| Province of residence | Unweighted | Weighted |
|---|---|---|
| Percentage | ||
| Newfoundland and Labrador | 22.8 | 21.2 |
| Prince Edward Island | 22.8 | 20.6 |
| Nova Scotia | 29.4 | 26.5 |
| New Brunswick | 28.5 | 25.2 |
| Quebec | 32.6 | 28.1 |
| Ontario | 31.0 | 28.7 |
| Manitoba | 32.2 | 29.0 |
| Saskatchewan | 29.3 | 26.6 |
| Alberta | 26.9 | 24.9 |
| British Columbia | 30.9 | 29.0 |
| Canada | 29.9 | 28.0 |
Catalogue number: 892000062022003
Release date: May 24, 2022 Updated: January 25, 2023
In this video, you will learn the answers to the following questions:
(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "Statistics 101 Confidence intervals".)
Statistics 101: Confidence intervals
Have you heard this before…
(Text on screen: 37% of Canadians anticipate working from home for the foreseeable future, based on an online survey of 2,000 Canadian adults, with a margin of error of +/- 2.0 percentage points, 19 times out of 20. Do you know what "a margin of error of +/- 2.0 percentage points, 19 times out of 20" means? This is an example of a confidence interval.)
You have probably heard on the radio or television or read in the newspaper a statement like this:
37% of Canadians anticipate working from home for the foreseeable future, based on an online survey of 2,000 Canadian adults, with a margin of error of +/- 2.0 percentage points, 19 times out of 20.
But what exactly does it mean and why is the information presented in this way?
Working with statistics involves an element of uncertainty, and in this video we will see how confidence intervals and their underlying concepts help us understand and measure this uncertainty.
The statement above actually presents an example of a confidence interval, even though at first glance it does not look like an interval. The interval in this case is 37% +/- 2.0% - in other words, the interval goes from 35% to 39%.
At the end of this presentation you will be able to read similar statements and understand that they represent confidence intervals. You will also understand what a "margin of error" is, and what is meant by the phrase "19 times out of 20".
As pre-requisite viewing for this video, make sure you've watched our other Statistics 101 videos called "Exploring measures of central tendency" and "Exploring measures of dispersion".
Learning goals
(Text on screen: In this video, you will learn the answers to the following questions: What are confidence intervals? Why do we use confidence intervals? What factors have an impact on a confidence interval?)
By the end of this video you will understand what confidence intervals are, why we use them, and what factors have an impact on them.
Understanding the measures of central tendency and the measures of dispersion before watching this video will help you to understand confidence intervals.
Steps of a data journey
(Text on screen: Supported by a foundation of stewardship, metadata, standards and quality.)
(Diagram of the Steps of the data journey: Step 1 - define, find, gather; Step 2 - explore, clean, describe; Step 3 - analyze, model; Step 4 - tell the story. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)
This diagram is a visual representation of the data journey from collecting the data; to exploring, cleaning, describing and understanding the data; to analyzing the data; and lastly to communicating with others the story the data tell.
Step 2: Explore, clean, and describe; Step 3: Analyze and model; and Step 4: Tell the story
(Diagram of the Steps of the data journey with an emphasis on Step 2: Explore, clean, and describe; Step 3: Analyze and model; and Step 4: Tell the story.)
Confidence intervals are helpful in steps 2, 3 and 4 of the data journey.
What is a Confidence Interval?
(text on screen:
Presents a range of possible values, rather than a single estimated value.
Represents the uncertainty resulting from the use of a sample.
The width of the confidence interval is related to the level of uncertainty.)
(Figure 1 demonstrating an example of confidence interval: the average grade on a math test in a class of 100 students. The estimated value is 70%, the lower bound is at 60% and the upper bound is at 80%. The values included between the lower and the upper bounds represent the confidence interval.)
A confidence interval is a range of possible values for something that we want to estimate – for example, what is the average grade on a math test in a particular class of 100 students. It is typically based on a sample that is representative of the population; however the sample is often small compared to the population. In the example here we have math grades for a sample of 10 students from a class of 100 students.
Since the estimate is based on a sample, there remains some uncertainty about the true value. The confidence interval accounts for this uncertainty by including a range of values, and not just the estimate itself. The more uncertainty there is, the wider the confidence interval will be.
Why do we use confidence intervals?
(Figure 1 demonstrating a young man wondering why we use confidence intervals.)
In statistics, we often estimate a value for a total population using a sample.
The value derived from the sample is not the true value, but an estimate of it.
Confidence intervals example
(Figure 1 demonstrating a class of 100 students, and a sample of 10 students. Figure 2 demonstrating the confidence interval, with an estimated value of 70%, a lower bound at 60%, an upper bound at 80% and a true value of 73%.)
In this example we have a class of 100 students, each with a percentage grade for a math test.
The class average for the math test is 73%. However, we are not looking at the marks of everyone in the population, but only those of a sample of 10 people. Taking a random sample we obtain an estimated average grade of 70%, with a confidence interval of + or – 10%. In this example, our estimate of 70% is different from the true average of 73%, but the true average is within the confidence interval.
Confidence intervals example
(Figure 1 demonstrating a class of 100 students, and a sample of 10 students. Figure 2 demonstrating the confidence interval, with an estimated value of 65%, a lower bound at 55%, an upper bound at 75% and a true value of 73%.)
By taking another random sample, we obtain a different estimated average grade of 65%, which is again not equal to the true average of 73%, but the confidence interval of 55% to 75% still contains the true average.
Confidence intervals example
(Figure 1 demonstrating a class of 100 students, and a sample of 10 students. Figure 2 demonstrating the confidence interval, with an estimated value of 78%, a lower bound at 68%, an upper bound at 88% and a true value of 73%.)
A third sample of the same class obtains an estimated average grade of 78%. This estimate again differs from the true average of 73%, but again the confidence interval contains the true average.
Estimated Value
(Figure demonstrating a confidence interval, with the estimated value highlighted in the centre.)
The estimated value from the sample is usually at the centre of the confidence interval.
Estimated Value
(Figure demonstrating a confidence interval, highlighting the lower and upper bounds of the interval at equal distance from the estimated value.)
The upper and lower bounds of the confidence interval are then an equal distance above and below the estimated value.
Estimated Value
(Figure demonstrating a confidence interval, highlighting the margin of error below and above the estimated value.)
The distance from the estimated value to the upper or lower bound is called the margin of error.
The size of the margin of error reflects the uncertainty about the true value. More uncertainty means a larger margin of error.
Factors having an impact on a confidence interval
(Figure demonstrating different coloured people with question marks on their heads.)
There are three factors that determine the width of the confidence interval from a sample survey – the confidence level, the variability within the population, and the size of the sample.
These factors will now be described one by one.
Confidence level
(Figure demonstrating an estimated value and two confidence intervals, a first one with a 95% confidence level and a second one, with a 99% confidence level.)
The confidence level tells us how certain we are that the interval contains the true population value.
With a 95% confidence level, we are 95% confident that the confidence interval contains the true value. In other words, if we were to repeat the survey many times, the interval would contain the true value 19 times out of 20.
With a 99% confidence level, we are 99% confident that the confidence interval contains the true value. Note that the higher level of confidence requires a longer confidence interval.
Variability within the population
(Figure demonstrating grades on math test for two different groups, a Regular Math class and an Enriched Math class.)
By variability of a population we mean how different population members are, one from another.
In the example shown here the grades of students in the Enriched Math class are less variable than the grades of students in the Regular Math class. In the Regular Math Class, grades vary from 54% to 87%. In the Enriched Math class, grades vary from 86% to 96% – about one third the variability of the Regular Math class.
If variability is high in the population, then it will be high in the sample. If we had two different random samples from the population, then the difference between the two different estimates would also tend to be larger. So higher variability in the population leads to higher variability in the samples, which leads to higher variability in the estimates. This larger variability for the estimates is reflected in a larger margin of error, so that the confidence interval is wider.
Similarly, if variability is lower in the population, then it will be lower in the sample, and the estimate will have lower variability, leading to a smaller margin of error and a narrower confidence interval.
Size of the sample
(Figure demonstrating a class of 100 students.)
A larger sample will produce more precise estimates – that is, estimates with lower variability.
For example, in a class of 100 students, the average of a sample of size 20 would have smaller variability than the average of a sample of size 10. The average of a sample of size 50 would have still smaller variability.
So the larger the sample size, the smaller the variability of the estimate, the smaller the margin of error, and the shorter the confidence interval.
Let's look at an example…
Example - sample of size 10
(Figure demonstrating a class of 100 students, and a sample of 10 students, with an estimated average grade of 64%, and the true class average of 73%.)
The average class grade is 73%.
The average for the random sample of 10 students is 64%.
Example - sample of size 50
(Figure demonstrating a class of 100 students, and a sample of 50 students, with an estimated average grade of 71%, and the true class average of 73%.)
As we see in this example, with a much larger sample size, the variability of the estimator is much smaller, and it would tend to be much closer to the true value. The confidence interval would then be narrower.
Knowledge check
Now it's your turn. How would you interpret the following statement:
According to a recent study, adults living in a specific city weighed an average of 75 kg, with a margin of error of -/+ 10 kg, 9 times out of 10.
What is the estimated value? What is the confidence interval? What is the confidence level?
Take a moment to think about all the information included in this sentence.
Answer
First, we can conclude that the estimated value was obtained using a sample of the population. Second, we understand that the estimated average weight is 75 kg, and that the confidence interval ranges from 65 kg to 85 kg. The confidence interval is quite large, which may suggest a small sample size, high variability in the weight of individuals, or even both.
The confidence level is 90%, or 9 times out of 10. This means that if a random sampling were to be repeated many times, the confidence interval would contain the true value 9 times out of 10. A higher confidence level, 95%, as an example, would require an even wider confidence interval.
Recap of key points
To summarize what we learned today: confidence intervals can help understand and measure the uncertainty associated with estimated values from samples; data coming from samples do not provide true values, but estimated values; the length of the confidence interval can vary based on the size of the sample, the variability of the population and the confidence level required.
(The Canada Wordmark appears.)
You probably heard on
the radio or television
or read in the newspaper
a statement like this:
37% of Canadians anticipate working
from home for the foreseeable future,
based on an online survey of 2000
Canadian adults, with a margin
of error of plus or minus two
percentage points, 19 times out of 20.
But what exactly does it mean
and why is the information
presented in this way?
Working with statistics involves
an element of uncertainty,
and in this video we will see how
confidence intervals and their
underlying concepts help us understand
and measure this uncertainty.
The statement above actually presents
an example of a confidence interval,
even though at first glance it
does not look like an interval.
The interval in this case is
37% plus or minus 2%
In other words,
the interval goes from 35% to 39%.
By the end of this presentation,
you will be able to read similar
statements and understand that they
represent confidence intervals.
You will also understand what a
"margin of error" is, and what is meant
by the phrase "19 times out of 20".
