Source: OKLAHOMA STATE UNIVERSITY submitted to NRP
DEVELOPING AND IMPROVING BIOENERGY CROP MODELS
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
HATCH
Reporting Frequency
Annual
Accession No.
1004396
Grant No.
(N/A)
Cumulative Award Amt.
(N/A)
Proposal No.
(N/A)
Multistate No.
(N/A)
Project Start Date
Oct 16, 2014
Project End Date
Sep 30, 2019
Grant Year
(N/A)
Program Code
[(N/A)]- (N/A)
Recipient Organization
OKLAHOMA STATE UNIVERSITY
(N/A)
STILLWATER,OK 74078
Performing Department
Plant & Soil Sciences
Non Technical Summary
Bioenergy crops will be grown in both traditional and non-traditional areas around the world and require intensive and longitudinal experimental studies to evaluate production potential and environmental and economic consequences. Crop models can reduce the costs involved in conducting field experiments by identifying key treatments and by extrapolating results to other regions and environmental conditions. We currently lack mechanistic models of many bioenergy crops and the intent is to develop new bioenergy crop models by providing data sets from growth chamber, greenhouse, and field experiments. Models developed would answer questions such as the what? when? and where? for bioenergy production in current and future climates. Bioenergy crop models will serve as decision support systems for producers, industry, researchers and policy makers and also act as teaching tools/aids.
Animal Health Component
80%
Research Effort Categories
Basic
10%
Applied
80%
Developmental
10%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
1021620102070%
1321630107030%
Knowledge Area
102 - Soil, Plant, Water, Nutrient Relationships; 132 - Weather and Climate;

Subject Of Investigation
1620 - Warm season perennial grasses; 1630 - Summer annual grasses;

Field Of Science
1070 - Ecology; 1020 - Physiology;
Goals / Objectives
The objectives of this project are to: 1) Identify current bioenergy crop models and simulation capabilities. 2) Develop model parameters and crop coefficients for bioenergy crops in existing models. 3) Develop new models for advanced bioenergy production feedstocks. 4) Model relative production potential of various biofuel crops in various areas and land-types (e.g. existing crop and pasture vs. marginal lands). 5) Produce multi-model assessment of the impacts of bioenergy crops on land use change and food security. 6) Conduct multi-model assessment of the impacts of climate change on bioenergy crop production.
Project Methods
Cellulosic bioenergy feedstocks for Oklahoma are either native that have not been managed or managed elsewhere but introduced species to Oklahoma. Bioenergy crop models will be developed for predicting crop production in Oklahoma. Agronomic adaptation of bioenergy crops in Oklahoma and best management practices will be evaluated using field research stations across the state by conducting replicated, multi-location small plot tests. Growth and developmental traits for developing model parameters of bioenergy crops will be quantified through field, green house and controlled environment studies. Both field and controlled environments will be used to evaluate bioenergy crop performance under current and future climates and necessary input will be provided for breeding crops for future climates. Data from available resources such as MESONET, GIS, and SURRGO will be used.

Progress 10/16/14 to 09/30/19

Outputs
Target Audience:1) Graduate and undergraduate students 2) Researchers Changes/Problems:Weather played a major role in initiating field activities in the study region. A new project on evaluating Camelina production had to be abandoned due to unseasonal rainfall during spring planting. What opportunities for training and professional development has the project provided?Participated in the following training activities: 1) LEAD 21 Class 15 leadership development program. June to Oct 2019. 2) Hyperspectral sensor training at Headwall Photonics. Oct 2019 3) DOE BETO's BioRestore Workshop, Argonne National Labs. September 2019. 4)Conducted the NIFA funded Sustainable Bioenergy Workshop along with Drs. Haggard, Sallee, Robinson, and Baker, from 12-24 JUNE 2019 in Stillwater, OK. A total of 20, K-12 teachers, from around the USA were trained. How have the results been disseminated to communities of interest?Results were disseminated through Peer-reviewed publications, MS and Doctoral thesis work, presentations at regional and national meetings and participation in training workshops. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? The project demonstrated the use of bioenergy models (empirical) to estimate the amount of C sequestered by bioenergy crops in Oklahoma. The protocols and modeling approach developed for switchgrass can be expanded to 48 contiguous US states.New collaborative projects were developed with USDA-ARS to introduce new summer biomass/forage crops and to monitor soil gas fluxes in biomass crops. Part of the project time was also spent on professional leadership development under the LEAD 21 program. The final year of the project was also used at disseminating the information at multiple scientific meetings and developing a new project proposal to expand on the identified knowledge gaps in the study. Under Objective 2: 1) Major activities completed/experiments conducted. Switchgrass root characteristics were measured over the entire soil profile (0-110cm) at different row spacings and algorithms were developed. We also evaluated different root characteristics and their distribution pattern as influenced by ecotype and (lowland and upland cultivars) cultivar difference and determine the relationship between aboveground and belowground biomass. 2) Data collected: Measurement of root parameters was carried out with an image analysis system (winRHIZO) with a grey level image type with 100 dpi resolution. 3) Summary statistics and discussion of results Root length (RL) was found to be higher at narrow spacing (19.05 cm) by 24.86% in August, while higher spacing (76.2 cm) was higher in 2nd harvest (December) by 26% at upper depth(0-10cm). Similarly, root length density (RLD) was observed to be 42% higher at lower depths ranging from 20-110cm, under narrow spacing (19.05 cm), and at the end of the growing season. Root weight density (RWD) was significantly higher in 2nd harvest than in 1st harvest (August) over the entire soil profile at higher spacing by 28.4%. Similar results in average diameter were also observed. Greater increase in average diameter by 29%, 41% and 12.5% at upper depth in 19.05cm, 38.1cm and 76.2cm row spacing, respectively, was recorded by the end of the growing season. However, specific root length (SRL) was higher by 42% with 19.05cm spacing than 76.2cm spacing and highly correlated with an average diameter (R2=0.96). More than 75% of the total root was of fine roots (0-2mm diameter) and were more in narrow spacing during the growing season while higher in wider spacing at end of the season. No significant relation was observed in root biomass at different spacing practices but above-ground biomass was significantly higher (15 Mg ha-1) in 76.2 cm spacing than with narrow spacing (11 Mg ha-1). Variation in switchgrass root characteristics owing to different cultivation practices will be an important determinant in C sequestration as well as in soil quality improvement. Lowland cultivars have lower RLD in comparison to upland cultivars by 39% in all soil depth but have increased RLD in lower depth, also higher RLD was observed in 2nd harvest of lowland cultivars. 87% of RLD was found in upper depth i.e.0-10cm while 83% of RLD resulted during 2nd harvest at the same depth. Similarly, Lowland cultivars increase about 52% while upland cultivars increase up to 40 % in 2nd harvest all over soil profile but still have a lower RWD than upland cultivars. Lowland cultivars did not produce more root biomass in the surface which accounts for a high percentage of the root mass, even though higher root biomass is found in deeper depth. Lowland Cultivars have a higher average diameter than upland cultivars in all soil depth from 0 to 110 cm. About 0.8-1.2mm average diameter results in low land cultivars while 0.8-1.0mm average diameter founds in upland cultivars. The lowland cultivar Alamo results around 15 DT ha-1 whereas upland cultivar Southlow, Cave-in-rock, Blackwell about 8 dry t ha-1 . Similarly, in comparison with belowground root biomass Upland cultivar, Blackwell and Cave-in-rock have higher root biomass i.e. about 7 dry t ha-1 while low land cultivars result in about 4 dry t ha-1 . The fine root proportions found to be increased by more than 150cm of total root length among low land cultivars while upland cultivars have decreased fine root proportions but as a whole upland cultivar has higher fine root proportions than lowland cultivars. Around 15% of coarse root diameter (2.0-2.5mm) was found to be increased among lowland cultivars while more than 25% increase in the coarse root of diameter 2.5-3.0mm was found in upland cultivars at the end of the growing season. 4) Key outcomes or other accomplishments realized. Root growth parameters for modeling switchgrass were developed. The project goals under 1, 4, 5, and 6 were achieved during previous years of the project period.

