The breakthrough in super hybrid rice yield has significantly contributed to China's and global food security. However, the inherent conflict between high productivity and environmentally sustainable agriculture poses substantial challenges. Issues such as water scarcity, energy crises, escalating greenhouse gas emissions, and diminishing farm profitability threaten longterm agricultural sustainability. In response, we applied a holistic food-carbon-nitrogen-water-energy-profit (FCNWEP) nexus framework to comprehensively assess the sustainability of distinct crop management strategies across three sub-sites in Central China. Field experiments were conducted in Hubei and Hunan provinces from 2017 to 2021 using a widely adopted elite super hybrid rice cultivar (Y-liangyou 900). Four crop management treatments were implemented: a control (CK, 0 kg N ha-1), conventional crop management (CCM, 210-250 kg N ha-1, 7:3 basal:mid-tiller fertilizer ratio), and two integrated crop management (ICM) treatments (ICM1, 180-210 kg N ha-1, 5:2:3 basal:mid-tiller:panicle initiation fertilizer ratio; ICM2, 240-270 kg N ha-1, 5:2:2:1 basal:mid-tiller:panicle initiation:flowering fertilizer ratio). Variables assessed included grain yield, carbon footprint, nitrogen footprint, water footprint, energy footprint, nitrogen use efficiency, and economic benefits. Our results showed significant yield variations, with ICM2 consistently outperforming CCM and ICM1 across all three sites. In Jingzhou, Suizhou, and Changsha, ICM2's grain yield was 30.2, 24.7, and 13.3% higher than CCM, respectively. Net profits under ICM2 exceeded those of CCM and ICM1 by 31.8 and 115.2% in Jingzhou, 32.2 and 109.9% in Suizhou, and 15.4 and 34.0% in Changsha, respectively. Integrated crop management, particularly ICM2, demonstrated improved nitrogen and energy use efficiency, leading to reduced carbon, nitrogen, water, and energy footprints. Overall, composite sustainability scores derived from the FCNWEP framework indicated that both ICM2 and ICM1 exhibited higher sustainability levels compared to CCM. This study provides valuable insights into practical management methodologies and offers recommendations for enhancing agricultural sustainability.

Understanding local environmental resources is key to easing resource pressure and achieving sustainable crop production under climate change. Using multi-source data and a crop model, the integrated environmental resource endowment, encompassing climatic conditions, blue water availability and soil properties, for maize and wheat, and how harvest areas align with these resources is quantified. Over the past 20 years, maize shifted northward with climate changes, while wheat's high endowment regions moved west but its harvest area moved east. Notably, both crops show increasing spatial misalignment with water resources, with about 84% of maize and 90% of wheat areas facing water scarcity and requiring extra water to maintain yields. This growing mismatch between where crops are grown and where resources, especially water, are abundant highlights the need for smarter, resource-informed crop placement and water management. Aligning crops with local environmental capacity represents an opportunity to ease pressure on finite resources, strengthen food security, protect ecosystems, and ensure long-term economic sustainability.

First passage time models describe the time it takes for a random process to exit a region of interest and are widely used across various scientific fields. Fast and accurate numerical methods for computing the likelihood function in these models are essential for efficient statistical inference of model parameters. Specifically, in computational cognitive neuroscience, generalized drift diffusion models (GDDMs) are an important class of first passage time models that describe the latent psychological processes underlying simple decision-making scenarios. GDDMs model the joint distribution over choices and response times as the first hitting time of a one-dimensional stochastic differential equation (SDE) to possibly time-varying upper and lower boundaries. They are widely applied to extract parameters associated with distinct cognitive and neural mechanisms. However, current likelihood computation methods struggle in common application scenarios in which drift rates dynamically vary within trials as a function of exogenous covariates (e.g., brain activity in specific regions or visual fixations). In this work, we propose a fast and flexible algorithm for computing the likelihood function of GDDMs based on a large class of SDEs satisfying the Cherkasov condition. Our method divides each trial into discrete stages, employs fast analytical results to compute stage-wise densities, and integrates these to compute the overall trial-wise likelihood. Numerical examples demonstrate that our method not only yields accurate likelihood evaluations for efficient statistical inference, but also considerably outperforms existing approaches in terms of speed.

