MANAGING COMPLEX AGRICULTURAL PORTFOLIO RISK

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: COMPLETE
• Funding Source: HATCH
• Reporting Frequency: Annual
• Accession No.: 1017800
• Project Start Date: Oct 4, 2018
• Project End Date: Sep 27, 2021
• Program Code: [(N/A)]- (N/A)

Recipient Organization
SOUTH DAKOTA STATE UNIVERSITY
PO BOX 2275A
BROOKINGS,SD 57007

Performing Department
Economics

Non Technical Summary
Real-time risk assessment that leads to optimal management decisions is becoming more critical to maintain a competitive advantage. The increasing challenge to real-time risk assessment is collecting and quantifying risk using real-time data that leads to enhanced optimal risk management decisions. Furthering the challenge is there are more sophisticated formal (contractual) and informal (non-contractual) relationships in the agri-food sector that have created greater complexities to assessing risk and risk reduction. Thus, decision-makers experience greater uncertainty to what their risk exposure there is when there are exogenous changes to important factors that affect risk. This condition is especially acute with larger, volatile agricultural portfolios where there are specific asset attributes that are important to the value of the assets (e.g. wheat protein, greenhouse gas (GHG) reduction). To assess and manage the agricultural portfolio, real-time calculations of value-at-risk (VaR) measures will be quantified and utilized to enhance optimal risk management decisions. The measures will provide greater transparency on the risk reduction of various contractual relationships (informal and formal) given asset attributes embedded in the relationships. This research will enable agricultural firms (ranging from individual farms to corporations) to implement flexible risk management strategies that reduces risk when there are changes to external factors. The research will lead to more accurate and timely risk assessments by using real-time data and new methods that identify more optimal risk management strategies.

Animal Health Component

60%

Research Effort Categories

Basic

30%

Applied

60%

Developmental

10%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
601	6030	3010	80%
601	6220	3010	20%

Knowledge Area
601 - Economics of Agricultural Production and Farm Management;

Subject Of Investigation
6030 - The farm as an enterprise; 6220 - Marketing, processing, and supply firms other than cooperatives;

Field Of Science
3010 - Economics;

Keywords

value at risk (var)

agricultural risk

new generation grain contracts

characteristic values

spatial

new institutional economics

predictive analytics

machine learning

real-time analysis

hedging

futures

options

vine copulas

real-time data

optimal portfolios

corn

soybeans

Goals / Objectives
Real-time risk assessment that leads to optimal management decisions is becoming more critical to maintain a competitive advantage. However, there are increasing challenges to real-time risk assessments because of larger data collection necessary and because the larger data collections must utilize methods that can quantify risk using real-time data that leads to enhanced, optimal risk management decisions. Moreover, there are more sophisticated formal (contractual) and informal (non-contractual) relationships in the agri-food sector that have created greater complexities to assessing strategies for risk reduction. Thus, decision-makers experience greater uncertainty to what their risk exposure is when there are exogenous changes. This condition is especially acute with larger, volatile agricultural portfolios where there are specific asset attributes that are important to the value of the assets (e.g. wheat protein, greenhouse gas (GHG) reduction). To assess and manage the agricultural portfolio, real-time calculations of value-at-risk (VaR) measures will be quantified and utilized to enhance optimal risk management decisions. These measures will provide greater transparency on the risk reduction of various contractual relationships (informal and formal) given asset attributes embedded in the relationships.Complex, formal contracts to manage agriculture risk and profitability will be studied. This includes examining the use of complex grain marketing contracts, both over-the-counter products coupled with futures and vanilla options strategies and insurance products. In addition, the research will focus on how contracts specify the valuation of commodities given various asset attributes. This research will provide greater transparency to the value contributions of asset attributes and VaR to those attributes. Risk management of the individual enterprises and the complete business portfolio, including potential alternative crops when applicable, will also be explored.Informal relationships along the agricultural supply chain will also be investigated. As the agricultural industry experiences greater concentration, and changes in consumer preferences, issues relating to informal relationships including producer, consumer, and processor bargaining power and governance rules will be explored. This includes the emergence of various initiatives and strategic alliances that are aimed at better meeting changes to consumer preferences by specifying management practices or specific asset attributes using product labels (e.g. GHG reduction and sustainability).Both formal and informal relationships will be studied under varying scenarios of exogenous factors, such as weather and policy impacts. Under each scenario, VaR will be quantified, potential risk reduction from contracts to manage risk will be quantified, and farm and enterprise profitability will be examined. Risk will be measured assuming alternative scenarios of exogenous factors, including changes to institutions (e.g. organizations, contracts, laws, norms, rules) that are spatially distinct. This will enable agricultural firms to implement flexible risk management strategies to reduce risk when there are changes to exogenous factors. The research will lead to quicker risk assessment using real-time data and rapidly identify more optimal risk management strategies. Specific objectives of this project include:Objective 1: Quantify agricultural value at risk for various firms Objective 2: Quantify agricultural risk and risk reduction of formal and informal contracts or relationships and examine potential implicationsObjective 3: Identify optimal agricultural risk management portfolios using risk adjusted measuresObjective 4: Develop tools to calculate agricultural value at risk in real-timeObjective 5: Measure agricultural value characteristics of specific asset attributes

