Approach

We examine the global-scale capacity for technologies, dietary transitions, and reductions in food waste to create agricultural systems with net negative GHG emissions using the global food system model used in the EAT-Lancet analysis (see section titled ‘Modelling food system emissions’ for more information). We account for the GHG emissions, CDR, and agricultural land cover change by examining historic spatial patterns of agricultural land cover and maps of C in organic biomass and soils. In addition, we examine how food system GHGs might be reduced by consumer-driven dietary transitions, reductions in food loss and waste, closing crop yield gaps (assumed in all scenarios), and the introduction of technologies that reduce emissions from food production or that increase rates of CDR on agricultural lands. Our focus is on 1) diets that vary in the proportion of plant to animal products [11], given that animal products generally produce more emissions than plant derived products, 2) emission reduction and CDR technologies representative of food system wide intervention, spanning cradle-to-grave and land-to-sea (Table 1), and 3) reductions in food discarded by retailers and consumers, which have been shown to have very high mitigation potential [1].

We selected technologies for which there was peer reviewed literature and potential to scale this century, acknowledging, however, that most climate smart technologies in the agricultural sector currently remain limited in their uptake. All estimates of climate change mitigation potential (i.e., combined C benefits from both emission reduction and CDR strategies) were reported in tons CO₂ per hectare. If available, we also reported nitrous oxide (N₂O) and CH₄ (reported as CO₂eq). We provide estimated climate change mitigation potential based on a select suite of technologies, however additional development of more novel technologies, for which there is great potential [18], will increase the climate change mitigation potential of this lever.

Technologies were categorized according to whether they were reducing GHG emissions or acting as CDR strategies (Table 1). We carefully evaluated potential redundancies to avoid the double-counting of climate change mitigation potential. For each technology, we then modelled four scenarios based on a 25, 50, 75, and 100% global adoption rate and assessed the life cycle of agricultural GHG emissions from now to 2050 (see below for a more in-depth description of the model; Table 4). We chose these adoption rates because they give a range of the climate change mitigation potential of different strategies at various intervals of adoption, ranging from no adoption (0%, the ‘business-as-usual’ scenario) to complete adoption (100% adoption). Ultimately, the exact intervals we chose (25%, 50%, 75%, and 100%) are arbitrary, but having five rates of adoption of food system strategies provides enough resolution for readers, policy-makers, and the like to (a) understand the relative effectiveness of different strategies when implemented at the same adoption rate, but also (b) to compare the GHG benefit of different strategies when adopted at different rates (e.g., is adding biochar at 25% adoption rate more or less effective than a 50% adoption of a flexitarian diet?). Furthermore, providing a range of adoption rates is an advance beyond other food system emission modeling studies, which typically apply an adoption rate of 100%.

The food system model

We used outputs from the projections resulting from the EAT-Lancet food system model, which connects food consumption across regions [11]. The EAT-Lancet model is based on the partial equilibrium multi-market food system model named International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT), which is a network of economic, crop, livestock, and water models [11,20]. It projects food demand, food production, and crop land for >40 commodities in >150 regions (that are approximately equivalent to countries, it does not provide subnational estimates) from 2010 to 2050 based on associations with changes in income and population. The EAT-Lancet food system model (henceforth referred to as the “food system model”) reformulates IMPACT such that food demand is an input parameter and food production is an output parameter. Equations used in this model are detailed in the appendix of Willett et al. [11]. The food system model adjusts relationships such as trade flows, processing, feed requirements, and demand for other associated commodities (i.e., oils, sugar, etc.) based on dietary changes. These data are translated into climate impacts based on country specific analyses of CH₄ and N₂O emissions for crops and livestock, and CO₂ emissions for seafood. We used this model for two primary reasons. First, the crop production, crop yield, crop land use, and diet demand projections are publicly available. Second, the EAT-Lancet analysis has gained widespread use and attention in the academic and policy spheres. Using the EAT-Lancet food system model, which has been extensively published and is widely known in the food system and policy spheres, provides more robust estimates than would creating our own food system model, whilst also ensuring the results of our analysis are more comparable to those published previously.

The BAU scenario assumes a middle-of-the-road pathway (i.e., Shared Socioeconomic Pathway 2) for economic growth, dietary trends, and rates of population increase. Projected increase in crop yields reflect potential rates of technology change and interactions between commodities and countries, whilst international food trade continues along gradients of comparative advantage in producer and consumer surplus. In this scenario, global population is projected to grow to 9.2 billion individuals in 2050, from 6.9 billion individuals in 2010. Similar to other analyses, diets transition to include more calories in total (an ~88% estimated increase in global food production from 2010 to 2050) as well as more calories from animal-sourced foods as populations become more affluent.

