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159 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The Utility of Satellites and Autonomous Remote Sensing Platforms for Monitoring Offshore Aquaculture Farms: A Case Study for Canopy Forming Kelps

The emerging sector of offshore kelp aquaculture represents an opportunity to produce biofuel feedstock to help meet growing energy demand. Giant kelp represents an attractive aquaculture crop due to its rapid growth and production, however precision farming over large scales is required to make this crop economically viable. These demands necessitate high frequency monitoring to ensure outplant success, maximum production, and optimum quality of harvested biomass, while the long distance from shore and large necessary scales of production makes in person monitoring impractical. Remote sensing offers a practical monitoring solution and nascent imaging technologies could be leveraged to provide daily products of the kelp canopy and subsurface structures over unprecedented spatial scales. Here, we evaluate the efficacy of remote sensing from satellites and aerial and underwater autonomous vehicles as potential monitoring platforms for offshore kelp aquaculture farms. Decadal-scale analyses of the Southern California Bight showed that high offshore summertime cloud cover restricts the ability of satellite sensors to provide high frequency direct monitoring of these farms. By contrast, daily monitoring of offshore farms using sensors mounted to aerial and underwater drones seems promising. Small Unoccupied Aircraft Systems (sUAS) carrying lightweight optical sensors can provide estimates of canopy area, density, and tissue nitrogen content on the time and space scales necessary for observing changes in this highly dynamic species. Underwater color imagery can be rapidly classified using deep learning models to identify kelp outplants on a longline farm and high acoustic returns of kelp pneumatocysts from side scan sonar imagery signal an ability to monitor the subsurface development of kelp fronds. Current sensing technologies can be used to develop additional machine learning and spectral algorithms to monitor outplant health and canopy macromolecular content, however future developments in vehicle and infrastructure technologies are necessary to reduce costs and transcend operational limitations for continuous deployment in an offshore setting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Active microbial biomass decreases, but microbial growth potential remains similar across soil depth profiles under deeply-vs. shallow-rooted plants

Climate-smart land management practices that replace shallow-rooted annual crop systems with deeply-rooted perennial plants can contribute to soil carbon sequestration. However, deep soil carbon accrual may be influenced by active microbial biomass and their capacity to assimilate fresh carbon at depth. Incorporating active microbial biomass, dormancy, and growth in microbially-explicit models can improve our ability to predict soil's capacity to store carbon. But, so far, the microbial parameters that are needed for such modeling are poorly constrained, especially in deep soil layers. Here, we used a lab incubation experiment and growth kinetics model to estimate how microbial parameters vary along 240 cm of soil depth in profiles under shallow- (soy) and deeply-rooted (switchgrass) plants 11 years after plant cover conversion. We also assessed resource origin and availability (total organic carbon, 14 C, extractable organic carbon, specific UV absorbance of K 2 SO 4 extractable organic C, total nitrogen, total dissolved nitrogen) along the soil profiles to examine associations between soil chemical and biological parameters. Even though root biomass was greater and rooting depth was deeper under switchgrass than soy, resource availability and microbial growth parameters were generally similar between vegetation types. Instead, depth significantly influenced soil chemical and biological parameters. For example, resource availability and total and relative active microbial biomass decreased with soil depth. Decreases in the relative active microbial biomass coincided with increased lag time (response time to external carbon inputs) along the soil profiles. Even at a depth of 210–240 cm, microbial communities were activated to grow by added resources within a day. Maximum specific growth rate decreased to a depth of 90 cm and then remained consistent in deeper layers. Our findings show that >10 years of vegetation and rooting depth changes may not be long enough to alter microbial growth parameters, and suggest that at least a portion of the microbial community in deep soils can grow rapidly in response to added resources. Our study determined microbial growth parameters that can be used in microbially-explicit models to simulate carbon dynamics in deep soil layers.