Project Methods
Few studies have specifically implemented VaR analyses focused on the agricultural industry. In the late 1980s, major financial firms began using VaR to manage their portfolio risk (Linsmeier & Pearson, 2000). In 1997, the Securities and Exchange Commission (SEC) established VaR (Linsmeier & Pearson, 1997) as an approved method for risk reporting (Manfredo & Leuthold, 2001). The release of J.P. Morgan's Risk Metrics software platform along with the published technical paper was accepted by many at that time as the industry standard for estimating VaR (Manfredo & Leuthold, 2001). This spurred the usage of VaR in estimating and managing risk, particularly in the financial sector. During this time period, Ohio State University and the University of Illinois at Urbana-Champaign developed an AgRisk program that analyzed VaR for pre-harvest marketing strategies for corn, wheat, and soybeans (Manfredo & Leuthold, 2001). To the best of my knowledge, these platforms are no longer published or supported on the internet.The agricultural research that has been done was focused on value at risk analyses on segments further from the farm-gate. Manfredo and Leuthold (2001) examined VaR focused on cattle feeding margins. Hawes, Wilson, and Dahl (2005) explained how VaR could be utilized for agricultural end-users. However, over the last decade, little attention has been focused on researching the optimal modeling structure to measure agricultural value at risk, nor in developing a platform to enable agricultural firms to frequently monitor value at risk. However, over this period agricultural risk has become more complicated to manage as a vast array of risk management options have emerged, including new generational grain marketing contracts coupled with greater insurance options choices for producers. TeSlaa, Elliott, Elliott, and Wang (2017) examined the performance of the producer accumulator contact in corn and soybean markets. There is a critical need for VaR to be analyzed at the firm level, which at the farm level would include modeling specific spatial factors such as farm yields, basis risk, and input price risk given farm level relationships. As the toolbox of risk management products has expanded, this greater complexity has often resulted in lenders not understanding their clients risk exposure and need for credit lines to manage risk. Enabling agricultural firms to better monitor and manage their risk will provide creditors with greater transparency into the firm's risk management strategies. VaR estimates the probability that a portfolio will lose a certain amount in a defined time-frame at a chosen level of certainty. The financial sector actively uses VaR for managing and reporting risk exposure for multiple assets, and stress-testing firms' portfolios. It allows for the risk of an entire portfolio of assets to be assessed in a single value. At any period, an agricultural firm, including producers, can observe their margin, forecast its volatility, and consequently calculate the VaR of their margin (Manfredo & Leuthold, 2001). VaR allows for the measurement of risk of a current portfolio including production and price risk, along with any currently implemented risk management strategies, which could include futures and options, over-the-counter products (e.g., new generation grain marketing contracts), insurance products, and farm programs. VaR can aid with decisions relating to hedging, managing cash flows, deciding on asset allocation or exposure (including crop selection and allocation decisions at the farm level) (Manfredo & Leuthold, 2001). VaR allows for different scenarios of potential risk management strategies to be explored to determine the degree of risk reduction for a given portfolio. This allows a firm to evaluate the marginal risk benefit of adding another risk management dimension. The VaR method allows for risk exposure to be measured in real-time for a firm given market (e.g., price changes) and production (e.g., changes in yield expectations over the production cycle) changes.To assess agricultural risk, additional research must be performed to better understand the impacts of various formal and informal relationships. In addition, research must identify influential exogenous factors in the agricultural sector. The types of formal relationships that will be explored can include complex grain marketing contracts, future and options marketing strategies, cash contracts, agricultural exchange-traded funds (ETFs), insurance products, cooperative membership, and alternative crop contracts. Informal relationship issues related to governance and bargaining power along the agricultural supply chain may also be examined. The entities in informal relationships may include commodity boards' use of checkoffs, commodity associations, strategic alliances, and other state or federal initiatives that alter marketing risk in agriculture. Analyses will incorporate spatial dynamic factors that contribute to risk management decisions. The impact of exogenous factors, which may include weather, policy, and social media information on risk will be considered.Objective 1: Quantify agricultural value at risk for various firmsTo measure value-at-risk (VaR) a combination of risk modeling approaches will be used (historical simulation/Monte Carlo simulation /variance-covariance approach). Real-time data from Bloomberg terminals will be used, as well as data from the CME Group. Risk of price, yield, and other relationships with covariates that have interacting dependence structures can be estimated and simulated using vine copulas that provide the flexibility to more accurately model these relationships. Furthermore, predictions on most likely outcomes can use machine learning techniques and other methods utilized in the field of predictive analytics. The VaR models that are developed will provide risk assessments that will enable firms to better understand their risk exposure and the marginal benefits of various risk management strategies. The research will provide specific measures of portfolio value at risk using real time data and approximate the value of risk reduction from utilizing marketing contracts and strategies.Objective 2: Quantify agricultural risk and risk reduction of formal and informal contracts or relationships and examine potential implications (methods in objective 1 will also be used): Option pricing formulas for vanilla and exotic options can be used using the Matlab Financial packages and other financial packages for similar programs to estimate the likely value changes and risk reductions in more complex grain marketing contracts. Game theory modeling, simulation, predicative analytics, regression analysis, and case study approaches can be used to understand formal and informal, strategic relationship risks, and changes to risk, given alternative governance rules.Objective 3: Identify optimal agricultural risk management portfolios using risk adjusted measures Ratios such as the Sharpe ratio and Sortino ratio will be estimated to provide comparisons of risk-adjusted return of alternative risk management strategies.Objective 4: Develop tools to calculate agricultural value at risk in real-time (methods in objective 1, 2 and 3 will also be used): Available cloud computing services allow greater use of sophisticated analytical tools and programmable data collection to measure risk and distribute risk assessments of firms using online/mobile apps or by providing add-on tools to spreadsheet programs like excel.Objective 5: Measure agricultural value characteristics of specific asset attributes Information resulting from hedonic price modeling, specifying product characteristics and market factors, and conjoint analysis, using a survey-based approach, will allow for value decompositions relating to specific asset attributes.

