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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Long‐term continuous cropping affects ecoenzymatic stoichiometry of microbial nutrient acquisition: a case study from a Chinese Mollisol

Abstract BACKGROUND Soil‐ and plant‐produced extracellular enzymes drive nutrient cycling in soils and are assumed to regulate supply and demand for carbon (C) and nutrients within the soil. Thus, agriculture management decisions that alter the balance of plant and supplemental nutrients should directly alter extracellular enzyme activities (EEAs), and EEA stoichiometry in predictable ways. We used a 12‐year experiment that varyied three major continuous grain crops (wheat, soybean, and maize), each crossed with mineral fertilizer (WCF, SCF and MCF, respectively) or not fertilized (WC, SC and MC, respectively, as controls). In response, we measured the phospholipid fatty acids (PLFAs), EEAs and their stoichiometry to examine the changes to soil microbial nutrient demand under the continuous cropping of crops, which differed with respect to the input of plant litter and fertilizer. RESULTS Fertilizer generally decreased soil microbial biomass and enzyme activity compared to non‐fertilized soil. According to enzyme stoichiometry, microbial nutrient demand was generally C‐ and phosphorus (P)‐limited, but not nitrogen (N)‐limited. However, the degree of microbial resource limitation differed among the three crops. The enzymatic C:N ratio was significantly lower by 13.3% and 26.8%, whereas the enzymatic N:P ratio was significantly higher by 9.9% and 42.4%, in MCF than in WCF and SCF, respectively. The abundances of arbuscular mycorrhizal fungi and aerobic PLFAs were significantly higher in MCF than in WCF and SCF. CONCLUSION These findings are crucial for characterizing enzymatic activities and their stoichiometries that drive microbial metabolism with respect to understanding soil nutrient cycles and environmental conditions and optimizing practices of agricultural management. © 2021 Society of Chemical Industry.

Chen, Xu↗
DOE OSTI2024

Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits

Abstract Plant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency of genetic improvement in future breeding cycles. However, generating the genetic marker data required to apply genomic prediction at the early stages of a breeding program remains costly for many public‐sector breeding programs as well as for many plant breeders operating in developing countries. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties requires plant breeders to make selection decisions without knowing the future environments those crop varieties will encounter in farmers’ fields. Therefore, both lower cost and higher accuracy methods for prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the 21 st century. To address this challenge, we conducted linked yield trials of 752 public maize ( Zea mays ) genotypes in two distinct environments. We developed and trained a phenotypic prediction model to predict yield from manually scored plant traits. The phenotypic prediction approach we employed outperformed genomic prediction in predicting yields in a second environment, with 8.7%–63% higher R 2 and 4%–13% less root mean square error than the genomic prediction. The phenotypic prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Root water uptake of biofuel crops revealed by coupled electrical resistivity and soil water content measurements

Abstract Biofuel crops, including annuals such as maize ( Zea mays L.), soybean [ Glycine max (L.) Merr.], and canola ( Brassica napus L.), as well as high‐biomass perennial grasses such as miscanthus ( Miscanthus × giganteus J.M. Greef & Deuter ex Hodkinson & Renvoiz), are candidates for sustainable alternative energy sources. However, large‐scale conversion of croplands to perennial biofuel crops could have substantial impacts on regional water, nutrient, and C cycles due to the longer growing seasons and differences in rooting systems compared with most annual crops. However, due to the limited tools available to nondestructively study the spatiotemporal patterns of root water uptake in situ at field scales, these differences in crop water use are not well known. Geophysical imaging tools such as electrical resistivity (ER) reveal changes in water content in the soil profile. In this study, we demonstrate the use of a novel coupled hydrogeophysical approach with both time domain reflectometry soil water content and ER measurements to compare root water uptake and soil properties of an annual crop rotation with the perennial grass miscanthus, across three growing seasons (2009–2011) in southwest Michigan, USA. We estimated maximum root depths to be between 1.2 and 2.2 m, with the vertical distribution of roots being notably deeper in 2009 relative to 2010 and 2011, likely due to the drought conditions during that first year. Modeled cumulative ET of both crops was underestimated (2–34%) relative to estimates obtained from soil water drawdown in prior studies but was found to be greater in the perennial grass than the annual crops, despite shallower modeled rooting depths in 2010 and 2011.

