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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Predicting chronic postsurgical pain: current evidence and a novel program to develop predictive biomarker signatures

Chronic pain affects more than 50 million Americans. Treatments remain inadequate, in large part, because the pathophysiological mechanisms underlying the development of chronic pain remain poorly understood. Pain biomarkers could potentially identify and measure biological pathways and phenotypical expressions that are altered by pain, provide insight into biological treatment targets, and help identify at-risk patients who might benefit from early intervention. Biomarkers are used to diagnose, track, and treat other diseases, but no validated clinical biomarkers exist yet for chronic pain. To address this problem, the National Institutes of Health Common Fund launched the Acute to Chronic Pain Signatures (A2CPS) program to evaluate candidate biomarkers, develop them into biosignatures, and discover novel biomarkers for chronification of pain after surgery. This article discusses candidate biomarkers identified by A2CPS for evaluation, including genomic, proteomic, metabolomic, lipidomic, neuroimaging, psychophysical, psychological, and behavioral measures. Acute to Chronic Pain Signatures will provide the most comprehensive investigation of biomarkers for the transition to chronic postsurgical pain undertaken to date. Data and analytic resources generatedby A2CPS will be shared with the scientific community in hopes that other investigators will extract valuable insights beyond A2CPS’s initial findings. This article will review the identified biomarkers and rationale for including them, the current state of the science on biomarkers of the transition from acute to chronic pain, gaps in the literature, and how A2CPS will address these gaps.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Intraspecific Diversity in Thermal Performance Determines Phytoplankton Ecological Niche

ABSTRACT Temperature has a primary influence on phytoplankton physiology and ecology. We grew 12 strains of Gephyrocapsa huxleyi isolated from different‐temperature regions for ~45 generations (2 months) and characterised acclimated thermal response curves across a temperature range. Even with similar temperature optima and overlapping cell size, strain growth rates varied between 0.45 and 1 day −1 . Thermal niche widths varied from 16.7°C to 24.8°C, suggesting that strains use distinct thermal response mechanisms. We investigated the implications of this thermal intraspecific diversity using an ocean ecosystem simulation resolving phytoplankton thermal phenotypes. Model analogues of thermal ‘generalists’ and ‘specialists’ resulted in a distinctive global biogeography of thermal niche widths with a nonlinear latitudinal pattern. We leveraged model output to predict ranges of the 12 lab‐reared strains and demonstrated how this approach could broadly refine geographic range predictions. Our combination of observations and modelled biogeography highlights the capacity of diverse groups to survive temperature shifts.

Krinos, Arianna I. [Department of Biology Woods Ho↗
DOE OSTI2026

The macroevolution of filamentation morphology across the Saccharomycotina yeast subphylum

Saccharomycotina is a subphylum of ascomycete fungi with diverse asexual growth morphologies. Filamentous growth can comprise linear and branched budding cells that do not undergo cell separation, termed pseudohyphae, or tubular filaments with septa that perforate allowing movement of organelles, termed true hyphae. We integrated phenotypic, genomic, metabolic, and environmental data on isolation sources from 1051 species to examine the variation and evolutionary history of filamentation across Saccharomycotina and determine whether these data could predict filamentation types. We found that 63.37% of strains can form filaments; 6.56% true hyphae, 42.40% pseudohyphae, and 14.39% both true hyphae and pseudohyphae. The distributions of species that can produce true hyphae or filament were more strongly correlated with the yeast phylogeny than the distribution of species with pseudohyphae. Ancestral state reconstruction suggested that true hyphal and pseudohyphal morphologies evolved several times, that most yeast ancestors likely produced pseudohyphae or lacked filaments, and that the Saccharomycotina last common ancestor likely produced pseudohyphae but not true hyphae. Machine learning models trained on genomic and metabolic features predicted filament morphologies with ∼70% accuracy. Connecting the evolution of morphologies to their genomic, physiological, and ecological characteristics will enrich our understanding of how the diversity of lifestyles evolved in Saccharomycotina.