Progress 10/04/18 to 09/27/21

Outputs
Target Audience:The target audience has included a wide array of domestic and international agricultural stakeholders and academics. The dissemination of this research has reached agricultural producers, cooperative members, agricultural leaders, and policymakers, as well as both researchers/instructors and undergraduate/graduate students. These individuals have been reached through presentations, workshops, publications, and web applications. Specifically, on-going research findings have been shared at numerous statewide South Dakota meetings. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?This project has provided opportunities for researchers to attend the Western Agricultural Economics Annual Meeting and American Agricultural Economics Meeting. How have the results been disseminated to communities of interest?The research results have been disseminated to interested parties by journal articles, publications, presentations, and web applications. Attendees of our presentations and webinars included ag producers, policy makers, ag students, and ag lenders. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Objective 1: Quantify agricultural value at risk for various firms. (75% Accomplished) The producer value-at-risk models were enhanced to calculate precision agricultural risk as an operation portfolio in real-time by incorporating location specific production risk and price risk. Commodity price risks include both futures and basis price risks, with exposure to downward price risk being of concern. Commodity production risk occur during the growing cycle for crops with production risk decreasing as harvest time is approached. The models also incorporate the operation's current risk management portfolio, which may include futures, options, cash marketing strategies, and insurance coverage. In addition, the analysis can incorporate an operation's input costs to calculate net income VaR. The producer has the ability to examine how the operation's Ag VaR might be altered with the adoption of other risk management strategies given changes in market prices for futures and options. These models allow producers to optimize their risk management given the daily change in price and production risk. These models more accurately measure the operations' s Ag VaR allowing users to more optimally choose risk management strategies that fit their tolerance to risk, and potentially reducing costs associated with duplicating risk reduction through multiple risk management products. Objective 2: Quantify agricultural risk and risk reduction of formal and informal contracts or relationships and examine potential implications. (45% Accomplished) Web applications for agricultural stakeholders to monitor risk factors Four web applications have been developed that allow agricultural stakeholders to monitor risk factors that affect the profitability of their operation. Enabling producers the ability to monitor various risk factors allows them to make better management decisions by having access to more information, thus contributing to their ability to remain economically viable. We have had over 1,870 unique visitors to our web applications since May of 2021. Risk Reduction of New Generation Grain Contracts Our research adds to the current literature on agricultural strategy performance in corn and soybean commodity markets in two ways. (1) It constructs and prices a subset of NGGCs, including the Accumulator contract, thus providing transparency between the issuer and producer with regard to the theoretical underpinnings of contract terms and exposing the agricultural sector to opportunities to construct novel risk management contracts through exotic options that may provide better flexibility in risk management. (2) It provides both the risk and return performance of the producer Accumulator and alternative NGGCs through back-testing. We found that the Accumulator portfolios in both commodity markets had the highest standard deviation in daily value changes between 2008 and 2017. In contrast, the Price Protector contracts and Minimum Price contracts offered the most risk reduction. The amount of risk reduction did depend on the volatility in the market largely due to changes in fundamental carryover stocks and adverse weather during the growing season. Tariff impacts to corn, soybeans, and wheat The BSTS model that we developed generally found that the impact of Chinese soybean tariffs on nearby CBOT U.S. soybean prices was a loss of approximately $0.31 to $0.61 per bushel from June 2018 to June 2019, with a mean expected loss of $0.46. This assumed that Brazil soybean prices increased 3.5% because of the tariff and adjusted the price series downward accordingly.When we multiplied the tariff impact estimate (-$0.46 per bushel) by U.S. soybean production in 2018 (4.5 billion bushels), we found that there was approximately a $2 billion loss to U.S. soybean producers in 2018. When we estimated the tariff effect to the nearby CBOT corn price using the BSTS model, we found that the estimated impact was approximately a loss of $0.10 per bushel. The 95% confidence range was a $0.04 to $0.15 per bushel loss from June 2018 to June 2019. The effect on corn prices was significant (p=0.004), which is similar to the soybean result. Given the BSTS estimate of tariff effects, we estimate that there was approximately a $1.4 billion loss to corn producers in the U.S. during 2018. When we estimated the tariff effects on nearby CBOT wheat prices, we found that the estimated impact was approximately a loss of $0.91 per bushel. The 95% confidence range was from a $0.51 to $1.11 per bushel loss. Again, the tariff effect on wheat prices was significant (p=0.001). Given the BSTS estimate of tariff effects, we estimate that there was approximately a $1.6 billion loss to wheat producers in the U.S. However, these values for all commodities could be larger or smaller depending on the number of bushels sold during the marketing year when the tariff impacts were greater. Objective 3: Identify optimal agricultural risk management portfolios using risk adjusted measures (40% Accomplished) This objective was met with applied research in the two areas of measuring VaR and analyzing NGGCs. The advanced models related to measuring and monitoring VaR (Objective 1) have led to novel tools and education for agricultural producers that more accurately identified optimal agricultural risk management portfolios. When analyzing NGGCs and considering risk as well as return, the Accumulator had the second highest Sharpe ratio in corn and the third highest in soybeans. The best risk-adjusted return was found for the Price Plus contract when examining the entire period. The type of market largely determined the ranking of the NGGCs, as years with tight carryover stocks reduced the risk-adjusted ranking of the Price Plus contract. Objective 4: Develop tools to calculate agricultural value at risk in real-time (60% Accomplished) Ag VaR Tool The advanced models related to measuring and monitoring VaR (Objective 1) led to novel tools and education for agricultural producers. The methods and system of the Ag VaR tool were submitted as an invention disclosure through the SDSU Technology Transfer Office. We have entered into a technology development license agreement with a firm to pursue commercialization of the tool, along with private sector partnerships. A patent application has been filed at the U.S. Patent and Trademark Office covering the methods and system of the Ag VaR tool. We have developed a private sector partnership with an affiliated company to the firm we are collaborating with to pursue commercialization of the tool. Through this relationship, we have engaged with producers in a grower network focused on identify preservation which was created by the company. We have continued to pursue various efforts with the firm to pursue commercialization. These efforts have included developing a business plan and pitch deck presentations. Monitoring Tariff Impacts We developed a web application to monitor the real-time Chinese tariff impact estimates on U.S. agricultural commodities. The data are periodically updated for users to generate new 'nowcasts' of tariff impacts using the data streaming capabilities from our trading lab. The application allows users to examine a certain commodity and market (e.g., futures or cash markets), date range, and data to begin measuring the impact of tariffs. Objective 5: Measure agricultural value characteristics of specific asset attributes (15% Accomplished) Two aspects relating to this objective have been completed. A conceptual framework on how to measure the financial risk of commodity characteristics has begun. In addition, a preliminary survey on value characteristics of camelina and carinata was developed along with a list of potential processors with contact information.