Kuhl, Alexandria S.↗
DOE OSTI2023

Amino mapping: possibility to visualize amino-N compounds in the rhizosphere of Zea Mays L.

Abstract Understanding N uptake by plants, the N cycle, and their relationship to soil heterogeneity has generated a great deal of interest in the distribution of amino-N compounds in soil. Visualization of the spatial distribution of amino-N in soil can provide insights into the role of labile N in plant-microbial mechanisms of N acquisition and plant N uptake, but until now, it has remained technically challenging. Here, we describe a novel technique to visualize the amino-N distribution at the root-soil interface. The technique is based on time-lapse amino mapping (TLAM) using membranes saturated with the fluorogenic OPAME reagent (O-phthalaldehyde and β-mercaptoethanol). OPAME in the membrane reacts with organic compounds containing a NH 2 functional group at the membrane-soil interface, generating a fluorescent product visible under UV light and detectable by a digital camera. The TLAM amino-mapping technique was applied to visualize and quantify the concentration of amino-N compounds in the rhizosphere of maize (Zea MaysL.). A ten times greater amino-N concentration was detected in the rhizosphere compared to non-rhizosphere soil. The high content of amino-N was mainly associated with the root tips and was 3 times larger than the average amino-N content at seminal roots. The amino-N rhizosphere was 2 times broader around the root tips than around other parts of the roots. We concluded that TLAM is a promising approach for monitoring the fate of labile N in soils. However, the technique needs to be standardized for different soil types, plant species, and climate conditions to allow wider application.

Agriculture↗
DOE OSTI2021

Subfield crop yields and temporal stability in thousands of US Midwest fields

Understanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed of 5520 yield maps from 768 fields provided by farmers was used to investigate the influence of subfield yield distribution skewness on temporal variability. The data are used to test two intuitive algorithms for mapping stability: one based on standard deviation and the second based on pixel ranking and percentiles. The analysis of yield monitor data indicates that yield distribution is asymmetric, and it tends to be negatively skewed (p < 0.05) for all of the four crops analyzed, meaning that low yielding areas are lower in frequency but cover a larger range of low values. The mean yield difference between the pixels classified as high-and-stable and the pixels classified as low-and-stable was 1.04 Mg ha –1 for maize, 0.39 Mg ha –1 for cotton, 0.34 Mg ha –1 for soybean, and 0.59 Mg ha —1 for wheat. The yield of the unstable zones was similar to the pixels classified as low-and-stable by the standard deviation algorithm, whereas the two-way outlier algorithm did not exhibit this bias. Furthermore, the increase in the number years of yield maps available induced a modest but significant increase in the certainty of stability classifications, and the proportion of unstable pixels increased with the precipitation heterogeneity between the years comprising the yield maps.

59 BASIC BIOLOGICAL SCIENCES↗
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 OSTI2021

Supermolecular structures of recrystallized starches with amylopectin side chains modified by amylosucrase to different chain lengths

Understanding the supermolecular structures of recrystallized starches is imperative to manufacture novel starches with controllable physicochemical properties enabling novel applications. In this study, native amylopectin (AP) from maize was modified to different chain lengths using amylosucrase from Neisseria polysaccharea. Native AP granules showed well-organized lamellar structures with a periodicity of ca. 9.0 nm whereas modified starches formed heterogeneous structures with the average crystallite size ranging from 19.77 to 29.88 nm. Acidic treatments eroded amorphous domains in both native AP granules and modified starch particles, producing nanocrystals composed of double helices. A-type nanocrystals (~40 nm) from native AP granules showed quadrangular shape with an acute angle of ca. 60°, while irregular B-type nanocrystals were observed for the modified starches. Molecular characterizations suggested that the nanocrystals had relatively uniform chain lengths (DP 16.5–25.2), and the helix length (the thickness of nanocrystals) was positively correlated to the thermal stability of starches.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Land use-land cover gradient demonstrates the importance of perennial grasslands with intact soils for building soil carbon in the fertile Mollisols of the North Central US

