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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A multi-product landscape life-cycle assessment approach for evaluating local climate mitigation potential

Increasing demand for land-based climate mitigation requires more efficient management of agricultural landscapes for competing objectives. Here we develop methods for assessing trade-offs and synergies between intensification and carbon-sequestering conservation measures in annual crop production landscapes using the DayCent ecosystem model and the Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) life-cycle assessment (LCA) model. We compiled county-scaled crop yields, fertilizer application rates, and tillage intensity for a corn–soybean farming case study landscape in the US state of Iowa. Using DayCent, we estimated a baseline soil organic carbon (SOC) accrual rate of 0.29 Mg C ha -1 y -1 driven by historical increases in crop productivity and reductions in tillage intensity. We then simulated the effects of management interventions targeted toward intensification (stover removal) and SOC sequestration (tillage intensity reduction and winter cover crop addition) individually and in combination. We propose a new multi-product landscape–LCA approach that analyzes marginal changes in corn grain, corn stover, and soybean production from the landscape in terms of their value for biofuel production (corn ethanol, soy biodiesel, and cellulosic ethanol from stover) and associated net displacement of conventional fossil-derived fuel use. This enables us to evaluate both intensification and sequestration effects in common CO 2 -equivalent mitigation units. We also used DayCent-simulated yields under the different land management scenarios to estimate farm-level costs and revenues. Our results show that intensification via collecting 30% of corn stover for biofuel production would increase the total greenhouse gas (GHG) mitigation potential of this landscape by 0.93 Mg CO 2 e ha -1 y -1 and provide $49 ha -1 y -1 of additional net revenue from biomass sales, but would reduce the baseline SOC accumulation rate by approximately 40%. In contrast, integrated approaches that include co-adoption of winter cover cropping and/or tillage intensity reduction would result in increased rates of SOC accumulation above the baseline, achieving simultaneous improvements in both farm profits and the overall GHG mitigation potential of the landscape.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The reference genome and abiotic stress responses of the model perennial grass Brachypodium sylvaticum

Abstract Perennial grasses are important forage crops and emerging biomass crops and have the potential to be more sustainable grain crops. However, most perennial grass crops are difficult experimental subjects due to their large size, difficult genetics, and/or their recalcitrance to transformation. Thus, a tractable model perennial grass could be used to rapidly make discoveries that can be translated to perennial grass crops. Brachypodium sylvaticum has the potential to serve as such a model because of its small size, rapid generation time, simple genetics, and transformability. Here, we provide a high-quality genome assembly and annotation for B. sylvaticum, an essential resource for a modern model system. In addition, we conducted transcriptomic studies under 4 abiotic stresses (water, heat, salt, and freezing). Our results indicate that crowns are more responsive to freezing than leaves which may help them overwinter. We observed extensive transcriptional responses with varying temporal dynamics to all abiotic stresses, including classic heat-responsive genes. These results can be used to form testable hypotheses about how perennial grasses respond to these stresses. Taken together, these results will allow B. sylvaticum to serve as a truly tractable perennial model system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Progress and challenges in sorghum biotechnology, a multipurpose feedstock for the bioeconomy

Abstract Sorghum [Sorghum bicolor (L.) Moench] is the fifth most important cereal crop globally by harvested area and production. Its drought and heat tolerance allow high yields with minimal input. It is a promising biomass crop for the production of biofuels and bioproducts. In addition, as an annual diploid with a relatively small genome compared with other C4 grasses, and excellent germplasm diversity, sorghum is an excellent research species for other C4 crops such as maize. As a result, an increasing number of researchers are looking to test the transferability of findings from other organisms such as Arabidopsis thaliana and Brachypodium distachyon to sorghum, as well as to engineer new biomass sorghum varieties. Here, we provide an overview of sorghum as a multipurpose feedstock crop which can support the growing bioeconomy, and as a monocot research model system. We review what makes sorghum such a successful crop and identify some key traits for future improvement. We assess recent progress in sorghum transformation and highlight how transformation limitations still restrict its widespread adoption. Finally, we summarize available sorghum genetic, genomic, and bioinformatics resources. This review is intended for researchers new to sorghum research, as well as those wishing to include non-food and forage applications in their research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Isolation of potential plant growth-promoting bacteria from nodules of legumes grown in arid Botswana soil