Publications

• Type: Conference Papers and Presentations Status: Other Year Published: 2020 Citation: Elliott, L., and M.S. Elliott. 2020. Demonstration of ag value-at-risk web tools. Prairie Aquatech. June 23. Brookings, SD.
• Type: Journal Articles Status: Published Year Published: 2019 Citation: Elliott, M.E., L.E. Elliott, T.W. Wang, and D.M. Malo. 2019. A change in highest and best use policy in South Dakota has a sizable Impact on agricultural land assessments. Choices. Quarter 4. Available online: http://www.choicesmagazine.org/choices-magazine/submitted-articles/a-change-in-highest-and-best-use-policy-in-south-dakota-has-a-sizable-impact-on-agricultural-land-assessments
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Elliott, M. S., Elliott, L.M., and Wang, T. 2020. Meandering water in the Prairie Pothole region of South Dakota. Journal of the ASFMRA, 18-29. Available at: https://higherlogicdownload.s3.amazonaws.com/ASFMRA/aeb240ec-5d8f-447f-80ff-3c90f13db621/UploadedImages/Journal/ASFMRAJournal_2020_Final.pdf
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Elliott, M., and Elliott, L. 2020. Using data analytics and decision tools in student and extension agribusiness education. Applied Economics Teaching Resources. 2(2). Available at: https://www.aaea.org/publications/applied-economics-teaching-resources/volumes/volume-2-2020/volume-2-issue-2-march-2020
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Elliott, M., and Elliott, L. 2020. Developing R Shiny web application for extension education. Applied Economics Teaching Resources. 2(4). Available at: https://www.aaea.org/publications/applied-economics-teaching-resources/volumes/volume-2-2020/volume-2-issue-4-october-2020
• Type: Conference Papers and Presentations Status: Other Year Published: 2019 Citation: OBrien, D., M.S. Elliott, and L. M. Elliott. 2019. South Dakota regional basis. Ag Horizons Conference. Dec 12. Pierre, SD.
• Type: Conference Papers and Presentations Status: Other Year Published: 2019 Citation: Elliott, M.S., L.M. Elliott, D.D. Malo, and T. Wang. 2019. Ag land highest and best use determinations. Ag Horizons Conference. Dec 11. Pierre, SD.
• Type: Conference Papers and Presentations Status: Other Year Published: 2019 Citation: Elliott, L., and M.S. Elliott. 2019. Demonstration of ag value-at-risk web tools. South Dakota Innovation Partners. Dec 13. Brookings, SD.
• Type: Other Status: Published Year Published: 2020 Citation: Elliott, M. S., and Elliott, L. M. 2020. South Dakota grain basis tool. Brookings, SD: SDSU Extension. May 14. Available at: https://extension.sdstate.edu/south-dakota-grain-basis-tools
• Type: Other Status: Published Year Published: 2020 Citation: Davis, J, Elliott, M. S., and Elliott, L. M. 2020. 2020 Planting decisions. Brookings, SD: SDSU Extension. April 7. Available at: https://extension.sdstate.edu/2020-planting-decisions
• Type: Other Status: Published Year Published: 2020 Citation: Elliott, M. S., and Elliott, L. M. 2020. South Dakota grain net income tool. Brookings, SD: SDSU Extension. April 21. Available at: https://extension.sdstate.edu/south-dakota-grain-net-income-tool
• Type: Other Status: Published Year Published: 2020 Citation: Elliott, M. S., Elliott, L. M. and Wang, T. 2020. South Dakota ag land soil tables tool. Brookings, SD: SDSU Extension. June 22. Available at: https://extension.sdstate.edu/south-dakota-ag-land-soil- tables-tool
• Type: Other Status: Published Year Published: 2020 Citation: Elliott, M. S., and Elliott, L. M. 2020. South Dakota interactive grain report tool. Brookings, SD: SDSU Extension. July 8. Available at: https://extension.sdstate.edu/interactive-grain-report-tool

Progress 10/01/19 to 09/30/20

Outputs
Target Audience:The target audience has included a wide array of domestic and international agricultural stakeholders and academics. The dissemination of this research has reached agricultural producers, cooperative members, agricultural leaders, and policymakers, as well as both researchers/instructors and undergraduate/graduate students. These individuals have been reached through presentations, workshops, publications, and web applications. Specifically, on-going research findings have been shared at numerous statewide South Dakota meetings. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?This project has provided opportunities for researchers to attend the Western Agricultural Economics Annual Meeting and American Agricultural Economics Meeting. How have the results been disseminated to communities of interest?The research results have been disseminated to interested parties by journal articles, publications, presentations, and web applications. Attendees of our presentations and webinars included ag producers, policy makers, ag students, and ag lenders. What do you plan to do during the next reporting period to accomplish the goals?Objective 1: Quantify agricultural value at risk for various firms We plan to make additional enhancements to the models to evaluate agricultural value at risk in a more holistic manner and micro-level for agricultural firms. Objective 2: Quantify agricultural risk and risk reduction of formal and informal contracts or relationships and examine potential implications We will continue to examine agricultural risk and risk reduction of formal and informal contracts, including NGGCs to see the risk reduction, when coupled with other risk management strategies. Objective 3: Identify optimal agricultural risk management portfolios using risk adjusted measures We will continue identifying optimal agricultural risk management portfolios by examining the inclusion of other risk management products. Objective 4: Develop tools to calculate agricultural value at risk in real-time We will continue to develop enhancements to the agricultural value at risk so that it encompasses more components of the firm's risk. In addition, we will continue to pursue commercialization of the tool, along with developing private sector partnerships. Objective 5: Measure agricultural value characteristics of specific asset attributes We plan to continue further develop a conceptual framework on how to measure the financial risk of commodity characteristics.