Saccharomycotina↗
DOE OSTI2020

Linking crop traits to transcriptome differences in a progeny population of tetraploid potato

Background: Potato is the third most consumed crop in the world. Breeding for traits such as yield, product quality and pathogen resistance are main priorities. Identifying molecular signatures of these and other important traits is important in future breeding efforts. In this study, a progeny population from a cross between a breeding line, SW93–1015, and a cultivar, Désirée, was studied by trait analysis and RNA-seq in order to develop understanding of segregating traits at the molecular level and identify transcripts with expressional correlation to these traits. Transcript markers with predictive value for field performance applicable under controlled environments would be of great value for plant breeding.Results: A total of 34 progeny lines from SW93–1015 and Désirée were phenotyped for 17 different traits in a field in Nordic climate conditions and controlled climate settings. A master transcriptome was constructed with all 34 progeny lines and the parents through a de novo assembly of RNA-seq reads. Gene expression data obtained in a controlled environment from the 34 lines was correlated to traits by different similarity indices, including Pearson and Spearman, as well as DUO, which calculates the co-occurrence between high and low values for gene expression and trait. Our study linked transcripts to traits such as yield, growth rate, high laying tubers, late and tuber blight, tuber greening and early flowering. We found several transcripts associated to late blight resistance and transcripts encoding receptors were associated to Dickeya solani susceptibility. Transcript levels of a UBX-domain protein was negatively associated to yield and a GLABRA2 expression modulator was negatively associated to growth rate.Conclusion: In our study, we identify 100’s of transcripts, putatively linked based on expression with 17 traits of potato, representing both well-known and novel associations. This approach can be used to link the transcriptome to traits. We explore the possibility of associating the level of transcript expression from controlled, optimal environments to traits in a progeny population with different methods introducing the application of DUO for the first time on transcriptome data. We verify the expression pattern for five of the putative transcript markers in another progeny population.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Tangent functional connectomes uncover more unique phenotypic traits

Functional connectomes (FCs) containing pairwise estimations of functional couplings between pairs of brain regions are commonly represented by correlation matrices. As symmetric positive definite matrices, FCs can be transformed via tangent space projections, resulting into tangent-FCs. Tangent-FCs have led to more accurate models predicting brain conditions or aging. Motivated by the fact that tangent-FCs seem to be better biomarkers than FCs, we hypothesized that tangent-FCs have also a higher fingerprint. We explored the effects of six factors: fMRI condition, scan length, parcellation granularity, reference matrix, main-diagonal regularization, and distance metric. Our results showed that identification rates are systematically higher when using tangent-FCs across the “fingerprint gradient” (here including test-retest, monozygotic and dizygotic twins). Highest identification rates were achieved when minimally (0.01) regularizing FCs while performing tangent space projection using Riemann reference matrix and using correlation distance to compare the resulting tangent-FCs. Such configuration was validated in a second dataset (resting-state).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

High-throughput functional trait testing for bacterial pathogens

Functional traits are characteristics that affect the fitness and metabolic function of a microorganism. There is growing interest in using high-throughput methods to characterize bacterial pathogens based on functional virulence traits. Traditional methods that phenotype a single organism for a single virulence trait can be time consuming and labor intensive. Alternatively, machine learning of whole-genome sequences (WGS) has shown some success in predicting virulence. However, relying solely on WGS can miss functional traits, particularly for organisms lacking classical virulence factors. We propose that high-throughput assays for functional virulence trait identification should become a prominent method of characterizing bacterial pathogens on a population scale. This work is critical as we move from compiling lists of bacterial species associated with disease to pathogen-agnostic approaches capable of detecting novel microbes. We discuss six key areas of functional trait testing and how advancing high-throughput methods could provide a greater understanding of pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Multi-strain analysis of Pseudomonas putida reveals the metabolic and genetic diversity of the species

