Agricultural Commodities in the Context of the Russia-Ukraine War: Evidence from Corn, Wheat, Barley, and Sunflower Oil

Round 1

Reviewer 1 Report

Supplement the introduction of the paper with an additional review of literature from the researched area. 

Additionally compare the research results with others from the same research fileld.

More clearly indicate the scientific application of the obtained results.

Author Response

1. Supplement the introduction of the paper with an additional review of literature from the researched area. 

Ihle, R., Bar‐Nahum, Z., Nivievskyi, O., & Rubin, O. D. (2022). Russia’s invasion of Ukraine increased the synchronisation of global commodity prices. Australian Journal of Agricultural and Resource Economics, 66(4), 775-796.

Svanidze, M., Götz, L., & Serebrennikov, D. (2022). The influence of Russia's 2010/2011 wheat export ban on spatial market integration and transaction costs of grain markets. Applied Economic Perspectives and Policy, 44(2), 1083-1099.

Ahn, S., Kim, D., & Steinbach, S. (2023). The impact of the Russian invasion of Ukraine on grain and oilseed trade. Agribusiness.

„In this context, Ihle et al., (2022) documents the Russian invasion of Ukraine through a concordance index that includes 15 key global commodities. The results indicates that due to this conflict international trade commodities prices show stronger synchronization.“ – (Introduction – 50,….,53)

„On the other hand, Svanidze et al., (2022) investigated the price effects of the 2010 Russian wheat export ban. They conclude that wheat world prices skyrocketed due to export restrictions while price transmission was evident on the other agricultural commodities as well.“ - (Introduction – 53,…,56)

„The Russian invasion of Ukraine generated an imbalance in international trade, where Latin American countries were the most prominent beneficiaries (Ahn et al., 2023).“ – (Literature review – 179,180)

2. Additionally compare the research results with others from the same research fileld.

“Compared to Ihle et al., (2022), who analyzes a wider group of agricultural commodities, our study focuses only on the main four of Russia and Ukraine agricultural industry. At the same time, their study investigates price synchronization while ours shocks through VAR and VECM methods.” (Discussion – 689,..,693)

“Contextualizing the conflict in Ukraine, Svanidze et al., (2022) used the 2010 wheat export ban to Russia as a trigger for the prices of other agricultural commodities. In contrast, our study covers a more extended period (January 1990 to August 2023) and focuses only on Corn, Wheat, Barley, and Sunflower Oil. However, both studies use VECM as a model for disequilibrium correction, one for weekly series and the other for monthly series.” (Discussion – 679,…,684).

3. More clearly indicate the scientific application of the obtained results.

Recognizing the limitations that forecasts maintain, results provide modest signals for the relevant agencies, international regulatory authorities, retailers, and low-income countries. At the same time, stakeholders get informed about their price behaviour and the causal relationship they hold with each other. (Abstract – 23,…,26)

Dear Mr/Mrs Reviewer, thank you very much for your comments, which have made our work take a better shape.

Reviewer 2 Report

Dear Authors,

Following are some comments in order to improve your manuscript:

Line 5-7 - Affiliations are missing, Add address.

Line 17 - You need to expand on the acronyms when using it for the first time in the text - Vector Autoregressive (VAR) and Vector Error Correction Model (VECM). 

Line 22 - JEL Classification?

Intext citations needs to be as per MDPI style. Please see other papers. It is in numerical format. Following article is relevant to your area of research https://doi.org/10.3390/foods11142098.

Line 114-116 - Why are you going that far to 1990? Is there any specific reason? can't it be last decade? Please explain.

Line 117-118 - Why only 10 months? why not next 5-10 years?

Line 261 - You have used quite a few acronyms without expanding them. How the readers should know them? Please correct this and the rest of acronyms throughout this paper.

Figure 2 resolution need to improve. It is not readable. Also, all figure captions need to be below the figures.

