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Supplementary Material
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Agronomy Journal
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Winter wheat yield gaps and patterns in China
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Shuang Sun1,2, Xiaoguang Yang1*, Xiaomao Lin2*, Gretchen F. Sassenrath2, and
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Kenan Li3
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College of Resources and Environmental Sciences, China Agricultural University, No.2 Yuanmingyuan West Rd., Haidian District, Beijing 100193, China Department of Agronomy, Kansas State University, 2108 Throckmorton Plant Sciences Center, Manhattan, KS 66506, USA 3
College of Air Traffic Management, Civil Aviation University of China, No.2898 Jinbei Rd., Dongli District, Tianjin 300300, China
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Corresponding author: Xiaoguang Yang College of Resources and Environmental Sciences China Agricultural University, Beijing 100193, China Tel: +86-10-6273 3939, Fax: +86-10-6273 3939 Email:
[email protected] Xiaomao Lin Department of Agronomy Kansas State University, Kansas 66506, USA Tel: +1 7855326816, Fax: +1 7855326094 Email:
[email protected]
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This file includes:
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Classification of yield trend pattern and data analysis
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Example of using more than one cultivar in simulation
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How to use the weighting factors
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Supplemental Tables S1–S3
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Supplemental Figure S1
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References
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Classification of yield trend pattern and data analysis
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The wheat yield trends were classified for each county according to the global crop
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yield trends identified by Ray et al. (2012): yields never improved, yields stagnated,
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yields collapsed, and yields increasing. Regions where no significant yield improvement
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occurred over the 30-year study period were classified as ‘yields never improved’.
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Regions in which previous yield improvements have slowed or ceased were classified as
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‘yields stagnated’. Regions where yields decreased since the 1980s or initially increased
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and then declined to the 1980s level were classified as ‘yields collapsed’. If yields were
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still increasing, the regions were classified as ‘yields increasing’ (Ray et al. 2012). For
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each yield time series for each county, four linear regression models, an intercept-only
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model (Eq. 1), a linear model (Eq. 2), a quadratic model (Eq. 3), and a cubic model (Eq.
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4), were fitted for each county:
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Yield = k
(1)
Yield = at 2 + bt + k
(3)
Yield = at + k
(2)
Yield = at 3 + bt 2 + ct + k
(4)
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where Yield predictant represents the actual county-average yield in kg ha-1 and t
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represents year; a, b, and c are the regression coefficients and k is the intercept. The best-
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fit model for yield data was chosen based on the Akaike Information Criterion (AIC) for
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each model (Akaike 1974) (Eq. 5); the model with the minimum AIC was chosen as the
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best for the county evaluated:
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RSS
AIC=nlog(
n
)+2
(5)
2
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where RSS denotes residual sum of squares for a sample size of n and
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parameters in regression.
is the number of
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The accuracy and significance of model fit was then determined by the F-test with p-
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value
