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. 2020 Oct;61(10):1065–1072.

Factors associated with the adoption of technologies by the Canadian dairy industry

Murray D Jelinski 1,✉, David F Kelton 1, Chris Luby 1, Cheryl Waldner 1
PMCID: PMC7488376  PMID: 33012822

Abstract

Data generated from Statistics Canada’s 2016 Census of Agriculture and Census of Population were used to describe the adoption of 8 technologies by the Canadian dairy industry: computer/laptop, smartphone/tablet, auto-steering, auto-feeding, auto-environment, robotic milking, global positioning systems (GPS), and geographical information systems (GIS). Logistic regression was used to analyze the adoption of each technology by geographical region, operators’ gender, operators’ age, herd size, and number of operators per farm. Gender and age were marginally related to the level of adoption of each technology, whereas the number of operators per dairy farm and farm size were associated with increased adoption of most technologies. Quebec had the smallest average farm size, but the highest levels of adoption for 5 of 8 technologies.

Introduction

The dairy industry has a long history of supporting research dedicated to nutrition, management practices, and technologies. Exemplars are the Journal of Dairy Science, a journal that has been in existence for more than 100 years (1), and the Society of Dairy Technology, which has been publishing on advances in dairy technologies since 1943 (2). Furthermore, agricultural conferences on precision livestock farming frequently have sessions dedicated to new and innovative dairy technologies. On this point, several terms such as precision agriculture, precision farming, and smart farming have entered the agricultural lexicon to describe the use of technologies. One proposed definition of precision dairy farming is “the use of information and communication technologies for improved control of fine-scale animal and physical resource variability to optimise economic, social and environmental dairy farm performance” (3). Regardless of the definitions or the terminology used, precision or smart agricultural technologies are comprised of both information and communication technology (ICT) and decision support systems (DSSs). The ICT component includes data acquisition (sensors), recording, and the communication of information, while the DSSs are software systems that interface with operators to inform management’s decision-making process (3,4). While precision dairy farming is frequently categorized as the utilization of “Big Data,” these terms are not synonymous because precision dairy farming lacks some of the central components for fulfilling the definition of Big Data, namely the volume, variety, and velocity of the data (5). Currently, much of the data are generated from individual animals; however, to fulfill the volume and variety criteria more data must be generated and integrated across a broad population of animals, farms, as well as milk processors, retailers, and consumers. This will be challenging since it requires the integration and sharing of data across different acquisition and processing systems, which are frequently provided by different manufacturers and suppliers. The other component that is missing is velocity, which refers to how frequently the data are generated with the ultimate goal being for the decision-making processes to be made in real time.

Like other facets of agriculture, Canada’s dairy industry has been consolidating, resulting in fewer but larger operations (6), which is also a global phenomenon (7,8). A main driver of consolidation has been the ability for larger operations to capture economies of scale. For instance, a US study found that costs per hundredweight of milk sold were twice as high for the smaller (< 50 head) versus larger dairies (> 500 head), with per unit costs continuing to decline as herd size increased (9). Consolidation in American dairies is also being driven by improvements in individual animal milk production; from 1991 to 2010 individual animal production increased by 42%, while the number of dairy farms decreased by 66% (10). A similar trend has occurred in Canada with individual animal production increasing by 9.7% between 2011 and 2016 (11).

A number of demographic factors are associated with the adoption of technologies and increased milk production. A US study found that increasing farm size as well as dairy operators having a college education were 2 factors associated with the adoption of productivity-influencing technologies, management practices, and production systems (TMPPS) (10). This is supported by an Australian study reporting that larger operations (> 500 head) were 2 to 5 times more likely to adopt precision dairy farming technologies than smaller dairies (12). And a review article on precision agricultural (PA) practices reported that increasing farm size, producers’ level of education, familiarity with computers, and the perception that the adoption of PA will lead to improved competitiveness and greater profitability were all associated with adopting PA practices (13).

