Lab Comparison Study Completed by David Granatstein
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1 Washington State University Cooperative Extension The Compost Connection for Washington Agriculture WSU CSANR May 1997 Funded by a grant from the W.K. Kellogg Foundation No. 4 Lab Comparison Study Completed by David Granatstein As part of our effort to expand the practical information for agricultural use of compost in Washington State, Dave Bezdicek, Mary Fauci and I undertook a simple comparison study of the analysis of the same compost by several different testing laboratories. Many testing labs already participate in various refereed programs to help them maintain quality assurance. Our goal was to see how great the range of results would be for compost testing as normally done by a lab. Some labs treat compost as a soil, others treat it as a plant material. Labs may use different methods for the same parameter. We hoped to learn how much variation existed currently and thus determine the need for more standardized methods in our region so end users of compost could make the best use of analytical results. We did not seek to judge labs on the quality of their work (see the accompanying article on the German lab study) and our comparison does not allow us to make such a determination. We chose two different composts to use in the study: a high N, high E.C. chicken manure compost (Nielsen), and a low nutrient, low E.C. yardwaste compost (Cedar Grove). A sample of each product was taken from a field pile, thoroughly mixed, and then subsampled into plastic bags. The Nielsen compost was sent to (go to LAB STUDY on page 3) German Government Evaluates Compost Testing Labs by David Granatstein Public agencies, particularly land-grant universities, have conducted refereed analysis programs with soil testing laboratories for years. The goal of such programs is quality assurance. In 1993, the German Federal Compost Quality Assurance Organization funded a comprehensive study of the quality of work of approved compost testing labs. The results of this study are reported in the Summer 1996 issue of Compost Science and Utilization. The study encompassed 95 different labs which were asked to test for 31 different parameters. The first task for the researchers was the creation of a book of standardized test methods to be used. Then a green waste and biowaste compost was selected as the standard material for analysis. A large sample of the compost was mixed as thoroughly as possible at the production site, sieved to <10 mm, the sieved fraction again mixed, and then 400 subsamples were pulled, bagged, and numbered. Each lab was randomly assigned 4 numbers, and thus tested 4 subsamples from the original homogeneous sample. Once the data sets were returned from each lab, the researchers conducted a thorough and complex statistical analysis. Their goal was to separate the variation due to subsampling from the variation due to quality of work in the labs. The preliminary analysis showed that the lab variation was by far the predominant factor.
2 2 The Compost Connection Further analysis indicated that the variations in results between labs were significantly greater than the variation within the lab. Each lab was rated for the number of error analyses, and from this a ranking system of lab quality was established using four categories: Category I (18% of labs, no errors, specifically recommended by the government); Category II (55% of labs, satisfied requirements without limitations); Category III (13% of labs, approved subject to restrictions); and Category IV (15% of labs, more than 5 errors, lose federal approval for compost analysis). The researchers examined the statistical profile for each of the 31 parameters measured, and then used the coefficient of variability (CV) for each parameter to make comparisons among parameters. From this, they divided the test parameters into three groups. Group 1 contains those parameters with relatively low CV (<10%), Group 2 with moderate CV (10-19%), and Group 3 with high CV (20% or more). The Group 1 parameters are thus regarded as the most stable and reliable measurements, such that a single measurement would be more likely to reflect the actual level in the compost compared to parameters in the other groups. Classification of Test Parameters by CV Group 1 Water content Loss on ignition ph Group 2 Rotting degree Copper Zinc Salt content Group 3 Foreign matter Stone content Germinable seeds Bulk density Total N Total P Total Ca Total K Total Mg Soluble Mg Lead Nitrate Ammonium Soluble P Nickel Cadmium Chromium Soluble K Plant tolerance Mercury Most of the Group 1 parameters are relatively easy to analyze. The low CV for bulk density was a surprise, but is attributed to the method used and the fine screen size (<10 mm) of the compost. Several labs had problems with the plant tolerance and rotting degree methods. Except for copper and zinc, all heavy metal tests fell in the highest variability group. This has significant ramifications for trying to set standards for heavy metals in compost and the resultant land loading rates. With the high degree of variation inherent in the analyses, the error band around a given metal standard needs to be quite wide. For example, the German limit for lead in compost is set at 150 mg/kg. Based on the lab study, regulators would need to approve composts with lead content up to 210 mg/kg since there is this much spread statistically. Nitrate, ammonium, soluble phosphorus, and soluble potassium also had high variability. This is less a concern from a regulatory standpoint but is an issue in terms of nutrient content for the end user. The researchers were pleased with their study, as a primary objective was to classify labs, exclude unsuitable ones, and recommend exemplary ones. Their results also provide assistance to those interpreting compost analyses by indicating which parameters are relatively reliable using a single analysis and which might need multiple samples tested for a reliable estimate. Reference: W. Bidlingmaier and A. Maile Results of a parallel interlaboratory test of the analysis of compost. Compost Science and Utilization (4:18-37).