Rising temperatures pose a significant threat to crop production worldwide. While many studies have examined the effects of warming on crop yields, the role of soils in crop-climate interactions is often overlooked. This study investigates how soil properties influence crop yield responses to increased growing season temperatures, using an ensemble of nine global gridded crop models and 37 CMIP6 climate models under three shared socioeconomic pathway scenarios. Our findings show that, during 1980-2010, a 1 degrees C increase in temperature resulted in yield changes of -2.3 % for maize and + 3.0 % for soybean. However, under future climate scenarios of 2050-2080, yields are projected to decline by -6.6 % to -7.5 % for maize and -8.9 % to -10.7 % for soybean. Soil properties account for 51 % and 59 % of the spatial variations in temperature sensitivity for global maize and soybean yields, respectively, with soil organic carbon (SOC) emerging as the most influential factor. Improving SOC through farming and soil conservation practices is expected to reduce warming-induced yield losses by 0.5-1.1 % degrees C- 1 for maize and 1.3-2.5 % degrees C- 1 for soybean, particularly in dryland areas. These benefits diminish under more extreme warming scenarios (i.e., SSP585 vs. SSP126), but can be amplified by adopting new crop varieties with fixed growing seasons. Our findings highlight the buffering effects of SOC on crop responses to warming, suggesting a promising soil-based solution for building resilience in global food production under climate change.

Large-scale and difficult to decompose greenhouse gas (GHG) have challenged the agrifood sector to achieve net zero. While mulching perturbs GHG emissions and soil carbon sequestration, its potential in reducing GHG emissions remains unexplored. Here, we combined life cycle assessment with process-based model to evaluate carbon footprints and net environmental and economic benefits of maize, wheat and rice under no-mulching (CK), plastic film (PM) and straw mulching (SM) in China. We show that carbon footprint of rice was highest, methane (CH4) and nitrogen fertilizer were main contributor. In China, GHG emissions of three crops were 668 Tg CO2 eq yr(-1) (rice: 38 %, maize: 33 %, wheat: 29 %), SM increased yield (12 %), agricultural profit (9 %) and reduced emissions (9 %), which promotes agricultural co-development, but PM needs to be combined with other mitigation measures. Our study paved ways for comprehensively understanding the impact of mulching, and developing effective strategies towards sustainable future.

Maize possesses exceptional diversity and undergoes rapid and extensive genetic changes during breeding. New genotypes impact soil microbiota, and respond differently to current climates compared with older genotypes in diverse environments, assessment of such interactions was a key novelty of the present study. Here, we investigated associations between genetic relationship, plant traits and soil bacterial and fungal composition based on six decades of maize breeding in China. Soil microbiome of six maize cultivars, each representing a popular variety developed each decade from the 1950s to 2000s, were collected from a long-term field experiment (established in 2012) and a pot experiment. Microbial community shifts were deduced from the taxonomic co-occurrence and co-exclusion network dynamics across maize growth stages. As expected, cultivar replacement influenced the soil bacterial and fungal composition (P < 0.001). At flowering, different maize genotype groups had distinctive bacterial community structure in the rhizosphere and root-zone soil. Aboveground dry matter, plant height and leaf area were plant traits that best explained the bacterial community variance (29.0 % in rhizosphere and 19.3 % in root-zone soil; P = 0.01) among maize cultivars. Specific root length showed a negative correlation with the gene copy numbers of alpha-Proteobacteria. The major maize cultivar from the 2000s (M-00s) had relatively more cultivar-enriched bacterial taxa, with a greater proportion of the genera Acidibacter and Variibacter in root-zone soil. Furthermore, the M-00s cluster contained the most phoD-genes related to phosphorus cycling at harvest, and had the highest bacteria/fungi ratio in the root zone at elongation and flowering. The predominant taxa in the biggest module changed with cultivar replacement, from Proteobacteria in the older maize cultivars to Acidobacteria in the M-00s cultivar. The contemporary M-00s cultivar may attract beneficial bacteria and fungi while reducing contact with other fungi, which improves soil nitrogen and phosphorus availability. If the plant-associated microbiome could serve as an extended phenotype, then specific gene locus in the maize genome could be targeted to optimize maize breeding for sustainable farming systems.