In North America, the relationship between herd size and adoption of technologies appears to be confounded by other factors. Barkema et al (8) reported that the adoption of automated milking systems (AMSs) was higher in Canada than in the United States, even though US dairies tend to be larger than Canadian dairies. These authors surmised that lower US milk prices and lower labor costs deterred the adoption of AMSs by US producers. Other researchers have posited that the slower uptake of the use of AMSs by US dairies may be related to the lack of service providers and that the initial AMSs were better suited for smaller dairies (14). However, several factors may accelerate the adoption of AMSs in the US. First, the introduction of AMS parlors has led to an increase in the adoption of these units by larger dairies. Secondly, changing immigration policies are projected to exacerbate labor shortages in agricultural sectors that are intensive users of labor (15), which will have ramifications for the US dairy industry since immigrant labor accounts for more than half of the labor force (16). In general, labor shortages in both Canada and the US will undoubtedly lead to increased adoption of AMSs in North America.

Both the Census of Agriculture and Census of Population are administered concurrently every 5 y, providing data that describe the socioeconomic traits of Canada’s agricultural operators. Historically, the Census of Agriculture’s technology section was restricted to a few questions relating to computer usage (17). The 2016 Census, however, was the first to inquire about the adoption of technologies by all the commodity groups (18). The timing of this change is somewhat fortuitous since it provides a baseline for the adoption of dairy technologies before the implementation of both the Canada-European Union Comprehensive Economic and Trade Agreement (CETA) and the Canada-United States-Mexico Agreement (CUSMA). These agreements will increase foreign access to Canada’s domestic market, thereby encouraging Canadian dairy producers to adopt more technology in order to become more competitive.

The objective of this study was to examine the relationship between operator/farm characteristics and the adoption of 8 technologies.

Materials and methods

Data were generated from Statistic’s Canada (StatsCan) 2016 Census of Agriculture (18), with a customized data extraction of the answers to the following question: “In 2015, which of the following TECHNOLOGIES were used on this operation? Computers/laptops for farm management, Smartphones/tablets for farm management, automated steering (auto-steer), GPS technology, GIS mapping (e.g., soil mapping), greenhouse automation, robotic milking, automated environmental controls for animal housing, automated animal feeding, Other technologies (specify), and None of the above.” Greenhouse automation and “other technologies” were excluded because of very low adoption, 0.8% and 0.6%, respectively (19). The following 8 technologies were analyzed: computer/laptop, smartphone/tablet, auto-steering, auto-feeding, auto-environment, GPS, and GIS. Robotic milking is used synonymously with automated milking systems (AMSs).

Statistics Canada defined a farm operator as any person who, as of May 10, 2016, was responsible for the management decisions in controlling the function of an agricultural operation, including owners, tenants, or hired managers (20). While dairy operators were defined as producers who reported having dairy cows and dairy replacement heifers. Statistics Canada’s customized data extraction resulted in a cross-tabulation of the adoption of the 8 technologies by 5 operator/farm characteristics: operators’ gender (female and male); number of dairy operators per farm (1 and ≥ 2); operator age (< 26 y, 26 to 30 y, 31 to 35 y, 36 to 40 y, 41 to 45 y, 46 to 50 y, 51 to 55 y, 56 to 60 y, and > 60 y); herd size (≤ 50, 51 to 100, 101 to 200, 201 to 300, 301 to 500, and > 500 dairy cows and replacement heifers > 1 y old); and geographical region (Canada, Atlantic provinces, Quebec, Ontario, and western Canada).

The extracted data were provided in a commercial spreadsheet (Microsoft Excel v. 12; Microsoft Corporation, Redmond, Washington, USA) and then imported into a statistical program for analyses (IBM SPSS Statistics Ver 25; IBM Corporation, Armonk, New York, USA). Descriptive statistics were used to assess the adoption of each technology by each of the 5 operator/farm characteristics. The mean adoption of each technology was calculated by dividing the total number of operators using each technology (numerator) by the total number of operators reporting to the census in each category (denominator). The operators’ ages were reduced from 9 to 5 age groups: (< 31, 31 to 40, 41 to 50, 51 to 60, ≥ 61 y). Univariate analyses were performed for each technology by operator/farm characteristic. A generalized estimating equation (GEE) with a logit link function and binomial distribution was used to examine factors associated with the adoption of each of the 8 technologies, correcting for clustering associated with herds within a geographical region (Atlantic provinces, Ontario, Quebec, western provinces). All 5 operator/farm characteristics were included in all 8 multivariable models. Data were excluded when the denominator was 0 or was rounded off as 0 by Statistics Canada, or in a few instances in which the rounding procedure resulted in a numerator greater than the denominator. The number of valid combinations of data for each analysis are reported.