3 3 The Compost Connection (from LAB STUDY, page 1) all eight labs in the study, while the Cedar Grove compost was only sent to six labs, due to cost constraints. We chose the labs based on our sense of their use by the agricultural or compost industry in the region. The following labs were used: findings are similar to those in the German lab study. Thus, for the highly variable parameters, compost producers might consider creating a database of results over time that can better illustrate the potential range of values to customers than a single result. A&L Western Agric. Lab, Modesto, CA Cascade Analytical, Wenatchee, WA Custom Dairy Services, Lynden, WA Northwest Agricultural Consultants, Kennewick, WA Soil and Plant Lab, Santa Clara, CA Soiltest Farm Consultants, Moses Lake, WA University of Idaho ASL, Moscow, ID Wallace Labs, El Segundo, CA We chose the following10 parameters for testing by each lab: ph, electrical conductivity (E.C.), total N, ammonium-n, nitrate-n, total P, available P, total K, organic matter, and carbon:nitrogen ratio. These are standard tests for all labs and for most composts. We did not specify what method to use, nor did we inform the lab that this was part of a study. The samples were shipped to labs on 8/30/96 and testing by all labs was completed during the first two weeks of September. The results are summarized in Table 1 (see pg. 4). Where a particular test result looked questionable, based on my judgement, I contacted the lab and in most cases received a corrected result. I also calculated the coefficient of variability (the standard deviation divided by the mean) as a measure of general variability for each parameter tested. Lower Cvs are more desirable, and a CV below 10% is excellent. Parameter variability was not always similar for the two composts. For example, the C.V. for ammonium-n for the Nielsen compost was 18% compared to 150% for the Cedar Grove compost. In general, the low variability parameters were ph, total N, total P, and organic matter. Results for these should be relatively reliable from a typical lab. In contrast, test results for E.C., ammonium-n, nitrate- N, and available P are highly variable. Some of this does have to do with different methods. These All labs but one used the combustion method for determining organic matter. The other method (Walkley-Black) greatly underestimated the organic matter in both composts when compared to the other labs. Most labs used standard soil test methods for readily available P. However, one lab used a citrate soluble P method, which extracts more P than the readily available methods and thus produces a higher result. The citrate method attempts to account for some of the potential P release by microbes in addition to the readily available P. This simple study provides a very cursory look at compost testing in the region. More standardization of compost analysis would be useful for the compost industry. The Compost Council, a national organization based in Alexandria, VA, has produced a draft book of methods for compost analysis and is promoting the idea of some degree of standardization. In the meantime, compost users should ask for as many test results as possible when evaluating compost from a vendor. Contributors to this project and to this newsletter edition include David Bezdicek and Mary Fauci, WSU Crops & Soils, and Patty Dauer and David Granatstein, WSU Tree Fruit Research and Extension Center. Newsletter Editor: David Granatstein Disclaimer: The mention of specific products or companies in this report does not constitute a recommendation for or against their use. Information is provided to help end users make decisions appropriate to their specific needs and conditions.
4 Table 1. Laboratory Comparison for Compost Testing - Aug/Sept Nielsen chicken manure compost: Lab Lab Lab Lab Lab Lab Lab Lab C.V. A B C D E F G H Mean % ph E.C. (mmho/cm) Total N % dry NH4-N ppm dry n.d NO3-N ppm dry 162 < Total P % dry [2.65] Avail. P ppm Total K % dry n.d. n.d [3.06] [2.6] Organic matter % C:N ratio 14.3 n.d n.d. n.d Cedar Grove yard waste compost: ph n.d. n.d E.C. (mmho/cm) n.d. n.d Total N % dry n.d. n.d NH4-N ppm dry n.d. n.d NO3-N ppm dry 324 < n.d. n.d [409] Total P % dry n.d. n.d [0.26] Avail. P ppm n.d. n.d Total K % dry n.d. n.d. 0.6 n.d. n.d [0.41] Organic matter % n.d. n.d C:N ratio 23.2 n.d n.d. n.d. n.d. 16 Notes: All numbers are as reported from the lab, except for numbers in [ ] which are revised results for the entry above based on conversations with the lab. Available P - Lab H test is for citrate soluble P, which extracts more P than the methods used by the other labs. Organic matter - Lab E used Walkley-Black method, other labs used combustion; the W-B method tends to underestimate on high O.M.matrices like compost. The mean and C.V. were calculated without outlier data. n.d. = not determined; CV = coefficient of variability
