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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Data for Optimizing Bioenergy Sorghum Productivity and Nutrient Removal in Illinois: Impact of Nitrogen Fertilization Under Diverse Marginal Conditions

Bioenergy sorghum ( Sorghum bicolor L. Moench) is a promising crop for contributing to the United States bioenergy supply. However, the varying limitations of the marginal lands targeted for its cultivation present a management challenge. This two-year study aimed to investigate how the limitations associated with marginal cropland impact the effects of nitrogen fertilization on the yield of bioenergy sorghum and the uptake of 11 macro- (N, P, K, Ca, Mg, and S) and micronutrients (Fe, Mn, Zn, Cu, and B). The study contrasted prime cropland in central Illinois (Urbana) with three marginal cropland sites in southern (Ewing) and central Illinois (Fairbury and Pesotum). These marginal cropland sites are characterized by varying limitations, including low soil fertility (P and K limitations), leaching and erosion, and flooding, respectively. Four nitrogen rates (0, 56, 112, and 168 kg N ha−1) were tested under eight environments. The average yields and ranges of sorghum biomass were 20.2 (17.0–23.2) Mg ha−1 in Urbana, 18.1 (13.1–19.8) Mg ha−1 in Ewing, 13.8 (9.0–17.3) Mg ha−1 in Fairbury, and 23.3 (14.6–33.0) Mg ha−1 in Pesotum. Optimal N rates were 56 N in Pesotum and 112 N in Urbana, Ewing, and Fairbury. Tissue macronutrient contents in Urbana were generally higher than in the marginal croplands, while micronutrient contents did not show discernible trends. Increasing N rate generally correlated with the macronutrient removal except in Ewing. Comparable sorghum biomass yields were observed between prime and marginal croplands (averaging 18.3 Mg ha−1), but optimal N rates varied between 56 N and 112 N. This suggests that yield gaps can be narrowed by applying the optimal N rates for the respective locations. However, increased removals of macronutrients, especially P and K, with increasing yields indicate the need to revise fertilizer recommendations, particularly for soils deficient in these nutrients. Our study suggests that while sorghum production on marginal cropland is feasible, N management needs to be adapted to the unique limitations associated with various types of marginal cropland.

Biomass Analytics↗
DOE OSTI2024

Bird Species Use of Bioenergy Croplands in Illinois, USA—Can Advanced Switchgrass Cultivars Provide Suitable Habitats for Breeding Grassland Birds?

Grassland birds have sustained significant population declines in the United States through habitat loss, and replacing lost grasslands with bioenergy production areas could benefit these species and the ecological services they provide. Point count surveys and autonomous acoustic monitoring were used at two field sites in Illinois, USA, to determine if an advanced switchgrass cultivar that is being used for bioenergy feedstock production could provide suitable habitats for grassland and other bird species. At the Brighton site, the bird use of switchgrass plots was compared to that of corn plots during the breeding seasons of 2020–2022. At the Urbana site, the bird use of restored prairie, switchgrass, and Miscanthus × giganteus was studied in the 2022 breeding season. At Brighton, Common Yellowthroat, Dickcissel, Grasshopper Sparrow, and Sedge Wren occurred on switchgrass plots more often than on corn; Common Yellowthroat and Dickcissel increased on experimental plots as the perennial switchgrass increased in height and density over the study period; and the other two species declined over the same period. At Urbana, Dickcissel was most frequent in prairie and switchgrass; Common Yellowthroat was most frequent in miscanthus and switchgrass. These findings suggest that advanced switchgrass cultivars could provide suitable habitats for grassland birds, replace lost habitats, and contribute to the recovery of these vulnerable species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Comparative Economic Analysis Between Bioenergy and Forage Types of Switchgrass for Sustainable Biofuel Feedstock Production: A Data Envelopment Analysis and Cost–Benefit Analysis Approach

