Improving Efficiency In Benchmarking And Farming Management Benchmarking Applications: The case study must showcase findings on how benchmarking improves d

Improving Efficiency In Benchmarking And Farming Management Benchmarking Applications: The case study must showcase findings on how benchmarking improves dairy farming. The report can be based on the practitioners, farmers, receiving benchmark reports to review to improve operations. In this case study, show the exploration of benchmarking in calf management through benchmarking. I attached some sources, APA format. J. Dairy Sci. 101:3323–3333
https://doi.org/10.3168/jds.2017-13596
© American Dairy Science Association®, 2018.
How benchmarking motivates farmers to improve dairy calf management
Christine L. Sumner, Marina A. G. von Keyserlingk, and Daniel M. Weary1
Animal Welfare Program, University of British Columbia, 248-2357 Main Mall, Vancouver, BC V6T 1Z4, Canada
ABSTRACT
Dairy calves often receive inadequate colostrum for
successful transfer of passive immunity and inadequate
milk to achieve their potential for growth and avoid
hunger, but little is known about what motivates farmers to improve calf management around these concerns.
Our aim was to assess if and how access to benchmarking reports, providing data on calf performance and
peer comparison, would change the ways in which farmers think about calves and their management. During
our study, 18 dairy farmers in the lower Fraser Valley
(British Columbia, Canada) each received 2 benchmark
reports that conveyed information on transfer of immunity and calf growth for their own calves and for
other farms in the region. Farmers were interviewed
before and after receiving their benchmarking reports
to gain an understanding of how they perceived access
to information in the reports. We conducted qualitative analysis to identify major themes. Respondents
generally saw having access to these data and peer
comparisons favorably, in part because the reports
provided evidence of how their calves were performing.
Benchmarking encouraged farmers to make changes in
their calf management by identifying areas needing attention and promoting discussion about best practices.
We conclude that some management problems can be
addressed by providing farmers better access to data
that they can use to judge their success and inform
changes.
Key words: animal welfare, extension, theory of
planned behavior, attitude
INTRODUCTION
Understanding the role of information in identifying
and improving management on farms is a key area of
interest in animal welfare research. Research aimed at
adoption of practices to reduce welfare risks on farms
has indicated that a lack of information is a barrier.
Received July 29, 2017.
Accepted January 5, 2018.
1
Corresponding author: danweary@mail.ubc.ca
For instance, Leach et al. (2010a) reported that welfare
problems such as lameness are more likely to persist
on dairy farms when farmers underestimate the extent
of the problem within their herd. Similarly, Becker et
al. (2013) found that farmers could underestimate the
severity of pain in treating foot problems because they
lack an understanding of how to assess pain in cows.
Dairy calves face several risks in the early weeks of
their lives, including inadequate colostrum for transfer
of passive immunity (Windeyer et al., 2014), and inadequate milk to achieve their potential for growth and
avoid hunger (reviewed by Khan et al., 2011). The technical solutions to these problems are well known; what
lacks is an understanding of the factors that limit adoption of these solutions on farms. Specifically, there is a
lack of research on how farmers view these concerns and
what motivates them to make decisions when it comes
to managing their calves. Increasing farmer awareness
and education on health-related practices, such as colostrum management, may encourage improvement in
welfare outcomes for calves (Heinrichs and Kiernan,
1994; Kehoe et al., 2007; Beam et al., 2009). The provision of information can influence a person’s attitude
and behavior toward a phenomenon (as reviewed by
Glasman and Albarracín, 2006). In addition to attitudes, understanding a person’s beliefs about who may
influence their decision-making and how much control
they have in making decisions are key factors in understanding a person’s motivation (Ajzen, 1991).
One way of providing information is through benchmarking. Benchmarking is the process of measuring
performance using specific indicators and then comparing performance with that of peers with the intention
of improving on those indicators (Fong et al., 1998).
The key concept is to use data to identify performance
gaps and drive improvements. Although often used to
increase efficiency (Anderson and McAdam, 2004),
benchmarking can also be used to motivate changes
not directly linked with economic outcomes (Magd and
Curry, 2003).
