Developments in machine learning series: Issue three

By: Nicholas Denis, Statistics Canada

Editor's Note: This series showcases new and interesting research developments in machine learning from around the world. Hopefully you can find something that will help you with your work.

This month's topics:

Advanced generative modelling now available for tabular data

Denoising diffusion probabilistic models can be applied to any tabular dataset and handle any feature type to generate synthetic data that can sometimes be more effective than real data.

Figure 1: TabDDPM scheme for classification problem; t, y, and l denote a diffusion timestep, a class label, and logits, respectively.

Figure 1: TabDDPM scheme for classification problem; t, y, and l denote a diffusion timestep, a class label, and logits, respectively.

Data passing through TabDDPM. A tabular data instance, x, is composed of numerical (num) and categorical (cat) features. Numerical features are transformed using the quantile transformer, and categorical features are one-hot encoded. The forward noise process is computed and corrupts the transformed input at timestep, t, which is used as input into TabDDPM. TabDDPM is a multi-layer perceptron (MLP) and conditioned on the diffusion timestep, t, and class label, y. The output of TabDDPM is the estimated noise added to the numerical features, ε, and the estimated uncorrupted (original) one-hot encoded categorical features.

The flowchart has the following elements:

  1. Quantile Transformer (xnum: 0.8, -3.0, 1.2)
    1. Forward to 3.
  2. One-hot Encoder (xcat1, xcat2)
    1. Forward to 3.
  3. X in (Xnumnorm, Xcat1ohe), Xcat2ohe
    1. Forward to 4.
  4. MLP
    1. t forward to 4.
    2. Y forward to 4.
    3. Forward to 5.
  5. ε, lcat1, lcat2
    1. ε forward to 7.
    2. lcat1, lcat2 forward to 6.
  6. Softmax
    1. From lcat1 forward to 7.
    2. From lcat2 forward to 7.
  7. X out (ε, Xcat1ohe, Xcat2ohe)

What's new? Denoising diffusion probabilistic models (DDPM) produce incredible results in text-to-image generation (e.g. Imagen, Stable Diffusion, DALL-E 2) and have been applied to tabular datasets. This has enabled synthetic tabular data instances to be generated comprising both numerical and categorical features.

How it works: A typical DDPM uses a forward Gaussian noising process and a reverse (learned) denoising process to transform pure noise sampled from a standard multivariate normal distribution to a synthetic data instance. Similarly, tabular data with diffusion models (TabDDPM) also includes a multinomial diffusion process that allows DDPM to be applied to categorical, ordinal and Boolean features typically found in tabular data.

Why does it work? For a quick review of DDPM models and the Gaussian diffusion process, please see Developments in Machine Learning Series: Issue two. Here, we'll focus on the multinomial diffusion process:

  • Let x0{0,1}K be a one-hot encoded variable of K dimensions (categories).
  • A forward diffusion process, q(xt|xt-1), over T time steps is written as:

q(xtxt-1)Cat(xt;(1-βt)xt-1+βt/K

q(xT)Cat(xT;1/K)

q(xt|x0)=Cat(xt;αt¯x0+(1-(αt¯)/K)

Where {βt}Tt=1, is a noise schedule.

  • The posterior, q(xt-1|xt,x0) is well defined, and the reverse diffusion process is approximated using a neural network: ρθ(xt-1|xt) The network can maximize the variational lower bound (KL divergences within the reverse diffusion process).
  • Numerical features are transformed using the quantile transformer, and categorical (ordinal, Boolean, etc) features are transformed to one-hot encodings. Gaussian and multinomial diffusion models are trained on each feature type, respectively.

Results: Several baseline generative models are compared, including generative adversarial networks (GAN) and a variational autoencoder (VAE), specifically designed for tabular datasets. There are 15 tabular datasets that are used. The authors use a formalized and standardized approach to evaluate the quality of the synthetic data generated by a generative model, called ML Efficiency. The approach is worth explaining in detail for anyone interested in evaluating the quality of synthetic datasets.

First, the real data is fit to a CatBoost classifier using a fixed hyperparameter tuning budget and a hyperparameter optimization tool called Optuna. Once the optimal hyperparameters are found, a CatBoost model is trained using the synthetic data. The F1 score of the model, evaluated on real data is called the ML Efficiency score. This metric measures the quality of synthetic data by its ability to be used instead of real data for downstream ML tasks.

  • TabDDPM beat the other deep learning approaches in 14/15 datasets
  • On 5/15 datasets, a CatBoost model trained on synthetic data from TabDDPM achieved higher test accuracy than a CatBoost model trained on real data.

Why does it matter? DDPMs are producing realistic synthetic data in other modalities. Sampling high-quality synthetic tabular data instances can have applications in data augmentation – generating and sharing synthetic versions of sensitive datasets that can guarantee privacy, imputation and balance class imbalanced datasets.

But… When tested, synthetic minority oversampling technique (SMOTE) consistently outperformed TabDDPM, despite being an older, simpler technique that generates novel data instances by taking convex combinations of data instances from within the dataset. Moreover, TabDDPM is computationally expensive to train, especially with their Optuna hyperparameter optimization approach.

Our opinion: With access to a GPU (graphics processing unit) for model training, TabDDPM is a great way to generate high-quality synthetic tabular data and will remain a strong baseline for future improvements in tabular DDPMs.

Filling in the blanks is all you need

Researchers from Facebook AI Research have achieved state-of-the-art performance on the ImageNet-1K dataset by applying the same pre-training task to computer vision datasets, as was applied to the successful language models.

Figure 2: Masked autoencoder architecture. 75% of the pixel patches are masked and then reconstructed as a pretraining task.

Figure 2: Masked autoencoder architecture. 75% of the pixel patches are masked and then reconstructed as a pretraining task.

An image of masked autoencoder architecture.

Input image of a pixelated flamingo, with most of the pixels greyed out. Forward to encoder containing all the unmasked pixels. Forward to decoder, containing only masked pixels. Forward to the final target – an image of the pixelated flamingo, no masked pixels.

What's new?  Masked autoencoders (MAE) combine the Vision Transfer (ViT) architecture and the masked token prediction pre-training task from BERT (Bidirectional Encoder Representations from Transformers)-style language models to produce state-of-the-art performance on various computer vision tasks and datasets.

How does it work? BERT-style language models encode and represent natural language via a Masked Language Model pre-training task. When given a large amount of text data, a sentence is fed into the model with 15% of the input word tokens masked. By correctly inferring the masked words, BERT-style models capture the semantics of the text inputs, making them useable for downstream natural language processing tasks, like text classification. MAE uses the same approach, except instead of masking words, 75% of the image's pixel patches are masked and reconstructed.

Why does it work? MAE uses an asymmetric encoder and decoder architecture. Both the encoder and decoder are transformer models. Since 75% of the image patches are masked, only 25% are processed by the encoder, making it more computationally efficient than traditional ViTs for standard supervised learning. The decoder also has a smaller architecture and reconstructs the masked patches. By minimizing the reconstruction loss, the model learns to extract global information about an image, including the masked patches, solely from the ‘visible' patch pixels. After pre-training is complete, the decoder is discarded, and the encoder acts as a pre-trained model which can be used as-is to transform inputs for linear classification or can be fine-tuned.

Results:

  • Using the ImageNet-1K (IN1K) dataset, MAE pre-training and then fine-tuning for 50 epochs achieved higher accuracy than the previous state-of-the-art fully supervised model.
  • Using various standard ViT architectures, MAE consistently outperformed previous state-of-the-art self-supervised pre-training techniques on the IN1K dataset for classification, on the COCO dataset for object detection and instance segmentation, and on the ADE20k dataset for semantic segmentation.
  • When evaluated for transfer learning, MAE outperformed all previous state-of-the-art accuracy results for various iNaturalist and Places datasets; even by as much as 8%.

Our opinion: Pre-training techniques are becoming increasingly powerful and can now outperform standard supervised learning paradigms. MAE is not only impressive in its results but is also impressively simple. This paper doesn't use any bells, whistles, or any special tricks. Moreover, they use the basic ViT architecture, and expect significant result improvements by using advanced vision transformer architectures.

Though relatively new, MAE has been applied to spatiotemporal data, Multi-modal data, tabular data, and is here to stay with applications beyond pre-training for supervised learning. It's been used for data imputation and can be used for semi-supervised learning. Pre-trained MAE models can also be downloaded and used by visiting Github Facebookreseearch/mae.

Masked autoencoders for tabular data imputation

Researchers turn an imputer into an imputer. Wait, is that right?

Figure 3: Overall, framework of ReMasker.

Figure 3: Overall, framework of ReMasker.

During the fitting stage, for each input, in addition to its missing values, another subset of values (re-masked values), is randomly selected and masked out. The encoder is applied to the remaining values to generate its embedding, which is padded with mask tokens and processed by the decoder to re-construct the re-masked values. During the imputation staged, the optimized model is applied to predict the missing values.

Fitting stage:

Input, encoder, embedding, forward to padded embedding, decoder and reconstructed value.

Imputation stage:

Input, encoder, embedding forward to padded embedding, decoder and imputed value.

What's new? As described above, MAEs are trained to impute missing data. Researchers decided to do the unthinkable and apply MAE to tabular data in order to… impute missing data.

How does it work? MAE and the masked token prediction task were originally developed to pre-train an encoder that could be fine-tuned for downstream classification. In the original MAE implementation, the decoder was discarded after pre-training. In ReMasker, the encoder and decoder are kept, and then applied to actual missing values in the dataset to infer their values.

Why does it work? ReMasker is trained with a tabular dataset that has actual missing values. During training, each input is masked via a masking mechanism and produces an input with a mixture of visible values, masked values, and missing values. The visible values enter the encoder (transformer architecture), and the output tokens are combined with learnable mask tokens and positional encodings for the masked and missing features. These then pass through the decoder (transformer architecture). The model then infers the original value of the masked features, but not the actual missing values.

At test time no additional masking is used, and the data instances with missing features are applied to ReMasker to infer the missing values. Just as the authors of the original MAE paper suggest, the encoder learns to represent the global information of an image from the visible pixel patches, the authors of ReMasker claim that the encoder learns a "missingness-invariant" representation of the data. In doing so, ReMasker learns to represent tabular data instances in a discriminative manner that is robust to missing features.