The GEE analyses provided statistically significant differences among groups with common operator/farm characteristics as well as the measure of effect (difference between estimates of the mean). Complete census data were available for the analysis; therefore, the GEE model was used to generate predicted probabilities of adopting the management tool for each category with 95% confidence interval (CI). The absolute differences in the probabilities of those adopting and those not adopting each management tool were determined for each factor with 95% CI. Residuals were examined for outliers. No interactions were evaluated for any model. Level of significance was P < 0.05 (2-tailed).

Results

Statistics Canada reported 26 935 dairy operators on 12 895 dairies in 2016 with most (n = 20 180; 75%) co-managing with another operator. Only 505 (7%) of the single dairy operators were female; therefore, most (93%; n = 6650) were co-managing with ≥ 1 male operator(s). The greatest number of operators (n = 5500; 20%) were in the oldest age cohort (≥ 61 y), 26% (n = 1415) of which were single operators. Conversely, the youngest cohort, ≤ 30 y of age, represented only 9% (n = 2315) of all operators, 19% (n = 450) of which were single operators.

While a GEE was generated for each of the 8 technologies, only the results from 5 technologies are provided in Tables 1 to 5. The 3 technologies not shown (auto-environment, auto-steer, and GIS) either had low adoption and/or there was minimal variation by geographical region, gender, age of operator, number of operators/farm, or herd size.

Table 1.

Proportion and 95% confidence interval (95% CI) of producers who were using computers and/or laptops, stratified by region, number of operators/herd, gender, age, and herd size (breeding heifers and cows) and absolute differences in proportions among groups.

95% CI Difference from reference group 95% CI
Mean Lower Upper Lower Upper P-value
Region < 0.001
 Atlantic 0.69 0.66 0.73 −0.04 −0.04 −0.03 < 0.001
 Ontario 0.68 0.63 0.73 −0.05 −0.07 −0.03 < 0.001
 Quebec 0.74 0.69 0.79 0.01 0.00 0.03 0.121
 Western Canada 0.73 0.69 0.77 Reference
Operators
 1 Operator 0.64 0.58 0.71 −0.13 −0.17 −0.09 < 0.001
 ≥ 2 Operators 0.77 0.74 0.80 Reference
Gender
 Female 0.70 0.65 0.75 −0.02 −0.05 0.00 0.080
 Male 0.72 0.68 0.76 Reference
Operator age (years) < 0.001
 ≤ 30 0.69 0.63 0.75 0.05 −0.06 0.15 0.397
 31 to 40 0.75 0.73 0.77 0.11 0.04 0.17 0.002
 41 to 50 0.73 0.70 0.77 0.09 0.04 0.14 < 0.001
 51 to 60 0.73 0.64 0.81 0.08 0.07 0.09 < 0.001
 ≥ 61 0.65 0.56 0.72 Reference
Herd size
 ≤ 50 0.50 0.46 0.54 −0.19 −0.30 −0.08 0.001
 51 to 100 0.72 0.68 0.75 0.03 −0.08 0.13 0.617
 101 to 200 0.83 0.79 0.86 0.14 0.07 0.21 < 0.001
 201 to 300 0.79 0.75 0.83 0.10 0.04 0.16 0.001
 301 to 500 0.70 0.50 0.84 0.01 −0.12 0.13 0.932
 ≥ 501 0.69 0.60 0.77 Reference
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Table 2.

Proportion and 95% confidence interval (95% CI) of producers who were using smartphones and/or tablets, stratified by region, number of operators/herd, gender, age, and herd size (breeding heifers and cows) and absolute differences in proportions among groups.

