Peer review of: Yield reduction under climate warming varies among wheat cultivars in South Africa (2020)

Reviewers' Comments: 
 
Reviewer #1: 
Remarks to the Author: 
General comments: 
The submitted manuscript presents a statistical analysis on the high temperature effect on wheat 
yields in South Africa. The variety-specific data examined here are potentially novel and useful to 
address the scientific question considered here and lead to implications for breeding targets. The 
manuscript is mostly clearly written. However, I have several concerns from the methodological point 
of view, as listed below. Given the current manuscript, rationales are not sufficient or in part lacking to 
conclude that this study is technically sound; and the conclusions are supported by evidence. 
 
Major concerns: 
1. My main concern is that whether only limited samples in terms of varieties and locations are used 
to estimate the yield sensitivity to the temperature bin >30degC, although the total sample size is 
quite large (18,881). Figures S1 and Table S1 suggested that wheat grown in Western Cape is rarely 
exposed to temperatures >30degC. In Free State, the exposure to the high temperatures may happen 
for some varieties but not for all varieties as many varieties complete their crop duration by up to the 
end of September in which the high temperatures rarely occur. The incidences of temperatures 
>30degC are shown in Fig. S1, but it is likely that these data came from limited cultivars and locations 
and probably the sample size is very small. If this is the case, the estimates of yield sensitivity to the 
high temperatures do not represent the average of various varieties examined here. Subsequently, 
the comparison across the varieties with low and high sensitivities presented becomes meaningless in 
this case. The author(s) is strongly encouraged to provide rationale that their estimates on the 
temperature bin >30degC is derived based on a wide-ranging varieties and locations. 
2. I am not convinced how the findings of this study have implications in particular for Sub-Saharan 
Africa (SSA), whereas the author(s) sometimes emphasize it in main text (for instance, “… as a major 
wheat producer and consumer, shocks to South African wheat production via heat extremes likely 
affect food security outcomes in South Africa and throughout the southern SSA region” (line 48-50)). 
In reality, South Africa is not necessarily a major wheat producer in the world. According to wheat 
production and trade data in 2017 available in FAO statistical database, South African wheat 
production is ranked 5th among African countries following to North and West African countries (e.g., 
Egypt, Morocco, Ethiopia and Algeria). In 2017, South Africa produced wheat of 1.5 million tons (Mt) 
and imported 1.7 Mt mainly from Russia and Germany (Table 8 in 
https://apps.fas.usda.gov/newgainapi/api/report/downloadreportbyfilename?filename=Grain%20and
%20Feed%20Annual_Pretoria_South%20Africa%20-%20Republic%20of_3-25-2019.pdf). Wheat 
exports from South Africa were relatively small (0.08 Mt). Given this situation, the present study may 
have implications for domestic production in South Africa, but possible implications for the remaining 
region in SSA as well as those for international scientific communities are unclear. 
3. Sowing date shifts are widely considered as an adaptation measure in crop production. However, 
this study assumes that the crop durations are unchanged in a warmer climate (+1 to +3 degC). This 
assumption is unreasonable and unrealistic, and more importantly leads to an overestimation in yield 
impacts associated with the high temperatures. Therefore, the yield impacts estimates presented need 
be revised by incorporating adaptation measures that would occur thanks to less additional costs for 
producers. 
4. Although I am not sure specific targets in wheat breeding in South Africa, drought tolerance rather 
than heat tolerance has long been a major target worldwide. This thought is in part supported by the 
fact that South Africa have frequently experienced droughts and literature (e.g., 
https://www.grainsa.co.za/screening-bread-wheat-lines-for-drought-tolerance). However, Table S3 
shows that no yield effect of precipitation (and precipitation squared). This suggests that the high 
temperature effect and drought effect in this study may not be well differentiated each other. To 

remedy this concern, I would suggest the author(s) providing Fig. 5 for drought (or precipitation) to 
show that known drought-tolerant cultivars have lower precipitation effect. Such an additional analysis 
can increase the reliability and confidence of the findings of this study. 
 
Technical comments: 
5. In Table S5, I would appreciate it if the statistical significance of heat effect >30degC for each 
variety is presented. If the estimated cultivar-specific heat effects that are not significant are included 
in analysis, it is hard to interpret Fig. 5 b and 5c. I would suggest conducting a separate analysis for 
all samples and subset with significant heat effect. 
6. In Fig. S5, on average, the estimated yield impacts for spring wheat are always more severe than 
those for winter wheat. Although the difference across the wheat type is not significant (Fig. S5), it 
would be nice if labels (winter, facultative or spring) are added to Table S2 to enable readers thinking 
about possible reasons for this non-existence of the significance. As the crop durations are almost the 
same across the cultivars (Table S2), it is hard to make a distinction (which cultivar is which type?). 
 