As pre-requisite viewing for this video,
make sure you've watched our other
Statistics 101 videos called "Exploring
measures of central tendency" and
"Exploring measures of dispersion".
In this video you will learn the
answers to the following questions:
What are confidence intervals?
Why do we use confidence intervals?
and what factors have an impact
on a confidence interval?
This diagram is a visual representation
of the data journey from collecting
the data to exploring, cleaning,
describing and understanding the data.
To analyzing the data and
lastly to communicating with
others the story the data tell.
Confidence intervals are helpful in
steps 2, 3 and 4 of the data journey.
A confidence interval is a range of
possible values for something that
we want to estimate, for example,
what is the average grade on a math test
in a particular class of 100 students.
It is typically based on a sample
that is supposed to be representative
of the population.
However, the sample is often
small compared to the population.
In the example here,
we have math grades for a sample of 10
students from a class of 100 students.
Since the estimate is based on a sample,
there remains some uncertainty
about the true value.
The confidence interval accounts for
this uncertainty by including a range of
values, and not just the estimate itself.
The more uncertainty there is,
the wider the confidence interval will be.
In statistics, we often estimate a value
for a total population using a sample.
The value derived from the sample
is not the true value,
but an estimate of it.
In this example,
we have a class of 100 students,
each with a percentage grade
for a math test.
The class average for the math test is 73%.
However,
we are not looking at the marks
of everyone in the population,
but only those of a sample of 10 people.
Taking a random sample shaded
in red in the figure,
we obtain an estimated average of 70% with
a confidence interval of plus or minus 10%.
In this example
our estimate of 70% is different
from the true average of 73%,
but the true average is within
the confidence interval.
By taking another random sample,
we obtain a different estimated
grade average of 65%,
which is again not equal
to the true average of 73%,
but the confidence interval of 55% to
75% still contains the true average.
A third sample of the same class obtains
an estimated average grade of 78%.
This estimate again differs
from the true average of 73%,
but again the confidence interval
contains the true average.
The estimated value of the
sample is usually at the center
of the confidence interval.
The upper and lower bounds of
the confidence interval are
then equally distanced above
and below the estimated value.
The distance from the estimated
value to the upper or lower bound
is called the margin of error.
The size of the margin of error reflects
the uncertainty about the true value.
More uncertainty means a
larger margin of error.
The location of a confidence interval
is determined by the estimated value,
which is normally the central
value of the interval.
There are three factors that
determine the width of the confidence
interval from a sample survey -
the confidence level,
the variability in the population,
and the size of the sample.
These factors will now be described one by one.
The confidence level tells us how
certain we are that the interval
contains the true population value.
For a 95% confidence interval,
we are 95% confident that the
interval contains the true
value. In other words,
if we were to repeat the survey many times,
the interval would contain the
true value 19 times out of 20.
For a 99% confidence interval,
we are 99% confident that the
interval contains the true value.
Note that the higher level of confidence
requires a longer confidence interval.
A longer confidence interval generally
has a higher confidence level.
A shorter confidence interval generally
has a lower confidence level.
By variability of a population
we mean how different population
members are, from one another.
In the example shown here
the grades of students in the Enriched
Math class are less variable than the
grades of students in the Regular Math class.
In the Regular Math class,
grades vary from 54% to 87%.
In the Enriched Math class,
grades vary from 86% to 96% -
about 1/3 of the variability
of the Regular Math class.
If variability is high in the population,
then it will be high in the sample.
If we had two different random
samples from the population,
then the difference between the
two different estimates would
also tend to be larger.
So higher variability in the population
leads to higher variability in the samples,
which leads to higher variability
in the estimates.
This large variability for the
estimates is reflected in a
larger margin of error, so that
the confidence interval is wider.
Similarly,
if variability is lower in the population,
then it will be lower in the sample,
and the estimate will
have a lower variability,
leading to a smaller margin of error
and a narrower confidence interval.
A larger sample will produce
more precise estimates -
that is, estimates with lower variability.
For example,
in a class of 100 students,
the average grades of a sample
size of 20 would have smaller
variability than a sample size of 10.
The average grades of a sample size of 50
would have smaller variability even still.
So the larger the sample size,
the smaller the variability of the estimate,
the smaller the margin of error, and
the shorter the confidence interval.
Let's look at an example.
In this example,
we take the same class of 100 students.
The average class grade is 73%.
If we select a sample of 10 students,
the average for those ten students is 64%.
Now when we look at a sample size of
50 for the same class of 100 students,
a much larger sample size,
the variability of the estimate is
much smaller, and it would tend to
be much closer to the true value.
So the confidence interval
would then be narrower.
Now it's your turn.
How would you interpret the
following statement:
According to a recent study,
adults living in a specific city weight
on average 75 kg, with a margin
of error of plus or minus 10 kg,
9 times out of 10.
Take a second to think for yourself.
What's the estimated value?
What's the confidence interval?
What's the confidence level?
Pause the video if you have to and
hit play when you're ready to answer.
First, we can conclude that the
estimated value was obtained
using a sample of the population.
Second, we understand that the estimated
average weight is 75 kg,
and that the confidence interval ranges
from 65 kg to 85 kg.
The confidence levels quite large,
which may suggest a small sample size,
high variability in the weight
of the individuals, or both.
The confidence level is 90%,
or 9 times out of 10.
This means that if a random sampling
was to be repeated many times,
the confidence interval would contain
the true value 9 times out of 10.
A higher confidence level,
95%, for example,
would require an even
wider confidence interval.
Let's summarize what we learned today.
Confidence intervals represent
the uncertainty resulting
from the use of a sample.
Data coming from samples do not provide
true values, but estimated values.
And finally, the length of the
confidence interval depends
on the size of the sample,
the variability of the population,
and the confidence level.
Please give us feedback so we can better provide content that suits our users' needs.
Catalogue number: 892000062022001
Release date: May 24, 2022
In this video, you will be introduced to data ethics, why they are important, and the 6 guiding principles of data ethics implemented by Statistics Canada, throughout the Data Journey.
(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "Data Ethics An Introduction")
Gathering, exploring, analyzing and interpreting data are essential steps in producing information that benefits society, the economy and the environment. To properly conduct these processes, data ethics must be upheld in order to ensure the appropriate use of data.
(Text on screen: By the end of this video, you should have a better understanding of the following:
In this video, you will be introduced to data ethics, why they are important, and the 6 guiding principles of data ethics implemented by Statistics Canada, throughout the Data Journey.
(Text on screen: Supported by a foundation of stewardship, metadata, standards and quality
Diagram of the Steps of the data journey: Step 1 - define, find, gather; Step 2 - explore, clean, describe; Step 3 - analyze, model; Step 4 - tell the story. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)
This diagram is a visual representation of the data journey from collecting the data; to exploring, cleaning, describing and understanding the data; to analyzing the data; and lastly to communicating with others the story the data tell.
Data ethics are relevant throughout all steps of the data journey.
So what are data ethics exactly? Data Ethics allow data users to address questions about the appropriate use of data throughout all steps of the data journey.
This field of study is used to ensure collected data always have a specific purpose, and that each new project or data acquisition has the best interests of both society and the individual at heart.
Slide 5: There Are Lots Of Ways To Gather Data…
With the rapid growth of data associated with the digital age, data gathering approaches have also evolved.
Along with the more traditional survey-based approach, some alternative data gathering methods include:
These data are then used to create useful information such as statistics, and to train algorithms for artificial intelligence and machine learning. But with big data comes big responsibility…
When deciding to embrace such evolving data gathering methods as administrative sourcing, web scraping, apps and crowdsourcing, there is a responsibility to maintain focus on such perennial ethical challenges as:
There are many ways to address these ethical challenges, at Statistics Canada, we use the following 6 guiding principles:
Let's look at these principles in more detail.
Benefits to society means that statistical activities must allow governments, businesses and communities to make informed decisions and manage resources effectively, ultimately aiming to clearly benefit the lives of Canadians.
A census of population is fundamental to any country's statistical infrastructure. In Canada, the census is currently the only data source that provides high-quality population and dwelling counts based on common standards and at low levels of geography, as well as consistent and comparable information on various population groups.
(Text on screen: It is important to find a balance between respecting privacy and producing information
It is also important to consider the practical aspects of security, and how potential breaches may affect the well-being of Canadians).
When statistical activities require personal information, the consideration of both privacy and security is mandatory. The appropriate measures must always be taken in order to protect personal information while still ensuring the data can be used to create meaningful information.
Firstly, there is a fine balance between respecting privacy and producing information. Projects that intrude into the private lives of Canadians must justify why this information is important enough to warrant this intrusion, and be able to explain how using this data will ultimately provide benefits. In other words, we must ensure that our statistical activities are not intruding into the lives of Canadians any more than necessary, and to always justify whatever intrusion we consider necessary.
Furthermore, when designing a data-gathering approach, we have a moral obligation to protect the confidentiality and data of Canadians. Part of the data ethics exercise also consists in ensuring that projects have considered potential security threats and have prepared accordingly.
(Text on screen: Study on the sexual orientation of individuals in management positions.
Questions related to gender, marital status and sex are pertinent, even if intrusive.
Questions about salary, criminal antecedents and health conditions are intrusive and not directly tied to the project, so they must be justified.
Strict IT and Information Management measures must be taken during all stages of working with this data, as they are personal and sensitive.)
Let's imagine we are trying to have a better picture of the sexual orientation of individuals in management positions. If we conduct a survey, then questions related to gender, marital status and sex are pertinent, even if intrusive. If we were to ask questions about salary, age and nationality, we would have to justify why these variables are necessary.
To avoid any breach of personal information, strict IT and Information Management measures must be taken during all stages of working with data - the collection, retention, use, disclosure and disposal of information, in order to protect the confidentiality of this vulnerable population as well as the integrity of the project.
Statistical activities undertaken for the benefit of society have the responsibility to be transparent about where the data come from, how they are used and the steps that are taken to ensure confidentiality.
At Statistics Canada's Trust Centre for example, you will find a list of all current surveys and statistical programs, together with their methodologies, goals and data sources. Making these projects available is important not only so that Canadians can consult how statistical activities are conducted to determine if a project is in their best interest, but also so they can keep the agency accountable and point out whenever Statistics Canada ever encroaches upon the limits of its mandate.
The Data Quality principle means that the data used to create statistical information must be as representative and accurate as possible. Maintaining this expectation means ensuring that biases and errors do not compromise the potential benefits of a project or mislead data users.
(Text on screen: Low response rates can lead to biasedestimates or samples too small to meet the information need.
Statistics Canada decides to start using alternative data sources.
If sources are biased, they may lead to uninformed measures and policies.)
When conducting a survey, low response rates can lead to biased estimates or samples too small to meet the information need. Take data surrounding employment among individuals with disabilities for example. If the response rate for survey affects the quality of the estimates, Statistics Canada might decide to start using alternative data sources, such as administrative data acquired from industrial associations or labor unions.
