Journal of the NACAA
ISSN 2158-9429
Volume 3, Issue 1 - July, 2010

Spatial Analysis of Precision Agriculture Data: Role for Extension

Farmers using spatial technologies have enhanced ability to conduct on-farm trials, thus providing an opportunity for Extension professionals to work with them. Three sources of evidence were used to provide insights into Extension’s role in analyzing precision agriculture data. These sources included a case study, a data analysis service, and data analysis workshops. A three-year case study intensively followed the decision making process of five farmers who conduct on-farm trials using yield monitor technology. A pilot yield monitor data analysis service was offered over the same three-year period. Two yield monitor data analysis workshops were conducted for farmers and consultants. Topics relating to Extension’s role were included in the final case study interview to evaluate what role Extension may play in the analysis of on-farm data.

 

Precision agricultural technologies such as yield monitors and global positioning systems (GPS) are being used by farmers. More than 20% of corn, rice, and soybean planted acres in the US are harvested with a combine equipped with a yield monitor (Griffin, 2009). Whipker and Akridge (2009) report that 92% of service providers use GPS guidance with manual control and 56% use automated guidance. Spatial analysis is defined as explicitly modeling the spatial heterogeneity and dependence of the data for making statistical inference and farm-management decisions, typically utilizing regression analysis.

 

Case study evidence was compiled from three sources: (1) direct observation, (2) formal interviews, (3) workshop participation. The main source of evidence was the three-year formal case study. Casual direct observational evidence was collected by the researchers over the project period and formal face-to-face interview data were collected at the end of the project (Griffin et al., 2008). Case study evidence was supplemented by the three-year Yield Monitor Data Analysis Service and Yield Monitor Data Analysis Workshops. Research was funded by a United States Department of Agriculture Sustainable Agriculture Research and Education (USDA-SARE) Graduate Student Research Grant. 

 

Qualitative case study research methods (Yin, 2003) have been used in farm management research in general and farmers’ use of precision agricultural technology in particular (Popp et al., 2002; Griffin et al., 2008; Urcola, 2003). Case study methods were used to evaluate each farmer as a unit of analysis during the three-year project. 

 

Five farmers conducting on-farm trials in Indiana, Illinois, Kentucky and Ontario, Canada initially volunteered to collaborate in yield monitor data analysis during Purdue’s Top Farmer Crop Workshop (see http://www.agecon.purdue.edu/topfarmer/). Farmers were selected based on their expertise in conducting on-farm trials with yield monitors and were identified as innovators who sought appropriate analysis techniques. Three topics pertaining to Extension were discussed with case study farmers. These topics included: 1) expected source of spatial analysis services, 2) farmers’ willingness-to-pay for spatial analysis services, and 3) the expected role of Extension.

 

Case study farmers have at least seven years experience mapping yields. Farmers D, F, and W received a spatial analysis of their on-farm trials. Farmers P and T did not receive a spatial analysis report prior to the final interview; however, these five farmers were not expected to differ from one another otherwise with respect to use of precision agriculture and conducting on-farm trials.  The relevant demographics of all five farmers are summarized in Table 1.

 

The principal investigators worked closely with Farmers D, F, and W, making farm visits and providing spatial analysis reports including production recommendations based upon on-farm trial data over a three-year period.

				Experience with technology since the year
Farm		Region	Attended YMA*	Began Farming	Adopted Computer	Began Internet/ email use	Began Yield map use	Began conducting OFT**	Adopted Lightbar	Adopted Automated guidance		Began using GIS analysis mapping
Experimental Group***
D		Central Illinois	Both	1979	1997	1998	2001	1987	2003	2009		2001
F		Central Indiana	No	1995	1995	1998	2000	1996	1999	2003		2000
W		Western Kentucky	No	1986	1986	2003	1996	2003	1998	2005		2003
Comparison Group***
P		Western Kentucky	2005	1987	1980	1997	1995	1997	2003	2005		2000
T		Ontario Canada	Both	1995	1990	1990	1994	1995	2003	2005		1995

Several possible sources exist for a spatial analysis service if and when it becomes common. Farmers D, F, P, and T suggested that farmers conduct their own spatial analysis if interest and time were available and farm size was sufficient (Table 2). Farmers W, P, and T suggested Extension may have a primary role in providing spatial analysis services if the farmer opts to outsource. Other third-party spatial analysts may include dedicated private consultants or a packaged bundle from service providers. 

