Solved – Dealing with heteroscedasticity in mixed models

I collected crop yield data for many years across multiple locations, which are nested under provinces and some associated weather data. I am interested in making a model and then using new weather data to predict yield across these locations.

Here's a model I fitted

library(lme4)   Linear mixed model fit by maximum likelihood  ['lmerMod'] Formula:  log(yield) ~ x1 + I(x1^2) + x2 +  I(x2^2) + x3 + I(x3^2) +                x4 + I(x4^2) + x5 + I(x5^2) +                x6 + I(x6^2) + x7 + I(x7^2) +                x8 + I(x8^2) + x9 + x10 +                I(x10^2) + x11 + I(x11^2) + year +               (year | province/location) Data: dat   AIC      BIC   logLik deviance df.resid  -11495.4 -11261.8   5777.7 -11555.4    17735   Scaled residuals:    Min      1Q  Median      3Q     Max  -4.4276 -0.5241  0.1373  0.6644  4.7144   Random effects: Groups            Name        Variance  Std.Dev. Corr  location:province (Intercept) 0.0028622 0.05350                           year        0.0005911 0.02431  0.46  province          (Intercept) 0.0028255 0.05316                           year        0.0003357 0.01832  -0.52 Residual                      0.0296151 0.17209        Number of obs: 17765, groups:   location:province, 174; province, 14  Fixed effects:             Estimate  Std. Error t value (Intercept) 7.77016483  0.01642658 473.024 x1          0.01490593  0.00286908   5.195 I(x1^2)    -0.00034555  0.00070202  -0.492 x2         -0.00093958  0.00300568  -0.313 I(x2^2)    -0.00008501  0.00056272  -0.151 x3          0.03302874  0.00226069  14.610 I(x3^2)     0.00664297  0.00142503   4.662 x4         -0.04088568  0.00252176 -16.213 I(x4^2)    -0.01398973  0.00139918  -9.998 x5          0.01764166  0.00374300   4.713 I(x5^2)    -0.00298363  0.00135861  -2.196 x6          0.00964780  0.00405505   2.379 I(x6^2)    -0.00359899  0.00123643  -2.911 x7          0.01127387  0.00352026   3.203 I(x7^2)    -0.00175782  0.00052768  -3.331 x8         -0.05147980  0.00392094 -13.129 I(x8^2)     0.00166814  0.00077124   2.163 x9          0.00018234  0.00132609   0.138 x10         0.02124123  0.00385797   5.506 I(x10^2)    0.00174181  0.00087155   1.999 x11        -0.02645305  0.00417074  -6.343 I(x12^2)    0.00045353  0.00092663   0.489 year        0.13482956  0.00617583  21.832  plot(mod) 

Since the residuals are obs – fit, I understand that as the fitted value increases, the residuals tend to move from positive to negative which means at a higher value of observed y, the model underpredicts and vice versa.

I would like some guidance on what are the possible reasons for this systematic lines in the plot and how to best deal with them?

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