Skip to main content
ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Publications at this Location » Publication #362016

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 the standardized precipitation and evaporation index (SPEI) as a potential management tool for grasslands

Author
item Starks, Patrick - Pat
item Steiner, Jean - Retired ARS Employee
item Neel, James - Jim
item Turner, Kenneth - Ken
item Northup, Brian
item Gowda, Prasanna
item Brown, Michael - Retired ARS Employee

Submitted to: Agronomy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/5/2019
Publication Date: 5/9/2019
Citation: Starks, P.J., Steiner, J.L., Neel, J.P., Turner, K.E., Northup, B.K., Gowda, P.H., Brown, M.A. 2019. Assessment of the standardized precipitation and evaporation index (SPEI) as a potential management tool for grasslands. Agronomy. 9(235). https://doi.org/10.3390/agronomy9050235.
DOI: https://doi.org/10.3390/agronomy9050235

Interpretive Summary: Early warning of detrimental climate impacts (particularly drought) on forage production would allow for tactical decision-making for management of pastures, supplemental feed/forage resources, and livestock. The standardized precipitation and evaporation index (SPEI) has been shown to be correlated with production of various cereal and vegetable crops and with aboveground tree mass. Its correlation with aboveground grassland or forage mass (AGFM) is less clear. We statistically analyzed SPEI at 1-, 2-, and 3-month timescales, for its potential as a classification variable in discriminant analysis and nominal logistic regression. We found that the 2- and 3- month timescale SPEIs provided no advantage over that of the 1-month timescale SPEI. Classification error assessments from the analyses suggested that the discriminant analysis better predicted class membership from the SPEI than did the nominal logistic regression. The discriminant analysis predicted a false condition of adequate forage availability 11% of time and a false condition of below adequate forage availability 16% of the time. The nominal logistic regression predicted a false condition of adequate forage availability 6% of the time and a false condition of below adequate forage 27% of the time. The results suggest that the SPEI has potential for use as a predictive tool for AGFM, and, thus, for grassland and livestock management.

Technical Abstract: Early warning of detrimental climate impacts (particularly drought) on forage production would allow for tactical decision-making for management of pastures, supplemental feed/forage resources, and livestock. The standardized precipitation and evaporation index (SPEI) has been shown to be correlated with production of various cereal and vegetable crops and with aboveground tree mass. Its correlation with aboveground grassland or forage mass (AGFM) is less clear. We statistically analyzed SPEI at 1-, 2-, and 3-month timescales, for its potential as a classification variable in discriminant analysis and nominal logistic regression. We found that the 2- and 3- month timescale SPEIs provided no advantage over that of the 1-month timescale SPEI. Assessment of the confusion matrices from the analyses suggested that the discriminant analysis better predicted class membership from the SPEI than did the nominal logistic regression. The discriminant analysis predicted a false condition of adequate forage availability 11% of time and a false condition of below adequate forage availability 16% of the time. The nominal logistic regression predicted a false condition of adequate forage availability 6% of the time and a false condition of below adequate forage 27% of the time. The results suggest that the SPEI has potential for use as a predictive tool for AGFM, and, thus, for grassland and livestock management.