Publications

  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Singh, B., Singh, S.K., Matcha, S.K., Kakani, V.G., Wijewardana, C., Chastain, D., Gao, W., Reddy, K.R. 2019. Parental environmental effects on seed quality and germination response to temperature of Andropogon gerardii. Agronomy, 9: 304. https://doi.org/10.3390/agronomy9060304
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Zhou, Y., Gowda, P.H., Wagle, P., Ma, S., Neel, J.P.S., Kakani, V.G., Steiner, J.L. 2019. Climate effects on tallgrass prairie responses to continuous and rotational grazing. Agronomy, 9: 219. https://doi.org/10.3390/agronomy9050219
  • Type: Journal Articles Status: Accepted Year Published: 2019 Citation: Singh, H., Northup, B.K., Baath, G.S., Gowda, P.P., Kakani, V.G. 2019. Greenhouse mitigation strategies for agronomic and grazing lands of the US Southern Great Plains. Mitigation and Adaptation Strategies for Global Change. In Press. DOI: 10.1007/s11027-019-09894-1
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Ma, S., Zhou, Y., Gowda, P.H., Dong, J., Zhang, G., Kakani, V.G., Wagle, P., Chen, L., Flynn, K.C., Jiang, W. 2019. Application of the water-related spectral reflectance indices: A review. Ecological Indicators, 98: 68-79. DOI: 10.1016/j.ecolind.2018.10.049
  • Type: Journal Articles Status: Accepted Year Published: 2019 Citation: Flynn, K.C., Zhou, Y., Gowda, P.H., Moffet, C.A., Wagle, P., Kakani, V.G. 2019. Burning and Climate Interactions Determine Impacts of Grazing on Tallgrass Prairie Systems. Rangeland Ecology & Management. In Press. https://doi.org/10.1016/j.rama.2019.10.002.
  • Type: Theses/Dissertations Status: Published Year Published: 2019 Citation: Katta, J.R. 2019. EVALUATING PHOTOSYNTHETIC RESPONSES OF SORGHUM (GRAIN & FORAGE) AND SOYBEAN TO TEMPERATURE STRESS. MS Thesis. Submitted to Oklahoma State University.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Baath, G.S., V.G. Kakani, P. H. Gowda, A. C. Rocateli, B. K. Northup, H. Singh, J. R. Katta. 2019. Guar responses to temperature: Estimation of cardinal temperatures and photosynthetic parameters. Industrial Crops and Products. 111940. https://doi.org/10.1016/j.indcrop.2019.111940. Available online 22 November 2019.


Progress 10/01/17 to 09/30/18

Outputs
Target Audience:Scientists; Industry; Graduate and Undergraduate students Changes/Problems:No major changes and problems during the project period. What opportunities for training and professional development has the project provided? Conducted the NIFA funded Sustainable Bioenergy Workshop along with Drs. Haggard, Sallee, Robinson, and Baker, from 18-22 JUNE 2018 in Stillwater, OK. A total of 20, K-12 teachers, from around the USA were trained. Attended from Nov 4 to 8th 2018 the International Annual Meetings of ASA, CSSA, and SSSA, Baltimore, MD. In 2018 - assisted the National Research Foundation of South Africa in reviewing an application submitted for the rating from Dr. T. Mabhaudhi, (Crop modeling; Crop physiology; Crop production; Crop water use; Climate change) who is associated with a Higher Education/Research Institution in South Africa. How have the results been disseminated to communities of interest?Results were disseminated through Oral and Poster presentations atASA-CSSA-SSSA, and AGU annual meetings. A website was developed as described above. What do you plan to do during the next reporting period to accomplish the goals? Improve DSSAT crop simulation model to simulate biomass production. Improve the OKCROp website. Develop and publish manuscripts based on the protocols and methodologies developed during the project period. Develop collaborations through proposals and research activities to enhance the applicability of weather database to other crops.

