Site-Specific Management Guidelines

1) Site-Specific Use of the Environmental Phosphorus Index Concept

Phosphorus (P) loss to surface water can have negative impacts on the environment. The risk of such loss depends on both source (added fertilizer and manure, soil P) and transport factors (erosion and surface runoff). Fields at risk are those where areas of high P application or soil P coincide with zones of active surface runoff or erosion. A P index has been developed to rank field vulnerability to P loss so that high risk fields may be identified for site-specific management. The index provides a framework that can be regionally adapted to prevailing topography, geology, and climatic conditions and requires only readily available data. This fact sheet describes the technical basis of the index and shows its application to a watershed in Pennsylvania.

2) Management Zone Concepts

Varying the application rates of plant nutrients and other crop inputs across variable fields makes good agronomic sense. For every input some reasonable strategy must be used to guide that application. Grid soil sampling for phosphorus (P) and potassium (K) has greatly improved the accuracy of fertilizer application, although even greater accuracy can be attained by considering additional site characteristics within sub-regions of fields. A "management zone" is a sub-region of a field that expresses a relatively homogeneous combination of yield-limiting factors for which a single rate of a specific crop input is appropriate. Spatial information that is most helpful in defining management zones should be quantitative (numerical), densely or continuously sampled, stable over time, and directly related to crop yield. The basis for accurate and profitable application of crop inputs…uniform or variable-rate…will continue to be a clear understanding of the agronomic factors that directly affect crop growth and yield.

3) Profitability of Site-Specific Farming

When is site-specific farming (SSF) profitable? What makes it profitable or not? This Guideline looks at both variable rate (VR) input applications and yield mapping. It demonstrates basic budgeting methods to measure average profitability. Profitability results from nine field research studies show that high-value crops give the biggest payoff to VR fertilizer application. Many yield map benefits come from whole-field improvements such as drainage, land leveling, windbreaks, and fencing. Farmers and agribusinesses should remember that because SSF practices are site-specific, their profitability potential also will be site-specific.

4) How to Determine an Accurate Soil Testing Laboratory

Many people have said that soil testing laboratories are good enough considering the amount of field variability. With the advent of global positioning system (GPS) technology, soil sampling variability is minimized. Now, soil testing laboratories must be more accurate and reproducible to match the improved accuracy and precision achieved with GPS soil sampling. How can you know if the laboratory you are using produces good numbers? You can determine laboratory accuracy by: 1) knowing the difference between accuracy and precision, 2) know what questions to ask a laboratory, and 3) setting realistic expectations of laboratory quality.

5) Developing Management Zones to Target Nitrogen Applications

Whether the goal is to determine the level of soil nitrogen (N) or the soil yield potential, management zones for N fertilizer management can be constructed using a variety of tools, including topography, aerial photographs, satellite imagery, soil electrical conductivity sensors, yield maps, and intensive soil survey data. For the producer starting out, viewing satellite images and/or aerial photos that are relatively inexpensive to obtain and comparing them with landscape features would be a good place to start. More than one layer of information may be necessary to determine similarities between patterns and identify nutrient management areas. Often there are logical reasons for N patterns to exist in fields, and these patterns are stable between years. Zones can be constructed and managed for N using a fraction of the number of soil samples required to reveal the same zones through grid sampling. Zone sampling results in lower sampling costs for variable-rate fertilizer application and allows precision farming to be much more practical for producers of commodity crops. Using these principles, the next step would be to develop computer models to automate the zone development process and eliminate reviewing several maps for each field by the producer in order to decide where to draw zone boundaries.

6) Global Positioning System Receivers

The global positioning system (GPS) and GPS receivers provide the means to determine position at locations anywhere on earth. Developed by the U.S. Department of Defense (DOD) and used for many civilian purposes, from fishing to flying, GPS has also made precision farming a reality. A typical configuration for on-farm agricultural applications includes a GPS receiver and antenna, a differential correction receiver and antenna, and cables to interface differentially-corrected (DGPS) data from the receiver to other electronic equipment such as a yield monitor or a variable rate controller. Accurate, automated position tracking with GPS receivers allows farmers and agricultural service providers to record geo-referenced data and to apply variable rates of inputs to smaller areas within larger fields.

