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Recent studies show that current trends in yield improvement will not be sufficient to meet projected global food demand in 2050, and suggest that a further expansion of agricultural area will be required. However, agriculture is the main driver of losses of biodiversity and a major contributor to climate change and pollution, and so further expansion is undesirable. The usual proposed alternative—intensification with increased resource use—also has negative effects. It is therefore imperative to find ways to achieve global food security without expanding crop or pastureland and without increasing greenhouse gas emissions. Some authors have emphasized a role for sustainable intensification in closing global ‘yield gaps’ between the currently realized and potentially achievable yields. However, in this paper we use a transparent, data-driven model, to show that even if yield gaps are closed, the projected demand will drive further agricultural expansion. There are, however, options for reduction on the demand side that are rarely considered. In the second part of this paper we quantify the potential for demand-side mitigation options, and show that improved diets and decreases in food waste are essential to deliver emissions reductions, and to provide global food security in 2050.

Over 35% of the Earth’s permanent ice-free land is used for food production and, both historically and at present, this has been the greatest driver of deforestation¹ and associated biodiversity loss. Food demand has increased globally with the increase in global population and its affluence. Globally, the demand for food will undoubtedly increase in the medium-term future. The United Nations’ Food and Agriculture Organization (FAO) has projected that cropland and pasture-based food production will see a 60% increase by 2050, calculated in tonnages weighted by crop prices². Another study³ projected a ∼100% increase in cropland-based production, measured in calories, and including both food and livestock feed. The difference between the two studies can be partly explained by shifts towards more cropland-grown livestock feed (as opposed to pasture-based), as countries become richer.

Because agriculture is not on track to meet this demand, according to current trends in yields⁴, it has been widely suggested that we should strengthen global efforts in sustainable intensification of agriculture^{5, 6, 7, 8}. This involves an increase in crop yields while also improving fertilizer, pesticide and irrigation use-efficiency. The existence of yield gaps—the difference between yields achieved in best-practice agriculture and average yields in each agro-climatic zone—suggests that the scope for sustainable intensification is large. Yield gaps are wide in some developing countries, notably in Sub-Saharan Africa, but also exist in developed countries^{9, 10}. However, to complement these supply-side options, demand-side measures may also be necessary^{6, 7, 8, 11, 12, 13}.

The objectives of this paper are to estimate the environmental consequences of the increasing food demand by 2050, and to quantify the extent to which sustainable intensification and demand reduction measures could reduce them. Previous quantitative studies have examined future food systems and their impacts on land use¹⁴. However, few have touched on sustainable intensification³ or demand-side reductions^{12, 15, 16}. The types of model used in these studies include multiple regression analysis³, partial equilibrium models (such as the IMPACT (ref. 17) and GLOBIOM (ref. 18) models), and Integrated Assessment models (such as IMAGE; ref. 19). We based our calculations on a transparent, data-based biophysical analysis, which allows us to vary the key drivers of future land use, including those on the demand side. Our scenario based on current trends predicts a higher need for agricultural expansion than previous models²⁰. Reasons include using less optimistic projections for future agricultural productivity⁴, and not including barriers for land-use conversions. Our methodology is described in more detail in Supplementary Notes 1–2, Figs 1–8, and Tables 1–20. A comparison between our approach and previous studies is detailed in Supplementary Notes.

Our approach uses a model of the current global land system, with 2009 as a base year, based on empirical data. Two key components of this model are: an analysis of land distribution, which enables us to allocate land-use change, and determine natural ecosystem losses and GHG emissions; and a map of agricultural biomass flows, which is required to represent the demand-side options. In Fig. 1 we visualize the land system in 2009 with two Sankey diagrams, one for each component: Fig. 1a shows the distribution of land use, which connects to a representation of agricultural biomass flows (Fig. 1b). Sankey diagrams act as a visual accounting system and facilitate communication to a wide array of stakeholders in land use and management, by illustrating magnitudes, flows and efficiencies.

a, Major global biomes are traced onto three classes of land for agricultural suitability. 40% of the total ice-free land area is suitable for agriculture, of which about half is already in agricultural use for either pasture or cropping. b, Pasture and cropland areas support agricultural biomass growth, which we follow through harvesting and processing stages, to the delivery of final services. In both panels the width of each line is proportional to the magnitude of flow. Black lines show losses.

