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1 This article was downloaded by: [Dr Heitor Cantarella] On: 01 November 2011, At: 05:13 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: Registered office: Mortimer House, Mortimer Street, London W1T 3JH, UK Communications in Soil Science and Plant Analysis Publication details, including instructions for authors and subscription information: Variability of Soil Analysis in Commercial Laboratories: Implications for Lime and Fertilizer Recommendations Heitor Cantarella a, José A. Quaggio a, Bernardo van Raij a & Mônica F. de Abreu a a Agronomic Institute of Campinas (IAC), Campinas, Brazil Available online: 31 Oct 2011 To cite this article: Heitor Cantarella, José A. Quaggio, Bernardo van Raij & Mônica F. de Abreu (2006): Variability of Soil Analysis in Commercial Laboratories: Implications for Lime and Fertilizer Recommendations, Communications in Soil Science and Plant Analysis, 37:15-20, To link to this article: PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

2 Communications in Soil Science and Plant Analysis, 37: , 2006 Copyright # Taylor & Francis Group, LLC ISSN print/ online DOI: / PLENARY PAPER Variability of Soil Analysis in Commercial Laboratories: Implications for Lime and Fertilizer Recommendations Heitor Cantarella, José A. Quaggio, Bernardo van Raij, and Mônica F. de Abreu Agronomic Institute of Campinas (IAC), Campinas, Brazil Abstract: Data of soil analysis of 20 samples of 84 commercial laboratories were used to estimate discrepancies among results and analyze the implications for fertilizer recommendations. More than 90% of the laboratories had all results of basic routine analysis of individual samples within the confidence interval (CI). Laboratories with the best performance in the proficiency test (grade A) had only 2.9 and 4.0% of the results outside the CI for the basic and the micronutrient set, respectively. However, the corresponding figures for grade C and D laboratories were 26.2% (basic) and 20.7% (micronutrients). Lime recommendation for a soil with 32% of soil base saturation reached the target value of % in 74% of the cases. In about 90% of the cases, fertilizer recommendations were on or close to the target rates Sizeable deviations of the fertilizer recommendations for P and K that could affect profit occurred in less than 5% of the results reported. Keywords: Quality control, soil proficiency test, soil testing INTRODUCTION Soil analysis is widely used as a diagnostic tool to assess soil fertility and to determine the rates of application of lime, fertilizer, and other amendments. Soil analysis is a common practice throughout the world, but its degree of Received 14 February 2005, Accepted 7 May 2005 Address correspondence to Heitor Cantarella, Agronomic Institute of Campinas (IAC), P.O. Box 28, , Campinas, SP, Brazil. cantarella@iac.sp. gov.br 2213

3 2214 H. Cantarella et al. success depends on the quality of the method of analysis and on calibration and interpretation (Peck and Soltanpour 1990). In Brazil, considerable effort has been made to develop or adapt methods of analysis suitable for tropical soils from the agronomic point of view (Raij, Cantarella, and Quaggio 1994) and to establish fertilizer recommendation guidelines for a variety of crops (Cantarella, van Raij, and Quggio 1998; Quaggio, Cantarella, and van Raij 1998). However, even with adequate methods to evaluate soil reaction and nutrient bioavailability, the results of soil analysis are subject to several sources of variations due to the heterogeneity of the material and the complex nature of the extracting procedures (Klesta and Bartz 1996). In addition, the low prices of analyses charged by laboratories turns soil testing into a low-profit activity that limits investment in equipment and sometimes quality. Furthermore, the spatial variability of chemical and physical characteristics of soils in the field and the problems of representativeness associated with field sampling add to the wide variation of results commonly seen in soil analysis. Sometimes the acceptable variations of analytical results are confounded with lack of laboratory quality, jeopardizing the value of soil testing and nutrient management. A set of methods of soil analysis was launched in Brazil in 1983, which includes a procedure based on the extraction of Phosphorus (P), Calcium (Ca), Magnesium (Mg), and Potassium (K) with a mixture of cation and anion exchange resin (Raij, Quaggio, and da Silva 1986; Raij et al. 2001). In 1984, a soil exchange program or soil proficiency test was created to encourage the uniform use of the new methods and to improve the analytical quality of the results. The proficiency test has been running now for 20 years and comprises 89 public and private laboratories. The objective of this article is to assess the percentage of results outside the established confidence interval and the effect of these deviations on lime and fertilizer recommendations for a crop of economic importance such as maize. MATERIAL AND METHODS Data used in this study were from the soil proficiency test (SPT) run by Instituto Agronomico (IAC) in Brazil. Every year, 12 soil samples, chosen to cover a wide range of analytical results, are supplied to laboratories in Brazil, Uruguay, and Paraguay as part of IAC s SPT. Four of the 12 samples are sent with 3 replicates so that a total of 20 samples are analyzed annually. Individual participant laboratories may be evaluated for one or more of three sets of determinations, using established analytical methods: basic routine (organic matter; ph in CaCl 2 ; SMP-buffer total acidity (H þ Al); KCl-extractable Al 3þ ; resin-extractable P, K, Ca, and Mg; monocalcium-phosphate-extractable SO 4 -S); micronutrients (hot-water-extractable B, DTPA-TEA-extractable Cu, Fe, Mn, and Zn); and texture (clay, silt, and sand).

