Proficiency Testing of Feed Constituents: A Comparative Evaluation

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Proficiency testing of feed constituents: a comparative evaluation of European and developing country laboratories and its implications for animal production H.P.S. Makkar, I. Strnad, and J. Mittendorfer J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b02452 • Publication Date (Web): 20 Sep 2016 Downloaded from http://pubs.acs.org on September 21, 2016

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Journal of Agricultural and Food Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

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Journal of Agricultural and Food Chemistry

Proficiency Testing of Analytical Chemistry Laboratories

z-score =

𝑥𝑙𝑎𝑏 −𝑥𝑅𝑇 𝑆𝐷𝑅𝑇

𝑥𝑙𝑎𝑏 , laboratory result 𝑥𝑅𝑇 , mean of all results of the ring test 𝑆𝐷𝑅𝑇 , standard deviation

| z | ≤ 2: satisfactory result 2 < | z | < 3: doubtful result

ACS Paragon Plus Environment

| z | > 3: unsatisfactory result


1 2 3

Proficiency testing of feed constituents: a comparative evaluation of European and developing country laboratories and its implications for animal production

4 H. P .S. MAKKAR1, I. STRNAD2, J. MITTENDORFER2

5 6 7 8 9 10

1,

Food and Agriculture Organization of the United Nations (FAO), Animal Production and Health division, Rom, Italy 2,

Austrian Agency for Health and Food Safety (AGES), Institute for Animal Nutrition and Feed, Wieningerstrasse 8, 4020 Linz

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Corresponding author: Harinder P.S. Makkar

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E-mail: [email protected]

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Tel: +390657054944

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Fax: +390657055749

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Short title: Proficiency testing for feed analysis

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Proficiency testing of feed constituents: a comparative evaluation of European and developing country laboratories and its implications for animal production

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ABSTRACT

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Proficiency tests, with two feed samples each year, for various constituents (proximate, macro-

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and micro-minerals, feed additives and amino acids) were conducted in 2014 and 2015. A total

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of 40 and 50 European and 73 and 63 developing country feed analysis laboratories participated

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in the study in 2014 and 2015 respectively. The data obtained from these two sets of

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laboratories in each year enabled a comparison of the performance of the European and

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developing country laboratories. Higher standard deviation and several folds higher coefficient

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of variation were obtained for the developing country laboratories. The coefficients of

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variation for chemical composition parameters, macro-minerals, micro-minerals and amino

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acids were higher by up to 9-fold, 14-fold, 10-fold and 14-fold respectively for the developing

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country laboratories compared with the European laboratories in 2014, while the corresponding

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values for the 2015 were 4.6-fold, 4.4-fold, 9-fold and 14-fold higher for developing county

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laboratories. Also higher number of outliers were observed for developing countries (2014:

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7.6–8.7% vs. 2.9–3.0%; 2015: 7.7–9.5% vs. 4.2–7.0%). The results suggest higher need for

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developing country feed analysis laboratories to improve the quality of data being generated.

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The likely impact of higher variability of the data generated in developing countries towards

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safe and quality preparation of animal diets, their impact on animal productivity and possible

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ways to improve the quality of data from developing countries are discussed.

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Key words: Proficiency test, ring test, feed analysis, feed chemical composition, mineral

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analysis

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INTRODUCTION

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Feed is the base of the livestock production systems, impacting almost every sector of the

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livestock production – from animal reproduction, health and welfare – to farm economic

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viability, environment, animal product safety and quality. The feed cost can account for up to

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70% of the total costs for the production of an animal product. High feed costs can wipe out a

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livestock rearing operation. Good nutrition (protein, energy and minerals in the right amounts)

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increases animal production, reproductive efficiency (higher cyclicity, lower age at first calving

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and lower calving interval) and higher productive life1,2, which result in higher profitability to

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farmers. Feed nutrients (70 to 90% of nitrogen and phosphorus) are lost into the environment

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through manure, which if not managed properly can lead to environmental pollution. A number

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of welfare problems in animals are elicited by the feeding of poor quality or unsafe feeds.

