Impact of Climate Change on Food Security in Kazakhstan

Round 1

Reviewer 1 Report

The case study presented by Wang and co-workers indicating the impact of climate change on the staple food production. The authors have provided significant research output in the matter however there are several suggestions should be implemented before the publication of the study-

1.     Line 37: Abbreviation for MDGs is missing.

2.     Line 47: Explain in a few lines about the common concerns that are mentioned here.

3.     Line 75: Check the spelling of yield.

4.     Line 84 to line 87: Representing this sentence in a diagram form will be better.

5.     Line 90 to line 94: Represent this sentence in two different circular diagrams by mentioning their percentage share.

6.     Line 282: Check the spelling of prevent.

7.     Line 303: Abbreviate SPEI and SPI.

8.     Line 335: The conclusion should also present few lines on the importance of the tests and models are considered in this study.

9.     Line 335: This section should include some challenges involved in this study.

 

Author Response

Thank you very much for careful examination and pointing out our shortcomings. Our manuscript will get better with your detailed requirements. We have our native English-speaking colleagues check and polish the full text this time.

1. The abbreviation and full name of MDGs have been completed.
2. We have fully expanded the content of common climate research topics.

Climate change is closely associated with food security, and several attempts have been made to avoid potential risks of climate change on crop production to maintain stable food supply [4]. A study on the vulnerability of global crop yields to climate change shows that climate change could reduce global crop yields by 3–12% by mid-century and 11–25% by the of the century [5]. Climate change found to negatively impact household food security in regions of Africa where crop production is heavily dependent on environmental factors [6]. Further, extreme temperatures can have an impact on food availability and prices [7]. An important way to reduce food security risks is to accurately assess and predict the impact of climate change on food production [8]. 

3. We are sorry for always writing the wrong "yield",
4. We use a table to summarize the topography of Kazakhstan.
5. We have replaced the proportion of various grains in Kazakhstan with a pie chart.
6. We are sorry for the misspelling of the word "prevent" again.
7. We added the full names of "SPEI" and "SPI".
8. We add importance testing and panel regression to the conclusion.
9. We add the challenges of this study to the conclusion.

The upward trend of wind speed and PY are obvious throughout Kazakhstan; while WY and BY have increased in the southeast. T1, T2, T3, P4, P5, T6, T7, T8, W3, W4, W5 are important factors affecting staple crops’ yield. According to climatic characteristics, Kazakhstan can be divided into high-latitude and low-latitude regions. In this study, we also determined the crop management strategies for wheat, barley, and potato yields, to combat yield loss due to climate change. The challenge of this study is that the screening of important data inevitably leads to the loss of some information. For example, the effects of P2, P6, P8, and P10 cannot be reflected in the regression. In addition, macro-level studies are unable to separate late-maturing and early-maturing  potato, spring and winter wheat from statistical results.

Reviewer 2 Report

REVIEW

On the article on the Coercion and Countermeasures of Climate Change to Food 2 Security: a Case of Kazakhstan and authors:  Danmeng Wang, Ruolai Li, Guoxi Gao2, Nueryia Jiakula, Shggs Ayken, Shilin Li, Ping Ma and  Yongzhong Feng, proposed for publication in MDPI - Agriculture.

Climate changes also lead to changes in the agro-climatic resources of agricultural territories. In different regions, these changes have a different effect on the conditions for growing agricultural crops, which is why the study of the relationship between the changed conditions and the productivity of the countries' main agricultural crops is particularly important for the nutrition of the population. The topic is significant, which is proven by the established trends of change, which give reason to look for the effects of climate changes in the region of Kazakhstan, indicated in the abstract. The presented development is precisely shaped and all parts are subordinated to the purpose of the study. The location and the methods used are comprehensively presented, which are well chosen to answer the questions related to the purpose of the study. The term of the study is also indicated. The results are presented in detail and are well visualized. In the Discussion section, both an analysis of the factors influencing the yield of major agricultural crops and related works and an evaluation of the selected models are presented.

Notes to authors:

- The source of data is indicated in material and methods. The food crops' data for 2003-2020 comes from "Agriculture, Forestry and Fisheries in the Republic of Kazakhstan" [16], and the precipitation data comes from "Environmental Protection and Sustainable Development in Kazakhstan". At the same time, the temperature and wind speed data for 1990-2020 are from the NCEI (National Center for Environmental Information) website of the NOAA (National Oceanic and Atmospheric Administration). What is the actual study period?

- The graphs of Figure 2 can be represented as points; there is no need to have connecting lines.

- In the presence of irrigation, the study of the influence of precipitation on yields is incorrect. Therefore, I recommend the use of some agrometeorological indices for a complex assessment, which can provide information on the deficits of humidification - ETo or BAH (Balance of Atmospheric Humidification).

- The findings in 4.2 about the significance of temperatures, wind and precipitation for the formation of BY, WY and PY yield indicators raise doubts. Temperatures can't possibly matter more than rainfall.

Author Response

Thank you very much for affirmation of our method choice and view presentation. The research data behind this article is quite complex, involving yields of 3 major crops and 36 meteorological indicators. We have been thinking for a long time, how to find the most important information for research, and accurately target the problems and suggestions. We are very happy that you think the presentation is logical and not cluttered, and your affirmation gives us great confidence.

Regarding the four questions you mentioned later, we have also carefully checked and reflected on why there is a paradox with previous research. We have found the reason and edited some results and discussions.

(1) In the data source, we have added a specific description of the experimental data.

This study involves 18 years (2003-2020) of data from 17 regions (14 states and 3 cities in Fig1), with a total of 281 available samples.

(2) Figure 2 has been modified to remove the lines, and Figure 2 is now a point diagram.

(3) We deleted the reference to irrigation in the results analysis.

(4) We checked the algorithm and data and found that the conclusion that temperature is more important than precipitation is indeed wrong.

The reason for this data result is that in order to predict the accuracy of the positive and negative relationship, the panel regression model preferentially reports the unstandardized coefficients. And we didn't notice this detail, habitually treat them as standardized coefficient analysis. Thank you so much for pointing out this serious and significant mistake for us. We have rewritten the panel regression analysis and explained the problem so that readers will not be confused.

Panel regression results:

T2, T7 and P4 significantly promote WY, while T5, T6, T10 and W3 inhibite. There are large climatic differences between high- and low-latitude areas. In high latitudes, T7 becomes an inhibitory effect on WY, while it is still a promoting effect in low latitudes. In high-latitude areas, the effect of W5 on WY become significant; in the low-latitude areas, the effect of P5 on WY become significant. T2, T8 and P4 significantly promote BY, while T3, T5 and T10 inhibite. At high latitudes, T8 become the inhibitory effect on BY, and the promoting effect of W5 become significant. At low latitudes, the promoting effect of T7 become significant, and W4 have a significant inhibitory effect. For potatoes, the overall fitting effect is very poor, because the varieties and planting cycle between regions are extremely different. In high latitudes, T1, T6, T7 promote PY, while T5 and P5 inhibite; in low latitudes, T3 and T7 promote PY, while T8, T10, and W4 inhibite.

In order to obtain a more accurate positive and negative relationship between independent and dependent variables, this section uses unstandardized coefficients instead of standardized coefficients. Unstandardized coefficients cannot reflect the relative importance of indicators, and the importance analysis refers to the results of random forests.

Significance discussion:

The significant instability of P4 and P5 should still be caused by the large regional differences. Because when Kazakhstan is divided into northeast, south and west regions, the significance of P4,P5 and P6 is very high.

Author Response File: Author Response.docx