As the world population increases, improvements in crop growth and yield will be needed to meet rising food demands, especially in countries that have not developed agricultural practices optimized for their own soils and crops. In many African countries, farmers improve agricultural productivity by applying synthetic fertilizers and pesticides to crops, but their continued use over the years has had serious environmental consequences including air and water pollution as well as loss of soil fertility. To reduce the overuse of synthetic amendments, we are developing inocula for crops that are based on indigenous soil microbes, especially those that enhance plant growth and improve agricultural productivity in a sustainable manner. We first isolated environmental DNA from soil samples collected from an agricultural region to study the composition of the soil microbiomes and then used Vigna unguiculata (cowpea), an important legume crop in Botswana and other legumes as “trap” plants using the collected soil to induce nitrogen-fixing nodule formation. We have identified drought-tolerant bacteria from Botswana soils that stimulate plant growth; many are species of Bacillus and Paenibacillus . In contrast, the cowpea nodule microbiomes from plants grown in these soils house mainly rhizobia particularly Bradyrhizobium , but also Methylobacterium spp. Hence, the nodule microbiome is much more limited in non-rhizobial diversity compared to the soil microbiome, but also contains a number of potential pathogenic bacteria.

Kosty, Melissa↗
DOE OSTI2021

Nitrification is a minor source of nitrous oxide (N 2 O) in an agricultural landscape and declines with increasing management intensity

Abstract The long‐term contribution of nitrification to nitrous oxide (N 2 O) emissions from terrestrial ecosystems is poorly known and thus poorly constrained in biogeochemical models. Here, using Bayesian inference to couple 25 years of in situ N 2 O flux measurements with site‐specific Michaelis–Menten kinetics of nitrification‐derived N 2 O, we test the relative importance of nitrification‐derived N 2 O across six cropped and unmanaged ecosystems along a management intensity gradient in the U.S. Midwest. We found that the maximum potential contribution from nitrification to in situ N 2 O fluxes was 13%–17% in a conventionally fertilized annual cropping system, 27%–42% in a low‐input cover‐cropped annual cropping system, and 52%–63% in perennial systems including a late successional deciduous forest. Actual values are likely to be <10% of these values because of low N 2 O yields in cultured nitrifiers (typically 0.04%–8% of NH 3 oxidized) and competing sinks for available in situ. Most nitrification‐derived N 2 O was produced by ammonia‐oxidizing bacteria rather than archaea, who appeared responsible for no more than 30% of nitrification‐derived N 2 O production in all but one ecosystem. Although the proportion of nitrification‐derived N 2 O production was lowest in annual cropping systems, these ecosystems nevertheless produced more nitrification‐derived N 2 O (higher V max ) than perennial and successional ecosystems. We conclude that nitrification is minor relative to other sources of N 2 O in all ecosystems examined.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Toward sustainable production of value‐added bioenergy and industrial oils in oilseed and biomass feedstocks

Abstract Plant fatty acids are used for food, feed, fuel, and industrial materials. Structurally and chemically diverse fatty acids, referred to as unusual or specialized fatty acids, are found in the seed oils of diverse plant species. Many unusual fatty acids have potential use as alternative and renewable sources of biofuels and bio‐based industrial feedstocks due to their variant structures' physical or functional properties. Oils enriched in these fatty acids can increase the value of oilseed crops and provide co‐products that can be readily extracted from lignocellulosic materials in biomass crops. Here, we describe recent progress in strategies for enhancement of oil production and quality in oilseed crops and the extension of this knowledge for metabolic engineering of biomass crops. Successful implementation of these strategies, in part, through the use of emerging synthetic biology tools, will provide added product value for the economic sustainability of biomass crops as bioenergy feedstocks.

