Costs of transitioning to net-zero emissions under future climates

doi:10.21203/rs.3.rs-2939816/v1

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Costs of transitioning to net-zero emissions under future climates

https://doi.org/10.21203/rs.3.rs-2939816/v1

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Land managers are challenged with balancing priorities for agri-food production, greenhouse gas (GHG) abatement, natural conservation, social and economic license to operate. We co-designed pathways for transitioning farming systems to net-zero emissions under future climates. Few interventions enhanced productivity and profitability while also reducing GHG emissions. Seaweed (Asparagopsis) feed supplement and planting trees enabled the greatest mitigation (67–95%), while enterprise diversification (installation of wind turbines) and improved feed-conversion efficiency (FCE) were most conducive to improved profitability (17–39%). Mitigation efficacy was hampered by adoptability. Serendiptiously, the least socially acceptable option – business as usual and purchasing carbon credits to offset emissions – were also the most costly options. In contrast, stacking synergistic interventions enabling enteric methane mitigation, improved FCE and carbon removals entirely negated net emissions in a profitable way. We conclude that costs of transitioning to net-zero vary widely (-64% to + 30%), depending on whether interventions are stacked and/or elicit productivity co-benefits.

Earth and environmental sciences/Environmental social sciences/Climate-change impacts

Earth and environmental sciences/Environmental social sciences/Climate-change adaptation

Earth and environmental sciences/Environmental social sciences/Climate-change mitigation

Scientific community and society/Social sciences/Interdisciplinary studies

Earth and environmental sciences/Environmental social sciences/Sustainability

Climate emergency

Net-Zero 2050

Livestock

Carbon Storage

Soil Carbon

Food Security

Environmental Stewardship

Social license to operate

adoptability

Increasing atmospheric greenhouse gases concentrations (GHG) evokes global warming, intensifying the global water cycle and increasing the risk of extreme events^1,2. During the last four decades, the frequency of natural disasters borne by extreme events has almost quadrupled, causing the equivalent of US$280B in crop and livestock production losses³. However, regional variation in the effects of climate change, including seasonal and regional patterns of precipitation may - in some cases - realise benefits, such as reduced frequencies of extreme cold⁴ and reduced waterlogging in arable landscapes⁵.

Carefully conceived adaptations may enable food systems transformation, but often only if due consideration is given to a wide range of socioeconomic, institutional and cultural factors in the co-design process^6,7. As a corollary, few bona fide examples of food systems transformations exist, perhaps because research has traditionally progressed in a reductionist fashion, with primarily unidisciplinary and isolated foci instead of multidisciplinary system approach. Research designed to address only one GHG emissions reduction intervention has given rise to a phenomenon called ‘Carbon Myopia’, representing studies in which singular interventions are evaluated and rated based primarily on carbon removals or GHG emissions avoidance⁸. Effects of, or interactions caused by, such interventions on or with extraneous social factors, such as prosperity, productivity, risk, environmental stewardship and social license, are often downplayed or ignored completely, even though such collectively factors determine the whether an intervention will be sustainable, and ultimately, successful^8,9. Compared with unidisciplinary approaches however, multi- and transdisciplinary work (cross discipline and cross institutional, respectively) tends to be more difficult to lead, and more costly in time and money to execute, and hence the majority of GHG emissions mitigation research continues to progress in siloed pockets^8–10.

The bulk of past climate change adaptation and mitigation work for the livestock sector has however been premised primarily upon biophysical lenses. Such studies have examined, for example, (1) evaluation of GHG emissions of cropping and livestock systems¹¹, (2) comparisons of GHG emissions from model ensembles¹², (3) and the influence of genotype by management by environment combinations on GHG emissions and productivity^13–16. Much less work for sheep and beef systems has focused on how interventions aimed at adaptation and/or mitigation influence productivity, profitability and GHG emissions, although similar efforts for other sectors (such as dairy and grains) indicate that conclusions drawn differ considerably when economics are also taken into account^17,18. While land managers have multiple potential opportunities to reduce GHG emissions (e.g. through carbon removals, GHG emissions avoidance, or GHG emissions mitigation), scientific literature that develops, contrasts and identifies economic pathways to carbon neutral farming systems is scarce. Here, our aim is to (1) co-develop a range of management, genetic, environmental, livestock and landscape interventions for both adapting livestock systems while reducing GHG emissions, (2) analytically cost (economically and biophysically) a range of plausible pathways to net-zero emissions and (3) balance these with the needs of stakeholders within the ‘reality’ of a working farm and industry co-designing interventions with a ‘regional reference group’ (RRG) of industry experts and practitioners to ensure relevance, credibility and legitimacy of our proposed adaptation/mitigation interventions¹⁰. We calibrated our models and social research using two real farms in southern Australia, refining analytical methods based on feedback from the RRG. We then explored the impact of singular and stacked (bundled) interventions on productivity, profitability, GHG emissions and adoptability¹⁹. Stacked interventions were categorised into groups based on similarity of intent, including ‘Low Hanging Fruit’ (simple, reversible, immediate changes that could be made to the farm system), ‘Towards Carbon Neutral’ (interventions primarily designed to reduce GHG emissions), ‘Income Diversification’ (enabling revenue generation from enterprises other than livestock to reduce dependence on rainfall as a primary source of income) and ‘Transformational Adaptations’ (long-term, innovative, restructuring, cross-sectoral, potential economic, social and environmental high risks). While we exemplify our methods from two case study farms, the approach could be generically adapted to any location, production system or transdisciplinary problem.

The nexus between productivity, profitability and net greenhouse gas emissions

In comparing sheep and beef production systems in 2030 and 2050, we revealed that (1) few individual interventions elicited significant impact on the three dimensions of productivity, most likely annual average profitability (herein profit) and GHG emissions and (2) the impacts of the production system and intervention were greater than the impacts of climate change per se (Figs. 1, 2).

Interventions targeting livestock enteric methane (methane produced by fermentation in the gut) were most promising for reducing GHG emissions, such as the seaweed feed additive Asparagopsis taxiformis, which under the assumed conditions of the analysis could reduce on-farm CO₂e by 46–72% under future climates (Fig. 1a, 1b, Fig. 2a, 2b, Tables S1-S4). However, Asparagopsis when used as a feed supplement, it was identified as one of the costliest singular interventions, reducing profits by $23–25/Mg CO₂e mitigated (Fig. 1c, 1d, Fig. 2c, 2d). Interventions that were considered most adoptable by the group of expert practitioners (the RRG) often had the lowest mitigation potential (Figs. 1, 2).

