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This article in AJ

  1. Vol. 93 No. 1, p. 250-260
     
    Received: Dec 8, 1999


    * Corresponding author(s): w-payne@tamu.edu
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doi:10.2134/agronj2001.931250x

Assessing Simple Wheat and Pea Models Using Data from a Long-Term Tillage Experiment

  1. William A. Payne *,
  2. Paul E. Rasmussen,
  3. Chengci Chen and
  4. Robert E. Ramig
  1. Oregon State Univ., Columbia Basin Agric. Res. Cent., P.O. Box 370, Pendleton, OR 97801

Abstract

Fresh green pea (Pisum sativum L.) and winter wheat (Triticum aestivum L.) are grown in rotation with intensive tillage in northeast Oregon. We evaluated simple yield models for both crops using data from a long-term experiment that included four tillage treatments and soil water content measurements. Yields were affected by tillage for some of the 21 yr of study, but there was no consistent ranking among tillage treatments from year to year and no effect when data were pooled. Standardizing pea yields to a tenderometer reading of 100 failed to improve detection of treatment effect. Tillage affected wheat water use (ET) for three of the study years and water use efficiency (WUEET) for one. Conservation tillage reduced pea ET for seven of the study years. For combined years, however, there was no tillage effect on yield, ET, or WUEET of either crop. Wheat yield was better predicted from ET (R2 = 0.41) than by the Leggett model, which uses March soil water storage and spring rain (R2 = 0.12) Yield prediction was improved when ET was divided by seasonal mean daily water vapor pressure deficit (VPD) (R2 = 0.53) Multiple-regression equations using monthly rain predicted wheat yield well (R2 = 0.62), but coefficients differed among data sets. A model using monthly rain and heat degree day sum (HDDS) predicted pea yield much better (R2 = 0.65) than ET-based equations (R2 = 0.30), suggesting that pea yield is limited by factors other than water. For wheat, ET/VPD-based models should replace the Leggett model. However, for pea, multiple regression models predict yield better than ET-based models.

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Copyright © 2001. American Society of AgronomyPublished in Agron. J.93:250–260.