Pseudomonas putida is a gram-negative bacterial species increasingly utilized in biotechnology due to its robust growth, ability to degrade aromatic compounds, solvent tolerance, and genetic tractability. In this study, we report a comprehensive multi-strain analysis of 164 P. putida strains based on the reconstruction of a pan-putida metabolic network and the formulation of strain-specific genome-scale metabolic models (GEMs). We performed whole-genome sequencing and hybrid assembly for 40 strains, contributing a ~8% increase to the available genomic data for P. putida . Furthermore, high-throughput phenotypic profiling using the Biolog phenotype microarray system for 24 strains on 190 unique carbon sources, along with 15 aromatic compounds not present on Biolog plates, yielded 4,920 unique strain-phenotype measurements. These data were leveraged to curate GEMs for 24 representative strains, including a refined model for strain KT2440, which comprised 1,480 genes and 2,191 metabolites, achieving a prediction accuracy of 91.2% in carbon utilization. Systematic comparison of genomes and GEMs revealed both conserved core pathways and significant allelic and functional divergence across strains, highlighting strain-specific variation in aromatic degradation. While pathways for protocatechuate and phenylacetate degradation were widely conserved, metabolic capabilities for compounds such as ferulate, phenol, and cresols varied markedly, suggesting adaptation to distinct ecological niches. Alleleome analysis of enzymes, such as PcaI and PcaJ, revealed distinct, functionally similar clades, indicating possible convergent evolution or horizontal gene transfer. These results provide computable resources and informative models for selecting P. putida strains with desired traits for biomanufacturing and bioremediation and offer insights into the evolution and phylogeny of the P. putida species.

aromatics utilization↗
NASA NTRS2019

Prenatal 2g Exposure Alters Placental Expression of Stress-Related Genes

Extra-terrestrial colonization is of growing interest to space agencies and private entities, emphasizing the importance of research on reproduction and development in the absence of Earth's 1G. Maternal stressors can modify offspring development, exerting significant lifespan and crossgenerational changes through prenatal programming. The space environment is stressful, therefore exposure to altered gravity during pregnancy may impact later life outcomes in offspring. In ground-based studies, we exposed pregnant rats to continuous +G (above Earth gravity), and observed overweight and elevated anxiety in adult male (but not female) offspring, common phenotypes associated with prenatal maternal stress. Here we hypothesize that exposure to increased gravity during pregnancy elicits changes in the expression of stress-related genes in placenta that may mediate emergence of later life outcomes. While the placenta transports maternal factors to the fetus and produces endogenous fetal hormones, stress-induced changes at the placental-uterine interface may also alter communication between mother and fetus, facilitating prenatal transmission of unfavorable later life outcomes and cross-generational epigenetic alterations. Maternal stress elevates maternal glucocorticoids however placental 11b-hydroxysteroid dehydrogenase type 2 (HSD11B2) buffers fetal exposure by converting cortisol/corticosterone into inactive metabolites. Maternal stress during pregnancy down-regulates this enzyme and can induce epigenetic changes in placental and fetal tissues accounting for heightened adult HPA reactivity. Past studies have shown a placenta-specific increase in DNA methyltransferase (DNMT3a) mRNA in stressed mothers, an effect with implications for genome-wide epigenetic changes that may account for diverse phenotypic outcomes following maternal stress. Here we exposed groups of pregnant rats to one of five gravity loads (1, 1.5, 1.75 and 2G) and analyzed placental samples during late gestation. We predicted a systematic dose-response relationship between gravity load and the expression of the HSD11B2 and DNMT3 genes, thereby linking maternal exposure to altered gravity during pregancy with maternal stress.

Kalotia, Simranjit K.↗
DOE OSTI2025

Rational Modulation of Plant Root Development Using Engineered Cytokinin Regulators

Achieving precise control over quantitative developmental phenotypes is a key objective in plant biology. Recent advances in synthetic biology have enabled tools to reprogram entire developmental pathways; however, the complexity of designing synthetic genetic programs and the inherent interactions between various signaling processes remains a critical challenge. Here, we leverage Type-B response regulators to modulate the expression of genes involved in cytokinin-dependent growth and development processes. We rationally engineered these regulators to modulate their transcriptional activity (i.e., repression or activation) and potency while reducing their sensitivity to cytokinin. By localizing the expression of these engineered transcription factors using tissue-specific promoters, we can predictably tune cytokinin-regulated traits. As a proof of principle, we deployed this synthetic system in Arabidopsis thaliana to either decrease or increase the number of lateral roots. The simplicity and modularity of our approach makes it an ideal system for controlling other developmental phenotypes of agronomic interest in plants.