Results: The authors perform experiments using 12 tabular datasets from the UCI Machine Learning Repository, consider three different mechanisms to sample missing values, and compare ReMasker to 13 state-of-the-art imputation techniques. Performance metrics included Root Mean-Squared Error of imputed vs. real values, Wasserstein Distance of imputed vs. real data, and Area Under the Receiving Operating Characteristic (AUROC) curve, measuring effectiveness of imputed data for downstream classification tasks.

  • ReMasker consistently matched or outperformed almost all the baseline imputing approaches, most or all of the time.
  • The closest imputing approach was HyperImpute, a new and state-of-the-art imputation approach that uses an ensemble of imputation methods and selects the best imputation model for each column of a dataset.
  • Empirical evidence suggested that ReMasker performance increases as the dataset size increases, as well when the number of features increases.
  • ReMasker is robust to the amount of missing values, performing consistently from 10% to 70% of features missing.
  • Since HyperImpute is an Ensembling approach, the authors included ReMasker as a possible base model within HyperImpute and showed that it further improved the results of HyperImpute.
  • To test their hypothesis that by applying MAE pre-training on tabular data, it allows the encoder to learn "missing-ness invariant" representations of the data, the authors computed the Centered Kernel Alignment between the encodings of the full data and the encodings of the data with missing values between 10% and 70%. They found the similarity was robust and only slightly decreased as the proportion of missing features increased, providing evidence in support of their hypothesis.

What's the context? Real world datasets contain missing data for many reasons. The use of data for ML models, official statistics, or inference of any kind must address how to or how not to use data with missing values. Imputation is important and an active area of research within ML and statistical methods communities.

Our opinion: Given that the MAE pre-training technique explicitly performs imputation, it was only a matter of time before a researcher tested its applicability for imputation on tabular datasets. This was a good first look into how MAE could be used for such purposes.

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Retail Commodity Survey: CVs for Total Sales December 2022

Retail Commodity Survey: CVs for Total Sales December 2022
Table summary
This table displays the results of Retail Commodity Survey: CVs for Total Sales ( December 2022). The information is grouped by NAPCS-CANADA (appearing as row headers), and Month (appearing as column headers).
NAPCS-CANADA Month
202209 202210 202211 202212
Total commodities, retail trade commissions and miscellaneous services 0.67 0.55 0.61 0.55
Retail Services (except commissions) [561]  0.66 0.56 0.59 0.55
Food at retail [56111]  0.52 0.39 0.37 0.42
Soft drinks and alcoholic beverages, at retail [56112]  0.53 0.53 0.53 0.47
Cannabis products, at retail [56113] 0.00 0.00 0.00 0.00
Clothing at retail [56121]  2.02 0.84 0.77 0.72
Footwear at retail [56122]  1.60 2.66 1.37 1.39
Jewellery and watches, luggage and briefcases, at retail [56123]  4.56 5.07 3.82 3.28
Home furniture, furnishings, housewares, appliances and electronics, at retail [56131]  0.89 1.05 1.04 0.89
Sporting and leisure products (except publications, audio and video recordings, and game software), at retail [56141]  2.38 2.26 2.25 1.85
Publications at retail [56142] 5.33 5.57 6.05 4.69
Audio and video recordings, and game software, at retail [56143] 0.27 0.45 0.37 0.28
Motor vehicles at retail [56151]  2.51 2.05 2.22 2.29
Recreational vehicles at retail [56152]  4.34 4.05 5.51 4.38
Motor vehicle parts, accessories and supplies, at retail [56153]  1.83 1.66 1.73 1.74
Automotive and household fuels, at retail [56161]  1.47 1.63 1.58 1.77
Home health products at retail [56171]  3.37 2.80 3.08 3.40
Infant care, personal and beauty products, at retail [56172]  2.56 2.52 2.48 2.39
Hardware, tools, renovation and lawn and garden products, at retail [56181]  2.12 1.87 2.27 2.41
Miscellaneous products at retail [56191]  2.25 2.60 2.52 2.09
Total retail trade commissions and miscellaneous services Footnote 1 2.03 2.27 2.03 1.79
Footnote 1

Comprises the following North American Product Classification System (NAPCS): 51411, 51412, 53112, 56211, 57111, 58111, 58121, 58122, 58131, 58141, 72332, 833111, 841, 85131 and 851511.

Return to footnote 1 referrer

Quarterly Survey of Financial Statements: Weighted Asset Response Rate - fourth quarter 2022

Weighted Asset Response Rate
Table summary
This table displays the results of Weighted Asset Response Rate. The information is grouped by Release date (appearing as row headers), 2021, Q4, and 2022, Q1, Q2, Q3, and Q4 calculated using percentage units of measure (appearing as column headers).
Release date 2021 2022
Q4 Q1 Q2 Q3 Q4
quarterly (percentage)
February 23, 2023 80.9 79.3 79.2 76.9 55.2
November 23, 2022 80.9 76.2 76.1 56.2 ..
August 25, 2022 80.9 75.0 55.7 .. ..
May 25, 2022 77.3 56.7 .. .. ..
February 23, 2022 54.2 .. .. .. ..
.. not available for a specific reference period
Source: Quarterly Survey of Financial Statements (2501)

National Travel Survey: C.V.s for Person-Trips by Duration of Trip, Main Trip Purpose and Country or Region of Trip Destination – Q3 2022

National Travel Survey: C.V.s for Person-Trips by Duration of Trip, Main Trip Purpose and Country or Region of Trip Destination – Q2 2022
Table summary
This table displays the results of C.V.s for Person-Trips by Duration of Trip, Main Trip Purpose and Country or Region of Trip Destination. The information is grouped by Duration of trip (appearing as row headers), Main Trip Purpose, Country or Region of Trip Destination (Total, Canada, United States, Overseas) calculated using Person-Trips in Thousands (× 1,000) and C.V. as a units of measure (appearing as column headers).
Duration of Trip Main Trip Purpose Country or Region of Trip Destination
Total Canada United States Overseas
Person-Trips (x 1,000) C.V. Person-Trips (x 1,000) C.V. Person-Trips (x 1,000) C.V. Person-Trips (x 1,000) C.V.
Total Duration Total Main Trip Purpose 96,687 A 89,513 A 5,501 A 1,673 A
Holiday, leisure or recreation 47,517 A 43,556 A 3,020 A 941 A
Visit friends or relatives 33,317 A 31,644 A 1,114 B 559 B
Personal conference, convention or trade show 901

C

854 C 38 E 9 E
Shopping, non-routine 4,926 B 4,221 B 705 B ..  
Other personal reasons 5,182 B 4,946 B 178 C 58 E
Business conference, convention or trade show 1,338 B 1,054 C 236 C 47 D
Other business 3,506 B 3,238 B 209 C 58 D
Same-Day Total Main Trip Purpose 56,721 A 54,697 A 2,025 B ..  
Holiday, leisure or recreation 24,868 A 23,960 A 908 B ..  
Visit friends or relatives 20,092 A 19,789 A 303 C ..  
Personal conference, convention or trade show 410 C 399 C 10 E ..  
Shopping, non-routine 4,588 B 3,948 B 640 B ..  
Other personal reasons 3,816 B 3,724 B 92 D ..  
Business conference, convention or trade show 419 D 419 D ..   ..  
Other business 2,529 C 2,458 C 71 E ..  
Overnight Total Main Trip Purpose 39,966 A 34,817 A 3,476 A 1,673 A
Holiday, leisure or recreation 22,649 A 19,596 A 2,112 A 941 A
Visit friends or relatives 13,225 A 11,855 A 811 B 559 B
Personal conference, convention or trade show 491 C 455 D 28 D 9 E
Shopping, non-routine 338 C 274 D 65 D ..  
Other personal reasons 1,366 B 1,222 B 86 C 58 E
Business conference, convention or trade show 919 B 635 C 236 C 47 D
Other business 977 B 780 B 139 C 58 D
..
data not available

Estimates contained in this table have been assigned a letter to indicate their coefficient of variation (c.v.) (expressed as a percentage). The letter grades represent the following coefficients of variation:

A
c.v. between or equal to 0.00% and 5.00% and means Excellent
B
c.v. between or equal to 5.01% and 15.00% and means Very good.
C
c.v. between or equal to 15.01% and 25.00% and means Good.
D
c.v. between or equal to 25.01% and 35.00% and means Acceptable.
E
c.v. greater than 35.00% and means Use with caution.

National Travel Survey: C.V.s for Visit-Expenditures by Duration of Visit, Main Trip Purpose and Country or Region of Expenditures – Q3 2022

National Travel Survey: C.V.s for Visit-Expenditures by Duration of Visit, Main Trip Purpose and Country or Region of Expenditures, including expenditures at origin and those for air commercial transportation in Canada, in Thousands of Dollars (x 1,000)
Table summary
This table displays the results of C.V.s for Visit-Expenditures by Duration of Visit, Main Trip Purpose and Country or Region of Expenditures. The information is grouped by Duration of trip (appearing as row headers), Main Trip Purpose, Country or Region of Expenditures (Total, Canada, United States, Overseas) calculated using Visit-Expenditures in Thousands of Dollars (x 1,000) and c.v. as units of measure (appearing as column headers).
Duration of Visit Main Trip Purpose Country or Region of Expenditures
Total Canada United States Overseas
$ '000 C.V. $ '000 C.V. $ '000 C.V. $ '000 C.V.
Total Duration Total Main Trip Purpose 36,793,954 A 27,322,218 A 5,334,902 A 4,136,834 A
Holiday, leisure or recreation 22,080,413 A 15,843,037 A 3,653,467 B 2,583,908 B
Visit friends or relatives 8,584,594 A 6,844,993 A 685,916 B 1,053,685 B
Personal conference, convention or trade show 408109 C 319737 C 56828 E 31544 E
Shopping, non-routine 984,304 B 839,543 B 144,761 C ..  
Other personal reasons 1,678,451 B 1,349,129 B 216,021 D 113,300 E
Business conference, convention or trade show 1,301,969 B 815,153 B 385,972 C 100,844 D
Other business 1,756,114 B 1,310,626 B 191,936 C 253,552 D
Same-Day Total Main Trip Purpose 7,298,086 A 6,967,837 A 310,254 B 19,995 E
Holiday, leisure or recreation 3,706,152 A 3,539,091 A 147,066 C 19,995 E
Visit friends or relatives 1,760,108 B 1,722,371 B 37,737 C ..  
Personal conference, convention or trade show 53,926 C 53,650 C 276 E ..  
Shopping, non-routine 834,440 B 723,630 B 110,811 C ..  
Other personal reasons 490,914 B 478,357 B 12,557 E 324  
Business conference, convention or trade show 97,421 D 97,421 D ..   .. E
Other business 355,124 C 353,317 C 1,808 E ..  
Overnight Total Main Trip Purpose 29,495,867 A 20,354,381 A 5,024,647 A 4,116,839 A
Holiday, leisure or recreation 18,374,260 A 12,303,946 A 3,506,401 B 2,563,913 B
Visit friends or relatives 6,824,486 A 5,122,622 A 648,179 B 1,053,685 B
Personal conference, convention or trade show 354,183 C 266,087 C 56,552 E 31,544 E
Shopping, non-routine 149,864 C 115,914 C 33,950 D ..  
Other personal reasons 1,187,537 B 870,772 B 203,464 D 113,300 E
Business conference, convention or trade show 1,204,547 B 717,731 B 385,972 C 100,844 D
Other business 1,400,990 B 957,309 B 190,129 C 253,552 D
..
data not available