If these new sources are biased, the unreliable information resulting from them may lead to uninformed measures and policies, which may cause more harm than good.
When conducting statistical activities, it is necessary to consider all the potential risks that a statistical activity may pose to the well-being of individuals or specific groups.
When acquiring and linking a large amount of data, detailed descriptions of smaller sub-populations of society might become available for analysis. These detailed clusters can sometimes magnify what is happening at the lowest level of geography. While this may sound harmless, it is important to remember these clusters of data might reveal information such as ethnicity and socio-economic status. Putting any sub-population under a microscope can raise ethical issues. For instance, studies on criminality have to be worded in careful manner so as to not reinforce stereotypes, and results have to be shared with caution to ensure that the information is informative and not taken as an indictment of a specific population group.
In order to maintain the trust of the public, the use of data for the benefit of society should occur only by implementing such best practises as assuring confidentiality, protecting personal information, producing representative data, and being accountable. By making this our mandate, we can ensure that our statistical activities remain socially acceptable in the eyes of the public. If we have social acceptability, any partnership and any approach we undertake becomes and opportunity to show that we follow our mandate and helps the agency promote its objectives and maintain the trust of the public in the long term.
To illustrate when trust really matters, imagine we are trying to gather information on recreational cannabis use by Canadian youth, via voluntary crowdsourcing, and that this is happening before cannabis was legalized. One can only expect respondents to provide accurate, reliable data if they trust the institution responsible for guarding their responses and preserving confidentiality. In this case, they must trust their data is not going to be shared with anyone, including peers, parents and even legal authorities.
(Figure 1 showing a table with the 6 guiding principles: Benefits Canadians, Trust and Sustainability, Privacy and Security, Data Quality, Transparency and Accountability and Fairness and Do No Harm)
In summary, Data Ethics is the field of study that addresses questions about the appropriate use of data.
With advances in data gathering techniques comes ethical challenges regarding access to and use of data.
There are 6 guiding principles you can use to address ethical concerns:
(The Canada Wordmark appears.)
Gathering, exploring,
analyzing and interpreting data
are essential steps in producing
information that benefits society,
the economy and the environment.
To properly conduct these processes,
data ethics must be upheld
in order to ensure the
appropriate use of data.
In this video, you will be introduced to
data ethics, why they are important,
and the six guiding principles of
data ethics implemented by Statistics
Canada throughout the data journey.
This diagram is a visual representation
of the data journey from collecting
the data; to exploring, cleaning,
describing and understanding
the data; to analyzing the data;
and lastly to communicating with
others the story the data tell.
Data ethics are relevant throughout
all steps of the data journey.
So what are data ethics exactly?
Well, data ethics allow data users
to address questions about the
appropriate use of data throughout
all steps of the data journey.
This field of study is used to
ensure collected data always
have a specific purpose,
and that each new project or data
acquisition has the best interests of
both society and the individual at heart.
With the rapid growth of data
associated with the digital age,
data gathering approaches have also evolved.
Along with the more traditional survey-
based approach, some alternative
data gathering methods include:
earth observation data, scanner data,
Administrative data and web scraping.
These data are then used to create
useful information such as statistics,
and train algorithms for artificial
intelligence and machine learning.
But with more data comes more responsibility...
When deciding to embrace such evolving data
gathering methods as administrative sourcing,
web scraping apps and crowdsourcing,
there is a responsibility to maintain focus
on such perennial ethical challenges as:
protecting privacy and confidentiality,
balancing privacy intrusion
versus public good,
recognizing the potentially
harmful impacts of using bias data,
and ensuring data quality
to avoid misinformation.
There are many ways to address
these ethical challenges,
at Statistics Canada,
we use the following 6 guiding principles:
Data are used to benefit Canadians.
Data are used in a secure and private manner.
Data acquisitions and processing
methods are transparent and accountable.
Data acquisitions and processing methods
are trustworthy and sustainable.
The data themselves are of high quality.
And any information resulting from the
data are reported fairly and do no harm.
Let's look at these principles
in more detail.
Benefits to society means that statistical
activities must allow governments,
businesses and communities to make informed
decisions and manage resources effectively,
ultimately aiming to clearly
benefit the lives of Canadians.
A census of population is fundamental to
any country's statistical infrastructure.
In Canada, the Census is currently
the only data source that provides
high-quality population and dwelling
counts based on common standards
and at low levels of geography,
as well as consistent and comparable
information on various population groups.
When statistical activities
require personal information,
the consideration of both privacy
and security is mandatory.
The appropriate measures must
always be taken in order to protect
personal information while still
ensuring the data can be used to
create meaningful information.
First, there's a fine balance between
respecting privacy and producing information.
Projects that intrude into the private
lives of Canadians must justify why
this information is important enough
to warrant this intrusion, and be
able to explain how using this data
will ultimately provide benefits.
In other words,
we must ensure that our statistical
activities are not intruding into
the lives of Canadians any more
than absolutely necessary,
and to always justify whatever
intrusion we may consider necessary.
Furthermore,
when designing a data-gathering approach,
we have a moral obligation to protect the
confidentiality and data of Canadians.
Part of the data ethics exercise also
consists in ensuring that projects
have considered potential security
threats and have prepared accordingly.
Let's imagine we're trying to have a
better picture of the sexual orientation
of individuals in management positions.
If we conduct a survey,
then questions related to gender,
marital status and sex are pertinent,
even if intrusive.
If we were to ask questions about salary,
age, and nationality, we would have to justify
why these variables are necessary.
To avoid any breach of
such personal information,
strict IT and Information Management
measures must be taken during
all stages of working with data -
such as the collection, retention, use,
disclosure, and disposal of information,
in order to protect the confidentiality
of such a vulnerable population as
well as the integrity of the project.
Statistical activities undertaken
for the benefit of society have the
responsibility to be transparent
about where the data come from,
how they are used and the steps that
are taken to ensure confidentiality.
At Statistics
Canada's Trust Center for example,
you'll find a list of all current
surveys and statistical programs,
together with their methodologies,
goals and data sources.
Making these projects available is important
not only so that Canadians can
consult how statistical activities
are conducted to determine if a
project is in their best interest,
but also so they can keep
the agency accountable.
The Data Quality principle means
that the data used to create
statistical information must be as
representative and accurate as possible.
Maintaining this expectation means
ensuring that biases and errors do
not compromise the potential benefits
of a project or mislead data users.
When conducting a survey,
low response rates can lead to
biased estimates or samples too
small to meet the information needed.
Take data surrounding employment
among individuals with disabilities
for example.
If the response rate for the survey
affects the quality of the estimates,
Statistics Canada might decide
to start using alternative data
sources, such as administrative
data acquired from industrial
associations or labor unions.
If these new sources are biased,
the unreliable information resulting
from them may lead to uninformed
measures and policies, which
may cause more harm than good.
When conducting statistical activities,
it is necessary to consider all the
potential risks that a statistical
activity may pose to the well-being
of individuals or specific groups.
When acquiring and linking
a large amount of data,
detailed descriptions of smaller
sub-populations of society might
become available for analysis.
These detailed clusters can
sometimes magnify what is happening
at the lowest level of geography.
While this may sound harmless,
it is important to remember these clusters
of data might reveal information such
as ethnicity and socio-economic status.
Putting any sub-population under a
microscope which can raise ethical issues.
For instance,
studies on criminality have to
be worded very carefully so as
not to reinforce stereotypes,
and results have to be shared
with caution to ensure that the
information is informative and
not taken as an indictment of
a specific population group.
In order to maintain the trust of the public,
the use of data for the benefit
of society should only occur by
implementing best practices such
as assuring confidentiality,
protecting personal information,
producing representative data,
and being accountable.
By making this our mandate,
we can ensure that our statistical
activities remain socially
acceptable in the eyes of the public.
If we have social acceptability,
any partnership in any approach we
undertake becomes an opportunity
to show that we follow our mandate
and help the agency promote its
objectives and maintain the trust
of the people for the long term.
To illustrate when trust really matters,
imagine we're trying to gather
information on recreational cannabis
use by Canadian youth, via voluntary
crowdsourcing, and that this is
happening before cannabis was legalized.
One can only expect respondents
to provide accurate,
reliable data if they trust the
institution responsible for guarding their
responses and preserving confidentiality.
In this case, they must trust their data
is not going to be shared with anyone,
including peers,
parents or even legal authorities.
In summary, Data Ethics is the field
of study that addresses questions
about the appropriate use of data.
With advances in data gathering
techniques comes ethical concerns
regarding access to and use of data.
There are six guiding principles you
can use to address ethical concerns:
benefits to Canadians,
privacy and security,
transparency and accountability,
trust and sustainability,
data quality,
fairness and do no harm.
Please give us feedback so we can better provide content that suits our users' needs.
| Geography | Month |
|---|---|
| 202203 | |
| % | |
| Canada | 0.6 |
| Newfoundland and Labrador | 1.8 |
| Prince Edward Island | 0.9 |
| Nova Scotia | 1.2 |
| New Brunswick | 2.1 |
| Quebec | 1.5 |
| Ontario | 1.2 |
| Manitoba | 1.4 |
| Saskatchewan | 2.9 |
| Alberta | 1.3 |
| British Columbia | 1.7 |
| Yukon Territory | 1.0 |
| Northwest Territories | 1.3 |
| Nunavut | 1.6 |
This survey is being conducted by Statistics Canada, on behalf of Agriculture and Agri-Food Canada to monitor the financial situation of Canadian farms, and establish new policy.
Your information may also be used by Statistics Canada for other statistical and research purposes.
Your participation in this survey is required under the authority of the Statistics Act.
Data are collected under the authority of the Statistics Act, Revised Statutes of Canada, 1985, Chapter S-19.
By law, Statistics Canada is prohibited from releasing any information it collects that could identify any person, business, or organization, unless consent has been given by the respondent, or as permitted by the Statistics Act. Statistics Canada will use the information from this survey for statistical purposes only.
To enhance the data from this survey and to reduce the reporting burden, Statistics Canada may combine the acquired data with information from other surveys or from administrative sources.
To reduce respondent burden, Statistics Canada has entered into data-sharing agreements with provincial and territorial statistical agencies and other government organizations, which have agreed to keep the data confidential and use them only for statistical purposes. Statistics Canada will only share data from this survey with those organizations that have demonstrated a requirement to use the data.
Section 11 of the Statistics Act provides for the sharing of information with provincial and territorial statistical agencies that meet certain conditions. These agencies must have the legislative authority to collect the same information, on a mandatory basis, and the legislation must provide substantially the same provisions for confidentiality and penalties for disclosure of confidential information as the Statistics Act. Because these agencies have the legal authority to compel businesses to provide the same information, consent is not requested and businesses may not object to the sharing of the data.
