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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Genetic variation in Zea mays influences microbial nitrification and denitrification in conventional agroecosystems

Nitrogenous fertilizers provide a short-lived benefit to crops in agroecosystems, but stimulate nitrification and denitrification, processes that result in nitrate pollution, N 2 O production, and reduced soil fertility. Recent advances in plant microbiome science suggest that genetic variation in plants can modulate the composition and activity of rhizosphere N-cycling microorganisms. Here we attempted to determine whether genetic variation exists in Zea mays for the ability to influence the rhizosphere nitrifier and denitrifier microbiome under “real-world” conventional agricultural conditions. To capture an extensive amount of genetic diversity within maize we grew and sampled the rhizosphere microbiome of a diversity panel of germplasm that included ex-PVP inbreds (Z. mays ssp. mays), ex-PVP hybrids (Z. mays ssp. mays), and teosinte (Z. mays ssp. mexicana and Z. mays ssp. parviglumis). From these samples, we characterized the microbiome, a suite of microbial genes involved in nitrification and denitrification and carried out N-cycling potential assays. Here we are showing that populations/genotypes of a single species can vary in their ecological interaction with denitrifers and nitrifers. Some hybrid and teosinte genotypes supported microbial communities with lower potential nitrification and potential denitrification activity in the rhizosphere, while inbred genotypes stimulated/did not inhibit these N-cycling activities. These potential differences translated to functional differences in N 2 O fluxes, with teosinte plots producing less GHG than maize plots. Taken together, these results suggest that Zea genetic variation can lead to changes in N-cycling processes that result in N leaching and N 2 O production, and thereby are selectable targets for crop improvement. Understanding the underlying genetic variation contributing to belowground microbiome N-cycling into our conventional agricultural system could be useful for sustainability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Latent Dirichlet Allocation modeling of environmental microbiomes

Interactions between stressed organisms and their microbiome environments may provide new routes for understanding and controlling biological systems. However, microbiomes are a form of high-dimensional data, with thousands of taxa present in any given sample, which makes untangling the interaction between an organism and its microbial environment a challenge. Here we apply Latent Dirichlet Allocation (LDA), a technique for language modeling, which decomposes the microbial communities into a set of topics (non-mutually-exclusive sub-communities) that compactly represent the distribution of full communities. LDA provides a lens into the microbiome at broad and fine-grained taxonomic levels, which we show on two datasets. In the first dataset, from the literature, we show how LDA topics succinctly recapitulate many results from a previous study on diseased coral species. We then apply LDA to a new dataset of maize soil microbiomes under drought, and find a large number of significant associations between the microbiome topics and plant traits as well as associations between the microbiome and the experimental factors, e.g. watering level. This yields new information on the plant-microbial interactions in maize and shows that LDA technique is useful for studying the coupling between microbiomes and stressed organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Advancing Tassel Detection and Counting: Annotation and Algorithms

Tassel counts provide valuable information related to flowering and yield prediction in maize, but are expensive and time-consuming to acquire via traditional manual approaches. High-resolution RGB imagery acquired by unmanned aerial vehicles (UAVs), coupled with advanced machine learning approaches, including deep learning (DL), provides a new capability for monitoring flowering. In this article, three state-of-the-art DL techniques, CenterNet based on point annotation, task-aware spatial disentanglement (TSD), and detecting objects with recursive feature pyramids and switchable atrous convolution (DetectoRS) based on bounding box annotation, are modified to improve their performance for this application and evaluated for tassel detection relative to Tasselnetv2+. The dataset for the experiments is comprised of RGB images of maize tassels from plant breeding experiments, which vary in size, complexity, and overlap. Results show that the point annotations are more accurate and simpler to acquire than the bounding boxes, and bounding box-based approaches are more sensitive to the size of the bounding boxes and background than point-based approaches. Overall, CenterNet has high accuracy in comparison to the other techniques, but DetectoRS can better detect early-stage tassels. The results for these experiments were more robust than Tasselnetv2+, which is sensitive to the number of tassels in the image.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Using Reanalysis in Crop Monitoring and Forecasting Systems

Weather observations are essential for crop monitoring and forecasting but they are not always available and in some cases they have limited spatial representativeness. Thus, reanalyses represent an alternative source of information to be explored. In this study, we assess the feasibility of reanalysis-based crop monitoring and forecasting by using the system developed and maintained by the European Commission- Joint Research Centre, its gridded daily meteorological observations, the biased-corrected reanalysis AgMERRA and the ERA-Interim reanalysis. We focus on Europe and on two crops, wheat and maize, in the period 1980-2010 under potential and water-imited conditions. In terms of inter-annual yield correlation at the country scale, the reanalysis-driven systems show a very good performance for both wheat and maize (with correlation values higher than 0.6 in almost all EU28 countries) when compared to the observations-driven system. However, significant yield biases affect both crops. All simulations show similar correlations with respect to the FAO reported yield time series. These findings support the integration of reanalyses in current crop monitoring and forecasting systems and point to the emerging opportunities linked to the coming availability of higher-resolution reanalysis updated at near real time.

