Source: KANSAS STATE UNIV submitted to NRP
FARM BUSINESS MANAGEMENT AND BENCHMARKING: KANSAS, ILLINOIS, AND KENTUCKY COLLABORATION
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
OTHER GRANTS
Reporting Frequency
Annual
Accession No.
1001315
Grant No.
2013-38504-21432
Cumulative Award Amt.
$500,000.00
Proposal No.
2013-05143
Multistate No.
(N/A)
Project Start Date
Sep 1, 2013
Project End Date
Aug 31, 2015
Grant Year
2013
Program Code
[FBMB]- Farm Business Management and Benchmarking Program
Recipient Organization
KANSAS STATE UNIV
(N/A)
MANHATTAN,KS 66506
Performing Department
Ag Economics
Non Technical Summary
Many states are collecting farm financial data through their own farm analysis programs. However, benchmarking at the state level is incomplete. The lack of cooperation among individual state farm analysis programs is important because it prevents benchmarks from being developed which would be broader than state-level measures. Furthermore, limiting the calculation of financial benchmarks to members of the association can introduce bias if the sample is not representative of all farmers in the state. We propose to develop a consistent set of benchmarks and educational materials based on the farm- level data from Kansas, Illinois, and Kentucky to improve farmers' management skills and to help them compete in a world marketplace. The states involved in this project have three of the largest farm analysis databases in the country with over 8,000 farms across the three states. We plan to develop a program that will address the following three objectives: Advance data gathering methods and conduct research on cost of production, farm profitability factors, and farm policy impacts. Improve the profitability and competitiveness of small and medium-sized farms and ranches by providing access to high quality, uniform farm business management benchmarking information. Improve producers' abilities to successfully manage their agricultural operations through periods of high risk, volatility, and financial stress. ? Data from these three states are currently collected and analyzed individually at the state level. By utilizing the data together, a common set of benchmarks and analysis can be developed. Thus, when farmers examine their current state-level benchmark, they will have a similar benchmark from the other two states for comparison purposes. In addition, profitability and risk analysis will be conducted with the state data and farm census data. ? The common benchmarks and analysis will be distributed to the current farm members of each state farm management association as well as through the nationally recognized websites AgManager and farmdoc. Training of farm analysis field staff and direct farmer training will also occur in each state.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
6016030301050%
6025399301050%
Knowledge Area
601 - Economics of Agricultural Production and Farm Management; 602 - Business Management, Finance, and Taxation;

Subject Of Investigation
5399 - Structures, facilities, and equipment, general/other; 6030 - The farm as an enterprise;

Field Of Science
3010 - Economics;
Goals / Objectives
The long-term goal of this project is to provide improved benchmarks and relevant training so that farmers can identify weak points in their operation for their particular farm type, farm size, organizational structure, and debt structure in order to compete in a world marketplace. Our objective in this proposal is to advance data gathering across the three individual associations to create improved financial benchmarks and research-based educational materials, and to provide training programs and materials based on the farm analysis data programs in Kansas, Illinois, and Kentucky. The specific goals are: 1) Advance data gathering and conduct research on cost of production, farm profitability ?factors, and farm policy. (RFA Objective 2) 2) Improve the profitability and competitiveness of small and medium-sized farms and ?ranches by providing access to high quality, uniform farm business management ?benchmarking information. (RFA Objective 5) 3) Improve producers' abilities to successfully manage their agricultural operations through ?periods of high risk, volatility, and financial stress. (RFA Objective 6)
Project Methods
The current data from the Kansas and Illinois and Kentucky farm management associations will be combined together to develop the new benchmarks. In addition, the farm program data will be analyzed with ARMS and census data. This combined data will be the basis for all the benchmarks that are distributed. Survey feedback will be conducted using an external evaluator services to support our overall evaluation effort. The Office of Educational Innovation and Evaluation (OEIE) was established in 2000 and is affiliated with Kansas State University, housed within the College of Education. This group will conduct the surveys.

