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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A meta-analysis of global cropland soil carbon changes due to cover cropping

Including cover crops within agricultural rotations may increase soil organic carbon (SOC). However, contradictory findings generated by on-site experiments make it necessary to perform a comprehensive assessment of interactions between cover crops, environmental and management factors, and changes in SOC. In this study, we collected data from studies that compared agricultural production with and without cover crops, and then analyzed those data using meta-analysis and regression. Our results showed that including cover crops into rotations significantly increased SOC, with an overall mean change of 15.5% (95% confidence interval of 13.8% to 17.3%). Whereas medium-textured soils had highest SOC stocks with (mean = 39 Mg ha-1) and without (mean = 37 Mg ha-1) cover crops, fine-textured soils showed the greatest increase in SOC after the inclusion of cover crops (mean change of 39.5%). Coarse-textured (11.4%) and medium-textured soils (10.3%) had comparatively smaller changes in SOC. Cover crop mixtures resulted in greater increases in SOC compared to mono-species cover crops, and using legumes resulted in greater SOC increases than grasses and broadleaf species. Cover cropping was associated with significant SOC increases in shallow soils (= 30 cm), but not in subsurface soils (> 30 cm). The regression analysis revealed that SOC changes from cover cropping were correlated with improvements in soil quality, specifically decreased runoff and erosion and increased mineralizable carbon, mineralizable nitrogen, and soil nitrogen. Soil carbon change was also affected by annual temperature, number of years after start of cover crop usage, latitude, and initial SOC concentrations. Finally, the mean rate of carbon sequestration from cover cropping across all studies was 0.56 Mg ha-1 yr-1. If 15% of current global cropland were to adopt cover crops, this value would translate to 0.17 Pg of carbon sequestered per year or ~1-2% of current fossil fuels emissions. Altogether, these results indicated that inclusion of cover crops into agricultural rotations can enhance soil carbon concentrations, improve many soil quality parameters, and serve as a potential sink for atmosphere CO2.

Cover crop, soil organic carbon, soil quality, soi↗
DOE OSTI2022

Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China

Croplands play a critical role in regulating the energy and moisture exchanges between the land surface and atmosphere. However, the interactions between cropland and climate are usually poorly represented due to a lack of detailed representation in crop types and field management. Here, we coupled the Noah-MP-Crop model with the state-of-the-art Weather Research and Forecasting (WRF) model to explore and evaluate the crop growth dynamics in response to climate variations across Northeast China. The default parameters of the crop model were not exactly suitable for the agricultural ecosystems in Northeast China. The detailed cropland distribution, and crop phenology parameters including growing degree days (GDD) and planting (harvesting) date were first created using multi-source remote sensing products and reanalysis data, and was then successfully used to simulate the growth and yield for corn and soybean and associated energy exchanges. We also optimized and calibrated other crop parameters using the time-series of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface products. The modified crop model substantially improved the simulation of crop growth, plant physiology, and biomass accumulation for both corn and soybean. Coupling the localized dynamic crop model into the WRF led to considerable decreases in the simulated mean-absolute-errors (MAEs) and biases of the leaf area index, evapotranspiration, and gross primary production compared with the MODIS observed values. Compared with the statistical yield from each province, the modified crop model underestimated the corn yield from 11.1% to 48.6%, whereas overestimated the soybean yield from 16.5% to 162.6%.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data for Attributing Differences of Solar-Induced Chlorophyll Fluorescence (SIF)-Gross Primary Production (GPP) Relationships between Two C4 Crops, Corn and Miscanthus

Information to characterize the solar-induced chlorophyll fluorescence (SIF)-gross primary production (GPP) relationship in C4 cropping systems remains limited. The annual C4 crop corn and perennial C4 crop miscanthus differ in phenology, canopy structure and leaf physiology. Investigating the SIF-GPP relationships in these species could deepen our understanding of SIF-GPP relationships within C4 crops. Using in situ canopy SIF and GPP measurements for both species along with leaf-level measurements, we found considerable differences in the SIF-GPP relationships between corn and miscanthus, with a stronger SIF-GPP relationship and higher slope of SIF-GPP observed in corn compared to miscanthus. These differences were mainly caused by leaf physiology. For miscanthus, high non-photochemical quenching (NPQ) under high light, temperature and water vapor deficit (VPD) conditions caused a large decline of fluorescence yield (ΦF), which further led to a SIF midday depression and weakened the SIF-GPP relationship. The larger slope in corn than miscanthus was mainly due to its higher GPP in mid-summer, largely attributed to the higher leaf photosynthesis and less NPQ. Our results demonstrated variation of the SIF-GPP relationship within C4 crops and highlighted the importance of leaf physiology in determining canopy SIF behaviors and SIF-GPP relationships.