95% CI Difference from reference group 95% CI
Mean Lower Upper Lower Upper P-value
Region < 0.001
 Atlantic 0.54 0.51 0.57 −0.09 −0.10 −0.07 < 0.001
 Ontario 0.56 0.53 0.60 −0.06 −0.07 −0.05 < 0.001
 Quebec 0.62 0.59 0.66 0.00 −0.01 0.02 0.780
 Western Canada 0.62 0.60 0.65 Reference
Operators
 1 Operator 0.55 0.52 0.58 −0.08 −0.10 −0.06 < 0.001
 ≥ 2 Operators 0.62 0.59 0.66 Reference
Gender
 Female 0.58 0.55 0.61 −0.01 −0.01 0.02 0.277
 Male 0.59 0.56 0.63 Reference
Operator age (years) < 0.001
 ≤ 30 0.66 0.60 0.71 0.16 0.10 0.23 < 0.001
 31 to 40 0.65 0.62 0.68 0.16 0.10 0.22 < 0.001
 41 to 50 0.60 0.57 0.63 0.11 0.05 0.17 < 0.001
 51 to 60 0.53 0.49 0.57 0.04 0.00 0.08 0.038
 ≥ 61 0.49 0.45 0.54 Reference
Herd size < 0.001
 ≤ 50 0.33 0.30 0.37 −0.35 −0.48 −0.22 < 0.001
 51 to 100 0.50 0.47 0.53 −0.19 −0.34 −0.03 0.021
 101 to 200 0.63 0.58 0.68 −0.05 −0.23 0.12 0.556
 201 to 300 0.67 0.61 0.73 −0.01 −0.16 0.13 0.870
 301 to 500 0.68 0.59 0.76 0.00 −0.12 0.11 0.939
 ≥ 501 0.69 0.53 0.81 Reference
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Table 3.

Proportion and 95% confidence interval (95% CI) of producers who were using global positioning systems (GPS), stratified by region, number of operators/herd, gender, age, and herd size (breeding heifers and cows) and absolute differences in proportions among groups.

95% CI Difference from reference group 95% CI
Mean Lower Upper Lower Upper P-value
Region < 0.001
 Atlantic 0.26 0.24 0.29 −0.20 −0.21 −0.19 < 0.001
 Ontario 0.42 0.39 0.46 −0.04 −0.05 −0.02 < 0.001
 Quebec 0.28 0.24 0.32 −0.18 −0.20 −0.17 < 0.001
 Western Canada 0.46 0.43 0.49 Reference
Operators
 1 Operator 0.32 0.28 0.36 −0.07 −0.09 −0.05 < 0.001
 ≥ 2 Operators 0.39 0.36 0.42 Reference
Gender
 Female 0.32 0.26 0.38 −0.07 −0.12 −0.02 0.010
 Male 0.39 0.37 0.40 Reference
Operator age (years) < 0.001
 ≤ 30 0.34 0.30 0.37 0.01 −0.04 0.01 0.329
 31 to 40 0.40 0.34 0.45 0.07 0.03 0.12 0.001
 41 to 50 0.36 0.32 0.39 0.04 0.02 0.05 < 0.001
 51 to 60 0.35 0.31 0.39 0.03 0.01 0.05 0.012
 ≥ 61 0.32 0.30 0.35 Reference
Herd size < 0.001
 ≤ 50 0.13 0.11 0.15 −0.32 −0.43 −0.21 < 0.001
 51 to 100 0.26 0.25 0.26 −0.19 −0.31 −0.06 0.003
 101 to 200 0.40 0.39 0.42 −0.04 −0.17 0.08 0.481
 201 to 300 0.46 0.39 0.54 0.02 −0.11 0.14 0.783
 301 to 500 0.51 0.48 0.54 0.07 −0.03 0.16 0.181
 ≥ 501 0.45 0.33 0.57 Reference
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Table 4.

Proportion and 95% confidence interval (95% CI) of producers who were using auto-feeding technology, stratified by region, number of operators/herd, gender, age, and herd size (breeding heifers and cows) and absolute differences in proportions among groups.