The interplay between intensification, waste reduction and dietary preferences, informed our choice for six parameter combinations for scenarios in 2050 (Table 1). The probabilities of these key variables are unknown. We examine sustainable intensification to the point of yield-gap closures as the scenario that best represents the collection of supply-side management changes that improve food supply and reduce environmental impact. It includes improved irrigation efficiency and eliminates over-fertilization. Food waste and dietary change are the two most prominent demand-side measures proposed in previous studies^{12, 34, 35} and have been shown to have a large potential, so we have selected these two for closer examination in our study. Changes in agricultural biomass flows and land distributions in the six scenarios are shown in Supplementary Fig. 9. For each scenario we estimated four indicators: forest losses, carbon emissions (from land-use change and agricultural production), fertilizer use and irrigation use (Table 2).

Full table

Future land-use predictions are based on a model that describes the physical characteristics of global land-use and agricultural systems. This model was composed by collecting and fitting together the empirical data from many global datasets. It has two crucial components: the land-use distribution analysis and the agricultural biomass flow map. The analysis of land-use distribution was achieved by overlaying data on global biomes²¹, current land use^{22, 23, 46} and agricultural suitability¹⁰ in a Geographical Information System.

The agricultural biomass flow map allows us to model changes in food supply chains explicitly, together with livestock management systems, agricultural waste, food waste and dietary preferences. It is constructed in the manner of a material flow analysis, so that the flows always add up to the total vegetation growth on cropland and pasture, measured as net primary productivity (NPP) in grams of carbon. It follows the allocation of agricultural vegetation biomass to harvest, residues, losses and ecosystems in the first instance, and then to food, feed, fibre, fuel, soil recycling, losses and intermediate steps. This biomass flow map is first parameterized with 2009 data. FAOSTAT statistics²⁴ provide most of the data, supplemented by some characterization of livestock feed systems²⁵, agricultural residue quantification and uses^{25, 47}, and losses at each stage^{26, 29}.

The model with these two major components was used to assess the consequence of future food demands and changes in the agricultural systems in 12 global regions. Calculations can be described conceptually as the following sequence:

Future consumption for each commodity in a region was calculated as a product of the per capita future dietary preferences associated with socio-economic changes as projected by the FAO (ref. 2) and regional population from the UN mid-range projections³⁶. Aggregated by carbon mass, these add up to a 57% increase in food consumption, underpinned by a 75% increase in cropland productivity. Healthy dietary preferences^{37, 38, 39, 40} are taken as an alternative.

Required future production is calculated on the basis of the predicted future consumption and the characterized agricultural biomass flow map. We assume that agricultural systems in 2050 are different from those of today, in terms of the increased share of cropland-grown feed for livestock, and improved livestock efficiency. Trade between regions is assumed to remain the same. Changes in agricultural waste are implemented at this stage.

Future cropland area is a result of the required future production and yields. The Current Trends (CT) scenarios assume yields in each region will continue to increase linearly at current rates, which are taken from a recent global yield study⁴. The Yield Gap (YG) scenarios assume that sustainable intensification will achieve yield gap closures in all regions, achieving the current potentially attainable yields for their agro-ecological zone. Yield gaps for each region and crop are taken from the GAEZ study¹⁰.

Future pasture area is a result of future demand for grazing and the assumed livestock stocking densities. Unfortunately there are no statistics that could be used to estimate possible stocking densities on global levels. We compared results from a global dynamic vegetation model, a previous livestock energy model²⁵, and livestock product statistics²⁴, to determine that some regions can significantly increase densities (Latin America, SE Asia), whereas in others they are already very high (W. Europe, N. America). Because of many unknowns (about stocking densities as well as livestock management systems), pasture areas are highly uncertain.

The location of future cropland and pasture expansions (or retractions) is based on the land suitability component of the land distribution analysis, described above. Losses of ecosystems and GHG emissions are also dependant on the distribution of agricultural expansion over current land use and biomes in each region.

Fertilizer and irrigation use is estimated on the basis of current trends in their uses and total cropland area for each scenario. The YG scenarios assume an increase in irrigation use efficiency, whereas fertilizer use is set at high enough levels to support optimum yields.

GHG emissions from land-use change (LUC) are calculated on the basis of the ‘before and after’ land carbon pools, which depend on the biome and land use. We used the published methodology and parameters to obtain GHG values of ecosystems⁴⁸. Only emissions from agriculture expansion and contraction are included.

GHG emissions from agriculture associated with fertilizer use and production, rice paddy methane emissions, emissions from enteric fermentation and manure management, as well as energy use in mechanization, are also calculated. Calculations are based on scaling up today’s emissions^{49, 50} linearly with emission sources.

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