We also used the other food system scenarios analyzed in the EAT-Lancet report to investigate how changes to food supply and demand might interact with CDR technologies. These scenarios included different assumptions on dietary transitions (both amount of food consumed and type of food consumed), amounts of food loss and waste, and faster than BAU trends in crop yield increases. We analyze these scenarios to examine the potential climate change mitigation potential of technological implementation relative to other food system changes, as well as how the climate change mitigation potential of technology implementation might increase or decrease as other parts of the food system change. A further description of these scenarios is in the following paragraphs.

The alternative diet scenario we also analyzed is where the population slowly transitions to a flexitarian diet by 2050. The flexitarian diet (as described in the EAT-Lancet report, and also known as the EAT-Lancet diet), is where dietary composition meets best recommendations for human health as described in epidemiological and nutrition literature. This diet is predominantly plant-based and contains moderate amounts of dairy, eggs, meat, and fish.

Currently, it is estimated that one third of all food production is lost or wasted [21]. To examine the climate change mitigation potential of reductions in food loss and waste, and the interaction this may have with CDR technologies on agricultural landscapes, we included two food loss and waste scenarios: the first is the BAU scenario, or where current rates of food loss and waste continue into the future; and the second is where rates of food loss and waste throughout the entire food supply chain are reduced by 50% by 2050.

We assumed that yield gaps, or the difference between current yields and potentially attainable yields (as estimated in Mueller et al 2012; [22]), are closed by 2050. This assumption is intended to show that climate mitigation can be achieved in concert with increases in food production, however, we acknowledge that closing yield gaps is a more complicated aim than this scenario reflects. We assume the GHG impact per unit of food produced for potentially attainable yields is identical to current yields, which is a commonly used assumption in food system models, including Tilman and Clark (2014); Balzelj et al (2014); Springmann et al (2016); Springmann et al (2018); and Willett et al (2019). We made this assumption to increase comparability to other analyses but acknowledge that yield increases could both potentially increase (e.g., through excess fertilizer application and increased irrigation) and decrease (e.g., through better fertilizer management, land sparing, and increases in soil organic matter) GHG emissions per unit of food produced [23,24].

We used these diet, loss and waste, and yield scenarios to illustrate how different food system transformations affect overall food system GHG emissions and the potential emissions reductions of implementing CDR technologies at a global scale. We recognize that some of these scenarios may be challenging to implement and will have knock affects (and feedbacks) that permeate throughout the food system. For example, diet transitions towards lower-meat diets could reduce the cost of meat in the short-term resulting from surplus supply, which could have a rebound effect of increasing meat consumption. Similarly, rapid increases in crop yields could decrease the cost of food and result in increased diet demand, while also influencing the countries in which commodities are produced by affecting the relative economic costs of agricultural production across commodities and countries. While understanding these knock-on and rebound effects of food system transformation is integral to implementing them in real-life, they are beyond the scope of the current analysis.

All food system scenarios were implemented uniformly on a country-by-country basis. As such, for example, in the half flexitarian diet scenario, half the population of each country adopts the flexitarian diet whereas half the population continues to consume the BAU diet.

Estimating food system GHG emissions from production and land use change

We estimated the GHG emissions from the global food system for each of the diet, crop yield, and food loss and waste scenarios. This includes GHG estimates from agricultural production, as well as potential GHG emissions resulting from agricultural expansion into natural habitats and GHG sequestration from abandoning agricultural land.

To estimate emissions from agricultural production, we paired estimates of food production, consumption, and land use provided in the supplements of the EAT-Lancet analysis with results from an agro-environmental meta-analysis of life cycle assessments (LCAs) that estimated the GHG emissions per unit of food produced for different agricultural commodities [25]. The LCA meta-analysis used here has a system boundary of cradle to retail store, and provides GHG estimates from five agricultural activities: 1) fertilizer application, 2) manure management, 3) enteric fermentation, 4) methane from rice production, and 5) on-farm energy use. We then estimated emissions from food production, at both the national and global scale, by pairing estimates of the GHG emissions per unit of food produced with estimates of food production. Using this approach, we estimate global food production emissions were 10.08 Gt CO₂eq yr^-1 in 2010, which is similar to existing estimates of GHG emissions from food production [26,27].