4 Variability of Soil Analysis in Commercial Laboratories 2215 Analytical results are electronically transferred bimonthly to the coordinator of the SPT and are evaluated statistically. The statistical procedures were described by Quaggio, Cantarella, and van Raij (1994). In short, data of all laboratories are used to calculate average (x), standard deviation (s), and coefficient of variation (CV) for each determination of a given sample. An acceptance or confidence interval is calculated as x + 1s (if CV 40%), x + 1.5s (if 20%, CV, 40%), or x + 2.0s (if CV 20%). Minimum values of acceptance intervals are established based on the precision of specific analytical methods, for example, x + 2 mmol c /dm 3 for KCl-extractable Al. Results outside the interval receive one asterisk ( ) and are removed from the data set for a second turn of calculations. Again, values outside the new acceptance interval are marked with (the second for those who received an asterisk in the first round). A third round of calculations is performed if CV. 20%. At the end of the year, a general evaluation of the laboratories is carried out. Accuracy and precision indexes are calculated for each set of determinations (basic, micronutrients, and texture). The accuracy index is based on the ratio of the results that fall outside the acceptance interval to the total number of determinations performed by the individual laboratory; the precision index is calculated with the CV estimated for the three replications of the four soil samples analyzed in different periods of the year (Quaggio, Cantarella, and van Raij 1994). An overall performance or excellence index (EI) is calculated with the accuracy and the precision indexes (Quaggio, Cantarella, and van Raij 1994). Individual laboratories are classified for each set of determinations and are graded A (EI 90%), B (70% EI, 90%), C (50% EI, 70%), or D (EI, 50%). Variability of Analytical Results and Lime and Fertilizer Recommendations Results obtained by 66 commercial laboratories that performed all determinations of the basic analyses were used to estimate the lime and the P and K fertilizer rates recommended for a maize crop (6 t/ha yield goal) utilizing the guidelines of fertilizer recommendations of the estate of São Paulo (Raij et al. 1996). Seven soil samples were chosen to include one acidic soil (ph CaCl2 ¼ 4.3; soil base saturation of 32% at ph 7.0) for lime recommendation and soils belonging to the very low, low, and high classes of resin-extractable P and exchangeable K in order to generate recommendations for P and K, respectively. Data of 25 field experiments set up to calibrate yield responses of maize to P and K fertilization based on results of soil analysis (Raij et al. 1981; Raij, Feitosa, and Quggio 1983) provided the response curves (one for each class of soil interpretation) that were used to estimate the yield increases that would be obtained with the recommendations generated by each laboratory.