Park, Kiyoul↗
DOE OSTI2025

Homoeolog expression divergence contributes to time of day changes in transcriptomic and glucosinolate responses to prolonged water limitation in Brassica napus

SUMMARY Water availability is a major determinant of crop production, and rising temperatures from climate change are leading to more extreme droughts. To combat the effects of climate change on crop yields, we need to develop varieties that are more tolerant to water‐limited conditions. We aimed to determine how diverse crop types (winter/spring oilseed, tuberous, and leafy) of the allopolyploid Brassica napus , a species that contains the economically important rapeseed oilseed crop, respond to prolonged water limitation. We exposed plants to an 80% reduction in water and assessed growth and color on a high‐throughput phenotyping system over 4 weeks and ended the experiment with tissue collection for a time course transcriptomic study. We found an overall reduction in growth across cultivars but to varying degrees. Diel transcriptome analyses revealed significant accession‐specific changes in time‐of‐day regulation of photosynthesis, carbohydrate metabolism, and sulfur metabolism. Interestingly, there was extensive variation in which homoeologs from the two parental subgenomes responded to water limitation across crop types that could be due to differences in regulatory regions in these allopolyploid lines. Follow‐up experiments on select cultivars confirmed that plants maintained photosynthetic health during the prolonged water limitation while slowing growth. In two cultivars examined, we found significant time of day changes in levels of glucosinolates, sulfur‐ and nitrogen ‐rich specialized metabolites, consistent with the diel transcriptomic responses. These results suggest that these lines are adjusting their sulfur and nitrogen stores under water‐limited conditions through distinct time of day regulation.

Ricono, Angela [University of Minnesota St. Paul M↗
DOE OSTI2024

Root-associated bacterial communities and root metabolite composition are linked to nitrogen use efficiency in sorghum

ABSTRACT The development of cereal crops with high nitrogen use efficiency (NUE) is a priority for worldwide agriculture. In addition to conventional plant breeding and genetic engineering, the use of the plant microbiome offers another approach to improving crop NUE. To gain insight into the bacterial communities associated with sorghum lines that differ in NUE, a field experiment was designed comparing 24 diverse Sorghum bicolor lines under sufficient and deficient nitrogen (N). Amplicon sequencing and untargeted gas chromatography–mass spectrometry were used to characterize the bacterial communities and the root metabolome associated with sorghum genotypes varying in sensitivity to low N. We demonstrated that N stress and sorghum type (energy, sweet, and grain sorghum) significantly impacted the root-associated bacterial communities and root metabolite composition of sorghum. We found a positive correlation between sorghum NUE and bacterial richness and diversity in the rhizosphere. The greater alpha diversity in high NUE lines was associated with the decreased abundance of a dominant bacterial taxon, Pseudomonas . Multiple strong correlations were detected between root metabolites and rhizosphere bacterial communities in response to low N stress. This indicates that the shift in the sorghum microbiome due to low N is associated with the root metabolites of the host plant. Taken together, our findings suggest that host genetic regulation of root metabolites plays a role in defining the root-associated microbiome of sorghum genotypes differing in NUE and tolerance to low N stress. IMPORTANCE The development of crops that are more nitrogen use-efficient (NUE) is critical for the future of the enhanced sustainability of agriculture worldwide. This objective has been pursued mainly through plant breeding and plant molecular engineering, but these approaches have had only limited success. Therefore, a different strategy that leverages soil microbes needs to be fully explored because it is known that soil microbes improve plant growth through multiple mechanisms. To design approaches that use the soil microbiome to increase NUE, it will first be essential to understand the relationship among soil microbes, root metabolites, and crop productivity. Using this approach, we demonstrated that certain key metabolites and specific microbes are associated with high and low sorghum NUE in a field study. This important information provides a new path forward for developing crop genotypes that have increased NUE through the positive contribution of soil microbes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Global Market and Economic Welfare Implications of Changes in Agricultural Yields Due to Climate Change

