4.7 Article

Evaluation of the Oceanic Nino Index as a decision support tool for winter wheat cropping systems in the Texas High Plains using SWAT

Journal

COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 151, Issue -, Pages 331-337

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2018.06.030

Keywords

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Funding

  1. Ogallala Aquifer Program
  2. USDA-Agricultural Research Service
  3. Kansas State University
  4. Texas A&M AgriLife Research
  5. Texas A&M AgriLife Extension Service
  6. Texas Tech University
  7. West Texas AM University

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The semi-arid Texas High Plains has experienced decreasing well capacities due to decades of pumping with negligible recharge. Improvements in irrigation system efficiency and advances in drought tolerant crop varieties have improved water use efficiency. However, a gradual transition of irrigated lands to dryland management systems is expected for many areas in the region within the coming decades. Producers may elect to allocate more acreage to dryland crops such as winter wheat during this transition. Precipitation forecasting approaches may aid producers when considering planting acreage and additional inputs. Classifications of the El Nino Southern Oscillation (ENSO) periods have been associated with seasonal fluctuations of precipitation in North America. In this study, the Oceanic Nino Index (ONI), used to classify El Nino and La Nina phases of the ENSO, was evaluated for predicting growing season precipitation for winter wheat using measured data from the USDA-ARS Conservation and Production Laboratory (CPRL) for 1950-2015. Although not statistically significant, probability exceedance plots revealed a tenable correlation between precipitation and ONI classifications. Corresponding winter wheat yields were also simulated using the Soil and Water Assessment Tool (SWAT) model using both continuous and single-year scenarios designed to determine the effects of antecedent soil water resulting from inter-seasonal precipitation and residual soil water. The sporadic and uncertain nature of precipitation appeared to outweigh the ONI signal for prediction of precipitation for the winter wheat growing season in the Texas High Plains. However, simulated minimum yield values associated with El Nino phase classifications were nearly three times those of La Nina and phase neutral values, suggesting that ONI-based predictions of El Nino conditions have a lower probability of reduced yields. This finding in part supports the use of the ONI as a decision support tool for the planting of increased acreage and/or additional inputs for winter wheat crops in the Texas Panhandle. Additional analysis using long term precipitation data from multiple sites in the region would provide a more comprehensive determination of the efficacy of the ONI as a management tool.

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