Adoption of technology and management practices by Canadian cow-calf producers

Abstract

Statistics Canada’s 2016 census data were analyzed to determine the proportion of Canadian cow-calf producers who had adopted the use of 7 different technologies and 2 different grazing/feeding management practices, collectively referred to as “management tools.” The 4 most commonly used management tools were rotational grazing, in-field winter grazing/feeding, smartphones/tablets, and computers/laptops. Differences in the adoption of these technologies by geographical region, number of producers/operations, herd size, operator gender, and operator age were examined using logistic regression. Estimates of the mean proportion of producers in eastern (65%) and western (60%) Canada using rotational grazing were similar (P = 0.24). However, a greater proportion of producers in western Canada versus eastern Canada were using in-field winter grazing/feeding (P < 0.001), smartphones/tablets (P < 0.001), and computers/laptops (P = 0.002). Adoption of all 4 tools was higher on farm operations with ≥ 2 operators versus those with 1 operator (P < 0.001). Larger herd size was associated with higher adoption rates across all 4 management tools. The effect of gender on adoption rates was equivocal.

Introduction

Research in the early 1960s led to the development of the “diffusion model” to help explain the adoption of technologies by agricultural producers (1). The premise was that the dissemination of information is a critical driver for the adoption of innovative technologies and practices. Subsequent research in the early 1980s concluded that even though producers might be well-informed and eager to adopt new technologies, economic constraints may preclude their adoption (2). A more contemporary study of cattle producers found that unfamiliarity and perceived non-applicability were the main reasons why they chose not to adopt various best management practices (3). Insufficient labor and profitability were also identified in a study that examined factors associated with the adoption of different grazing strategies for beef cattle production in the southern US (4). In general, producers must anticipate a positive net return (economically, environmentally, or socially) in order to adopt a new innovation.

One of the few sources of data relating to the adoption of technologies by Canadian agricultural producers comes from Statistics Canada’s (StatsCan) Census of Agriculture. The Census of Agriculture is mandatory under the Statistics Act; therefore, all individuals who produce agricultural products intended for sale must complete the census (5). The data represent all commodities, geographical locations, and varying sizes of farming operations (6). While agricultural data have been generated from censuses spanning many decades, changing agricultural practices have resulted in the survey questions evolving over time, which is problematic when it comes to comparing data across time. For example, the use of computers for farm management increased from 2.7% to 59.6% from 1986 to 2011 (7). The earliest census data, however, provide no information as to how the computers were being used for farm management. It was not until the 2001 census did StatsCan begin to ask whether computers were being used for specific activities such as bookkeeping, payroll, livestock record-keeping, word processing, Internet, and e-mail (8). Then, in 2011, the census reverted to only asking whether the farm operator had a high-speed Internet connection (9). More recently, the 2016 census included a new section titled “technologies,” which included the use of smartphones/tablets for farm management, computers/laptops for farm management, GPS technology, and GIS mapping, among other technologies (10). Thus, this latest census provides the first opportunity to gain insight into the uptake of a broad array of technologies by Canadian cow-calf producers.

The objectives of this study were to i) determine what proportion of Canadian cow-calf beef producers had adopted the technologies described in the 2016 census as well as 2 feeding/grazing management practices (rotational grazing and in-field winter grazing/feeding); and ii) identify operator (producer) and operation (farm) characteristics associated with the adoption of these technologies and feeding/grazing management practices.

Materials and methods

All data used in the analyses were obtained from Statistics Canada’s 2016 Census of Agriculture using a customized data extraction, with producer and herd parameters determined by the authors. The analyses were restricted to farm operators with cow-calf beef operations; operators (producers) were defined by Statistics Canada (StatsCan) as any person responsible for the management decisions for an agricultural operation as of May 10, 2016 (10). Cow-calf operations were defined as farms, ranches, or other operations that returned a census indicating that they had cows or heifers (1 y or older) for beef purposes. The census survey also inquired about how many hours/weeks each operator worked, if the operator earned off-farm income, and if the farm remunerated family and non-family employees; however, these parameters were not included in the current analyses. Additional information on how the census was administered, analyzed, and reported can be found elsewhere (11).

