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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Land‐based climate solutions for the United States

Abstract Meeting end‐of‐century global warming targets requires aggressive action on multiple fronts. Recent reports note the futility of addressing mitigation goals without fully engaging the agricultural sector, yet no available assessments combine both nature‐based solutions (reforestation, grassland and wetland protection, and agricultural practice change) and cellulosic bioenergy for a single geographic region. Collectively, these solutions might offer a suite of climate, biodiversity, and other benefits greater than either alone. Nature‐based solutions are largely constrained by the duration of carbon accrual in soils and forest biomass; each of these carbon pools will eventually saturate. Bioenergy solutions can last indefinitely but carry significant environmental risk if carelessly deployed. We detail a simplified scenario for the United States that illustrates the benefits of combining approaches. We assign a portion of non‐forested former cropland to bioenergy sufficient to meet projected mid‐century transportation needs, with the remainder assigned to nature‐based solutions such as reforestation. Bottom‐up mitigation potentials for the aggregate contributions of crop, grazing, forest, and bioenergy lands are assessed by including in a Monte Carlo model conservative ranges for cost‐effective local mitigation capacities, together with ranges for (a) areal extents that avoid double counting and include realistic adoption rates and (b) the projected duration of different carbon sinks. The projected duration illustrates the net effect of eventually saturating soil carbon pools in the case of most strategies, and additionally saturating biomass carbon pools in the case of forest management. Results show a conservative end‐of‐century mitigation capacity of 110 (57–178) Gt CO 2 e for the U.S., ~50% higher than existing estimates that prioritize nature‐based or bioenergy solutions separately. Further research is needed to shrink uncertainties, but there is sufficient confidence in the general magnitude and direction of a combined approach to plan for deployment now.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Molecular mechanism of Rubisco activase: Dynamic assembly and Rubisco remodeling

Ribulose-1,5-bisphosphate (RuBP) carboxylase-oxygenase (Rubisco) enzyme is the limiting step of photosynthetic carbon fixation, and its activation is regulated by its co-evolved chaperone, Rubisco activase (Rca). Rca removes the intrinsic sugar phosphate inhibitors occupying the Rubisco active site, allowing RuBP to split into two 3-phosphoglycerate (3PGA) molecules. This review summarizes the evolution, structure, and function of Rca and describes the recent findings regarding the mechanistic model of Rubisco activation by Rca. New knowledge in these areas can significantly enhance crop engineering techniques used to improve crop productivity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Data from: Land-based climate solutions for the United States

Meeting end-of-century global warming targets requires aggressive action on multiple fronts. Recent reports note the futility of addressing mitigation goals without fully engaging the agricultural sector, yet no available assessments combine both nature-based solutions (reforestation, grassland and wetland protection, and agricultural practice change) and cellulosic bioenergy for a single geographic region. Collectively, these solutions might offer a suite of climate, biodiversity, and other benefits greater than either alone. Nature-based solutions are largely constrained by the duration of carbon accrual in soils and forest biomass; each of these carbon pools will eventually saturate. Bioenergy solutions can last indefinitely but carry significant environmental risk if carelessly deployed. We detail a simplified scenario for the U.S. that illustrates the benefits of combining approaches. We assign a portion of non-forested former cropland to bioenergy sufficient to meet projected mid-century transportation needs, with the remainder assigned to nature-based solutions such as reforestation. Bottom-up mitigation potentials for the aggregate contributions of crop, grazing, forest, and bioenergy lands are assessed by including in a Monte Carlo model conservative ranges for cost-effective local mitigation capacities, together with ranges for (a) areal extents that avoid double counting and include realistic adoption rates and (b) the projected duration of different carbon sinks. The projected duration illustrates the net effect of eventually saturating soil carbon pools in the case of most strategies, and additionally saturating biomass carbon pools in the case of reforestation. Results show a conservative end-of-century mitigation capacity of 110 (57 – 178) Gt CO2e for the U.S., ~50% higher than existing estimates that prioritize nature-based or bioenergy solutions separately. Further research is needed to shrink uncertainties but there is sufficient confidence in the general magnitude and direction of a combined approach to plan for deployment now.

