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The integrated Future Estimator for Emissions and Diets (iFEED)

Any good modeller knows that all models are wrong (and, yes, some are useful). Opinions on the utility of models vary and can be a barrier to effective working across disciplines. Unless we work effectively across the full range of relevant research and stakeholder expertise, we are unlikely to successfully address the grand challenges that face society. Sustainably adapting to climate change is one such challenge.

An Integrated Assessment Framework (IAF) incorporates models, data and expert judgment with analysis from across the natural and social sciences, in order to make a balanced and sound assessment. The framework is constructed using a modelling mindset, so that a clear picture emerges which accounts for the strengths and weaknesses of each component method.

The integrated Future Estimator for Emissions and Diets (iFEED) is the IAF developed as part of the GCRF-AFRICAP programme. You can read a blog about the approach here: https://africap.info/scientists-mixing-modelling-data-and-expertise-to-feed-africas-future/

iFEED is being used to describe and quantify climate-smart, nutrition secure futures that have been defined through participatory scenario workshops. These workshops set out four comprehensive possible futures that iFEED is being used to explore.

For Malawi, these scenarios contrast high vs low climate risk (RCP8.5 vs RCP2.6) and poor vs good policy implementation. 

 

Click on the image below for details of the four scenarios.

iFEED results are expressed via descriptors (see figure below), which come from mixes of various methods, from model-driven (food production, climate-smartness) through to data- and expertise- driven (trade and nutrition, pests).

 

Modelling methods

  • The General Large Area Model for annual crops (GLAM; Challinor et al. 2004) is used to assess changes to average crop yields and crop failures in the future. The crops simulated are maize, soybean, potato and groundnut.
  • There are approximately 432,000 simulations per crop and country (100 years, 10 productivity levels (YGP), 12 irrigation levels (from rainfed to fully irrigated), 18 climate models, 2 RCPs)
  • Climate change impacts on a wider range of agricultural commodities are estimated using mean results from these simulated crops – the C3 photosynthetic pathway results (groundnut, soybean and potato) are used as average impacts for other C3 crops, and maize impacts are used for C4 photosynthetic pathway crops.
  • Subsequent work will analyse changes to livestock production, the nutrition security implications of these future land use patterns and the emissions associated with them.
  • iFEED results will be disseminated using an online user-interface tool, allowing the user to firstly explore country-level outcomes and secondly to explore in detail the implications of the four scenarios modelled.
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Land use and emissions

Changes to agricultural land use are a key driver of changes in productivity in iFEED. The land use scenarios being explored are defined by taskforce input, including changes to extent of agricultural land and crop diversification. This taskforce input results in a range of modelling options being explored, as shown below.

Emissions associated with future land use patterns

  • The varying land use inputs as mandated by the taskforces will result in contrasting agricultural futures, not only in terms of food production but also in terms of emissions. The climate smartness of these land use futures therefore needs to be quantified, just as the impacts on nutrition security are quantified.
  • The soil organic carbon and greenhouse gas emission changes associated with simulated yield, land use and livestock changes will therefore be calculated.
  • Land use change is expressed as grassland going to arable crop land and vice versa.
  • Livestock changes are calculated based on changes to the amount of food available in 2050 to support livestock: pasture, crop production and cropland residues are all accounted for.
  • Yield changes will be based on the average yield change across crops in every grid cell.
  • Taken together, these factors will provide an estimate of how each possible future ranks in terms of emissions. The climate smartness of the futures can be assessed.
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People-power: taskforces and integration workshops

People are just as central to the iFEED approach as the models and data it employs. The GCRF-AFRICAP project is working in four countries: South Africa, Tanzania, Zambia and Malawi. The photo below was taken at the Malawi Participatory Scenarios Workshop.

Our participatory scenario workshops, taskforces and integration workshops address the challenges of translating idealised modelling into the real-world policy arena and vice-versa.

Each country has an iFEED taskforce, composed of a range of individuals who have expertise in climate smart agriculture, nutrition security and agricultural and food systems policy. Representatives of AFRICAP’s in-country partner organisations are included to ensure policy-relevance.