To estimate GHG emissions from cropland expansion and cropland abandonment, we paired the estimates of changes in cropland use for each country with estimates of below and above ground biomass. To do this, we first estimated spatial changes in cropland extent at a 2.25 km² resolution using satellite data from 2002 to 2012 (MODIS; [28]). We then overlaid this historic spatial change in cropland extent with estimates of above-ground and below-ground C stores in natural land covers based on IPCC Tier 1 methodology [29,30], further 40% of soil organic C stores are lost following conversion to agriculture, which is in line with recent estimates of the proportion of soil organic C stores lost following conversion from forest to cropland [31]. This allowed us to derive country-specific estimates of average C stores per hectare in areas that experienced cropland expansion or abandonment in the ten years between 2002 and 2012 (i.e., a different value for cropland expansion and cropland abandonment for each country).

We validated this approach using current estimates of GHG emissions from cropland expansion into natural habitats. We did so by pairing historic changes in cropland extent from 2006–2010 as reported by the Food and Agriculture Organization (FAO) with the derived country-level average C stores described above. Using this approach, we estimated land use change (LUC) GHG emissions from changes in cropland extent and location to be an average of 4.0 Gt CO₂eq yr^-1 from 2006 to 2010. This is within the range of existing estimates of agriculture-related land cover change emissions [26,32]. We do not estimate LUC emissions associated with changes in pastureland because satellite images often cannot differentiate pasturelands from nature grasslands or savannahs.

We used the same approach to project LUC GHG emissions in each food system scenario. Specifically, we paired the country-specific estimates of cropland LUC GHG emissions per hectare with the country-specific projections of cropland demand from the food system scenarios. In doing so, we amortized the potential GHG sequestration on abandoned croplands over a 100-year period because the full sequestration potential of abandoned agricultural lands is often only realized over a period of several decades to a century. This approach provides a conservative estimate of the GHG sequestration in abandoned croplands because we only account for the GHG sequestration potential that occurs during the time period of the analyses (e.g., before 2050).

Estimating the climate change mitigation potential of implementing climate smart technologies in agriculture

We estimated the climate change mitigation potential of incorporating technologies to the food system for each food system scenario. We included two broad types of technologies: ones that reduce emissions from food production, and ones that increase the rate of CDR on croplands and pasturelands.

We incorporated six technologies that reduce emissions from food production (see description of the technologies above and Table 1), which resulted in 63 unique emission reduction technology combinations (Table F in S1 Text). These technologies reduce food production GHG emissions from specific GHG emissions sources in food systems (e.g., microalgal feed additives in cattle rations reduce methane emissions from enteric fermentation). To account for the climate change mitigation potential of each technology, we therefore paired the individual technologies with the emissions source they target (e.g., microalgal feed additives in cow rations reduce enteric emissions) and then estimated climate change mitigation potential from this emissions source.

The technologies targeting emissions from food production reported their climate change mitigation potential as the percent of emissions from a specific source they reduce (e.g., 46% average reduction in methane emissions for enteric fermentation in the case of microalgal additives in cattle feed). As such, we calculated the climate change mitigation potential of a given tech as GHG emissions * (1 –tech benefit), where GHG emissions denotes the GHG emissions in the food system from the given source before technology is applied, and tech benefit is a value between 0 and 1 that denotes the extent to which the tech reduces food production emissions. In this case, values of 0.25, 0.5, 0.75, and 1 would reducing emissions from the targeted source by 25%, 50%, 75%, and 100%, respectively.

We incorporated five technologies that increase rates of CDR on existing or abandoned croplands and pasturelands, resulting in a total of 31 unique CDR technology combinations (Table G in S1 Text). The CDR benefit of many of these technologies scale linearly with land use. For these technologies, the total CDR benefit was thus calculated as the product of estimated CDR per hectare and total cropland area and was assumed to remain consistent over time.

In contrast to the other CDR technologies, we implemented agroforestry as a CDR practice only on abandoned agricultural lands to avoid competition with food production. Agroforestry is only possible in locations with adequate precipitation, with potential implementation only occurring in locations that receive >1 meter of precipitation per year (which is approximately equivalent to the precipitation threshold needed for establishment of trees). We therefore assumed that agroforestry would only be implemented in locations with cropland abandonment and in locations where annual precipitation is estimated to be at least 1 meter per year under both current and projected climates in Representative Concentration Pathway (RCP) 4.5 and 8. 5 [33]. To avoid double-counting CDR on abandoned croplands, we do not include natural rates of GHG sequestration on abandoned agricultural lands (as described above) in the locations and scenarios where agroforestry is implemented on abandoned croplands.

