Comparisons of alternative on-farm trial designs indicated that limited replication were sufficient for making production decisions if the appropriate spatial statistics were used to analyze the data. Five farms from Illinois, Indiana, Kentucky, and Canada collaborated in on-farm research. Select farmers received a spatial analysis report of their on-farm trials over the three-year project period and all farmers were the subject of a case study. Case study results indicated that farmers who received a spatial analysis of their on-farm trials were more confident in their data and production decisions than before participating in this research of on-farm trial spatial analysis.
Precision agriculture is information technology applied to agriculture, using technologies such as global positioning systems (GPS) and geographic information systems (GIS) to apply appropriate rates of inputs at specific locations. Precision agriculture technologies have spread rapidly in the Midwestern United States with 36% of corn and 29% of soybean area harvested in 2001 and 2002, respectively, with a yield monitor (Griffin et al., 2004). Numerous results on the profitability of precision agriculture have been reported (see Lambert and Lowenberg-DeBoer, 2000 for an exhaustive review).
Several studies used spatial statistics to analyze data from conventional designs. Bongiovanni and Lowenberg-DeBoer (2002) studied corn response in on-farm data, showing that explicitly modeling the spatial error structure identified patterns lost in typical analysis when spatial correlations are ignored. Hurley et al. (2001) uses a similar spatial regression method. Lambert et al. (2002; 2004) showed spatial econometrics (Anselin, 1988) and spatial regression using geostatistics (Cressie, 1993) yielded similar results.
The fundamental idea of on-farm trial design is that under certain circumstances the statistical model can be mathematically identical for data from numerous strips and/or subplots, and for data from a smaller number of non-replicated blocks. With the spatial error model, the same information could be extracted from a simpler design with treatments in larger non-replicated blocks by explicitly modeling the spatial error structure, instead of eliminating it.
Precision agriculture technologies such as GPS, yield monitors and other sensors provide many low-cost geo-referenced observations per acre. Because of the increased ease of gathering data, precision agriculture renewed interest in field-scale on-farm trial comparisons (Urcola, 2003) although experimental designs and analyses were developed for 20th century technology (Nielsen, 2000; Brouder and Nielsen, 2000; Nafziger, 2005). Instantaneous yield monitors have provided opportunities for this type of research to be implemented without interfering with other field operations. Farmers have incentive for conducting their own on-farm trials. On-farm field-scale trials provide farmers opportunity to
1) verify university Extension recommendations and industry claims under a wide range of production conditions,
2) to verify if small-plot experiment station research applies to field scales under local environmental and management practices,
3) gain local information to fine tune production practices, and
4) test equipment, systems or technology packages.
Farmers conduct field-scale on-farm comparisons of new products and alternative systems with a range of experimental designs including large non-replicated blocks such as split-field, strip-trials and split-planter trials. While data from strip and split-planter trials has some value to classically trained agronomists and statisticians, unreplicated comparisons are often dismissed as unreliable sources of information because of lack of power in determining treatment differences. Evidence from Indiana suggests that unreplicated split-field comparisons are the most common design used by farmers especially for farmers with yield monitor technology (Urcola, 2003).
Short-term outcomes include:
Farmer-collaborators receive assistance in farm management decision making.
Identification of alternative experimental designs conducive to on-farm planned comparisons.
Farmer-collaborators provided with the opportunity to experience the advantages of appropriate on-farm trial designs.
Identification of appropriate statistical analysis methods appropriate for research using precision farming technologies.
A renewed relationship among farmers, researchers, and Extension developed.
Intermediate outcomes include:
Farmer-collaborators are able to make better decisions from participatory research.
Farmer-collaborators empowered to use local information instead of depending on external information sources for large geographic regions.
Farmer-collaborators are able to decide whether industry claims are true, improving confidence in production information.
Reduction of over-application of inputs, reducing pollution.
Strengthened community relationships will emerge from cooperation between farmer-collaborators and universities.
Long-term systemic changes:
More farmer-collaborators conduct on-farm trials.
More farmers conduct on-farm research in partnership with universities.
Reduction of reliance upon generalized recommendations based upon large geographical scales.
Increased confidence in farm management decisions based on localized information.