Climatic diversification - purchasing a farm in a distinctively different climatic zone - and altering lambing/calving times yielded the greatest improvement in productivity (16–18%), while enterprise diversification (capital investment to enable grapevine/wind turbines enterprises), pasture renovation with deep-rooted legumes and improvements in animal genetic feed-conversion efficiency (FCE) were most conducive to improved profit (17–39%). Interventions that achieved the greatest gains in productivity and profit tended to do little influence to reduce GHG emissions, underlining the challenges inherent in decoupling the tight linkage between productivity and GHG emissions.

Improving FCE - considered akin to good farm management practice by increasing pasture utilisation and liveweight gain per unit utilisation in a sustainable way increased profit ($70–250/Mg CO₂e mitigation; Fig. 1c, 1d, Fig. 2c, 2d, Tables S1-S4), with only modest impacts on productivity (0–6% increase), and on GHG emissions mitigation (-9-15% reduction). Transformational improvement in animal genetic feed conversion efficiencies (TFCE) promises increases in livestock production and farm profits by 8–39% and reducing net GHG emissions by 11–17%, though is aspirational according to the RRG because further livestock genetic science is needed to improve FCE before such genotypes could be widely available (Fig. 3a, 3d, Fig. 4a, 4d).

The modelled climate change scenarios had significant implications for the extent of carbon removal on the case study farms. By 2050, GHG mitigation potential associated with improving soil carbon stocks was reduced by 6–13% for interventions that expanded farm area covered by deep-rooted perennial legumes (in this case lucerne or Medicago sativa), and by 20–40% for carbon sequestered by planting native vegetation (Figs. 1, 2, Tables S1-S4). Planting trees decreased profit per unit CO₂ mitigated compared with incorporating lucerne into pastures (Fig. 1c, 1d and Fig. 2c, 2d). This was because lucerne enabled pasture growth and livestock production, whereas trees reduced productive pasture area and livestock carrying capacity.

Biochar was considered to be highly adoptable by the RRG and was proposed as a livestock feed supplement based on anecdotal evidence suggesting that use of biochar (1) improved liveweight gain, (2) reduced enteric methane and (3) enriched organic carbon content of manure. In line with the people-centric nature of this research, we conducted on-farm experiments with free-choice biochar, fed ad libitum over 12 months to beef steers. Little impact of biochar was observed on either liveweight gains or manure organic carbon content (Fig. S1). From the modelling results, we showed that biochar feed supplement would potentially reduce net GHG emissions by 8% and increase profit by 18%, saving $290 Mg CO₂e^− 1 per year (Fig. 1). However, the effect of feeding biochar differed across production systems (cf. Figure 1c, d with Fig. 2c, d). By assuming minimal effects of biochar feed supplement on sheep liveweight gains and wool production, the elevated costs of implementation reduced profits by 10% despite an 18% reduction in GHG emissions for both climate horizons (Fig. 2).

To buffer against the possibility of reduced rainfall under future climates, income diversification avenues that were independent of rainfall were co-designed. These interventions included planting a small irrigated area of grapevines on the sheep farm, hosting wind turbines on the beef farm, and climatic diversification by purchasing a block of land for cattle farming in a distinctively different climatic zone. While wind turbines, developing irrigated grapevines and purchasing another beef cattle farm improved farm profits by 12–18%, 20% and 15% respectively (Figs. 1 and 2), effects on productivity and profit varied widely. Buying an extra beef farm in a diverse agro-climatic region improved production by 15% (Fig. 1), but this came with a cost of increased associated GHG emissions (net and emissions intensity, Tables S1-S2).

The RRG provided insights into income diversification interventions. For example, purchasing a farm in a diversified climatic zone (north-eastern Tasmania, compared with the beef farm that was located some 400 km away in the north-west of the state) would require additional labour, costs of transporting cattle between regions, sometimes added infrastructure on the new farm, and higher management skills coordinating separate farm enterprises. Still, many farmers do precisely this, profitably. Growing irrigated grapevines requires specialist input, and on-ground evidence such as existing successful grape-growing, to identify suitable microclimates. The option of hosting wind turbines on the property requires proximity to three-phase powerlines (to feed into the main electricity grid) as well as high prevailing windspeeds. These conditions are not usually common or widespread. Despite this however, the sheep case study farmer was pursuing investment in irrigated grapevines, while the beef farmer had signed a lease for a company to lease part of his land for wind turbines.

Contextualised adaptation-mitigation bundles: stacking interventions

We next co-designed and stacked together contextualised bundles of inteventions, each group based on synergies of outcome intended (i.e., interventions were constructed and the outcomes modelled, Figs. 3 and 4). Simple, immediately actionable and relatively reversible changes to the farm systems were stacked together into a ‘Low Hanging Fruit (LHF)’ theme improved annual productivity (15–16%) and increased profit by 19–25% but increased GHG emissions by 6–18% compared with the baseline scenarios under future climates.

A Towards Carbon Neutral (TCN) package was co-designed with the intent of improving productivity and reducing year-on-year GHG emissions. This bundle of interventions combined the LHF package with mitigation interventions (methane inhibition vaccine, planting trees and renovating pastures with deep-rooted legumes). The TCN package respectively increased livestock productivity by 18–20% (beef farm) and by 36–40% (sheep farm) under future climates (Tables S5-S8). Despite added costs associated with buying land and planting trees and the costs of a theoretical CH₄ vaccine inoculation (Table S9), biophysical changes realised from pasture renovation increased profits by 33–37% and 60–68% for the beef and sheep farms, respectively. The TCN package reduced net GHG emissions by 37–69% for the beef farm (Fig. 3) and 29–34% for the sheep farm (Fig. 4), diluting emission intensities by 30–50% (Tables S5-S8). While the TCN package was highly ranked in terms of profit, production and GHG emissions evidenced by equally distributed ternary plots (Fig. 3c, 3f, Fig. 4c, 4f), the incorporation of strategies such as the methane inhibition vaccine (which is not commercially available) and its accompanying social concerns, reduced the adoptability of TCN overall.