95% CI Difference from reference group 95% CI
Mean Lower Upper Lower Upper P-value
Region < 0.001
 Atlantic 0.28 0.25 0.30 0.02 0.00 0.04 0.011
 Ontario 0.20 0.17 0.22 −0.06 −0.07 −0.04 < 0.001
 Quebec 0.47 0.43 0.52 0.22 0.18 0.25 < 0.001
 Western Canada 0.26 0.24 0.27 Reference
Operators
 1 Operator 0.27 0.25 0.30 −0.03 −0.06 −0.01 0.004
 ≥ 2 Operators 0.31 0.27 0.34 Reference
Gender
 Female 0.29 0.26 0.32 −0.01 −0.03 0.01 0.252
 Male 0.30 0.27 0.32 Reference 0.27 0.32
Operator age (years) < 0.001
 ≤ 30 0.30 0.26 0.35 0.03 −0.03 0.10 0.335
 31 to 40 0.28 0.24 0.32 0.01 −0.04 0.06 0.697
 41 to 50 0.32 0.29 0.35 0.05 0.00 0.10 0.056
 51 to 60 0.28 0.26 0.31 0.01 −0.01 0.04 0.267
 ≥ 61 0.27 0.23 0.31 Reference
Herd size < 0.001
 ≤ 50 0.10 0.07 0.12 −0.30 −0.38 −0.23 0.000
 51 to 100 0.29 0.24 0.33 −0.11 −0.23 0.01 0.072
 101 to 200 0.40 0.37 0.43 0.00 −0.11 0.11 0.995
 201 to 300 0.38 0.36 0.40 −0.02 −0.11 0.08 0.732
 301 to 500 0.29 0.20 0.40 −0.10 −0.16 −0.05 < 0.001
 ≥ 501 0.40 0.31 0.49 Reference
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Table 5.

Proportion and 95% confidence interval (95% CI) of producers who were using automated milking systems, stratified by region, number of operators/herd, gender, age, and herd size (breeding heifers and cows) and absolute differences in proportions among groups.

95% CI Difference from reference group 95% CI
Mean Lower Upper Lower Upper P-value
Region < 0.001
 Atlantic 0.08 0.07 0.09 −0.03 −0.03 −0.02 < 0.001
 Ontario 0.08 0.07 0.09 −0.03 −0.03 −0.02 < 0.001
 Quebec 0.10 0.08 0.12 0.01 −0.02 −0.01 0.302
 Western Canada 0.11 0.10 0.12 Reference
Operators
 1 Operator 0.09 0.08 0.10 −0.01 −0.03 0.00 0.147
 ≥ 2 Operators 0.10 0.08 0.12 Reference
Gender
 Female 0.10 0.08 0.11 0.01 0.00 0.01 0.070
 Male 0.09 0.08 0.10 Reference
Operator age (years) < 0.013
 ≤ 30 0.09 0.06 0.12 0.00 −0.02 0.02 0.901
 31 to 40 0.11 0.09 0.14 0.02 0.01 0.04 0.000
 41 to 50 0.09 0.08 0.10 0.00 −0.03 0.02 0.679
 51 to 60 0.09 0.08 0.10 0.00 −0.02 0.02 0.812
 ≥ 61 0.09 0.08 0.10 Reference
Herd size < 0.000
 ≤ 50 0.01 0.01 0.01 −0.14 −0.21 −0.08 0.003
 51 to 100 0.07 0.05 0.09 −0.08 −0.18 0.02 0.102
 101 to 200 0.15 0.14 0.17 0.00 −0.09 0.10 0.918
 201 to 300 0.21 0.17 0.26 0.06 −0.05 0.17 0.264
 301 to 500 0.18 0.11 0.28 0.03 −0.11 0.17 0.699
 ≥ 501 0.15 0.08 0.27 Reference
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There were several trends across the 8 technologies. Quebec dairy operators had the highest adoption for 5 of the 8 technologies. Quebec operators were the least likely to be using GPS technology, while western Canadian and Ontario producers were the highest adopters of this technology. Increasing farm size was generally associated with increasing adoption and farms with ≥ 2 operators were consistently more likely to be adopters of all technologies compared to single operator farms. There was little to no association between gender and the adoption of most technologies. While there were significant differences in adoption by operator age, the measure of effect was often small.