After reading the article, it seems that the title is not the right fit as the article only focuses on prices of corn, wheat, barley and sunflower oil. The current title is misleading, therefore, authors need to come up with a better title. 

Author Response

Line 5-7 - Affiliations are missing, Add address.

All affiliations are located in the manuscript.

Line 17 - You need to expand on the acronyms when using it for the first time in the text - Vector Autoregressive (VAR) and Vector Error Correction Model (VECM). 

The following acronyms are corrected.

Line 22 - JEL Classification?

JEL Classification: F14, F51 (Place in the abstract section)

Intext citations needs to be as per MDPI style. Please see other papers. It is in numerical format. Following article is relevant to your area of research https://doi.org/10.3390/foods11142098.

All intext citations are now in the style required by MDPI.

Line 114-116 - Why are you going that far to 1990? Is there any specific reason? can't it be last decade? Please explain.

Dear Mr. Reviewer, thank you for your comment. We completely agree with you, but since our series is monthly, we cannot shorten the series. However, for the forecasts to have more observations, we have extended the series until 1990 because the data for these four agricultural commodities exist only monthly.

Line 117-118 - Why only 10 months? why not next 5-10 years?

Dear Mr. Reviewer, thank you very much for the comment. Since our VAR model is built with monthly data, this has limited us from making forecasts monthly. We tried to make forecasts for over a year, but the margin of error increased a lot, making the predictions unviable.

-Other scholars could build VAR and VECM with annual data that would enable them to provide forecasts in a longer time interval. (Conclusion section – 733,734)

Line 261 - You have used quite a few acronyms without expanding them. How the readers should know them? Please correct this and the rest of acronyms throughout this paper.

Dear Mr. Reviewer, thank you very much for the comment. The acronyms are corrected throughout the entire manuscript.

Figure 2 resolution need to improve. It is not readable. Also, all figure captions need to be below the figures.

Dear Mr. Reviewer, thank you very much for this comment. It is very accurate that the result of the figures could be more optimal, but in R studio, this is the maximum result of the figures that could be extracted. It also has to do with the word format of the magazine.

Dear Mr. Reviewer, thank you very much for this comment. All the captions in the manuscript are now below the figures.

After reading the article, it seems that the title is not the right fit as the article only focuses on prices of corn, wheat, barley and sunflower oil. The current title is misleading; therefore, authors need to come up with a better title. 

Dear Mr. Reviewer, thank you for this very important comment. Very precisely, the title does not correspond to the content of the paper. For this reason, we have proposed this title:

“Agricultural commodities in the context of the Russia-Ukraine war: Evidence from Corn, Wheat, Barley, and Sunflower Oil”.

Dear Mr/Mrs reviewer, we would like to thank you for the comments and suggestions that increase the quality of this study.

Reviewer 3 Report

1. I am happy to see the significance of the work and original work but simultaneously I am not happy with the English part of this. 2. There is no doubt about it that paper is timely; context of the Russia-Ukraine war. 3. Please highlight few more points on the theoretical contribution of this study and also the background of this study. 4. The research gap should be emphasized. 5. There are so many abbreviations. Please prepare a table for this. 6. Because the approached topic is widely discussed in the prior literature, a more comprehensive review is necessary. 7. Please clarify about Data Collection and Sample Characteristics 8. First, the time-frame should be extended. 9. The practical implications formulated within the very last section are quite suitable, but a more compelling discussion is necessary. In view of the aforementioned comments and observations, I consider that the paper requires serious improvements.

Comments for author File: Comments.pdf

Author Response

1. I am happy to see the significance of the work and original work but simultaneously I am not happy with the English part of this.

Dear Mr. Reviewer, thank you for your comments. During this period, we have tried to improve the English part of the article as much as possible with Grammarly premium.

2. There is no doubt about it that paper is timely; context of the Russia-Ukraine war.
3. Please highlight few more points on the theoretical contribution of this study and also the background of this study.

