Fig 1.
Linear mixed effects models of county-level maize yields (2000–2014) against statewide annual mean of maize grain yield (environmental index).
Thick black lines show state mean fixed effect. Gray lines show individual county relationships (best linear unbiased predictors, BLUPs), as estimated from the random effects structure. Number of observations, by state: NIL = 1215, NMI = 705, NMN = 780, NPA = 840.
Fig 2.
County-level maize yield volatility (coefficient of variation) in each state over 2000–2014.
Minnesota (top left state) had the least yield volatility and Pennsylvania (far right) the most. Illinois (second from left) had the greatest variation in yield volatility.
Fig 3.
Conceptual framework underpinning structural equation modeling approach to investigate effects of climate and edaphic factors on maize yield stability.
Climate data was restricted to growing season periods. Edaphic variables were separated into two components representing complementary soil properties: soil fertility and soil moisture retention.
Fig 4.
Best supported structural equation models showing effects of climate and edaphic factors on county-level maize minimum yield potential (downside risk) and temporal variability (volatility) for each state.
(A) Illinois (χ2 = 5.2, df = 2, P = 0.07, N = 81), (B) Michigan (χ2 = 2.8, df = 2, P = 0.24, N = 47), (C) Minnesota (χ2 = 0.68, df = 1, P = 0.41, N = 52), (D) Pennsylvania (χ2 = 0.75, df = 2, P = 0.69, N = 56). Model fit values indicate good support for each model. Double-headed arrows denote covariances among variables, whereas single-headed arrows denote standardized regression coefficients. Black arrows show positive relationships; grey arrows show negative relationships. P < 0.05 (*), P < 0.01 (**), P < 0.001 (***).