This project was implemented in collaboration with five farmers. A Farmer Advisory Panel (FAP) consisting of farmers experienced in precision agriculture and on-farm research guided this project. The principle investigators (PIs) and FAP brainstormed together on what research was needed and what could be changed in on-farm trial designs. Specific treatments to test were up to farmer-collaborators as the PIs were interested in the development of experimental trial designs and the necessary analysis methods.
The FAP were involved from brainstorming to presentation of results, meeting at the beginning of this three-year project for preliminary brainstorming and initial decision-making, and again in Year 3 for evaluation and final case study interview. In addition to face-to-face meetings, frequent electronic and mail communication occurred. Once yield data had been collected, the PIs analyzed the data and provided a spatial analysis report to the farmer (see Appendix for sample report).
To accomplish the research objectives, farmers from Indiana, Illinois, Kentucky, and Ontario were included in a case study. Case study methods were used to evaluate each farmer-collaborator as a unit of analysis. Three broad information gathering techniques included direct observation of farmer, participant-observation of farmers during the project, and open-ended interviews, with the latter as the primary source of data. The PI made multiple farm visits, analyzed on-farm trial data, and provided farmers with spatial analysis reports. Semi-structured personal interviews loosely followed an interview script allowing respondents to comment on specific questions while providing the opportunity to openly remark on their experiences (see Dissertation Appendix C for sample interview script). Farmers were observed and interviewed concerning unbiased local production recommendation information from spatial analysis, experimental designs, and their farm management decision making process.
Case study farmers were initially identified as innovators who sought out better analysis techniques. They were selected based upon their expertise in conducting on-farm trials with yield monitors. All five farmers have at least six years experience mapping yields and annually test production practices using on-farm trials.
The five farmers were included in a multiple case study consisting of two groups. Three farmers were introduced to spatial analysis over the three-year project period. This group is referred to as the “experimental group”. This group learned about spatial statistics and they received spatial analysis reports on some of their on-farm trials from university staff involved in the USDA/SARE project. The case study “control group” comprised two farmers that did not receive a spatial analysis for their on-farm trials. For many topics such as adoption of new technology, use of precision agricultural methods, and conducting on-farm trials, the two groups of farmers were very similar. The experimental group includes Farmer D, Farmer F, and Farmer W, while the control group includes Farmer P and Farmer T.
Farmer D produces irrigated corn, soybean, popcorn, green beans, and seed corn in Illinois. Illinois River bottom soils and variable topography influences yield response to inputs. Farmer D is a graduate of Illinois State University. Manual GPS lightbar navigation has been used for four years; however, no automated guidance has been used. Variable rates of lime, phosphorus, and potassium have been made over the past five years. Farmer D has been using computers and the Internet for 10 years. His first yield monitor was purchased off the back of a flatbed trailer at an auction in 2000, and he began collecting georeferenced yield data the following year.
Farmer F grows corn and soybean under strip-till production in Indiana. Farmer F is a graduate of Purdue University and has been using computers for more than 12 years and the Internet for nearly 10 years. Manual lightbar navigation was used for four years prior to adopting automated guidance four years ago. The highest level of GPS accuracy, RTK-GPS, has been used for automated guidance the last three years and is currently used on four tractors. Yield mapping has been used for seven years. Variable rate applications of lime, phosphorus, and potassium have been used for four years.
Farmer W produces corn and soybean in Kentucky. Farms are rolling hills with eroded hilltops and depression areas prone to reduced yields in wet years. Farmer W has been practicing no-till production for 20 years; however, many fields were extensively tilled prior to Farmer W’s management practices. Lightbar navigation has been used for nine years and automated guidance for two. Farmer W and his wife have advanced degrees in Agricultural Economics from Purdue University. Farmer W stated that the first piece of farm machinery purchased was a personal computer in 1986 with the Internet and email being used for the last four years.
Farmer P grows corn and soybean in Kentucky. A graduate of University of Kentucky, Farmer P has been using computers for farm management for 27 years, with the Internet over the past ten. Manual lightbar navigation was used four years ago with automated guidance used on equipment for the last two years. Variable rates of lime and seeds have been used for eight and 10 years, respectively. On-farm trials have been a management practice for 10 years.
