Determining Key Agricultural Strategic Factors Using AHP-MICMAC

1.                    

In   my opinion, the article is valuable because it introduces an integrated   method using MICMAC and AHP techniques to deal with understanding the key   strategic variables of agricultural system. It gives an innovative approach   to understanding complex relationships in the agricultural environment.

However,   the chapter Conclusions is lacking a synthetic, brief discussion of the   obtained research results, which would constitute a valuable summary of the   article allowing for making conclusions.

After   expanding the Conclusions chapter, because of the interesting presentation   and discussion of the research problem.

Modified:

Thanks   for your nice feedback. We have tried to address your concern about the “Conclusion   section”. In this regard we added a   synthetic and brief discussion of the research results as follows:

“Agricultural systems, especially in developing   countries, are typically complex, and when forming strategies and scenarios,   available methods have failed to reveal the essence of such complex systems.   Therefore, the main objective of this study was to address this problem by   using an integrated method. We integrated the MICMAC and AHP methods, using   the MICMAC to determine the various classifications of variables and the AHP   method to apply weight to these different variables. The case of the   agricultural system of Iran was used to indicate an application of this new   integrated method. The results revealed that the various types of variables   in agricultural systems, from "actual direct influence" to   "potential indirect dependence", did not present similar influences   or dependencies on each other. As a result, the ranks of key variables may   change by applying the weight of different classification types of variables.   Additionally, the AHP-MICMAC method allows us to have a total priority for   each variable that helps policy and decision makers to recognize the most   important variable according to its dependency and influence on other   variables.

For example, in Iran case, based on the total   priority scores of the strategic variables, “farmers organizing and   institutionalizing”, “farmers' knowledge, awareness, and skills” and   “disasters” respectively are three main variables that describe the conditions   and the dynamics of the other variables of agricultural system. Therefore,   they have a critical role in agricultural growth and development. “Government   policies and programs” is the most important intermediate variable for   agricultural development. It means, the instability of the policies and   programs will have high flow throughout the rest variables of the system.   “farmers' interest and motivation”, “Storage facilities”, and “crop   insurance” are three main high depended variables that are influenced by both   input and intermediate variables. There also are some variables, such as   “agricultural support system”, “water efficiency”, “agricultural research”,   “pricing system”, “rural welfare and comforting”, “agricultural land area”,   “transportation and communications”, and “trade incentives and restrictions”,   that they should be recognized and studied more closely in the future.

According to expert opinion, the use of the   AHP-MICMAC method has led to a more realistic ranking of the variables and   this combination has been able to improve results. It then facilitates the   ranking of the variables according to their different types of influences and   dependency weights. Without a doubt, any improvement in our understanding of   the key variables of a system will lead to forming better scenarios and   strategies for development of that system. Although the AHP-MICMAC method is   more capable of illustrating the complexities among the variables than many   other current methods, it still needs to be developed further so that it can   better reflect the interdependency of variables including economic, social,   environmental, religious, etc. which can lead to risky, diverse and complex   agriculture in developing countries such as Iran. In this regard, performing   a study in order to compare the effectiveness of various methods, such as   system dynamic modeling, AHP-MICMAC or cross impact analysis to display these   complexities, is very crucial. ”

Please   see pages 15-16.

2.                    

Lines   155-164 - I suggest reducing the size of the font (unification with the whole   text).

Unified:

The   size of the fonts was reduced.  

Please   see page 5.

3.                    

Table   7 - it could be better discussed and explained in the text.

Discussed:

Thanks   for your comment. Constructing this Table will help researcher to calculate   the overall priority of each variable. We discussed and explained this table   in text as follows:

“   7) Compute the matrix of the variables’ scores (construct the matrix S):   Matrix S is a matrix that includes the matrix R_norm and the vector   of criteria weights (w). Table (7) represents a part of this matrix. The   first row is include the criteria weights and the rest rows are include the   normalized scores of the variables. Constructing this Table will help   researcher to calculate the overall priority of each variable.”  

Please   see page 10

4.                    

Figure   4 is missing the (b) symbol / sign.

Modified:

Thanks   for your comment. Symbol (b) was added to Figure 4.

Please   see page 14

5.                    

The   Conclusions is too general, it should be further elaborated. Please refer in   more detail to the obtained research results.

Modified:

Thanks   for your comment. Conclusion section improved as follows:

“Agricultural systems, especially in developing   countries, are typically complex, and when forming strategies and scenarios,   available methods have failed to reveal the essence of such complex systems.   Therefore, the main objective of this study was to address this problem by   using an integrated method. We integrated the MICMAC and AHP methods, using   the MICMAC to determine the various classifications of variables and the AHP   method to apply weight to these different variables. The case of the   agricultural system of Iran was used to indicate an application of this new   integrated method. The results revealed that the various types of variables   in agricultural systems, from "actual direct influence" to   "potential indirect dependence", did not present similar influences   or dependencies on each other. As a result, the ranks of key variables may   change by applying the weight of different classification types of variables.   Additionally, the AHP-MICMAC method allows us to have a total priority for   each variable that helps policy and decision makers to recognize the most   important variable according to its dependency and influence on other   variables.

For example, in Iran case, based on the total   priority scores of the strategic variables, “farmers organizing and   institutionalizing”, “farmers' knowledge, awareness, and skills” and   “disasters” respectively are three main variables that describe the   conditions and the dynamics of the other variables of agricultural system.   Therefore, they have a critical role in agricultural growth and development.   “Government policies and programs” is the most important intermediate   variable for agricultural development. It means, the instability of the   policies and programs will have high flow throughout the rest variables of   the system. “farmers' interest and motivation”, “Storage facilities”, and   “crop insurance” are three main high depended variables that are influenced   by both input and intermediate variables. There also are some variables, such   as “agricultural support system”, “water efficiency”, “agricultural   research”, “pricing system”, “rural welfare and comforting”, “agricultural   land area”, “transportation and communications”, and “trade incentives and   restrictions”, that they should be recognized and studied more closely in the   future.

According to expert opinion, the use of the   AHP-MICMAC method has led to a more realistic ranking of the variables and   this combination has been able to improve results. It then facilitates the   ranking of the variables according to their different types of influences and   dependency weights. Without a doubt, any improvement in our understanding of   the key variables of a system will lead to forming better scenarios and   strategies for development of that system. Although the AHP-MICMAC method is   more capable of illustrating the complexities among the variables than many   other current methods, it still needs to be developed further so that it can   better reflect the interdependency of variables including economic, social,   environmental, religious, etc. which can lead to risky, diverse and complex   agriculture in developing countries such as Iran. In this regard, performing   a study in order to compare the effectiveness of various methods, such as   system dynamic modeling, AHP-MICMAC or cross impact analysis to display these   complexities, is very crucial.”

1.                    

In my opinion, the paper in its current form is well   written. If anything, I would suggest the authors including some information   on the advantages and limitations (potential subjective nature, for example)   of both methods.

Corrected:

Thanks to your nice comment, we added some more   information about the advantages and limitations of these method as follows:

“Each of these methods alone has advantages and   limitations for example MICMAC can investigate multiple variables at the same   time, but it does not give an overall priority score for each variable. On   other side, AHP considers only direct impact of variables, but it gives and   overall priority score for each variable. This study has tried to overcome   these constraints and to consider their advantages by combining them and   proposing an integrated method. It is our hope that this new integrated   method will supply instructions for the development of agriculture, and find   wider applications in complex systems.”

Please see page 2.