More specifically, the adoption of AMS (robotic milking) was consistently low (6% to 11%) by region, herd size, number of operators/farm, and operator age (Table 5). The highest estimated mean adoption rate was in western Canada (11%), which was greater than both Ontario (8%) and Atlantic Canada (8%) (P ≤ 0.001), but not different from Quebec (10%) (P = 0.302). Although there were significance differences in adoption by region, the measures of effect were very small. Unlike most other technologies, the adoption of AMS was not related to the number of operators per operation (P = 0.147), nor to gender (P = 0.070). The adoption of AMS by age was steady at 9%, except for the 31 to 40 y cohort, which had higher adoption at 11% (P < 0.013). Herd size had an effect on the adoption of AMS (P < 0.001), with the smallest herds (≤ 50 head) having the lowest adoption at 1%, while 21% of herds with 201 to 300 head had adopted this technology.

For the 3 technologies not shown, 20% to 30% of operators used auto-environment technology. Adoption was lowest in western Canada and highest in Quebec (P < 0.001). The oldest operators (> 60 y) were less likely to use environmental technology compared to all other age cohorts (P < 0.001). Generally, adoption of auto-environment increased with increasing herd size. Auto-steer was more commonly used in western Canada than in all other regions (P < 0.001) and there was a clear trend for increasing adoption with increasing herd size. Although younger operators were more likely to use auto-steer technology than the > 60 y cohort, the differences in the measures of effect were very small. The adoption for GIS across all regions ranged from 12% to 15%; however, the largest farms (> 500 head) had the highest adoption rate (36%; P < 0.001).

Discussion

The 2016 Census was the first time Statistics Canada included a section dedicated to technologies. This section, however, was designed to capture data on all commodities, not just dairy. Thus, some technologies such as auto-steer, GIS, and GPS were not particularly relevant, while reproductive technologies such as estrus detection and sexed semen were not included. Although the list of technologies provided by the Census of Agriculture was short and non-specific to the dairy industry, the 8 technologies serve as a proxy for the adoption of technology by Canadian dairy operators.

In describing the factors influencing the adoption of precision agriculture for dry-land farming, Pierce et al (21) coined the phrase “do the right thing, in the right place, at the right time, and in the right way.” However, there are a number of steps needed before arriving at the point of adoption. The first step is rooted in the diffusion theory, which explains how innovative technologies are first communicated amongst operators before being adopted (22). This well-recognized theory is supported by a European study that found that access to information from extension services, service providers, and those selling the technology (vendors) was a determinant for the adoption of precision agricultural (PA) technologies (13). Thus, there is a need for intermediaries or “translators” such as nutritionists, veterinarians, technicians, and vendors to participate in the diffusion of information. Although awareness of the technology is important, other factors such as return on investment, total cost of the investment, and ease of use must also be factored into the decision (23). The last point is salient because the installation of precision dairy farming technologies often results in a steep learning curve (3).

As previously discussed, there is a strong correlation between increasing farm size and the adoption of PA technologies. Therefore, it is paradoxical that Quebec producers, whose average herd size is the smallest in Canada (24), led the nation in adoption of computers/laptops, smartphones/tablets, auto-feeding, auto-environment, and auto-milking technologies. This may be explained by the diffusion of information within the Quebec dairy industry. Consider that ~10 700 dairies in Canada ship milk, 48% of which are in Quebec (25). This concentration of dairies presumably results in more interactions between dairy producers as well as more vendors and technicians servicing the industry. Furthermore, the province subsidizes both veterinary and dairy extension services, resulting in a relatively high number of dairy veterinarians and consultants. And while many provinces have moved away from providing extension services, Quebec has created a knowledge translation service within their milk recording program (Valacta). With the 2019 launch of Lactanet (a partnership between Valacta, Canwest DHI, and the Canadian Dairy Network), some of these services may be offered nationally in the future, although when and how this will happen has yet to be determined. The density of dairy farms coupled with the number of translators has created an ideal environment for the diffusion of technology.