Feedstock Production↗
DOE OSTI2026

Data for Immediate Impacts of Soybean Cover Crop on Bacterial Community Composition and Diversity in Soil Under Long-Term Saccharum Monoculture

Saccharum yield decline results from long-term monoculture practices. Changes in cropping management can improve soil health and productivity. Below-ground bacterial community diversity and composition across soybean (Glycine max (L.) Merr) cover crop, Saccharum monoculture (30+ year) and fallowed soil were determined. Near full length (~1,400 base pairs) of 16S rRNA gene sequences were extracted from the rhizospheres of sugarcane and soybean and fallowed soil were compared. Higher soil bacterial diversity was observed in the soybean cover crop than sugarcane monoculture across all measured indices (observed operationational taxonomic units, Chao1, Shannon, reciprocal Simpson and Jackknife). Acidocateria, Proteobacteria, Bacteroidetes and Planctomycetes were the most abundant bacterial phyla across the treatments. Indicator species analysis identified nine indicator phyla. Planctomycetes, Armatimonadetes and candidate phylum FBP were associated with soybean; Proteobacteria and Firmicutes were linked with sugarcane and Gemmatimonadetes, Nitrospirae, Rokubacteria and unclassified bacteria were associated with fallowed soil. Non-metric multidimensional scaling analysis showed distinct groupings of bacterial operational taxonomic units (97% identity) according to management system (soybean, sugarcane or fallow) indicating compositional differences among treatments. This is confirmed by the results of the multi-response permutation procedures (A = 0.541, p = 0.00045716). No correlation between soil parameters and bacterial community structure was observed according to Mantel test (r = 211865, p = 0.14). Use of soybean cover-crop fostered bacterial diversity and altered community structure. This indicates cover crops could have a restorative effect and potentially promote sustainability in long-term Saccharum production systems.

Field Data↗
DOE OSTI2023

The Impact of Crop Rotation and Spatially Varying Crop Parameters in the E3SM Land Model (ELMv2)

Abstract Earth System Models (ESMs) are increasingly representing agriculture due to its impact on biogeochemical cycles, local and regional climate, and fundamental importance for human society. Realistic large scale simulations may require spatially varying crop parameters that capture crop growth at various scales and among different cultivars, as well as common crop management practices, but their importance is uncertain, and they are often not represented in ESMs. In this study, we examine the impact of using constant versus spatially varying crop parameters using a novel, realistic crop rotation scenario in the Energy Exascale Earth System Model (E3SM) Land Model version 2 (ELMv2). We implemented crop rotation by using ELMv2's dynamic land unit capability, and then calibrated and validated the model against observations collected at three AmeriFlux sites in the US Midwest with corn soybean rotation. The calibrated model closely captured the magnitude and observed seasonality of carbon and energy fluxes across crops and sites. We performed regional simulations for the US Midwest using the calibrated model and found that spatially varying only a few crop parameters across the region, as opposed to using constant parameters, had a large impact, with the carbon fluxes and energy fluxes both varying by up to 40%. These results imply that large scale ESM simulations using spatially invariant crop parameters may result in biased energy and carbon fluxes estimation from agricultural land, and underline the importance of improving human‐earth systems interactions in ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Optimizing pollinator conservation and crop yield among perennial bioenergy crops

In order to both combat the decline of biodiversity and produce food, fuel, and fiber for a growing human population, current agricultural landscapes must transition into diversified, multifunctional systems. Perennial cellulosic biofuel crops have potential to meet both of these challenges, acting as multifunctional systems that can enhance biodiversity. What is not well understood, and what we test here, are the tradeoffs among different perennial crops in their performance as biofuels and in biodiversity conservation. Working in an established bioenergy experiment with four native, perennial, cellulosic biofuel crop varieties - ranging from monoculture to diverse restoration planting - we tested the effect of biofuel crop management on flower communities, pollinator communities, and crop yield. The greatest abundance and diversity of pollinators and flowers were in treatments that were successional (unmanaged), followed by restored prairie (seeded mix of native grasses and forbs), switchgrass, and a mix of native grasses. However, biofuel crop yield was approximately the inverse, with native grasses having the highest yield, followed by switchgrass and prairie, then successional treatments. Restored prairie was the optimal biofuel crop when both pollinator conservation and crop yield are valued similarly. We add to mounting evidence that policy is needed to create sustainable markets that value the multifunctionality of perennial biofuel systems in order to achieve greater ecosystem services from agricultural landscapes.

