Quality Assurance and Quality Control in a Soil Test Laboratory
David H. Hardy, Rao Mylavarapu, and Tony Provin
Introduction
Soil testing laboratories provide soil fertility recommendations for farmers and
consultants to make management decisions in crop production. Labs are also used
relevant to environmental issues such as phosphorus loss and heavy metal loading as
governed by state and federal regulations. Although end uses of soil test data may be
extremely different, the primary goal of the lab is to provide reliable, consistent, and
valid data. The need for accurate results imposes great demand on labs in terms of quality
assurance and quality control.
Quality assurance (QA) in a laboratory is governed at the management level to
ensure that laboratory data provided to the customer meet a defined level of confidence.
This implies that data are generated under statistical control by trained staff in the
laboratory where activities are driven by standard operating procedures (SOPs), good
laboratory practices (GLPs), frequent inspection, and thorough documentation to ensure
quality control. Quality control (QC) occurs in the laboratory. The quality assessment of
data is essentially a monitoring process of internal or external reference standards
meeting defined statistical limits to ensure accuracy and precision.
Here, we provide helpful, applied information based on practices found to be
important in the operation of public soil testing labs analyzing routine grower samples for
agronomic recommendations. We begin this chapter discussing detection limits of
chemical measurements to provide a general understanding of this topic as pertaining to
instruments and methods. Afterward, quality assessment and quality control are discussed
in detail.
Limits Associated with Chemical Measurements
Laboratories generate thousands of data daily with quantitative instruments but
are obligated to report values only when certainty exists that a given value is real or
measurable with confidence. This is especially true for environmental labs involved in
ultra-trace measurements. For soil testing labs performing agronomic tests, there is more
leniency in reporting in general but an understanding of chemical measurements as
related to “detection limits” is necessary and beneficial.
Three limits associated with chemical measurement are— limit of detection
(LOD), limit of quantitation (LOQ), and limit of linearity (LOL). These may be defined
as follows:
LOD: the concentration of a given analyte where there is certainty of a non-zero
measurement. The term method detection limit is also used to describe LOD.
LOQ: a concentration above the LOD where quantitative measurement is
meaningful for a given analyte and the method. The term method reporting limit
(MRL) is sometimes used to describe LOQ.
LOL: the highest concentration or upper limit, of the linear calibration range, both
for the analyte and the method.
We can further think about detection limits as related to instruments as well as
methods. Instrument manufacturers provide specified detection limits for a given analyte.
This instrumental LOD is typically lower than what can be attained in a soil testing lab
where a given analyte such as phosphorus is present in the sample matrix with other
constituents after extraction.
For extraction methods of elements from soils, we refer to the LOD as being the
method detection limit (MDL). The MDL is approached in soil testing by analyzing
reagent or extractant blanks. A measureable amount of an analyte may exist simply by
performing the method itself due to small concentrations of elements being present in
reagents, extraction vessels and so forth. Blanks are probably most important in
measurements of low concentrations and in micronutrient analysis. In routine soil fertility
testing, methods should be evaluated with blanks to determine if significant contributions
occur. Occasionally, preparation blanks must be used due to the inability of the
laboratory to exactly duplicate the analyzed solution matrix. This may be due to the
laboratory accepting solutions for analyses and the client failed to provide adequate
matrix solution for the development of standards.
Although it is customary to not report zero values, some soil testing labs when
testing soils for agronomic recommendations may do so since reporting a non-value such
as below MDL may be confusing to growers. Given the variability in soil fertility across
an agricultural field and methods used in sample collection relative value and subsequent
recommendation are most important. For environmental testing, values below the MDL
should be qualified and reported as such. More common in agronomic laboratories to
establish a MRL that is sufficiently low enough to insure all agronomic fertilizer or
remediation requirements/recommendations can be utilized. In this instance, a less than
symbol is placed in front of the MRL.
With this understanding, we now discuss assessing data for accuracy and
precision.
Quality Assessment of Accuracy and
Precision through Statistical Control of Data
2
Accuracy and precision are two words used when qualifying data. Accuracy is
defined as the closeness of the analytical value to the “true quantitative value” for a given
analyte. If one thinks of a target, great accuracy is hitting the bull’s eye. A lab should
define its confidence in reporting a value.
Precision is a measure of the consistency of reproducing a value; it is a measure
of scattering of data around the mean. Tightly clustered data indicate great precision but
precision does not suggest accuracy. When data are tightly clustered but inaccurate,
random error is small but there is a strong indication of bias or occurrence of systematic
error.
“Control” or “check” samples, also referred to as internal reference samples are
commonly used in labs to assess accuracy and precision. Check samples are often used
by labs that analyze a large number of samples to circumvent the very high cost of the
purchase of prepared reference samples. The following guidelines should enable someone
to prepare a “check” sample for quality assessment.