5 2216 H. Cantarella et al. The cost of fertilizer and the profit due to fertilization were calculated based on average market prices and were both expressed as kg/ha of grain. Lime requirement (LR), in t/ha of limestone, to reach V ¼ 70% was calculated as LR ¼ [(70 Vs) CEC]/1000, where Vs is the soil base saturation, in percent, and CEC is reported in mmol c /dm 3 (Raij et al. 1996). Limestone recommendations based on the analytical results of each laboratory were used to estimate the actual base saturation that would be reached if the lime material were applied to the soil with the chemical characteristics considered correct in the SPT (average value after discarding results outside the confidence interval in two or three rounds of calculations, as mentioned previously). RESULTS AND DISCUSSION About 8.4% of all determinations (n ¼ 13575) of the basic analytical set carried out by 84 laboratories fell outside the confidence interval. However, when only the laboratories that received the lower grade (C) are considered, 26.2% of the results were out, that is, only about 74% of the results could be considered acceptable (Table 1). For the micronutrient set, 79% of the results produced by grade C laboratories were within the confidence interval; the corresponding figure for all laboratories participating in the micronutrient monitoring program (41 institutions and 4100 samples analyzed) was 89% (Table 2). The percentage of acceptable results of texture determinations of 43 laboratories was 85%, but this number fell to 68% of the results for participant institutions graded C and D (Table 3). These figures are similar to those of other performanceassessment programs, which show that about 80 to 95% of the laboratories generally present results within the confidence limits (Wolf, Jones, and Hood 1996; Wiethölter 1997). There were great differences in performance of laboratories because those best ranked (graded A) delivered 96 to 97% of the results of basic, micronutrients, and texture determinations within the acceptance intervals (Tables 1 3). These figures are much lower for laboratories that received poor grades. The grading system, initiated in 1989, was suggested by the participant institutions because there was no official accreditation system for soil analysis laboratories in Brazil. It was agreed upon that only laboratories graded A or B would be listed in the SPT publications, which are used by the State Extension Service and others as a local guide of soil analysis service (Cantarella and Abreu 2004). However, laboratories may continue to operate, regardless of the grades obtained in the SPT. Usually 10% of the laboratories do not reach the A or B grade for the basic and the micronutrient tests, but the figure rises to 30% for the texture determinations. Laboratories that fail to obtain satisfactory results in one set of determination do not necessarily have poor performance in the rest, although institutions graded A in one set tend to be well evaluated (grades A or B) in the

6 Table 1. Grade Percentage of samples of the soil proficiency test outside the confidence interval for different soil characteristics Resin-P Organic matter ph Exchangeable K Ca Mg Hþ Al Al SO S Average of all determinations A B C All A þ B Notes: Laboratories were graded A (best) to C according to the overall performance. Each laboratory analyzed 20 samples (total 180 determinations) in Number of laboratories per grade: A ¼ 36; B ¼ 28; C ¼ 6; not evaluated ¼ 14. Total ¼ 84. Variability of Soil Analysis in Commercial Laboratories 2217

7 2218 H. Cantarella et al. Table 2. Percentage of samples of the soil proficiency test outside the confidence interval for micronutrient determinations Grade B Cu Fe Mn Zn Average of all determinations A B C All A þ B Notes: Laboratories were graded A (best) to C according to the overall performance. Each laboratory analyzed 20 samples (total 100 determinations) in Number of laboratories per grade: A ¼ 15; B ¼ 23; C ¼ 3; not evaluated ¼ 43. Total ¼ 84. other sets. For the basic and the micronutrient sets most of those graded C or D were small, low-revenue commercial laboratories, underequiped or with old equipment and that remain in the borderline of grades B and C in most years. Occasionally laboratories that usually perform well may receive low grades in a given year (data not shown). However, many well-evaluated laboratories in the chemical essays have had poor results in the texture determinations, probably because the monitoring program for texture is new (started in 2000) and adjustments for this type of analysis are still being made by the participant laboratories. Besides, a standard method for dispersion of clay in highly oxidic tropical soils that suits routine analysis is not well established and different published procedures are being used. Soil texture is the only set of determination for which methods are not yet standardized within the Table 3. Percentage of samples of the soil proficiency test outside the confidence interval for micronutrient determinations Grade Clay Silt Sand Average of all determinations A B C D All A þ B C þ D Notes: Laboratories were graded A (best) to D according to the overall performance. Each laboratory analyzed 20 samples (total 60 determinations) in Number of laboratories per grade: A ¼ 13; B ¼ 16; C ¼ 8; D ¼ 6; not evaluated ¼ 41. Total ¼ 84.