09 BIOMASS FUELS↗
DOE OSTI2021

Long-term variability of root production in bioenergy crops from ingrowth core measurements

Long-term determination of root biomass production upon land-use conversion to biofuel crops is rare. To assess land-use legacy influences on belowground biomass accumulation, we converted 22-year-old Conservation Reserve Program (CRP) grasslands and 50+-year-old agricultural (AGR) lands to corn (C), switchgrass (Sw) and restored prairie (Pr) biofuel crops. We maintained one CRP grassland as a reference (Ref). We hypothesized that land-use history and crop type have significant effects on root density, with perennial crops on CRP grasslands having a higher root biomass productivity, while corn grown on former agricultural lands produce the lowest root biomass. Methods The ingrowth core method was used to determine in situ ingrowth root biomass, alongside measurements of aboveground net primary productivity (ANPP). Ancillary measurements, including air temperature, growing season length and precipitation were used to examine their influences on root biomass production. Important Findings Root biomass productivity was the highest in unconverted CRP grassland (1716 g m-2 yr-1) and lowest in corn fields (526 g m-2 yr-1). All perennial sites converted from CRP and AGR lands had lower root biomass and ANPP in the first year of planting but peaked in 2011 for switchgrass and a year later for restored prairies. Ecosystem stability was higher in restored prairies (AGR-Pr: 4.3 ± 0.11; CRP-Pr: 4.1 ± 0.10), with all monocultures exhibiting a lower stability. Root biomass production was positively related to ANPP (R2 = 0.40). Overall, attention should be given to root biomass accumulation in large-scale biofuel production as it is a major source of carbon sequestration.

Environmental Sciences & Ecology↗
DOE OSTI2023

A functional trait framework for integrating nitrogen‐fixing cover crops into short‐rotation woody crop systems

Abstract Developing approaches to simultaneously maximize short‐rotation woody crop (SRWC) productivity while minimizing footprints associated with intensive management is imperative to profitable and sustainable bioenergy production systems. Intercropping nitrogen (N)‐fixing cover crops in SRWC systems is an overlooked approach to sustainably intensify SRWC production by increasing N availability using less environmentally costly inputs. Here, we discuss how functional traits (e.g., seasonal activity, lifespan, leaf habit, soil exploration) of cover crops and SRWCs may interact through space and time influencing access to light, water, and nutrients to provide a framework for successful integration of cover crops into SRWCs. Next, we summarize the literature on intercropping forest plantations with N‐fixing cover crops to identity research gaps and outline future research needs and opportunities. And then, using empirical N demand and productivity data from SRWCs and cover crop N inputs from the literature, we illustrate how SRWC leaf habit (conifer evergreens and deciduous hardwoods) would influence successful integration of cover crops and potential N fixation. We estimate that integrating cover crops into SRWCs could supply 27% and 72% of the N demand across a 10‐year rotation for an evergreen and a deciduous hardwood, respectively. These figures suggest these integrated SRWC systems may approach a virtual minimal external N input when other biogeochemical cycles are considered. The guiding principles presented here are grounded in ecological theory and provide a framework for sustainable intensification of forest production.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Data for Clumping Index Estimation With 30°-tilted Cameras in Row Crops: Evaluation of Methods and Segment Size Effects

The clumping index (CI) quantifies the spatial distribution of foliage elements and is essential for accurately estimating the plant area index (PAI), canopy radiative transfer, and photosynthesis. Traditionally, the finite-length averaging method (LX), the gap size distribution method (CC), and a combined approach of CC and LX (CLX) have been applied to instruments like TRAC and digital hemispherical photography to estimate CI. However, a comprehensive evaluation of these methods in row crops remains limited, especially regarding the influence of segment size on CI. Meanwhile, digital cameras offer a cost-effective and user-friendly solution for canopy measurements in row crops, yet their application in this context remains underexplored. In this study, we employed a new approach using a 30°-tilted digital camera to estimate CI in corn and soybean fields, applying the LX, CC, and CLX methods. We systematically assessed the performance of these three methods by combining field measurements in real-world fields with simulations using the LESS 3D radiative transfer model. Our results showed that CLX applied to the whole image and 45° segment offered accurate estimation of CI (bias within ±0.1, RMSE < 0.2) and PAI (bias within ±0.4, RMSE < 1) in real-world fields and LESS simulations. The accuracy of the LX method was highly sensitive to segment size, with the best performance observed at the 15° segment (PAI bias within ±0.4). In contrast, the CC method remained stable across different segment sizes, and its performance was generally comparable to that of LX, except at the 15° segment. Across view zenith angles, CI derived from CC generally showed a continuous increase, while those from LX and CLX followed a rising trend at small zenith angles but began to decline at 68°, likely due to an increasing proportion of no-gap segments. Seasonally, LX tended to show decreasing CI during early growth stages but increased as the canopy matured, whereas CC and CLX showed gradually increasing CI before plateauing at peak PAI. The 30°-tilted camera effectively captured CI variations across different angles and growth stages, making it a practical and robust instrument for row crop canopy structure analysis. Applying these CI methods to digital cameras offers a low-cost and accessible CI estimation alternative, improving canopy structure monitoring accuracy in row crops.

