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Background

Drivers of unsustainable development.

Smallholders in many rural and peri-urban environments in arid and semi-arid Latin America are caught in a vicious cycle of unsustainability. Those producing for markets experienced decreasing prices of agricultural products and rising prices of inputs causing a major decline in family income over the past two decades. The typical response of farmers has been to increase the intensity of production by increasing input application and share of cash crops, and by taking up farming on marginal parts of their properties. This intensification required substantial inputs of labour and capital and put a heavy burden on soil and water resources. READ MORE

Systems diversification and systems thinking for co-innovation.

Alternative developmental tracks are possible where socio-economic improvements are combined with improved natural resource use [1]. Many of these alternatives share socio-economic and agro-ecological diversification of livelihoods as an important direction for improved income and resource use [2, 3]. Economic diversification concerns choice of markets, product transformation, distribution channels and off-farm sources of income. Agro-ecological diversification refers to the balance between crop and livestock production, the types of crops and animals, and the technologies with which they are produced. READ MORE

Integration of fragmented information.

Over the past decade agricultural land use in arid and semi arid environments has been topic of research activities at universities and national and international research institutions throughout Latin America. A substantial amount of experimental information on relations between field-level management and production of feed and food was created, in more recent years supplemented by information on relations between management and natural and agro-resource use, in particular related to soil erosion [e.g. 10]. A major problem remains to mobilize and integrate these essentially fragmentary pieces of information to the benefit of innovation of farm livelihoods in the context of various unsustainability drivers. This lack of integration compromises the quality and efficacy of research interventions by system scientists.
A novel category of models using the systems approach emerged over the past ten to fifteen years as result of convergence of agro-ecological sciences and agricultural economics: bio-economic models. READ MORE

Relevant insights and developments in social and natural sciences.

In the more linear and top-down tradition of extension studies, it was proposed that processes of adoption of innovations progressed through the stages of awareness, interest, evaluation, trial and adoption. However, in the context of co-innovation it is no longer useful to refer to a ‘fixed’ externally supplied innovation, as the prime idea is that innovations are to be ‘designed’ by the resource managers themselves. ‘Designs’ may originally have a rather abstract or vague shape and may crystallize over time into concrete and coherent technical devices, maps, practices, or agreements. In such co-innovation process the following basic ‘tasks’ may be distinguished: READ MORE

References cited in this section

Drivers of unsustainable development.

Smallholders in many rural and peri-urban environments in arid and semi-arid Latin America are caught in a vicious cycle of unsustainability. Those producing for markets experienced decreasing prices of agricultural products and rising prices of inputs causing a major decline in family income over the past two decades. The typical response of farmers has been to increase the intensity of production by increasing input application and share of cash crops, and by taking up farming on marginal parts of their properties. This intensification required substantial inputs of labour and capital and put a heavy burden on soil and water resources: consequently erosion of arable land, desertification of overgrazed pastures, dwindling soil organic matter levels, high levels of soil borne diseases, pollution of water by nutrients and pesticides have become increasingly common throughout Latin America. These ecological constraints frequently lead to disappointing yield levels, prompting farmers to further increase production intensity to the extent of endangering their livelihood, both ecologically and through debt. Such forms of agricultural intensification have consequences beyond the farm scale as erosion soil mining and deforestation affect natural resource quality in entire regions. Rural populations in search of scarce sources of off-farm income end up in cities causing social imbalances. A major cause of this cascade of negative developments is that the adaptation of farmers to changing conditions is mostly incremental, short-term oriented and only rarely involves strategic re-design of their rural livelihood strategies as a whole. As a result, livelihoods become locked-in on unsustainable development tracks.

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Systems diversification and systems thinking for co-innovation.

Alternative developmental tracks are possible where socio-economic improvements are combined with improved natural resource use [1]. Many of these alternatives share socio-economic and agro-ecological diversification of livelihoods as an important direction for improved income and resource use [2, 3]. Economic diversification concerns choice of markets, product transformation, distribution channels and off-farm sources of income. Agro-ecological diversification refers to the balance between crop and livestock production, the types of crops and animals, and the technologies with which they are produced. Decisions on diversification by rural stakeholders interact with the available human and physical resources of the farm and with the socio-economic and political environment. Together, these components, their interactions and the feedbacks between them constitute a complex ecosystem where various rural actors intervene with their own objectives and priorities. Systems thinking provides the means to structure these components and their interactions and assess consequences of changes in systems management. It enables the assessment of alternative options and the understanding of trade-offs between objectives. The latter is important to reveal conflicts between alternatives and to provide directions for promising alternative development tracks.