09 BIOMASS FUELS↗
DOE OSTI2026

CLM5 Simulations of Soil Moisture and Grain Carbon for CONUS at 0.125 degrees

This dataset provides 0.125° gridded simulations of soil moisture and crop grain carbon for the Contiguous United States (CONUS), generated using the Community Land Model version 5 (CLM5) with the biogeochemistry module enabled. The data covers a historical baseline (1980–2015) and mid-century future projections (2020–2055). Future projections are organized into two sets of scenarios to distinguish the impacts of different drivers: (1) Atmospheric Only (ATM-only): These scenarios apply future atmospheric forcings while holding land use and land cover change (LULCC) at historical baseline levels. The atmospheric forcings represent moderately versus severely hotter/drier atmospheric conditions (dynamically downscaled perturbed thermodynamics simulations based on CMIP6 SSP245 and SSP585 warming signals), each with cooler versus hotter Earth System Model temperature sensitivity instantiations. These scenarios are identified in the file names as atm45cooler, atm45hotter, atm85cooler, and atm85hotter. (2) Coupled Atmospheric and Land-Use (LAND+ATM): These scenarios apply future atmospheric forcing together with future LULCC by pairing atmospheric pathways with lower versus higher population/economic growth scenarios representing Shared Socioeconomic Pathways 3 and 5 (SSP3 and SSP5). These scenarios are identified in the file names as atm45cooler_ssp3, atm45hotter_ssp3, atm45cooler_ssp5, atm45hotter_ssp5, atm85cooler_ssp3, atm85hotter_ssp3, atm85cooler_ssp5, and atm85hotter_ssp5. Please refer to the README file for detailed information on file structure, variables, units, and data formats.

Yao, Lili [Pacific Northwest National Laboratory] ↗
DOE OSTI2022

Chapter 9.12 - Weather, Climatic and Ecological Impacts of Onshore Wind Farms

Wind power is an environmentally sustainable technology that is likely to be part of the solution to the climate change, air pollution, and energy security problems. Despite many positive benefits, the rapid development of wind power has raised concerns about some potential adverse environmental impacts. While converting wind?s kinetic energy into electricity, wind turbines (WTs) modify properties of the atmospheric boundary layer (ABL) including the vertical profiles and surface-atmosphere exchanges of energy, momentum, mass, moisture, and trace gases. Given the current installed capacity and the projected installation worldwide, wind farms (WFs) are likely becoming a major driver of manmade land use change on Earth. Hence, understanding WT-atmosphere-surface interactions and assessing potential environmental impacts of WFs are of significant scientific, societal and economic importance. Here we review our progress in assessing potential impacts of onshore wind power on weather, climate and vegetation activity. A consensus is emerging based on observations and modeling studies that WFs cause a local to regional warming effect, particularly at nighttime, while the impacts on precipitation, wind patterns, crop yields and vegetation activity are uncertain. The warming effect results simply from vertical heat redistribution within the ABL due to turbine-enhanced vertical turbulent mixing in the wakes. At the global scale, with a substantial installation of WFs, mesoscale and climate models predicted large regional changes but small global impacts on temperature, while the impacts on precipitation, clouds, wind patterns and large-scale circulation have large uncertainties and are region specific and scale dependent. Despite increasing number of research efforts, our assessment of potential WF impacts is still very limited. Although the WF impacts are mostly local and limited to the near-surface ABL, this is the layer where we live and plants grow. Hence, more studies are needed to improve our understanding of WT-atmosphere-surface interactions and our capability to model and project the weather, climatic and ecological impacts of large WFs.

atmospheric boundary layer↗
DOE OSTI2023

Biophysical Impact of Land-Use and Land-Cover Change on Subgrid Temperature in CMIP6 Models

In this study, we investigate the air temperature response to land-use and land-cover change (LULCC; cropland expansion and deforestation) using subgrid land model output generated by a set of CMIP6 model simulations. Our study is motivated by the fact that ongoing land-use activities are occurring at local scales, typically significantly smaller than the resolvable scale of a grid cell in Earth system models. It aims to explore the potential for a multimodel approach to better characterize LULCC local climatic effects. On an annual scale, the CMIP6 models are in general agreement that croplands are warmer than primary and secondary land (psl; mainly forests, grasslands, and bare ground) in the tropics and cooler in the mid–high latitudes, except for one model. The transition from warming to cooling occurs at approximately 40°N. Although the surface heating potential, which combines albedo and latent heat flux effects, can explain reasonably well the zonal mean latitudinal subgrid temperature variations between crop and psl tiles in the historical simulations, it does not provide a good prediction on subgrid temperature for other land tile configurations (crop vs forest; grass vs forest) under Shared Socioeconomic Pathway 5–8.5 (SSP5–8.5) forcing scenarios. A subset of simulations with the CESM2 model reveals that latitudinal subgrid temperature variation is positively related to variation in net surface shortwave radiation and negatively related to variation in the surface energy redistribution factor, with a dominant role from the latter south of 30°N. We suggest that this emergent relationship can be used to benchmark the performance of land surface parameterizations and for prediction of local temperature response to LULCC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Cut–and–chip harvester material capacity and fuel performance on commercial-scale willow fields for varying ground and crop conditions