Estimates contained in this table have been assigned a letter to indicate their coefficient of variation (c.v.) (expressed as a percentage). The letter grades represent the following coefficients of variation:

A
c.v. between or equal to 0.00% and 5.00% and means Excellent.
B
c.v. between or equal to 5.01% and 15.00% and means Very good.
C
c.v. between or equal to 15.01% and 25.00% and means Good.
D
c.v. between or equal to 25.01% and 35.00% and means Acceptable.
E
c.v. greater than 35.00% and means Use with caution.

National Travel Survey Q3 2022: Response Rates

National Travel Survey: Response Rate – Q2 2022
Table summary
This table displays the results of Response Rate. The information is grouped by Province of residence (appearing as row headers), Unweighted and Weighted (appearing as column headers), calculated using percentage unit of measure (appearing as column headers).
Province of residence Unweighted Weighted
Percentage
Newfoundland and Labrador 19.4 17.2
Prince Edward Island 19.1 17.2
Nova Scotia 26.7 23.7
New Brunswick 25.0 21.8
Quebec 29.4 25.8
Ontario 27.5 25.4
Manitoba 28.6 25.6
Saskatchewan 26.0 23.2
Alberta 25.6 23.9
British Columbia 29.2 27.5
Canada 26.9 25.4

Data quality, concepts and methodology: Explanatory notes on direct program payments to agriculture producers 2022

Payments Enhancing Receipts

Explanatory notes for programs which existed prior to 2007 can be found in the discontinued Direct Payments to Agriculture Producers publication (21-015-X).

Agricultural Revenue Stabilization Account (ARSA) (2000 to 2002)

The objective of the Agricultural Revenue Stabilization Account program was to offer a risk management tool to farming operations in Quebec, based on the operation's gross income. To this effect, the program established two individual funds, for contributions from participants and La Financière agricole du Québec, and made provisions for withdrawals from these funds to compensate for reductions in farm income. The ARSA was a program developed and administered by La Financière agricole du Québec.

Following the introduction of the Canadian Agricultural Income Stabilization Program, La Financière agricole du Québec terminated this program in the 2002 program year. Consequently, participants had five years to make withdrawals from their account, at an annual minimum of 20% of the government contribution held on February 1st, 2005.

AgriInvest (2008 to present)

This program was created under the Growing Forward policy framework (2007 – 2013) and has continued under Growing Forward 2 (2013 – 2018) and the Canadian Agricultural Partnership (effective April 1, 2018). AgriInvest replaces part of the coverage that had been available under the Canadian Agricultural Income Stabilization (CAIS) program, and, operates similar to the former Net Income Stabilization Account (NISA) program.

Through government and producer contributions, AgriInvest provides cash flow to help producers manage small income declines, as well as provide support for investments to mitigate risks or improve market income. Producers can deposit up to 100% of their Allowable Net Sales, with the first 1% matched by governments. The limit on matching government contributions is $10,000 per AgriInvest account. AgriInvest is administered by the Federal government in all provinces except Quebec.

Agri-Québec (2011 to present)

Agri-Québec is a self-directed risk management program offered to all farming and aqua-farming operations in Quebec. The program allows participants to deposit an amount in an account under their name, in order to receive matching contributions from La Financière agricole du Québec. Participants can then withdraw the funds from the accounts, based on their operational needs. Agri-Québec is managed jointly by the provincial and federal governments, as it is similar and complimentary to AgriInvest.

Agri-Québec Plus (2015 to present)

The Agri-Québec Plus program offers additional financial assistance to eligible operations. Agri-Québec Plus complements AgriStability by offering a coverage level of 85% of the reference margin rather than 70%. The program covers agriculture products that are not covered or not associated with the ASRA program (Farm Income Stabilization Program) and are not supply-managed. Participation in the program is linked to the respect of environmental requirements.

AgriRecovery (2008 to present)

The AgriRecovery framework is part of a suite of federal-provincial-territorial (FPT) Business Risk Management (BRM) tools under the Canadian Agricultural Partnership (replacing Growing Forward 2, as of 2018).

AgriRecovery was designed to provide quick, targeted assistance to producers in case of natural disasters, with a focus on the extraordinary costs producers must take on to recover from disasters. Federal and provincial governments jointly determine whether further assistance beyond existing programs already in place is necessary, and what form of assistance should be provided. AgriRecovery initiatives are cost-shared on a 60:40 basis between the federal government and participating provinces or territories. The assistance provided will be unique to the specific disaster situation and often unique to a province or region. Examples of programs included in AgriRecovery are the 2017 and 2018 Canada-BC Wildfire Recovery Initiatives, and the 2017 Canada-Quebec Hail Assistance Initiative.

AgriStability (2007 to present)

This program was created under the Growing Forward policy framework (2007 – 2013) and has continued under Growing Forward 2 (2013 – 2018) and the Canadian Agricultural Partnership (effective April 1, 2018). AgriStability was developed as a margin-based program that provides income support when a producer experiences a large margin decline. AgriStability has replaced part of the coverage that had been provided under the Canadian Agricultural Income Stabilization (CAIS) Program.

AgriStability is delivered in Manitoba, New Brunswick, Nova Scotia, Newfoundland and Labrador and Yukon by the Federal government. In British Columbia, Saskatchewan, Alberta, Ontario, Quebec, and Prince Edward Island, AgriStability is delivered provincially.

AgriStability Program Enhancements (2021 to 2022)

Through the Prince Edward Island Agriculture Insurance Corporation, the Department of Agriculture and Land announced some important changes to the AgriStability Program. These changes will provide increased support to all producers for the 2021 and 2022 program years:

  • The coverage level is being increased to 85% from 70%, reducing the margin loss a producer must incur to trigger a payment.
  • The compensation rate is being increased from 70% to 80% of the loss covered.
  • The 20% late participation penalty for the 2021 program year will be covered and paid by the province.

All changes will be 100% covered by the PEI government, including the federal portion of the cost-share.

Assiniboine Valley Producers Flood Assistance Program (2007 to 2011)

This Province of Manitoba program provided financial assistance for Assiniboine Valley agricultural producers who experienced crop loss or the inability to seed a crop in 2005 and 2006 along the Assiniboine River from the Shellmouth Dam to Brandon, due to flooding. This program also provided assistance in 2011, following flooding in 2010.

These programs were managed through the Manitoba Agricultural Service Corporation (MASC).

Beef Herd Renewal and Improvement Program (2022 to 2023)

The objective of this program is to grow, renew and improve the New Brunswick beef cow herd. Applicants will be eligible for financial assistance of $300 per retained bred heifer above a 5% replacement rate. Eligible numbers of heifers will be determined based on current mature cow numbers, defined as cows that have already had at least one calf. Included as payments in the series «Direct Program Payments to Producers» is the compensation paid for the basic herd growth.

Beekeepers Financial Assistance Program (2014)

Due to harsh winter conditions in Ontario in 2014, and other pollinator health issues, Ontario's bee colonies experienced higher than normal mortality rates. To help offset these losses, the Ontario Ministry of Agriculture and Food provided one-time financial assistance of $105 per hive to beekeepers who have 10 hives or more and lost over 40 per cent of their colonies between Jan. 1, 2014, and Oct. 31, 2014.

British Columbia Raspberry Replant Program (2022 to 2023)

The B.C. Raspberry Replant Program is a cost-sharing funding program which is intended to revitalize, regenerate and increase the competitiveness of the B.C. raspberry industry in local and global markets. This program is being delivered by the Ministry of Agriculture and Food with input from the Raspberry Industry Development Council.

Canada-Ontario General Top-Up Program (2005 to 2007)

This was a special top-up payment program which provided whole farm coverage to the Canadian Agricultural Income Stabilization (CAIS) Program participants in Ontario, who were automatically enrolled. All commodities eligible for CAIS payment were covered under this program. In order to qualify, participants must have experienced a decline in their program year production margin as calculated by the CAIS Program Administrator and be eligible to receive the government portion of the CAIS payment. The Ontario Ministry of Agriculture, Food and Rural Affairs were responsible for the overall administration of the program.

Canadian Agricultural Income Stabilization (CAIS) Program (2004 to 2008)

The CAIS program was available to producers across Canada and provided assistance to those producers who had experienced a loss of income as a result of bovine spongiform encephalopathy (BSE) or other factors. The program integrated stabilization and disaster protection into a single program, helping producers protect their farming operations from both small and large drops in income.

Canadian Agricultural Income Stabilization Inventory Transition Initiative (CITI) (2006 to 2007)

CITI was a one-time federal government injection of $900 million into Canada's Agriculture and Agri-food industry. The funds were delivered to producers by recalculating how the Canadian Agricultural Income Stabilization (CAIS) program valued inventory change for the 2003, 2004, and 2005 CAIS program years.

Canadian Agricultural Income Stabilization Ontario Inventory Transition Initiative (2006 to 2019)

The Ontario Inventory Transition Payment was an additional one-time payment from the province of Ontario, for the Canadian Agricultural Income Stabilization (CAIS) program participants, as it transitioned to a new method of valuing inventory for CAIS.