Select soils similar in soil chemistry, texture and organic matter to those analyzed
for clients
o Gather several different check samples to encompass a range of results
Dry, grind and screen to 2mm size just as preparing clients’ samples
Thoroughly mix the sample to acquire homogeneity.
o An affordable, effective method of mixing is through placement of the
ground, screened soil on a clean tarp; the sample is split and mixed by
pulling alternate corners of the tarp toward the center. This effectively
divides the sample in half upon each pull.
o Another method of mixing is through use of a cement mixer.
Subsample the thoroughly mixed sample 30 times to acquire subsamples for soil
analysis. Samples should be run in slugs of 10 times on three different days.
After collection of 30 subsamples from the prepared check soil as above, each are
tested for analytes using SOPs of the lab. A time of at least 4 hours should
separate each analysis; some labs analyze samples on different days.
o Labs preparing check soils sometimes exchange samples with other labs
using same methods in an effort to attain the best estimate of a true value.
Variability in measurements will come from inherent variability within the
samples, each step in sample preparation related to equipment (scoops, pumps) or
technician technique, and instrumentation. If a lab has multiple instruments, check
samples should be analyzed on each instrument to compare results. Bias can be
investigated by repeatedly analyzing the samples using different chemists on different
instruments and on different days to further establish baseline data— the “true” values for
the check samples.
Statistical analysis of the data from the analysis, the mean (X) and standard
deviation (s), is used to establish the required accuracy to accept data. For example, data
for phosphorus found in Table 1 are used to define the following limits.
3


Upper Warning Limit (UWL): X + 2s
Lower Warning Limits (LWL): X - 2s


Upper Control Limit (UCL): X + 3s
Lower Control Limits (LCL): X - 3s
Assuming a normal distribution of data about the mean, these limits include 95%
and 99% of the population for warning and control, respectively.
Table 1. Phosphorus data from a bulk check soil sample used to establish accuracy.a
Sample
P
Sample
P
Sample
P
ppm
ppm
ppm
1
8.63
2
8.13
3
7.48
6
7.92
7
7.55
8
7.42
11
7.86
12
7.56
13
8.15
15
8.18
17
7.64
18
7.93
Mean = 7.83; s = 0.31, n = 20
LWL = 7.21, UWL = 8.44, LCL = 6.91, UCL = 8.75
a
Twenty subsamples extracted and analyzed by ICP.
Sample
4
9
14
19
P
ppm
7.92
7.55
7.96
7.77
Sample
5
10
15
20
P
ppm
7.75
7.41
7.71
8.03
The analytical limits are typically plotted to create X-Quality control charts as seen in
Figure 1.
Figure1. Phosphorus data from Table 1 plotted as an X-Quality control chart.
10
9.5
UCL
P mg dm -3
9
8.5
UWL
8
7.5
LWL
7
LCL
6.5
6
0
5
10
15
Observation Number
4
20
25
During daily lab analyses if data for a “check” sample falls outside of the
established warning or control limits for an analyte, it warrants investigation. Lab
supervisors and chemists should determine the cause and the necessary corrective action.
Consider if





