Search NASA⌕ Search

SEARCH · Search NASA

Results for “wheat yield”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
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 NTRS1987

USU research helps agriculture enter the space age

Research at the Utah State University College of Agriculture that is relevant to the space life sciences is reviewed. Specific programs detailed are gravitropism of dicot stems, maximization of wheat yields for use in space exploration, and plant development processes in wheat in microgravity.

Non-NASA Center↗
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↗
NASA NTRS1979

LACIE sampling design

The sampling design in LACIE consisted of two major components, one for wheat acreage estimation and one for wheat yield prediction. The acreage design was basically a classical survey for which the sampling unit was a 5- by 6-nautical mile segment; however, there were complications caused by measurement errors and loss of data. Yield was predicted by sampling meteorological data from weather stations within a region and then using those data as input to previously fitted regression equations. Wheat production was not estimated directly, but was computed by multiplying yield and acreage estimates. The allocation of samples to countries is discussed as well as the allocation and selection of segments in strata/substrata.

Feiveson, A. H.↗
DOE OSTI2022

Long-term integrated soil-crop management improves soil microbial community structure to reduce GHG emission and increase yield

Integrated soil-crop management (ISCM) has been shown as an effective strategy to increase efficiency and yield while its soil microbial community structure and function remain unclear. We evaluated changes in soil physicochemical factors, bacterial community structure responses, and the contributions of soil properties and bacterial communities to summer maize-winter wheat yield and GHG emissions through an ISCM experiment [T1 (local smallholder farmers practice system), T2 (improved management system), T3 (high–yield production system), and T4 (optimized management system)], which could provide scientific guidance for sustainable development of soil in summer maize-winter wheat rotation system. The results showed that the optimized ISCM could improve the soil quality, which significantly changed the soil bacterial community structure to reduce GHG emissions and increase yield. The co-occurrence network density of T3 was increased significantly. The Acidobacteria (class) and OM190 (class) were enriched in T2 and T4. The Frankiales (order) and Gaiellales (order) were enriched in T3. However, the changes in different crop growth stages were different. At the wheat jointing stage and maize mature stage, T4 could enhance carbon-related functional groups, such as aromatic hydrocarbon degradation and hydrocarbon degradation, to increase the soil organic carbon content. And at the maize tasseling stage, T4 could enhance nitrogen-related functional groups. And soil bacteria structure and function indirectly affected annual yield and GHG emission. T2 and T4 exhibited a similar soil microbial community. However, the yield and nitrogen use efficiency of T2 were reduced compared to those of T4. The yield of T3 was the highest, but the GHG emission increased and soil pH and nitrogen use efficiency decreased significantly. Therefore, T4 was a suitable management system to improve soil quality and soil bacterial community structure and function to decrease GHG emissions and increase the yield of the summer maize-winter wheat rotation system.

Yu, Ningning↗
DOE OSTI2023

Mycorrhizal effects on crop yield and soil ecosystem functions in a long‐term tillage and fertilization experiment

Summary It is well understood that agricultural management influences arbuscular mycorrhizal (AM) fungi, but there is controversy about whether farmers should manage for AM symbiosis. We assessed AM fungal communities colonizing wheat roots for three consecutive years in a long‐term (> 14 yr) tillage and fertilization experiment. Relationships among mycorrhizas, crop performance, and soil ecosystem functions were quantified. Tillage, fertilizers and continuous monoculture all reduced AM fungal richness and shifted community composition toward dominance of a few ruderal taxa. Rhizophagus and Dominikia were depressed by tillage and/or fertilization, and their abundances as well as AM fungal richness correlated positively with soil aggregate stability and nutrient cycling functions across all or no‐tilled samples. In the field, wheat yield was unrelated to AM fungal abundance and correlated negatively with AM fungal richness. In a complementary glasshouse study, wheat biomass was enhanced by soil inoculum from unfertilized, no‐till plots while neutral to depressed growth was observed in wheat inoculated with soils from fertilized and conventionally tilled plots. This study demonstrates contrasting impacts of low‐input and conventional agricultural practices on AM symbiosis and highlights the importance of considering both crop yield and soil ecosystem functions when managing mycorrhizas for more sustainable agroecosystems.

Peng, Zhenling↗
NASA NTRS2018

Can Egypt Become Self-Sufficient in Wheat?

