“Renewed effort at agroforestry modeling” needed in a changing climate

How can we predict the future performance of agroforestry systems when faced with higher temperatures, higher concentrations of carbon dioxide and possible changes in precipitation?climate analogue map

In a special issue of Current Opinion in Environmental Sustainability scientists from the World Agroforestry Centre and CSIRO evaluate methods that are currently available to project climate change impacts on agroforestry systems, and they call for a renewed effort at agroforestry modeling.

“It is especially urgent to have rigorous methods that can show how climate change will affect agroforestry when agroforestry is being widely promoted for its ability to improve food security, help farmers adapt to climate change and contribute to climate change mitigation through carbon sequestration,” stresses Eike Luedeling, Senior Decision Analyst with the World Agroforestry Centre and lead author of the article.

“Agroforestry systems – like all other agricultural and natural ecosystems – are going to be affected by climate change,” says Luedeling. “Adapting these systems will require an understanding of how existing and potential future systems will perform in future climates.”

In their article, Luedeling and colleagues compare the challenges and opportunities provided by 3 different projection methods: process-based models, species distribution models and climate analogue analysis.

The scientists suggest that process-based models are the best bet for robust and credible projections. They are the only approach that can comprehensively capture the effects of both increased carbon dioxide and changing climates. However, process-based models have high data requirements. The climate sensitivity of all components of an agroforestry system and their many interactions need to be incorporated, which Luedeling says is “no easy task”.

While several robust models for monoculture crops are now available to help in adaptation planning, there are no equally robust models for tree-based farming systems. According to Luedeling, the main reason for this is that agroforestry systems are more complex. Not only do trees take a long time to grow, but agroforestry systems comprise annual and perennial plants that are often integrated with livestock.

“Modeling has to look at the effects of changing temperatures, humidity and carbon dioxide concentrations on all components of an agroforestry system.”

To develop specific process-based models capable of simulating all the processes in an agroforestry system will require a substantial investment in time and effort and there is no guarantee that a single model will be transferable across a range of climatic and environmental settings.

“When all relevant processes are properly incorporated, process-based models are a powerful tool and can predict the performance of agroforestry systems even in places or climates where the particular type of agroforestry system has never been observed.”

The other projection methods analyzed – species distribution modeling and climate analogue analyses – were viewed by the scientists as less reliable because they generate predictions based on present performance or distribution and therefore do not take into account elevated concentrations of carbon dioxide. However, the authors point out that species distribution modeling and climate analogue analyses have the advantage of being much cheaper, faster, easier and more flexible, and they are still valuable tools for adaptation planning.

Species distribution models determine the environmental niche of a species, system, biome or genotype which then allows its distribution in the environment or future geographic space to be mapped.

The scientists believe that applying species distribution models to agroforestry systems could provide a conservative projection of the potential distribution of the climatic niche of a particular agroforestry system, but there are challenges associated with their use.

“The current distribution of agroforestry systems may not be sufficiently known or particular agroforestry systems may not be found under present conditions because the soil conditions are not suitable or there is no marketing infrastructure in place even if the climate is suitable.”

Climate analogue analysis – which searches for different locations where the current climate is similar to that which is predicted for a given site in the future – is unlikely to be a successful approach in predicting the impacts of climate change on agroforestry systems, say the scientists, because in its current form it does not account for differences in land use, land cover or socio-economic conditions between locations. Also, its focus on suitability for a particular system does not allow conclusions to be drawn about a system’s future performance.

“There are many shortcomings in quantifying requirements of complex systems and their components,” outlines Luedeling. “Important factors such as soil type, farm size, market orientation or cultural preferences may differ between target and analogue sites.”

While the scientists generally favor process-based models for predicting the performance of agroforestry under climate change, they believe it may be possible to combine different projection approaches from all 3 methods, drawing on their specific strengths. But of course, much more research will be needed to further investigate how this can be applied.

Download the article:

Luedeling, E, Kindt, R, Huth, N I, Koenig, K (2014) Agroforestry systems in a changing climate — challenges in projecting future performance. Current Opinion in Environmental Sustainability 6:1-7


Kate Langford

Kate Langford is a consultant writer with close to 20 years’ experience in communicating natural resource, environmental and land management issues for various government and non-government organizations. She previously worked as Communications Specialist for the World Agroforestry Centre in Kenya and has worked in Indonesia, Laos, Vietnam and Australia. She holds a Bachelor of Science and a Graduate Diploma in Scientific Communication.

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