For this survey, there are Section 11 agreements with the provincial statistical agencies of Newfoundland and Labrador, Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta and British Columbia. The shared data will be limited to information pertaining to business establishments located within the jurisdiction of the respective province.
Section 12 of the Statistics Act provides for the sharing of information with federal, provincial or territorial government organizations. Under Section 12, you may refuse to share your information with any of these organizations by writing a letter of objection to the Chief Statistician, specifying the organizations with which you do not want Statistics Canada to share your data and mailing it to the following address:
Chief Statistician of Canada
Statistics Canada
Attention of Director, Enterprise Statistics Division
150 Tunney's Pasture Driveway
Ottawa, Ontario
K1A 0T6.
You may also contact us by email at Statistics Canada Help Desk: statcan.esd-helpdesk-dse-bureaudedepannage.statcan@canada.ca or by fax at 613-951-6583.
For this survey, there are Section 12 agreements with the Prince Edward Island Statistical agency as well as with the ministère de l'Agriculture, des Pêcheries et de l'Alimentation du Québec, the Ontario Ministry of Agriculture, Food and Rural Affairs, and Agriculture and Agri-Food Canada.
For agreements with provincial and territorial government organizations, the shared data will be limited to information pertaining to business establishments located within the jurisdiction of the respective province or territory.
Use the Previous and Next buttons located at the bottom right of each page.
Do not use the navigation buttons at the top of your browser or the corresponding shortcut keys.
Based on your answers to certain questions, the questionnaire will automatically skip any questions or sub-questions that do not apply to your situation.
After 2 hours of inactivity, your session will time out. You will not be able to access any unsaved information.
If you cannot complete the questionnaire in one session, you can save the information you have entered by pressing the Save and finish later button at the bottom left of any page on which you are asked to enter information. You can then resume your session at another time.
Please be advised that information from partially completed or unsaved questionnaires may be retained and used for statistical and research purposes.
A help button is available for certain questions. Press this button for additional information or clarification.
For this questionnaire
Reporting instructions
Additional information that may be useful in the completion of this electronic questionnaire include the operation's:
Also:
Other information about this questionnaire
Additional information about this survey can be found by selecting the following link:
1. Verify or provide the business or organization's legal and operating name and correct where needed.
Note: Legal name modifications should only be done to correct a spelling error or typo.
Note: Press the help button (?) for additional information.
Legal name
Operating name (if applicable)
2. Verify or provide the contact information of the designated business or organization contact person for this questionnaire and correct where needed.
Note: The designated contact person is the person who should receive this questionnaire. The designated contact person may not always be the one who actually completes the questionnaire.
First name
Last name
Title
Preferred language of communication
Postal code or ZIP (Zone Improvement Plan) code Example A9A 9A9 or 12345-1234
Country
Email address Example: user@example.gov.ca
Telephone number (including area code) Example: 123-123-1234
Extension number (if applicable)
Fax number (including area code) Example: 123-123-1234
3. Verify or provide the current operational status of the business or organization identified by the legal and operating name above.
Why is this business or organization not currently operational?
3a Seasonal operations
When did this business or organization close for the season?
Date:
Example: YYYY-MM-DD
When does this business or organization expect to resume operations?
Date:
Example: YYYY-MM-DD
3b Ceased operations
When does this business or organization cease operations?
Date:
Example: YYYY-MM-DD
Why did this business or organization cease operations?
3c Sold operations
When was this business or organization sold?
Date:
Example: YYYY-MM-DD
What is the legal name of the buyer?
3d Amalgamated with other businesses or organizations
When did this business or organization amalgamate?
Date:
Example: YYYY-MM-DD
What is the legal name of the resulting or continuing business or organization?
What are the legal names of the other amalgamated businesses or organizations?
3e Temporarily inactive but will re-open
When did this business or organization become temporarily inactive?
Date:
Example: YYYY-MM-DD
When does this business or organization expect to resume operations?
Date:
Example: YYYY-MM-DD
Why is this business or organization temporarily inactive?
3f No longer operating due to other reasons
When did this operation or organization cease operations?
Date:
Example: YYYY-MM-DD
Why did this business or organization cease operations?
4. Verify or provide the current main activity of the business or organization identified by the legal and operating name above.
Note: The described activity was assigned using the North American Industry Classification System (NAICS).
Note: Press the help button (?) for additional information, including a detailed description of this activity complete with example activities and any applicable exclusions.
Descriptions and examples
This is the current main activity
This is not the current main activity
Provide a brief but concise description of this business or organization's main activity e.g. breakfast cereal manufacturing, shoe store, software development
5. You indicated that is not the current main activity
Was this business or organization's main activity ever classified as: ?
6. Search and select the industry classification code that best corresponds to the business or organization's main activity.
How to search:
To reduce the number of questions in this questionnaire, Statistics Canada can use data on this operation's revenues and expenses as submitted to the Canada Revenue Agency (CRA).
1. Are you an operator or a person normally authorized to provide tax data, such as revenues and expenses, to CRA for this operation?
With your permission, Statistics Canada will share this operation's tax data on revenues and expenses with Agriculture and Agri-Food Canada and your provincial Ministry of Agriculture.
Statistics Canada does not share names, addresses or any other direct identifiers that could identify you or this operation.
If you do not grant us permission to share this information, you will be required to provide the revenues and expenses data later on in this questionnaire.
2. Do you give Statistics Canada permission to share this operation's tax data on revenues and expenses with Agriculture and Agri-Food Canada and your provincial Ministry of Agriculture?
3. What is the end date of this operation's fiscal year?
If financial statements are not available for 2021, please use your best estimates throughout the survey.
Year
Month
Day
4. On this operation's fiscal end date, how many operators 18 years of age and over, who participate in the or financial decisions, were responsible for this operation?
An operation may have more than one operator but only one questionnaire is to be completed for each operation.
Exclude.
Number of operators
5. What was the age of the oldest operator on fiscal year-end date?
Age
6. What was the gender of the oldest operator on fiscal year-end date?
7. What was the age of the youngest operator (minimum age of 18) on fiscal year-end date?
Age
8. What was the gender of the youngest operator (minimum age of 18) on fiscal year-end date?
9. As of fiscal year-end date, how many years has the most experienced operator on this operation managed a farm business? Managing refers to controlling the decisions to produce and invest in a farm business. If you are the only operator, please enter your years of experience managing a farm.
Years managing a farm business
10. On fiscal year-end date, how many members were in the operator's family?
Please choose one operator (if there are more operators than just yourself), plus the operator's spouse/partner and all kids who live on the farm or whose address is still that of the farm.
Exclude operator's parents and siblings.
Number of people in operator's family
11. On fiscal year-end date, what was the legal operating arrangement of this operation?
12. Which method of accounting does this operation use to report farm revenue and expenses?
13. In 2021, which of the following was this operation's major source of gross revenue? -Major source of gross revenue usually accounts for more than 50% of total revenues.
14. In 2021, which of the following was the main source of gross revenue for crops?
The main source of gross revenue within the crops group may be less than 50% of total revenues.
15. In 2021, what was the major source of beef revenue?
16. In 2021, which of the following was the main source of gross revenue for cattle owned by this operation? The main source of gross revenue within the beef group may be less than 50% of total revenues.
17. In 2021, which of the following was the main source of gross revenue for custom or contract cattle?
18. In 2021, which of the following was the main source of gross revenue for hogs?
The main source of gross revenue within the hogs group may be less than 50% of total revenues.
19. In 2021, which of the following was the main source of gross revenue for poultry?
The main source of gross revenue within the poultry group may be less than 50% of total revenues.
20. What unit of measure will be used to report land areas? The unit of measure chosen here will be used in subsequent questions.
Acres
Hectares
Arpents
21. In 2021, of the total area of workable and non-workable land for this operation, how much was: Enter "0", if there is no value to report.
a. owned land
Include all land owned by this operation whether or not it is used for farming.
b.rented or leased land from others (with or without a written agreement or payment)
Include government land, crop-sharing agreements and pastureland rented or leased.
c.rented or leased land to others (with or without a written agreement or payment)
22.In 2021, what was the total area of cropland operated by this farm business?
Include:
Exclude:
Enter "0", if there is no value to report.
23. In 2021, what was the estimated market value of a(n) acre of cropland?
Market value is the estimated price at which the land would sell for today.
24. In 2021, did this operation rent any cropland from others?
Exclude land rented for pasture.
25. In 2021, which of the following types of farm machinery and equipment did this operation purchase or lease?
Include new and used equipment.
Exclude short-term rentals and repairs and replacement parts (these are operating expenses).
Select all that apply.
26. In 2021, what was the amount of money invested for the following farm machinery and equipment (purchased or leased)?
Report the full purchase or lease price before trade-in or down payment.
Exclude:
Enter "0", if there is no value to report.
Cars used in the farm business
Trucks used in the farm business
Other equipment used for transportation
Grain drying equipment
Tractors of various types
Harvesting machinery
Seeding equipment
Other machinery and equipment
27. In 2021, did this operation invest any money farm improvements or other assets?
Include:
Exclude:
28. In 2021, what was the amount of money invested for the following?
Exclude:
Enter "0", if there is no value to report.
29. In 2021, how much money did this operation receive from government programs (both federal and provincial) to reduce the cost of any capital investment to increase environmental performance, including energy efficiency?
Enter "0", if there is no value to report.
30. In 2021, did this operation sell or trade-in any of the following assets?
Select all that apply
31. In 2021, what was the selling price for the following?
Exclude GST, PST, HST, and Quebec Sales Tax.
Enter "0", if there is no value to report.
32. What was the estimated market value of the following assets of this operation on the fiscal year-end date?
Market value is the estimated price at which the assets would sell for today.
Exclude:
Enter "0", if there is no value to report.
If precise figures are not available, please provide your best estimates.
Livestock and poultry assets
33. On this operation's fiscal year-end date, did this operation have any long-term debts?
Long-term debt are liabilities your operation owes that are payable one year after this operation's fiscal year-end date.Include:
Exclude:
34. On this operation's fiscal year-end date, what was the amount of your operation's long-term debt to the following?
Enter "0", if there is no value to report.
35. On this operation's fiscal year-end date, did this operation have any short-term debts?
Short-term debts are liabilities that need to be paid within a year from your fiscal year end date.
Short-term debt includes short-term portion of long-term debt.
Include:
Exclude:
36. On this operation's fiscal year-end date, what was the amount of your operation's short-term debt to the following?
Enter "0", if there is no value to report.
37. The amount reported in provincial government agencies (Question 34f) may have been obtained through a financial institution, but guaranteed by the provincial government. Please confirm the lender.
38. In 2021, did this operation custom or contract feed any livestock or poultry for others?
Custom or contract feeding is where livestock or poultry are fed and raised by the operation for somebody else.
Include custom or contract grazing.
