Comparison of Causality of Temperature and Precipitation on Italian Ryegrass (Lolium Multiflorum Lam.) Yield between Cultivation Fields via Multi-Group Structural Equation Model Analysis in the Republic of Korea
Round 1
Reviewer 1 Report
With the Changes made, I Think the manuscript is good enough.
Author Response
Reviewer 1
With the Changes made, I Think the manuscript is good enough.
Response: Thanks for your comment. I will do my best until it is published.
Reviewer 2 Report
Much effort and improvements were made to clarify the analytical approach and justify the imposed SEM structures. Some more editing is required to improve the readability of this article. Some points need to be better clarified and there are syntax and grammatical problems, notably with some of the newly added text. Examples are provided in minor comments below.
Note that all line references are for the version showing tracked changes.
lns 31-33: It is unclear what is being referred to with "the structure". Please clarify this and that, I think, this sentence is explaining future research.
lns 56-58: The countries did not develop these models but models were developed for crops in these countries- please re-phrase. Also, omit "through many studies and are leading the world". Its irrelevant and unsubstantiated here.
Lns 59-61: How are economic and ecological “outcomes” used as indicators in these models and then why does this lead to your next conclusion supporting your approach? These sentences and logical progression of ideas are unclear.
Ln 80: Change “are more complex than mankind knows” to “are extremely complex”.
Ln 81: SEM cannot ‘identify’ this complexity per se. Rephrase.
Ln 157: What does “where water is hold with facility” mean?
Ln 324: What are “other groups”? Specify.
Lns 352-326: “Otherwise, it is suggested that the role scheme based on growth stages instead of seasons.” This sentence is very challenging to interpret. Re-phrase.
Lns 326-327: Incomplete sentence.
Lns 327-358: Again, this sentence structure is problematic and challenging to understand.
Ln 339: “it plans”? What is “it” referring to here?
Author Response
Reviewer 2
Much effort and improvements were made to clarify the analytical approach and justify the imposed SEM structures. Some more editing is required to improve the readability of this article. Some points need to be better clarified and there are syntax and grammatical problems, notably with some of the newly added text. Examples are provided in minor comments below.
Response: The all comments were checked carefully.
lns 31-33: It is unclear what is being referred to with "the structure". Please clarify this and that, I think, this sentence is explaining future research.
Response: rewrote. Lines 30-32
In the future, the structure established in this study will be expanded by adding variables related to soil physical properties from soil information system and cultivation management from survey sheets.
lns 56-58: The countries did not develop these models but models were developed for crops in these countries- please re-phrase. Also, omit "through many studies and are leading the world". Its irrelevant and unsubstantiated here.
Response: rewrote the explanation of the models specifically, and removed "through many studies and are leading the world". Line 56-61.
The potential increase in biomass could be estimated by a single model in the erosion-productivity impact calculator (EPIC) [9]. In the decision support system for agrotechnology transfer- cropping system model (DSSA-CSM), soil, crop, weather and management were considered as a primary scientific components [10]. The agricultural production system simulator (APSIM) were developed mainly for food and cash crops to simulate growth and development using many economic, biological and environmental modules as an indicator [11,12].
Lns 59-61: How are economic and ecological “outcomes” used as indicators in these models and then why does this lead to your next conclusion supporting your approach? These sentences and logical progression of ideas are unclear.
Response: inputted the idea of logical connection between this study and others. Lines 62-64
These models have the advantage of using various and precise indicators, but the module focuses on calculations to make accurate predictions instead of describing the relationships between the indicators.
Ln 80: Change “are more complex than mankind knows” to “are extremely complex”.
Response: corrected. Line 83
are more complex than mankind knows -> are extremely complex
Ln 81: SEM cannot ‘identify’ this complexity per se. Rephrase.
Response: rewrote. Lines 83-84
The complexity caused by various cause-and-effect relationships can be efficiently constructed by using SEM
Ln 157: What does “where water is hold with facility” mean?
Response: rewrote. Line 158
where water is hold with facility -> with a large water holding capacity
Ln 324: What are “other groups”? Specify.