The initial analysis examined 7 technologies: i) the use of computers/laptops for farm management; ii) use of smartphones/tablets for farm management; iii) GPS technology; iv) GIS mapping; v) auto-steer; vi) auto-feeding; and vii) automated environmental controls for animal housing (Step 23 of the Census of Agriculture). Two feeding/grazing management practices from a separate section of the census (Step 13) were also examined: rotational grazing and in-field winter grazing/feeding. The respondents were asked if they had used these technologies or grazing/feeding practices in 2015. These 7 technologies and 2 management practices are herein collectively referred to as “management tools” and the proportion of producers using each tool was considered the adoption rate.

The number of producers using each management tool was stratified by gender (female and male); number of beef cattle operators per farm (1 and ≥ 2); producer 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); and herd size (< 51, 51 to 100, 101 to 200, 201 to 300, 301 to 500, and > 500 breeding female cattle). Herd size data were determined by summing the number of beef cows and replacement heifers (> 1 y of age) per operation. StatsCan provided the extracted data at the provincial level with the Atlantic provinces being reported as 1 region. These data were then collated into 3 regions: Canada, eastern Canada (Atlantic provinces, Quebec, and Ontario), and western Canada (Manitoba, Saskatchewan, Alberta, and British Columbia). Eastern and western Canada are herein referred to as “East” and “West,” respectively.

The extracted data were provided in a commercial spreadsheet (Microsoft Excel v. 12; Microsoft Corporation, Redmond, Washington, USA) and then exported to a statistical program for analyses (IBM SPSS Statistics ver 24; IBM Corporation, Armonk, New York, USA). Descriptive statistics were generated to gain an understanding of the relative adoption of each management tool. Generalized estimating equations (GEE) with a logit link function and binomial distribution were then used to examine factors associated with the adoption of each management tool, correcting for clustering associated with herds within the 7 geographical codes (6 provinces and Atlantic Canada). The following factors were used in each of the final multivariable models: region (East or West), number of operators/farm (1 or ≥ 2), gender, producer age, and herd size. The total number of producers using the management tool in each category was the numerator for the model, and the total number of producers reporting to the census in each category was the denominator. Because complete census data were used for the analysis, the model could be used to generate predicted probabilities of adopting the management tool for each category with 95% confidence intervals (CI). The absolute differences in the probabilities of those adopting and those not adopting each management tool were then determined for each factor with 95% CI. Residuals were examined for outliers. As this was intended as an exploratory analysis of factors associated with all of the most common management tools no interactions are reported.

Results

In 2016, Canada had 72 820 cow-calf producers, 19 315 (26.5%) in the East and 53 505 (73.5%) in the West. Of these, 30 065 (41.3%) were sole operators: 27 245 (90.6%) males and 2820 (9.4%) females. The age distribution of all producers was: < 26 y (1.8%), 26 to 30 y (3.1%), 31 to 35 y (4.8%), 36 to 40 y (6.3%), 41 to 45 y (7.3%), 46 to 50 y (9.7%), 51 to 55 y (15.1%), 56 to 60 y (16.5%), and > 60 y (35.5%). The distribution of herds based on size (number of cows and replacement heifers) was: < 51 (60.4%), 51 to 100 (16.8%), 101 to 200 (13.4%), 201 to 300 (4.9%), 301 to 500 (3.1%), and > 500 (1.3%). In summary, 35.5% of producers were > 60 y of age and most herds (60.4%) had < 51 breeding female cattle. Herds with 1 reported operator were 1.12 (95% CI: 1.10 to 1.13; P < 0.0001) times more likely to have < 51 breeding females than herds with ≥ 2 operators. To put the producer data in context with the number of farm operations, there were 53 837 farms reporting beef cows in 2016 (12), or an average of 1.4 operators/cow-calf operation.

