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August 1, 2024
Breakout Sessions
… 130%
Inde
x (20
15=1
00)
Total Crops Food Crops Feed Crops
U.S. Population and Food
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
Poultry & Eggs …
February 23, 2015
Commodity Program Papers
aid farmers with their
marketing and risk decisions. This … contrast, if a
non-program crop was planted on base acreage … 2009-2012 plantings for each crop (take the total acres planted …
January 21, 2015
Commodity Program Papers
to aid farmers with their
marketing and risk management decisions … must consider whether such marketing
strategies are appropriate … the KSU estimated 2014/15
Marketing Year Average (MYA) wheat …
February 20, 2015
Commodity Program Papers
records. For most, this will be crop insurance records. These … FSA. If you
don’t have crop insurance records, there … to aid farmers with their marketing and risk decisions. This …
Breakout Sessions
feedstock
production and markets; the adoption, intensification … examination of individual
crop production practices to examining … production practices to examining cropping systems. This change in
paradigm …
Breakout Sessions
a
reduction in irrigated crop acres of approximately 44,500 … technology allowed irrigated crop
producers to maintain profitability … shifting to a higher valued crop mix.
Comparisons of farm …
October 15, 2018
2018 Crop Insurance Workshop Presentations
‐‐How It Really Works‐‐
Federal Crop Insurance Corporation Pilot Insurance
Program
Risk Management Agency
1
2015 Top 10 Commodities Nationwide by
Liability
2
Rank … Expanding operations provision allows for up to 35% growth over historic
average with insurance company approval
11
• Costs for market readiness operations may be left in the approved revenue
– …
February 3, 2021
Farm Profitability, KFMA Research
2021.01
only guide to prices is crop futures and both yields and … Because most of the 2021 crop is not even planted yet … forecast model: yields, prices,
crop acres, expenses …
August 1, 2008
KFMA Newsletters
of a prior year soybean crop still in inventory,
Joe … bushels of last year’s crop equating to around
$10,000 … commodities. In contrast, crop-share landlords
and accrual …
September 1, 2015
KFMA Newsletters
2015 E‐Newsletter
http://www.agmanager.info/kfma/ September 2015 E‐newsletter 2
long‐running Purdue CropLife survey reports on the perceptions of agricultural input suppliers and retailers (Erickson
and Widmar, 2015). They indicated that GPS guidance was nearly ubiquitous for service providers but analogous
technologies were not adopted by a critical mass of farmers. Given the lack of information on the profitability of
precision agricultural technologies, the KFMA databank has a unique opportunity to fill this gap.
How KFMA will collaborate
No studies have adequately reported the whole‐farm benefits of precision agriculture based on empirical data. The
KFMA precision agriculture study will be the first study capable of assessing 1) the whole‐farm profitability of before and
after adoption of precision agriculture, 2) the whole‐farm profitability differences between adopters and non‐adopters,
and 3) the probability of transitioning from one bundle of technologies to another bundle of technologies.
This study relies upon KFMA farms being “tagged” for each level of precision agriculture technology for the specific years
that the technology was used. During the enrollment process, the KFMA farmer will complete a worksheet grid
regarding what technologies and years the technology was used; then the KFMA economist will transpose the farmer’s
response onto another sheet that the KFMA office staff will enter into the computerized database. In subsequent years,
the KFMA member and KFMA economist would simply verify that the ‘tagging’ is still relevant or update it accordingly.
The true ‘tagging’ occurs with the computerized database. It is anticipated that the total time for the KFMA farmer and
KFMA economist to complete the forms will be less than 20 minutes; and during the time that the KFMA farmer is
completing their form, the KFMA economist is likely performing some other task.
Each KFMA farm will eventually be tagged for adoption of each precision technology as 1) adopted, 2) not adopted, or 3)
null/unknown. Initially, the list of technologies will be limited to the most common with additional technologies added
once a critical mass of farmers have adopted. The year that the farm began using each technology, and the year that
they ceased using it in the event the technology became obsolete or abandoned, will be noted. This simple tagging of
each farm by KFMA economists allows further analysis.
KFMA allows unique assessment of precision agriculture profitability
KFMA will be the first to report actual whole‐farm profitability from adoption and use of precision agricultural
technology. In addition to being able to assess the impact of precision agricultural technologies across farms, the KFMA
databank allows the evaluation of the technology before and after adoption. In analytical terms, this means that both
cross‐sectional and time‐series analysis can be conducted in a panel dataset. The panel databank also allows us to
determine how farms transition from one bundle of technologies to another bundle; and how whole‐farm
characteristics influence and are influenced by these changes.
Expected Outcomes
Where statistically valid and confidential results are available (i.e. a large enough sample of both adopters and non‐
adopters such that no individual farms can be identified), reports will be made to several audiences. Results of this
study will first be made available to KFMA members and staff, then posted to the AgManager.info website as
newsletters and Extension publications, and submitted for peer reviewed in scientific publications. The results are
expected to be useful for KFMA farmer members who are considering the adoption of precision agricultural
technologies. In addition to the publications, results will be disseminated across Kansas at Extension events as well as at
national and international conferences. Since the Kansas results from KFMA are the only source of farm‐level
information on profitability of precision agriculture, the international community will be interested.
Background on Principal Investigator
The principal investigator, Terry Griffin, has a long track record of evaluating the profitability of precision agriculture. His
master’s thesis surveyed farmers and agricultural industry of the status of precision farming in Arkansas in 1999. His
http://www.agmanager.info/kfma/ September 2015 E‐newsletter 3
doctoral dissertation adapted spatial statistical techniques to analyze yield monitor data; and evaluated the farm
management implications in a series of case studies across North America. Griffin joined the K‐State Department of
Agricultural Economics February 2015 with a focus on cropping systems economics and specifically precision agriculture.
Using the KFMA databank, he will be able to provide even greater insights into the profitability of precision agricultural
technology.
The K‐State Precision Agriculture Team has been formed with members across the College of Agriculture. In addition to
Terry Griffin from the Department of Agricultural Economics, Ajay Sharda from the Deaprtment of Biological and
Agricultural Engineering, Lucas Haag and Ignacio Ciampitti from the Department of Agronomy have led this effort. This
team will disseminate the results of this KFMA study across Kansas and to precision agriculture meetings across the US
and internationally. For more information on this project, please email Terry Griffin at twgriffin@ksu.edu or call
501.249.6360.
References
Erickson, Bruce and Widmar, David. 2015 Precision Agricultural Services Dealership Survey Results. Sponsored By
CropLife Magazine And The Center For Food And Agricultural Business. Purdue University. July 2015.
Schimmelpfennig, David and Ebel, Robert. 2011. On the Doorstep of the Information Age: Recent Adoption of Precision
Agriculture. Economic Information Bulletin No. (EIB‐80) 31 pp, August 2011. Available at:
http://www.ers.usda.gov/publications/eib‐economic‐information‐bulletin/eib80.aspx#.U7LuLfldXbA
Comparison …