Context: As China's breadbasket, the Yangtze River Basin plays a critical role in global food security, yet its agricultural stability is increasingly threatened by the climate emergency. Objective: This study aimed to (1) identify the critical climatic variables that drive wheat yield variability across Yangtze River Basin; (2) reveal the key climatic variables that contribute to regional yield gaps, and (3) quantify yield increase and associated environmental costs under current nitrogen application rates and estimate the nitrogen fertiliser required to achieve full yield potential with improved genetic traits. Methods: We combined process-based crop model knowledge, fine-scale municipal wheat yield data (1980-2020) and statistical analytics to benchmark regional yield gaps under historical and future climates and we then analysed 8192 hypothetical genotypes with diverse yet realistic ranges of phenology, growth, radiation use efficiency and yield components to assess their impact on reducing yield gaps and associated environmental costs. Results and conclusion: We found that many arable areas exhibited statistically significant correlations (R2 = 0.94) between climate and wheat yield variability. Changes in precipitation explained the largest proportion of yield variance (R2 = 0.38), while temperature (R2 = 0.31) and solar radiation (R2 = 0.29) exerted nearly identical influences on wheat yield fluctuations. Yield gaps were well explained by photothermal quotients, which ranged from 0.55 to 1.14 across the Yangtze River Basin. Due to increased extreme climatic events, simulated rainfed wheat yields decreased by 12 % and 24 % in 2050s under emissions scenarios SSP245 and SSP585, respectively. Adoption of cultivars with optimised traits (higher RUE and larger grain size) could increase grain yield by 61-80 % under current and future climate. However, trait-optimisation for yield revealed clear trade-offs for nitrogen demand (16-36 % increase), N2O emissions (30-50 % increase), greenhouse gas emission intensity (24-35 % increase) and nitrogen-use efficiency (13-21 % decrease), heralding dire ramifications for breeding programs that optimize traits for productivity benefits alone. Significance: We advocate that crop breeding programs include sustainability indicators encompassing nitrogen demand, use, and use-efficiency to avoid environmental impacts associated with increasing yields to feed a burgeoning global population.

Context: The winter wheat-summer maize rotation in China's Huang-Huai-Hai (HHH) farming region has been plagued by the long-standing problem of excessive nitrogen (N) application, driven by ambiguous N fertilizer recommendations, due to insufficient understanding of inter-seasonal N turnover and long-term N accumulation effects in the crop-soil system. Objective: Our aims were (1) to calibrate the STICS model for the wheat-maize rotation in the HHH region, (2) to elucidate the annual N turnover characteristics affected by seasonal differences in N use, mineralization and leaching, and (3) to determine optimal N rates considering long-term cumulative effects of N fertilizer management. Methods: Data from an 11-year field experiment were used to calibrate and evaluate the STICS model. Scenario analysis contrasting different N rates for wheat and maize, as well as their pairwise combinations, was conducted to identify optimal N ranges for each season, aiming for high yields and nitrogen-use efficiency (NUE) in concert with low N surplus. Results: STICS well captured the dynamics of shoot biomass (rRMSE: 20-22 %), N uptake (rRMSE: 22-28 %) and soil water content (rRMSE: 19-24 %) under different N treatments, respectively. STICS relatively well simulated crop yields under various N rates, with rRMSE of 9-13 % in the short term, and 13-15 % in the long term. Simulated N mineralization was higher in maize seasons than in wheat seasons due to the higher temperature and soil moisture, leading to a greater N surplus and increased leaching under the current management. Our simulations revealed optimal N rates of 180 kg N ha-1 for wheat and 164 kg N ha-1 for maize, which were 12 % and 38 % lower than contemporary N use in the region, achieving a long-term stable annual yield of 18.2 Mg ha-1, along with an annual NUE of 78 % and an N surplus of 80 kg N ha-1 per year. Conclusions: High residual soil N from the wheat season and strong N mineralization during the maize season suggest that less N fertilizer can be applied to maize without influencing yield level. Implications: A systematic perspective and consideration of long-term N turnover within crop rotations provided by crop models and field observations are crucial for improving N management in the wheat-maize rotation in the HHH region of China.

To enhance the quality of forage-grain ratoon rice (FGR) and promote the sustainable development of both rice and livestock industries in southern China, this study investigates the microbial fermentation dynamics of co-ensiling FGR with maize or sorghum-sudangrass hybrid. Results demonstrate that co-ensiling with maize significantly improved fermentation quality, reducing fiber content and enhancing lactic acid production, compared with sorghum-sudangrass hybrid. The optimal FGR-to-maize ratio of 75:25 yielded the lowest neutral detergent fiber and acid detergent fiber values and the highest lactic acid concentration (39.37 g/kg DM). Co-ensiling promoted the growth of beneficial lactic acid bacteria (LAB), particularly Lactobacillaceae and Sporolactobacillaceae, thus enhancing fermentation efficiency. Additionally, inoculation with Lactobacillus plantarum improved silage stability by promoting LAB growth and inhibiting the growth of undesirable Enterobacter species. This study offers a sustainable strategy to optimize rice straw utilization for livestock feed, reduce dependence on imported forages, and support agricultural sustainability in China.

