Regenerative agriculture amplifies productivity and profitability while negating greenhouse gas emissions

Regenerative agriculture amplifies productivity and profitability while negating greenhouse gas emissions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Regenerative agriculture amplifies productivity and profitability while negating greenhouse gas emissions Matthew Harrison, Albert Muleke, Karen Christie-Whitehead, Michelle Cain, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5703590/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Nature Food → Version 1 posted You are reading this latest preprint version Abstract The broad philosophy comprising regenerative agriculture can be deconstructed into several underpinning components, including adaptive multi-paddock grazing (AMP), improved biodiversity, silvopasture, and minimal use of cultivation and synthetic fertilisers. Here, we use sheep farms positioned across a rainfall gradient to examine how pasture species diversity, antecedent SOC and AMP influence soil organic carbon (SOC) accrual, greenhouse gas (GHG) emissions, pasture production and enterprise profit. Compared with light grazing intensities for long durations, high-intensity short-duration cell grazing with long spelling periods (AMP) amplified pasture productivity, improving SOC accrual and GHG abatement, increasing profit per animal and hectare. Renovation of pastures with high-yielding, low-emissions ecotypes enhanced pasture production and carbon removals, albeit to a lesser extent than that realised from AMP. Adaptive grazing management, where animals were moved in response to pasture residual, evoked the greatest SOC accrual and GHG abatement, but also increased supplementary feed costs. Low stocking rates with longer spelling periods between grazing events were the most profitable, highlighting the need for agile, proactive grazing management adapted in line with seasonal conditions. We conclude that (1) whole farm stocking rate and seasonal rainfall quantum have greater influence on pasture production, SOC, GHG and profit compared with species diversity and grazing management, (2) individual pasture species – rather than species diversity – have greater bearing on sward production, (3) notwithstanding carbon removals via improved SOC, CH 4 from enteric fermentation dominates farm GHG profiles, and (4), AMP can catalyse SOC accrual and sward production compared with lighter stocking conducted for longer durations, but only when whole farm stocking rate is harmonised with long-term sustainable carrying capacity, with the latter being a function of plant-available water capacity and drought frequency. Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation Scientific community and society/Agriculture Earth and environmental sciences/Natural hazards Earth and environmental sciences/Ecology/Climate-change ecology Cell grazing biodiversity soil carbon natural capital mitigation ecosystems services extreme event drought climate change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction In the absence of land use change, livestock production systems dominate GHG emissions from the agricultural sector 1 , 2 , 3 , 4 . In the quest to uncover technologies and practices that improve carbon reductions and removals, ‘regenerative agriculture’ has gained significant momentum, being espoused not only for GHG mitigation 5 but also for purported ecosystems services, such as improved soil organic carbon (SOC) stocks and biodiversity 6 , 7 , 8 , 9 , 10 . Regenerative agriculture can be deconstructed into manifold underpinning components including, inter alia , (1) holistic farm management that puts humans and ecosystems at the centre, acknowledging indigenous heritage, (2) integration of trees, forage, and domesticated livestock grazing in a mutually synergistic way, e.g., silvopasture, (3) adaptive grazing conducted for short periods at high grazing intensities, with long spelling between subsequent grazing events ie., adaptive multi-paddock grazing (AMP), (3) increased biodiversity, (4) minimal or zero use of synthetic fertiliser and pesticides, (5) application of organic amendments as natural fertilisers, such as compost, biochar and/or livestock manure, (6) cover crops, (7) no cultivation, and (8) perenniality, particularly pasture ecotypes that endure for more than one year 10 , 11 , 12 , 13 , 14 . In short, regenerative agriculture can be conceptualised as a construct that aspires to emulate natural systems by minimising, and recovering from, the influence of anthropogenic activities on agrifood production systems. When implemented holistically, regenerative agriculture may be conducive to GHG emissions abatement 15 through improved pasture productivity 16 and/or increased SOC sequestration 12 , 16 , 17 , 18 , although the extent to which such phenomena occur is contextualised by agroecological region and historical enterprise management. As well, concurrent implementation of multiple regenerative constituents may have additive benefits. For instance, some scholars opine that integration of improved grazing (rotational or seasonal grazing, or destocking) in concert with increased botanical composition increase SOC accrual compared with conventional grazing practices conducted in isolation 19 . Others contend that high intensity grazing regimes combined with high sward diversity can reduce GHG emissions relative to low intensity grazing regimes, even when swards contain a diversity of species 20 , 21 . Although it is known that pasture production and ecotype presence/absence depend on rainfall 7 , 21 , 22 , less is known of how regenerative grazing and botanical composition impact on sward productive potential, or how these relationships vary across a rainfall gradient. This is important, because a significant proportion of global livestock production occurs semi-arid or arid zones 23 , 24 . Relative benefit conferred by any intervention to a farming system can be conceptualised as a function of historical management and climate relative to the influence of climate and management post intervention. For example, SOC stocks can be increased when historical (antecedent) SOC stocks are low, regardless of the type of intervention 25 , 26 . In contrast, when antecedent SOC stocks are high, it is difficult to further increase SOC stocks, regardless of practice change, soil characteristics or climate. Indeed, when SOC is near the ceiling level determined by agroclimatic region, carbon inputs, and soil type; SOC can be easily lost, with SOC often falling towards a long-term equilibrium mediated by seasonal net primary production 25 , 27 . Such phenomena may explain why meta-analyses of the effects of grazing management have shown contrasting conclusions across studies 27 . In part this may be due to extreme weather events – particularly drought – which can negate years of SOC 17 , 26 . These results suggest that impacts of regenerative agriculture on SOC would be more robust if assessments account for long-term climatic variability, environment and management. Here we systematically assess three constituents of regenerative agriculture—pasture species diversity, antecedent SOC and AMP—using sheep production systems positioned in regions across southern Australia, ranging from low to very high annual rainfall. Because farm business prosperity depends on the balance between revenue and costs, we also examined how regenerative grazing impacted on income from livestock and supplementary feed costs. As occurs when proponents enrol in carbon markets, we assumed financial revenue when net farm GHG emissions diminished relative to status quo management (hereafter, ‘Baseline’), and we imposed a carbon tax when GHG emissions increased relative to Baseline emissions. As carbon removals via improved SOC can be offset by concurrent changes in other GHG, such as higher methane (CH₄) emissions borne by increased stocking rates 28 , we also assessed changes in net farm GHG, accounting for CH 4 , CO 2 and N 2 O. We examined farming systems interventions through a factorial lens accounting for botanical composition, grazing regime and antecedent SOC level (Table 1 ). Real case study farms were strategically chosen based on location within dominant national sheep production zones, and whether farm managers classified their enterprises as "conventional (C)" or "regenerative (R)" agricultural management. Use of case studies in this way was a deliberate choice to provide real baseline systems and practices for evaluating biophysical variables, GHG emissions, economics, biodiversity and natural capital across predominant sheep production regions of Australia. Baseline information was used to calibrate a range of biophysical, economic and ecological models. Farm systems interventions and SOC were simulated using the Sustainable Grazing Systems (SGS) model version 5.4.3 29, 30 . Outputs from SGS were used to model GHG emissions in the Sheep & Beef GHG Accounting Framework, which follows the Australian Greenhouse Gas National Inventory (SB-GAF) 31 . Gross margins were computed using outputs from SGS, SB-GAF and the literature (Tables 1 – 2 ). The underpinning motivation of our study was to examine how pasture ecotype diversity, antecedent SOC and grazing regime impacted on long-term pasture productivity, SOC sequestration and GHG emissions of sheep production systems positioned along a rainfall gradient. 2. Methods 2.1. Climate and soil characteristics Historical daily climate data including maximum and minimum temperature, rainfall and solar radiation for a 100-year period (1 January 1924–31 December 2023) were obtained as SILO patch-point data 32 for each site (Table 1 ). The most representative soil types for each farm were selected using the Digital Atlas of Australian Soils 33 . To account for variability in antecedent soil organic carbon (SOC) stocks, we configured antecedent SOC stocks for upper (0–30 cm) and lower (30–50 cm) soil profiles. 2.2. Farming systems 2.2.1. Regenerative farm R1, Kulin, Western Australia Farm R1 was located near Kulin, Western Australia (WA), and self-declared as regenerative agriculture practitioner. The enterprise used large mobs and implemented rotational grazing. The farm purchased rams and sold wethers either between February and April or in August (late summer or mid-winter). Approximately 80% of wether lambs were marketed at 130 days of age, while the remaining 20% were sold at 210 days. Ewes were culled and sold between 5 to 8 years of age. Soils were primarily sandy loams and medium clays (Tables 1 – 3 ). 2.2.2. Conventional management farm C2, Meningie, South Australia Case study C2 was situated in the Meningie region of South Australia (SA) and self-classified as a ‘conventional management’ practitioner (Tables 1 – 3 ). Stock were moved from each paddock on 7–21 day intervals based on pasture production. The farm purchased breeding ewes at five years of age and did not retain offspring. Around 1,040 ewe lambs were sold from October at 60 kg (7.5 months of age). Approximately 1,040 wether lambs were sold from mid-August upon attainment of 46 kg or not later than September (6 months of age). Breeding ewes were cast for age at 7–8 years. 2.2.3. Conventional management farm C3, Casterton, Victoria Case study C3 was located near Casterton, Victoria, and practiced ‘conventional management’ of wool and meat production enterprises. The sheep flock comprised 2,200 ewes and 480 replacement ewes per age group. Lambing meat flock ewes were set stocked from July to September, while the merino wool ewes were rotationally grazed from August to October (Table 4 ). Wool flock ewes lamb in mid-August, and the lambs are weaned in mid-November, whereas the meat flock ewe lambs in mid-July and wean in early November. All ewe lambs transitioned to the meat flock two weeks post-weaning, while wether lambs were sold from mid-July upon reaching 45 kg or not later than mid-November (13 months). All ewe and wether lambs (2,300 head) were sold from early December at 38 kg or not later than early February (6.5 months of age). Ewes were cast for age at 5–6 years. 2.2.4. Regenerative farm R4, Winslow, Victoria Case study R4 was located near Winslow, Victoria, and self-classified as a regenerative agriculture practitioner. The farm operated a merino wool enterprise, comprising 2,532 ewes and 884 replacement ewes for each age group. The farm had 3,484 ha of arable pasture that was rotationally grazed. Ewes lambed in early-September and lambs were weaned in late November. All lambs (1,340 head) were sold from May to November at 27 kg liveweight (LWT). Ewes were cast for age at 5–6 years. 2.1. Biophysical modelling Farming systems and alternative management decisions were simulated using the biophysical model SGS (v5.4.3) 8 , 34 , 35 , which accounts for daily soil water, nutrient balance, botanical composition, pasture growth, plant senescence, ground cover, animal growth, animal purchases and sales, and livestock pasture and supplementary feed utilisation 29 , 36 . The model accounts for movement of livestock between paddocks based on management rules that allow animals to graze as separate flocks or herds (e.g., mature animals vs. young, weaned stock). Supplementary feeding can be applied to maintain animals within a certain body weight or achieve target liveweight while receiving supplementary feed within a paddock or containment pen. Supplementary feed comprising varied between farms, including lupins (24 MJ/kg), barley straw (11 MJ/kg), whole barley grain (14 MJ/kg), faba beans (12.8 MJ/kg), grass silage (12 MJ/kg) and grass hay (7.6 MJ/kg). Simulations were initialised with current farm practices, for example, joining and weaning dates, livestock numbers and corresponding liveweights, pasture species, grazing management, sale date(s), shearing time, and were ran continuously from 1 January 1924 to 31 December 2023. Atmospheric carbon dioxide concentration was set at 380 ppm to reflect average carbon dioxide (CO₂) concentration during the simulation period. To standardise comparisons between farm sizes and stocking rates, we present long-term average annual dry sheep equivalent (DSE) per hectare of grazed farm area, with 1 DSE representing a two-year old 45 kg merino wether consuming 7.6 MJ ME/day. Table 1 Case study farm climate (1924–2023), pasture species, production and fertilisation. Farms are ordered from low to high average annual rainfall from left to right, abbreviated by location (WA = Western Australia, SA = South Australia and VIC = Victoria) and second by management (C = conventional, R = regenerative management). Farm characteristics WA (R1) SA (C2) VIC (C3) VIC (R4) Nearest township Kulin Meningie Casterton Winslow Latitude (°S) -32.80 -35.70 -37.430 -38.20 Longitude (°E) 118.25 139.35 141.268 142.50 Rainfall (mm/year) 353 447 657 734 Avg min/max temperature (°C) 9.7/23.5 10/21.2 8.8/19.2 8.7/18.5 Soil type Sandy loam, medium clay Sand Clay Sandy loam Pasture production (kg DM/ha/year) 5,621 5,479 8,212 12,374 Pasture fertilisation 47 kg/ha SSP¹ 10 kg/ha Urea, 43 kg/ha SSP, 8 kg/ha DAP² 167 kg/ha SSP 32 kg/ha MAP³ ¹SSP = Single Superphosphate, ² DAP = Diammonium Phosphate, ³ MAP = Monoammonium Phosphate. Table 2 Baseline farming system characteristics. Farms are abbreviated first by location (WA = Western Australia, SA = South Australia, and VIC = Victoria) and second by management (C = conventional, R = regenerative agricultural management). Values shown are averaged per annum unless stated otherwise. Parameters WA (R1) SA (C2) VIC (C3) VIC (R4) Livestock genotype