Impacts
What was accomplished under these goals? There are several approaches to estimate crop yield at a location. One other approach is by using a remote sensing technique where the yield of a crop is determined by analyzing the satellite or unmanned aircraft imagery. But it only provides information which is subjected to that snapshot and it might not be valid for all seasons. Crop simulation using DSSAT allows the user to understand the pattern throughout the year and make decisions accordingly. So decision making using DSSAT is more reliable than remote sensing technique. Broader impact:This research will help significantly improve the agricultural systems in Oklahoma and similar regions facing high levels of uncertainty in weather and soil conditions. Better management decisions for biological and natural systems would improve systems performance under inherently dynamic climates and can potentially transform agricultural system management decision-making, improving system productivity and sustainability of the ecosystem. The research outcomes directly impact economic activities pertaining to managed ecosystems that are of national and global significance. The framework developed will be disseminated through discipline-specific open source repositories and communities. The following two specific goals were addressed during this year. 2) Develop model parameters and crop coefficients for bioenergy crops in existing models. 3) Develop new models for advanced bioenergy production feedstocks. The description below is also available in a rPDF format with figures included https://www.dropbox.com/s/8mhvt34b50it5vg/Pannala_report.pdf?dl=0 The proposed system "OKCrop" serves as a platform for integrating multiple, heterogeneous, and high-volume data central to understand the agricultural systems in Oklahoma. Outcomes from such a framework are of immense value to the US food, water, and energy sector, helping end users sustain agro and natural ecosystems and their services. Crop simulation is necessary for the adaptation of global climate change for agriculture. DSSAT, Decision Support System for Agrotechnology Transfer, is one of the most popular crop simulation systems, which contains models of 42 crops. It has been in use for more than 30 years in over 150 countries worldwide. Serving custom inputs to DSSAT based on user preference is not an easy process. Inputs to this system would be users geographic location, type of crop and year of plantation. The proposed project solves this problem by simplifying the process of crop simulation. Gridded Soil Survey Geographic (gSSURGO) Database for Oklahoma produced by the USDA Natural Resources Conservation Service (NRCS) was used to identify soil properties. This project will help significantly improve the agricultural system management decision-making, improving system productivity and sustainability of the ecosystem in Oklahoma and similar regions facing high levels of uncertainty in weather and soil conditions. Technical Approach:This application has a REST based architecture style and is built using the latest programming/ scripting languages. The web application has three main components which are Web Interface Application Server Backend Process Web Interface: The web interface of this application is responsible to take the user inputs. These inputs include the type of crop that the user wants to run the simulation on, year of plantation and the location of the crop. The location is obtained by GoogleMaps interface where the user can select the exact geographical location of the crop by moving the marker on the map. The following technologies are used for developing the web interface of this application. HTML & CSS Hypertext markup language is the standard markup language for creating web pages. HTML describes the structure of the web page semantically and originally included cues for the appearance of the document. Cascading Style Sheets (CSS) is used to define the styles for the web page and variations in design and layouts. Bootstrap which is an open source front end framework is used to beautify the website. JavaScript If HTML dictates the content of a page and CSS dictates the style of a page, JavaScript dictates the behavior of a page. It manipulates both CSS and HTML attributes of a page, changing content and the way things look when a user interacts with it. The major advantage of using JavaScript is its ability to run on any browser. This cross-browser support is made seamless with libraries like jQuery. JavaScript in the front end is responsible for filling the HTML table with output values which are generated after the simulation is completed. CanvasJS It is a JavaScript framework for creating visually rich charts that run across various devices. In this project, CanvasJS is used to generate Line graphs and scatter plots for various parameters of plant growth and evaporation. These graphs are generated dynamically based on user selection. The data required to create these graphs are in the output files which are generated after each simulation. The data from the output files is obtained by using JavaScript in the front end and then the graphs are created. Application Server The application server handles all the requests made by the user and serves the appropriate response to the web interface. It is built using JavaScript frameworks like Express.js and Node.js. Express.js Express.js builds on the underlying capability of Node, by providing a web application server framework. This framework provides a wrapper around a lower-level Node interface: giving the developer, a convenient means to handle routing and HTTP operations (such as GET and POST). Express.js facilitates a simplified and more elegant solution than (re-)implementing these services directly using Node. Backend Process DSSAT software is deployed on the server and is used in the backend to run simulations based on the user inputs. In this method, the capabilities of the standalone software are integrated on to a web application so that the user can access the features of decision support system without any prior knowledge of using the software and also eliminating the necessity to install the software. Based on the location and crop type selected by the user, the nearest weather station and soil ID available for that latitude and longitude in the station information file is obtained by the script written in Node.js. Then the values of weather station and soil ID are updated in the file which serves as input for the simulation. A fully functional Beta version of the website (www.okcm.okstate.edu)for producers, managers and policy makers is undergoing testing. A creative component report was submitted by Yashwanth Pannala, as part of MS program in Computer Science. A poster was presented at the Creative Component Symposium in Computer Science Department. Pannala, Y., J.P. Thomas, V.G. Kakani, C. Crick. 2018. OkCrop-A Web-based decision support system for the producer to the policymaker. Presented at Creative Component Symposium, Department of Computer Science, Oklahoma State University, Stillwater OK. Nov 30, 2018. An operational website combined with a cell phone interface is being developed. Users will be able to select crop, location (Google Maps) and planting dates. Advanced options for irrigation and fertilizer inputs will be provided along with other management options. In addition, users will be able to see yield forecasts based on current, normal and project weather patterns.

