Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1016/j.compag.2018.06.030 |
Evaluation of the Oceanic Nino Index as a decision support tool for winter wheat cropping systems in the Texas High Plains using SWAT | |
Marek, Gary W.1; Baumhardt, R. L.1; Brauer, D. K.1; Gowda, P. H.2; Mauget, S. A.3; Moorhead, J. E.1 | |
通讯作者 | Marek, Gary W. |
来源期刊 | COMPUTERS AND ELECTRONICS IN AGRICULTURE
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ISSN | 0168-1699 |
EISSN | 1872-7107 |
出版年 | 2018 |
卷号 | 151页码:331-337 |
英文摘要 | 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. |
类型 | Article |
语种 | 英语 |
国家 | USA |
收录类别 | SCI-E |
WOS记录号 | WOS:000440119900034 |
WOS关键词 | SOUTHERN-OSCILLATION ; GRAIN-YIELD ; WATER-USE ; EL-NINO ; PRECIPITATION |
WOS类目 | Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications |
WOS研究方向 | Agriculture ; Computer Science |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/208471 |
作者单位 | 1.USDA ARS, Conservat & Prod Res Lab, PO Drawer 10, Bushland, TX 79012 USA; 2.USDA ARS, Grazingslands Res Lab, 7207 West Cheyenne, El Reno, OK 73036 USA; 3.USDA ARS, Cropping Syst Res Lab, 3810 4th St, Lubbock, TX 79401 USA |
推荐引用方式 GB/T 7714 | Marek, Gary W.,Baumhardt, R. L.,Brauer, D. K.,et al. Evaluation of the Oceanic Nino Index as a decision support tool for winter wheat cropping systems in the Texas High Plains using SWAT[J],2018,151:331-337. |
APA | Marek, Gary W.,Baumhardt, R. L.,Brauer, D. K.,Gowda, P. H.,Mauget, S. A.,&Moorhead, J. E..(2018).Evaluation of the Oceanic Nino Index as a decision support tool for winter wheat cropping systems in the Texas High Plains using SWAT.COMPUTERS AND ELECTRONICS IN AGRICULTURE,151,331-337. |
MLA | Marek, Gary W.,et al."Evaluation of the Oceanic Nino Index as a decision support tool for winter wheat cropping systems in the Texas High Plains using SWAT".COMPUTERS AND ELECTRONICS IN AGRICULTURE 151(2018):331-337. |
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