The demand and supply mechanism moves prices toward equilibrium and adjusts market excesses. The outbreak of the COVID-19 pandemic gave a boost to the prices of agricultural products, while the Ukraine-Russia war took them to another level. For this purpose, we have chosen four agricultural products that derive precisely from this conflict. Therefore, the modest role of this work is part of the theoretical contributions that emphasize the significance of the events and their context. At the same time, the results document from a historical perspective how these four critical agricultural products for Ukraine and Russia have influenced each other. (Introduction-77,..,84)

4. The research gap should be emphasized.

Recognizing the importance of these four agricultural commodities for global food security. It is the first empirical work to analyze this concern in the context of the Russia-Ukraine war (Literature review section, 270,.., 272).

5. There are so many abbreviations. Please prepare a table for this.

All abbreviations have been corrected within the manuscript.

6. Because the approached topic is widely discussed in the prior literature, a more comprehensive review is necessary.

„In this context, Ihle et al., (2022) documents the Russian invasion of Ukraine through a concordance index that includes 15 key global commodities. The results indicates that due to this conflict international trade commodities prices show stronger synchronization.“ – (Introduction – 50,…,53)

„On the other hand, Svanidze et al., (2022) investigated the price effects of the 2010 Russian wheat export ban. They conclude that wheat world prices skyrocketed due to export restrictions while price transmission was evident on the other agricultural commodities as well.“ - (Introduction – 53,…,56)

„The Russian invasion of Ukraine generated an imbalance in international trade, where Latin American countries were the most prominent beneficiaries (Ahn et al., 2023).“ – (Literature review – 179,180)

“Compared to Ihle et al., (2022), who analyzes a wider group of agricultural commodities, our study focuses only on the main four of Russia and Ukraine agricultural industry. At the same time, their study investigates price synchronization while ours shocks through VAR and VECM methods.” (Discussion – 689,..,692)

“Contextualizing the conflict in Ukraine, Svanidze et al., (2022) used the 2010 wheat export ban to Russia as a trigger for the prices of other agricultural commodities. In contrast, our study covers a more extended period (January 1990 to August 2023) and focuses only on Corn, Wheat, Barley, and Sunflower Oil. However, both studies use VECM as a model for disequilibrium correction, one for weekly series and the other for monthly series.” (Discussion – 679,…, 683).

7. Please clarify about Data Collection and Sample Characteristics

The identical models were applied to measure the causal relationship among variables from January 1, 1990, to July 1, 2022. The data were collected from the St. Louis FED database (FRED, 2022) based on monthly series. Moreover, each one of them is presented in the same measurement unit, such as US Dollars per metric ton. The individual prices indicate a representative global benchmark of four selected agricultural commodities. The market prices are determined by worldwide exporters and traded regularly on exchanges. Further, the frequencies stand on average monthly prices denominated in nominal U.S. Dollars. At the same time, each variable contains a total of 391 observations and is analyzed in identical currency.

8. First, the time-frame should be extended.

Since our series dates from the early 90s, they carry two shocks that have been important for food security. From January 1, 1995, to January 1, 1996, corn prices increased by 47%, wheat by 27%, and barley by 45%, while sunflower oil prices dropped by 13%. The inflation presented in food commodities during this period is mainly linked to weather conditions and labor shortages in the agricultural sector (Light and Shevlin, 1998). From 1990 to 1995, the former communist countries that were considered the major world suppliers of agricultural commodities were conducting structural reforms in the economy. Among those reforms was the agricultural sector as well, where productivity dropped dramatically due to free market initiatives in the early 90s. The other spikes are related to the global financial crisis of 2008/09, where the prices of these four commodities almost tripled. The financial meltdown of this period, in addition to the devastating effects on the financial system, was quickly transferred to the real economy. The recession of that time spillover to the global economy mainly due to the globalization and interconnected world financial system. (Data section, 314,….., 327)

9. The practical implications formulated within the very last section are quite suitable, but a more compelling discussion is necessary. In view of the aforementioned comments and observations, I consider that the paper requires serious improvements.