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Nutrient deficiency or excess in feed can lead to metabolic disorders in ruminants such as

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acidosis and lameness causing welfare issues; whilst breeding animals of monogastric species,

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which are restrict-fed to optimise health and production, may suffer from chronic hunger3. In

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addition, these imbalanced nutrient situations also increase enteric methane emissions4. Society

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demands production of animal products in a manner that is least damaging to the environment,

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takes welfare needs of animals into account and brings benefits that are equitable.

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Precision feeding or a properly balanced diet, free of undesirable substances, fed in the right

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amount that meets the animal’s nutrient requirement avoids physical and psychological

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suffering from hunger and thirst; and is crucial for optimal performance, economic viability of

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livestock operation (by decreasing feed cost), decreasing environment pollutants (by decreasing

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release of nitrogen and phosphorus in manure and enteric methane)5,6. Precision, or balanced

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feeding, results in more animal products from less feed. It will also translate to less use of

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energy, land and natural resources7,8. In order to practice precision or balanced feeding, a pre-


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requisite is the availability of good quality data on chemical composition of feed ingredients

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and feeds. Therefore the laboratories should conduct analyses using the right methods in the

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right manner, using good laboratory practices including the use of internal quality control

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samples.

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A proficiency test is an inter-laboratory test that allows the evaluation of the performance of

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laboratories, and is based on analysis of similar homogeneous samples. It is critical to ensuring

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quality of analyses being performed in a laboratory. A proficiency test is an element of external

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quality assurance (EQA). EQA promotes both quality improvement and standardisation of the

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test procedures. Both EQA and internal quality control (IQC) are essential elements to good

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laboratory practices. It is in the interest of the laboratories to assess their performance,

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especially using proficiency tests, since it allows them to evaluate their performance vis-a-vis

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their peers and is a valued step towards certification and accreditation. It also gives assurance to

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the customers that the results they get are the right ones.

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The International Analytical Group, Section Feeding Stuffs (IAG) has many years of

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experience in conducting proficiency tests (also called ring test) for European feed analysis

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laboratories. In 2014 and 2015 the Food and Agriculture Organization of the United Nations

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(FAO) invited laboratories from developing countries for participation in this exercise. FAO’s

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vast networks in different countries were used to deliver the samples to feed analysis

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laboratories located in different parts of the world. The main goal of conducting the proficiency

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test was to enable laboratories to assess and improve their feed analysis performance. The IAG

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had also conducted the proficiency test in 2014 and 2015 for European countries using the

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same test samples as used for developing countries laboratories. As a result of this proficiency

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test, it was considered pertinent and prudent to compare the performance of the two groups of

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countries: European and developing countries; and to use the results of the study to develop

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strategies and means to enhance quality of data emerging from feed analysis laboratories.



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Materials and Methods

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Test material

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The proficiency tests are generally conducted with one or two common samples, which are

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used by all participating laboratories. The four samples (two per year) used for the proficiency

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test were commercial products available in 20 kg paper bags.

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Sample 1; 2014 and 2015: Green meal pellets. This feed belonged to the category of forages

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and roughages and this matrix was especially selected for analysis of different fibre fraction

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parameters. Analyses conducted on this sample were the classical proximate parameters

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including mineral and trace elements.

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Sample 2; 2014: Compound feed for pigs. This was a compound feed for weaned piglets (25%

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inclusion rate). Besides analysis of classical proximate parameters this compound feed was

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especially intended for the analysis of different feed additives (enzymes, vitamins, organic

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acids and antioxidants). For the proficiency test this sample was spiked with formic acid (2%)

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and butylated hydroxytoluene (BHT) (50 mg/kg). In developing countries there were too few

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laboratories that analyzed these parameters; therefore, theses parameters were not taken for

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comparisons in this paper.

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Sample 2; 2015: Complete feed for poultry. Besides analysis of classical parameters this mixed

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feed was especially selected for the analysis of different feed additives (enzymes, vitamins,

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organic acids, coccidiostats and antioxidants).