The economic welfare effects of climate change on global agriculture will be mediated by several complex biophysical and economic processes. For a given emissions scenario, these include: (1) the response of the climate system to anthropogenic forcing, (2) the response of crop yields to climate system and carbon dioxide changes, given baseline improvements in crop yields, (3) the response of agricultural markets to crop yield changes, and (4) the economic welfare implications of such market responses. In this paper, we use information about the first two processes from available climate-crop model comparison studies to analyze implications for the third and fourth processes. Applying the range of crop yield changes in a Global Integrated Assessment Model (GCAM) highlights several important economic relationships. First, we find a consistent relationship between global cropland area and yield change that is approximately orthogonal to the relationship between regional cropland area and yield change. Second, we find that the change in economic welfare, expressed as total surplus change per unit economic output, peaks during the 21st century. Third, we find that, at the global level, changes in yield affect both producer surplus and consumer surplus. Specifically, surplus changes to producers and consumers are always opposite in sign, although which economic actors gain or lose varies with the sign of yield change for any given commodity. Taken together, these results contribute to a growing body of research on climate-induced changes on agriculture by highlighting several economic relationships that are robust to differences in the underlying biophysical responses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

AmeriFlux FLUXNET-1F US-CF2 CAF-LTAR Cook West

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-CF2 CAF-LTAR Cook West. This is the FLUXNET version of the carbon flux data for the site US-CF2 CAF-LTAR Cook West produced by applying the standard ONEFlux (1F) software. Site Description - CF2 has operated since May 2017 at the R.J. Cook Agronomy Farm outside of Pullman, Washington, and monitors the conventionally tilled side of a paired-catchment study that is part of the Longterm Agroecosystem Research (LTAR) site common experiment. CF2 is located in “Cook West,” a field that has been in conventional tillage since prior to 1998 and constitutes the Prevalent treatment of the common experiment. In this case, conventional tillage involves chisel plowing after the winter wheat phase of rotations (approximately every 3-4 years) and occasional harrowing. CF2 is paired with the CF1 tower location in “Cook East,” which has been in no-till since 1998 and represents the Alternative treatment of the common experiment. Both fields are planted in the same crops every year. Cook Agronomy Farm is in the high precipitation agroecological zone of the Columbia Plateau’s dryland cropping region. Crop rotations are wheat-based and include winter wheat, spring wheat, chickpea, spring canola, and winter peas. Wheat is the principal cash crop, with other crops grown in rotation for diversity, nutrient, and pest management. Soils are predominantly silt loam texture Mollisols in the Palouse, Thatuna, Naff soil series. The CF2 tower replaced US-RC2, which operated 2012-2016 in a neighboring field that was also conventionally tilled. CF2 and RC2 have distinct footprints, aspects, and soil series compositions.

Huggins, Dave↗
DOE OSTI2020

Influence of Vegetation Canopy Architecture on Dynamics of Spatiotemporal Light Distribution and its Effect on Land-Biosphere-Atmosphere Fluxes of Energy, Carbon, and Water