Merino Merino Merino Merino Farm-grown supplementary feed Barley straw Grass silage Grass hay Barley, Faba beans Purchased supplementary feed Lupins - Faba beans Barley grain Total supplement feed (t DM) 151 115 243 65 Average mature ewe weight (kg) 74 77 52 50 Number of mature ewes sold 360 502 941 814 Liveweight of mature ewes at sale (kg) 77 84 54 55 Mature wethers sold - - - 677 Liveweight of wethers at sale (kg) - - - 55 Lambs sold 1,212 2,082 3,372 1,340 Liveweight of lambs at sale (kg) 46 50 41 27 Shearing dates Main flock: 30-Jan, weaners: 1-Feb Main flock: 1-Jul, weaners: 15-Sep Main flock & weaners: 30-Apr Main flock & wethers: Dec-15, weaners: 15-Dec Total wool sold (t greasy wool) 15 10 18 41 Average fleece diameter (micron) 21 22 18 17 Replacement animals purchased - 524 ewes - - Table 3 Long-term sward growth and development were modelled for three pasture diversity themes, comprising the Baseline (blue rows), individual pasture ecotypes (green rows) and combined pasture species (brown rows). Farms are abbreviated first by location (WA = Western Australia, SA = South Australia and VIC = Victoria) and second by management (C = conventional, R = regenerative management). Botanical compositions WA (R1), 353 mm/year SA (C2), 447 mm/year VIC (C3), 657 mm/year VIC (R4), 734 mm/year Baseline swards Annual ryegrass, subterranean clover, weeds Perennial ryegrass, lucerne, cocksfoot Perennial ryegrass, subterranean clover, phalaris Subterranean clover, phalaris, perennial ryegrass Swards with individual species Individual species Perennial ryegrass, lucerne, phalaris, tall fescue, white clover, cocksfoot Subterranean clover, phalaris, weeds, white clover, tall fescue, annual ryegrass Annual ryegrass, tall fescue, weeds, cocksfoot, white clover, lucerne White clover, tall fescue, annual ryegrass, weeds, lucerne, cocksfoot Least productive individual pasture Weeds Weeds Weeds Weeds Most productive individual pasture Annual ryegrass Subterranean clover Subterranean clover Subterranean clover Swards with multiple species Least productive three species Weeds, tall fescue, white clover Weeds, annual ryegrass, perennial ryegrass Weeds, annual ryegrass, perennial ryegrass Weeds, annual ryegrass, perennial ryegrass Least productive five species Annual ryegrass, subterranean clover, capeweed, perennial ryegrass, phalaris Perennial ryegrass, lucerne, cocksfoot, subterranean clover, white clover Perennial ryegrass, subterranean clover, phalaris, annual ryegrass, capeweed Subterranean clover, phalaris, perennial ryegrass, white clover, annual ryegrass Most productive three species Lucerne, annual ryegrass, cocksfoot Subterranean clover, phalaris, lucerne Subterranean clover, phalaris, lucerne Subterranean clover, phalaris, lucerne Most productive five species Annual ryegrass, subterranean clover, capeweed, perennial ryegrass, tall fescue Perennial ryegrass, lucerne, cocksfoot, subterranean clover, annual ryegrass Perennial ryegrass, subterranean clover, phalaris, annual ryegrass, lucerne Subterranean clover, phalaris, perennial ryegrass, white clover, lucerne 2.2. Greenhouse gas (GHG) emissions Long-term average livestock numbers, liveweight, diet quality, supplementary feed, meat and wool production and SOC modelled in SGS were entered into Sheep & Beef GHG Accounting Framework (SB-GAF) 31 to quantify average annual GHG emissions over 100 years. SB-GAF uses Australian National Greenhouse Accounts Inventory methods to model whole-farm CH₄, N₂O and CO₂ emissions. The ratio of farm-grown and purchased supplementary feed was used to determine Scope 1 (direct) GHG emissions at the enterprise level. Scope 2 GHG were classed as those emissions derived from consumption of electricity. Scope 3 emissions were classed as indirect upstream and downstream GHG emissions generated in the wider economy. Carbon sequestration per annum was enumerated in carbon dioxide equivalents (CO₂-e) with SOC converted into CO 2 -e using a conversion factor of 3.67, and a 100-year global warming potential (GWP) of 28 for CH 4 and 265 for N 2 O. Net farm GHG emissions were calculated as the sum of gross emissions minus the sum of the carbon sequestration each year. Scope 3 emissions from purchased supplementary feeds were attributed to each flock based on SGS outputs, allowing calculations of net GHG emissions and emissions per unit product ( viz . emissions intensity). We allocated net GHG emissions in direct proportion to the mass of protein of meat and wool produced by the enterprise 37 then divided these values by annual wool and meat production to determine wool (kg CO 2 e/kg clean wool) and meat (kg CO 2 e/kg liveweight) emissions intensity. 2.3. Economic and financial analyses Economic and financial analyses were conducted using outputs from SGS, SB-GAF and data from the literature. Gross margins were calculated per DSE and per grazing hectare to standardise comparisons across farms. We compute costs associated with supplementary feeding and income associated with changes in net GHG emissions. We assume that interventions that reduce net farm GHG relative to the status quo farm systems (hereafter, Baseline systems) result in carbon income, whereas interventions that increase net farm GHG relative to Baseline operations invoke a carbon tax. To account for market volatility, we calculate income/costs associated with low and high prices ( $ 38/tonne CO 2 -e and $ 100/t CO₂e respectively) for (1) SOC and (2) GHG emissions (Tables S9, S10, S11, S14 and S15). Other farm costs were assumed to be consistent with the Baseline (Table S13). All economic currency are expressed in Australian dollars (AUD). 2.4. Farming systems interventions We devised farming systems interventions (treatments) to deconstruct constituent principles underpinning the broader ‘regenerative agriculture’ philosophy: AMP, antecedent SOC and pasture species diversity 25 . The pasture species diversity intervention was designed to examine effects of ecotype diversity (or lack thereof) compared with Baseline botanical compositions (Table 3 ). Several botanical compositions were then simulated, first including swards with individual pasture species, then by combining the most and least productive botanical compositions (Table 3 ). For the AMP intervention we contrasted short, intense grazing with conventional long duration, lower stocking rates, including six grazing regimes with levels configured relative to Baseline farming systems. Adaptive grazing management was explored by initiating and terminating grazing duration as a function of seasonal conditions based on pasture characteristics (Table 4 ). To examine effects of historical (antecedent) SOC stocks, two soil organic matter (SOM) levels were simulated. These were designed to represent low or high SOC (0.5% SOM/0.3% SOC and 10% SOM/6% SOC, respectively) in the 0–30 cm and 30–50 cm soil layers. 3. Results 3.1. Pasture ecotype, not diversity, drives sward productivity and SOC accrual The most productive botanical compositions comprised only three high-yielding pasture species (Tables 3 , 5 and Fig. 1 ); adding further species to the sward reduced productivity. These results demonstrate that pasture ecotype, not pasture species diversity, had greater impact on seasonal sward production. Over the long term, swards with the three most productive ecotypes had 7% greater annual production, whereas the least productive swards (with three pasture types) had seasonal productivity that was 39% lower (Table 5 and Fig. 1 ). Table 4 Grazing management interventions. Farms are abbreviated by location (WA = Western Australia, SA = South Australia and VIC = Victoria) and management (C = conventional, R = regenerative management). Grazing regime Indicator WA (R1), 353 mm/year SA (C2), 447 mm/year VIC (C3), 657 mm/year VIC (R4), 734 mm/year Level 1 : Set stocked (Baseline) N o . paddocks 1 1 1 1 Paddock area (ha) 3,484* 865 600 440 Stocking rate (DSE/ha) 2 5 17 25 Days on paddock 120 120 120 120 Rest period (days) 120 120 120 120 Level 2 : low intensity grazing with long rest periods (12 paddocks 14 day) N o . paddocks 12 12 12 12 Paddock area (ha) 290 72 50 37 Stocking rate (DSE/ha) 29 56 200 273 Days on paddock 14 14 14 14 Rest period (days) 132 132 132 132 Level 3 : high intensity grazing with long rest periods (30 paddocks 12 day) N o . paddocks 30 30 30 30 Paddock area (ha) 116 29 20 15 Stocking rate (DSE/ha) 73 141 500 682 Days on paddock 12 12 12 12 Rest period (days) 348 348 348 348 Level 4 : low intensity grazing with short rest periods (12 paddocks 1 day) N o . paddocks 12 12 12 12 Paddock area (ha) 290 72 50 37 Stocking rate (DSE/ha) 29 56 200 295 Days on paddock 1 1 1 1 Rest period (days) 11 11 11 11 Level 5 : high intensity grazing with short rest periods (30 paddocks 1 day) N o . paddocks 30 30 30 30 Paddock area (ha) 116 29 20 15 Stocking rate (DSE/ha) 73 141 500 682 Days on a paddock 1 1 1 1 Rest period (days) 29 29 29 29 Level 6 : adaptive rotational grazing based on leaf stage N o . paddocks 30 30 30 30 Paddock area (ha) 116 29 20 15 Stocking rate (DSE/ha) 73 141 500 682 Leaf stage eligible for grazing (N o . leaves) 3 3 3 3 Days on paddock 1.5 1.5 1.5 1.5 Pasture biomass (t DM/ha) 3.0 3.0 3.0 3.0 *As maximum area for a single paddock in SGS is 1,000 ha, the set stocked grazing regime for R1 (WA) was allocated 2,440 DSE to ensure a representative Baseline stocking rate of 2.44 DSE/ha. Stocking rates for all other grazing regimes were calculated based on grazed area and number of sheep across the whole farm. Higher pasture resulted in lower need for supplementary feed. Average supplementary feed requirements were lowest for more productive swards comprising three high-yielding pasture types, ranging from 18 to 21 kg/animal/year for the WA (R1) and VIC (C3) farms, respectively. In contrast, the least productive individual pasture species resulted in the highest supplementary feed requirements (74–114 kg/animal/year; Table 5 ). More productive pastures sequestered more soil carbon. The most productive swards with three species sequestered the highest absolute SOC (7–41 t C/ha) and relative SOC (4–7 t C/ha), while the least productive swards resulted in SOC loss (-5 to -20 t C/ha) and relative SOC (-8 to -41 t C/ha; Table 5 , Fig. 2 ). Table 5 Long-term average pasture productivity, supplementary feed, liveweight and SOC accrual for four regions depicted along a rainfall gradient. Absolute SOC accrual was calculated as the change in SOC stocks over 100 years relative to the initial year (1924). Relative SOC was computed as the change in absolute SOC compared with the Baseline over the same period. Farms are abbreviated by location (WA = Western Australia, SA = South Australia and VIC = Victoria) and management (C = conventional, R = regenerative management). Botanical compositions WA (R1), 353 mm/year SA (C2), 447 mm/year VIC (C3), 657 mm/year VIC (R4), 734 mm/year Monthly pasture growth rate (kg/ha/day) Baseline 9.2 9.8 15.8 19.2 Least productive individual species 6.2 6.0 6.1 9.0 Least productive three species 6.3 6.1 14.0 14.2 Least productive five species 8.5 10.6 14.0 17.5 Most productive individual species 8.9 11.3 15.8 19.3 Most productive three species 9.8 11.7 16.4 20.0 Most productive five species 9.0 10.8 15.1 19.1 Annual pasture production (kg/ha/yr) Baseline 3,386 3,608 5,898 7,045 Least productive individual species 2,300 2,211 2,236 3,296 Least productive three species 2,328 2,253 2,300 5,190 Least productive five species 3,242 3,554 5,120 6,411 Most productive individual species 3,275 3,748 5,886 7,058 Most productive three species 3,738 3,923 6,004 7,298 Most productive five species 3,302 3,920 5,381 6,982 Absolute SOC (t C/ha) Baseline 3 10 37 21 Least productive individual species -5 -8 -3 -20 Least productive three species -4 -5 -1 -2 Least productive five species 1 9 31 11 Most productive individual species 2 12 32 19 Most productive three species 7 17 41 29 Most productive five species 5 15 34 23 Relative SOC (t C/ha) Least productive individual species -8 -18 -40 -41 Least productive three species -7 -15 -38 -23 Least productive five species -2 -1 -6 -10 Most productive individual species -1 2 -5 -2 Most productive three species 4 7 4 7 Most productive five species 2 5 -3 2 Annual supplementary feed intake (kg/animal/yr) Baseline 24 47 19 18 Least productive individual species 74 74 127 114 Least productive three species 49 54 108 58 Least productive five species 24 35 25 29 Most productive individual species 19 20 25 21 Most productive three species 18 19 21 18 Most productive five species 22 28 24 19 Mature liveweight (kg) Baseline 55 56 56 56 Least productive individual species 53 53 53 54 Least productive three species 54 54 55 55 Least productive five species 54 55 55 54 Most productive individual species 56 56 56 56 Most productive three species 57 57 57 58 Most productive five species 56 56 56 56 Higher SOC accrual associated with greater pasture production translated into lower net GHG emissions, notwithstanding greater CH 4 produced in regions with higher stocking rates. More productive swards with three high-yielding species reduced net GHG emissions by 6%, while the least productive swards increased net GHG emissions by 13% (Table S1 and Fig. 3 ). Annual pasture production, stocking rate and GHG emissions increased along a rainfall gradient (Tables 5 , S1 and Fig. 3 ). In general, R4 had the highest pasture production, which was accompanied by higher net GHG emissions (exceeding 5.7 t CO₂e/ha/yr), primarily driven by CH 4 emissions (Fig. 2 ). In contrast, the farm in the lowest rainfall zone (R1) had the lowest pasture production and net GHG emissions (0.4–0.6 t CO₂e/yr) due to lower stocking rates (Table S1 and Fig. 3 ). These findings indicate that rainfall and stocking rate have a greater impact on net GHG emissions compared with pasture species diversity. 3.2. Soil carbon accrual more a function of antecedent SOC than type of intervention Lower antecedent SOC evoked greater SOC accrual in upper soil layers (0–30 cm), which increased absolute SOC gains over 100 years (Table S2 and Figure S1 ). In contrast, high antecedent SOC resulted in SOC loss, diminishing absolute SOC stocks from − 60 t C/ha for the low-rainfall farm C2 to -28 t C/ha for the high-rainfall farm R4 (Table S2 and Figure S1 ). In deep soil layers (30–50 cm), SOC accrual was less impacted by antecedent SOC (-11 to 2 t C/ha across farms; Table S3), suggesting that most carbon storage would occur in, or be lost from, upper soil layers. Farm C3, located in the high-rainfall zone with clay-rich soils, accumulated more SOC in topsoil for both low antecedent SOC (0.3%) and Baseline scenarios, resulting in the lowest relative SOC change of 15 t C/ha (Table S2). Conversely, the farm in the lowest rainfall zone with sandy loam and medium clay soils (R1 in WA), had a greater relative SOC change of 29 t C/ha (Table S2). In all cases, high antecedent SOC (6%) resulted in subsequent SOC decline (Table S2). Low (0.3%) antecedent SOC stocks manifested in greater SOC sequestration and lower net emissions (Table S4 and Fig. 4 ). In contrast, higher antecedent SOC increased net GHG emissions, with GHG increasing along a rainfall gradient (0.7 t CO₂e/ha/yr for R1 to 6.3 t CO₂e/ha/yr for R4; Table S4 and Fig. 4 ). Elevated net GHG emissions at high antecedent SOC were associated with increased enteric methane emissions and soil C emissions resulting from high SOC loss (Table S4 and Fig. 4 ). Relative to Baseline farm systems, the reduction in net GHG at low antecedent SOC was greater (-25%) for farms in low-rainfall zones (e.g. R1), compared with farms in high rainfall zones (e.g. -12% GHG abatement for C3; Table S4). This suggests that SOC accrual at low antecedent SOC levels are likely to elicit greater proportional carbon reductions from extensified production systems in low-rainfall regions compared with intensified farming systems in high-rainfall zones. Higher antecedent SOC evoked greater net GHG emissions relative to current farming practices (Table S4) mainly due to CO 2 emissions from loss of SOC, and to a lesser extent enteric CH 4 emissions. Effects of SOC accrual on consequent pasture growth were small (Table S5 and Fig. S9). 