The initial analysis involved graphing the proportion of producers who had adopted the use of the 7 technologies and 2 grazing/feeding management practices (Figure 1). Management tools such as GIS, environmental controls for animal housing, and auto-feeders had low adoption rates (< 6.0%) in all provinces and the GPS and auto-steer technologies were primarily adopted by operators in the West. Therefore, these lesser used technologies were not examined further. Multivariable models were then developed for each of the 4 most commonly used management tools: rotational grazing, in-field winter grazing/feeding, smartphones/tablets, and computers/laptops. Tables 1 to 4 summarize the associations between herd attributes and the reported use of each management tool.

Figure 1.

Percentage of cow-calf producers by province and region (Atlantic Canada) that have adopted each of the 9 management tools.

Table 1.

Estimates of the mean (95% CI) proportion of producers who were using rotational grazing, stratified by region, number of producers/herds, 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
East	0.65	0.58	0.71	0.05	−0.03	0.12	0.24
West	0.60	0.56	0.64	Reference
Producers
1 Operator	0.60	0.55	0.64	−0.06	−0.07	−0.04	< 0.001
≥ 2 Operators	0.65	0.62	0.69	Reference
Gender
Male	0.62	0.58	0.66	−0.01	−0.02	0.00	0.009
Female	0.63	0.59	0.67	Reference
Producer age (years)							< 0.001
< 26	0.59	0.54	0.64	0.01	−0.01	0.04	0.32
26 to 30	0.60	0.53	0.67	0.02	−0.03	0.07	0.36
31 to 35	0.64	0.60	0.68	0.06	0.02	0.1	0.003
36 to 40	0.65	0.61	0.68	0.07	0.05	0.09	< 0.001
41 to 45	0.66	0.62	0.69	0.08	0.06	0.1	< 0.001
46 to 50	0.65	0.61	0.70	0.08	0.06	0.09	< 0.001
51 to 55	0.63	0.59	0.66	0.05	0.03	0.07	< 0.001
56 to 60	0.63	0.59	0.67	0.05	0.04	0.07	< 0.001
> 60	0.58	0.54	0.62	Reference
Herd size							< 0.001
< 51	0.49	0.45	0.53	Reference
51 to 100	0.58	0.53	0.63	0.09	0.06	0.12	< 0.001
101 to 200	0.61	0.57	0.65	0.12	0.07	0.17	< 0.001
201 to 300	0.67	0.61	0.72	0.18	0.11	0.24	< 0.001
301 to 500	0.67	0.62	0.72	0.18	0.12	0.25	< 0.001
> 500	0.72	0.65	0.78	0.23	0.15	0.32	< 0.001

Table 4.

Estimates of the mean (95% CI) proportion of producers who were using computers/laptops for farm management, stratified by region, number of producers per 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
East	0.61	0.57	0.65	−0.06	−0.10	−0.02	0.002
West	0.67	0.63	0.71	Reference
Producers
1 Operator	0.58	0.54	0.63	−0.11	−0.12	−0.09	< 0.001
≥ 2 Operators	0.69	0.66	0.72	Reference
Gender
Male	0.64	0.60	0.67	−0.01	−0.02	0.00	0.014
Female	0.65	0.61	0.68	Reference
Producer age							< 0.001
< 26	0.61	0.55	0.66	0.12	0.08	0.16	< 0.001
26 to 30	0.66	0.62	0.70	0.17	0.14	0.19	< 0.001
31 to 35	0.70	0.68	0.72	0.21	0.18	0.24	< 0.001
36 to 40	0.71	0.67	0.75	0.22	0.21	0.23	< 0.001
41 to 45	0.68	0.65	0.72	0.19	0.17	0.22	< 0.001
46 to 50	0.68	0.64	0.71	0.18	0.17	0.2	< 0.001
51 to 55	0.61	0.57	0.65	0.12	0.11	0.14	< 0.001
56 to 60	0.60	0.55	0.65	0.11	0.09	0.13	< 0.001
> 60	0.49	0.46	0.52	Reference
Herd size							< 0.001
< 51	0.51	0.49	0.53	Reference
51 to 100	0.60	0.57	0.62	0.09	0.07	0.1	< 0.001
101 to 200	0.66	0.63	0.68	0.14	0.13	0.16	< 0.001
201 to 300	0.68	0.66	0.70	0.17	0.15	0.19	< 0.001
301 to 500	0.70	0.62	0.77	0.19	0.13	0.25	< 0.001
> 500	0.69	0.62	0.75	0.18	0.13	0.23	< 0.001