Multiple combinations of stacked interventions facilitated profitable transitioning of farm systems to net-zero emissions (Figs. 3ad; 4ad). The four carbon neutral packages (CN1-4) were co-designed with consideration to various areas of trees planting, adoption (or not) of livestock genotypes with transformational gains in FCE (TFCE) and/or renovation of pastures with the deep-rooted perennial legume, lucerne. For the beef farm, feeding of Asparagopsis, planting trees and TFCE were most promising (CN1 and CN2), facilitating not only carbon neutrality but also increasing productivity by 13% with a possible 30% profit gain under 2050 climates (Fig. 3). For the sheep farm, productivity and profitability gains associated with carbon neutral GHG positions were more likely to be realised with stacking of Asparagopsis feed, planting trees and renovating pastures with lucerne, such that CN3 and CN4 increased production and profit by 8% relative to the baselines, respectively (Fig. 4).

Costs of transitioning to net-zero emissions under future climates

The potential effects of a carbon market existing were analysed in which GHG emissions were taxed and offsets credited, respectively. The case study farmers simply paying the tax on the net CO₂e^− 1 from their farm systems, with no practice changes to reduce GHG emissions, reduced farm profits by 64% and 33% for the beef and sheep farms, respectively (Fig. 5). Using Asparagopsis as a feed supplement would reduce operating profit by 7–8%. Paying a carbon tax on net residual GHG emissions would improve profit by 58% (beef farm) or 25% (sheep farm) relative to the baseline farm in which all net GHG emissions were taxed (Fig. 5c, d, g, h). When feeding of Asparagopsis was stacked with purchasing an extra farm that was planted with trees (ASP + PT), a further 38–87% net GHG emissions were offset (Fig. 5a, b, e, f). Relative to the baseline farm in which all residual GHG emissions were taxed, ASP + PT improved profits by 34%/68% for the sheep/beef farm.

The CN packages intervention stacked TFCE (CN1 and CN2) or lucerne in the pasture mix (CN3 and CN4) with ASP + PT to reduce GHG emissions, while further reducing the burden of taxes on emissions carbon taxes. For the beef farm, there was little difference in net GHG emissions after implementing TFCE (CN1) and lucerne in the pasture sward (CN3), both with residual GHG emissions of ~ 1,000 Mg CO₂e (Fig. 5a, b). Profits after paying the carbon tax were greater for the CN1 package (Fig. 5c) compared with the CN3 package (Fig. 5d), and were three times greater than the baseline farm, even after paying a tax on residual GHG emissions. Additional land for tree plantings was required for the beef farm’s CN1 and CN3 packages to become net-zero (CN2 and CN4 packages; Fig. 5a, b). For the sheep farm, the lucerne CN3 package achieved net-zero, with net sequestration of ~ 1,400 Mg CO₂e (Fig. 5f) and pre-carbon tax profit of $1,366K (Fig. 5h), which slightly declined if surplus carbon offsets were sold (Fig. 5h).

The RRG highlighted potential difficulties in implementing CN packages (Table S10), while the results clearly demonstrate that adoption of mitigation practices were at least three times more profitable for the beef farm and 1.5 times more profitable for the sheep farm, relative to the ‘do nothing different scenario’, where the two farming systems conducted business as usual and all their net GHG emissions were subjected to carbon taxes.

A new framework for co-designing bundles of mitigation-adaptation interventions

This reseach was conducted using co-design framework that embodied the best available science for effective climate action, along with the critically important engagement of farmers and other interested parties. The aim was to analyse potential implications for farm systems that could flow from the key outcomes of the COP27²⁰ being achieved. The people-centric framework meant engaging end-users who would be directly or indirectly affected by the climate crisis and policies responding to it, to develop fit-for-purpose farm interventions and thematic innovation bundles^8,21. The flexible design of this framework, using integrated, interchangeable and numerical and social systems thinking is adaptable to explore sustainability indicators associated with multuple analogous production systems, locations or climatic horizons. This co-design framework and research builds end-user awareness, knowledge and confidence to increase chances of adoption, which also engenders social license for landholders to operate stemming from public recognition of producers agri-environmental stewardship²².

The cost of transitioning farm systems to net-zero emissions

The findings from the modelled analyses of case study farms suggest that if the future climates eventuated, and the social cost of on-farm GHG emissions was to be brought home to producers, profitable individual and combined options exist to change their systems and reduce their emissions whilst maintaining and even increasing their profits in the future compared with the current profit of their farm businesses. Eventually, the most socially unacceptable option – continuing business as usual and purchasing carbon credits to offset net farm emissions – was also the most costly option. Furthermore, relying on carbon credits to offset a GHG emissions may be perceived as transferring responsibility to someone else. From an ethical standpoint, producers must take ownership of their farm’s carbon emissions and strive to reduce them within their own operations.

For the case study farms analysed, stacking together interventions enable improved pasture growth and some added soil carbon sequestration (e.g. renovation with lucerne, and ignoring additionality), and adopting future animal genotypes that are able to gain more weight with the same or less amount of feed (FCE, TFCE), along with planting small areas of trees relative to farm size (50–220 ha in this case and subject to permanence considerations), in combination, went some considerable way towards negating all farm emissions. When interventions to reduce GHG emissions delivered a productivity co-benefit – such as improved metabolisible energy supply per unit area with legumes, or shade and shelter provided by trees – on the case study farms there both carbon neutrality as well as 8% gains in productivity and profit were possible under future climates. As ever, these positive findings are tempered by the assumptions and limits of the analyses, practical barriers to adoption, development and acquisition of new knowledge required to develop some of the technologies needed and to implement changes in farm practices, and, ultimately by consumer needs and expectations and the public policies that will be implemented. These extraneous factors may influence adoptability, as shown by feedback from the RRG on some of the mitigation/adaptation bundles here (e.g. purchasing and extra farm or leasing part of the land to wind turbine companies to generate electricity).

