Generic algorithm for multicriteria ranking of crop technological options based on the “Technique for Order of Preference by Similarity to Ideal Solution” using ShinyApps
Many agricultural research and development programs aiming at enhancing tradeoffs related to different adoption, management and policy decisions face a methodological problem in which multi-criteria ranking is used to reach acceptable compromises between different objectives (e.g. those of farms, re...
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| Veröffentlicht in: | MethodsX Jg. 8; S. 101519 |
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01.01.2021
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| Abstract | Many agricultural research and development programs aiming at enhancing tradeoffs related to different adoption, management and policy decisions face a methodological problem in which multi-criteria ranking is used to reach acceptable compromises between different objectives (e.g. those of farms, research managers, donors or policy makers). A typical situation is where many farm management options will result in different conflicting economic, social and environmental impacts. Ranking these options and the choice of those to promote is challenging. The literature provides a set of methodological solutions that need background data organization and simulation through coding using different computing software. Here, we provide a generic solution and friendly interface, made on Shiny (an R-package) based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We apply this method for ranking different crop technological products of grain legumes and dry cereals based on their respective impacts on poverty, child malnutrition and economic benefits in more than 40 countries in eight different geographic zones across South Asia and Sub-Saharan Africa.
• The developed algorithms and interface can help rank different options based on the weights (preferences) of their respective outcome indicators.
• The interface allows for changing the weights (preferences) and automatically generates new ranking tables and graphs accordingly, which can serve for scenario simulations, which saves time compared to manually performing these calculations.
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| AbstractList | Many agricultural research and development programs aiming at enhancing tradeoffs related to different adoption, management and policy decisions face a methodological problem in which multi-criteria ranking is used to reach acceptable compromises between different objectives (e.g. those of farms, research managers, donors or policy makers). A typical situation is where many farm management options will result in different conflicting economic, social and environmental impacts. Ranking these options and the choice of those to promote is challenging. The literature provides a set of methodological solutions that need background data organization and simulation through coding using different computing software. Here, we provide a generic solution and friendly interface, made on Shiny (an R-package) based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We apply this method for ranking different crop technological products of grain legumes and dry cereals based on their respective impacts on poverty, child malnutrition and economic benefits in more than 40 countries in eight different geographic zones across South Asia and Sub-Saharan Africa. • The developed algorithms and interface can help rank different options based on the weights (preferences) of their respective outcome indicators. • The interface allows for changing the weights (preferences) and automatically generates new ranking tables and graphs accordingly, which can serve for scenario simulations, which saves time compared to manually performing these calculations.Many agricultural research and development programs aiming at enhancing tradeoffs related to different adoption, management and policy decisions face a methodological problem in which multi-criteria ranking is used to reach acceptable compromises between different objectives (e.g. those of farms, research managers, donors or policy makers). A typical situation is where many farm management options will result in different conflicting economic, social and environmental impacts. Ranking these options and the choice of those to promote is challenging. The literature provides a set of methodological solutions that need background data organization and simulation through coding using different computing software. Here, we provide a generic solution and friendly interface, made on Shiny (an R-package) based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We apply this method for ranking different crop technological products of grain legumes and dry cereals based on their respective impacts on poverty, child malnutrition and economic benefits in more than 40 countries in eight different geographic zones across South Asia and Sub-Saharan Africa. • The developed algorithms and interface can help rank different options based on the weights (preferences) of their respective outcome indicators. • The interface allows for changing the weights (preferences) and automatically generates new ranking tables and graphs accordingly, which can serve for scenario simulations, which saves time compared to manually performing these calculations. Many agricultural research and development programs aiming at enhancing tradeoffs related to different adoption, management and policy decisions face a methodological problem in which multi-criteria ranking is used to reach acceptable compromises between different objectives (e.g. those