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

^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

View larger version (24K): [in a new window]   Fig. 1. Comparison of yield estimates derived from Landsat with farmer-reported values in 2001 and 2003. Regression statistics for 2001: n = 80, root mean square error (RMSE) = 0.37 t ha–1, and R2 = 0.78. For 2003: n = 47, RMSE = 0.64 t ha–1, and R2 = 0.60. For all data: n = 127, RMSE = 0.49 t ha–1, and R2 = 0.76.

View larger version (21K): [in a new window]   Fig. 2. Cumulative average daily temperature for 2001 and 2003 during the wheat growing season in the Yaqui Valley. Also shown is the average for 1984–2003.

View larger version (12K): [in a new window]   Fig. 3. (A) Regression tree model for 2001 survey. (B) Comparison of yield estimates with regression tree model predictions (R2 = 0.44).

View larger version (14K): [in a new window]   Fig. 4. (A) Regression tree model for 2003 survey. (B) Comparison of yield estimates with regression tree model predictions (R2 = 0.52).

View larger version (10K): [in a new window]   Fig. 5. Regression tree model for 2003 survey for fields in (A) deep clay (DC) and (B) compacted clay (CC) soils. R2 = 0.40 on DC and 0.45 on CC.