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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Development and characterization of a wild emmer wheat backcross introgression population for hard winter wheat improvement

Abstract Wild emmer wheat (Triticum turgidumsubsp.dicoccoides) is the tetraploid progenitor of hexaploid bread wheat (Triticum aestivumL.) and is known to be a valuable source of genetic variation for wheat improvement. However, direct evaluation of wild emmer diversity for agronomic potential has limited value unless performed in the backgrounds of adapted cultivars. Here, we present a genetic characterization of a population of 1601 backcross recombinant inbred lines, with an average genome composition of 75% bread wheat and 25% wild emmer. Low‐coverage whole‐genome sequencing allowed introgressions and aneuploidies to be identified at a relatively low cost per sample. We identified a relatively large proportion of small introgressions (median length 38 Mb), and we found introgressions to be distributed across all chromosomes. Approximately 44% of genotyped progeny carried at least one aneuploidy, with monosomies being by far the most common. This population, which we have denoted as the Great Plains Wild Emmer/Hard Winter Wheat introgression population (GPWEW‐IP), is, to our knowledge, the first introgression population developed through the direct hybridization of wild emmer wheat and US‐adapted hard winter wheat. We believe that this population represents a valuable resource for wheat breeders and will accelerate the discovery and integration of useful variation from wild emmer wheat.

Genetics & Heredity↗
NASA NTRS2016

Incorporating Yearly Derived Winter Wheat Maps Into Winter Wheat Yield Forecasting Model

Wheat is one of the most important cereal crops in the world. Timely and accurate forecast of wheat yield and production at global scale is vital in implementing food security policy. Becker-Reshef et al. (2010) developed a generalized empirical model for forecasting winter wheat production using remote sensing data and official statistics. This model was implemented using static wheat maps. In this paper, we analyze the impact of incorporating yearly wheat masks into the forecasting model. We propose a new approach of producing in season winter wheat maps exploiting satellite data and official statistics on crop area only. Validation on independent data showed that the proposed approach reached 6% to 23% of omission error and 10% to 16% of commission error when mapping winter wheat 2-3 months before harvest. In general, we found a limited impact of using yearly winter wheat masks over a static mask for the study regions.

MODIS↗
NASA NTRS1986

Testing and validating the CERES-wheat (Crop Estimation through Resource and Environment Synthesis-wheat) model in diverse environments

CERES-Wheat is a computer simulation model of the growth, development, and yield of spring and winter wheat. It was designed to be used in any location throughout the world where wheat can be grown. The model is written in Fortran 77, operates on a daily time stop, and runs on a range of computer systems from microcomputers to mainframes. Two versions of the model were developed: one, CERES-Wheat, assumes nitrogen to be nonlimiting; in the other, CERES-Wheat-N, the effects of nitrogen deficiency are simulated. The report provides the comparisons of simulations and measurements of about 350 wheat data sets collected from throughout the world.

Otter-Nacke, S.↗
NASA NTRS2017

The International Heat Stress Genotype Experiment for Modeling Wheat Response to Heat: Field Experiments and AgMIP-Wheat Multi-Model Simulations

The data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.

Wheat; Field Experimental Data; Heat Stress; Simul↗
NASA NTRS2023

AgMIP-Wheat Multi-Model Simulations on Climate Change Impact and Adaptation for Global Wheat

The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (http://doi.org/10.5281/zenodo.4027033). Two scientific publications have been published based on some of these data here.

Agricultural Model Intercomparison and Improvement↗
NASA NTRS1976

Wheat signature modeling and analysis for improved training statistics: Supplement. Simulated LANDSAT wheat radiances and radiance components

Simulated scanner system data values generated in support of LACIE (Large Area Crop Inventory Experiment) research and development efforts are presented. Synthetic inband (LANDSAT) wheat radiances and radiance components were computed and are presented for various wheat canopy and atmospheric conditions and scanner view geometries. Values include: (1) inband bidirectional reflectances for seven stages of wheat crop growth; (2) inband atmospheric features; and (3) inband radiances corresponding to the various combinations of wheat canopy and atmospheric conditions. Analyses of these data values are presented in the main report.

Malila, W. A.↗
NASA NTRS1979

CCEA second-generation wheat yield model for hard red wheat in North Dakota

The problem of estimating wheat yields for years with unusual planting dates and/or unusual phenological development is investigated. A yield model developed for hard red spring wheat in North Dakota using historical yields for crop reporting districts in conjunction with meteorological predictor variables based on weekly data is assessed.