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The content of the 20 kg paper bags (7 in number) was thoroughly mixed, subsequently split in

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portions of about 0.7 kg9, which were filled into plastic bags and sealed. The samples were sent

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to the participating laboratories by express means and these were received in the laboratories


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within 15 days of dispatch. The homogeneity of the samples was tested and validated according

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to Thompson and Wood10 by determining protein, crude fibre and fat using a near infrared

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spectroscopy (NIRS) apparatus (Foss NIR Spectrometer InfraXact Lab) and selected elements

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using ICP-MS respectively. The laboratories stored the samples in a cool and dark place. The

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analyses were completed within one month of receiving the samples. The analyses conducted

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by the participants are given in tables, which were for general chemical constituents, macro

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minerals, trace elements, feed additives and amino acids. Metabolizable energy estimation was

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also included. Laboratories were asked to specify the analytical methods applied. Most

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laboratories followed this requirement. Either the laboratories referred to the international

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standard methods (e.g. ISO, EU or AOAC) or described the method.

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Proficiency test

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The proficiency test by the International Analytic Group (IAG) is conducted annually with two

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samples (green meal and compound feed) and is organized by the Institute for Animal Nutrition

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and Feed, Austria Agency for Health and Food Safety (AGES). This proficiency test is

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conducted without prescribing any methods of analysis in order to evaluate the laboratories

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competence. Public and private laboratories were invited to participate. All laboratories are

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provided with excel-sheets for reporting the analytical results. Each result should be the mean

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of such a number of replicates the laboratory normally uses in feed analysis, expressed on dry

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matter basis and reported with 3 significant digits in the units indicated. The participants are

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asked to give information about the methods used by them in a standardized format, comprising

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of three parts. Part 1 seeks information on: Digestion and extraction techniques; Part 2 on

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Separation, pre-concentration and clean up; and Part 3 on Method of detection. Alternatively



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the applied international standard procedure could be reported. All laboratories followed the

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standard published protocols.

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This proficiency test is being conducted since 1977 and a number of European laboratories

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have been participating in the test on a regular basis. Based on the results of the proficiency

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tests for many years, it can be assumed that the participating laboratories have integrated

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quality control systems and follow good laboratory practices and generate quality data.

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In 2014 and 2015 proficiency tests was organized jointly by FAO and IAG and conducted by

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AGES. The laboratories participated in the proficiency test in response to an open invitation

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sent through various FAO networks and the FAO website. The main aim of this joint test

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between European and developing country laboratories was to enable laboratories in

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developing countries to assess and improve their feed analysis performance. Both groups of

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laboratories, European and developing countries were provided with the same samples for the

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proficiency test. The annual IAG proficiency test (European laboratories) was separately

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evaluated and discussed. The data of European and developing countries were evaluated

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together, in order to get a good population of laboratories and a holistic view of the results.

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Additionally, an extra internal evaluation was done for the comparison of both groups of

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laboratories.

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In 2014, in total 113 laboratories from 42 countries participated in the study, the distribution of

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which was: developing countries, 73 laboratories from 28 developing countries; European

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countries, 40 laboratories from 14 European countries. These laboratories were both from

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public and private sectors. Several laboratories which were neither from Europe nor from

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developing countries (e.g. Australia) were excluded for the comparison. In 2015, in total 113

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laboratories from 49 countries participated. In this year, for comparison of the two groups 63


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laboratories from 27 developing countries and 50 laboratories from 22 European countries were

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taken.

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Statistical evaluation

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As part of the proficiency test the following parameters were calculated: average, standard

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deviation, coefficient of variation. Before calculating these parameters, Dixon outlier test11 with

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critical values, at 5% error, as listed in Wernimont12 was used to identify outliers, which were

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eliminated from the calculation.

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The calculated parameters are described below:

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Average of all results (x) as: x =

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Standard deviation (SD) measures the amount of variation or dispersion from the

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average.

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Coefficient of variation is a relative measure of dispersion. It is the ratio of the

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standard deviation to the mean (x) expressed as a percentage. It provides information

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of variation in percentage comparable to other parameters.