Modeling↗
DOE OSTI2020

Alley cropping affects perennial bioenergy crop root distribution, carbon, and nutrient stocks

This study quantified root biomass distribution; root accrual of C, N, P, and K; and changes in soil organic carbon (SOC) associated with alley-cropped switchgrass (Panicum virgatum L.), prairie cordgrass (Spartina pectinata Bosc ex Link), intermediate wheatgrass [Thinopyrum intermedium (Host) Barkworth and Dewey ‘Rush’], and a native polyculture planted between rows of ‘NM6’ poplar (Populus maximowiczii × P. nigra) and ‘Fish Creek’ willow (Salix purpurea) at two Minnesota sites (Empire and Granada). After 4 yr since establishment, SOC declined at each site but was not influenced by species selection. NM6 poplar–prairie cordgrass systems had among the highest root biomass, C, and nutrient accrual, with up to 16.3 Mg root biomass, 7.0 Mg C, 175 kg N, 31 kg P, and 97 kg K ha -1 . Fine roots were the largest fraction of belowground biomass, although course roots were also a large fraction for poplar and prairie cordgrass. Tree roots extended to 6 m into the crop alley, although 85–89% were within 1 m of tree rows, depending on tree species. Crop fine root biomass was reduced up to 67% at 1 m from tree rows and 20% at 3.5 m and was up to 142% greater in willow than in poplar alleys. Total root C was predominated by poplars at Empire regardless of herbaceous crop type, whereas the proportions of tree and crop root C varied by crop at Granada. These results suggest that prairie cordgrass is well suited to alley cropping and that, due to the competitive ability of poplars, productivity, C sequestration, and nutrient accrual may be greater in willow systems in the long term.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An assessment of the harvesting and fuel performance of a single-pass cut-and-chip harvester in commercial-scale short-rotation poplar crops as influenced by crop and weather conditions

There is a need for data on commercial-scale harvesting operations in hybrid poplar short rotation crops to better understand costs and logistics, especially for modeling large scale biorefineries. An investigation was conducted on the in-field performance of a single-pass cut-and-chip harvester operating in commercial fields for over 370 individual wagon loads representing a range of crop and field conditions. Mean material capacity peaked at 70 Mg h -1 in dry conditions in lower biomass (<60 Mg ha -1 ), but was only 28 Mg h -1 during wet conditions and often higher standing biomass. Wet ground conditions require the harvester to divert additional power to maintaining forward movement, which results in decreased material capacity and increased fuel consumption. Crops with higher standing biomass had taller trees that do not always feed as smoothly into the harvester, slowing forward progress and lowering material capacity. Mean crop specific fuel consumption, L of fuel per Mg of biomass processed, generally decreased as standing biomass increased. When standing biomass was above 40 Mg ha -1 mean crop specific fuel consumption (FCC) was 1.69 L Mg -1 in dry conditions and 3.98 L Mg -1 in wet conditions, but when standing biomass was below 40 Mg ha-1 FCC increased drastically (as high as 5 L Mg -1 ) because the harvester is putting more effort into forward speed instead of material processing. Developing relationships between material capacity and fuel consumption based on standing biomass and ground conditions at representative scales are essential for conducting environmental and economic analyses of these systems.