Egypt produces half of the 20 million tons of wheat that it consumes with irrigation and imports the other half. Egypt is also the world's largest importer of wheat. The population of Egypt is currently growing at 2.2% annually, and projections indicate that the demand for wheat will triple by the end of the century. Combining multi-crop and -climate models for different climate change scenarios with recent trends in technology, we estimated that future wheat yield will decline mostly from climate change, despite some yield improvements from new technologies. The growth stimulus from elevated atmospheric CO2 will be overtaken by the negative impact of rising temperatures on crop growth and yield. An ongoing program to double the irrigated land area by 2035 in parallel with crop intensification could increase wheat production and make Egypt self-sufficient in the near future, but would be insufficient after 2040s, even with modest population growth. Additionally, the demand for irrigation will increase from 6 to 20 billion m3 for the expanded wheat production, but even more water is needed to account for irrigation efficiency and salt leaching (to a total of up to 29 billion m3). Supplying water for future irrigation and producing sufficient grain will remain challenges for Egypt.

Asseng, Senthold↗
NASA NTRS1982

Comparison of the CEAS and Williams-type barley yield models for North Dakota and Minnesota

The CEAS and Williams type models were compared based on specified selection criteria which includes a ten year bootstrap test (1970-1979). Based on this, the models were quite comparable; however, the CEAS model was slightly better overall. The Williams type model seemed better for the 1974 estimates. Because that year spring wheat yield was particularly low, the Williams type model should not be excluded from further consideration.

Leduc, S.↗
NASA NTRS2015

Is There Ecological Information in Optical Polarization Data?

Optical linear polarization? In remote sensing it's due to specular reflection. The first surface that incident light encounters - a smooth water surface or the waxy first surface of a leaf's cuticle, if it's even somewhat smooth (i.e. shiny) - will specularly reflect and linearly polarize the incident light. We provide three examples of the types of ecological information contained in remotely sensed optical linear polarization measurements. Remove the surface reflection to better see the interior. The linearly polarized light reflected by leaf surfaces contains no information about cellular pigments, metabolites, or water contained in the leaf interiors of a plant canopy, because it never enters the leaf interior to interact with them. Thus, for purposes of remotely sensing the leaf interiors of a plant canopy, the linearly polarized light should be subtracted from the total reflected light, because including it would add noise to the measurement. In particular 'minus specular' vegetation indices should allow improved monitoring of a plant canopy's physiological processes. Estimate plant development stage and yield. Wheat and sorghum grain heads, following emergence, rapidly extend upward and very quickly tower over nearby leaves, partially blocking our view of the sunlight reflected by those leaf surfaces. The resulting decrease in the amount of surface reflected and polarized sunlight, if monitored over time, potentially allows per-field estimates of the dates of the heading and flowering development stages to be interleaved with weather data in models, which is key to better estimating per-field grain yield. Similar polarization changes may occur in other grasses, such as oats, barley, corn and rice, each a crop so widely grown that it potentially affects climate at the regional scale. Wetlands Mapping. The sunlight specularly reflected by surface waters is blindingly bright, spectrally flat and polarized - all of which telegraphs that the ground area is inundated. Inundated soils exchange methane with the atmosphere; non-inundated soils, carbon dioxide. Aquatic plants growing through the water surface pipe the soil-produced methane via the stomata to the atmosphere, enhancing exchanges rates by factors of 10-20 compared to ebullition (bubbling) or diffusion through the water column to the atmosphere. Thus, mapping wetland areas into three community types - inundated areas with emergent vegetation, open water and uplands - provides potentially key information to water, carbon and energy budgets at landscape to global scales.

Water↗
NASA NTRS2018

Remote Sensing Based Yield Monitoring: Application to Winter Wheat in United States and Ukraine

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100% of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (15–18%). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7%) while for Ukraine it is 0.27 t/ha (8.4%).

remote sensing↗
NASA NTRS2018

Remote Sensing Based Yield Monitoring: Application to Winter Wheat in United States and Ukraine

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100 percent of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (tonnes per hectare) (15-18 percent). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7 percent) while for Ukraine it is 0.27 t/ha (8.4 percent).