ABSTRACT The capacity to produce switchgrass efficiently and cost‐effectively across diverse environments can be pivotal in achieving the short‐ and medium‐term Sustainable Aviation Fuel targets set by the U.S. Department of Energy. This study evaluated the economic performance of forage‐ and bioenergy‐type switchgrass cultivars and their response to N fertilization under diverse marginal environments across the US Midwest that included Illinois (IL), Iowa (IA), Nebraska (NE), and South Dakota (SD). Data Envelopment Analysis (DEA) was used to evaluate the efficiency of 23 Decision‐Making Units (DMUs)—cultivar types and N fertilization rate combinations—while a cost–benefit analysis calculated their profitability over 5 years. Results showed that two energy‐type cultivars—“Independence” and “Liberty”—were superior economically to the forage cultivars. Independence performed best with the highest profit margin when fertilized at 56 kg N ha −1 , particularly in the US hardiness zone 6a (Urbana, IL). Liberty exhibited the highest profit margins in hardiness zone 5b (Madrid, IA, and Ithaca, NE) at 56 kg N ha −1 and showed exceptional profitability with 28 kg N ha −1 in hardiness zone 6b (Brighton, IL). Switchgrass cultivar “Carthage” showed better efficiency score and profitability results in hardiness zone 4b (South Shore, SD) at 56 kg N ha −1 . The profit trends observed in current study sites may indicate broader patterns across similar US hardiness zones. This study provides valuable insights for decision‐makers to optimize input strategies for biomass production of bioenergy switchgrass to meet renewable energy demands.

Arshad, Muhammad Umer [Department of Crop Sciences↗
DOE OSTI2026

Linking Plant and Microbial Traits to Soil Carbon for Reliable and Resilient Bioenergy Systems

Bioenergy systems in the United States offer a dual opportunity to supply renewable feedstocks while enhancing ecosystem services such as hydrologic regulation, erosion control, and soil carbon (C) storage. National assessments highlight the potential to grow perennial energy crops to improve soil function and ecosystem resilience. Realizing this potential requires understanding the ecological mechanisms that govern how C is added, transformed, and stabilized in soils. Plant traits determine the quantity, depth, and chemistry of organic inputs, while microbial processes—including carbon use efficiency, necromass formation, and trophic interactions—mediate their transformation and partitioning among soil carbon pools. These biological pathways are shaped by soil physical and chemical properties, including aggregation, texture, and mineralogy, and by environmental drivers such as temperature, moisture, and disturbance, leading to context-dependent outcomes across landscapes. Management practices that diversify feedstocks, minimize disturbance, and maintain soil cover can promote both biomass production and C retention, while microbial amendments and rhizosphere engineering offer emerging, but often context-dependent, tools to optimize plant–microbe interactions. Trade-offs between biomass yield and soil carbon storage may arise when systems favor rapid aboveground productivity at the expense of belowground inputs and microbial processing, underscoring the importance of trait combinations that support both functions. Advances in monitoring, reporting, and verification—spanning precision agriculture, remote sensing, and biosensing—are improving predictive capacity through microbial-explicit process models and model–experiment (ModEx) frameworks. By connecting soil, plant, and microbial processes with advances in modeling and biosensing, this review outlines research priorities focused on trait-based parameterization and ModEx integration. These priorities will support the design of bioenergy systems that are both reliable and resilient, enhancing renewable energy production and ecosystem sustainability.

bioenergy systems↗
DOE OSTI2025

Integrating AI and Multi-sensor Technology for better Bioenergy Preprocessing

Integrating multi-sensor identification with machine learning significantly enhances bioenergy preprocessing by using non-destructive, multi-spectral sensors to improve feedstock purity and quality. This approach not only reduces preprocessing costs but also ensures a more efficient and sustainable bioenergy production process.

09 - BIOMASS FUELS↗
DOE OSTI2025

Synthetic microbial communities: Bridging research and application in second-generation bioenergy feedstock microbiomes

The sustainable production of purpose-grown bioenergy feedstocks is essential in transitioning away from fossil fuels. Synthetic communities (SynComs) are consortia of microorganisms that can be used as biological interventions to support objectives like plant growth and stress tolerance. This review examines the state of knowledge regarding microbiomes and SynComs of second-generation bioenergy feedstocks, focusing on the rhizosphere. We first provide an overview of second-generation feedstocks, including switchgrass (Panicum virgatum), miscanthus (Miscanthus × giganteus), sorghum (Sorghum spp.), sugarcane (Saccharum spp.), and poplar (Populus spp.), and summarize our current understanding of their plant-soil-microbiome ecology. We next discuss considerations in the objectives, design, and evaluation of SynComs to enhance feedstock production, and then critically review the literature around their use. Our literature analysis revealed that SynCom performance varied substantially between controlled pilot experiments and field trials, possibly due to system complexity that could not be fully considered in their design and pilot evaluation. We identified a gap in the use of SynComs to support the unique sustainability objectives of biofuel feedstock agriculture, presenting an opportunity to leverage these additional microbial traits in SynCom designs. Finally, we emphasize the importance of targeted research to identify the ecological principles that govern the assembly, activation, and persistence of microbes in the feedstock rhizosphere, thereby enhancing our capacity to manage microbiomes under diverse environmental conditions and ensure their functionality. Beyond biofuels, SynComs are a promising microbiome management strategy for crop production; however, an ecologically informed design and evaluation of SynComs are advised.