14C↗
DOE OSTI2023

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

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

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications

Agriculture is under pressure to provide food for a growing population and the feedstock required to drive the bioeconomy. Methods to breed and genetically modify plants are inadequate to keep pace. When engineering crops, traits are painstakingly introduced into plants one-at-a-time, combine unpredictably, and are continuously expressed. Synthetic biology is changing these paradigms with new genome construction tools, computer aided design (CAD), and artificial intelligence (AI). “Smart plants” contain circuits that respond to environmental change, alter morphology, or respond to threats. Further, the plant and associated microbes (fungi, bacteria, archaea) are now being viewed by genetic engineers as a holistic system. Historically, plant health has been enhanced by many natural and laboratory-evolved soil microbes marketed to enhance growth, provide nutrients, or confer pest/stress resistance. Synthetic biology has expanded the number of species that can be engineered, increased the complexity of engineered functions, controlled environmental release, and assembled stable consortia. New CAD tools will manage genetic engineering projects spanning multiple plant genomes (nucleus, chloroplast, mitochondrion) and the thousands of genomes of associated bacteria/fungi. Here, this review covers advanced genetic engineering techniques to drive the next agricultural revolution, as well as push plant engineering into new realms for manufacturing, infrastructure, sensing, and remediation.

Clauer, Phillip [Massachusetts Inst. of Technology↗
DOE OSTI2021

SLOPE daily and 250 m gross primary productivity (GPP) for the CONUS, 2000-2019, Carbon Monitoring System (CMS)

The SatelLite Only Photosynthesis Estimation (SLOPE) GPP product is a daily, 250 m GPP dataset covering the Contiguous United States (CONUS) from 2000 to present with 1 day latency. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time, and observation-based GPP products. This product has been developed to address this critical gap, leveraging a number of MODIS land and atmosphere products. There are three distinct features of the SLOPE GPP production algorithm. (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRv (SANIRv) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Crop Data Layer (CDL) product to predict dynamic C4 crop fraction. PAR, SANIRv and C4 fraction are used to drive a parsimonious model with only two slope parameters to estimate GPP along with a quantitative uncertainty on a per-pixel and daily basis. The slope GPP product has a R2 = 0.84 and a root-mean-square error (RMSE) of 1.65 gC m-2 d-1, evaluated against from 50 AmeriFlux eddy covariance sites (332 site-years). Archived data from 2000 to 2019 are publicly available at the NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). Data from 2020 are available from the authors upon request. All data are projected in the standard MODIS Land Integerized Sinusoidal tile map projection. Each processing tile is in size of 4800 pixels by 4800 pixels, representing approximately 1200 km by 1200 km land region. In addition to the GPP product, SLOPE PAR, SANIRV, and C4 fraction, along with their uncertainties, are also released.

Jiang, Chongya↗
DOE OSTI2021

Estimating induced land use change emissions for sustainable aviation biofuel pathways

Sustainable aviation fuels (SAFs) are expected to play an essential role in achieving the aviation industries’ goal of carbon-neutral growth. However, producing biomass-based SAFs may induce changes in global land use and the associated carbon stock. The induced land use change (ILUC) emissions, as a part of the full life-cycle emissions for SAF pathways, will affect whether and to what extent SAFs reduce emissions compared with petroleum-based jet fuels. Here, we estimate the ILUC emission intensity for seventeen SAF pathways considered by the International Civil Aviation Organization (ICAO), covering five ASTM-certified technologies, nine biomass-based feedstocks, and four geographical regions. We introduce the SAF pathways into a well-established computable general equilibrium (CGE) model, GTAP-BIO, and its coupled emission accounting model, AEZ-EF, to study economy-wide implications of SAF production and estimate ILUC emissions intensity for each pathway. The estimated SAF ILUC emission intensities, using a 25-year amortization period, range from -58.5 g CO 2 e MJ -1 for the USA miscanthus alcohol (isobutanol)-to-jet (ATJ) pathway to 34.6 g CO 2 e MJ -1 for the Malaysia & Indonesia palm oil Hydrotreated Esters of Fatty Acids (HEFA) pathway. Notably, the vegetable oil pathways tend to have higher ILUC emission intensities due to their linkage to palm expansion and peatland oxidation in Southeast Asia. The cellulosic pathways studied provide negative ILUC emissions, mainly driven by the high carbon sequestrations in crop biomass and soil. Using the core life-cycle emissions established by ICAO, we show that fifteen of the assessed pathways have a lower full life-cycle emission intensity than petroleum-based jet fuels (89 g CO 2 e MJ -1 ), offering promising options to reduce aviation emissions.