The dynamic spatiotemporal distribution of light and micro climatic variables (wind speed, atmospheric CO 2 concentration, humidity, pressure, and air temperature) within the plant canopy as determined by the canopy architecture play a critical role in land-biosphere-atmosphere (LBA) photosynthetic carbon uptake, transpiratory water loss and energy partitioning. Vegetation models that include effects of sun and shade flecks, leaf temperature and energy balance partitioning between sensible and latent heat fluxes, and reduced turbulent mixing in dense vegetation canopies can accurately predict canopy performance, particularly its shaded leaves, which can constitute 80% of leaf area and contribute up to 50% of carbon update and 65% of water loss. However, current vegetation models with their bulk characterization of shaded leaves poorly represent the complex three dimensional (3D) canopy architecture are insufficient to capture the spatiotemporal dynamics of shaded leaves. Here, we propose to employ an x-ray computed tomography (XCT) imaging technique in combination with an automated, high throughput, image processing pipeline (3DArch, to be developed) to obtain an explicit 3D triangulated surface mesh of a plant canopy (Fig 1). 3DArch would provide a much needed technological jump in 3D LBA modeling where computer generated artificial 3D models suffer from lack of high resolution detailed canopy architecture parameters. The 3D canopy surface mesh output from 3DArch is coupled with an explicit 3D forward ray tracing model (fastTracer) to obtain a high resolution representation of the dynamic spatiotemporal light environment within the canopy over the course of a day (Fig 1). The results from the light attenuation model are then coupled with a process based multi-scale bio-physical canopy and leaf model of photosynthesis (PlantGro) to get fluxes of carbon, water and energy at the leaf and canopy scale, in addition to estimating whole plant carbon uptake, and water loss. Our proposed model framework would be the first of its kind to provide a breakthrough capability in the field of process-based plant modeling that bridges the key technological gap in shaded leaf modeling through explicit representation of 3D canopy architecture. Computer generated artificial 3D model canopy simulations on grasses show that the canopy light distribution does not follow an exponential behavior as assumed in most 1D vegetation models, but rather exhibits a heterogeneous behavior, with a small but significant portion of interior canopy leaves experiencing peak light levels due to sun flecks while the rest of the canopy receives substantially lower light levels. These results indicate a bias in traditional models that overestimates shaded leaf contribution towards whole canopy photosynthesis by up to 15% and underestimate transpiration by up to 12% with an associated increase in sensible heat fluxes since photosynthesis, transpiration and energy partitioning are tightly coupled processes. However, these test results need to be validated using the 3D architecture obtained from a real plant imaged in 3D using 3DArch, rather than an artificial model plant. Our proposed model framework would allow us to identify inefficiencies in canopy light distribution and prescribe optimal canopy architectures (leaf optical properties, leaf area, leaf angles, row spacing, planting density, etc.) to traditional and genetic engineering based crop breeders, for maximizing target functions such as light use efficiency, water use efficiency, drought tolerance, crop yield, crop resilience etc., with important applications in biofuel crops such as sorghum, and switchgrass. Our model can also be applied for designing optimal canopy architecture that increase surface albedo as a mitigation strategy for global warming and projections of crop yield for future food and water security under climate change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Abiotic and Biotic Damage of Microalgae Generate Different Volatile Organic Compounds (VOCs) for Early Diagnosis of Algal Cultures for Biofuel Production

Open microalgal ponds used in industrial biomass production are susceptible to a number of biotic and abiotic environmental stressors (e.g., grazers, pathogens, pH, temperature, etc.) resulting in pond crashes with high economic costs. Identification of signature chemicals to aid in rapid, non-invasive, and accurate identification of the stressors would facilitate targeted and effective treatment to save the algal crop from a catastrophic crash. Specifically, we were interested in identifying volatile organic compounds (VOCs) that can be used to as an early diagnostic for algal crop damage. Cultures of Microchloropsis gaditana were subjected to two forms of algal crop damage: (1) active grazing by the marine rotifer, Brachionus plicatilis, or (2) repeated freeze–thaw cycles. VOCs emitted above the headspace of these algal cultures were collected using fieldable solid phase microextraction (SPME) fibers. An untargeted analysis and identification of VOCs was conducted using gas chromatography-mass spectrometry (GC-MS). Diagnostic VOCs unique to each algal crop damage mechanism were identified. Active rotifer grazing of M. gaditana was characterized by the appearance of carotenoid degradation products, including β-cyclocitral and various alkenes. Freeze–thaw algae produced a different set of VOCs, including palmitoleic acid. Both rotifer grazing and freeze–thawed algae produced β-ionone as a VOC, possibly suggesting a common stress-induced cellular mechanism. Importantly, these identified VOCs were all absent from healthy algal cultures of M. gaditana. Early detection of biotic or abiotic environmental stressors will facilitate early diagnosis and application of targeted treatments to prevent algal pond crashes. Thus, our work further supports the use of VOCs for monitoring the health of algal ponds to ultimately enhance algal crop yields for production of biofuel.