Compensation for animal losses (1981 to present)

Formerly a program under the Animal Disease and Protection Act, this compensation program is now administered by the Canadian Food Inspection Agency in accordance with requirements established under the Health of Animals Act. Producers in all provinces are compensated when farm animals infected with certain contagious diseases are ordered to be slaughtered. Compensation also includes applicable transportation and disposal costs and compensation for animals injured during testing.

Cost of Production Payment (COP) (2007 to 2010)

This program helped non-supply managed commodities producers with the rising cost of production. This federal program was based on producers' net sales for 2000-2004 (or in the case of new producers: payments were based on average net sales for 2005-2006).

Cover Crop Protection Program (CCPP) (2006 to 2008)

The CCPP was a Government of Canada initiative designed to provide financial assistance to agricultural producers who were unable to seed commercial crops as a result of flooding in the spring of 2005 and/or 2006.

Crop Insurance (1981 to present)

Crop Insurance (now referred to as 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 to which the federal government contributes a portion of total premiums and administrative costs. Premiums for most crop insurance programs are cost-shared: 40 per cent by participating producers, 36 per cent by the federal government and 24 per cent by the province, while administrative costs are funded by governments, 60 per cent by the federal government and 40 per cent by the province.

AgriInsurance plans are developed and delivered by each province to meet the needs of the producers in that province. AgriInsurance helps to cover production losses as well as losses from poor product quality. Both yield and non-yield based plans are offered. These plans cover traditional crops such as wheat, corn, oats and barley as well as horticultural crops such as lettuce, strawberries, carrots and eggplants. Some provinces also provide coverage for bee mortality as well as maple syrup production. The provinces constantly work to improve their programs by adjusting existing plans and implementing new ones to meet changing industry requirements.

Crop Loss Compensation (1981 to present)

Crop loss compensation programs are generally one element of a province's Wildlife damage compensation programs, which can also include separate Waterfowl damage and Livestock predation programs. This Big Game program reduces the financial loss incurred by producers in these provinces from wildlife damage to eligible crops, and can include compensation for wildlife excreta contaminated crops and silage in pits and tubes. In some provinces damage to honey producers and leafcutter bee products is also included.

Also see Livestock predation compensation, Waterfowl damage and Wildlife damage compensation programs.

Cull Animal Program (2003 to 2006)

This program was intended to assist farmers with the additional cost of feeding surplus animals while the US border was closed to Canadian animals over 30 months of age. With the goal of discouraging on-farm slaughter and encouraging movement of mature animals to domestic markets in an orderly fashion.

Cull Breeding Swine Program (2008)

This federally funded program for 2008, administered by the Canadian Pork Council, was designed to help restructure the industry to bring it in line with market realities. The objective was to reduce the national breeding herd size by up to 10% over and above normal annual reductions. Producers were eligible to receive a per head payment for each animal slaughtered as well as reimbursement for slaughter and disposal costs. Producers had to agree to empty at least one barn, and not restock for a three year period.

Dairy Direct Payment Program (2019-2023)

The objective of the Dairy Direct Payment Program is to support dairy producers as a result of market access commitments made under recent international trade agreements, namely the Canada–European Union Comprehensive Economic and Trade Agreement (CETA) and the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP).

In August 2019, the federal government announced that it will make available $1.75 billion to supply-managed milk producers. Up to $345 million in direct payments was made available in 2019-2020.

In November 2020, the government announced the payment schedule for the remaining $1.405 billion in direct payments over the next three years:

  • $468 million in 2020-21
  • $469 million in 2021-22
  • $468 million in 2022-23

The Canadian Dairy Commission (CDC) has been mandated to deliver the program.

Drought Assistance for Livestock Producers (2007 to 2008)

This program was enacted in 2007, to assist livestock owners in Northern B.C. who suffered economic hardship in 2006 due to drought. Drought conditions in the summer of 2006 reduced hay and forage yields by up to 50% and producers were left with higher costs for feed, water and other expenses.

Eurasian Wild Boar Transition Assistance Initiative (2021 to 2022)

The purpose of this Initiative was to provide financial support to producers of EWB to assist them in transitioning out of the production of EWB and into other forms of production. The need for this type of financial support arose out of the passage of a regulation under the Invasive Species Act, 2015 that designated Ontario as an invasive species control area for wild pigs, including the possession and movement of EWB.

Farm Emergency Response Grant Program (2022)

A one-time grant of $2,500 will be sent to registered farms in Central, Northern and Eastern Nova Scotia that experienced financial losses due to infrastructure or crop damage, livestock loss or extended power outages due to Hurricane Fiona. Funding will also be available to registered farms that experienced storm damage outside the most impacted regions.

Fed Cattle Set Aside Program (2005 to 2006)

The program was part of a national strategy to assist Canada's cattle industry to reposition itself to help ensure its long-term viability.

Financial Assistance for Replanting Apple Orchards (2020-2022)

The financial assistance for replanting apple orchards program is being implemented to support the apple industry during the transition period following the termination of the Farm Income Stabilization Insurance Program coverage.

The program is intended to support development of the apple industry from a sustainable development perspective, as a complement to other government assistance available to the apple industry.

More specifically, this program is intended to provide financial support to apple businesses in their apple orchard replanting projects.

Eligible businesses may receive financial assistance of $5,000 per replanted hectare for up to four hectares. If the business is deemed eligible for one of the grants under the Financial Support Program for Aspiring Farmers on the date that they submitted their application to the program, the assistance is increased to $6,250 per eligible hectare.

Frost loss Program (2018-2019)

The Frost Loss Program helped Nova Scotia Farmers recover from crop and financial losses from the frost in June 2018.

This program provided financial assistance in addition with other Business Risk Management programs that were available, such as AgriInsurance.

Golden Nematode Disaster Program (2007 to 2009)

The objective of this program was to assist producers affected by Golden Nematode with the costs of disposing potatoes and a per hectare support payment to assist potato producers and producers of nursery and greenhouse crops with extraordinary costs not covered under existing programs. The program was funded by the federal government.

Grains and Oilseeds Payment (GOPP) (2006)

The Grains and Oilseeds Payment Program was a one-time program for producers of grains, oilseeds, or special crops, to help address the severe economic hardships they were facing.

Hazelnut Renewal Program (2020 to present)

This provincially funded program provides funds to remove infected trees to mitigate the spread of Eastern Filbert Blight and to provide incentives for the planting of new disease-resistant hazelnut trees in British Columbia.

Types of Program Funding:

  • Hazelnut Renewal: Funding to provide incentives for the planting of new Eastern Filbert Blight (EFB) resistant hazelnut trees in British Columbia.
  • Removal of EFB Infected Hazelnut Trees: Funding to remove infected trees to mitigate the spread of EFB and to protect new orchards.

Hog Transition Fund (2008)

This program was designed to assist Nova Scotia hog producers who were having financial difficulties due to declining market prices in 2006-2007. The program was administered through Pork Nova Scotia.

Lake Manitoba Flood Assistance Program (2011 to present)

This program was designed to provide financial compensation to crop and livestock producers affected by the flooding of Lake Manitoba in 2011. Part A - Lake Manitoba Pasture Flooding Assistance Component and Part B - Lake Manitoba Transportation and Crop/Forage Loss Component, are included. This program is funded entirely by the provincial government.

Livestock Insurance Programs (1991 to present)

The Livestock Insurance Programs include a number of provincially administered livestock insurance programs. These programs include:

The Cattle Price Insurance Program (2009 to present), designed to provide Alberta cattle producers with an effective price risk management tool reflective of their risk. As of 2014, this program is now referred to as the Western Livestock Price Insurance Program.

Dairy Livestock Insurance (1991 to present), implemented to assist Nova Scotia producers when a number of cattle were lost due to disease outbreaks. The program continues to exist for situations resulting in a significant loss in production, causing a loss of revenue.

The Hog Price Insurance Program (2011 to present), designed to provide Alberta hog producers with protection against unexpected declines in Alberta hog prices, over a defined period of time. As of 2014, this program is now referred to as the Western Livestock Price Insurance Program.

Livestock Insurance in Newfoundland and Labrador (1991 to present) compensates producers for the death or injury to sheep, goats, dairy cattle or beef cattle caused by dogs or other predators.

Livestock Insurance in Prince Edward Island (2009 to present) offers two types of coverage: compensation to cattle producers for the death of an animal due to disease, as well as compensation to dairy producers whose production levels fall beneath a set threshold, causing a loss of income.

The Overwinter Bee Mortality Insurance (2012 to present) insures Manitoban beekeepers against unmanageable wintering losses, including weather-related damages, diseases and pests. As of 2014 the data for this program is included in Crop Insurance.

Poultry Insurance (2008 to present) compensates Nova Scotia producers for the loss of poultry (which includes broilers, breeders, breeder pullets, layer pullets, commercial layers and integrated layers) to the disease infectious laryngotracheitis (ILT).

The Western Livestock Price Insurance Program (WLPIP) (2014 to present) enables livestock producers to purchase price protection on cattle and hogs in the form of an insurance policy. It offers protection against an unexpected drop in prices over a defined period of time, and is available to producers in British Columbia, Alberta, Saskatchewan and Manitoba.

Administration costs are covered by the federal and provincial governments through Growing Forward 2. Premiums will be fully funded by producers, but any deficit after four years will be made up by the federal government. The four-province program will be managed by the Alberta Agriculture Financial Services Corp, which ran the pre-existing Cattle and Hog Price Insurance programs in Alberta. Crop insurance entities in Manitoba and Saskatchewan will deliver the WLPIP in those provinces. The Business Risk Management Branch of the British Columbia Ministry of Agriculture delivers the program in that province.

Additional notes on the Livestock Insurance Programs

Producer premiums for the Prince Edward Island Livestock Insurance and Dairy Livestock Insurance in Nova Scotia (as of 2006) are partially subsidized by the provincial and federal governments.

Premiums are not subsidized for the Cattle Price Insurance Program, the Hog Price Insurance Program, Livestock Insurance in Newfoundland and Labrador, Poultry Insurance program in Nova Scotia, or the Western Livestock Price Insurance Program. However, the costs of administrating the programs are funded by provincial governments and/or Crown Corporations.