09 BIOMASS FUELS↗
DOE OSTI2020

Willow Biomass Crops Are a Carbon Negative or Low-Carbon Feedstock Depending on Prior Land Use and Transportation Distances to End Users

Few life cycle assessments (LCAs) on willow biomass production have investigated the effects of key geographically specific parameters. This study uses a spatial LCA model for willow biomass production to determine spatially explicit greenhouse gas (GHG) emissions and energy return on investment (EROI), including land use conversion from pasture and cropland or grassland. There were negative GHG emissions on 92% of the land identified as suitable for willow biomass production, indicating this system’s potential for climate change mitigation. For willow planted on cropland or pasture, life cycle GHG emissions ranged from −53.2 to −176.9 kg CO2eq Mg-1. When willow was grown on grassland the projected decrease in soil organic carbon resulted in a slightly positive GHG balance. Changes in soil organic carbon (SOC) associated with land use change, transportation distance, and willow yield had the greatest impacts on GHG emissions. Results from the uncertainty analysis exhibited large variations in GHG emissions between counties arising from differences in these parameters. The average EROI across the entire region was 19.2. Willow biomass can be a carbon negative or low-carbon energy source with a high EROI in regions with similar infrastructure, transportation distances, and growing conditions such as soil characteristics, land cover types, and climate.

09 BIOMASS FUELS↗
DOE OSTI2021

A daily, 250 m and real-time gross primary productivity product (2000–present) covering the contiguous United States

Abstract. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time and observation-based GPP products. To address this critical gap, here we leverage a state-of-the-art vegetation index, near-infrared reflectance of vegetation (NIRV), along with accurate photosynthetically active radiation (PAR), to produce a SatelLite Only Photosynthesis Estimation (SLOPE) GPP product for the contiguous United States (CONUS). Compared to existing GPP products, the proposed SLOPE product is advanced in its spatial resolution (250 m versus >500 m), temporal resolution (daily versus 8 d), instantaneity (latency of 1 d versus >2 weeks) and quantitative uncertainty (on a per-pixel and daily basis versus no uncertainty information available). These characteristics are achieved because of several technical innovations employed in this study: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRV (SANIRV) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Cropland Data Layer (CDL) product to predict dynamic C4 crop fraction. Through developing a parsimonious model with only two slope parameters, the proposed SLOPE product explains 85 % of the spatial and temporal variations in GPP acquired from 49 AmeriFlux eddy-covariance sites (324 site years), with a root-mean-square error (RMSE) of 1.63 gC m−2 d−1. The median R2 over C3 and C4 crop sites reaches 0.87 and 0.94, respectively, indicating great potentials for monitoring crops, in particular bioenergy crops, at the field level. With such a satisfactory performance and its distinct characteristics in spatiotemporal resolution and instantaneity, the proposed SLOPE GPP product is promising for biological and environmental research, carbon cycle research, and a broad range of real-time applications at the regional scale. The archived dataset is available at https://doi.org/10.3334/ORNLDAAC/1786 (download page: https://daac.ornl.gov/daacdata/cms/SLOPE_GPP_CONUS/data/, last access: 20 January 2021) (Jiang and Guan, 2020), and the real-time dataset is available upon request.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Belowground plant allocation regulates rice methane emissions from degraded peat soils