Impacts
What was accomplished under these goals? Objective 1: Quantify agricultural value at risk for various firms. (40% Accomplished) The producer value-at-risk models were further developed to calculate an operation's value-at-risk more accurately in real-time by incorporating location specific production risk and price risk. Commodity price risks include both futures and basis price risks, with exposure to downward price risk being of concern. Commodity production risk occur during the growing cycle for crops with production risk decreasing as harvest time is approached. The models also incorporate the operation's current risk management portfolio, which may include futures, options, cash marketing strategies, and insurance coverage. In addition, the analysis can incorporate an operation's input costs to calculate net income VaR. The producer has the ability to examine how the operation's Ag VaR might be altered with the adoption of other risk management strategies given changes in market prices for futures and options. These models allow producers to optimize their risk management given the daily change in price and production risk. These models more accurately measure the operations' s Ag VaR allowing users to more optimally choose risk management strategies that fit their tolerance to risk, and potentially reducing costs associated with duplicating risk reduction through multiple risk management products. Objective 2: Quantify agricultural risk and risk reduction of formal and informal contracts or relationships and examine potential implications. (40% Accomplished) Four web applications have been developed that allow agricultural stakeholders to monitor risk factors that affect the profitability of their operation. Enabling producers the ability to monitor various risk factors allows them to make better management decisions by having access to more information, thus contributing to their ability to remain economically viable. These applications bring together data from various sources, enabling stakeholders easy accessibility to this information with a simple click within the applications to access data associated with specific geographic points. In addition, these applications provide stakeholders with the ability to easily interact with the data, and allow users to visualize the data within the application. Three of these web applications allow stakeholders to monitor their risk related to basis, net income, and fundamental supply and demand indicators with slightly delayed data. The other web application allows users to map and download data related to the following: baseline land assessed values, the NRCS land capability class, the percent of sand silt and clay, the NRCS crop productivity index, the amount of usable forage expected, the expected animal units months that can be supported, the probability the soil will mostly be cropland, and the percent of the soil that has been cropped since 2010. We have had over 1,100 unique visitors to our web applications since April of 2020. Objective 3: Identify optimal agricultural risk management portfolios using risk adjusted measures (40% Accomplished) The advanced models related to measuring and monitoring VaR (Objective 1) have led to novel tools and education for agricultural producers that more accurately identified optimal agricultural risk management portfolios. These models have been built into a web application that provides an interface for producers to customize the models to measure and monitor their risks through visualizations. Objective 4: Develop tools to calculate agricultural value at risk in real-time (40% Accomplished) The advanced models related to measuring and monitoring VaR (Objective 1) led to novel tools and education for agricultural producers. The tools and education allow a manager of a farm to monitor VaR when adopting multiple complex marketing contracts and insurance plans, and adjust their VaR to be aligned with their risk preferences. The methods and system of the Ag VaR tool were submitted as an invention disclosure through the SDSU Technology Transfer Office and a provisional patent application has been filed. We have entered into a technology development license agreement with a firm to pursue commercialization of the tool, along with private sector partnerships. We have continued to pursue various efforts with the firm to pursue commercialization. These efforts have included developing a business plan and pitch deck presentations. Objective 5: Measure agricultural value characteristics of specific asset attributes (15% Accomplished) We have started to develop a conceptual framework on how to measure the financial risk of commodity characteristics.