Leduc, S. K.↗
NASA NTRS1981

Winter wheat: A model for the simulation of growth and yield in winter wheat

The basic ideas and constructs for a general physical/physiological process level winter wheat simulation model are documented. It is a materials balance model which calculates daily increments of photosynthate production and respiratory losses in the crop canopy. The partitioning of the resulting dry matter to the active growing tissues in the plant each day, transpiration and the uptake of nitrogen from the soil profile are simulated. It incorporates the RHIZOS model which simulates, in two dimensions, the movement of water, roots, and soluble nutrients through the soil profile. It records the time of initiation of each of the plant organs. These phenological events are calculated from temperature functions with delays resulting from physiological stress. Stress is defined mathematically as an imbalance in the metabolite supply; demand ratio. Physiological stress is also the basis for the calculation of rates of tiller and floret abortion. Thus, tillering and head differentiation are modeled as the resulants of the two processes, morphogenesis and abortion, which may be occurring simulaneously.

Baker, D. N.↗
NASA NTRS2020

Impacts of 1.5 °C and 2.0 °C global warming above pre-industrial on potential winter wheat production of China

Keeping global temperatures below 2.0 °C above pre-industrial condition and pursuing efforts toward the more ambitious 1.5 °C goal in the late 21st century was the main target from the Paris Agreement in 2015. Here we assessed the likely challenges for the China’s winter wheat production under 1.5 °C and 2.0 °C increase of global temperature, with four wheat crop models (CERES-Wheat, Nwheat, WheatGrow, and APSIM-Wheat) and the latest climate projections from the Half a degree Additional warming, Prognosis and Projected Impacts project (HAPPI). Instead of using average “winter type” wheat cultivar, and same management and soil inputs for whole region, location-specific winter wheat cultivars with local agronomic information were calibrated for each of the representative wheat growing area of China, allowing a better spatial agronomic representation of the whole wheat planting area. The mean growing season temperature (GST) during the winter wheat vegetative stage was projected to increase by 0.6 to 1.4 °C for the 1.5 °C scenario, and 0.9 to 1.8 °C for the 2.0 °C scenario, while during the reproductive stage was decreased between 0 and 0.9 °C for the 1.5 °C scenario and -0.3 and 1.1 °C for the 2.0 °C scenario. Growing season duration (GSD) for the whole period was shortened by 6 to 15 days for the 1.5 °C scenario and 8 to 18 days for the 2.0 °C scenario, as a result of higher GST under global warming. Increase in GST and decrease in GSD was more obvious in the Southwest Subregion (SWS) than subregions in the north. The shortening GSD for the whole wheat growth period was mostly from the shortening vegetative period, as no appreciable difference in number of days from anthesis to maturity was found for the whole regions. Although there is variability among models, the indication is that wheat yields were projected to increase in the North Subregion (NS), the Huang-Huai Subregion (HHS), and the Middle-lower Researches of Yangzi River Subregion (MYS), but to decrease in the SWS under two warming scenarios. The effects of elevated CO2 concentration were mostly beneficial and tended to offset the negative impacts of increasing temperature at both global warming scenarios, with a rate of 7-14% yield increase per 100-ppm, except for locations with GST of baseline higher than 11 °C. Aggregating to regional wheat production, the total winter wheat production of China was projected to increase by 2.8% (1.6% to 3.0%, 25th percentile to 75th percentile) and 8.3% (7.0% to 9.6%, 25th percentile to 75th percentile) under 1.5 °C and 2.0 °C scenarios, and most of increase was observed in the north subregions due to the largest wheat planting area. Our results will lay the foundation for developing adaptation strategies to future climate change to ensure China and global wheat supply and food security.

Winter wheat↗
DOE OSTI2023

Recognition of glycoside hydrolase 12 proteins by the immune receptor RXEG1 confers Fusarium head blight resistance in wheat

Summary Fusarium head blight (FHB), caused by Fusarium graminearum , is a devastating disease in wheat ( Triticum aestivum ) that results in substantial yield losses and mycotoxin contamination. Reliable genetic resources for FHB resistance in wheat are lacking. In this study, we characterized glycoside hydrolase 12 (GH12) family proteins secreted by F. graminearum . We established that two GH12 proteins, Fg05851 and Fg11037, have functionally redundant roles in F. graminearum colonization of wheat. Furthermore, we determined that the GH12 proteins Fg05851 and Fg11037 are recognized by the leucine‐rich‐repeat receptor‐like protein RXEG1 in the dicot Nicotiana benthamiana . Heterologous expression of RXEG1 conferred wheat responsiveness to Fg05851 and Fg11037, enhanced wheat resistance to F. graminearum and reduced levels of the mycotoxin deoxynivalenol in wheat grains in an Fg05851/Fg11037‐dependent manner. In the RXEG1 transgenic lines, genes related to pattern‐triggered plant immunity, salicylic acid, jasmonic acid, and anti‐oxidative homeostasis signalling pathways were upregulated during F. graminearum infection. However, the expression of these genes was not significantly changed during infection by the deletion mutant ΔFg05851/Fg11037 , suggesting that the recognition of Fg05851/Fg11037 by RXEG1 triggered plant resistance against FHB. Moreover, introducing RXEG1 into three other different wheat cultivars via crossing also conferred resistance to F. graminearum . Expression of RXEG1 did not have obvious deleterious effects on plant growth and development in wheat. Our study reveals that N. benthamiana RXEG1 remains effective when transferred into wheat, a monocot, which in turn suggests that engineering wheat with interfamily plant immune receptor transgenes is a viable strategy for increasing resistance to FHB.

