Benchmarking Growth Performance and Feed Efficiency
of Commercial Rainbow Trout Farms in Ontario, Canada
by
James Owen Skipper-Horton
A Thesis
presented to
The University of Guelph
In partial fulfilment of requirements
for the degree of
Master of Science
in
Animal and Poultry Science
Guelph, Ontario, Canada
© James Owen Skipper-Horton, May, 2013
ABSTRACT
BENCHMARKING GROWTH PERFORMANCE AND FEED EFFICIENCY
OF COMMERCIAL RAINBOW TROUT FARMS IN ONTARIO, CANADA
James Owen Skipper-Horton Advisor:
University of Guelph, 2013 Professor Dominique P. Bureau
Ontario cage culture operations produce the majority of farmed rainbow trout in Canada, using a diverse
range of management practices that are expected to result in substantial variation in trout performance
across the industry. A preliminary survey of performance data was undertaken, resulting in data from 5
commercial sites between 2008 and 2012. Commercial performance was somewhat poorer than expected,
particularly for mortality rates, thermal-unit growth coefficients, and economic feed conversion ratios
(average weighted values of 12%, 0.165, and 1.36, respectively). Substantial variability in all
performance parameters within and across operations suggests that continued production monitoring and
benchmarking could be highly valuable for improving the economic sustainability of the sector. For
future benchmarking efforts to be effective, improvement and standardization of data collection methods
is needed. As such, a number of recommendations are provided to the industry for the refinement and
standardization of performance recording protocols used by Ontario producers.
iii
DEDICATION
This thesis is dedicated to Ontario cage growers, whose hard work and dedication to the
betterment and well-being of the industry and the environmental sustainability of its operations far too
often goes unnoticed and unappreciated.
iv
ACKNOWLEDGEMENTS
First and foremost, I would like to thank my advisor, Dominique Bureau, for his steadfast support
and continuous encouragement throughout this undertaking. His immense confidence in the abilities of
his students is a vital component of their successes at Guelph and throughout global aquaculture
thereafter. Thank you to the Ontario cage growers who provided data for this project. It is safe to say
that this project would not have been possible without their assistance and generosity. Thank you to my
committee members Andy Robinson and Roy Danzmann for their guidance, particularly during the
editing of this thesis. Thank you to Flavio Schenkel and Gord Vander Voort for their time and effort in
the development of this project’s statistical aspects. Thank you to the wonderful people of the Fish
Nutrition Research Laboratory for their striving to cultivate a warm, inviting, and entertaining work
environment. Thank you to my parents Gord Horton and Brenda Skipper and my siblings Katharine,
Rachel, and Graeme Skipper-Horton. Your love is the most important thing in my life, and I carry it with
me wherever I go. I must also thank all of my friends for continuing to inspire and challenge me to be a
better person. Thank you to Brenden Hurley and Adam Wray for teaching me the art of inquiry and the
joy that it offers. Thank you to Jason Hanson and Dominic Raiola for their years of unquestionable work
ethic. Finally, for providing financial support for the completion of this project and my development as a
researcher I must also thank both the OMAFRA Highly Qualified Personnel and the MITACS Accelerate
programs.
v
TABLE OF CONTENTS
1 - GENERAL INTRODUCTION .......................................................................................................................... 1
1.1 - Objectives ......................................................................................................................................... 2
2 - LITERATURE REVIEW .................................................................................................................................. 3
2.1 - Introduction ...................................................................................................................................... 3
2.2 - Overview of Rainbow Trout and Its Production in Ontario ............................................................. 3
2.3 - Challenges for the Ontario Trout Industry ........................................................................................ 5
2.4 – Benchmarking .................................................................................................................................. 8
2.5 - Animal Recording Systems .............................................................................................................. 9
2.6 - Benchmarking and Animal Recording Systems in Aquaculture .................................................... 14
2.7 - Thesis Objectives ............................................................................................................................ 16
3 – MATERIALS AND METHODS ........................................................................................................................ 17
3.1 - Data Survey .................................................................................................................................... 18
3.2 - Database Structure and Organization ............................................................................................. 18
3.3 - Use of Excel Functions ................................................................................................................... 19
3.4 - Experimental Units (Lots and Lotgroups) ...................................................................................... 19
3.5 - Commercial Lot ID Codes .............................................................................................................. 20
3.6 - Interval (INT) and Cumulative (CUMUL) Values ......................................................................... 22
3.7 - Database Performance Variables and Parameters of Interest ......................................................... 23
3.7.1 - Initial Inventories (ININVENT, # fish; ININVENTHATCH and ININVENTBC, # fish) ..... 24
3.7.2 - Temperature (TEMP, °C) ........................................................................................................ 25
3.7.3 - Degree Days (DD) ................................................................................................................... 26
3.7.4 - Initial Body Weight (IBW, g) .................................................................................................. 26
3.7.5 - Final Body Weight (FBW, g) .................................................................................................. 26
3.7.6 - Average Body Weight (ABW, g) ............................................................................................ 27
3.7.7 - Mortalities (MORTS, # fish) ................................................................................................... 29
3.7.8 - Transfer In (TRANSFIN, # fish) ............................................................................................. 30
3.7.9 - Transfer Out (TRANSFOUT, # fish) ....................................................................................... 30
3.7.10 - Harvests (HARV, # fish) ....................................................................................................... 30
3.7.11 - Inventory (INVENT, # fish) .................................................................................................. 31
3.7.12 - Standing Biomass (STANBIOM, kg) .................................................................................... 32
3.7.13 - Initial Biomass (INBIOM, kg; INBIOMHATCH and INBIOMBC, kg) ............................... 32
3.7.14 - Harvested Biomass (HARVBIOM, kg) ................................................................................. 32
3.7.15 - Biomass In (BIOMIN, kg) ..................................................................................................... 32
3.7.16 - Biomass Out (BIOMOUT, kg) .............................................................................................. 33
vi
3.7.17 - Biomass Gain (BIOMGAIN, kg) ........................................................................................... 33
3.7.18 - Mortality Rate (MORTRATE, %; MORTRATEHATCH and MORTRATEBC, %) ........... 34
3.7.19 - Thermal-Unit Growth Coefficient (TGC, Growth Rate) ....................................................... 34
3.7.20 - Model-Based ABW Estimations ............................................................................................ 36
3.7.21 - Feed Served (FEEDSERV, kg) .............................................................................................. 37
3.7.22 - Feed Served/Fish (FEEDFISH, g/fish) .................................................................................. 39
3.7.23 - Feed Type (FEEDTYPE, FEEDID) and corresponding Feed Type Amount
(FEEDTYPEAMNT, kg) .................................................................................................................... 39
3.7.24 - Protein Served (PROSERV, kg) ............................................................................................ 40
3.7.25 - Protein Distributed/Fish (PROFISH, g/fish) .......................................................................... 41
3.7.26 - Nitrogen/Fish (NFISH, g/fish) ............................................................................................... 41
3.7.27 - Economic Feed Conversion Ratio (ECONFCR; ECONFCRHATCH and ECONFCRBC) .. 42
3.7.28 - Biological Feed Conversion Ratio (BIOFCR) ....................................................................... 42
3.7.29 - Nitrogen Retention Efficiency (NRE) ................................................................................... 43
3.8 - Comparison of Traditional and Modified Thermal-Unit Growth Coefficient Models ................... 43
3.9 - Modelling Upper and Lower Size Bounds in Relation to Model-Estimated ABW ........................ 44
3.10 - Data Smoothing for Regression Analysis ..................................................................................... 45
3.11 - Weighted Averages ....................................................................................................................... 46
3.12 - Statistical Analysis ....................................................................................................................... 46
4 - RESULTS .............................................................................................................................................................. 47
4.1 - Survey Summary ............................................................................................................................ 47
4.2 - Summary of Production Parameters ............................................................................................... 52
4.2.1 - Sample Sizes ............................................................................................................................ 52
4.2.2 - Initial Inventory ....................................................................................................................... 52
4.2.3 - Initial Body Weight ................................................................................................................. 53
4.2.4 - Final Body Weight ................................................................................................................... 53
4.2.5 - Days ......................................................................................................................................... 54
4.2.6 - Temperature ............................................................................................................................. 55
4.2.7 - Thermal-Unit Growth Coefficient ........................................................................................... 55
4.2.8 - Mortality Rate .......................................................................................................................... 59
4.2.9 - Feed Distribution ..................................................................................................................... 63
4.2.10 - Economic Feed Conversion Ratio ......................................................................................... 64
4.2.11 - Biological Feed Conversion Ratio ......................................................................................... 66
4.2.12 - Nitrogen Retention Efficiency ............................................................................................... 68
4.3 - Site Rankings .................................................................................................................................. 71
4.4 - Comparison of Traditional and Modified Thermal-Unit Growth Coefficient Models ................... 72
4.5 - Upper and Lower Size Bounds in Relation to Model-Estimated ABW ......................................... 73
vii
5 - DISCUSSION ....................................................................................................................................................... 75
5.1 – Conclusion ..................................................................................................................................... 85
6 – GENERAL DISCUSSION AND INDUSTRY RECOMMENDATIONS ....................................................... 87
7 - REFERENCES ..................................................................................................................................................... 94
8 - APPENDIX ......................................................................................................................................................... 100
8.1 – Glossary ....................................................................................................................................... 100
viii
LIST OF TABLES
Table 4.1: Descriptive statistics of various production parameters, analyzed across sites. ......................................... 48 Table 4.2: Average site values for various production parameters .............................................................................. 49 Table 4.3: Average site values for various production parameters, calculated for each cohort year (ie. year in which
cohort or series of common lotgroups was initially stocked) .............................................................................. 50 Table 4.4: Average values for various production parameters, calculated using total lot and lotgroup values (ie. from
stocking to completion of harvest). ..................................................................................................................... 51 Table 4.5: Results of least squares analysis of fixed effect (ie. site) on global lot values for various performance
parameters ........................................................................................................................................................... 57 Table 4.6: Results of least squares means for multiple comparisons of fixed effect levels (ie. sites) for global lot
thermal-unit growth coefficient values.. ............................................................................................................. 57 Table 4.7: Results of least squares means for multiple comparisons of fixed effect levels (ie. sites) for global lot
mortality rates (MORTRATEHATCH) .............................................................................................................. 61 Table 4.8: Commercial Feed IDs, percent crude protein and crude lipid for each of the feed types served across sites
during the study period. ...................................................................................................................................... 63 Table 4.9: Rankings of each commercial site for various production parameters ...................................................... 71 Table 4.10: Comparison of the residual sums of squares between producer-estimated average body weights and
corresponding model predictions, the latter performed using either traditional or modified thermal-unit growth
coefficient (TGC) models. .................................................................................................................................. 72
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LIST OF FIGURES
Figure 4.1: Average lot initial inventory for each site ................................................................................................. 53 Figure 4.2: Box plots of final average body weights from all lots of each site. ........................................................... 54 Figure 4.3: Histogram of thermal-unit growth coefficients from lots of Site A and all other commercial sites
combined. ............................................................................................................................................................ 58 Figure 4.4: Box plots of lot thermal-unit growth coefficients from each site ............................................................. 58 Figure 5.5: Average lot mortality rates for each site.................................................................................................... 61 Figure 4.6: Median values and 10
th and 90
th percentiles of live interval mortality rates (ie. aggregated across sites,
divided into 50 day periods of grow-out) ............................................................................................................ 62 Figure 4.7: Median values of live interval mortality rates and corresponding water temperatures (ie. aggregated
across sites, divided into 50 day periods of grow-
out)……………………………………………………………………… ........................................................... 62 Figure 4.8: Box plots of lot economic feed conversion ratios for each site ................................................................ 65 Figure 4.9: Box plots of lot biological feed conversion ratios for each site.. .............................................................. 67 Figure 4.10: Median values and 10
th and 90
th percentiles of live interval biological feed conversion ratios (ie.
aggregated across sites, divided into 50 g body weight stanzas) ........................................................................ 67 Figure 4.11: Live interval biological feed conversion ratios, aggregated across sites. ................................................ 68 Figure 4.12: Box plots of lot nitrogen retention efficiency values for each site. ......................................................... 70 Figure 4.13: Live interval nitrogen retention efficiency values, aggregated across sites............................................. 70 Figures 4.14a and 4.14b: Producer-estimated average body weights from Site A and expected corresponding size
distribution ranges, plotted relative to model-predicted average body weights .................................................. 74
1
1 - GENERAL INTRODUCTION
The global production of fed aquatic species has increased at an annual rate of approximately
eight percent since 1980 (FAO, 2012), now generating almost 50% of all fish and shellfish consumed
globally (FAO, 2012). The global aquaculture industry is incredibly diverse, with over 250 species being
produced to varying scales by more than an estimated 500,000 operations. Management strategies (eg.
feeds and feeding strategies, genetic backgrounds, etc.) and environmental conditions (eg. water
temperature, dissolved oxygen, etc.) tend to vary from one site to the next. While industry growth is
expected to continue throughout the coming decades, aquaculture operations continue to face a number of
common challenges which collectively threaten the industry’s economic sustainability. Feed and
production costs continue to rise while product prices stagnate, diminishing operational profit margins.
Public scrutiny towards the safety of consumed aquatic products and potential environmental impacts
continues to increase. To ensure the sustainability of the aquaculture sector, solutions are needed to
produce more efficient, faster-growing, and disease-resistant animals that result in high-quality, safe, and
marketable products for consumers.
Decades of experience in other terrestrial livestock sectors have demonstrated the value in the
systematic, standardized, industry-wide recording and evaluation of animal performance (ie. animal
recording systems). Standardized animal recording systems permit systematic, reliable comparisons of
performance achieved by animals within and across operations, a function referred to as benchmarking.
Through comparing various performance parameters of one operation against those of its competitors,
benchmarking facilitates identification of operational strengths and weaknesses as well as the levels of
performance achieved across an industry (Giacomini, 2008; McDougal, 2012). Benchmarking ultimately
provide producers with the context needed to understand the true extent of their operational performance.
Given the immense diversity present in global aquaculture operations, there exists tremendous potential
value for aquaculture producers in performance benchmarking.
2
The Ontario rainbow trout industry provides a good example of the operational diversity
characterizing aquaculture. Of the 4,060 total annual tonnes produced in Ontario, approximately eighty
percent is raised on a small number of open-water cage sites situated in the North Channel and Georgian
Bay of Lake Huron. Across these sites there is considerable diversity in management strategies and
environmental variables (eg. feeds and feeding strategies, stocking and harvest sizes, inventory
adjustment schemes, water temperature profiles, etc.). Performance measuring and recording techniques
currently used in Ontario are highly variable amongst producers, particularly with respect to body weight
and temperature sampling. As a result of this operational diversity and the resulting difficulty in merging
idiosyncratic datasets there has to date been no attempt to survey commercial performance data across
Ontario. In absence of information, there has been no systematic evaluation of trends in trout
performance, thus precluding any attempts at performance benchmarking. Producers thus lack
perspective as to the breadth of trout performance achieved across the industry, and thus their own
position along this spectrum. Therefore, there is an immediate need within the Ontario rainbow trout
industry for systematic surveying and evaluation of commercial performance data.
1.1 - Objectives
The general objective of this thesis was to carry out a preliminary, systematic survey of
performance data from Ontario trout cage culture operations and to examine variability within and across
production sites. In addition to this, preliminary analysis of commercial data with current mathematical
growth and linear mixed models will be performed. These exercises would also serve to highlight the
challenges associated with the collection and analysis of idiosyncratic commercial farm data. With an
inventory of the functional limitations of the collected data, this thesis would then be able to offer
recommendations to the industry for the refinement and standardization of recording methods to improve
upon future data quality and its suitability for evaluative models and benchmarking schemes.
3
2 - LITERATURE REVIEW
2.1 - Introduction
The global production of fed aquatic species has increased at an annual rate of approximately
eight percent since 1980, now generating approximately 50% of all fish and shellfish consumed globally
(FAO, 2012). Aquaculture is a young and globally diverse industry currently comprised of more than 250
species produced by more than 500,000 small, medium, and large operations around the World (Rana,
1997). Production strategies vary significantly within and amongst these species and operations, with
differences in feed types and feeding strategies, scales of production, culture practices, genetic
backgrounds, and environmental conditions. The industry is expected to continue its growth throughout
the coming decades (FAO, 2012). However, there are many common challenges to the profitability of the
varied operations that are increasingly compromising the industry’s economic sustainability. The costs of
feed and production for aquaculture operations are ever increasing, in most cases without proportionate
increases in product prices and/or productivity, restricting operational profitability. Meanwhile, there is
increasing public scrutiny as to the safety of consumed aquatic products and the environmental effects of
their production, limiting their market value and creating conflicts between multiple users of a given body
of water.
2.2 - Overview of Rainbow Trout and Its Production in Ontario
Rainbow trout is the most extensively cultivated cold freshwater species in the World, with global
2011 production estimated at approximately 770, 000 metric tons, or $3.84 billion (FAO, 2013). Since
the early 1980s, when the cage culture of trout was first introduced to Lake Huron, Ontario has produced
the majority of rainbow trout (Oncorhynchus mykiss) in Canada. While a variety of production systems
(ie. cages, tanks, raceways, and ponds) contribute to the sum of Ontario’s production, approximately 80%
4
of the 4,060 total annual tonnes is raised on a small number of cage farms (Statistics Canada, 2011),
where trout are raised from fingerlings to market size. Ontario cage systems provide superior water
quality and historically suitable water temperature profiles, permitting greater stocking densities and
ultimately greater farm profitability.
Rainbow trout are raised to a market size of approximately one to two kilograms, after which the
majority are processed and sold as fillets in Ontario and the US. Trout are fed mostly extruded pellets,
sometimes formulated to contain levels of nutrients specific to a given life stage or production strategy;
trout diets distributed in Ontario may contain anywhere from 34-52% protein and 16-26% lipids. Feed is
manufactured mostly by a company in Ontario and a small group of companies located on the East Coast.
Feed companies use an ever-changing blend of fisheries-, animal by-product-, and plant-based ingredients
(eg. fish meal, fish oil, poultry by-products, feather meal, wheat, corn gluten meal, etc.), the relative
proportions of each depending on a number of physical, biological, and economic considerations.
There is considerable diversity in management strategies and culture environments within and
across Ontario sites. The open-water cages in which Ontario rainbow trout are raised, situated in the
North Channel and Georgian Bay of Lake Huron, experience extreme fluctuations in water temperatures
and environmental conditions over time and at varying depths and locales. Fingerlings are purchased
from a number of private hatcheries which have, for the most part, independently developed their
breeding programs. Fingerlings are stocked and market fish harvested each over a wide range of average
body weights. Stocking densities and inventory adjustment schemes (eg. grading, splitting) exhibit
marked differences amongst sites. Distributed feeds tend to vary amongst sites in ingredient and nutrient
compositions, while feeding strategies also differ (eg. number of times per day, hand vs. mechanized
feeding, feeding guidelines, etc.). Such varying input mixes are likely to result in substantial differences
in trout performance (eg. growth rates, feed efficiency, and potentially mortality rates) within and
amongst sites.
5
The monitoring and evaluation of performance variables is inherently more challenging when
raising animals in open bodies of water. For instance, determining weight distributions within a cage is
complicated when only a small proportion of the animals can be seen or weighed at any given time.
Similarly, determining the amount of feed consumed within a cage is challenging when the amount of
uneaten feed cannot be observed or weighed. Water temperatures fluctuate constantly within an Ontario
trout cage, due for example to the presence of thermoclines and the movement and mixing of water that
occurs within such large bodies of fresh water as Georgian Bay. Temperature extremes also vary greatly
from one season to the next, with surface waters typically frozen in the winter and reaching maximum
temperatures of 20-25°C in the summer.
With such challenges to the accurate monitoring of relevant performance parameters, Ontario
trout producers are believed to have developed recording methods that function to serve their own needs
in site management and that are compatible with the tools and resources available to them. For example,
producers are suspected to sample water temperatures at varying depths and at varying times of day, as
permitted by their own schedules. There are also a variety of methods used within the industry for
estimating the average body weight of a cage, some of which are believed to result in selection bias. For
instance, some producers offer small amounts of feed into their cages during body weight sampling to
entice trout to the water’s surface, at which point the fish are captured with a dip net for subsequent
weighing. It is believed that feed enticement might select for more aggressive and thus larger fish (Gord
Cole, personal communication, July 2011).
2.3 - Challenges for the Ontario Trout Industry
Although market prices for trout have increased somewhat in recent years, they have for the most
part remained stagnant since the 1980s, in part due to competition from the extensively produced Atlantic
salmon. In fact, prices for trout were lower from 2001-2005 than from 1996-2000 (Cummings et al.,
6
2007). This coupled with rising feed and production costs have contributed to diminishing producer
profit margins. As a result, improvements to productivity (eg. growth rates) and feed efficiency are goals
common to all producers.
Optimizing management strategies to achieve maximum fish performance is complicated in open-
water cage settings by a range of dynamic biological and environmental variables and their interaction.
That is, a number of exogenous and endogenous factors (eg. water temperature, feed composition,
species, strain, life stage, etc.) and other management variables (eg. feeding strategies, stocking size,
stocking density, etc.) contribute and interact to influence fish performance at any one time. The effects
of such factors and management variables and their interactions on fish performance have thus been the
focus of significant research efforts across the World (Azevedo et al., 2004a; Dumas et al., 2007a; Dumas
et al., 2007b; Encarnação et al., 2006; McKenzie et al., 2012; Overturf et al., 2012; Wilkinson et al.,
2010).
Knowledge gleaned from these types of experimentally-based studies has helped global
aquaculture producers achieve substantial gains to the productivity and feed efficiency of their operations
(Asche et al., 1999; Muir, 2005; Naylor et al., 2009). However, producer application of these scientific
advancements is complicated in commercial settings by an array of biological and environmental factors
that interact to affect or limit the intended outcomes or benefits of such knowledge transfer. Ontario
producers thus need solutions that are tailored to the idiosyncrasies of their own production environments.
Ideally, commercial-scale trials comparing production systems, fish strains, feeds, and feeding strategies
could be performed locally as needed to better support farm management decisions. However,
commercial-scale studies would be impractical and cost-prohibitive (Parker, 1998). Therefore, solutions
are needed to continuously improve performance and productivity of trout culture operations in a manner
which is responsive to dynamic and fluctuating production environments and market demands.
The consumer market for Ontario trout demands a product of a certain size and quality, available
on a year-round basis. Producers tend to target specific market weights to meet the demands of their
7
buyers and to capture particular price points. For producers to provide processing plants with expected
dates of harvest, thus enabling processing plants to coordinate harvest schedules amongst cage culture
operations to ensure a steady flow of product to the marketplace, producers will often attempt to forecast
(ie. model) the growth of their fish throughout the course of grow-out periods. However, failure to
achieve modelled growth targets and anticipated harvest schedules often result in producers having to
prematurely harvest their fish in order to provide their processors, and ultimately the marketplace, with a
timely product. Early harvests might come at a cost to the producer through smaller trout sizes and thus
lower product prices. Therefore, there is a need for refinement of current growth models using
commercial data. Furthermore, while larger fish will typically sell at a higher price, the most profitable
harvest weight is by no means certain. Experimental evidence suggests that feed efficiency decreases as
trout approach maturation or market size (Azevedo et al., 2004), although the extent of this process in
commercial settings is not known. As such, exploration of longitudinal trends in commercial feed
efficiency data is now needed in order to support optimization of this trade-off between market prices and
feed efficiency.
With the diversity in management strategies and environmental conditions observed amongst
Ontario cage culture operations, there is expected to be similarly large variability in performance
parameters within and across sites. While producers track the performance of their own fish throughout
each production cycle, there has been little collaborative effort and sharing of data amongst producers in
this regard. As a result, there are no industry standards for the performance traits which they hope to
improve, and thus no understanding of their own performance relative to these standards. A systematic
survey of performance data from Ontario cage culture operations is now needed. Not only would this
permit the aforementioned investigations into longitudinal growth and feed efficiency data, but would
most importantly offer producers insight as to the variability in performance experienced across the
industry. With access to industry standards and ranges in performance parameter values across sites,
producers would have yardsticks against which they could compare their own production performance.
Such across-site performance comparisons (ie. benchmarking) would provide producers with immediate
8
perspective and context as to the true level of their performance, ultimately helping to guide and prioritize
management interventions and the efficient use of limited resources.
2.4 – Benchmarking
The comparison of organizational performance against that of competitors and/or accepted standards
and modelled estimations is a function known as benchmarking (Giacomini, 2008; McDougal, 2012).
Such comparative analyses provide important perspectives for organizations. That is, while most
producers will evaluate their own performance internally, their true performance might not be fully
understood or appreciated until compared with that of others outside of the organization (Bilbrey, 2012).
Benchmarking serves to highlight both the strengths and weaknesses of an organization, helping
decision-makers identify their operational areas most likely to gain from management intervention
(Giacomini, 2008). Typically, producers are provided with minimum and maximum levels for a range of
indicators (eg. biological, economic, etc.) and their position or ranking along this spectrum (Giacomini,
2008). Benchmarking thus serves as an unbiased analytical tool for identifying the facets of operation
with the greatest economic potential for improvement (Giacomini, 2008). It is a systematic data-yielding
process whereby producers continuously isolate and identify superior performance either from within or
outside the organization, striving to understand and adapt the practices contributing to this performance
(American Productivity and Quality Centre, 1997).
When applied in the context of established standardized recording and evaluation frameworks,
benchmarking can be an effective analytical tool for identifying an organization’s strengths and
weaknesses, and thus improving its performance incrementally over time (McDougal, 2012). A common
concern with benchmarking is that there is limited value in comparisons amongst firms with idiosyncratic
performance recording methods or with differing organizational structures and management strategies
9
(Parker, 1998). In these instances, the effectiveness of strategy analysis might be limited by differences
amongst organizations in the relative value of their inputs or by misrepresentation of relative values
through irregular or non-standardized recording and evaluation schemes (Parker, 1998). Thus, to enable
“apples to apples” comparisons and effective benchmarking, it is essential that not only the methods used
to measure and record performance parameters be consistent amongst organizations, but so too the
models and methods for evaluating and comparing performance (McDougal, 2012).
2.5 - Animal Recording Systems
There are many challenges to accurately monitoring and evaluating the performance of aquatic
animals and their highly variable environmental conditions. As a result, recording methods of such
industries as the Ontario trout cage culture industry comprise substantial differences amongst operations.
Differences amongst cage culture operations in recording methods and management practices (eg. genetic
backgrounds, stocking densities, feeding practices, timing of initial cage stocking, lengths of grow-out
periods, etc.) pose substantial challenges to systematic and effective performance benchmarking.
However, such challenges in irregular recording methods and non-standardized datasets are not unlike
those that once limited improvements to the performance and economic viability of terrestrial livestock
industries in the former half of the 20th century. The collection of early genetic information on dairy
animals began over 125 years ago with the establishment of breed associations (Agriculture and Agri-
Food Canada, 2009). While breed associations were aware of the potential for identifying superior
genetics, they were knowingly limited by a lack of established methods for measuring animal
characteristics that would permit proper ranking of animals over time (Black, 1936). A seminal paper on
the state of livestock was anonymously published in 1935, outlining the factors most limiting livestock
improvement and breeding, “paralyzing movement toward any practical goal” (Harris, 1998). These
factors are likely limiting the effective performance benchmarking of aquaculture operations today:
10
The use of standards that are incomplete and in some cases inaccurate.
Lack of yardsticks to supplement or revise existing standards.
Large gaps in the knowledge of animal genetics.
Factors limiting experimentation, namely costs.
Following WWII, the newly created Food and Agriculture Organization (FAO) sought to
overcome these limitations, capturing the potential value in global standards for animal recording
methods and the resulting wealth of data from subsequent collaboration amongst breeders. Shortly
thereafter, the FAO thus created the International Committee for Animal Recording (ICAR) to realize
these benefits. With a primary goal of comparing animal performance across regions and identifying
superior genetics, the committee, comprised of participating countries’ many breed organizations and
academic institutions, was to mobilize and exchange available expertise and address technical issues
limiting the adoption of standardized recording systems for dairy cattle (Rosati, 2011). ICAR has since
served as a platform for sharing the learned experiences of member organizations in performance
recording and evaluations (Boulesteix et al., 2004; Cromie et al., 2008; Wickham, 2008), benchmarking
(Baier, 2008; Giacomini, 2008), and genetic improvement services (Norman et al., 2008; Schaeffer, 2008;
Woodward and Van der Lende, 2008). By providing requirements to ensure a satisfactory level of
uniformity in recording and evaluation techniques, ICAR has enabled and supported decades of work in
performance comparisons and genetic evaluations that have been essential to the concurrent
improvements in animal performance and productivity.
As summarized by the FAO (1998), “animal recording is a systematic process that leads to
outcomes that facilitate a comparison of production alternatives [ie. benchmarking], the availability of
baseline information on the performance of animals, animal management decisions and genetic
improvement that are beneficial to the governments and policy-makers, producers and by extension the
consumers.” The development of recording systems for extensive animal production sectors occurred
11
over decades in conjunction with that of other production technologies (Holst, 1999; Djemali, 2004). The
development of recording systems provided the foundation and structure for concurrent development and
dissemination of almost all high productive cattle and swine breeds raised today (Bougler, 1990; Harris,
1998). As a result of these successes, ICAR’s portfolio of species has since expanded to include beef
cattle, sheep, goat, and buffalo (Rosati, 2011); ICAR is now represented by 87 member organizations
(eg. breed associations, producer cooperatives, academic institutions, etc.) from 51 countries (Rosati,
2011).
An animal recording system is a critical tool for farm management, comprised of a series of
systematic processes involving the collection of information on animals, entry of data into a standardized
database, data processing and evaluation, and the interpretation and distribution of results (Flammant,
1998). Such interpretation of results is typically done in the context of comparing multiple operations and
their various production scenarios, improving management or optimization of production inputs,
identifying animals to be bred for more productive future generations, and/or other related extension
services (FAO, 1998; Flamant, 1998; Wasike et al., 2011). All such components of recording systems
can be performed by one or many organizations, with any combination of private, public, or academic
entities fulfilling these roles.
It is well established that animal recording systems are the primary technologies driving
extension services (eg. benchmarking, genetic improvement, etc.) for continuous and sustainable
improvements to the productivity and profitability of animal production sectors (Mackechnie, 1995).
There are many examples of recording systems and benchmarking programs established across the World
that have successfully demonstrated their usefulness in improving the economic viability of industries.
The Canadian dairy industry established its Record of Performance (R.O.P.) program in 1905 to permit
unbiased performance evaluations. This benchmarking program has since expanded in popularity to
include recording of over 70% of dairy cattle raised in Canada today (Agriculture and Agri-Food Canada,
2009). LAMPLAN is a genetic evaluation system involved with the recording of over 100,000 Australian
12
meatsheep per year (Banks and Kinghorn, 1997). The program began with a single district officer
measuring growth rate and backfat thickness, processing the data, and providing advice to producers
(Harris, 1985). Boer goat is bred for its meat in South Africa and distributed internationally, the
successes of its production tied to genetic improvement enabled by a standardized recording system and
database framework (Holst, 1999). The French dairy sheep industry employs ICAR procedures in its
improvement program, achieving annual gains in milk yield of up to 2.4%, values similar to those of
dairy cattle (Barillet et al., 1996; Holst, 1999).
With the widespread adoption and successes of animal recording systems and enabled extension
services, there is great incentive for producers to invest in such frameworks. However, economic
incentives have not always proven sufficient for such investments, as recording systems typically require
significant human, economic, and technical capital, and provide relatively minimal short-term economic
returns (Garrick and Golden, 2008; Geroski, 1995; Wasike et al., 2011). For example, employees must be
trained in any implemented measurement and recording techniques and indoctrinated in the culture of
continuous improvement. Upgrades to infrastructure are required to comply with established performance
recording protocols, and technicians must be hired to train and review on-farm recording techniques, to
update and maintain computer databases, and to process and evaluate commercial data for the purposes of
benchmarking and other such extension services (Baier, 2008).
The preliminary adoption and establishment of recording systems and evaluative frameworks
within an animal sector is further challenged by the distinct and unique characteristics amongst producer
methods for measuring and recording performance. In absence of an established animal recording
system, producers develop methods that are compatible with the resources available to them and that
serve their own objectives in day-to-day operations (Wasike et al., 2011). Such idiosyncrasies often result
in records appearing “incoherent” relative to one another, complicating not only the aggregation of
datasets into standardized databases, but also the systematic process of treating data and applying it to
evaluative models and frameworks (Wasike et al., 2011). As producers continue to function within their
13
own unique recording schemes in lieu of established protocols, differences in recording methods and
dataset structures are reinforced and added to over time, further challenging the eventual merging of past
records (Garrick and Golden, 2008; Mocquot et al., 2004; Parker, 1998; Wasike et al., 2011).
Inconsistencies or gaps in datasets may also lead to the loss of valuable data following their
integration into a standardized database, limiting the short-term value of extension services and
improvement programs (Garrick and Golden, 2008). For example, the National Beef Consortium
attempted to standardize database frameworks for across-herd evaluations amongst breed associations in
the early 2000s (Garrick and Golden, 2008). Its hope was to transition the sector’s “data-driven”
approach to improvement (ie. adding parameters to databases as they are needed) to a goal-driven one, for
example by including parentage information in all frameworks for across-breed EPD evaluations (Garrick
and Golden, 2008). Without standardized registration systems across breed associations, it was
determined that multiple identification codes for individual parents would preclude the complete use of
their data in evaluations, leading to wasted information (Garrick and Golden, 2008). Thus far, a lack of
leadership and common objectives amongst breed organizations has indeed prevented homogenization of
registration systems and complete use of information.
Such obstacles to the adoption of standardized animal recording systems have been partially
overcome in the past through collaborations between breeder associations and/or producer cooperatives
with academic institutions, beginning in the 1970s (Harris, 1998; Zimmerman, 2008). Such
collaborations provide mutual benefits in that researchers gain access to valuable swaths of commercial
data on which to apply and further develop their breeding techniques, statistical models, etc., while
producers in turn benefit from these academic advancements and their practical application. With such
functions being performed by academic institutions, and also with governments occasionally providing
funding for the costs of framework implementation (eg. animal recording infrastructure, integration of
datasets into standardized databases, commercial-scale growth trials, etc.), such collaborations often help
minimize the initial financial barriers that may discourage producers from participating in animal
14
recording systems (Garrick and Golden, 2008). In the past, swine breed associations have partnered with
federal research services and Purdue University (Stewart et al., 1991), while American dairy coops and
the US National Beef Consortium have collaborated with four separate land-grant universities (Garrick
and Golden, 2008; Zimmerman, 2008). The University of Guelph had for decades performed genetic
evaluations for the conformation traits of dairy cattle in coordination with the provincial government and
the Holstein Association of Canada (Lazenby and Stanley, 1997). This partnership enabled substantial
improvements to the methodologies and statistical procedures for genetic improvement strategies (eg.
linear mixed models, random regressions, test day models, etc.).
2.6 - Benchmarking and Animal Recording Systems in Aquaculture
The state of standardized performance recording and evaluation in aquaculture is not unlike that of
livestock in the former half of the 20th century. Indeed, the factors discussed as limiting livestock
improvement in 1935 (ie. the use of standards that are incomplete, lack of yardsticks to supplement or
revise existing standards, etc.) are today limiting that of global aquaculture. For most regions, species,
and/or production systems there has been little to no attempt to standardize performance recording and
evaluation methods. There exist few commercial yardsticks or benchmarks for any culture variety.
National or interregional databases for performance data do not exist, perhaps outside of the Federation of
European Aquaculture Producers (F.E.A.P.). Where performance recording systems do exist, they are
typically established by a company as a proprietary tool for internal benchmarking and production
planning.
The lack of benchmarking programs and animal recording systems in aquaculture is due largely
to a few main factors. First, aquaculture is a relatively young industry. For example, the Norwegian
salmon farming industry was one of the first to develop modern intensive cage culture operations, the
advancement of its practices initiated only in the 1970s (Coull, 1993). The animal recording systems used
15
in extensive terrestrial livestock sectors are, alternatively, the result of over a century of progress in the
field; progress paralleled by concurrent industry growth. Secondly, aquaculture is the most diverse form
of animal production in the World with over 250 species cultured (Rana, 1997). As has occurred with
terrestrial livestock sectors, local and/or regional differences in producer goals and objectives has limited
the sorts of interregional collaborations in aquaculture that are required for standardization of recording
systems, database frameworks, and evaluation schemes. Finally, the development of national breeding
programs has been limited in aquaculture, with only 8.2% of 2010 global production derived from
improved stocks (Gjedrem et al., 2012; Neira, 2010; Rye et al., 2010). While breeding programmes are
not the only motivation for establishing standardized recording systems, they indeed necessitate and are a
large impetus for such efforts (Garrick and Golden, 2008; Rosati, 2011).
The diversity characterizing aquaculture – both of cultured species and production environments – is
both a limiting factor and impetus for performance benchmarking and the adoption of standardized animal
recording systems. Similar to the present extent of diversity in genetics, culture techniques, and
environmental conditions within global aquaculture is the extent of diversity in its recording and
evaluation systems. This variety in recording methods not only complicates the merging of past datasets
into standardized databases, but also the process of systematically treating and applying data to
standardized models and frameworks for performance evaluations and benchmarking (Wasike et al.,
2011). However, the diversity amongst production and management environments also creates
tremendous potential value for performance benchmarking. For every cultured species there is
considerable global variety in production techniques and animal performance, providing vast assemblages
of benchmarks and alternative management scenarios against which producers can compare their own
performance, isolate superior management strategies, and eventually identify superior genetics. As was
captured long ago in other animal sectors, the potential value in recording systems and comparative
benchmarking must now be captured in aquaculture.
16
There have to date been relatively few published accounts of performance benchmarking in
aquaculture. Bolton-Warberg and FitzGerald (2012) performed preliminary analyses on growth
trajectories of different North Atlantic cod strains to compare prospective value for producers. They
assessed and compared such simple parameters as days-post-hatch to harvest weight, and developed
growth models to project growth trajectories of the various cod strains in a range of temperature profiles.
Soares et al. (2011) developed a system for benchmarking weekly mortality rates on Scottish Atlantic
salmon farms, applying the parameter as an indicator of animal health. Production data has been used for
examination of economic functions and optimizations, risk analysis and related functions (Guttormsen,
2002), analysis of inefficiencies (eg. Asche et al., 2009), and for guiding of policy development (Asche,
1997), but little has been done in terms of practical benchmarking of performance criteria.
While the private sector has begun providing services and technologies in performance recording
and management (eg.Fishtalk™ by Akva (Europe), Aquabench® (Chile)), benchmarking services remain
limited or simplistic in their capabilities. Furthermore, corresponding methodologies and results of
benchmarking efforts are not made available to the public. Documentation of experiences and challenges
in the preliminary performance benchmarking of aquatic species is valuable to the global industry given
the ubiquitous variability in current recording and evaluation methods and the inevitable complications
arising from benchmarking of idiosyncratic datasets. Cataloguing of lessons and experiences amongst
ICAR members has been fundamental to their development of recording systems and methodologies for
benchmarking functions (Guellouz et al., 2004; Sattler, 2008), ensuring advancement of the most current
state of the art.
2.7 - Thesis Objectives
There is a need for performance benchmarking of the Ontario trout industry to provide producers
perspective as to the range of values achieved across the industry and their own performance relative to
17
this. As such, the initial objective of this thesis was to perform a preliminary, systematic survey of
commercial performance data for Ontario trout and to examine variability within and across operational
sites. This thesis would also initiate the application of data to current mathematical growth and
nutritional models in order to explore the potential for model refinement with commercial data. In
addition to this, preliminary evaluation of longitudinal (ie. time-series) data for various performance
parameters (eg. feed efficiency, mortality rates, etc.) would provide further insight into trends in
commercial data and the limitations of the dataset when applied to evaluative models. These exercises
would thus serve to illustrate the functional shortcomings of the current Ontario dataset and the highly
irregular sampling and recording methods from which it was derived. With a working knowledge of the
dataset’s practical limitations, this thesis would then conclude with recommendations to Ontario
producers as to the adjustment and standardization of their recording methods in order to enable
continuous improvement to the quality and value of the commercial dataset for purposes of performance
benchmarking.
18
3 – MATERIALS AND METHODS
*Glossary provided in Appendix 1.
3.1 - Data Survey
Data was gathered from five commercial open-water cage sites located in Georgian Bay, Ontario,
from September 2008 to June 2012. Included in the dataset were ancillary values reported by affiliated
hatcheries and processors/processing plants over the same period of time. Data from one experimental
cage site (Experimental Lakes Area (ELA)), in operation from 2003 to 2007, was also included in the
analysis for comparison.
3.2 - Database Structure and Organization
The preliminary surveying and evaluating of performance data from Ontario trout cage culture
operations was the primary goal of this thesis. This being a novel effort, it was necessary to first design
and construct a database framework in Microsoft Excel for the storage and evaluation of data, and to
develop associated methodologies for the systematic refinement and incorporation of data into this
framework. Methods for recording and evaluating trout performance in Ontario have never been
systematically coordinated for means of benchmarking and animal improvement. The survey’s resulting
dataset thus provides a valuable exercise in testing the adaptability of the database framework to variable
production settings and in applying the dataset to integrated mathematical growth and nutritional models
(eg. Fish-PrFEQ system (Cho and Bureau, 1998)), any associated challenges providing insight into
practical limitations of the dataset and current commercial recording methods. Protocols for treating and
19
incorporating into the database the many idiosyncrasies amongst producer sampling and reporting
methods will be discussed where appropriate with descriptions of performance variables below.
Ontario trout producers often report data at different time intervals (eg. daily, monthly). To reconcile
these differences and to facilitate evaluations at varying time scales (eg. descriptive summary statistics,
time-series models, etc.), the performance database was structured into three spreadsheet formats, each
representing production lots at a different time scale:
1) Daily Format (DAILYFORM), in which each day of grow-out (ie. stocking to harvest) is
designated its own row/entry. Each site is designated its own DAILYFORM spreadsheet.
2) Management Event Format (MANEVFORM), in which data is summarized for intervals of time
between average body weight sampling events, fish movement events (eg. transfers, size grades,
etc.), and hatchery- or processing plant-reported body weights. Each event is designated its own
row/entry and each site its own MANEVFORM spreadsheet.
3) Summary Format (SUMFORM), in which data is summarized in one row/entry for the complete
grow-out stage of a given lot (ie. “global” values, from cage stocking to harvest). Data from all
sites is compiled into one SUMFORM spreadsheet.
3.3 - Use of Excel Functions
A number of Excel functions were used to facilitate inter-format functioning and compatibility.
For example, ROUND was used to maintain consistency amongst formats in number of decimals. Other
important functions will be discussed as appropriate with descriptions of performance variables below.
3.4 - Experimental Units (Lots and Lotgroups)
In Ontario cage culture, trout are typically stocked in relatively large numbers into one cage and
subsequently split or graded (ie. ordered into separate cages according to specific size class/grade) into
20
multiple other cages as the fish increase in size. There were thus two classes of production units in this
thesis considered as “experimental units” for purposes of performance evaluation: “lots” (ie. cages, from
initial stocking to harvest), for which fish are tracked and evaluated through a single cage over time, and
“lotgroups” that had once comprised a common singular cage at time of stocking and are evaluated as a
singular aggregate of lots as they are split and redistributed over time. To summarize certain variables by
lot from stocking to harvest, a number of corollary assumptions and data modifications were required to
utilize data from periods of time in which multiple separate lots shared a singular cage. For example, if a
cage was graded into two empty cages whose data was to be summarized on a per lot basis, feed and
mortality totals from the initial period of time as one shared cage had to be divided amongst its
succeeding two lots. Periods of time before or between fish movement events will subsequently be
referred to as “inter-movement periods.” To verify and/or validate results across lots, variables were thus
also assessed across aggregate lotgroups for comparison, the calculation of which did not require
accessory assumptions and data alterations.
3.5 - Commercial Lot ID Codes
Unlike with other animal production systems (beef, dairy, swine, sheep, etc.), fish cannot be cost-
effectively identified individually on a commercial scale. Fish were thus identified by a) the country in
which their grow-out cycle occurred, b) their species, c) their hatchery/strain, d) the site of their grow-out,
and e) the cage(s) or tank number(s) in which they were held during grow-out (ie. from stocking as
fingerlings to harvest at market size). To explain the commercial lot ID system, an example ID code from
the Ontario dataset will be provided and described sequentially in a stepwise manner, beginning with
identification of the country in which fingerlings were raised. Unless explained otherwise, all listed
hatchery and site alphanumeric codes were unique to this system, developed and assigned to
21
hatcheries/sites as part of this thesis.
Example ID Code:
124-OM-E2E3E1-C05093-2-5-6
Piecemeal description of ID code:
“124”
As is used by ICAR, the ISO 3166-1 numeric system was used by this system to identify country
(ie. of grow-out). “124” is the code for Canada.
“124-OM”
Species code was listed after country code, “OM” representing Oncorhynchus mykiss (rainbow
trout).
“124-OM-E2-C05093”
Fingerlings purchased for grow-out were sold by a hatchery designated as “E2,” “E” being the
letter assigned to the company name, “2” being the company’s specific hatchery site.
Fish were stocked into Site C in May of 2009 (C0509). Month and year were listed directly after
site letter(s).
Fish were stocked initially into cage 3, as recorded by the site technician (C05093).
Tank/lot/cage numbers assigned by site technicians were retained for use in this system to avoid
confusion. Initial cage number was listed following date of stocking.
“124-OM-E2E3-C05093-2”
A group of fish from 124-OM-E2-C05093 were moved into cage 2 (C05093-2). A hyphen was
used to signal the movement of fish, its new cage number listed thereafter.
A group of fish from a cage other than cage 3 was also stocked into cage 2 at the same time, its
hatchery code identified as E3 (E2E3). In instances such as these, where an empty cage was
stocked with fish from multiple other cages, or an existing group of fish was supplemented with
fish from another cage, the ID code of the fish group with the largest standing biomass was
retained and assigned to all fish sharing the same cage. If the smaller group of fish had a
22
different hatchery code, as is the case in this example, the hatchery code of the smaller group was
added to the retained ID sequence (E2E3).
“124-OM-E2E3E1-C05093-2-5”
A group of fish from 124-OM-E2E3-C05093-2 was moved into cage 5 (C05093-2-5).
At the same time, a second group of fish (ie. with lesser biomass than the first) was also moved
into cage 5, thus assigned the ID code of the first group.
The second group of fish was from a different hatchery than the first, its hatchery code thus added
to the existing ID sequence (E2E3E1).
3.6 - Interval (INT) and Cumulative (CUMUL) Values
Performance variables were evaluated for individual lots in MANEVFORM on an interval and/or
cumulative basis. Interval values in MANEVFORM were calculated from one management event (eg.
weight sampling event, fish transfer, etc.) to the next, whereas cumulative values were calculated relative
to Day 0 (day of stocking). All values in SUMFORM were inherently cumulative.
It was not possible to calculate cumulative values for lotgroups in MANEVFORM. That is,
cumulative values in MANEVFORM were calculated up to the date of a specific management event. As
the multiple lots of a particular lotgroup would have had differing management event schedules, there
were generally no common dates to which cumulative lotgroup totals could have been calculated.
Cumulative values in MANEVFORM were either simple running totals of a variable (eg. feed/fish,
degree days), or more elaborate calculations referencing the cumulative totals of other variables (eg.
thermal-unit growth coefficients, economic feed conversion ratio, biological feed conversion ratio, etc.).
Following the movement of fish into an empty cage, formulas for all cumulative totals of the new group
had to be manually adjusted to reference those of its previous cage on the day of fish movement.
Estimating cumulative values for lots in MANEVFORM was complicated by frequent movement of
fish. For example, if an empty cage was filled with fish from three different lots on the same day, there
23
would be three cumulative values for a specific variable that could represent that of the new cage. It was
determined that in these instances, the cumulative total (and current lot ID, as previously described) of the
group with the largest standing biomass would be retained to represent that of the new cage. If a cage
was ever supplemented with fish from another lot, the lot receiving fish would retain its own cumulative
values to date, these values now also representing that of the supplementary group.
Values in SUMFORM were typically copied directly from cumulative totals on the day of reported lot
final body weight in MANEVFORM, unless calculated within SUMFORM or otherwise explained with
descriptions of specific performance variables below.
3.7 - Database Performance Variables and Parameters of Interest
Descriptions, Formulas, and Other Relevant Information
For all formulas listed, subscripts X, Y, and 0 represent values at the start of an interval, end of an
interval, and time of stocking (ie. day 0), respectively. Subscript INT refers to the value of a variable
summed or averaged over a given interval of time (ie. in MANEVFORM). Subscript CUMUL refers to a
cumulative value, totalled to the end of a particular interval. Subscript GLOBAL refers to a lot’s final
cumulative value (eg. thermal-unit growth coefficient, temperature, biological feed conversion ratio, etc.),
calculated from initial to final reported body weights. Subscript TOTAL refers to a value (eg. harvested
biomass (kg), feed distributed (kg), etc.) totalled from stocking to the completion of harvest, which at
times extended beyond the date of reported final body weight. Subscript WEEK refers to a value
summarized over a given week in DAILYFORM. Subscripts used in combination and separated by a
comma (eg. X,0) indicate that either value could be used depending on whether the formula is for interval
24
or cumulative calculations. In these instances, the first listed subscript is for interval calculations, the
second for cumulative.
3.7.1 - Initial Inventories (ININVENT, # fish; ININVENTHATCH and ININVENTBC, #
fish)
SUMFORM; lot and lotgroup totals
Initial inventory estimates (ie. number of fish at time of stocking or Day 0) were reported in
SUMFORM and used in calculation of other variables as described below (eg. mortality rates, economic
feed conversion ratio, etc.). Lot and lotgroup ININVENT was calculated both as hatchery
(ININVENTHATCH) and back-calculated (ININVENTBC) estimates, the former reported by hatcheries,
the latter calculated as the sum of harvested number of fish and mortalities within each lot and lotgroup.
ININVENTHATCH estimates were reported by hatcheries only for entire lotgroups (ie.
separation of lotgroups into succeeding lots occurs later in grow-out cycles at the discretion of site
managers). Furthermore, back-calculation of ININVENTBC estimates was performed in DAILYFORM
only for entire lotgroups, as the required separation of inter-movement period mortalities into their
succeeding lots was performed in SUMFORM. Thus, to estimate lot ININVENT values, lotgroup
ININVENTHATCH and ININVENTBC estimates were divided amongst their successive lots based on
ratios of lot inventories resulting from lotgroup divisions (eg. grading, splitting, etc.). A separate column
entitled “Inventory Post-Movement” was included in SUMFORM, the values copied directly from
corresponding lot Inventory values in MANEVFORM following lotgroup divisions. Inventory Post-
Movement values were in turn used in calculation of relative lot ratios, the resulting ratios multiplied by
corresponding lotgroup ININVENT values to estimate lot ININVENT values. These lot ININVENT
values were entitled “Ratio-Based Initial Inventory (Hatchery or Back-calculated)” in SUMFORM.
25
3.7.2 - Temperature (TEMP, °C)
DAILYFORM; daily, weekly, or monthly averages
MANEVFORM; interval averages
SUMFORM; lot averages
Surface temperatures, provided by producers, were entered daily in DAILYFORM, where
possible. Separate columns for TEMP at 5m and 10m were included in DAILYFORM, as there was
occasional sampling at these depths. In the case of reported surface freezing, surface temperatures were
recorded as 1°C.
Average interval temperatures were calculated in DAILYFORM for inclusion into
MANEVFORM. As fish are typically not fed the day before weight sampling, interval TEMP was
averaged from the day of one event up to and including the day prior to the subsequent event. Given the
sparseness of TEMP data provided by Site A, if an interval of Site A had less than four recorded
temperatures in a given week, weekly temperature averages throughout the entire interval were
calculated, the average of which then used to represent interval average. This was done to limit the
influence of data sparseness on final interval TEMP. If an interval had too infrequent TEMP sampling
(eg. consistently once or twice per week), or daily sampling at varying depths, the lot’s TEMP and growth
data was excluded from TGC analysis. Site A temp data was sparsely recorded at varying depths from
2008-2009, necessitating the use of data from the closest public water treatment plant, sampled at a depth
of 20m. From 2010 onwards, efforts in temp recording at Site A increased overall but remained
inconsistent.
26
3.7.3 - Degree Days (DD)
MANEVFORM; INT and CUMUL totals
SUMFORM; lot totals
Degree days were calculated on an interval basis in MANEVFORM as TEMPINT*DAYSINT.
3.7.4 - Initial Body Weight (IBW, g)
SUMFORM; lot values
Initial body weights were taken mostly from hatchery reports and reported in SUMFORM.
Hatchery estimations typically involve larger sample sizes (n) than do site estimations and were thus the
preferred source for IBW values. In the case where fish were stocked without a reported hatchery-
estimated body weight, the first producer-estimated body weight was selected as IBW, the date of this
estimate used as Day 0.
3.7.5 - Final Body Weight (FBW, g)
SUMFORM; lot values
Final body weights were taken from processing plant data and reported in SUMFORM. As with
IBW, processor-estimated values were suspected to be more accurate than producer-estimated values due
to their use of larger sample sizes. Harvesting processes in Ontario are not size-selective (ie. no pre-
harvest size segregation), so processor-reported average body weights were believed to be fairly
representative of the entire cage average on any given date.
The number of days required to harvest a cage depends on many factors. Occasionally a cage
might be partially harvested for further grow-out and completion of harvest at a later date. The reporting
27
over time of multiple processor-estimated body weights for a single lot complicates the reporting of its
FBW. FBW was thus reported as the first processor-estimated body weight after which no further grow-
out rations were distributed (ie. only maintenance feeding or <1% BW/day thereafter). It was believed
that summarizing values (eg. mortalities, feed, etc.) to this date permitted the most accurate representation
of trout performance to the range of market sizes currently targeted by Ontario producers.
Sites reporting data on a monthly basis still typically reported individual dates of harvest and
associated harvest data (eg. average body weight, harvested biomass (kg), etc.), assisting in the
identification of lot FBW. For these sites, it was assumed that there was no further distribution of grow-
out rations following a particular harvest date if there was no gap between subsequent harvest dates
greater than three days in length (Site I manager, personal communication, February 2012).
3.7.6 - Average Body Weight (ABW, g)
DAILYFORM; reported on day of sample
MANEVFORM; reported on day of sample
All hatchery-, site-, and processor-estimated body weights were included in DAILYFORM and
MANEVFORM. Model-based estimations reported by producers were not included. While the
calculations behind producer-estimated values were not always reported, average body weights were
typically calculated by producers as (total sampled biomass(kg)*1000)/n, regardless of the sampling
method.
ABW was reported chronologically in MANEVFORM. Unless a lot was partially harvested for a
subsequent period of further growth, the chronological sequence of a lot in MANEVFORM would
conclude with the first reported processor-estimated ABW to follow the completion of grow-out rationing
(ie. FBW). Following this, a second chronological sequence of all processor-estimated ABW values was
28
initiated for the same lot in MANEVFORM, beginning with the first reported processor-estimated ABW,
in order to investigate any trends in size variation during harvests.
If an event was reported in MANEVFORM without a corresponding producer-estimated ABW,
model-predicted ABW was entered to maintain proper functioning of the spreadsheet. Model-predicted
ABW was copied and pasted directly from corresponding values in MANEVFORM, using whichever of
the two growth models (ie. “traditional” and “modified” thermal-unit growth coefficient models, as
introduced below) that provided the best fit between its model ABW predictions and each site’s set of
producer-estimated ABW values, the methodologies for determining which are elaborated upon below.
As harvests are often prolonged events (ie. spanning multiple months), processor-estimated ABW
values were provided on a weekly basis in DAILYFORM as Harvested
Biomass(kg/WEEK)/Harvests(#fish/WEEK) and copied directly into the corresponding secondary
chronological sequence of MANEVFORM. If any two consecutive harvest days during the week of
interest were greater than 3 days apart, possible feeding/growth during this time required that the
calculation be separated and two processor ABW values be provided instead of one.
Most producers sample lots infrequently or on a very limited basis when they are nearing harvest,
and reported ABW around this time are often derived from processor reports. If a management event (eg.
fish movement) occurred near a harvest date without an accompanying ABW estimate, processor
estimates from the closest harvest date were used as the reported ABW in MANEVFORM, given that the
following two criteria were met: 1) that no grow-out ration was distributed between the two dates, and
that b) the two dates were no more than 3 days apart (as suggested by one site manager), assuming that a
larger gap could have permitted further feeding and/or growth.
For some sites reporting data in monthly totals, reports nearing harvest periods often included end
of month producer-estimated ABW that had in actuality been taken from the nearest available processor-
estimated ABW. The time gap between the two values was at times multiple weeks, limiting the value of
29
processor-estimated values in accurately representing end of month ABW values in MANEVFORM.
Furthermore, as a goal of this thesis was to explore the nature of data variability and potential bias in site
sampling methods, it was important that processor-estimated ABW values not be reported in
MANEVFORM as producer estimates. Mistakenly identifying these data points as producer estimates
might also confound other evaluations involving producer-estimated ABW (eg. producer-estimated
cumulative TGC, producer-estimated biological FCR, etc.). Therefore, if producer-estimated ABW
reported at the end of a month ever matched the nearest processor-estimated ABW to the nearest whole
number, the producer-estimated ABW was removed from MANEVFORM and replaced with a model-
predicted ABW (ie. only to maintain spreadsheet functioning).
3.7.7 - Mortalities (MORTS, # fish)
DAILYFORM; daily or monthly totals
MANEVFORM; INT and CUMUL totals
SUMFORM; lot and lotgroup totals
Interval mortalities were totalled in DAILYFORM, for copying into MANEVFORM, from the day
after one management event up to and including the day of the next. Totalling to the day of the secondary
event ensured that a complete emptying of fish from one cage would result in an inventory of zero in
DAILYFORM and MANEVFORM on the day of fish movement. MORTS reported in SUMFORM were
totalled until completion of harvest, not just until the date of reported FBW. Lots with 100% mortality
were excluded from all analyses.
If a site reported monthly totals and a reported fish movement event occurred between reports (ie.
on a day outside of the first or last of the month), total mortalities for that lot’s month were adjusted to a
daily rate (ie. morts/day, calculated as MORTSMONTH/DAYSMONTH), and mortalities up to and including
the day of the fish movement event were calculated as (morts/day)*DAYSINT.
30
Cumulative MORTS in MANEVFORM were separated by inter-movement period, rather than
totalling values from IBW to FBW. This was to facilitate subsequent division of inter-movement totals in
SUMFORM amongst the individual lots resulting from a given movement event. To do this, cumulative
inter-movement period totals in MANEVFORM were proportioned amongst their succeeding lots through
multiplication of a given total by corresponding lot inventory ratios.
3.7.8 - Transfer In (TRANSFIN, # fish)
DAILYFORM; daily totals
MANEVFORM; INT totals
Transfer In was reported as the number of fish transferred into a cage. If a transfer event
occurred over multiple days with no concurrent distribution of grow-out rations, TRANSFIN was totalled
for a single interval entry in MANEVFORM.
3.7.9 - Transfer Out (TRANSFOUT, # fish)
Transfer Out was summarized and reported in the same manner as TRANSFIN.
3.7.10 - Harvests (HARV, # fish)
DAILYFORM; daily totals
MANEVFORM; INT totals
Daily harvests were reported in DAILYFORM, in which interval totals were calculated for
31
copying into MANEVFORM. For sites reporting data on a monthly basis, HARV values were totalled
for each month and reported in MANEVFORM at each month’s end. For all other sites (ie. daily
reporting), the sum of all fish harvested from a given lot were reported in the same MANEVFORM
entry/row as that of the first reported processor-derived ABW (ie. FBW), unless this date was followed by
a fish movement event or continued distribution of grow-out rations and further harvesting at a later date.