8 Variability of Soil Analysis in Commercial Laboratories 2219 SPT. The average performance of texture determinations of laboratories in the SPT is gradually improving: 39% of the laboratories obtained C or D grades in 2000; in 2003, this figure was lowered to 33%, despite the increasing number of participating institutions. A similar trend was observed in the first years of this monitoring program with the basic set of determinations (Quaggio, Cantarella and van Raij 1994). Significant progress in laboratory quality has been observed in the United States Western States Proficiency Testing Program (Robert Miller, Colorado State University, personal communication), although quick improvements due to proficiency tests sometimes do not happen (Cools et al. 2004). About 96% of the ph determinations were within the acceptance interval (99% and 71% for laboratories graded A and C, respectively), which makes it the most consistent determination of the basic set (Table 1). On the other extreme was sulfate-s, for which 83% of all results were within the acceptance interval but only 60% of the S analysis reported by the C-graded participants were within the acceptable interval. Around 90% of the resin-p and exchangeable K determinations were acceptable. The best-rated laboratories produced 98 and 96% of the results for P and K within the acceptance range, but these figures dropped to 69 and 75% for the C-graded laboratories. These are important differences because these determinations are among the most used to define fertilizer recommendations. Results of Ca, Mg, Al, H þ Al, and organic matter were between those of ph and K (Table 1). For the micronutrient set of analyses, B was the least-consistent determination because 17% of the results were outside the acceptance range for laboratories rated A and B, but this figure increased to 38% for C-graded labs; Zn and Cu determinations were less variable (Table 2). Boron and zinc are the most deficient micronutrients in Brazilian soils and soil analysis plays an important role for diagnostic purposes. Silt, usually measured as the difference between soil mass and clay plus sand contents, accumulates the errors of those determinations. Therefore, it is already expected that silt presents the greatest variation in the texture set (Table 3). The variability of results found in the SPT is comparable to that of other soil proficiency testing programs. Comparatively high variability for P determination using other extractant solutions were reported by Wolf, Jones, and Hood (1996) in North America, Rayment (1993) in Australia, and Cools et al. (2004) in Europe, whereas ph is usually more consistent among laboratories (Wolf, Jones, and Hood 1996; Rayment 1993). The low prices of soil analysis in many cases limit the capacity of laboratories to invest in modern equipment and quality control. In Brazil in the past, government institutions charged very low prices to promote soil testing so that nowadays farmers are reluctant to pay more for the service. The problem of low prices of soil testing will remain as one of the obstacles for high-quality analysis.

9 2220 H. Cantarella et al. Lime and Fertilizer Recommendation Data on the variation of fertilizer recommendation for P and K of six soil samples analyzed by 66 commercial laboratories enrolled in the SPT are presented in Tables 4 and 5. Despite the variations of laboratory output, the majority of the results of soil analysis fell within the correct (according to the SPT s statistics) class of soil interpretation: 54 to 86% of the data for resin-p and 87 to 96% for exchangeable K. For instance, in a soil with high P (46 mg/dm 3 ), only 4% of the laboratories reported results in the low or very low classes (Table 4), whereas up to 11% of the results for K were in a class immediately above or below. In the case of P, a larger percentage of the data was in the neighboring soil interpretation class. The same rate of fertilizer is recommended for soil tests belonging to a given interpretation class, which contributes to decrease the effect of analytical data variation. For most samples, the effect of variation of laboratory results on rates of P and K fertilizer recommended was relatively small (20 kg/ha of P 2 O 5 or K 2 O) compared Table 4. Phosphorus fertilizer recommendations for corn a for soil samples analyzed by 66 commercial laboratories participating in the soil proficiency test Soil interpretation Results in each soil interpretation range b (%) P fertilizer recommended (kg/ha P 2 O 5 ) Estimated yield increase (kg/ha maize) Cost of fertilizer (kg/ha maize) Profit (kg/ha maize) Sample 1 (very low P, 6 mg/dm 3 ) VL L M Sample 2 (low P, 12 mg/dm 3 ) VL L M Sample 3 (high P, 46 mg/dm 3 ) VL L M H a Rates recommended according to the Fertilizer Recommendation Guide for the State of São Paulo, Brazil (Raij et al. 1996) for maize for 6 t/ha yield goal. Yield increases were estimated with response curves obtained with 25 field experiments calibrated for resin-extracted P (Raij et al. 1983). Grain to fertilizer price ratio was 6.5 kg maize/kg P 2 O 5. b Range of values of soil analysis interpretation, in mg/dm 3 : very low (VL) 0 to 6; low (L) 7 to 15; medium (M) 16 to 40; high (H) 41 to 80.