3.3. Adaptive grazing management increases SOC accrual relative to set stocking Over the long term, adaptive grazing management evoked the greatest absolute SOC storage (44 t C/ha) in upper soil layers (Table S6, Fig. 5 ), while low intensity grazing with short rest periods had the lowest absolute SOC accrual (26 t C/ha) and relative SOC storage (8 t C/ha). Farms in high rainfall zones had the highest absolute and relative SOC stocks, whereas C2 had the lowest SOC stocks (Table S6). Greater SOC gains for AMP on C3 were attributed to the higher rainfall and clay-rich soils, which are more conducive to long-term SOC sequestration in topsoil (30 cm). On the other hand, low rainfall and sandy soils was conducive to lower SOC accrual for C2 (Table S6 and Fig. 5 ). 3.1. Regenerative grazing improves pasture production and carbon removals Adaptive grazing had the highest pasture growth rates, with growth rates increasing as annual rainfall increased (Fig. 7 and Table S8). Relative to Baseline systems, supplementary feed intakes were higher under adaptive grazing for low rainfall zones (Tables S8 and S4). Grazing regimes with shorter rest periods had lower supplementary feed intakes, ranging between 6 and 22 kg/animal/yr. High SOC accrual of the adaptive grazing regime tended to reduce GHG emissions (Table S7 and Fig. 6 ) while high intensity grazing intensity with short rest periods had the highest net GHG emissions, primarily due to increased CH 4 emissions (Fig. 6 , Table S7). Across farms and grazing regimes, adaptive grazing management on C3 had the highest carbon removals relative to the Baseline , while high intensity grazing with short rest periods had the least effect on net GHG emissions (Table S12). In contrast, the high-rainfall farm (R4) had the highest net GHG across grazing regimes, despite having greater gains in SOC than low-rainfall farms (Fig. 6 and Table S7). These results highlight that fact that per unit grazing area, enteric CH 4 dominate farm GHG profiles and outweigh carbon removals associated with SOC accrual. 3.2. Sward productivity has greater bearing on gross margins than carbon prices Compared with the Baseline , more productive swards increased along a rainfall gradient, which increased gross margins (Fig. 8 , S10). The relative change in gross margins was more influenced by carbon prices than by rainfall or livestock prices (Figs. 8 , S2 and S3). The most productive botanical compositions with three high-yielding pasture species were more profitable, with high-rainfall zone farms having the highest gross margins (R4), and low rainfall farms the least (Figure S10). The increased relative gross margins for low-rainfall farms were primarily attributed to lower stocking rates and reduced costs associated with supplementary feed (Figure S5 and Table S14), GHG emissions (Figure S6 and Table S15) and slight gains in SOC revenue (Figs. 9 and S4). Despite significant carbon income for high-rainfall farms (Fig. 9 ), GHG emissions abatement costs outweighed economic gains associated with SOC improvement (Figure S4). The lowest productivity botanical compositions were least economically viable (Figures S2, S3, S10), with larger relative gross margin losses for high-rainfall farms underpinned by increased GHG emissions costs (Table S14). 3.3. High intensity short grazing events with long spell periods increase profit Gross margins per grazed area were more influenced by rainfall and stocking rate than gross margins per DSE (Fig. 10 and Table S10). Gross margins per DSE were higher on low-stocked farms located in drier regions compared with intensified farms (Fig. 10 and Table S10). Farms in high-rainfall zones with higher stocking rates had higher gross margins per unit area relative to farms in low-rainfall zones, even though the high density farms incurred larger GHG emission costs. Despite higher SOC income and lower GHG emission costs under AMP (Table S15), higher supplementary feed costs associated with this intervention reduced gross margins per DSE and grazed area compared with low intensity grazing with short rest periods (Fig. 11 and Tables S10, S14). Greater gross margins for low intensity grazing regimes with short rest periods were primarily attributed to the relatively reduced supplementary feed intake (Tables S8 and S14). Over the long-term, high intensity grazing with long rest periods in high-rainfall regions had the lowest profit associated with higher supplementary feed costs (Tables S8, S10 and S15). 4. Discussion Sustainable livestock carrying capacity must be calibrated on production during climatic adversities Proponents of regenerative agriculture advocate several underpinning constituents, including increased biodiversity, cell grazing, little or no cultivation and reduced synthetic inputs. When bundled, identification of those components having benefit, detriment, or de minimus influence can be fraught, due to interactions and feedbacks occurring within the system. As such, the objective of this study was to extricate effects of pasture species diversity, antecedent SOC and AMP on pasture productivity, GHG emissions and enterprise profitability of farms positioned across a range of agroecological environments, from cool and wet in southern Victoria to hot and arid in inner Western Australia. We discovered that adaptive grazing regimes were more effective in enhancing pasture productivity, increasing SOC stocks, and mitigating GHG emissions compared with increasing pasture species diversity (Table S12). This led to higher average gross margins per DSE and per grazed area across most scenarios (Figures 11 and S8). While adaptive grazing regimes demonstrated the greatest potential for improving pasture production and reducing GHG emissions through improved SOC sequestration compared with contemporaneous systems (Tables S6, S7, S8, Figures 6, 7), in some cases adaptive grazing also incurred higher costs in supplementary feeding, reducing profit (Figures 11 and S8). In contrast, lower supplementary feed requirements for low intensity grazing regimes with longer rest periods often led to greater gross margins per area on highly stocked farms in wetter regions and increased profit per DSE on lower stocked farms in drier regions (Figures 11 and S8). These results demonstrate that even though a particularised grazing regime can be beneficial in terms of GHG abatement and SOC accrual 38, 39, 40 , the same intervention can have economic downside when pasture feed supply falls 41, 42, 43 . Our results clearly highlight the need for adaptive management of stocking rate not just in response to short-term vicissitudes of the weather ( viz . drought frequency) but also to whole farm carrying capacity, which in turn is a function of prevailing climate and plant available water capacity. As such, the sustainable carrying capacity should be calibrated to the most extreme seasonal expectation (or series of climatic adversities), which would be expected to vary between regions and with the changing climate 44, 45, 46 . Pasture ecotype, not diversity, drives production, SOC accrual and carbon removals Integrating high-yielding pasture species (such as subterranean clover) into pasture swards mitigated GHG emissions and enhanced pasture production. Swards containing subterranean clover, phalaris and lucerne increased annual pasture production by +7% and liveweight by +2% relative to Baseline species, whereas low production swards (comprising weeds, cocksfoot and annual ryegrass) reduced pasture production by -39% compared with existing pasture species (Table 5). These findings are consistent with studies demonstrating increased pasture production over summer–autumn through use of high-yielding species 47, 48, 49 . The primary distinction in productivity between the highest and lowest performing botanical compositions in the present study was largely attributed to peak growth rates in spring, with minimal production differences during winter and early autumn (Figure 1), suggesting that additional measures may be necessary to address seasonal feed shortages. Integration of dual-purpose crops, such as wheat and canola, into grazing rotations on mixed cropping-livestock enterprises may help alleviate such feed gaps by supplying high-nutritive forage from early autumn to mid-winter (May to August) 44, 50 and enhance livestock performance 51 . Other prospective interventions to mitigate feed gaps include the use of summer-active perennial pastures, forage shrubs, and the utilisation of crop residue/stubble during winter and summer/early autumn 52, 53 . Tactical adjustment in feed demand may also help better match the seasonal distribution of feed supply, which in southern Australia often peaks in spring. As such, lambing in late winter, and sale of superfluous stock in early summer, would be expected to better align flock feed demand with seasonal feed supply (e.g. Figure 1). Improving lambing rates through ewe genotypes with greater fecundity can increase lambing rates, reduce whole farm emissions intensity and improve farm profit, provided non-breeding animals are sold prior to summer 54, 55, 56 . Antecedent SOC has greater bearing on SOC accrual than climate or practice change Effects of SOC stocks prior to any intervention should be carefully considered if the aspiration is to improve SOC stocks post practice change, as is the case for most soil carbon markets. We showed that low antecedent SOC levels evoked greater SOC sequestration in topsoil compared with higher antecedent SOC concentrations over 100 years (Figure S1). These findings align with past work 25, 57, 58, 59 , suggesting that potential for improving SOC stocks exists in contexts where antecedent stocks are low (0.3%) across agro-ecological regions and livestock farming systems. In line with previous work, we showed that neither pasture species diversity nor grazing regime had significant impact on deep SOC 2, 23 . High SOC sequestration achieved from low antecedent SOC stocks reduced net farm GHG emissions (Figure 4). However, antecedent SOC had relatively little impact on pasture productivity and supplementary feed intake (Fig. S8). These findings underscore trade-offs between sustainability outcomes (GHG abatement vs pasture production) and profitability, highlighting the need to assess multiple indicators associated with any purported innovation, such as regenerative agriculture. These results also indicate that degraded ecosystems provide the most attractive opportunity for not just improved carbon storage and GHG mitigation, but also for increased biodiversity and natural capital 14, 34, 42 . Our results suggest that no single intervention can benefit all farming systems, because systems vary in their current state, from degraded to ecologically pristine 60, 61 . Regenerative grazing has clear benefits over conventional set stocking We showed that adaptive grazing management mitigated GHG emissions through superior SOC sequestration compared with conventional set stocking traditionally conducted at lighter stocking rates for longer periods. After a century of AMP, SOC stocks were higher (Tables S6 and S12) compared with set stocking conducted for the Baseline systems, whereas low intensity grazing with short rest periods manifest in lowest SOC storage (26 t C/ha). Low intensity, short rest grazing tended to reduce pasture recovery periods, reduce soil carbon inputs and reduce SOC sequestration. While these results are context-specific, they align with hitherto work documenting increased soil carbon and nitrogen under AMP 62, 63 , with sequestration potential modulated by agroecological context 61, 64 . Previous work also highlights the importance of rainfall, particularly drought (Fig. S9), on SOC sequestration potential 65, 66, 67 . In general, sites with minimal cultivation, greater rest periods between grazing events, and afforded long-term growth of perennial vegetation tend to have greater soil organic matter, carbon and nitrogen 63 . AMP increased herbage productivity relative to pasture species diversity, more so as rainfall increased (Table S12). Adaptive grazing had the highest herbage growth rates (Table S8), which translated to higher annual pasture production (+35%), and increased SOC sequestration. In contrast, high intensity grazing with long rest periods had the lowest herbage growth rates and only a modest 16% increase in annual pasture production compared with the Baseline . Despite this, adaptive grazing also increased supplementary feed requirement for farms in drier regions, while high intensity grazing with long rest periods increased intakes for highly stocked farms in wetter regions. Compared with increasing pasture species diversity, AMP had much greater bearing on profit margins per animal (DSE) and per unit of grazed land across most scenarios (Figures 11 and S8). This finding is consistent with prior research that has documented improved financial performance under AMP due to lower production costs, including reduced reliance on inputs such as herbicides 68 . Even though the adaptive grazing regime achieved the highest levels of pasture productivity and GHG mitigation through superior SOC sequestration, it incurred higher supplementary feed costs, which in some cases diminished profits. In contrast, low intensity grazing regimes with shorter rest periods had lower supplementary feed costs, offsetting the elevated GHG abatement expenses and yielding greater profit. The least productive pasture species and set stocking (when sheep prices were low) or high intensity long rest grazing (when sheep prices were high) had the lowest financial returns (Fig. 10). 5. Concluding remarks Should future legislation be imposed on farms for exceeding an arbitrary GHG emissions threshold, our results suggest that AMP - in concert with selection of appropriate pasture ecotypes and calibration of whole farm stocking rate to expected pasture production under drought – can improve SOC accrual, reduce GHG emissions, and enhance enterprise prosperity. We demonstrated that high intensity short duration grazing had greater impact on pasture production SOC accrual and economic returns than did pasture species diversity. In general, short spelling periods were more profitable than longer spelling periods, but longer spelling periods resulted in greater SOC accrual. Although adaptive grazing management had greater potential for GHG mitigation, increased herbage production and SOC accumulation, this regime evoked higher supplementary feed costs, which reduced profit per hectare and DSE. Low intensity grazing intensities with shorter rest periods generated higher profit margins per hectare and DSE due to lower supplementary feed costs, offsetting larger GHG abatement taxes, particularly in high-rainfall regions. We revealed that agroecological environment and stocking rates exerted the greatest influence on pasture productivity, GHG emissions and profit per area. In most scenarios, the least productive swards and high intensity grazing regimes were the least economically viable. We conclude that (1) contextualized evaluation of multiple sustainability metrics while carefully controlling for historical management, climate and contemporary status is more likely to elicit benefits in carbon sequestration, agri-food production and enterprise profitability, (2) strategic and tactical management of stocking rate considering likely pasture production, feed demand and seasonal climate forecast can reduce supplementary feed costs and potentially carbon emissions taxes, and (3) regenerative grazing can reduce GHG emissions and improve profit, provided whole farm stocking rate is in harmony with long-term productive potential. Declarations Data availability All data are available from the corresponding author on request. Competing interests The authors declare no conflict of interest. 