Research on the Coordination between Agricultural Production and Environmental Protection in Kazakhstan Based on the Rationality of the Objective Weighting Method

Round 1

Reviewer 1 Report

All comments are available in a separate PDF file.

Comments for author File: Comments.pdf

Author Response

1. 140‘272×106km2’ is a mistake

Reply: The text has been revised to "2.72×10⁶ km²".

2. 142-145 no need to enumerate because the whole territory of the county has been taken into account (all states), the names in the list do not match the names on the map.

Reply: We have deleted this map and the list of states. The names of states on this map were further not well marked. South Kazakhstan was renamed to Turkestan in 2019. We have added this explanation to section 3.1.

3. 157-162 ‘These indicators are selected because they show the mutual influence of environmental protection and agricultural development.’ In which way exactly these indicators show the mutual influence?

Reply: This sentence was inappropriately written and caused confusion so that it has been changed to ‘These indicators were selected because they provide a comprehensive overview of the current agricultural environment and the level of agricultural development’.

4. What is the remaining ‘0.79%’

Repla: The text in parentheses answers this question, and we corrected the number format. The modifications to the manuscript are as follows:

In 2019, the output value of plant production was 2,817,660.6 million₸, and the output value of animal husbandry was 2,319,496.7 million₸, accounting for 99.21% (the remaining 0.79% referring to agricultural services) of the total agricultural output value of 5,177,893.7 million₸.

Reply: Thank you very much for pointing out the lack of details and information in Table 2. We made the following additions and modifications to the table for completeness and clarity.

5. 175-176 The expression of ‘indicator z’ was inappropriate. 1-9 formulas require literature.

Reply: We have made modifications to the formula and changed the description:

Cj is the information volume of indicator j; each indicator has n data, and there are p indicators in total; xij is value i of indicator j, and r_jj’ is the correlation coefficient between indicator j and the other indicators. Formulas 1-9 have been supplemented with literature.

6. 202 Maybe you can use ‘CRITIC method results’ instead of ‘CW’s results’?

Reply: ‘CRITIC method results’ has been replaced with ‘CW’s’.

7. 203 ‘is the most important investment’ according to which criteria？

Reply: We have adjusted the narrative order to prevent misunderstandings.

For EPI, the CRITIC method result presents the most reasonable allocation. From 2010 to 2019, Kazakhstan invested an average of 61,050.18 million ₸ for CSAQ, 21,040.26 million ₸ for WSI, and 1,177.91 million ₸ for HPR annually. Thus, investments in the safety of climate and air (CSAQ) were most important in agriculture-related environmental protection in Kazakhstan, followed by water and soil improvement (WSI), while investments in habitat (HPR) were the least.

8. 223-232 There is a misunderstanding about the expression of APR as a productivity indicator, while the analysis of results involves the content of production value.

Reply: First, there was no explanation of the ‘tenge symbol (₸)’ in Table 2, might have caused confusion. Second, what we wanted to convey is that planting is more relevant in Kazakhstan's agricultural production than animal husbandry. In addition to the production value, data on the production area also adds to the objective evidence. The original passage was rewritten to:

“For APR, CW was selected. As mentioned in the indicator selection instructions in section 3.1, planting contributed more than animal husbandry to the agricultural development in Kazakhstan. From 2015 to 2019, the average annual output value of the planting industry was 2,270,226.3 million ₸, and that of animal husbandry was 1,854,466.2 million ₸. The output value of the planting industry was about 1.2 times that of the animal husbandry. In addition, it is extremely difficult to improve the productivity of animal husbandry. The pastures in Kazakhstan are mainly distributed in the horizontal central area. In 2019, the grazing area of Karagandy, Aktobe, and East-Kazakhstan accounted for 43% of the total. However, the semi-desert and semi-savannah areas are not suitable to support the excessive demand for forage; the fodder planted area reached 2,178.8 kha. Thus, changes in crop yields could be more easily achieved and should be more impactful than changes in livestock yields. Only CW fit this actual situation.”

9. 263 ‘EP level are growing much faster than AP levels, by about three time’,
is wrong.
Reply: We made a mistake in describing the results.
This sentence was changed to ‘AP levels are growing much faster than EP levels.’

10. Table3, CCD level set the boundaries of the classes? Source?
Reply: We have added the CCD level content to the CCD literature review in section 2.2. We have further added a reference after the title in Table 3.

Author Response File: Author Response.pdf

Reviewer 2 Report

The subject addressed in the manuscript is very interesting and important for the agricultural production sector and the environment. The applied analyzes were adequate. However, this reviewer understands that the way the results and the writing were approached/explored were not adequate. As this is a research from a certain region, the results presented should have a greater scientific basis, as well as the methodology should be more consistent and robust.

Author Response

The subject addressed in the manuscript is very interesting and important for the agricultural production sector and the environment. The applied analyzes were adequate. However, this reviewer understands that the way the results and the writing were approached/explored were not adequate. As this is a research from a certain region, the results presented should have a greater scientific basis, as well as the methodology should be more consistent and robust.