Cell signaling↗
DOE OSTI2024

Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel

Abstract Estimates of plant traits derived from hyperspectral reflectance data have the potential to efficiently substitute for traits, which are time or labor intensive to manually score. Typical workflows for estimating plant traits from hyperspectral reflectance data employ supervised classification models that can require substantial ground truth datasets for training. We explore the potential of an unsupervised approach, autoencoders, to extract meaningful traits from plant hyperspectral reflectance data using measurements of the reflectance of 2151 individual wavelengths of light from the leaves of maize ( Zea mays ) plants harvested from 1658 field plots in a replicated field trial. A subset of autoencoder‐derived variables exhibited significant repeatability, indicating that a substantial proportion of the total variance in these variables was explained by difference between maize genotypes, while other autoencoder variables appear to capture variation resulting from changes in leaf reflectance between different batches of data collection. Several of the repeatable latent variables were significantly correlated with other traits scored from the same maize field experiment, including one autoencoder‐derived latent variable (LV8) that predicted plant chlorophyll content modestly better than a supervised model trained on the same data. In at least one case, genome‐wide association study hits for variation in autoencoder‐derived variables were proximal to genes with known or plausible links to leaf phenotypes expected to alter hyperspectral reflectance. In aggregate, these results suggest that an unsupervised, autoencoder‐based approach can identify meaningful and genetically controlled variation in high‐dimensional, high‐throughput phenotyping data and link identified variables back to known plant traits of interest.

Tross, Michael C.↗
DOE OSTI2023

New insights into the natural history of bronchopulmonary dysplasia from proteomics and multiplexed immunohistochemistry

Bronchopulmonary dysplasia (BPD) is a disease of prematurity related to the arrest of normal lung development. The objective of this study was to better understand how proteome modulation and cell-type shifts are noted in BPD pathology. Pediatric human donors aged 1–3 yr were classified based on history of prematurity and histopathology consistent with “healed” BPD (hBPD, n = 3) and “established” BPD (eBPD, n = 3) compared with respective full-term born (n = 6) age-matched term controls. Proteins were quantified by tandem mass spectroscopy with selected Western blot validations. Multiplexed immunofluorescence (MxIF) microscopy was performed on lung sections to enumerate cell types. Protein abundances and MxIF cell frequencies were compared among groups using ANOVA. Cell type and ontology enrichment were performed using an in-house tool and/or EnrichR. Proteomics detected 5,746 unique proteins, 186 upregulated and 534 downregulated, in eBPD versus control with fewer proteins differentially abundant in hBPD as compared with age-matched term controls. Cell-type enrichment suggested a loss of alveolar type I, alveolar type II, endothelial/capillary, and lymphatics, and an increase in smooth muscle and fibroblasts consistent with MxIF. Histochemistry and Western analysis also supported predictions of upregulated ferroptosis in eBPD versus control. Finally, several extracellular matrix components mapping to angiogenesis signaling pathways were altered in eBPD. Despite clear parsing by protein abundance, comparative MxIF analysis confirms phenotypic variability in BPD. This work provides the first demonstration of tandem mass spectrometry and multiplexed molecular analysis of human lung tissue for critical elucidation of BPD trajectory-defining factors into early childhood.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

High School Citizen Scientists Use AI/ML to Predict Intra-Ocular Pressure From Gene Expression Data for Spaceflown Mice