7) Variable Rate Equipment--Technology for Weed Control

Sprayer controllers have been developed by agricultural equipment vendors to minimize variation of applied rates of chemicals within fields. The control systems that allow these devices to compensate for changes in vehicle speeds now also provide the potential to apply variable rates of pesticides according to preplanned maps. The types of sprayer systems and controllers capable of variable rate control are discussed here, along with their advantages and disadvantages. Communications between task computers used to store maps and these sprayer controllers are also discussed.

8) Standardization and Precision Agriculture--'The Promised Land'

Progress toward increased use of electronic systems for precision farming applications will be enhanced by the introduction of standards for electronic communications on agricultural equipment and translation of spatial data formats. The standard J1939 will provide a uniform approach to communications on tractors and implements. The Transfer Support Layer (TSL) specification will allow for transparent use of maps from many different software companies on any system that adheres to the TSL specification.

9) Yield Monitor Accuracy

Field and laboratory experiments were done to investigate the performance of an impact-based yield sensor. Laboratory tests showed that the accuracy of the yield sensor was affected by sudden grain flow changes. The yield sensor had a quick response to flow variations; however, it did not provide consistent readings when grain flow variations were abrupt. In field tests, the yield monitor showed yield trends quite reasonably. The yield monitor accuracy was higher at a constant combine ground speed compared to varying speeds based on weights from individual strips. Yield monitor calibration plays a key role in obtaining the best possible accuracy from the yield monitor.

10) The Pioneer Split-Planter Comparison Method

The Pioneer Split-Planter Comparison Method is a simple, low-cost technique for making treatment evaluations using a global positioning system (GPS)-equipped yield monitor in whole fields. Comparisons can be made between two hybrids, varieties, or agronomic treatments applied in alternating strips throughout a field. Careful attention to products or practices tested, harvest direction in sloping fields, and accurate load designation will help insure meaningful results. Pooling results from similar comparisons made at multiple locations is much preferred to relying on single-location results. The ability to bring a yield difference map into a geographic information system (GIS) and overlay it with other spatial data layers will greatly increase the value of the map as a crop management tool.

11) Earth Model--Calculating Field Size and Distances between Points Using GPS Coordinates

An ever-increasing number of farmers have global positioning system (GPS) receivers on their combines. When not harvesting, GPS receivers are useful for more than locating one’s favorite fishing spot. They can be tools for determining the distance between two points or to accurately determine the acres in a field that is to be rented. The distance between two sampling points and the area of a field can be found using GPS coordinates and knowledge of the Earth Terrestrial Coordinate System. Because GPS latitude and longitude are in terrestrial coordinates, determining the distance in length measurements (feet, meters, yards, kilometers, and miles) rather than degrees, between two points is not trivial. The objective of this guideline is to provide a method that farmers, ranchers, or agricultural practitioners can use to calculate distances between points and to calculate the size of a field using Excel, a commonly available spreadsheet. A more detailed description for calculating distance and area is found in Carlson (1999), in which the mathematics and assumptions used to create the model used in this guideline are described. A Basic programming language computer program is also available in that paper.

12) Assessing Crop Nitrogen Needs with Chlorophyll Meters

One of the most difficult challenges facing farmers is to determine the appropriate fertilizer nitrogen (N) application rate. Regardless of source, most N in soil is eventually transformed to the nitrate (NO3) form. To minimize NO3 leaching, cropping systems and management practices must minimize excess NO3 in the soil and the potential for percolation below the root zone. The problem is basically one of synchronizing soil N availability (from all N sources) with crop N needs. This task is complicated because it is difficult to accurately predict climatic variables that influence crop growth, soil microbial activity, and NO3 leaching.

13) Identifying Good Candidates for Precision Phosphorus Management

1. Findings from this study are applicable to fields with phosphorus (P) frequency distributions similar to that shown in Figure 1. In fields with high soil test P variability, precision management of P produced the greatest level of profitability when the composite soil test P level was in the high to very high soil test P categories. 2. Average soil P test level and prior field histories can be used as a decision aid to reduce economic risks associated with adopting precision farming techniques. 3. Appropriate P response models and yield goals must be used to accurately assess potential profitability associated with precision P management.

14) Selecting a DGPS for Making Topography Maps

The CEO of your company decides he wants you to start offering precision farming services. Based on this mandate, you purchase a code-phase differentially corrected global positioning system (DGPS), and you use it for grid sampling, applying variable rate fertilizers, and yield monitoring. After a while you notice that your competitor is superimposing soil nutrient, pH, and yield information on topography maps, and is thereby improving his ability to identify management zones (Figure 1). Based on the need to stay competitive, you decide to use your code-phase DGPS system, purchased for locating soil sample grid points, to develop topography maps. After spending $5,000 to attend a geographic information systems (GIS) training workshop for a week, you develop your first topography map (Figure 2). Why doesn’t it look like your competitor’s maps? What went wrong?

15) Scouting for Weeds

The concept behind scouting for weeds is to provide accurate and timely information needed to make intelligent, cost effective decisions. Moreover, scouting is a key component in the design of effective weed management strategies that help to manage risks by providing information needed to optimize the correct timing of herbicides and accurately monitor weed management successes and failures (Wallace, 1994). This requires one to think about dynamic and flexible weed management systems to meet challenging demands. Adaptive sampling strategies (rather than fixed strategies such as grid sampling) are flexible and build on previous information and experience. Adaptive approaches also result in more dynamic data gathering systems that can be used to determine if the current weed management system is or is not meeting your goals. We can also assess if given weed species are increasing or decreasing in density and area. Being able to adjust sampling strategies based on previous observations is critical and must be taken into account each year. Experience coupled with flexibility is the key to obtaining reliable data needed to make intelligent site-specific weed management decisions. However, we must recognize that here is no single scouting strategy that is best in all situations and that each strategy has advantages and disadvantages.

16) Remote Sensing: Photographic vs. Non-Photographic Systems

The intention of site-specific management is to optimize grower inputs on areas much smaller than the entire field. These areas may be as small as a few square meters in size. To manage a field on such a scale, data would have to be collected on a similar or smaller scale. To collect the data by hand would be very time consuming, labor intensive, and destructive. This is the role remote sensing systems can play in site-specific management. The purpose of this guideline is to compare the advantages and disadvantages for two types of remote sensing systems: photographic and non-photographic. It is also intended to provide basic information about remote sensing and how to deal with the data obtained.

17) Setting Up On-Farm Experiments

The ability to perform on-farm experiments has been greatly improved with the advent of yield monitoring and differentially-corrected global positioning system (DGPS) equipment. However, care should be exercised when planning a particular experiment to remove sources of variation that might confound the interpretation of the data. In addition, the use of yield monitor data may require more observations or larger loads to adequately capture the random variation in the field. Simple statistical tests, like the t statistic, are appropriate when analyzing paired data such as the side by side split-planter design or strip data from yield monitors.

18) Simple On-Farm Comparisons

Many farmers continuously experiment with new farming approaches to optimize profitability. To determine if management changes make a difference, it is necessary to compare one treatment versus another. The selection of a method for conducting the experiment and making comparisons is critical to minimize incorrect interpretation of experimental results. It is not necessary to implement complicated experimental or statistical methods to determine if a change in management will improve production. However, if proper techniques are not used, results can be misleading, and a change in management might not lead to an increase in productivity. The purpose of this guideline is to provide a framework for conducting simple on-farm experiments. These techniques can be used for any comparisons where two factors need to be compared with statistical precision.

19) Area-Wide Management Zones for Insects

Corn rootworm area-wide management is not for everyone. However, if you go into it with an understanding of the potential infrastructure/coordination problems and the knowledge that intense program oversight is needed, it can successfully reduce corn rootworm populations and provide sustainable economic and sociological benefits to the individuals involved. Farmers working together to manage a problem can overcome many of the difficulties that might be encountered. A proactive and futuristic approach to pest management can yield many unexpected benefits related to total farm management.