SynCom↗
DOE OSTI2024

Aboveground woody biomass estimation of young bioenergy plantations of Populus and its hybrids using mobile (backpack) LiDAR remote sensing

Woody aboveground biomass (AGB) including short-rotation Populus is used as a feedstock for renewable and carbon-neutral bioenergy. While woody AGB can be estimated with allometric equations requiring labor-intensive field data, remote sensing technologies like mobile terrestrial light detection and ranging (LiDAR) can estimate woody AGB quickly and accurately. Therefore, the goals of this study were to develop a model to predict woody AGB of 2-year-old Populus spp. from three taxa (P. deltoides, P. deltoides × P. maximowiczii and P. deltoides × P. trichocarpa) using allometric (height and diameter at breast height (DBH)) or LiDAR-derived metrics from a mobile terrestrial (backpack) system. Likewise, we sought to compare LiDAR-estimated tree height and DBH with field-measured values. We found that a taxa-specific model containing LiDAR-measured tree height, crown volume, and taxa interactions with the height of the 10 th percentile, and the density of the lowest interval (density metric 0) explained 84 % of the variation in woody AGB with a root mean square error (RMSE) of 28.7 % and performed slightly better than the allometric model. The best model excluding taxa had a slightly higher RMSE but lower bias than the allometric model. LiDAR-derived tree heights were highly correlated with field-measured heights, but DBH could not be estimated accurately. Therefore, terrestrial mobile LiDAR systems can accurately estimate woody AGB and tree height of Populus in short rotation systems to aid in the fast and efficient quantification of woody bioenergy production and renewable energy resources.

AGB↗
DOE OSTI2024

Provincial-scale assessment of direct air capture to meet China’s climate neutrality goal under limited bioenergy supply

China has large, estimated potential for direct air carbon capture and storage (DACCS) but its deployment locations and impacts at the subnational scale remain unclear. This is largely because higher spatial resolution studies on carbon dioxide removal (CDR) in China have focused mainly on bioenergy with carbon capture and storage. This study uses a spatially detailed integrated energy-economy-climate model to evaluate DACCS for 31 provinces in China as the country pursues its goal of climate neutrality by 2060. We find that DACCS could expand China’s negative emissions capacity, particularly under sustainability-minded limits on bioenergy supply that are informed by bottom-up studies. But providing low-carbon electricity for multiple GtCO 2 yr -1 DACCS may require over 600 GW of additional wind and solar capacity nationwide and comprise up to 30% of electricity demand in China’s northern provinces. Investment requirements for DACCS range from 330 to 530 billion dollars by 2060 but could be repaid manyfold in the form of avoided mitigation costs, which DACCS deployment could reduce by up to $6 trillion over the same period. Enhanced efforts to lower residual CO 2 emissions that must be offset with CDR under a net-zero paradigm reduce but do not eliminate the use of DACCS for mitigation. For decision-makers and the energy-economy models guiding them, our results highlight the value of expanding beyond the current reliance on biomass for negative emissions in China.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

OPEN-Augmented Reality GUI for Bioenergy Crop Phenotyping and Precision Agriculture (Donald Danforth Plant Science Center Final Scientific Technical Report)

The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2019

Freshwater Requirements of Large-Scale Bioenergy Plantations for Limiting Global Warming to 1.5°C

Limiting mean global warming to well below 2 °C will probably require substantial negative emissions (NEs) within the 21st century. To achieve these, bioenergy plantations with subsequent carbon capture and storage (BECCS) may have to be implemented at a large scale. Irrigation of these plantations might be necessary to increase the yield, which is likely to put further pressure on already stressed freshwater systems. Conversely, the potential of bioenergy plantations (BPs) dedicated to achieving NEs through CO2 assimilation may be limited in regions with low freshwater availability. This paper provides a first-order quantification of the biophysical potentials of BECCS as a negative emission technology contribution to reaching the 1.5 °C warming target, as constrained by associated water availabilities and requirements. Using a global biosphere model, we analyze the availability of freshwater for irrigation of BPs designed to meet the projected NEs to fulfill the 1.5 °C target, spatially explicitly on areas not reserved for ecosystem conservation or agriculture. We take account of the simultaneous water demands for agriculture, industries, and households and also account for environmental flow requirements (EFRs) needed to safeguard aquatic ecosystems. Furthermore, we assess to what extent different forms of improved water management on the suggested BPs and on cropland may help to reduce the freshwater abstractions. Results indicate that global water withdrawals for irrigation of BPs range between ~400 and ~3000 km(exp 3) yr(exp -1), depending on the scenario and the conversion efficiency of the carbon capture and storage process. Consideration of EFRs reduces the NE potential significantly, but can partly be compensated for by improved on-field water management.

