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precag_thumb.gifTo many, Precision Farming seems like an oxymoron. With mud up to the axles and 400 acres left to plough, precision seems worlds away. Yet site-specific management makes sense to an exponentially growing number of farmers.  And production agriculture is at the cutting edge of geotechnology applications through mapping and analyzing the inherent variability in field conditions and linking the derived spatial relationships to management action.

To many, Precision Farming, more formally termed Precision Agriculture, seems like an oxymoron. With mud up to the axles and 400 acres left to plough, precision seems worlds away. Yet site-specific management makes sense to an exponentially growing number of farmers.

Mapping and analyzing the inherent variability in field conditions and linking the derived spatial relationships to management action places production agriculture at the cutting edge of geotechnology applications. Couple this with the U.S. Department of Labor’s recognition that Geotechnology is one of the three “mega-technologies” of the 21st Century (biotechnology and nanotechnology are the other two) and it’s apparent that seat-of-the-pants farming is increasingly in front of the computer.

Precision Farming focuses on sound crop production, applying geotechnology to effectively understand and manage the dynamic flows and cycles within a landscape perspective. This broader perspective, in both scope and geography, promises to enhance agriculture sustainability and stewardship throughout the world.

Site-Specific Management

Until the 1990s, maps played a minor role in production agriculture. Most soil maps and topographic sheets were too generalized to apply at the farm level. As a result, the principle of whole-field management based on broad averages of field data, dominated management actions. Weigh-wagon and grain elevator measurements established a field’s yield performance, and soil sampling determined the average nutrient levels for an intact field. Farmers used such data to determine best overall seed varieties, fertilization rates and a bushel of other decisions that all treated an entire field as a uniform whole within its boundaries.

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

Precision Farming Components

Site-specific farming isn’t just a bunch of pretty maps, it’s a set of new geospatial technologies and procedures for linking mapped variables to appropriate management actions. Such procedures integrate several key elements: the Global Positioning System (GPS), Remote Sensing (RS) imagery, Geographic Information Systems (GIS) software, and “on-the-fly” data collection devices and variable-rate implements (Robotics) as depicted in Figure 1.

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Figure 1 –  Precision Farming’s “Component Technologies” with a background image overlaid with yield, soils, and high resolution elevation surfaces as viewed in Google Earth. CLICK ON THE IMAGE TO ENLARGE IT IN A NEW WINDOW.

Mid-range GPS receivers can easily establish positions within a field within 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 “feet-down digitizing” devices enable farmers to sketch conditions, such as weed infestations, on a digital map or aerial photo backdrop while standing in a field. Downloading GIS data to mobile mapping devices lets a farmer see maps that summarize complex conditions and relationships throughout a field. These technologies also can be used to extend yield visualization to analyze relationships among yield variability and field conditions.

Once established, these relationships derive a “prescription” map of management actions required for each location in a field—often every few feet or so. The final element, variable-rate implements, automatically detects a tractor’s position through GPS, continuously locates it on the prescription map, and then varies the application rate of field inputs, such as fertilizer blend or seed spacing, according to precise instructions for each location. Combining technologies such as GPS, GIS and intelligent devices and implements provides the mechanisms to manage field variability. The maturation and commercialization of these technologies have made the concept not only possible, but increasingly practical.

In early applications, most of the analysis involved visual interpretations of yield maps. By viewing a map, 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.

Precision farming’s foundation occurs at the cognitive level, where desktop mapping is used to manage and store mapped data. At the analysis level, map analysis is used to discover relationships among variables such as yield and soil nutrient levels. This step is analogous to a farmer’s visceral visions of relationships, but uses the computer and digital maps to establish more detailed mathematical and statistical relationships. Although this step is a somewhat uncomfortable “leap of scientific faith,” it extends data visualization by investigating the detailed coincidence of the variation patterns among mapped data sets. The results relate yield goals to specific levels of farm inputs as in traditional agricultural research, but tailored to a farmer’s “backyard.”

The synthesis level evaluates newly derived relationships to formulate management action. 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 linked with a section of old drainage tile in need of repair. Further analysis might locate areas in which simulated yield increases under drier conditions, justifying the installation of additional drainage tiles.

The Precision Farming Process

The Precision Farming process can be broken into four steps: 1) Data Logging, 2) Point Sampling, 3) Data Analysis and 4) Spatial Modeling (Figure 2).

Data Logging continuously records measurements, such as crop yield, as a tractor moves through a field. 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 four miles per hour translates into roughly six feet between estimates. With differential positioning, the coordinates are accurate to about a meter, resulting in thousands of on-the-fly samples per field.

But the paired yield measurement is for a location far behind the harvester, because it takes several seconds for crop material to pass from the cutters on a harvester to the yield monitor. To complicate matters, the mass flow and speed of the harvester change constantly as different terrain and crop conditions are encountered. The precise placement of GPS/yield records aren’t reflected as much in the accuracy of the GPS receiver as in the “smarts” of yield mapping software to correct for the time lag induced offset.