There was no difference (P = 0.24) in the adoption rate of rotational grazing between the East and West (Table 1). However, a greater proportion of producers in the West were using in-field winter grazing/feeding (Table 2), smartphones/tablets (Table 3), and computers/laptops (Table 4). The adoption of all 4 management tools was higher on farms having ≥ 2 operators (Tables 1 to 4). Operations that listed a female as being involved in the operation, as opposed to farms managed solely by males, had higher adoption rates for rotational grazing, in-field winter feeding/grazing, and computers/laptops. While gender-associated differences were significant, the measure of effect was marginal (1% to 2%).

Table 2.

Estimates of the mean (95% CI) proportion of producers who were using in-field winter grazing/feeding, stratified by region, number of producers/herds, 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
East	0.39	0.32	0.46	−0.19	−0.26	−0.11	< 0.001
West	0.57	0.51	0.63	Reference
Producers
1 Operator	0.46	0.40	0.51	−0.05	−0.06	−0.03	< 0.001
≥ 2 Operators	0.50	0.45	0.56	Reference
Gender
Male	0.47	0.41	0.53	−0.02	−0.03	−0.01	< 0.001
Female	0.49	0.44	0.54	Reference
Producer age							< 0.001
< 26	0.47	0.37	0.58	0.06	−0.02	0.13	0.13
26 to 30	0.50	0.43	0.57	0.08	0.03	0.13	0.003
31 to 35	0.51	0.43	0.58	0.09	0.05	0.13	< 0.001
36 to 40	0.52	0.47	0.57	0.11	0.07	0.14	< 0.001
41 to 45	0.51	0.46	0.57	0.10	0.07	0.13	< 0.001
46 to 50	0.49	0.44	0.53	0.07	0.05	0.09	< 0.001
51 to 55	0.45	0.42	0.49	0.04	0.03	0.04	< 0.001
56 to 60	0.45	0.40	0.50	0.03	0.01	0.05	0.002
> 60	0.42	0.38	0.45	Reference
Herd size							< 0.001
< 51	0.29	0.26	0.32	Reference
51 to 100	0.39	0.35	0.43	0.11	0.007	0.15	< 0.001
101 to 200	0.49	0.43	0.55	0.21	0.14	0.27	< 0.001
201 to 300	0.58	0.52	0.64	0.29	0.23	0.36	< 0.001
301 to 500	0.59	0.49	0.68	0.31	0.19	0.42	< 0.001
> 500	0.56	0.45	0.66	0.27	0.17	0.38	< 0.001

Table 3.

Estimates of the mean (95% CI) proportion of producers who were using smartphones/tablets for farm management, stratified by region, number of producers/herds, 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
East	0.47	0.41	0.53	−0.14	−0.20	−0.08	< 0.001
West	0.61	0.56	0.67	Reference
Producers
1 Operator	0.50	0.44	0.56	−0.08	−0.11	−0.06	< 0.001
≥ 2 Operators	0.58	0.54	0.63	Reference
Gender
Male	0.55	0.51	0.59	0.02	0.00	0.03	0.081
Female	0.54	0.48	0.59	Reference
Producer age							< 0.001
< 26	0.58	0.49	0.65	0.27	0.22	0.32	< 0.001
26 to 30	0.65	0.61	0.70	0.35	0.33	0.36	< 0.001
31 to 35	0.67	0.63	0.70	0.36	0.34	0.38	< 0.001
36 to 40	0.65	0.59	0.70	0.34	0.32	0.37	< 0.001
41 to 45	0.60	0.55	0.65	0.30	0.28	0.32	< 0.001
46 to 50	0.54	0.49	0.60	0.24	0.21	0.26	< 0.001
51 to 55	0.47	0.43	0.50	0.16	0.14	0.18	< 0.001
56 to 60	0.42	0.37	0.47	0.11	0.09	0.13	< 0.001
> 60	0.31	0.27	0.34	Reference
Herd size							< 0.001
< 51	0.39	0.36	0.42	Reference
51 to 100	0.47	0.43	0.52	0.08	0.06	0.11	< 0.001
101 to 200	0.53	0.49	0.57	0.14	0.12	0.16	< 0.001
201 to 300	0.59	0.53	0.65	0.20	0.16	0.24	< 0.001
301 to 500	0.64	0.58	0.68	0.25	0.22	0.27	< 0.001
> 500	0.63	0.55	0.71	0.24	0.18	0.31	< 0.001

