6.7 Tracking Field Operations for Economic Analysis and Identity-Preservation
Investigators
Carl Dillon, Agricultural Economics, cdillon@uky.edu
Russ Morgan, Agricultural Economics, pfagroup@apex.net
John Fulton, Biosystems and Agricultural Engineering, jfulton@bae.uky.edu
Steve Issacs, Agricultural Economics, sisaacs@uky.edu
Samuel G. McNeill, Biosystems and Agricultural Engineering, smcneill@uky.edu
Michael D. Montross, Biosystems and Agricultural Engineering, montross@bae.uky.edu
Scott A. Shearer, Biosystems and Agricultural Engineering, shearer@bae.uky.edu
Timothy S. Stombaugh, Biosystems and Agricultural Engineering, tstomb@bae.uky.edu
Introduction
With the advent of the global positioning system, agriculture has entered an era where tracking inputs and the resulting final product on a square-meter-basis are now practical. With this new technology comes an obligation to accumulate and archive these spatially referenced data for subsequent analyses. Unfortunately, end users of this technology are less than pleased with the current results, as precision agriculture requires a new level of management. And, until the data recording and tracking requirements can be reined in, adopters of this technology will continue to ask for evidence of the profitability of this technology. Hence, most producers will fall back to making equipment management decisions based on information provided in the popular press, from input suppliers, research universities, or producer testimonials. In reality, decisions regarding equipment purchases or implementation of new crop production practices are as unique and varied as the operations in which they will be integrated. From a practical perspective, it will be increasingly important to know exactly how changes in production practices affect the profitability of a specific producer.
With the purchase of each new piece of precision agriculture equipment, a new burden is created for producers requiring them to expand the time devoted to data collection and archiving. Further, the failure to track pertinent ancillary data may reduce the value of this new data. For example, while producers spend a significant portion of time calibrating yield monitors and faithfully download yield data on a routine basis, they fail to keep records of where specific varieties or hybrids are planted. The ideal situation will involve single point entry of data, and the porting of this data to both GIS and accounting software. If successful, this technology should pave the way for detailed analyses and the production of reports regarding farm profitability, machinery costs and productivity, along with the potential for increasing revenue via IP specialty grains. Central to the simultaneous accomplishment of these activities is a standardized methodology for entering and archiving field operations data.
Implementations of a "field operations data model" (FODM) will enable producers to track all field operations such as seed varieties or hybrids planted, chemical compounds and application rates, and yield performance information. To automate much of this data collection GPS receivers are mounted on machinery to track the placement of inputs. Handheld computers called Personal Digital Assistants (PDAs) can be used to record field/operation-specific data, ultimately limiting paper notes that must be rentered into the database at a later date. Further, low-cost microcontrollers and short-range wireless devices offer the potential to eliminate much of the operator data entry. A number of companies produce microcontrollers for less than $20 a piece (MicroChip Technologies, Inc. and Motorola Corp.). These devices can be mounted on field machinery to facilitate collection of field track at planting, or to track inputs such as seed at planting using bar code readers.
Crop production and marketing in the U.S. is based on a commodity, bulk grain production system. With precision agriculture, spatial data can be accessed from databases and used to generate sub-field level economic analysis. A natural extension of a FODM is to production of identity preserved (IP) crops. To market IP crops a producer must have a record of the variety or hybrid and where it was produced within a field. Data collected and stored in the FODM is sufficient to produce a complete IP solution. A farmer could sell grain from the bin with a known production history that includes planting, chemical application, harvest, and storage data.
Objectives
The primary intent of a FODM is to link third-party software packages, and this forms the basis of this project. A FODM will be implemented on Kentucky farms with the primary intent of linking precision agriculture data to economic analysis capabilities. Specifically, the following objectives will be addressed:
1. Develop and implement a software and hardware architecture that facilitates effective entry and accumulation of field operations data;
2. Develop an interface between the FODM and traditional accounting and GIS software for the purpose of generating spatially-referenced economic reports at user defined scales and formats;
3. Utilize the FDOM and GIS to develop machinery performance, efficiency and cost analysis capabilities; and
4. Establish identity-preserved (IP) procedures and protocols for tracking agricultural products from seed to market using the FODM.