10 Variability of Soil Analysis in Commercial Laboratories 2221 Table 5. Potassium fertilizer recommendations for corn a for soil samples analyzed by 66 commercial laboratories participating in the soil proficiency test Soil interpretation Results in each soil interpretation range b (%) K fertilizer recommended (kg/ha K 2 O) Estimated yield increase (kg/ha maize) Cost of fertilizer (kg/ha maize) Profit (kg/ha maize) Sample 4 (very low, 0.4 mmol c /dm 3 ) VL L H Sample 5 (low, 1.1 mmol c /dm 3 ) VL L M H Sample 6 (high, 4.3 mmol c /dm 3 ) M H a Rates recommended according to the Fertilizer Recommendation Guide for the State of São Paulo, Brazil (Raij et al. 1996) for maize for 6 t/ha yield goal. Yield increases were estimated with response curves obtained with 25 field experiments calibrated for exchangeable K (Raij et al. 1981). Grain to fertilizer price ratio was 4.5 kg maize/kg K 2 O. b Range of values of soil analysis interpretation, in mmol c /dm 3 : very low (VL) 0 to 0.7; low (L) 0.8 to 1.5; medium (M) 1.6 to 3.0; high (H) 3.1 to 6.0. to the target value (Tables 4 and 5). In about 90% of the cases, fertilizer recommendations for P or K would reach the target or nearby rates. Differences of fertilizer rates can be higher for crops that require larger amounts of nutrients. However, data of experimental response curves (Raij et al. 1981; Raij, Feitosa, and Quaggio 1983) indicated that the effect on yield increases is usually small. The reason is that the rates of fertilizers that maximize profits, which are taken into account in recommendation guidelines, usually are near the point of maximum yield, where the slope of the response curve is relatively smooth, that is, in a position where yield increases are little affected by variations of nutrient application. For soils with P and K concentration in the high or very high soil interpretation classes, the effect of variation of soil analysis as long as the results are in a neighboring class is still lower because yield gains due to fertilizer application are small, as are the fertilizer recommended rates. Therefore, even when a large numeric variation in soil analysis occur, provided that results do not fall in very different soil interpretation classes, the effects on yield and profit are relatively small (Tables 4 and 5). Only a

11 2222 H. Cantarella et al. reduced number of laboratories released discrepant analytical results that would lead to fertilizer recommendations that would bring substantial money losses to farmers. In general, the same thing applies to lime recommendation. However, soils with high exchangeable acidity and low percentage of base saturation may be subject to high variations of lime recommendation rates (Table 6) because of the variability of H þ Al results. In the example of Table 6, soil base saturation varied from,17% to 67%, although 75% of the results were close to the most likely value estimated in the STP (V ¼ 32%). The majority of the lime recommendations (58%) were in the range of 3.7 to 4.8 t/ha (most likely value ¼ 4.3 t/ha), and only 7% of the recommendations were too low (,2.4 t/ha) or too high (.6.0 t/ha) (Table 6). In most cases (74% of the samples), the variation of results of soil analysis and of the lime rate recommended would cause small deviations from the target values of soil base saturation. Lime recommendation would reach the target value of V ¼ % in 74% of the cases and a target value of V ¼ % in 92% of the cases. However, the consequences of errors in lime application may not only lead to direct monetary losses but also bring about changes in nutrient availability that may be difficult to fix, especially if rates higher than those actually needed are prescribed (Tables 7 and 8). A survey by Wiethölter (1997) showed that in southern Brazil, 77% of the laboratories produced results of soil analysis that would give rise to correct lime recommendations; for N, P, and K fertilization, acceptable recommendations would occur in 80% of cases. Similar results were obtained by Peltovuori (1999) in a simulation study to assess the precision of soil testing for P in Finland: the observed variation corresponded approximately to a maximum error of one P class in a seven-step classification system, and the largest deviation from a correct P fertilization recommendation was 10 kg/ha. Table 6. Distribution frequency of soil base saturation obtained based on results of soil analysis of a sample analyzed by 66 commercial laboratories participating in the soil proficiency test Soil base saturation (V%) Distribution frequency (%),

12 Variability of Soil Analysis in Commercial Laboratories 2223 Table 7. Distribution of lime requirement calculated based on results of soil analysis of a sample analyzed by 66 commercial laboratories participating in the soil proficiency test Estimated lime requirement (t/ha) Distribution frequency (%), Gross errors of fertilizer recommendations due to inadequate results of soil analysis can be minimized if records of previous analysis of the area are available. However, this does not decrease laboratory responsibility for the results and recommendations issued. There is much to improve, but even with the variations presently observed in soil analysis, good laboratories can offer very useful analytical results with Table 8. Distribution of soil base saturation that would actually be reached based on results of soil analysis of a sample analyzed by 66 commercial laboratories participating in the soil proficiency test Soil base saturation reached a (V%) Distribution frequency (%) a The values of lime recommendation obtained with the results of soil analysis supplied by each laboratory were used to estimate the soil base saturation that would be reached considering the real or most likely soil characteristics (V ¼ 32%, CEC ¼ 114 mmol c /dm 3 ; lime needed to reach V ¼ 70% of the CEC ¼ 4, 3 þ limestone/ha), that is, disregarding the error of analysis of individual laboratories.