Artificial Intelligence (AI) and Machine Learning (ML) have increasingly become pivotal in biological and biomedical research, largely due to the culture of open data sharing and its associated benefits. The methodologies inherent in AI/ML are particularly adept at identifying and forecasting biological phenotypes from the vast amounts of data generated by next-generation sequencing technologies. These techniques offer substantial promise for advancing research in space biosciences and for the development of automated systems for monitoring space health. Nevertheless, there are crucial aspects to consider when training, validating, and testing machine learning models in both biological research and clinical contexts. It is essential that Open Science principles, including data sharing and the availability of open-source code, are complemented by high-quality, publicly accessible training resources. These resources should focus on best practices and include modules based on real-world scientific cases and data to ensure that future AI/ML practitioners gain practical experience with genuine problems. Addressing this knowledge gap, we have designed, developed, and delivered both interactive and self-paced training programs for citizen scientists worldwide, enabling them to utilize AI/ML for space biology research. This initiative was made possible through generous funding from a Transformation to Open Science Training grant. The interactive training sessions, conducted this summer, utilized AI/ML techniques to analyze data from the Open Science Data Repository, specifically targeting the effects of spaceflight on ocular structure and function. The dataset OSD-583, from the Rodent Research 9 mission, provides experimental data detailing the ocular responses of mice subjected to a 35-day spaceflight, compared with ground control counterparts. Using OSD-583 as observational data, our summer training participants applied AI/ML methods to predict intraocular pressure from RNA-seq data and identify the genes most predictive of the observed responses. Further analysis through pathway enrichment and gene set enrichment revealed that these genes are involved in molecular and cellular processes contributing to retinal degeneration.

James Casaletto↗
DOE OSTI2023

Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling

Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Cyanobacterial photosystem II reaction center design in tobacco chloroplasts increases biomass in low light

Abstract The D1 polypeptide of the photosystem II (PSII) reaction center complex contains domains that regulate primary photochemical yield and charge recombination rate. Many prokaryotic oxygenic phototrophs express two or more D1 isoforms differentially in response to environmental light needs, a capability absent in flowering plants and algae. We report that tobacco (Nicotiana tabacum) plants carrying the Synechococcus (Synechococcus elongatus PCC 7942) low-light mutation (LL-E130Q) in the D1 polypeptide (NtLL) acquire the cyanobacterial photochemical phenotype: faster photodamage in high light and significantly more charge separations in productive linear electron flow in low light. This flux increase produces 16.5% more (dry) biomass under continuous low-light illumination (100 μE m−2 s−1, 24 h). This gain is offset by the predicted lower photoprotection at high light. By contrast, the introduction of the Synechococcus high-light mutation (HL-A152S) into tobacco D1 (NtHL) has slightly increased photoprotection, achieved by photochemical quenching, but no apparent impact on biomass yield compared to wild type under the tested conditions. The universal design principle of all PSII reaction centers trades off energy conversion for photoprotection in different proportions across all phototrophs and provides a useful guidance for testing in crop plants. The observed biomass advantage under continuous low light can be transferred between evolutionarily isolated lineages to benefit growth under artificial lighting conditions. However, removal of the selective marker gene was essential to observe the growth phenotype, indicating growth penalty imposed by use of the particular spectinomycin-resistance gene.

Zhang, Yuan (ORCID:0000000291785207)↗
DOE OSTI2023

Mass Spectrometry–Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Dynamics of phage-host interactions in Bacteroides fragilis resolved by single-cell transcriptomics

The interactions between lytic phages and their hosts are typically studied in bulk culture, which obscures cell-cell differences in infection susceptibility or expression of protective factors. Here, we use bacterial single-cell RNA sequencing to profile the transcriptomes of ~50,000 cells from cultures of a human pathobiont, Bacteroides fragilis, infected with a lytic bacteriophage. From a single sampling, we quantified the asynchronous progression of phage infection in individual bacterial cells and reconstructed the infection timeline, characterizing both host and phage transcriptomic changes as infection unfolded. Further, we discovered phenotypic subpopulations of bacteria that remained uninfected. Each cell’s vulnerability to phage infection was influenced by expression of multiple genetic loci, most prominently phase-variable capsular polysaccharide (CPS) biosynthesis pathways and an operon predicted to encode fimbrial genes. These findings uncovered genome-wide phase variation and stochasticity that enable bacterial survival and re-growth without acquiring additional mutations. Overall, we establish bacterial single-cell RNA sequencing as a powerful platform for investigating the dynamics of host-phage interactions and revealing the roles of phase variation and stochasticity in bacterial defenses.