Carbon-rich peat soils have been drained and used extensively for agriculture throughout human history, leading to significant losses of their soil carbon. One solution for rewetting degraded peat is wet crop cultivation. Crops such as rice, which can grow in water-saturated conditions, could enable agricultural production to be maintained whilst reducing CO 2 and N 2 O emissions from peat. However, wet rice cultivation can release considerable methane (CH 4 ). Water table and soil management strategies may enhance rice yield and minimize CH 4 emissions, but they also influence plant biomass allocation strategies. It remains unclear how water and soil management influences rice allocation strategies and how changing plant allocation and associated traits, particularly belowground, influence CH 4 -related processes. We examined belowground biomass (BGB), aboveground biomass (AGB), belowground:aboveground ratio (BGB:ABG), and a range of root traits (root length, root diameter, root volume, root area, and specific root length) under different soil and water treatments; and evaluated plant trait linkages to CH 4 . Rice (Oryza sativa L.) was grown for six months in field mesocosms under high (saturated) or low water table treatments, and in either degraded peat soil or degraded peat covered with mineral soil. We found that BGB and BGB:AGB were lowest in water saturated conditions where mineral soil had been added to the peat, and highest in low-water table peat soils. Furthermore, CH 4 and BGB were positively related, with BGB explaining 60% of the variation in CH 4 but only under low water table conditions. Our results suggest that a mix of low water table and mineral soil addition could minimize belowground plant allocation in rice, which could further lower CH 4 likely because root-derived carbon is a key substrate for methanogenesis. Minimizing root allocation, in conjunction with water and soil management, could be explored as a strategy for lowering CH 4 emissions from wet rice cultivation in degraded peatlands.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Estimating Fine-Resolution Shortwave Broadband Albedo of Croplands from Harmonized Landsat and Sentinel-2 Data

Altered surface albedo due to land-cover conversions and management is a significant driver of global climate change. Albedo can be directly measured at ground stations, and remote sensing data can be used to scale-up albedo values to regional and global levels. Some previous studies have retrieved fine-resolution (10–30 m) instantaneous albedo and coarse-resolution (500–1000 m) daily mean albedo from remote sensing data, but they all required the input of Moderate Resolution Imaging Spectroradiometer (MODIS) albedo information at 500-m resolution, and none have assembled both instantaneous and daily albedo based exclusively on fine-resolution satellite data. Here, to address this issue, we compiled 387 instantaneous and 346 daily albedo records using field net radiometer measurements from the bioenergy croplands at the W. K. Kellogg Biological Station in southwest Michigan. We then connected these albedo records with a suite of variables derived from harmonized Landsat and Sentinel-2 data through two machine learning algorithms (random forest regression and extreme gradient boosting) to retrieve clear-sky instantaneous and daily shortwave broadband albedo. The performance statistics indicate reasonable accuracy of model results [root-mean-square error (RMSE)] around or below 0.03 except for snow-covered surfaces), suggesting that the retrieval of both instantaneous and daily albedo based exclusively on fine-resolution satellite data is promising. To facilitate the use of fine-resolution albedo products at the global level, future efforts need to include more albedo records of diverse surface cover types, as well as to accurately model daily albedo for cloudy days to address the “clear-sky bias.”

Harmonized Landsat and Sentinel-2↗
DOE OSTI2020

Draft genome of multiple resistance donor plant Sinapis alba: An insight into SSRs, annotations and phylogenetics

Sinapis alba is a wild member of the Brassicaceae family reported to possess genetic resistance against major biotic and abiotic stresses of oilseed brassicas. However, the resistance nature of S. alba was not exploited generously due to the unavailability of usable genome sequences in public databases. Therefore, the present study was conducted to assemble the first draft genome from raw whole genome shotgun sequences with annotation and develop simple sequence repeat markers for molecular genetics and marker-assisted breeding. Results The raw genome sequences had 96x coverage on the Illumina platform with 170 Gbp data. The developed assembly by SOAPdenovo2 has ~459 Mbp genome size covered in 403,423 contigs with an average size of 1138.04 bp. The assembly was BLASTX with Arabidopsis thaliana which showed 32.9% positive hits between both plants. The top hit species distribution analysis showed the highest similarity with A. thaliana. A total of 809,597 GO level annotations were recorded after BLASTX results, and 34,012 sequences were annotated with different enzyme codes grouped under seven classes. The gene prediction tool AUGUSTUS identified 113,107 probable genes with an average size of 684 bp. The biochemical pathway annotation assigned 16,119 potential genes to 152 KEGG maps and 1751 enzyme codes. The development of potential SSRs from the de-novo assembly yielded 70731 unique primer pairs. Out of 159 randomly selected SSR markers for validation, 149 successfully amplified in S. alba. However, 10 SSR markers did not amplify during the validation experiment. Conclusion The annotated genome assembly with a large number of SSRs was developed in the present study. To the best of our knowledge, this is the first report of S. alba genome assembly development, annotation, and SSRs mining to date. The data presented here will be a very important resource for future crop improvement programs, especially for resistant breeding.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Object-oriented analysis as a foundation for building climate storylines of compounding short-term drought and crop heat stress