13 2224 H. Cantarella et al. acceptable deviations. In addition, spatial variability of fertilizer and lime distribution in the field and errors in soil sampling are usually greater than those that take place in analytical procedures. Therefore, recommendations based on soil analysis are preferable to those that do not take into account the diagnosis of soil fertility. REFERENCES Cantarella, H. and de Abreu, M.F. (2004) Evaluation of the performance of laboratories in In 19th Annual Meeting of IAC Soil Proficiency Testing Program for Laboratories of Soil Analysis; Instituto Agronomico: Campinas, Brazil (in Portuguese). Cantarella, H., van Raij, B., and Quaggio, J.A. (1998) Soil and plant analysis for lime and fertilizer recommendations in Brazil. Communications in Soil Science and Plant Analysis, 29: Cools, N., Delanote, V., Scheldeman, X., Quataert, P., de Vos, B., and Roskams, P. (2004) Quality assurance and quality control in forest soil analyses: A comparison between European soil laboratories. Accreditation and Quality Assurance, 9: Klesta, E.J. and Bartz, J.K. (1996) Quality assurance and quality control. In Methods of Soil Analysis, Part 3: Chemical Methods; Bartels, J.M. and Bigham, J.M. (eds.), Soil Science Society of America: Madison, Wisconsin, Peck, T.R. and Soltanpour, P.N. (1990) The principles of soil testing. In Soil Testing and Plant Analysis, 3rd ed.; Westerman, R.L. (ed.), Soil Science Society of America: Madison, Wisconsin, 1 9. Peltovuori, T. (1999) Precision of commercial soil testing practice for phosphorus fertilizer recommendations in Finland. Agricultural and Food Science in Finland, 8: Quaggio, J.A., Cantarella, H., and van Raij, B. (1994) Evolution of the analytical quality of soil testing laboratories integrated in a sample exchange program. Communications in Soil Science and Plant Analysis, 25: Quaggio, J.A., Cantarella, H., and van Raij, B. (1998) Phosphorus and potassium soil test and nitrogen leaf analysis as a base for citrus fertilization. Nutrient Cycling in Agroecosystems, 52: Raij, B. van, Feitosa, C.T., Cantarella, H., Camargo, A.P., Dechen, A.R., Alves, S., de Sordi, G., Veiga, A.A., Petinelli, A., and Nery, C. (1981) Soil analysis to discriminate corn response to fertilizer. Bragantia, 40: (in Portuguese). Raij, B. van, Feitosa, C.T., and Quaggio, J.A. (1983) Soil testing as a basis of phosphorus recommendations for maize. In Proceedings of the International Congress on Phosphorus Compounds; Institut Mondial du Phosphate (IMPHOS): Brussels, Raij, B.van, Cantarella, H., Quaggio, J.A., and Furlani, A.M.C. (1996) Recommendations of Fertilizer and Lime for the State of São Paulo; Instituto Agronomico: Campinas, Brazil (in Portuguese). Raij, B.van, Cantarella, H., and Quaggio, J.A. (1994) Soil testing and plant analysis in Brazil. Communications in Soil Science Plant Analysis, 25: Raij, B. van, Quaggio, J.A., and da Silva, N.M. (1986) Extraction of phosphorus, potassium, calcium and magnesium from soils by an ion-exchange resin procedure. Communications in Soil Science and Plant Analysis, 17:

14 Variability of Soil Analysis in Commercial Laboratories 2225 Raij, B. van, de Andrade, J.C., Cantarella, H., and Quaggio, J.A. (2001) Chemical Analysis for Evaluation of the Fertility of Tropical Soils; Instituto Agronomico: Campinas, Brazil (in Portuguese). Rayment, G.E. (1993) Soil analysis a review. Australian Journal of Experimental Agriculture, 33: Wiethölter, S. (1997) Quality control of soil analysis of ROLAS and its effects on lime and fertilizer recommendations. In 29th Annual Meeting of the Official Network of Soil and Plant Analysis Laboratories of the States of Rio G. do Sul and Santa Catarina; ROLAS: Guaíba, Brazil (in Portuguese). Wolf, A.M., Jones, J.B., and Hood, T. (1996) Proficiency testing for improving analytical performance in soil testing laboratories: A summary of results from the council s soil and plant analysis proficiency testing programs. Communications in Soil Science and Plant Analysis, 27: