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At least 1,063 records · Page 59

Site-specific field management adaptation is key to feeding the world in the 21st century

Rapid climate change and growing population threaten global food security across the globe. Several studies have proposed early planting, increased irrigation, and increased fertilizer applications as climate adaptation strategies, yet none have considered combined and site-specific field management strategies as a comprehensive solution. Here, we analyzed non-irrigated wheat yield responses to climate change and field management adaptation using a mechanistic crop model evaluated against observed global non-irrigated wheat-yield over 3-year intervals spanning 13 years at 3749 sites (RMSE = 36 gC m -2 ). Early planting with later-maturing varieties provided the most benefit to future yields among the proposed field management adaptation strategies. Improved water use efficiency from increased CO 2 led to relatively low benefits of additional irrigation. Here we estimated that spatially heterogeneous adaption strategies had the potential to improve global wheat yields by 91% by 2100 compared to the present day. The yield improvements from combined field adaptation strategies were larger than the sum of improvements from the individual strategies. These synergistic benefits were shown to result from complementary processes regulating nutrient and water uptake, physiological tolerance to heat stress, and internal carbon and nutrient cycling.

54 ENVIRONMENTAL SCIENCES↗

Editorial special issue: Advancing foundational sun-induced chlorophyll fluorescence science

The first research on the possibilities offered by chlorophyll-a fluorescence to track the daily course of CO 2 assimilation by leaves dates back to the last century (Kautsky and Hirsch, 1931). In the second half of the XXth Century, the development of the field of active fluorescence, which relies mainly on the pulse amplitude modulation (PAM) fluorimetry technique, allowed the unraveling of the relationship between the yield of chlorophyll-a fluorescence and photochemistry (linear electron transport), which is modulated by a third process known as non-photochemical quenching (Genty et al., 1989). Since then, active fluorescence has been regularly used in ecophysiology, forestry, and crop sciences to understand the plant response to stress (Schreiber, 2004). Since the beginning of the 2000s, the development of portable field spectrometers allows for measuring passive sun-induced fluorescence (SIF) at strong solar or telluric absorption features, which do not rely on the use of artificial excitation light (e.g., Meroni and Colombo, 2006; Meroni et al., 2009; Perez-Priego et al., 2005). Since then, the field has rapidly evolved, and now fluorescence is measurable with automated field spectrometers in the field (Grossmann et al., 2018; Gu et al., 2019b; Rossini et al., 2010), airborne platforms (Rascher et al., 2015; Zarco-Tejada et al., 2000), and satellites (Guanter et al., 2012; Sun et al., 2018). These advances offer the potential to couple ecosystem-scale measurements of CO 2 fluxes and SIF to probe new aspects related to ecosystem structural impacts on SIF andphotosynthesis processes at different time-scales and on different ecosystems (e.g., Damm et al., 2010; Magney et al., 2019; Porcar-Castell et al., 2021). While SIF has been proven as a good proxy of gross primary productivity (GPP), mainly across large spatial and temporal gradients, a series of studies showed that different factors could confound this relationship, namely different canopy structures (e.g., Dechant et al., 2020; Migliavacca et al., 2017), stress conditions (e.g., Martini et al., 2022; Wieneke et al., 2018; Wohlfahrt et al., 2018), nutritional conditions (Cendrero-Mateo et al., 2015; Martini et al., 2019), species-specific differences (e.g., Van Wittenberghe et al., 2013) and light regimes (e.g., Liu and Liu, 2018). At the same time, the modeling of SIF developed rapidly (Gu et al., 2019a; Han et al., 2022; Han et al., 2021; van der Tol et al., 2014; van der Tol et al., 2009) and with an increasing degree of realism, offering the possibility to retrieve important vegetation parameters from remote sensing (Pacheco-Labrador et al., 2019; Verrelst et al., 2015; Verrelst et al., 2016; Zhang et al., 2014). Excellent and comprehensive reviews of the field are by (Mohammed et al., 2019; Porcar-Castell et al., 2021; Porcar-Castell et al., 2014; Sun et al., 2023a; Sun et al., 2023b). Despite the exponential increase in the number of publications in the field, significant progress is still needed. Here, this special issue aims to report foundational SIF science, including theoretical modeling and measurement-based research that is urgently needed to unleash the full potential of SIF for physiological and ecological applications at scales spanning from leaf to globe. The 14 articles collected in this special issue reported on the following specific aspects: first, technical capabilities enabling spectrally resolved SIF observations and their inter-comparability in space and time; second, theoretical developments in SIF-photosynthesis relationships to correctly interpret the signal and extract mechanistic information on vegetation structure and function; third, evaluation of the potential of SIF to track process beyond photosynthesis, such as transpiration; fourth, large scale-applications of SIF observations; and finally, upscaling fluorescence from leaf to canopy scales.