Progress 09/01/13 to 08/31/15

Outputs
Target Audience:The target audience included farmers, lenders, policy makers, and member farmers within the farm management associations. Publications and presentations were developed and were based on the financial benchmarking information collected by the respective farm management associations. These publications were published on AgManager.info and FarmDoc.com. These sites have a wide audience reach of not only farmers but nearly anyone connected to agriculture. Much of the information from this project was internal to the farmer members of the respective farm management associations. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Training is an on-going part of the farm analysis program. Kansas meets with their farm management economists twice a year to provide training on the software and to provide general farm management. This past spring, Brian Briggeman provided a half-day training on the use of financial ratios and the use of the DuPont method to help explain how the financial ratios work together. How have the results been disseminated to communities of interest?Many of the reports and analysis available from farmdoc and AgManager use data taken from the respective farm analysis programs. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Activity 1: Uniform Financial Benchmarks Across Associations Considerable time has been spent collecting farm financial data from each state and making sure the financial ratios follow the standards of the Farm Financial Standards Committee. Each state is providing a set of pre-calculated farm financial ratios for each of their farms for the last 10 years. Kansas State University is collecting the data and developing a database of the farm financial ratios. To facilitate the development of reports and data analysis, other farm characteristics are being collected in addition to the farm financial ratios. This other information includes: Farm location (both state and crop reporting district); The type of business organization (corporation, individual, or partnership); Crop or livestock farm, Sub-type of crop or livestock (beef, crop, dairy, hog, mix, poultry, sheep), Total farm assets, Total capital managed (includes the value of rented land), Gross revenue, Age of farmer, Total crop acres, Total pasture acres, Work days, The percentage of land rented, The percentage of pasture acres, and The percentage of irrigated cropland. These other characteristics can be used to filter the data and produce a subset of data from which the farm financial ratios are produced. The computer program Quantrix is being used to store the data and then filter the data as desired. Quantrix is used as it has as cloud option to host the data and have the database available to anyone to use. Unfortunately, the cloud use terms of Quantrix have changed and users must be registered with a limit to the number of registered users. Future funding of this project will allow Kansas State University to program the web interface internally that will eliminate the user accessibility problem. The farm analysis program at Kansas State currently uses a web interface for much of its internal reporting so a reporting ability available to anyone is an achievable goal. Figure 1 below shows how the Quantrix program (and future web version) works to generate a subset of data from which the financial ratios are produced. A set of checkboxes is used to select whether a farm with the given data characteristic is included (e.g., corporation). The sliders at the bottom are used to select the range of values for other data characteristics (e.g., total assets for the farm within a range of values). Once the parameters are set, Quantrix will filter the data and produce a median value for the chosen farm financial ratio. To protect user confidentially, only the median value is shown from the subset of data and the subset of data has to have a minimum number of farms. In the lower right hand corner of the screen, the program shows the number of farms that meet the set of criteria chosen. The database is complete for Kansas Farms and the Illinois data is in place but not fully in the database yet. The Kentucky data will be forthcoming. With the database as it exists in the Quantrix program, generating a set of reports about financial ratios is a trivial matter. By changing a set of parameters, a whole new subset of data is selected and the farm financial ratios for the last 10 years are produced. Rerunning a new set of parameters only takes a few minutes. Figure 1. Quantrix modeling tool for producing farm financial ratios Activity 2: Representative Benchmarks from the U.S. Census of Agriculture A publication comparing farm analysis data to Census data is currently in draft form. This publication is very similar to a publication developed by Michael Langemeier from the 2007 Census. http://agmanager.info/about/contributors/Presentations/Langemeier/2007_CensusComparison_KFMA.pdf Activity 3: Cost of Production, Profitability, and Policy Analysis Research Currently each state has a set of publications about production, profitability, and policy analysis based on their own state's data. These can be accessed from the farmdoc and AgManager web sites. Activity 4: Explore the possibility for data aggregation across associations These discussions are on-going. The Kansas system and the Illinois system (Kentucky uses the Illinois system) both have their strengths and weaknesses. While each systems serves it respective state, a new combined system would effectively be the farm management database for the entire country. This should probably be the ultimate goal of these farm benchmarking grants. Activity 5: Farm management association software upgrades and improvements Considerable resources have been devoted to upgrading and improving the farm analysis database software. Kansas, in the fall of 2014, completed the development of a new SQL relational database for asset depreciation. Until last fall Kansas was still using a COBOL flat file system that generated much of the depreciation data on the fly. As a result, very little depreciation data was available to analyze. Now, the new Kansas system keeps tract of each individual data for every asset on every farm. This new depreciation database will allow us to answer such question as optimal machinery level, when should equipment be replaced, when should a second combine be added to a farm, etc. In addition to the depreciation changes, work has progressed on adding new reports and modifying old ones. Kansas has made changes to help provide farmers information on benchmarks and to help run special reports for unique situations. The web interfaces have been improved and many farmer reports can now be run in real time. Also, considerable programming time was devoted to improving the database so that all the farm financial ratios can be calculated. The Kansas system has some limitations about recording the current portion of loans that is being addressed.