Publications

  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Dhakal, K., V.G. Kakani*, and E. Linde. 2018. Climate Change Impact on Wheat Production in the Southern Great Plains of the US Using Downscaled Climate Data. Atmospheric and Climate Sciences, 8: 143. DOI: 10.4236/acs.2018.82011
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Makaju, S.O, Y. Wu, M.P. Anderson, V.G. Kakani, M.W. Smith, L. Liu. 2018. Yield-height correlation and QTL localization for plant height in two lowland switchgrass populations. Frontiers of Agricultural Science and Engineering 5: 118-128. DOI: 10.15302/J-FASE-2018201
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Lindsay, K.R., M.P. Popp, C.P. West, A.J. Ashworth, A.C. Rocatelie, R. Farris, V.G. Kakani, F.B. Fritschi, V.S. Green, M.W. Alisoni, M.J. Maw, and L. Acosta-Gamboai. 2018. Predicted harvest time effects on switchgrass moisture content, nutrient concentration, yield, and profitability. Biomass and Bioenergy. 108: 74-89. DOI: 10.1016/j.biombioe.2017.09.017
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Lee, D.K., E. Aberle, E.Anderson, W. Anderson, B. Baldwin, D. Baltensperger, M. Barrett, S. Bonos, J. Bouton, C. Brummer, P. Burks, C. Chen, C. Daly, J. Egenolf, R. Farris, J. Fike, R. Gaussoin, J. Gill, K. Gravois, M. Halbleib, A. Hale, W. Hanna, K. Harmoney, E. Heaton, R. Heiniger, L. Hoffman, C. Hong, V.G. Kakani, R. Kallenbach, B. Macoon, J. Medly, A. Missaoui, R. Mitchell, K. Moore, J. Morrison, G. Odvody, R. Ogoshi, J Parrish, L. Quinn, Ed Richard, B. Rooney, B. Rushing, R. Schnell, M. Sousek, S. Staggenborg, T. Tew, G. Uehara, D. Viands, T. Voigt, D. Williams, L. Williams, L. Wilson, A. Wycislo, Y. Yang, V. Owens. 2018. Biomass Production of Herbaceous Energy Crops in the United States: Field Trial Results and Yield Potential Maps from the Multiyear Regional Feedstock Partnership. GCB Bioenergy. 10: 698⿿716. https://doi.org/10.1111/gcbb.12493
  • Type: Journal Articles Status: Submitted Year Published: 2019 Citation: Singh, B., S.K. Singh, S.K. Matcha, V.G. Kakani, C. Wijewardana, W. Gao, D. Chastain, and K.R. Reddy. 2018. Effect of parental environments on seed quality and germination responses to temperatures of Andropogon gerardii. Crop & Pasture Science (Submitted - In Review)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: 1Zhou, Y., P.H. Gowda, P. Wagle, V.G. Kakani, T. Yildrim. 2018. Examine the impact of different managements on grassland productivity at global FLUXNET sites. In B33H Use Remote Sensing and Eddy Covariance Techniques to Quantify the Impacts of Climate and Managements on Grassland Posters. AGU Fall Meeting, 10-14 Dec, 2018, Washington, D.C., USA.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Baath, G.S., V.G. Kakani, J.R. Katta, P.H. Gowda. 2018. Photosynthetic Responses of New Summer Forage Legumes for Oklahoma. [CD-ROM]. ASA, CSSA, and SSSA, Baltimore, MD.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Baath, G.S., P.H. Gowda, V.G. Kakani, A.C. Rocateli, and H. Singh. 2018. Hardeep SinghForage Yield of Three Warm-Season Annual Legumes across an Irrigation Gradient. [CD-ROM]. ASA, CSSA, and SSSA, Baltimore, MD.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Singh, H. T.P. Kandel, P. H. Gowda, V.G. Kakani, B. K. Northup. 2018. Emissions of CO2 and N2O after Termination of Legume Green Manures at Contrasting Soil Moisture Conditions. [CD-ROM]. ASA, CSSA, and SSSA, Baltimore, MD.


Progress 10/01/16 to 09/30/17

Outputs
Target Audience:Scientists, Industry;graduate and undergraduate students are the targeted audience. Changes/Problems:No major changes and problems during the project period. What opportunities for training and professional development has the project provided? ASA-CSSA-SSSA Annual Meeting, Oct 15-19, 2015, Minneapolis, MN. AGU Fall Meeting, 11-15 Dec, 2017, New Orleans, LA, USA. How have the results been disseminated to communities of interest?Results were disseminated through Oral and Poster presentations at OK EPSCoR, ASA-CSSA-SSSA, and AGU annual meetings. What do you plan to do during the next reporting period to accomplish the goals? Improve DSSAT crop simulation model to simulate biomass production. Conduct multi-model comparison for biomass crop simulation. Develop and publish manuscripts based on the protocols and methodologies developed in Year 1 and 2 of the project. Develop collaborations through proposals and research activities to enhance applicability of weather database to other crops. Submit research proposals and generate funds to hire postdocs, graduate and undergraduate students.