Of the 8 technologies analyzed, the 3 most closely aligned with the dairy industry were auto-feeding, auto-environmental control, and automated milking systems (AMSs). However, auto-feeding was not defined by Statistics Canada, hence it could have been interpreted as feed mixing, feed delivery, or pushing up the feed to the cattle. Similarly, auto-environment did not specify whether it applied to cow housing or calf housing. Furthermore, the census asked about the use of robotic milking, whereas AMS is perhaps a more encompassing term. That said, dairy producers should appreciate the clear delineation between using and not using robotic milkers. Despite these limitations, a common finding to most of the 8 technologies was that farms with ≥ 2 operators had a higher rate of adoption than single operator farms, which is likely confounded by herd size. It was also common to find no difference in adoption by gender; however, this finding requires some explanation. Farms were not segregated by those operated solely by females or males; rather, 93% of female operators were managing alongside male operators; therefore, it is difficult to parse the effect of gender. Similarly, operator age also had a marginal effect on the adoption of most technologies with the exception being smartphones/tablets and computers/laptops. This lack of age effect is likely related to 75% of dairies having multiple operators, who by Statistics Canada’s definition are responsible for farm management decisions. Farming in Canada is still very much a family enterprise; therefore, the operators undoubtedly represent multiple generations, which conceals the effect of age on the adoption of technologies. Regarding the use of smartphones/tablets, there was a clear trend for younger operators to be more intensive users of these devices; however, this may be related to these devices being personal use items. Overall, adoption of smartphones/tablets and computers/laptops was higher in the dairy industry compared to the Canadian cow-calf industry (26), which may reflect dairy operators being more comfortable with computers and software applications.

While there were statistical differences in the estimates of the mean for each AMS parameter, the measure of effects was negligible for gender, number of operators per farm, and operators’ age. The only determinants of significance were geographical region and herd size. The estimates of the means by geographical region show that dairies in western Canada and Quebec had the highest adoption of AMS compared to Ontario and the Atlantic region. These relationships, however, are probably confounded by other management practices, such as housing. Automated milking systems are not used in tie-stall (pipeline) systems, a housing practice that is much more common in Quebec (41%) and Ontario (31%) and rarely used in the Atlantic region (3%) or western Canada (2%) (27). Furthermore, only 7% of Canadian herds milked > 80 cows using a pipeline system and average herd size is greatest in western Canada (27). There are multiple factors that favored the adoption of AMS in western Canada; however, these same factors should have been barriers to adoption in Quebec. As previously noted, the adoption of all technologies by the Quebec dairy producers could be related to a greater diffusion of information. In addition, Quebec’s average herd size is ~62 milking animals (24), which is the approximate capacity of most AMSs. Automatic milking systems technology, therefore, is a very good fit for many of Quebec’s dairies, which not only alleviates labor constraints but may also increase their competitiveness. A Canadian study reported that AMSs reduced the amount of time spent each day on milking activities by 62%, while increasing milk yield (28). Although the adoption of AMSs remains relatively low, both economics and demographics are important drivers for the increased adoption of AMSs, which should be evident by the next census in 2021.

Biosensors are a cost-effective means of monitoring an animal’s physiology and/or environment and are considered by some to be the 4th revolution in agriculture (29). The concept of integrating nanotechnologies or molecular diagnostics into wearable biosensors, which provide data in real-time, is no longer a futuristic construct (30). Rather, technologies are currently being used to monitor health, reproduction, metabolic parameters, milk production, and as an aid in genomic selection (31–37). The breadth of these technologies underscores the challenges that Statistics Canada will encounter when attempting to gather technology data from across all agricultural sectors. Fortunately, Canada has a cohort of collaborative dairy researchers located in all regions of the country who have the means to investigate the adoption of novel dairy management practices and technologies. A case in point is the 2015 National Dairy Study, which captured detailed benchmarking data on all facets of dairy production: health, welfare, management, and production (38).

While the Census of Agriculture data have their limitations, they also has some notable strengths. Specifically, they capture data from all dairy producers and farms, eliminating biases associated with surveys. Secondly, the data can be analyzed in conjunction with the Census of Population data, thereby providing data on the demographics of the dairy operators. For the current study, only 5 farm and farming operator characteristics were chosen; however, a similar analysis could have been performed using a range of other parameters such as farm income, land base, and farm ownership.

This study provided an overview of the adoption of technologies by the dairy industry. Clearly, some technologies were of particular relevance to the dairy industry, whereas others were more applicable to dry-land farming and other livestock commodity groups. This was the first time that Statistics Canada dedicated a section of the census to technologies, and therefore the current study provides a baseline for future census data. CVJ

Footnotes

Use of this article is limited to a single copy for personal study. Anyone interested in obtaining reprints should contact the CVMA office (hbroughton@cvma-acmv.org) for additional copies or permission to use this material elsewhere.

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