Technologies targeting CDR on agricultural lands are often economically profitable if applied infrequently (every five years at most). We thus assumed that technologies increasing rates of CDR were applied once every five years (as opposed to annually). Similarly, we assumed that agroforestry is at most implemented on 1/5 of the area potentially suitable for agroforestry. The total CDR benefit of these technologies would be larger if applied more frequently, for instance once every three years, or alternatively across a larger geographic area. Biochar, compost, and rock amendments use different mechanisms for C sequestration (biochar adds stable forms of organic C to the soil which increases organo-mineral associations and subsequent C storage, compost promotes plant growth and photosynthesis which transforms atmospheric CO₂ into soil organic matter, and silicate rock weathering converts atmospheric CO₂ into inorganic C in soils or soil pore water), and thus we assumed additive effects when co-implemented, however, this assumption remains to be tested in the field [34].

We paired every possible combination of technologies reducing emissions from food production with the technology combinations that increase rates of CDR while accounting for technologies that are not mutually compatible. We further assume technology is implemented at four different rates: 25%, 50%, 75%, and 100%. Note that because of economic limits on implementation of technologies targeting CDR, 100% adoption for these technologies corresponds with application of the technologies once every 5 years (or for agroforestry, that 1/5 of abandoned cropland is converted to agroforestry). We further assumed that implementing these technologies does not affect yields in either crop or livestock systems, and that they do not affect total cost of agricultural production. All analyses were performed using R version 3.6.0.

Model parametrization for technology data

We examined the peer-reviewed literature to estimate the efficacy of each of the chosen technologies (Table 1). When meta-analyses or comprehensive literature studies were available, we used the reported mean values of climate change mitigation potential. For technologies where no meta-analysis or literature review had been conducted, we either a) used data from individual studies in instances where published literature were scarce (e.g., seaweed farming and hydrogen powered fertilizer production) or b) conducted literature syntheses and calculated mean values in instances where published literature were plentiful but meta-analyses were lacking (e.g., biochar and rock amendments). For each technology (Table 1), we collected literature values on GHG emissions reductions (% reduction) and CDR (metric tonnes CO₂/ha), or related values that can be converted to emission reduction or CDR units. When multiple values were collected, we used the mean value in our analysis. For technologies that reduced only CH₄ or N₂O, we converted these emission values to CO₂eq. Technology values applied in the model can be found in Tables 2 and 3). We constrained soil amendment applications based on the global availability of source material (e.g., biomass availability limited compost applications to croplands as opposed to croplands and pasture lands).

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Table 2. Emission reduction technology land use applications, emissions form, mean emissions reductions (%) and source used in food system model.

https://doi.org/10.1371/journal.pclm.0000181.t002

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Table 3. Carbon sequestration technology land use applications, mean carbon sequestration (tonnes CO₂/ha) and source used in food system model.

https://doi.org/10.1371/journal.pclm.0000181.t003

We conducted a literature survey to determine the CDR rate of rock amendments. Peer-reviewed journal articles of enhanced silicate weathering studies were compiled using the search term “enhanced silicate weathering” in Google Scholar, and additional papers were obtained by following citations and by the suggestion of colleagues. Paleoclimate studies or those solely focused on nutrient liberation from rock amendments were omitted, as were laboratory studies of dissolution rates in suspended solution. This is a nascent field; therefore, studies were not excluded based on mineral used, cropping system, or other study design factors. If different rock types or application rates were used in a single publication, these were treated as separate studies. This yielded a total of 8 discrete studies from 5 papers, including five modeled estimates and three from mesocosm studies, from which CDR data were extracted. If only visual results were included, numerical values were extracted using WebPlotDigitizer [35]. Carbon dioxide removal on a per soil mass or per unit area basis was converted to Gt of CO₂ storage per year on a global scale.