20) Estimating the Time of Weed Emergence

The basis for timing of many weed control operations is seedling emergence. However, weeds rarely, if ever, emerge in a synchronous and uniform flush. Instead, they emerge in "fits and starts," depending upon weather, soil, and management conditions. Recently developed computer software permits site-specific prediction of weed emergence and early seedling growth using on-farm weather data. Armed with this information, producers and crop consultants can estimate current weed emergence on a daily basis. They also can forecast forthcoming emergence. The software is sensitive to tillage system and soil type. Consequently, the variability of emergence, due to these factors, across fields can be estimated easily. Recognition of the asynchrony in timing of weed emergence helps producers and consultants make better management decisions for spatially variable fields.

21) Field Testing Management Zones for VRT

Developing accurate variable-rate technology (VRT) fertilizer application maps is critical in implementing precision farming management. Intensive grid soil sampling has traditionally been used to develop application maps. Research at the University of Nebraska found that in many cases where the spatial distribution is rather complex, much finer grid densities than those currently used commercially are required to produce accurate maps of nutrient levels for fertilizer applications (Gotway et al., 1996). However, the cost and labor intensity associated with intensive grid sampling suggests other approaches may be more feasible. Management zone technology may provide a more economical method of developing VRT application maps.

22) Potential Applications of Remote Sensing

Since the development of remote sensing nearly 60 years ago, there have been many applications for agriculture. Some have proved effective, while others have not succeeded in assisting farmers with problem solving. Profit margins for individual farmers are typically slim; therefore, farmers are likely to take seriously any technology advances that will help increase those margins. So far the use of remote sensing data has proven most economical for the high value crops where the risks are greater per acre. Remote sensing has not been perceived as cost-effective for Midwest crops where weather is the greatest variable and therefore not manageable. Recent advances in the spatial, spectral and temporal resolution of remote sensing (Johannsen et al., 1998) as well as potential positive changes in cost and availability of remotely sensed data may make it a profitable tool for more farmers. There are some practical applications of remote sensing that are often overlooked by many farmers and consultants. The purpose of this Guideline is to highlight those applications.

23) Getting Specific with Site-Specific Nutrient Management

Over simplification of site-specific nutrient management can lead to reduced profits and production. Currently, site-specific nutrient management typically involves applying a definite set of recommendations to different areas in a field, based upon a few factors, such as soil test levels and yield goals. However, if these recommendations do not consider other site-specific factors that influence response to nutrient application, substantial opportunities to increase profits and production may be lost. Standard university recommendations for nitrogen (N) and phosphorus (P) were evaluated for profitability potential within a field and compared to actual crop response needs. Site-specific university recommendations produced an average net return of $75/A, while actual crop response suggested that a return of $100/A was possible with the right nutrient management decisions. Yield and crop response at this location were impacted by drainage and compaction. Proper evaluation of these yield-limiting factors and appropriate management changes based on readily available information could make site-specific nutrient management more profitable.

24) Grain Protein Sensing to Identify Nitrogen Management in Spring Wheat

Wheat producers in the northern Great Plains market grain under a quality payment system that provides economic incentives for optimizing grain protein. Protein concentration in grain is greatly influenced by the level of nitrogen (N) fertility. However, there is significant spatial variation in N fertility in a field. Conventional uniform application ignores this variability and leads to over-fertilization in some areas and under-fertilization in others. Therefore, the question arises as to whether grain protein can be optimized on a site-specific basis by accounting for spatial variability of N fertility within individual fields.

25) Weed Biology and Precision Farming

Weeds, and methods used to control weeds, can have negative economic and environmental impacts. With precision agriculture, growers can take advantage of the patchy nature of weeds by targeting management efforts only where they are needed instead of wasting expensive and potentially hazardous inputs where weeds are not present. Weeds are patchy because weed spread, survival, and reproduction are variable within a field and over time. Weed patches stay in about the same place from year to year, even though weed density within a patch may vary. This Guideline describes the biological basis for weed patchiness and discusses how human-aided dispersal and manipulation of field conditions contribute to the spread of weeds.