Background
The development of precision agriculture software has not kept pace with the evolution of equipment. In some respects, the market is mature, and any development costs must be recouped from the existing customer base. In consideration of this trend, and the trend of increasing farm size, it is reasonable to expect a slowed development pace by software providers. However, if software developers band together to begin using the FODM, it may be possible for unrelated software application (e.g., GIS, and cost accounting) to use the common database. To this end MapShots, Inc. in conjunction with the A*E*A (Agricultural Electronics Association) have proposed a data model for field operations (Macy, 2001). The envisioned model was developed in response to the increasing regulatory requirements for farmers to track Confined Animal Feeding Operations (CAFO) nutrient management and the cultivation practices associated with Genetically Modified Organisms (GMOs). Further impetus for the data model is the ever-expanding realm of sensors in the area of precision agriculture. Each time new parameters are sensed and logged (e.g. soil electrical conductivity) software developers must write additional code that supports data transfer from a proprietary logging device to the office PC. There is also a trend where service providers generate data for integration into the on-farm database. The basis of the envisioned FODM is the ability to accept field scale data as well geographically referenced data within the same framework. Further, that the FODM is robust enough to enable expansion to included data from new sensing technologies.
The solution proposed in Macy (2001) supports: 1) transfer for field operations data to third party software, 2) configuration of proprietary field devices from third party software, and 3) the transfer of data among third party software packages. The latter feature supports transfer of data from the service provider to the producer. Macy (2001) proposed the FODM in response to the needs indicated above. He began by identifying resources such as people, equipment, and products. Second, he describes the data to be collect and implements the concept of "virtual sensors." These virtual (and/or real) sensors are used to spatially track products at the time of application. And lastly, from an inventory control perspective, all products must pass through a "container."
Macy (2001) advocates an XML database structure. This document database has advantages in that it is self-describing (markup describes the structure and type names of the data), portable (Unicode), and it describes data in tree or graph structures. XML exhibits disadvantages in that it is verbose and access to the data may be slow because of parsing and text conversion. This implementation is accomplished using Component Object Modules (COM). With a COM implementation it is possible to move data from a proprietary field data collection device to the FODM via a standard programming interface (e.g. drivers).
The project investigators have experimented with collecting spatial field operation data at planting. A simple electronic relay was used to sense whether the ground drive clutch on the planter was engaged, and pass this information to a parallel port on a laptop computer for logging. These data were used to evaluate field efficiency, compute overlap and skip percentages, and map seed variety. During harvest, combines equipped yield monitors and GPS receivers provide machinery performance data within the yield file. Since every data point is stamped with GPS time, field capacities can be computed for the combine. Walker (2002) demonstrated how grain flow-rate and combine forward speed data could be used to evaluate combine throughput efficiency and draw conclusions about proper header and combine size.
Information within GIS databases facilitates economic analyses and cost accounting data to be generated at user defined scales. FODMs provide the base physical data that enables a user to develop economic and financial reports. Consequently, the establishment of information useful for both the control function of management (in the sense of assessing past performance) and the planning function of management (in the sense of permitting comparative analysis and predictions of economic performance) embodied within the FODM.
Many growers are interested in producing non-genetically modified (non-GM) grains at a premium. Non-GM corn generally commands premiums in the range of $0.02 and $0.15 per bushel while premiums for non-GM soybeans range from $0.20 to $0.25 $ per bushel over the Chicago Board of Trade prices (Illinois Specialty Farm Products, 2002). The American Corn Growers Association (ACGA, 2001) reports that over half of the elevators surveyed require segregation of GM varieties either upon arrival at the elevator or at the farm. Almost 20 percent reported offering premiums for non-GM corn or soybeans. The yet unresolved question is how elevators will verify non-GM crops. The default at this time is a laboratory test for GM organisms.
Methods
The primary focus of this project is to assemble and implement a hardware/software compliment that is capable of utilizing the structure of the FODM proposed by Macy (2001) as noted under Objective 1. It will be incumbent on the researchers to work with software vendors to translate the FODM as necessary for each application. Further, it may be desirable to develop specialized databases and input forms for PDA devices to streamline on-farm data accumulation.