Prior to 2005, Dairy Livestock Insurance in Nova Scotia and Livestock Insurance in Newfoundland and Labrador were reported under Programs funded by the private sector.

Livestock Predation Compensation Program

Manitoba (1999 to present) - This program compensates livestock producers in Manitoba for losses from injury or death of eligible livestock that resulted from losses due to natural predators such as black bear, cougar, wolf or coyote. Compensation is available to 100% of the assessed value of the animal, for a confirmed loss due to predation and to 50% of the value for a probable loss. In respect for livestock injured, the payment will be the lesser of the veterinary treatment or the value of the livestock. The government of Manitoba pays 60% of program payments and the Government of Canada 40%. Administration costs are cost-shared 50/50 between the Government of Canada and the Government of Manitoba.

Saskatchewan (2010 to present) - Under the Wildlife Damage Compensation Program, the Saskatchewan Compensation for Livestock Predation compensates producers for livestock killed or injured by predators. The first 80 percent of the program funding is cost-shared by federal and provincial governments. The provincial government contributes the remaining amount. The program is administered by the Saskatchewan Crop Insurance Corporation. Other components of the Wildlife Damage Compensation Program include Waterfowl damage compensation and Crops loss compensation (reported separately).

Also see Crop loss compensation, Waterfowl damage and Wildlife damage compensation programs.

Manitoba Ruminant Assistance Program (2008)

This one-time payment for 2008, funded jointly by the province of Manitoba and the federal government, allowed cattle producers to receive a direct payment of up to 3% of historical net sales. The payment, administered by the Manitoba Agricultural Services Corporation (MASC), was provided to all ruminant producers and was in proportion to the size of the producer's livestock operations.

Manitoba Spring Blizzard Livestock Mortalities Assistance Program (2011 to 2012)

The 2011 Manitoba Spring Blizzard Mortalities Assistance program provided assistance to Manitoba producers who experienced livestock losses following the blizzard that hit April 29th and 30th, 2011. Compensation is provided for animal deaths that occurred, as a result of the storm, between April 29th and May 5th 2011. This program is funded and administered by Manitoba Agriculture, Food and Rural Initiatives (MAFRI).

Marketing and Vineyard Improvement Program (MVIP) (2015-2016)

This program provides funds for eligible vineyard improvements to enable growers in Ontario to produce quality grapes in order to respond to the growing demands of Ontario wine manufacturers and to adapt ongoing and emerging vineyard challenges. This payment will be overseen by Agricorp (a provincial crown corporation) and was created under the Wine and Grape Strategy to promote Ontario VQA (Ontario's Wine Authority) and support vineyard production improvements. Only certain non-capital payments to producers are included in the Direct payments data series (e.g. wine grape vine removal, land preparation, etc.).

Measure to support grain corn producers in mitigating the impact of the 2019 rise in propane prices in Québec (2020)

The measure to support grain corn producers in mitigating the impact of the 2019 rise in propane prices in Québec was meant to help reduce the repercussions on grain corn production due to the rise in prices of propane which is used to dry grain corn. This measure covered grain corn not yet harvested by November 19, 2019, the date when Canadian National Railway employees went on strike.

Financial assistance was provided in the form of a maximum flat rate of $23.50 per hectare of eligible grain corn areas for up to $50,000 per farm business.

Net Income Stabilization Account (NISA) (1991 to 2009)

The Net Income Stabilization Account (NISA) was established in 1991 under the Farm Income Protection Act.

The purpose of NISA was to encourage producers to save a portion of their income for use during periods of reduced income. Producers could deposit up to 3% of their Eligible Net Sales (ENS) annually in their NISA account and receive matching government contributions. The federal government and several provinces offered enhanced matching contributions over and above the base 3% on specified commodities. All these deposits earn a 3% interest bonus in addition to the regular rates offered by the financial institution where the account is held.

Most primary agricultural products were included in the calculation of Eligible Net Sales (sales of qualifying commodities minus purchases of qualifying commodities), the main exception being those covered by supply management (dairy, poultry and eggs).

The NISA account was comprised of two funds. Fund No. 1 which held producer deposits while Fund No. 2 contained the matching government contributions and all accumulated interest earned on both Fund 1 and Fund 2. Included as payments in the series «Direct Program Payments to Producers» were the producer withdrawals from Fund 2.

Nova Scotia Beef Kickstart Program (2008)

This one-time payment for 2008 provided funding for Nova Scotia's beef industry with the goal of helping the sector move toward greater economic self-sustainability.

Nova Scotia Margin Enhancement Program (2007 to 2008)

This initiative introduced in 2006, was a provincial initiative that provided additional income support to Nova Scotia producers. Using 2003 CAIS program data, reference margins of CAIS participants were increased by 10%.

Ontario AgriStability Top Up Program (2022)

The purpose of the Program is to help offset losses resulting from factors beyond the control of farmers in Ontario, including those as a result of increased market uncertainty exacerbated by the COVID-19 Pandemic, by providing Ontario's forty percent (40%) share of an increase of ten percent (10%) to the maximum Payment Benefit provided under AgriStability.

Ontario Cattle, Hog and Horticulture Program (OCHHP) (2008)

This one-time payment for 2008, funded by the province of Ontario, was to assist farmers suffering from multiple financial pressures due to the stronger Canadian dollar, and lower market prices. Payments for cattle and hog producers were based on 12% of their historic allowable net sales, while payments for horticulture were based on 2% of allowable net sales.

Ontario Cost Recognition Top-up Program (2007 to 2010)

This program was a 40% matching provincial contribution to the federal Cost of Production Payment Program. This program was a direct payment to producers in recognition of rising production costs over the previous few years. The Ontario Top-Up Program payments were distributed after the payment details regarding the federal program were released.

Ontario Duponchelia Assistance Program (2008)

The purpose of this initiative was to provide financial support to horticulture producers in the Niagara Region of Ontario affected by Duponchelia, a reportable pest. The initiative provided a federal share (60%) of financial compensation to assist these producers in addressing plant replacement costs and in dealing with extraordinary expenses incurred due to quarantine measures imposed by the Canadian Food Inspection Agency (CFIA).

Ontario Edible Horticulture Crop Payment (2006)

This one-time payment compensates Ontario producers of edible horticulture crops for losses experienced on their 2005 crop.

Ontario Edible Horticultural Support Program (2018-2019)

This program provided financial support to Ontario producers of edible horticulture products (small and medium-size agricultural operators) to adjust to the changing small business environment. This program was funded by the Government of Ontario and the payments were based on net sales of edible horticulture. Self-Directed Risk Management Program participants were enrolled automatically.

Ontario Special Beekeepers Fund (2007 to 2008)

The Special Beekeepers Fund, enacted in June, 2007, provided direct compensation to beekeepers who suffered higher than normal hive losses during the winter of 2006. The assistance was designed to help bring Ontario's bee population back to near-normal levels, and beekeepers back to normal business.

PEI Pollination Expansion Program (2021 present)

The Prince Edward Island Department of Agriculture and Land has established the PEI Pollination Expansion Program to support the sustainable increase of local honey bee colonies that are available for the pollination of wild blueberries and other fruit crops and the advancement of the beekeeping sector through strategic industry initiatives.

PEI Potato Seed Recovery Program (2020)

The purpose of the Potato Seed Recovery Program was to offset extraordinary costs and a loss in revenue for Island seed potato producers impacted by the pandemic. This payment was a $1.19 million fund and was a provincially funded program.

Porcine Epidemic Diarrhea Programs (PED)

Prince Edward Island (2014) - The Prince Edward Island PED program provided financial aid to hog farmers for increased sanitation and screening measures to help combat the pig virus. This was a cost-shared program between the federal and provincial governments under Growing Forward 2. The program was administered by the PEI Hog Board.

Québec (2015 to present) - Emergency Fund Program in Response to Porcine Epidemic Diarrhea (PED) and Swine Delta Coronavirus (SDCV) in Québec. The purpose of this program is to provide assistance to affected operations, up to a maximum of $20,000 per production site, to cover certain additional expenses required to combat this disease and prevent it from spreading. The program is financed by La Financière agricole and administered by the Québec swine health team (EQSP). The fund has a maximum budget of $400,000.

Portage Diversion Fail-Safe compensation program (2014 to 2015)

This program was designed to provide financial assistance to Manitoba agricultural producers affected by the 2014 flooding due to the operation of the Portage diversion fail-safe. This program was fully funded by the Manitoba Government and administrated by the Manitoba Agricultural Services Corporation (MASC).

Post-tropical Storm Dorian Response Program (DRP) (2020-2021)

The Prince Edward Island Department of Agriculture and Land had established the Post-tropical Storm Dorian Response Program (DRP) to provide financial support to corn, crambe, and tree fruit producers who had incurred extraordinary costs due to Dorian which were not covered by existing Business Risk Management programs.

Prince Edward Island Beef Industry Initiative (2007 to 2008)

This one-time payment for 2008 was designed to assist beef producers in Prince Edward Island to adjust to current market conditions and develop improved quality in their herds. The program provided immediate assistance to producers to help mitigate risk and provided genetics and enhanced herd health incentives. Payments were based on a combination of their average net sales and December 2007 inventory.

Prince Edward Island Hog Transition Fund (2008)

This program was designed to reduce hog numbers through a buyout program. It provided funds for producers to transition out of hog production.

Privately funded programs

Private hail insurance (1981 to present)

Private Hail Insurance is purchased by agricultural producers to protect themselves against the loss of their crops due to hail. Hail insurance is privately funded through producer premiums and producers may have the option to extend coverage for damage to crops due to loss through fire, depending on the insurance provider.

Other Private Programs (2011 to present)

Alberta Hog and Cattle Levy Refund (2011 to present)

In May 2011, Alberta Pork announced it would refund 85 cents for every dollar of levies it had collected from producers during the 2010-2011 fiscal year to assist producers coping with rising feed costs and small profit margins.

Legislation regarding levies in Alberta also changed in 2011. Levies for pork, beef, lamb, and potato producers had been mandatory until a change is legislation gave these producers the right to ask for a refund of the levies paid. Since that time, estimates for the hog and cattle levies refunded have been produced.

