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This article in AJ

  1. Vol. 97 No. 1, p. 241-249
     
    Received: Mar 4, 2004
    Published: Jan, 2005


    * Corresponding author(s): dlobell@stanford.edu
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doi:10.2134/agronj2005.0241

Combining Field Surveys, Remote Sensing, and Regression Trees to Understand Yield Variations in an Irrigated Wheat Landscape

  1. David B. Lobell *a,
  2. J. Ivan Ortiz-Monasteriob,
  3. Gregory P. Asnera,
  4. Rosamond L. Naylorc and
  5. Walter P. Falconc
  1. a Dep. of Global Ecol., Carnegie Inst. of Washington, Stanford, CA 94305, and Dep. of Geol. and Environ. Sci., Stanford Univ., Stanford, CA 94305
    b Int. Maize and Wheat Improvement Cent. (CIMMYT), Wheat Progr., Apdo. Postal 6-641, 06600 Mexico D.F., Mexico
    c Cent. for Environ. Sci. and Policy, Inst. for Int. Studies, Stanford Univ., Stanford, CA 94305

Abstract

Improved understanding of the factors that limit crop yields in farmers' fields will play an important role in increasing regional food production while minimizing environmental impacts. However, causes of spatial variability in crop yields are poorly known in many regions because of limited data availability and analysis methods. In this study, we assessed sources of between-field wheat (Triticum aestivum L.) yield variability for two growing seasons in the Yaqui Valley, Mexico. Field surveys conducted in 2001 and 2003 provided data on management practices for 68 and 80 wheat fields throughout the Valley, respectively, while yields on these fields were estimated using concurrent Landsat satellite imagery. Management–yield relationships were analyzed with t tests, linear regression, and regression trees, all of which revealed significant but year-dependent impacts of management on yields. In 2001, an unusually cool year that favored high yields, N fertilizer was the most important source of between-field variability. In 2003, a warmer year with reduced irrigation water allocations, the timing of the first postplanting irrigation was found to be the most important control. Management explained at least 50% of spatial yield variability in both years. Regression tree models, which were able to capture important nonlinearities and interactions, were more appropriate for analyzing yield controls than traditional linear models. The results of this study indicate that adjustments in management can significantly improve wheat production in the Yaqui Valley but that the relevant controls change from year to year.

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