The Precision Farming Primer  

© 1999
Precision Farming Primer

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An Overview of Precision Farming -- introduces spatial relationships as the basis of precision farming
Underlying Principles -- describes the difference between whole-field and site-specific farming
Unusual Blend of technologies -- introduces the technologies supporting precision farming
Processing Precision Farming Data -- identifies the different levels in precision farming
Technical Issues -- introduces the four steps of the precision farming process
Current Reality and Future Directions -- looks at the opportunities of site-specific management

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An Overview of Precision Farming     (return to top of Introduction)

To many, precision farming might appear an oxymoron. With mud up to axles and 400 acres left to plow, precision seems worlds away. Yet site-specific management makes sense to a rapidly growing number of farmers as years of experience confirm the variability in field conditions and yield. Mapping and analyzing this variability and linking the spatial relationships to management action places production agriculture at the cutting edge of GIS applications. Its use down on the farm is both down to earth and downright ambitious.

Underlying Principles     (return to top of Introduction)

Until the 1990s maps played a minor role in production agriculture. Soil maps and topographic sheets, for the most part, were too generalized for application at the farm level. Acquisition of spatial data with the detail and information farmers needed for operations were beyond reach. The principle of whole-field management, based on broad averages of field data, dominated management actions. Weigh-wagon, or grain elevator measurements, established a field’s yield performance. Soil sampling determined the typical nutrient levels within a field. From these and other data the best overall seed variety was chosen and a constant rate of fertilizer applied, as well as a bushel of other decisions—all treating the entire field as uniform within its boundaries.

Site-specific management, on the other hand, recognizes the variability within a field and is about doing the right thing, in the right way, at the right place and time. It involves assessing and reacting to field variability by tailoring management actions, such as fertilization levels, seeding rates and variety selection, to match changing field conditions. It assumes that managing field variability leads to both cost savings and production increases, as well as improved stewardship and environmental benefits.

Unusual Blend of Technologies     (return to top of Introduction)

Site-specific farming isn’t just a bunch of pretty maps, but a set of new technologies and procedures linking mapped variables to appropriate management actions. It requires the integration of several key elements: the global positioning system (GPS), on-the-fly data collection devices, geographic information systems (GIS) and variable-rate implements. Modern GPS receivers are able to establish positions within a field to about a meter. When connected to a data collection device, such as a yield/moisture meter, these data can be "stamped" with geographic coordinates. Several portable "heads-up" digitizing devices allow farmers to sketch conditions, such as weed infestations, on a map or aerial photo backdrop. A GIS is used to map the field data so a farmer can see the conditions throughout a field. The GIS also can be used to extend map visualization of yield to analysis of the relationships among yield variability and field conditions. Once established these relationships are used to derive a "prescription" map of management actions required for each location in a field. The final element, variable rate implements, notes a tractor’s position through GPS, continuously locates it on the prescription map, then varies the application rate of field inputs, such as fertilizer blend or seed spacing, in accordance with the instructions on the prescription map. Combining the technologies of GPS, GIS and intelligent devices and implements (IDI) provides the mechanisms for managing field variability. The maturation and commercialization of these technologies have made the concept practical.

Processing Precision Farming Data     (return to top of Introduction)

To date, most analysis has been visual interpretations of yield maps. By viewing a map, all sorts of potential relationships between yield variability and field conditions spring to mind. These "visceral visions " and explanations can be drawn through the viewer’s knowledge of the field. More recently, data visualization is being extended through map analysis at three levels: cognitive, analysis and synthesis.

The foundation of precision farming occurs at several levels:

The cognitive level manages and stores mapped data on the desktop. The analysis level discovers relationships among the mapped variables, such as yield and soil nutrient levels. This step is analogous to a farmer’s visceral visions of relationships but uses the computer to establish more detailed mathematical and statistical connections. Although this step is somewhat an uncomfortable "leap of scientific faith," it extends data visualization by investigating the coincidence of the patterns of variation among a set of maps. The results relate yield goals to specific levels of farm inputs—traditional agricultural research, but tailored to a farmer’s "backyard."

The synthesis level evaluates newly derived relationships to formulate management actions for a new location (change in space) or another year at the same place (change in time). The result is a prescription map used to guide the intelligent implements as they "variable rate control" the application of field inputs. Or, the analysis might discover an area of abnormally low yield as aligning with a section of old drainage tile in need of repair. Further analysis might locate areas whose simulated yield increases under drier conditions justify installation of additional drainage tiles.