Response: inputted. Lines 312-314
If not, it is proposed to explore other groups that could be applicable to the same structures, such as climate classification, terrain classification
Lns 352-326: “Otherwise, it is suggested that the role scheme based on growth stages instead of seasons.” This sentence is very challenging to interpret. Re-phrase.
Response: rewrote. Lines 315-316
Otherwise, growth stages may be used for the purpose of dividing the whole growth period instead of the seasons.
Lns 326-327: Incomplete sentence.
Response: rewrote. Lines 310-311, 314-315, 316-317
The first is to cultivate in a different environments that can be compared, such as upland fields and paddy fields with rice-winter crop rotation system.
The second is the distinct seasonal role of the cropping system, such as autumn seeding, overwintering and spring harvesting.
The final condition is to construct complex causality structures with various cause-and-effect relationships.
Lns 327-358: Again, this sentence structure is problematic and challenging to understand.
Response: rewrote. Lines 317-319
SEM is effective when many variables are linked continuously through three or more cause-and-effect relationships that the direct/indirect effects can be estimated.
Ln 339: “it plans”? What is “it” referring to here?
Response: corrected. Line 330
Meanwhile, it is planned to expand the structure by adding variables related to soil physical properties from soil information system and related to cultivation management collected from the survey sheet to the structure centered on climatic variables.
Author Response File: 
Author Response.docx
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
Round 1
Reviewer 1 Report
Much editing is required to improve the quality of English and permit a more effective review of the study. Furthermore, the authors should address the major comments below. I also provided some minor suggestions to improve on the clarity of the manuscript.
Major comments:
The SEM needs better explanation. Why do autumn and spring climate variables allowed to covary, as indicated in Figure 1? There are a lot of assumptions going into the path directions between precipitation and temperature within the seasons. The causality from the latent precipitation variables to latent temperature variables needs to be explained. For instance, on lines 22-24, its stated precipitation has an indirect effect on yield via temperature. Yet, I missed any discussion in the manuscript on why this is or could be.
As the authors present this analytical approach to being novel, I think there should be at least a discussion as to how the SEM approach permitted improved understanding of these systems, in contrast to previous efforts.
Minor comments:
ln 19: "big data" is vague. It would be more effective to specify number of observations or data.
ln 25-26: Please improve this sentence. "while after wintering" is stated twice. I suggest replacing the second "while after wintering" to "between the two field types" (I assume that is what is being referred to as "somewhat similar").
ln 30: This final statement: “contribute to expanding the structure in conjunction with other big data in the future” is very difficult to understand. Please re-work for clarity.
lns 70-71: What do you mean by the effects being more complex than natural ecosystems? This doesn't seem intuitive.
ln: 95: Why call climate data big data? Why not just climate data?
ln 97: Change "this data" to "these data".
lns 134-135: These results are t-test comparisons. Why make "due to" statements? At least indicate that this is what you are suggesting is causing this difference.
lns 145-147: Again, how are causal conclusions being drawn from Table 1 which reports only results from t-tests?
ln 157: What is meant by "all climatic variables are not independent mutually"? Why mutually?
lns 161-163: Please improve this sentence. I find it very challenging to understand.
ln 185 and elsewhere: What do you mean by "effective"? I don’t think this word is used correctly here.
ln 186: difference in what?
ln 194: Table 3 caption - Specify these are path coefficients. Correct?
ln 209: Specify what is meant by "prepare for wintering".
224-225: Figure 2 caption - explain what the arrow types mean, specifically the dashed arrows. It suggests that the paths were retained despite non-significant coefficients in the model.
ln 236: "lean to the right" is only a relative direction in the figure and doesn't really say anything. Need to describe more concretely.
lns 236-237: What fluctuations?
Lns 244-246: Figure 4 caption needs a lot more explanation. Explain the gradient, the red and blue lines on the left. What are the red ovals on the right map (very coarsely) indicating?
Author Response
# Reviewer 1’s comments
Comments and Suggestions for Authors
Much editing is required to improve the quality of English and permit a more effective review of the study. Furthermore, the authors should address the major comments below. I also provided some minor suggestions to improve on the clarity of the manuscript.