Biochar can both remove CO₂ from the atmosphere and reduce N₂O emissions and was thus treated as both a CDR and GHG reduction technology (Table 1). We conducted a meta-analysis to determine biochar’s emission reduction in croplands. A literature search for biochar studies that examined N₂O emission reductions was conducted in Google Scholar using the search terms “biochar” and “N₂O” or “greenhouse gas”. Only studies on cropping systems and with study durations longer than 14 days were extracted for data. Different types of biochar showed no significant effects on emission reduction performance, but biochar application rates were weighted. In total, the N₂O emission data of 248 experimental treatments from 31 peer-reviewed articles were selected. Numerical data given by figures were extracted using WebPlotDigitizer [35]. The gas emission reduction percent was calculated by , which generated a mean GHG reduction of 42.44% (Table 2). Biochar CDR data were obtained by Mayer et al. [17], which synthesizes data from multiple sources. CDR rate was reported in gigatonnes CO₂eq and converted based on application area; Mayer et al. report a CDR rate of 1.05 Pg C/yr, which converts to 3.89 Gt CO₂eq/yr. Dividing this by current cropland area (~1.27 billion hectares), results in 3.06 metric tonnes CO₂/ha (Table 3).

Literature on the impact of compost application on CDR rates was obtained from a review paper by Martinez-Blanco et al. [36] who synthesized the results from 90 articles. The review found that in croplands, compost amendments sequestered 2% to 53% of C applied in compost, depending on the time period being considered (100 years and 1 year, respectively) [36]. We used mean estimates of compost C content (30.61%) [37] to calculate CDR in metric tonnes CO₂eq/ha, based on the California Department of Food and Agriculture recommended cropland compost application rate of 4 tons/ha, which provided a range of 0.082–2.16 metric tonnes CO₂eq/ha from which we used the mean (1.12 metric tonnes CO₂eq/ha; Table 3).

Agroforestry (defined here as a natural resource management system that deliberatively integrates woody perennials (trees, shrubs, palms, bamboos, etc.) on farms and in the agricultural landscapes [38]) can increase both above and below ground C, including soil C. Since we include agroforestry on abandoned agricultural lands adjacent to farms, we consider this agroforestry as trees are deliberately planted within agricultural landscapes. Agroforestry data for combined above ground biomass and below ground soil C was sourced from Kim et al. [39] who synthesized the results of 56 papers and 109 observations on the climate change mitigation potential of agroforestry. This comprehensive study was used to extract mean CDR rate for agroforestry of 7.2 t C/ha, which was converted to 26.42 metric tonnes CO₂/ha (Table 3).

The impact of improved feed on reductions of CH₄ from enteric fermentation in both grass fed and grain fed livestock were obtained from a review paper by Meale et al. [40] For grain fed livestock, we selected feed supplements with the highest impact, including microalgae [40], which ranged from a 25–80% reduction in CH₄, and seaweed feed, which ranged from a 17–63% reduction in CH₄ [41], we thus used a mean reduction value of 46.25%. For grass fed livestock, while a variety of forage strategies were identified, that included early season forage, leguminous forage, and genetically modified forage, these strategies were similar in that increased digestibility (higher nitrogen to lignin ratio) was key to reductions in CH₄ emissions [40]. Such strategies were applied for pasture only, rather than rangelands, as pastures are more likely to be managed and/or planted. Alfalfa pastures (78% alfalfa (Medicago sativa L.), 22% meadow bromegrass (Bromus biebersteinii)) were found to reduce emissions 25% compared to grass pastures, while early season pasture was found to reduce methane emissions up to 45%, thus we used a mean reduction value of 35%. We assume that emissions reductions on early-season pasture and alfalfa-grass pasture represent improvements that can be achieved through intensive grazing and pasture management to increase forage digestibility. Due to uncertainty over the split between pasture-based and grain-based cattle production systems, we further assumed that the average benefit of enteric fermentation technologies was equivalent to the average benefit of pasture-based and grain-based technologies (e.g., a 40.625% reduction overall, with a 35% reduction in pasture-based and 46.25% reduction in grain-based systems; Table 2). This assumption provides an absolute upper bound of the potential emission reductions, which is aligned with the approach we took on all other technologies. There is a need for further research, given that a linear assumption may not translate, given seasonal effects and lifecycle feeding dynamics for both grain- and grass-fed livestock.

Greenhouse gas emissions reductions from the adoption of anaerobic dairy manure digestion were obtained from a meta-analysis conducted by Miranda et al. [42] based on 30 studies, primarily located in Europe. The authors stated that components of emissions calculations varied by paper, but they included emissions from anaerobic digesters, storage, field application, offset of fossil fuels, and offset of mineral fertilizers. Percent relative changes in CH₄, CO₂, and N₂O emissions were evaluated, with nearly all emissions reductions resulting from changes in CH₄ production (78.5% reduction for CH₄ [n = 20] and 76.0% reduction for all GHGs [n = 24]; calculated as best estimates using the Kernal density distribution). Because these values were nearly comparable, only CH₄ was selected for use in our model analysis (Table 2).

Reduced trawling consists mainly of converting trawling equipment to pot fishing technology, thereby reducing the impact on ocean floors and energy costs from drag incurred on the fishing vessel as a result of trawling. We sourced data on reduced trawling from a literature synthesis of 148 animal based food studies by Hilborn et al. [43], 29 of which were for capture fisheries. In 2010, fish production was assumed to be 40% non-trawling fisheries, 10% trawling fisheries, and 50% aquaculture (FAO State of Fisheries 2018) [44]. We estimated the CO₂ emission reductions of decreasing trawling by assuming that all trawling fish (if 100% management was 100% adopted) was instead substituted by an equivalent amount of fish caught via non-trawling technologies, which resulted in a emission reduction value of 58.97% (Table 2).

Haber‐Bosch (HB) nitrogen fertilizer production accounts for ~1% to 2% of the world’s energy usage translating to 1.4% of energy-based CO₂ emissions or 0.5 Gt CO₂/yr [45]. Developing industrial‐scale processes to synthesize C neutral fertilizers via hydrogen generation from renewables (solar, wind, and hydropower) can reduce the upstream GHG emissions and cut energy costs [15,16]. Given the projected doubling of HB fertilizer production from 100 Tg N/yr (in 2000) to 200 Tg N/yr by 2050 [46], we estimate that HB fertilizer production will result in 1 Gt of CO₂ emissions by 2050 under BAU practices. We assumed that the climate change mitigation potential of electrified HB production scales linearly against this value, with an emission reduction factor of 47% (Table 2), or 0.47 Gt CO₂ emissions reductions compared to the baseline.

At the time of analysis, only one study was known to have analyzed the CDR potential of seaweed farming [47]. Using current global oceanographic, biological, and production data, Froehlich et al. [47] explore the potential of seaweed aquaculture to offset C emission through CDR now and in the future. We extracted a CDR value of 11.1 metric tonnes CO₂eq/ha directly from the text (Table 3).

Dietary transition effects on gross and net GHG emissions worldwide

We compared a baseline BAU against which the climate change mitigation potential of different interventions can be examined. Our BAU scenario assumes that diets, agricultural systems, and the global population continue to shift along historic middle-of -the-road development trajectories. The model results point to a 75% increase in GHG emissions from food production between 2010 to 2050, or an increase of 10.5 Gt CO₂eq/year in 2010 to 18.4 Gt CO₂eq/year in 2050 under the BAU case (see Methods). This increase in GHG emissions from food production is similar to past estimates and reflects a combination of land-use change, CH₄ and nitrous oxide (N₂O) emissions from agriculture and food waste [1,3].

Previous research suggests that such GHG emissions can be reduced by ~50–70% via worldwide adoption of diets with smaller contributions of animal sourced foods than average diets, such as a Mediterranean, pescatarian, or vegetarian diet [11,12]. Our model suggests a similar magnitude of global GHG emissions abatement via the adoption of a flexitarian diet, which is higher in fruits, vegetables, legumes, whole grains, and nuts, and lower in red meat, eggs, and starchy vegetables (potatoes) than the current average global diet (see the EAT-Lancet report for more details) [11]. In terms of the maximum capacity, if the entire human population adopted a flexitarian diet by 2050, we estimate a reduction in gross GHG emissions of 8.2 Gt CO₂eq, which is separated between 2.4 Gt CO₂eq of reductions in N₂O, 3.4 Gt CO₂eq of reductions in CH₄, and 2.4 Gt CO₂eq of reductions in production-related CO₂ emissions annually (Fig 1). Our model also suggests a maximum capacity of 1.1 Gt CO2/yr of gross CDR with 100% adoption of flexitarian diets with full crop yield gaps closure (Fig 1B), reflecting the magnitude of land savings and increased terrestrial C sinks compared to the BAU case. However, it is important to note these CDR benefits will only be realized if abandoned agricultural lands remain outside of human use in the long term and the C sinks remains resilient to climate impacts.

Our findings do not reveal an obvious pathway through which adoption of flexitarian diets can achieve net negative GHG emissions in the future food system. Net negative emissions are not apparent under dietary shifts in the model because gross CDR potential does not eclipse the magnitude of gross GHG emissions with the adoption of flexitarian diets, absent any other mitigation approaches (Fig 1). However, that dietary transitions would happen in the absence of new management practices and technology deployment is highly unlikely–and so we address the ensemble of possibilities involving technology and consumer decisions, both singly and systematically across scenarios, below.

Gross and net GHG emissions from global technology deployment

We examine the potential for technology deployment to alter gross GHG emissions reductions and gross CDR under the assumption that diets follow BAU trajectories (see Methods; Table 1). We distill our analysis into interventions that can reduce gross GHG emissions (Fig 1A) from those involved in gross CDR (Fig 1B), both singly and in combination (i.e., ‘All Technologies’). We estimate that technologies aiming to reduce gross GHG emissions during agricultural production could mitigate between 1.5 (25% adoption, which corresponds with application on 25% of agricultural lands every 5 years) and 6.0 (100% adoption) Gt CO₂eq /yr in 2050 (Fig 1A), or ~2.2 Gt CO₂eq /yr fewer gross GHG reductions estimated for 100% adoption of flexitarian diets by 2050 (cf. Fig 1). The benefit of individual production technologies ranged from marginal (i.e., <0.1 Gt CO₂eq /yr) in the case of new seafood farming practices (i.e., trawling management), to substantial (>1.0 Gt/yr) for converting Haber-Bosch nitrogen fertilizer production to hydrogen-power (1.1 Gt CO₂eq /yr), supplementing feed with additives that reduce methane emissions in livestock systems (1.7 Gt CO₂eq /yr), and applying biochar to cropland soil that reduce N₂O emissions (2.3 Gt CO₂eq /yr). Anaerobic manure digesters are estimated to have a moderate GHG benefit (0.9 Gt CO₂eq/yr) (Fig 1A).

For technologies that increase CDR in croplands, we estimate climate change mitigation potential ranging from 6.9 (25% deployment) to 27.5 (100% deployment) Gt CO₂eq/yr when all technologies are implemented and whilst assuming that yield gaps—the difference between current yields and potentially attainable yields—are fully closed (Fig 1B; Table 4). The low-end gross CDR estimate is ~ six times greater than the land sparing benefits of dietary changes; the high-end estimate is ~27 times greater. At 25% deployment potential, organic soil amendments (i.e., compost and biochar) sequester 0.3 Gt CO₂eq/yr globally, assuming applications occur once every five years in cropland soil. Amending cropland soil every five years with silicate rock dust (known as enhanced weathering), which accelerates the formation of long-lived carbonates, results in 1.3 (25% deployment) to 5.2 (100% deployment) Gt CO₂eq/yr [48]. Seaweed farming, whereby seaweed is farmed at the ocean’s surface and buried in the deep ocean, removed between 2.7 (25% deployment) to 10.7 (100% deployment) Gt CO₂/yr. In addition, our model projects unrivaled CDR capacity via agroforestry (tree planting in farmlands, which can sequester above and below ground C), varying between 2.6 (at 25% adoption) to 10.3 (at 100% adoption) Gt CO₂eq/yr (Fig 1B). We assume that any cropland abandoned that is presently suitable to tree growth is available for agroforestry thereby avoiding conflict between land needed for food production to meet human nutritional demands and land set aside for CDR. The magnitude of CDR from agroforestry would potentially be higher if extended into lands currently used for food production through practices such as hedgerows or intercropping.

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Table 4. Adoption rate scenario (technology, dietary change, and food loss and waste reduction global adoption rate) data generated (gross or net GHG benefit), and assumptions (caloric consumption, yield gaps) organized by figure.

BAU stands for business-as-usual, ER stands for emissions reduction, CDR stands for carbon dioxide removal, and GHG stands for greenhouse gas.

https://doi.org/10.1371/journal.pclm.0000181.t004

Simultaneously implementing technologies that reduce GHG emissions and remove C from the atmosphere demonstrates a capacity for food systems to result in net negative emissions of 13 Gt CO₂eq /yr in 2050 if fully deployed. This scenario assumes BAU diets, no change in food loss or waste, and 50% of technology deployment at scale. This contrasts with the dietary lever, for which our model indicates that complete de-carbonization of the agricultural sector is not possible and that net negative emissions cannot be achieved if not paired with technological implementation. However, because neither dietary choice nor technology deployment operate in isolation, and further because relying on any single intervention whilst ignoring others increases the potential risk of not meeting global GHG targets, we therefore examine the climate change mitigation potential of simultaneously implementing technologies and dietary transitions below.