In these cases, HARV was totalled from the date of the first processor-estimated ABW until the day of
movement or continued distribution of grow-out rations. Following this, the continuation of harvest
initiated the next interval, its HARV value totalled until the completion of harvest, another fish movement
event, or any further distribution of grow-out rations. This process was repeated until completion of a
lot’s harvest.
3.7.11 - Inventory (INVENT, # fish)
DAILYFORM; daily values
MANEVFORM; INT values
Lot inventory was calculated in DAILYFORM as INVENTX – MORTSINT-TRANSFOUTINT. If
sites reported data on a monthly basis, fish movement events within a month (ie. not on the first or last
day of month) necessitated that the INVENT formula for each day of the month be changed to INVENTX
– (MORTSMONTH/DAYSMONTH). As will be described with “Feed Served” below, this was to enable
estimation of total lot feed distribution (kg) up to the fish movement date, the calculation of which
required that feed(g)/fish/day estimates (as calculated by the Fish-PrFEQ system (Cho and Bureau, 1998))
be multiplied by corresponding daily INVENT values. INVENT was calculated in MANEVFORM as
INVENTX-MORTSINT-TRANSFOUTINT+TRANSFININT-HARVINT. INITINVENTBC was used as
INVENT0 in both DAILYFORM and MANEVFORM.
32
3.7.12 - Standing Biomass (STANBIOM, kg)
MANEVFORM; INT values
Standing Biomass was calculated in MANEVFORM as (ABWY/1000)*INVENTY.
3.7.13 - Initial Biomass (INBIOM, kg; INBIOMHATCH and INBIOMBC, kg)
SUMFORM; lot and lotgroup values
Initial Biomass was calculated in SUMFORM as ININVENT*IBW, where either hatchery or
back-calculated estimates of ININVENT were used (INBIOMHATCH and INBIOMBC, respectively).
3.7.14 - Harvested Biomass (HARVBIOM, kg)
DAILYFORM; day totals
SUMFORM; lot and lotgroup totals
Harvested biomass was reported in DAILYFORM and SUMFORM. DAILYFORM values were
taken directly from processor reports, the total amount for a given lot/lotgroup then calculated in
DAILYFORM for direct copying into SUMFORM.
3.7.15 - Biomass In (BIOMIN, kg)
MANEVFORM; INT totals
Biomass In was calculated in MANEVFORM as (TRANSFINY*ABWY)/1000.
33
3.7.16 - Biomass Out (BIOMOUT, kg)
MANEVFORM; INT and CUMUL totals
Biomass Out was calculated on an interval basis in MANEVFORM as
(TRANSFOUTINT+HARVINT)*ABWY. ABWY was used rather than an average of ABWX and ABWY in
part because any transfer event always signalled the end of an interval (ie. on the same date as ABWY).
Furthermore, unless data was reported on a monthly basis, HARV values were totalled for intervals of
time over which little growth would have likely occurred, this time period coinciding with ABWY. For
sites with monthly reporting, ABWX values for months with harvests would more likely be site-sampled
estimates, and ABWY more likely processor-estimated or in some cases model-predicted values. ABWY
values were thus assumed to likely be the more accurate representation of the month’s ABW rather than
ABWX or an average of ABWX and ABWY.
3.7.17 - Biomass Gain (BIOMGAIN, kg)
MANEVFORM; INT and CUMUL totals
SUMFORM; lot and lotgroup totals
Biomass Gain was calculated on an interval basis in MANEVFORM as
(STANBIOMY+BIOMOUTINT,CUMUL)-(STANBIOMX,0+BIOMININT). BIOMGAIN was calculated in
SUMFORM as HARVBIOMTOTAL – INBIOM.
34
3.7.18 - Mortality Rate (MORTRATE, %; MORTRATEHATCH and MORTRATEBC,
%)
MANEVFORM; INT values
SUMFORM; lot and lotgroup values
Mortality rates were calculated in MANEVFORM as (MORTSINT/INVENTX)*100. Mortality
rates in SUMFORM were calculated as (MORTSTOTAL/ININVENT)*100, using both hatchery and back-
calculated initial inventories for all estimations (ie. MORTRATEHATCH and MORTRATEBC,
respectively).
3.7.19 - Thermal-Unit Growth Coefficient (TGC, Growth Rate)
3.7.19.1 - Traditional TGC (TGCTRAD)
MANEVFORM; INT and CUMUL values
SUMFORM; lot values
“Traditional” thermal-unit growth coefficients were calculated as (FBW(g)1/3
- IBW(g)1/3
) / Σdegree-
days (Iwama and Tautz, 1981), which translated into MANEVFORM as
(((ABWY^(1/3))-(ABWX,0^(1/3)))/DDINT,CUMUL)*100 and into SUMFORM as
(((FBW^(1/3))-(IBW^(1/3)))/DDGLOBAL)*100.
35
3.7.19.2 - Modified TGC (TGCMOD)
MANEVFORM; CUMUL values
The “Modified” thermal-unit growth coefficient model incorporates into the traditional TGC model
three unique growth stanzas of rainbow trout observed in controlled experimental settings, as described in
Dumas et al.2007a. TGCMOD values were calculated in MANEVFORM, the appropriate formula for
which chosen by Excel (ie. through VLOOKUP) as a function of ABWY and the corresponding growth
stanza. Growth stanzas were classified as 1) IBW to the first producer-estimated ABW greater than 500g
(1STABW>500), and 2) 1STABW>500 to FBW. In order to calculate TGCMODCUMUL values in
MANEVFORM, separate columns were included in SUMFORM for 1STABW>500 and cumulative
degree days to this date (DDAT1STABW>500). These values were then included in a separate
“Summary” worksheet of a given MANEVFORM and selected for use in TGCMODCUMUL calculations
through use of Excel`s VLOOKUP function. Excel’s IF function was used to apply the correct
TGCMOD formula dependent upon whether ABWY was greater or less than 500g. If a given ABWY was
blank, the formula was edited to select the previous most recently producer-estimated ABW value instead.
TGCMODCUMUL values in MANEVFORM were calculated as:
=IF(ABWY>=500,100*((ABWY^0.967-1STABW>500^0.967)/(DDCUMUL-DD@1STABW>500)),100*
((ABWY^(1/3)-IBW^(1/3))/DDCUMUL))
in which formulas for the two stanzas are separated by a comma.
To enable TGCMOD-predicted ABW values (as described below), two additional columns for
TGCMOD growth rates of the two stanzas were included in SUMFORM (“TGCMOD<500” and
“TGCMOD>500”). Their values were copied and pasted directly from TGCMODCUMUL in
MANEVFORM, using those values corresponding to the first producer-estimated ABW before 500g
(1STABW<500; first stanza) and the reported FBW (second stanza). These were then entered into
36
separate “Summary” worksheets within each MANEVFORM and selected for use in TGCMOD-
estimated ABW through use of VLOOKUP.
3.7.20 - Model-Based ABW Estimations
3.7.20.1 - TGCTRAD-Estimated ABW (ABWTGCTRAD, g/fish)
MANEVFORM; INT values
TGCTRAD-Estimated ABW was calculated in MANEVFORM using VLOOKUP to select the
required IBW and TGC growth rates for each lot, previously copied from SUMFORM and pasted into a
separate “Summary” worksheet of each MANEVFORM. ABWTGCTRAD was calculated using the
formula:
=((IBW^(1/3))+(TGCTRADGLOBAL/100)*DDCUMUL)^3
3.7.20.2 - TGCMOD-Estimated ABW (ABWTGCMOD, g/fish)
MANEVFORM; INT values
TGCMOD-Estimated ABW was calculated in MANEVFORM using VLOOKUP to select the
required values for IBW, 1STABW>500, DDAT1STABW>500, TGCMOD<500, and TGCMOD>500,
all previously copied directly from SUMFORM into separate “Summary” worksheets of each
MANEVFORM. As shown in the formula for ABWTGCMOD, IF was used to apply the correct growth
stanza formula as determined by ABWY:
37
=IF(ABWY>=500,((1STABW>500^0.967)+(TGCMOD>500/100)*(DDCUMUL-
DDAT1STBW>500))^(1/0.967),((IBW^(1/3))+(TGCMOD<500/100)*DDCUMUL)^3
in which formulas for the two stanzas are separated by a comma.
ABWTGCTRAD and ABWTGCMOD predictions on the dates of reported IBW and FBW, as
well as ABWTGCMOD on the dates of reported 1STABW<500 and 1STABW>500, will always match
exactly with corresponding producer-estimated ABW values due to use of these estimations in
calculation of growth rates. That is, because calculation of TGC growth rates requires the use of
estimated (ie. not model-predicted) IBW, 1STABW<500, 1STABW>500, FBW, and corresponding
DDCUMUL values, these ABW values will always equal corresponding model-predicted ABW values. As
TGCMOD involves calculation of two separate stanza growth rates, ABWTGCMOD will typically equal
four sampled ABW estimates per lot, as opposed to the two of ABWTGCTRAD. To avoid this having an
effect on evaluations of site sampling and/or model accuracy (ie. residual sum of squares analysis), two
additional columns for ABWTGCTRAD and ABWTGCMOD were included in MANEVFORM in which
values corresponding to IBW, FBW, 1STABW<500, and 1STABW>500 were excluded.
3.7.21 - Feed Served (FEEDSERV, kg)
DAILYFORM; daily or monthly totals
MANEVFORM; INT and CUMUL totals
SUMFORM; lot and lotgroup totals
Interval feed totals were calculated in DAILYFORM and copied directly into MANEVFORM.
Since fish are typically fasted before sampling, interval totals were calculated in DAILYFORM from the
day of one management event up to and including the day prior to the subsequent event. FEEDSERV
38
values reported in SUMFORM were totalled until completion of harvest, not just until the date of reported
FBW.
Cumulative FEEDSERV values in MANEVFORM were separated by inter-movement period,
rather than totalling values from IBW to FBW. This was to facilitate subsequent division of inter-
movement totals in SUMFORM amongst the individual lots resulting from a given movement event. To
do this, cumulative inter-movement totals in MANEVFORM were divided amongst the separate lots
resulting from a given movement event. Inter-movement period totals were proportioned amongst their
succeeding lots through multiplication of a given total by corresponding lot inventory ratios, as with
MORTS/lot calculations in SUMFORM.
If a site reported monthly feed totals and a reported fish movement event occurred between
reports (ie. on a day outside of the first or last of the month), a number of steps were taken to determine
feed distribution up to the movement date. In DAILYFORM, predicted feed(g)/fish was recorded for
each day of the month of interest using the Fish-PrFEQ bioenergetics model (Cho and Bureau, 1998).
Next, predicted feed (kg/day) for each day of the month was recorded in DAILYFORM by multiplying
PrFEQ-predicted feed(g)/fish/day by each day’s corresponding estimated inventory. Next, predicted daily
feed (kg/day) for the month was totalled from the start of the month to the day prior to that of fish
movement, and from the day of fish movement to the end of the month. A ratio of these values was then
multiplied by the site-reported feed total (kg) for the month to determine the amount distributed before
and after the fish movement event.
39
3.7.22 - Feed Served/Fish (FEEDFISH, g/fish)
MANEVFORM; INT and CUMUL totals
SUMFORM; lot totals
Feed served/fish was calculated on an interval basis in MANEVFORM as
FEEDSERVINT/((INVENTX+(INVENTX-MORTSINT))/2)), the formula accounting for mortalities
occurring during the given interval.
3.7.23 - Feed Types (FEEDTYPE, FEEDID) and corresponding Feed Type Amounts
(FEEDTYPEAMNT, kg)
MANEVFORM; FEEDTYPE IDs and corresponding interval totals
SUMFORM; FEEDTYPE IDs and corresponding lotgroup totals
A feed ID code was provided with every listed FEEDSERV value in MANEVFORM and
SUMFORM. As multiple feed types were at times distributed to an individual lot over one interval of
time, there were multiple FEEDTYPE columns in each MANEVFORM and the SUMFORM.
FEEDTYPE columns were numbered sequentially, the feed types for a given interval ordered into the
columns by the amounts to which they were distributed (ie. most-distributed type in FEEDTYPE #1).
Every FEEDTYPE column in both formats was accompanied by an adjacent FEEDTYPEAMNT column
for accompanying totals. FEEDTYPEAMNT columns were numbered sequentially in the same manner
as FEEDTYPE.
Feed types were determined using feed manufacturers’ feeding guidelines/tables in which feed
types (ie. pellet size and composition) are recommended for specific ranges of fish size. Using
VLOOKUP, the formula for FEEDTYPE linked a given lot’s most recently reported ABW (ie. ABWX) in
its appropriate MANEVFORM to a specific size range found on the feeding table provided by the site’s
feed manufacturer(s), entered into a separate worksheet of the same MANEVFORM. The FEEDID
40
associated with the identified size range was in turn presented by VLOOKUP in the original
MANEVFORM entry. Unless the serving of multiple feed types over one interval was suspected to have
occurred, FEEDTYPEAMNT values associated with the determined FEEDTYPE were copied directly
from corresponding FEEDSERV totals.
Sites reporting data on a monthly basis did not report which feed types were served to specific
lots, but rather the feed types and respective amounts served to the entire site. When sites reported
distribution of a “special” feed (eg. medicated, experimental, or from an alternative manufacturer), the
distributed amount for the month was proportioned amongst specific lots depending on whether they met
the special feed’s particular feeding criteria at the start of the month (eg. transfer or stocking feeds,
dependent on fish size, etc.). The reported distributed amounts of special feeds were proportioned
amongst eligible lots based on lot ratios of STANBIOMX. When the resulting amount of special feed for
a given lot was less than that lot’s reported monthly FEEDSERV, the outstanding amount was assigned
one of the site’s standard feed types through use of VLOOKUP, as described above.
FEEDTYPEAMNT values for each FEEDTYPE were totalled for lotgroups in MANEVFORM
using Excel’s PivotTable function, the values copied directly into SUMFORM. Values were totalled until
the completion of each lot’s harvest, rather than just to the dates of reported FBW.
3.7.24 - Protein Served (PROSERV, kg)
MANEVFORM; INT totals (provided for each FEEDTYPE)
SUMFORM; lotgroup totals (provided for each FEEDTYPE)
Columns for protein served were included in MANEVFORM and SUMFORM, located adjacent
to each FEEDTYPE and FEEDTYPEAMNT column, and numbered sequentially in the same manner.
Using VLOOKUP, a FEEDID entry listed in MANEVFORM and/or SUMFORM was linked with that
41
found in a diet composition table of a separate “Feed Information” worksheet, the diet’s value for percent
protein then selected for multiplication with the corresponding FEEDTYPEAMNT to determine
PROSERV.
3.7.25 - Protein Distributed/Fish (PROFISH, g/fish)
MANEVFORM; INT and CUMUL totals
SUMFORM; lot totals
Protein distributed/fish was calculated on an interval basis in MANEVFORM as
∑PRODISTINT/((INVENTX+(INVENTX-MORTSINT))/2), the formula accounting for interval mortalities.
∑PRODIST was the sum of all PROSERV values (ie. for each feed type) listed for a given lot and
interval.
3.7.26 - Nitrogen/Fish (NFISH, g/fish)
MANEVFORM; CUMUL totals
SUMFORM; lot totals
Nitrogen/Fish was calculated in MANEVFORM and SUMFORM as PROFISHCUMUL/16.
42
3.7.27 - Economic Feed Conversion Ratio (ECONFCR; ECONFCRHATCH and
ECONFCRBC)
MANEVFORM; INT and CUMUL values
SUMFORM; lot and lotgroup values
Economic FCR was calculated as feed served (kg)/(harvested biomass(kg)-initial biomass(kg)),
which translated into MANEVFORM as FEEDSERVINT,CUMUL/BIOMGAININT,CUMUL, and into
SUMFORM as FEEDSERVTOTAL/(HARVBIOMTOTAL – INBIOM), where INBIOM was estimated using
either ININVENTHATCH (ie. ECONFCRHATCH) or ININVENTBC (ie.ECONFCRBC).
3.7.28 - Biological Feed Conversion Ratio (BIOFCR)
MANEVFORM; INT and CUMUL values
SUMFORM; lot values
Biological FCR was calculated as (feed/fish(g))/(growth/fish(g)), which translated into
MANEVFORM as (FEEDFISHINT,CUMUL)/(ABWY-ABWX,0), and into
SUMFORM as (FEEDFISHGLOBAL)/(FBW-IBW). VLOOKUP was used to select lot ABW0 (ie.IBW) in
MANEVFORM, pasted from SUMFORM into an adjacent “Summary” worksheet of each
MANEVFORM.
43
3.7.29 - Nitrogen Retention Efficiency (NRE)
MANEVFORM; INT and CUMUL values
SUMFORM; lot values
Nitrogen retention efficiency was calculated as (nitrogen served/fish(g))/(nitrogen gain/fish(g)),
which translated into MANEVFORM and SUMFORM as
(((0.164*ABWY-0.214)/16)-((0.164*IBW-0.214)/16))/NFISHCUMUL)*100. Whole-body nitrogen content
was modelled using results from Dumas et al., 2007b. VLOOKUP was used to select IBW in
MANEVFORM, pasted from SUMFORM into an adjacent “Summary” worksheet of each
MANEVFORM.
3.8 - Comparison of Traditional and Modified Thermal-Unit Growth Coefficient
Models
Residual sum of squares (RSS) was used to assess the relative fit of ABWTGCMOD and
ABWTGCTRAD (ie. model-predicted body weights) to producer-estimated lot ABW values. RSS was
calculated for and across sites as ∑((OBS-PRE)2), where OBS was producer-estimated ABW and PRE
was model-predicted ABW. Processor-estimated ABW values that were reported prior to those used as
FBW in a given lot MANEVFORM sequence were also included in the RSS analysis (ie. used as
additional OBS values).
44
3.9 - Modelling Upper and Lower Size Bounds in Relation to Model-Estimated
ABW
In order to visually inspect the extent of deviation of producer-estimated ABW relative to
corresponding growth model predictions, it was necessary to develop reasonable expectations for upper
and lower size “bounds” relative to model-predicted ABW (ie. largest and smallest fish relative to
modelled predictions). To do this, data from the Alma Aquaculture Research Station (AARS) was used
to model within-cage size variability/distribution with increasing body weight. Expectations for “bounds”
or observation limits in relation to measured or observed values are critical components of such practical
statistical tools as data censoring (Bewick, 2004; Cole, 2008) and statistical process control charts (De
Vries and Reneau, 2010). Site data was found to be inappropriate for modelling within-cage size
variability, as technicians typically sample only a small proportion of a given lot at a time (eg. <1%) and
do not report the weights of individual fish. AARS data was deemed a suitable substitute for use in
modelling commercial cage size variability for two reasons: 1) AARS technicians feed their fish in a
fashion generally similar to that of site technicians (ie. two feeds/day, occasional hand-feeding, feeding to
satiety, mixed distribution of ration sizes determined by previous amounts, etc.), and 2) AARS
technicians do little to mitigate competition or other factors known to influence size variability, allowing
natural deviation in growth trajectories to persist over time.
All past growth trials from AARS records were considered for analysis. To be included in the
analysis, growth trials had to meet two conditions: 1) that the tank in question was fed a nutritionally
complete diet (ie. “control” or “commercial” diet), and that 2) at least 25 individual fish from the tank
were weighed at a time (given that tanks typically do not contain more than 25-50 fish at one time. The
mean, first and third quartiles, and interquartile range (IQR; 3rd-1st quartiles) were calculated for each
tank. Three relationships were then plotted in Excel: 1) Mean tank BW against Mean tank BW (linear for
point of comparison), 2) Mean tank BW against Mean tank BW+1.5*IQR (ie. upper bound), and 3) Mean
tank BW against Mean tank BW-1.5*IQR (ie. lower bound). A variety of models were fitted to plots 2
45
and 3 in Excel (upper and lower bounds, respectively). The equations for the closest fitting models of
each were then used to construct upper and lower size bounds relative to model-predicted ABW, ABW
serving as the independent variable (x) in formulas for the two bounds.
3.10 - Data Smoothing for Regression Analysis
The degree of random variability of untreated time-series data made these values inappropriate
for refined modelling efforts. However, to enable simple regression analysis of time-series data for
MORTRATEHATCH, BIOFCR, and NRE, values were treated by both iterative and non-iterative data
smoothing procedures.
Interval values for each parameter were aggregated across commercial sites and years prior to
application of smoothing procedures. Interval values to which smoothing procedures were applied were
non-cumulative for MORTRATEHATCH and cumulative for BIOFCR and NRE. Mortality rates on days
of fish movement and/or harvest were excluded due to associated inflation of values and confounding
variations in fish movement schedules amongst sites. BIOFCR and NRE values that used both producer-
and processor-estimated average body weights in their calculation were removed due to potentially
confounding differences in the extent of the two methods’ sampling biases. Remaining
MORTRATEHATCH values were divided into 50 day segments (eg. 0-50 days, 50-100 days, etc.) and
BIOFCR and NRE values into 50 g segments. “Residuals” (ie. parameter values outside of each
segment’s range of μ+/- 2.5*σ) were non-iteratively removed for all three parameters and iteratively
removed for both BIOFCR and NRE. The iterative procedure was applied until the closest possible fit
was achieved with any model regressions available on Microsoft Excel, as determined by the coefficient
of determination (R2).
46
3.11 - Weighted Averages
Weighted averages were provided for various performance parameters, whereby global or total
lot values were weighted by their corresponding HARVBIOM. Weighted averages were calculated as:
=∑(HARVBIOMLOT*VALUELOT)/∑HARVBIOM
where VALUELOT is the determined global or total lot value for the performance parameter of interest.
3.12 - Statistical Analysis
Differences amongst sites in lot values were examined through use of the linear mixed model:
yij= Si + SYj(i) + eij
where yij is the production record (ie. TGC, MORTRATEHATCH, ECONFCRHATCH, BIOFCR, and
NRE) of the ith
site in the jth
season-year, Si is the fixed effect of the ith site, SYj(i) is the random effect of
the jth season-year nested within the i
thsite, and eijis the random error associated with the production
record yij.
SAS PROC GLM (SAS Software) was used to fit the linear mixed model. Least squares analysis
was used to determine differences between levels of the fixed effect (ie. Sites), where the appropriate
error term in Site hypothesis testing was determined using the TEST option of PROC GLM Random
statement. The Scheffé adjustment for multiple hypothesis tests was used to control the family-wise error
rate at 5%. Mortality rate values were arcsine-transformed prior to PROC GLM analysis due to
percentages.
47
4 - RESULTS
4.1 - Survey Summary
Production data from five commercial sites was comprised of 80 lotgroups which resulted in 126
individual lots. Commercial data spanned from September 2009 (initial stocking) to June of 2012 (final
harvest), with 43% of lots stocked in the spring (ie. April-June) and 57% stocked in the fall (ie.
September-November). Data from the Experimental Lakes Area (ELA) spanned from 2003 to 2007, with
stocking occurring either in May or June and removal the following October or November.
Commercial producers stocked cages with either mixed sex diploid, all female diploid, or triploid
populations during the period of study. Some producers elected to raise one of these types of populations
exclusively, while others raised a combination of these populations simultaneously (ie. in separate cages).
For example, one site raised entirely all female diploid populations, while roughly 83% of another site’s
lotgroups were all triploid, 8% were mixed sex diploids, and only 8% were all female diploids.
Unless noted otherwise, all reported or mentioned results are for commercial sites only (ie.
excluding ELA results). Descriptive statistics and benchmark values (ie. averages, weighted averages) of
various production parameters from the commercial dataset and the ELA are provided in Table 4.1.
Average parameter values for individual sites (including ELA) are provided in Table 4.2; average site
values for each year of production (ie. year in which cohort or common series of lotgroups was initially
stocked) are provided in Table 4.3. To compare methodologies of lot and lotgroup summaries, average
lot and lotgroup values of relevant production parameters are provided in Table 4.4.
48
Table 4.1: Descriptive statistics of various production parameters, analyzed using aggregate lot values (ie. either global or
total lot values, depending on the parameter) from the commercial dataset. Parameter values from the Experimental
Lakes Area (ELA), averaged across years, are included for comparison.
Production
Parameter
Average Weighted
AverageA
ELA
Average
Count Standard
Deviation
Min Max 10th
Percentile
90th
Percentile
Initial Body Weight
(g) 33.2 36.1 103.1 80.0 24.7 6.9 110.0 10.0 74.1
Final Body Weight (g) 1127.5 1163.6 955.1 126 293.3 530.8 2416.4 760.7 1439.0
Days (#) 468 477 158 126 110 185 719 307 589
TemperatureB
(°C) 10.1 10.1 18.0 59 1.9 7.3 15.4 8.0 12.8
Thermal-Unit Growth
CoefficientC
(TGC) 0.165 0.165 0.185 59 0.026 0.109 0.206 0.132 0.193
Mortality Rate,
HatcheryD
(%) 14 13 9 46 11 3 52 27 6
Mortality Rate, Back-
CalculatedE
(%) 17 15 n/a 46 12 3 63 31 7
Economic
FCR,HatcheryF
(feed
(kg):biomass
gain(kg))
1.40 1.36 1.25 113 0.32 0.90 3.93 1.65 1.19
Economic FCR, Back-
CalculatedG
(feed
(kg):biomass
gain(kg)) 1.39 1.35 n/a 113 0.31 0.90 3.89 1.63 1.18
Biological FCR
(feed(g)/fish:
gain(g)/fish) 1.29 1.29 1.22 126 0.17 0.79 1.98 1.48 1.12
Nitrogen Retention
Efficiency (%) 28.2 28.2 27.6 126 3.4 19.0 43.2 24.3 31.8
ALot values were weighted by harvested biomass (kg).
BGlobal lot values for Temperature evaluated in exclusion of Site A data.
CGlobal lot values for Thermal-Unit Growth Coefficient ((FBW1/3-IBW1/3)(∑DegreeDays)-1) evaluated in exclusion of Site A
data. D
Total lot values for Mortality Rate, Hatchery (MORTRATEHATCH, % of hatchery-estimated initial inventory) evaluated in
exclusion of Site A data. E
Total lot values for Mortality Rate, Back-calculated (MORTRATEBC, % of back-calculated initial inventory) evaluated in
exclusion of Site A data. F
Total lot values for Economic Feed Conversion Ratio, Hatchery (ECONFCRHATCH) calculated using hatchery-estimated
initial biomass. G
Total lot values for Economic Feed Conversion Ratio, Back-calculated (ECONFCRBC) calculated using back-calculated initial
biomass.
49
Table 4.2: Average site values for various production parameters, calculated using either global or total lot values,
depending on the parameter. Parameter values from the Experimental Lakes Area (ELA), averaged across years, are
included for comparison.
Production Parameter
Average Weighted
AverageA
Site A Site B Site C Site D Site E ELA
CountB
126 126 67 15 9 12 23 5
Initial Body Weight (g) 33.2 36.1 21.1 30.0 46.3 76.6 58.5 103.1
Final Body Weight (g) 1127.5 1163.6 1090.2 1000.5 1229.8 1093.6 1296.4 955.1
Days (#) 468 477 497 362 517 530 404 158
Temperature (°C) 10.1 10.1 8.0 10.3 9.5 8.5 10.9 18.0
Thermal-Unit Growth
CoefficientC
(TGC) 0.165 0.165 0.197 0.188 0.143 0.134 0.174 0.185
Mortality Rate, HatcheryD
(%) 14 13 3 10 21 17 11 9
Mortality Rate, Back-
CalculatedE
(%) 17 15 3 11 22 22 12 n/a
Economic FCR, HatcheryF
(feed
(kg):biomass gain(kg))
1.40 1.36 1.30 1.21 1.67 1.67 1.46 1.25
Economic FCR, Back-
CalculatedG
(feed (kg):biomass
gain(kg)) 1.39 1.35 1.29 1.20 1.66 1.64 1.44 n/a
Biological FCR (feed(g)/fish:
gain(g)/fish) 1.29 1.29 1.28 1.11 1.48 1.44 1.28 1.22
Nitrogen Retention Efficiency
(%) 28.2 28.2 27.9 30.7 25.9 25.9 29.1 27.6
ALot values were weighted by harvested biomass (kg).
BCount of lots used in calculation of site Final Body Weight average values reported as Lot Count.
CThermal-Unit Growth Coefficient ((FBW1/3-IBW1/3)(∑DegreeDays)-1) evaluated using global lot values.
DMortality Rate, Hatchery (MORTRATEHATCH, % of hatchery-estimated initial inventory) evaluated using total lot values.
EMortality Rate, Back-calculated (MORTRATEBC, % of back-calculated initial inventory) evaluated using total lot values.
FTotal lot values for Economic Feed Conversion Ratio, Hatchery (ECONFCRHATCH) calculated using hatchery-estimated
initial biomass. G
Total lot values for Economic Feed Conversion Ratio, Back-calculated (ECONFCRBC) calculated using back-calculated initial
biomass.
50
Table 4.3: Average site values for various production parameters, calculated for each cohort year (ie. year in which
cohort or series of common lotgroups was initially stocked).
Production Parameter
Site A Site B Site C Site D Site E
2008 2009 2010 2009 2010 2009 2010 2009 2010 2009 2010 2011
Initial Body Weight (g) 27.2 21.6 22.9 40.9 23.1 58.4 38.0 98.4 62.7 33.7 71.1 57.9
Final Body Weight (g) 1176.8 1007.8 1124.7 1073.4 936.7 1415.1 998.1 1192.8 994.3 1698.0 1242.3 964.5
Days (#) 530 476 500 352 372 576 442 527 533 550 403 260
Temperature(°C) 7.2 7.6 9.5 10.6 10.1 8.7 10.5 8.1 8.9 9.7 10.7 12.5
Thermal-Unit Growth
CoefficientA
(TGC)
0.197 0.202 0.181 0.189 0.186 0.146 0.140 0.139 0.128 0.166 0.169 0.189
Mortality Rate,
HatcheryB
(%)
5 3 2 6 14 16 27 23 11 15 10 7
Mortality Rate, Back-
CalculatedC
(%)
6 3 2 7 14 19 26 29 16 17 11 9
Economic FCR,
HatcheryD
(feed
(kg):biomass gain(kg))
1.39 1.30 1.25 1.20 1.21 1.81 1.50 1.71 1.69 1.73 1.35 1.33
Economic FCR, Back-
CalculatedE
(feed
(kg):biomass gain(kg))
1.38 1.29 1.24 1.19 1.21 1.77 1.51 1.65 1.62 1.71 1.34 1.30
Biological FCR
(feed(g)/fish:
gain(g)/fish)
1.35 1.28 1.24 1.16 1.07 1.55 1.39 1.37 1.52 1.36 1.28 1.21
Nitrogen Retention
Efficiency (%)
26.4 27.7 28.9 30.0 33.2 25.2 26.7 27.5 24.3 28.0 29.3 30.1
AThermal-Unit Growth Coefficient ((FBW1/3-IBW1/3)(∑DegreeDays)-1) evaluated using global lot values.
BMortality Rate, Hatchery (MORTRATEHATCH, % of hatchery-estimated initial inventory) evaluated using total lot values.
CMortality Rate, Back-calculated (MORTRATEBC, % of back-calculated initial inventory) evaluated using total lot values.
DTotal lot values for Economic Feed Conversion Ratio, Hatchery (ECONFCRHATCH) calculated using hatchery-estimated
initial biomass. E
Total lot values for Economic Feed Conversion Ratio, Back-calculated (ECONFCRBC) calculated using back-calculated initial
biomass.
51
Table 4.4: Average values for various production parameters, calculated using total lot and lotgroup values (ie. from
stocking to completion of harvest).
Production Parameter Lot
Average
Lotgroup
Average
Lot
Weighted
AverageA
Lotgroup
Weighted
Average
Mortality Rate, HatcheryB
(%) 14 14 13 14
Mortality Rate, Back-CalculatedC
(%) 17 16 15 16
Economic FCR, HatcheryD
(feed (kg):biomass gain(kg)) 1.40 1.36 1.36 1.37
Economic FCR, Back-CalculatedE
(feed (kg):biomass gain(kg)) 1.39 1.34 1.35 1.35
ALot and lotgroup values were weighted by harvested biomass (kg).
BMortality Rate, Hatchery (MORTRATEHATCH, % of hatchery-estimated initial inventory) evaluated using total lot values.
CMortality Rate, Back-calculated (MORTRATEBC, % of back-calculated initial inventory) evaluated using total lot values.
DTotal lot values for Economic Feed Conversion Ratio, Hatchery (ECONFCRHATCH) calculated using hatchery-estimated
initial biomass. E
Total lot values for Economic Feed Conversion Ratio, Back-calculated (ECONFCRBC) calculated using back-calculated initial
biomass.
52
4.2 - Summary of Production Parameters
4.2.1 - Sample Sizes
The number of global lot values included in the analysed dataset (ie. lot count or sample size)
varied substantially amongst production parameters and sites, as seen in Tables 1 and 2, respectively.
Inconsistencies in lot values and the backgrounds of their calculation necessitated their occasional
exclusion from analysis, contributing to the observed differences in lot counts for performance
parameters. For instance, global TGC values for certain lots from Site A were excluded due to
inconsistent and sporadic depths at which daily temperature sampling occurred. Or, due to the relative
frequency of fish movement on certain sites (eg. transfers, grades, splits, etc.), data had to be aggregated
across lots for calculation of ECONFCR and MORTRATE values, lowering the total lot counts for these
parameters. Site A typically contributed the largest lot counts for individual performance parameters and
years, except when excluded entirely from analysis of certain parameters (eg. TGC, mortality rates).
4.2.2 - Initial Inventories
As seen in Fig. 4.1, hatchery-estimated initial inventories (ININVENTHATCH) were consistently
larger than back-calculated initial inventories (ININVENTBC). There were substantial differences
between hatchery-estimated and back-calculated initial inventories (ININVENTHATCH and
ININVENTBC, respectively), the extent of which depended on the site. For instance, the average
difference for site C was 2865 fish, while that for Site D was 9749 fish. As a result of the large
differences between ININVENTHATCH and ININVENTBC, other parameters calculated using initial
inventories (ie. mortality rates, economic FCR) were reported as two separate values, the difference
between which was proportional to that of the two corresponding initial inventory values.
53
Figure 4.1: Lot initial inventories were estimated using either hatchery reports or back-calculated using harvest and
mortality data. Average site values are plotted for both. Lower error bars represent standard error of back-calculated
values from each site, while upper error bars represent standard error of hatchery-estimated values. *Due to relatively
frequent fish movement on Site B (eg. grades, transfers, etc.), values were available for only 9 of the 15 total lots. **Back-
calculated values were not estimable for the Experimental Lakes Area (ELA).
4.2.3 - Initial Body Weight
Fingerling initial body weights (IBW, stocking weights) averaged 33 g across lots (n=80,
σ=24.7), or 36 if weighted by HARVBIOM, both of which were considerably lower than the ELA
average (103, n=5, σ=51.3). Commercial IBW varied substantially, with minimum and maximum lot
values of 7 and 110 g, respectively. Site averages appeared to be evenly distributed between 21 (Site A)
and 77 g (Site D).
4.2.4 - Final Body Weight
Fish were harvested commercially at an average body weight (FBW) of 1128g across lots (n=126,
σ=293.3 g), or 1164g if weighted by HARVBIOM, both values somewhat different from the ELA average
of 955.1g (n=5, σ=102.8). As apparent in Fig. 4.2, lot FBW values varied considerably within and
amongst sites, ranging across sites from approximately half a kilogram (531g lot minimum) to almost two
0
10000
20000
30000
40000
50000
60000
A B* C D E ELA**
Init
ial I
nve
nto
ry (
# fi
sh)
Site ID
Hatchery-Estimated
Backcalculated
54
and a half kilograms (2416.4g lot maximum). Average FBW was considerably different amongst sites,
with the smallest average being 1001g (Site B, n=15, σ=155.3) and the largest being 1296g (Site E, n=9,
σ=513.1). Site averages varied considerably amongst years as well. For instance, average FBW values
from Site C were 1415g and 998g for 2009 and 2010, respectively, whereas from Site D they were 1698g
and 1242g for the same years, respectively.
Figure 4.2: Box plots of final average body weights from all lots of each site, including the Experimental Lakes Area
(ELA). Whiskers represent minimum and maximum values from each site.
4.2.5 - Days
The average commercial grow-out cycle, from stocking to harvest (ie. Days, IBW to FBW), was
468 days long (n=126, σ=110), or 477 days if weighted by HARVBIOM. Both values were substantially
different from the ELA average (158 days, n=5, σ=12). The shortest commercial lot grow-out cycle was
185 days, and the longest 719 days. Amongst site averages, Site B had the shortest grow-out cycle (362
days, n=15, σ=58) and Site D the longest (530 days,n=15, σ=117), a difference of almost half a year.
Average site values varied considerably amongst years. For instance, average grow-out cycles for 2009
and 2010 were 576 and 442 days for Site C, respectively, and 550 and 403 days for Site E, respectively.
0
500
1000
1500
2000
2500
3000
A B C D E ELA
Fin
al B
od
y W
eig
ht
(g)
Site ID
Mean
55
4.2.6 - Temperature
Water temperatures were typically sampled within one metre of the surface by farm technicians,
although some producers occasionally reported temperatures at alternative depths. Global lot water
temperatures averaged 10.1°C across lots when excluding data from Farm A (n=59, σ=1.9), and 9.1°C
across lots when including it (n=115, σ=1.9). These values remained unchanged after weighting by
HARVBIOM. Global lot values from the ELA averaged 18.0°C across years (n=5, σ=0.9), for
comparison. Global Site A lot values, the daily values of which were provided largely by a nearby water
treatment plant and sampled at a depth of 20m, averaged 8.0 across lots (n=56, σ=1.9). Many Site A
global lot values were excluded from analysis of Site A averages due to relatively inconsistent and
sporadic sampling at varying depths.
Water temperatures varied substantially over time and amongst sites. The minimum and
maximum global lot temperatures from the dataset, excluding Site A, were 7.3 (Site D, 2010) and 15.4°C
(Site E, 2011), respectively. The maximum interval temperature was 22.0°C (Site B), averaged from
7/13/2010 to 8/3/2010. Site averages ranged from 8.5°C (Site D, n=12, σ=1.0) to 10.9°C (Site E, n=23,
σ=2.3). Yearly averages differed to varying extents, depending on the site. For instance, 2009 and 2010
averages were 10.6°C and 10.1°C for Site B, respectively, while those of Site C were 8.7°C and 10.5°C,
respectively. It should be noted that the average length of grow-out cycles also varied to differing extents
amongst years, possibly influencing the corresponding global lot temperature values.
4.2.7 - Thermal-Unit Growth Coefficient (TGC)
Results from Proc GLM demonstrate that 84% of the variation in TGC was explained by the
mixed model and that the fixed effect (ie. site) had a significant influence on TGC values (p<0.0001).
Results of model fitting for relevant performance parameters are provided in Table 4.5.
56
Global TGC values averaged 0.165 across lots when excluding data from Site A (n=59, σ=0.026).
This value remained the same after weighting by HARVBIOM. For comparison, TGC values from the
ELA averaged 0.185 across years (n=5, σ=0.020). The range of TGC values from Site A was distinct
from all other sites, seen best in a histogram of all commercial global lot values (Fig. 4.3). Global lot
TGC values averaged 0.179 across lots when including data from Site A (n=107, σ=0.027). TGC could
not be calculated for lotgroups.
Global lot TGC values demonstrated considerable variation amongst all sites, observed visually
through the contrasting ranges of values in the box plots of Fig. 4.4. Excluding Site A data, minimum and
maximum commercial global lot values were 0.109 and 0.206, respectively. Site TGC averages were
notably different, ranging from a minimum of 0.134 (Site D, n=12, σ=0.014) to a maximum of 0.188 (Site
B, n=15, σ=0.005), excluding Site A data. Significant differences between sites in TGC values, as
determined through least squares means analysis, are indicated in Fig. 4.4. Results of least squares means
analysis for the fixed effect on TGC values are provided in Table 4.6.
The extent of variation between yearly averages depended on the site. For instance, Site B
averages for 2009 and 2010 were 0.189 and 0.186, respectively, while for Site D these averages were
0.139 and 0.128, respectively. For 2009 and 2011, Site E averages were 0.166 and 0.189, respectively,
resulting in the largest difference between any two yearly averages of a given site.