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Supplementary Files Mulekeetal2025DELIGHTEDSuppInfo24Dec2024.docx Regenerative agriculture amplifies productivity and profitability while negating greenhouse gas emissions Cite Share Download PDF Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Nature Food → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5703590","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":406376594,"identity":"8bf43fff-04ca-4097-b493-bfa0857f04ba","order_by":0,"name":"Matthew Harrison","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABIElEQVRIie2RMUvEMBiGvxLoLRHXynH+A6FSqIrF39IQiJsKt9zgUDi4LqJrxT9REZwjAW+pdI24HBQ6nwhSoXh+FRcld+LmkAcSkpAn75cEwGL5p5Cu87suxrb+teokywVn/E3ZSMhflM+B/EXZSstpdTJSsNN7qOezNjoK9PHdcwPRIJek9gxKWHBnnBUK9s4Og4xNxDDUnPQpiCCXbmhUJCprkyesRxBgiWK3mkMfQLFcglkpK1TeUSlrAnGr2E3GyVsDC1R6r0ZFdykJKhpTYhcP97jrUZCoUHOKrrav6P2C+hpT8C4sK6pwn/o8uFR0uGssjM1e6KnY9EtBnKaN2EXKqsdmdDA4n6bXeslDd9Af8+6byIr9FovFYlnJB7SvZ5YXhsObAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-7425-452X","institution":"University Of Tasmania","correspondingAuthor":true,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Harrison","suffix":""},{"id":406376595,"identity":"796c15e0-1410-4945-827f-02e82381e8fb","order_by":1,"name":"Albert Muleke","email":"","orcid":"","institution":"Cranfield University","correspondingAuthor":false,"prefix":"","firstName":"Albert","middleName":"","lastName":"Muleke","suffix":""},{"id":406376596,"identity":"18886921-807f-4ebd-96cb-c7653a01dfba","order_by":2,"name":"Karen Christie-Whitehead","email":"","orcid":"https://orcid.org/0000-0003-1469-8748","institution":"University of Tasmania, Tasmanian Institute of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Karen","middleName":"","lastName":"Christie-Whitehead","suffix":""},{"id":406376597,"identity":"af027b24-bf8b-44b4-9eec-0e2a9ae8bfeb","order_by":3,"name":"Michelle Cain","email":"","orcid":"","institution":"University of Cranfield","correspondingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"","lastName":"Cain","suffix":""},{"id":406376598,"identity":"c9a7f39b-aed5-4142-abdb-c1546b19847a","order_by":4,"name":"Paul Burgess","email":"","orcid":"","institution":"University of Cranfield","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Burgess","suffix":""},{"id":406376599,"identity":"144b8233-d029-4616-9ac6-775c312c4b42","order_by":5,"name":"Katy Wiltshire","email":"","orcid":"","institution":"University of Cranfield","correspondingAuthor":false,"prefix":"","firstName":"Katy","middleName":"","lastName":"Wiltshire","suffix":""},{"id":406376600,"identity":"6bfee8b0-9481-4907-ad84-6ada0cc62128","order_by":6,"name":"Ke Liu","email":"","orcid":"","institution":"University of Tasmania, Tasmanian Institute of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Liu","suffix":""},{"id":406376601,"identity":"981ec12f-83f1-44a6-b4b6-72b77116a2ac","order_by":7,"name":"Georgios Pexas","email":"","orcid":"","institution":"University of Cranfield","correspondingAuthor":false,"prefix":"","firstName":"Georgios","middleName":"","lastName":"Pexas","suffix":""}],"badges":[],"createdAt":"2024-12-24 05:30:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5703590/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5703590/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43016-026-01331-2","type":"published","date":"2026-03-13T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74902269,"identity":"da5bffa3-f5dd-40b8-b7af-031d85e4b13d","added_by":"auto","created_at":"2025-01-28 07:32:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":333686,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly pasture growth rates for the most and least productive botanical compositions, as well as each \u003cem\u003eBaseline\u003c/em\u003e. Most (3) and Least (3) represent the most and least productive botanical compositions each comprising of three pasture species, while Most (5) and Least (5) indicate the highest and lowest productive swards, each with five pasture species.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5703590/v1/f68a7cacff1f767476843c2a.png"},{"id":74901657,"identity":"cd2d6e20-0e92-4016-a07e-6507faaa312a","added_by":"auto","created_at":"2025-01-28 07:24:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":201441,"visible":true,"origin":"","legend":"\u003cp\u003eLong-term soil organic carbon (SOC) in the soil surface (0-30 cm) for the most and least productive botanical compositions. Most (3) and Least (3) represent the most and least productive botanical compositions, each comprising three pasture species, while Most (5) and Least (5) indicate the highest and lowest productivity swards with five pasture species.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5703590/v1/b404d8b98c22d94dd2d550b3.png"},{"id":74902257,"identity":"032f16a4-4b79-4c42-9954-44bf1cebee8d","added_by":"auto","created_at":"2025-01-28 07:32:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":105619,"visible":true,"origin":"","legend":"\u003cp\u003eAverage annual net GHG emissions (points), soil carbon (CO₂) emissions, enteric methane (CH₄), and nitrous oxide (N₂O) for the most and least productive swards relative to each \u003cem\u003eBaseline\u003c/em\u003e. Most and least productive (3) represent the most productive botanical compositions, each with three pasture ecotypes.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5703590/v1/0a2b33143956747be5da9b4d.png"},{"id":74901672,"identity":"b17ef0e2-cb81-4f13-8635-68c9d381502d","added_by":"auto","created_at":"2025-01-28 07:24:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":105805,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of low (0.3%) and high (6%) antecedent SOC in the soil surface on average annual net GHG emissions (points), enteric and manure methane (CH₄), soil nitrous oxide (N₂O), soil carbon (CO₂) emissions and SOC accrual.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5703590/v1/6c55c3104f537d89b5f1cbc4.png"},{"id":74901674,"identity":"3045b0b3-13c7-4843-8482-abceb4216575","added_by":"auto","created_at":"2025-01-28 07:24:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":178093,"visible":true,"origin":"","legend":"\u003cp\u003eLong-term (1924-2023) soil organic carbon (SOC) accumulation in the soil surface (0-30 cm) formultiple grazing regimes at low (0.3%) and high (6%) antecedent SOC levels, across four case study farms positioned along a rainfall gradient in southern Australia.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5703590/v1/50a0e220f666f82b73e7e5e4.png"},{"id":74901676,"identity":"e4e58748-9ce1-41aa-9596-e669f0b771a8","added_by":"auto","created_at":"2025-01-28 07:24:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":332813,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of grazing regime on soil organic carbon (SOC) in the soil surface (0-30 cm) and annual net farm GHG emissions (red points).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5703590/v1/1c4d9239c71a0707a3ed89d8.png"},{"id":74901637,"identity":"9828a493-16f2-4151-901d-10a6d34d7e65","added_by":"auto","created_at":"2025-01-28 07:24:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":274625,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal pasture growth rates for several grazing regimes with varying intensity, duration and spelling periods.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-5703590/v1/da0e879a932bae041605e763.png"},{"id":74901673,"identity":"12411a6d-1c65-42c0-849a-e8d8844640ab","added_by":"auto","created_at":"2025-01-28 07:24:04","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":117887,"visible":true,"origin":"","legend":"\u003cp\u003eChange in gross margins relative to \u003cem\u003eBaseline\u003c/em\u003e gross margins for high and low sheep and carbon prices.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-5703590/v1/516c9efa053daacb77f7c6fa.png"},{"id":74901640,"identity":"514bc65e-41e2-4831-9298-da5b62ec4c3d","added_by":"auto","created_at":"2025-01-28 07:24:01","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":102226,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of botanical composition on financial income from soil carbon credits for low and high production pastures.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-5703590/v1/be3e83c4f6a7cea2371e8325.png"},{"id":74901660,"identity":"78b24ba3-5ec2-4566-bfc4-7f327f42958d","added_by":"auto","created_at":"2025-01-28 07:24:03","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":216912,"visible":true,"origin":"","legend":"\u003cp\u003eGross marginsper dry sheep equivalent (DSE) and per grazed area as a function of grazing regime.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-5703590/v1/1992e46eff52d28524777c3e.png"},{"id":74901639,"identity":"f5807881-127a-4379-8512-f8a1d5fbb7da","added_by":"auto","created_at":"2025-01-28 07:24:01","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":249902,"visible":true,"origin":"","legend":"\u003cp\u003eRelative change in gross margins per grazed area as a function of costs associated with supplementary feeding and GHG emissions. Relative change in gross margins is shown for high and low sheep prices and is compared across all scenarios.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-5703590/v1/fefb2f477fb924edea145520.png"},{"id":104614933,"identity":"69fc858f-8686-47a4-843a-f6c78e51219b","added_by":"auto","created_at":"2026-03-14 07:06:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3865968,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5703590/v1/3b193b8a-2eec-4a57-ac46-52a3ff27b546.pdf"},{"id":74902260,"identity":"7d045efa-7c59-41ec-b73f-f6a581ad6296","added_by":"auto","created_at":"2025-01-28 07:32:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2222378,"visible":true,"origin":"","legend":"Regenerative agriculture amplifies productivity and profitability while negating greenhouse gas emissions","description":"","filename":"Mulekeetal2025DELIGHTEDSuppInfo24Dec2024.docx","url":"https://assets-eu.researchsquare.com/files/rs-5703590/v1/c3118cbed2d25bf1b932786c.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Regenerative agriculture amplifies productivity and profitability while negating greenhouse gas emissions","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the absence of land use change, livestock production systems dominate GHG emissions from the agricultural sector\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In the quest to uncover technologies and practices that improve carbon reductions and removals, \u0026lsquo;regenerative agriculture\u0026rsquo; has gained significant momentum, being espoused not only for GHG mitigation\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e but also for purported ecosystems services, such as improved soil organic carbon (SOC) stocks and biodiversity\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Regenerative agriculture can be deconstructed into manifold underpinning components including, \u003cem\u003einter alia\u003c/em\u003e, (1) holistic farm management that puts humans and ecosystems at the centre, acknowledging indigenous heritage, (2) integration of trees, forage, and domesticated livestock grazing in a mutually synergistic way, e.g., silvopasture, (3) adaptive grazing conducted for short periods at high grazing intensities, with long spelling between subsequent grazing events ie., adaptive multi-paddock grazing (AMP), (3) increased biodiversity, (4) minimal or zero use of synthetic fertiliser and pesticides, (5) application of organic amendments as natural fertilisers, such as compost, biochar and/or livestock manure, (6) cover crops, (7) no cultivation, and (8) perenniality, particularly pasture ecotypes that endure for more than one year\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In short, regenerative agriculture can be conceptualised as a construct that aspires to emulate natural systems by minimising, and recovering from, the influence of anthropogenic activities on agrifood production systems.\u003c/p\u003e \u003cp\u003eWhen implemented holistically, regenerative agriculture may be conducive to GHG emissions abatement\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e through improved pasture productivity\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and/or increased SOC sequestration\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, although the extent to which such phenomena occur is contextualised by agroecological region and historical enterprise management. As well, concurrent implementation of multiple regenerative constituents may have additive benefits. For instance, some scholars opine that integration of improved grazing (rotational or seasonal grazing, or destocking) in concert with increased botanical composition increase SOC accrual compared with conventional grazing practices conducted in isolation\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Others contend that high intensity grazing regimes combined with high sward diversity can reduce GHG emissions relative to low intensity grazing regimes, even when swards contain a diversity of species\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Although it is known that pasture production and ecotype presence/absence depend on rainfall\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, less is known of how regenerative grazing and botanical composition impact on sward productive potential, or how these relationships vary across a rainfall gradient. This is important, because a significant proportion of global livestock production occurs semi-arid or arid zones\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRelative benefit conferred by any intervention to a farming system can be conceptualised as a function of historical management and climate relative to the influence of climate and management post intervention. For example, SOC stocks can be increased when historical (antecedent) SOC stocks are low, regardless of the type of intervention\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In contrast, when antecedent SOC stocks are high, it is difficult to further increase SOC stocks, regardless of practice change, soil characteristics or climate. Indeed, when SOC is near the ceiling level determined by agroclimatic region, carbon inputs, and soil type; SOC can be easily lost, with SOC often falling towards a long-term equilibrium mediated by seasonal net primary production\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Such phenomena may explain why meta-analyses of the effects of grazing management have shown contrasting conclusions across studies\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In part this may be due to extreme weather events \u0026ndash; particularly drought \u0026ndash; which can negate years of SOC\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. These results suggest that impacts of regenerative agriculture on SOC would be more robust if assessments account for long-term climatic variability, environment and management.