 

Question	Experimental Group			Comparison Group
	D	F	W	P	T
What advice would you have for farmers considering on-farm trials for the first time?	Be careful with data, calibrate yield monitor. Tend to little details.	Make sure to devote enough time to trials.	Consult Extension researchers for guidance on experimental designs.	Prepare for extra time commitment, be patient; garbage in garbage out.	Start slow. Do not expect too much. Yield monitors are tools with limitations. Bounce ideas off someone with experience.
Who do you expect to perform the software portion of spatial analysis of on-farm trials?	The farmer.	The farmer, some farmers may outsource.	Extension because unbiased. Do not want to send hybrid trials to seed companies.	The farmer depending upon skill level and interest. Probably consultants or Extension.	Centrally located. Large farmers may have someone in-house. Latest research associated with Extension.
What would you expect to pay for full-service spatial analysis?	$3 per acre.	$5 per acre or $500 flat fee per experiment. Doubtful to be percentage of expected value.	Percentage of predicted value. Up to several hundred dollars if timely.	$2 per acre. Maybe on a per experiment basis up to $500.	$5 to $10 per acre or 40 to 50% of net.

Farmers’ willingness-to-pay for spatial analysis services may dictate the source of the service. Case study farmers suggested three different fee structures for spatial analysis including a fee per acre or per experiment, and a percentage of net benefits. Farmers suggested per acre fees ranging from $3 to $10 and per experiment fees of $500 (Table 2). Farmer T suggested 40 to 50% of the estimated value in net returns from the spatial analysis.  

The per acre estimates are in the appropriate range for rudimentarily analyses. However, the suggested fees are not expected to entice qualified analysts to offer inferential spatial statistical analysis unless paid across all farm acreage or at least the acreage on which the decision is to be implemented. If suggested per acre fees were intended to cover spatial statistical analysis of field-scale experiments, it can be inferred that public service programs are the likely source of spatial analysis service and not the private sector unless cost-effective automation is developed.

 

The range of suggested spatial analysis service fees are greater than current fees charged for yield monitor data analysis. Nearly 40% of service providers offer yield data analysis at approximately $1 per acre with only 20% of those reporting making a profit (Whipker & Akridge, 2009). Although the exact nature of “yield data analysis” was not defined, it is suspected the survey question was interpreted as printing yield maps rather than any quantitative data evaluation. 

 

Case study subjects suggested that Extension may facilitate much of the opportunity that exists in on-farm trials and spatial analysis including: 1) organize farmer peer group meetings similar to marketing clubs, 2) link farmers to applied researchers, 3) advise on experiment designs, and 4) help farmers select treatments to test (Table 3). The benefits of Extension facilitating the spatial analysis service include access to scientific expertise and analysis supported by public funds. All case study subjects valued the direct linkage with applied university researchers. Farmers did not suggest spatial analysis be directly offered as an Extension service.

Question	D	F	W
What is the role of Extension?	Supporting role like in marketing clubs, maybe develop yield monitor data analysis clubs by facilitating and setting up farmer peer groups.	Doubtful local Extension would have a role or would facilitate spatial analysis or farmer peer groups. Farmers contact individual professors for specific issues.	Recommendations on experimental designs.

 

 

 

 

 

	November 2005	March 2007
Farmers	11	5
Industry		11
University		3

 

 

 

 

 

Initially, the spatial data analysis service may be facilitated by or offered as an Extension program due to the high costs of specialized human capital and the relatively low farmer willingness-to-pay. Yield monitor data analysis has a public good aspect because it is expected to lead to more efficient crop production using fewer and more carefully targeted agricultural inputs, and also to increased economic activity in rural areas due to more prosperous farmers. Based on the potential public good of yield monitor data analysis, it is reasonable for Extension to invest resources at least until private services are able to enter the market. Extension has a track record of working one-on-one or at least in small groups with interested clientele; whereas private services are likely to 1) target specific clientele based on potential revenue streams or 2) use of computerized automation of the process in conjunction with other services.