Publications

  • Type: Journal Articles Status: Published Year Published: 2014 Citation: Paulson, N. and G. Schnitkey (2014). Farmland Value and Rental Rate Behavior in Illinois. Journal of the American Society of Farm Managers and Rural Appraisers, 2014:251-264.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2014 Citation: Ibendahl, G. Graphical presentation of KFMA financial ratios. KFMA newsletter, July 2014. Ibendahl, G. A comparison of KFMA and ERS net farm income KFMA newsletter, July 2014. Ibendahl, G., and K. Herbel. KFMA Farmer Age Characteristics. Presentation at the 2014 KSRE Annual Conference. Part of a full session called Tight Farm Finances and Planning Successful Farm and Ranch Transition. Dan OBrien and Gregg Hadley, organizers. Ibendahl, G. Machinery Costs in Kansas Risk and Profit conference presentation, Manhattan, KS August 21-22, 2014. Ibendahl, G. KFMA Net Farm Income by Farm Type, Size, Age, and Debt Characteristics. Presentations at the Ag Lenders Conference, October 7, Garden City, KS and October 8, Manhattan, KS. Radio interview at KSU. 5/21/14.?Ibendahl, G. A Comparison of KFMA and ERS net farm income. Radio interview at KSU, 7/17/14?
  • Type: Conference Papers and Presentations Status: Published Year Published: 2014 Citation: Ibendahl, G. 2014 Selected Paper. Characteristics That Make a Farm Consistently Profitable. Southern Agricultural Economics Meetings in Dallas, TX, 2/1  2/4/14. Ibendahl, G. 2014 Organized Symposium. Risk Management Training for Beginning Farmers and Ranchers. Part of Three Case Studies on Innovative Risk Management Extension Programming. Organizer: Scott Fausti. AAEA Annual Meeting in Minneapolis, MN, 7/27-29, 2014. Ibendahl, G. 2014 Selected Paper. Evaluating How Machinery Costs Affect Net Farm Income. NC-1177 Agricultural and Rural Finance Markets in Transition, Kansas City, MO. October 2014. Schnitkey, G. Projected 2015 Corn Revenue with Comparisons to Revenues from 2010 to 2014. farmdoc daily (5):73, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, April 21, 2015. Schnitkey, G. "Capital Purchases and Lower Net Incomes." farmdoc daily (4):230, Department of Agricultural and Consumer Economics, University of Illinois at Urbana- Champaign, December 2, 2014. Zulauf, C., G. Schnitkey, J. Coppess, and N. Paulson. The Forgotten Variable: Yield and the Choice of Farm Program Option. farmdoc daily (4):225, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, November 20, 2014. Schnitkey, G. Projected Corn Gross Revenues Down in 2014 and 2015. farmdoc daily (4):203, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, October 21, 2014.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2015 Citation: Schnitkey, G. Projected 2015 Corn Revenue with Comparisons to Revenues from 2010 to 2014. farmdoc daily (5):73, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, April 21, 2015. Schnitkey, G. Projected Corn Gross Revenues Down in 2014 and 2015. farmdoc daily (4):203, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, October 21, 2014. Schnitkey, G. Will Non-Land Costs Decrease in 2015? farmdoc daily (4):178, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, September 16, 2014. Schnitkey, G. "Historic and Projected Cash Rents as Percentages of Crop Revenues." farmdoc daily (4):150, Department of Agricultural and Consumer Economics, University of Illinois, August 12, 2014. Li, X. and N. Paulson. 2014 Selected Paper. Is Farm Management Skill Persistent? AAEA Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota. Kuethe, T. and N. Paulson. 2014 Selected Paper. Crip Insurance Use and Land Rental Agreements. AAEA Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota. Kuethe, T., B. Briggeman, N. Paulson, and A. Katchova. A Comparison of Data Collected Through Farm Management Associations and the Agricultural Resource Management Survey, Agricultural Economics Staff Paper #485, 2014, University of Kentucky.
  • Type: Journal Articles Status: Published Year Published: 2014 Citation: Kuethe, T.H., B. Briggeman, N. Paulson, and A.L. Katchova (2014) A Comparison of Data Collected through Farm Management Associations and the Agricultural Resource Management Survey. Agricultural Finance Review 74(4):492-500.
  • Type: Journal Articles Status: Published Year Published: 2014 Citation: Ibendahl, G. and M. Langemeier (2014). Characteristics That Help a Farm Achieve Long-term Viability. Journal of the American Society of Farm Managers and Rural Appraisers, 2014:240-250.
  • Type: Journal Articles Status: Published Year Published: 2014 Citation: Langemeier, M., and G. Ibendahl (2014). Crop Machinery Benchmarks. Journal of the American Society of Farm Managers and Rural Appraisers, 2014:204-213. Ibendahl, G. (Forthcoming). The Effects of Machinery Costs on Net Farm Income. Journal of the ASFMRA
  • Type: Other Status: Published Year Published: 2015 Citation: Gibson, Heather (Spring 2015). Completion of Masters Thesis. "The Relationship between Cash Rents and Land Values in Kansas." Department of Agricultural Economics, Kansas State University.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2015 Citation: Zulauf, C., A. Hershey, G. Schnitkey, and G. Ibendahl Analysis: ARC-IC vs. ARC-CO Payments Using Illinois and Kansas Farm Management Association Data. Farmdoc publication, January 2015. Herbel, K., and M Dikeman. 2014 KFMA Executive Summary. May 2015.