assessment of heat unit availability and potential lint yield of cotton in Oklahoma. Applied Engineering in Agriculture. 36(6):943-954. https://doi.org/10.13031/aea.14006.
DOI: https://doi.org/10.13031/aea.14006

Interpretive Summary: Oklahoma is increasingly becoming a major cotton producing state. Despite the increased interest in cotton cultivation in Oklahoma, there is little information available on production feasibility across all seventy-seven counties of the state. In this study, we utilized a model, based on heat units or number of degree-days needed for cotton to reach maturity, to estimate the potential cotton yields (PCYs) for all Oklahoma counties using 38 years (1981-2018) of air temperature data. The results indicated that many areas in Oklahoma have conducive thermal conditions for cotton production in most years. The PCYs generally increased from the northern to the southern counties of the state, and long-term averages ranged from 407 to 2472 kg ha-1. However, estimated PCYs in each county varied from one year to the next depending on the prevailing air temperature conditions. In general, low and high PCYs coincided with years characterized by cool and warm growing seasons, respectively. Results also indicated that delaying planting by just one week from optimal planting date significantly reduced PCYs. Therefore, we recommend that producers avoid delays in planting as much as possible in order to maximize cotton yield. The findings of this research contribute to the goals of the USDA Long-Term Agroecosystem Research (LTAR) Network that seeks to understand and enhance the sustainability of agriculture in the United States.

Technical Abstract: With the expansion of planted area, Oklahoma is increasingly becoming a major cotton producing state in the United States (U.S.). However, the feasibility of growing cotton in all counties of Oklahoma has not been determined. In this study, a heat unit based model was used to estimate the potential cotton yields (PCYs) for all 77 counties of Oklahoma using 38 years (1981-2018) of air temperature data. The long-term total heat units (THUs) were more than 1000 °C·d in 99% of counties, an indication that many areas in Oklahoma may have conducive thermal conditions for cotton production in most years. Similar to the THUs, the PCYs generally increased from the northern to the southern counties of the state, and long-term averages ranged from 407 to 2472 kg ha-1. About 97% of the counties achieved long-term average PCYs of at least 1000 kg ha-1. However, the results showed significant inter annual variability of the estimated PCYs in each county over the 38-year period. Low and high PCYs mostly coincided with years characterized by cool and warm growing seasons, respectively. A significant reduction of PCY by delaying planting by just one week from the optimized planting date was observed, an indication of the need for producers to carefully consider this variable to maximize cotton yield. As THUs were the only factor considered for calculating PCYs in this study, future research should incorporate other variables such as rainfall and heat stress in order to improve PCY estimations.