The Malawi taskforce consists of representatives of Lilongwe University of Agriculture and Natural Resources, the National Planning Commission, the National Statistical Office, MwAPATA Institute, Concern Worldwide, Care Malawi, National Smallholder Farmers' Association of Malawi (NASFAM) , Chancellor College, Trocaire, Total LandCare, Save the Children, Welthungerhilfe, the Farmers Union, Department of Disaster Management Affairs and the Civil Society Agriculture Network.

Each taskforce is mandated to directly inform country-specific implementation of iFEED. The table below shows modelling decisions made based on taskforce input, which is coordinated according to a protocol. For example, if multiple taskforce members disagree, then the majority opinion is selected. Otherwise, AFRICAP researchers select from the choices presented, by using the literature and by seeking consensus opinion. These choices are then presented to the taskforces for final agreement.

The integration workshop is a key part of the process for developing a consensus assessment of each scenario. This consensus is expressed via five descriptors (see top left box), which take the form of calibrated statements, such as those used in the Intergovernmental Panel on Climate Change processes, and assessments of opportunities and challenges.

Once the integrated assessment is complete, the taskforce works with the research team to co-developspathways to policy-preferred outcomes, based on the results. It also works to disseminate results to policy-makers throughout the project.

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Policies to maximise production

The box to the right, 'Climate-induced changes in crop productivity', shows that with currently-available adaptation, a 5% national production decrease is projected for high climate risk (RCP8.5). For low climate risk (RCP2.6) the reduction is 1%.

The projected population of 37 million people in Malawi in 2050 (medium UN population projection) strongly suggests that adaptations beyond those that are currently available will be required to maintain food security.

A very effective agricultural policy might be expected to maintain recent yield trends, develop and breed crops that are resilient to the new climates, and manage land to maximise production (e.g. by switching crops, as shown in the box to the right).

Using this idealised framework to compare a highly effective agricultural policy scenario with a highly ineffective one highlights the potential value – albeit an unattainable upper limit – of policy. The figure below shows the yield changes for these two situations, for both maize and soybean. Whilst both of these ends of the spectrum represent unlikely situations – the reality will be somewhere in the middle – the value of effective policy is clear.

The figure above shows yield change for high climate risk (RCP8.5) with effective and ineffective agricultural policy. The yield increases are large, but consistent with recent decades. The analysis does not account for any unprecedented future plateau in technology trends, nor of any systematic impact of extremes on yield trends.

Yield changes are only part of what policy could do to increase food production in the face of climate change. Our idealised framework shows that a very highly effective policy could, in theory, produce an eightfold increase in national crop production.

This increase comes from yield changes due to adaptation (diversification options, irrigation, and new varieties; as explored in the top right box, 'Climate-induced changes in crop productivity'), coupled with crop area expansions and investment in high-yielding varieties to maintain current yield trends. The figure below breaks down these production increases by cause.

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Climate-induced changes in crop productivity

Preliminary results below show that crop yields in Malawi fall in the majority of future scenarios. These results include currently-available adaptation measures. The change is manifest as both mean yield decreases and increases in variability, e.g. crop failure rates. On average across all crops, the yield decrease under RCP8.5 is approximately 5%.

Irrigation and new crop varieties that are drought-resistant show reduced negative impacts. Eliminating water stress by increasing irrigation from the baseline levels, or introducing new drought resistant varieties, more than halves the negative impacts of RCP8.5 (the average yield loss is just 2%).

New adapted varieties that are adjusted to warmer mean temperatures not only avoid yield losses on average – they result in overall yield gains of 5%.

Crop diversification is also a sensible strategy to reduce climate risk. In the example shown below, a switch to soybean is clearly beneficial, in terms of both higher mean yields and reduced incidence of crop failures.

The range of options available to policy for maintaining food production in the face of climate change are explored in the column on the left.

The figure above shows yield changes from 2000 (1990-2010 average) to 2050 (2040-2060 average). Range across climate models includes adaptation of sowing windows and varieties (although only varying between the varieties currently available).

The figure above shows the increase in the number of extremely hot days in Malawi. It shows the seasonal cycle of the number of days per month in which the daily mean temperature is greater than 35degC for the RCP8.5 greenhouse gas concentrations scenario. The baseline period shown in blue covers 1990-2010, the future period shown in brown covers 2040-2060.