Publications

• Type: Journal Articles Status: Published Year Published: 2019 Citation: Elliott, M.E., L.E. Elliott, T.W. Wang, and D.M. Malo. 2019. A change in highest and best use policy in South Dakota has a sizable Impact on agricultural land assessments. Choices. Quarter 4. Available online: http://www.choicesmagazine.org/choices-magazine/submitted-articles/a-change-in-highest-and-best-use-policy-in-south-dakota-has-a-sizable-impact-on-agricultural-land-assessments
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Elliott, M. S., Elliott, L.M., and Wang, T. 2020. Meandering water in the Prairie Pothole region of South Dakota. Journal of the ASFMRA, 18-29. Available at: https://higherlogicdownload.s3.amazonaws.com/ASFMRA/aeb240ec-5d8f-447f-80ff-3c90f13db621/UploadedImages/Journal/ASFMRAJournal_2020_Final.pdf
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Elliott, M., and Elliott, L. 2020. Using data analytics and decision tools in student and extension agribusiness education. Applied Economics Teaching Resources. 2(2). Available at: https://www.aaea.org/publications/applied-economics-teaching-resources/volumes/volume-2-2020/volume-2-issue-2-march-2020
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Elliott, M., and Elliott, L. 2020. Developing R Shiny web application for extension education. Applied Economics Teaching Resources. 2(4). Available at: https://www.aaea.org/publications/applied-economics-teaching-resources/volumes/volume-2-2020/volume-2-issue-4-october-2020
• Type: Conference Papers and Presentations Status: Other Year Published: 2020 Citation: Elliott, L., and M.S. Elliott. 2020. Demonstration of ag value-at-risk web tools. Prairie Aquatech. June 23. Brookings, SD.
• Type: Conference Papers and Presentations Status: Other Year Published: 2019 Citation: OBrien, D., M.S. Elliott, and L. M. Elliott. 2019. South Dakota regional basis. Ag Horizons Conference. Dec 12. Pierre, SD.
• Type: Conference Papers and Presentations Status: Other Year Published: 2019 Citation: Elliott, M.S., L.M. Elliott, D.D. Malo, and T. Wang. 2019. Ag land highest and best use determinations. Ag Horizons Conference. Dec 11. Pierre, SD.
• Type: Conference Papers and Presentations Status: Other Year Published: 2019 Citation: Elliott, L., and M.S. Elliott. 2019. Demonstration of ag value-at-risk web tools. South Dakota Innovation Partners. Dec 13. Brookings, SD.
• Type: Other Status: Other Year Published: 2020 Citation: Elliott, M. S., and Elliott, L. M. 2020. South Dakota grain basis tool. Brookings, SD: SDSU Extension. May 14. Available at: https://extension.sdstate.edu/south-dakota-grain-basis-tools
• Type: Other Status: Published Year Published: 2020 Citation: Davis, J, Elliott, M. S., and Elliott, L. M. 2020. 2020. Planting decisions. Brookings, SD: SDSU Extension. April 7. Available at: https://extension.sdstate.edu/2020-planting-decisions
• Type: Other Status: Published Year Published: 2020 Citation: Elliott, M. S., and Elliott, L. M. 2020. South Dakota grain net income tool. Brookings, SD: SDSU Extension. April 21. Available at: https://extension.sdstate.edu/south-dakota-grain-net-income-tool
• Type: Other Status: Published Year Published: 2020 Citation: Elliott, M. S., Elliott, L. M. and Wang, T. 2020. South Dakota ag land soil tables tool. Brookings, SD: SDSU Extension. June 22. Available at: https://extension.sdstate.edu/south-dakota-ag-land-soil- tables-tool
• Type: Other Status: Published Year Published: 2020 Citation: Elliott, M. S., and Elliott, L. M. 2020. South Dakota interactive grain report tool. Brookings, SD: SDSU Extension. July 8. Available at: https://extension.sdstate.edu/interactive-grain-report-tool

Progress 10/04/18 to 09/30/19

Outputs
Target Audience:The target audience has included a wide array of domestic and international agricultural stakeholders and academics. The dissemination of this research has reached agricultural producers, cooperative members, agricultural leaders, and policymakers, as well as both researchers/instructors and undergraduate/graduate students. These individuals have been reached through presentations, workshops, videos, and publications. Specifically, on-going research findings have been shared at the American Agricultural Economic Association Annual Meeting and numerous statewide South Dakota meetings. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?This project has provided opportunities for researchers to attend the Western Agricultural Economics Annual Meeting and American Agricultural Economics Meeting. In addition, training has been provided to undergraduate students who have assisted with this project. Two undergraduates students (Devin Brand, B.S. Agricultural Business, 2019; Jana Thorstenson, B.S. Agricultural Business, 2019) assisted in developing datasets and surveys, and reviewing literature. In addition, the students helped in disseminating research results to stakeholders through publications and workshops. How have the results been disseminated to communities of interest?The research results have been disseminated to interested parties by presentations, videos, and publications. In addition, in April 2019 we hosted "Managing the Margin" workshops for ag producers to help them understand marketing strategies and alternative marketing contracts. We also provided a webinar on the same topic. Attendees included ag producers, policy makers, ag students, and ag lenders. Lisa Elliott was the lead presenter and Matt Elliott assisted in portions of the presentations. What do you plan to do during the next reporting period to accomplish the goals?Objective 1: Quantify agricultural value at risk for various firms We plan to make additional enhancements to the models in order to evaluate agricultural value at risk in a more holistic manner for agricultural firms. Objective 2: Quantify agricultural risk and risk reduction of formal and informal contracts or relationships and examine potential implications We will continue to examine agricultural risk and risk reduction of formal and informal contracts, including NGGCs to see the risk reduction, when coupled with other risk management strategies. Objective 3: Identify optimal agricultural risk management portfolios using risk adjusted measures We will continue identifying optimal agricultural risk management portfolios by examining the inclusion of other risk management products. Objective 4: Develop tools to calculate agricultural value at risk in real-time We will continue to develop enhancements to the agricultural value at risk so that it encompasses more components of the firm's risk. In addition, we will continue to pursue commercialization of the tool, along with developing private sector partnerships. Objective 5: Measure agricultural value characteristics of specific asset attributes We plan to continue further developing the survey for characteristic values for camelina and carinata, along with performing a pre-test of the survey.