The need of the case study farms to buy carbon credits to offset their farm GHG was reduced as additional interventions were combined, especially when such interventions catalysed improved animal performance (CN packages, Fig. 5). The results reiterated the enduring imperative faced by livestock producers to adapt profitably to the changing climatic, technical and economic circumstances in which they farm so as to avoid, reduce and remove GHG emissions (e.g., through interventions such as enteric CH₄ vaccines, feed additives such as Asparagopsis, breeding low CH₄ emitting animals, nitrification inhibitors, balancing dietary energy to protein⁸), as well as, if possible and profitable, offset farm emissions through revegetation and increasing soil organic carbon (SOC). Depending on cost, purchasing additional land with the explicit objective of planting trees to offset livestock emissions and feeding a CH₄ inhibitor such as Asparagopsis in combination with TFCE (CN1 and CN2) or lucerne (CN3 and CN4) in the pasture mix showed a promising avenue for improving profit while also achieving carbon neutrality. As the need for non-agricultural industries to also offset their GHG emissions increases, the price of arable land is likely to increase in line with public pressure to maintain or improve institututional and organisational carbon removals²³. As a corollary, carbon insetting (practices to reduce GHG within the value chain) may become higher priority, feasible and profitable for some land managers selling carbon credits, rather than seeking new arable land elsewhere. The CN packages examined with and without emission trading schemes were more profitable than the baselines business-as usual scenarios where existing and emerging technologies could deliver the necessary abatement to reach net zero by 2050, opening the door to new markets spurred by increasing consumer preferences for low carbon products²⁴.

Flexibity in systems and management is key to responding to change in timely and effectively ways, while adopting. Tactical and strategic whole farm management plans therefore need to dynamically include new available technologies, practices, and market and climatic trends²⁵. To be effective in Nationally-Determined Contributions, forests require a minimum level of ‘permanence’ (e.g. 25–100 years, depending on carbon market) ²⁶, potentially consuming arable zones that go a long way towards fulfilling the growing global need for protein, fibre and starch. An en masse land-use conversion from commodity-based production to that designed for ecosystems services may have perverse outcomes, such as diminished food security or increase poverty; phenomena exacerbated by a burgeoning global population. Countries or regions that prioritise carbon and/or environmental outcomes may invoke carbon leakage²⁷, wherein commodity-based production shifts to other regions or nations, potentially causing land clearing (e.g., substantial release of GHG emissions in developing nations in South America), and thus the atmosphere perceived more GHG emissions that would have occurred had the original land not been locked up for carbon or biodiversity purposes.

Simple changes to systems raise productivity and profitability but further practices are required to reduce emissions to net-zero

An important insight from this study, and the case study farms analysed, was that CN packages may simultaneously increase farm profitability while offsetting GHG emissions. Under the assumed level of performance, feeding livestock with the modelled use of Asparagopsis was a promising adaptation, decreasing enteric CH₄ emissions by 80% (46–72% reduction in total net GHG emissions on farm). However, there is much uncertainty about how Asparagopsis will perform. The rates of enteric CH₄ mitigation assumed in this analysis were relatively high, though lower than some published results²⁸, plus this CH₄ mitigation quantum remains to be observed in practice. Some projections suggest a $1.5 billion seaweed industry in Australia, creating 9,000 jobs and up to 10% national GHG emissions reduction per year by 2040, making a substantial contribution to the UN Sustainable Development Goals²⁹. Apart from farm level challenges of feeding Asparagopsis as a potential solution for reducing CH₄ emissions important questions remain about its impact on health and the environment. For example, are the species of seaweed used for CH₄ mitigation invasive, and what implications could this have for local ecosystems? When fed to animals, could bromoform, the potent CH₄ inhibitor derived from seaweed, have negative effects on consumer health? And what are the potential environmental consequences of scaling up the manufacture of synthetic bromoform, given its impact on ozone depletion? These questions highlight the need for further research and careful consideration of the benefits and risks associated with using seaweed as a means of reducing GHG emissions^28,30.

Stacking the transformational FCE (TFCE) into CN packages (CN1, CN2) increased animal performance in the beef farm and decreased costs of production (i.e. 50–88% reduction in supplementary feeding), improving profits (Figs. 3, 4). However, reduced wool production associated with TFCE eroded overall livestock production for the sheep farm, similar to results seen by others³¹. On the other hand, lower pasture intakes may reduce enteric CH₄ emissions and increase residual biomass and litter fall, potentially improving SOC stocks. Despite these prospective emergent economic and environmental complementarities, benefits elicited from genetic improvement have historically only been observed after 10–20 years of sustained investment^32,33.

The expert group of practitioners involved in the research were keen on including legumes (e.g., lucerne and Talish clover) into existing grass pastures (CN3 and CN4 packages). Sturludóttir, et al. ³⁴ further demonstrated the well-established reality that mixing grasses with legumes improved herbage yield, dry matter digestibility and crude protein in pastures from Northern Europe and Canada, while reducing the invasion of weeds compared to monocultures. The nitrogen yield advantage from grass-legume mixtures supported by symbiotic N₂ fixation³⁵, given the close linkage between C and N cycling in grazing systems³⁶, are in effect also mechanisms increasing SOC stocks^37–39. However, excessive proportions of legumes within swards come with animal welfare concerns, with excessive soluble protein and nitrate contents linked to ruminant bloat and even animal deaths, particularly for cattle but less so for sheep⁴⁰.

The unique insights arising from this research include:

Singular interventions elicit little improvements in productivity and profitability or GHG emissions;

Under future climate conditions, feeding Asparagopsis and planting trees had the greatest emissions benefits, but also came with the greatest costs;

Spatial and climatic diversification (purchasing an extra farm block in a different climatic zone) as well as hypothetical transformational improvements in animal feed-conversion efficiency (TFCE) promise to deliver the greatest benefits for productivity and profitability;

Stacking of interventions explicitly aimed at (1) reducing enteric methane, (2) carbon removals and (3) improving productivity were often the most profitable and productive, while also having the least GHG emissions;

Continuing business as usual and purchasing carbon credits to negate all farm GHG emissions was a more costly option than using suites of interventions;

Appropriately contextualised bundles of interventions to local conditions like planting trees, renovating pastures with deep rooted legumes and adopting high FCE animal genotypes not only have the potential to reduce farm business GHG emissions to net zero, but also improved profitability and productivity gains;

Genuinely transformational solutions will be combinations and permutations of changes in individual farm systems that reduce GHG emissions, maintain and improve future productivity and profit, strengthen social license to operate through public recognition of producers' agri-environmental stewardship.