26) Interpreting Remote Sensing Data

Can remote sensing fulfill its 30-year-old promise for enhancing profitability in agriculture? The answer is still not clear-cut for everyone, but a combination of past experience and technological improvements is now making it profitable to incorporate remote sensing into many farm and ranch enterprises. This guideline explores some of the basic analysis options for agricultural applications of remote sensing data. Once remote sensing data have been collected, the user must interpret the data to derive the information needed to help make decisions. It is a given that the data and information will have some associated error. As a producer or service provider, you must decide what amounts of certainty are necessary and whether or not the information is worth the investment.

27) Spatial Variability in Corn and Soybean Insect Pests: Precision Farming and Insect Pest Management for the Future

Public and private research effort is being invested in site-specific insect pest management, but progress in this area lags behind other aspects of site-specific agriculture. Intensive grid-sampled information about insect dispersion in soybean and corn fields provides valuable knowledge, but the usefulness of the information is overshadowed by problems related to implementing precision farming programs for insects. The existence of field level spatial variability in populations of key pests of soybean and corn suggests that a site-specific approach to IPM is possible. The necessary GIS/GPS capabilities are available, but have not been effectively combined into systems incorporating economical scouting methods or real-time monitoring and mapping of pest variability. It has been suggested that optical sensors might be applied to detection of canopy-dwelling insect pests such as the bean leaf beetle. Targeted sampling can be directed by analysis of remotely sensed aerial images that identify anomalous areas indicative of severe pest infestations, provided the cost of the imagery can be kept at reasonable levels and still provide rapid turn around. It will take time to overcome the barriers associated with site-specific insect management, but because of the potential benefits of this technology, research in this area will continue to move forward.

28) Strategic Approach to Site-Specific Systems

Site-specific management must be approached logically and systematically. Like any major change, it is easier to break the process into manageable action steps that ultimately build to a complete management system. A strategy is offered in contrast to a generalized recipe of tools and practices. A successful strategy considers farmer goals and characteristics of the individual farm, farmer, and fields before decisions about tools and practices are made. A strategic approach for site-specific management might include the following. Define goals. • List the decisions that must be made to reach the goals. • Determine the data needed to support the decisions. • Determine the tools needed to collect/manage/interpret the data. • Determine requirements for implementation. • Inventory the human, physical, and information resources available. • Make adjustments to meet projected future needs. • Collect and interpret the data needed. • Modify the production plan based on the interpretation of the data collected. • Implement the improved plan. • Repeat the process.

29) Geographic Information Systems (GIS) in Site-Specific Systems

The collection and management of data from site-specific crop and soil management systems soon overwhelm the standard farm record system. Geographic information systems (GIS) provide a systematic approach to managing the large amounts of data accumulated, along with the tools necessary for analysis and interpretation. This Guideline reviews some example data sets used to characterize a field and how GIS can help organize and manage the data so that they can be more effectively used in various management decisions.

30) Soil Electrical Conductivity Mapping

Soil electrical conductivity (EC) mapping is a simple, inexpensive tool that precision farmers can use to quickly and accurately characterize soil differences within crop production fields. Soil EC is a measurement that correlates to soil properties affecting crop productivity, including soil texture, cation exchange capacity (CEC), drainage conditions, organic matter (O.M.) level, salinity, and subsoil characteristics. With field verification, soil EC can be related to specific soil properties that affect crop yield, such as topsoil depth, pH, salt concentrations, and water-holding capacity. Soil EC maps often visually correspond to patterns on yield maps and can help explain yield variation. The EC data can also be correlated with yield, elevation, plant population, surface hydrology, or remotely sensed data with a suitable geographic information system (GIS). Other uses of soil EC maps include guiding directed soil sampling, assigning variable rates of crop inputs, fine-tuning Natural Resources Conservation Service (NRCS) soil maps, improving the placement and interpretation of on-farm tests, salinity diagnosis, and planning drainage remediation.

