Benchmark Sampling to Monitor Soil Fertility and Assess Field Variability Doug Keyes, Norwest Labs, Edmonton, AB dougk@norwestlabs.com Grant Gillund, Kenlund Consulting, Smoky Lake, AB ggillund@telusplanet.net Doug Penney, Edmonton, AB dpenney@telusplanet.net John Ashworth, Norwest Labs, Edmonton, AB johna@norwestlabs.com Problem The value of a soil test as a management tool is determined largely by the extent to which the sample taken represents the soil (area) to be managed. The traditional sampling strategy seeks to obtain a sample with average concentrations/properties for an area by taking about 20 cores at random from across a field. It does not exactly constitute random sampling however because there is an attempt made to cover the whole field, so sample points tend to be evenly spaced, and there may be some selection exercised, the avoidance of obviously different areas such as low areas. The problem with the traditional sampling approach is that the frequency distribution of extractable nutrient concentrations is often positively skewed, which means that the average value is higher than the mode (most frequent) value for a field (Penney et.al., 1996). In other words, most of the field has lower extractable nutrient values than the average values obtained from the composite sample. In terms of soil properties, this average sample can be misleading because the combining of samples from different soil types creates a fictitious soil with properties that may be quite different from anything encountered in the field. The modal value is, by definition, the most accurate representation of the field. Inaccuracy resulting from a positively skewed distribution can lead to below optimum fertilization rates being recommended when the conventional approach to sampling is used. Inherent soil variability and the selection of sampling points can cause problems when conventional sampling is used. A situation often arises wherein one person samples a field and gets one set of lab results and then another person samples the same field and gets different results. Similarly, with conventional sampling technique, year-to-year sampling of the same field may not accurately reflect changes that have occurred. In these situations, the farmer is understandably concerned and, unfortunately, loses confidence in the whole soil testing process, which he sees as being imprecise. With the advent of precision farming, there is an even greater need for accurate and precise soil test measurements. Management zones or gradients must be identified and appropriate rates of fertilizer determined for each. Background and Research Soil testing has long been used to evaluate soil fertility status (extractable macro- and micro-nutrients) and soil quality parameters (ph, salinity, organic matter and texture). The reliability of a soil test depends on analytical and sampling accuracy and precision. The accuracy of an analysis depends on how closely results match those obtained using a standard procedure. Running standard samples of known value along with each batch of samples monitors accuracy. Analytical precision is measured by how closely repeated measures on the same sample match one another. Selected samples run in duplicate monitor analytical precision. Sampling accuracy is not as easily defined nor measured as analytical accuracy. An early bulletin suggested taking samples from 10 locations within a field (Wyatt, 1921). Later work indicated more samples are needed (Cameron et.al. 1971). Surprisingly little information on
within-field soil variability was available until precision farming sparked interest in this area of research. Accuracy can be thought of as how well the sample represents the area in question. The first step is to decide; what is the area in question: is it the whole field, the whole field minus certain atypical areas, or is it a particular soil type or landscape position within a field? Seldom do we have much soil data on the area in question. Only with extensive sampling, such as grid sampling, can we obtain data against which to measure any less extensive (and more economical) sampling methodology. Sampling precision can be determined relatively easily by repeated sampling of the area in question. The other component of soil testing accuracy is the validity of the test calibration; the test results for extractable soil nutrients are of limited use without suitable criteria for comparison. Field research, usually on small plots, indicates the test level at which extra crop yield or quality will be gained by adding a particular nutrient and, ideally, the optimum amount to apply for any particular test level. Calibration accuracy depends on the amount of research done on the nutrient, the crop and the growing region. Accuracy is important not only for determining the appropriate rates of nutrients to apply. It also improves the usefulness of the data as a means of monitoring nutrients and soil quality, and it provides a reliable record of soil test data that can be used to evaluate the impact of field management (fertility, crop rotation, tillage, etc) on soils. The difficulty of obtaining representative soil samples arises because soils are variable. Soil properties and constituents vary in complex ways and at different spatial scales. For example, low areas of a field may be high in nitrate (large-scale variability) while phosphate concentrations are much higher in the fertilized seed row than between rows (small-scale