Reanalysis↗
NASA NTRS2021

Methodology to Assess the Changing Risk of Yield Failure Due to Heat and Drought Stress Under Climate Change

While the understanding of average impacts of climate change on crop yields is improving, few assessments have quantified expected impacts on yield distributions and the risk of yield failures. Here we present the relative distribution as a method to assess how the risk of yield failure due to heat and drought stress (measured in terms of return period between yields falling 15% below previous five year Olympic average yield) responds to changes of the underlying yield distributions under climate change. Relative distributions are used to capture differences in the entire yield distribution between baseline and climate change scenarios, and to further decompose them into changes in the location and shape of the distribution. The methodology is applied here for the case of rainfed wheat and grain maize across Europe using an ensemble of crop models under three climate change scenarios with simulations conducted at 25 km resolution. Under climate change, maize generally displayed shorter return periods of yield failures (with changes under RCP 4.5 between −0.3 and 0 years compared to the baseline scenario) associated with a shift of the yield distribution towards lower values and changes in shape of the distribution that further reduced the frequency of high yields. This response was prominent in the areas characterized in the baseline scenario by high yields and relatively long return periods of failure. Conversely, for wheat, yield failures were projected to become less frequent under future scenarios (with changes in the return period of −0.1 to +0.4 years under RCP 4.5) and were associated with a shift of the distribution towards higher values and a change in shape increasing the frequency of extreme yields at both ends. Our study offers an approach to quantify the changes in yield distributions that drive crop yield failures. Actual risk assessments additionally require models that capture the variety of drivers determining crop yield variability and scenario climate input data that samples the range of probable climate variation.

Climate risk assessment↗
NASA NTRS2022

Impacts of Climate Change on Global Food Trade Networks

Countries' reliance on global food trade networks implies that regionally different climate change impacts on crop yields will be transmitted across borders. This redistribution constitutes a significant challenge for climate adaptation planning and may affect how countries engage in cooperative action. This paper investigates the long-term (2070–2099) potential impacts of climate change on global food trade networks of three key crops: wheat, rice and maize. We propose a simple network model to project how climate change impacts on crop yields may be translated into changes in trade. Combining trade and climate impact data, our analysis proceeds in three steps. First, we use network community detection to analyse how the concentration of global production in present-day trade communities may become disrupted with climate change impacts. Second, we study how countries may change their network position following climate change impacts. Third, we study the total climate-induced change in production plus import within trade communities. Results indicate that the stability of food trade network structures compared to today differs between crops, and that countries' maize trade is least stable under climate change impacts. Results also project that threats to global food security may depend on production change in a few major global producers, and whether trade communities can balance production and import loss in some vulnerable countries. Overall, our model contributes a baseline analysis of cross-border climate impacts on food trade networks.

climate change↗
NASA NTRSDate not supplied

Climate change signal in global agriculture emerges earlier in new generation of climate and crop models

Potential climate-related impacts on future crop yield are a major societal concern first surveyed in a harmonized multi-model effort in 2014. We report here on new 21st-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean, and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5 to -6% (SSP126) and +1 to -24% (SSP585) — explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9 shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts — when the change signal emerges from the noise — consistently occurs earlier in the new projections for several main producing regions before 2040. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

climate change↗
NASA NTRSDate not supplied

Machine Learning Emulators and Empirical Models Combining Climate and Global Crop Models for Seasonal Agricultural Production