Stenzel, Fabian↗
DOE OSTI2025

Enhancing Hydrogen Production from Bioenergy Crops via Photoreforming

Photoreforming perennial bioenergy crops (willow, Miscanthus , and poplar) has the potential to produce H 2 with reduced environmental impacts. To understand the compositional effects of the biomass on the average rate of H 2 production over the first 30 min of reaction ( r H 2 ), the r H 2 values of model biomass component (i.e., cellulose, hemicellulose, and lignin) mixtures were compared with those from the raw biomass. The higher cellulose or hemicellulose content in multicomponent mixtures resulted in higher r H 2 , whereas lignin reduced the hydrogen production rate. However, with raw biomass, the ratio of biomass components alone did not determine the r H 2 via photoreforming, with rates of hydrogen production for different varieties of willow ranging between 1.9 μmol h -1 and 12.3 μmol h -1 , 11.8 μmol h -1 for a poplar, and 6.8 μmol h -1 for a miscanthus biomass. In addition, comparable r H 2 values of raw poplar and its extracted cellulose via an IonoSolv treatment indicated the possibility of using raw biomass materials without delignification for generating H 2 via photoreforming. Importantly, r H 2 was positively correlated with the interaction between water and the biomass, as assessed by NMR relaxation via an examination of the T 1 /T 2 ratio. A stronger water-biomass interaction resulted in a higher r H 2 . Genetic modification of biomass has been suggested as a putative way to improve the r H 2 of biomass with an enhanced interaction with water. This research enhances the understanding of factors influencing H 2 production from lignocellulosic biomass by photoreforming and supports the breeding and management of perennial biomass crops to maximize H 2 yields while minimizing land area requirements.

biomass↗
DOE OSTI2025

Combining biotechnology with sustainability: feasibility of bioenergy sorghum in generating high-value bioproducts

Biofuels generated through fermentation of plant-derived lignocellulosic biomass are currently not competitive with fossil fuels. Here, a framework is described for the engineering of valuable coproducts to improve the economic feasibility of biofuel production. To accomplish this goal, the desirable traits of the bioenergy crop Sorghum bicolor are harnessed via modern engineering approaches. A robust analysis is provided to highlight the scientific basis, costs, regulatory challenges, and lucrative nature of coproduct development. Sustainable biofuel production could also have far-reaching societal impacts, especially in the context of our rapidly changing global climate. This perspective suggests that the path to profitable biofuel production is attainable and encourages consideration of similar approaches by the biotechnology industry.

09 BIOMASS FUELS↗
DOE OSTI2024

Estimated attribution of the RFS program on soybean biodiesel in the U.S. using the bioenergy scenario model

Biofuels are supported by various governmental policies in the U.S. and globally as an alternative transportation fuel for environmental, geopolitical, and economic reasons. Much debate surrounds the effectiveness of these policies as well as the overall net environmental effect of increased biofuels use. In the U.S., recent studies have shown that the Renewable Fuels Standard (RFS) Program, overall, may not have been the leading driver of the ethanol industry from 2005 to 2020, contrary to common perception. Similar scrutiny has not been applied to biodiesel. Here, this study uses the Bioenergy Scenario Model, a well-vetted system dynamics model, to retrospectively apportion historical biodiesel production between the RFS Program and other potentially influential drivers, such as the economics of biodiesel vs. diesel, the Biodiesel Tax Credit (BTC), California's Low Carbon Fuel Standard, and other factors. From 2002 to 2020 about 36% of biodiesel production can be attributed to the RFS Program, 35% to the BTC, and the rest to other factors. Thus, the overall effect of the RFS Program appears much larger on biodiesel than on corn ethanol. The finding that the same policy may have very disparate effects on different biofuels helps inform the design of future policies on biofuels.

