<table><caption>**Table 7 (continued)**</caption><thead><tr><th></th><th>Decision support framework</th><th>Context-based principle</th><th>Pluralistic principle</th><th>Goal-oriented principle</th><th>Interactive principle</th></tr></thead><tbody><tr><td>15.</td><td>smallholder agroforestry farms Van der Wolf et al. 2019 DESIRE-DSS<br><a href="">Schwilch et al., 2012</a><br>Interdisciplinary, multi-scaled, and integrated socio-ecological approach, including MARS DSS<br>Berger et al. 2019</td><td></td><td>X</td><td>X</td><td>X</td></tr><tr><td>16.</td><td>WET-WIN<br><a href="">Arias-Hidalgo et al., 2013</a><br>iFEED</td><td>X</td><td>X</td><td></td><td>X</td></tr><tr><td>17.</td><td>Jennings et al. 2022</td><td></td><td>X</td><td>X</td><td>X</td></tr></tbody></table>

## 5. Conclusions

Co-production can be practiced in support of planning, management or investment decisions with the aim of improving these decisions from the point of view of climate change adaptation. The frameworks we analyzed draw on multiple decision resources, and the processes are designed in a way that attempts to integrate them following a number of strategies responding to co-production principles. As we found, these can be applied at different stages of the process – from preliminary study, through designing project architecture, selecting participants and implementing participatory methods. We identified numerous useful entry points, some of them not yet sufficiently used, such as jointly developing monitoring and evaluation procedure that can contribute to responding to goal-oriented, but also to context-based and interactive principles. Next to what we found there's also, and more importantly, what we did not find. What is missing from most of the analyzed frameworks is thinking of selection criteria not only for non-academic participants but also for participating researchers. It is important to reflect on the characteristics of researchers that could support co-production and strengthen the framework implementation and its impact: as researchers, are we open to mutual learning? are we ready to recognize “different types of knowing”? are we committed to giving non-academic participants more important role in the overall process? Yet, the work that we, as individuals, may be ready to do to embrace co-production in our research practice may not be sufficient. There is no simple method to follow some of the principles without rethinking how research is planned and financed and then trying to remove the constraints that current practice poses on achieving processes that would be truly demand-driven or truly responsive to what emerges from interactions with participants. What are the enabling factors for co-production at the institutional level requires further reflection, which could be fueled by the propositions from responsible innovation or open innovation literatures.

Almost all decision-support frameworks we analyzed are designed to be applied either at policy or farm level. They are focused on bridging the usability gap or the science-policy gap. In only one case (the Multidisciplinary Working Groups in Senegal) the framework is deployed in parallel at the two levels, thus creating an opportunity to address both of these gaps. It may be interesting to better explore the potential of this kind of set-up and to imagine the interaction between

the two levels, so that the third gap – between policy and practice – is also tackled and that the three dimensions of the space come together so that sustainable solutions can be found.

The four co-production principles proposed by Norström et al. (2020) made it possible for us to identify both the useful entry points from past experiences and the areas that should be thought of if one wanted to design a co-production process for decision support. This suggests they can be used as a check-list to guide the process of co-construction of decision support. If we are serious about working with the co-construction concept, we should try to best use the potential of including different actors in creating decision-support in order to increase chances of bridging the gaps between science, policy and practice.

## Data availability

No data was used for the research described in the article.

## Acknowledgements

The work was supported by the CGIAR Research Program on Climate Change Agriculture and Food Security (CCAFS), and by the ClimBeR initiative of One CGIAR, both supported by the Action Plan between France and CGIAR on climate change from the French Ministry of Europe and Foreign Affairs.

## References

Adiku, S.G.K., MacCarthy, D.S., Hathie, I., Diancoumba, M., Freduah, B.S., Amikuzuno, J., Traore, P.C.S., Traore, S., Koomson, E., Agali, A., Lizaso, J.I., Fatondji, D., Adams, M., Tigana, L., Diarra, D.Z., N'diaye, O., Valdivia, R.O., 2014. Climate change impacts on West African agriculture: an integrated regional assessment (CIWARA). In: Handbook of Climate Change and Agroecosystems, ICP Series on Climate Change Impacts, Adaptation, and Mitigation. IMPERIAL COLLEGE PRESS, pp. 25–73. https://doi.org/10.1142/9781783265640.0014.