57
Table 4.5: Results of least squares analysis of fixed effect (ie. site) on global lot values for various performance
parameters, analyzed through Proc GLM (SAS software).
Performance Parameter Number of
Observations
F Value Pr>F Model
R-Square
Thermal-Unit Growth
CoefficientA
(TGC)
112 11.84 <0.0001 0.839
Mortality Rate, HatcheryB
(%) 118 12.14 <0.0001 0.765
Economic FCR, HatcheryC
(feed
(kg):biomass gain(kg))
118 3.33 0.0226 0.517
Biological FCR (feed(g)/fish:
gain(g)/fish)
131 4.46 0.0080 0.528
Nitrogen Retention Efficiency
(%)
131 2.44 0.0743 0.489
AThermal-Unit Growth Coefficient ((FBW1/3-IBW1/3)(∑DegreeDays)-1) evaluated using global values for lots.
BMortality Rate, Hatchery (MORTRATEHATCH, % of hatchery-estimated initial inventory) evaluated using total values for
lots. MORTRATEHATCH values were arcsine-transformed prior to analysis, due to percentages. C
Total lot values for Economic Feed Conversion Ratio, Hatchery (ECONFCRHATCH) calculated using hatchery-estimated
initial biomass.
Table 4.6: Results of least squares means for multiple comparisons of fixed effect levels (ie. sites) for global lot thermal-
unit growth coefficient values. P values for differences between sites are listed, with a significance level of 5% used.
Site ID A B C D E ELA
A - 0.941 0.006 0.001
0.094 0.974
B - - 0.065 0.017 0.708 1.000
C - - - 0.995 0.371 0.298
D - - - - 0.112 0.137
E - - - - - 0.961
ELA - - - - - -
58
Figure 4.3: Histogram of global lot TGC values from Site A and all other commercial sites.
Figure 4.4: Global lot thermal-unit growth coefficient values, displayed as box plots for each site. Error bars represent
minimum and maximum lot values from each site. Sites with common letters lack statistically significant differences in
TGC values.
0
5
10
15
20
25
0.12 0.14 0.16 0.18 0.20 0.22 0.24 More
Fre
qu
en
cy
Thermal-Unit Growth Coefficient ((FBW1/3-IBW1/3)(∑DegreeDays)-1)
Site A
Other
a
ab
bc
abc abc
0.000
0.050
0.100
0.150
0.200
0.250
A B C D E ELA
Ther
mal
-Un
it G
row
th C
oef
fici
ent
((FB
W1
/3-
IBW
1/3)(∑DegreeDays)
-1)
Site ID
Mean
c
59
4.2.8 - Mortality Rates
Results from Proc GLM demonstrate that 77% of the variation in MORTRATEHATCH was
explained by the mixed model and that the fixed effect (ie. site) had a significant influence on
MORTRATEHATCH values (p<0.0001). As MORTRATEBC values were not available for ELA, results
for the fixed effect for MORTRATEHATCH will thus be used to represent those of mortality rates in
general. Due to extensive fish movement amongst cages, Site B mortality rates were summarized on a
per year basis. Therefore, Site B had only two data points (ie. 2009 and 2010) for each of
MORTRATEHATCH and MORTRATEBC.
When excluding data from Site A, commercial MORTRATEHATCH and MORTRATEBC
values averaged 14% (n=46, σ=10.8) and 17% (n=46, σ=12.0) across lots, respectively. These values
were influenced substantially when weighted by HARVBIOM, decreasing to 13% and 15%, respectively.
MORTRATEHATCH and MORTRATEBC values were similar when averaged across lotgroups rather
than lots (14% and 16%, n=22 and n=23, σ=9.9 and σ=10.8, respectively). Lotgroup averages were
influenced less by weighting, resulting in values of 14% and 16%, respectively. For comparison, ELA
MORTRATEHATCH values were slightly smaller, averaging 9% across years (n=5, σ=5.1). When
including data from Site A, average MORTRATEHATCH and MORTRATEBC values across lots
decreased to 8%(n=113,σ=9.1) and 9% (n=113, σ=10.3), respectively.
As illustrated in Fig. 4.5, global back-calculated mortality rates (MORTRATEBC) were
consistently higher than global hatchery-estimated mortality rates (MORTRATEHATCH). Lot mortality
rates were highly variable within and amongst sites, as visually illustrated through the wide range of site
means and large standard error bars of Fig. 4.5. Average global lot mortality rates varied considerably
amongst sites. For example, the lowest commercial site MORTRATEHATCH average (ie. excluding Site
A) was 10% (Site B, n=2, σ=5.1), while the highest was 21% (Site C, n=9, σ=13.4). Significant
differences between sites in MORTRATEHATCH values, as determined through least squares means
60
analysis, are indicated in Fig. 4.5. Results of least squares means analysis for the fixed effect (ie. site) on
MORTRATEHATCH values are provided in Table 4.7.
When excluding Site A data, the range of lot MORTRATEHATCH and MORTRATEBC values
(ie. 10th-90
thpercentiles) from the remaining dataset was 21% and 25%, respectively. Yearly mortality
rates also varied considerably within sites. For example, Site B values averaged 7% and 14% in 2009 and
2010, respectively, while at Site D these values were 29% and 15%, respectively.
Interval MORTRATEBC values, following treatment with a non-iterative data smoothing
procedure, demonstrated a general but inconsistent decline across sites over time, as seen in Figs.4.6 and
4.7. MORTRATEBC values demonstrated large variability over time, as observed through the 10thth
percentile curve of Fig. 4.6. Following an initial and early decline in MORTRATEBC and associated
variability (ie. after 75 days), these values tended to fluctuate to a noticeable extent from 225-475 days,
with for instance the second-highest MORTRATEBC median value and 10thth percentiles occurring at
275 days, as seen best in Figs. 4.7 and 4.6, respectively. Corresponding median temperatures (ie.
following removal of values corresponding to removed MORTRATEBC values) followed a similar
pattern of decline as that of mortality rates, as seen in Fig. 4.7, with an initial decline followed by a period
of increased values from 325-425 days.
61
Table 4.7: Results of least squares means for multiple comparisons of fixed effect levels (ie. site) for global lot mortality
rate values (MORTRATEHATCH). P values for differences between sites are listed, with a significance level of %5 used.
Site ID A B C D E ELA
A - 0.877 0.006 0.039
0.028 0.810
B - - 0.935 0.998 1.000 0.999
C - - - 0.958 0.601 0.563
D - - - - 0.984 0.914
E - - - - - 0.991
ELA - - - - - -
Figure 4.5: Global lot mortality rate values were calculated using both hatchery-estimated and back-calculated initial
inventories (MORTRATEHATCH and MORTRATEBC, respectively). Average values are plotted for each site. Lower
error bars represent standard error of global MORTRATEHATCH values for each site, while upper error bars represent
1 standard error of global MORTRATEBC values. Sites with common letters lack statistically significant differences in
MORTRATEHATCH values. *Due to relatively frequent fish movement at Site B (eg. transfers, grades, etc.), values were
aggregated across all lots and summarized for each year of production, thus reducing the number of Site B values for
MORTRATEHATCH and MORTRATEBC to two each (ie. 2009 and 2010).
a
ab
b b
b
a
0
5
10
15
20
25
30
A B* C D E ELA
Mo
rtal
ity
Rat
e (%
of
init
ial i
nve
nto
ry)
Site ID
BackcalculatedInitial Inv.
Hatchery-EstimatedInitial Inv.
62
Figure 4.6: Mortality rates were calculated for lots on an interval basis as mortalitiesINT/inventoryX, where mortalitiesINT
are lot mortalities recorded over any given interval and inventoryX is the lot’s back-calculated inventory (# fish) at the
beginning of the interval. Aggregate commercial values were divided into 50 day periods of grow-out, to which a non-
iterative data smoothing procedure was applied. The resulting median values and 10th and 90th percentiles of each 50 day
period are shown.
Figure 4.7: Mortality rates were calculated for lots on an interval basis as mortalitiesINT/inventoryX, where mortalitiesINT
are lot mortalities recorded over any given interval, and inventoryX is the lot’s back-calculated inventory (# fish) at the
beginning of the interval. Aggregate commercial interval mortality rates and temperatures were divided into 50 day
periods of total grow-out, to which a non-iterative data smoothing procedure was applied. The resulting median values of
each 50 day period are shown.
-1
0
1
2
3
4
5
6
7
0 100 200 300 400 500 600 700
Mo
rtal
ity
Rat
e (
% o
f in
itia
l in
ven
tory
)
Days (#)
Median
90th Percentile
10th Percentile
0
2
4
6
8
10
12
14
16
18
20
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 200 400 600
Tem
pe
ratu
re (
°C)
Mo
rtal
ity
Rat
e (
% o
f in
itia
l in
ven
tory
)
Days (#)
Median Mortality Rate
Median Temperature
63
4.2.9 - Feed Distribution
A variety of feeds with varied nutritional profiles were distributed amongst commercial sites, as
documented in Table 4.8. Feeding strategies also differed amongst sites; whereas some sites retained
uniform dietary composition throughout their standard grow-out cycle, others adjusted dietary
composition with body size. For example, standard diets used at one site shifted in composition from
47% protein and 17% lipid, distributed in early stages (eg. ABW<50g), to 44% protein and 24% lipid,
distributed in later stages (eg.ABW>1000g).
Table 4.8: Commercial Feed IDs, percent crude protein and crude lipid for each of the feed types served across sites
during the study period. Data was taken from commercial feed labels.
Commercial
Feed ID
Crude
Protein
(%)
Crude
Lipid
(%)
AA25 47.0 17.0
AA35 46.0 19.0
AAA50 44.0 24.0
AAA75 44.0 24.0
BB25 48.0 24.0
BC30 48.0 26.0
CA30 48.0 18.0
CA75 44.0 18.0
CB25 48.0 18.0
EA 45.0 22.0
EB 44.0 22.0
EC 44.0 22.0
ED 45.0 20.0
EE 46.0 20.0
EF 47.0 18.0
64
4.2.10 - Economic Feed Conversion Ratios
Results from Proc GLM demonstrate that 52% of the variation in ECONFCRHATCH was
explained by the mixed model and that the fixed effect (ie. site) had a significant influence on
ECONFCRHATCH values (p=0.0226). As ECONFCRBC values were not available for ELA, results for
the fixed effect for ECONFCRHATCH will thus be used to represent those for economic FCR values in
general. Due to extensive fish movement amongst cages, Site B values were summarized on a per year
basis. Therefore, Site B only had two data points (ie. 2009 and 2010) for each of ECONFCRHATCH and
ECONFCRBC.
Lot ECONFCRHATCH and ECONFCRBC values averaged 1.40 (n=113, σ=0.32) and 1.39
(n=113, σ=0.31) across sites, respectively. Weighting lot values by HARVBIOM resulted in values of
1.36 and 1.35, respectively. Average ECONFCRHATCH from the ELA was 1.25 (n=5, σ=0.06), for
comparison. Summarizing commercial values by lotgroup resulted in average ECONFCRHATCH and
ECONFCRBC values of 1.36 (n=67, σ=0.19) and 1.35 (n=68, σ=0.18) across sites, respectively, or 1.37
and 1.35 if weighted by HARVBIOM. Differences between lot and lotgroup ECONFCR values were
greater than those of MORTRATE values.
Although there were no statistically significant differences found between ECONFCRHATCH
values of any two sites, there was still substantial variation in both ECONFCRHATCH and
ECONFCRBC values within and across lots. For example, Site B ECONFCRHATCH and
ECONFCRBC averaged 1.21 (n=2, σ=0.01) and 1.20 (n=2, σ=0.01), respectively, while site C averaged
1.67 (n=9, σ=0.09) and 1.66 (n=9, σ=0.08), respectively. Minimum global lot values across sites were
0.90 for both ECONFCRHATCH and ECONFCRBC (Site A), while maximum values were 3.93 and
3.89 (Site E) for ECONFCRHATCH and ECONFCRBC, respectively. Such variation amongst sites and
the large ranges over which each site spanned may be observed in the box plots of Fig. 4.8.
65
Differences in yearly averages of ECONFCRHATCH and ECONFCRBC tended to depend on the
site. For example, average ECONFCRHATCH values for Site B were 1.20 and 1.21 in 2009 and 2010,
respectively, while for Site E they were 1.71 and 1.34, respectively.
Figure 4.8: Global lot economic feed conversion ratios (ECONFCRHATCH) were calculated as feed(kg) :
(harvested biomass(kg)-initial biomass(kg)), where initial biomass was estimated using hatchery reports.
Values are displayed as box plots for each site. Error bars represent minimum and maximum lot values for
each site. Sites with common letters lack statistically significant differences in ECONFCRHATCH
values.*Due to relatively frequent fish movement at Site B (eg. transfers, grades, etc.), values were aggregated
across all lots and summarized for each year of production, thus reducing the number of global
ECONFCRHATCH values to two (ie. 2009 and 2010).
a a
a a
a
a
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
A B* C D E ELA
Eco
no
mic
Fee
d C
on
vers
ion
Rat
io
(fee
d(k
g):b
iom
ass
gain
(kg)
)
Site ID
Mean
66
4.2.11 - Biological FCR
Results from Proc GLM demonstrate that 53% of the variation in BIOFCR was explained by the
mixed model and that the fixed effect (ie. site) had a significant influence on BIOFCR values (p=0.0080).
Global lot BIOFCR values averaged 1.29 across sites (n=126, σ=0.17). Weighting lot values by
harvested biomass did not change this average. For comparison, BIOFCR values from the ELA were
slightly smaller, averaging 1.22 across years (n=5, σ=0.12). BIOFCR could not be calculated for
lotgroups.
As observed through the box plots and large whiskers of Fig. 4.9, global lot BIOFCR values were
variable within and amongst sites, although there were no significant differences found between any two
sites. The minimum global lot value across sites was 0.79 (Site B) while the maximum was 1.98 (Site C).
The lowest site average was 1.11 (Site B, n=15, σ=0.11), while the highest was 1.48 (Site C, n=9,
σ=0.24). Differences between average yearly BIOFCR values tended to depend on the site. For instance,
2009 and 2010 averages for Site B were 1.16 and 1.07, while for Site C they were 1.55 and 1.39.
Treatment of all commercial cumulative BIOFCR values with a non-iterative and iterative data
smoothing procedure removed 4% and 19% of values, respectively. The iterative data smoothing
procedure required 10 iterations, after which no additional values could be removed. Following
treatment with these procedures, BIOFCR tended to steadily increase with ABW, as observed in Fig. 4.10
(non-iterative) and Fig. 4.11 (iterative). For example, the regression of Fig. 4.11 gave BIOFCR values of
approximately 1.00 at an ABW of 0g and 1.20 at an ABW of 1400g. The median curve of Fig. 4.10
provided similar values. The trend of increasing BIOFCR, while visually apparent, was not statistically
strong, as suggested by the coefficient of determination provided in Fig. 4.11 (R2=0.179). Values were
highly variable despite the applied smoothing procedures, as observed through the irregular and sporadic
10th percentile curve of Fig. 4.10 and the generally expansive range of values observed in Fig. 4.11. The
67
breadth of BIOFCR values spanned in Fig. 4.11 was particularly large over the body weight range of 0-
200g.
Figure 4.9: Global lot biological feed conversion ratios, displayed as box plots for each site. Error bars represent
minimum and maximum values for each site. Sites with common letters lack statistically significant differences in
BIOFCR values.
Figure 4.10: Interval biological feed conversion ratios were calculated cumulatively for lots (ie. from Day 0). Aggregate
commercial values were divided into 50 g body weight stanzas, to which a non-iterative data smoothing procedure was
applied. The resulting median values and 10th and 90th percentiles of each 50 g stanza are shown.
a
a
a
a a
a
0
0.5
1
1.5
2
2.5
A B C D E ELA
Bio
logi
cal F
CR
(f
eed
(g)/
fish
:gai
n(g
)/fi
sh)
Site ID
Mean
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
0 500 1000 1500
Bio
logi
cal F
CR
(fe
ed(g
)/fi
sh:g
ain
(g)/
fish
)
Average Producer-Estimated Body Weight (g)
Median
90th Percentile
10th Percentile
68
Figure 4.11: Lot interval biological feed conversion ratios were calculated cumulatively (ie. from Day 0). An iterative
data smoothing procedure was applied to aggregate commercial values until the closest possible fit was achieved with any
model regressions available on Microsoft Excel, as determined by the coefficient of determination (R2). The remaining
values and model fitting results are shown.
4.2.12 - Nitrogen Retention Efficiency
Results from Proc GLM demonstrate that 49% of the variation in NRE was explained by the
mixed model and that the fixed effect (ie. site) did not have a significant influence on BIOFCR values
(p=0.0743).
Global lot NRE values averaged 28.2 across sites (n=126, σ=3.4). Weighting lot values by
harvested biomass did not change this average. For comparison, NRE values from the ELA were similar,
averaging 27.6 across years (n=5, σ=5.3). NRE could not be calculated for lotgroups.
As observed through the box plots and large whiskers of Fig. 4.12, global lot NRE values were
variable within and amongst sites. The minimum global lot value across sites was 19.0 (Site C) while the
maximum was 43.2 (Site B). The lowest site average was 25.9 (Sites C and D, n=9 and 12, σ=3.7 and
4.3, respectively), while the highest was 30.7 (Site B, n=15, σ=1.93). Differences between average yearly
y = 0.986e0.0001x R² = 0.179
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0 200 400 600 800 1000 1200 1400
Cu
mu
lati
ve B
iolo
gica
l FC
R
(fee
d(g
)/fi
sh:g
ain
(g)/
fish
)
Average Producer-Estimated Body Weight (g)
69
NRE values appeared to be less than those of other parameters. For instance, the largest difference
between any two yearly averages of the same site was between 2009 and 2010 Site B values (30.0 and
33.2, respectively).
Treatment of all commercial cumulative NRE values with an iterative data smoothing procedure
removed 9.6% of values in 20 iterations, after which no additional values could be removed. Following
treatment, NRE values tended to decline gradually with increasing ABW, as observed in Fig. 4.13. For
example, the regression providing the closest fit to the smoothed dataset (ie. polynomial, order 2) gave
NRE values of approximately 37.0 at an ABW of 0g and 29.0 at an ABW of 1450g. The trend of
decreasing NRE, while apparent through visual inspection, was not statistically strong (R2=0.0818). The
expansive range of values seen in Fig. 4.13 demonstrates the large variability in NRE values, persistent
despite the smoothing procedure. This variability was found to be especially large in the ABW range of
0-200g.
70
Figure 4.13: Lot interval nitrogen retention efficiency values were calculated cumulatively (ie. from Day 0). An iterative
data smoothing procedure was applied to aggregate commercial values until the closest possible fit was achieved with any
model regressions available on Microsoft Excel, as determined by the coefficient of determination (R2). The remaining
values and model fitting results are shown.
Figure 4.12: Global lot nitrogen retention efficiency values, displayed as box plots for each site. Error bars
represent minimum and maximum values for each site. Sites with common letters lack statistically significant
differences in nitrogen retention efficiency values.
a
a
a
a a a
0
5
10
15
20
25
30
35
40
45
50
A B C D E ELA
Nit
roge
n R
eten
tio
n E
ffic
ien
cy (
%)
Site ID
Mean
y = -3E-07x2 - 0.0031x + 34.514 R² = 0.0818
0
10
20
30
40
50
60
0 200 400 600 800 1000 1200 1400
Cu
mu
lati
ve N
itro
gen
R
eten
tio
n E
ffic
ien
cy (
%)
Average Producer-Estimated Body Weight (g)
71
4.3 - Site Rankings
Table 4.9: Rankings of each commercial site for various production parameters. Rankings are determined by site
averages for global lot values.
ARankings were the same for MORTRATEHATCH and MORTRATEBC, thus listed as one mortality rate parameter.
BMortality data from Site A deemed unreliable.
CTemperature data from Site A deemed unreliable.
DEconomic Feed Conversion Ratio, Hatchery (ECONFCRHATCH) calculated using hatchery-estimated initial biomass.
