A Fuzzy-AHP Multi-Criteria Decision-Making Approach for a Sustainable Supply Chain of Rice Farming Stakeholders in Edu-Patigi LGA, Kwara State, Nigeria
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1. Introduction
2. Literature Review
2.1. Food Security
2.2. Sustainable Supply Chain Management
2.3. Triple Bottom Line
3. Methodology
3.1. Study Site
3.2. Data Collection Method
Data were drawn from responses to a structured questionnaire administered to small-scale farmers who are members of the Rice Farmers Association of Nigeria (RIFAN) within the Edu-Patigi local government area of Kwara state. These farmers fall under the jurisdiction of the Kwara State Ministry of Agriculture, Agricultural Development Project Extension (ADP); the association is recognized by the government and it consults the government on policies regarding rice fields, cultivation, warehousing, and exports. Five RIFAN members’ responses about the 21 questionnaire items that focused on stakeholders’ decision-making activities were used for the study. A 6-point Likert rating scale was adopted, ranging from 1 (strongly disagree) to 6 (strongly agree).
3.3. Multi-Criteria Decision Making
is a set of ordered pairs, where X is a subset of the real numbers 𝓡, and μA (x) is called the membership function, which assigns to each object “x” a grade of membership ranging from zero to one.
Triangular Fuzzy Numbers (TFNs)
With −∞ < p′; ≤ q′ ≤ r′ ≤ ∞
3.4. Fuzzy Analytical Hierarchy Process (AHP)
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Define the problem: Clearly define the problem you want to solve and identify the criteria and sub-criteria that will be used to evaluate the alternatives.
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Create a pairwise comparison matrix: Create a matrix that compares each criterion and sub-criterion with every other criterion and sub-criterion. Use a scale to rate the relative importance of each pair.
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Calculate the priority vectors: Use the pairwise comparison matrix to calculate the priority vectors for each criterion and sub-criterion.
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Calculate the consistency ratio: Check the consistency of the pairwise comparison matrix by calculating the consistency ratio. A consistency ratio of less than 0.1 is considered acceptable.
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Calculate the fuzzy weights: Use the priority vectors and the triangular fuzzy numbers to calculate the fuzzy weights for each criterion and sub-criterion.
3.5. Selection Criteria
3.5.1. Social Criteria
3.5.2. Economic Criteria
3.5.3. Environmental Criteria
The criteria adopted in this study are in line with the overall sustainable development goals and applicable to the area of study. The criteria obtained from the questionnaire were converted to scores in order to reflect the rice farmers’ decisions.
4. Results
5. Discussion
Tax payment, quality assurance, and government intervention weigh <0.1; this is an indication that they are not prioritized decisions among the respondents and there are no platforms to enable tax payment and quality assurance. Quality assurance should be a means of checking produce for color, stones, and brokenness. A sound quality assurance platform will give rice produced in the region a competitive edge in the international market. Government intervention weighs 0.057, which implies that no policies guide the activities of the RIFAN members and other stakeholders across the rice value chain.
Chemicals such as NPK are used to manage weeds, although the recommended proportion was not measured in this research. Power usage and seed selection weighed 0.082 and 0.055, respectively; this is <0.1, which indicates that little attention is paid to decisions on the choice of seedlings and power usage. RIFAN members indicated that they use fuel because there is no good supply of electricity. The weight of seed selection implies that members obtain seeds from unverified suppliers; such seedlings affect productivity and grain quality, a reason for poor-quality grain.
6. Conclusions
This study adopted MCDM fuzzy-AHP to assess decision making in relation to the adoption of sustainable practices in the rice supply chain from the perspective of small-scale RIFAN members (rice farmers) in the Edu-Patigi local government region of Kwara state, Nigeria. The model enabled the identification and prioritization of the sustainability-related practices embraced by the RIFAN members towards the drive for food security within the region. Water usage and supplier diversity ranked highest, followed by a safe working environment across the three dimensions of sustainability. However, to improve the performance of the small-scale RIFAN members, government as a stakeholder in the rice supply chain should promote the adoption of sustainable practices across the rice value chain, drawing on specific indicators such as those identified in the SRP. The SRP is a voluntary initiative that aims to ensure sustainability through the efficient utilization of resources and best management practices across the rice value chain.
This would enable rice farmers to check for the presence of pests and beneficial organisms and guide the use of pesticides. The pesticides applied per area of land used for rice farming would also be recorded. With regard to water for irrigation, given the need for high-quality water, the source must be free from contamination. Rice farmers need to source seedlings, fertilizers, and chemicals from designated, government-approved centers or suppliers. This would ensure the use of quality inputs that must be used in the right quantity. There is also a need to enhance efficiency in the nitrogen and phosphorus make-up of the chemicals used and the ADP team should guide users in this regard. Rice farmers should be encouraged to use both organic and inorganic fertilizers.
Lastly, the government should establish a platform to empower women to make decisions relating to their well-being such as personal and household income, labor inputs, agricultural production inputs, information and capacity building, and violence against women.
The limitation of this study is that the analysis was based on the opinions of a single category of stakeholders in the rice value chain. The views expressed are thus not representative of those of other stakeholders. Nonetheless, the research contributes to the body of knowledge by assessing the priorities and adoption of sustainable practices among rice farmers for SSCM using a fuzzy-AHP MCDM model. Another limitation is the criteria used. However, the number of criteria were reduced to ensure the use of the most relevant ones to assess the prioritization of sustainable practices among the RIFAN members.
Future research should include the views of stakeholders across the rice value chain, with more criteria included for the best possible rankings. The model proposed in this study could be used for other agricultural output or stakeholders across the value chain of other crops towards the adoption of SSC practices to drive food security in Nigeria.