Shrub willow (Salix spp.) is capable of producing commercially attractive amounts of biomass in short rotations, but harvesting costs and logistics remain a concern. There is a particular need for information about harvesting operations on larger, commercial short–rotation woody crop systems. Another recent issue on commercial fields in northern New York is commercial growers conducting harvests during the growing season rather than the recommended dormant season when fields may be too wet to harvest. This study evaluated and modeled the in–field performance of a cut–and–chip harvester for almost 700 wagonloads of chips operating in commercial willow fields in a wider array of crop and field conditions than have been previously reported. Analysis indicated that the time of harvest (leaf–on or leaf–off) and whether site conditions were wet or dry affected the harvester's material capacity. Mean material capacity was greatest for leaf–off, dry conditions (71.8 Mg/hr) and lowest for leaf–on harvests, which were similar for wet (30.4 Mg/hr) and dry conditions (29.7 Mg/hr). Mean crop specific fuel consumption ranged between 1.3 and 3.3 L/Mg, but can get considerably higher for standing biomasses below 40 Mg/ha. Wet ground conditions and leaf–on harvests tend to decrease material capacity and increase fuel consumption as the harvester has to divert power to forward movement and material processing. Relationships for material capacity and fuel consumption based on standing biomass, time of harvest and ground conditions will be essential for evaluating and modeling the economic and environmental impacts of commercial–scale willow operations.

09 BIOMASS FUELS↗
DOE OSTI2023

Field‐scale analysis of miscanthus production indicates climate change may increase the opportunity for water quality improvement in a key Iowa watershed

Abstract The Raccoon River Basin is the primary source for drinking water in Iowa's largest city and plays a major role in the Mississippi River Basin's high nutrient exports. Future climate change may have major impacts on the biological, physiological, and agronomic processes imposing a threat to ecosystem services. Efforts to reduce nitrogen (N) loads within this basin have included local litigation and the implementation of the Iowa Nutrient Reduction Strategy, which suggest incorporating bioenergy crops (i.e., miscanthus) within the current corn–soybean landscape to reach a 41% reduction in nitrate loads. This study focuses on simulating N export for historical and future land use scenarios by using an agroecosystem model (Agro‐IBIS) and a hydrology model (THMB) at the 500‐m resolution, similar to the scale of agricultural fields. Model simulations are driven by CMIP5 climate data for historical, mid‐century, and late‐century under the RCP 4.5 and 8.5 warming projections. Using recent crop profit analyses for the state of Iowa, profitability maps were generated and nitrogen leaching thresholds were used to determine where miscanthus should replace corn–soybean area to maximize reductions in N pollution. Our results show that miscanthus inclusion on low profit and high N leaching areas can result in a 4% reduction of N loss under current climate conditions and may reduce N loss by 21%–26% under future climate conditions, implying that water quality has the potential continue to improve under future climate conditions when strategically implemented conservation practices are included in future farm management plans.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Marginal Damage of Methane Emissions: Ozone Impacts on Agriculture

Methane directly contributes to air pollution, as an ozone precursor, and to climate change, generating physical and economic damages to different systems, namely agriculture, vegetation, energy, human health, or biodiversity. The methane-related damages to climate, measured as the Social Cost of Methane, and to human health have been analyzed by different studies and considered by government rulemaking in the last decades, but the ozone-related damages to crop revenues associated to methane emissions have not been incorporated to policy agenda. Using a combination of the Global Change Analysis Model and the TM5-FASST Scenario Screening Tool, we estimate that global marginal agricultural damages range from ~ 423 to 556 $2010/t-CH 4 , of which 98 $2010/t-CH 4 occur in the USA, which is the most affected region due to its role as a major crop producer, followed by China, EU-15, and India. These damages would represent 39–59% of the climate damages and 28–64% of the human health damages associated with methane emissions by previous studies. The marginal damages to crop revenues calculated in this study complement the damages from methane to climate and human health, and provides valuable information to be considered in future cost-benefits analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Coupled machine learning–ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N 2 O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N 2 O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high-emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N 2 O fluxes from US cropland. Trained and validated on ~12,000 N 2 O chamber measurements at 17 US Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N 2 O at both training (R 2 = 0.84, RMSE = 16.4 g N ha −1 d −1 ) and held-out testing sites (R 2 = 0.84, RMSE = 6.2 g N ha −1 d −1 ). Analyses identified six dominant N 2 O drivers: soil organic carbon (SOC), NH 4 + , NO 3 - , water-filled pore space, temperature, and aboveground biomass production. Wet, warm soils produced large N 2 O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N 2 O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