Exclude animals owned by this operation.
a. Yes
b. No
37. In 2021, of this operation's total gross farm revenue, how much was from custom or contract feeding?
Include revenue received for the total number of livestock and poultry custom or contract fed or grazed for the whole year.
Exclude animals owned by this operation.
Enter "0" if there is no value to report.
40. Please indicate the types of livestock and poultry custom or contract fed in 2021.
Select all that apply.
39. Please indicate the total number of livestock and poultry custom or contract fed in 2021.
Include all cycles.
Exclude animals owned by the operation.
42. In 2021, what was this operation's total expense for wages and salaries?
Include:
Exclude dividends paid to shareholders.
Enter "0", if there is no value to report.
43. Of the $X in total wages and salaries, how much was paid to the following?
Enter "0", if there is no value to report.
a. Family
If this operation has more than one operator, report for one operator and that operator's family.
Include:
Exclude wages and salaries paid to hired workers not in the family, family not residing in the same dwelling and any withdrawals from owner's equity.
b. Hired help
Include:
Exclude:
44. In 2021, what was the total gross farm revenue (before expenses) of this operation?
Include:
Exclude:
Enter "0", if there is no value to report.
45. In 2021, of the $X in total gross revenue, how much was from the following?
The following selected revenue items may not add to the amount reported in the previous question.
Enter "0", if there is no value to report.
46. In 2021, what were the total farm operating expenses?
Include wages and salaries previously reported.
Exclude:
Enter "0", if there is no value to report.
47. In 2021, what were the farm operating expenses for the following?
The following selected expenses will not necessarily sum to the amount reported in the previous question.
Enter "0", if there is no value to report.
Important
The purpose of the following questions is to assess the reliance of farm operators on non-farm activities for income.
When answering these questions, please refer only to off-farm income, assets and debts that are not related to this operation.
48. In 2021, what was the family's income from the following sources?
Refer to the T1 and T4 forms of all members of the operator's family (if possible).
Note: Press the help button (?) for additional information, including the definition of family.
49. Based on your experience over the last 5 years, how would you rate each of the following risks faced by this operation?
a. Weather
b. Crop insects, pests and diseases
c. Livestock diseases
d. Commodity prices
e. Input prices
f. Interest rates on loans
g. Border closures or access to markets
h. Exchange rate
i. Government policies / programs / regulations
j. Labour
50. In 2021, did this operation participate in the AgriInvest program?
AgriInvest is a self-managed producer-government savings account that allows producers to set money aside which can be used to recover from small income shortfalls, or to make investments to reduce on-farm risks.
51. In 2021, did this operation deposit into and/or withdraw money from AgriInvest?
Please refer to this operation's 2021 AgriInvest Annual Statement of Account.
52. In 2021, what were this operation's total AgriInvest deposits?
Please refer to this operation's 2021 Annual Statement of Account.
Exclude government contributions.
53. In 2021, what were this operation's total AgriInvest withdrawals?
Please refer to this operation's 2021 Annual Statement of Account.
54. In 2021, what were the AgriInvest withdrawals used for?
Select all that apply.
55. What were the ending balances in this operation's AgriInvest account as of 2021?
Please refer to this operation's 2021 AgriInvest Annual Statement of Account.
a. Balance in Fund 1 (operator's deposits)
b. Balance in Fund 2 (government contributions)
56. In 2021, which of the following reasons describe why this operation did not participate in the AgriInvest program?
Select all that apply.
57. Based on your experience, how would you rate your level of satisfaction of the following characteristics of the Agrilnvest program?
a. Delivery
Administrators communicate effectively, respond to program changes in a timely way, are accurate in processing claims, etc.
b. Timeliness
Time it takes to assess the eligible amount and to receive government contribution.
c. Responsiveness
Effective in helping you recover small income losses or make on-farm investments.
d. Predictability
Likelihood of payments and the dollar amount are predictable.
58. Did this operation participate in AgriStability in any of the following years?
AgriStability provides support when you experience a large margin decline.
Select all that apply.
59. In 2021, which of the following reasons describe why this operation did not participate in the AgriStability program?
Select all that apply.
60. Based on your experience, how would you rate your level of satisfaction of the following characteristics of the AgriStability program?
a. Delivery
Administrators communicate effectively, respond to program changes in a timely way, are accurate in processing claims, etc.
b. Timeliness
Time it takes to assess the eligible amount and to receive government contribution.
c. Responsiveness
Effective in helping you recover small income losses or make on-farm investments.
d. Predictability
Likelihood of payments and the dollar amount are predictable.
61. Do you intend to enrol in the 2022 AgriStability program?
a. What are the reasons?
Select all that apply.
62. Did this operation participate in AgriInsurance (also known as provincial crop insurance or production insurance) in any of the following years?
AgriInsurance is a federal-provincial-producer cost-shared program that stabilizes a producer's income by minimizing the economic effects of production losses caused by natural hazards. AgriInsurance is a provincially delivered program.
Select all that apply.
63. In 2021, which of the following reasons describe why this operation did not participate in the AgriInsurance program?
64. Based on your experience, how would you rate your level of satisfaction of the following characteristics of the AgriInsurance program?
a. Delivery
Administrators communicate effectively, respond to program changes in a timely way, are accurate in processing claims, etc.
b. Timeliness
Time it takes to assess the eligible amount and to receive government contribution.
c. Responsiveness
Effective in helping you recover small income losses or make on-farm investments.
d. Predictability
Likelihood of payments and the dollar amount are predictable.
65. Has this operation ever participated in the Advance Payments Program (APP)?
Advance Payments Program (APP) is a federal loan guarantee program which provides agricultural producers with easy access to low-interest cash advances.
66. Why did this operation borrow money through the Advance Payments Program (APP)?
Select all that apply.
67. In 2021, did this operation participate in the Advance Payments Program (APP)?
68. Will this operation participate in the Advance Payments Program (APP) in coming years?
69. In 2021, which of the following tools / programs did the operation use to manage business risks?
Select all that apply.
** FOR REFERENCE YEAR 2021, PLEASE GO TO QUESTION 75 DIRECTLY**
70. For your operation, how important are each of the following government-funded risk management programs in providing an effective means of managing business risk and disaster situations (caused by weather, low commodity prices, etc.) at your farm operation?
a. AgriStability
b. AgriInvest
c. AgriInsurance
d. Advance Payments Program (or Cash Advance Program)
71. For your operation, how important are each of the following government-funded risk management programs in providing an effective means of managing business risk and disaster situations (caused by weather, low commodity prices, etc.) at your farm operation?
a. Make use of market-based or private risk management tools (production/marketing contracts, hedging, options, WLPIP, private insurance)
b. Diversification of farm production
c. Off-farm income or other income sources
d. Other
72. You've indicated there were other risk management tools / strategies providing effective means of managing business risk and disaster situations (caused by weather, low commodity prices, etc.) at your farm operation. What are these other risk management tools / strategies?
73. How has this operation's Business Risk Management (BRM) strategy shifted in the last five years?
BRM programs include AgriInvest, AgriStability, AgriInsurance and the Advance Payments Program.
74. In recent years, how much of a role did Business Risk Management (BRM) program(s) play in this operation's investment decisions?
BRM programs include AgriInvest, AgriStability, AgriInsurance and the Advance Payments Program.
75. In the last three years, 2019 to 2021, did this operation implement any of the following new or significantly improved products or practices?
Innovation means implementing a new or significantly improved product, practice or process on your farming operation. Innovations must be new to your operation but need not be new to the industry.
a. Crop products
Include crop varieties, cultivars or hybrids.
b. Livestock products
Include types or breeds raised.
c. Production practices
Include:
d. Approaches to marketing
Include types of contracts, futures/options, direct marketing.
e. Business management practices
Include:
Although the following questions are voluntary, your answers are important as they provide valuable information regarding energy use and hiring practices.
81. In the last three years, 2019 to 2021, which of the following energy-saving practices or technologies did this operation implement?
Select all that apply.
82. For 2022 or 2023, which of the following energy-saving practices or technologies are you planning to implement?
83. In 2021, what was the amount spent on heating energy for farming activities?
Include mechanical drying of grains, heating of any barn areas, greenhouse heating, heating water for farming use.
Exclude heating energy expenses for the farm house.
Please complete for all energy types that you used.
84. In 2021, did you grow any grains, oilseeds, or pulses that required the use of fuel or electric- powered dryers?
85. In 2021, did you own a fuel or electric-powered grain dryer?
86. In 2021, what type of energy did your grain dryers use?
If you had multiple dryers, please select the type of energy most used overall.
87. In 2021, how many acres of the following crops did you harvest?
88. In 2021, for the following crops, how many acres were electrically or fueled dried by your own dryers or by someone else's?
89. What was the total energy cost to mechanically dry your crops using your own dryers?
If precise figures are not available, please provide your best estimates.
Total energy cost (Can$)
90. In 2021, did you have any employees that were not any of the operator's family members?
An operator's family is defined as an operator, an operator's spouse or common-law partner and children residing in the same dwelling.
91. In 2021, did you hire or attempt to hire any new employees?
92. In 2021, did you attempt to hire any employees?
93. In 2021, for what type of employment did you hire or attempt to hire?
Select all that apply
94. In 2021, how would you rate your experience in recruiting employees?
a. Full-time year round
b. Part-time year round
c. Full-time seasonal
d. Part-time seasonal
95. In 2021, which methods did you use to recruit employees?
Exclude Temporary Foreign Workers.
Select all that apply.
96. In 2021, what strategies did you use to recruit employees?
Exclude Temporary Foreign Workers.
Select all that apply.
97. In 2021, did you hire or attempt to hire any temporary foreign workers?
98. How important is it for your operation to retain employees year-over-year?
99. Which of the following practices do you use to retain employees?
Select all that apply.
100. How concerned are you with turn-over among your employees?
101. Indicate any changes or events that affected the reported values for this business or organization compared with the last reporting period.
Select all that apply.
102. Statistics Canada may need to contact the person who completed this questionnaire for further information.
Is this the best person to contact?
Who is the best person to contact about this questionnaire?
103. How long did it take to complete this questionnaire?
Include the time spent gathering the necessary information.
104. Do you have any comments about this questionnaire?
200 characters available
By: Kenneth Chu, Statistics Canada
The Data Science Division of Statistics Canada recently completed a number of research projects to evaluate the effectiveness of a statistical technique called functional principal component analysis (FPCA) as a feature engineering method in order to extract temporal trends from Synthetic Aperture Radar (SAR) satellite time series data.
These projects were conducted in collaboration with the Agriculture Division and the Energy and Environment Statistics Division of Statistics Canada, as well as the National Wildlife Research Centre (NWRC) of Environment and Climate Change Canada. These projects used Sentinel-1 data. Sentinel-1 is a radar satellite constellation of the European Space Agency’s Copernicus program. It collects SAR imagery data that captures information about Earth’s surface structures of the imaged area. Sentinel-1 provides global year-round coverage under all weather conditions of the imaged area. The data are unaffected by day and night, have high spatial resolution (approximately 10m x 10m) and a fixed data capture frequency of one data captured every 12 days, for each imaged area. Consequently, Sentinel-1 data are highly suitable for scientific studies as the Earth’s surface structures hold important information.