31) Yield Monitors—Basic Steps to Ensure System Accuracy and Performance

According to today’s yield monitor manufacturers, most users should obtain accuracy within +/- 3 percent, if the system is properly installed, maintained and calibrated. Items that operators must be conscious of and attend to for good results can be summarized as follows: Proper calibration of the mass-flow sensor using multiple loads acquired according to the manufacturer’s recom-mendations. Inspection of the system sensors, particularly those affected by crop conditions, during the harvest. Verification and, if necessary, calibration of the ground speed sensor. Verification and calibration of moisture and temperature sensors. Correct entry of the operating information such as crop type, field, and header width for each field into the system console. Proper use of the software to extract and process the yield data. New technology often requires time and experience to ensure all things are operating at peak performance. The items discussed here are lessons learned with field experience.

32) Trouble-Shooting Yield Monitor Systems

In spite of extensive calibration, yield monitors can still have problems recording the data. When yield monitors stop working, trouble-shooting to solve the problem can be difficult, especially when the time to harvest the crop is at hand. The objective of this guideline paper is to provide information for trouble-shooting yield monitors.

33) Bt Corn and Insect Resistance Management: What Are They?

Transgenic corn with resistance to European corn borer (ECB) has been commercially available since 1996. Inserting a gene from the soil bacterium Bacillus thuringiensis (Bt) has genetically modified the corn plant to produce a protein that is toxic to moth larvae. Bt corn is valuable because it provides yield protection, reduces ear molds, and at least in some areas of the U.S., reduces the use of chemical insecticides. Grower response to transgenic corn has been positive. Over-use of Bt corn, however, could lead to ECB becoming resistant to Bt protein. In 2000, approxi-mately 25 percent of the total corn acreage in the U.S. contained the Bt gene. Growers must practice insect resistance management (IRM) to ensure that this technology will be available to future generations of growers.

34) Site-Specific Soil Compaction Mapping Using a Digital Penetrometer

Soil compaction is generally defined as an increase of the natural density of soil at a particular depth. A density increase translates into less pore space, less plant available water, slower water transport, and a decrease in the root's ability to penetrate the compacted zone as it seeks out water and nutrients. Similarly, the increase in density due to compaction can serve to retard or divert the flow of water, resulting in ponding or excessive runoff. These factors may limit yield and inhibit effective site management for many crops. compaction can be measured with penetrometers. Recent advances in digital penetrometer systems can provide users with a simple way to map soil characteristics over large areas in the field. This guideline discusses the impact of compaction on crop growth, methods to measure compaction, and techniques to solve compaction problems.

35) Obtaining Soil Information Needed for Site-Specific Management Decisions

County soil surveys contain a compendium of information about soil and climatic conditions within a region. The soil surveys are available from local Natural Resources Conservation Service (NRCS) offices. Most are in the process of being digitized (http://www.ftw.nrcs.usda.gov/ssur.data.html). Boundaries of the different soils are usually drawn on an aerial photograph. Most soil surveys are Order 2, with scales of 1:12,000 to 1:31,680 and a minimum size delineation of 1.5 to 10 acres. Order 2 soil surveys were not developed for site-specific management, and research evaluating the ability to use them for site-specific management has been mixed. They can be personalized by developing a new map based on the Order 2 soil survey as well as experiences, visual observations, and measured values. Interactions between soil and climatic conditions influence land productivity and weed, disease, and nutrient spatial and temporal variability. By understanding these interactions, our ability to manage risk, increase productivity, and protect the environment can be improved. We must recognize that there is no single strategy for incorporating soils information into the decision process. This guideline discusses different approaches for developing experience-modified soils maps which can be used for a variety of site-specific management decisions.

36) Variable-Rate Nitrogen Management for Corn Production - Success Proves Elusive

Adoption of variable-rate nitrogen (N) management by North American corn producers is low, despite the potential economic and environmental benefits of this practice. A major obstacle is that recommended N fertilizer rates based on yield goal are often poorly correlated with actual economically optimum N rates. Nitrogen response patterns are often field- and season-specific and can vary widely within the same field, further complicating adoption. Side-by-side comparisons of uniform and variable-rate N management have revealed no consistent advantages for either strategy in yields achieved, profitability, whole-field N usage, or N-use efficiency by plants. In the future, a better understanding of temporal variation in N soil test levels, better crop simulation models, and improved N sensing and application equipment may assist growers in capturing the benefits of site-specific N management.