Scenarios for achieving net negative GHG emissions in the future food system

We examine the option space for joint implementation of dietary, technological, and food loss and waste changes to result in a food system with net negative emissions, recognizing the common pitfalls in either and barriers to regional decision making (see Discussion). We examine the model across both a discrete set (Fig 2) and full ensemble (Fig 3) of global modeling scenarios. The BAU diet scenario combined with closed yield gaps and with >50% adoption in combination with ensemble of technologies results in C neutrality by 2050 (Fig 2); additionally, halving food loss and waste, which could involve a set of policies, technologies and consumer habits [49], results in 6.7 Gt of net negative GHG emissions under the same assumptions (see Methods). Full scale-up of technological interventions (100%), combined with a halving of food loss and waste and closing yield gaps, results in net negative emissions of >20 Gt CO₂eq/yr from the world’s food system in 2050 (BAU diet scenario) which increases to >30 Gt CO₂eq/yr with 100% adoption of a flexitarian diet (Fig 2). Although our model does not simulate net negative GHG emissions from the food system under dietary shifts, uptake of flexitarian diets increases the magnitude of net negative GHG emissions under all technology deployment scenarios (Fig 2), while also offering co-benefits to other aspects of environment and health that would not be realized with technological interventions singly (see Discussion).

Beyond these discrete simulations, we examine the full option-space for net negative GHG emissions from the 2050 food system, spanning +20 to -33Gt CO₂eq emissions annually on a global scale (Fig 3). It should be noted that technologies such as agroforestry and seaweed farming have a substantial influence on these findings given their high CDR potential (~10 Gt CO₂eq/yr). For example, removing seaweed farming from this analysis would effectively move the isoline for net-zero emissions (Fig 3) from ~45% adoption to ~70% adoption under a BAU diet scenario. The climate change mitigation potential of agricultural interventions are in some cases strongly affected by dietary choice: technologies targeting GHG emissions from animal sourced foods become less important as consumers shift to flexitarian diets and the global production of livestock decreases. While interventions that reduce CH₄ emissions from livestock (e.g., feed alternatives), and those targeting manure emissions (i.e., anaerobic digesters) and fertilizer N₂O emissions are sensitive to dietary choice, those targeting C sequestration through soil interventions, ocean trawling, and agroforestry are more reliant on the rate at which agricultural yields are increased and food loss and waste is decreased. It is important to note, however, that the GHG sequestration benefits of technologies that sequester C in soils or biomass (e.g., biochar applications, agroforestry) were based on primary analyses that examined these benefits in the short (several years) to medium (25 years) time durations. It is possible that in the longer term (> several decades), the annual emissions benefits of these technologies might reduce as C in living biomass and in soils saturates.

Spatial patterns across countries

While global analyses help to identify the total potential benefit of different strategies, it is important to downscale results, because food policies are often implemented at country or regional scales. We therefore explore the climate change mitigation potential in each country in a scenario with 50% adoption of technology and flexitarian diets by 2050, combined with a halving of food loss and waste (Figs 4 and 5). We focus on this middle path scenario as a reasonable compromise between tractability and complexity in our country-scale analysis, recognizing that there are vast numbers of options with varied amounts of dietary change, reductions in food loss and waste, and technological interventions, and further that full implementation of any single lever might not be possible due to taste preferences, sociocultural values, and the economics of dietary transitions and technology implementation.

We estimate the largest climate change mitigation potential per capita in: (1) areas with high rates of ruminant production and caloric consumption, especially New Zealand, Brazil, and the United States; (2) countries where ruminant abundance is moderate to high, with high GHG emissions per unit of production, such as central Asia; and (3) areas with yield gaps where agricultural intensification can reduce land use change emissions, benefiting natural CDR, principally parts of West Africa and Southeast Asia. In contrast, our findings highlight smaller per capita benefits in countries that consume smaller amounts of meat in general (and ruminant meat in particular), or where meat consumption might increase when transitioning to a flexitarian diet (as occurs in some low-income countries) (Fig 4).

Across regions, we estimate the largest national climate change mitigation potential in countries with large populations and a large amount of land devoted to agricultural production, such as China, Brazil, the US, India, and Indonesia. Many of these countries have high GHG emissions, with food sector innovations contributing to overall curbing GHG emissions. For instance, in China we estimate over 1 Gt CO₂eq/yr in GHG savings by halving food loss and waste, transitioning halfway to a flexitarian diet, and implementing all technologies at 50% of their potential adoption rate (Fig 5). Importantly, given the potential for unwanted outcomes to emerge with either/or strategies, it’s critical that decision makers consider a mix of options that are locally anchored in a co-benefit space that considers human and environmental health, economic livelihoods, and climate mitigation and adaptation.