Our results suggest that FPCA could be a very effective tool to numerically extract seasonal temporal trends captured in a Sentinel-1 image time series, in a large-scale and high-granular fashion. This article gives a summary of the FPCA-based method and its feature extraction results from Sentinel-1 seasonal time series data.
I will focus on one of the study areas of research, namely the Bay of Quinte area in Ontario, located near Belleville. The objective of this project is to give a more accurate wetland classification. The following is an optical satellite image (rather than a radar satellite image) of the Bay of Quinte area, downloaded from Google Maps.
Figure 1: Optimal satellite image of Bay of Quinte, Ontario. Downloaded from Google Maps.
Optimal satellite image of Bay of Quinte, Ontario, located in the center at the top of the image. The rest shows a generally rural sprawl of the surrounding area and includes several townships and the highways that connect them. The townships pictured include (clockwise from the top left): Rossmore, Fenwood Gardens, Mountain View, Crofton, Elmbrook, Picton, Gilbert Mills, Huff’s Corners, Bowermans and Allisonville.
In Figure 1, note the large body of water at the top-centre of the image and the rectangular agricultural fields. Also note the island near the top-centre and the waterway that separates it from the mainland.
Next, examine Figure 2, which is a land cover map of the Bay of Quinte area.
Figure 2: Land cover map of Bay of Quinte, ON. This image is created from 2019 RADARSAT-2 time series data (Banks et al., 2019). The colours indicate different land cover types: Blue indicates water, pink indicates shallow water, cyan shows marsh, red indicates forest, green is for swamp area and brown indicates agricultural land.
Land cover map of Bay of Quinte, ON, from 2019 RADARSAT-2 time series data. The colours indicate different land cover types. The Bay of Quinte is blue, indicating water and is mostly at the top centre of the image. The outline of the Bay of Quinte is in Cyan, which denotes marsh area, and includes a few pink spots within it, indicating shallow water. The surrounding land area is mainly red, which denotes forest area, and two semi large spots in the lower centre of the image are green, highlighting a swamp area.
Figure 3 is a red, green, blue (RGB) rendering of the first three functional principal component (FPC) scores computed from 2019 Sentinel-1 time series data for the Bay of Quinte area. Note the remarkable granularity and the high-concordance level with the land cover map in Figure 2.
Figure 3: RGB rendering of the first three FPC scores computed from 2019 Sentinel-1 time series data for the Bay of Quinte area.
Land cover map of Bay of Quinte. The colours indicate different land cover types. The Bay of Quinte is blue, indicating water. The outline of the Bay of Quinte is pink, and the surrounding land area is mainly orange. This image is similar to the rendering from Figure 2.
The land cover map in Figure 2 was created from RADARSAT-2 (one of Canadian Space Agency’s SAR Earth Observation satellites) time series data from 2019, using the methodology described in Banks et al. (2019). It’s well corroborated with ground truth collected via field observations. A notable observation here is that the waterway separating the island in the top-centre of the image and the mainland, in fact contains two types of wetland – marsh in cyan and shallow water in pink. These two wetland types are not easily distinguishable in Figure 1. The article reported that SAR seasonal time series data contain sufficient information f or wetland classification, but the methodology described involved a high degree of manual decision-making informed by expert knowledge (e.g., data captures for which dates or statistics to use, etc.). It was labour-intensive and it wasn’t clear on how to automate that methodology and scale it to a regional, provincial or pan-Canada scale.
The motivation behind the Bay of Quinte project was to seek an automatable and effective methodology for numerically extracting temporal trends from seasonal SAR time series data in order to facilitate downstream land cover classification tasks.
FPCA has the potential to be a powerful methodology for extracting dominant trends from a collection of time series (Wang et al. 2016). Therefore, we carried out a feasibility study on the use of FPCA as a feature extraction technique for the pixel-level SAR seasonal time series data. The resulting features are FPC scores, at the pixel-level. Our preliminary results suggest that the FPCA-based methodology could be remarkably effective. Figure 3 is the RGB rendering of the first three FPC scores (first FPC score is the red channel, second FPC score is green, third FPC score is blue (Wikipedia 2022); computed from the 2019 Sentinel-1 time series data for Bay of Quinte. Note the remarkable granularity of Figure 3 and its high level of concordance with Figure 2. This strongly suggests that these FPCA-based extracted features may significantly facilitate downstream land cover classification tasks. The rest of this article will explain how Figure 3 was generated from Sentinel-1 time series data.
A quick overview of the procedure is as follows:
The collection of training data (from the locations/pixels from the Bay of Quinte area) were carefully and strategically chosen to contain specific properties.
The Sentinel-1 data have two polarizations – vertical transmit and vertical receive (VV), and vertical transmit and horizontal receive (VH). These two polarizations can be regarded as two observed variables measured by the Sentinel-1 satellites.
Different Earth surface structures are detected with different sensitivities in polarizations. For example, rough surface scattering, such as those caused by bare soil or water, is most easily detected in the VV polarization. On the other hand, volume scattering, commonly caused by leaves and branches in a forest canopy, is most easily detected in VH and HV polarizations. Finally, a type of scattering called double bounce, commonly caused by buildings, tree trunks, or inundated vegetation, is most easily detected in the HH polarization (NASA 2022). For the rest of this article, we will focus on VV polarization.
Figure 4: Sentinel-1 VV time series data of the training locations and corresponding ribbon plots for easy temporal trend visualization. (a): 2017 data. Left panel group: Line plots of the Sentinel-1 VV time series data grouped by known land cover types. Right panel group: Corresponding ribbon plots, with the black curve in each panel indicating the panel-specific mean curve, and the ribbon indicating one standard deviation above and below the panel-specific mean curve. (b), (c): 2018, 2019 data, respectively.
Each panel corresponds to one of the six wetland types for 2017, 2018 and 2019 – marsh, swamp, water, forest, agriculture and shallow water. Each panel contains 1000 training time series, corresponding to 1000 distinct training locations, except for the bottom panel, which has only 518 time series. The horizontal axis shows the observation dates. They are 12 days apart, except the occasional gaps. The following four points are observed for the 2017, 2018 and 2019 time series data:
We give a conceptual explanation of FPCA via a comparison to the familiar ordinary principal component analysis (OPCA).
The OPCA is:
How does FPCA (more precisely, the particular flavour of FPCA we used) differ from OPCA ?
Why can FPCA capture temporal trends ?
Then, for each function in , express it as a linear combination of the FPC. The coefficients of this linear combination are the FPC scores of the given function with respect to the FPC.
Consequently, the functions in , the FPC, and the FPC scores can be organized as follows:
As more FPCs are included, the approximations above should increase in accuracy.
We explain how the FPC scores that underlie Figure 3 were computed in the Bay of Quinte project.
This FPCA-based feature extraction workflow is illustrated in Figure 5.
Figure 5: Schematic of FPCA-based feature extraction workflow.
FPC Features diagram begins with a square for New Time Series Data that feeds into the R package, which contains FPC 1, FPC 2, etc. A square for Training Time Series Data also feeds up to the R package. From there, the R package points to the next step which is are the Features or FPC Score. And from there extracted features are generated, which can be used in downstream processing or analytical tasks, e.g., visualization via RGB rendering, land use classification, land use change Detection.
If you recall, the so-called “trained FPCA-based feature extraction engine” is really just the global mean function and the ordered sequence of FPCs (functions defined on a common time interval) computed from the training data. Figure 6 displays the first seven FPCs computed from the 2017, 2018 and 2019 training Sentinel-1 VV time series data from the Bay of Quinte study area.
Figure 6: Graphical representations of the first seven functional principal components as functions of time.
Seven FPC line graphs computed from the 2017, 2018 and 2019 training Sentinel-1 time series data. In each panel, the horizontal axis represents the “date index,” where 1 refers to New Year’s Day, 2 refers to January 2, and so on. The vertical axis represents the value of the respective FPC scores. FPC 1 – Variability captured = 85.044%; FPC 2 – Variability captured = 7.175%; FPC 3 – Variability captured = 2.229%; FPC 4 – Variability captured = 1.677%; FPC 5 – Variability captured = 1.407%; FPC 6 – Variability captured = 1.162%; FPC 7 – Variability captured = 0.917%. A further explanation is below.
These are graphical representations of the first seven FPCs as functions of time. The (counting from top) panel visualizes the FPC. Recall that each FPC is, first and foremost, a (continuous) function of time, viewed as a vector in a certain vector space of (-integrable) functions defined on a certain time interval.
In each panel, the grey curve indicates the mean curve of the spline interpolations of all the VV time series (across all training locations and across years 2017, 2018 and 2019). The FPC turn out to be eigen vectors of a certain linear map (related to the “vari ance” of the training data set) from to itself (Wang et al. 2016). In each panel, the orange curve is the function obtained by adding to the mean curve (in gray) the scalar multiple of the functional principal component , where is the eigenvalue corresponding to . The blue curve is obtained by subtracting the same multiple of from the mean curve. Note as well that the first principal component explains about 85.04% of the variability in the training data (VV time series), the second component explains about 7.18%, and so on.
Next, note that the first functional principal component resembles a horizontal line (orange curve in top panel); it captures the most dominant feature in the training time series, which turns out to be the large near-constant difference in VV values between the Water/Shallow Water time series and those of the rest of the (non-water) land cover types. This large near-constant difference is clearly observable in the line and ribbon plots in Figures 4, 5, and 6.
Lastly, note that the second functional principal component (orange curve in second panel) has an early-season trough and a late-season peak, which captures the Shallow Water trend, as observable in the Shallow Water ribbon plots in Figures 4, 5, and 6.
We show here the scatter plots of the first FPC score against the second for the training data. Note the well separation of the water and shallow water training locations from those of the other land cover types, and note the consistency across years of this well separation.
Figure 7: 2017 FPC scores – training
Scatter plots of the first FPC score against the second for the 2017, 2018 and 2019 training time series data. The FPC scores are regarded as the extracted features. Each data point in this plot corresponds to a training location, with its colour indicating its wetland type. The horizontal and vertical axes correspond respectively to the first and second FPC scores derived from the VV time series. We emphasize that, while each plot shows only the scores of their corresponding training time series year, the FPC scores were computed simultaneously for all VV time series across all years and all wetland types.
The data points are partially clustered by wetland type; in particular, the water and shallow water separate very well from the rest of the wetland types. The horizontal (which is the dimension corresponding to the first FPC) separation between water/shallow water from the rest. This is a manifestation of the large vertical difference between water/shallow water and the rest of the non-water wetland types in terms of the original VV values (refer to figures 4). The first FPC captures this large vertical difference consistent throughout the growing season, and this explains why the first FPC resembles a flat line (orange curve that would appear in the top panel of Figure 6).