EEconomic Feed Conversion Ratio, Back-calculated (ECONFCRBC) calculated using back-calculated initial biomass.
Site rankings for various production parameters, as determined by site averages of global lot
values, are provided in Table 4.9. Site B was ranked first for 4 of the 6 listed parameters and second for
the other two. Site A was ranked higher than Site B for mortality rates and TGC values. However, Site A
was believed to have incomplete records of mortalities, and its water temperatures were sampled at a
depth of 20m (ie. typically colder than surface temperatures), factors which possibly inflated Site A`s
rankings for these parameters. Site A was ranked either second or third for all other parameters, giving
reason to believe that Site A performed relatively well in general. Site E was ranked either second or
third for all parameters, while sites C and D were ranked either fourth or fifth for all parameters. Site C
appeared to have the worst performance, ranking better than last for only parameter (TGC).
Production Parameter
Site Rank (#)
Site
A
Site
B
Site
C
Site
D
Site
E
Mortality RateA
(% of initial inventory) 1B
2 5 4 3
Thermal-Unit Growth Coefficient (growth rate) 1C
2 4 5 3
Economic FCR, ECONFCRHATCHD
(feed served(kg):biomass
gain(kg))
2 1 4T 4T 3
Economic FCR, ECONFCRBCE(feed served(kg):biomass
gain(kg)) 2 1 5 4 3
Biological FCR (feed(g)/fish:(avg. weight gain(g)/fish) 2T 1 5 4 2T
Nitrogen Retention Efficiency (%) 3 1 4T 4T 2
72
4.4 - Comparison of Traditional and Modified Thermal-Unit Growth Coefficient
Models
The residual sums of squares obtained through comparing producer-estimated and model-
predicted ABW values suggests that, overall, the modified TGC model better predicted ABW than did the
traditional TGC model (Table 4.10). Total RSS across commercial sites was less for the modified TGC.
Total RSS from the ELA was also less for the modified TGC. The traditional TGC was better able to
predict producer-estimated ABW values of Sites B and D.
Table 4.10: Comparison of the residual sums of squares between producer-estimated average body weight values and
corresponding model predictions, the latter performed using either traditional or modified thermal-unit growth
coefficient (TGC) models.
Site ID
Residual Sums of Squares
Traditional TGC Modified TGC
A
4.5X106 2.6X10
6
B
3.6X104 4.1X10
4
C
1.9X106 1.4X10
6
D
2.2X106 2.7X10
6
E
9.4X106 3.7X10
6
F
4.5X106 2.6X10
6
Commercial Total 1.8 X107 1.0X10
7
Experimental Lakes Area 8.9X104 8.7X10
4
73
4.5 - Upper and Lower Size Bounds in Relation to Model-Estimated ABW
To qualitatively evaluate trends and/or potential biases in producer-estimated ABW, these values,
as well as expectations for upper and lower ABW limits, were plotted relative to corresponding model
predictions for ABW. Data from Site A is included as an example, with TGCTRAD predictions
displayed in Fig. 4.14a and TGCMOD predictions in Fig. 4.14b.
As observed in Figs. 4.14a and 4.14b, producer-estimated ABW values from Site A tended to
deviate considerably from model predictions. The majority of producer-estimated values tended to lie
somewhere between model predictions and upper ABW limits. A greater number of producer estimations
were found outside of upper limits when plotted relative to TGCTRAD predictions, although more
producer estimations were plotted relative to TGCTRAD predictions overall. Since TGCMOD is divided
into two growth stanzas (ie. <500g and >500g), a break in values is observed around 500g in Fig. 4.14b.
These values were intentionally removed, as model predictions on either side of 500g matched producer-
estimated ABW values exactly.
For values less than 500g, producer estimations appeared to be fairly evenly balanced around
both TGCTRAD and TGCMOD predictions, with greater balance potentially occurring when plotted
relative to TGCMOD predictions. When greater than 500g, producer-estimated ABW values were less
balanced, deviating away from both TGCTRAD and TGCMOD predictions towards the plotted upper
limits. The extent of this deviation from TGCTRAD and TGCMOD predictions was not discernibly
different.
74
Figure 4.14a and 4.14b: The Fish-PrFEQ bioenergetics model and either the traditional or modified thermal-unit growth
coefficient models (Figs. 4.14a and 4.14b, respectively) were used to predict lot average body weight values (ABW),
plotted here in a linear fashion. Corresponding producer-estimated lot ABW values and expectations for upper and
lower individual body weight limits were plotted relative to model predictions in order to gain insight into behaviours of
producer-estimated body weights. Upper and lower size limits were modelled through the use of cage size variability data
from the Alma Aquaculture Research Station (AARS). All values from Site A are provided in Fig. 4.14a and 4.14b as an
example.
0
500
1000
1500
2000
2500
0 500 1000 1500 2000
Mo
del
-Pre
dic
ted
Ave
rage
Bo
dy
Wei
ght
(g)
Model-Predicted Average Body Weight (g)
B)
Modified TGC-PredictedABW
Producer-Estimated ABW
Upper ABW Limit
Lower ABW Limit
0
500
1000
1500
2000
2500
0 500 1000 1500 2000
Mo
del
-Pre
dic
ted
Ave
rage
Bo
dy
Wei
ght
(g)
Model-Predicted Average Body Weight (g)
A)
Traditional TGC-Predicted ABW
Producer-Estimated ABW
Upper ABW Limit
Lower ABW Limit
75
5 - DISCUSSION
Datasets provided by sites differed in a number of ways. First, datasets differed substantially in
size (ie. number of lots). For instance, Site A provided over half of the commercial lot values analyzed
for certain production parameters (ie. ECONFCR and BIOFCR). While the differences in sizes of site
datasets is largely a reflection of their scales of production, certain inconsistencies in site data required
modification and/or exclusion of lot values from analysis, compounding the existing variation in dataset
sizes. For example, the frequent movement of fish on certain sites complicated the tabulating of feed and
biomass gain data, necessitating its aggregation into across-site cohort totals (ie. production year totals),
thus decreasing the number of units for analysis. The years for which data was available also varied
amongst sites. For instance, only Site A provided data for the 2008 cohort, while Site E offered the only
complete lot values for the 2011 cohort. Data was provided by producers in a number of formats (eg.
field records from archives, photocopies of records, electronic spreadsheet transfers, online databases,
etc.). Questionable or incomplete records were excluded from analysis, partially contributing to
inconsistencies in the sizes of datasets and the periods of time represented.
As could be observed in Fig. 4.1, there were considerable differences between hatchery-estimated
and back-calculated initial inventories, and thus so too the production parameters using initial inventories
in their calculation (eg. MORTRATE and ECONFCR). The source of these differences is not clear.
Back-calculated initial lot inventories were estimated using total lot values for mortalities and harvests
(ie. the latter provided by processing plants). Hatchery technicians and processors typically estimate
relevant inventories by dividing total stocked or harvested biomass by average body weight, the latter
estimated using sub-samples of the population of interest. There is usually considerable variation in lot
body weights at stocking and harvest and thus reason to doubt the accuracy of both hatchery and
processor inventory estimates. Back-calculated inventory estimates may also be influenced by the
dedication with which producers record mortalities (ie. partial recording of mortalities will result in
smaller back-calculated initial inventories). While producers typically use hatchery estimations in the
76
calculation of relevant parameters throughout a grow-out cycle, uncertainties in both back-calculated and
hatchery initial inventories need to be addressed, and one method decided upon, for future benchmarking
efforts.
Results demonstrate considerable differences within and amongst sites in the length of grow-out
cycles, an expected outcome given the difference in production strategies across sites. The length of a
grow-out cycle is the result of a number of considerations (eg. water temperature profiles, fish
performance, targeted biomass or average body weight at harvest, etc.). The differences in lot grow-out
cycle lengths amongst sites are thus, not surprisingly, similar to those of lot temperatures and final body
weights. For instance, many of the shorter grow-out cycles corresponded to smaller final body weights
and/or higher average lot temperatures. Site B had the shortest average grow-out cycle, the smallest
average final body weight, and the second-highest average lot temperature, suggesting that this producer
might be focused on exploiting the warm growing seasons through shorter grow-out cycles.
Similar to the length of grow-out cycles, the producer’s choice of harvest weight (ie. FBW)
depends on many factors. While larger fish will typically fetch a higher price point for producers,
rainbow trout have also been shown in laboratory settings to preferentially catabolize dietary protein for
energy as they approach maturation size (ie. market size, >1000g) (Azevedo et al., 2004), a costly
endogenous process given the high costs of feed (Cheng et al., 2004). Producers are believed to be aware
of this process, and results of the current study likely suggest it is indeed occurring in commercial settings
as well (Figs. 4.11 and 4.13). The trade-off between market prices and feed efficiency might partially
explain the diversity in final body weights, such as the differences in average values between Site B
(μ=1000.5g) and Site E (μ=1296.4g). Within-site variation in harvest weights was also considerably large
(eg. Site C and Site E of fig. 4.2), suggesting that many factors contribute to the timing of harvest. For
instance, processors have mentioned that deviation from forecasted growth trajectories and corresponding
harvest schedules sometimes force producers to harvest prematurely to ensure the continuous and timely
flow of products to the marketplace (Geoff Cole, personal communication, December 2011). Such
77
realities suggest the need for continued refinement of growth models with commercial data as well as the
continued optimization of harvest schedules in regards to market prices and an improved understanding of
changes to feed efficiency as a function of body weight.
While the observed variation in average lot temperatures is to be expected given the different
lengths of grow-out cycles, there were considerable differences in temperature sampling and reporting
methods amongst sites that could have influenced this variation. Water temperatures throughout a cage
constantly fluctuate, especially given the presence of thermoclines and the mixing of large bodies of water
in Georgian Bay. While producers will typically sample temperatures from the surface of their cages (ie.
within one metre of the surface), these temperatures will likely change considerably throughout any given
day and are not necessarily representative of the water in which fish are most likely to be found. This for
example might be true during periods of frozen surface water, which typically last multiple months, and
for which water temperatures were generalized for this study as 1°C. Or, during hot summer months
when technicians continue to report surface water temperatures despite trout remaining below
thermoclines. Site A temperature data was either provided by a local water treatment plant (ie. for 2008
and 2009, sampled at a depth of 20m), or sampled inconsistently and sporadically at a number of depths
(ie. 2010), necessitating the exclusion of certain data points from analysis. There may also have been
questionable data from sites reporting monthly values. For these sites, management events (eg. transfers,
harvests) triggered the end of an interval (ie. as would be reported in MANEVFORM). However, interval
temperatures could not be calculated up to the specific day of any event. That is, only cumulative
temperature values for the entire calendar month were ever reported. Such an inaccuracy might influence
both global lot temperature and TGC values. Therefore, there are many issues remaining with
commercial methods for water temperature sampling that need to be resolved.
Commercial TGC values (μ=0.165) were for the most part lower than what is achieved in
laboratory settings (Dumas et al., 2007a), not surprising given the variability in environmental and
management conditions experienced in commercial settings. Aside from Site A, only Site B achieved an
78
average TGC better than that of the ELA, while only Site D was found to have values significantly lower
than those of the ELA. The mixed model was able to explain the variation in TGC relatively well
(R2=0.839), permitting a reasonable level of confidence in statistical interpretations of site differences.
The lot TGC values from Site A were believed to be inflated due to the temperature data for 2008 and
2009 cohorts being sampled at a depth of 20m, at which water is generally colder than at higher depths.
The variation within and amongst sites in growth rates is not surprising given the dramatic
differences amongst sites in genetic backgrounds of fish, final harvested body weights, feeds and feeding
strategies, and other management variables (eg. stocking densities, inventory adjustment schemes, etc.).
Fingerlings are provided to producers by a number of private hatcheries, each having developed its own
breeding program independently. Overturf et al. (2003) observed distinct differences in growth rates of
five North American strains of rainbow trout raised in laboratory settings. Furthermore, the growth rates
of rainbow trout with genetic backgrounds likely similar to those of commercial Ontario trout were
observed to change with life stage (Dumas et al., 2007a), suggesting that the observed differences
amongst sites in final average body weights (ie. harvest weights) might influence corresponding
differences in growth rates.
Producer-estimated average body weights were plotted against corresponding model predictions
to gain insight into the behaviour of producer estimates and to test the performance of the modified
thermal-unit growth coefficient model in commercial settings (Dumas et al., 2007a). The modified TGC
was generally better able to predict producer-estimated ABW (Table 4.10), supporting the observations of
Dumas et al. (2007a). Nonetheless, regardless of the model used for predictive comparisons, producer
ABW estimations appeared to be biased towards larger fish, particularly for body weights greater than
500g (Figs. 4.14a and 4.14b). This was anticipated, as producers are known to prefer feed enticement and
dip-netting as their main method for body weight sampling, a process believed to select for more
aggressive and thus larger fish (Gord Cole, personal communication, July 2011).
79
There were two individual sites (Sites B and D) for which the traditional TGC model better
predicted producer-estimated ABW. The traditional TGC was found in laboratory settings to
overestimate body weights greater than 500g (Dumas et al., 2007a). As producer ABW estimations were
suspected of having an increasing bias towards larger trout as a function of increasing body weight, the
traditional TGC might have in fact been expected to better predict such biased body weight estimations.
Nevertheless, producer estimations exhibited considerable deviation from both traditional and modified
TGC model predictions (Figs. 4.14a and 4.14b), suggesting that standardization of weight and
temperature sampling methods and continued refinement of growth models with an improved commercial
dataset is needed.
Ontario producers typically cite their cumulative mortality rates at 5% of initial inventories or less
(Gord Cole, personal communication, July 2011). As such, this study’s results for mortality rate
parameters, regardless of their weighting schemes, were unexpectedly high. For instance, average
MORTRATEHATCH and MORTRATEBC values were 12.4% and 14.1%, respectively, when weighted
by lot harvested biomass. However, these values are actually somewhat similar to mortality rates
published for rainbow trout and other salmonids. Overturf et al. (2003) recorded cumulative values of
4.8, 6.0, and 11.5% from three commercial strains of American rainbow trout grown in laboratory settings
to a weight of 350g, while Soares et al. (2011) recorded 24% mortality of Atlantic salmon raised
commercially in Scotland to an average body weight of 4.5 to 5.0 kg.
While the differences in site mortality rates might be expected given the differences in genetic
backgrounds of fish and their demonstrated influence on mortality rates (Overturf et al., 2003),
differences in the completeness of site mortality records were believed to have possibly had an effect on
the observed variation in mortality rates as well. While producers always closely monitor the health
status of their animals, this attentiveness does not necessarily guarantee the accurate recording of
mortalities. For example, Site A was suspected of having incomplete or inaccurate mortality recording,
necessitating the removal of its values from industry averages. Reported mortalities might be particularly
80
misrepresented during periods of high occurrence (eg. initial stocking, peak summer temperatures, etc.),
when accurate recording requires relatively extra time and effort. While the inaccurate recording of
mortalities would not necessarily limit the producer’s responsiveness to health issues, it will undoubtedly
limit the effectiveness of subsequent benchmarking efforts. For example, the accuracy of mortality
recording was believed to influence the extent of differences between site ININVENTHATCH and
ININVENTBC values, and thus MORTRATEHATCH and MORTRATEBC values as well.
Uncertainties in these parameters limit the value of comparative benchmarking exercises. Thus, re-
dedication to accurate mortality recording by all participating producers is needed.
Interval mortality rates, aggregated across Ontario and treated with a non-iterative smoothing
procedure, demonstrated a general decrease over time (Fig. 4.7), agreeing with observations from Soares
et al., 2011. However, despite the exclusion of interval values corresponding to fish movement events
and associated increases in mortalities (eg. transfers, grades, etc.), mortality rates tended to fluctuate
throughout grow-out cycles. As observed in Fig. 4.7, this might be related to increased summer water
temperatures often occurring near the middle of grow-out cycles. As cages were stocked at different
times throughout the year (eg. approximately 57% in the fall, 43% in the spring), periods of temperature
extremes would occur at varying stages of grow-out cycles, helping to explain the fluctuations in
mortality rates observed over time in the aggregated dataset. The mortality rate curves of Figs. 4.6 and
4.7 offer benchmarks against which producers can compare mortality rates of their own fish, helping to
evaluate the effectiveness of management strategies (eg. stocking densities, quality of fingerlings, feeding
intensity, etc.). Furthermore, the median and 10th and 90
thpercentile curves of Fig. 4.6 offer three
different health scenarios for current and prospective producers to incorporate into production plans (ie.
average, poor, and good health statuses, respectively), aiding producers in risk management and
budgeting of expected losses.
Commercial economic FCR values were generally higher than those of the ELA (ie. only site B
had an average value lower than that of ELA). However, this might have been anticipated given the
81
substantially greater final body weights of the commercial sites and the demonstrated negative
relationship between final body weight and feed efficiency (Figs. 4.10 and 4.11, Azevedo et al., 2004).
Although none of the sites were found to have significantly different values, 48.3% of the variation in
FCRECONHATCH was not explained by the mixed model, suggesting that improved data quality might
facilitate improved recognition of site differences.
Qualitative observation of economic FCR values (ie. ECONFCRHATCH values, Fig. 4.8)
suggests substantial variation within and amongst farms. This was an expected outcome given the
variation in final body weights, feed compositions (eg. Table 4.8), feeding strategies, and genetic
backgrounds. Despite the lack of statistically significant differences amongst sites, average values and
descriptive statistics for economic FCR parameters may be used as benchmarks against which producers
can compare the performance of their own animals. However, there were possible sources of error in
economic FCR calculations that should be noted for their potential influence on any conclusions reached
by producers. For instance, technicians sometimes approximate the total feed distributed to each lot on
any given day by “eyeballing” the volume displaced from the given feed receptacle (eg. feed silo, feed
buckets, etc.), using experience to equate the estimated volume into weight.
Distributions of lot biological FCR values were similar to those of economic FCR. While visual
inspection found substantial variation in values within and amongst sites (ie. Fig. 4.9), there were no
statistically significant differences found amongst sites. As with economic FCR, almost half of the
variation in global biological FCR values was unexplained by the mixed model, suggesting that
implementation of and industry commitment to standardized recording techniques could improve the
strength of statistical interpretations. The sites believed to have recorded mortalities with a reasonable
level of accuracy (ie. Sites B-E) had large differences between average ECONFCR and BIOFCR values,
demonstrating the high sensitivity of cumulative ECONFCR values relative to BIOFCR values to
mortalities. That is, while mortalities will influence ECONFCR values through loss of biomass gain (ie.
82
causing higher ECONFCR values), BIOFCR can account for these mortalities, as values are summarized
on a per fish basis.
There were sources of uncertainty in BIOFCR values that might be reduced through
standardization of sampling methods. BIOFCR values require estimates of average initial and final lot
body weights. Hatchery and processing plant estimates were used to represent these values rather than
producer estimates as they sampled relatively larger groups of fish and sampled from entire ranges of lot
body weights, thus reducing the likelihood of sampling bias. Nonetheless, average body weights from
hatcheries and processing plants were estimated using only sub-samples of all fish handled on any given
day. Furthermore, the harvesting process is a multi-day affair, with processor-reported average body
weights fluctuating dramatically from one day to the next. Although this study applied a systematic
method for identifying final body weights from these ranges of daily values, there still remained a strong
chance that these values were not entirely accurate representations of their corresponding lot’s average
harvested body weight.
While trends in site NRE values were similar to those of both biological and economical feed
conversion ratios, there were differences worthy of discussion. First, it should be noted that NRE was the
only production parameter on which the fixed effect (ie. Site) of the mixed model was not found to have a
significant effect (Pr>F=0.0743). This suggests that the relatively large random error in NRE values -
likely associated with inaccuracies in the use of feed manufacturers’ feeding guidelines in assignment of
feed types to reported feed totals - greatly limits the confidence in any conclusions regarding trends in
NRE values.