Agrivoltaic systems (AVS) - wherein solar photovoltaics (PV) and agriculture are co-located on the same land parcel - offer a sustainable approach to achieving the Sustainable Development Goals (SDGs) by enabling concurrent renewable electricity and agri-food production. Here, we elucidate plausible co-benefits and tradeoffs of agri-food production and electricity generation in AVS across manifold socio-enviro-economic contexts, with the aim of understanding the contextualized interplay between AVS implementation and progress towards the SDGs. We modeled three AVS designs with varying solar panel densities (high, mid, low) at case study locations in Australia, Chad, and Iran using various models (System Advisor Model for PV and GrassGro for livestock systems). The findings suggest that in regions conducive to high biomass production per unit area, such as in parts of Australia, AVS design with high solar panel density can reduce meat production by almost 50%, which can jeopardize food security and impede achieving SDG 2 (Zero Hunger). In these regions, AVS design with low solar panel density enables meeting SDGs aligned with agri-food production and renewable energy generation. In contrast, in semi-arid regions, such as Iran, AVS design with a high density of solar panels can improve agricultural production via the alleviation of water deficit, thereby supporting the prioritization of solar power generation, with food production as a co-benefit. In developing countries such as Chad, AVS can enhance economic development by providing electricity, food, and financial benefits. We call for policymakers to incentivize AVS deployment in such regions and stimulate public and private investment to enable progress towards SDGs.

Context: North China Plain (NCP) is characterized with sporadic seasonal rainfall patterns and scarce surface water resources that challenge the consistency of winter wheat production. Farmers in the NCP tend to fertilize crops with luxury N to obtain high yield, but such practices have accelerated depletion rates of scarce water supplies. However, the influences of long-term N treatments on water use and use-efficiency in the winter wheat production are still not fully understood, which is important for optimizing N rates and irrigation to promote agricultural green development. Objective: The purpose of this study was to quantify the impacts of different N rates on water consumption and water use efficiency (WUE) in winter wheat using 11-year experimentation. Methods: This study was focused on a winter wheat-summer maize cropping system in an 11-year field experiment in the NCP, which comprised five N rates in the wheat production season, namely 0, 60, 120, 180 and 240 kg N ha-1, hereafter recorded as N0, N60, N120, N180 and N240, respectively. Results: Fertilization longitudinally increased the yield and WUE of winter wheat, but also ramped water use. Water consumption and WUE under the N60-N240 treatments were 17-38 % and 186-333 % higher than that in the N0 treatment, respectively. Soil water extraction primarily occurred in the 0-120 cm soil depth, and which was predominantly concentrated within the 0-60 cm soil layer before flowering. It primarily reflected by root distribution abundance and yield formation. Greater yield and aboveground biomass under high N treatments were directly proportional to larger amount of water consumption in both vegetative and reproductive stages. Conclusions: Increased N rates resulted in higher wheat yield and WUE but also caused larger amount of water consumption, while low N inputs significantly reduced water consumption but led to reduced WUE in the longterm. The optimal N rate to achieve high levels of wheat yield and WUE with low water consumption is 180 kg N ha-1 (N180) in the study. Implications: Application of N fertilizer is beneficial for improving WUE, however, N application may result in accelerated premature senescence when water is limiting, the appropriate N rates should thus consider soil water availability as well as potential for the crop to receive water from rainfall or irrigation.

While irrigation is generally required for most summer crops in the Mediterranean region, increasingly scarce water supplies are leading to a demand for more efficient irrigation infrastructure. Here, we assess how three irrigation volumes—100 mm/week (simulating excess water), 55 mm twice per week (moderate supply), and a variable amount adjusted on a weekly basis according to crop water demand (AMP) applied once or twice weekly via drip irrigation—impacted the growth, yield, and ear traits of a local maize variety under low-input farming in central Portugal. We found that irrigation management significantly influenced grain yield and irrigation water use efficiency (IWUE), with the 55 mm treatment applied twice weekly achieving the highest yield (3504 kg ha−1) and IWUE (7.2 kg ha−1 mm−1). The highest irrigation treatment (100 mm/weekly) impaired yield (996 kg ha−1 and 1973 kg ha−1, when water was applied in one or two events), likely due to nutrient leaching, and resulted in the lowest IWRU (1.2 kg ha−1 mm−1 and 2.5 kg ha−1 mm−1, respectively). Biweekly applications tended to increase crop height. Irrigation rate and frequency significantly affected kernel number and size, but not total ear weight or cob-to-ear weight ratio. These findings highlight the importance of irrigation frequency based on crop water demand over blanket approaches based on volume alone.