37) Estimating Corn Yield Losses from Unevenly Spaced Planting

Agronomists and corn growers have long assumed that evenly spaced stands of corn have a greater yield potential than unevenly spaced stands. The uniformity of spacing between plants can easily be determined by using a commonly used statistic, standard deviation (SD). This statistic is available within most spreadsheets. By measuring the SD of plant uniformity, yield loss due to non-uniform plant spacing can be estimated using the following equation: Yield loss = (present plant spacing SD –2.0) X (4 bu/A/in. of SD improvement) This guide discusses how to measure stand variability and develops criteria for determining if recalibration of planter meters is needed.

38) Collecting Representative Soil Samples for Nitrogen and Phosphorus Fertilizer Recommendations

Soil fertilizer recommendations in modern crop production rely on laboratory analysis of representative soil samples. Regardless of where the samples were collected (grid points, management zones, or whole fields) the accuracy and precision of the fertilizer recommendation can be improved by considering the factors that influence nutrient variability in the design of the sampling protocol. As each producer’s crop production enterprise varies, it is recommended that producers select approaches that are suited for their operation. The objectives of this guide are to discuss how management influences nutrient variability and to provide insight into how to design soil sampling protocols that provide good fertilizer recommendations.

39) A ‘Cookbook’ Approach for Determining the ‘Point of Maximum Economic Return’

Many agronomists and producers have been conducting on-farm experiments that are designed to determine the impact of different fertilizer rates or plant populations on crop yields. These data are usually analyzed by plotting the input (fertilizer or population rate) vs. output (yield). The point of maximum yield may be picked directly off the plot. To make the results of these experiments more useful, the point of maximum economic return should be calculated. The point of optimum economic return is determined by: 1. Conducting a yield response experiment; 2. Converting the yield response data to a functional relationship, outputcorn yield= f(input levels); 3. Knowing or estimating the costs of your inputs and outputs; and 4. Using calculus to determine where the change in the value of the input equals the change in the value of the output. The goal of this Guideline is to provide an easy-to-follow approach for calculating the point of maximum economic return.

40) Selecting the Appropriate Satellite Remote Sensing Product for Precision Farming

Given the large number of satellite remote sensing products available, it is difficult to select the appropriate one because each satellite has different revisit times, delivery schedules, ordering requirements, pixel resolutions, sensors, and costs. Some satellites collect on a regular schedule (Landsat), while other satellites (IKONOS, QuickBird, and SPOT) need advance programming (tasking). To obtain high resolution satellite information promptly from QuickBird or SPOT, either High Priority or Rush Tasking may need to be purchased. However, high-resolution (small pixel size) data are not needed for all agricultural problems. The purpose of this Guideline is to provide direction on how to select an appropriate satellite-based remote sensing product.

41) Determining the “Best” Approach to Identify Nutrient Management Zones: A South Dakota Example

Productivity zones, yield stability maps, and management zone maps based on elevation, electrical conductivity, yields, and remote sensing may be developed using a variety of different approaches. Producers frequently ask: Which method for identifying zone boundaries is best? The answer to this question depends on the criteria used to evaluate the zone boundaries. At least three different criteria for assessing management zone boundaries are used. These criteria are: • The ability to group areas with similar soil test results into the same zone; • The ability to group areas with similar yields into the same zone; and • The ability to improve fertilizer recommendations. For the two South Dakota fields used in this study, if the goal was to group areas with similar yields into the same zone, then zones based on personalized soil surveys were best. However, if the goal was to minimize nitrogen (N) and phosphorus (P) recommendation errors, then this was accomplished by using multiple years of yield monitor data to develop landscape specific yield goals, sampling old homesteads separately from the rest of the field, and grid-cell soil sampling to fine-tune N and P recommendations. Similar analysis can be conducted in your field if you have multiple years of yield data and an understanding of soil nutrient variability.