Impacts
What was accomplished under these goals? The following specific objectives were met: 1) Identify current bioenergy crop models and simulation capabilities. 2) Develop model parameters and crop coefficients for bioenergy crops in existing models. Major Activities Completed Simulated switchgrass carbon sequestration potential across Oklahoma Simulated wheat yield under current and future (2040-2060) climates Graduated a PhD student Significant results achieved, including major findings, developments, or conclusions (both positive and negative); and Regional, automated meteorological networks, such as the Oklahoma Mesonet, can potentially provide high quality forcing data for gridded models, but proven methods of interpolating weather variables between the station locations are needed. Four interpolation methods were compared for creating daily gridded weather datasets for agro-hydrological applications. Daily meteorological variables from the Oklahoma Mesonet for the period 1997-2014 were interpolated using geoprocessing tool in ArcGIS with python as the scripting language. Ordinary kriging (OrKr) and empirical Bayesian kriging (EBKr) with and without the use of climate imprints (CI) were compared. Cross-validation metrics for all interpolation approaches showed R2 values of 0.99 and 0.98 for maximum (TMAX) and minimum (TMIN), with mean absolute error (MAE) ranging from ±0.45-0.50? for TMAX and ±0.77-0.80? for TMIN. Likewise, for SRAD R2 values of 0.94 and 0.93 showed overall good prediction accuracy with MAE values 1.00 MJ m-2 d-1 and 1.01 MJ m-2 d-1 for EBKr and OKr respectively. However, for rainfall, all methods yielded R2 values ≤0.67, suggesting a need for more effective interpolation methods. Therefore, based in its lower computational time and lower input data requirement, OKr appears preferable to the other approaches tested here to meet the daily weather data for gridded models in Oklahoma and other regions with similar monitoring networks. Monitoring net ecosystem carbon dioxide (CO2) exchange (NEE) using flux towers is very common but the measurements are valid at the scale of tower footprint. Alternative ways to quantify and extrapolate NEE measurements have not been explored in detail. Empirical relationships were developed based on our previous measurements of eddy CO2 fluxes of a switchgrass (Panicum virgatum L.) ecosystem from 2011-2014 to quantify seasonal (April to October) NEE using detailed meteorological measurements obtained from the Oklahoma Mesonet, and predicted NEE values for potential switchgrass production sites in Oklahoma, USA. The purpose here is not to calibrate the model for making prediction in future, but to explore if model parametrizations based on high temporal frequency meteorological forcing can be used to construct reliable estimates of NEE for evaluating source-sink status of organic carbon. Based on eddy covariance measurements, empirical models, a) rectangular hyperbolic light-response curve and b) temperature response functions were fitted to estimate GEP and ER on a seasonal scale. Seasonal average net ecosystem production (NEP) ranged from 2.3 t C ha-1-6.9 t C ha-1. Model performance was validated by comparing estimates of NEE with measured biomass for three additional locations. No correlation was observed between rainfall and NEP. However, results based on a simple linear model analysis suggested that there were significant differences in NEP between years. Overall, the results from this study indicate that this new scaling-up exercise involving high temporal resolution meteorological data may be useful tool for assessing spatiotemporal heterogeneity of switchgrass production and the potential to sequester C in Oklahoma grasslands. Wheat (Triticum aestivum L.) is dominant grain crop across the Southern Great Plains of North America. In the past, variation in wheat yields are attributed to seasonal weather patterns such as drought. However, there is lack of study analyzing wheat production on a finer scale quantifying the differences between actual (Ywf), attainable (Ya), and potential (Yp) yields. A high-resolution modeling framework was developed to simulate wheat yield in wheat growing areas of Oklahoma under three different scenarios of nitrogen and water limitation. Data from field experiments on wheat crop (cultivar Duster) during the years 20013-2014 and 2014-2015 were used to calibrate CERES-wheat crop growth model of the DSSAT v4.6. Simulation was performed using 35 years of historical weather data across wheat growing landscapes at 800 m × 800 m resolution in a high-performance computing environment. County-aggregated summary from our investigation revealed that magnitudes of grain yield derive from CERES-wheat, follow the county level yield patterns reported by the USDA-NASS. Moreover, variations in yield gap across different scenarios suggest that wheat yield in this region could increase substantially with sufficient optimum management for grain yield. In conclusion, a properly calibrated CERES-wheat model could be useful in predicting anthesis date, maturity date, and grain yield of wheat crop with considerable accuracy and therefore can be recommended for decision-support in the Southern Great Plains and similar regions. Gradually developing climatic and weather anomalies due to increasing atmospheric greenhouse gases concentration can pose threat to farmers and resource managers. There is a growing need to quantify the effects of rising temperature and changing climatic on crop yield and assess impact on a finer scale so that specific adaptation strategies pertinent to that location can be developed. Our work aims to quantify and evaluate the influence of future climate anomalies for winter wheat (Triticum aestivum L.) yield under the Representative Concentration Pathways 6.0 and 8.5 using downscaled climate projections from different models and their ensembles. Marksim downscaled daily data of maximum (TMax) and minimum (TMin) air temperature, rainfall, and solar radiation (SRAD) from different Couppled Model Intercomparison Project General Circulation Models (CMIP5 GCMs) were used to simulate the wheat productivity in water and nitrogen limiting and non-limitng conditions for the future period 2040-2060. The potential impacts of climate changes on winter wheat production across Oklahoma was investigated. Climate change predictions by the downscaled GCMs suggested increase in air temperature and decrease in total annual rainfall. This will be really critical in a rain-fed and semi-arid agro-ecological region of Oklahoma. Under the assumed climate change, predicted average wheat yield during 2040-2060, we observed increase in wheat yield, compared with the baseline years 1980-2014. Our results indicate that downscaled GCMs can be used for different climate projection scenarios for regional crop yield assessment.

