Stories 2 - MAgPIE stories #2

In this story, I will talk about the experiences of using a global model to answer policy relevant questions for India. Under the Food, Agriculture, Biodiversity and Land-Use project, our team in undertaking model development and research exercise to pursue interesting policy questions for India such as introduction of a biofuel policy, policy shocks that focus on water use reduction as well as closing the yield gap for India.

China initiates a suite of water policies – water redlines to address the escalating water scarcity, while the impacts of water policies on food and environment systems are yet assessed. Here we develop detailed scenarios of the water redlines regarding water quantity–water cap and irrigation efficiency to assess their impacts using an agro-economic land system model (MAgPIE). The results show that water cap and irrigation efficiency redlines can reduce agricultural water use, without influencing food security. However, China would need to increase import of crops and livestock products to meet the water cap in 2030. Water cap redline can induce additional CO2 and CH4 emissions due to cropland expansion, but fewer N2O emissions because of less fertilizer use. By contrast, improving irrigation efficiency has a reversed effect, with a reduction of CO2 and CH4 emissions but an increase of N2O emissions. The impacts of the synergy of the two redlines are dominated by the water cap redline.

This project aims to fundamentally re-orientate the expectations, actions and disclosure reporting of both private and public actors toward an ‘inevitable’ business-as-usual world in which a transition to a net zero pathway comes late and abruptly, and involves significant economic disruption. IPR provides greater detail on the food and land use system in modelling its interaction with the energy system and economy. This reveals how critical assumptions on food and land use emissions and Nature-Based Solutions (NBS) are to achieving climate goals.

MagPIE outputs were used as an input into a NBS model, which combined MAgPIE insights with NBS literature to estimate NBS supply. Customised inputs on regions, diet, bioenergy and carbon pricing were used to specify the scenario for the modelling.

The landscape perspective is key for understanding how future land-use changes might affect the supply of crucial nature’s contribution to people (NCP). Here, I will show examples of how MAgPIE can be applied to assess how global land-use dynamics affect the supply of critical regulating NCP that underpin the resilience and long-term productivity of farmed landscapes. A key aspect of this work is the preparation of future land-cover maps at field-scale (300x300 Meter spatial resolution at the equator) by linking the Spatial Economic Allocation Landscape Simulator (SEALS) with MAgPIE. SEALS utilizes adjacency relationships, physical suitability and constraints on cultivation for land cover allocation. The MAgPIE-SEALS framework can be used to derive fine-scale changes in landscape structure, pollination, and soil erosion, for providing nuanced global assessments of changes in multiple material and regulating NCP across different land-use futures.

This study assessed how much biodiversity loss can be halted in a Circular Economy (CE) scenario by 2050. We modelled four sectors – food & agriculture, buildings, forestry, and textiles. Levers include replacing meat and dairy with alternative proteins, defining new timber demand projections to simulate wood re-use and recycling, reducing urban areas to account for increases in urban density, and reducing fibre demand to simulate clothing re-use and recycling. The CE scenario halts and starts to reverse biodiversity loss. The food & agriculture lever had the greatest impact (73%) of the four sectors, followed by buildings (10%), textiles (9%) and forestry (8%). Key magpie developments include making minor edits to the food module to allow for the specification of exogenous diet shift scenarios in in every time period as well as editing the BII of some agricultural and forest areas using the PREDICTS database to help simulate regenerative practices.