Impacts
What was accomplished under these goals? Objective 1: Quantify agricultural value at risk for various firms. (20% Accomplished) Preliminary models were developed that allow individual producers to calculate their operation's value-at-risk in real-time by incorporating location specific production risk and price risk. The models also incorporate the operation's current risk management portfolio, which may include futures, options, cash marketing strategies, and insurance coverage. In addition, the analysis can incorporate a operation's input costs to calculate net income VaR. The producer has the ability to examine how the operation's Ag VaR might be altered with the adoption of other risk management strategies given changes in market prices for futures and options. These models allow producers to optimize their risk management given the daily change in price and production risk. Objective 2: Quantify agricultural risk and risk reduction of formal and informal contracts or relationships and examine potential implications. (30% Accomplished) Risk Reduction of New Generation Grain Contracts When we were conducting extension workshops with crop producers on marketing strategies, we received feedback from agricultural producers that they did not understand the risk management benefits of marketing contracts. This issue spurred us to conduct research on new generation grain contracts (NGGCs) to provide information to the agricultural sectors, including extension educators, risk management educators, grain merchants, and market researchers regarding the effectiveness of NGGCs as risk management tools. We also wanted to show how novel NGGC contracts can be constructed to manage risk. Our research adds to the current literature on agricultural strategy performance in corn and soybean commodity markets in two ways. (1) It constructs and prices a subset of NGGCs, including the Accumulator contract, thus providing transparency between the issuer and producer with regard to the theoretical underpinnings of contract terms and exposing the agricultural sector to opportunities to construct novel risk management contracts through exotic options that may provide better flexibility in risk management. (2) It provides both the risk and return performance of the producer Accumulator and alternative NGGCs through back-testing. We found that the Accumulator portfolios in both commodity markets had the highest standard deviation in daily value changes between 2008 and 2017. In contrast, the Price Protector contracts and Minimum Price contracts offered the most risk reduction. The amount of risk reduction did depend on the volatility in the market largely due to changes in fundamental carryover stocks and adverse weather during the growing season. Tariff impacts to corn, soybeans, and wheat The BSTS model that we developed generally found that the impact of Chinese soybean tariffs on nearby CBOT U.S. soybean prices was a loss of approximately $0.31 to $0.61 per bushel from June 2018 to June 2019, with a mean expected loss of $0.46. This assumed that Brazil soybean prices increased 3.5% because of the tariff and adjusted the price series downward accordingly. In the model we explored, the tariff was found to have a very significant impact (p=0.002), and there was little probability that divergence in U.S. soybean prices from prices in other markets happened by chance after mid-June 2018. Stated another way, a significant event occurred in June-July 2018 that caused the observed series of nearby CBOT soybean prices to diverge from the covariates that was different from how the series evolved with the covariates previously. When we multiplied the tariff impact estimate (-$0.46 per bushel) by U.S. soybean production in 2018 (4.5 billion bushels), we found that there was approximately a $2 billion loss to U.S. soybean producers in 2018. When we estimated the tariff effect to the nearby CBOT corn price using the BSTS model, we found that the estimated impact was approximately a loss of $0.10 per bushel. The 95% confidence range was a $0.04 to $0.15 per bushel loss from June 2018 to June 2019. The effect on corn prices was significant (p=0.004), which is similar to the soybean result. Given the BSTS estimate of tariff effects, we estimate that there was approximately a $1.4 billion loss to corn producers in the U.S. during 2018. When we estimated the tariff effects on nearby CBOT wheat prices, we found that the estimated impact was approximately a loss of $0.91 per bushel. We did not make any adjustments to the wheat prices in other markets that were used as covariates. The 95% confidence range was from a $0.51 to $1.11 per bushel loss. Again, the tariff effect on wheat prices was significant (p=0.001). Given the BSTS estimate of tariff effects, we estimate that there was approximately a $1.6 billion loss to wheat producers in the U.S. However, these values for all commodities could be larger or smaller depending on the number of bushels sold during the marketing year when the tariff impacts were greater. Objective 3: Identify optimal agricultural risk management portfolios using risk adjusted measures (40% Accomplished) When analyzing NGGCs and considering risk as well as return, the Accumulator had the second highest Sharpe ratio in corn and the third highest in soybeans. The best risk-adjusted return was found for the Price Plus contract when examining the entire period. The type of market largely determined the ranking of the NGGCs, as years with tight carryover stocks reduced the risk-adjusted ranking of the Price Plus contract. Objective 4: Develop tools to calculate agricultural value at risk in real-time (20% Accomplished) Ag VaR Tool The models related to measuring and monitoring VaR (Objective 1) led to novel tools and education for agricultural producers. The tools and education allow a manager of a farm to monitor VaR when adopting multiple complex marketing contracts and insurance plans, and adjust their VaR to be aligned with their risk preferences. The methods and system of the Ag VaR tool were submitted as an invention disclosure through the SDSU Technology Transfer Office and a provisional patent application has been filed. We have entered into a technology development license agreement with a firm to pursue commercialization of the tool, along with private sector partnerships. Monitoring Tariff Impacts We developed a web application to monitor the real-time Chinese tariff impact estimates on U.S. agricultural commodities. The data are periodically updated for users to generate new 'nowcasts' of tariff impacts using the data streaming capabilities from our trading lab. The application allows users to examine a certain commodity and market (e.g., futures or cash markets), date range, and data to begin measuring the impact of tariffs. Results are shown in a graph and quantified loss values are shown in boxes. Below these results, a description of the method used, an explanation of what is being reported, and a more detailed analysis and model report are provided. Users can make changes to the model and perform sensitivity analyses of variables used in the model. Although discretion must be applied when determining which components to make interactive for users without compromising model specifications, these applications are flexible and can be designed according to developers' preferences. Objective 5: Measure agricultural value characteristics of specific asset attributes (10% Accomplished) A preliminary survey on value characteristics of camelina and carinata was developed along with a list of potential processors with contact information.