The impact of land use change and agricultural management on the cycling of soil organic carbon (SOC) is not well understood, limiting our ability to manage for, and accurately model, soil carbon changes at both local and regional scales. To address this issue, here we combined long-term soil incubations with acid-hydrolysis and dry combustion to parse total SOC (C t ) into three operationally defined SOC pools (active, slow, and recalcitrant) from 9 long-term sites with varying land uses on current and former tallgrass prairie soil. Land uses represented a gradient of soil disturbance histories including remnant prairie, restored prairie, grazed pasture, annual crop rotations, and continuous maize. Dry combustion was used to estimate total carbon (C t , physical), while acid hydrolysis of both the active (C a ) and slow (C s ) pools was used to estimate a recalcitrant carbon pool (C r , chemical). Non-linear modeling of CO 2 efflux data from the long-term incubations was then used to estimate C a , and the decomposition rates of both C a and C s (k a and k r , biological). The size of the slow pools C s was then defined mathematically as C t- (C a + C r ). Remnant prairie had the highest C t , while cool-season pasture and a 35-y-old restored prairie had higher C t than the other agricultural systems. All agricultural systems, including pasture, had the highest fraction of C t as C r (~50%), whose mean residence time (MRT) in these soils is ≥500 years (Paul et al., 2001a) demonstrating that this fraction persists, while the more labile fractions were lost over the course of a few months (C a ) to a few decades (C s ) as a result of tillage-intensive agriculture. The two- to four-decade MRT time of C s indicated a pool likely to be more responsive to the 20 to 40 years of land-use practices used at some of the sites. The C s pool was largest in the remnant- and 35-y-old prairies indicating significant C accrual and stabilization compared to the agricultural ecosystems. Interestingly, the remnant prairie maintained the highest C a pool as well, demonstrating the strong connection between the quantity of fresh C inputs and the potential for long-term C stabilization and accrual. The accumulation of C in active (≈labile) pools as a first step toward long-term stabilization highlights the tenuous nature of early carbon gains, which can be quickly lost in response to climate change or poor management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields

Estimating soil organic carbon (SOC) stocks in agricultural fields is essential for environmental and agronomic research, management, and policy. Stratified sampling is a classic strategy for estimating mean soil properties, and has recently been codified in SOC monitoring protocols. However, for the specific task of estimating the SOC stock of an agricultural field, concrete guidance is needed for which covariates to stratify on and how much stratification can improve estimation efficiency. It is also unknown how stratified sampling of SOC stocks compares to modern alternatives, notably doubly balanced sampling. To address these gaps, we collected high-density (average of 7 samples ha -1 ) and deep (average of 75 cm) measurements of SOC stocks at eight commercial fields under maize-soybean production in two US Midwestern states. We combined these measurements with a Bayesian geostatistical model to evaluate stratified and balanced sampling strategies that use a set of readily-available geographic, topographic, spectroscopic, and soil survey data. We examined the number of samples needed to achieve a given level of SOC stock estimation accuracy. While stratified sampling using these variables enables an average sample size reduction of 17% (95% CI, 11% to 23%) compared to simple random sampling, doubly balanced sampling is consistently more efficient, reducing sample sizes by 32% (95% CI, 25% to 37%). The data most important to these efficiency gains are a remotely-sensed SOC index, SSURGO estimates of SOC stocks, and the topographic wetness index. We conclude that in order to meet the urgent challenge of climate change, SOC stocks in agricultural fields could be more efficiently estimated by taking advantage of this readily-available data, especially with doubly balanced sampling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Soil carbon change in intensive agriculture after 25 years of conservation management