Farmer T grows corn, soybean, dry edible beans, and wheat in Southwest Ontario. The farmer was considered to be an innovator with the first automated boom sprayer in Ontario and mapping yields for 13 years. Manual lightbar navigation has been used for four years and automated guidance for two years. Variable rates of nitrogen, phosphorus, and potassium fertilizer have been used for eight years. Farmer T earned a B.S. from the University of Guelph, and an advanced degree in Agricultural Economics from Purdue University. He began using computers and the Internet extensively 17 years ago.
The farmer-collaborators and researchers gained experience in what works for this type of research over the three year project on on-farm planned comparisons. Challenges from both field implementation and computer statistical analysis were overcome and have been documented.
The graduate student has completed and successfully defended his Ph.D. dissertation, completing his Ph.D. in December 2006, however continues to work with the farmer-collaborators and Farmer Advisory Panel as well as other farmers and research in his new role as an Assistant Professor and Extension Economist with University of Arkansas Cooperative Extension Service.
* Identification of alternative experimental designs conducive to on-farm planned comparisons.
The graduate student’s Ph.D. dissertation (Griffin, 2006) expanded the earlier simulation work on alternative experimental designs as presented in Griffin et al. (2005). Traditional and alternative farm-level experimental designs have been implemented on farmer fields. Datasets are have been analyzed and results communicated with farmer-collaborators (for examples see Griffin (2006)). Some farm management decisions are made prior to harvest including hybrid seed purchases, thus final decisions on hybrids must be made even before the end of harvest to secure discounts and reserve limited supplies.
On the basis of 2004, 2005, and 2006 experiences, more practical experimental designs were described in Griffin (2006) that was easier to implement at the farm level and easier to analyze. Benefits of each experimental design were documented from both the farmers’ and analysts’ perspectives. Some of the problems cited for designs derived from small plot experimentation such as split-planter trials are that the analyst may be required to count passes if the design is not electronically recorded with software and GPS and that mathematical problems arise from defining which observations are considered neighbors for purposes of spatial analysis. In addition, the spatial variability is not overcome even with the many replications from split-planter trials as determine from simulations reported in Griffin et al. (2005). Split-field designs seem to work the best, but some opposition by traditional field scientists has emerged due to the lack of replication, which are deemed by some to be universally required.
* Farmer-collaborators provided with the opportunity to experience the advantages of appropriate on-farm trial designs.
The Farmer Advisory Panel gathered for a final meeting on February 28, 2007 in conduction with the Yield Monitor Data Analysis Workshop on March 1, 2007 on Purdue University (Nistor and Florax, 2007) campus similar to the workshop held November 13-14, 2005 (Erickson, 2005).
* Identification of appropriate statistical analysis methods for on-farm research using precision farming technologies.
Several spatial and traditional analysis methods were conducted on farmers’ datasets to compare the differences regarding benefits and ease. From theoretical simulations, several methods have been proven to be superior to others under field-scale conditions (Griffin et al., 2005; Griffin, 2006). These statistical models were evaluated in this analysis to demonstrate the potential erroneous decisions that the farm managers would have made if traditional analysis were used rather than the appropriate spatial analysis. Hence, the question of what is the cost of a wrong decision was addressed. Statistical diagnostics are used to ascertain which models fit the data and to correctly specify the experimental model. Spatial statistical methods used in spatial analysis include: general spatial model, spatial regimes, spatial error model, spatial lag model, cross regression, and geostatistical methods.
Each farmer-collaborator was interviewed face to face as the final case study data collection phase for this research this year.
* Farmer-collaborators are able to make better decisions from participatory research.
Theory and simulation has shown that spatial analysis of on-farm trials lead to better decisions than non-spatial analysis of the same data. Therefore, farmers using spatial analysis have information appropriate for addressing farm management decisions. In addition to knowing analysis results are better, farmers using spatial analysis stated that they had more confidence in their data and their decisions than before spatial analysis.
* Farmer-collaborators are empowered to use local information instead of depending on external information sources for large geographic regions.
With decreased funding for localized field research and an overall shift to regional or multiple state research, more reliance on local on-farm trials have occurred. One farmer suggested that his primary source of quantitative information was the on-farm trails.
* Farmer-collaborators able to decide whether industry claims are true, which should improve confidence in production information.
Case study farmers introduced to spatial analysis had more confidence in their on-farm trial data, information, and decisions. Farmers made decisions quicker and made more decisions than before using spatial analysis.
Work is on-going.