54 ENVIRONMENTAL SCIENCES↗

Limited potential of irrigation to prevent potato yield losses in Germany under climate change

Climate models project higher temperatures and a substantial net water deficit during the summer months over Germany until the end of the 21st century. The agricultural sector is particularly vulnerable to changing weather and climate conditions. In this study, we examine the role of irrigation for potato yields as a climate change adaptation measure in Northeast Lower Saxony (Germany). The region represents the largest irrigated area in Germany and is one of the main growing areas of potatoes. We develop a statistical multivariate regression model to investigate whether regional climate and irrigation data at the monthly and county level are suitable to describe potato yield variability from 1978 to 2018. Irrigation is estimated based on the climatic water balance and crop water demand. These estimates are calibrated against irrigation data from local agencies to account for realistic monthly water withdrawals. We analyze future yield changes based on climate model projections for the 21st century and different irrigation scenarios. We find that the potato yield will decrease significantly by 18% on average until 2050 with climate projection uncertainty of ±6.4% under the high emission scenario RCP 8.5 and without irrigation. Irrigation at current levels could compensate for about 54% of these yield losses. Doubling the amount of irrigation minimizes yield losses by as much as 80%. Under the low (middle) emission scenario RCP 2.6 (RCP 4.5), potato yield are projected to decrease moderately by 4.5% ± 3.4% (5.4% ± 2.8%) with irrigation at current levels. We show that the yield losses mainly originate from higher temperatures during the summer. Our analysis indicates that the capacity of irrigation to minimize yield losses is limited. Strengthening climate change mitigation efforts may be more effective in preventing yield losses. However, the effect of elevated CO 2 might buffer the high yield losses, especially in RCP 8.5. The studies supports farmers and decision makers in assessing future risks in potato yields. It can guide policymakers in allocating economic resources to cope with climate change.

60 APPLIED LIFE SCIENCES↗

Unveiling the drivers contributing to global wheat yield shocks through quantile regression

Sudden reductions in crop yield (i.e., yield shocks) severely disrupt the food supply, intensify food insecurity, depress farmers' welfare, and worsen a country's economic conditions. Here, we study the spatiotemporal patterns of wheat yield shocks, quantified by the lower quantiles of yield fluctuations, in 86 countries over 30 years. Furthermore, we assess the relationships between shocks and their key ecological and socioeconomic drivers using quantile regression based on statistical (linear quantile mixed model) and machine learning (quantile random forest) models. Using a panel dataset that captures spatiotemporal patterns of yield shocks and possible drivers in 86 countries, we find that the severity of yield shocks has been increasing globally since 1997. Moreover, our cross-validation exercise shows that quantile random forest outperforms the linear quantile regression model. Despite this performance difference, both models consistently reveal that the severity of shocks is associated with higher weather stress, nitrogen fertilizer application rate, and gross domestic product (GDP) per capita (a typical indicator for economic and technological advancement in a country). While the unexpected negative association between more severe wheat yield shocks and higher fertilizer application rate and GDP per capita does not imply a direct causal effect, they indicate that the advancement in wheat production has been primarily on achieving higher yields and less on lowering the possibility and magnitude of sharp yield reductions. Hence, in the context of growing extreme weather stress, there is a critical need to enhance the technology and management practices that mitigate yield shocks to improve the resilience of the world food systems.