Allen, W., Cruz, J., Warburton, B., 2017. How decision support systems can benefit from a theory of change approach. Environ. Manag. 59, 956–965. https://doi.org/10.1007/s00267-017-0839-y.

Andrieu, N., Howland, F., Acosta-Alba, I., Le Coq, J.-F., Osorio-Garcia, A.M., Martinez-Baron, D., Gamba-Trimino, C., Loboguerrero, A.M., Chia, E., 2019. Co-designing climate-smart farming systems with local stakeholders: a methodological framework for achieving large-scale change. Front. Sustain. Food Syst. 3, 37. https://doi.org/10.3389/fsufs.2019.00037.

Antle, J.M., Jones, J.W., Rosenzweig, C.E., 2017. Next generation agricultural system data, models and knowledge products: introduction. Agr. Syst. 155, 186–190.

Arias-Hidalgo, M., Villa-Cox, G., Griensven, A.V., Solórzano, G., Villa-Cox, R., Mynett, A. E., Debels, P., 2013. A decision framework for wetland management in a river basin context: the “Abras de Mantequilla” case study in the Guayas River basin, Ecuador. Environ. Sci. Policy 34, 103–114. https://doi.org/10.1016/j.envsci.2012.10.009.

Barnhart, B.L., Golden, H.E., Kasprzyk, J.R., Pauer, J.J., Jones, C.E., Sawicz, K.A., Hoghooghi, N., Simon, M., McKane, R.B., Mayer, P.M., Piscopo, A.N., Ficklin, D.L., Halama, J.J., Pettus, P.B., Rashleigh, B., 2018. Embedding co-production and addressing uncertainty in watershed modeling decision-support tools: successes and challenges. Environ. Model. Software 109, 368–379. https://doi.org/10.1016/j.envsoft.2018.08.025.

Berger, C., Bieri, M., Bradshaw, K., Brümmer, C., Clemen, T., Hickler, T., Kutsch, W.L., Lenfers, U.A., Martens, C., Midgley, G.F., Mukwashi, K., 2019. Linking scales and disciplines: an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management. Climatic Change 156, 139–150.

Berkes, F., Colding, J., Folke, C., 2000. Rediscovery of traditional ecological knowledge as adaptive management. Ecol. Appl. 10, 1251–1262.

Berthet, E.T., Hickey, G.M., Klerkx, L., 2018. Opening design and innovation processes in agriculture: insights from design and management sciences and future directions. Agr. Syst. 165, 111–115.

Biesbroek, G.R., Swart, R.J., Carter, T.R., Cowan, C., Henrichs, T., Mela, H., Morecroft, M.D., Rey, D., 2010. Europe adapts to climate change: comparing national adaptation strategies. Glob. Environ. Chang. 20, 440–450. https://doi.org/10.1016/j.gloenvcha.2010.03.005.

Boon, W.P.C., Moors, E.H.M., Kuhlmann, S., Smits, R.E.H.M., 2011. Demand articulation in emerging technologies: intermediary user organisations as co-producers? Research Policy 40, 242–252. https://doi.org/10.1016/j.respol.2010.09.006.

Boon, W.P.C., Chappin, M.M.H., Perenboom, J., 2014. Balancing divergence and convergence in transdisciplinary research teams. Environ. Sci. Policy 40, 57–68. https://doi.org/10.1016/j.envsci.2014.04.005.

Bradshaw, G.A., Borchers, J.G., 2000. Uncertainty as information: narrowing the science-policy gap. Conservation Ecology 4 (1), 7 [online] URL: http://www.consecol.org/vol4/iss1/art7/.
<table><caption>**Table 7 (continued)**</caption><thead><tr><th></th><th>Decision support framework</th><th>Context-based principle</th><th>Pluralistic principle</th><th>Goal-oriented principle</th><th>Interactive principle</th></tr></thead><tbody><tr><td>15.</td><td>smallholder agroforestry farms Van der Wolf et al. 2019 DESIRE-DSS<br><a href="">Schwilch et al., 2012</a><br>Interdisciplinary, multi-scaled, and integrated socio-ecological approach, including MARS DSS<br>Berger et al. 2019</td><td></td><td>X</td><td>X</td><td>X</td></tr><tr><td>16.</td><td>WET-WIN<br><a href="">Arias-Hidalgo et al., 2013</a><br>iFEED</td><td>X</td><td>X</td><td></td><td>X</td></tr><tr><td>17.</td><td>Jennings et al. 2022</td><td></td><td>X</td><td>X</td><td>X</td></tr></tbody></table>