* Reduction of over-application of inputs will reduce pollution potential.
Work is on-going.
* Strengthened community relationships will emerge from cooperation between farmer-collaborators and universities.
Farmer-collaborators and researchers contact each other directly at this point. In the future, it is expected that local Extension professionals will become a larger part of the relationship. Due to the yield monitor data analysis workshop on November 14, 2005, farmer advisory panel (FAP) members contact each other directly to gain feedback into their decisions and identify issues and potential solutions. Farmers have begun contacting one another directly for feedback and to share information.
Long-term systemic changes:
* More farmer-collaborators conduct on-farm trials.
On-going: The USDA-ARMS survey has asked farmers for their uses of yield monitors. In the 2002 ARMS survey, on-farms trials ranked third in the uses of yield monitors by farmers. In the future, the USDA-ARMS survey may assist in tracking information on how yield monitors and spatial analysis influence farmers’ use of on-farm trials over time.
* More farmers conduct on-farm research in partnership with universities.
Case study farmers stated their relationship with university Extension and research was strengthened due to involvement with this participatory research.
* Reduce reliance upon generalized recommendations based on large geographic scales.
Case study evidence has indicated farmers rank their on-farm trial information over regional recommendations. Some farmers used on-farm trial data as the primary source of quantitative information.
* Increased confidence in farm management decisions based on localized information.
These long-term objectives will continue to be evaluated after the end of this project.
Anselin, L. 1988. Spatial Econometrics: Methods and Models, Kluwer Academic Publishers, Drodrecht, Netherlands.
Bongiovanni, R. 2002. A Spatial Econometric Approach to the Economics of Site-Specific Nitrogen Management in Corn Production. PhD Dissertation. Purdue University, West Lafayette, IN.
Brouder, Sylvie, and Robert Nielsen, 2000 “On-Farm Research,” in Precision
Farming Profitability, J. Lowenberg-DeBoer and K. Erickson, eds., Purdue University Agricultural Research Programs, p. 103-112.
Cressie, Noel A.C. 1993. Statistics for Spatial Data. John Wiley & Sons: New York.
Erickson, B. 2005. Workshop Helps Farmers Utilize One Of Their Key Resources:
Information. November 2005 Site-specific Management Center Newsletter, Available on-line at: http://www.purdue.edu/ssmc
Griffin, Terry, “Decision Making from On-Farm Experiments: Spatial Analysis of Precision Agriculture Data,” Ph.D. Dissertation, Department of Agricultural Economics, Purdue University, West Lafayette, IN, 2006.
Griffin, T.W., Lambert, D.M., and Lowenberg-DeBoer, J. 2005. Testing Appropriate On-Farm Trial Designs and Statistical Methods for Precision Farming: A Simulation Approach. Proceedings of the 7th International Conference on Precision Agriculture and Other Precision Resources Management, ASA/SSSA/CSSA, Madison, WI.
Griffin, T.W., J.M. Lowenberg-DeBoer, D.M. Lambert, J. Peone, T. Payne and S.J. Daberkow. 2004. Precision Farming: Adoption, Profitability, and Making Better Use of Data. Paper presented at the 2004 Triennial North Central Farm Management Conference, July 14-16, 2004, Lexington, Kentucky.
Hurley, T., Kilian, B. and H. Dikici. 2001. The Value of Information for Variable Rate Nitrogen Applications: A Comparison of Soil Test, Topographical, and Remote Sensing Information. Selected Paper, AAEA Annual Meeting, Chicago, IL, August 5-8, 2001. Available on line at: (http://agecon.lib.umn.edu/cgi-bin/pubview.pl)
Lambert, D.M., Lowenberg-DeBoer, J. and Bongiovanni, R. 2004. A Comparison of Four Spatial Regression Models for Yield Monitor Data: A Case Study from Argentina. Precision Agriculture 5 pp. 579-600.
Lambert, D., Lowenberg-DeBoer, J., and R. Bongiovanni. 2002. Spatial Regression, an Alternative Statistical Analysis for Landscape Scale On-farm trials: Case Study of Variable Rate N Application in Argentina. P.C. Robert et al., ed. In: Proceedings of the 6th International Conference on Precision Agriculture. ASA/CSSA/SSSA Madison, WI.