To facilitate implementation of the FODM, two farm cooperators will be identified in each year of the investigation for a total of six cooperators during the course of the project. In return for their time and interests, these cooperators will be equipped with the necessary compliment of software, PDA and GPS data logging capabilities for the duration of the project. It will be incumbent on the cooperators to provide GPS capabilities for planting, spraying, and harvesting operations, and in the latter case yield monitoring capabilities. It is also expected that the farm cooperators will participate in the Kentucky Farm Business Management Program sponsored by the Cooperative Extension Service through the University of Kentucky, College of Agriculture.
The crop input and performance data obtained in Objective 1 will be coupled with producer entered economic data including output prices (e.g., corn price) and input prices (e.g., fertilizer prices, interest rates on operating capital). To the extent that commodity price may be influenced by IP premiums, there will be a linkage of work completed her with Objective 4. This data will be combined with existing farm accounting software (e.g., Quicken, Quickbooks or Farmworks) and programs (Kentucky Farm Business Management Program) as appropriate. Procedures for the development of economic reports, including net return maps being developed in ongoing research efforts within Agricultural Economics, will be used in defining the algorithm for generating cost accounting results. To the extent that existing commercial software is impractical for achieving the desired results, programs will be developed to accomplish Objective 2. Envisioned economic and cost accounting reports to be generated (Objective 2) will include net returns maps, risk maps, economic performance assessments (e.g., net returns per unit of resource such as labor, capital investment, or per unit production costs) and financial reports (e.g., balance sheets, and income statements on user defined scales).
A major challenge in logging field operations data is to obtain implement status, and therefore another essential goal of this project will be to develop a simple, universal logging system for multiple field operations. This system will be comprised of a relatively inexpensive single chip device with compact flash data storage. It will be designed for boot-up when the machine is started, thereby relieving the equipment operator of any data logging responsibilities. These capabilities will be replicated for all farm cooperators. FODM analysis procedures will be developed to extract machinery performance features such as machine field capacity and efficiency, variable costs, and down time as noted under Objective 3. The nature of software and development will limit the availability of machinery performance features to the second third years of the investigation.
With the implementation of the FODM the migration to IP crop production practices, under Objective 4, is nearly instantaneous. Contamination on non-GMO crops (primarily corn) from surrounding fields is a major concern and therefore GIS coverages will be generated from the FODM that indicate high risk contamination areas. The description of these areas will be based on FODM entries that include field boundaries, crop type, planting date, etc. During harvest the field name, load, and time information will be archived and in turn transferred from the combine, to the grain cart, to the truck, and finally to the scale house at the elevator. IP data will be transferred between microcontrollers on each vehicle via wireless communications. Drying conditions, storage location and post-harvest treatments will be recorded using a PDA at the storage facility. At market time the producer will query the FODM to retrieve information that included variety or hybrid, chemical applications, harvest date, yield, moisture content, drying conditions, and storage treatments. Economic analyses can be performed to show the returns as a function of production practices. The nature of software and development will limit the availability of IP tracking functions to the second third years of the investigation.
Expected Benefits
The primary focus of this project is to assemble and implement a hardware/software compliment that is capable of utilizing the structure of the FODM. Envisioned economic and cost accounting reports to be generated will include net returns maps, risk maps, economic performance assessments and financial reports at user defined scales. A simple, universal logging system will be developed for logging multiple field operations. FODM analysis procedures will be developed to extract machinery performance features (e.g. field capacity and efficiency, variable costs, and down time) from the data model. In addition GIS coverages will be generated from the FODM that indicate high risk GM contamination areas based model entries that include field boundaries, crop type, planting date, etc. It is the implementation of the FODM on Kentucky farms that will tie precision agriculture field practices to comprehensive cost accounting and economic analyses models -- the essential missing link!
Deliverables
Producer meetings, and workshops sponsored by the Extension service will be used to demonstrate the implementation of the FODM, economic analyses, and to provide information about IP crop production using the FODM. Drawings, specification lists, and software tools will be published and made available via the Internet to encourage the private sector to produce hardware and software for use by farmers. Extension publications and referred journal articles will be developed and published as appropriate based on the results of this study.