\u003c/p\u003e \u003cp\u003eHere we systematically assess three constituents of regenerative agriculture\u0026mdash;pasture species diversity, antecedent SOC and AMP\u0026mdash;using sheep production systems positioned in regions across southern Australia, ranging from low to very high annual rainfall. Because farm business prosperity depends on the balance between revenue and costs, we also examined how regenerative grazing impacted on income from livestock and supplementary feed costs. As occurs when proponents enrol in carbon markets, we assumed financial revenue when net farm GHG emissions diminished relative to \u003cem\u003estatus quo\u003c/em\u003e management (hereafter, \u0026lsquo;Baseline\u0026rsquo;), and we imposed a carbon tax when GHG emissions increased relative to Baseline emissions. As carbon removals via improved SOC can be offset by concurrent changes in other GHG, such as higher methane (CH₄) emissions borne by increased stocking rates \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, we also assessed changes in net farm GHG, accounting for CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO.\u003c/p\u003e \u003cp\u003eWe examined farming systems interventions through a factorial lens accounting for botanical composition, grazing regime and antecedent SOC level (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Real case study farms were strategically chosen based on location within dominant national sheep production zones, and whether farm managers classified their enterprises as \"conventional (C)\" or \"regenerative (R)\" agricultural management. Use of case studies in this way was a deliberate choice to provide real baseline systems and practices for evaluating biophysical variables, GHG emissions, economics, biodiversity and natural capital across predominant sheep production regions of Australia. Baseline information was used to calibrate a range of biophysical, economic and ecological models. Farm systems interventions and SOC were simulated using the Sustainable Grazing Systems (SGS) model version 5.4.3\u003csup\u003e29, 30\u003c/sup\u003e. Outputs from SGS were used to model GHG emissions in the Sheep \u0026amp; Beef GHG Accounting Framework, which follows the Australian Greenhouse Gas National Inventory (SB-GAF)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Gross margins were computed using outputs from SGS, SB-GAF and the literature (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The underpinning motivation of our study was to examine how pasture ecotype diversity, antecedent SOC and grazing regime impacted on long-term pasture productivity, SOC sequestration and GHG emissions of sheep production systems positioned along a rainfall gradient.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Climate and soil characteristics\u003c/h2\u003e \u003cp\u003eHistorical daily climate data including maximum and minimum temperature, rainfall and solar radiation for a 100-year period (1 January 1924\u0026ndash;31 December 2023) were obtained as \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSILO\u003c/span\u003e patch-point data\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e for each site (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The most representative soil types for each farm were selected using the Digital Atlas of Australian Soils\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. To account for variability in antecedent soil organic carbon (SOC) stocks, we configured antecedent SOC stocks for upper (0\u0026ndash;30 cm) and lower (30\u0026ndash;50 cm) soil profiles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Farming systems\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Regenerative farm R1, Kulin, Western Australia\u003c/h2\u003e \u003cp\u003eFarm R1 was located near Kulin, Western Australia (WA), and self-declared as regenerative agriculture practitioner. The enterprise used large mobs and implemented rotational grazing. The farm purchased rams and sold wethers either between February and April or in August (late summer or mid-winter). Approximately 80% of wether lambs were marketed at 130 days of age, while the remaining 20% were sold at 210 days. Ewes were culled and sold between 5 to 8 years of age. Soils were primarily sandy loams and medium clays (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Conventional management farm C2, Meningie, South Australia\u003c/h2\u003e \u003cp\u003eCase study C2 was situated in the Meningie region of South Australia (SA) and self-classified as a \u0026lsquo;conventional management\u0026rsquo; practitioner (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Stock were moved from each paddock on 7\u0026ndash;21 day intervals based on pasture production. The farm purchased breeding ewes at five years of age and did not retain offspring. Around 1,040 ewe lambs were sold from October at 60 kg (7.5 months of age). Approximately 1,040 wether lambs were sold from mid-August upon attainment of 46 kg or not later than September (6 months of age). Breeding ewes were cast for age at 7\u0026ndash;8 years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Conventional management farm C3, Casterton, Victoria\u003c/h2\u003e \u003cp\u003eCase study C3 was located near Casterton, Victoria, and practiced \u0026lsquo;conventional management\u0026rsquo; of wool and meat production enterprises. The sheep flock comprised 2,200 ewes and 480 replacement ewes per age group. Lambing meat flock ewes were set stocked from July to September, while the merino wool ewes were rotationally grazed from August to October (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Wool flock ewes lamb in mid-August, and the lambs are weaned in mid-November, whereas the meat flock ewe lambs in mid-July and wean in early November. All ewe lambs transitioned to the meat flock two weeks post-weaning, while wether lambs were sold from mid-July upon reaching 45 kg or not later than mid-November (13 months). All ewe and wether lambs (2,300 head) were sold from early December at 38 kg or not later than early February (6.5 months of age). Ewes were cast for age at 5\u0026ndash;6 years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4. Regenerative farm R4, Winslow, Victoria\u003c/h2\u003e \u003cp\u003eCase study R4 was located near Winslow, Victoria, and self-classified as a regenerative agriculture practitioner. The farm operated a merino wool enterprise, comprising 2,532 ewes and 884 replacement ewes for each age group. The farm had 3,484 ha of arable pasture that was rotationally grazed. Ewes lambed in early-September and lambs were weaned in late November. All lambs (1,340 head) were sold from May to November at 27 kg liveweight (LWT). Ewes were cast for age at 5\u0026ndash;6 years.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003e2.1. Biophysical modelling\u003c/h3\u003e\n\u003cp\u003eFarming systems and alternative management decisions were simulated using the biophysical model SGS (v5.4.3)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, which accounts for daily soil water, nutrient balance, botanical composition, pasture growth, plant senescence, ground cover, animal growth, animal purchases and sales, and livestock pasture and supplementary feed utilisation\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The model accounts for movement of livestock between paddocks based on management rules that allow animals to graze as separate flocks or herds (e.g., mature animals vs. young, weaned stock). Supplementary feeding can be applied to maintain animals within a certain body weight or achieve target liveweight while receiving supplementary feed within a paddock or containment pen. Supplementary feed comprising varied between farms, including lupins (24 MJ/kg), barley straw (11 MJ/kg), whole barley grain (14 MJ/kg), faba beans (12.8 MJ/kg), grass silage (12 MJ/kg) and grass hay (7.6 MJ/kg). Simulations were initialised with current farm practices, for example, joining and weaning dates, livestock numbers and corresponding liveweights, pasture species, grazing management, sale date(s), shearing time, and were ran continuously from 1 January 1924 to 31 December 2023. Atmospheric carbon dioxide concentration was set at 380 ppm to reflect average carbon dioxide (CO₂) concentration during the simulation period. To standardise comparisons between farm sizes and stocking rates, we present long-term average annual dry sheep equivalent (DSE) per hectare of grazed farm area, with 1 DSE representing a two-year old 45 kg merino wether consuming 7.6 MJ ME/day.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCase study farm climate (1924\u0026ndash;2023), pasture species, production and fertilisation. Farms are ordered from low to high average annual rainfall from left to right, abbreviated by location (WA\u0026thinsp;=\u0026thinsp;Western Australia, SA\u0026thinsp;=\u0026thinsp;South Australia and VIC\u0026thinsp;=\u0026thinsp;Victoria) and second by management (C\u0026thinsp;=\u0026thinsp;conventional, R\u0026thinsp;=\u0026thinsp;regenerative management).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWA (R1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSA (C2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIC (C3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVIC (R4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNearest township\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeningie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCasterton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWinslow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatitude (\u0026deg;S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-32.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-35.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-37.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-38.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitude (\u0026deg;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall (mm/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvg min/max temperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.7/23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10/21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.8/19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.7/18.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandy loam, medium clay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSandy loam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePasture production (kg DM/ha/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12,374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePasture fertilisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 kg/ha SSP\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 kg/ha Urea, 43 kg/ha SSP, 8 kg/ha DAP\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167 kg/ha SSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 kg/ha MAP\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026sup1;SSP\u0026thinsp;=\u0026thinsp;Single Superphosphate, \u0026sup2; DAP\u0026thinsp;=\u0026thinsp;Diammonium Phosphate, \u0026sup3; MAP\u0026thinsp;=\u0026thinsp;Monoammonium Phosphate.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline farming system characteristics. Farms are abbreviated first by location (WA\u0026thinsp;=\u0026thinsp;Western Australia, SA\u0026thinsp;=\u0026thinsp;South Australia, and VIC\u0026thinsp;=\u0026thinsp;Victoria) and second by management (C\u0026thinsp;=\u0026thinsp;conventional, R\u0026thinsp;=\u0026thinsp;regenerative agricultural management). Values shown are averaged per annum unless stated otherwise.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWA (R1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSA (C2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIC (C3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVIC (R4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLivestock genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMerino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMerino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMerino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMerino\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm-grown supplementary feed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBarley straw\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrass silage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrass hay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBarley, Faba beans\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchased supplementary feed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLupins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFaba beans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBarley grain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal supplement feed (t DM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage mature ewe weight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of\u0026nbsp;mature ewes sold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiveweight of mature ewes at sale (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMature wethers sold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiveweight of wethers at sale (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLambs sold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiveweight of lambs at sale (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShearing dates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMain flock: 30-Jan, weaners: 1-Feb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMain flock: 1-Jul, weaners: 15-Sep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMain flock \u0026amp; weaners: 30-Apr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMain flock \u0026amp; wethers: Dec-15, weaners: 15-Dec\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal wool sold (t greasy wool)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage fleece diameter (micron)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReplacement animals purchased\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e524 ewes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLong-term sward growth and development were modelled for three pasture diversity themes, comprising the \u003cem\u003eBaseline\u003c/em\u003e (blue rows), individual pasture ecotypes (green rows) and combined pasture species (brown rows). Farms are abbreviated first by location (WA\u0026thinsp;=\u0026thinsp;Western Australia, SA\u0026thinsp;=\u0026thinsp;South Australia and VIC\u0026thinsp;=\u0026thinsp;Victoria) and second by management (C\u0026thinsp;=\u0026thinsp;conventional, R\u0026thinsp;=\u0026thinsp;regenerative management).