60 APPLIED LIFE SCIENCES↗

Reliability metrics and their management implications for open pond algae cultivation

The prevalence of contaminating organisms in outdoor algae cultivation, with the often-associated dramatic crop failures, necessitates the need for metrics describing production system reliability. Standard metrics for algae cultivation reliability are critically needed to be able to map improvements in operational parameters, but do not currently exist. In this work, we present a set of standard metrics including mean time to failure (MTTF) and mean time between failures (MTBF) as the basis for the calculation of pond failure rate (FR) and reliability coefficient (RC). Metrics associated with the numbers of contaminating organisms such as abundance ratio (AR), prevalence of infection (PI), and mean intensity of infection (MII) are also relevant. These metrics, based on measured experimental values of outdoor pond performance during the Algae Testbed Public-Private Partnership (ATP3) Unified Field Studies (UFS), provide a basis for quantifying pond failure and provide insight into potential pond management and contaminant mitigation strategies. From these reliability metrics applied to this dataset, we are able to link operational parameters, such as harvest frequency and inoculum source, to pond reliability for different algae strains and seasons, and from an assessment of AR, provide contamination thresholds beyond which a culture may be unrecoverable. Ultimately, the implementation of widely-practiced and simple-to-calculate algae pond reliability metrics calculated from rapid and easy to collect data or observations will reduce risk and uncertainty in large-scale algae deployment and aid in the development of integrated pest management (IPM) strategies.

09 BIOMASS FUELS↗

Heterologous expression of phosphite dehydrogenase in the chloroplast or nucleus enables phosphite utilization and genetic selection in Picochlorum spp.

Microalgae present a path to ameliorate problems associated with climate change via capture and reduction of CO2 to sustainable fuels and chemicals. Picochlorum is a genus of algae recently recognized for potential application in these regards due to its high productivity, thermotolerance, and halotolerance. Foundational genetic tools have recently been established in this genus. However, at present, genetic markers are limited, hindering genetic throughput and trait stacking approaches. To expand the suite of genetic tools and markers available for this genus, we sought to heterologously express the phosphite dehydrogenase (ptxD) gene from Pseudomonas stutzeri WM88 in both the nucleus and chloroplast of Picochlorum renovo and Picochlorum celeri. Additionally, the resultant strains allow for utilization of phosphite as a sole phosphorous source and as a nuclear and plastidial selection marker for genetic engineering. Growth analysis indicated comparable growth and composition when transgenic algae were grown in media containing phosphite as a sole phosphorus source, as compared to the conventionally used phosphate. Combined, these results expand the genetic toolbox available to the Picochlorum genus and present a potential crop protection and biocontainment strategy.

09 BIOMASS FUELS↗

A novel approach to build algal consortia for sustainable biomass production

In the last decade, microalgae have reemerged as a feedstock for biofuels and a diverse suite of bioproducts. Yet, considerable challenges must be overcome before algal biofuels and bioproducts become technoeconomically viable. At present, single algal strain selected for particular phenotypic traits, such as maximum specific growth rate or lipid content, are commonly scaled for cultivation in open, outdoor raceway ponds due to the low capital costs of these systems. Although this monoculture approach may maximize the production of end products, monocultures are particularly susceptible to crashes associated with environmental and biological variability. An approach that has been proposed to generate more productive and stable microalgal crops is the use of eco-engineered communities, or consortia. Yet, attempts to construct productive consortia have not been consistently successful. We argue that failures stem from the lack of an eco-engineering approach to design species combinations. Here, we used an in silico method to build consortia before testing their performance against monocultures. Focusing on consortia of Nannochloropsis and Microchloropsis, we measured growth of strains along gradients of light, temperature, and salinity and used a functional dispersion approach to generate over 8000 functionally-diverse consortia combinations. We tested the 50 most functionally diverse consortia in a laboratory experiment and found that consortia overwhelmingly outperformed monocultures. Indeed, overyielding (OY) and a positive net biodiversity effect (NBE) was found respetively in 8%-86% and 88-92% of consortia combinations over the different experimental phases. To our knowledge, this is the first application of an in silico approach to design functionally diverse consortia before laboratory and field testing. Furthermore, our results highlight the importance of employing a functional diversity approach for consortia design.