variability). To deal with soil variability we tested a system of sampling whereby the large-scale variability would be eliminated by confining the sampling to a small area, and small-scale variability would be dealt with in the usual way, by taking a large number of samples for compositing. We call this approach benchmark sampling because it is intended to provide a reference point from which to evaluate the remainder of the field and a reference point for determining year-to-year changes. The problem of skewed distributions of nutrient values is less in samples taken from benchmarks than from fields because with large-scale variability eliminated, there is less overall variability. In this respect, the benchmark sampling approach reflects the methodology used in soil test calibration where crop response in small plots is related to a composite sample of the experimental area. Our initial results are reported in the Western Canada Agronomy Workshop, 1995. Not surprisingly, we found greater precision when sampling was confined within a benchmark site ¼ acre in size than when samples were taken from the entire field. How well a benchmark might represent the field in general was evaluated by comparing results from several benchmark sites on the same field to repeated random samples taken throughout the field. Generally, the results from the benchmark sites fell within the range of results that could be expected from conventional sampling, indicating that it is not difficult to find a benchmark site that represents the field just as well as the conventional sample. Year-to-year changes in soil test levels were not necessarily less with benchmark sampling, but because the benchmark sample results are more reproducible, the differences that did occur more likely reflected true changes in the soil rather than changes caused by sampling variability.
Depending where it is situated, a benchmark site may or may not accurately represent the area in question, but it does accurately reflect the benchmark itself. In contrast, there is no assurance that a conventional sample represents any particular area. The main issue with benchmark sampling is choice of site(s). Based on our research and subsequent follow up with fertilizer dealers, this is not an insurmountable problem. We found that even with sites chosen arbitrarily, the results in most cases represented the field as well as, or better than the conventional samples. To test how benchmark sampling would compare to conventional sampling as a routine method, we asked fertilizer dealers and agronomists to sample fields first using their normal sampling procedure, and then by selecting an area they felt would be representative of the field and take a sample from it according to a prescribed benchmark sampling protocol. The relationship between benchmark and conventional samples were closer for the water-soluble nutrients, nitrate and sulphate, probably because they tend to be distributed according to landscape position (e.g. mid-slope) which are easily identified in the field. The correlations (r 2 ) were 0.60 for nitrate, 0.54 for sulphate and potassium, and 0.34 for phosphorus. The only means of evaluating the benchmark sampling was in relation to the results for conventional samplings. It is important to point out, however, that the results from the conventional sampling are not necessarily the best or the correct result. Cases in point were two fields where sulphate with conventional sampling showed >20 ppm (the highest number the lab reports for sulphate) whereas the corresponding benchmark test showed considerably less. This is a common problem with conventional sampling, particularly for sulphate one or two cores from an area with very high concentrations can have a dramatic effect on the results. The consequence can be serious. If a crop such as canola, that is very sensitive to S deficiency, is grown without adding sulphur, yield and quality will be sacrificed. There is increasing interest in managing nutrients in manure. Spreading manure, particularly solid manure from windrows in the field, causes extremely high variability. Research done on the application of solid cattle manure with a beater spreader however indicates (Kennedy et al., 2000) that the greatest variability is at a scale smaller than a benchmark site (100 ft across). Benchmark sampling, therefore, could capture the additional variability associated with manure application. Research done in relation to precision farming, particularly intensive grid sampling, provides detailed information on the distribution of soil properties (Penney et. al., 1996; Clay et.al., 2000). It is usually apparent from studying these fields that there are some parts of them that are more deficient than others in some particular nutrient, and with this knowledge more profitable uniform application rates could be determined. It follows that with strategically placed benchmark sites it should be possible to estimate the range in soil properties and thereby arrive at a better uniform application rate without the expense of an intensive sampling program. In some fields, depending on the landscape and the field history, it may be possible to go one step further and use the results from benchmark sites as the basis for variable rate fertilizer applications. For example, Clay et, al. (2000), found that sampling by soil type, with some historical information on the field, showed higher potential profits than sampling using a 90 m grid.