We present results from several connected efforts to apply machine learning methods to estimates of seasonal agricultural production anomalies around the world. First, we apply the XGBoost Random Forest method to fit emulators that mimic global crop models participating in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI). These are the same models used in the agricultural sector simulations of the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). These emulators use 8 climate variables split across 5 sub-seasonal representations of the growing season for each ½ degree grid cell around the world for maize, wheat, rice and soybeans. Emulators are useful for estimating conditions that have not already been simulated by GGCMI (e.g., in a seasonal prediction model) and also to diagnose model differences and capabilities. For example, emulators of the pDSSAT maize model tend to be more reliant on mean temperatures than the LPJmL model, and few models have strong responses to cold extremes. Second, we use a similar XGBoost approach to fit empirical models for national production data for the top 20 producing countries according to the United Nations Food and Agricultural Organization (FAO). Models utilize both climate observations and the GGCM models as predictors, resulting in skillful models for many (but not all) top producing-countries. The patterns of climate and crop model features selected indicate regions and systems that are better or worse simulated by the GGCMs. For example, information in cold extreme predictors is often combined with GGCM output predictors to provide sensitivity that models may underrepresent.

machine learning↗
DOE OSTI2022

Identification of quantitative trait loci for sorghum leaf blight resistance

Sorghum leaf blight and northern corn leaf blight, both caused by Exserohilum turcicum {(Pass.) K. J. Leonard and Suggs [syn. Setosphaeria turcica (Luttr.) K. J. Leonard and Suggs.]}, are major diseases of sorghum [Sorghum bicolor (L.) Moench] and maize (Zea mays L.), respectively. Examining the genetic architecture of resistance in sorghum will lead to a better understanding of the relationship between resistance in sorghum and maize, which can ultimately enhance management options in both crops. In 2018 and 2019, we evaluated two sorghum recombinant inbred line (RIL) populations for resistance to E. turcicum. The BTx623 × IS3620C and BTx623 × SC155 populations consisted of 235 and 81 RILs, respectively. Resistance in both populations was moderately to highly heritable. We identified a total of six quantitative trait loci (QTL) across the two populations. Three QTL with small- to moderate-effect sizes were identified in the BTx623 × IS3620C population. Three QTL, including a large-effect QTL on chromosome three that explained 24% of the variation, were identified in the BTx623 × SC155 population. We compared the identified QTL with the position of northern corn leaf blight candidate genes and found eight candidate resistance gene orthologs that colocalize with the sorghum leaf blight QTL. There were also several nucleotide-binding leucine-rich repeat encoding genes within the candidate intervals. Understanding host resistance in multiple species furthers our understanding of the Exserohilum turcicum patho-system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Diversifying and perennializing plants in agroecosystems alters retention of new C and N from crop residues

Abstract Managing soils to retain new plant inputs is key to moving toward a sustainable and regenerative agriculture. Management practices, like diversifying and perennializing agroecosystems, may affect the decomposer organisms that regulate how new residue is converted to persistent soil organic matter. Here we tested whether 12 years of diversifying/perennializing plants in agroecosystems through extended rotations or grassland restoration would decrease losses of new plant residue inputs and, thus, increase retention of carbon (C) and nitrogen (N) in soil. We tracked dual‐labeled ( 13 C and 15 N), isotopically enriched wheat ( Triticum aestivum ) residue in situ for 2 years as it decomposed in three agroecosystems: maize–soybean (CS) rotation, maize–soybean–wheat plus red clover and cereal rye cover crops (CSW2), and spring fallow management with regeneration of natural grassland species (seven to 10 species; SF). We measured losses of wheat residue (C wheat and N wheat ) in leached soil solution and greenhouse gas fluxes, as well as how much was recovered in microbial biomass and bulk soil at 5‐cm increments down to 20 cm. CSW2 and SF both had unique, significant effects on residue decomposition and retention dynamics that were clear only when using nuanced metrics that able to tease apart subtle differences. For example, SF retained a greater portion of C wheat in 0–5 cm surface soils (155%, p = 0.035) and narrowed the C wheat to N wheat ratio ( p < 0.030) compared to CS. CSW2 increased an index of carbon‐retention efficiency, C wheat retained in the mesocosm divided by total measured, from 0.18 to 0.27 (49%, p = 0.001), compared to CS. Overall, we found that diversifying and extending the duration of living plants in agroecosystems can lead to greater retention of new residue inputs in subtle ways that require further investigation to fully understand.