Changes in soil organic carbon (SOC) and nitrogen (SON) are strongly affected by land management but few long-term comparative studies have surveyed changes throughout the whole soil profile. We quantified 25-year SOC and SON changes to 1 m in 10 replicate ecosystems at an Upper Midwest, USA site. We compared four annual cropping systems in maize (Zea mays)-soybean (Glycine max)-winter wheat (Triticum aestivum) rotations, each managed differently (Conventional, No-till, Reduced input, and Biologically based); in three managed perennial systems (hybrid Poplar (Populus × euramericana), Alfalfa (Medicago sativa), and Conifer (Pinus spp.); and in three successional systems (Early, Mid- and Late succession undergoing a gradual change in species composition and structure over time). Both Reduced input and Biologically based systems included winter cover crops. Neither SOC nor SON changed significantly in the Conventional or Late successional systems over 25 years. All other systems gained SOC and SON to different degrees. SOC accrual was fastest in the Early successional system (0.8 ± 0.1 Mg C ha –1 y –1 ) followed by Alfalfa and Conifer (avg. 0.7 ± 0.1 Mg C ha –1 y –1 ), Poplar, Reduced input, and Biologically based systems (avg. 0.4 ± 0.1 Mg C ha –1 y –1 ), and Mid-successional and No-till systems (0.3 and 0.2 Mg C ha –1 y –1 , respectively). Over the most recent 12 years, rates of SOC accrual slowed in all systems except Reduced input and Mid-successional. There was no evidence of SOC loss at depth in any system, including No-till. Rates of SON accrual ranged from 64.7 to 0.8 kg N ha –1 y –1 in the order Alfalfa ≥ Early successional > Reduced input and Biologically based ≥ Poplar > No-till and Conifer > Mid-successional systems. Pyrogenic C levels in the Conventional, Early, and Late successional systems were similar despite 17 years of annual burning in the Early successional system (~ 15 % of SOC to 50 cm, on average, and ~40 % of SOC from 50 to 100 cm). Results underscore the importance of cover crops, perennial crops, and no-till options for sequestering whole profile C in intensively managed croplands.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Hydrologic applicability of satellite-based precipitation estimates for irrigation water management in the data-scarce region

Reliable precipitation estimates are crucial for planning and managing water resources, monitoring hydrologic extremes, and fulfilling irrigation water requirements. Accurate precipitation estimates are particularly challenging in complex mountain terrains, where monitoring gauges are often sparsely distributed due to their remote locations, and high installation and long-term operation costs. Recent advances in satellite-based precipitation estimates offer promising opportunities to improve our understanding of hydrologic processes and their applications for irrigation water management. Several datasets are available varying considerably in terms of their data sources, quality control methods, estimation procedure, and spatiotemporal resolutions. Choosing the most suitable dataset for a particular application is a complex task. In this study, we (1) evaluate the performance of six satellite-based precipitation estimates (SPEs): i) CHIRPS v2.0, ii) CMORPH v1.0, iii) ERA5, iv) IMERG v6, v) MSWEP v2.8, and vi) PERSIANN-CDR against the gauge precipitation using continuous statistical and categorical indices, (2) integrate SPEs with a calibrated semi-distributed hydrologic model to predict streamflow, and (3) demonstrate practical implications of improved streamflow prediction for irrigation water management in the central Himalayan region, Nepal. Our results illustrate that satellite-based precipitation estimates have competitive performance in capturing a wide range of rainfall characteristics, with demonstrated variability across river basins and time scales. Further, there are no significant discrepancies observed in satellite-based precipitation estimates for estimating irrigation water requirements for the three major crops (maize, wheat, and paddy) during the cropping period across the selected river basins, showing a greater promise for irrigation water management planning and decision making.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Integrating fine root diameter and watershed mapping to characterize rhizosphere hydrology

Root morphology and soil hydraulic characteristics were integrated using watershed distance mapping to show water distribution and uptake across the plant-soil interface. Poplar (Populus deltoides, P. trichocarpa), maize (Zea mays), juniper (Juniperus virginiana), grape (Vitis rotundifolia) and maple (Acer saccharum) seedlings were grown in sand, after which root diameter and soil water dynamics were assessed via sequential neutron radiography. Three local soil regions (root-soil interface or edge, rhizosphere, bulk soil) were classified based on both radial distance from the root surface and diameter of the nearest root, from which changes in water content and distribution were characterized using digital image processing. Water content dynamics across the rhizosphere showed two different species-independent processes: a consistently elevated water content at the root-soil edge interface which increased with root diameter, and hysteresis as the rhizosphere transitioned to bulk soil (~0.5 cm from the root), independent of root diameter. Water uptake per unit root surface area declined exponentially with root diameter, independent of species. Results highlight the species-independent hydrologic characteristics of the rhizosphere and the potential for evaluating them in a local spatially connected soil context. Avenues for improved integration of soil and root characteristics are discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Combining organic amendments with enhanced rock weathering shifts soil carbon storage in croplands