Heinz payment (2013)

Due to the closure of the Ontario Heinz processing plant in 2013, Heinz has paid a one-time 'goodwill' payment to compensate the farmers that were under contract to deliver processing tomatoes in 2013. The payment was to help offset costs that farmers may have incurred in preparing for the 2013 crop.

Programme d'aide pour les inondations en Montérégie (2011 to 2012)

This program provided financial assistance to agricultural enterprises affected by the floods of spring 2011, in the Richelieu valley. Compensation was offered to producers for loss of income due to flooded farmland, and/or losses due to unseeded acreage.

Programme d'appui à la replantation des vergers de pommiers au Québec (2007 to 2010)

The first component of this MAPAQ (Ministère de l'Agriculture, des Pêcheries et de l'Alimentation du Québec) program offered replanting help in order to improve efficiency, profitability as well as competitiveness. The objective of the second component was to compensate apple producers for the loss of apple trees due to winter-kill (frost) in 1994.

Provincial Stabilization Programs (1981 to present)

Under provincial stabilization programs, payments are made in order to support producer incomes affected by small profit margins, or low prices, for selected commodities. Provincial stabilization programs are partly funded by the provincial government, either directly through the subsidization of producer premiums, or indirectly by absorbing a part, or the whole, of the cost of administering the program. These programs are optional, and producers are required to pay premiums in order to participate.

Farm Income Stabilization Program (ASRA) (1981 to present)

The Farm Income Stabilization Insurance Program is designed to guarantee a positive net annual income to producers in Quebec. Producers participating in the program receive funds when the average selling price falls below a stabilized income, which is based on the average production cost in a specific sector. ASRA is complementary to AgriStability, but participation in AgriStability is not mandatory. Payments under ASRA decrease in accordance to amounts paid out through AgriStability. ASRA premiums are partially funded by the provincial government, which pays two thirds of the cost of premiums, while producers pay the remaining third.

Ontario Risk Management Program (RMP) (2007 to present)

ORMP is a provincial program that offers compensation to Ontario producers for losses of income caused by fluctuating market prices and rising production costs. Commodities eligible for compensation include a variety of grains and oilseeds, as well as certain livestock, including cattle, calves, hogs and sheep. The program also offers compensation for unseeded acres, under certain conditions. In order to participate in this program, producers must also participate in AgriStability, as well as Production Insurance (for grains and oilseeds). Payments made under ORMP count as an advance on the provincial portion of AgriStability for the corresponding program year. Because ORMP is provincially funded, it has no impact on the federal portion of AgriStability payments. ORMP premiums are partly funded by the provincial government, which pays 40% of the cost of premiums, while producers pay the remaining 60%.

Saskatchewan Cattle and Hog Support Program (2009)

This program helped producers retain their breeding herds and address immediate cash flow needs.

Saskatchewan Feed and Forage Program - 2011 (2011 to 2012)

This program provided compensation to producers who had to transport additional feed to their livestock, or transport their livestock to alternate locations for feeding and grazing, due to feed shortages caused by excess moisture. In addition, financial assistance was provided to producers who had to reseed hay, forage or pasture land that had been damaged by excess moisture. This provincially-funded program replaces the initial Saskatchewan Feed and Forage Program (2010-2011), which was jointly offered by the provincial and federal governments, as part of AgriRecovery.

Self-Directed Risk Management (SDRM) (2005 to present)

SDRM is a provincial program designed to help Ontarian horticultural producers manage farm operation risk. Under the program, over 150 edible horticultural crops are eligible for coverage, including fruits, vegetables, mushrooms, herbs and spices, nuts, honey and maple products. To be eligible, producers must also participate in AgriStability, and meet the minimum amount of allowable net sales (ANS). Participating producers can deposit up to a maximum of 2% of their ANS into an account, and have their contribution matched by the provincial government. Payments made under SDRM count as an advance on the provincial portion of AgriStability for the corresponding program year. Because SDRM is provincially funded, it has no impact on the federal portion of AgriStability payments. Amounts received under Production Insurance for a crop also covered by SDRM will be deducted from SDRM payments.

Shoal Lakes Agriculture Flooding Assistance Program (2011)

The purpose of this program was to provide financial support to agriculture producers affected by chronic flooding in the Shoal Lakes Complex in the Interlake of Manitoba.

  1. Land payments on a per acre basis were provided to farm operators to compensate for lost income related to agricultural production that cannot be realized due to flooded acres in 2010 and 2011.
  2. Financial assistance for transportation costs incurred between April 1, 2011 and March 15, 2012 to those farm operators who needed to transport feed to livestock or livestock to feed, due to the flooding.

This payment was administered by the Manitoba Agriculture Corporation (MASC), with the assistance of Manitoba Agriculture, Food & Rural Initiatives (MAFRI).

Support Program for the Eradication of Chronic Wasting Disease in Cervids (CWD) (2019 to present)

This program implemented by La Financière agricole du Québec offers financial aid to cervid producers affected by the measures taken to eradicate CWD.

There are two categories of aid under this program:

  • The first compensates cervid producers ordered to slaughter and dispose of animals under the Animal Health Protection Act.
  • The second financially supports cervid producers required to implement sanitary measures stipulated under the Animal Health Protection Act.

Surplus Potato Management Response (2022)

The Surplus Potato Management Response was cost-shared between the federal and provincial governments and aimed to support PEI potato farmers impacted by trade disruptions. The Prince Edward Island Potato Board delivered the plan on behalf of the governments to manage potatoes that had been rendered surplus. Only the Destruction Program was included in the Direct payments data series.  The growers received up to 8.5 cents per pound to assist with the costs of environmentally-sound destruction of surplus potatoes.

Syndrome de dépérissement postsevrage (SDP) (2008 to 2010)

This MAPAQ (Ministère de l'Agriculture, des Pêcheries et de l'Alimentation du Québec) program granted financial support to Quebec feeder hog operations affected by Post Weaning Multisystemic Wasting Syndrome (PMWS).

Transitional Production Adjustment Program (1996) (1993 to 1997 and 1999 to 2008)

Under the Tree Fruit Revitalization Program, British Columbia orchardists were guaranteed specific annual revenue per acre during the first three years, following replant of orchards to new high density tree fruit varieties.

Tree Fruit Replant Program (previously known as Tree fruit grafting/budding and replant program) (2008 to 2011, 2012 to present)

In 2008, the Transitional Production Adjustment Program ended and the Tree fruit grafting/budding and replant program started. In July 2007, the federal and provincial governments jointly announced that they were investing $8 million to help British Columbia's tree fruit and grape industries adapt to changing markets. The cost was shared (60% federal, 40% provincial) and the program lasted for three years.  In 2012, the provincial government invested an additional $2 million to replant tree fruit orchards to expand domestic markets through high-quality products by targeting the planting of premium varieties. The program, which also includes a grafting and budding component, concluded in 2014. The 2015 program is the first year of a 7 year commitment by British Columbia of $8.4 million announced in Nov 2014. This is a British Columbia Agriculture Department program that shares the administration of the program with the British Columbia Fruit Growers Association under contract until 2016.

2019-2020 British Columbia AgriStability Enhancement Program

The British Columbia government is offering greater coverage to farmers who have lost income due to weather, trade challenges or natural disaster. The Program includes:

  • Increasing the compensation rate, for all farms, from 70% to 80% on income margin losses greater than 30%. In other words, B.C. will be adding 14.3% to every AgriStability payment.
    • An AgriStability payment is triggered when a producer's current margin (allowable income less allowable expenses) drops more than 30% below their average historical margin (referred to as Reference Margin)
  • Eliminating the Reference Margin Limit (RML) which reduced compensation for some farms.
    • Farms which have wide margins due to low eligible expenses will no longer have their compensation reduced due to the RML.

Unseeded Acreage Payment - 2006 (2006 to 2007)

This program provided a payment to Saskatchewan farmers who experienced excess moisture conditions prior to June 20, 2006 and were unable to seed 95% of the acres they would normally intend to seed.

Waterfowl Damage (1981 to present)

Waterfowl damage payment programs are designed to compensate producers for crop losses caused by waterfowl. Compensation is also available for cleaning excreta contaminated grain in some provinces, and for prevention management.

Also see Crop loss compensation, Livestock predation compensation and Wildlife damage compensation programs.

Wildlife Damage Compensation Program

British Columbia (2002 to present) - The British Columbia Wildlife Compensation program is part of an Agricultural Environment Partnership Initiative that includes the following programs: The Waterfowl Damage to Forage Fields in Delta, Wild Predator Loss Control and Compensation Program for Cattle and East Kootenay Agriculture Wildlife Pilot Project. These programs are designed to compensate producers for the losses incurred to crops and livestock due to wildlife.

New Brunswick (2014 to present) - This cost-shared program compensates producers who suffer livestock or crop losses due to wildlife. Compensation is available for specified crops and livestock for damage caused by eligible wildlife. The maximum compensation per producer is $50,000 per year. The New Brunswick Agricultural Insurance Commission (NBAIC) administers this program, applicants are not required to be an insurance client to receive compensation.

Nova Scotia (2008 to present) - This cost-shared program, announced in 2008, will help address some of the risks experienced by Nova Scotia farmers regarding damage to eligible agricultural products because of the activities of wildlife, including wildlife predation on livestock and damage to crops. Applicants are not required to have crop insurance.

Ontario (2008 to present) - The Ontario Wildlife Damage Compensation Program provides financial assistance to eligible applicants whose livestock and poultry have been injured or killed by wolves, coyotes, bears and other species of wildlife identified in the program guidelines, or whose bee-colonies, bee-hives and bee-hive related equipment have been damaged by bears, raccoons, deer and skunks. The program was funded by the provincial government up to the fiscal year of 2008/2009 and became part of Growing Forward - a federal, provincial and territorial initiative starting from fiscal year 2009/2010, when cost-sharing of the program began between the governments of Canada and Ontario.

Also see Crop loss compensation, Livestock predation compensation and Waterfowl damage programs.

Making data visualizations accessible to blind and visually impaired people

By: Jessica Lachance, Elections Canada

Editor's note: The information discussed in this article are presented as possible solutions for accessible data visualizations, including some exciting forward-thinking solutions many people don't have access to today. None of the tools discussed in this article should be construed as an official recommendation, nor should they be considered for implementation in your environment without a thorough review.