Extreme precipitation poses a significant threat to crop production, often underestimated by process-based models. State-of-the-art models also struggle with high-resolution spatial applications due to process complexity. Here, we developed a Knowledge-Guided Machine Learning (KGML) framework that integrates machine learning with a waterlogging-enabled APSIM (Agricultural Production Systems sIMulator) to simulate wheat yield change under climate change in the Yangtze River Basin, China. Using transfer learning, this KGML framework transferred waterlogging processes to eight gridded crop models, enabling more accurate yield projections. We found that KGML could accurately replicate the behavior of the improved APSIM model under waterlogging conditions, achieving an R2 of 0.83 and an RMSE of 272.3 kg/ha for yield loss simulations. Soil properties were identified as the primary factors influencing yield losses under waterlogging, highlighting the importance of optimizing soil conditions to mitigate the adverse impacts of excessive water. Across different scenarios, the improved crop model ensembles projected greater crop yield losses compared to the original simulated outputs, with additional losses (compared to the historical period) around 5.9%–7.3% during the two periods. Although global climate models were the primary source of uncertainty in T1 (2029–2059), crop models contributed more to uncertainty in T2 (2069–2099). The improved ensemble reduced uncertainty from crop models compared to the original. This study highlights the potential of KGML to improve crop models, offering valuable insights for climate impact assessments and resource management. We believe our results can help national and local authorities make informed crop yield decisions under climate change.

Effective parameter calibration is vital for accurate crop model simulations but is challenged by extensive parameter sets and inherent uncertainties in agroecosystem models. This study systematically evaluated three global sensitivity analysis (GSA) methods (Morris, Sobol-Martinez, and eFAST) alongside three parameter optimisation algorithms (Nelder-Mead simplex, DREAM-zs, and L-BFGS-B) to enhance parameter estimation and simulation accuracy in APSIM Next Generation, a widely used agroecosystem model. Sensitivity analysis results indicated that the Morris method identified the broadest set of influential parameters due to its inclusive parameter selection strategy, while Sobol-Martinez provided more targeted parameter identification by clearly distinguishing impactful parameters. Conversely, eFAST was highly selective, pinpointing fewer parameters of highest impact, beneficial for computational efficiency. Among the optimisation methods, the Bayesian DREAMzs algorithm consistently produced superior model predictions across evaluated output variables, including phenology, biomass, leaf area index, and grain yield, outperforming the frequentist Nelder-Mead and gradientbased L-BFGS-B methods. However, DREAM-zs required significantly higher computational resources, particularly at higher iteration settings. The uncertainty analysis revealed that interactions among sensitivity analysis methods, optimisation algorithms, and wheat genotypes dominated the sources of uncertainty in parameter estimation. This underscores the necessity of carefully selecting and integrating complementary sensitivity analysis and optimisation methods tailored to specific modelling objectives. Our findings demonstrated a robust methodological framework to improve calibration accuracy, reduce predictive uncertainty, and ultimately support more reliable agricultural decision-making.

It has become common to compare crop model results in multi-model simulation experiments. In general, one observes a large variability in such studies, which reduces the confidence one can have in such models. It is important to understand the causes of this variability as a first step toward reducing it. For a given data set, the variability in a multi-model study can arise from uncertainty in model structure or in parameter values for a given structure. Previous studies have made assumptions about the origin of parameter uncertainty, and then quantified its contribution, generally finding that parameter uncertainty is less important than structure uncertainty. However, those studies do not take account of the full parameter variability in multi-model studies. Here we propose estimating parameter uncertainty based on open-call multi-model ensembles where the same structure is used by more than one modeling group. The variability in such a case is due to the full variability of parameters among modeling groups. Then structure and parameter contributions can be estimated using random effects analysis of variance. Based on three multi-model studies for simulating wheat phenology, it is found that the contribution of parameter uncertainty to total uncertainty is, on average, more than twice as large as the uncertainty from structure. A second estimate, based on a comparison of two different calibration approaches for multiple models leads to a very similar result. We conclude that improvement of crop models requires as much attention to parameters as to model structure.