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=

∗ 100

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Results which are outside the mean ± 2 x standard deviation boundaries were marked with an

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"s" (so called straggler) but remain in the set of evaluation data. Values presented as ‘below the

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limit of detection’ were not taken into account in the evaluation.

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Average and standard deviation are accepted as assigned values for calculation of the z-value13.



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An often used quantitative criterion for the evaluation of the laboratory performance is the z-

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score14 and the International Harmonized Protocol of the Proficiency Testing of Analytical

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Chemistry Laboratories15. This value was calculated by dividing the difference between the

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laboratory result (xlab) and the mean of all results of the ring test (xRT) for a particular parameter

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by the standard deviation.

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z-score =

| |

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The following internationally accepted classification is used:

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| z | ≤ 2: satisfactory result

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2 < | z | < 3: doubtful result

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| z | > 3: unsatisfactory result

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Using this approach, all doubtful or unsatisfactory results must be critically examined by the

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laboratory staff for all possible sources of error. Measures should be taken to avoid errors and

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improve results.

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For the detection of the significant difference between the means and the standard deviations of

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the two groups Students-t test and F-test were used respectively16. Both tests were applied with

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a probability of 95%. Following the international protocol for proficiency tests17 only

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parameters with equal or more than 8 participants were taken into account for the comparison

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between the two groups. The parameters for which data were available from 6 and 7

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participants have also been given the table but not taken for comparison.

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RESULTS


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The evaluation of the proficiency test results show that the number of laboratories of

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developing countries analysed mainly proximate analysis of feed constituents and macro-

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minerals. The laboratories that also analysed trace elements and amino acids were few.

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Carotenoid, xanthophyll, sugar, selenium, mercury, nickel, fluoride, iodine, vitamin D3,

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enzymes, antioxidants, coccidiostats were analysed only in 3 laboratories in developing

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countries at a maximum; and hence not included in the proficiency tests..

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Proximate analysis

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Tables 1 and 2 present mean and standard deviation (SD) for chemical composition data for the

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forage and the compound feed sample, respectively.

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For the forage sample mean values differed significantly (P < 0.05) only for acid detergent

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lignin (ADL) in 2014; and for crude ash and crude fibre in 2015. The CV for ADL observed

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was as high as 89% (Table 1). Determination of ADL occurs in several steps and is therefore

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prone to errors. Challenges associated with determination of fibre fractions and higher

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deviations in determination of these parameters have also been observed in previous ring tests

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conducted by IAG. High values of CV for ADL (up to 42%) have been reported18,19. Amongst

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the fibre fractions, generally highest CV has also been observed for ADL in other proficiency

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tests for feed analysis19,20. Points worthy to note are significant differences between SD of the

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two groups and overall higher SD for the values obtained by developing country laboratories

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(Table 1). Only SD of protein (Dumas) was comparable in 2015 between European and

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developing country laboratories.

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For the compound feed sample, means for crude fibre, crude ash and HCl insoluble ash (a

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measure of soil/sand contamination) in 2014 and protein (Kjeldahl), crude ash, HCl insoluble

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ash and, crude fat and metabolizable energy in 2015 differed significantly for the two groups



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of laboratories. While for all parameters the SD values were significantly different between the

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two groups and always higher for developing countries, suggesting higher spread of values for

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these countries. This is also reflected in several fold higher coefficient of variation (CV) of

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values obtained by developing country laboratories (Table 2). Only SD of protein (Dumas) was

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comparable between the two groups of the laboratories in 2015.

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Macro- and trace-minerals, feed additives and amino acids

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A pattern similar to that reported for proximate analysis parameters was obtained for both

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macro- and trace-minerals in both samples: mean values are statistically similar for most of the

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analyses (except for iron, copper and two amino acids in 2014; potassium, iron, copper and

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three amino acids in 2015) but SD values are significantly different. Standard deviation as well

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as CV values are several folds higher for developing country laboratories (Tables 3–6).

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Amino acid also followed the similar pattern (Table 7), measured only for the compound feed

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sample. A number of feed additives such as Vitamin A, E and D3, phytase, BHT, organic acids,

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coccidiostats were not or seldom analysed in developing countries.