Wang, Zongkuan↗
NASA NTRS1997

Very high CO2 reduces photosynthesis, dark respiration and yield in wheat

Although terrestrial CO2 concentrations, [CO2] are not expected to reach 1000 micromoles mol-1 for many decades, CO2 levels in closed systems such as growth chambers and glasshouses, can easily exceed this concentration. CO2 levels in life support systems in space can exceed 10000 micromoles mol-1 (1%). Here we studied the effect of six CO2 concentrations, from ambient up to 10000 micromoles mol-1, on seed yield, growth and gas exchange of two wheat cultivars (USU-Apogee and Veery-l0). Elevating [CO2] from 350 to 1000 micromoles mol-1 increased seed yield (by 33%), vegetative biomass (by 25%) and number of heads m-2 (by 34%) of wheat plants. Elevation of [CO2] from 1000 to 10000 micromoles mol-1 decreased seed yield (by 37%), harvest index (by 14%), mass per seed (by 9%) and number of seeds per head (by 29%). This very high [CO2] had a negligible, non-significant effect on vegetative biomass, number of heads m-2 and seed mass per head. A sharp decrease in seed yield, harvest index and seeds per head occurred by elevating [CO2] from 1000 to 2600 micromoles mol-1. Further elevation of [CO2] from 2600 to 10000 micromoles mol-1 caused a further but smaller decrease. The effect of CO2 on both wheat cultivars was similar for all growth parameters. Similarly there were no differences in the response to high [CO2] between wheat grown hydroponically in growth chambers under fluorescent lights and those grown in soilless media in a glasshouse under sunlight and high pressure sodium lamps. There was no correlation between high [CO2] and ethylene production by flag leaves or by wheat heads. Therefore, the reduction in seed set in wheat plants is not mediated by ethylene. The photosynthetic rate of whole wheat plants was 8% lower and dark respiration of the wheat heads 25% lower when exposed to 2600 micromoles mol-1 CO2 compared to ambient [CO2]. It is concluded that the reduction in the seed set can be mainly explained by the reduction in the dark respiration in wheat heads, when most of the respiration is functional and is needed for seed development.

NASA Discipline Life Support Systems↗
NASA NTRS2015

Improving the Timeliness of Winter Wheat Production Forecast in the United States of America, Ukraine and China Using MODIS Data and NCAR Growing Degree Day Information

Wheat is themost important cereal crop traded on international markets and winter wheat constitutes approximately 80% of global wheat production. Thus, accurate and timely production forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Becker-Reshef et al. (2010) developed an empirical generalized model for forecasting winter wheat production. Their approach combined BRDF-corrected daily surface reflectance from Moderate resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG) with detailed official crop statistics and crop typemasks. It is based on the relationship between the Normalized Difference Vegetation Index (NDVI) at the peak of the growing season, percent wheat within the CMG pixel (area within the CMG pixel occupied by wheat), and the final yields. This method predicts the yield approximately one month to six weeks prior to harvest. In this study, we include Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data in order to improve the winter wheat production forecast by increasing the timeliness of the forecasts while conserving the accuracy of the original model. We apply this modified model to three major wheat-producing countries: the Unites States (US), Ukraine and China from 2001 to 2012. We show that a reliable forecast can be made between one month to a month and a half prior to the peak NDVI (meaning two months to two and a half months prior to harvest), while conserving an accuracy of 10% in the production forecast.

Production↗
NASA NTRS1977

Investigations of spectral separability of small grains, early season wheat detection, and multicrop inventory planning

The author has identified the following significant results. LANDSAT data from seven 5 by 6 segments having crop type information were analyzed to determine the potential for spectral separation of spring wheat from other small grains as an alternative to the primary LACIE procedure for estimating spring wheat acreage. Within segment field-center, classification accuracies for spring wheat vs. barley tended to be best in mid-July when crop color changes were in progress. When correlations were made for differences in atmospheric haze, data from several segments could be aggregated, and results that approached within segment accuracies were obtained for selected dates. LACIE field measurement spectral reflectance data provided information on both wheat development patterns and the importance of various agronomic factors on wheat reflectance, the most important being availability of soil moisture. To investigate early season detection for winter wheat, reflectance of developing wheat patterns was simulated through reflectance modeling and was analyzed along with field measured reflectance from a Kansas site. The green component development of the wheat field was analyzed as a function of data throughout the season. A selected threshold was not crossed by all fields until mid-April. These reflectance data were shown to be consistent actual LANDSAT data.