Response: Before we submitted this manuscript, we had already taken English correction services from an English calibration company for words and grammar. However, we paid more attention to better representations and descriptions for the readers in the first revision. Moreover, we checked the reviewer’s comments carefully.
Major comments:
The SEM needs better explanation. Why do autumn and spring climate variables allowed to covary, as indicated in Figure 1? There are a lot of assumptions going into the path directions between precipitation and temperature within the seasons. The causality from the latent precipitation variables to latent temperature variables needs to be explained. For instance, on lines 22-24, its stated precipitation has an indirect effect on yield via temperature. Yet, I missed any discussion in the manuscript on why this is or could be.
Response: Since the figure 1 led a misunderstanding for the structure, it was modified to include an all paths and error terms, and description was added. Lines 183-193
The two-way arrow in Figure 1 represents the covary of the error term, not between variables. Therefore, the interaction between the error terms indicates dependency between variables, and it does not represent an effect between variables. The effect between variables could be reflected by the paths between factors in structural part. In general, longitudinal data, sometimes referred to as panel data, track the same sample at different points in time. In this study, growing days, accumulated temperature, precipitation amount and precipitation days were measured by same method (same circulation) at different season. Therefore, the variables were set by longitudinal form in SEM.
The climate variables were set by longitudinal form in SEM because they were calculated by the same method at different seasons. Thus, the residual terms in measurement part ( ) were paired based on the season and variables, respectively. The D matrix in Eq. (1) indicates the interaction between residual terms, for example, was interaction between and . These interactions help to precisely estimate the coefficient of seasonal effects (green colored paths) by reflecting the longitudinal structure. Although autumn and the next spring are not continuous seasons, winter is not a growing season for winter crops, therefore, the seasonal effect was given only between growing seasons in this study. Hence, it was hypothesized that if autumn growing period was long enough, autumn growth and development would lead to yield through growth and development in the next spring, else if, yield would depend largely on spring growth and development.
Response: For the path directions between precipitation and temperature within the seasons, the reason to set the form from precipitation to temperature was inputted in the new paragraph. Lines 194-203
The direction of the arrow between temperature and precipitation within seasons (blue colored paths) was determined to take into account the precipitation mechanism. As a rule, the mechanism in the order of temperature increase, water evaporation from the atmosphere and surface, moisture condensation in the atmosphere, and precipitation does not occur in a short time [31]. In particular, the growing season of winter crops in Korea is not as high in atmosphere and surface temperature as in summer, so the mechanism from temperature to precipitation occurrence will take longer time. In contrast, a physical rationale of the relationship from precipitation to temperature is that precipitation may lead to soil moisture which may, in turn, affect temperature by controlling the partitioning between the sensible and latent heat fluxes [32], and it will happen in a relatively short time. Therefore, we judged that the direction from precipitation to temperature was appropriate.
As the authors present this analytical approach to being novel, I think there should be at least a discussion as to how the SEM approach permitted improved understanding of these systems, in contrast to previous efforts.
Response: Added the difference from previous studies, crop growth model, to the introduction. Lines 55-60
Since the 1990s, countries such as the Netherlands, the United States, and Australia have developed a model for precise prediction of crop productivity through many studies and are leading the world [9,10]. The crop growth models were developed mainly for food and cash crops to simulate growth and development using many economic and ecological outcomes as an indicator [11,12]. Therefore, in this study, we focused on various relationships between variables rather than on the variable generation.
Minor comments:
ln 19: "big data" is vague. It would be more effective to specify number of observations or data.
Response: Inputted. Line 16
The sample size in this study can be marked for the forage data, not climate big data. Because, before merging the climate big data to forage data, we couldn’t count the sample size. In the server of weather information system, the information have been recorded six times in every day. We just download the information by approach to the server through open-API. Thus, sample size (n=728) was inputted for the forage data.
The raw data (n=728) on forage
ln 25-26: Please improve this sentence. "while after wintering" is stated twice. I suggest replacing the second "while after wintering" to "between the two field types" (I assume that is what is being referred to as "somewhat similar").
Response: corrected. Line 25
while after wintering -> between the two field types
ln 30: This final statement: “contribute to expanding the structure in conjunction with other big data in the future” is very difficult to understand. Please re-work for clarity.