A previous study from our group evaluated benchmarking to improve lameness outcomes for mature
dairy cows (Chapinal et al., 2013), but that study was
retrospective, did not include controls, and assessed
3323
3324
SUMNER ET AL.
only biological outcomes (e.g., lameness). Another
study compared calf mortality on 2 farms and suggested
that the comparison of the underperformer with a high
performer helped identify management and employee
training not previously thought as pertinent to calf
mortality (Khade and Metlen, 1996). In a companion
paper to the current study (Atkinson et al., 2017), we
showed how benchmarking calf growth and transfer of
immunity resulted in some farms changing their management in ways that improved outcomes related to
both measures. To our knowledge, no previous work
has assessed the effect of benchmarking on farmer perceptions toward their animals and their motivation to
improve.
The aim of the current study was to describe how
benchmarking motivates farmers to make changes in calf
management. We used a qualitative, interview-based
approach to assess how access to benchmarking reports,
providing data on transfer of immunity and growth,
would change the ways in which farmers thought about
calves and making changes in their management.
MATERIALS AND METHODS
This study was approved by the University of British
Columbia Behavioral Research Ethics Board under #
H14-03196. All participants provided written consent.
Study Design
This interview study was designed from a critical
realist perspective that emphasized understanding the
meaning that people attach to a phenomenon and the
context within which this occurs (Manicas, 2006; Maxwell, 2012). For the current study, we were interested in
understanding the phenomenon of farmer perspectives
about factors related to the benchmarking process that
motivated them to make changes in calf management,
the specific context of our intervention study. Following the framework of Maxwell (2012), this approach
allowed us to identify mechanisms within the situation
(i.e., the benchmarking study) that cause a particular
outcome (i.e., why farmers made changes). Specifically,
we were interested in describing the mechanism(s) of
change that motivated farmers to improve calf management based on the provision of information in the
benchmark reports about their calves and those of their
peers participating in the study.
We purposively recruited from 18 commercial dairy
farms in the lower Fraser Valley of British Columbia
that participated in a benchmark study (see Atkinson
et al., 2017 for details concerning recruitment, biological measures and outcomes, and report delivery). We
interviewed individuals responsible for calf care, includJournal of Dairy Science Vol. 101 No. 4, 2018
ing farm owners, herd managers, and calf managers.
Choosing these individuals allowed us to fulfill 2 criteria
with sampling consistent with Maxwell (2012): (1) they
can best help us answer our research question because
they participated in the benchmark study, and (2) they
were the individuals who could best speak about the
calf management on each farm.
During the study, each farm received 2 reports 10
wk apart. These reports described serum total protein
from calf blood samples and average daily gains (as
estimated from heart-girth tapings) and information
on management practices on all study farms. Reports
provided data on the individual calves and graphically
presented data to facilitate interpretation. Each report
was presented by the herd veterinarian who used examples of other study findings (e.g., on the effects of
increasing milk ration on calf growth) and props (e.g.,
a colostrometer for testing colostrum quality) to facilitate the discussion. Examples of the content found in
these reports can be found at https://​figshare​.com/​s/​
7af49a9205a47ceb1363.
Interview Guide, Data Collection, and Participants
We used the theory of planned behavior to develop
the interview guide for semi-structured interviews.
The theory of planned behavior constructs (attitudes,
subjective norms, and perceived behavioral control) are
key to understanding a person’s motivation to perform
a behavior (Ajzen, 1991). According to the theory of
planned behavior, “attitudes” are positive or negative evaluations of a behavior, “subjective norms” are
the perceived social expectation toward performing a
behavior, and “perceived behavioral control” refers to
perceived ease or difficulty toward performing a behavior (Ajzen, 1991). The theory of planned behavior
has been used as a framework to provide structure for
open-ended qualitative inquiries (Goh, 2009; Borges et
al., 2014), including with dairy farmers and decisionmaking (Hötzel and Sneddon, 2013; Brennan et al.,
2016). For our study, we developed questions for interviews that occurred before and after farmers received
their benchmark reports. During the initial interviews,
we asked farmers a series of open-ended questions and
follow-up probes about calf management (How do you
think your calf management is going?), how they felt
about making decisions about their calves (How easy or
difficult is it for you to make decisions about how you
manage your calves?), how they felt about collecting
data on their calves (Can you tell me about benefits or
challenges you think there are with collecting data on
your calves?), and how they felt about comparing their
own farm performance against their peers (Who influences the way you manage your calves?). Farmers were
FARMER MOTIVATION TO IMPROVE CALF MANAGEMENT
also asked questions about the priority given to calves
on the farm in general (What priority are calves given
on your farm?). In the subsequent interviews, after
receiving 2 benchmark reports, we asked farmers the
same questions from the first interviews, but adjusted
the wording with respect to farmers now having access
to data on their calves and peer comparison. We also
asked farmers for any additional thoughts on the topic.