Enhanced rock weathering (ERW) involves applying crushed silicate minerals to cropland soils to remove carbon dioxide and stabilize the global climate. If practiced widely, ERW has the potential to mitigate climate change and improve soil health and crop productivity. However, most ERW studies emphasize inorganic carbon (IC) chemistry, using model-based estimates and short-term mesocosms. Limited field data exist on how ERW interacts with organic amendments to affect organic carbon (C) cycling in soils. In a three-year field study in conventionally managed, irrigated maize fields, we monitored how key soil variables responded to crushed rock-alone, and in combination with compost and/or biochar. We measured weathering indicators (pH, major cations, and IC contents) and organic fractions, including particulate organic matter (POM), mineral-associated organic matter (MAOM), microbial biomass C, and water-extractable organic C. Rock-alone treatments increased weathering proxies (pH and IC) and showed an increasing trend in POM and MAOM, relative to control. In contrast, combining crushed rock with organic amendments resulted in lower soil organic C and nitrogen (N) concentrations (in both POM and MAOM) compared to organic amendments alone, though IC increased in the rock+compost treatment. Combining rock with both compost and biochar (compost/biochar) significantly lowered MAOM-N compared to compost/biochar alone. Overall, co-applying rock with organic inputs may promote weathering and C accrual but slow the accrual rate of organic C and N relative to organic amendments alone. Quantifying these trade-offs over multiple years and scales is critical to integrating ERW with existing soil health practices and climate mitigation strategies.

Biological and medical sciences↗
DOE OSTI2026

NMR of Fully and Partially 13 C-Enriched Biomass Enhances Pendent Group Structural Characterization

Traditional solution-state NMR experiments may either fail or yield unsatisfactory results when employing fully- 13 C-labeled biomass due to complications arising from 13 C– 13 C coupling. Constant-time analogs of HSQC experiments mitigate such issues and deliver enhanced sensitivity. A rarely reported CT-HSQC-TOCSY experiment allows the proton coupling network to deliver much of the same value as the parent experiment on unlabeled or 10–15%- 13 C-labeled biomass polymers but with enhanced sensitivity. In the absence of a viable HMBC analog for long-range correlations, a relayed C–C experiment, i.e., via directly bonded 13 C-labeled networks, enables the reliable assignment of coupled carbons, with the added advantage of correlating the more elusive quaternaries. A C–C-FLOPSY experiment takes advantage of fully- 13 C-labeled materials for mapping extensive carbon networks in the complex polymer mixtures inherent in biomass. Various pendent groups (tricin units, cis- and trans-p-coumarates, and p -hydroxybenzoates) that adorn lignins, and the cis- and trans-ferulates on arabinoxylan polysaccharides, are exquisitely revealed in spectra from isolated lignins or whole-cell-wall materials from maize, sorghum, and poplar.

biopolymers↗
DOE OSTI2020

Lignin Monomers from beyond the Canonical Monolignol Biosynthetic Pathway: Another Brick in the Wall

Lignin is conventionally defined as being formed by the oxidative polymerization of three main monolignols, p-coumaryl alcohol, coniferyl alcohol, and sinapyl alcohol, that are derived from the general phenylpropanoid biosynthetic pathway. Many other phenolic compounds that are also derived from the phenylpropanoid pathway are also known to perform as genuine lignin monomers in many plants, as is the case of the monolignol ester conjugates, phenolic compounds arising from the truncated biosynthesis of monolignols, or ferulate esters. Recent investigations, however, have indicated that phenolic compounds arising from beyond the canonical phenylpropanoid pathway, namely flavonoids, hydroxystilbenes, and hydroxycinnamic amides, may also behave as authentic lignin monomers and are incorporated into the lignin in some plants, further challenging the traditional definition of lignin. This is the case of the flavone tricin that is incorporated into the lignin of grasses and other monocots, the hydroxystilbene piceatannol (together with resveratrol and isorhapontigenin, at lower levels) that has been found in the lignins of palm fruit shells, their respective O-glucosides (astringin, piceid, and isorhapontin) that are present in the lignin of Norway spruce bark, or the ferulic amides feruloyltyramine, incorporated into the lignin of tobacco and potato tubers, and diferuloylputrescine, which appears to be incorporated into maize kernel lignin. These valuable compounds are potentially available in high amounts and at low cost and may be obtained from the waste products from the processing of agricultural or forest biomass.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Hot or Not? An Evaluation of Methods for Identifying Hot Moments of Nitrous Oxide Emissions From Soils

