Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions
Climate-smart interventions in agriculture have varying costs and environmental and economic impacts. Their implementation requires appropriate investment decisions by policy makers that are relevant for current as well as future scenarios of agro-ecology, climate and economic development. Decision...
| Autores principales: | , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
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Elsevier
2018
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/92571 |
| _version_ | 1855540987706736640 |
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| author | Aggarwal, Pramod K. Thornton, Philip K. Dunnett, Alex Shirsath, Paresh Bhaskar Joshi, Pramod K. Pal, Barun Deb Khatri-Chhetri, Arun Ghosh, Joydeep |
| author_browse | Aggarwal, Pramod K. Dunnett, Alex Ghosh, Joydeep Joshi, Pramod K. Khatri-Chhetri, Arun Pal, Barun Deb Shirsath, Paresh Bhaskar Thornton, Philip K. |
| author_facet | Aggarwal, Pramod K. Thornton, Philip K. Dunnett, Alex Shirsath, Paresh Bhaskar Joshi, Pramod K. Pal, Barun Deb Khatri-Chhetri, Arun Ghosh, Joydeep |
| author_sort | Aggarwal, Pramod K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Climate-smart interventions in agriculture have varying costs and environmental and economic impacts. Their implementation requires appropriate investment decisions by policy makers that are relevant for current as well as future scenarios of agro-ecology, climate and economic development. Decision support tools are therefore needed to assist different stakeholders to prioritize and hence implement appropriate strategic interventions. These interventions transform agriculture ecosystems to climate-resilient, adaptive and efficient. This paper outlines the mathematical modelling framework of one such, the Climate Smart Agricultural Prioritization (CSAP) toolkit. This toolkit employs a dynamic, spatially-explicit multi-objective optimization model to explore a range of agricultural growth pathways coupled with climate-adaptation strategies to meet agricultural development and environmental goals. The toolkit consists of three major components: (i) land evaluation including assessment of resource availability, land suitability, yield and input-output estimation for all promising crop production practices and technologies for key agro-ecological units; (ii) formulation of scenarios based on policy views and development plans; and (iii) land-use optimization in the form of linear programming models. Climate change and socio-economic drivers condition the land evaluation, technological input-output relations, and specification of optimization objectives that define modelled scenarios. By integrating detailed bottom-up biophysical, climate impact and agricultural-emissions models, CSAP is capable of supporting multi-objective analysis of agricultural production goals in relation to food self-sufficiency, incomes, employment and mitigation targets, thus supporting a wide range of analyses ranging from food security assessment to environmental impact assessment to preparation of climate smart development plans. |
| format | Journal Article |
| id | CGSpace92571 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace925712025-12-08T10:11:39Z Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions Aggarwal, Pramod K. Thornton, Philip K. Dunnett, Alex Shirsath, Paresh Bhaskar Joshi, Pramod K. Pal, Barun Deb Khatri-Chhetri, Arun Ghosh, Joydeep climate change agriculture food security climate-smart agriculture adaptation agricultural production agroecology mathematical models optimization methods decision-support systems agricultural research investment policies land allocation land use climate change adaptation climate change mitigation agricultural development environmental impact assessment agroecosystems resilience Climate-smart interventions in agriculture have varying costs and environmental and economic impacts. Their implementation requires appropriate investment decisions by policy makers that are relevant for current as well as future scenarios of agro-ecology, climate and economic development. Decision support tools are therefore needed to assist different stakeholders to prioritize and hence implement appropriate strategic interventions. These interventions transform agriculture ecosystems to climate-resilient, adaptive and efficient. This paper outlines the mathematical modelling framework of one such, the Climate Smart Agricultural Prioritization (CSAP) toolkit. This toolkit employs a dynamic, spatially-explicit multi-objective optimization model to explore a range of agricultural growth pathways coupled with climate-adaptation strategies to meet agricultural development and environmental goals. The toolkit consists of three major components: (i) land evaluation including assessment of resource availability, land suitability, yield and input-output estimation for all promising crop production practices and technologies for key agro-ecological units; (ii) formulation of scenarios based on policy views and development plans; and (iii) land-use optimization in the form of linear programming models. Climate change and socio-economic drivers condition the land evaluation, technological input-output relations, and specification of optimization objectives that define modelled scenarios. By integrating detailed bottom-up biophysical, climate impact and agricultural-emissions models, CSAP is capable of supporting multi-objective analysis of agricultural production goals in relation to food self-sufficiency, incomes, employment and mitigation targets, thus supporting a wide range of analyses ranging from food security assessment to environmental impact assessment to preparation of climate smart development plans. 2018-08 2018-05-16T11:45:11Z 2018-05-16T11:45:11Z Journal Article https://hdl.handle.net/10568/92571 en Open Access Elsevier Dunnett A, Shirsath PB, Aggarwal PK, Thornton P, Joshi PK, Pal BD, Khatri-Chhetri A, Ghosh J. 2018. Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions. Ecological Modelling 381:23-35. |
| spellingShingle | climate change agriculture food security climate-smart agriculture adaptation agricultural production agroecology mathematical models optimization methods decision-support systems agricultural research investment policies land allocation land use climate change adaptation climate change mitigation agricultural development environmental impact assessment agroecosystems resilience Aggarwal, Pramod K. Thornton, Philip K. Dunnett, Alex Shirsath, Paresh Bhaskar Joshi, Pramod K. Pal, Barun Deb Khatri-Chhetri, Arun Ghosh, Joydeep Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions |
| title | Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions |
| title_full | Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions |
| title_fullStr | Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions |
| title_full_unstemmed | Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions |
| title_short | Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions |
| title_sort | multi objective land use allocation modelling for prioritizing climate smart agricultural interventions |
| topic | climate change agriculture food security climate-smart agriculture adaptation agricultural production agroecology mathematical models optimization methods decision-support systems agricultural research investment policies land allocation land use climate change adaptation climate change mitigation agricultural development environmental impact assessment agroecosystems resilience |
| url | https://hdl.handle.net/10568/92571 |
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