Study overview

Farming systems were co-designed using integrated, cross-disciplinary framework¹⁰. A Regional Reference Group (RRG) of experts and industry practitioners was involved in the co-design of biophysical, environmental, and economic interventions (Fig. 6). Co-designed interventions (singularly and in combination) were further examined using a social science lens, including assessment of adoption barriers, social license to operate, and new skills needed to adopt them. This co-design framework was used to quantify and stack individual whole farm adaptations on top of the baseline farm system, each intervention iteratively refined by discussing results with the RRG over several cycles¹⁰.

To showcase this approach, farm systems across two regions of Tasmania, Australia, were selected to be modelled: a sheep production system (hereafter ‘sheep farm’) in the low rainfall zone in central Tasmania and a beef production system (hereafter ‘beef farm’) in the relatively high rainfall zone of northwestern Tasmania. Individual interventions aimed at income diversification and/or transformational were suggested by the RRG. Transformational adaptations were considered to be longer-term, higher risk interventions with some degree of irreversibility. These adaptations were stacked together in a mutually synergistic way based on commonality of intended outcomes. Incremental adaptations were defined as those that do not significantly alter the status quo. Income diversification interventions were designed such that new income streams would be derived that were independent of rainfall in the location of the current farm system, as rainfall was perceived to be a climatic index that would change under future climates, and these livestock systems relied primarily on pasture produced from rainfall. Income diversification was thus classified as those interventions affording either climatic diversification or enterprise diversification. A multitude of approaches and software packages were used to simulate farm systems (Fig. 6). Future climate projections²² accounted for increased frequency and severity of extreme weather events. The whole-farm model GrassGro® (version 3.3.10⁴¹) was used to simulate daily pasture and livestock production and was driven by historical and future climate horizons. Soil organic carbon sequestration were simulated using RothC model (version 26.3 in Microsoft Excel format⁴²) with GrassGro outputs, while FullCAM (version 4.1.6⁴³) was invoked to estimate tree carbon sequestration. Net farm GHG emissions were calculated using Sheep Beef-Greenhouse Accounting Framework (SB-GAF version 1.4⁴⁴) using outputs from GrassGro®, RothC and FullCAM. The @Risk model⁴⁵ was used to account for market volatility using a partial budgeting approach (i.e. Earnings Before Interest and Taxes and herein referred to as operating profit or most likely annual profitability) to compare the costs and income benefits of incremental, income diversification and transformational adaptions faced by a farm business.

Historical and future climates

The beef farm was located at Stanley in the cool temperate zone of north-western Tasmania (40° 43' 41''S 145° 15' 43''E), while the sheep farm was located in the Midlands, west of Campbell Town (41°56'30"S 147°25'02"E). Stanley and Campbell Town have long-term mean and standard deviation annual rainfall of 807 ± 139 mm and 499 ± 103 mm, respectively, with average daily temperatures of 16.5°C and 16.7°C in January and 9.1°C and 6.5°C in July, respectively (Fig. S2). Daily historical climate data for the baseline period of 1 January 1980 to 31 December 2018 was sourced from SILO meteorological archives (http://www.longpaddock.qld.au/silo). Data from SILO was used to generate future climate data following Harrison et al. ²² using a stochastic approach to account for changes in climatic extremes, including heatwaves, droughts and extreme rainfall events²². Future climate projections were downscaled from global circulation models (GCMs) to regional and farm-scale⁴⁶. To generate future climate data, (1) we estimated mean changes in future climates projected for a region based on ensembles of global climate models (GCMs), (2) accounted for historical climate characteristics (obviated by raw GCM data) and (3) generated climatic projections with increased variability. Future climate projections for 2030 and 2050 were developed using monthly regional climate scaling factors (Table S14) from GCMs provided by Harris et al. ⁴⁶ based on Representative Concentration Pathway (RCP) 8.5. Atmospheric CO₂ concentrations were set at 350 ppm, 450 ppm and 530 ppm for the historical, 2030 and 2050 climate scenarios, respectively⁴⁷.

People-Centred Design: the Regional Reference Group (RRG)

During an iterative process with a RRG, we sense-checked model assumptions and results. Model outputs discussed with the RRG included pasture growth rates, stocking rates, livestock production, wool production, supplementary feeding, costs, income, depreciation, net cash flows, and wealth. When RRG consensus was reached for results for each historical period (1986 to 2005), several biophysical and economic models were run for 26-year periods (first six years of data discarded to allow for model initialisation) centered on 2030 (2022 to 2041) and 2050 (2042 to 2061). Over several workshops, we gleaned RRG thinking and feedback on incremental, systems and transformational adaptation and mitigation opportunities in light of qualified holistic impacts of climate change. Assuming the recommendations from the RRG, we explored individual adaptations to understand their potential effects on productivity, profitability and offsetting of GHG emissions and acceptability within the industry. Sequentially, several adaptations were combined into four distinct themes; ‘Low Hanging Fruit’, ‘Towards Carbon Neutral’, ‘Income Diversification’ and ‘Carbon Neutral’; outcomes from these themes were compared with the baseline scenario (detailed Fig. 5 and Table 1). Based on RRG advice, we refined model parameters to reflect feasibility and magnitude of variables simulated for each theme of adaptation. This process (1) ensured that model results were realistic, (2) provided the research team with nascent knowledge relating to opportunities for adaptation and mitigation of climate change from expert practitioners, (3) engender end-user confidence in the analytical process and results and (4), provided end-users with credible, legitimate and fit-for-purpose adaptation/mitigation modelled interventions. Detailed information about the baselines and adaptation process is below and in the supplementary information (Tables S15-S18, Fig. S3).

Pasture and livestock production

The model GrassGro® enables simulation of ruminant grazing enterprises of southern Australia by combining biophysical (climate, soils, pastures and livestock), farm management (soil fertility, paddock size and layout, pasture grazing rotations, stocking rate and animal management) and economic data (gross margin). GrassGro® has been used to explore the effects of climate, soil, pasture, herd/flock management and adaptation for predicted climate change impacts on livestock productivity and profitability⁴⁸ in pasture-based industries across Australia^17,49, North America and Northern China^50,51. GrassGro® computes soil moisture, pasture production, pasture quality [Crude Protein (%CP) and Dry Matter Digestibility (%DMD)] on a daily basis for each pasture species, paddock and farm. Other variables calculated by the model include sward characteristics, pasture cover, pasture persistence, pasture availability, pasture intake, feed supplement requirements, liveweight change, and feed carry-over effects from year to year. We initialised and parameterized GrassGro® using baseline information collated from each case study farmer.