 

Although local county Extension offices are not expected to conduct analyses or serve as a dropping point for data, yield monitor data may be analyzed at specialized workshops similar to whole-farm linear programming (LP) services provided by Purdue’s Top Farmer Crop Workshop where farmers have the option to “farm in the computer”. The LP modeling service has been offered at no additional cost to workshop participants for over 30 years (Candler et al., 1970; McCarl et al., 1977). The model is expensive to update and requires specialized skills to help participants input their farm information and interpret the results. Similar services would be cost prohibitive in the private sector. With current estimated costs to conduct spatial analysis based on computational resources and human capital requirements, fees suggested by case study farmers would not entice qualified analysts to offer the service. However, opportunity exists for Extension to offer the service since human capital costs may be considered a public good until computational and software limitations are reduced. 

 

Case study evidence suggests that Extension should at least offer guidance on selecting treatments to test at the farm-level, designing experiments, and interpreting spatial analysis results for on-farm crop production trials using precision agricultural technology. Extension may offer spatial statistical analysis services until private sector analysis services develop, potentially with the assistance from university Extension programs. Although Extension does not commonly offer computer analysis services to its clientele there are precedents such as the Purdue Top Farmer Crop Workshop, which has provided whole-farm linear programming analysis for farmers.   Given current costs of offering spatial statistical analysis and the farmer willingness-to-pay, no private sector firms are expected to enter the market; Extension therefore has an opportunity to generate a public good by filling that niche until costs drop and farmers realize the value, so that private providers can enter the market. Once private providers of spatial analyses exist, Extension can provide technical training to the private firms in statistical analysis. Regardless of the source of spatial analysis services, Extension is expected to teach farmers to interpret spatial analysis results. 

A United States Department of Agriculture – Sustainable Agriculture Research and Education (USDA-SARE) Graduate Student Grant Program funded this project. The authors thank the farmer-collaborators who have assisted in this research.

 

Candler W., Boehlje, M. & Saathoff, R. 1970. Computer software for farm management extension. American Journal of Agricultural Economics. 52(1), 71-80.

 

Erickson, B. 2005. Workshop helps farmers utilize one of their key resources:

information. November 2005 SSMC newsletter. Retrieved February 1, 2010 from: http://www.agriculture.purdue.edu/ssmc/Frames/SSMCNewsletter11_05.pdf

 

 

 

Griffin, T. W. & Lambert, D. M. 2005. Teaching interpretation of yield monitor data analysis: lessons learned from Purdue’s 37th top farmer crop workshop. Journal of Extension [On-line], 43(3) Article 3IAW5. Retrieved February 1, 2010 from: http://www.joe.org/joe/2005june/iw5.shtml

 

 

Lowenberg-DeBoer, J., & Griffin, T. W. 2003. The 2004 Top Farmer Crop Workshop adds yield map analysis. September 2003 SSMC newsletter. Retrieved February 1, 2010 from: http://www.agriculture.purdue.edu/ssmc/Frames/sept03PrecisionAg_TopFarmeWkshp.htm

 

McCarl, B. A., Candler, W. V., Doster, D. H., & Robbins, P. R. 1977. Experiences with farmer oriented linear programming for crop planning. Canadian Journal of Agricultural Economics. 25(1977), 17-30.

 

Nistor, A. & Florax, R. J. G. M. 2007. Farmers and consultants receive training in spatial analysis of yield monitor data. Site-Specific Management Center website April 2007 Newsletter. Retrieved February 1, 2010 from: http://www.agriculture.purdue.edu/ssmc/Frames/SSMCnewsletter4_2007.pdf

 

Popp, J., Griffin, T. W., & Pendergrass, E. 2002. How cooperation may lead to consensus assessing the realities and perceptions of precision farming in your state. Journal of the American Society of Farm Managers and Rural Appraisers. 65(1) pp.26-31. Retrieved February 1, 2010 from: http://portal.asfmra.org/userfiles/file/journal/popp26_31.pdf

 

Urcola, H. 2003. Economic value added by yield monitor data from the producer’s own farm in choosing hybrids and varieties. Purdue University, M.S. Thesis.

Whipker, L. D, and Akridge, J. T. 2009. 2009 Precision agricultural services dealership survey results. Staff Paper No. 08-09. Department of Agricultural Economics, Purdue University, West Lafayette, Indiana.   Retrieved February 1, 2010 from http://www.agriculture.purdue.edu/ssmc/publications/2009_Precision_Report.pdf

 

Yin, R. K. 2003. Case study research: design and methods (3rd ed.). Beverly Hills, CA: Sage Publishing. 204 pp.