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Appendix S1. Crop model evaluation
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Systematic differences in crop yield response to climate change resulting from heterogeneity
in cropping intensity
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A. J. Challinor, B. Parkes and J. Ramirez-Villegas
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GLAM has successfully been run over the study region for both groundnut (Vermeulen et al.,
2013) and maize(Garcia-Carreras et al., 2014, Parkes et al., 2014). In the current study, direct
evaluation of GLAM yields is not possible, since there is with only one year of data, leaving no
data remaining after calibration for an independent test. Instead, GLAM was run with two
values of the yield gap parameter (CYG): 1.0, which is the maximum value, and 0.5. Both of
these simulations were compared to observations from Monfreda et al. (2008). The results,
presented in Fig. S8, suggest that calibrated values of CYG for groundnut would be likely to lie
between 0.5 and 1. Calibrated values for maize appear more likely to be below 0.5. Evaluation
of the ability of GLAM to reproduce the observed niche effect is presented in the main paper.
A. Observed maize
B. Observed groundnut
C.Simulated maize CYG=1
D. Simulated groundnut CYG=1
E. Simulated maize CYG=0.5
F. Simulated groundnut CYG=0.5
Supplementary Figure 8. Observed and simulated GLAM yield (in kg ha-1). Observed yields (A,
B) are taken from the M3-Crops dataset (Monfreda et al., 2008). Simulated yields use the
GLAM model with CYG = 1.0 (C, D) and CYG = 0.5 (E, F).
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For EcoCrop model evaluation, a set of suitability simulations using observed climatological
means from WorldClim was conducted. Following Ramirez-Villegas et al. (2013) and IPCC
(2014), four evaluation analyses were performed in order to test the ability of EcoCrop to
discriminate between presence and absence and also its ability to predict the geographic trend
in suitability within the study area. First, EcoCrop simulations were plotted and visually
inspected against the global harvested areas of the M3-Crops dataset (Monfreda et al., 2008).
Second, the area under the Receiving Operating Characteristic curve (Anderson et al., 2003,
Elith & Leathwick, 2009, Manel et al., 2001) was calculated using the harvested areas to define
areas of presence and absence. Areas of absence were defined as those with zero fraction
harvested. The rest of land areas were considered areas of presence. Third, at a threshold of
zero suitability, the true positive rate (TPR), the false negative rate (FNR) and the true negative
rate (TNR) were calculated. Fourth, density plots of suitability and observed crop yields
(Monfreda et al., 2008) were produced to compare yields and suitability of all areas, and also
of areas with high cropping intensity.
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Results indicated that, in general, EcoCrop simulated suitability agrees well with observed crop
distribution (Fig. S9). Groundnut suitability was greater than that of maize in very dry areas but
the opposite was true for wetter areas in the south of West Africa.
A. Maize area harvested
B. Groundnut area harvested
C. Maize WorldClim suitability
D. Groundnut WorldClim suitability
E. Maize CASCADE suitability
F. Groundnut CASCADE suitability
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Figure S9. Area harvested and simulated crop suitability. Top plots show the fractional area
harvested from Monfreda et al. (2008). Middle plots show suitability as simulated using
observed meteorology from WorldClim. Bottom plots show simulated suitability using 12-km
explicit convection CASCADE data (Birch et al., 2014).
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AUC values for both crops and meteorological datasets were above 0.7, and, in particular for
maize, also above 0.8. This suggested good discrimination ability in the model. At zero
suitability, the prediction of positives was relatively good (66.2 – 73.4 % for maize, 65.9 – 66.2
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% for groundnut). The false positives are difficult to assess given the lack of real environmental
absence data. The prediction of negatives was also accurate, with TNR values above 70 %, and
generally low false negative rates (see Table A1).
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Table A1 Evaluation metrics of EcoCrop, showing Area Under Curve, True Positive Rate, True Negative
Rage and False Negative Rage.
Crop /
Observed
12-km
Metric
Maize
Groundnut
Maize
Groundnut
AUC
0.863
0.802
0.824
0.743
TPR
73.4
67.1
66.2
65.9
TNR
99.2
88.1
81.0
70.0
FNR
0.75
11.9
18.9
29.9
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Frequency plots of suitability (Fig. S10) indicated agreement in the spatial variation of
predicted suitability and observed yields across the study area. This agrees with GLAM
simulations, for which we found a similar geographic pattern. For maize, the areas where the
greatest cropping intensity occurs coincided with areas of very high suitability. The same was,
however, not true for groundnut, for which the areas of greatest cropping intensity are
generally of low crop suitability. This may be attributed to groundnut being generally grown in
areas where potential yields are lower [see Figure S8 and Monfreda et al. (2008)]. This in turn
may be caused by cultural or market preferences, or because the most suitable areas are quite
simply used for other crops such as maize, bananas, plantains or sorghum.
A. Maize
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B. Groundnut
Figure S10. Frequency plots of EcoCrop suitability for all 12-km grid cells and for those with
high cropping intensity, for maize and groundnut. Vertical dotted lines are the means of
suitability for each of the two cases. In panel B, these means overlap.
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References
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