High rainfall beef production system

The beef farm ran a self-replacing cow and calf operations on a land area of 569 ha. This enterprise comprised 367 mature cows calving in late winter (1 Aug with 95% weaning rate, first calving at two years of age) assuming a typical replacement rate of around 20% each year (74 heifers). Home-bred non-replacement heifers and steers were sold at 25 months (1 Sep) at approx. 550 and 600 kg, respectively. An additional 115 of weaners were purchased at 6 months of age (1 Feb) at approx. 200 kg liveweight (LW) and were sold at 25 months (1 Sep) at approx. 600 kg LW. A group of 155 steers was also purchased at 16 months of age (1 Feb) at approx. 375 kg LW each year and sold at 28 months (31 Jan) at approx. 545 kg LW. Before being cast for age on 10 Feb, mature cows were retained for five lactations. Pasture species mainly comprised perennial ryegrass (Lolium perenne L.) and white clover (Trifolium repens L.) but also cocksfoot (Dactylis glomerata L.), subclover (Trifolium subterraneum L.) and lucerne (Medicago sativa). According to the Northcote classification⁵², the soil type defined in GrassGro was Uc2.3. In addition, 5% of farm area (20 ha lucerne/ryegrass and 8 ha ryegrass/cocksfoot/white clover pastures) was irrigated between 21 Nov and 31 Mar each year (20mm/event on a 14-day interval) to replicate long-term average irrigation water applied. To either maintain LW (cows) or achieve target LWs (all other stock), production feeding rules were implemented in GrassGro using hay (dry matter digestibility (DMD) of 77% and crude protein (CP) of 20%)). While all stock grazed rainfed pastures, home-bred steers were also given access to irrigated pastures throughout the year. Further information can be found in the Supplementary Material (Table S15).

Low-rainfall sheep production system

The sheep farm ran a self-replacing Merino superfine wool, prime lamb and, secondary, a beef cattle enterprise grazing 3,170 ha and consisted of 49% native grasslands, 48% rainfed developed pastures and 3% centre pivot irrigation (introduced grasses and legumes). A total of 4,600 ha of native woodlands were also present on the farm that were not subjected to grazing. According to the Northcote classification⁵², the soil type defined in GrassGro was Dy5.61. The modelled rainfed pastures were composed of pure stands of phalaris (Phalaris aquatica L.) or phalaris-subclover mixtures. One paddock of lucerne was used for grazing and hay production and another paddock of dual-purpose wheat (Triticum aestivum L.) was grazed for four months prior to grain production. Both of these paddocks were irrigated from 1 Sep to 31 Mar with 18 mm of water per application to fill the soil profile to 95% of field capacity when soil water deficit reached 50%.

The sheep farm ran 24,750 animals, grouped in two flocks: a self-replacing Merino flock (SMF) and a prime lamb flock (PLF). The SMF comprised three groups: 5,300 mature superfine Merino ewes, 7,500 wethers and 5,500 replacement ewes and wethers. The SMF ewes were first lambed at 2 years of age and retained for three lambings before entering the PLF for two more annual births before being cast at 7 years old (16 Dec). Before wethers were cast for age (14 Oct), the animals were retained for five years. The non-replacement wether lambs and ewes were sold 1 Feb. A total of 3,450 Merino ewes were mated with White Suffolk rams in the PLF; the 2,950-lamb progeny were sold in mid-December at 27 kg LW. The sheep (except prime lambs) were all shorn on 20 Jul, clean fleece weight (CFW) were 3.3–4.1 kg with fibre diameters of 17.4–18.1µm (variation in CFW and micron depended on stock class and age). Further details are provided on maintenance and production feeding rules, as well as grazing rotations in Supplementary Material (Table S14). The beef cattle herd consisted of 340 mature cows and 60 replacement heifers per age group. Two-year-old mature cows calved (30 Aug) and were retained for eight years before being cast for age. After weaning date (1 Apr), steers (150 head) were sold at 18 months of age (28 Feb at ~ 460 kg LW) while non-replacement heifers (90 head) were sold at 200 kg LW.

Net farm greenhouse gas emissions

The Sheep-Beef Greenhouse Accounting Framework (SB-GAF version 1.4⁴⁴), which incorporates Intergovernmental Panel on Climate Change methodology and is detailed in the Australian National Greenhouse Gas Inventory, was used to calculate net farm greenhouse gas emissions. Use of outputs from biophysical models^22,48 as SB-GAF inputs has been previously shown to be reliable for beef⁵³ and sheep enterprises⁵⁴. Twenty-year seasonal mean data from GrassGro was used as input data for SB-GAF. To convert CH₄ and N₂O into carbon dioxide equivalents (CO₂e), SB-GAF assumes 100-year global warming potentials (GWP₁₀₀) of 28 and 265, respectively. Greenhouse gas outputs were calculated as net farm emissions (Mg CO₂e/annum) and emissions intensity (Mg CO₂e/Mg product). Greenhouse gas emissions considered included CH₄ from livestock enteric fermentation and manure; N₂O from nitrogenous (N) fertiliser, waste management, urinary deposition and indirect N emissions via nitrate leaching and ammonia volatilisation; CO₂ from synthetic urea applications, electricity and diesel consumption, as well as CO₂e pre-farm embedded emissions for fertiliser and supplementary feed. Annual electricity and diesel consumption are computed as a function of location, enterprise type, cultivation and machinery use, as well as livestock numbers and use of farm infrastructure. According to Wiedemann et al. ⁵⁵, the allocation of emissions between meat and wool was based on protein mass ratio.