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A further indicator for the difference between European and developing country laboratories is

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the percentage of outliers. In 2014 for the forage sample the outliers for European and

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developing country laboratories were 2.9% and 7.6% respectively; and for the compound feed

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sample the outliers were 3.0% and 8.7% respectively. In 2015 for the forage sample the outliers

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for European and developing country laboratories were 4.2% and 7.7% respectively; and for

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the compound feed sample the outliers were 7.0% and 9.5% respectively (data not shown in

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tables). Greater numbers of outliers were produced by developing country laboratories.

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DISCUSSION

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This is the first time that a proficiency test for feed analysis has been conducted in a systematic

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manner and at an extensive scale for developing country laboratories. The objective was to

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evaluate the laboratory performance of participating laboratories, aid them in locating

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systematic errors, which is often hard to achieve by other means, and identify assays that need

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improvement in the participating laboratories. The basic elements of a proficiency test are that

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the test samples sent through a proficiency testing programme should not be given any special

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treatment and that the analyses on them should be conducted following the same set of

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procedures including the number of replicates and the standard methods as for the samples

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analysed in routine. These internationally accepted basic elements formed the basis of the

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current study. Also following the internationally accepted procedure, the assays that had z

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values of 2 < | z | < 3 and | z | > 3 were identified as the ones producing doubtful and

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unsatisfactory results, which were informed to the laboratories to critically examine for all

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possible errors and follow good laboratory practices and integrate quality control methods as

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described in 13.

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Higher SD, several fold higher CV of almost all analyses, higher percentage of outliers for

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developing country laboratories, and a significant difference (P < 0.05) for SD between

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European and developing country laboratories for a large number of analyses (Tables 1–7)

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demonstrate that there is a greater need for developing country laboratories to improve the

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quality of analysis. As some examples the CV of ADL, crude fat and acid insoluble ash (2014

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forage sample) of 88%, 40% and 13% in developing countries are unacceptably high. Similarly

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CV of micro-minerals of 40 to 98% in developing countries illustrate a big scope for

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improvement in the manner these assays are being conducted (Tables 1–7). It may be noted that

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in proficiency tests there is no obligation regarding methodology used in the assays since the

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main issue is to test the performance of the procedures that are in routine use at each laboratory.



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As for 2014, the data analysed from the proficiency testing in 2015 showed higher number of

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outliers for developing countries. The data also illustrated higher standard deviation and several

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folds higher coefficient of variation for assays conducted in developing country laboratories.

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For developing country feed analysis laboratories the coefficients of variation for chemical

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composition parameters, macro-minerals and micro-minerals were higher by up to 2.4-fold,

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2.7-fold and 2.7-fold respectively, compared with those for European feed analysis laboratories

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for the forage sample (the values for 2014 being 4.2-fold, 8.4-fold, and 4.1-fold respectively);

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and for the compound feed the values for chemical composition parameters, macro-minerals,

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micro-minerals and amino acids were higher by 2.6-fold, 2.3 fold, 2.5-fold, and 4.5-fold for

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2015 and 3.4-fold, 3.3-fold, 1.8-fold, and 5.0-fold for 2014 respectively (Table 8). Unlike in

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2014, the standard deviation of protein determined by Dumas method was comparable between

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the two groups of the laboratories in 2015 (Tables 1 and 2). Although lower variations in the

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results were obtained in 2015, but still the variations for developing countries are higher than

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for European countries. Again highlighting that developing countries need to pay higher

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attention towards integrating quality control systems and follow good laboratory practices in

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feed analysis laboratories. A similar proficiency test is currently taking place and it is also

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planned to follow the progress of the laboratories that participated in all three proficiency tests.

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In the past, at a number of fora, experts visiting developing country laboratories have expressed

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the need to introduce the use of internal control samples in the analyses and for them to follow

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good laboratory practices such as regular standardization of spectrophotometers, frequent

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calibration of balances, pipettes and thermometers, and use of properly washed glassware,

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among others. The results of this study corroborate these views originated from field

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observations, and illustrate the need to strengthen quality assurance systems in these

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laboratories. Without a robust quality control system, the laboratory personnel are unable to

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evaluate the quality of the results being generated.