of farms, research managers, donors or policy makers). A typical situation is where many farm management options will result in different conflicting economic, social and environmental impacts. Ranking these options and the choice of those to promote is challenging. The literature provides a set of methodological solutions that need background data organization and simulation through coding using different computing software. Here, we provide a generic solution and friendly interface, made on Shiny (an R-package) based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We apply this method for ranking different crop technological products of grain legumes and dry cereals based on their respective impacts on poverty, child malnutrition and economic benefits in more than 40 countries in eight different geographic zones across South Asia and Sub-Saharan Africa.• The developed algorithms and interface can help rank different options based on the weights (preferences) of their respective outcome indicators.• The interface allows for changing the weights (preferences) and automatically generates new ranking tables and graphs accordingly, which can serve for scenario simulations, which saves time compared to manually performing these calculations. Many agricultural research and development programs aiming at enhancing tradeoffs related to different adoption, management and policy decisions face a methodological problem in which multi-criteria ranking is used to reach acceptable compromises between different objectives (e.g. those of farms, research managers, donors or policy makers). A typical situation is where many farm management options will result in different conflicting economic, social and environmental impacts. Ranking these options and the choice of those to promote is challenging. The literature provides a set of methodological solutions that need background data organization and simulation through coding using different computing software. Here, we provide a generic solution and friendly interface, made on Shiny (an R-package) based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We apply this method for ranking different crop technological products of grain legumes and dry cereals based on their respective impacts on poverty, child malnutrition and economic benefits in more than 40 countries in eight different geographic zones across South Asia and Sub-Saharan Africa. • The developed algorithms and interface can help rank different options based on the weights (preferences) of their respective outcome indicators. • The interface allows for changing the weights (preferences) and automatically generates new ranking tables and graphs accordingly, which can serve for scenario simulations, which saves time compared to manually performing these calculations. [Display omitted] Many agricultural research and development programs aiming at enhancing tradeoffs related to different adoption, management and policy decisions face a methodological problem in which multi-criteria ranking is used to reach acceptable compromises between different objectives (e.g. those of farms, research managers, donors or policy makers). A typical situation is where many farm management options will result in different conflicting economic, social and environmental impacts. Ranking these options and the choice of those to promote is challenging. The literature provides a set of methodological solutions that need background data organization and simulation through coding using different computing software. Here, we provide a generic solution and friendly interface, made on Shiny (an R-package) based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We apply this method for ranking different crop technological products of grain legumes and dry cereals based on their respective impacts on poverty, child malnutrition and economic benefits in more than 40 countries in eight different geographic zones across South Asia and Sub-Saharan Africa. • The developed algorithms and interface can help rank different options based on the weights (preferences) of their respective outcome indicators. • The interface allows for changing the weights (preferences) and automatically generates new ranking tables and graphs accordingly, which can serve for scenario simulations, which saves time compared to manually performing these calculations. Image, graphical abstract |
| ArticleNumber | 101519 |
| Author | Swamikannu, Nedumaran Frija, Aymen Ismail, Firas Gbegbelegbe, Sika Ouerghemmi, Hassen |
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| References | Arega, Nedumaran, Gbegbelegbe, Mausch, Carberry (bib0001) 2019 Belenson, Kapur (bib0002) 1973; 24 Ramón-Canul, Margarito-Carrizal, Limón-Rivera, Morales-Carrrera, Rodríguez-Buenfil, Ramírez-Sucre, Cabal-Prieto, Herrera-Corredor, Ramírez-Rivera (bib0009) 2020; 10959 Mahmoud, Y. M. (2015). Package “topsis” Title TOPSIS method for multiple-criteria decision making (MCDM) Description Evaluation of alternatives based on multiple criteria using TOPSIS method. Rosegrant, Sulser, Mason-D’Croz, Cenacchi, Nin-Pratt, Dunston, Zhu, Ringler, Wiebe, Robinson, Willenbockel (bib0007) 2017 Hwang, Yoon (bib0004) 1981 Shih, Shyur, Lee (bib0010) 2007; 45 Kim, Park, Yoon (bib0005) 1997; 50 Mason-D’Croz, Sulser, Wiebe, Rosegrant, Lowder, Nin-Pratt, Willenbockel, Robinson, Zhu, Cenacchi, Dunston, Robertson (bib0008) 2019; 116 Yeh (bib0012) 2002; 9 Yadav, Karmakar, Kalbar, Dikshit (bib0011) 2019; 9 El Allaki, Christensen, Vallières (bib0003) 2019; 165 10.1016/j.mex.2021.101519_bib0006 Mason-D’Croz (10.1016/j.mex.2021.101519_bib0008) 2019; 116 Kim (10.1016/j.mex.2021.101519_bib0005) 1997; 50 Yadav (10.1016/j.mex.2021.101519_bib0011) 2019; 9 El Allaki (10.1016/j.mex.2021.101519_bib0003) 2019; 165 Rosegrant (10.1016/j.mex.2021.101519_bib0007) 2017 Yeh (10.1016/j.mex.2021.101519_bib0012) 2002; 9 Shih (10.1016/j.mex.2021.101519_bib0010) 2007; 45 Arega (10.1016/j.mex.2021.101519_bib0001) 2019 Belenson (10.1016/j.mex.2021.101519_bib0002) 1973; 24 Ramón-Canul (10.1016/j.mex.2021.101519_bib0009) 2020; 10959 Hwang (10.1016/j.mex.2021.101519_bib0004) 1981 |