The figure above shows maize and soybean crop failure rates for RCP2.6 and RCP8.5 compared to the historical period. A crop failure is defined as 1.5 standard deviations below the historical mean.

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Trade and nutrition outcomes

Increasing domestic production in the face of climate change is only one element of a policy for nutrition security. International trade will ultimately determine what food is available domestically.

A trade and nutrition trade-off analysis is conducted within iFEED, in order to assess the way in which particular trade patterns would result in particular nutrition security outcomes.

Preliminary results for Malawi are shown above.

With ineffective [agricultural] policies out to 2050, the combination of population growth and the 15% climate-induced reduction in domestic food production will fail to meet the national nutrient requirements. Under these conditions, and in the absence of more imported food, nutrition security will become substantially worse. 

In the contrasting scenario of very effective agricultural policies, 2050 population-level nutrient requirements are mostly met. Whilst imports of certain crops may still be needed to boost zinc, iron and fat intake, export potential for many other crops could grow.

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Abstract

Food system outcomes depend upon a range of interacting factors. Domestic climate change impacts, land-use patterns and dietary habits all play a role in determining local supply and demand. National policy influences all of these factors, as well as influencing outcomes through policy on international trade, climate change mitigation targets and domestic nutrition security. The complexity of the inter-related set of climate change and food security risks and responses means that no single modelling system can be used to assess the functioning of food systems under climate change. Here, we present a new integrated assessment framework (IAF) that combines crop-climate-emissions modelling with analyses of the FAO food balance sheets and trade data in order to map out food system outcomes under a range of climate, technological, policy and market futures.

The IAF uses scenarios of land use, productivity and trade that have been developed jointly with stakeholders and form the basis of an empirical examination of the interactions between mitigation, food production and sustainable nutrition through modelling. The scenarios are being used to test hypotheses and, in consultation with stakeholders, develop pathways towards a) sustainable sinks and sources of greenhouse gas emissions, b) climate resilient agricultural food production systems, and c) sustainable nutrition security in 2050. The novel methodology proposed draws on existing tools and expertise to address the timely and fundamental question of how Paris-compliant agriculture can be achieved alongside the SDGs.

The outcome of this process is an assessment of national nutrition security, greenhouse gas emissions and resilience to extremes for a range of policy-oriented future land use patterns. Assessed trade-offs include indications of how plausible trade futures affect national nutrition security. Initial results suggest, amongst other things, that climate-induced losses to crop yields can be mitigated by possible expansion and diversification of cropland.

 

Food system outcomes depend upon a range of interacting factors. Domestic climate change impacts, land-use patterns and dietary habits all play a role in determining local supply and demand. National policy influences all of these factors, as well as influencing outcomes through policy on international trade, climate change mitigation targets and domestic nutrition security. The complexity of the inter-related set of climate change and food security risks and responses means that no single modelling system can be used to assess the functioning of food systems under climate change. Here, we present a new integrated assessment framework (IAF) that combines crop-climate-emissions modelling with analyses of the FAO food balance sheets and trade data in order to map out food system outcomes under a range of climate, technological, policy and market futures.

The IAF uses scenarios of land use, productivity and trade that have been developed jointly with stakeholders and form the basis of an empirical examination of the interactions between mitigation, food production and sustainable nutrition through modelling. The scenarios are being used to test hypotheses and, in consultation with stakeholders, develop pathways towards a) sustainable sinks and sources of greenhouse gas emissions, b) climate resilient agricultural food production systems, and c) sustainable nutrition security in 2050. The novel methodology proposed draws on existing tools and expertise to address the timely and fundamental question of how Paris-compliant agriculture can be achieved alongside the SDGs.

The outcome of this process is an assessment of national nutrition security, greenhouse gas emissions and resilience to extremes for a range of policy-oriented future land use patterns. Assessed trade-offs include indications of how plausible trade futures affect national nutrition security. Initial results suggest, amongst other things, that climate-induced losses to crop yields can be mitigated by possible expansion and diversification of cropland.

 

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