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Implications for developing countries

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Developing countries need to address this issue seriously because it could adversely impact

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their export and increase wastage of feed and food items for not meeting the quality and safety

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standards. Contaminated feed has often resulted in food of animal origin being recalled and/or

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destroyed, causing huge economic losses and negatively affecting food security.

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Generation of sound data is fundamental to implementation of nutritional principles and for

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getting benefits from them. Also for sustainable development of the livestock sector, generation

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of sound chemical composition data of feed ingredients and mixed or compounded feed is vital.

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Feed industries are neither able to resource good quality ingredient nor prepare balanced feeds

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without having reliable chemical and nutritional value data on feed ingredients.

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In addition, without sound data on chemical composition, precision feeding or balanced feeding

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approaches that demand nutrient provision, as per the nutrient requirements of the animal

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cannot be used. Unbalanced feeding results in lower profit to farmers, production below the

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genetic potential of animals, reproductive problems for example longer first age of calving and

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longer calving interval, animal being more prone to metabolic diseases such as milk fever and

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ketosis, shorter productive life, poor animal health and welfare, and excessive amounts of

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pollutants released to the environment6,7. Feeding of unbalanced diets could have severe

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implications for developing countries and this could be avoided by generating good data.

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Furthermore, ensuring good laboratory practices will enhance the health and safety of the

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laboratory workers, protect the environment from laboratory-discharged pollutants and increase

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efficiency of the laboratories in developing countries. Other spin-offs will be enhanced research

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and education capabilities of students graduating from R&D institutions, promotion of a better

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trading environment between developing and developed countries, increased quality of

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research, and meeting of the requirements of international standards. It will also enhance



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confidence of the customers towards analytical laboratories serving them that all technical,

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administrative and human factors that influence the quality of the results being generated are

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under continuous supervision with the aim to prevent non conformity and identify opportunities

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for improvement.

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Suggested measures to improve quality of the data

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Governments should consider increasing investment for improving laboratory infrastructure

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and laboratory proficiency. Development of sound training programmes and their effective

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execution is warranted. Higher donors’ attention to this issue and provision of greater funds for

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developing capacity of laboratory personnel are needed.

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Many functional laboratories can improve the quality of the data without much investment. A

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culture of generation of quality data needs to be practiced through a change of mindset that

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generation of any data is not important. This can be achieved by integrating quality control

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systems and following good laboratory practices. FAO has taken a number of steps in this

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direction: produced various manuals on quality control systems in feed analysis

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laboratories13,21,22; distributed them at no-cost; made them available on FAO website for free

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downloading, arranged on-line courses23 on risk management and quality control enhancement;

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and by organized proficiency tests every year. FAO has also been providing relevant

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educational support to the laboratories through its network of experts24. However, these efforts

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need to be translated into a movement within countries by formulating government policies, for

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example setting up of a body overseeing quality of data being generated by laboratories and the

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laboratory operations; and by supporting laboratories through investments and capacity

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development. The efforts of International organizations will only be catalytic towards


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furthering a culture of generation of quality data. Large impact can only be generated by

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government actions and policies, including promoting public-private partnerships.

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The donors, besides supporting efforts that enhance quality control systems in laboratories,

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should also demand putting in place of a proper control mechanism for the data being generated

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by the laboratories in the framework of their sponsored projects. Similarly, journals while

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considering the work for publication in the journal should also seeking information from

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authors on the quality control set up in their research laboratories. Certification and

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accreditation of laboratories by an outside agency may be encouraged but most laboratories in

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developing countries do not have funds to achieve this. Use of internal standards will enable the

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laboratory personnel to evaluate if they are producing quality data. Also a creation of a network

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of laboratories within a country and running of annual in-country proficiency tests would

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contribute to furthering the proficiency of laboratories, at a relatively low cost.

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The progress made in improving the quality of data generated could be assessed, both at the

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levels of a country and participating laboratory, by annual monitoring of z-scores. External

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quality assessment or proficiency testing and internal quality control are critical to ensuring

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quality of test practice. These processes permit laboratories to monitor and assess their long-

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term performance, which could be compared to those of peer laboratories for proficiency

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testing.