DVI↗
NASA NTRS2021

Global irrigation contribution to wheat and maize yield

Irrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY). At global scale, ΔY is 34 ± 9% for wheat and 22 ± 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30–47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts.

Irrigation↗
NASA NTRS1988

Exploring the limits of crop productivity. I. Photosynthetic efficiency of wheat in high irradiance environments

The long-term vegetative and reproductive growth rates of a wheat crop (Triticum aestivum L.) were determined in three separate studies (24, 45, and 79 days) in response to a wide range of photosynthetic photon fluxes (PPF, 400-2080 micromoles per square meter per second; 22-150 moles per square meter per day; 16-20 hour photoperiod) in a near-optimum, controlled-environment. The CO2 concentration was elevated to 1200 micromoles per mole, and water and nutrients were supplied by liquid hydroponic culture. An unusually high plant density (2000 plants per square meter) was used to obtain high yields. Crop growth rate and grain yield reached 138 and 60 grams per square meter per day, respectively; both continued to increase up to the highest integrated daily PPF level, which was three times greater than a typical daily flux in the field. The conversion efficiency of photosynthesis (energy in biomass/energy in photosynthetic photons) was over 10% at low PPF but decreased to 7% as PPF increased. Harvest index increased from 41 to 44% as PPF increased. Yield components for primary, secondary, and tertiary culms were analyzed separately. Tillering produced up to 7000 heads per square meter at the highest PPF level. Primary and secondary culms were 10% more efficient (higher harvest index) than tertiary culms; hence cultural, environmental, or genetic changes that increase the percentage of primary and secondary culms might increase harvest index and thus grain yield. Wheat is physiologically and genetically capable of much higher productivity and photosynthetic efficiency than has been recorded in a field environment.

NASA Discipline Number 61-10↗
NASA NTRS1976

LACIE performance predictor FOC users manual

The LACIE Performance Predictor (LPP) is a computer simulation of the LACIE process for predicting worldwide wheat production. The simulation provides for the introduction of various errors into the system and provides estimates based on these errors, thus allowing the user to determine the impact of selected error sources. The FOC LPP simulates the acquisition of the sample segment data by the LANDSAT Satellite (DAPTS), the classification of the agricultural area within the sample segment (CAMS), the estimation of the wheat yield (YES), and the production estimation and aggregation (CAS). These elements include data acquisition characteristics, environmental conditions, classification algorithms, the LACIE aggregation and data adjustment procedures. The operational structure for simulating these elements consists of the following key programs: (1) LACIE Utility Maintenance Process, (2) System Error Executive, (3) Ephemeris Generator, (4) Access Generator, (5) Acquisition Selector, (6) LACIE Error Model (LEM), and (7) Post Processor.

Source record↗
NASA NTRS1978

Proceedings of Plenary Session: The LACIE Symposium

A technology assessment of the LACIE data processing and information systems was discussed during the Large Area Crop Inventory Experiment Symposium. Crop inventories of wheat yield in the United States as well as several other nations (such as the U.S.S.R., Canada, etc.) were discussed, along with the methodology involved in acquiring this data.

Source record↗
NASA NTRS1979

Ancillary data acquisition for LACIE

The design, implementation, and operational functions of the three phases of LACIE supported the data needs of all other elements of the project and required several types of data in addition to LANDSAT multispectral digital data. The nonelectronic data base consisted of statistical data, printed reports, periodicals, ground observed data received from intensive test sites and operational segments, and full-frame multispectral scanner CIR photographs. The following data were collected for the test sites in the United States and Canada: land use inventories, periodic crop observations, solar radiometer measurements, rainfall, and wheat yield for selected fields.

Spiers, B. E.↗
NASA NTRS1979

Development of a winter wheat adjustable crop calendar model

The author has identified the following significant results. Least squares techniques were applied for parameter estimation of functions to predict winter wheat phenological stage with daily maximum temperature, minimum temperature, daylength, and precipitation as independent variables. After parameter estimation, tests were conducted using independent data. It may generally be concluded that exponential functions have little advantage over polynomials. Precipitation was not found to significantly affect the fits. The Robertson triquadratic form, in general use for spring wheat, yielded good results, but special techniques and care are required. In most instances, equations with nonlinear effects were found to yield erratic results when utilized with averaged daily environmental values as independent variables.

Baker, J. R.↗