Reply: Thank you very much for your valuable comments, which refer to two major problems of this study, which we tackled by carefully revising various parts of our manuscript.

First, the presentation of the results and the writing were often inadequate, and the results require a sounder scientific basis. We, thus, carefully checked the text and found many locations at which the expression is vague and incomplete. We have made the following changes.

• We added more information on CCD grading to section 2.2. to make the division of CCD levels in the results more convincing.

“In a study on the temporal and spatial changes of CCD between the socio-economic and ecological environment of the Loess Plateau, the CCD level was divided into good coordination (0.8-0.9), moderate coordination (0.7-0.8), primary coordination (0.6-0.7), tiny coordination (0.5-0.6), and mild dissonance (0.4-0.5) [28]. In a CCD study of the public demand and government supply in Chinese urban agglomerations, 0-0.3 indicated a serious im-balance, 0.3-0.5 indicated a moderately low, 0.5-0.8 indicated a moderately high, and 0.8-1 indicated a perfect coordination [29]. In a study on the CCD of environmental and social benefits in urban wetland parks, a CCD value between 0.55-0.75 was considered to reflect a moderate coordination, as the average was 0.63 [30].”

The caption of Table 2 has been amended with many details, and the introduction of the indicator selection has been modified.

“The selection of evaluation indicators was realized by optimizing the mature and di-verse green agriculture evaluation system, combined with data from Kazakhstan's agricultural and environmental Statistical Yearbooks from 2010 to 2019 [31-32]. Environmental protection includes investments in the improvement of climate, water, soil, and ecological zones; achievements in fallow and land recovery rates, which are both closely related to agricultural production; and the status of afforestation, forest coverage, protected areas, and environmental protection agencies. In 2019, the output value of plant production was 2,817,660.6 million ₸, and the output value of animal husbandry was 2,319,496.7 million ₸, accounting for 99.21% (the remaining 0.79% referring to agricultural services) of the total agricultural output value of 5,177,893.7 million ₸. Therefore, the production efficiency of the main planting and animal husbandry products was chosen to represent the agricultural production capacity. The profitability of planting and animal husbandry, labor productivity, and the agricultural salary level were used to represent the economic benefits of agriculture. These indicators were selected because they provide a comprehensive overview of the current agricultural environment and the level of agricultural development.”

• We added information about the actual production status of Kazakhstan to the results section, making the selection of the objective weight method results more convincing. For example, the following three paragraphs were revised.

“For EPI, the CRITIC method results presented the most reasonable allocation. From 2010 to 2019, Kazakhstan invested an average of 61,050.18 million₸ in CSAQ, 21,040.26 million₸ in WSI, and 1,177.91 million₸ in HPR annually. Thus, investments in the safety of climate and air (CSAQ) were most important in agriculture-related environmental protection in Kazakhstan, followed by water and soil improvement (WSI), while the least was invested in habitat (HPR). Only CW fit the actual situation;”

“From 2015 to 2019, the average annual output value of the planting industry was 2,270,226.3 million ₸, and that of animal husbandry was 1,854,466.2 million ₸. The output value of the planting industry was about 1.2 times that of the animal husbandry. In addition, it is extremely difficult to improve the productivity of animal husbandry. The pastures in Kazakhstan are mainly distributed in the horizontal cen-tral area. In 2019, the grazing area of Karagandy, Aktobe, and East-Kazakhstan ac-counted for 43% of the total. However, the semi-desert and semi-savannah areas are not suitable to support the excessive demand for forage; the fodder planted area reached 2,178.8 kha. Thus, changes in crop yields could be more easily achieved and should be more impactful than changes in livestock yields. Only CW fit this actual situation. EW did not capture the relationship among indicators, and the statis-tical method of egg production is different, so that the volatility is large, causing inter-ference. This is widely diverge from the actual production. In fact, the total eco-nomic output of poultry farming only accounts for 27% of the livestock industry. FW weakens the effects of cereals and F&V because of their strong association and their yields having higher environmental requirements compared with po-tatoes”

“For AEB, the FW program was the most suitable. The economic volume of the planting industry was 1.2 times that of animal husbandry, so that the profitability of the planting industry has a higher probability of affecting the economic benefits of Kazakhstan's agriculture. The allocation of CW was not in line with reality. However, EW was suboptimal with respect to the different statistical methods for the agricultural wage level (LP).!

The second comment is related to the consistency and robustness of the used method.

We have thought about this intensively. The innovation of our research is that we consider the limitations of three objective weights and the weight calculation bias caused by these limitations. Through our literature review and data analysis, we could confirm that the objective evaluation method is always out of touch with the actual situation and research needs. At this time, the subjective choice becomes extremely important, especially when the simple averaging process cannot ideally alleviate the serious limitations of the objective weighting method (such as in APR, where the weight distribution is seriously disconnected from the actual production and limited by EW on egg productivity, which cannot be well alleviated, even if the weights of the three methods are averaged). Our research highlights the limitations of objective evaluation methods and urges researchers to pay more attention to the actual situation. We hope that the more detailed information about the actual production materials added to the results section make the selection of weights more reasonable.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The authors provided the corrections requested by the reviewers.