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The results discussed here have implications for both monogastric and ruminant production

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systems. The unsound data could adversely impact trade, increase feed wastage, render

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precision feeding ineffective, and make feed industries incapable to resource good quality

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ingredients or prepare good feeds. Unbalanced feeding results in: decrease in profit to farmers,

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production below the genetic potential of animals, reproductive problems, metabolic diseases,

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shorter productive life, poor animal health and welfare, and excessive amounts of pollutants



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released to the environment. Spin-offs of improved data quality will be enhanced research and

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education capabilities of students. The study will enable putting in place mechanisms in

378

developing countries to improve quality of chemical composition data of feed resources.

379

In conclusion, the chemical composition values of animal feeds, which reflect their nutritional

380

value, have a wide variation around the mean and a higher percentage of outliers for developing

381

countries. The quality of data from feed analysis laboratories in developing country laboratories

382

needs improvement. Developing countries need to address this issue seriously because it could

383

adversely impact their export and increase wastage of feed and food items by not meeting the

384

required quality and safety standards. Investment in improving skills of laboratory staff and

385

laboratory infrastructure, coupled with putting in place a practice of following good laboratory

386

practices, are expected to improve the quality and reliability of the data. Also there is a need to

387

further strengthen already ongoing training programs and to establish formal training programs,

388

if they do not exist, for laboratory staff that provide effective training on methods for

389

determination of chemical composition of feeds and feed ingredients. Technical Without sound

390

data, countries will not be able to fully exploit the benefits, in terms of enhanced animal

391

productivity, decreased environmental pollution and other social benefits, of using sound

392

nutritional concepts and practices in the livestock sector.

393 394

ACKNOWLEDGEMENT

395

We thank FAO representative in the countries of the participating laboratories for their help in

396

making available the proficiency test samples to the participating laboratories. Also thanks to

397

Claudia Pecanka from AGES; Antonella Falcone and Enrico Masci from FAO Headquarter in

398

Rome; and FAO Representatives in countries in which the participating laboratories were


Page 18 of 30

Page 19 of 30


399

located for their excellent support in distribution of the proficiency samples. Authors also thank

400

Dr. Johan De Boever for useful suggestions.

401 402

REFERENCES

403

1. Hersom, M.J. Opportunities to enhance performance and efficiency through nutrient

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synchrony in forage-fed ruminants. J. Anim. Sci. 2008, 86, E306–E317. 2. Delgadillo, J.A.; Martin; G.B. Alternative methods for control of reproduction in small

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ruminants: A focus on the needs of grazing industries. Animal Frontiers 2015, 5, 57–65.

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3. FAO. Impact of animal nutrition on animal welfare – Expert Consultation 26−30 September

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2011 – FAO Headquarters, Rome, Italy. Animal Production and Health Report. No.1.

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Rome 2012. (http://www.fao.org/docrep/017/i3148e/i3148e00.pdf) (30 May 2016).

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4. Flachowsky, G.; Kamphues, J. Carbon footprints for food of animal origin: What are the

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most preferable criteria to measure animal yields? Animals 2012, 2, 108–126. 5. Nahm, K.H. Efficient feed nutrient utilization to reduce pollutants in poultry and swine manure. Critical Rev. Environ. Sci.Technol. 2012, 32, 1–16.

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6. Garg, M.R.; Sherasia, P.L.; Bhanderi, B.M.; Phondba, B.T.; Shelke, S.K,; Makkar, H.P.S.

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efficiency, feed nitrogen use efficiency, rumen microbial protein supply, parasitic load,

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immunity and enteric methane emissions of milking animals under field. Anim. Feed Sci.

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Technol. 2013, 179, 24–35.

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7. Makkar, H.P.S. Precision feeding – a developing country perspective, in Precision livestock farming ’13, Berckmans, D.; Vandermeulen, J. Eds. 2013, pp. 95–105.