Lambert, D.M. and Lowenberg-DeBoer, J. 2000. Precision Agriculture Profitability Review. Site-specific Management Center, Purdue University, West Lafayette, IN, USA. Available on-line at: http://www.purdue.edu/ssmc
Nafziger, E. 2005. “On-Farm Research,” Chapter 21, Illinois Agronomy Handbook, University of Illinois, Urbana-Champaign, 2005.
Nielsen, R. 2000. “Opportunities for On-Farm Variety Performance Testing Using GPS Enabled Technologies”, in J. Lowenberg-DeBoer and K. Erickson, Eds, Precision Farming Profitability, Purdue University, Agricultural Research Program, 2000, p. 12-18.
Nistor, A. and 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. http://www.purdue.edu/ssmc
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.
Educational & Outreach Activities
Griffin, T.W. 2006. Decision-Making from On-Farm Experiments: Spatial Analysis of Precision Agriculture Data. Ph.D. Dissertation, Purdue University, West Lafayette, IN, USA.
“Teaching Interpretation of Yield Monitor Data Analysis: Lessons Learned From Purdue’s Top Farmer Crop Workshop,” with Dayton Lambert in Journal of Extension 43(3): June 2005.
“Economics of Lightbar and Auto-Guidance GPS Navigation Technologies” with J. Lowenberg-DeBoer, and D.M. Lambert. 2005. Proceedings of the 5th European Conference on Precision Agriculture. Uppsala, Sweden.
“Case study of on-farm trials, spatial analysis and farm management decision making” with C.L. Dobbins and J. Lowenberg-DeBoer. 2007. Proceedings of the 6th European Conference on Precision Agriculture. Skiathos, Greece.
Works in progress anticipated for peer reviewed journals
“Case Study Evidence of New Opportunities for Farm Management Analysts in Spatial Analysis of On-farm Trial Data”
“Whole Farm Profitability Impact from Implementing and Harvesting On-farm Trials with Precision Agriculture Technologies: A Linear Programming Model”
“On-farm Trials and Spatial Analysis of Precision Agriculture Data: Role for Extension”
“Case Study of On-Farm Experiments and Farm Management Decision Making”
“Testing For Appropriate On-Farm Trial Designs And Statistical Methods For Precision Farming: A Simulation Approach” with D.M. Lambert and J. Lowenberg-DeBoer. 2004 Proceedings of the 7th International Conference on Precision Agriculture and Other Precision Resources Management, ASA/SSSA/CSSA, Madison, WI.
“Precision Farming: Adoption, Profitability, And Making Better Use Of Data” with J.M. Lowenberg-DeBoer, D.M. Lambert, J. Peone, T. Payne and S.J. Daberkow Paper presented at Triennial North Central Farm Management Conference, July 14-16, 2004, Lexington, KY.
“Testing Appropriate On-Farm Trial Designs and Statistical Methods for Cotton Precision Farming,” with Glenn Fitzgerald, Dayton Lambert, Lowenberg-DeBoer, Edward Barnes, and Robert Roth [On-line] Beltwide Cotton Conference, January 4 – 7, 2005, New Orleans, LA. Available at: http://www.cottoninc.org
“Field-Scale Experimental Designs and Spatial Econometric Methods for Precision Farming: Strip-Trial Designs for Rice Production Decision Making” with Raymond Florax and Jess Lowenberg-DeBoer, Southern Agricultural Economics Association Annual Meeting, Orlando, FL, February 2006.
“Improving Farm Management Decision Making: Experiences From Spatial Analysis of Yield Monitor Data From Field-Scale-On-Farm Trials” with Raymond Florax and Jess Lowenberg-DeBoer, 8th International Conference on Precision Agriculture and Other Resource Management, Minneapolis Minnesota, July, 2006.
“Local Spatial Autocorrelation in Precision Agriculture Settings Accounting for Micro-Scale Topography Differences” with Raymond Florax and Jess Lowenberg-DeBoer, 8th International Conference on Precision Agriculture and Other Resource Management, Minneapolis Minnesota, July, 2006.
“Case Study Evidence of New Opportunities for Farm Management Specialists in Spatial Analysis of On-farm Trial Data” with C.L. Dobbins and J. Lowenberg-DeBoer. 2007. Presented to the National Farm Management Conference June 12-14, 2007 in Rochester, MN.