Introduction: Crops are vulnerable to precipitation and heat extremes during late spring through summer. Methods: We analyzed for a north-central U.S. region short-term drought and agricultural heat stress during April-May-June-July. We used the 4-km Parameter Elevation Regression on Independent Slopes Model (PRISM) for observations, aggregated to a 25-km grid, and two 25-km Regional Climate Model version 4 (RegCM4) simulns used either GFDL- or MPI-GCM boundary conditions. We chose 1981-2000 as our contemporary time period, and 2041- 2060 as our scenario time period, which used the Representative Concentration Pathway 8.5 emissions scenario. We used object-oriented analysis to identify events of interest in observations and simulations by identifying objects in a space-time domain that meet specified criteria, such as exceeding a heat-stress temperature threshold. The event diagnosis allowed analysis of compound events, occurring when temperature and drought objects overlap. Results: Identified objects yielded events that can undermine agricultural productivity and which are thus relevant to decision makers, making them building blocks for possible climate storylines. The observations and simulations showed similar spatial distributions of event frequencies across the analysis region. However, the simulations attained this distribution by having fewer events that tend to cover larger areas compared to observed events, suggesting that the effective resolution of the simulations was coarser than their 25-km grids. Short-term drought frequency increased and heat-stress frequency decreased in transitioning to the scenario climate. When compounding occurred heat-stress events generally preceded the short-term drought events. The overlapping, compound events tended to be more extreme compared to non-overlapping events of either type. Discussion: The information yielded projected changes in these agriculturally motivated events. One prominent conditional behavior emerging from the work was that a heat-stress event should be a warning to watch for potential drought, as both could compound each other to more intense levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Water vapor transport through bioenergy grass residues and its effects on soil water evaporation

Miscanthus is a productive perennial grass that is suitable as a bioenergy crop in “marginal” lands (e.g., eroded soils) with low water holding capacity. However, little is known about the impact of miscanthus residues on vapor transport and soil water budgets. Laboratory experiments were conducted to measure the vapor conductance through miscanthus residues and its effect on soil water evaporation. The ranges for the length, width, and thickness of residue elements were 0.5–9.0, 0.1–0.5, and 0.1–0.5 cm, respectively. Average residue areal, bulk, and skeletal densities were 0.88 kg m –2 , 24 kg m –3 , and 1006 kg m –3 , respectively, giving a porosity of 0.98 m 3 m –3 . A power function described the decrease in conductance with increasing residue load. The corresponding conductance for a residue load of 0.88 kg m –2 was 1.6 mm s –1 . During the first days of a 60-day drying experiment, cumulative evaporation showed logarithmic decay with increasing residue load. Conversely, cumulative evaporation during the last days of the study showed little difference between treatments. Measurements indicated that there is a “critical” residue load (~1.0 kg m –2 ) beyond which evaporation no longer decreases appreciably when the soil is under the stage 1 evaporation regime. Results suggest that soil water conservation in marginal lands may be accomplished by maintaining moderate amounts of bioenergy grass residue covering the soil. Determining “critical” loads for different residue types is a knowledge gap that merits further research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Tracking cropland transitions: A comparative analysis of U.S. land cover change data