09 BIOMASS FUELS↗
DOE OSTI2025

The Carbon Budget of Land Conversion: Sugarcane Expansion and Implications for a Sustainable Bioenergy Landscape in Southeastern United States

The expansion of sugarcane onto land currently occupied by improved (IMP) and semi-native (SN) pastures will reshape the U.S. bioenergy landscape. We combined biometric, ground-based and eddy covariance methods to investigate the impact of sugarcane expansion across subtropical Florida on the carbon (C) budget over a 3-year rotation. With 2.3- and 5.1-fold increase in productivity over IMP and SN pastures, sugarcane displayed a C use efficiency (CUE; i.e., fraction of gross C uptake allocated to plant growth) of 0.59, well above that of pastures (0.31–0.23). Sugarcane also had greater C allocation to aboveground productivity and hence, harvestable biomass relative to IMP and SN. Cane heterotrophic respiration over the 3-year rotation (903 ± 335 gC m −2 year −1 ) was 1% and 14% higher than IMP and SN pastures, respectively. These soil C losses responded largely to disturbance over the first year after conversion (1510 ± 227 gC m −2 year −1 ) but declined in subsequent years to an average 599 ± 90 gC m −2 year −1 —well below those of IMP (933 ± 140 gC m −2 year −1 ) and SN (759 ± 114 gC m −2 year −1 ) pastures—despite a significant 40%–61% increase in soil C inputs. Soil C inputs, however, shifted from root-dominated in pastures to litter-dominated in sugarcane, with only 5% C allocation to roots. Reduced decomposition rates in sugarcane were likely driven by changes in the recalcitrance and distribution rather than the size of the newly incorporated soil C pool. As a result, we observed a rapid shift in the net ecosystem C balance (NECB) of sugarcane from a large source immediately following conversion to approaching the net C losses of IMP pastures only 2 years after conversion. The environmental cost of converting pasture to sugarcane underscores the importance of implementing management practices to harness the soil C storage potential of sugarcane in advancing a sustainable bioeconomy in Southeastern United States.

09 BIOMASS FUELS↗
DOE OSTI2025

Bioenergy sorghum stem density increases threefold following internode elongation due to continued accumulation of lignified cell walls and complex regulation of genes involved in cell wall biosynthesis

Bioenergy sorghum is a highly productive drought tolerant C4 grass that accumulates ~ 80% of its harvested biomass in ~ 4 m long stems comprised of > 40 internodes that develop sequentially during an extended vegetative growth phase. Following elongation of each internode, internode density increases ~ threefold to fourfold primarily due to the accumulation of cell walls composed of cellulose, glucuronoarabinoxylan and lignin. Lignin accumulates initially on cell walls of sclerenchyma cells surrounding vascular bundles and later on cell walls of the stem rind and stem core pith parenchyma. Many genes involved in cell wall biosynthesis were expressed continuously during the stem internode densification process whereas others showed dynamic patterns of expression (high to low, low to high). Several CESA genes involved in primary cell wall cellulose synthesis were expressed in the stem rind and core throughout the stem densification phase. In contrast, CESA genes involved in secondary cell wall biogenesis were expressed continuously in the stem rind but downregulated in the stem core shortly after completion of internode elongation. Overall, accumulation of cell wall biomass in elongated internodes during stem densification increases stem mechanical strength and biomass bulk density while modifying biomass composition in ways that could impact the amount and release of cellulosic sugars and lignin-derived bioproducts.

09 BIOMASS FUELS↗
DOE OSTI2024

Sustainable strategies to achieve industrial ethanol titers from different bioenergy feedstocks: scale-up approach for better ethanol yield

Hydrothermal pretreatment is a promising approach to lignocellulosic biomass processing for enzymatic hydrolysis and high-yield bioethanol fermentation, as it reduces downstream inhibitor content and the amount of toxic byproducts generated. In this paper, the ethanol yield and productivity of an engineered xylose-fermenting strain of Saccharomyces cerevisiae were tested on lignocellulosic hydrolysates produced with varying citrate buffer concentration, solid loading, supplemental nitrogen source, and feedstock of origin, and a semi-integrated bioprocess which integrates enzymatic hydrolysis and bioethanol fermentation was developed. The greatest ethanol yields (g p /g s ) of 0.490 ± 0.008, 0.460 ± 0.001, 0.420 ± 0.002 and 0.410 ± 0.002 were obtained from bioenergy sorghum (BES), Miscanthus × giganteus (MG), energy cane (EC), and oilcane (OC), respectively. In addition, an equivalent of 291 L, 253.54 L, 257.8 L, and 260.3 L of bioethanol were produced per ton of BES, MG, EC, and OC, respectively, by using urea as a nitrogen source in a bioreactor.

09 BIOMASS FUELS↗
DOE OSTI2026

Contrasting Carbon–Water–Energy Dynamics in Perennial and Annual Bioenergy Agroecosystems Using Eddy Covariance and Interpretable Machine Learning

Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site-years of high-frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem-scale carbon, water, and energy fluxes. Using an interpretable machine-learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short-term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem-scale carbon–water–energy coupling. By integrating long-term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data-driven basis for improving crop and Earth-system models and for guiding bioenergy landscape design under future climate scenarios.

Accumulated Local Effects↗