The first round of interviews took place before delivery of the first benchmark report on all 18 farms,
with 21 people interviewed. We conducted follow-up
interviews after farms had received their second benchmark report on 16 of the 18 farms, interviewing a total
of 19 people (2 farms were not included in the second
round of interviews due to scheduling conflicts). When
a farm had multiple interviewees, they agreed to be
interviewed together. Interviews were audiotaped and
conducted by the first author on the participants’ own
farms.
Data Analyses
Interviews were professionally transcribed and transcripts were compared with the original audio files to
ensure fidelity. We used Nvivo (version 10.2.2; QSR
International, Burlington, MA) for data analysis. We
treated the participants as a single case bound by context (participating in the calf benchmark study and geographic location) and conducted a within-case analysis
using a variable-oriented approach based on a priori
theoretical constructs of the theory of planned behavior
(Ajzen, 1991) and inductive analysis. Consistent with
a within-case approach, all farmers were treated as a
single entity rather than using comparison among farmers. This approach allowed us to identify major themes
that arose based on identifying phenomena related to
the variables of interest and the patterns that emerged
between these phenomena (Miles et al., 2014).
We analyzed interview data using a 2-step coding
process: the first step involved condensing the raw data
into groupings (codes) and the second step condensed
these groupings into “more meaningful and parsimonious units of analysis” (categories; based on Miles et al.,
2014, p. 86). During the first step, data were organized
using an a priori list of codes based on the theory of
planned behavior constructs (attitudes, social norms,
and perceived behavioral control) and a code for “values.” This was followed by the development of more
specific codes to further distinguish the different topics
that emerged during the analysis. For example, data
classified under the “attitudes” code were separated
into attitudes about calf management and attitudes
about benchmarking. The lead author and another
trained individual developed a list of code definitions
3325
that were then used to code a subset of the interviews;
discrepancies on how data were coded were discussed
until consensus was reached. The lead author then
coded the remaining interviews. During the second step
of the analysis, sections of the transcripts labeled with
more than one code were further organized into categories based on the relationship between the overlapping
codes. These categories were then organized into the
3 major themes reported in the Results section of this
paper. Quotes were selected to represent examples of a
given category within each theme; we specifically identified statements reflective of many responses and that
more clearly expressed a given concept. Quotes have
been modified for length and clarity: ellipses indicate
where text was omitted to reduce quote length, and
brackets indicate the authors’ additions to the text.
Validity
To stay consistent with our critical realist approach,
we used a framework that organizes concepts based
on descriptive validity (what is reported as seen and
heard), interpretive validity (the participant’s perspective), and theoretical validity (meaning is explained
through concepts and their relation; Maxwell, 2012). For
concerns about the descriptive validity of our study, we
relied on audio recordings of all interviews and checked
all transcripts against the original recordings. For concerns about the interpretive validity, the lead author
and sole interviewer made repeat visits to the farms
for interviews and report meetings to establish rapport
with the participants, employed repeat interviews that
helped confirm respondents’ answers to questions, used
multiple researchers to analyze the data to minimize
researcher bias, and maintained an ongoing log of notes
documenting the research process. For concerns with
theoretical validity, we used an established theory to
frame our data collection and analysis, and we used an
analytic framework to develop our themes (from codes
to categories, and finally themes) based on Miles et al.
(2014).