Bacteria↗
DOE OSTI2024

Distinct function of Chlamydomonas CTRA-CTR transporters in Cu assimilation and intracellular mobilization

Abstract Successful acclimation to copper (Cu) deficiency involves a fine balance between Cu import and export. In the green alga Chlamydomonas reinhardtii, Cu import is dependent on a transcription factor, Copper Response Regulator 1 (CRR1), responsible for activating genes in Cu-deficient cells. Among CRR1 target genes are two Cu transporters belonging to the CTR/COPT gene family (CTR1 and CTR2) and a related soluble protein (CTR3). The ancestor of these green algal proteins was likely acquired from an ancient chytrid and contained conserved cysteine-rich domains (named the CTR-associated domains, CTRA) that are predicted to be involved in Cu acquisition. We show by reverse genetics that Chlamydomonas CTR1 and CTR2 are canonical Cu importers albeit with distinct affinities, while loss of CTR3 did not result in an observable phenotype under the conditions tested. Mutation of CTR1, but not CTR2, recapitulates the poor growth of crr1 in Cu-deficient medium, consistent with a dominant role for CTR1 in high-affinity Cu(I) uptake. On the other hand, the overaccumulation of Cu(I) (20 times the quota) in zinc (Zn) deficiency depends on CRR1 and both CTR1 and CTR2. CRR1-dependent activation of CTR gene expression needed for Cu over-accumulation can be bypassed by the provision of excess Cu in the growth medium. Over-accumulated Cu is sequestered into the acidocalcisome but can become remobilized by restoring Zn nutrition. This mobilization is also CRR1-dependent, and requires activation of CTR2 expression, again distinguishing CTR2 from CTR1 and consistent with the lower substrate affinity of CTR2. One sentence summary Regulation of Cu uptake and sequestration by members of the CTR family of proteins in Chlamydomonas.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2010

Agricultural Productivity Forecasts for Improved Drought Monitoring

Water stresses on agricultural crops during critical phases of crop phenology (such as grain filling) has higher impact on the eventual yield than at other times of crop growth. Therefore farmers are more concerned about water stresses in the context of crop phenology than the meteorological droughts. However the drought estimates currently produced do not account for the crop phenology. US Department of Agriculture (USDA) and National Oceanic and Atmospheric Administration (NOAA) have developed a drought monitoring decision support tool: The U.S. Drought Monitor, which currently uses meteorological droughts to delineate and categorize drought severity. Output from the Drought Monitor is used by the States to make disaster declarations. More importantly, USDA uses the Drought Monitor to make estimates of crop yield to help the commodities market. Accurate estimation of corn yield is especially critical given the recent trend towards diversion of corn to produce ethanol. Ethanol is fast becoming a standard 10% ethanol additive to petroleum products, the largest traded commodity. Thus the impact of large-scale drought will have dramatic impact on the petroleum prices as well as on food prices. USDA's World Agricultural Outlook Board (WAOB) serves as a focal point for economic intelligence and the commodity outlook for U.S. WAOB depends on Drought Monitor and has emphatically stated that accurate and timely data are needed in operational agrometeorological services to generate reliable projections for agricultural decision makers. Thus, improvements in the prediction of drought will reflect in early and accurate assessment of crop yields, which in turn will improve commodity projections. We have developed a drought assessment tool, which accounts for the water stress in the context of crop phenology. The crop modeling component is done using various crop modules within Decision Support System for Agrotechnology Transfer (DSSAT). DSSAT is an agricultural crop simulation system, which integrates the effects of soil, crop phenotype, weather, and management options. It has been in use for more than 15 years by researchers, growers and has become a de-facto standard in crop modeling communities spanning over 100 countries. The meteorological forcings to DSSAT are provided by NASA s National Land Data Assimilation System (NLDAS) datasets. NLDAS is a framework that incorporates atmospheric forcing and land parameter values along with land surface models to diagnose and predict the state of the land surface.

Limaye, Ashutosh↗