AI↗
DOE OSTI2022

Long‐term yields in annual and perennial bioenergy crops in the Midwestern United States

Abstract Many yield predictions in perennial bioenergy species have been made based on data collected during the establishment phase of growth or a limited number of long‐term studies. Few studies compare multiple perennial crops with the dominant agricultural vegetation of the landscape over long time periods. Here, we present the results of 11 years of perennial crop management on fertile agricultural soils in central Illinois, compared with conventional row crop maize/soybean ( Zea mays L., Glycine max L.) production. We examined the long‐term productivity and drought susceptibility of Miscanthus x giganteus Greef et. Deu. ex. Hodkinson et Renvoize (miscanthus), Panicum virgatum L., Cave‐in‐Rock cultivar (switchgrass), and a native prairie mix, in contrast to annual maize/soybean agriculture. Long‐term yields for miscanthus and switchgrass failed to reach initial predictions made during the establishment phase; however, in miscanthus, the 11 th year of production shows little progressive yield loss with age, exceeding the modeled limit for the onset of age‐related decline. Harvest timing and differences in yields from hand and machine harvests in perennial crops likely contribute to overestimates of potential yields. Application of fertilizer to mature miscanthus resulted in significant increases in yield after a severe drought, though modeled effects of management and drought in miscanthus point to a more complex mechanism for yield response.

09 BIOMASS FUELS↗
DOE OSTI2022

Citrus FRIGIDA cooperates with its interaction partner dehydrin to regulate drought tolerance

SUMMARY Drought is a major environmental stress that severely affects plant growth and crop productivity. FRIGIDA ( FRI ) is a key regulator of flowering time and drought tolerance in model plants. However, little is known regarding its functions in woody plants, including citrus. Thus, we explored the functional role of the citrus FRI ortholog ( CiFRI ) under drought. Drought treatment induced CiFRI expression. CiFRI overexpression enhanced drought tolerance in transgenic Arabidopsis and citrus, while CiFRI suppression increased drought susceptibility in citrus. Moreover, transcriptomic profiling under drought conditions suggested that CiFRI overexpression altered the expression of numerous genes involved in the stress response, hormone biosynthesis, and signal transduction. Mechanistic studies revealed that citrus dehydrin likely protects CiFRI from stress‐induced degradation, thereby enhancing plant drought tolerance. In addition, a citrus brassinazole‐resistant (BZR) transcription factor family member ( CiBZR1 ) directly binds to the CiFRI promoter to activate its expression under drought conditions. CiBZR1 also enhanced drought tolerance in transgenic Arabidopsis and citrus. These findings further our understanding of the molecular mechanisms underlying the CiFRI ‐mediated drought stress response in citrus.

Xu, Yuan‐Yuan↗
DOE OSTI2024

Engineered reduction of S-adenosylmethionine alters lignin in sorghum

Abstract Background Lignin is an aromatic polymer deposited in secondary cell walls of higher plants to provide strength, rigidity, and hydrophobicity to vascular tissues. Due to its interconnections with cell wall polysaccharides, lignin plays important roles during plant growth and defense, but also has a negative impact on industrial processes aimed at obtaining monosaccharides from plant biomass. Engineering lignin offers a solution to this issue. For example, previous work showed that heterologous expression of a coliphage S -adenosylmethionine hydrolase (AdoMetase) was an effective approach to reduce lignin in the model plant Arabidopsis. The efficacy of this engineering strategy remains to be evaluated in bioenergy crops. Results We studied the impact of expressing AdoMetase on lignin synthesis in sorghum ( Sorghum bicolor L. Moench). Lignin content, monomer composition, and size, as well as biomass saccharification efficiency were determined in transgenic sorghum lines. The transcriptome and metabolome were analyzed in stems at three developmental stages. Plant growth and biomass composition was further evaluated under field conditions. Results evidenced that lignin was reduced by 18% in the best transgenic line, presumably due to reduced activity of the S -adenosylmethionine-dependent O -methyltransferases involved in lignin synthesis. The modified sorghum features altered lignin monomer composition and increased lignin molecular weights. The degree of methylation of glucuronic acid on xylan was reduced. These changes enabled a ~20% increase in glucose yield after biomass pretreatment and saccharification compared to wild type. RNA-seq and untargeted metabolomic analyses evidenced some pleiotropic effects associated with AdoMetase expression. The transgenic sorghum showed developmental delay and reduced biomass yields at harvest, especially under field growing conditions. Conclusions The expression of AdoMetase represents an effective lignin engineering approach in sorghum. However, considering that this strategy potentially impacts multiple S -adenosylmethionine-dependent methyltransferases, adequate promoters for fine-tuning AdoMetase expression will be needed to mitigate yield penalty.