Applied Questions 1. How many benchmark sites are required per field? This depends on the field. A benchmark should be thought of as representing a soil type within the field. If there is a dominant soil that covers most of the field then one benchmark site may be adequate. Generally, there will be more than one significant soil type, however, as well as transitions from one type to another. Two or three benchmarks are likely needed for most fields: for example, a mid-slope site, an upper-slope site and perhaps a level area. Furthermore, as a rule of thumb, there should be at least one sample for every 80 acres regardless how the field is sampled. 2. How does one mark the site? The best way to mark the site is with a GPS (Global Positioning System). Alternatively, the corner of the site could be located by triangulation, measuring out from two points at the field edge. Remember that the site must be situated well in from field margins to avoid atypical soil conditions. 3. Where should benchmark sites be located? Benchmark sites should be selected with the same considerations that are given to conventional sampling in terms of areas to avoid: low spots, old brush piles, field margins, field entrances, corners where equipment turns, old fence lines etc. Beyond these considerations, the site should be situated on soil that looks like or produces like the majority of the area. It is probably better to select the site when the crop is growing and differences are more evident. The site should also be uniform with respect to slope and soil colour to minimize variation within the benchmark site. 4. Can benchmark sites be used for precision farming? One big advantage of benchmark samples is that they provide information specific to the soil type on which they are situated; varying the rate of fertilizer application according to soil type has obvious advantages. When a benchmark site is chosen, it is assumed to represent particular areas of the field, but before benchmark-specific data can be used to support variable rate application it is critical that this assumption is validated. This would be particularly important if differences in test values varied widely among benchmark sites. 5. How does one sample a benchmark site? The entire benchmark site should be covered with 20 to 25 samples taken for compositing. Within the approximately 100 x 100 site, samples may be taken in a 4 by 5 or 5 by 5 array, or any other convenient pattern that assures the entire site is covered. Benchmark sampling lends itself to hand sampling because once at the site there is minimal walking required to take the sample. 6. Will benchmark sampling work for manured fields? Manured fields tend to have higher nutrient levels, particularly phosphate, and greater variability than other fields due to uneven application of manure nutrients. Solid manure is especially difficult to apply evenly. Benchmark sampling is appropriate for manured fields but lower precision may have to be accepted because of the greater variability; alternatively, precision can be improved by increasing the number of cores taken.
7. Can benchmark sites be used for anything other than soil testing? The concept of a benchmark site to acquire information on a field could be extended to anything that needs to be monitored on a consistent basis: plant tissue testing; crop emergence; weed populations, etc. - anything that is affected by soil type. Conclusion The value of soil testing for farm management is limited by our ability to obtain representative samples using conventional soil sampling methods. Benchmark sampling is an alternative sampling technique that can more accurately represent the greater part of a field. It provides a means of identifying and quantifying variability in soil properties within a field. It is also less subject to sampling error, thus allowing for more reliable year-to-year monitoring. Specialized knowledge is not required to use benchmark sampling; anyone with soil sampling experience and able to use a GPS can take benchmark samples. Finally, benchmark sampling may be the only economical way of collecting soil information to support precision farming decisions. References Cameron, D. R., M. Nyborg, J.A. Toogood, D.H. Laverty. 1971. Accuracy of Field Sampling for Soil Tests. Can. J. Soil Sci. 51:165-175. Clay, D.E., J. Chang, C.G. Carlson, D. Malo, S.A. Clay and M. Ellsbury. 2000. Precision Farming Protocols. II. Comparison of Sampling Approaches for Precision Phosphorus Management. Commun. Soil Sci. Plant Anal. 31:2969-2985. Kennedy B., G. Gillund and P. Penny-Stuart. 2000. Practical Manure Spreading. Farming For the Future on Farm Demonstration Program, Project #98NE14. Keyes, D. and G. Gillund. 1995 Benchmark Sampling for Agricultural Fields. In Western Canada Agronomy Workshop, July 5 th 7 th, 1995, Red Deer, Alberta. Penney, D.C., R.C. McKenzie, S.C. Nolan and T.W. Goddard. 1996. Use of crop yield and soillandscape attributes maps for variable rate fertilization. Great Plains Soil Fertility Conference Proceedings, Denver, Colorado, March 5-6, 1996. Wyatt, F.A. 1921. Soil Sampling: Field Husbandry circular No. 11. Department of Extention, University of Albereta, Edmonton, Alberta.