All analytes are involved or if only one analyte is involved
New standards are recently involved
The problem is isolated to one instrument or technician
A recent trend in data is occurring
The “check sample” is becoming segregated relative to particle size.
One element, especially a micronutrient, may sometimes fall just outside of limits
without concern. If the check sample is segregated, new control limits may need to be
established. Whatever the case may be, it is imperative that a thorough discussion and
investigation among supervisors and chemists occurs to ascertain if a problem exists.
The frequency of check samples is a laboratory management decision. There is a
monetary cost associated with check samples, both in time and analytical overhead. In
most soil labs, a check sample is analyzed for every 20 to 30 grower samples. The checks
are inserted and analyzed as part of the daily work with grower samples. This allows for
measurement of systematic error, especially if the checks are “blind” so the analyst
cannot differentiate the check from grower samples. Blind check samples are possibly
preferred but check samples that are not blind can also serve a valuable role too. They
can identify a problem during the actual analysis, before numerous samples are
potentially analyzed in error. Even if the check samples are not true blind samples, the
analyses data, means and standard deviation criteria should not be shared with laboratory
technicians. Limiting these data to the laboratory manager, director and QA officer will
better insure check sample integrity.
Although check samples are most common and helpful to monitor accuracy on a
daily basis, it is recommended that labs participate in proficiency testing programs that
are external to the lab. These programs involve analysis of various soil samples on a
systematic basis (usually quarterly) throughout the year. Typically, multiple labs using
various methods are involved and data from labs are statistically analyzed to estimate a
“true” value for analytes. This approach is excellent as long as the population of
participating labs for a given method is not too small. Proficiency programs are
affordable and voluntarily utilized by many private and public labs across North America
as well as internationally. Two examples of proficiency soil testing programs available in
the United States are the North American Proficiency Testing (NAPT) program
administered by the Soil Science Society of America and the Agricultural Laboratory
Proficiency Testing (ALP) program. These proficiency testing programs are not
certification programs which do exist but are much more costly. The samples used in
these programs have been finely ground to reduce heterogeneity and allow the laboratory
to pinpoint mechanistic and analytical difficulties within the laboratory.
5
Accuracy can also be evaluated through use of standard reference materials
(SRMs) that offer certified values of analytes. However, SRMs are expensive and with
few exceptions fail to provide mean data from commonly used soil testing methods.
Check samples can also be used to evaluate precision or repeatability in acquiring
the same data over time. Precision can also be estimated by analyzing duplicates of
grower samples or other unknowns.
As an example in monitoring precision, we use K data presented in Table 2 from
11 grower samples analyzed twice (duplicates— Xanalysis1, Xanalysis2) by the lab. The
absolute relative difference percent (RD%) is calculated where X = K data as follows.
RD% = 100 | [(Xanalysis1 - Xanalysis2) ÷ (Xanalysis1 + Xanalysis2)÷ 2) ] |
The mean and the standard deviation of the RD% values are calculated. The upper
control limit is calculated as the mean %RD + 3s which is equal to 22.61 for our
example. Data for K are under statistical control for precision if the calculated %RD fall
between 0 and 21.98
Table 2.Potassium data acquired from duplicate analysis of grower soil samples.
Sample
1
2
Analysis 1
Analysis 2
-3
------------------K mg/dm ---------------8.6
8.1
9.8
9.7
%RD
6.0
1.0
3
5.5
4.9
11.5
4
21.7
19.5
10.7
5
17.4
16.8
3.5
6
59.8
63.2
5.5
7
5.9
5.7
3.4
8
7.9
7.5
5.2
9
4.1
3.4
18.7
10
3.5
3.4
2.9
11
16.1
15.0
7.1
Mean %RD = 6.87%, s = 5.04, Mean %RD + 3s = 21.98
The %RD values of the 11 samples can be plotted as a range chart (R chart) as
presented in Figure 2 with the upper control limit. Although these data are from duplicate
analyses of grower samples, the data for check samples presented in Table 1 could be
used for similar purposes.
6
Data can also be evaluated for precision by calculating the absolute difference
between duplicate measurements: | Xanalysis1 - Xanalysis2 |. The mean of the absolute average
difference (R) is calculated and control limits are set for as follows:



UCL = 3.267 R for a 99% confidence interval
UWL = 2.512 R for a 95% confidence interval
LWL & LCL = 0
A range chart (R) chart (not shown) can be used to plot the absolute differences and the
associated limits. All data should fall under the UCL for it to be considered under
statistical control. Also, data points should be randomly distributed within the warning
limits when under a state of statistical control.
Figure 2. Potassium data presented in Table 2 plotted as a range
25
UCL = 21.98%
Relative Difference %
20
15
10
5
0
0
2
4
6
8
10
12
Observation Number
Quality Control in the Laboratory
In any laboratory, consistency in daily work regardless of a task is extremely
important to attain precise data. Quality control is a method to minimize errors and foster
reproducibility in all aspects of lab work. The following practices and guidelines are
important in attaining good quality control.
Standard Operation Procedures
7
The foundation of QC in daily work is by use of standard operating procedures
(SOPs) for both technical and nontechnical work. The word “standard” implies an
operation is performed the same way on each occasion. Any part of a procedure where
personal preference or judgment of chemists could be imposed is to be included in the
SOP.
At a minimum, SOPs in soil testing are needed for sample receiving and drying,
sample grinding, sample scooping, solution preparation, soil acidity measurements- pH
and exchangeable acidity, soil extraction, element analysis by ICP, soil disposal, and
dishwashing.
An SOP needs to describe information in sufficient detail such that a competent
person unfamiliar with the method or task could obtain acceptable results and / or
conduct a reliable review of results using the procedure. The SOP for a given task needs
to include: title page with reference method, lab name/location, authorization signature,
and approval dates; table of contents; scope of application; summary of method;
safety/waste handling; interferences; apparatus/equipment; reagents/chemicals; sample
collection, preservation, shipment, and storage; calibration; sample preparation;
procedure; calculations; quality control; data validation; and preventative maintenance.
Guidance for preparing SOPs is found at http://www.epa.gov/quality/qs-docs/g6-final.pdf
Instruments and Equipment— Operation, Maintenance, & Records
Highly optimized, operational instrumentation or equipment is essential to
producing consistent, accurate data. Users should refer to the operation manual for the
manufacturer’s guidelines in correct operation, calibration, maintenance and trouble
shooting. Many manufacturers offer on-site training at the purchase of an instrument as
well as regional seminars; laboratories should take advantage of such training
opportunities.
Table 3 lists guidelines for daily operation of laboratory equipment in a highvolume lab setting for non-research samples. A more stringent calibration schedule may
be desirable for labs analyzing small volumes of samples or research samples.
Records on equipment are extremely important. A record of purchase, repair,
maintenance and calibration should be archived for all equipment and instruments,
including items such as automated pipettes. The operator’s manual can be used if space
allows for non-daily information. An instrument log can be used for daily calibration.
Each entry should be dated and initialed. Details are needed as to who performed a given
activity.
As equipment ages, repairs often become more frequent. Management needs to be
informed of increasing downtime and potential need for replacement. Sensitivity of
analysis too becomes important for some instruments such as ICPs.
Chemicals, Standards, and Lab Solutions
8
Analytical methods should specify the quality or grade of reagents needed to
achieve desired results. Instruments that rely on gases for their operation will specify the
purity of gas needed in the operator’s manual; if not found, consult the manufacturer.
Just as any instrument, a record of chemical purchase is needed. Chemical
inventory records should be kept so lab supervisors are aware of supply on hand.
Ordering should be adequate to prevent uninterrupted lab operation. An inventory of
chemicals for the entire lab should occur yearly, preferably after the lab’s busiest season.
Care should be taken in disposal of chemicals no longer needed due to changes in
methods or expiration dates of the chemical.
Upon receipt, each chemical container should be inspected as to its contents and
physical soundness, dated and initialed only by designated chemists, preferably
supervisors. An expiration date should be circled or written on the container. The
container should be properly stored according to the material safety data sheet notebook
(MSDS). A copy of the MSDS should be placed in the MSDS laboratory notebook with
replacement of the out-dated copy if needed. At the time of opening a container for initial
use, the date opened should be recorded and initialed on the container.
Table 3.Guidelines for equipment calibration and maintenance.
Equipment
ICP
Calibration
After initial warm-up
Check calibration
every 30 samples
Special Concerns
Temperature
Humidity
A recalibration is
necessary if periodic
QC samples fail to be
within control limits
Profile 2-hr intervals
Maintenance
Schedule routine
professional
maintenance yearly
or as suggested by
manufacturer
All unknown
since run since
last acceptable QC
sample must reanalyzed
Significant changes in
torch, tubing, gas
flow, or method
Spectrophotometers After initial warm-up
pH Meter & probes
Check calibration
every 30 samples
Hourly or every 6090 samples, read back
samples frequently
9
Temperature
Check probes
frequently for wear;
make sure electrodes
are filled.
Conductivity Meter
Balances
pH 4.0 and 7.0,
consider 10.0 for
saline soils with high
pH
Check known
samples for
calibration frequently
Temperature and
fouling of probe
surfaces
Daily prior to use
Check frequently for
wear and tear of the
probes and
as per the
manufacturers
specifications
Clean after each use
Annual certification
& cleaning by
qualified company
Macrodispensers
Automated Pipettes
Soil scoops
Daily using balance
and DI water or
solution of known
density
Each work session
and should be
calibrated quarterly.
Micropipettes should
be calibrated
quarterly.
Annually
Wear of scoop top
Logbooks are needed for preparation of standards or solutions such as extractants.
Entries in the logbook should include:







Time / date of preparation
Receptacle container for the solution being prepared
Name, amount and lot of chemicals used
order mixed in preparation
Required measurements (for example, initial pH for buffered solutions)
Associated soil samples analyzed
Chemist’s initials
Again, careful attention should be given to chemical expiration dates relative to the date
on the container and also to the longevity of the use for the end solution in preparation.
Lastly any chemical or reagent removed from its original container if unused should
never be returned back to it.
Chemist Notebooks and Laboratory Records
10
The purpose of laboratory records whether in a chemist notebook or a logbook is
to provide a historical reproduction of an occurrence. Without documentation, a chemist
cannot prove or possibly remember what was done. Records shall:









Allow for historical reproduction
Always be entered with blue or black ink, never pencil
Be accurate, legible, objective, and recorded in a permanent manner
Be recorded immediately
Follow an established retention policy
Protect and maintain integrity and security in data
Record any modification in procedure
Contain information on all calculations to the final result if needed
Document levels of review
Occasionally in documentation, errors will occur. Guidelines to follow when this
occurs are:






Draw a single line through the error such that original entry remains legible
Write the correction adjacent to the error
Never use white-out
Initial and date change
Offer a reason for the change if confusion is likely
Use revisions to electronic files if involved to maintain history
Labware and Glassware
In many high volume labs, plastic has replaced glassware vessels in many day-today operations. Plastic is affordable, usually unbreakable, and can often be used without
contamination if properly cleaned; in some cases disposable glassware offers more of an
economical advantage than washing, especially for certain tasks. Some metal
contaminates, primarily zinc, can be present in high volume disposable plastic
manufactured products. Laboratories should purchase these supplies in sufficient
quantities to reduce the number of manufacture production runs or lots. Prior to use of
the new lot, multiple blank extractions should be conducted and compared to existing
inventory.
Most plastic vials used in daily extractions can be configured in a grouping of ten
to twelve bottles that will allow for ease of handling as well as washing. Automated
washing stations allow for grouped bottles to be thoroughly washed efficiently.
Manifolds with nozzles that allow for bottles to rest upon with water controlled by a footpedal in such an apparatus has proved effective. Three levels of rinse are made with the
first two being with tap water and the last being made with deionized, distilled water.
Filtering devices can be washed similarly at a sink. Labware should be washed before
it becomes dry. Many high volume labs use “solo” cups that are dispensable for pH.
11
When glassware is involved, scrub to remove any residue, soak if needed, wash
with phosphate-free detergent and triple rinse. If standards are prepared in volumetric
flasks, the flasks should be soaked in a 0.1 N HCl for a few hours, then rinsed thoroughly
with deionized water.
Volumetric pipettes can be washed using standard pipette washers and should be
washed after each use. These should also be soaked in an dilute (not to exceed 1N HCl)
acid bath prior to washing. All pipettes should be thoroughly dried prior to use.
Store any labware not in use in a clean environment as dust-free as possible.
When possible, closed cabinets or drawers are best. Any stored or new labware should
be thoroughly washed prior to use.
Daily Traceability of Samples and Laboratory Processes
The importance of records in logbooks and lab notebooks has already been
mentioned. The documentation of chain of events for the progression of various steps of
receiving, handling and analysis that a sample undergoes is extremely beneficial and
important to attain, although routine samples may not require a formal chain-of-custody.
In the end, it is important to know what happened to each sample, who performed each
step, where solutions were attained, what instruments were used, when each task was
performed and so forth. This is of tremendous assistance if error is found in the analysis
and also to attain accountability of technicians or chemists. Table 4 lists guidelines for
sample traceability for various lab processes.
Table 4.Guidelines in tracking lab processes.
Lab Process
Sample
receiving
Technician / Chemist Activity
Record client name, shipment
information
Method of Record
Logbook / bar code scanner /
hardcopy submittal forms, LIMS
system
Date / time of arrival
Method of shipment
Condition of samples
Location of storage if needed
Lab ID
assignment
Special considerations if quarantine
Organize working groups of samples Soil receiving logbook
Designate QA/QC position
Lab ID assigned to sample box/bag
12
Lab ID assigned / recorded on
information sheet
Grinding
Scoop
Extraction
ICP analysis
pH / BpH
Date / time of process
Grind soil samples
Label vials with lab IDs for tests
Scoop samples and controls
Addition of extractant
Filtering
Assign samples to instrument
Additions of water / buffer
Read pH / BpH
Grinding logbook
Sample Prep logbook
Extractant / solutions logbook
ICP logbook or LIMS
Meter / electrode logbook or LIMS
Meter / electrode logbook or LIMS
Training & Error Prevention
Written, formal SOPs are excellent tools to train new employees and refresh
experienced chemists. Again, training offered by instrument and chemical manufacturers
is valuable too. The QAQC and/or laboratory manager should maintain a logbook
documenting all trainings to insure existing and new staff are fully aware of all laboratory
procedures and changes in methodology, instrumentation and similar differences which
could impact overall laboratory performance.
An important part of quality in laboratory work is a prepared worker, not only
from a technical perspective but also with mental attitude. Prior knowledge of work
volume expectations is extremely important. In labs as any work environment,
emergencies and illness will occur and employees may be absent unexpectedly. Having
chemists with a wide range of abilities in the event of employee illness can be very
beneficial. Cross-training to allow for such flexibility and also to avoid monotony
sometimes associated with lab work is recommended.
No matter how diligent workers are, errors will occur. Anticipating errors through
a thorough understanding of specific work is extremely important. Data should be
reviewed by the technicians generating the data and further reviewed by supervisors/QA
officers to prevent erroneous data from being reported to users. A conscientious chemist
can prevent generation of poor quality data before data are submitted to further scrutiny,
thus resulting in efficiency.
Some known errors that may occur in soil testing labs are listed below. Details are
given as to the lab location or work related, actual error, result if uncorrected; and action
to minimize the potential for recurring error.

