| Literature DB >> 32639991 |
Olivier Mora1, Chantal Le Mouël2, Marie de Lattre-Gasquet3,4,5, Catherine Donnars1, Patrice Dumas6, Olivier Réchauchère1, Thierry Brunelle7, Stéphane Manceron1, Elodie Marajo-Petitzon2, Clémence Moreau8, Marc Barzman1, Agneta Forslund9, Pauline Marty1.
Abstract
Facing a growing and more affluent world population, changing climate and finite natural resources, world food systems will have to change in the future. The aim of the Agrimonde-Terra foresight study was to build global scenarios linking land use and food security, with special attention paid to overlooked aspects such as nutrition and health, in order to help explore the possible future of the global food system. In this article, we seek to highlight how the resulting set of scenarios contributes to the debate on land use and food security and enlarges the range of possible futures for the global food system. We highlight four main contributions. Combining a scenario building method based on morphological analysis and quantitative simulations with a tractable and simple biomass balance model, the proposed approach improves transparency and coherence between scenario narratives and quantitative assessment. Agrimonde-Terra's scenarios comprise a wide range of alternative diets, with contrasting underlying nutritional and health issues, which accompany contrasting urbanization and rural transformation processes, both dimensions that are lacking in other sets of global scenarios. Agrimonde-Terra's scenarios share some similarities with existing sets of global scenarios, notably the SSPs, but are usually less optimistic regarding agricultural land expansion up to 2050. Results suggest that changing global diets toward healthier patterns could also help to limit the expansion in agricultural land area. Agrimonde-Terra's scenarios enlarge the scope of possible futures by proposing two pathways that are uncommon in other sets of global scenarios. The first proposes to explore possible reconnection of the food industry and regional production within supranational regional blocs. The second means that we should consider that a 'perfect storm', induced by climate change and an ecological crisis combined with social and economic crises, is still possible. Both scenarios should be part of the debate as the current context of the COVID-19 pandemic shows.Entities:
Mesh:
Year: 2020 PMID: 32639991 PMCID: PMC7343151 DOI: 10.1371/journal.pone.0235597
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1An overview of Agrimonde-Terra’s foresight method.
Fig 2The five Agrimonde-Terra scenarios.
Quantitative model inputs for scenarios’ simulation.
| Scenarios | Initial | Metropolization | Regionalization | Healthy | Communities | ||||
|---|---|---|---|---|---|---|---|---|---|
| Model input variables | 2007–09 | 2050 | 2050 | 2050 | 2050 | ||||
| Population (billion) | 6.7 | 9.7 | |||||||
| Impact on crop yields (%) | CC impacts on crop yields in 2050 | ||||||||
| Wheat | - | -13 | -6 | 0 | -6 | ||||
| Maize | - | -8 | -4 | 0 | -4 | ||||
| Rice | - | -13 | -7 | 0 | -7 | ||||
| Soyabean | - | -20 | -10 | 0 | --10 | ||||
| Sugar plants | - | +13 | +7 | 0 | +7 | ||||
| Pulses | - | -15 | -7 | 0 | -7 | ||||
| Fruits&vegetable | - | -11 | -6 | 0 | -6 | ||||
| Impact on cultivable area | (GAEZ 1–4 | ||||||||
| 4341 | 4459 | 4400 | 4341 | 4400 | |||||
| Daily calories/cap (kcal) | 2802 | 3132 | 2785 | 2843 | 2507 | ||||
| Diet pattern (% share) | Ultrap | Animp | |||||||
| Meat | 7.8 | 6.3 | 11.0 | 6.9 | 6.1 | 6.9 | |||
| Dairy&eggs | 6.0 | 6.3 | 7.8 | 6.3 | 5.7 | 6.3 | |||
| Aquatic animals | 1.2 | 1.2 | 1.1 | 1.3 | 1.1 | 1.3 | |||
| Pulses | 2.6 | 1.3 | 2.8 | 3.6 | 7.1 | 3.6 | |||
| Wheat, maize, rice | 42.7 | 40.8 | 37.0 | 40.4 | 34.5 | 40.4 | |||