McDaniel, Marshall D.↗
DOE OSTI2024

Root exudation links root traits to soil functioning in agroecosystems

Root exudation is a key process for plant nutrient acquisition, but the controls on root exudation and its relationship to soil C and N processes in agroecosystems are unclear. We hypothesized that root exudation rates would be related to root morphological traits, N fertilization, and soil moisture. We also anticipated that root exudation would be correlated with bulk soil enzyme activity. Root exudation, root traits, and bulk soil extracellular enzyme activity were assessed in maize (Zea mays L.), soybean (Glycine max (L.) Merr.), biomass sorghum (Sorghum bicolor (L.) Moench), giant miscanthus (Miscanthus × giganteus), and switchgrass (Panicum virgatum L.). Measurements were taken in situ during two growing seasons with contrasting precipitation regimes, and N fertilization rate was varied in sorghum during one year. Specific root exudation (per unit root surface area) was negatively related to root diameter and was generally higher in annuals than perennials. Sorghum N fertilization did not affect root exudation rates, and soil moisture regime had no effect on annual root exudation rates within maize, sorghum, and miscanthus. Specific root exudation was negatively related to bulk soil C- and N-degrading soil enzyme activities. Intrinsic plant characteristics appeared more important than environmental variables in controlling in situ root exudation rates. The relationships between root diameter, root exudation, and soil C and N processes link root morphological traits to soil functions and demonstrate the potential tradeoffs among plant nutrient acquisition strategies in agroecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Energy balance in the DSSAT-CSM-CROPGRO model

One potential way to improve crop growth models is for the models to predict energy balance and evapotranspiration (ET) from first principles, thus serving as a check on “engineered” ET methodology. In this paper, we present new implementations and the results of an energy balance model (EBL) developed by Jagtap and Jones (1989) and then implemented in DSSAT’s CROPGRO (CG-EBL) model by Pickering et al. (1995) as a linked energy balance-photosynthesis model that has not been field-tested until now. The energy balance code computes evapotranspiration and other energy balance components, as well as a canopy air temperature, based on three sources (sunlit leaves, shaded leaves, soil surface). Model performance was evaluated with measured biomass and energy fluxes from two sites in Nebraska, namely, the US-Ne2 irrigated maize-soybean rotation field and the US-Ne3 rainfed maize-soybean rotation field, which are part of the Ameriflux eddy covariance network (htt ps://ameriflux.lbl.gov/sites). After implementing new aerodynamic resistances and the stomatal conductance model of the Ball–Berry–Leuning, crop growth, evapotranspiration and soil temperature were simulated well by the EBL model. The EBL improved ET predictions slightly over the often-used FAO56 method [Penman–Monteith (Allen et al., 1998)] for 4 of the 5 years evaluated for both irrigated and rainfed conditions. Further, a significant improvement was achieved using EBL for the simulation of soil temperature at the various depths compared to STEMP, the original subroutine in DSSAT for simulating soil temperature. Compared to the other available DSSAT methods, the EBL explicitly simulates the impacts of crop morphology, physiology and management on the crop’s environment and energy and mass exchange, which in turn directly affect the water use and irrigation requirements, phenology, photosynthesis, growth, sterility, and yield of the crop.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Root exudate composition from different plant species influences the growth of rhizosphere bacteria

Plant roots release exudates that fuel microbial activities and can structure rhizosphere microbial communities, but how different plant species use their root exudate to potentially select for different soil microbes in the rhizosphere is not well understood. Here, we investigated how root exudate from plants of three diverging lineages, Lactuca sativa (lettuce), Brassica juncea (mustard cabbage), and Zea mays (maize) influence the growth of their own rhizosphere bacteria (host) and those from other plant species (non-host) in growth bioassays. We found that on the community level, lettuce rhizosphere bacteria grew better in non-host exudate, but mustard cabbage and maize rhizosphere bacteria grew similarly well in both host and non-host exudate. However, individual bacteria taxa showed strong preferences for exudate from different plant species. The bacterial growth patterns were independent of C and N quantity, suggesting that certain exometabolic compounds may drive the growth patterns. Our results demonstrate that root exudate from a given plant species have the potential to stimulate or suppress soil bacteria and hint at a mechanism that different plant species use to select for their specific suite of rhizosphere bacteria. Furthermore, these findings contribute to our broader understanding of how root exudate quality could be a mechanism that plants use to select for distinct microbial communities in the rhizosphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Empirical Evidence for the Potential Climate Benefits of Decarbonizing Light Vehicle Transport in the U.S. with Bioenergy from Purpose-Grown Biomass with and without BECCS