Introduction

Throughout the 21st century, the amount and variety of data available to citizens, researchers, industry, and government has grown exponentially (see: Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2020, with forecasts from 2021 to 2025). With that growth comes an expectation we'll use the data to inform policies, business decisions and consumer choice.

The human mind can't efficiently interpret this amount of raw data, yet we need to summarize our data to understand its features. The dominant tools to help us understand data are data visualizations. Visualizations range from simple static images to interactive software that lets you choose specific data and display curated summaries.

Despite their benefits, data visualizations present significant barriers for Blind and visually impaired (BVI) people. These barriers have left BVI people less able to effectively participate in public discourse, in their workplace, or make informed choices. For example, current alt-text guidelines aren't always based in well-researched evidence. This leads to gaps that don't allow BVI people to glean statistical information at the same speed as sighted users. How does someone understand what it means to "flatten the curve" if you can't see the associated graph? (See: Making data visualizations more accessible).

We'll further explore these key needs and the methods used to help improve the accessibility of data visualizations and provide non-visual alternatives to present data. We'll then study a few solutions that work for a number of visualization types.

Current accessibility guidelines

The Web Content Accessibility Guidelines (WCAG) 2.1 are the international web accessibility standard. Web accessibility is founded on four main principles – content should be perceivable, operable, understandable and robust (see: Understanding the Four Principles of Accessibility). The Canada.ca Content Style Guide, which bases their rules on WCAG 2.1, says that writers should include a "long description" for charts and a shorter alt-text for a high-level description. For charts, it suggests that an HTML table could be used as the long description.

But researchers and advocates argue that this doesn't always meet BVI people's needs, and the HTML tables require BVI users to exert more effort (or to require a higher cognitive load) to think through answers to simple questions such as "which data series is the maximum?". Sighted users can easily glean the information at-a-glance. Also, alt-texts don't always provide sufficient detail, especially when it comes to the spatial information of the graph.

Features of accessible data visualizations

We found that each researcher uniquely defined what makes a data visualization operable and what information is necessary to make it understandable and robust. The following dimensions must be considered to make data visualizations accessible.

Data-related tasks should require an equal cognitive load and equal effort: As noted above, HTML tables require a higher cognitive load to identify statistical features, like the minimum or maximum. That's not to say HTML tables aren't valuable. In general, BVI users appreciate this feature, and most sighted users do, too. However, when the HTML table is the only interface it can sometimes be overwhelming.

Provide information at varying levels of complexity: Not all data-related tasks are equal. Information with varying levels of complexity can be shown with a visualization. One interesting article from Lundgard and Satyanarayan (see: Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content) defines four distinct levels of semantic content a data visualization description could be conveying.

The four levels are:

  1. Listing visualization construction properties (e.g., axes, chart type, colours)
  2. Reporting statistical concepts and relations
  3. Identifying perceptual and cognitive phenomenon
  4. Extracting domain-specific insights

Their study found that both Blind and sighted participants found level 3 content most useful. Blind participants found level 2 content to be more useful than their sighted counterparts but found level 4 content much less useful.

These different semantic levels also align with Shneiderman's Visual Information-Seeking mantra to "Overview first, zoom and filter, then details-on-demand." as written in his highly influential work in the dawn of online visualizations (see: Schneiderman's Mantra | Data Visualization). This also applies to BVI users – they want to picture the overview of the chart, zoom in to it, and filter it to get statistical concepts, relations, and details.

Paint a mental picture: Some existing guidelines state that details like graph colours, or axes' descriptions should be ignored to reduce cognitive load. But, multiple studies found these to be important as they helped BVI users to picture the charts. Doing so helped them communicate results with sighted colleagues and helped them understand data visualizations as a statistical tool or when coming across a less common chart type.

BVI users want to know "what the author wants you to know": The majority of BVI users found levels 2 and 3, where we start to understand the gist of the data, are also where the current accessibility guidelines are most lacking. Understanding trends and features are easier to remember when presented with physical descriptions of the graph. This mental picture help users to easily recall where to find statistical information like the minimum, the maximum or where two lines intersect.

Present data objectively: BVI participants in studies emphasized that accessible descriptions shouldn't contain editorialized content associated with level 4 semantic content. BVI users should be able to verify any claims made about the data themselves, and not be given an editorialized view, beyond what a sighted user would have access to. They also expressed a preference for descriptions which use an objective tone.

Make sure the solution is appropriate for web-browsing: We can also consider the scope of the solution. A disability dongle is a well-intentioned solution that prioritizes form over its function as an accessibility device. Making accessible data visualizations don't need a fancy new tool. Ideally, solutions should be compatible with common technology for BVI people, like braille displays and screen readers. When solutions require costly software or hardware to operate, it reduces their operability and robustness.

Alternative accessible solutions

We described six key needs of BVI users when engaging with data visualizations:

  1. Provide information at varying levels of complexity
  2. Paint a mental picture of the visualization
  3. Let BVI users know what you, as the author, want them to know
  4. Present the data objectively
  5. Integrate accessibilities tool BVI users already have or can be easily integrated within the browser
  6. Have a solution that works for many different kinds of graphs

We've already described how dominant recommendations fall short. This section describes some of the alternative accessible solutions for data visualizations and their pros and cons.

Sonification

Sonification refers to the use of sound scales to map data in a way that is analogous to colour scales. Sounds can change pitch, tenor, or volume to represent changes in data. Research into graph sonification focuses on how to map data to sounds to optimize BVI user understanding.

Sonification, like visualization, can be processed in parallel, making it well-suited for multidimensional data. With integrations of a 3D soundscape device, sonification can even be used to plot data with spatial relationships, like choropleths. Check out the Data Sonification Archive for examples.

BVI users found that sonification helped them "visualize" the graph, but there was a learning curve to overcome. Lack of standards also creates challenges for designers trying to integrate sonification scales into the online environment, though several new open-source libraries (TwoTone Data Sonification, Highcharts Sonification Studio) promise to create sonification scales, using data.

Haptics

Haptics refer to the sense of touch, for example through sensations of force or friction against one's finger. Haptics can help users feel the ups and downs of a line graph or the height of a bar graph against gridlines.

Research into haptic visualization argued that many BVI people learn through touch, therefore tactile representation of the graph can make it easier to link what they may have learned in school to what is being presented in front of them. But in practice, that's not always the case. Some users found the different frictions "disturbing" and "confusing" leading them to incorrectly perceive the layout of a complex graph. This could be due to the range of values perceived by the eyes is "orders of magnitude" greater than what can be perceived by touch. So, while haptics may be useful in gaining the gist of a graph, it's not well-suited to identifying precise data points.

Another drawback is that without an easily available library to work with, the average website designer isn't able to create a data visualization that maps to haptic feedback. The lack of haptic libraries and the need for a specialized haptic device could result in just creating a disability dongle.

Accessible descriptions

Accessible descriptions involve writing a description of the data visualization. While this sounds like alt-texts in WCAG 2.1, the strategies below take it a step further than the current guidelines.

Alt-texts are not all bad. One study asked participants to evaluate the quality of alt-texts found in academic journals, and the participants appreciated when they contained information recommended by alt-text guidelines. Where these guidelines fall short are in the first three points mentioned in the introduction. Alt-texts are great at conveying information like the subject and graph type, but don't give BVI users a summary of the data or statistical features.

For designers, it would be a tedious, if not impossible task to write out these descriptions. More so when the charts are interactive and allow users to choose different views of a graph. As a result, research for the next generation of accessible descriptions focuses on three possibilities:

  1. Allowing BVI users to "navigate" a visualization, as they do for a current webpage
  2. Programmatically generate natural language descriptions
  3. Presenting an interactive query mode where users can ask questions about the data in natural language

Navigable Scalable Vector Graphics

HTML structures a webpage into hierarchical sections, and each tag describes the content. Navigating these structures are familiar to BVI users who use screen-readers to navigate the web.

Some researchers and web developers are promoting the use of HTML elements to create data visualizations and present accessible descriptions for BVI people. Scalable Vector Graphics (SVGs) have been popular for programmatically building data visualizations online, and are used by Chart.js, D3.js - Data-Driven Documents, Google Charts, and others. When done right, each tag in an SVG can be organized so a tree of shapes can be traversed by screen-readers in a meaningful way (for example: Semiotic, Highcharts' accessibility module, Accessibility in d3 Bar Charts | a11y with Lindsey).

Natural language generation

Natural language generation is a domain of machine learning that automatically generates text that sounds like a human wrote it. This could be used to create a description of a graph and a summary of its data. Several libraries currently exist (like VoxLens, evoGraphs) but are limited to 2D charts, like bar or line charts.

Interactive query models

Interactive query modes allow users to control how much information they receive at once. These natural language interfaces allow users to ask questions of varying complexity without overwhelming them and complement other methods described in this article.

Interactive query models can also be intuitive to learn. In one study, VoxLens users were given the option to sonify a graph or use an interactive dialogue. Most preferred the interactive dialogue.

But interactive query modes, especially in natural language, push the limits of our current computational capacities, especially for nuanced or context-dependent analysis of data. The current challenge for both natural language generation and interactive query models is the capacity to make a solution that is robust for even complex visualizations.

Multi-modal visualizations

Multi-modal, as the name implies, is a combination of the previously described techniques to communicate the data. The main advantages are:

  1. The weakness of one method can be reinforced by another method
  2. Multi-modal solutions account for a wider range of user preferences; and
  3. Having multiple sensory inputs can reduce the cognitive load it takes to understand data

The most common multi-modal solution is the combination of sonification and haptics. Because touch is a familiar learning method for some BVI people, it can reinforce the information being provided by sonification, which is less known. Conversely, sound hints can reinforce haptic reception, like playing a sound when a user switches haptically to a different data series noted by different frictions. However, the multi-modal approach that use haptics suffer from the same drawback as only using haptics – they require specialized hardware.

Multi-modal approaches that combined sonification and accessible descriptions found better success. VoxLens saw a 122% increase in task accuracy and a 36% reduction in total interaction time.

In summary, multi-modal approaches that combined sonification and accessible descriptions create accessible data visualizations that require an equitable cognitive load, provide information at all levels of semantic content and lightweight for web-browsing, if the limitations to the kind of data visualizations supported can be overcome.