Economically and agro-ecologically diversified livelihood options do not come as validated technology packages waiting to be adopted by farmers. Solutions to complex problems need to be built up in situ in farmers’ fields and communities. Farmers and other resource users have always invented and experimented, using the materials and concepts that they have had at hand. However, farmers are increasingly confronted with situations for which their experience provides little guidance. Here, researchers can play a role in supporting the strengthening of resource users’ own learning capabilities so that they can make better-informed decisions to adaptively manage their land. In such processes, researchers themselves learn by being able to analyze the many experiments that farm practices represent and by submitting their approaches to close scrutiny by local actors. This collective learning process about sustainable development is what is called co-innovation in this proposal. Besides farmers, farmers’ organizations and researchers it involves actors such as extension agents, private sector and policy makers.

In the literature, a substantial number of examples of collaboration of scientists and natural resource managers (usually farmers) can be found [see e.g. overview in 4, 5, 6]. Usually these examples deal with a few components of the system – integrated pest management, integrated crop management – in a rather qualitative manner. In several other cases [7, 8, 9], quantitative and integrative tools made essential contributions to an ongoing learning process. Such cases, however, are still rare and our understanding on how quantitative approaches affected learning and how quantitative and qualitative tools together supported the learning process is in its infancy.

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Integration of fragmented information.

Over the past decade agricultural land use in arid and semi arid environments has been topic of research activities at universities and national and international research institutions throughout Latin America. A substantial amount of experimental information on relations between field-level management and production of feed and food was created, in more recent years supplemented by information on relations between management and natural and agro-resource use, in particular related to soil erosion [e.g. 10]. A major problem remains to mobilize and integrate these essentially fragmentary pieces of information to the benefit of innovation of farm livelihoods in the context of various unsustainability drivers. This lack of integration compromises the quality and efficacy of research interventions by system scientists.

A novel category of models using the systems approach emerged over the past ten to fifteen years as result of convergence of agro-ecological sciences and agricultural economics: bio-economic models. These models allow powerful integrated analyses of existing and alternative land use patterns in terms of multiple land use objectives at the farm or regional scale [11]. In these models land use alternatives are characterized by their resource inputs and by their output of products and positive or negative contribution to natural resources. The models show which trade-offs exist between objectives and where win-win situations can be expected. Their integrated approach makes these models excellent tools for bringing together fragmented information from scientific sources, but also from expert knowledge. Weakness in many bio-economic studies is the lack of application of the integrated farm and regional models in situ with farmers or policymakers. Critical assessment of the potential of these models to support strategic decision making constitutes a priority challenge, as was recently also concluded in the MULTAGRI project commissioned by the European Commission under the FP6 Framework Programme (www.multagri.net) [12].

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Relevant insights and developments in social and natural sciences.

In the more linear and top-down tradition of extension studies, it was proposed that processes of adoption of innovations progressed through the stages of awareness, interest, evaluation, trial and adoption. However, in the context of co-innovation it is no longer useful to refer to a ‘fixed’ externally supplied innovation, as the prime idea is that innovations are to be ‘designed’ by the resource managers themselves. ‘Designs’ may originally have a rather abstract or vague shape and may crystallize over time into concrete and coherent technical devices, maps, practices, or agreements. In such co-innovation process the following basic ‘tasks’ may be distinguished: 1) raising awareness of a problematic situation; 2) mobilizing interest in a network of stakeholders; 3) socio-technical design and redesign which involves experiential and social learning and exploration among stakeholders. The three tasks are intertwined and need to be addressed symmetrically. The term ‘task’ expresses better than the term ‘stage’ that we are talking about fields of activity that are worked on simultaneously and/or that many iterations are likely to occur. Quantitative system methodologies are particularly suited to enhance learning related to the third task. In this task, the quality of models to organize local and generic information in ecosystem building blocks and creating different ‘designs’, can be exploited for better-informed discussion and negotiation. How this is to be organized as part of all three tasks remains an outstanding research question [13].