Point Sampling uses a set of dispersed samples to characterize field conditions (e.g., phosphorous, potassium and nitrogen levels). Surface modeling estimates map values between the sample points to establish continuous map variables. Concerns in the procedure include issues of sampling frequency, pattern and interpolation technique. The cost of soil lab analysis often dictates “smart sampling” based on terrain and previous data to balance spatial variability, the number of samples and a farmer’s budget. In addition, techniques for evaluating alternative interpolation techniques and selecting the “best” map using residual analysis are available in some soil mapping systems.

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Figure 2 – Precision Farming’s “Processing Steps”(fertility example) involve procedures for 1) Data Logging, 2) Point Sampling, 3) Data Analysis and 4) Spatial Modeling. CLICK ON THE IMAGE TO ENLARGE IT IN A NEW WINDOW.

 

Note that the data derived by the two approaches are radically different— a “direct census” of yield that consists 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.

Data Analysis involves GIS processing to uncover spatial relationships among crop yield and factors that affect crop development, such as soil type, nutrient levels, Ph, organic matter and the like. 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) vs. potassium levels (independent variable). A GIS enables users to regress an entire map of corn yield on a stack of soil nutrient maps to derive the production function related to the mapped variables. But should you? Technical concerns, such as variable independence and autocorrelation, have yet to be thoroughly addressed. Statistical measures assessing results of the analysis, such as a spatially responsive correlation coefficient, await discovery and full acceptance by the statistical community, let alone the farm community.

Spatial Modeling uses the derived spatial relationships to drive decision support systems, such as determining a spatially specific fertility program that is implemented on-the-fly throughout a field. In theory, spatial modeling evaluates the relationships established during the data analysis phase to determine “optimal” actions, such as the blend of phosphorous, potassium and nitrogen to be applied at each location in the field.

In current practice, however, these translations are primarily 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 for each location—an “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 generate maps of optimal actions.

The Growing Precision Farming Revolution

Geospatial technology has evolved rapidly within production agriculture. In less than 15 years the application has moved from inception to an operational reality on millions of acres. It is becoming increasingly difficult to buy a tractor that isn’t GPS-enabled with on-board navigation and on-the-fly instrumentation. Even at the low-end of adoption, there’s emphasis on real-time display of yield maps by linking GPS with yield monitors. Valuable insight is gained by visualizing field variability, particularly when yield maps for several years are considered. More advanced applications involve analyzing soil nutrient maps to derive a prescription map used in variable rate fertilizer control.

The infrastructure for Precision Farming is in place. Most manufacturers offer options with their vehicles and implements and a growing cadre of service providers offers advice to farmers interested in adopting the new technology. Opportunities abound in one of GIS’s more important applications and, quite literally, we will all benefit from its fruits.

Future farmers are plugged into the planet as never before. They use conduits of digital information, piping data to and from their farm fields, as they forge new information flows and links in the farm-to-food chain. And they are constantly working in new ways with new communities of suppliers and customers.

Behind every technology is a philosophy. Most farmers adopting site-specific technologies do so to discover ways to cut their costs, to use inputs appropriate to the productive capacity of the site, and to optimize their outputs for a safe and stable supply of food and fiber.

They’re not just “farming by the numbers,” but they are able to apply more science to the art of farming. They are the front-line integrators of information and technology but don’t want to become entrapped by data-driven technologies—they expect to be empowered with decision-making tools. They’re turning information technologies and geographic information systems into geographic management systems as part of a toolbox of overall farm management tools and techniques aimed at reducing risk and optimizing efficiency.

This becomes even more important when you consider the future structure of agriculture. The industry is moving toward consolidation and vertical integration, along with the adoption of information technologies and biotechnologies. As a result, there may be fewer farmers. This suggests forward-thinking producers must forge new links on the farm-to-food chain.

“What you know about what you can grow” will become the key to farm management. The agricultural industry will become increasingly involved in planting, growing, harvesting and processing “information” along with value-added crops.

Site-specific management drives farmers to accurate record keeping, which will direct their precision farming decision making. This must be the next transition: from Precision Farming to “appropriate agriculture”—doing the right thing at the right time in the right place in the right way. At the heart of it all will remain temporal and spatial decision making–made more effective by GIS working in tandem with other spatial information technologies.


Authors:
Joseph K. Berry is Keck Scholar in Geosciences, University of Denver, Colorado, Jorge A. Delgado is Soil Scientist, USDA-ARS, Soil Plant Nutrient Research, Fort Collins, Colo., and Rajiv Khosla is GPS/ Assistant Professor, Colorado State University, Fort Collins, Colo.

Editor’s Note:
This is the first installment in a two-part series about the use of precision land management techniques. Read the second installment here .
References:

1)    Who’s Minding the Farm , GeoWorld, February 1998, Vol. 11, No. 2, pgs. 46-51. . J.K. Berry and Grant Mangold.

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