“Case Study of On-Farm Experiments and Farm Management Decision Making” with C.L. Dobbins and J. Lowenberg-DeBoer. 2007. Selected paper presented to the Annual Meeting of the American Agricultural Economics Association in Portland, Oregon July 29-August 1, 2007.
“Need for Spatial Analysis in On-Farm Research” presented to the Top Farmer Crop Workshop at Purdue University, West Lafayette, IN July, 2007 to 120 farmers.
“Case Study Evidence of New Opportunities for Farm Management Specialists in Spatial Analysis of On-farm Trial Data” presented at the National Farm Management Conference aka Triennial North Central Extension Committee in Rochester, Minnesota June 12, 2007 to 45 Extension professionals and farm business analysts.
“Making the Most of Yield Monitor Data for On-farm Trials” presented to “Basic Concepts & Agricultural Applications of GPS/GIS” Professional Development (Technology) Workshop for Vocational Agriculture Teachers at University of Arkansas at Monticello, June 29, 2007.
“GPS Guidance on the Farm” presented to “Basic Concepts & Agricultural Applications of GPS/GIS” Professional Development (Technology) Workshop for Vocational Agriculture Teachers at University of Arkansas at Monticello, June 29, 2007.
“Profitability of Precision Agriculture” presented to “Basic Concepts & Agricultural Applications of GPS/GIS” Professional Development (Technology) Workshop for Vocational Agriculture Teachers at University of Arkansas at Monticello, June 29, 2007.
“Precision Agriculture: How farmers are making the most of the technology” presented to the SERA-35 Delta States Farm Management Group on May 24, 2007.
“On-Farm Research: Need for Spatial Analysis” presented to InfoAg Midsouth February 7-8, 2007 at Starkville, Mississippi, USA.
“Collecting and Analyzing Data” presented to Oklahoma State University Partners in Research Workshop January 31, 2007 at Northeastern State University, Broken Arrow, OK to 45 farmers.
“Making the Most of Yield Monitor Data for On-farm Trials” presented to Indiana CCA Conference to 85 farmers, consultants, and salespeople, Indianapolis Marriott East, Indianapolis, Indiana, December 19-20, 2006.
“Spatial and Temporal Variability in Nutrient Concentrations and the Role of Precision Agriculture” presented to 125 University of Arkansas Cooperative Extension Service County Agricultural Agents, Arkansas 4-H Center, Ferndale, Arkansas as part of the Soil Fertility and Plant Nutrition In-service Training December 12-13, 2006.
“Using precision technology for on farm trials” presented to Cotton Inc. Precision Cotton Workshop November 2, 2006 in Memphis, TN.
“Making the Most of Spatially Dependent Data” presented to 16 members of the Advanced Center for Management, Innovation, and Technology for Agriculture from University Federico Santa Maria, Santiago, Chile Hosted by the Site-Specific Management Center at Purdue University October 12-13, 2006.
“Decision-Making from On-Farm Experiments: Spatial Analysis of Precision Agriculture Data” presented to Danish Agricultural Delegation to Purdue University, West Lafayette, IN.
“Precision Farming Technologies” to 112 producers as the Keynote at the Illinois GPS Workshop June 23, 2006 at the Interstate Center in Bloomington, IL.
“On-farm Research with GPS: Making the most of on-farm trials” to 40 producers as a breakout session at the Illinois GPS Workshop June 23, 2006 at the Interstate Center in Bloomington, IL.
“Effectively Using GPS in Management” to 40 extension professors with J. Lowenberg-DeBoer presented to Purdue Cooperative Extension Service Agriculture and Natural Recourses In-service Training in Lebanon Indiana, October 2005.
“Whole-farm planning with GPS navigation technologies” presented as the “base case” to 99 farmers at the Top Farmer Crop Workshop July 2004, Purdue University, West Lafayette, IN.
“On-Farm Experimentation: Making the most out of your planned comparisons for improved crop management” presented to 6 invited farmers, April 2005 at Starr Farm, Connersville, IN.
“Using Yield Monitor Analysis to Refine Nitrogen Application Decision Making” presented to 125 farmers at the Top Farmer Crop Workshop July 2005, Purdue University, West Lafayette, IN.