Author Contributions
Conceptualization, A.O.B. and T.P.M.; methodology, A.O.B.; software, A.O.B.; validation, A.O.B. and T.P.M.; formal analysis, A.O.B.; investigation, A.O.B.; resources, A.O.B.; data curation, A.O.B.; writing—original draft preparation, A.O.B.; writing—review and editing, T.P.M.; visualization, T.P.M.; supervision, T.P.M.; project administration, A.O.B.; funding acquisition, T.P.M. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The study was conducted in accordance with and approved by the Ethics Committee of the University of KwaZulu-Natal (Humanities and Social Science Research Ethics Committee—HSSREC (HSSREC/00003546/2021 and 11/11/2021).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The data presented in this study are available on request from the corresponding author.
Acknowledgments
The authors acknowledge support from the University of KwaZulu-Natal and RIFAN members of Edu-Patigi local government area of Kwara state who participated in the research.
Conflicts of Interest
The authors declare no conflicts of interest.
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Figure 1.
Conceptualization of research.
Figure 1.
Conceptualization of research.
Triangular fuzzy [34].
Figure 3.
SRP indicators.
Figure 3.
SRP indicators.
Table 1.
Assessment, standard values, corresponding triangular fuzzy numbers, and the inverse values.
Table 1.
Assessment, standard values, corresponding triangular fuzzy numbers, and the inverse values.
Definition | Standard Values | Fuzzy Number | Inverse Values of the Fuzzy Number |
---|---|---|---|
Disagree | 1 | 1, 1, 1 | 1/1, 1/1, 1/1 |
Slightly agree | 3 | 1, 3, 5 | 1/5, 1/3, 1/1 |
Agree | 5 | 3, 5, 7 | 1/7, 1/5, 1/3 |
Strongly agree | 7 | 5, 7, 9 | 1/9, 1/7, 1/5 |
Extremely agree | 9 | 7, 9, 11 | 1/11, 1/9, 1/7 |
Intermediate values | 2, 4, 6, 8 | (1, 2, 4), (3, 4, 5) (5, 6, 7) (7, 8, 9) | (1/4, 1/2, 1/1), (1/5, 1/4, 1/3), (1/7, 1/6, 1/5), (1/9, 1/8, 1/7) |
Table 2.
Corresponding assessment using the Satty scale and Triangular fuzzy scale.
Table 2.
Corresponding assessment using the Satty scale and Triangular fuzzy scale.
Assessment | Weighting | Importance Intensity (Satty Scale) | Triangular Fuzzy Scale | Triangular Fuzzy Reciprocal |
---|---|---|---|---|
Slightly Agree | 30 | 3 | 2, 3, 4 | ¼, 1/3, 1/2 |
Agree | 50 | 5 | 4, 5, 6 | 1/6, 1/5, 1/4 |
Slightly Agree | 30 | 3 | 2, 3, 4 | ¼, 1/3, 1/4 |
Disagree | 10 | 1 | 1, 1, 1 | 1, 1, 1 |
Slightly Agree | 30 | 3 | 2, 3, 4 | ¼, 1/3, 1/2 |
Agree | 50 | 5 | 4, 5, 6 | 1/6, 1/5, 1/4 |
Disagree | 10 | 1 | 1, 1, 1 | 1, 1, 1 |
Table 3.
Averaged weight criterion (Mi) and normalized weight criterion (Ni) of each assessment.
Table 3.
Averaged weight criterion (Mi) and normalized weight criterion (Ni) of each assessment.
Rating | Mi | Ni | Rank |
---|---|---|---|
R1 | 0.048 | 0.045 | 6 |
R2 | 0.096 | 0.089 | 4 |
R3 | 0.167 | 0.155 | 3 |
R4 | 0.096 | 0.089 | 4 |
R5 | 0.239 | 0.221 | 2 |
R6 | 0.057 | 0.053 | 5 |
R7 | 0.377 | 0.349 | 1 |
Total | 1.080 |
Table 4.
Overall rating of the Social criteria for the five respondents.
Table 4.
Overall rating of the Social criteria for the five respondents.
Nodes | Criteria | Weight | Rank |
---|---|---|---|
SOC-1 | Quality assurance | 0.063 | 6 |
Storage system | 0.129 | 3 | |
SOC-2 | Job creation | 0.247 | 2 |
Skills acquisition | 0.115 | 4 | |
Safe working environment | 0.323 | 1 | |
SOC-3 | Government intervention | 0.057 | 7 |
Tax payment | 0.067 | 5 |
Table 5.
Overall rating of the Economic criteria for the five respondents.
Table 5.
Overall rating of the Economic criteria for the five respondents.
Nodes | Criteria | Weight | Rank |
---|---|---|---|
ECO-1 | Supplier diversity | 0.349 | 1 |
Transparency | 0.028 | 7 | |
Quality input | 0.102 | 5 | |
ECO-2 | Loan repayment | 0.049 | 6 |
Adoption of new technology | 0.21 | 2 | |
Financial decisions | 0.108 | 4 | |
ECO-3 | Profitability | 0.129 | 3 |
Table 6.
Overall rating of the Environmental criteria for the five respondents.
Table 6.
Overall rating of the Environmental criteria for the five respondents.
Nodes | Criteria | Weight | Rank |
---|---|---|---|
ENV-1 | Water Usage | 0.349 | 1 |
Land usage | 0.168 | 2 | |
Weed management | 0.128 | 4 | |
ENV-2 | Processing method | 0.140 | 3 |
ENV-3 | Power usage | 0.055 | 6 |
Grain wastage | 0.049 | 7 | |
Seed selection | 0.082 | 5 |
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