The practical implications of this research might be twofold. First, countries facing food security issues must address the problem of agricultural commodities in the context of the events. Second, state authorities should not separate forecasts from agricultural products' influences on each other.

Dear Mr/Mrs reviewer, we would like to thank you for the comments and suggestions that increase the quality of this study.

Reviewer 4 Report

I’m listing some comments, questions, doubts, and observations that I believe are worth to be considered:

 1.      The justification of the chosen commodities (wheat, corn, barley and sunflower oil) is just the fact that Ukraine and Russia are the main global suppliers? Why is any energy commodity not considered (natural gas for  example) or any fertilizers commodity since the three markets are interconnected?

2.      2.      The period of study is from January 1990 to August 2022. It is a wide covered period, considering the monthly type of the series, so there are some important structural breaks included (the crisis of 2008, the Covid-19 pandemic,  the first months of the conflict Russia – Ukraine). Not accounting for them is not a quite realistic assumption (if you made it so!) theoretically, and empirically it provides some nonreliable findings. More specifically:

a)     The Augmented Dickey-Fuller unit root test and the others are not appropriate for structural breaks due to the low power. There are unit root tests to apply in the presence of structural breaks.

b)     Pearson’s correlation coefficients are calculated for the whole period, without making a distinction for subperiods following the structural breaks (you already identified there are structural breaks in the series but do not account for them in your analysis).

c)      The Johansen’s cointegration test also should account for the structural breaks introducing some time-dummy variables for each identified structural break.

d)     You run VAR and also VECM approach? Theoretically, if the series are cointegrated (as you showed with the Johansen’s cointegration test), a VECM representation is the most appropriate for representing the system (you can identify both the short and long run relationships) and get some first hints about some causality linkages.

e)     Granger causality analysis is a pair-wise analysis or a derivation for VECM analysis? You looked for linear causality only? Did you consider some non-linearity since the agricultural commodity market is complex in per se?

3.      3.      You stated in the title: the Russia – Ukraine conflict…How did you assess any change in the series relationships before and after the conflict (considering that you have just the first months of it included in your analysis)?

4.      Too much content explaining the descriptive statistics and a superficial theoretical framework is provided for each used technique.

     What I can suggest firmly, is to reconsider the research design (you can’t report VAR and VECM simultaneously, cointegrated series are better represented through VECM). There is a clear structure to follow: series stationarity > cointegration > (if positive) VECM > FEVD, IRFs and Granger causality; >(if negative) VAR > FEVD, IRFs and Granger causality. Then, for the forecasting procedure, you need to validate empirically the chosen models, if not the forecasted values are not reliable

5.      Consider consolidating the structure of your manuscript in a more elegant and clear way.

6.      Avoid, If possible, too much auto reference!

Author Response

1. The justification of the chosen commodities (wheat, corn, barley and sunflower oil) is just the fact that Ukraine and Russia are the main global suppliers? Why is any energy commodity not considered (natural gas for  example) or any fertilizers commodity since the three markets are interconnected? To give a comprehensive description of the findings, future research might analyse energy commodities but also agricultural fertilizers. This could show broader picture of the impact that energy commodities and artificial fertilizer hold on agricultural commodities. (Conclusion section, 730…., 732)

2. The period of study is from January 1990 to August 2022. It is a wide covered period, considering the monthly type of the series, so there are some important structural breaks included (the crisis of 2008, the Covid-19 pandemic,  the first months of the conflict Russia – Ukraine). Not accounting for them is not a quite realistic assumption (if you made it so!) theoretically, and empirically it provides some nonreliable findings. More specifically:
3. a)     The Augmented Dickey-Fuller unit root test and the others are not appropriate for structural breaks due to the low power. There are unit root tests to apply in the presence of structural breaks.

Dear Mr/Mrs reviewer, thank you for your very accurate comments. We have used Augmented Dickey Fuller Test (ADF) limiting it to two lags. Since ADF is a test that requires the number of lags to be determined. To strengthen if our data pass the stationarity test, we also applied Phillip Peron test (PP) and KPSS test. In this context, PP and KPSS compared to ADF tests automatically in R studio determines the number of lags.