42) Using Remote Sensing to Develop Weed Management Zones in Soybeans

Crop scouting should provide accurate, timely, and cost effective information about diseases, insects, nutrient deficiencies, and weeds in production fields. Approaches for weed scouting include examining edges of fields or driving across fields in an X or W pattern to determine weed species present. Often weeds or weed species are spatially aggregated and using traditional approaches usually will not produce enough information for site-specific weed management recommendations. Remote sensing can be used to guide ground-scouting activities and identify the extent of weed patches. Ground-truthed remote sensing information can be used to develop effective weed management strategies and monitor weed management successes and failures. Four critical decisions that should be considered to integrate remote sensed data into agronomic management include: • Feasibility of using remote sensing as a field-scouting tool; • Reflectance bands used to distinguish weed-infested and weed-free areas in soybeans; • When to collect the remote sensed data; and • Spatial resolution needed for weed patch detection. This guide provides information to help answer these questions.

43) The First Step in Precision Agriculture: Sampling Old Farmsteads Separately from the Rest of the Field

Many of the small farms that dotted the countryside a hundred years ago had enclosures where horses, cows, and hogs were kept. Manure from animals contained in these enclosures still impacts soil properties today. Most fertilizer recommendations rely on the collection of representative soil samples. Aerial photographs stored by USDA Farm Services Agency (FSA) offices provide clues to past management. The objective of this Guideline is to demonstrate the importance in sampling old homesteads separately from the rest of the field. In grid soil sampled fields located in South Dakota and Missouri, historical aerial photographs were used to identify old homesteads. By sampling the old homesteads separately from the rest of the field, the 80% confidence interval of a 20 core composite sample was reduced by as much as 97% and the soil test phosphorus (P) levels were decreased. These results were attributed to excluding areas with very high P concentrations from the composite sample. Improved sampling protocols constitute a savings for producers because the soil test results are more representative of the crops needs.

44) Characterizing Soil Variability Using On-the-Go Sensing Technology

One of the major objectives of precision agriculture technologies is the site-specific management of agricultural inputs to increase profitability of crop production, improve product quality, and protect the environment. Information about the variability of different soil attributes within a field is essential to the decision-making process. The inability to obtain soil characteristics rapidly and inexpensively remains one of the biggest limitations of precision agriculture. Numerous researchers and manufacturers have attempted to develop sensors for measuring soil properties on-the-go. These sensors have been based on electrical and electromagnetic, optical and radiometric, mechanical, acoustic, pneumatic, and electrochemical measurement concepts. The major benefit of on-the-go sensing has been the ability to quantify the heterogeneity (non-uniformity) of soil within a field and to adjust other data collection and field management strategies accordingly. As new on-the-go soil sensors are developed, different real-time and map-based variable rate soil treatments may become economically feasible.

45) Developing Productivity Zones from Multiple Years of Yield Monitor Data

The ability to instantaneously record yield data at harvest has been widely available to agricultural producers since the mid-1990s. Yield is the ultimate integrator of landscape and climatic variability and therefore should provide useful information for identifying management zones. However, due to year-to-year climatic variation, identifying useful management zones based on a single year’s yield map is difficult. Increasing the number of years used to define zones may be a solution to this problem. A technique to define a type of management zone known as a productivity zone based on multiple years of yield monitor data involves creating common grid-cells across years and then calculating mean yield and standard deviation maps. ‘Mean yield’ maps created from multiple years of data may be used to determine yield goals and fertilizer recommendations, while standard deviation maps may be used to identify areas requiring corrective management. The preferred method for explaining yield variability used a combination of average yields and standard deviation to delineate productivity zones.

46) GNSS-Based Auto-Guidance in Agriculture

Auto-guidance, also called auto-steer, of tractors and self-propelled agricultural machines that is based on a global navigation satellite system (GNSS) represents one currently available technology that can provide significant benefits for crop production in diverse growing environments. Once producers use auto-guidance equipment, they seldom want to return to conventional practices. Newer, improved versions of auto-guidance products provide better operation functionality, which prevents the frustration and fears that early adopters experienced. There is an on-going effort to define and quantify performance of auto-guidance systems so that users of this technology could better select the most suitable option for a given farm operation.