| Other cereals | 3.0 | 0.8 | 4.0 | 5.3 | 12.6 | 5.3 | |||
| Fruits&vegetable | 6.3 | 5.1 | 6.7 | 6.2 | 15.0 | 6.2 | |||
| Roots&tuber | 6.1 | 6.0 | 6.4 | 8.9 | 3.7 | 8.9 | |||
| Sugar&sweeteners | 8.3 | 13.0 | 7.7 | 7.1 | 2.5 | 7.1 | |||
| Vegetable oils | 10.0 | 14.0 | 10.0 | 5.8 | 8.3 | 5.8 | |||
| Other products | 6.0 | 5.2 | 5.5 | 8.2 | 3.4 | 8.2 | |||
| Crop yields (ton/ha) | Crop yields not including climate change impacts | ||||||||
| Wheat | 2.8 | 4.3 | 3.6 | 3.4 | 3.4 | 3.3 | 3.1 | 2.8 | |
| Maize | 5.3 | 8.7 | 7.1 | 6.6 | 7.1 | 6.6 | 6.0 | 5.3 | |
| Rice | 4.2 | 4.6 | 4.4 | 4.4 | 4.4 | 4.3 | 4.3 | 4.2 | |
| Soyabean | 2.5 | 3.2 | 3.2 | 3.0 | 2.8 | 2.7 | 2.7 | 2.5 | |
| Sugar plants | 68.5 | 90.2 | 78.1 | 75.7 | 76.2 | 74.3 | 72.8 | 68.5 | |
| Pulses | 0.9 | 1.3 | 2.0 | 1.7 | 2.5 | 2.1 | 1.2 | 0.9 | |
| Fruits&vegetable | 14.3 | 19.7 | 21.5 | 19.7 | 30.1 | 26.2 | 17.6 | 14.3 | |
| Feed to output ratios | Feed to output ratios in kg dry matter feed per kg output of animal product | ||||||||
| Bovine meat | 66.8 | 42.9 | 42.9 | 43.0 | 43.0 | 43.0 | 43.0 | 60.6 | |
| Small ruminant meat | 38.0 | 23.6 | 23.6 | 23.2 | 23.2 | 23.2 | 23.2 | 36.1 | |
| Dairy | 3.3 | 2.9 | 2.9 | 2.9 | 2.9 | 2.9 | 2.9 | 3.2 | |
| Pork meat | 5.4 | 5.0 | 5.0 | 5.4 | 5.4 | 5.4 | 5.4 | 5.4 | |
| Poultry meat | 4.4 | 3.9 | 3.9 | 4.4 | 4.4 | 4.4 | 4.4 | 4.4 | |
1GAEZ 1–4: GAEZ land categories 1 to 4, based on the Suitability Index (see S1 File).
2The ‘Communities’ scenario uses the regional food diet assumption but in a context of lower incomes per capita.
3The ‘Metropolization’ scenario was simulated with two alternative food diet assumptions: Transition towards diets based on ultra-processed products (Ultrap variant) and Transition towards diets based on animal products (Animp variant).
4The ‘Regionalization’, ‘Healthy’ and ‘Communities’ scenarios were simulated with two alternative cropping and livestock systems assumptions: Sustainable intensification for cropping systems and conventional intensification for livestock systems (technology variant A); Agroecology for cropping and livestock systems (technology variants B and D); Sustainable intensification for cropping systems and agroecology for livestock systems (technology variant C); Agroecology for cropping and livestock systems, but in a context of lower R&D investments (AE variant); Collapse of cropping systems and backyard livestock (Collapse variant).
Fig 3Consequences of the Agrimonde-Terra and SSP scenarios on world cropland area (Panel A) and world pastureland area (Panel B). Sources: GlobAgri-AgT simulation results and SSP database [49–50] (https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=20). Agrimonde-Terra scenarios include the impacts of differentiated pathways of climate change (Table 1). They are thus compared to SSP/RCP (Radiative Concentration Pathway) combinations of similar mitigation levels (respectively, RCP2.6, RCP4.5 and RCP6.0). The RCP8.5 option would fit better for comparison of SSP5 with Metropolization, however results for SSP5-RCP8.5 do not exist in the SSP database; Regarding SSP-RCP results, those reported are REMIND-MAGPIE-SSP5-RCP6.0, MESSAGE-GLOBIOM-SSP2-RCP4.5, IMAGE-SSP1-RCP2.6, GCAM4-SSP4-RCP4.5, AIM/CGE-SSP3-RCP6.0.
Fig 4Food diets in 2010 and in 2050 under alternative diet assumptions at the world level and for selected world regions (kcal available/cap/day).
Sources: Calculated from GlobAgri-AgT database. Each bar represents food available per food groups in kcal/cap/day at the world level (the first bar of each group) and for eight world regions: Brazil-Argentine; Canada-USA; European Union (UE 27); Former Soviet Union (FSU); China; India; West Africa; East, Central and South Africa (ECS Africa).