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8. Beever, D.E.; Drackley, J.K. Feeding for optimal rumen and animal health and optimal feed

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conversion efficiency: The importance of physical nutrition, in Optimization of feed use

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11. Wernimont, G.T. Use of Statistics to Develop and Evaluate Analytical Methods (W. Spendley, editor), AOAC, Arlington, VA. 1985, Table A-9, p. 156. 12. FAO. Quality assurance for animal feed analysis laboratories, FAO Animal Production and

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13. ISO/IEC-17043. Conformity Assessment – General requirements for proficiency testing, International Organization for Standardization, Geneva, 2010. 14. ISO-13528. Statistical methods for use in proficiency testing by Inter-laboratory Comparison, International Organization for Standardization, Geneva (2005).

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15. Thompson, M.; Ellison, S.R.; Wood, R. The international harmonized protocol for the

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proficiency testing of analytical chemistry laboratories (IUPAC Technical Report). Pure

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Appl. Chem. 2006, 78, 145–196.

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16 Sachs, L. Angewandte Statistik, Anwendung statistischer Methoden, Springer; 7. Auflage (5. März 1992). 17. Horwitz, W. Protocol for the design, conduct and interpretation of collaborative studies. Pure Appl Chem 1988, 60, 855–864.

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Nutrizione Animale 1992, 17, 285–295.

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19. Bovera, F.; Spanghero, M.; Galassi, G.; Masoero, F.; Buccioni A. Repeatability and

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determinations. Ital. J. Anim. Sci. 2003, 2, 41-50.

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Table 1 Chemical composition obtained for the forage samples in 2014 and 2015 by the two groups of laboratories (values without parentheses are for 2014, while values in square parentheses are for 2015) European countries

Protein Kjeldahl, g/kg

No. of labs 25 [30]

Mean

SD (CV)

112 [145]

2.50x (2.23) [2.38x (1.65)] x

Developing countries No. of labs 59 [49]

Mean

SD (CV)

113 [143]

9.05y (8.04) [8.54y (5.98)] y

Protein Dumas, g/kg

19 [27]

114 [148]

3.08 (2.70) [7.71 (5.21)]

11 [7]

115 [144]

5.00 (4.37) [6.50 (4.51)]

Crude fibre, g/kg

22 [30]

261 [250]

8.16x (3.13) [11.9x (4.77)]

49 [43]

256 [246]

27.4y (10.69) [21.9y (8.93)]

ADFOM, g/kg

20 [25]

318 [313]

17.8 (5.60) x [21.4 (6.83)]

37 [25]

321 [314]

42.3 (13.20) y [44.4 (14.16)]

NDFOM, g/kg

20 [26]

521 [488]

17.0x (3.26) [24.6x (5.03)]

36 [25]

540 [478]

52.2y (9.65) [44.7y (9.35)]

ADL, g/kg

20 [17]

50.7 [59.7]

a

5.39 (10.63) x [5.17 (8.67)]

27 [15]

79.8 [54.1]

70.6 (88.56) y [21.3 (39.44)]

Crude ash, g/kg

30 [37]

115a a [110 ]

3.94x (3.41) x [2.33 (2.11)]

65 [58]

111b b [105 ]

5.60y (5.03) y [5.68 (5.41)]

16

49.0

2.28 x (4.65)

23

49.2

6.51y (13.23)

[21]

[29.0]

[1.6 (5.53)]

[21]

[29.4]

[3.72 (12.66)]

25

26.1

2.79 x (10.71)

43

29.5

11.8 y (40.13)

[33]

[30.6a]

[3.8 x (12.4)]

[43]

[33.5b]

[7.82 y (23.31)]

HCl insoluble ash, g/kg

Crude fat (including hydrolysis), g/kg

x

x

x

b

y

y

y

473 474 475 476 477 478

ADFOM , acid detergent fibre corrected for organic matter; NDFOM, neutral detergent fibre corrected for organic matter; ADL, acid detergent fibre; values in round parentheses are coefficient of variation (CV) in % . n, number of observation Within a row, means with different superscripts a and b differ at P

Proficiency Testing of Feed Constituents: A Comparative Evaluation

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