Nalepka, R. F.↗
NASA NTRS1998

Photosynthetic capacity and dry mass partitioning in dwarf and semi-dwarf wheat (Triticum aestivum L.)

Efficient use of space and high yields are critical for long-term food production aboard the International Space Station. The selection of a full dwarf wheat (less than 30 cm tall) with high photosynthetic and yield potential is a necessary prerequisite for growing wheat in the controlled, volume-limited environments available aboard long-term spaceflight missions. This study evaluated the photosynthetic capacity and carbon partitioning of a full-dwarf wheat cultivar, Super Dwarf, which is routinely used in spaceflight studies aboard U.S. space shuttle and NASA/Mir missions and made comparisons with other dwarf and semi-dwarf wheat cultivars utilized in other ground-based studies in plant space biology. Photosynthetic capacity of the flag leaf in two dwarf (Super Dwarf, BB-19), and three semi-dwarf (Veery-10, Yecora Rojo, IBWSN 199) wheat cultivars (Triticum aestivum L.) was assessed by measuring: net maximum photosynthetic rate, RuBP carboxylation efficiency, chlorophyll concentration and flag leaf area. Dry mass partitioning of carbohydrates to the leaves, sheaths, stems and ear was also assessed. Plants were grown under controlled environmental conditions in three replicate studies: slightly enriched CO2 (370 micromoles mol-1), high photosynthetic photon flux (1000 micromoles m-2 s-1; 58 mol m-2 d-1) for a 16 h photoperiod, 22/15 degrees C day/night temperatures, ample nutrients and water provided by one-half strength Hoagland's nutrient solution (Hoagland and Arnon, 1950). Photosynthetic capacity of the flag leaf was determined at anthesis using net CO2 exchange rate versus internal CO2 concentration curves measured under saturating light (2000 micromoles m-2 s-1) and CO2 (1000 micromoles mol-1). Dwarf wheat cultivars had greater photosynthetic capacities than the taller semi-dwarfs, they averaged 20% higher maximum net photosynthetic rates compared to the taller semi-dwarfs, but these higher rates occurred only at anthesis, had slightly greater carboxylation efficiencies and significantly increased chlorophyll concentrations per unit leaf area. The reduced-height wheat had significantly less dry mass fraction in the stem but greater dry mass partitioned to the ear than the taller semi-dwarfs (Yecora rojo, IBWSN-199). Studies with detached heads confirm that the head is a significant sink in the shorter wheat cultivars.

Non-NASA Center↗
NASA NTRS2021

The Arya Crop Yield Forecasting Algorithm: Application to the Main Wheat Exporting Countries

Wheat is the most important commodity traded in the international food market. Thus, accurate and timely information on wheat production can help mitigate food price fluctuations. Within the existing operational regional and global scale agricultural monitoring systems that provide information on global crop yield and area forecasts, there are still fundamental gaps: #1. Lack of quantitative Earth Observation (EO) derived crop information, #2. Lack of global but detailed (national or subnational level) and timely crop production forecasts and #3. Lack of information on forecast uncertainties. In this study we present the Agriculture Remotely-sensed Yield Algorithm (ARYA) an EO-based method, advancing the state of EO data application and usage (addressing gap #1) to forecast wheat yield. The algorithm is based on the evolution of the Difference Vegetation Index (DVI) using MODIS data at 1km resolution and the Growing Degree Days (GDD) from reanalysis data. Additionally, we explore how Land Surface Temperature (LST) can be included into the model and whether this parameter adds any value to the model performance when combined with the optical information. ARYA is implemented at the national and subnational level to forecast winter wheat yield in the main wheat exporting countries of US, Russia, Ukraine, France, Germany, Australia and Argentina from 2001 to 2019 (covering over 70% of wheat exports globally) in a timely manner by providing daily forecasts (addressing gap #2). The results show that ARYA provides yield estimations with RMSE’s within 0.3 ± 0.1 t/ha at national level and 0.6 ± 0,1 t/ha at subnational level after Day Of the Year (DOY) 140 (mid May) in the Northern Hemisphere and DOY 280 (beginning of October) in the Southern Hemisphere. This means that ARYA can provide crop yield estimates of wheat yield with 5-15 % error at national and 7-20 % error at subnational level starting from 2 to 2.5 months prior to harvest.

Agriculture↗