1. b)     Pearson’s correlation coefficients are calculated for the whole period, without making a distinction for subperiods following the structural breaks (you already identified there are structural breaks in the series but do not account for them in your analysis).

Dear Mr/Mrs reviewer, thank you very much for the suggestion. Figure A.1 in the appendix tests for structural breaks which shows that our series do not exceed the 95% confidence band, which shows that our model is valid. Well, in future studies, it would be much better if the series were divided into different periods.

1. c)      The Johansen’s cointegration test also should account for the structural breaks introducing some time-dummy variables for each identified structural break.
2. d)     You run VAR and also VECM approach? Theoretically, if the series are cointegrated (as you showed with the Johansen’s cointegration test), a VECM representation is the most appropriate for representing the system (you can identify both the short and long run relationships) and get some first hints about some causality linkages.

Dear Mr/Mrs reviewer, thank you very much for the suggestion. Your statement that in this case VECM is more suitable for reporting the results is very accurate. For this purpose, we have placed a more pronounced focus on the results of the VECM model. However, most studies also report the results of VAR, which only allow for comparison.

1. e)     Granger causality analysis is a pair-wise analysis or a derivation for VECM analysis? You looked for linear causality only? Did you consider some non-linearity since the agricultural commodity market is complex in per se?

Dear Mr/Mrs reviewer, thank you very much for the suggestion. We have analysed granger tests as a pair wise analysis of VECM.

3.     You stated in the title: the Russia – Ukraine conflict…How did you assess any change in the series relationships before and after the conflict (considering that you have just the first months of it included in your analysis)?

However, the series stand from the early 90s and includes only a few months of the war in Ukraine. In this context, we could not divide the series before and during the conflict in Ukraine due to the lack of data for this period. (The section “Estimated forecasts with VAR and VECM fanchart.” – 650…,652)

4. Too much content explaining the descriptive statistics and a superficial theoretical framework is provided for each used technique.

What I can suggest firmly, is to reconsider the research design (you can’t report VAR and VECM simultaneously, cointegrated series are better represented through VECM). There is a clear structure to follow: series stationarity > cointegration > (if positive) VECM > FEVD, IRFs and Granger causality; >(if negative) VAR > FEVD, IRFs and Granger causality. Then, for the forecasting procedure, you need to validate empirically the chosen models, if not the forecasted values are not reliable

It is evident that after the series passes unit root tests and maintains co-integration, the VECM results are more reliable. However, the VAR estimation results are set only to compare with those of the VECM. (Section (Section - “Estimated VECM results” – 554, …, 556)

5. Consider consolidating the structure of your manuscript in a more elegant and clear way.

Dear Mr/Mrs. Reviewer, we have tried our best to organize the manuscript as best as possible. We completely agree with you that manuscript is not organized in the best possible form.

6. Avoid, If possible, too much auto reference!

Aliu, F., Bajra, U., & Preniqi, N. (2021). Analysis of diversification benefits for cryptocurrency portfolios before and during the COVID-19 pandemic. Studies in Economics and Finance.

We have removed this auto reference from the manuscript, as we considered that it does not fit the context of the work.

Dear Mr/Mrs. Reviewer, thank you for the significant comments on the statistical part, which, as young researchers, will enable us to improve in future studies.

Reviewer 5 Report

The paper focuses on the Russian invasion of Ukraine on February 24, 2022, which accelerated agricultural commodity 8 prices and raised food insecurities around the world. Ukraine and Russia are the main global sup-9 pliers of wheat, corn, barley, and sunflower oil. For this purpose, we investigate the relationship 10 between these four agricultural commodities and at the same time predict their future performance. Overall, the paper is  an interesting piece of work, it needs a lot of work to be done to reach the conclusions that the authors have. 