Abstract Effectively quantifying hot moments of nitrous oxide (N 2 O) emissions from agricultural soils is critical for managing this potent greenhouse gas. However, we are challenged by a lack of standard approaches for identifying hot moments, including (a) determining thresholds above which emissions are considered hot moments, and (b) considering seasonal variation in the magnitude and frequency distribution of net N 2 O fluxes. We used one year of hourly N 2 O flux measurements from 16 autochambers that varied in flux magnitude and frequency distribution in a conventionally tilled maize field in central Illinois, USA, to compare three approaches to identify hot moment thresholds: standard deviations (SD) above the mean, 1.5x the interquartile range (IQR), and isolation forest (IF) identification of anomalous values. We also compared these approaches on seasonally subdivided data (early, late, and non‐growing seasons) versus the whole year. Our analyses revealed that 1.5x IQR method best identified N 2 O hot moments. In contrast, using 2 or 4 SD both yielded hot moment threshold values too high, and IF yielded threshold values too low, leading to missed N 2 O hot moments or low net N 2 O fluxes mischaracterized as hot moments, respectively. Furthermore, seasonally subdividing the data set not only facilitated identification of smaller hot moments in the late‐ and non‐growing seasons when N 2 O hot moments were generally smaller but it also increased hot moment threshold values in the early growing season when N 2 O hot moments were larger. Consequently, of the methods evaluated here, we recommend using the 1.5x IQR method on whole year data sets to identify N 2 O hot moments.

Stuchiner, Emily R. [Institute for Sustainability,↗
DOE OSTI2023

Genome analyses reveal population structure and a purple stigma color gene candidate in finger millet

Finger millet is a key food security crop widely grown in eastern Africa, India and Nepal. Long considered a ‘poor man’s crop’, finger millet has regained attention over the past decade for its climate resilience and the nutritional qualities of its grain. To bring finger millet breeding into the 21 st century, here we present the assembly and annotation of a chromosome-scale reference genome. We show that this ~1.3 million years old allotetraploid has a high level of homoeologous gene retention and lacks subgenome dominance. Population structure is mainly driven by the differential presence of large wild segments in the pericentromeric regions of several chromosomes. Trait mapping, followed by variant analysis of gene candidates, reveals that loss of purple coloration of anthers and stigma is associated with loss-of-function mutations in the finger millet orthologs of the maize R1/B1 and Arabidopsis GL3/EGL3 anthocyanin regulatory genes. Proanthocyanidin production in seed is not affected by these gene knockouts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Microbially mediated mechanisms underlie soil carbon accrual by conservation agriculture under decade-long warming

Increasing soil organic carbon (SOC) in croplands by switching from conventional to conservation management may be hampered by stimulated microbial decomposition under warming. Here, we test the interactive effects of agricultural management and warming on SOC persistence and underlying microbial mechanisms in a decade-long controlled experiment on a wheat-maize cropping system. Warming increased SOC content and accelerated fungal community temporal turnover under conservation agriculture (no tillage, chopped crop residue), but not under conventional agriculture (annual tillage, crop residue removed). Microbial carbon use efficiency (CUE) and growth increased linearly over time, with stronger positive warming effects after 5 years under conservation agriculture. According to structural equation models, these increases arose from greater carbon inputs from the crops, which indirectly controlled microbial CUE via changes in fungal communities. As a result, fungal necromass increased from 28 to 53%, emerging as the strongest predictor of SOC content. Collectively, our results demonstrate how management and climatic factors can interact to alter microbial community composition, physiology and functions and, in turn, SOC formation and accrual in croplands.

59 BASIC BIOLOGICAL SCIENCES↗