09 BIOMASS FUELS↗

Bringing solar to agriculture: An interdisciplinary design and analysis of a Concord grape agrivoltaic system

Agrivoltaics presents an opportunity to integrate solar photovoltaics (PV) with agricultural production, but crop-specific challenges and operational constraints remain underexplored. This study develops and evaluates a Concord grape agrivoltaic system in the Lake Erie American Viticulture Area, where vineyards face economic pressures and land use conflicts with solar development. Starting with vertical, tracking, and overhead PV systems, we model photosynthetic photon flux density (PPFD) reduction in grapevines and power generation losses from shading using the Agrivoltaic Radiation Tool (ART). Based on these results, which indicate 0.47 % annual grapevine PPFD loss for vertical designs, 1.6 % for tracking, and up to 25 % for the overhead systems, the vertical and tracking designs are selected for further computational fluid dynamics (CFD) analysis to evaluate airflow interactions. CFD results show that vertical panels do not significantly impact airflow through the grapevine canopy, and that tracking systems in horizontal position may enhance airflow compared to a vine-only scenario. Considering operational constraints for tracking systems, the vertical design is selected for an economic evaluation to reveal key financial outcomes for solar developers (14-year payback period) and growers ($408 reduction in financial losses per acre annually). A sensitivity analysis quantifies uncertainty in power generation (±8.8 %) and PPFD (±5.0 %), ensuring model robustness across different vineyard conditions. Furthermore, these findings provide quantitative evidence for the feasibility of Concord grape agrivoltaics, demonstrating a synergistic opportunity for dual-use solar while preserving cultural heritage in grape-growing regions.

14 SOLAR ENERGY↗

Evaluation of artificial neural network performance for classification of potato plants infected with potato virus Y using spectral data on multiple varieties and genotypes

Potato virus Y (Potyviridae, PVY) is a plant virus that poses a significant threat to potato producers on a global basis. The pathogen has disrupted seed potato supplies and negatively impacted yield and quality of commercial potato crops. The potato industry currently manages PVY infection levels via insecticide applications, regional seed certification programs that rely on field scouting to visually assess individual plants for infection status, and destructive and costly tissue sampling coupled with laboratory assays. Despite these efforts, PVY continues to confound potato industry stakeholders resulting in economic harm. Remote sensing and machine learning provide for the development of new tools to more accurately detect and spatially quantify PVY-infected plants versus the current state of the art. However, there is a need to understand how the occurrence of many different potato varieties impact the dynamics of developing models to detect potato plants impacted with PVY and their potential effectiveness. This study evaluates classification modelling outcomes using spectral datasets collected in different temporal and spatial environments (greenhouse and a production field) on multiple potato varieties consisting of labelled instances of plants infected with PVY and those not infected with the virus. A modelling framework was developed to support iterative modelling runs using artificial neural network (ANN) architectures configured as binary classifiers to develop sample populations to support statistical analysis on model performance using specific spectral subsets. When using spectral data to detect PVY-infected plants, ANN models achieved the highest mean accuracy of 0.894 on a single variety. Conversely, the same ANN model architecture only achieved a mean accuracy of 0.575 on a spectral data set representing 29 potato breeding lines. Additionally, statistical analysis indicates spectral regions including the red edge, near infrared and shortwave infrared contain more important spectral features for the ANN classifier introduced in this research.

60 APPLIED LIFE SCIENCES↗

Wild bees and natural enemies prefer similar flower species and respond to similar plant traits

Designing wildflower habitats to support beneficial insects providing pollination and pest control services is important for supporting sustainable crop production. It is often desirable to support both groups of insects, making the selection of resource plants for insect conservation programs more challenging. Moreover, the process of selecting resource plants is complicated by the array of possible options in each region, and the need to provide resources over the entire growing season. Identifying traits shared by resource plants that are attractive to both bees and natural enemies can reduce the need to evaluate new plants in each region, by providing a guide for the types of plants expected to be rewarding to these insects. Using insect visitation data collected from replicated common garden plantings of native wildflower and shrub species from the Great Lakes region of the United States, we found a high degree of correlation between the abundance of bees and natural enemies visiting native plant species. These results were used to identify a set of 15 plant species that can provide resources for these insects throughout the summer. Across all tested species, pollen quantity per flower and the week of bloom were positively correlated with some, but not all, taxonomic groupings of beneficial insects. In contrast, floral area was consistently positively associated with visitation of both natural enemies and wild bees. This trait is easy to document and can allow for efficient local testing of potential resource plants, providing a faster path to implementing insect conservation in working landscapes.