5 Summary of 1996 On-Farm Tests : Compost by Patty Dauer Twelve apple orchard sites in central Washington State were monitored in 1996 to begin to scientifically quantify the effects of compost. These sites can be categorized as : 1) existing orchard to which compost was applied as a topdress; 2) old orchard site replanted in 1996 with a surface application of compost either before or after planting; 3) old or new orchard site, incorporating compost in the planting hole; and 4) new orchard site planted with a surface application of compost either before or after planting. Existing Orchards. Five orchard sites were topdressed with compost in areas where the orchardist felt the trees were growing poorly. In all cases the compost was applied by hand shovel or bucket around the base of the tree. Composts included Nielsen chicken, Stutzman chicken, Sunland chicken, Lincoln chicken, and Cedar Grove yard waste. Rates varied from lb/tree wet weight. The composts did vary considerably in moisture content. The following measurements were taken, but not all at each site: leaf N, leaf greenness (SPAD), % increase in trunk cross-sectional area, leader growth, fruit diameter, fruit yield per tree, and soil moisture. In only one orchard did compost application have a significant effect upon leaf N and SPAD readings. This orchard is organically managed, and relies primarily upon compost for tree fertilization. The other sites used conventional fertilizers as well as compost. No significant increase in trunk growth was found after one season, but some sites did show a trend of greater tree growth with higher rates of compost. Trunk growth ranged from 9-40 % increase in cross-sectional area. We monitored one sandy site for soil moisture using several different hand-held moisture probes. The check plots with no compost were consistently drier at 6 and 12 depths than where compost was surface applied. However, none of the probes proved very satisfactory as a quick and reliable measure of soil moisture. Replant : old ground, surface application. At three orchard sites, compost was applied in bands. One grower used a compost spreader to band compost on the tree row after planting. At the other sites, an adapted lime spreader was used to band compost on the tree row before planting, followed by incorporation. Composts tested were Stutzman chicken, Lincoln chicken, Lincoln cow feedlot, and Cedar Grove yardwaste. Rates ranged from 0-45 tons/acre on a wet basis. Leonardite, a humic acid product, was also applied to some plots. Measurements taken included % trunk growth and soil moisture. No statistical differences were found after one season. However, in most cases, leonardite appeared to enhance trunk growth. Overall trunk growth ranged from % increase in cross-sectional area. Replant or new planting : compost in the hole. Two orchard sites were replants, one in 1995 and the other in A third site was planted for the first time. In all cases compost was added to and mixed in with the soil material placed into the augured tree hole around the tree roots. Materials added included leonardite, Stutzman chicken, Cedar Grove yard waste, Sunland chicken, off-site new soil, on-site old orchard soil, and a biosolid soil mix. Rates ranged from 0-40 % volume of the augured hole ( 0-20 gallons of compost/tree), and 0-2 cups of leonardite/tree. The Biosolid mix treatment was 100% volume of the augured hole. Measurements taken included % trunk growth, electrical conductivity of the soil in the planting hole, and leader growth. A few trees did not survive their treatments; adding high-n composts to the planting hole can raise soluble salts to levels which trees can not tolerate. At the newly planted site, trees with Cedar Grove treatments grew the best in an unreplicated trial, with leonardite further enhancing growth. In the 1995 replant study, re-using old orchard soil in the planting hole resulted in significantly poorer tree
6 6 The Compost Connection growth and no response to compost, even after soil fumigation. All treatments receiving new soil grew better, and there was a significant response to compost. This held true for the first season as well as the second. In the 1996 replant trial, which was adjacent to the 1995 trial, only new soil was added to the planting hole along with varying rates of compost. No significant trunk growth differences were observed. Trunk and leader growth measurements indicated that trees treated with the biosolid mix did not grow as well as the others. Planting on new ground, surface application. Two orchards utilizing compost were planted on new ground. At one, an adapted lime spreader was used to apply the composts in a band, with the compost tilled in before planting. At the second site, materials were applied 2 separate times after planting. Materials included Cedar Grove yard waste, BION (a product recovered from dairy manure), ground up fish, alfalfa, and organic minerals. Rates were 0-20 tons/acre for the Cedar Grove, and 5 gal/tree for BION. Measurements taken included % trunk growth, leaf N, leaf greenness, leader growth, and electrical conductivity of the soil. No significant differences were observed at the site where compost was applied before planting. All trees grew well, with trunk growth increases of %. The second site did show significant differences in % trunk growth. The BION treatment did best, followed by a mix of BION, fish, and alfalfa. Least growth was seen in the K-mag, rock P, sulfur, bloodmeal, and alfalfa mix. Trunk growth ranged from % increase. All tests will continue to be monitored for one or two more years. For more information, contact David Granatstein or Patty Dauer, Washington State University Tree Fruit Research and Extension Center, Wenatchee, WA. Tel. (509) x.222. Funding for this project comes from the W.K. Kellogg Foundation Integrated Farming Systems program through a grant to The Food Alliance. Cooperative Extension programs and employment are available to all without discrimination.
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