There are a growing number of land cover data available for the conterminous United States, supporting various applications ranging from biofuel regulatory decisions to habitat conservation assessments. These datasets vary in their source information, frequency of data collection and reporting, land class definitions, categorical detail, and spatial scale and time intervals of representation. These differences limit direct comparison, contribute to disagreements among studies, confuse stakeholders, and hamper our ability to confidently report key land cover trends in the U.S. Here we assess changes in cropland derived from the Land Change Monitoring, Assessment, and Projection (LCMAP) dataset from the U.S. Geological Survey and compare them with analyses of three established land cover datasets across the coterminous U.S. from 2008-2017: (1) the National Resources Inventory (NRI), (2) a dataset Lark et al. 2020 derived from the Cropland Data Layer (CDL), and (3) a dataset from Potapov et al. 2022. LCMAP reports more stable cropland and less stable noncropland in all comparisons, likely due to its more expansive definition of cropland which includes managed grasslands (pasture and hay). Despite these differences, net cropland expansion from all four datasets was comparable (5.18-6.33 million acres), although the geographic extent and type of conversion differed. LCMAP projected the largest cropland expansion in the southern Great Plains, whereas other datasets projected the largest expansion in the northwestern and central Midwest. Most of the pixel-level disagreements (86%) between LCMAP and Lark et al. 2020 were due to definitional differences among datasets, whereas the remainder (14%) were from a variety of causes. Cropland expansion in the LCMAP likely reflects conversions of more natural areas, whereas cropland expansion in other data sources also captures conversion of managed pasture to cropland. The particular research question considered (e.g., habitat versus soil carbon) should influence which data source is more appropriate.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Data for Yield from Iowa’s first commercial miscanthus fields: implications of spatial variability for productivity and sustainability beyond research plots

This dataset contains biomass yield measurements and associated vegetation index data collected from commercial Miscanthus × giganteus fields in eastern Iowa during the 2022–2023 growing seasons. The data support the analyses presented in the article: “Yield From Iowa's First Commercial Miscanthus Fields: Implications of Spatial Variability for Productivity and Sustainability Beyond Research Plots.” We collected 105 ground-truth biomass samples from four mature commercial fields (>4 years old) covering 92.81 ha. Samples were taken from 3 m² quadrats that were hand-harvested in alignment with commercial harvest timing. Stem biomass (excluding leaves) was weighed, moisture-corrected, and converted to dry-matter yield expressed in Mg DM ha⁻¹. Sampling locations were selected to capture spatial variability visible in aerial imagery and were recorded using RTK GPS. Each biomass observation was paired with vegetation indices derived from high-resolution PlanetScope satellite imagery (3 m resolution). Images were acquired throughout the growing season, and indices were calculated to evaluate their ability to predict end-of-season biomass yield. Statistical and machine learning approaches were used to identify key predictors, and a linear regression model based on end-of-July Green Normalized Difference Vegetation Index (GNDVI) was developed and evaluated. This repository includes the data used in that modeling workflow. Management practices, economic data, full imagery time series, and additional methodological details are described in the associated publication and are not included here. The dataset consists of three comma-separated value (CSV) files: 1. Combine_Groundtruth_Yield_VI_22_23.csv This file contains ground-truth biomass yield measurements and associated key vegetation index values collected during the 2022 and 2023 growing seasons. Rows: 105 observations Columns: Year — Year of observation (2022 or 2023) Field — Field location identifier Sample_number — Unique sample identifier GNDVI_End_Jul — Green Normalized Difference Vegetation Index calculated at end of July GNDVI_End_Aug — Green Normalized Difference Vegetation Index calculated at end of August NDRE_End_Aug — Normalized Difference Red Edge index calculated at end of August Biomass_Stem_Yield_MgDM/ha — Measured stem biomass yield (megagrams dry matter per hectare) 2. trainData_GNDVI.csv This file contains the subset of observations used to train the predictive relationship between July GNDVI and biomass yield. Rows: 76 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Stem_Yield_MgDM/ha — Observed stem biomass yield (Mg DM ha⁻¹) 3. testData_GNDVI.csv This file contains the test dataset used to evaluate model performance. Rows: 29 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Predicted_Yield_MgDM/ha — Model-predicted stem biomass yield (Mg DM ha⁻¹) Observed_Yield_MgDM/ha — Measured stem biomass yield (Mg DM ha⁻¹)

Potential yield, yield gap, in-field management, y↗