Can computer models stimulate learning about sustainable land use? Experience with LUPAS in the humid (sub-)tropics of Asia

In many rice-cultivating regions of east and south-east Asia, competition for land and water resources is strong and increasing. This calls for exploration of future technology and policy options in support of sustainable land (and water) use. Sustainable land use is a complex issue, that involves u...

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Autores principales: Paassen, A. van, Roetter, R.P., Keulen, H. van, Hoanh, Chu Thai
Formato: Journal Article
Lenguaje:Inglés
Publicado: 2007
Materias:
Acceso en línea:https://hdl.handle.net/10568/40904
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author Paassen, A. van
Roetter, R.P.
Keulen, H. van
Hoanh, Chu Thai
author_browse Hoanh, Chu Thai
Keulen, H. van
Paassen, A. van
Roetter, R.P.
author_facet Paassen, A. van
Roetter, R.P.
Keulen, H. van
Hoanh, Chu Thai
author_sort Paassen, A. van
collection Repository of Agricultural Research Outputs (CGSpace)
description In many rice-cultivating regions of east and south-east Asia, competition for land and water resources is strong and increasing. This calls for exploration of future technology and policy options in support of sustainable land (and water) use. Sustainable land use is a complex issue, that involves uncertainties about the dynamics of the biophysical system and the social system, as well as multiple perspectives. The capacity to identify options for sustainable and equitable development depends on the acquisition of knowledge and skills for (a) holistic analysis of the biophysical system dynamics, (b) analysis of the multiple positions, perceptions, values, beliefs and interests of the relevant stakeholders, (c) contemplation of the action needed to fill the gap between the desired socio-technical system and the perceived real-world situation. Learning is contextual and gradual: historically, agricultural scientists have moved from a reductionist to a holistic hard system perspective, while now slowly embracing the interpretive system perspective. Under the Ecoregional Initiative for the humid tropics of Asia, SysNet (1996-2000) trained scientists at four National Agricultural Research Systems (NARS) in trans-disciplinary analysis through development and application of the LUPAS (Land Use Planning and Analysis System) modelling framework. Aim was to develop a holistic land use analysis methodology for four different rice-cultivating regions. To ensure relevance for land use decision making, scientists organized meetings with planners and other stakeholders. Four NARS teams of 12-24 scientists acquired and/or increased expertise and skills outside their own disciplines, in modelling and improved data management, and learned to perform agro- ecological analyses. Regional planners were presented with region-specific data in support of formulation of area-specific technical and policy recommendations, and agricultural experts used LUPAS tools to extrapolate research results to other areas. Much learning was acquired within the technical-economic domain, but the exchange with stakeholders did not yet lead to a critical learning system approach. Nevertheless, NARS highly appreciated the new tools and knowledge, and made a significant step forward from mono-disciplinary to holistic agro-ecological analysis. Application of the LUPAS methodology has been expanded following finalization of the project, despite practical problems such as diminishing long-term funds and the high demand for IT people outside agriculture. NRM decision making demands reflexivity and skilful facilitation. In these processes, LUPAS modeling has a specific added value: it enhances long-term strategic thinking about sustainable land use and rural development issues.
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spelling CGSpace409042025-03-11T09:50:20Z Can computer models stimulate learning about sustainable land use? Experience with LUPAS in the humid (sub-)tropics of Asia Paassen, A. van Roetter, R.P. Keulen, H. van Hoanh, Chu Thai land use computer models ecosystems rice In many rice-cultivating regions of east and south-east Asia, competition for land and water resources is strong and increasing. This calls for exploration of future technology and policy options in support of sustainable land (and water) use. Sustainable land use is a complex issue, that involves uncertainties about the dynamics of the biophysical system and the social system, as well as multiple perspectives. The capacity to identify options for sustainable and equitable development depends on the acquisition of knowledge and skills for (a) holistic analysis of the biophysical system dynamics, (b) analysis of the multiple positions, perceptions, values, beliefs and interests of the relevant stakeholders, (c) contemplation of the action needed to fill the gap between the desired socio-technical system and the perceived real-world situation. Learning is contextual and gradual: historically, agricultural scientists have moved from a reductionist to a holistic hard system perspective, while now slowly embracing the interpretive system perspective. Under the Ecoregional Initiative for the humid tropics of Asia, SysNet (1996-2000) trained scientists at four National Agricultural Research Systems (NARS) in trans-disciplinary analysis through development and application of the LUPAS (Land Use Planning and Analysis System) modelling framework. Aim was to develop a holistic land use analysis methodology for four different rice-cultivating regions. To ensure relevance for land use decision making, scientists organized meetings with planners and other stakeholders. Four NARS teams of 12-24 scientists acquired and/or increased expertise and skills outside their own disciplines, in modelling and improved data management, and learned to perform agro- ecological analyses. Regional planners were presented with region-specific data in support of formulation of area-specific technical and policy recommendations, and agricultural experts used LUPAS tools to extrapolate research results to other areas. Much learning was acquired within the technical-economic domain, but the exchange with stakeholders did not yet lead to a critical learning system approach. Nevertheless, NARS highly appreciated the new tools and knowledge, and made a significant step forward from mono-disciplinary to holistic agro-ecological analysis. Application of the LUPAS methodology has been expanded following finalization of the project, despite practical problems such as diminishing long-term funds and the high demand for IT people outside agriculture. NRM decision making demands reflexivity and skilful facilitation. In these processes, LUPAS modeling has a specific added value: it enhances long-term strategic thinking about sustainable land use and rural development issues. 2007 2014-06-13T14:48:40Z 2014-06-13T14:48:40Z Journal Article https://hdl.handle.net/10568/40904 en Limited Access Van Paassen, A.; Roetter, R. P.; Van Keulen, H.; Hoanh, Chu Thai. 2007. Can computer models stimulate learning about sustainable land use? Experience with LUPAS in the humid (sub-)tropics of Asia. Agricultural Systems, 94:874-887.
spellingShingle land use
computer models
ecosystems
rice
Paassen, A. van
Roetter, R.P.
Keulen, H. van
Hoanh, Chu Thai
Can computer models stimulate learning about sustainable land use? Experience with LUPAS in the humid (sub-)tropics of Asia
title Can computer models stimulate learning about sustainable land use? Experience with LUPAS in the humid (sub-)tropics of Asia
title_full Can computer models stimulate learning about sustainable land use? Experience with LUPAS in the humid (sub-)tropics of Asia
title_fullStr Can computer models stimulate learning about sustainable land use? Experience with LUPAS in the humid (sub-)tropics of Asia
title_full_unstemmed Can computer models stimulate learning about sustainable land use? Experience with LUPAS in the humid (sub-)tropics of Asia
title_short Can computer models stimulate learning about sustainable land use? Experience with LUPAS in the humid (sub-)tropics of Asia
title_sort can computer models stimulate learning about sustainable land use experience with lupas in the humid sub tropics of asia
topic land use
computer models
ecosystems
rice
url https://hdl.handle.net/10568/40904
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