Site E slightly outperformed Site A in average NRE (Site A μNRE=27.9, Site E
μFCRECONHATCH=29.1). At first glance, this seems surprising considering that Site E ECONFCR values
appeared considerably larger than those of Site A (Site A μFCRECONHATCH=1.30, Site E
μFCRECONHATCH=1.46) and that nitrogen retention efficiency is closely related to feed efficiency in rainbow
83
trout (Azevedo et al., 2004). However, the relatively low average ECONFCR value of Site A may in fact
be more related to the site’s relatively low number of reported mortalities for each lot. Since ECONFCR
values are calculated at the entire cage level, an increase in mortalities would result in increased
ECONFCR values through a decrease in final calculated biomass gain. Alternatively, BIOFCR and NRE
values are calculated at the individual fish level and are thus less susceptible to mortalities. Sites B
through E reported considerably more total lot mortalities than Site A, while differences between
ECONFCR and BIOFCR values of these sites were also considerably larger, supporting this idea. Thus,
the susceptibility of ECONFCR values to mortality events could explain how both BIOFCR and NRE
values of Site A and Site E appear similar (Site A μBIOFCR=1.28, Site E μBIOFCR=1.28) while ECONFCR
values appear substantially different.
Cumulative interval BIOFCR and NRE values, aggregated across commercial sites, demonstrated
decreases in feed and nitrogen retention efficiency as a function of increasing body weight (Figs. 4.10,
4.11, and 4.13), supporting observations made in laboratory settings (Azevedo et al., 2004). The 90th
percentile BIOFCR curve of Fig. 4.10 provides a good benchmark of the superior level of feed efficiency
that is achievable on Ontario cage culture operations when producers provide an ideal blend of
management variables (eg. stocking densities, feed types, feeding strategies, etc.) and environmental
conditions for the given genetic background of the cultured trout.
Despite the employed data smoothing procedures, interval values of Figs. 4.11 and 4.13 were
highly sporadic and variable and regressions applied to the smoothed datasets were not statistically
strong. Calculation of these values required both producer-estimated interval body weights and back-
calculated interval inventories (ie. BIOFCR = feed served per fish(g): body weight gain per fish (g); NRE
= nitrogen served per fish : nitrogen gain per fish), thus also indirectly involving interval mortalities and
processing plant-estimated harvests (ie. for back-calculation of interval inventories). Furthermore, as
mentioned, calculations of NRE values required approximation of amounts of each distributed feed type
using the feeding guidelines provided by each feed producer. Thus, the lack of statistically strong trends
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is not surprising given the multiple potential sources of error. Improved and systematic sampling
methods, particularly with respect to average body weights, will be necessary to improve in this regard.
Improved industry BIOFCR and NRE curves will be important for two main reasons. First, given the
extent of variation in genetics, feed types and feeding strategies, producers require industry BIOFCR and
NRE curves for benchmarks, against which they can compare their own curves for insight into the
effectiveness of their particular strain(s) of trout and their various management and feeding strategies
used throughout the year. Secondly, accurate BIOFCR curves are needed to better support the producer’s
timing of harvest with regards to final market prices and feeding costs as a function of body weight.
Site rankings (ie. Table 4.9) provide a generalized and preliminary summary of the relative
performance of each site. While there is tremendous uncertainty and variability in performance
parameters, generalized trends such as these provide perspective for producers on the effectiveness of
their operations. For instance, Sites C and D could be said to be achieving the worst performance of the
five commercial sites, necessitating careful auditing of their various management practices. These
rankings must also be considered in the context of final body weights, knowing their likely influence on
feed efficiency and growth rates (Azevedo et al., 2004; Dumas et al., 2007a). For example, while Site E
was typically ranked somewhere in the middle for all performance parameters, it also produced on
average the largest final body weights, suggesting that its rankings might be slightly better if its fish were
harvested earlier (eg. average BIOFCR currently tied for second place). Refinement and standardization
of recording techniques will likely permit more meaningful evaluations of time-series data (eg. BIOFCR
curves, growth curves, etc.) and the effects of various management and environmental variables on trout
performance, ultimately enabling more advanced benchmarking exercises and more meaningful
conclusions for producers.
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5.1 – Conclusion
This preliminary survey of commercial Ontario rainbow trout performance data has found
substantial variation within and amongst Ontario sites in length of grow-out cycles, final body weights,
growth rates, mortality rates, and economic and biological feed conversion ratios, a likely result of diverse
management strategies and environmental conditions as well as idiosyncratic performance recording
techniques. Commercial performance was somewhat poorer than what is often reported by producers,
particularly for such parameters as mortality rates, thermal-unit growth coefficients, and economic feed
conversion ratios (average weighted values of 12.4%, 0.165, and 1.36, respectively). Variability in
performance across sites suggests that benchmarking could be highly valuable for improving the
economical sustainability of the sector. Descriptive statistics and site rankings for all performance
parameters were provided as benchmarks for producers to gain perspective as to the real effectiveness of
their production strategies. While statistically significant differences between sites were only found for
growth and mortality rates, there was considerable variation in all performance parameters that was not
explained by the linear mixed model, demonstrating the need for refinement and standardization of
performance recording systems for effective future benchmarking.
Exploration of time-series data suggests a decrease in feed and nitrogen retention efficiency with
increasing trout body weight, although there was little statistical confidence in these trends. Furthermore,
it was found that the modified thermal-unit growth coefficient model was better able to predict producer-
estimated average body weights than the traditional model, although this result was not conclusive.
Producer-estimated average body weights were found to deviate considerably from both traditional and
modified TGC model predictions, and were suspected of potential bias towards larger fish. These results
further suggest the need for standardized sampling methods in order to refine growth models and
evaluations of time-series data with continuously improved commercial datasets.
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This study has highlighted a number of inconsistencies amongst sites and potential sources of
error in performance recording and evaluation techniques, all of which are bottlenecks to the effective
performance benchmarking of the Ontario trout industry. Water temperatures are recorded inconsistently
over time and at varying depths, possibly misrepresenting the thermal energy available to trout over the
course of a grow-out cycle. Re-dedication to accurate mortality recording is needed by all producers.
Producer methods for estimating average body weights are likely biased towards larger fish, particularly
when body weights are greater than 500g. Finally, methods used in this study for determining distributed
amounts of different feed types were unreliable. Improvements to these areas will be of the utmost
priority for the preliminary standardization of trout performance recording systems in Ontario, necessary
for the effective continued performance benchmarking of the industry.
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6 – GENERAL DISCUSSION AND INDUSTRY RECOMMENDATIONS
It was the primary goal of this thesis to perform a preliminary survey and evaluation of
performance data from the Ontario trout cage culture industry. The survey has resulted in the generation
of the first ever industry benchmarks for a number of important performance parameters (eg. TGC,
MORTRATE, BIOFCR, ECONFCR, NRE). Weighted averages for these parameters offer producers
with industry standards against which they can compare their own levels of performance, while 90th
percentile values offer realistic and achievable performance targets. In addition, evaluation of
longitudinal time-series data has resulted in median and 90th percentile time-series curves for mortality
rates, biological feed conversion ratios, and nitrogen retention efficiencies that span the length of a
standard grow-out cycle. With these, producers are offered further insight into how their relative
production performance and operational effectiveness may change throughout the duration of grow-out
cycles. Finally, this survey has documented impressive variability within and amongst Ontario cage sites
in virtually all evaluated performance parameters, suggesting substantial opportunity for the continuous
improvement of operational performance and the reduction of operational variability in this regard.
Having illustrated the tremendous variability within and amongst Ontario cage culture operations
in performance parameter values, there is ample reason to continue performance benchmarking in order to
identify sources of unexpected or unexplained variability in performance, to further identify producers’
comparative strengths and weaknesses, and to pinpoint sources of superior performance and its
contributing factors. However, in order for continued benchmarking efforts to be of value to producers,
the methods used to record performance amongst sites need to be refined and standardized. Having
outlined the numerous idiosyncrasies and potential sources of error amongst current producer
performance recording schemes, there are thus numerous recommendations for the preliminary
standardization of Ontario trout performance recording systems.
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The implementation of modular, scalable, and expandable recording systems for the purposes of
performance benchmarking and maximum continuous performance improvement will not be immediately
possible given practical limitations in financial and human capital. As such, the following
recommendations are made with acknowledgement and appreciation of the day-to-day challenges in site
management and operational workloads. However, it must also be mentioned that a reality of
implementing animal recording systems and performing effective benchmarking services is a certain
investment in time and energy on the part of participating producers (Wasike et al., 2011). Without
dedicated efforts from site managers and technicians, the accuracy and consistency in performance data
by which effective benchmarking is driven are not possible, thus preventing targeted continuous
performance improvement.
Water temperatures were sampled inconsistently over time and at varying depths. While the
mixed model was best able to describe the variation in global TGC values, there is still substantial room
for improvement in this regard (R2=0.839), and thus so too the effectiveness of relevant benchmarking
exercises. It is recommended that daily water temperatures be sampled at depths of 0m (ie. surface), 5m,
and 10m. Sampling at a range of depths is important given their constant fluctuations and the presence of
thermoclines during important months of growth (ie. summer). Surface temperatures often provide little
to no value in summer months when trout prefer to remain below thermoclines. In an effort to be
systematic, and given the fluctuations in water temperatures throughout any given day, it is recommended
that technicians sample water temperatures in the morning. It is believed that most technicians already
sample in the morning prior to feeding.
Given the variation in mortality rate values, the differences between MORTRATEHATCH and
MORTRATEBC values, as well as the amount of variability in MORTRATEHATCH unexplained by the
mixed model (R2=0.765), it is believed that efforts in mortality recording were inconsistent amongst
producers. For more effective benchmarking of mortality rates, it is recommended that all mortalities be
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recorded, particularly during periods of extensive fish loss (eg. initial stocking, transfer events, extreme
summer temperatures, harvesting events, etc.).
It is recommended that hatchery-estimated initial inventories be used for future benchmarking
efforts to maintain consistency with current producer methods and to permit live feedback on trends in
site performance (ie. back-calculation of initial inventories would require final processor-estimated
harvest totals, thus precluding live feedback on site trends). Once producer re-dedication to accurate
mortality recording can be confirmed, use of back-calculated initial inventories may be considered. In the
meantime, incomplete mortality recording limits the reliability of back-calculated estimates.
Producer average body weight sampling methods were suspected of bias. This was suggested by
the tendency of producer-estimated ABW values to deviate from model predictions towards the larger
range of lot size distributions, although the extent to which these deviations was due to either sampling
biases or model shortcomings is not known (eg. Figs. 4.14a and 4.14b). Body weights are sampled by
Ontario producers primarily through feed enticement and subsequent dip-netting. Body weights are also
sampled to a lesser extent through aggregation of trout into seine nets, also usually requiring feed
enticement, and subsequent dip-netting from random locations within the seine net. It is the belief of
producers that feed enticement attracts the most aggressive (ie. largest) fish towards nets, thus inflating
estimated average body weights relative to their actual values (Gord Cole, personal communication, July
2011). Although feed enticement of trout into seine nets might still allow for bias, subsequent dip-netting
is done without bias and from a larger group of fish than would be accessible without use of seine nets.
Regardless of the sampling method, insufficient proportions of total cage populations are
currently being sampled at any one time. For example, it has been recommended for terrestrial livestock
industries in developing nations that in order to have reliable across-herd genetic evaluations, at least 50%
of animals must be recorded over time (Stewart et al., 1991). Current trout sampling methods usually
90
access less than 1% of all individuals within a lot at any one time (eg. largest samples from the current
dataset weighed approximately 500 fish from a cage of approximately 50,000 individuals).
The frequency of lot body weight sampling over a grow-out cycle is also currently limited. For
example, average lot body weights are usually not reported on days of fish movement. While these
movement events offer producers a good understanding of current lot size distributions, this
understanding appears to delay for multiple weeks the need for subsequent body weight sampling. Thus,
on either side of a fish movement event there are gaps in lot body weight information that together may
span up to multiple months in length. Also, producers often limit body weight sampling when nearing
harvest, opting instead for the “eyeballing” of body weights and relying on processor-reported data for
estimated final body weights. However, accurate performance data and performance benchmarking near
periods of harvest provide relatively greater value given the scale of feeding and thus feeding costs.
Better datasets are needed for trout of harvest size in order to more accurately define trends in growth
rates and feed efficiency as functions of body weight, thus providing better support for producers’
optimizing and forecasting of harvest schedules.
More frequent sampling from larger proportions of cages would require producer adoption of
seine netting (eg. monthly) and/or the incorporation of modern sampling tools capable of estimating cage
biomass and size distributions from within cages (eg. AKVA group biomass estimator). While the latter
might be preferable, such technology is prohibitively expensive for the small scale of operations
characterizing the independent trout producers of Ontario. Widespread adoption of frequent seine netting
is also not a realistic recommendation given that most producers rarely if ever use the technique as it is.
Thus, until industry conglomeration, vertical integration, and/or establishment of producer co-ops provide
the economies of scale and/or infrastructural capital necessary for the transaction of modern biomass and
body weight sampling tools, it is recommended that such tools be provided by the University of Guelph
and circulated amongst sites as a continuation of this research project.
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The accuracy of estimations regarding the total amounts of feed distributed amongst lots will not
likely be improved in absence of computerized feed delivery systems (eg. as often used by Atlantic
salmon producers). However, the accuracy of estimation of amounts of separate feed types could be
improved with relatively little additional effort expended by producers. As it stands there is little to no
reporting of feed type amounts for individual lots, necessitating the use of feeding guidelines provided by
feed manufacturers to assign feed types to reported lot feed totals. Simple annotation of feed types (eg.
Feed Type ID) alongside reported lot feed totals would allow for a more accurate representation of feed
type amounts, providing greater confidence in evaluation and benchmarking of relevant performance
parameters (eg. NRE).
The differences between commercial lot and lotgroup averages for mortality rate and economic
FCR parameters did not appear substantial. However, lot values were more greatly influenced by the
HARVBIOM weighting scheme. Lot values are derived from the same data as lotgroup values, but are
summarized as total values (ie. stocking to completion of harvest) for the individual lots resulting from
division of lotgroups (ie. splitting, grading, etc.). As such, there is to be greater expected variability in lot
values, especially when considering that lotgroups are often graded into multiple lots based on their
distinct size classes. Diverging performance trajectories amongst resulting lots would likely lead to
greater variability of lot values relative to lotgroup values, and thus greater sensitivity of average site lot
values to HARVBIOM weighting schemes.
Separation into lot values provided greater sample sizes and a larger dataset, and thus greater
degrees of freedom for statistical evaluations. Furthermore, lot values likely provided relatively superior
representation of the variability and range of industry performance parameter values. However,
separation into lot values required substantial amounts of time and effort to manually divide parameter
totals from inter-movement periods (eg. feed totals, mortality totals, initial inventories, etc.). Such
divisions were made all the more difficult when data was reported on a monthly basis, necessitating for
instance the use of bioenergetic models in the estimation of daily feed totals. While this was possible for
92
the scale of this project, and perhaps even necessary given the relatively small dataset, this might become
impractical when expanding these benchmarking efforts on a temporal and spatial scale. Furthermore,
while division into lot values was a reasonable exercise for this project given the relatively minimal
amount of fish movement common to Ontario trout culture, benchmarking efforts for other species and/or
production systems (eg. tilapia, carp, raceway systems, recirculating systems, etc.) might preclude these
divisions given the frequent movement of fish in these types of systems. For instance, mortality rate and
economic FCR parameter values for Site B had to be summarized on a per year basis due to the relatively
frequent movement of fish and the resulting difficulties in identifying distinct lotgroups and/or dividing
inter-movement totals amongst lots. Given the lack of obvious differences between lot and lotgroup
values, lotgroup totals should be sufficient both for continued benchmarking efforts in Ontario and for the
application of similar efforts to any additional species and/or production systems, especially if performed
on a larger scale.
Many producers are unfamiliar with biological FCR as a performance parameter, instead using
economic FCR as a rough indicator of site-level feed efficiency. Economic FCR values appeared to be
relatively sensitive to mortalities and fish movement events (eg. grades, transfers, etc.) occurring over the
period of interest. Since feed and mortality totals can be summarized for intervals between fish
movement events, relatively accurate estimations of feed(g)/fish(g) can be calculated and used in
biological FCR calculations. Cumulative feed/fish totals can thus be assigned to individual fish as they
are moved about from one lot to the next. Alternatively, economic FCR calculations require summation
of feed and biomass gain totals for any and all lots that have shared fish over a grow-out cycle. As
identification of common lots/lotgroups is not always possible, summarizing for entire cohorts (ie.
production years) is sometimes necessary, as was the case in this study for Site B. This is not ideal, as
sample sizes and/or degrees of freedom are reduced as a result. As benchmarking efforts expand
internationally, it is believed that the relative flexibility of biological FCR will make it a more valuable
93
parameter than economic FCR. It is hoped that Ontario producers will begin to familiarize themselves
with this parameter to allow for its preferred use in benchmarking efforts.
Following standardization of trout recording systems and incorporation of modern biomass
sampling tools, it is recommended that the improved dataset be continuously analyzed using more
advanced time-series models (eg. mixed models, random regressions, etc.) expanded to include more
effects (eg. pedigree, sex, strain, hatchery background, etc.). Expansion of evaluative models would
permit more accurate determination of differences between sites and strains as well as changes in
performance parameters (eg. TGC, BIOFCR, NRE, etc.) as functions of time and body weight. Such
efforts will enable more advanced benchmarking exercises and thus the output of more meaningful
conclusions for producers as to the relative performance of their animals. Furthermore, as standardized
recording systems are adopted by other regions and for use in other species and/or production systems,
performance benchmarks can be compared across regions for identification of superior genetics and/or
management strategies.
94
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8 - APPENDIX
8.1 – Glossary
ACRONYM TERM
1STABW<500 First Producer-Estimated Average Body Weight Greater Than 500g
1STABW>500 First Producer-Estimated Average Body Weight Less Than 500g
AARS Alma Aquaculture Research Station
ABW Average Body Weight (g)
ABWTGCMOD Modified TGC-Predicted Average Body Weight (g)
ABWTGCTRAD Traditional TGC-Predicted Average Body Weight (g)
BIOLFCR Biological Feed Conversion Ratio
BIOMGAIN Biomass Gain (kg)
BIOMIN Biomass In (kg)
BIOMOUT Biomass Out (kg)
CUMUL Cumulative (value)
DAILYFORM Daily Format (spreadsheet type)
DD Degree Days (#)
DD@1STABW>5
00
Cumulative Degree Days at 1STABW>500 (#)
ECONFCR Economic Feed Conversion Ratio
FBW Final Body Weight (g)
FEEDFISH Feed Served(g)/Fish
FEEDID Commercial Feed Identification Code
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FEEDSERV Feed Served (kg)
FEEDTYPE Feed Type
FEEDTYPEAMN
T
Feed Type Amount (kg)
HARV Harvests (# fish)
HARVBIOM Harvested Biomass (kg)
IBW Initial Body Weight (g)
INBIOM Initial Biomass (kg)
INBIOMHATCH Initial Biomass, Hatchery-Estimated Initial Inventory (kg)
INBIOMBC Initial Biomass, Back-calculated Initial Inventory (kg)
ININVENT Initial Inventory (# fish)
ININVENTBC Initial Inventory, Back-Calculated Estimate (# fish)
ININVENTHATC
H
Initial Inventory, Hatchery Estimate (# fish)
INT Interval (value)
INVENT Inventory (# fish)
MANEVFORM Management Event Form (spreadsheet type)
MORTRATE Mortality Rate (% of ININVENT)
MORTRATEBC Mortality Rate (% of ININVENTBC)
MORTRATEHAT
CH
Mortality Rate (% of ININVENTHATCH)
MORTS Mortalities (# fish)
NFISH Nitrogen(g)/Fish
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NRE Nitrogen Retention Efficiency
OBS Observed, Producer-Estimated Average Body Weight (g)
PRE Predicted, Model-Predicted Average Body Weight (g)
PROFISH Protein(g)/Fish
PROSERV Protein Served (kg)
STANBIOM Standing Biomass (kg)
SUMFORM Summary Format (spreadsheet type)
TEMP Temperature (°C)
TGC Thermal-Unit Growth Coefficient (growth rate)
TGCMOD Modified TGC (growth rate)
TGCTRAD Traditional TGC (growth rate)
TRANSFIN Transfer In (# fish)
TRANSFOUT Transfer Out (# fish)
103
104
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