Tian, Yang↗
DOE OSTI2023

A scalable framework for quantifying field-level agricultural carbon outcomes

Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in adopting certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N 2 O and CH 4 ) and changes in soil carbon stock are defined here as “agricultural carbon outcomes”. Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, and process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes = Environment (E) × Management (M) × Crop (C). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agricultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, and modeling), defined as the “System-of-Systems” solution. Such a “System-of-Systems” solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of environment variables (E), management practices (M), and crop conditions (C) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes; (3) systematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of individual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability, i.e. the ability of a solution to perform robustly with accepted accuracy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future research and development (R&D) priorities to achieve different components of the “System-of-Systems” solution, thus accomplishing the Environment×Management×Crop framework to quantify field-level agricultural carbon outcomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Using AI to build a hydrobiogeochemical soil model

Soil water content is a function of inputs from precipitation and outputs via evaporation, transpiration, lateral flow, and vertical percolation, and is sensitive to biogeochemical processes. As such, soils serve as an ideal integrator of atmospheric, hydrological, and biogeochemical processes affecting the water cycle. In addition, soil water retention capacity, infiltration rates, and hydraulic conductivity can buffer or exacerbate the effects of extreme precipitation events (e.g., flooding, runoff, subsurface transport, erosion, greenhouse gas emissions) and mitigate the impact of droughts and heat waves on land systems (e.g., fire, crop failure). However, integrating water cycle measurements spanning different land atmosphere compartments across scales is a fundamental barrier for numerical model predictability. A significant challenge is that each domain (soil, hydrology, biology, and atmosphere) typically collects different sets of data at different temporal and spatial frequencies/scales, and even different dimensionalities (2D vs 3D). To implement soil as an integrator of the water cycle in land models, we suggest that novel machine learning (ML) tools can be developed to effectively simulate complex landscapes across various domains and scales, extended to regions with sparse or no data. The ultimate goals are to improve predictive understanding of land-atmosphere interactions and to extend the predictability of current Earth System Models (ESMs) through better integration of hydrological and biogeochemical data. We envision a framework in which: (1) ML-aided data reconstructions enable the merger of data sources into a unified geospatial product; (2) automated detection techniques are used to improve the knowledge of complex soil processes and interactions; and (3) this knowledge is leveraged and incorporated into models through AI-based emulators to distinctly connect the land and atmospheric compartments of the water cycle in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Utility of near‐surface phenology in estimating productivity and evapotranspiration across diverse ecosystems

Abstract Agroecosystems, which include row crops, pasture, and grass and shrub grazing lands, are sensitive to changes in management, weather, and genetics. To better understand how these systems are responding to changes, we need to improve monitoring and modeling carbon and water dynamics. Vegetation Indices (VIs) are commonly used to estimate gross primary productivity (GPP) and evapotranspiration (ET), but these empirical relationships are often location and crop specific. There is a need to evaluate if VIs can be effective and, more general, predictors of ecosystem processes through time and across different agroecosystems. Near‐surface photographic (red‐green‐blue) images from PhenoCam can be used to calculate the VI green chromatic coordinate (G CC ) and offer a pathway to improve understanding of field‐scale relationships between VIs and GPP and ET. We synthesized observations spanning 76 site‐years across 15 agroecosystem sites with PhenoCam G CC and GPP or ET estimates from eddy covariance (EC) to quantify interannual variability (IAV) in the relationship between GPP and ET and G CC across. We uncovered a high degree of variability in the strength and slopes of the G CC ∼ GPP and ET relationships (R 2 = 0.1 ‐ 0.9) within and across production systems. Overall, G CC is a better predictor of GPP than ET (R 2 = 0.64 and 0.54, respectively), performing best in croplands (R 2 = 0.91). Shrub‐dominated systems exhibit the lowest predictive power of G CC for GPP and ET but have less IAV in slope. We propose that PhenoCam estimates of G CC could provide an alternative approach for predictions of ecosystem processes.