Conclusion

At the beginning of this article, we asked "How would you understand how to flatten the curve if you can't see the associated graph?". We looked at key considerations. We learned that providing a table of values wouldn't easily allow BVI users to find the maximums of each curve.

To balance the desire to add information without increasing the cognitive load, BVI users and researchers suggest having an accessible method of querying the data that would be beneficial for tasks like retrieving the minimum or maximum, or highlighting areas of the graph.

Data is getting more complex, and with the Accessible Canada Act requiring a barrier-free Canada by 2040 (see: Towards an Accessible Canada), we all have a role to play in ensuring our data visualizations are barrier-free.

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Referenced Tools

Further reading

Date modified:

Comparability and limitations of the data

Sources

Statistics are based on a census of all provincial and territorial liquor and cannabis authorities. Data are reconciled with annual reports of the liquor and cannabis authorities as well as other administrative sources and respondents are contacted to confirm or to explain variations.

Updates to the questionnaire and data tables in 2023

In 2023, the Control and Sale of Alcoholic Beverages program was updated to include cannabis and became the Control and Sale of Alcoholic Beverages and Cannabis. The related questionnaire, the "Government Liquor Authority: Report of Operations", was updated to include questions on non-medical cannabis sales and two new data tables were created to reflect the addition of cannabis.

Summary of the main updates to the questionnaire:

  • A fourth section was added to the questionnaire to collect data on the sale of non-medical cannabis by product type for both value and weight.
  • A fifth section was added to the questionnaire for cannabis finances.

The data from the fourth and fifth sections on the questionnaire are reflected in two new data tables. Table 10-10-0164 displays non-medical cannabis sales data by product and table 10-10-0165 displays government earnings derived from the sale of cannabis.

Updates to the questionnaire and CANSIM series in 2015

In 2015, the Control and Sale of Alcoholic Beverages program questionnaire, the “Government Liquor Authority: Report of Operations”, was updated after conducting qualitative testing involving field interviews with provincial and territorial liquor authorities'. Subsequently, the previous CANSIM tables were terminated and new CANSIM series were created to reflect these changes. A summary of the main updates are as follows.

A fourth beverage category was added to the questionnaire - Ciders, Coolers, and Other Refreshment Beverages (CCORB). International organizations including the World Health Organization publish alcohol statistics with four beverage categories: Spirits, Wine, Beer, and Other. Prior to the 2015 update, Ciders and Wine Coolers were included with Wines, Spirit Coolers were included with Spirits, and Beer Coolers were included with Beer. Other refreshment beverages not elsewhere classified could be reported as a Spirit, Wine, or Beer, at the discretion of the respondent. In aim of improving the comparability of these statistics, the fourth beverage category CCORB was added to the questionnaire and to its associated CANSIM series. As of fiscal year ending March 31st 2014, a majority of liquor authorities in Canada report sales using a variant of a fourth category that include ciders, coolers, “ready-to-drink” beverages, and/or other refreshment beverages.

Also in the beverage categories, the Beer category was updated to include four sub-categories: Light Beer, Regular Beer, Strong Beer, and Beer not elsewhere classified.

Absolute volume of sales of alcoholic beverages is calculated by multiplying the sales volume by the percentage of alcohol content for each product category. The percentages were modified in 2015 to reflect more accurate alcohol content based on an administrative data source obtained for this purpose. In the Spirits category, the percentage of alcohol content estimate is now 40% for Brandy, Gin, Rum, Whisky and Vodka. Spirit liqueurs are now classified at 20%, other spirits not elsewhere classified at 35%, and Alcohol at 90%. In the Wine category, the conversion rate is now estimated at 10% for sparkling wines. For non-sparkling wines, rosé wines are now estimated at 11%, white wines 12%, red wines 13%, fortified wines 18%, and other wines not elsewhere classified 15%. In the Beer category, light beer is now classified at 4%, regular beer 5% and strong beer 7%. For series continuity, beer not elsewhere classified is assigned an alcohol content of 5%, equivalent to a Regular Beer, and equivalent to the alcohol content assigned to Beer in the previous version of the questionnaire and CANSIM series. In the Ciders, Coolers, and Other Refreshment Beverages category, ciders are classified at 5.5%, wine coolers at 4.5%, spirit coolers at 6%, beer coolers at 7% and other refreshment beverages at 8%.

With the aim of improving the comparability of the number and types of retail outlets between each province and territory's diverse alcohol beverage distribution networks, the categorization of outlets section was updated. The previous version of the questionnaire classified outlets as either a government owned and operated liquor store, a liquor store agency, a wineries' retail outlet, or a breweries' retail outlet. The updated questionnaire and associated CANSIM series maintains the categories liquor store and liquor store agency, and updates wineries' and breweries' retail outlets into a broader third category: Other Retail Outlets. Other Retail Outlets includes wineries' and breweries' on-site and off-site retail outlets, ferment-on-premise facilities, general merchandise and grocery stores, and other retail outlets. This was done to better reflect the other types of retail outlets in operation across the country and to ensure full coverage of the distribution network.

The tax and other government revenue sections of the questionnaire and associated CANSIM series were updated to include all retail sales taxes, excise taxes, specific taxes on alcohol, and other reported and identifiable government revenues derived from the control and sale of alcoholic beverages. Retail sales taxes are estimated at the applicable rate by province and applied to gross sales. Specific taxes on alcoholic beverages are validated in corresponding provincial and territorial public accounts and annual reports of the liquor authority. Excise taxes are estimated using the reported excise taxes by product type from the Federal Public Accounts, and proportionally applying these amounts to each provincial and territorial share of sales in those categories by corresponding year. Additionally, the sales figures in all CANSIM series were revised to show the pre-tax sales. Previously, GST was included in the sales figures but other taxes were not.

Concepts and methods

Statistics on sales of alcoholic beverages by volume should not be equated with data on consumption. Sales volumes include only sales as reported by the liquor authorities and their agencies, including sales by wineries, breweries, and other outlets that operate under license from the liquor authorities. Consumption of alcoholic beverages would include all of these sales, as well as any unreported volumes of alcohol sold through ferment-on-premise operations or other outlets, and any unrecorded or illegal transactions. Statistics on sales of alcoholic beverages by dollar value should not be equated with consumer expenditures on alcoholic beverages. The sales data refer to the revenues received by liquor authorities and their agents, and a portion of these revenues include sales to licensed establishments such as bars and restaurants. The sales data do not, therefore, reflect the total amount spent by consumers on alcoholic beverages since the prices paid in licensed establishments are greater than the price paid by those establishments to the liquor authorities.

The value of sales of alcoholic beverages excludes all sales taxes, the value of returnable containers, and deposits. Per capita sales by value and volume are based on the population of inhabitants of 15 years of age and over. This is in accordance with the practice of Health Canada in presenting trends that are more realistic in the consumption of alcoholic beverages. This allows comparability with other countries, the Organization of Economic Co-operation and Development and the World Health Organization as they also present alcohol per capita data using the population of inhabitants of 15 years of age and over. The population estimates are based on CANSIM table 051-0001 Estimates of Population, by age group and sex for July 1, Canada, provinces, and territories, annual (persons).

Statistics on sales of non-medical cannabis do not account for the significant illicit cannabis market that still exists in Canada (Detailed household final consumption expenditure, provincial and territorial, annual). Sales values include only non-medical sales as reported by the cannabis authorities and outlets that operate under license from the cannabis authorities.

The value of sales of non-medical cannabis excludes all sales taxes. Per capita sales by value is based on the population of inhabitants of legal age to buy, use, possess, and grow recreational cannabis. The population estimates are based on table 17-10-0005-01 (formerly: CANSIM 051-0001) Estimates of Population, by age group and sex for July 1, Canada, provinces, and territories, annual (persons).

Monthly Survey of Food Services and Drinking Places: CVs for Total Sales by Geography - December 2022

Monthly Survey of Food Services and Drinking Places: CVs for Total Sales by Geography - December 2022
Table summary
This table displays the results of CVs for Total sales by Geography. The information is grouped by Geography (appearing as row headers), Month and percentage (appearing as column headers).
Geography Month
202112 202201 202202 202203 202204 202205 202206 202207 202208 202209 202210 202211 202212
percentage
Canada 0.15 0.68 0.82 0.94 0.38 0.55 0.70 0.47 0.15 0.15 0.17 0.13 0.15
Newfoundland and Labrador 0.57 0.98 1.60 1.62 1.56 1.70 0.62 0.60 0.55 0.54 0.67 0.58 1.20
Prince Edward Island 4.93 8.04 10.63 9.24 8.78 7.24 16.27 9.30 5.32 3.09 8.38 6.64 2.96
Nova Scotia 1.13 0.93 0.58 13.41 1.03 1.27 1.85 0.77 0.67 0.82 0.40 0.51 0.78
New Brunswick 1.69 8.61 13.21 0.89 0.69 1.38 0.67 0.60 0.56 0.54 0.61 0.91 0.66
Quebec 0.27 2.15 2.64 2.34 0.44 1.81 1.67 0.95 0.31 0.36 0.28 0.23 0.40
Ontario 0.20 1.19 1.04 1.17 0.67 0.89 1.37 0.87 0.27 0.26 0.21 0.18 0.22
Manitoba 0.50 4.84 0.59 0.57 0.48 1.04 0.76 4.08 0.54 0.61 0.40 0.60 0.61
Saskatchewan 0.74 1.38 1.19 1.16 1.70 1.23 7.67 4.35 1.41 0.89 1.34 1.29 0.64
Alberta 0.74 1.23 2.53 2.37 0.65 0.56 1.44 0.66 0.40 0.30 0.33 0.41 0.37
British Columbia 0.27 1.16 1.74 3.01 1.39 1.18 0.66 1.08 0.30 0.23 0.66 0.33 0.30
Yukon Territory 12.40 2.59 2.40 2.10 3.27 22.68 3.59 3.00 2.51 2.37 2.60 3.34 3.04
Northwest Territories 4.96 3.70 2.58 2.27 3.02 30.07 3.69 3.02 2.88 2.11 2.15 3.11 3.42
Nunavut 2.53 0.65 0.69 0.66 0.59 103.39 2.09 3.27 2.73 3.71 4.35 4.68 51.31