An understanding of cultivar effects on field greenhouse gas (GHG) emissions in rice ( L.) systems is needed to improve the accuracy of predictive models used for estimating GHG emissions and to evaluate the GHG mitigation potential of different cultivars. We compared CH and NO emissions, global warming potential (GWP = NO + CH), yield-scaled GWP (GWP = GWP Mg grain), and plant growth characteristics of eight cultivars within four study sites in California and Arkansas. Nitrous oxide emissions were negligible (<10% of GWP) and were not different among cultivars. Seasonal CH emissions differed between cultivars by a factor of 2.1 and 1.4 at one California and one Arkansas site, respectively. Plant growth characteristics were generally not correlated with seasonal CH emissions; however, the strongest correlations were observed for shoot and total plant (root + shoot) biomass at heading ( = 0.60) at one California site and for grain at maturity ( = -0.95) at one Arkansas site. Although differences in GWP and GWP were observed, there were inconsistencies across sites, indicating the importance of the genotype × environment interaction. Overall, the cultivars with the lowest CH emissions, GWP, and GWP at the California and Arkansas sites were the lowest and highest yielding, respectively. These findings highlight the potential for breeding high-yielding cultivars with low GWP, the ideal scenario to achieve low GWP, but environmental conditions must also be considered.

Our current understanding of the mechanisms driving spatiotemporal yield variability in rice systems is insufficient for effective management at the sub-field scale. The overall objective of this study was to evaluate the potential of precision management for rice production. The spatiotemporal properties of multiyear yield monitor data from four rice fields, representing varying soil types and locations within the primary rice growing region in California, were quantified and characterized. The role of water management, land-leveling, and the spatial distribution of soil properties in driving yield heterogeneity was explored. Mean yield and coefficient of variation at the sampling points within each field ranged from 9.2 to 12.1 Mg ha -1 and from 7.1 to 14.5 %, respectively. Using a k-means clustering and randomization method, temporally stable yield patterns were identified in three of the four fields. Redistribution of dissolved organic carbon, nitrogen, potassium and salts by lateral flood water movement was observed across all fields, but was only related to yield variability via exacerbating areas with high soil salinity. The effects of cold water temperature and land-leveling on yield variability were not observed. Soil electrical conductivity and/or plant available phosphorus were identified as the underlying causes of the within-field yield patterns using classification and regression trees. Our results demonstrate that while the high temporal yield variability in some rice fields does not permit precision management, in other fields exhibiting stable yield patterns with identifiable causes, precision management and modified water management may improve the profitability and resource-use efficiency of rice production systems.

Large CH and NO fluxes can occur from flooded rice ( L.) systems following end-of-season drainage, which contribute significantly to the total growing-season greenhouse gas (GHG) emissions. Field and laboratory studies were conducted to determine under what soil water conditions these emissions occur. In three field studies, GHG fluxes and dissolved CH in the soil pore water were measured before and after drainage. Across all fields, approximately 10% of the total seasonal CH emissions and 27% of the total seasonal NO emissions occurred following the final drain, confirming the importance of quantifying postdrainage CH and NO emissions. Preplant fertilizer N had no effect on CH emissions or dissolved CH; however, increased postdrainage NO fluxes were observed at higher N rates. To determine when postdrainage sampling needs to take place, our laboratory incubation study measured CH and NO fluxes from intact soil cores from these fields as the soil dried. Across fields, maximum CH emissions occurred at approximately 88% water-filled pore space (WFPS), but emissions were observed between 47 and 156% WFPS. In contrast, maximum NO emissions occurred between 45 and 71% WFPS and were observed between 16 and 109% WFPS. For all fields, gas samplings between 76 and 100% WFPS for CH emissions and between 43 and 78% WFPS for NO emissions was necessary to capture 95% of these postdrainage emissions. We recommend that frequent gas sampling following drainage be included in the GHG protocol of total GHG emissions.

Developing data standards on Version Control System platforms like GitHub enables collaboration and transparency.• Many standards do not use tools for collaboration: issue tracking, licensing, and automated website hosting (GitBook or GitHub Pages).• We make recommendations and provide templates for creating descriptive versioncontrolled data standard documentation on GitHub.

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Process‐based modeling of CH4 and N2O emissions from rice fields is a practical tool for conducting greenhouse gas inventories and estimating mitigation potentials of alternative practices at the scale of management and policy making. However, the accuracy of these models in simulating CH4 and N2O emissions in direct‐seeded rice systems under various management practices remains a question. We empirically evaluated the denitrification‐decomposition model for estimating CH4 and N2O fluxes in California rice systems. Five and nine site‐year combinations were used for calibration and validation, respectively. The model was parameterized for two cultivars, M206 and Koshihikari, and able to simulate 30% and 78% of the variation in measured yields, respectively. Overall, modeled and observed seasonal CH4 emissions were similar (R2 = 0.85), but there was poor correspondence in fallow period CH4 emissions and in seasonal and fallow period N2O emissions. Furthermore, management effects on seasonal CH4 emissions were highly variable and not well represented by the model (0.2–465% absolute relative deviation). Specifically, simulated CH4 emissions were oversensitive to fertilizer N rate but lacked sensitivity to the type of seeding system (dry seeding versus water seeding) and prior fallow period straw management. Additionally, N2O emissions were oversensitive to fertilizer N rate and field drainage. Sensitivity analysis showed that CH4 emissions were highly sensitive to changes in the root to total plant biomass ratio, suggesting that it is a significant source of model uncertainty. These findings have implications for model‐directed field research that could improve model representation of paddy soils for application at larger spatial scales.

The ongoing climate crisis merits an urgent need to devise management approaches and new technologies to reduce atmospheric greenhouse gas concentrations (GHG) in the near term. However, each year that GHG concentrations continue to rise, pressure mounts to develop and deploy atmospheric CO2 removal pathways as a complement to, and not replacement for, emissions reductions. Soil carbon sequestration (SCS) practices in working lands provide a low‐tech and cost‐effective means for removing CO2 from the atmosphere while also delivering co‐benefits to people and ecosystems. Our model estimates suggest that, assuming additive effects, the technical potential of combined SCS practices can provide 30%–70% of the carbon removal required by the Paris Climate Agreement if applied to 25%–50% of the available global land area, respectively. Atmospheric CO2 drawdown via SCS has the potential to last decades to centuries, although more research is needed to determine the long‐term viability at scale and the durability of the carbon stored. Regardless of these research needs, we argue that SCS can at least serve as a bridging technology, reducing atmospheric CO2 in the short term while energy and transportation systems adapt to a low‐C economy. Soil C sequestration in working lands holds promise as a climate change mitigation tool, but the current rate of implementation remains too slow to make significant progress toward global emissions goals by 2050. Outreach and education, methodology development for C offset registries, improved access to materials and supplies, and improved research networks are needed to accelerate the rate of SCS practice implementation. Herein, we present an argument for the immediate adoption of SCS practices in working lands and recommendations for improved implementation.

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