To support the co-innovation processes, ecosystem models will have to be operational; ready to be applied for emerging issues. Modelling, however, is technically complex and easily becomes a purpose in itself, losing the link to the demand for information [7]. Over the last decade, considerable progress was made in summarizing major ecological processes in mathematical models. In many fields, our understanding has advanced to the point where ‘standard’ approaches are being used, for instance to calculate crop growth under water limited conditions, to evaluate organic matter dynamics or to calculate farm economic balances. Such standard approaches are now being integrated in software environments to enable rapid access [e.g. 8]. Prototype software frameworks developed by the present proposal consortium and under construction in major European projects allow confidence in the availability of these tools for relatively rapid application in co-innovation. Nevertheless, prudent application of these models is called for, since many parameters are location-specific.

Analyses of projects where natural scientists engaged in co-innovation with quantitative systems models suggest a number of critical factors for success, i.e. actual changes in resource management by farmers: stakeholders, including scientists, share a sense of urgency of the problems addressed; stakeholders share a sense of mutual dependency and make a serious effort to understand each other’s frame of mind; scientists adopt a role as one of the stakeholders, rather than as ‘independent’ experts [14].

In this proposal, we set out from the premise that reversing unsustainable development of farm livelihoods should be treated as co-innovation: a learning process of scientists and other stakeholders, in which different types of learning are needed as part of the different co-innovation tasks. The outstanding scientific question is whether and how knowledge in ecosystems models can be unlocked for collective learning, as part of a wider set of participatory methods and approaches.

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References cited in this section.

[1] Anonymous, 2003. World agriculture: towards 2015/2030, An FAO Perspective. FAO, Rome, and Earthscan, London, January 2003.

[2] Renting, H., J.D. van der Ploeg, K. Knickel, 2004. Multifunctionality in European agriculture. In: Sustaining Agriculture and the Rural Environment. Governance, Policy and Multifunctionality. Floor Brouwer (Ed.). Edward Elgar. p. 81-103.

[3] Altieri, M.A., 2004. Linking ecologists and traditional farmers in the search for sustainable agriculture. Frontiers in ecology and the environment 2: 35-42.

[4] Horton, D., R. Mackay (Eds), 2003. Learning for the future: Innovative approaches to evaluating agricultural research. Special Issue Agricultural Systems 78: 119-336.

[5] Pretty J., H. Ward, 2001. Social capital and the environment. World development 29: 209-227.

[6] Bunde Veldhuizen, L. van, A. Waters-Bayer, H. de Zeeuw, 1997. Developing technology with farmers. A trainer’s guide for participatory learning. Zed Books, London, UK / ETC, Leusden, the Netherlands.

[7] McCown, R.L., Z. Hochman, P. S. Carberry, 2002. Probing the enigma of the decision support system for farmers: Learning from experience and from theory. Editorial to the Special Issue. Agricultural Systems 74: 1-10.

[8] Meinke H., W.E. Baethgen, P.S. Carberry, M. Donatelli, G.L. Hammer, R. Selvaraju, C.O. Stöckle, 2001. Increasing profits and reducing risks in crop production using participatory systems simulation approaches. Agricultural Systems 70: 493-513.

[9] Sterk, B., M.K. van Ittersum, C. Leeuwis, W.A.H. Rossing, H. van Keulen, G.W.J. van de Ven, 2005. Finding niches for whole-farm design models – contradictio in terminis? Agricultural Systems, in press – available on the internet.

[10] Carmona, H., P. Sosa, P. Davies, G. Cristiani, R. Puente, 1993. Manejo y desarrollo integrado de cuencas hidrográficas en la cuenca del río Santa Lucía: Plan Ejecutivo para el manejo de la microcuenca del embalse Canelón Grande. Programma de Cooperación Técnica FAO, Uruguay, Documento 5, 92pp.

[11] Pacini, C., G. Giesen, A. Wossink, 2004. The EU’s Agenda 2000 reform and the sustainability of organic farming in Tuscany: ecological-economic modelling at field and farm level Agricultural Systems 80: 171-197.

[12] Rossing, W.A.H., P. Zander, E. Josien, J.C.J. Groot, B.C. Meyer, A. Knierim, 2006. Integrative modeling approaches for analysis of impact of multifunctional agriculture: a review for France, Germany and the Netherlands. Agric. Ecosyst. Environ. in press.

[13] Leeuwis, C., R. Pyburn, N. Röling, 2002. Wheelbarrows full of frogs: social learning in rural resource management: international research and reflections. Koninklijke Van Gorcum. 479 pp.

[14] Leeuwis, C., 1999. Integral design: innovation in agriculture and resource management. Mansholt Studies Series, no. 15, Mansholt Institute / Backhuys Publishers. Wageningen University.

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