Most crop simulation models do not consider the effect of waterlogging despite its importance for crop performance. Here, we reviewed the impact of waterlogging during different wheat phenological stages on grain number per unit area, average grain size, and grain yield. Episodes of waterlogging from the onset of tillering to anthesis result in fewer, and during grain filling in lighter grains. To simulate such impacts, we implemented a new waterlogging module into the wheat crop simulation model DSSAT-NWheat, accounting for the effects of waterlogging on wheat root growth, biomass growth, and potential average grain size. The model incorporating the new waterlogging routine was tested using data from a controlled experiment, and it reasonably reproduced wheat yield responses to pre-anthesis waterlogging. A sensitivity analysis showed that the simulated impact of waterlogging on above ground biomass and roots, as well as leaf area index, grain number, and grain yield varied with phenological stages. The simulated crop was most sensitive to pre-anthesis waterlogging, consistent with experimental studies. The new waterlogging-enabled crop model is an initial attempt to consider the impact of excess rainfall and waterlogging on crop growth and final grain yield to reduce model uncertainties when projecting climate change impacts with increasing rainfall intensity.

While previous studies have primarily examined the impacts of singular interventions on GHG emissions reduction and carbon dioxide removals (CDR), few studies explore complementarities and antagonisms when multiple interventions are simultaneously operationalized. Here, the aim is to examine how stacking of two pathways for mitigation-CDR, via soil organic carbon (SOC) accrual and GHG emissions avoidance via antimethanogenic feed additives-impacts net GHG emissions associated with sheep production. A nonlinear programing approach is invoked to elicit optimal combinations of grazing management and antimethanogenic feed additives to maximize farm profit and/or minimize net GHG emissions. It is shown that stacking multiple interventions realizes that greater abatement and profit do any singular intervention. Adoption of 3-NOP feed supplement with 15- and 30-paddock high stocking rate systems is the most prospective stacked intervention for concurrent profit maximization and emissions minimization. It is contended that (1) increasing payments for farming of carbon and ecosystems services relative to that of wool and meat will stimulate participation in carbon markets, (2) economics of participation in carbon markets tend to be more favorable for larger farms than smaller farms due to economies of scale and (3) adoption of optimal grazing management and antimethanogenic feed additives can realize more profit from sheep production and carbon farming than enterprises that only derive income from sheep production.

Rice production faces increasing challenges from climate change and soil degradation. The conversion from double to single-cropping rice over the past decades has further threatened rice self-sufficiency in China. Understanding the spatial and temporal variations of rice yield across different rice-cropping systems is crucial for creating adaptation strategies. Here we used a process-based modelling approach combined with a nationwide field dataset from 1981 to 2020 to evaluate rice yield gaps and temporal yield variabilities for single and double rice-cropping systems, and further assessed their underlying determinants in China. We showed that single rice had the largest yield gap and the greatest temporal variability in yield, followed by late rice and early rice. The coefficient of variation (CV) for actual yield ranged from 6 % to 64 %, 4 % to 36 %, and 5 % to 28 % for single rice, late rice, and early rice, respectively. Regions with unstable yields were primarily located in southwestern (for single rice) and southern China (for late rice), and determinants of yield stability varied across subregions. Overall, the combined effects of climate and soil factors generally reduce yield stability. Improved management, such as appropriate sowing dates, precise fertilization, and cultivars with favorable traits, significantly enhanced the stability. Socio-economic factors including sufficient labor and advanced agricultural mechanization also contributed to closing yield gaps and stabilizing yield. This study provides spatial insights for developing region- specific strategies to ensure a sufficient and stable rice supply.

Land managers are challenged with the need to balance priorities in production, greenhouse gas (GHG) abatement, biodiversity and social license to operate. Here, we develop a transdisciplinary approach for prioritising land use, illustrated by co-designing pathways for transitioning farming systems to net-zero emissions. We show that few interventions enhanced productivity and profitability while reducing GHG emissions. Antimethanogenic feed supplements and planting trees afforded the greatest mitigation, while revenue diversification with wind turbines and adoption of livestock genotypes with enhanced feed-conversion efficiency (FCE) were most conducive to improving profit. Serendipitously, the intervention with the lowest social licence—continuing the status quo and purchasing carbon credits to offset emissions—was also the most costly pathway to transition to net-zero. In contrast, stacking several interventions to mitigate enteric methane, improve FCE and sequester carbon entirely negated enterprise emissions in a profitable way. We conclude that costs of transitioning to net-zero are lower when interventions are bundled and/or evoke productivity co-benefits.