Also recalling that the second FPC has a trend that resembles that of the shallow water (as noted in the ribbon plots in the bottom panels of figure 4 against the orange curve in the second panel in Figure 6). Recall also that the marsh roughly exhibits the opposite trend (as noted in the top panel of figures 4 against the blue curve in the second panel in Figure 6). The second FPC captures the trend exhibited by shallow water, and accordingly, in this scatter plot is the shallow water training locations (red) that extend significantly in the positive vertical direction. Conversely, since the marsh exhibits roughly the opposite trend, the marsh training locations (black) extend significantly downward in the present scatter plot.
Given that the FPCA hinges on approximating individual training data time series as linear combinations of a sequence of FPCs (themselves functions) that are learned from the training data time series as a group:
To assess the suitability of FPCA to a specific given time series data set, it is prudent to examine how well the FPCA-based approximations can actually approximate the original given time series data.
Figure 12 displays six randomly chosen Sentinel-1 training data time series and their FPCA approximations to give an impression of the goodness-of-fit of the FPCA approximations.
Figure 8: FPCA approximations of six training data time series.
FPCA approximations of six training data time series. In each panel, the horizontal axis represents the “date index,” where 1 refers to New Year’s Day, 2 refers to January 2, and so on. The vertical axis represents the value of the VV variable in the Sentinel-1 data. The black dots are the original time series data points. The blue curve is the B-spline interpolation. The red curve is the seven-term FPCA approximation of the B-spline interpolation (blue curve), where “seven-term” here means that the FPCA approximation is the sum of the global mean function and a linear combination. Panel 1 – year 2017, location: -77.210217019792_43.8920257051607; Panel 2 – year 2017, location: -77.2997875102733_44.0678018892809; Panel 3 – year 2018, location: -77.2373431411184_44.1006434402341; Panel 4 – year 2018, location: -77.2691161641941_43.9610969253399; Panel 5 – year 2019, location: -77.2663596884513_43.950887882021; Panel 6 – year 2019, location: -77.3141305185843_44.1218009272802
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This guide is designed to assist you as you complete the 2021 Farm Financial Survey. If you need more information, please call the Statistics Canada Help Line at the number below.
Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act.
Statistics Canada will use information from this survey for statistical purposes.
Help Line: 1-877-949-9492
Additional information that may be useful in the completion of this electronic questionnaire include the operation's 2021:
Also:
Legal Name
The legal name is one recognized by law, thus it is the name liable for pursuit or for debts incurred by the business or organization. In the case of a corporation, it is the legal name as fixed by its charter or the statute by which the corporation was created.
Modifications to the legal name should only be done to correct a spelling error or typo.
To indicate a legal name of another legal entity you should instead indicate it in question 3 by selecting 'Not currently operational' and then choosing the applicable reason and providing the legal name of this other entity along with any other requested information.
Operating Name
The operating name is a name the business or organization is commonly known as if different from its legal name. The operating name is synonymous with trade name.
Current main activity of the business or organization
This question verifies the business or organization's current main activity as classified by the North American Industry Classification System (NAICS). The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, Mexico and the United States. Created against the background of the North American Free Trade Agreement, it is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply-side or production-oriented principles, to ensure that industrial data, classified to NAICS, are suitable for the analysis of production-related issues such as industrial performance.
The target entity for which NAICS is designed are businesses and other organizations engaged in the production of goods and services. They include farms, incorporated and unincorporated businesses and government business enterprises. They also include government institutions and agencies engaged in the production of marketed and non-marketed services, as well as organizations such as professional associations and unions and charitable or non-profit organizations and the employees of households.
The associated NAICS should reflect those activities conducted by the business or organizational unit(s) targeted by this questionnaire only, as identified in the 'Answering this questionnaire' section and which can be identified by the specified legal and operating name. The main activity is the activity which most defines the targeted business or organization's main purpose or reason for existence. For a business or organization that is for-profit, it is normally the activity that generates the majority of the revenue for the entity.
The NAICS classification contains a limited number of activity classifications; the associated classification might be applicable for this business or organization even if it is not exactly how you would describe this business or organization's main activity.
Please note that any modifications to the main activity through your response to this question might not necessarily be reflected prior to the transmitting of subsequent questionnaires and as a result they may not contain this updated information.
Question 1, 2
The Canada Revenue Agency has a requirement that Statistics Canada keep a record of the name of the person who gave consent to share the farm operation's revenue and expenses data. As stated, your name will not be shared with Agriculture and Agri-Food Canada or your provincial agriculture agency.
With the respondent's consent, the following data (20 questions) will be obtained from the Canada Revenue Agency and shared with AAFC and your provincial ministry of agriculture:
Note: In the case of sharing data with provincial agriculture ministries, the data shared will be limited to information pertaining to farm operations within the jurisdiction of the province.
Question 3
Fiscal year-end refers to the last day of the twelve-month period a business uses as its income tax year.
If this operation's fiscal year-end is close to the survey date, e.g., April 30, 2021, and the current financial statements are not yet available, use the most recent financial statements, e.g., April 30, 2020.
Question 4
A farm operator is an individual responsible for the day-to-day operation of the farm, who participates in the decisions to borrow money, to rent, buy or sell assets and to manage debts.
Include farm owner-operators and hired managers.
Question 11
Definitions:
A sole proprietorship is a farming business in which there is one person that makes the business decisions, takes the risks and makes the profits.
A corporation is a farming business that has been registered with a province as a legal entity.
A partnership is an agreement to share the profits and losses of the business and is formally registered as such with the Canada Revenue Agency for tax purposes.
The partners jointly own the operation's property and share a joint bank account, joint accounting, single financing, use of the partnership name, etc.
A cooperative and communal operations is a farming business that is operated by a community of people. (All Hutterite colonies are considered to be cooperatives even if they are incorporated).
A joint venture is similar to a partnership, but is not exactly the same. The parties in a joint venture are usually separate businesses, which join together to accomplish a project. Each party contributes assets to the joint venture. Often, one of the partners is an operator and the others provides the use of capital for the operation.
A trust is when management and decision making powers of an investment or business is held by a third party. Therefore, the owners cannot make decisions concerning the business. However, the actual owners still receive the profits generated by the trust.
Family's percent ownership for corporations and partnerships:
Question 21
Rounding procedures for this section:
Workable land includes all arable or cleared land including area in field crops, vegetables, sod, nursery, fruits, berries and nuts, summerfallow, and tame or seeded pasture land.
Non-workable land includes land that is not or cannot be used for agricultural purposes plus land on which all farm buildings are located:
Question 22
Cropland is land which has not been left in its natural state. It is workable land which is tilled either annually or periodically to produce agricultural products. This includes field crops, land used to produce fruits and vegetables, greenhouses, sod and nursery production, etc.
Question 23
Cropland is land which has not been left in its natural state. It is workable land which is tilled either annually or periodically to produce agricultural products. This includes field crops, land used to produce fruits and vegetables, greenhouses, sod and nursery production, etc.
Market value is the most probable price an asset would bring in a competitive and open market under all conditions required for a fair sale, with buyer and seller each acting prudently and knowledgeably.
Sources for market value:
Question 24
Cropland rented from others is workable land that you rented in order to cultivate and produce agricultural products.
Include land rented for field crops, e.g., tame hay, wheat, canola, potatoes, sugar beets, land used to produce fruits and vegetables, greenhouses, sod and nursery production, etc.
Question 25 - 27
Capital investments are expenditures for long-term assets, generally expensed using capital cost allowance/depreciation. Although not depreciable, land and living things such as trees, shrubs and animals are capital investments.
Question 28
b) Construction and renovation of building and other structures includes construction of a new farmhouse or any renovations to the existing farmhouse or to any other farm building.
d) Land improvements include:
If this operation incurred expenses to create an environmental plan, these expenses should be reported as operating expenses.
f) Quota is an entitlement or right to sell or deliver a certain amount of an agricultural product. This pertains to six products: milk, table eggs, hatching eggs, chicken, turkey, and tobacco.
Question 30, 31
Capital sales arise from the sale or trade-in of capital assets, such as machinery, land, buildings, breeding and replacement livestock, and quota. This is opposed to farm revenue, which is the sale of products produced on the farm, such as livestock or crops.
Where farmland and farm buildings have been foreclosed on or transferred to a lender, use best estimate of the market value of the property. This might be obtained from: an assessment on a property tax bill, an estimate based on recent land sales in the area, or if money has been borrowed from a lending institution, the real estate value has likely been estimated.
Quota is an entitlement or right to sell or deliver a certain amount of an agricultural product. This pertains to six products: milk, table eggs, hatching eggs, chicken, turkey, and tobacco.
Breeding and replacement livestock includes the value of breeding and replacement livestock sold, excluding culls. Culls are female or male breeding stocks that are no longer productive. Do not report culls sold for slaughter as a capital sale (this is considered revenue).
Question 32
Assets refer to all tangible and intangible items of value owned by this operation. They are the sum of current assets, breeding and replacement livestock, market livestock, machinery and equipment, quota, land and buildings, and financial investments.
Please report market value — the most probable price an asset would bring in a competitive and open market under all conditions required for a fair sale, with buyer and seller each acting prudently and knowledgeably. If the exact market value is unknown, please give the best estimation.
a) Breeding and replacement livestock are animals that are expected to be on the operation for more than one year – animals acquired or raised for the production of progeny, or for the production of a livestock product.
b) Market livestock are animals that are expected to be on the operation for less than one year. Include all poultry as market livestock.
Short-term farm assets are cash and any other asset that, in the normal course of operations, is expected to be converted into cash or consumed in the production process within one year or within the normal operating cycle (where the cycle is longer than a year).
c) Accounts receivable are monies which are owed to the operation usually arising from the sale of goods or services, such as crops, livestock or custom work, and from program payments, to be paid to the operation within 12 months. If the money is to be repaid after 12 months or over a series of years, it is considered a long-term note receivable and should be reported as "other long-term farm assets" (section "l").
d) Cash and short-term investments include surplus cash (usually arising from the sale of crops or livestock) which is invested for a short period of time until such time as the cash is again required in the farm business, usually to purchase new inputs such as fertilizer or feeder livestock. Included are certificates of deposits with a maturity of less than 12 months; if the maturity is 12 months or more, the amount should be reported as a "long-term investment" (section "k").
e) Inventory – Supplies on hand (inputs) refer to farm supplies that are to be used in the farm business. Crops for sale are not considered inputs and should be reported as "crops for sale" (section "f").
f) Inventory – Crops for sale include all harvested crops destined for market and greenhouse and nursery horticulture products for market.
g) Other short-term farm assets includes notes receivable, which are claims issued as evidence of debt e.g., promissory note. Only report here the amount expected to be repaid within 12 months. Amounts to be repaid after 12 months should be reported as "other long-term farm assets" (section "l").