54 ENVIRONMENTAL SCIENCES↗

Utilizing plant synthetic biology to accelerate plant-microbe interactions research

Plant-microbe interactions are critical to ecosystem resilience and substantially influence crop production. From the perspective of plant science, two important focus areas concerning plant-microbe interactions include: 1) understanding plant molecular mechanisms involved in plant-microbe interfaces and 2) engineering plants for increasing plant disease resistance or enhancing beneficial interactions with microbes to increase their resilience to biotic and abiotic stress conditions. Molecular biology and genetics approaches have been used to investigate the molecular mechanisms underlying plant responses to various beneficial and pathogenic microbes. While these approaches are valuable for elucidating the functions of individual genes and pathways, they fall short of unraveling the complex cross-talk across pathways or systems that plants employ to respond and adapt to environmental stresses. Also, genetic engineering of plants to increase disease resistance or enhance symbiosis with microbes has mainly been attempted or conducted through targeted manipulation of single genes/pathways of plants. Recent advancements in synthetic biology tool development are paving the way for multi-gene characterization and engineering in plants in relation to plant-microbe interactions. Here, we briefly summarize the current understanding of plant molecular pathways involved in plant interactions with beneficial and pathogenic microorganisms. Then, we highlight the progress in applying plant synthetic biology to elucidate the molecular basis of plant responses to microbes, enhance plant disease resistance, engineer synthetic symbiosis, and conduct in situ microbiome engineering. Lastly, we discuss the challenges, opportunities, and future directions for advancing plant-microbe interactions research using the capabilities of plant synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗

Global Biodiversity Implications of Alternative Electrification Strategies Under the Shared Socioeconomic Pathways

Addressing climate mitigation while meeting global electrification goals will require major transitions from fossil-fuel dependence to large-scale renewable energy deployment. However, renewables require significant land assets per unit energy and could come at high cost to ecosystems, creating potential conflicts between global climate mitigation and biodiversity conservation. Here, we explore the potential biodiversity implications of alternative future global electrification pathways as depicted under the Shared Socioeconomic Scenarios (SSPs), i.e., alternative trends in societal development. We examined the intersection of high-resolution estimates of global energy densities for ten renewable and conventional technologies with global richness data to estimate technology-specific biodiversity footprints (species per GWh), whereas a Cumulative Biodiversity Impact (CBI) score was used to assess land and biodiversity outcomes of alternative scenarios. Downscaled electricity generation scenarios (2020-2100) were also constrained by alternative land conservation and energy development policies. Unexpectedly, variation among SSPs did not exhibit a clear tradeoff between global climate mitigation and CBI. Rather, CBIs were an outcome of total infrastructure development to meet electricity demand (from population growth and GDP) and the total magnitude of renewable energy development and storage technologies. Renewables assembled along a spectrum from land sharing to sparing. At the land sharing end, biomass-powered electricity from dedicated crops contributed the most to biodiversity impacts due to low energy density, whereas land-sparing technologies (solar) caused more-intense land degradation, but in smaller areas. Our results suggest that local land conservation practices and strategies promoting energy diversification could have greater implications for future biodiversity conflicts than global socioeconomic drivers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Interactions between U.S. biofuels policy and the Endangered Species Act