09 BIOMASS FUELS↗
DOE OSTI2022

Estimation of Corn Latent Heat Flux from High Resolution Thermal Imagery

Crop evapotranspiration (ET), which is directly related to latent heat flux, is also a key indicator in determining the water status of crops. In order to estimate the latent heat flux, two-source energy balance (TSEB) models have been developed for thermal imagery from satellite platforms. However, because of the coarse resolution of thermal sensors on the satellite, distinguishing soil and vegetation is difficult which complicates the calculation process and introduces errors in latent heat estimates. In this research, high-resolution thermal datasets (0.05 m) and corresponding RGB datasets (0.03 m) were used for calculating crop latent heat flux using an adapted TSEB model. The RGB datasets were used for supervised classification of soil and vegetation, and the classification results were then used to filter the thermal mosaics to separate vegetation and soil temperatures. The vegetation temperature is used for calculating latent heat flux and the results are validated against the ground reference measurements of latent heat using a handheld porometer. The objective of this research is to introduce a workflow including an adapted TSEB model which is customized for high resolution thermal images from unmanned aircraft systems (UAS) to estimate the latent heat flux of row crops in agricultural fields. Nine dates of data collection in 2018 and 2020 have been evaluated and the root mean square error (RMSE) varies between 16 to 106 W/m2 depending on the days after planting (DAP) and the time of measurement for each day. The results indicate that the workflow introduced here is able to provide estimates of instantaneous latent heat flux (evapotranspiration) measurements for row crops in agricultural fields which will enable people to make reliable decisions related to irrigation scheduling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Deep Convolutional Neural Networks Exploit High-Spatial- and -Temporal-Resolution Aerial Imagery to Phenotype Key Traits in Miscanthus

Miscanthus is one of the most promising perennial crops for bioenergy production, with high yield potential and a low environmental footprint. The increasing interest in this crop requires accelerated selection and the development of new screening techniques. New analytical methods that are more accurate and less labor-intensive are needed to better characterize the effects of genetics and the environment on key traits under field conditions. We used persistent multispectral and photogrammetric UAV time-series imagery collected 10 times over the season, together with ground-truth data for thousands of Miscanthus genotypes, to determine the flowering time, culm length, and biomass yield traits. We compared the performance of convolutional neural network (CNN) architectures that used image data from single dates (2D-spatial) versus the integration of multiple dates by 3D-spatiotemporal architectures. The ability of UAV-based remote sensing to rapidly and non-destructively assess large-scale genetic variation in flowering time, height, and biomass production was improved through the use of 3D-spatiotemporal CNN architectures versus 2D-spatial CNN architectures. The performance gains of the best 3D-spatiotemporal analyses compared to the best 2D-spatial architectures manifested in up to 23% improvements in R2, 17% reductions in RMSE, and 20% reductions in MAE. The integration of photogrammetric and spectral features with 3D architectures was crucial to the improved assessment of all traits. In conclusion, our findings demonstrate that the integration of high-spatiotemporal-resolution UAV imagery with 3D-CNNs enables more accurate monitoring of the dynamics of key phenological and yield-related crop traits. This is especially valuable in highly productive, perennial grass crops such as Miscanthus, where in-field phenotyping is especially challenging and traditionally limits the rate of crop improvement through breeding.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Data from: Nitrification is a minor source of nitrous oxide (N2O) in an agricultural landscape and declines with increasing management intensity