Long-term farm assets have a useful life of greater than one year. Such an asset, which can be either a tangible or an intangible item, is usually not purchased for resale, but is to be used over time to produce saleable products.
h) Sources for the market value of farmland and buildings:
j) Quota is the right to sell an agricultural product such as milk, poultry, eggs and tobacco. A quota can be a separate asset or can be attached to the land and buildings (poultry).
Quota has a market value and is considered an asset. Please ensure that the market value of quota is reported e.g., what the operation would have been able to sell its quota for on this operation's fiscal year-end date. It may help to multiply quota per animal by the number of animals owned by the operation on this operation's fiscal year-end date.
l) Other long-term farm assets include:
Question 33 - 36
Money owed to banks, caisses populaires, credit unions, trust companies, treasury branches, credit card debt, suppliers or government agencies.
Long-term includes mortgages and long-term loans. Also include long-term loans guaranteed by governments but obtained through a financial institution.
Financière agricole du Québec is considered a financial institution and amounts owed to it should be reported here.
Short-term includes balances on operating lines of credit, short-term loans, credit card balances, cash advances, and any overdue payments. Also include short-term loans guaranteed by governments but obtained through a financial institution and the short-term portion, due within one year of fiscal year-end date, of long-term liabilities.
Money owed to the Advance Payments Program (APP) – short-term only
The APP provides cash advances (up to $1,000,000) guaranteed by Agriculture and Agri-Food Canada (AAFC). Some APP loans may have 18-month repayment periods, however, for this survey, please record all money owed to APP as short-term debt.
Please record loans from the Commodity Loan Program (CLP) from the Agricultural Credit Corporation (ACC) here as well (Ontario only).
Money owed to machinery and supply companies or feed companies
Debts which are owed for the purchase of inputs, also referred to as accounts payable.
Include seed grain (input), fertilizer (input), feed (input), fuel for machinery, heating fuel, propane, purchase of vehicles or agricultural equipment financed by the dealer, monies owed to poultry suppliers (breeder-hatcheries or breed flock operations).
Money owed to family members or private individuals
Include parents, spouse, siblings, parent company, previous owner of the operation, another farm, e.g., for land rental or custom work, in-laws, sister company.
Amount of money owed to provincial agencies
Include agricultural credit / lending agencies, farm loan board.
Exclude:
All other short or long-term debt
Include:
Exclude amounts to be paid for:
Question 37
Loans guaranteed by provincial governments or agencies, e.g., Financière agricole du Québec and Investissement Québec, should be reported with the institutions (banks, caisses populaires, credit unions, trust companies or treasury branches) which disbursed the loans and not as government loans.
Question 38
Custom or contract feeding is where livestock or poultry are fed and raised by the operation for somebody else.
Question 39
Include revenue received for the total number of livestock and poultry custom or contract fed for the whole year.
Question 41
Report the total number of livestock fed for the whole year.
Include custom or contract grazing.
Exclude animals owned by this operation.
Please report for all cycles of livestock and poultry custom or contract fed. For example, a broiler producer could have five cycles of 10,000 broilers in the barns in one year. In this case, report the total number of broilers for the year (10,000 broilers X 5 cycles = 50,000).
Question 42
Dividends paid by a farm corporation to its owners are not farm operating expenses like wages and salaries. Dividends are paid after tax, while wages and salaries are deducted before tax.
Question 43
Wages and salaries paid to family
The operator's family is defined as an operator, an operator's spouse or common-law partner and children residing in the same dwelling. Children are included regardless of age or marital status as long as they do not have their own spouse, common-law partner or child living in the same dwelling.
Include children studying away from the home whose main address is still the farm address.
Exclude:
Question 44
Gross farm revenue represents the income received from the sale of agricultural commodities, as well as direct program payments made to support or subsidize the agriculture sector.
Please report the gross revenue before any deductions.
Question 45
g) Total amount received for program payments represent payments from government agencies to farm operations in the form of rebates, subsidies and stabilization payments.
AgriStability provides support when you experience a large margin decline. You may be able to receive an AgriStability payment when your current year program margin falls below 70% of your reference margin.
AgriInsurance (also known as provincial crop or production insurance) is a federal-provincial-producer cost-shared program that stabilizes a producer's income by minimizing the economic effects of production losses caused by natural hazards. AgriInsurance is a provincially delivered program.
i) All other farm revenue includes all farm revenue for this operation that does not fall into the other categories provided.
Include Patronage dividends - payments that a cooperative make to its members.
Agritourism - display gardens (flowers, herbs, etc.), food processing facilities, historical museums and displays, working farm/ranch, barn dances, corn mazes, corporate picnics, educational tours / workshops, entertainment/music, fairs, festivals, on-farm accommodations (guest ranch, picnic areas, restaurant, sugar shack, farm / ranch holidays), tours, retail sales, leisure/recreation (fishing, gardening, hiking, horseback riding, U-pick crops).
Question 46
Operating expenses are the business costs, generating a cash outlay, incurred by farm operators for goods and services used in the production of agricultural commodities in the fiscal year.
Operating expenses are reported on the operation's income statement and normally includes both direct and indirect production costs.
Include veterinary fees, medicine and breeding fees, e.g., artificial insemination.
Question 47
a) Fertilizer and lime expenses include all costs associated with the purchase of fertilizer and lime including spreading, if it is part of the cost.
b) Include all farm expenditures for pesticides (herbicides, insecticides, fungicides, etc.). If the application of pesticides is part of the cost, it is also included.
c) Seed and plants expenses include the value of seed and seedlings purchased by farmers through nurseries, elevators, seed houses, seed dealers and other farmers. The value of home-grown seed and the value of seed bought for resale are excluded. Seed cleaning and treatment costs are included if they are part of the purchase cost.
d) Feed, supplements and hay purchased by farmers, including hay and straw costs. The value of home-grown feed is excluded.
e) Fuel for machinery, trucks and automobiles expenses include petroleum, diesel oil and lubricants used for all types of machinery and equipment from tractors and combines to generators and irrigation pumps. Only the farm business share of automobiles and trucks is included.
f) Total interest paid on farm debt loans such as mortgages or credit from suppliers and private individuals.
g) Land rentals includes all land rented for cash, from others including land rented from governments and other sources.
h) Heating fuel is the cost of all heating fuels (natural gas, propane, furnace oil, etc.). Only the farm business portion of heating fuel is included.
i) Electricity is the cost of electricity for the farm business portion only.
Question 48
The operator's family is defined as an operator, an operator's spouse or common-law partner and children residing in the same dwelling. Children are included regardless of age or marital status as long as they do not have their own spouse, common-law partner or child living in the same dwelling.
Include children studying away from the home whose main address is still the farm address.
Exclude:
a) Employment Income
Include the amount of money for the gross wages and salaries (before deductions) for all family members. If an operator is involved in a second independent farm operation, the gross wages or salaries earned from that second farm should be reported here as well.
b) Net self-employment income
Business income includes income from any activity done for profit, for example, income from a service business. Exclude employment income as business income.
Professional fees are fees received for goods or services provided, whether money, something the same as money (such as credit units that have a notional monetary value), or something from bartering was received or will be received.
Fishing income includes income earned whether payable in cash, property or services from fishing for or catching shellfish, crustaceans or marine animals.
Fishing income does not include income earned from working as an employee in a fishing business.
f) Other examples of other income not from this operation include income from snow plowing or cutting weeds along a roadway.
Question 75
If this operation has innovated, but has not yet seen the results, mark the question as "yes"; the impact of the innovation is not relevant to this question.
Innovation means implementing a new or significantly improved product, practice or process on your farming operation. Innovations must be new to your operation but need not be new to the industry.
a) Crop products innovation involves new or significantly improved crops produced, varieties planted and cultivars created.
b) Livestock products innovation involves new or significantly improved livestock types and livestock breeds raised.
c) Production process and practice innovations involve new or significantly improved processes and/or practices regarding soil management, fertilizer application, irrigation and water management, and livestock handling.
d) Marketing practice innovation involves new or significantly improved approaches to marketing the farm's production, such as marketing and/or production contracts, futures and/or options and direct marketing.
e) Business management practices involve new or significantly improved approaches to meeting labour requirements, business ownership and/or partnerships, ways of acquiring inputs, and adding processing activities.
Thank you for your participation.
| Geography | Month | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202103 | 202104 | 202105 | 202106 | 202107 | 202108 | 202109 | 202110 | 202111 | 202112 | 202112 | 202202 | 202203 | |
| percentage | |||||||||||||
| Canada | 0.6 | 0.7 | 0.9 | 0.8 | 0.6 | 0.6 | 0.7 | 0.7 | 0.8 | 1.2 | 0.8 | 0.7 | 0.7 |
| Newfoundland and Labrador | 0.2 | 1.2 | 2.4 | 0.3 | 0.3 | 0.4 | 0.4 | 0.3 | 0.4 | 0.4 | 1.0 | 0.6 | 7.1 |
| Prince Edward Island | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Nova Scotia | 2.2 | 5.2 | 8.1 | 4.4 | 2.1 | 2.5 | 2.8 | 2.4 | 2.8 | 5.9 | 2.8 | 1.8 | 1.0 |
| New Brunswick | 1.0 | 1.1 | 1.6 | 2.9 | 2.4 | 2.1 | 2.4 | 2.2 | 4.0 | 1.4 | 3.2 | 0.5 | 1.5 |
| Quebec | 1.9 | 1.9 | 3.1 | 3.0 | 1.5 | 1.7 | 1.6 | 1.6 | 1.7 | 1.9 | 2.2 | 1.4 | 1.3 |
| Ontario | 0.9 | 1.1 | 1.2 | 0.9 | 0.8 | 1.0 | 1.1 | 1.1 | 1.3 | 2.1 | 1.3 | 1.2 | 1.2 |
| Manitoba | 0.9 | 2.8 | 5.2 | 1.6 | 0.8 | 1.0 | 1.1 | 1.7 | 1.2 | 1.5 | 1.7 | 1.6 | 1.7 |
| Saskatchewan | 1.2 | 0.8 | 0.5 | 0.6 | 0.6 | 1.3 | 1.6 | 1.0 | 0.8 | 0.5 | 0.9 | 0.3 | 0.4 |
| Alberta | 1.3 | 1.2 | 1.5 | 1.3 | 1.5 | 1.1 | 1.0 | 1.4 | 2.0 | 1.0 | 1.8 | 1.6 | 0.9 |
| British Columbia | 1.5 | 1.1 | 1.3 | 1.3 | 1.5 | 1.4 | 1.8 | 1.2 | 1.7 | 1.3 | 1.6 | 2.3 | 1.6 |
| Yukon Territory | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Northwest Territories | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Nunavut | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