Technical Issues     (return to top of Introduction)

The precision farming process can be viewed as four steps: data logging, point sampling, data analysis and spatial modeling (see fig. 0.1). Data logging continuously monitors measurements, such as crop yield, as a tractor moves through a field. Point sampling, on the other hand, uses a set of dispersed samples to characterize field conditions, such as phosphorous, potassium, and nitrogen levels. The nature of the data derived by the two approaches are radically different— a "direct census" of yield consisting of thousands of on-the fly samples versus a "statistical estimate" of the geographic distribution of soil nutrients based on a handful of soil samples.

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Figure 0.1. Flowchart of the precision farming process.

In data logging, issues of accurate measurement, such as GPS positioning and material flow adjustments, are major concerns. Most systems query the GPS and yield monitor every second, which at 4 mph translates into about 6 feet. With differential positioning the coordinates are accurate to about a meter. However the paired yield measurement is for a location well behind the harvester, as it takes several seconds for material to pass from the point of harvest to the yield monitor. To complicate matters, the mass flow and speed of the harvester are constantly changing when different terrain and crop conditions are encountered. The precise placement of GPS/Yield records are not reflected as much in the accuracy of the GPS receiver as in "smart" yield mapping software.

In point sampling, issues of surface modeling (estimating between sample points) are of concern, such as sampling frequency/pattern and interpolation technique. The cost of soil lab analysis dictates "smart sampling" techniques based on terrain and previous data be used to balance spatial variability with a farmer’s budget. In addition, techniques for evaluating alternative interpolation techniques and selecting the "best" map using residual analysis are available in some of the soil mapping systems.

In both data logging and point sampling, the resolution of the analysis grid used to geographically summarize the data is a critical concern. Like a stockbroker’s analysis of financial markets, the fluctuations of individual trades must be "smoothed" to produce useful trends. If the analysis grid is too course, information is lost in the aggregation over large grid spaces; if too small, spurious measurement and positioning errors dominate the information.

The technical issues surrounding mapped data analysis involve the validity of applying traditional statistical techniques to spatial data. For example, regression analysis of field plots has been used for years to derive crop production functions, such as corn yield (dependent variable) versus potassium levels (independent variable). In a GIS, you can use regression to derive a production function relating mapped variables, such as the links among a map of corn yield and maps of soil nutrients—like analyzing thousands of sample plots. However, technical concerns, such as variable independence and autocorrelation, have yet to be thoroughly investigated. Statistical measures assessing results of the analysis, such as a spatially responsive correlation coefficient, await discovery and acceptance by the statistical community, let alone the farm community.

In theory, spatial modeling moves the derived relationships in space or time to determine the "optimal" actions, such as the blend of phosphorous, potassium and nitrogen to be applied at each location in the field. In current practice, these translations are based on existing science and experience without a direct link to data analysis of on-farm data. For example, a prescription map for fertilization is constructed by noting the existing nutrient levels (condition) then assigning a blend of additional nutrients (action) tailored to each location forming a if-(condition)-then-(action) set of rules. The issues surrounding spatial modeling are similar to data analysis and involve the validity of using traditional "goal seeking" techniques, such as linear programming or genetic modeling, to calculate maps of the optimal actions.

Current Reality and Future Directions     (return to top of Introduction)

The application of GIS within production agriculture has been rapid. Since its inception in the early 90s, precision farming has moved from a fledgling idea to operational reality on millions of acres. Its current expression emphasizes the generation of yield maps by linking GPS with on-the-fly yield monitors. Valuable insight is gained by visualizing field variability, particularly when yield maps for several years are considered. More advanced applications include analysis of soil nutrient maps to derive a prescription map used in variable rate control of fertilizer, terrain analysis for variable seeding rates and spatial modeling for timing and spot application of herbicides/pesticides.

The infrastructure for precision farming is coming online. Most manufacturers offer precision farming options with their farm vehicles and implements. A growing number of service providers offer advice to farmers in their adoption of the new technology. At present, however, a full implementation of precision farming is in the hands of the developers and researchers. Advancements in the data analysis and spatial modeling phases await contributions from the GIS community. The considerable knowledge and methodologies of the agricultural science community need to be reviewed for their spatial inferences. Opportunities abound in one of GIS’s more important applications and we all benefit from precision farming’s fruits—check it out at your local super market.

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