Publications

  • Type: Conference Papers and Presentations Status: Submitted Year Published: 2017 Citation: Kakani, V.G., K.R. Reddy, D. Zhao, A.J. Foster, and P. Gowda. 2017. Universal Algorithms for Plant Phenotyping: Are we there yet? In B41J: Advanced Plant Phenotyping for Global Food Security: Lessons Across Measurement Scales from Leaf, to Field, to Remote Sensing I. AGU Fall Meeting, 11-15 Dec, 2017, New Orleans, LA, USA.
  • Type: Conference Papers and Presentations Status: Submitted Year Published: 2017 Citation: Dhakal, K., E. Linde, V.G. Kakani, P.D. Alderman, T.E. Ochsner, and B. Carver. 2017. Impact of Climate Change on Potential, Attainable, and Actual Wheat Yield in Oklahoma. In GC41D: Improving the Simulation of Climate Impacts on Agriculture: AgMIP and Related Research II Posters. AGU Fall Meeting, 11-15 Dec, 2017, New Orleans, LA, USA
  • Type: Conference Papers and Presentations Status: Submitted Year Published: 2017 Citation: Baker, J.H., Y. Wu, M.P. Anderson, V.G. Kakani, and L. Zhu. 2017. Molecular Marker Analysis of Progeny Origins in Sibling-Mating and Crossing Populations of Inbred Lowland Switchgrass. [CD-ROM]. ASA, CSSA, and SSSA, Tampa, FL
  • Type: Conference Papers and Presentations Status: Submitted Year Published: 2017 Citation: Kakani, V.G. 2017. NSF REU - Interdisciplinary Approach for Biobased Products and Energy. [CD-ROM]. ASA, CSSA, and SSSA, Tampa, FL
  • Type: Conference Papers and Presentations Status: Submitted Year Published: 2017 Citation: Kakani, V.G. and K. Dhakal. 2017. High Resolution Simulation of Winter Wheat Production in Future Climates of Oklahoma. [CD-ROM]. ASA, CSSA, and SSSA, Tampa, FL
  • Type: Conference Papers and Presentations Status: Submitted Year Published: 2017 Citation: Kakani, V.G. and K. Dhakal. 2017. Constructing Gridded Daily Oklahoma Mesonet DATA for AGRO-Hydrological Applications. [CD-ROM]. ASA, CSSA, and SSSA, Tampa, F
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Lindsay, K.R., M.P. Popp, C.P. West, A.J. Ashworth, A.C. Rocateli, R. Farris, V.G. Kakani, F.B. Fritschi, V.S. Green, M.W. Alisoni, M.J. Maw, and L. Acosta-Gamboai. 2017. Predicted harvest time effects on switchgrass moisture content, nutrient concentration, yield, and profitability. Biomass and Bioenergy
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Lee, D.K., E. Aberle, E.Anderson, W. Anderson, B. Baldwin, D. Baltensperger, M. Barrett, S. Bonos, J. Bouton, C. Brummer, P. Burks, C. Chen, C. Daly, J. Egenolf, R. Farris, J. Fike, R. Gaussoin, J. Gill, K. Gravois, M. Halbleib, A. Hale, W. Hanna, K. Harmoney, E. Heaton, R. Heiniger, L. Hoffman, C. Hong, V.G. Kakani, R. Kallenbach, B. Macoon, J. Medly, A. Missaoui, R. Mitchell, K. Moore, J. Morrison, G. Odvody, R. Ogoshi, J Parrish, L. Quinn, Ed Richard, B. Rooney, B. Rushing, R. Schnell, M. Sousek, S. Staggenborg, T. Tew, G. Uehara, D. Viands, T. Voigt, D. Williams, L. Williams, L. Wilson, A. Wycislo, Y. Yang, V. Owens. 2017. Biomass Production of Herbaceous Energy Crops in the United States: Field Trial Results and Yield Potential Maps from the Multiyear Regional Feedstock Partnership. GCB Bioenergy.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Foster, A.J., V.G. Kakani and J. Mosali. 2017. Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression. Precision Agriculture 18: 192-209. doi:10.1007/s11119-016-9455-8.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Wagle, P., N. Bhattarai, P.H. Gowda and V.G. Kakani. 2017. Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum. ISPRS Journal of Photogrammetry and Remote Sensing 128: 192-203. doi:10.1016/j.isprsjprs.2017.03.022.
  • Type: Theses/Dissertations Status: Submitted Year Published: 2017 Citation: Dhakal, Kundan. 2017. SPATIALLY EXPLICIT MODELING OF SWITCHGRASS AND WHEAT IN OKLAHOMA. PhD Dissertation


Progress 10/01/15 to 09/30/16

Outputs
Target Audience:Our target audience is scientists; Industry, and gradaute and undergradaute students. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Opportunities for training and professional development has been the ASA-CSSA-SSSA Annual Meeting, held on Nov 15-19, 2015, at Minneapolis, MN. How have the results been disseminated to communities of interest?So far, we have built databases for running simulations. The methodologies and protocols developed will be shared with research community at future opportunities. What do you plan to do during the next reporting period to accomplish the goals?My plan for the next reporting period will be to: Improve DSSAT crop simulation model to simulate switchgrass and sorghum production. Conduct multi-model comparison for biomass crop simulation. Publish results based on the protocols and methodologies developed in Year 1 of the project. Develop collaborations through proposals and research activities to enhance applicability of database to other crops. I will also submit research proposals and generate funds to hire postdocs, graduate and undergraduate students.

Impacts
What was accomplished under these goals? The following specific objectives were met: 1) Identify current bioenergy crop models and simulation capabilities. 2) Develop model parameters and crop coefficients for bioenergy crops in existing models. Major Activities Completed; Significant Outcomes and Key Outcomes Land Use Crop growing areas: The cropland data layer (CDL) is a pixel-based, high-resolution, land use map based on remotely sensed satellite images; produced annually by the USDA, National Agricultural Statistics Service. To identify potential wheat growing areas in Oklahoma, seven years of the USDA NASS CDL (2006 - 2012) were imported in ArcGIS. Those images were merged and pixels where wheat was not identified were masked out, resulting in a single raster depicting areas where only wheat was planted for those years. Switchgrass Potential Sites We aggregated six subclasses: switchgrass, fallow, pasture, shrubland, grassland, and pasture as defined in the 2008-2011 USDA-NASS Cropland Data Layer (CDL) to identify potential switchgrass production area in Oklahoma. The raster data were imported into ArcGIS and reclassified to show only potential switchgrass production area. Elevation information for Oklahoma was obtained from the USGS National Elevation Data (NED) (http://viewer.nationalmap.gov/viewer/). The elevation data (30 m resolution) were also used to extract slope. Gridded Soil Survey Geographic (gSSURGO) Database for Oklahoma produced by the USDA Natural Resources Conservation Service (NRCS) was used to identify soil properties http://datagateway.nrcs.usda.gov/GDGOrder.aspx). Gridded SSURGO (gSSURGO) is a modified, spatially-explicit database of the USDA-NRCS Soil Survey Geographic (SSURGO) database (Soil Survey, 2014). The gSSURGO data were prepared by merging traditional tabular database to the spatial vector database to create a Conterminous US-wide ArcGIS file raster geodatabase. The database offers greater storage capacity and greater spatial coverage than the SSURGO data. The data is offered both statewide and Conterminous United States tiles. We obtained information on several soil physical and chemical properties, for e.g., soil classification, slope, soil color, permeability, drainage class, albedo, soil organic, carbon, saturated hydraulic conductivity, drained upper and lower limit. Soil profile data by each horizon was created. The data included upper and lower horizon depths (cm); sand, silt, and clay percentage; bulk density (0.33 bar); pH in water etc. Quad UI tools from AgMIP, which is a desktop application for windows system converting crop modeling data to standard AgMIP format and then translating to compatible model-ready formats for multiple crop models. We used the Quad UI tool to generate soil database with correct column formatting for DSSAT. High Temporal Resolution Weather Database We acquired weather data from 2007 to 2014 from the Oklahoma Mesonet (http://mesonet.org/). Around 110 automated weather stations collect statewide weather data. A minimum of one site is located in each of the Oklahoma's 77 counties to ensure spatial meteorological differences across landscapes are captured really well. Mesonet stations record data at 5-minute intervals, which include relative humidity, air temperature, wind speed, wind direction, rain, barometric pressure, solar radiation, and soil temperature at different depths. We only used information on temperature, solar radiation, and precipitation to compute calculate average values for 30 minute and 60 minutes time interval. For creating input weather files for DSSAT, solar radiation data was converted from W m-2 to MJ m?2. Half -hourly and hourly time value data were converted into UNIX time format. Using UNIX time format has some benefits; it only uses numbers and does not include hyphen and colons reducing the length of characters used to denote a time. Daily Weather Data Our approach to handle the missing information for the weather variables was to first rely upon nearby NOAA-GHCN (Global Historical Climatology Network) stations (Menne, Durre, et al., 2012), if the data is available, and then on WeatherMan (Pickering, Hansen, et al., 1994). WeatherMan stochastically generates values for missing records using the means and variances of long-term weather record for a weather station. We identified the nearest GHCN site for each mesonet station and used its corresponding daily weather record for gap filling. In addition, weather record of the NOAA GHCN site prior to its corresponding mesonet site's establishment was appended to the mesonet site to generate a complete long-term weather profile for the station. Using records from previous day measurements did not work as expected because there were several instances where we observed minimum temperature greater than maximum temperature and vice versa. Gridded rainfall data was acquired from the National Weather Service (NWS) Advanced Hydrologic Prediction Service website (http://water.weather.gov/precip/p_download_new/). The NWS River Forecast Centers use computer applications for generating the most accurate rainfall estimates to use as input to hydrologic models. The data is pooled from multiple sensors such as radar images, rain gauge, and satellite images. The data undergoes thorough quality control before its final production. Precipitation estimates at the 12 continental United States RFCs mosaicked to create roughly 4km grids (Lawrence, Shebsovich, et al., 2003). Unlike point-based rainfall measurement from the Oklahoma Mesonet relying on a single sensor i.e. rain gauge, the NWS rainfall data is created combining multiple sensors, is continuous and also captures local climatological and geographical effects on rainfall. The data is available in both shapefile and network Common Data Form (NetCDF) format. Rainfall data was downloaded in shapefile format. Projection information was redefined its projection so that is which was downscaled to 800 ´ 800 m pixel size. This spatial scale was chosen to represent the scale at which land use and human influence could plausibly influence the establishment of aquatic plants. Climate grids were constructed for four climate variables (precipitation, maximum temperature, minimum temperature, and solar radiation) for the period 2007-2012, which require multiple steps of reformatting, processing, and cataloging. For temperature and solar radiation, we used mesonet data to create statewide raster surfaces. We used ordinary kriging as an interpolation method to generate surfaces on a daily interval for solar radiation (MJ m-2), maximum temperature (ºC), and minimum temperature (ºC) using the 118 mesonet sites.