The largest herds (> 500 head) had the highest adoption rate for rotational grazing (Table 1), while the next largest herds (301 to 500 head) had the highest adoption rates for the remaining 3 management tools. In general, increasing herd size was associated with an increase in the adoption of all 4 management tools. There was a consistent trend for the youngest (< 26 y) and oldest (> 60 y) cohorts to have the lowest rates of adoption of the 4 management tools (Tables 1 to 4). Overall, herd size accounted for the largest differences (measure of effect) in rotational grazing and in-field winter grazing/feeding, while producer age was associated with the largest differences in smartphone/tablet and computer/laptop use (Tables 1 to 4).

Discussion

There were no appreciable differences in the adoption of rotational grazing amongst producers in the East versus the West; however, producers in western Canada were more likely to incorporate the use of in-field winter grazing/feeding practices, smartphones/tablets, and computers/laptops in their cow-calf operations. After controlling for the other factors, operations with ≥ 2 operators/farm had higher adoption rates for all 4 management tools compared to single-operator farms. While no single age cohort had the highest adoption rates across all 4 management tools, the youngest and oldest cohorts were consistently the lowest adopters. The impact of gender on adoption of the management tools was significant, but the measure of effect was marginal. The models estimated that operations with a female operator(s) would increase the adoption of rotational grazing, in-field winter grazing/feeding practices, and computers/laptops use by 1% to 2% over the farms managed solely by male operators.

While there were no differences in the uptake of rotational grazing between eastern and western Canada, there were differences at the provincial level. Approximately 60% of producers in Quebec (QC) and British Columbia (BC) used rotational grazing, while < 50% of Saskatchewan (SK) and Ontario (ON) producers had adopted this practice (Figure 1). These findings suggest that unique factors (i.e., environmental, geographical, regulatory, herd size) may be influencing the use of rotational grazing at the provincial level.

There were clear differences in the adoption of in-field winter grazing/feeding strategies in the West (57%) compared to the East (39%). This may be related in part to climatic conditions and regulatory constraints. Eastern Canada tends to have warmer and wetter winters, resulting in more trampling, muddy field conditions, and the potential for damage to the forage stand. Deeper snow also precludes swath grazing and stockpiling, although bale grazing and bale unrolling could still occur in the deeper snow. Furthermore, Quebec has environmental legislation that precludes extensive winter grazing, hence stockpiling as well as swath and bale grazing are not allowed (13). A more detailed analysis is needed to determine where, and by whom, in-field winter grazing/feeding strategies are being practiced.

The marginal effect of having women involved in the management of the farm needs to be interpreted with caution. The effect may have been greater had single operator farms managed by males been compared to single operator farms managed by females. In the current analysis, farms with a combination of male and female operators were compared to male-only managed operations. The male versus female-only analysis could not be conducted due to the limited numbers of sole female operators when considered across all of the factors examined in this analysis. This is unfortunate since there is a paucity of contemporary data relating to women’s influence on agriculture, particularly in industrialized countries.

It has been suggested that women’s contributions to agriculture may be higher than reported. Specifically, the lack of visibility of wives and daughters working in agriculture has resulted in the under-reporting of their contributions to agriculture (14). An American study conducted in the 1980’s concluded that women’s contribution or participation in agriculture may be systematically under-reported and that their involvement with farming remains obscure because their contributions are ill-defined (15). Furthermore, women’s off-farm employment was more likely to be captured in greater detail by surveys than was on-farm work. While only 2% to 4% of females had the final authority when it came to decision-making, ~45% were involved in making major decisions such as purchasing land, equipment, and the timing of when to sell farm products. Whether this American study from the 1980s informs the current discussion remains unclear, and as such, is an area requiring additional research.