09 BIOMASS FUELS↗
DOE OSTI2021

Early season prediction of within-field crop yield variability by assimilating CubeSat data into a crop model

Accurate early season predictions of crop yield at the within-field scale can be used to address a range of crop production, management, and precision agricultural challenges. While the remote sensing of within-field insights has been a research goal for many years, it is only recently that observations with the required spatio-temporal resolutions, together with efficient assimilation methods to integrate these into modeling frameworks, have become available to advance yield prediction efforts. Here we explore a yield prediction approach that combines daily high-resolution CubeSat imagery with the APSIM crop model. The approach employs APSIM to train a linear regression that relates simulated yield to simulated leaf area index (LAI). That relationship is then used to identify the optimal regression date at which the LAI provides the best prediction of yield: in this case, approximately 14 weeks prior to harvest. Instead of applying the regression on satellite imagery that is coincident, or closest to, the regression date, our method implements a particle filter that integrates CubeSat-based LAI into APSIM to provide end-of-season high-resolution (3 m) yield maps weeks before the optimal regression date. The approach is demonstrated on a rainfed maize field located in Nebraska, USA, where suitable collections of both imagery and in-situ data were available for assessment. The procedure does not require in-field data to calibrate the regression model, with results showing that even with a single assimilation step, it is possible to provide yield estimates with good accuracy up to 21 days before the optimal regression date. Yield spatial variability was reproduced reasonably well, with a strong correlation to independently collected measurements (R 2 = 0.73 and rRMSE = 12%). When the field averaged yield was compared, our approach reduced yield prediction error from 1 Mg/ha (control case based on a calibrated APSIM model), to 0.5 Mg/ha (using satellite imagery alone), and then to 0.2 Mg/ha (results with assimilation up to three weeks prior to the optimal regression date). Such a capacity to provide spatially explicit yield predictions early in the season has considerable potential to enhance digital agricultural goals and improve end-of-season yield predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A global meta‐analysis of cover crop response on soil carbon storage within a corn production system

Abstract By influencing soil organic carbon (SOC), cover crops play a key role in shaping soil health and hence the system's long‐term sustainability. However, the magnitude by which cover crops impacts SOC depends on multiple factors, including soil type, climate, crop rotation, tillage type, cover crop growth, and years under management. To elucidate how these multiple factors influence the relative impact of cover crops on SOC, we conducted a meta‐analysis on the impacts of cover crops within rotations that included corn ( Zea mays L.) on SOC accumulation. Information on climatic conditions, soil characteristics, management, and cover crop performance was extracted, resulting in 198 paired comparisons from 61 peer‐reviewed studies. Over the course of each study, cover crops on average increased SOC by 7.3% (95% CI, 4.9%–9.6%). Furthermore, the impact of cover crop–induced increases in percent change SOC was evaluated across soil textures, cover crop types, crop rotations, biomass amounts, cover crop durations, tillage practices, and climatic zones. Our results suggest that current cover crop–based corn production systems are sequestering 5.5 million Mg of SOC per year in the United States and have the potential to sequester 175 million Mg SOC per year globally. These findings can be used to improve carbon footprint calculations and develop science‐based policy recommendations. Taken altogether, cover cropping is a promising strategy to sequester atmospheric C and hence make corn production systems more resilient to changing climates.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Quantifying cover crop effects on soil health and productivity

The dataset presented here supports the research paper entitled “A calculator to quantify cover crop effects on soil health and productivity”. Soil health (sometimes used synonymously with soil quality) is a concept that describes soil as a living system to sustain plants, animals, and human. Soil physical, chemical, and biological properties, along with their interactions, are required to quantify soil health. The use of cover crops in agricultural rotations may enhance soil health, yet there has been little progress in understanding how external factors such as climate, soil type, and agronomic practices affect soil and cash crop responses. In response, this dataset compiles measurements from 281 studies and provides an analysis of field-measured changes in 38 soil health indicators due to cover crop usage. Environmental and background indicators were also compiled to assess how climatic and management practices affect soil and cash crop responses to cover crops, with specific categories including climate type (tropical, arid, temperate, and continental), soil texture (coarse, medium, and fine), cover crop type (legume, grass, multi-species mixture, and other), and cash crop type (corn, soybean, wheat, vegetable, corn-soybean rotation, corn-soybean-wheat rotation, and other). An unbalanced analysis of variation was used to determine the hierarchy of most to least important factors that affected responsiveness of each soil health indicator. Based on the hierarchy structure, a soil health calculator was then developed to quantify the response of 13 parameters – erosion, runoff, weed suppression, soil aggregate stability, leaching, infiltration, microbial biomass carbon, soil bulk density, soil organic carbon, soil nitrogen, microbial biomass nitrogen, cash crop yield, and saturated hydraulic conductivity – to cover crops. The presented data in the calculator report the mean change in parameter values based on all combinations of climate, soil texture, cover crop type, and cash crop type.

54 ENVIRONMENTAL SCIENCES↗