Publications

• Type: Journal Articles Status: Published Year Published: 2019 Citation: Elliott, L., Elliott, M., Te Slaa, C., and Wang, Z. 2019. New generation grain contracts in corn and soybean commodity markets. Journal of Commodity Markets. (In Press). Available at: https://www.sciencedirect.com/science/article/pii/S2405851319300789
• Type: Journal Articles Status: Accepted Year Published: 2019 Citation: Elliott, M., Elliott, L., and Wang, T. 2019. A change in highest and best use policy in South Dakota shows a sizable impact to agricultural land assessments. Choices. (In Press). Available at: http://works.bepress.com/matthew-elliott/2/
• Type: Journal Articles Status: Accepted Year Published: 2019 Citation: Elliott, M., Elliott, L., and Wang, T. 2019. Meandering water in the Prairie Pothole region of South Dakota. Journal of ASFMRA (In Press).
• Type: Journal Articles Status: Accepted Year Published: 2019 Citation: Elliott, M., and Elliott, L. 2019. Using data analytics and decision tools in student and extension agribusiness education. Applied Economics Teaching Resources. (In Press).
• Type: Journal Articles Status: Under Review Year Published: 2019 Citation: Elliott, M., and Elliott, L. 2019. Divergence of USDA trade payments for corn, soybean, and wheat producers and Nowcasts of tariff impacts" AEPP. (Under review).
• Type: Conference Papers and Presentations Status: Other Year Published: 2019 Citation: Elliott, M. S., Elliott, L. M., Malo, D. D., and Wang, T. 2019. Ag land productivity assessment formula and soil research briefing. Ag land property tax assessment implementation and oversight task force. South Dakota Legislature. Pierre, SD. January 23.
• Type: Conference Papers and Presentations Status: Other Year Published: 2018 Citation: Elliott, M. S., Elliott, L. M., Malo, D. D., and Wang, T. 2018. Ag land productivity assessment formula and soil research finding. Ag land property tax assessment implementation and oversight task force. South Dakota Legislature. Pierre, SD. November 13.
• Type: Conference Papers and Presentations Status: Other Year Published: 2019 Citation: Elliott, M. and Elliott, L. 2019. Using data analytics and decision tools for agribusiness and extension education. American Agricultural Economic Association Annual Meeting. Atlanta, GA. July 23.
• Type: Conference Papers and Presentations Status: Other Year Published: 2019 Citation: Elliott, M.S. and Elliott, L. 2019. The economic impact of trade issues faced by agriculture. South Dakota Crop Insurance Conference. Brookings, SD. September 17.
• Type: Other Status: Other Year Published: 2019 Citation: Elliott, L. M., Elliott, M. S., Davis, J. B., and Sand, S. R. 2019. Managing the margin workshop. SDSU First Dakota National Bank e-Trading Education Laboratory. Brookings, SD. April 5.
• Type: Other Status: Other Year Published: 2019 Citation: Elliott M. and Elliott, L. 2019. Market-implied tariff impacts. South Dakota State University. Brookings, SD. http://agland.sdstate.edu/Tariff_web/.
• Type: Conference Papers and Presentations Status: Other Year Published: 2019 Citation: Elliott, L. M. 2019. Pre-planting marketing opportunities. Ag Economic Dialogues. South Dakota State University. Brookings, SD. March 1.
• Type: Conference Papers and Presentations Status: Other Year Published: 2018 Citation: Elliott, L. M. 2018. Crop market outlook and value at risk marketing tool. Ag Lenders Conference. Sioux Falls, SD. October 19.
• Type: Conference Papers and Presentations Status: Other Year Published: 2018 Citation: Elliott, L. M. 2018. Crop market outlook and value at risk marketing tool. Ag Lenders Conference. Watertown, SD. October 17.
• Type: Other Status: Other Year Published: 2018 Citation: Elliott M. and Elliott, L. 2018. Ag land highest and best use study. South Dakota State University. Brookings, SD. https://melliott-sdsu.shinyapps.io/r_app_HBU/ .
• Type: Conference Papers and Presentations Status: Other Year Published: 2018 Citation: Elliott, L. M., Elliott, M. S. 2018. Options/hedges/contracts: Managing revenue risk. First Dakota- Agrivision Beginning Farmer Program-Session II. Brookings, SD. June 14.
• Type: Conference Papers and Presentations Status: Other Year Published: 2019 Citation: Elliott, M. S., Elliott, L. M., Malo, D. D., and Wang, T. 2019. Ag land highest and best use determinations. South Dakota ASFMRA Education Meeting. Oacoma, SD. January 24.
• Type: Other Status: Published Year Published: 2019 Citation: Elliott, L. M. 2019. Winter wheat- outlook, risk, expected net income. Ag Economic Dialogues Webinar. Brookings, SD. September 16.
• Type: Conference Papers and Presentations Status: Other Year Published: 2018 Citation: Elliott, L. M., and Elliott, M. 2018. Returns/Impacts on land values and grain outlook. South Dakota ASFMRA Education Meeting. Oacoma, SD. January 24.
• Type: Conference Papers and Presentations Status: Other Year Published: 2018 Citation: Elliott, M. S., and Elliott, L. 2018. South Dakota crop production, marketing outlook, and trade impacts. South Dakota Agronomy Conference. South Dakota Agribusiness Association. Sioux Falls, SD. December 12.
• Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Elliott, M. S., Elliott, L. M., Malo, D. D., and Wang, T. 2018. Ag land highest and best use determinations. South Dakota Cattlemens Association Annual Meeting. Huron, SD. November 27.
• Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Elliott, M. and Elliott, L. 2018. Prevented planting\MFP impacts to SD grain crop value. Ag Dialogues. Sioux Falls, SD. August 20.
• Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Elliott, M. S., Elliott, L. M., Malo, D. D., and Wang, T. 2018. Ag land highest and best use determinations. Ag Lenders Conference. Sioux Falls, SD. October 19.
• Type: Other Status: Published Year Published: 2018 Citation: Elliott, M. S., & Elliott, L. M. 2018. Cost to import U.S. soybeans into China versus Brazil. Brookings, SD: iGrow. http://igrow.org/agronomy/profit-tips/cost-to-import-u.s.-soybeans-into-china-versus-brazil/
• Type: Other Status: Published Year Published: 2019 Citation: Elliott, L. M., Elliott, M. S., Davis, J. B., and Sand, S. R. 2019. Managing the ag margin webinar series. SDSU First Dakota National Bank e-Trading Education Laboratory. Brookings, SD. April 11.