The long-term contribution of nitrification to nitrous oxide (N2O) emissions from terrestrial ecosystems is poorly known and thus poorly constrained in biogeochemical models. Here, using Bayesian inference to couple 25 years of in situ N2O flux measurements with site-specific Michaelis-Menten kinetics of nitrification-derived N2O, we test the relative importance of nitrification-derived N2O across six cropped and unmanaged ecosystems along a management intensity gradient in the U.S. Midwest. We found that the maximum potential contribution from nitrification to in situ N2O fluxes was 13-17% in a conventionally fertilized annual cropping system, 27-42% in a low-input cover-cropped annual cropping system, and 52-63% in perennial systems including a late successional deciduous forest. Actual values are likely to be less than 10% of these values because of low N2O yields in cultured nitrifiers (typically 0.04 to 8% of NH3 oxidized) and competing sinks for available NH4+ in situ. Most nitrification-derived N2O was produced by ammonia oxidizing bacteria (AOB) rather than archaea (AOA), who appeared responsible for no more than 30% of nitrification-derived N2O production in all but one ecosystem. Although the proportion of nitrification-derived N2O production was lowest in annual cropping systems, these ecosystems nevertheless produced more nitrification-derived N2O (higher Vmax) than perennial and successional ecosystems. We conclude that nitrification is minor relative to other sources of N2O in all ecosystems examined.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Agriculture, bioenergy, and water implications of constrained cereal trade and climate change impacts

International trade increases connections and dependencies between countries, weaving a network of global supply chains. Agricultural commodity trade has implications for crop producers, consumers, crop prices, water and land uses, and other human systems. Interconnections among these systems are not always easy to observe when external impacts penetrate across multiple sectors. To better understand the interactions of non-linear and globally coupled agricultural-bioenergy-water systems under the broader economy, we introduce systematic perturbations in two dimensions, one human (restrictions on agricultural trade) and the other physical (climate impacts on crop yields). We explore these independently and in combination to distinguish the consequences of individual perturbation and interactive effects in long-term projections. We show that most regions experience larger changes in cereal consumption due to cereal import dependency constraints than due to the impacts of climate change on agricultural yields. In the scenario where all regions ensure an import dependency ratio of zero, the global trade of cereals decreases ~50% in 2050 compared to the baseline, with smaller decreases in cereal production and consumption (4%). The changes in trade also impact water and bioenergy: global irrigation water consumption increases 3% and corn ethanol production decreases 7% in 2050. Climate change results in rising domestic prices and declining consumption of cereal crops in general, while the import dependency constraint exacerbates the situation in regions which import more cereals in the baseline. The individual and interactive effects of trade perturbations and climate change vary greatly across regions, which are also affected by the regional ability to increase agricultural production through intensification or extensification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

High-shade dryland agrivoltaic conditions enhanced carbon uptake and water-use efficiency in zucchini ( Cucurbita pepo )

Introduction: The increasing global demand for food and energy is intensifying land-use competition. Agrivoltaic systems are a multifunctional land-use approach that vertically integrates the production of agricultural crops and solar power on the same land area. Most food crops are adapted to full-sun conditions, and the physiological responses of these crops to the novel microclimate under solar panels remain poorly understood. We hypothesized that the microclimate beneath the high-density photovoltaic system would influence carbon uptake, water use, and yield outcomes of zucchini summer squash.Methods: We conducted a field experiment in a hot, semi-arid climate on zucchini (Cucurbita pepo). Plants were grown under an agrivoltaic system with a 75% ground cover ratio (GCR) and in a full-sun control plot, each with two irrigation regimes (100 and 50%). We measured leaf-level photosynthesis, microclimate variables, and fruit yield at plant maturity and throughout the growing season.Results: The agrivoltaic array reduced photosynthetically active radiation (PAR) by ~79%, resulting in a cooler (−1.1 °C), more humid environment with higher soil moisture. These microclimatic conditions enhanced midday photosynthesis and daily cumulative carbon uptake. However, fruit yield was consistently lower under the panels, indicating a shift in carbon allocation toward vegetative growth. Photosynthesis was primarily driven by PAR across treatments, while soil moisture significantly influenced photosynthesis only in the control plots, suggesting water limitation was alleviated under the panels.Discussion: These findings highlight a trade-off between improved physiological performance and reduced yield under high-density agrivoltaics. While the system buffered heat and drought stress and improved overall plant function, excessive shade reduced reproductive output. Optimizing panel density or selecting crops cultivated for non-fruit yields will be essential for balancing food production and energy generation in dryland agrivoltaic settings.

14 SOLAR ENERGY↗