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2015 Citation: Kakani, V.G., P. Wagle, A.J. Foster, K. Dhakal, T.E. Ochsner and J.G. Warren. 2015. Greenhouse Gas Emissions from Bioenergy Cropping Systems in the Southern Great Plains. [CD-ROM]. ASA, CSSA, and SSSA, Madison, WI.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Foster, A.J., V.G. Kakani, J. Mosali. 2016. Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression. Precision Agriculture. First Online: 27 May 2016; DOI: 10.1007/s11119-016-9455-8.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Wagle, P., V.G. Kakani, R.L. Huhnke. 2016. Evapotranspiration and Ecosystem Water Use Efficiency of Switchgrass and High Biomass Sorghum. Agronomy Journal. 108:1007-1019.


Progress 10/16/14 to 09/30/15

Outputs
Target Audience:Bioenergy systems community, Agriculturla Model Improvement and Intercomparison community, formal classroom instruction for undergraduates and graduates. experiential learning opportunities for NSF REU students, instructed ethnic minorities as part of REU program and formal teaching. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? World Food Prize and Borlaug Dialogue Series, Oct 15-17, 2014 Provosts Initiative: Focus on General Education, 2014 ASA, CSSA and SSSA International Annual Meetings, Oct 31-Nov 6, 2014. South Central Climate Science Center 2014 Research Workshop, Dallas, TX, 20-21 Nov, 2015, Heat and Drought Wheat Improvement Consortium Meeting, Frankfurt, Germany, Nov 30 - Dec 4. ARPA-E Energy Innovation Summit, Washington D.C.,9-11 Feb 2015 Merging Crop Models and Genetics Workshop,Gainesville, FL,19-25 Jul, 2015 How have the results been disseminated to communities of interest?Explained the ongoing project activities to the participants (10) of the NSF-REU program hosted by the Biobased Products and Energy Center and lead by the project PI Dr. Kakani. Studentsattending theOSHRE 2014 Biology and Engineering for Sustainable Tomorrow Summer Academy (twenty five (25) 7th graders in areas of plant science and bioenergy) were exposed to various bioenergy related activities of teh project. What do you plan to do during the next reporting period to accomplish the goals?1) A manuscript will be developed and submitted using the NEE protocol. 2) Current perennial grass model in DSSAT will improved to simulate switchgrass growth and development. 3) Research finding will be disseminated through conferences and research workshops. 4) New project proposal will be submitted leveraging the data and results developed from this project.