“How to make the most of your on-farm trials with yield monitor data” presented to 125 farmers at the Top Farmer Crop Workshop July 2005, Purdue University, West Lafayette, IN.
“Making the Most of Yield Monitor Data” presented to 25 agronomists from Cazenave & Associates, Argentina August 2005, Purdue University, West Lafayette, IN.
Other farmers in the North Central Region and other locations may benefit from this research in a number of ways. This project led to a full day Yield Monitor Data Analysis to be held on March 1, 2007 on Purdue University campus as a follow-up to the workshop held November 14, 2005 on Purdue campus. Farmers and consultants from across the Midwest attended this workshop, with promotion primarily by word of mouth. Using evaluations from the March 1 workshop, the workshop is being revised and will be offered in additional locations across the United States.
The planned comparisons the farmers chose to use were important to the farmers and were intended to answer existing production questions. Several farmers were eager to get their yield data to the researcher for analysis and subsequent decision making as quickly as possible to begin planning for the 2005, 2006, and 2007 growing seasons. Wintertime decisions include which varieties to order. Farm management decisions are more urgent than in years past, with substantial price discounts available for hybrid purchases prior to harvest and the need to reserve certain hybrids, many hybrid and planting decisions are made before harvest is complete.
One farmer-collaborator reduced soybean seeding rates from 130,000 seeds per acre on 15 inch row spacing to 100,000 seeds per acre on the higher productivity soils, which have lead to reduced costs of production, increased planting timeliness and improved profitability. On the relatively poor soils, seeding rates remain at the current 130,000 seeds per acre in order to optimize profit. This farm ordered the appropriate amount of seed for the farm, a substantially smaller amount than if this experiment hand not been conducted. Reduced seeding rates increased planting timeliness during the planting season by reducing downtime for filling planters, thus increasing yields by planting in the optimal time period. A mathematical example based on this situation was modeled with linear programming methods and presented as the Base Case at the 2006 Top Farmer Crop Workshop.
In addition to the increased production efficiency that results from the on-farm experimentation for participating farms and their associated grower groups, a renewed relationship among innovative growers and universities is created. Many farmers are already contacting the researchers directly, which creates renewed opportunities for local Extension staff to work with these farmers. The relationships originated from the researcher-farmer, i.e. graduate student and major professor with farmer-collaborators and farmer advisory panel (FAP). Extension’s interest grew once they learned of the relationship and are becoming involved, and Extension is fostering relationships with new farmer-collaborators from their own experience.
As a result of this project, a procedure for analyzing yield monitor data has been developed into a protocol to allow other researchers, consultants, and farmers to conduct their own data handling and spatial analysis. This protocol is what we have decided worked best for us and is available on the Site-Specific Management Center website at http://www.purdue.edu/ssmc. This protocol has been updated to reflect advancements in farm software packages made over the last two years as well as advancements in spatial analysis techniques as a direct result of this research. In addition to the protocol, a recommendation to the farm software industry is being prepared to provide suggestion on what farmer software packages need to add to be able to perform yield monitor data analysis from start to finish including spatial statistical analysis.
From the techniques developed for site-specific data analysis gained from this study, two additional graduate student projects, one PhD and one MS, have utilized these techniques for their research. Both projects used precision agriculture data to analyze split-field or paired-field controlled drainage field-scale experiments in cooperation with farmer-collaborators. The graduate student of this project has provided guidance in the technical aspects of data gathering, data handling, and data analysis with GIS and spatial analysis.
See Appendix A for an example of a whole-farm economic analysis using linear programming techniques. In order to obtain the information in Appendix A, please contact the NCR-SARE office at email@example.com and request a paper copy of the final report for GNC03-020.
It is difficult to report on farmer adoption based on this case study; although a extensive follow up survey would be a worthy additional study. Farmers from the group receiving a spatial analysis of their on-farm trial results were very interested in continuing to receive the service.
Areas needing additional study
As previously mentioned, one needed additional study is a quantitative survey to ascertain how many farmers conduct their own on-farm trials, what analysis techniques are used, and how farm management decisions are made.
In terms of experimental designs and spatial statistical methods, this study only began to examine the possibilities for on-farm research. Rigorous studies will most likely occur for many years and generations of researchers. The PI’s are continuing simulation research to determine the most appropriate statistical methods for analyzing on-farm trial data as new and more advanced statistical methods are being developed.