Comments: 

-I think the paper is weak in the identification strategy. There are missing a number of variables that are determinants that are not taken into account by the authors. First determinant oil prices, second, population changes, I can continue with climate change, exchange rates, transportation costs, wars, COVID-19, stock market. All of these variables are not included in the specification. Obviously, the specification is problematic. 

-The span of the data is very long. There are a lot of events that happened and they need to be taken into account. You need to split the sample into subsamples and test the robustness. For instance, could you have a sample from 1990 to 2000 and then predict the next decade and compare their predictions with the actual predictions? and So on.  If you look at the figures/plots you can notice that the COVID-19 period was one of the main reasons that picked up the prices. How do you take into account the specification in the sample? 

-Did you test for the stationarity of the variables?  You mention in the paper that you do but the results of the tests do not appear in the manuscript. 

-Discussion needs to be done on why the VAR is the best method of forecasting and you do not use other methods like Simple time series like the Naive method, or more complicated Machine learning techniques.  

-I think the motivation of the paper needs to be different. For instance, you can claim that there are some events like the Ukrainian war, COVID, etc that might change food prices, so it will be interesting to investigate the research question and clarify clearly their contribution to the literature. Perhaps a table with other articles that they have done some similar and the methods they used for the forecasting. 

-The conclusions need to be rewritten and be more realistic. For instance, the focus could be on regions like Africa where the problems of food are more problematic and the cost will become higher and more propositions for the food security of the poor and discussion about the inequality that it can raise in the countries. 

-Some stylized facts could motivate the paper and persuade the reader of the contribution to the paper. 

Author Response

-I think the paper is weak in the identification strategy. There are missing a number of variables that are determinants that are not taken into account by the authors. First determinant oil prices, second, population changes, I can continue with climate change, exchange rates, transportation costs, wars, COVID-19, stock market. All of these variables are not included in the specification. Obviously, the specification is problematic. 

Dear Mr/Mrs Reviewer, we completely agree with your comments. In future studies, it might be very interesting to integrate these variables. Therefore, we are placing it in the literature review section as limitations of our work.

“However, to generate a more comprehensive approach, other scholars may include shocks such as COVID-19, climate change, exchange rates, transportation costs, and wars.” (Literature review – 272,273)

-The span of the data is very long. There are a lot of events that happened and they need to be taken into account. You need to split the sample into subsamples and test the robustness. For instance, could you have a sample from 1990 to 2000 and then predict the next decade and compare their predictions with the actual predictions? and So on.  If you look at the figures/plots you can notice that the COVID-19 period was one of the main reasons that picked up the prices. How do you take into account the specification in the sample? 

Dear Mr/Mrs Reviewer, thank you for this very meaningful comment. We thought of dividing them into two time series. However, because the frequencies are monthly, we did not have enough observations to run the VAR and VECM model. On the contrary, it would be ideal if we could divide the time series into two parts.

“To examine the shocks more accurately, isolating the frequencies into two diverse time periods might provide in depth investigation. Analyzing the series from the beginning of the 90s to the end of 2000 would provide more realistic results. Since this period corresponds with the structural reforms in the Eastern Europe countries. Considering the stability of the VAR and VECM model (data insufficiency), this since the frequencies were monthly.” – (Data section – 334,…,339)

-Did you test for the stationarity of the variables?  You mention in the paper that you do but the results of the tests do not appear in the manuscript. 

Dear Mr/Mrs Reviewer, Thank you very much for your comment. We have performed three unit root tests to return the data to Stationary.

On the other hand, the unit root tests are important for identifying the stationary issue of the time series. For this purpose, three types of tests were used to identify this prob-lem: ADF, PP, and KPSS. The four series do not pass stationary tests at the level but only after the first differentiation. After the first differentiation, ADF and PP tests indicate a p-values lower than 5% significance level, while for KPSS higher than 5%. (VAR estimation results section – 456,..,461)

-Discussion needs to be done on why the VAR is the best method of forecasting and you do not use other methods like Simple time series like the Naive method, or more complicated Machine learning techniques.  