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBotanical compositions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWA (R1), 353 mm/year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSA (C2), 447 mm/year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIC (C3), 657 mm/year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVIC (R4), 734 mm/year\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e \u003cb\u003eswards\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual ryegrass, subterranean clover, weeds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerennial ryegrass, lucerne, cocksfoot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePerennial ryegrass, subterranean clover, phalaris\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubterranean clover, phalaris, perennial ryegrass\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSwards with individual species\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndividual species\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerennial ryegrass, lucerne, phalaris, tall fescue, white clover, cocksfoot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubterranean clover, phalaris, weeds, white clover, tall fescue, annual ryegrass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual ryegrass, tall fescue, weeds, cocksfoot, white clover, lucerne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWhite clover, tall fescue, annual ryegrass, weeds, lucerne, cocksfoot\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeast productive individual pasture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeeds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeeds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeeds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeeds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMost productive individual pasture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual ryegrass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubterranean clover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubterranean clover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubterranean clover\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSwards with multiple species\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeast productive three species\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeeds, tall fescue, white clover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeeds, annual ryegrass, perennial ryegrass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeeds, annual ryegrass, perennial ryegrass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeeds, annual ryegrass, perennial ryegrass\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeast productive five species\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual ryegrass, subterranean clover, capeweed, perennial ryegrass, phalaris\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerennial ryegrass, lucerne, cocksfoot, subterranean clover, white clover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePerennial ryegrass, subterranean clover, phalaris, annual ryegrass, capeweed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubterranean clover, phalaris, perennial ryegrass, white clover, annual ryegrass\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMost productive\u0026nbsp;three species\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLucerne, annual ryegrass, cocksfoot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubterranean clover, phalaris, lucerne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubterranean clover, phalaris, lucerne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubterranean clover, phalaris, lucerne\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMost productive\u0026nbsp;five species\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual ryegrass, subterranean clover, capeweed, perennial ryegrass, tall fescue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerennial ryegrass, lucerne, cocksfoot, subterranean clover, annual ryegrass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePerennial ryegrass, subterranean clover, phalaris, annual ryegrass, lucerne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubterranean clover, phalaris, perennial ryegrass, white clover, lucerne\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Greenhouse gas (GHG) emissions\u003c/h2\u003e \u003cp\u003eLong-term average livestock numbers, liveweight, diet quality, supplementary feed, meat and wool production and SOC modelled in SGS were entered into Sheep \u0026amp; Beef GHG Accounting Framework (SB-GAF)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e to quantify average annual GHG emissions over 100 years. SB-GAF uses Australian National Greenhouse Accounts Inventory methods to model whole-farm CH₄, N₂O and CO₂ emissions. The ratio of farm-grown and purchased supplementary feed was used to determine Scope 1 (direct) GHG emissions at the enterprise level. Scope 2 GHG were classed as those emissions derived from consumption of electricity. Scope 3 emissions were classed as indirect upstream and downstream GHG emissions generated in the wider economy. Carbon sequestration per annum was enumerated in carbon dioxide equivalents (CO₂-e) with SOC converted into CO\u003csub\u003e2\u003c/sub\u003e-e using a conversion factor of 3.67, and a 100-year global warming potential (GWP) of 28 for CH\u003csub\u003e4\u003c/sub\u003e and 265 for N\u003csub\u003e2\u003c/sub\u003eO. Net farm GHG emissions were calculated as the sum of gross emissions minus the sum of the carbon sequestration each year. Scope 3 emissions from purchased supplementary feeds were attributed to each flock based on SGS outputs, allowing calculations of net GHG emissions and emissions per unit product (\u003cem\u003eviz\u003c/em\u003e. emissions intensity). We allocated net GHG emissions in direct proportion to the mass of protein of meat and wool produced by the enterprise\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e then divided these values by annual wool and meat production to determine wool (kg CO\u003csub\u003e2\u003c/sub\u003ee/kg clean wool) and meat (kg CO\u003csub\u003e2\u003c/sub\u003ee/kg liveweight) emissions intensity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Economic and financial analyses\u003c/h2\u003e \u003cp\u003eEconomic and financial analyses were conducted using outputs from SGS, SB-GAF and data from the literature. Gross margins were calculated per DSE and per grazing hectare to standardise comparisons across farms. We compute costs associated with supplementary feeding and income associated with changes in net GHG emissions. We assume that interventions that reduce net farm GHG relative to the \u003cem\u003estatus quo\u003c/em\u003e farm systems (hereafter, \u003cem\u003eBaseline\u003c/em\u003e systems) result in carbon income, whereas interventions that increase net farm GHG relative to \u003cem\u003eBaseline\u003c/em\u003e operations invoke a carbon tax. To account for market volatility, we calculate income/costs associated with low and high prices (\u003cspan\u003e$\u003c/span\u003e38/tonne CO\u003csub\u003e2\u003c/sub\u003e-e and \u003cspan\u003e$\u003c/span\u003e100/t CO₂e respectively) for (1) SOC and (2) GHG emissions (Tables S9, S10, S11, S14 and S15). Other farm costs were assumed to be consistent with the \u003cem\u003eBaseline\u003c/em\u003e (Table S13). All economic currency are expressed in Australian dollars (AUD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Farming systems interventions\u003c/h2\u003e \u003cp\u003eWe devised farming systems interventions (treatments) to deconstruct constituent principles underpinning the broader \u0026lsquo;regenerative agriculture\u0026rsquo; philosophy: AMP, antecedent SOC and pasture species diversity\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The pasture species diversity intervention was designed to examine effects of ecotype diversity (or lack thereof) compared with \u003cem\u003eBaseline\u003c/em\u003e botanical compositions (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Several botanical compositions were then simulated, first including swards with individual pasture species, then by combining the most and least productive botanical compositions (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the AMP intervention we contrasted short, intense grazing with conventional long duration, lower stocking rates, including six grazing regimes with levels configured relative to \u003cem\u003eBaseline\u003c/em\u003e farming systems. Adaptive grazing management was explored by initiating and terminating grazing duration as a function of seasonal conditions based on pasture characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). To examine effects of historical (antecedent) SOC stocks, two soil organic matter (SOM) levels were simulated. These were designed to represent low or high SOC (0.5% SOM/0.3% SOC and 10% SOM/6% SOC, respectively) in the 0\u0026ndash;30 cm and 30\u0026ndash;50 cm soil layers.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Pasture ecotype, not diversity, drives sward productivity and SOC accrual\u003c/h2\u003e \u003cp\u003eThe most productive botanical compositions comprised only three high-yielding pasture species (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); adding further species to the sward reduced productivity. These results demonstrate that pasture ecotype, not pasture species diversity, had greater impact on seasonal sward production. Over the long term, swards with the three most productive ecotypes had 7% greater annual production, whereas the least productive swards (with three pasture types) had seasonal productivity that was 39% lower (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGrazing management interventions. Farms are abbreviated by location (WA\u0026thinsp;=\u0026thinsp;Western Australia, SA\u0026thinsp;=\u0026thinsp;South Australia and VIC\u0026thinsp;=\u0026thinsp;Victoria) and management (C\u0026thinsp;=\u0026thinsp;conventional, R\u0026thinsp;=\u0026thinsp;regenerative management).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrazing regime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWA (R1),\u003c/p\u003e \u003cp\u003e353 mm/year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSA (C2),\u003c/p\u003e \u003cp\u003e447 mm/year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVIC (C3),\u003c/p\u003e \u003cp\u003e657 mm/year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIC (R4),\u003c/p\u003e \u003cp\u003e734 mm/year\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eLevel 1\u003c/b\u003e: Set stocked \u003cem\u003e(Baseline)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003csup\u003eo\u003c/sup\u003e. paddocks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaddock area (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,484*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStocking rate (DSE/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDays on paddock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRest period (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eLevel 2\u003c/b\u003e: low intensity grazing with long rest periods (12 paddocks 14 day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003csup\u003eo\u003c/sup\u003e. paddocks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaddock area (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStocking rate (DSE/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDays on paddock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRest period (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eLevel 3\u003c/b\u003e: high intensity grazing with long rest periods (30 paddocks 12 day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003csup\u003eo\u003c/sup\u003e. paddocks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaddock area (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStocking rate (DSE/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDays on paddock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRest period (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eLevel 4\u003c/b\u003e: low intensity grazing with short rest periods (12 paddocks 1 day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003csup\u003eo\u003c/sup\u003e. paddocks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaddock area (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStocking rate (DSE/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDays on paddock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRest period (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eLevel 5\u003c/b\u003e: high intensity grazing with short rest periods (30 paddocks 1 day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003csup\u003eo\u003c/sup\u003e. paddocks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaddock area (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStocking rate (DSE/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDays on a paddock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRest period (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eLevel 6\u003c/b\u003e: adaptive rotational grazing based on leaf stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003csup\u003eo\u003c/sup\u003e. paddocks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaddock area (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStocking rate (DSE/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeaf stage eligible for grazing (N\u003csup\u003eo\u003c/sup\u003e. leaves)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDays on paddock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePasture biomass (t DM/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*As maximum area for a single paddock in SGS is 1,000 ha, the set stocked grazing regime for R1 (WA) was allocated 2,440 DSE to ensure a representative Baseline stocking rate of 2.44 DSE/ha. Stocking rates for all other grazing regimes were calculated based on grazed area and number of sheep across the whole farm.\u003c/p\u003e \u003cp\u003eHigher pasture resulted in lower need for supplementary feed. Average supplementary feed requirements were lowest for more productive swards comprising three high-yielding pasture types, ranging from 18 to 21 kg/animal/year for the WA (R1) and VIC (C3) farms, respectively. In contrast, the least productive individual pasture species resulted in the highest supplementary feed requirements (74\u0026ndash;114 kg/animal/year; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMore productive pastures sequestered more soil carbon. The most productive swards with three species sequestered the highest absolute SOC (7\u0026ndash;41 t C/ha) and relative SOC (4\u0026ndash;7 t C/ha), while the least productive swards resulted in SOC loss (-5 to -20 t C/ha) and relative SOC (-8 to -41 t C/ha; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLong-term average pasture productivity, supplementary feed, liveweight and SOC accrual for four regions depicted along a rainfall gradient. Absolute SOC accrual was calculated as the change in SOC stocks over 100 years relative to the initial year (1924). Relative SOC was computed as the change in absolute SOC compared with the \u003cem\u003eBaseline\u003c/em\u003e over the same period. Farms are abbreviated by location (WA\u0026thinsp;=\u0026thinsp;Western Australia, SA\u0026thinsp;=\u0026thinsp;South Australia and VIC\u0026thinsp;=\u0026thinsp;Victoria) and management (C\u0026thinsp;=\u0026thinsp;conventional, R\u0026thinsp;=\u0026thinsp;regenerative management).