Soil organic carbon in grazed pastures

The Rothamsted Carbon model (RothC; version 26.3 in Microsoft Excel format⁴²) was used to simulate dynamic soil organic carbon (SOC). RothC has been used globally to model the impacts of climate and management on SOC stocks⁵⁶. RothC simulations are driven by historical and projected monthly means of temperature, rainfall and pan evaporation (see Historical and future climate data). Monthly average GrassGro outputs were input into RothC including dung and litter. Root residue C inputs were derived from GrassGro outputs considering litter, allocation of net primary production between plant components, active root length density and proportion of root by layer (0–30 cm and 30–100 cm depth) and dung excreted by animals. Further details about the link between GrassGro and RothC can be found in Supplementary Material (see subsection ‘Linking GrassGro and RothC model to account for soil carbon changes in long-term pastures’). Soil types primarily consisted of clay loam Red Ferrosols on the beef farm⁵⁷, and Dermosols on the slopes adjacent to native vegetation and Vertosols on the river flats on the sheep farm⁵⁸. Soil clay contents in the 0–30 cm and 30–100 cm layers were derived from the TERN-ANU Landscape Data Visualiser (https://maps.tern.org.au/#/) and historical SOC was sourced from regional sources⁵⁷. RothC simulates C transfers between several soil organic matter pools, including decomposable plant material (DPM), resistant plant material (RPM), fast and slow microbial biomass (BIOF and BIOS), humified organic matter (HUM) and inert organic matter (IOM) ⁴². RPM, HUM and IOM fractions were comparable to historical data for the three soil types across the two farms⁵⁷. Allocations across SOC pools given by Hoyle et al. ⁵⁹ for initial fractions of DPM, BIOF and BIOS were adopted here (1%, 2% and 0.2% of initial SOC stocks, respectively) and IOM fraction was similar to that reported by Falloon et al. ⁶⁰. Soil carbon decomposition rates at 30 cm were derived following Jenkinson and Coleman⁶¹, except for the decomposition rate for RPM, which was set to 0.17 following Richards and Evans⁴³, similar to the 0.15 reported by Cotching⁵⁷, such that decomposition rates constants for DPM, RPM, BIO and HUM were 10, 0.17, 0.66 and 0.02, respectively. At 30–100 cm, decomposition rates were calculated following Jenkinson and Coleman⁶¹; all values were lower than values at 0–30 cm, reflecting lower decomposition rates at depth. Decomposition rates constants for DPM, RPM, BIO and HUM were 0.33, 0.01, 0.02 and 0.00, respectively. To account for the C enrichment of manure by feeding biochar, a sub-model was incorporated to RothC (see more detail in supplementary materials subsection ‘Accounting for carbon changes in soil by enrichment of manure with biochar’, Fig. S4).

Tree growth, carbon in wood and soil carbon beneath tree canopies

We invoked the FullCAM model (version 4.1.6⁴³) to simulate dynamic temporal tree growth, along with carbon sequestration in biomass and in soils beneath trees. FullCAM is currently used in Australia’s National Carbon Accounting System and is driven using mean monthly temperature, rainfall and open-pan evaporation. Soil organic matter and carbon in FullCAM is simulated by RothC; all soil parameters were matched with those we used for RothC described above. FullCAM simulates C cycling between forest and soil components, including litter, surface and subsurface debris. We modelled planting of Tasmanian blue gum (Eucalyptus globulus L.) and ‘environmental’ plantings (combination of trees, understory and shrubs native to the region) for the beef and sheep farms, respectively. FullCAM simulations were run continuously from 2022 to 2062 by combining the climate data for the two future time frames, as opposed to two individual simulations commencing 2022 and 2042. We modelled planting of shelter belts for the beef farm and woody thickening of pre-existing woody vegetation for the sheep farm; livestock grazing beneath trees (silvopasture) was not permissible following advice from the RRG. The parameters assumed to simulate SOC changes for grapes were further explained in Supplementary Material (see subsection ‘Diversifying land use with grapes on a sheep farm’).

Economic analyses

In concert with GrassGro outputs, we used the @Risk Software⁴⁵ to stochastically simulate annual feed supply, changes in annual carrying capacity and added annual supplementary feed requirements, commodity prices and animal farm incomes, following approaches outlined in previous studies⁶². Long-term wool, meat and livestock prices adjusted for inflation were adopted from Thomas Elder Markets, Data and Consultancy ( http://thomaseldermarkets.com.au ). The probability distribution of each price variable was derived from analysis of the price data series using BestFit software (Accura Surveys Ltd) (Tables S9, S11-S13). Prices of livestock products were correlated. Economic assessments of the baseline and adaptations were assessed using the @Risk model. To account for economic risk and uncertainty, we performed Monte Carlo simulations using 10,000 iterations of runs of 10-year annual operating profit (Earnings Before Interest and Taxes), as well as measures of return on capital. To attribute a cost for carbon offsetting, we computed operating profit plus a carbon ‘tax’, in which each tonne of CO₂e was taxed at $60-$100/Mg CO₂e, following Stiglitz et al. ⁶³. Any carbon sequestration beyond net farm GHG emissions were ‘credited’ at $35/Mg CO₂e that of a carbon tax⁶⁴.

Normalised multidimensional impact assessments

Normalised multidimensional impact assessments were used to rank all interventions and climate horizons through integration of the relative benefit of each adaptation across economic, biophysical and environmental disciplines into a singular unified metric. Following principles outlined by Gephart et al. ⁶⁵, liveweight production, net operating profit (pre-carbon taxes) and net farm GHG emissions were selected for normalisation by the maximum value for each corresponding metric, such that normalised values ranged from 0 to 1. Normalised net farm GHG emissions were computed as the additive inverse of 1 [i.e., 1 - normalised net farm GHG emission factor] given that lower values for this specific metric are desired. Normalised multidimensional impact was calculated as the sum of three key normalised metrics with equal weighting for each metric, such that each normalised output value ranged from 0 (very low impact) to 3 (representing very high beneficial impact in each of the productivity, profitability and GHG emissions dimensions).