Response: rewrote. Lines 30-32
The structure is being expanded by adding variables related to soil physical properties from soil information system and cultivation management from survey sheets, respectively, to the structure established in this study.
lns 70-71: What do you mean by the effects being more complex than natural ecosystems? This doesn't seem intuitive.
Response: rewrote. Lines 77-78
The effect and flow of climates on crop productivity in the natural eco-system are more complex than mankind knows because of the mixed factors
ln: 95: Why call climate data big data? Why not just climate data?
Response: In this study, the terminology, ‘climate big data’, was used to distinguish from climate data. In the metadata, each experiment was carried out based on different seeding and harvesting dates, year, location, etc. Therefore, we judged that the terminology, ‘climate big data’, is appropriate, since it consisted of different 728 climate data due to 728 experiments over 30 years in 10 locations, while the terminology, ‘climate data’, is appropriate for a single experiment.
ln 97: Change "this data" to "these data".
Response: corrected. Line 99
This data -> these data
lns 134-135: These results are t-test comparisons. Why make "due to" statements? At least indicate that this is what you are suggesting is causing this difference.
Response: rewrote to focus on comparison. Lines 141-145
The yields were greater in the upland field than in the paddy field, while the dry-matter rate was greater in the paddy field (20.6%) than in the upland field (18.7%), which means that the paddy fields contained more soil moisture contents than the upland fields. This is because continuous water injection was required when growing crops in the paddy fields [28]. It is likely that the soil moisture content was still high while cultivating winter crops, even if the water was drained after the rice harvesting since the paddy fields also hold water for four to six months in the Republic of Korea.
lns 145-147: Again, how are causal conclusions being drawn from Table 1 which reports only results from t-tests?
Response: rewrote. Lines 149-153
Furthermore, all climate variables were different between upland fields and paddy fields (p < 0.05). Even if there were the same area, it is likely that the weather conditions are different depending on the topological features between upland fields and paddy fields. In general, the upland fields are distributed in hilly and slope areas, but the paddy fields are distributed on relatively low and flat land where water is hold with facility.
ln 157: What is meant by "all climatic variables are not independent mutually"? Why mutually?
Response: removed, ‘mutually’. Line 165
In the natural eco-system, all climatic variables are not independent,
lns 161-163: Please improve this sentence. I find it very challenging to understand.
Response: rewrote. Line 174-176,
The relationship between SPA and the fourth factor in the paddy fields was weak (|coefficient| < 0.7), which lowered the commonality for the spring precipitation factor.
ln 185 and elsewhere: What do you mean by "effective"? I don’t think this word is used correctly here.
Response: rewrote. Lines 218-220, Line 222, Line 223
Thus, the effect of precipitation was hypothesized that it could only be transferred to the yield through proper temperature and sufficient growing period.
ln 186: difference in what?
Response: inputted. Lines 226-227
In particular, the difference in path from temperature to yield between upland fields and paddy fields was remarkable in the autumn.
ln 194: Table 3 caption - Specify these are path coefficients. Correct?
Response: rewrote. Line 209
Table 3. Comparison of coefficient for pathways from factor to factor, from factor to variable, between upland fields and paddy fields
ln 209: Specify what is meant by "prepare for wintering".
Response: rewrote. Lines 243-247
The impacts of autumn temperature and precipitation on IRG yield in both fields were weak compared to spring temperature and precipitation, respectively, which means that yield was more sensitive to spring climate factors than autumn climate factors. The sensitivity means importance. Therefore, for IRG yield in both fields under the good wintering, the spring climate factors were more important for the IRG yield than the autumn climate factors.
lns 224-225: Figure 2 caption - explain what the arrow types mean, specifically the dashed arrows. It suggests that the paths were retained despite non-significant coefficients in the model.
Response: added the explanation of arrow in the caption of Figure 2, and removed the non-significant paths. Lines 260-263
Figure 2. Path diagram of climatic factors on Italian ryegrass yield in structural part (left is upland fields, right is paddy fields): direct effects from climate to yield (red), from precipitation to temperature (blue), seasonal effect from autumn to next spring (green)
ln 236: "lean to the right" is only a relative direction in the figure and doesn't really say anything. Need to describe more concretely.