| References_xml | – year: 2017 ident: bib0007 article-title: Quantitative foresight modeling to inform the CGIAR research portfolio | IFPRI publication-title: Quantitative foresight modeling to inform the CGIAR research portfolio (Project Report for USAID) – reference: Mahmoud, Y. M. (2015). Package “topsis” Title TOPSIS method for multiple-criteria decision making (MCDM) Description Evaluation of alternatives based on multiple criteria using TOPSIS method. – volume: 10959 year: 2020 ident: bib0009 article-title: Technique for order of preference by similarity to ideal solution (<scp>TOPSIS</scp>) method for the generation of external preference mapping using rapid sensometric techniques publication-title: J. Sci. Food Agric. jsfa. – year: 1981 ident: bib0004 article-title: Multiple Attribute Decision Making (Vol. 186) – volume: 116 start-page: 38 year: 2019 end-page: 53 ident: bib0008 article-title: Agricultural investments and hunger in Africa modeling potential contributions to SDG2 – Zero Hunger publication-title: World Dev. – volume: 9 start-page: 169 year: 2002 end-page: 181 ident: bib0012 article-title: A Problem-based Selection of Multi-attribute Decision-making Methods publication-title: Int. Trans. Oper. Res. – volume: 45 start-page: 801 year: 2007 end-page: 813 ident: bib0010 article-title: An extension of TOPSIS for group decision making publication-title: Math. Comput. Model. – year: 2019 ident: bib0001 article-title: Ex-ante Evaluation of Research and Technology Options for Grain Legumes and Dryland Cereals in Sub-Saharan Africa and South Asia – volume: 50 start-page: 23 year: 1997 end-page: 33 ident: bib0005 article-title: Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement publication-title: Int. J. Prod. Econ. – volume: 165 start-page: 36 year: 2019 end-page: 43 ident: bib0003 article-title: A modified TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) applied to choosing appropriate selection methods in ongoing surveillance for Avian Influenza in Canada publication-title: Prev. Vet. Med. – volume: 24 start-page: 65 year: 1973 end-page: 77 ident: bib0002 article-title: Algorithm for solving multicriterion linear programming problems with examples publication-title: Oper. Res. Q. – volume: 9 start-page: 217 year: 2019 end-page: 222 ident: bib0011 article-title: PyTOPS: A Python based tool for TOPSIS publication-title: SoftwareX – volume: 50 start-page: 23 issue: 1 year: 1997 ident: 10.1016/j.mex.2021.101519_bib0005 article-title: Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement publication-title: Int. J. Prod. Econ. doi: 10.1016/S0925-5273(97)00014-5 – ident: 10.1016/j.mex.2021.101519_bib0006 – volume: 165 start-page: 36 year: 2019 ident: 10.1016/j.mex.2021.101519_bib0003 article-title: A modified TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) applied to choosing appropriate selection methods in ongoing surveillance for Avian Influenza in Canada publication-title: Prev. Vet. Med. doi: 10.1016/j.prevetmed.2019.02.006 – volume: 116 start-page: 38 year: 2019 ident: 10.1016/j.mex.2021.101519_bib0008 article-title: Agricultural investments and hunger in Africa modeling potential contributions to SDG2 – Zero Hunger publication-title: World Dev. doi: 10.1016/j.worlddev.2018.12.006 – volume: 10959 year: 2020 ident: 10.1016/j.mex.2021.101519_bib0009 article-title: Technique for order of preference by similarity to ideal solution (TOPSIS) method for the generation of external preference mapping using rapid sensometric techniques publication-title: J. Sci. Food Agric. jsfa. – volume: 9 start-page: 169 issue: 2 year: 2002 ident: 10.1016/j.mex.2021.101519_bib0012 article-title: A Problem-based Selection of Multi-attribute Decision-making Methods publication-title: Int. Trans. Oper. Res. doi: 10.1111/1475-3995.00348 – volume: 24 start-page: 65 issue: 1 year: 1973 ident: 10.1016/j.mex.2021.101519_bib0002 article-title: Algorithm for solving multicriterion linear programming problems with examples publication-title: Oper. Res. Q. doi: 10.1057/jors.1973.9 – year: 1981 ident: 10.1016/j.mex.2021.101519_bib0004 – volume: 9 start-page: 217 year: 2019 ident: 10.1016/j.mex.2021.101519_bib0011 article-title: PyTOPS: A Python based tool for TOPSIS publication-title: SoftwareX doi: 10.1016/j.softx.2019.02.004 – volume: 45 start-page: 801 issue: 7–8 year: 2007 ident: 10.1016/j.mex.2021.101519_bib0010 article-title: An extension of TOPSIS for group decision making publication-title: Math. Comput. Model. doi: 10.1016/j.mcm.2006.03.023 – year: 2017 ident: 10.1016/j.mex.2021.101519_bib0007 article-title: Quantitative foresight modeling to inform the CGIAR research portfolio | IFPRI – year: 2019 ident: 10.1016/j.mex.2021.101519_bib0001 |
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| SubjectTerms | agricultural research algorithms children computer software farm management issues and policy malnutrition Method Multicriteria assessment poverty Ranking of options research and development Rstudio Shiny South Asia Sub-Saharan Africa Tradeoffs |
| Title | Generic algorithm for multicriteria ranking of crop technological options based on the “Technique for Order of Preference by Similarity to Ideal Solution” using ShinyApps |
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