Incremental, systemic, transformational adaptations and contextualised stacking of thematic interventions

The outcome of the co-design process was distinct adaptation/mitigation themes that were analysed individually or as combined interventions ‘stacked’ together (Table S17; Table 1). The “Low-Hanging Fruit” (LHF) intervention consisted of simple, immediate and reversible changes to existing farm systems that were considered good management practice and may occur over time in the absence of the present study. Incremental adaptations for LHF included changes in animal management/genetics, feedbase management, plant breeding and improved soil fertility (Tables 1 and S17). The second thematic adaptation was co-designed with an overarching aspiration of reducing net farm GHG emissions year on year, such that the trajectory of net farm GHG emissions over time diminished: “Towards Carbon Neutral” or TCN. Incremental adaptations within TCN comprised longer-term, more difficult, higher cost and sometimes irreversible interventions imposed on top of those in LHF including, but not limited to, pasture renovation with deep-rooted genotypes, injecting livestock with an enteric CH₄ inhibition vaccine and planting regionally appropriate trees on a portion of existing farmland or on newly purchased land. A third thematic adaptation “Income Diversification” (ID) was co-designed with the RRG in which income is derived from sources other than the current livestock farm system through options such as buying another block of land in a different agroclimatic region (climate diversification), leasing land to host a wind turbine farm or diversifying part of the farm area with grapes (climate diversification, reduce the vulnerability to market fluctuations). The fourth thematic adaptation/mitigation bundle, described as “Carbon Neutral” or CN, was created after co-designing pathways designed to reach net zero emissions (Fig. S3). A summary of each adaptation theme together with subset incremental adaptations are shown in Table 1 (further details provided in Tables S15-S18, Fig. S3).

Table 1

**Summarised thematic adaptations co-designed with a Regional Reference Group (RRG).** Each thematic adaptation comprised multiple stacked incremental adaptations suggested by the RRG; the extent to which each factor was varied from the baseline level was derived from feasible values from the literature. Abbreviations: LHF: Low-Hanging Fruit. TCN: Towards Carbon Neutral; this theme also included all incremental adaptations for LHF. ID: Income Diversification. CN. Carbon Neutral Package. SR: Stocking Rate. LW: Liveweight per head. FCE: Feed Conversion Efficiency. RD: Rooting Depth. SSP: Single Superphosphate fertiliser. N: Nitrogen fertiliser. Further details are provided in Tables S15-S18.
Theme	Incremental, systemic and transformational adaptations stacked into holistic adaptation themes
LHF	- Altered lambing/calving dates to better match seasonal pasture supply
	- Altered selling dates/SR/LW to better match seasonal pasture supply
	- Adopting pasture species with 10% improvements in maximum root depth⁶⁶
	- Increasing soil fertility with SSP and N by 3% [Harrison et al. ⁶⁷; all paddocks except the native pastures for the sheep farm]
	- Increasing FCE by 10% in 2030 and 15% in 2050, relative to baseline⁶⁸
	- Introduction of Talish clover (Trifolium tumens) to a proportion of the sheep farm⁶⁹
	- Removing cattle from the sheep farm and increasing rainfed introduced pasture area to the two sheep flocks
TCN	- Strategic manipulation of livestock selling dates/SR/LW to better match seasonal pasture supply
	- Pasture renovation with (and increased farm area of) lucerne pastures
	- Injecting animals with an enteric CH₄ inhibitor vaccine to reduce CH₄ by 30%⁷⁰ - Purchase 50 ha of land for the beef farm to establish a tree plantation of Tasmanian Blue Gums to offset livestock GHG emissions
	- Thickening of 200 ha of existing nature pasture (non-grazed) land for sheep farm with environmental plantings (trees, shrubs and understory species endemic to the region)
ID	- Buying an extra farm in a different agroclimatic region (by translocating cow calf systems to Gladstone, NE Tasmania to dedicate the current farm for backgrounding and finishing of weaners) - Diversifying land use with grapes by repurposing 30ha from the sheep farm to grow Pinot Noir and Chardonnay grapes (processed offsite and outside scope of the current project) - Hosting a wind farm (by leasing land for 12 wind turbines to generate an extra income, no insetting of CO₂ from turbines to reduce on-farm GHG emissions, in line with the business model of the wind turbine company)
CN	- Feeding red seaweed (Asparagopsis taxiformis) to offset CH₄ by 80%⁷¹ - Pasture renovation with (and increased farm area of) lucerne pastures - Purchase 55 to 85 ha of land for the beef farm to establish a tree plantation of Tasmanian Blue Gums to offset livestock GHG emissions - Thickening of 200 to 220 ha of existing nature pasture (non-grazed) land for sheep farm with environmental plantings (trees, shrubs and understory species endemic to the region) - Transformational increase in FCE, to 20% in 2030 and 30% in 2050, relative to baseline⁶⁸

Acknowledgements

We thank Meat and Livestock Australia, The University of Tasmania and the Tasmanian Institute of Agriculture for financial support (project P.PSH.1219). We acknowledge the important contributions of the Regional Reference Group members, and in particular to the two case study farmers, to the success of the NEXUS project.

Author contributions

F.B., K.M.C-W., B.M., B.C., M.A., M.T.M. conceiving and/or designing the project or output. F.B., K.M.C-W., N.B., M.T.M. acquired research data where the acquisition has required significant intellectual judgement, planning, design or output. F.B., K.M.C-W., N.B., M.T.M. contributing knowledge, where justified, including indigenous knowledge. F.B., K.M.C-W., B.M., N.B., M.T.M. analysing and/or interpreting research data. F.B., K.M.C-W., B.M., N.B., M.A., M.T.M. Drafting significant parts of research output/s or critically revising output/s to contribute to interpretation.

Declaration of interests

The authors declare no competing interests.

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Costs of transitioning to net-zero emissions under future climates

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Abstract

Figures

Introduction

Results

The nexus between productivity, profitability and net greenhouse gas emissions

Contextualised adaptation-mitigation bundles: stacking interventions

Costs of transitioning to net-zero emissions under future climates

Discussion

A new framework for co-designing bundles of mitigation-adaptation interventions

The cost of transitioning farm systems to net-zero emissions

Concluding remarks

Methods

Study overview

Historical and future climates

People-Centred Design: the Regional Reference Group (RRG)

Pasture and livestock production

High rainfall beef production system

Low-rainfall sheep production system

Net farm greenhouse gas emissions

Soil organic carbon in grazed pastures

Tree growth, carbon in wood and soil carbon beneath tree canopies

Economic analyses

Normalised multidimensional impact assessments

Incremental, systemic, transformational adaptations and contextualised stacking of thematic interventions

Declarations

References

Additional Declarations

Supplementary Files

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