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBotanical compositions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWA (R1),\u003c/p\u003e \u003cp\u003e353 mm/year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSA (C2),\u003c/p\u003e \u003cp\u003e447 mm/year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIC (C3),\u003c/p\u003e \u003cp\u003e657 mm/year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVIC (R4),\u003c/p\u003e \u003cp\u003e734 mm/year\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eMonthly pasture growth rate (kg/ha/day)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBaseline\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive individual species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive three species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive five species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive individual species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive three species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive\u0026nbsp;five species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnnual pasture production (kg/ha/yr)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBaseline\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive individual species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive three species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive five species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive individual species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive three species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive\u0026nbsp;five species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,982\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbsolute SOC (t C/ha)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBaseline\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive individual species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive three species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive five species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive individual species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive three species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive\u0026nbsp;five species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRelative SOC (t C/ha)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive individual species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive three species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive five species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive individual species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive three species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive\u0026nbsp;five species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnnual supplementary feed intake (kg/animal/yr)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBaseline\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive individual species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive three species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive five species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive individual species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive three species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive\u0026nbsp;five species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMature liveweight (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBaseline\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive individual species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive three species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast productive five species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive individual species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive three species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost productive\u0026nbsp;five species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHigher SOC accrual associated with greater pasture production translated into lower net GHG emissions, notwithstanding greater CH\u003csub\u003e4\u003c/sub\u003e produced in regions with higher stocking rates. More productive swards with three high-yielding species reduced net GHG emissions by 6%, while the least productive swards increased net GHG emissions by 13% (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnnual pasture production, stocking rate and GHG emissions increased along a rainfall gradient (Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, S1 and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In general, R4 had the highest pasture production, which was accompanied by higher net GHG emissions (exceeding 5.7 t CO₂e/ha/yr), primarily driven by CH\u003csub\u003e4\u003c/sub\u003e emissions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, the farm in the lowest rainfall zone (R1) had the lowest pasture production and net GHG emissions (0.4\u0026ndash;0.6 t CO₂e/yr) due to lower stocking rates (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings indicate that rainfall and stocking rate have a greater impact on net GHG emissions compared with pasture species diversity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Soil carbon accrual more a function of antecedent SOC than type of intervention\u003c/h2\u003e \u003cp\u003eLower antecedent SOC evoked greater SOC accrual in upper soil layers (0\u0026ndash;30 cm), which increased absolute SOC gains over 100 years (Table S2 and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In contrast, high antecedent SOC resulted in SOC loss, diminishing absolute SOC stocks from \u0026minus;\u0026thinsp;60 t C/ha for the low-rainfall farm C2 to -28 t C/ha for the high-rainfall farm R4 (Table S2 and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn deep soil layers (30\u0026ndash;50 cm), SOC accrual was less impacted by antecedent SOC (-11 to 2 t C/ha across farms; Table S3), suggesting that most carbon storage would occur in, or be lost from, upper soil layers. Farm C3, located in the high-rainfall zone with clay-rich soils, accumulated more SOC in topsoil for both low antecedent SOC (0.3%) and \u003cem\u003eBaseline\u003c/em\u003e scenarios, resulting in the lowest relative SOC change of 15 t C/ha (Table S2). Conversely, the farm in the lowest rainfall zone with sandy loam and medium clay soils (R1 in WA), had a greater relative SOC change of 29 t C/ha (Table S2). In all cases, high antecedent SOC (6%) resulted in subsequent SOC decline (Table S2).\u003c/p\u003e \u003cp\u003eLow (0.3%) antecedent SOC stocks manifested in greater SOC sequestration and lower net emissions (Table S4 and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, higher antecedent SOC increased net GHG emissions, with GHG increasing along a rainfall gradient (0.7 t CO₂e/ha/yr for R1 to 6.3 t CO₂e/ha/yr for R4; Table S4 and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Elevated net GHG emissions at high antecedent SOC were associated with increased enteric methane emissions and soil C emissions resulting from high SOC loss (Table S4 and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Relative to \u003cem\u003eBaseline\u003c/em\u003e farm systems, the reduction in net GHG at low antecedent SOC was greater (-25%) for farms in low-rainfall zones (e.g. R1), compared with farms in high rainfall zones (e.g. -12% GHG abatement for C3; Table S4). This suggests that SOC accrual at low antecedent SOC levels are likely to elicit greater proportional carbon reductions from extensified production systems in low-rainfall regions compared with intensified farming systems in high-rainfall zones. Higher antecedent SOC evoked greater net GHG emissions relative to current farming practices (Table S4) mainly due to CO\u003csub\u003e2\u003c/sub\u003e emissions from loss of SOC, and to a lesser extent enteric CH\u003csub\u003e4\u003c/sub\u003e emissions. Effects of SOC accrual on consequent pasture growth were small (Table S5 and Fig. S9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Adaptive grazing management increases SOC accrual relative to set stocking\u003c/h2\u003e \u003cp\u003eOver the long term, adaptive grazing management evoked the greatest absolute SOC storage (44 t C/ha) in upper soil layers (Table S6, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), while low intensity grazing with short rest periods had the lowest absolute SOC accrual (26 t C/ha) and relative SOC storage (8 t C/ha). Farms in high rainfall zones had the highest absolute and relative SOC stocks, whereas C2 had the lowest SOC stocks (Table S6). Greater SOC gains for AMP on C3 were attributed to the higher rainfall and clay-rich soils, which are more conducive to long-term SOC sequestration in topsoil (30 cm). On the other hand, low rainfall and sandy soils was conducive to lower SOC accrual for C2 (Table S6 and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3.1. Regenerative grazing improves pasture production and carbon removals\u003c/h3\u003e\n\u003cp\u003eAdaptive grazing had the highest pasture growth rates, with growth rates increasing as annual rainfall increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Table S8). Relative to \u003cem\u003eBaseline\u003c/em\u003e systems, supplementary feed intakes were higher under adaptive grazing for low rainfall zones (Tables S8 and S4). Grazing regimes with shorter rest periods had lower supplementary feed intakes, ranging between 6 and 22 kg/animal/yr.\u003c/p\u003e \u003cp\u003eHigh SOC accrual of the adaptive grazing regime tended to reduce GHG emissions (Table S7 and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) while high intensity grazing intensity with short rest periods had the highest net GHG emissions, primarily due to increased CH\u003csub\u003e4\u003c/sub\u003e emissions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table S7). Across farms and grazing regimes, adaptive grazing management on C3 had the highest carbon removals relative to the \u003cem\u003eBaseline\u003c/em\u003e, while high intensity grazing with short rest periods had the least effect on net GHG emissions (Table S12). In contrast, the high-rainfall farm (R4) had the highest net GHG across grazing regimes, despite having greater gains in SOC than low-rainfall farms (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Table S7). These results highlight that fact that per unit grazing area, enteric CH\u003csub\u003e4\u003c/sub\u003e dominate farm GHG profiles and outweigh carbon removals associated with SOC accrual.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Sward productivity has greater bearing on gross margins than carbon prices\u003c/h2\u003e \u003cp\u003eCompared with the \u003cem\u003eBaseline\u003c/em\u003e, more productive swards increased along a rainfall gradient, which increased gross margins (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, S10). The relative change in gross margins was more influenced by carbon prices than by rainfall or livestock prices (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, S2 and S3). The most productive botanical compositions with three high-yielding pasture species were more profitable, with high-rainfall zone farms having the highest gross margins (R4), and low rainfall farms the least (Figure S10). The increased relative gross margins for low-rainfall farms were primarily attributed to lower stocking rates and reduced costs associated with supplementary feed (Figure S5 and Table S14), GHG emissions (Figure S6 and Table S15) and slight gains in SOC revenue (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and S4). Despite significant carbon income for high-rainfall farms (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), GHG emissions abatement costs outweighed economic gains associated with SOC improvement (Figure S4). The lowest productivity botanical compositions were least economically viable (Figures S2, S3, S10), with larger relative gross margin losses for high-rainfall farms underpinned by increased GHG emissions costs (Table S14).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3. High intensity short grazing events with long spell periods increase profit\u003c/h2\u003e \u003cp\u003eGross margins per grazed area were more influenced by rainfall and stocking rate than gross margins per DSE (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e and Table S10). Gross margins per DSE were higher on low-stocked farms located in drier regions compared with intensified farms (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e and Table S10). Farms in high-rainfall zones with higher stocking rates had higher gross margins per unit area relative to farms in low-rainfall zones, even though the high density farms incurred larger GHG emission costs. Despite higher SOC income and lower GHG emission costs under AMP (Table S15), higher supplementary feed costs associated with this intervention reduced gross margins per DSE and grazed area compared with low intensity grazing with short rest periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e and Tables S10, S14). Greater gross margins for low intensity grazing regimes with short rest periods were primarily attributed to the relatively reduced supplementary feed intake (Tables S8 and S14). Over the long-term, high intensity grazing with long rest periods in high-rainfall regions had the lowest profit associated with higher supplementary feed costs (Tables S8, S10 and S15).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003ch2\u003eSustainable livestock carrying capacity must be calibrated on production during climatic adversities\u003c/h2\u003e\n\u003cp\u003eProponents of regenerative agriculture advocate several underpinning constituents, including increased biodiversity, cell grazing, little or no cultivation and reduced synthetic inputs. When bundled, identification of those components having benefit, detriment, or \u003cem\u003ede minimus\u003c/em\u003e influence can be fraught, due to interactions and feedbacks occurring within the system. As such, the objective of this study was to extricate effects of pasture species diversity, antecedent SOC and AMP on pasture productivity, GHG emissions and enterprise profitability of farms positioned across a range of agroecological environments, from cool and wet in southern Victoria to hot and arid in inner Western Australia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe discovered that adaptive grazing regimes were more effective in enhancing pasture productivity, increasing SOC stocks, and mitigating GHG emissions compared with increasing pasture species diversity (Table S12). This led to higher average gross margins per DSE and per grazed area across most scenarios (Figures 11 and S8). While adaptive grazing regimes demonstrated the greatest potential for improving pasture production and reducing GHG emissions through improved SOC sequestration compared with contemporaneous systems (Tables S6, S7, S8, Figures 6, 7), in some cases adaptive grazing also incurred higher costs in supplementary feeding, reducing profit (Figures 11 and S8). In contrast, lower supplementary feed requirements for low intensity grazing regimes with longer rest periods often led to greater gross margins per area on highly stocked farms in wetter regions and increased profit per DSE on lower stocked farms in drier regions (Figures 11 and S8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese results demonstrate that even though a particularised grazing regime can be beneficial in terms of GHG abatement and SOC accrual\u003csup\u003e38, 39, 40\u003c/sup\u003e, the same intervention can have economic downside when pasture feed supply falls\u003csup\u003e41, 42, 43\u003c/sup\u003e. Our results clearly highlight the need for adaptive management of stocking rate not just in response to short-term vicissitudes of the weather (\u003cem\u003eviz\u003c/em\u003e. drought frequency) but also to whole farm carrying capacity, which in turn is a function of prevailing climate and