Dear Mr/Mrs Reviewer, Thank you very much for your comment. Let's be honest, we still don't know the models associated with neural networks. Although in the future studies we have a lot of interest to implement.

“To measure the accuracy of these predictions, other studies can verify them through more complex models such as neural networks.” (Section Estimated forecasts with VAR and VECM fanchart -652,..,654)

-I think the motivation of the paper needs to be different. For instance, you can claim that there are some events like the Ukrainian war, COVID, etc that might change food prices, so it will be interesting to investigate the research question and clarify clearly their contribution to the literature. Perhaps a table with other articles that they have done some similar and the methods they used for the forecasting. 

Dear Mr/Mrs Reviewer, Thank you very much for your comment.

This study is mainly motivated by two shocks that accelerated the prices of agricultural commodities. First, the period of the COVID-19 pandemic, which limited people's movements, slowed down world trade and the migration of workers. Second, the Russian invasion of Ukraine that further raised food insecurity issues and impeded global inflation. (Literature review section – 274,.., 278)

-The conclusions need to be rewritten and be more realistic. For instance, the focus could be on regions like Africa where the problems of food are more problematic and the cost will become higher and more propositions for the food security of the poor and discussion about the inequality that it can raise in the countries. 

Dear Mr/Mrs Reviewer, Thank you very much for your comment.

“The results are addressed with particular emphasis to African countries where the food security problems are more pronounced.” (Conclusion section – 706,-707)

-Some stylized facts could motivate the paper and persuade the reader of the contribution to the paper. 

Dear Mr/Mrs Reviewer, thank you very much for your comments, which have made our work take a better shape.

Round 2

Reviewer 2 Report

Dear Authors

Following are some minor comments:

1) All authors are from the same institute and department so no need to repeat it three times. Write it as follows:

Florin Aliu, Jiri Kucera and Simona Haskova

Institute of Technology and Business in České Budějovice, Okružní 517/10, České Budějovice, 370 01, Czech republic, (add all email ids)

2) All Figure captions need to be below the figures. No need to touch table captions as they are fine.

3) This article might be useful https://doi.org/10.3390/foods11142098

4) Also, while responding to reviewers don't assume that all reviewers are male. You have quite often have used Mr Reviewer. You need to use gender inclusive language. 

Regards

Author Response

Dear Mr/Mrs reviewer, thank you very much for your comment.

We have made all the formal changes you suggested.

Thank you for the suggestion to use your suggested article in our article. This article was added to our manuscript after consideration.

We apologize for not using gender inclusive language.

Reviewer 5 Report

Thanks for the authors for their effort. However, the comments are not addressed as it is expected. 

I think the comments for additional variables like oil prices and exchange rates are not addressed properly. 

The sample can be split since you have monthly level data and not yearly level data. 

Stylized facts are not provided. 

Author Response

Comment 1

I think the comments for additional variables like oil prices and exchange rates are not addressed properly. 

Answer 1

Dear Mr/Mrs reviewer, thank you very much for your comment. We fully agree that we have not been able to integrate these two essential elements. The reason is mainly related to unrestricted VAR, which does not have restrictions in the system. Through this, it enables all variables to influence each other. Your valuable comment has helped us to implement the Structural VAR model in the future to measure only the impact of oil and exchange rates on agricultural commodities.

Comment 2

The sample can be split since you have monthly level data and not yearly level data. 

Answer 2

Dear Mr/Mrs. Reviewer, thank you very much for your comment. It is correct that it would be excellent if the data were split. During this period, we tried in the R program to split the series, but it was impossible. During our research, we noticed this is possible in some other programs, but in R studio, we have yet to find a code enabling splitting series.

Comment 3

Stylized facts are not provided. 

Answer 2

Dear Mr./Mrs. Reviewer, thank you very much for the comment. We also thank you for the comments regarding the statistical part that will open new perspectives in our studies.