The Renewable Fuel Standard (RFS) is the primary policy governing the use of biofuels in the United States. Following the 2007 enactment of the RFS, there has been substantial expansion in the use of cropland to produce biofuel feedstocks, including corn and soybeans. Both the conversion of land and the ongoing cultivation of these crops can, depending on management practices, lead to negative environmental outcomes including degraded water quality, increased water use, increased greenhouse gas emissions, and loss or degradation of wildlife habitat. These outcomes have the potential to affect threatened and endangered species protected under the U.S. Endangered Species Act (ESA). The ESA requires federal agencies to consult with the U.S. Fish and Wildlife Service or the National Marine Fisheries Service to ensure that an agency's actions are not likely to jeopardize the existence of imperiled species, nor result in the destruction or adverse modification of their habitat. However, formal consultation has not yet been completed between the U.S. Environmental Protection Agency—the agency charged with implementing the RFS—and the wildlife services, thereby hindering mitigation or remediation of potential impacts. This analysis explores the likely effects of the RFS on land use changes and critical habitat, illustrates example pathways by which threatened and endangered species may be affected, and proposes solutions to help redress this harm. The findings suggest a need for further consultation and assessment of the interactions between the RFS and ESA as well as additional research to support the species-conservation and policymaking processes.

09 BIOMASS FUELS↗

Trichoderma harzianum transcriptome in response to the nematode Pratylenchus brachyurus

The root-lesion nematode Pratylenchus brachyurus causes extensive damage in several crops of economic importance. Fungi of Trichoderma genus have been highlighted as biopesticide agents in the control of several plant diseases. Although it is already widely used in agriculture, there are few studies, especially at the molecular level, that evaluate T. harzianum in the control of P. brachyurus . The aim of the present study was to investigate how interaction with the nematode P. brachyurus influences gene expression of T. harzianum , by using RNA-Seq analysis. Of the 13,932 predicted genes in the T. harzianum genome, 2,922 (21%) were differentially expressed in the presence of P. brachyurus , in relation to the absence of the nematode. Among the differentially expressed genes, we found genes encoding Carbohydrate Active EnZymes (CAZy), MEROPS peptidases, and proteins related to secondary metabolite synthesis. 118 pathways were identified as related to the biosynthesis of secondary metabolites. Additionally, the analysis identified 136 metabolic pathways related to these differentially expressed genes, among which we highlight: aminobenzoate degradation, xenobiotic metabolism by cytochrome P450, and sesquiterpenoid and triterpenoid biosynthesis. Our results contribute to a better understanding of the response of T. harzianum to the nematode P. bracyurus , the potential for biocontrol by this fungus.

59 BASIC BIOLOGICAL SCIENCES↗

Discrete element modeling of switchgrass particles under compression and rotational shear

Switchgrass is a perennial herbaceous plant regarded as a biomass energy crop in the United States for its highadaptability and yield potential. Processing and handling of switchgrass particles are challenging due to the erratic mechanical and flow behavior originating from their intrinsic particulate properties. Here, we present a bonded-sphere discrete element model designed specifically for switchgrass particles. The model simultaneously captures three key particulate features, i.e., fibrous particle shapes, a wide range of particle sizes, and particle deformability. Realistic yet computationally efficient particle shape templates are created based on the image analysis data of switchgrass specimens. A fitting procedure is proposed to ensure both the particle width and length distributions are captured, a unique requirement for fibrous particles. Two full-scale numerical models, i.e., a uniaxial compression model and a Schulze ring shear model, are developed using information fromphysical experiments. The model is calibrated using experimental data of chopped-small switchgrass specimens, and then, is validated using data of chopped-large specimens in both compression and ring-shear tests. Numerical results show that the numerical models capture bulk densities accurately (with an error of 3%) while slightly underestimate the bulk friction angle. Furthermore, an extensive sensitivity analysis reveals that (1) switchgrass particles with rougher edges (due to different processing techniques) exhibit a higher shear strength and a lower flowability; (2) stiffer particles yield a lower bulk density (up to 21% lower) compared to more deformable particles, indicating particle deformability should be incorporated when modeling biomass flow in a preprocessing system.