plant available water capacity. As such, the sustainable carrying capacity should be calibrated to the most extreme seasonal expectation (or series of climatic adversities), which would be expected to vary between regions and with the changing climate\u003csup\u003e44, 45, 46\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003ePasture ecotype, not diversity, drives production, SOC accrual and carbon removals\u003c/h2\u003e\n\u003cp\u003eIntegrating high-yielding pasture species (such as subterranean clover) into pasture swards mitigated GHG emissions and enhanced pasture production. Swards containing subterranean clover, phalaris and lucerne increased annual pasture production by +7% and liveweight by +2% relative to \u003cem\u003eBaseline\u003c/em\u003e species, whereas low production swards (comprising weeds, cocksfoot and annual ryegrass) reduced pasture production by -39% compared with existing pasture species (Table 5). These findings are consistent with studies demonstrating increased pasture production over summer\u0026ndash;autumn through use of high-yielding species\u003csup\u003e47, 48, 49\u003c/sup\u003e. The primary distinction in productivity between the highest and lowest performing botanical compositions in the present study was largely attributed to peak growth rates in spring, with minimal production differences during winter and early autumn (Figure 1), suggesting that additional measures may be necessary to address seasonal feed shortages. Integration of dual-purpose crops, such as wheat and canola, into grazing rotations on mixed cropping-livestock enterprises may help alleviate such feed gaps by supplying high-nutritive forage from early autumn to mid-winter (May to August)\u003csup\u003e44, 50\u003c/sup\u003e and enhance livestock performance\u003csup\u003e51\u003c/sup\u003e. Other prospective interventions to mitigate feed gaps include the use of summer-active perennial pastures, forage shrubs, and the utilisation of crop residue/stubble during winter and summer/early autumn\u003csup\u003e52, 53\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTactical adjustment in feed demand may also help better match the seasonal distribution of feed supply, which in southern Australia often peaks in spring. As such, lambing in late winter, and sale of superfluous stock in early summer, would be expected to better align flock feed demand with seasonal feed supply (e.g. Figure 1). Improving lambing rates through ewe genotypes with greater fecundity can increase lambing rates, reduce whole farm emissions intensity and improve farm profit, provided non-breeding animals are sold prior to summer\u003csup\u003e54, 55, 56\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eAntecedent SOC has greater bearing on SOC accrual than climate or practice change\u003c/h2\u003e\n\u003cp\u003eEffects of SOC stocks prior to any intervention should be carefully considered if the aspiration is to improve SOC stocks post practice change, as is the case for most soil carbon markets. We showed that low antecedent SOC levels evoked greater SOC sequestration in topsoil compared with higher antecedent SOC concentrations over 100 years (Figure S1). These findings align with past work\u003csup\u003e25, 57, 58, 59\u003c/sup\u003e, suggesting that potential for improving SOC stocks exists in contexts where antecedent stocks are low (0.3%) across agro-ecological regions and livestock farming systems. In line with previous work, we showed that neither pasture species diversity nor grazing regime had significant impact on deep SOC\u003csup\u003e2, 23\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHigh SOC sequestration achieved from low antecedent SOC stocks reduced net farm GHG emissions (Figure 4). However, antecedent SOC had relatively little impact on pasture productivity and supplementary feed intake (Fig. S8). These findings underscore trade-offs between sustainability outcomes (GHG abatement vs pasture production) and profitability, highlighting the need to assess multiple indicators associated with any purported innovation, such as regenerative agriculture. These results also indicate that degraded ecosystems provide the most attractive opportunity for not just improved carbon storage and GHG mitigation, but also for increased biodiversity and natural capital\u003csup\u003e14, 34, 42\u003c/sup\u003e. Our results suggest that no single intervention can benefit all farming systems, because systems vary in their current state, from degraded to ecologically pristine\u003csup\u003e60, 61\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eRegenerative grazing has clear benefits over conventional set stocking\u003c/h2\u003e\n\u003cp\u003eWe showed that adaptive grazing management mitigated GHG emissions through superior SOC sequestration compared with conventional set stocking traditionally conducted at lighter stocking rates for longer periods.\u0026nbsp;After a century of AMP, SOC stocks were higher (Tables S6 and S12) compared with set stocking conducted for the \u003cem\u003eBaseline\u003c/em\u003e systems, whereas low intensity grazing with short rest periods manifest in lowest SOC storage (26 t C/ha). Low intensity, short rest grazing tended to reduce pasture recovery periods, reduce soil carbon inputs and reduce SOC sequestration. While these results are context-specific, they align with hitherto work documenting increased soil carbon and nitrogen under AMP\u003csup\u003e62, 63\u003c/sup\u003e, with sequestration potential modulated by agroecological context\u003csup\u003e61, 64\u003c/sup\u003e. Previous work also highlights the importance of rainfall, particularly drought (Fig. S9), on SOC sequestration potential\u003csup\u003e65, 66, 67\u003c/sup\u003e. In general, sites with minimal cultivation, greater rest periods between grazing events, and afforded long-term growth of perennial vegetation tend to have greater soil organic matter, carbon and nitrogen\u003csup\u003e63\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAMP increased herbage productivity relative to pasture species diversity, more so as rainfall increased (Table S12). Adaptive grazing had the highest herbage growth rates (Table S8), which translated to higher annual pasture production (+35%), and increased SOC sequestration. In contrast, high intensity grazing with long rest periods had the lowest herbage growth rates and only a modest 16% increase in annual pasture production compared with the \u003cem\u003eBaseline\u003c/em\u003e. Despite this, adaptive grazing also increased supplementary feed requirement for farms in drier regions, while high intensity grazing with long rest periods increased intakes for highly stocked farms in wetter regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared with increasing pasture species diversity, AMP had much greater bearing on profit margins per animal (DSE) and per unit of grazed land across most scenarios (Figures 11 and S8). This finding is consistent with prior research that has documented improved financial performance under AMP due to lower production costs, including reduced reliance on inputs such as herbicides\u003csup\u003e68\u003c/sup\u003e. Even though the adaptive grazing regime achieved the highest levels of pasture productivity and GHG mitigation through superior SOC sequestration, it incurred higher supplementary feed costs, which in some cases diminished profits. In contrast, low intensity grazing regimes with shorter rest periods had lower supplementary feed costs, offsetting the elevated GHG abatement expenses and yielding greater profit. The least productive pasture species and set stocking (when sheep prices were low) or high intensity long rest grazing (when sheep prices were high) had the lowest financial returns (Fig. 10).\u003c/p\u003e"},{"header":"5. Concluding remarks","content":"\u003cp\u003eShould future legislation be imposed on farms for exceeding an arbitrary GHG emissions threshold, our results suggest that AMP - in concert with selection of appropriate pasture ecotypes and calibration of whole farm stocking rate to expected pasture production under drought \u0026ndash; can improve SOC accrual, reduce GHG emissions, and enhance enterprise prosperity. We demonstrated that high intensity short duration grazing had greater impact on pasture production SOC accrual and economic returns than did pasture species diversity. In general, short spelling periods were more profitable than longer spelling periods, but longer spelling periods resulted in greater SOC accrual. Although adaptive grazing management had greater potential for GHG mitigation, increased herbage production and SOC accumulation, this regime evoked higher supplementary feed costs, which reduced profit per hectare and DSE. Low intensity grazing intensities with shorter rest periods generated higher profit margins per hectare and DSE due to lower supplementary feed costs, offsetting larger GHG abatement taxes, particularly in high-rainfall regions. We revealed that agroecological environment and stocking rates exerted the greatest influence on pasture productivity, GHG emissions and profit per area. In most scenarios, the least productive swards and high intensity grazing regimes were the least economically viable. We conclude that (1) contextualized evaluation of multiple sustainability metrics while carefully controlling for historical management, climate and contemporary status is more likely to elicit benefits in carbon sequestration, agri-food production and enterprise profitability, (2) strategic and tactical management of stocking rate considering likely pasture production, feed demand and seasonal climate forecast can reduce supplementary feed costs and potentially carbon emissions taxes, and (3) regenerative grazing can reduce GHG emissions and improve profit, provided whole farm stocking rate is in harmony with long-term productive potential.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eAll data are available from the corresponding author on request.\u003c/p\u003e\n \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003ch2\u003eCredit statement\u003c/h2\u003e \u003cp\u003eAM and KMCW, simulations; AM and MTH; writing first draft; KMCW; MC; KL; PB; KW; GP, MTH, writing second draft and editing; MTH, conceptualisation and supervision; all authors revised the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis project was funded by Australian Wool Innovation (AWI), who invests in research, development, innovation and marketing activities along the global supply chain for Australian wool (Project No. ON-00899). AWI is grateful for its funding, which is primarily provided by Australian wool growers through a wool levy and by the Australian Government which provides a matching contribution for eligible R\u0026amp;D activities.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHarrison MT, \u003cem\u003eet al.\u003c/em\u003e Carbon myopia: the urgent need for integrated social, economic and environmental action in the livestock sector. Global Change Biology, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor CA, Harrison MT, Telfer M, Eckard R. Modelled greenhouse gas emissions from beef cattle grazing irrigated leucaena in northern Australia. Animal Production Science 56, 594\u0026ndash;604 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang-Fung-Martel J, Harrison MT, Brown JN, Rawnsley R, Smith AP, Meinke H. 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Land Use Policy 26, 630\u0026ndash;639 (2009).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cell grazing, biodiversity, soil carbon, natural capital, mitigation, ecosystems services, extreme event, drought, climate change","lastPublishedDoi":"10.21203/rs.3.rs-5703590/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5703590/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe broad philosophy comprising regenerative agriculture can be deconstructed into several underpinning components, including adaptive multi-paddock grazing (AMP), improved biodiversity, silvopasture, and minimal use of cultivation and synthetic fertilisers. Here, we use sheep farms positioned across a rainfall gradient to examine how pasture species diversity, antecedent SOC and AMP influence soil organic carbon (SOC) accrual, greenhouse gas (GHG) emissions, pasture production and enterprise profit. Compared with light grazing intensities for long durations, high-intensity short-duration cell grazing with long spelling periods (AMP) amplified pasture productivity, improving SOC accrual and GHG abatement, increasing profit per animal and hectare. Renovation of pastures with high-yielding, low-emissions ecotypes enhanced pasture production and carbon removals, albeit to a lesser extent than that realised from AMP. Adaptive grazing management, where animals were moved in response to pasture residual, evoked the greatest SOC accrual and GHG abatement, but also increased supplementary feed costs. Low stocking rates with longer spelling periods between grazing events were the most profitable, highlighting the need for agile, proactive grazing management adapted in line with seasonal conditions. We conclude that (1) whole farm stocking rate and seasonal rainfall quantum have greater influence on pasture production, SOC, GHG and profit compared with species diversity and grazing management, (2) \u003cem\u003eindividual\u003c/em\u003e pasture species \u0026ndash; rather than species \u003cem\u003ediversity\u003c/em\u003e \u0026ndash; have greater bearing on sward production, (3) notwithstanding carbon removals via improved SOC, CH\u003csub\u003e4\u003c/sub\u003e from enteric fermentation dominates farm GHG profiles, and (4), AMP can catalyse SOC accrual and sward production compared with lighter stocking conducted for longer durations, but only when whole farm stocking rate is harmonised with long-term sustainable carrying capacity, with the latter being a function of plant-available water capacity and drought frequency.\u003c/p\u003e","manuscriptTitle":"Regenerative agriculture amplifies productivity and profitability while negating greenhouse gas emissions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-28 07:23:54","doi":"10.21203/rs.3.rs-5703590/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-food","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natfood","sideBox":"Learn more about [Nature Food](http://www.nature.com/natfood/)","snPcode":"","submissionUrl":"","title":"Nature Food","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d9af6003-9d4b-46a5-866d-99ac4931cb08","owner":[],"postedDate":"January 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":43336732,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation"},{"id":43336733,"name":"Scientific community and society/Agriculture"},{"id":43336734,"name":"Earth and environmental sciences/Natural hazards"},{"id":43336735,"name":"Earth and environmental sciences/Ecology/Climate-change ecology"}],"tags":[],"updatedAt":"2026-03-14T07:05:59+00:00","versionOfRecord":{"articleIdentity":"rs-5703590","link":"https://doi.org/10.1038/s43016-026-01331-2","journal":{"identity":"nature-food","isVorOnly":false,"title":"Nature Food"},"publishedOn":"2026-03-13 04:00:00","publishedOnDateReadable":"March 13th, 2026"},"versionCreatedAt":"2025-01-28 07:23:54","video":"","vorDoi":"10.1038/s43016-026-01331-2","vorDoiUrl":"https://doi.org/10.1038/s43016-026-01331-2","workflowStages":[]},"version":"v1","identity":"rs-5703590","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5703590","identity":"rs-5703590","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}