The Agricultural Model Intercomparison and Improvement Project (AgMIP)

Global changes, such as climate change and resource scarcity, pose considerable risks to agriculture. Decision-makers need tools to be able to identify and prioritise the best ways to adapt to these changes. Simulation models are key decision-making tools; they improve understanding of feedbacks and processes in agricultural systems. However, different models have different underlying assumptions and can give different solutions to the same problem.

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is an international collaborative initiative to improve agricultural simulation capacity (see figure). AgMIP is establishing research standards so future research can provide more consistent solutions across regions and models. It achieves this by identifying uncertainties in simulation models, understanding the reasons that models generate different projections of production for a particular site, and testing the performance of models across different sites and environments. We contribute to AgMIP Wheat team and AgMIP Global Economics team.

The AgMIP Wheat team addresses challenges that hamper ensemble modelling approaches. These challenges include datasets from different sources with ensembles of crop models and the multiple formats of simulated model outputs.

The AgMIP Global Economics team conducted extensive model intercomparison. The team considers model performance in assessing the effects of climate change, bioenergy policy, and socioeconomics on agriculture.

AgMIP aims to:

• improve agricultural modelling outcomes
• incorporate model improvements
• collaborate with regional experts
• utilise multiple models to explore uncertainty
• improve scientific and adaptive capacity in modelling.

Many AgMIP applications have been undertaken within the context of climate change impact research and global food security assessments.

Read more about research by the AgMIP Wheat team:

• Martre et al. (2018) The Hot Serial Cereal Experiment for modeling wheat response to temperature: field experiments and AgMIP-Wheat multi-model simulations. Open Data Journal for Agricultural Research 4, 28-34.
• Martre et al. (2017) The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations. Open Data Journal for Agricultural Research, 3.
• Asseng et al. (2015) Rising temperatures reduce global wheat production. Nature Climate Change 5, 143-148.
• Asseng et al. (2015). Benchmark data set for wheat growth models: field experiments and AgMIP multi-model simulations. Open Data Journal for Agricultural Research, 1, 1–5.
• Asseng et al. (2013) Uncertainty in simulating wheat yields under climate change. Nature Climate Change 3, 827–832.

The AgMIP simulations of worldwide wheat production using the point-scale crop models have been compared to similar methods. Read more about this research:

• Liu et al. (2016) Similar estimates of temperature impacts on global wheat yield by three independent methods. Nature Climate Change, 6, 1130-1136. 

The AgMIP Global Economics team is currently preparing for their third round of model Intercomparison. The first phase focused on understanding how the variability of climate change as expressed by climate and crop models could have varying impacts across a range of global economic models. Outputs of this work are in an article in PNAS (Nelson et al. 2014) and a special issue of Agricultural Economics. The second phase looked to expand the representation of varying socioeconomic assumptions in the global economic models to better understand the interactions of economic development, population growth, environmental policy, climate change, and food security. Outputs from Phase 1 and 2 have been highly cited, and are important inputs to a wide range of high level policy reports such as the Intergovernmental Panel on Climate Change’s Special Report on Climate Change and Land and Special Report on Global Warming of 1.5°C.  The third phase looks to build on this work with greater attention on climate variability, healthy and sustainable diets, and sector and region focused analysis.

Key outputs from Phase 1:

• Nelson et al. (2014) Climate change effects on agriculture: Economic responses to biophysical shocks. PNAS 111(9) 3274-3279.
• 8 articles in the Special Issue in Agricultural Economics on Modelling climate change and agriculture. 

Key outputs from Phase 2:

• Wiebe et al. (2015) Climate Change Impacts on Agriculture in 2050 under a Range of Plausible Socioeconomic and Emissions Scenarios. Environmental Research Letters 10(8), 085010.
• Hasegawa et al. (2018) Risk of Increased Food Insecurity under Stringent Global Climate Change Mitigation Policy. Nature Climate Change 8(8), 699–703.
• Ruane et al. (2018) Biophysical and Economic Implications for Agriculture of +1.5° and +2.0°C Global Warming Using AgMIP Coordinated Global and Regional Assessments. Climate Research 76(1), 17–39. 
• Rosenzweig et al. (2018) Coordinating AgMIP Data and Models across Global and Regional Scales for 1.5°C and 2.0°C Assessments. Phil Trans of the Royal Society A 376 (2119), 20160455.
• Stehfest et al. (2019) Key Determinants of Global Land-Use Projections. Nature Communications 10(1), 2166.

Contact Katharina Waha or Daniel Mason-D’Croz for more information.