09 BIOMASS FUELS↗

Sustainability assessment of alternative jet fuel for the U.S. Department of Defense

The United States Department of Defense (DoD) is considering replacing the 2.8 billion gallons of petroleum jet fuel consumed within the continental United States (CONUS) annually with alternative jet fuels to reduce vulnerability to price and supply fluctuations and improve sustainability. We evaluate the feasibility of replacing DoD CONUS jet fuel with alternative jet fuel from domestic feedstocks and assess the cost, greenhouse gas (GHG) emissions, energy balance, land, water, and fertilizer impacts of nine alternative jet fuel pathways. The feedstocks include terrestrial crops (soy, corn, wood, and grass), marine microalgae, and by-products (forestry residue, municipal solid waste, and waste oil) converted by extraction, fermentation, or thermo-chemical conversion, paired with upgrading steps. We found that domestic biomass feedstocks can meet the DoD CONUS jet fuel demand without significantly affecting food supply. Alternative pathways cost more than petroleum jet fuel ($0.78 l -1 and emitting 89 g CO 2 equivalent (CO 2e ) MJ -1 ), but many lowered GHG emissions. The forestry residue and waste oil pathways yielded the lowest costs ($\$0.92$ and $\$0.82$ l -1 , respectively), decreased emissions (23 and 35 g CO 2 e MJ -1 , respectively), and had negligible effects on land, water, and fertilizer resources. Finally, tradeoffs among sustainability metrics are further explored with a sensitivity analysis and by evaluating carbon-cost scenarios.

09 BIOMASS FUELS↗

Variations in cell wall traits impact saccharification potential of Salix famelica and Salix eriocephala

Increasing global populations, finite arable land, and the anthropogenic release of carbon dioxide into the atmosphere are driving the search for bio-based alternatives to the petroleum-derived fuels and chemicals that underpin the global economy. With rapid growth rates, a propensity for coppicing, and a wide geographic range across Canada, native shrub willows (Salix spp.) are an attractive source of low-input, high-volume biomass. To date, most willow research has focused on increasing yields and improving cropping systems, while comparatively little work has been done to assess the intrinsic diversity in cell wall traits and bioenergy potential. In this study, we characterized the cell wall composition and wood ultrastructure of 338 xylem samples from two Canadian willow species, Salix famelica and Salix eriocephala, harvested from a common garden experimental plot. Lignin content ranged from 17.5–25.1% in S. famelica and 18.6–24.3% in S. eriocephala. Following alkali pretreatment with 62.5 mM NaOH at 90 °C for 3 h and a 70-h enzymatic digestion with Accellerase 1000, glucose release ranged from 23.0–38.9 wt% in S. famelica and from 20.5–37.7 wt% in S. eriocephala, while xylose release ranged from 9.4–14.9% in S. famelica and from 9.5–15.2% in S. eriocephala. Here, partial least squares regression modelling showed that lignin content and composition were important negative regulators of glucose release. Overall, this work highlights the innate variability in cell wall traits of native willows and identifies potential genotypes that should be considered in future breeding and deployment strategies for Canadian bioenergy production.

59 BASIC BIOLOGICAL SCIENCES↗

Multi-objective optimization of sustainable aviation fuel production pathways in the U.S. Corn Belt

As a potential source of low-carbon transportation energy, biofuels offer certain advantages over vehicle electrification (e.g., lower societal vulnerability to grid failures, and improved range of sustainable aviation), but also several challenges, including cost, carbon intensity, and land usage. There are also well-founded concerns that biofuel supply chains could be disrupted if extreme weather events impact feedstock yields. In this paper, we explore the use of multi-objective optimization to identify biofuel production pathways that balance cost, greenhouse gas emissions, and supply vulnerability to extreme weather. We compare the use of three different many-objective evolutionary algorithms and linear programming in optimizing biomass cultivation decisions in the U.S. Corn Belt under weather uncertainty using historical, modeled, and synthetic yield data. We consider four feedstock choices (corn, soy, switchgrass, and algae) with two land types (agricultural and marginal lands) and evaluate decisions using three alternative spatial resolutions (ranging from the USDA agricultural district level to the state level). Results show that feedstock choice is the primary driver of objective performance (i.e., the position and shape of 3D, approximate Pareto frontiers). Spatial diversification is a less effective tool in reducing exposure to weather-caused drops in crop yield.

09 BIOMASS FUELS↗