## 5. Conclusions

Co-production can be practiced in support of planning, management or investment decisions with the aim of improving these decisions from the point of view of climate change adaptation. The frameworks we analyzed draw on multiple decision resources, and the processes are designed in a way that attempts to integrate them following a number of strategies responding to co-production principles. As we found, these can be applied at different stages of the process – from preliminary study, through designing project architecture, selecting participants and implementing participatory methods. We identified numerous useful entry points, some of them not yet sufficiently used, such as jointly developing monitoring and evaluation procedure that can contribute to responding to goal-oriented, but also to context-based and interactive principles. Next to what we found there's also, and more importantly, what we did not find. What is missing from most of the analyzed frameworks is thinking of selection criteria not only for non-academic participants but also for participating researchers. It is important to reflect on the characteristics of researchers that could support co-production and strengthen the framework implementation and its impact: as researchers, are we open to mutual learning? are we ready to recognize “different types of knowing”? are we committed to giving non-academic participants more important role in the overall process? Yet, the work that we, as individuals, may be ready to do to embrace co-production in our research practice may not be sufficient. There is no simple method to follow some of the principles without rethinking how research is planned and financed and then trying to remove the constraints that current practice poses on achieving processes that would be truly demand-driven or truly responsive to what emerges from interactions with participants. What are the enabling factors for co-production at the institutional level requires further reflection, which could be fueled by the propositions from responsible innovation or open innovation literatures.

Almost all decision-support frameworks we analyzed are designed to be applied either at policy or farm level. They are focused on bridging the usability gap or the science-policy gap. In only one case (the Multidisciplinary Working Groups in Senegal) the framework is deployed in parallel at the two levels, thus creating an opportunity to address both of these gaps. It may be interesting to better explore the potential of this kind of set-up and to imagine the interaction between

the two levels, so that the third gap – between policy and practice – is also tackled and that the three dimensions of the space come together so that sustainable solutions can be found.

The four co-production principles proposed by Norström et al. (2020) made it possible for us to identify both the useful entry points from past experiences and the areas that should be thought of if one wanted to design a co-production process for decision support. This suggests they can be used as a check-list to guide the process of co-construction of decision support. If we are serious about working with the co-construction concept, we should try to best use the potential of including different actors in creating decision-support in order to increase chances of bridging the gaps between science, policy and practice.

## Data availability

No data was used for the research described in the article.

## Acknowledgements

The work was supported by the CGIAR Research Program on Climate Change Agriculture and Food Security (CCAFS), and by the ClimBeR initiative of One CGIAR, both supported by the Action Plan between France and CGIAR on climate change from the French Ministry of Europe and Foreign Affairs.

## References

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Allen, W., Cruz, J., Warburton, B., 2017. How decision support systems can benefit from a theory of change approach. Environ. Manag. 59, 956–965. https://doi.org/10.1007/s00267-017-0839-y.

Andrieu, N., Howland, F., Acosta-Alba, I., Le Coq, J.-F., Osorio-Garcia, A.M., Martinez-Baron, D., Gamba-Trimino, C., Loboguerrero, A.M., Chia, E., 2019. Co-designing climate-smart farming systems with local stakeholders: a methodological framework for achieving large-scale change. Front. Sustain. Food Syst. 3, 37. https://doi.org/10.3389/fsufs.2019.00037.

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Arias-Hidalgo, M., Villa-Cox, G., Griensven, A.V., Solórzano, G., Villa-Cox, R., Mynett, A. E., Debels, P., 2013. A decision framework for wetland management in a river basin context: the “Abras de Mantequilla” case study in the Guayas River basin, Ecuador. Environ. Sci. Policy 34, 103–114. https://doi.org/10.1016/j.envsci.2012.10.009.

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Boon, W.P.C., Moors, E.H.M., Kuhlmann, S., Smits, R.E.H.M., 2011. Demand articulation in emerging technologies: intermediary user organisations as co-producers? Research Policy 40, 242–252. https://doi.org/10.1016/j.respol.2010.09.006.

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