Response: rewrote. Lines 274-277
In the paddy field (Figure 3 C, D), there were peaks with high yield in several places, not in certain locations. Especially in the autumn, DMY showed a tendency to increase in range of 7-14 of the daily temperature, but the trend stopped increasing because the daily temperature was not wide as spring temperature.
lns 236-237: What fluctuations?
Response: rewrote. Lines 277-279
Thus, yield fluctuations, which tend to increase IRG yield with increasing precipitation at high temperatures, were not clear and the yield trends were inconsistent in autumn and spring.
Lns 244-246: Figure 4 caption needs a lot more explanation. Explain the gradient, the red and blue lines on the left. What are the red ovals on the right map (very coarsely) indicating?
Response: added the gradient’s name in the left figure, inputted the explanation about red and blue marks in the caption, and removed the red ovals in the right map. Lines 283-286
Figure 4. Maps of cultivation suitability classification of Italian ryegrass based on autumn accumulated temperature (red: paddy fields, blue: upland fields) and topography in the Republic of Korea (left is cultivation suitability map, right is topography map)
Author Response File: Author Response.docx
Reviewer 2 Report
An interesting and well written manuscript. However, in the present form it only focus on Korea. I suggest the authors add sections in the manuscript so that they also present the results that are of relevance to the research community and to the agricultural community outside Korea.
I think in general the paper was good and well written and the experiments well carried out. My concern is that the paper only deals with cultivation in various parts of Korea which has limited interest for the majority of the readers, so I suggest to incorporate sections of what this might mean in the other parts of the world as well as information as to how the statistical methods used can be to benefit also other systems and cultivation regions.
Author Response
Review 2’s comments
Comments and Suggestions for Authors
An interesting and well written manuscript. However, in the present form it only focus on Korea. I suggest the authors add sections in the manuscript so that they also present the results that are of relevance to the research community and to the agricultural community outside Korea.
Response: In accordance with the reviewer’s proposal, we made the new subsection (3.4) newly in the results and discussion section, Lines 300-314.
3.4. proposals and implications
This study was carried out to compare the direct/indirect effects of temperature and precipitation on IRG yield between upland fields and paddy fields in the Republic of Korea via multi-group SEM. Although the results were focused on the Korean areas, the method of this study could be proposed to the research community outside Korea that is interested in the following conditions: First, cultivation under rice-winter crop rotation system in paddy field. Unfortunately, the rice-winter crop rotation system is popular only in southern China, Japan and Korea. If not, it is proposed to explore other groups that could be applicable to the same structures. Second, clear seasonal role of cropping system, such as autumn seeding, overwintering and spring harvesting. Otherwise, it is suggested that the role scheme based on growth stages instead of seasons. Finally, construction of complex causality structures with various cause-and-effect relationships. It is better to three or more continuously connected cause-and-effect relationships with many variables. As a form of group, it also could be considered as a multi-group SEM that aims to be compared, as well as a multi-level SEM that can be entered as an explanatory variable to estimate its effect rather than a classification, and a multi-stage SEM that can identify the flow according to an ordered group [33].
I think in general the paper was good and well written and the experiments well carried out. My concern is that the paper only deals with cultivation in various parts of Korea which has limited interest for the majority of the readers, so I suggest to incorporate sections of what this might mean in the other parts of the world as well as information as to how the statistical methods used can be to benefit also other systems and cultivation regions.
Response: This is also explained in the newly added section, Lines 315-324
In general, the structure of the SEM can be applied to various studies depending on the characteristics of measurements. For example, it is expected that if the measurements are related to agricultural economic feasibility, such as income, production costs, import prices, distribution costs, etc., part of the agricultural economic system can be structured. In this study, cultivation field types were considered for classification purposes; however, there was a limitation in that they could not be included in the structure as a variable. To overcome this limitation, it will be necessary to select and develop various measurements that can reflect the characteristics of the field types as a quantitative variable. Meanwhile, it plans to expand the structure by adding variables related to soil physical properties from soil information system and related to cultivation management collected from the survey sheet to the structure centered on climatic variables.
Author Response File: Author Response.docx