Climate mitigation scenarios limiting global temperature increases to 1.5 °C rely on decarbonizing vehicle transport with bioenergy production plus carbon capture and storage (BECCS), but climate impacts for producing different bioenergy feedstocks have not been directly compared experimentally or for ethanol vs electric light-duty vehicles. A field experiment at two Midwest U.S. sites on contrasting soils revealed that feedstock yields of seven potential bioenergy cropping systems varied substantially within sites but little between. Bioenergy produced per hectare reflected yields: miscanthus > poplar > switchgrass > native grasses ≈ maize stover (residue) > restored prairie ≈ early successional. Greenhouse gas emission intensities for ethanol vehicles ranged from 20 to -179 g CO 2 e MJ –1 : maize stover $\gg$ miscanthus ≈ switchgrass ≈ native grasses ≈ poplar > early successional ≥ restored prairie; direct climate benefits ranged from ~80% (stover) to 290% (restored prairie) reductions in CO 2 e compared to petroleum and were similar for electric vehicles. With carbon capture and storage (CCS), reductions in emission intensities ranged from 204% (stover) to 416% (restored prairie) for ethanol vehicles and from 329 to 558% for electric vehicles, declining 27 and 15%, respectively, once soil carbon equilibrates within several decades of establishment. Extrapolation based on expected U.S. transportation energy use suggests that, once CCS potential is maximized with CO 2 pipeline infrastructure, negative emissions from bioenergy with CCS for light-duty electric vehicles could capture >900 Tg CO 2 e year –1 in the U.S. In the future, as other renewable electricity sources become more important, electricity production from biomass would offset less fossil fuel electricity, and the advantage of electric over ethanol vehicles would decrease proportionately.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Impact of drought on global food security by 2050

Drought is a major driver of crop production loss, threatening global food security as the population rises toward 9 billion by 2050. Using a process-based crop model within an Earth system model, we assess drought-related impacts on maize, soybean, rice, and wheat production at global and country levels. We then develop a food insecurity index combining drought impacts with socio-economic factors. Our results indicate that by 2050, globally averaged drought losses for combined maize, soybean, rice, and wheat production are <2%, though soybean losses reach 3.6%. Despite these small average changes at the global scale, 62 countries experience maximum production losses over 10%, and 24 countries over 20%. Our index identifies regions at greatest risk, including large parts of South America, Africa, Eastern Europe, and Southeast Asia. These results illustrate the need for drought adaptation to mitigate future drought impacts on crop production.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Fertilizer management for global ammonia emission reduction

Crop production is a large source of atmospheric ammonia (NH 3 ), which poses risks to air quality, human health and ecosystems. However, estimating global NH 3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy. In this report we develop a machine learning model for generating crop-specific and spatially explicit NH 3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH 3 emissions from rice, wheat and maize fields in 2018 were 4.3 ± 1.0 Tg N yr -1 , lower than previous estimates that did not fully consider fertilizer management practices. Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH3 emissions by about 38% (1.6 ± 0.4 Tg N yr -1 ) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH3 emissions reductions of 47% (44–56%) for rice, 27% (24–28%) for maize and 26% (20–28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH3 emissions could increase by 4.0 ± 2.7% under SSP1–2.6 and 5.5 ± 5.7% under SSP5–8.5 by 2030–2060. However, targeted fertilizer management has the potential to mitigate these increases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Genome-wide discovery of G-quadruplexes in barley

G-quadruplexes (G4s) are four-stranded nucleic acid structures with closely spaced guanine bases forming square planar G-quartets. Aberrant formation of G4 structures has been associated with genomic instability. However, most plant species are lacking comprehensive studies of G4 motifs. In this study, genome-wide identification of G4 motifs in barley was performed, followed by a comparison of genomic distribution and molecular functions to other monocot species, such as wheat, maize, and rice. Similar to the reports on human and some plants like wheat, G4 motifs peaked around the 5' untranslated region (5' UTR), the first coding domain sequence, and the first intron start sites on antisense strands. Our comparative analyses in human, Arabidopsis, maize, rice, and sorghum demonstrated that the peak points could be erroneously merged into a single peak when large window sizes are used. We also showed that the G4 distributions around genic regions are relatively similar in the species studied, except in the case of Arabidopsis. G4 containing genes in monocots showed conserved molecular functions for transcription initiation and hydrolase activity. Additionally, we provided examples of imperfect G4 motifs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Climate impacts on global agriculture emerge earlier in new generation of climate and crop models

Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project’s Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to -6% (SSP126) and from +1% to -24% (SSP585)—explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts consistently occurs earlier in the new projections—before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

Jagermeyr, Jonas↗