RESULTS
Three major themes emerged during data analysis,
explaining how providing information about calf performance and peer comparison in benchmark reports
influenced farmer decision-making. Collectively, the
responses suggest that benchmarking motivated farmers to improve calf management because of the intrinsic
and instrumental value of having access to data and
peer comparisons. Additionally, farmers’ values about
their calves emerged as a key feature in how access
to data and peer comparison motivated them to imJournal of Dairy Science Vol. 101 No. 4, 2018
3326
SUMNER ET AL.
prove calf management. Finally, benchmarking calf
management shifted the social norms among farmers
around calf management. Our study design was based
on interviews before and after farmers received their
benchmark reports. The themes are organized to show
farmers’ perceptions of their calves, their calf management, and their peers before and after they had access
to data on their calves and peer comparison.
Theme 1: Improved Farmer Confidence from Access
to Data and Peer Comparison
Access to data on their calves and peer comparison
had intrinsic value (i.e., value in itself) for farmers
because it instilled a sense of confidence in their assessment of their calves and calf management. Before access to the benchmark reports, farmers’ perceptions of
success were based on methods of assessment that did
not rely on data. Once farmers received their benchmark data, reports improved their confidence because
it provided an additional measure of success.
Before Benchmark Reports. During the first set
of interviews, before recurring benchmark reports, farmers expressed both confidence and ambivalence about
managing their calves. Farmers described a range of
factors related to confidence in their ability to manage
calves. For example, low mortality for preweaned calves
was perceived as an indicator of success, “I think it’s
going excellent right now… There’s very little calf loss”
(Farmer 15). Another outcome of success was breeding
age for heifers. For example, when describing why they
felt their calf program was going well, the following
2 participants (both from Farm 4) included breeding
milestones and growth, “We’re able to raise our calves
bigger, quicker” (Farmer 4A) and, “We were breeding
them [heifers] at 13 months; we’ve changed a couple
little things, now we’re breeding them at 12 months”
(Farmer 4B).
Confidence in successful calf management also included the ability to identify sick calves based on visual
assessment. Farmers relied on a range of behavioral
indicators to assess the health of their calves. Farmer 5
described his observations as follows, “… if the calf’s
not drinking or slow-drinking, just look if it’s got a bit
of a snotty nose or droopy ears or it’s got scours. It’s
usually maybe a touch of pneumonia … And [it is] just
usually those two things, if you assess that pretty quick,
I don’t think we lose a calf a year.”
Activities related to calf management were also linked
to confidence, including the use of vaccine schedules,
hygiene protocols, and colostrum management. For example, when describing how well his calves were doing,
Farmer 1 reported, “I think we’ve thought it out pretty
Journal of Dairy Science Vol. 101 No. 4, 2018
well. We do a good job with cleanliness and tidiness,
capitalizing on things that need to be capitalized on.”
We also noted a degree of ambivalence about assessing and managing calves, often based upon the lack
of relevant data to support assessments. For example,
Farmer 11 commented on the lack of data for assessing
failure of passive transfer of immunity, “I never had
my calves tested … So, have I had problems with sick
calves before? Yes. Have I pinpointed it to my colostrum
management? No. So, to say that, I don’t really know if
I’ve had problems with my colostrum management before
… Kind of been like, ‘we’re okay’. But have I had a lot of
research to back up that ‘think we’re okay,’ no.” Having
data on calves was anticipated to address ambivalence.
Farmer 13 indicated the value of data in providing reassurance, “… it would just be a good relief to know you’re
raising good heifers.”
Ambivalence was also expressed in terms of questioning routine practices that were not supported with relevant data. Farmer 10 expressed mixed feelings about
the quality of colostrum fed to calves, given that quality was not tested. He stated, “If I have a healthy cow
through a healthy transition period that had a healthy
far-off period, [she] should have a healthy first, second
feeding for its baby. And I just trust that. Should I trust
that? Well that’s probably just me with my blinders on.”
The anticipated value in having data on their calves
was linked with personal values farmers expressed. For
example, Farmer 11 described the value of having data
on his calves in relation to identity, “I want to be a good
farmer … I want to do well at everything I do, so if I
see a benchmark that I’m not doing well, then I want to
figure out a way within economic reason to do a better
job … if I can change little things to do better, then I
absolutely will do that.” The calf’s intrinsic value was
also linked w…
Purchase answer to see full
attachment