Impacts
What was accomplished under these goals? 1) Identify current bioenergy crop models and simulation capabilities: Under this objective DSSAT crop model was evaluated for simulating high biomass sorghum and switchgrass. Current sorghum parameters were unable to simulate high biomass sorghum yield of biomass.Similarly, Almanac model ws evaluated for simulating sorghum and switchgrass. The model does not have parameters to simulate high biomass sorghum. The model partially simulated switchgrass yields, but was unable to accout for changes in soil moisture and air temperature. 2) Develop model parameters and crop coefficients for bioenergy crops in existing models. Under this objective sorghum parameters in DSSATwere modified and model was calibrated for simulating high biomass yield. An R2 of greater than 0.9 was achieved between observed and predicted biomass production. Further calibration is beingcarried outto evaluate model performance under multiple locations. 3) Develop new models for advanced bioenergy production feedstocks. Algorithms are being developed from field studies and data obtained from collaborators on SunGrant project. 4) Model relative production potential of various biofuel crops in various areas and land-types (e.g. existing crop and pasture vs. marginal lands). The DSSAT crop model was compiled and implemented on OSU Cowboy HPC system.The Net Ecosystem Exhange is being used currentlyas a surrogate for estimating relative biomass production potential in variou areas and land-types. A spatial data base of OK Mesonet was developed form 1994 to 2014 that includes 5 minute values ofTemperature, solar radiation, precipitation, soil mositure and relative humidity. Algorithms and protocols were developed to simulate NEE at 30 min interval for identified switchgrass production areas in the state of oklahoma. Initial NEE simulations aross the state demonstrate a high correlation between biomass production and NEE at 5 test sites in the state. Further model calibration and validation is required to enhancebiomass prediction. 5) Produce multi-model assessment of the impacts of bioenergy crops on land use change and food security. As the earlier goals are were developed this objective will be initiated in later years of the project. 6) Conduct multi-model assessment of the impacts of climate change on bioenergy crop production. A data base of various crop models capable of simulating bioenery crops is being developed. Climate change databse for OK is being developed using mesonet weather data and RCP projections.

Publications

  • Type: Journal Articles Status: Published Year Published: 2015 Citation: 1. Hivrale, V., V. Zheng, C.O.R. Puli, G. Jagadeeswaran, K. Gowdu, V.G. Kakani, A. Barakat, R. Sunkar. 2015. Characterization of drought- and heat-responsive microRNAs in switchgrass. Plant Science. doi: http://dx.doi.org/10.1016/j.plantsci.2015.07.018 2. Yimam, Y.T., T.E. Ochsner, V.G. Kakani. 2015. Evapotranspiration partitioning and water use efficiency of switchgrass and biomass sorghum managed for biofuel, Agricultural Water Management 155: 40-47. http://dx.doi.org/10.1016/j.agwat.2015.03.018. 3. Haankuku, C., F.M. Epplin, and V.G. Kakani. 2015. Industrial sugar beets to biofuel: Field to fuel production system and cost estimates. Biomass and Bioenergy. 80: 267-277. doi: http://dx.doi.org/10.1016/j.biombioe.2015.05.027 4. Mohammed, Y. A., W. Raun, V.G. Kakani, H. Zhang, R. Taylor, K.G. Desta, C. Jared, J. Mullock, J. Bushong, A. Sutradhar, M.S. Ali, and M. Reinert. 2015. Nutrient sources and harvesting frequency on quality biomass production of switchgrass (Panicum virgatum L.) for biofuel. Biomass and Bioenergy, 81, 242-248. doi: http://dx.doi.org/10.1016/j.biombioe.2015.06.027 5. Foster, A.J., K. Dhakal, V.G. Kakani, M.Gregory, J. Mosali. 2015. Fine and Coarse Scale Sampling of Spatial Variability within a Switchgrass Field in Oklahoma. International Journal of Plant and Soil Sciences. 6: 254-265. doi: 10.1007/s12155-014-9434-8
  • Type: Journal Articles Status: Under Review Year Published: 2015 Citation: 4. Foster, A.J., V.G. Kakani, J.Ge, and J. Mosali. 2015. Discriminant analysis of leaf and canopy scale hyperspectral reflectance of nitrogen treatments in switchgrass and high biomass sorghum. International Journal of Remote Sensing. [Revision Submitted] 5. Wagle, P., V.G. Kakani, R.L. Huhnke. 2015. Evapotranspiration and ecosystem water use efficiency of dedicated bioenergy feedstocks: switchgrass and high biomass sorghum. Agronomy Journal. [In review]
  • Type: Conference Papers and Presentations Status: Published Year Published: 2014 Citation: 1. Kakani, V.G. and P. Wagle. 2014. Ecosystem level responses of dedicated bioenergy feedstocks. 4th Pan-American Congress on plants and Bioenergy, University of Guelph Conference Center, Jun 4-7, 2014, Guelph, ON, Canada. 2. Kakani, V.G., P. Wagle, A.J. Foster, K. Dhakal, R.L. Huhnke, T.E. Ochsner and J.G. Warren. 2014. Sustainable Bioenergy Feedstock production in Southern Great Plains. In Annual meetings abstracts [CD-ROM]. ASA, CSSA, and SSSA, Madison, WI. 3. Dhakal, K. and V.G. Kakani. 2014. Spatially Explicit Regional Assessment of Climate Change Impacts on Oklahoma Wheat Production. In Annual meetings abstracts [CD-ROM]. ASA, CSSA, and SSSA, Madison, WI.
  • Type: Theses/Dissertations Status: Published Year Published: 2014 Citation: Wilson, T. 2014. Nitrogen management for climate change and mitigation in the southern great plains. Dissertation. Submitted to the Faculty of the Graduate College of the Oklahoma State University in partial fulfillment of the requirements for the Degree of DOCTOR OF PHILOSOPHY. December, 2014.
  • Type: Book Chapters Status: Published Year Published: 2015 Citation: 1. Kakani, V.G., T.R. Wheeler, P.Q. Craufurd and R.C.N. Rachaputi. 2015. Effect of High Temperature and Water Stress on Groundnut under Field Conditions. In: Combined Stresses in Plants: Physiological, Molecular and Biochemical Aspects, Ed. Mahalingam, R. Springer International Publication AG, Cham. 2. Kiniry, J.R., M.N. Meki, T.E. Schumacher, C.J. Zilverberg, F.B. Fritschi and V.G. Kakani. 2014. Modeling to Evaluate and Manage Water and Environmental Sustainability of Bioenergy Crops in the United States. In: Advances in Agricultural Systems Modeling, Practical Applications of Agricultural System Models to Optimize the Use of Limited Water 5:139-160 doi:10.2134/advagricsystmodel5.c6
  • Type: Conference Papers and Presentations Status: Published Year Published: 2015 Citation: 1. Kakani, V.G. 2015. Bioenergy: Challenges and Opportunities. Presented to Faculty and Students of Indian Agricultural Research Institute, 13th March 2015, Delhi, India.