Soil receiving; error— misalignment of samples / incorrect lab numbers as related
to information on the client information sheet; result— client receives incorrect
sample results and recommendations; action— worker carefully reviews work
after assigning lab numbers to sample box/bag and information sheets.
13

Soil receiving; error— not placing a control sample in a grouping of samples as
specified; result if uncorrected— loss of QA/QC for the group of samples
involved and uncertainty as to data quality; action— worker carefully reviews
work.

Grinding; error— placing samples out of sequence as samples are ground;
result— lab numbers and samples are now out of sequence so client will receive
incorrect data and recommendations; action— working in verified order without
deviation and review of lab number order before and after scooping.

Grinding; error action— samples not sufficiently dry, so soil adheres to the
grinder and contaminates samples; result— potential inaccurate results of
samples; action— thoroughly checking samples for dryness, further dry if
necessary, and clean grinder if needed.

Sample preparation or soil scooping; error— using the wrong size scoop for an
analysis; result— inaccurate data; action— paint handles of similar size scoops
the same color and store scoops of different sizes apart from one another.

Sample preparation or soil scooping; error— samples scooped out of order;
result— client receives incorrect sample results and recommendations; check
sample data fails; action— train workers to systematically scoop samples and to
ascertain control samples are scooped in correct positions; control samples should
be scooped as grower samples are scooped, not at one time.

Soil Extraction; error— filtrate is poured out of sequence; result— client receives
incorrect samples results and recommendations; action- label each extraction rack
with a color code and beginning lab number with the same done for the extraction
filtering apparatus.

ICP analysis; error— data are 0 or near 0 values for many elements due to
blocked tubing; result— client receives incorrect results and recommendations;
action— review data prior to importing into LIMS and free blockage.
References
Taylor, J. K. 1987. Quality Assurance of Chemical Measurements. Lewis Publishers, Inc.
Chelsea, MI.
Watson, M.E., J. Kotuby-Amacher, P. Chu, C. Focht, W. Greig, M. Nathan, T. Provin, K.
Reid, and D.A. Horneck. 2005. NAPT Quality Assurance / Quality Control Model Plan
for Soil Testing Laboratories. Soil Sci. Soc. of Am.
14
Hoskins, B. Chapter 1- Laboratory quality assurance programs. IN Recommended Soil
Testing Procedures for the Northeastern United States. 3rd. Ed. Northeastern Regional
Publication No. 493.
Reeuwijk, L. P. and V.J.G. Houba. 2001. Guidelines for quality management in soil and
plant laboratories. Food and Agriculture Organization of the United Nations FAO Soils
Bulletin 74. Daya Publishing House, Shastrinagar Delhi, India.
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