The potential effect of age on the adoption of management tools is of particular interest because the Canadian cow-sector is currently undergoing a major generational shift in farm ownership (16). Canada’s cow-calf sector had 12.4% fewer operations and 12.4% fewer producers in 2016 than in 2011. Furthermore, at the time of the last census, 35% of all cow-calf producers were > 60 y and for every 1 producer < 31 y of age there were ~7 producers > 60 y of age. The demographic profile of the Canadian cow-calf sector is such that over the coming decade the current cohort of cow-calf producers who comprise the baby boom generation (born between 1946 and 1964) will have retired, leaving a generation of millennials (Generation-Y) to manage the farm operations.

The millennial generation is defined by those persons born between the early 1980s and mid-1990s and is frequently referred to as “echo of the baby boom.” They are also considered to be technologically savvy, being referred to as the “digital generation” (17), “digital natives,” or “digital immigrants” (18). They are stereotyped as having a lower tolerance for delays and therefore seek readily accessible information, preferring graphics before text and having the opportunity to network and multi-task. Many veterinarians can attest to millennials having researched their disease or production problem before coming to seek advice. The exponential growth of information through social media and the internet could have significant implications with respect to how veterinarians charge for consulting services. While a relatively recent survey of Saskatchewan beef producers ranked veterinarians as the main source of information, 13.5% of respondents considered the internet in the top 3 sources for information (19). Veterinarians need to be cognizant that the next generation of operators may require different means of communication.

Age had a relatively small effect on the use of rotational grazing and in-field winter grazing/feeding but was a major factor when it came to use of smartphones/tablets for farm management. The estimate of the mean proportion of producers aged 31 to 35 using smartphones/tablets for farm management was 67% compared to 31% of producers > 60 y. The smartphone/tablet data, however, were difficult to reconcile with smartphone usage by the general population. While 58% of producers < 26 y of age used a smartphone/tablet in the operation of the farm, 94% of all Canadians 15 to 34 y of age own a smartphone (20). StatsCan has acknowledged that the respondents’ interpretation of what constitutes “use” may have led to under-reporting (21). The same may apply to the proportion of younger people using computers/laptops. Only 61% of producers < 26 y of age indicated that they were using computers/laptops to assist in farm management, whereas 71% of Canadians > 15 y of age own a laptop and 50% own a desktop computer (20). It will be of interest to see how adoption rates of these digital-based technologies increase over the coming decade as more millennials become the sole managers of the farming operation. The increased use of these technologies will also be aided by improvements in hardware, software, and new and more novel agricultural applications.

There was a consistent trend with respect to the effect of herd size. Generally, producers with larger herds had greater adoption rates, while the lowest adoption rates were in herds of < 51 breeding females. This relationship may be related, in part, to constraints on labor and profitability (4). In the current study, operations with ≥ 2 operators consistently had higher adoption rates for all 4 management tools compared to the farms operated by 1 person. Presumably the additional amount of available labor allowed or facilitated a higher adoption rate of all 4 management tools. However, profitability has also been shown to influence the adoption of newer technologies and larger operations tend to capture the economies of scale and therefore are potentially more profitable. Therefore, profitability may have confounded the effect of farm size on the adoption of the new technologies.

The 2016 Census of Agriculture was the first to include questions relating to a host of technologies. Of interest was the use of electronic devices such as smartphones/tablets and computers/laptops. Conceivably, the adoption of technology and innovative management practices will increase as the cow-calf sector continues to consolidate, resulting in fewer farms, fewer producers, but larger herds. Technology holds the potential to assist producers in managing more efficiently, allowing fewer producers to manage more animals. Veterinarians should anticipate that the next generation of producers, who are more technology savvy, will be more informed and also more willing to accelerate the adoption of technologies such as drones for surveilling land and livestock, GPS tracking of individual animals, enhanced hardware and software for data acquisition and reporting, to name a few. While a constantly evolving census questionnaire makes it difficult to compare data across time, it is equally important that the questions remain relevant to the changing technologies. CVJ