Environmental Sciences & Ecology↗
DOE OSTI2023

Establishment of a genome editing tool using CRISPR-Cas9 ribonucleoprotein complexes in the non-model plant pathogen Sphaerulina musiva

CRISPR-Cas9 is a versatile genome editing system widely used since 2013 to introduce site-specific modifications into the genomes of model and non-model species. This technology is used in various applications, from gene knock-outs, knock-ins, and over-expressions to more precise changes, such as the introduction of nucleotides at a targeted locus. CRISPR-Cas9 has been demonstrated to be easy to establish in new species and highly efficient and specific compared to previous gene editing strategies such as Zinc finger nucleases and transcription activator-like effector nucleases. Grand challenges for emerging CRISPR-Cas9 tools in filamentous fungi are developing efficient transformation methods for non-model organisms. In this paper, we have leveraged the establishment of CRISPR-Cas9 genome editing tool that relies on Cas9/sgRNA ribonucleoprotein complexes (RNPs) in the model species Trichoderma reesei and developed the first protocol to efficiently transform the non-model species, Sphaerulina musiva. This fungal pathogen constitutes a real threat to the genus Populus, a foundational bioenergy crop used for biofuel production. Herein, we highlight the general considerations to design sgRNAs and their computational validation. We also describe the use of isolated protoplasts to deliver the CRISPR-Cas9 RNP components in both species and the screening for targeted genome editing events. The development of engineering tools in S. musiva can be used for studying genes involved in diverse processes such as secondary metabolism, establishment, and pathogenicity, among many others, but also for developing genetic mitigation approaches. The approach described here provides guidance for potential development of transformation systems in other non-model spore-bearing ascomycetes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Soybean fallow and nitrification inhibitors: Strategies to reduce N 2 O emission intensities and N losses in Australian sugarcane cropping systems

The Australian sugar industry is facing mounting pressure to reduce the nitrogen (N) losses and nitrous oxide (N 2 O) emissions associated with N fertiliser use. Research has shown that N 2 O emissions from sugarcane (Saccharum officinarum L.) cropping systems can be reduced with the use of fertilisers coated with nitrification inhibitors, or by sowing legume crops in the fallows between sugarcane crop cycles. However, the efficacy of these two N management strategies across different climatic zones is still unclear, as results from field studies have been contradictory. The objectives of this study were therefore to use the DayCent model to assess the long-term effects of the two strategies (separately or in combination) on N 2 O emissions, N losses and yields. The model was parameterised using data from eight field experiments (39 treatments) conducted across four of the five main districts of the Australian sugarcane industry. A series of long-term scenarios embracing a range of N fertiliser rates (0- 160 kg N ha -1 applied as urea), climatic regions (Tropics and Subtropics), soil textures (fine- and coarse-textured soils) and N-loss risk scenarios (crop fertilised well before or close to the on-set of the wet season) were tested for both N management strategies. Simulations identified that the combined use of soybean (Glycine max L.) fallows and N fertiliser coated with the 3,4-dimethylpyrazole phosphate (DMPP) nitrification inhibitor was the most effective strategy to maintain or even increase current yields while significantly reducing N 2 O and cumulative N (mainly due to nitrate) losses. When used alone, the soybean fallow strategy on average reduced N losses over the entire crop cycle by 29 % compared with current industry best practice. However, this strategy showed a limited capacity to reduce N 2 O emissions over the entire cane crop cycle (median: -3% compared with current industry best practice), or even increased them in scenarios at high-risk of N losses in the Tropics. The use of urea coated with DMPP alone allowed current fertiliser N rates to be reduced by 30 % without any substantial yield penalty. In environments prone to high N losses, DMPP urea abated overall N losses and N 2 O emissions by 41 % and 30 %, respectively. This was the first study to use such an extensive dataset to parameterise a biogeochemical model for yields, N 2 O emissions and N losses in sugarcane cropping systems. Finally, the findings will be instrumental in guiding future research and policies designed to improve the profitability and environmental sustainability of this industry.

54 ENVIRONMENTAL SCIENCES↗