 
 
Reviewer #2: 
Remarks to the Author: 
This paper highlights the impacts of extreme heat events on wheat production in South Africa using a 
large dataset from a number of field plots from a national research center. Interestingly, they also 
show the potential adaptation benefit of using more advanced seeds. Overall, this paper provides 
important results that are valuable for scientists and policy makers when it comes to identifying ways 
to mitigate the impacts of heat on wheat production. 
 
I really liked how thorough the analysis is while reading the methods section, especially the 
robustness checks. The authors seem to have thought about almost all potential caveats. Well done! 
 
However, I have the following suggestions/concerns/queries: 
- The title does not really reflect what you have shown. While very subjective, I wonder whether the 
title should be more about the potential benefits of plant breeding to mitigate heat impacts? Whatever 
you go for, I think the paper could use a more convincing title. 
- abstract: would be good to end the abstract with a concluding sentence highlighting the importance 
of this paper. 
- introduction: overall, I believe that both the intro and discussion (see below) could use some 
rethinking/rewriting. There is quite a lot of focus on droughts, which are not the same as heat events. 
Paragraphs are quite busy. Would be good to include a bit more info on South Africa (who, when and 
where is wheat produced; import/export; what are the climate models saying about South africa). 
From my understanding, there is a string decrease in the area harvested as the country is more and 
more relying on imports. Also, it would be good to talk a bit more about irrigation here (you mention it 
in the discussion). Again, from what I understand, most of the wheat produced in the Free State is 
irrigated (not so in the Western Cape). And finally, there is no talk at all about soils. Is wheat grown in 
these two provinces on the same type of soil? different soils are better for keeping moisture. 
Something that can also be discussed later. (Maybe in the Suppl Mat, you can include some review on 
the strengths and weaknesses of the various methods that are used to assess the impacts of climate 
on crops such as Hertel, Thomas W., and Stephanie D. Rosch. Climate change, agriculture and 
poverty. The World Bank, 2010; or Lobell, David B., and Senthold Asseng. "Comparing estimates of 
climate change impacts from process-based and statistical crop models." Environmental Research 
Letters 12.1 (2017): 015001. but also experimental plots). 
 
- discussion: same comments as above, but I would also add that you could further discuss the 

caveats (methodological and others), and other issues such as uptake of new seeds. I assume that 
this mainly focused on large, commercial producers, but I may be wrong. But from my experience 
with smallholders, there is often a mistrust or other barriers in adapting newer breeds that farmers 
may not be familar with. I think it would be interesting to discuss these differences, and what may 
happen in times of extreme heat events (i suppose smallholders would then mainly rely on markets - 
if they actually consume wheat (see imports/exports info in introduction)). 
 
-methods 
it would be good to include a table or some other detailed summary of what data are available from 
ARC-SGI. Currently it is rather difficult to understand. 
one important concern I have is regarding the use of data from the weather stations, and the 
somewhat arbitrary buffer of 75km. For temperature, this may be OK (even here there may be big 
differences depending the landscapes these stations are in) but for rainfall there may be major 
differences, which also depend on elevation. In Fig 1, one can see that some stations are on the coast 
while the plots are further inland. Could you maybe compare your rainfall and temperature data with 
gridded datasets? Or create spatial data, taking into account elevation? 
Would it be useful to include other variables such as Growing Degree Days, or Coeff of Variation of 
Rainfall? 
Is there an argument to use these specific, 5C bins? Would the results look different with different 
bins? 
One concern I had from the beginning was spatial autocorrelation. I was glad to read on line 318 that 
you had also considered this. Not being an expert in this, I would like to know whether your approach 
is correct. Is there some analysis that can be done to measure existing spatial autocorrelation and 
highlight the impact of your approach? 
The stats are a bit beyond my understanding but I do have two queries: 1. what is the impact of 
having observations of different length in time? Does this temporal heterogeneity influence results? 2. 
you do analyse the impact of release year, but what does that really say about the type of cultivar. 
Are these heat tolerant seeds or do they focus more on specific pests/diseases? 
 
smaller comments: 
L202: I believe that ARC-SGI hasn't been defined yet. 
L202-213: should this be moved to the discussion? 
L286: typo 'iss' 
references: there are several where the formatting needs to change (i have identified issues with ref 
#17 and 48, please check others.) 
 
 
Figures: 
Fig1 - the label/title 'South Africa' is weird as one would think it represents the dark grey areas. 
Most figures: not sure why you use color for the barplots, especially fig 3: One would think that the 
different colors represent something else, and this would then need to be explained somewhere (in 
caption or legend) 
Fig S1: the caption mainly talks about the bottom figure. Provide info on top figure as well. Clearly 
indicate sub-figures. Label x-axis too. 
Fig S2: label both axes