Exploring the Effects of Precision Livestock Farming

Notification Mechanisms on Dairy Farmers

by

Muhammad Muhaiminul Islam

A Thesis

presented to

The University of Guelph

In partial fulfilment of requirements

for the degree of

Master of Science

in

Computer Science

Guelph, Ontario, Canada

© Muhammad Muhaiminul Islam, August 2020

ABSTRACT

EXPLORING THE EFFECTS OF PRECISION LIVESTOCK FARMING

NOTIFICATION MECHANISMS ON DAIRY FARMERS

Muhammad Muhaiminul Islam

University of Guelph, 2020

Advisor:

Stacey D. Scott

Modern dairy farms are increasingly adopting technologies to monitor animal health and

welfare and send notifications to farmers when issues arise. These precision livestock

farming (PLF) technologies promise increased animal health and farm productivity. Yet,

few studies exist on the effects of these technologies on those who use them. Studies

from Europe show the 24/7 nature of potential PLF notifications can make farmers feel

always “on call”, increasing their overall stress levels. This thesis conducted an initial

online survey of 18 dairy farmers in Ontario, Canada, to explore their experiences with

PLF notifications. Reported benefits of PLF technologies include improved animal

health and dairy products, labour benefits, and ease of data collection. The study also

uncovered weaknesses of PLF notifications, including information uncertainty and

overload, false alerts, inappropriate timing, and mismatches between information

content and communication mediums used. Design recommendations are presented to

improve PLF notification mechanisms.

iii

DEDICATION

This work is dedicated to my parents and my only lovely sister – thank you for your

guide, love and support.

iv

ACKNOWLEDGEMENTS

My most profound gratitude goes to my supervisor, Dr. Stacey D. Scott, for her support,

care and direction throughout the research. I am grateful to you for all the open doors

that you have managed for me during this degree procedure, and I anticipate

proceeding to work together.

I am gigantically thankful to Dr. David Flatla for being my advisory committee member

and providing tremendous support during my research. I am also thankful to my thesis

examiner Dr. Daniel Gillis for providing thoughtful feedback on my thesis document. I

must also thank Dr. Tom Wright and Dr. Evan Fraser for their support during my

research.

I would also like to thank Makinde Ayoola for helping and providing useful suggestions

to me throughout my research and other daily life situations while I moved to Canada. I

would like to thank my parents for boosting me to concentrate on my study and my

lovely sister for mental support. My gratitude to my friends who always helped me to

keep myself mentally healthy and without them it was not possible for me to stay in

Canada alone.

This research would not have been conceivable without the cooperation of the Ontario

dairy farmers, retailers and everyone who helped me in my research, especially in

distributing my online survey. My participants managed time from their busy schedule to

fill my survey, and I am cordially thankful to them for that.

Finally, I want to show my gratitude to the University of Guelph and NSERC for

providing funds to conduct this research.

v

TABLE OF CONTENTS

Contents

Abstract ................................................................................................................................................................. ii

Dedication ............................................................................................................................................................ iii

Acknowledgements .............................................................................................................................................. iv

Table of Contents................................................................................................................................................... v

List of Tables ......................................................................................................................................................... ix

List of Figures ......................................................................................................................................................... x

List of Acronyms ................................................................................................................................................. xiii

List of Appendices ............................................................................................................................................... xiv

Introduction .................................................................................................................................... 1

2.1 Motivation ................................................................................................................................................ 4

2.2 Research Questions, Objectives and Scope ................................................................................................ 6

2.3 Thesis Outline ........................................................................................................................................... 8

Background ..................................................................................................................................... 9

3.1 Systematic Literature Search ..................................................................................................................... 9

3.1.1 Inclusion and Exclusion Criteria ...................................................................................................... 10

3.2 Overview of PLF Technology .................................................................................................................... 11

3.2.1 Impact of Automated Milking Systems ........................................................................................... 13

vi

3.2.2 Impact of Mastitis Detection Technologies ..................................................................................... 16

3.2.3 Impact of Lameness Detection Technologies .................................................................................. 18

3.2.4 Impact of Estrus Detection ............................................................................................................. 19

3.2.5 Impact of Calving Time Detection................................................................................................... 21

3.2.6 Effects of Activity Monitoring Technologies.................................................................................... 22

3.3 Are PLF Notifications a Source of Stress? ................................................................................................. 27

3.4 Information Overload.............................................................................................................................. 28

3.5 Technologies are not User-centric ........................................................................................................... 29

3.6 Importance of Proper Notification Management ..................................................................................... 29

3.7 Summary ................................................................................................................................................ 31

Research Methodology ................................................................................................................. 33

4.1 Regional Context..................................................................................................................................... 33

4.2 Study Methodology ................................................................................................................................. 36

4.2.1 Survey Design ................................................................................................................................ 36

4.2.2 Survey Procedure .......................................................................................................................... 38

4.2.3 Survey Distribution ........................................................................................................................ 39

4.3 Interview Design ..................................................................................................................................... 44

4.4 Survey Participants ................................................................................................................................. 44

4.5 Data Collection and Analysis ................................................................................................................... 47

4.6 Summary ................................................................................................................................................ 49

Results .......................................................................................................................................... 50

5.1 Participants Receiving Notifications from Specific PLF Technologies ......................................................... 50

5.2 Type of Information Received by the Farmers .......................................................................................... 53

5.3 Actions Taken by the Farmers ................................................................................................................. 56

5.4 Benefits of PLF Technologies ................................................................................................................... 58

5.5 Challenges of PLF Technologies ............................................................................................................... 60

vii

5.6 Number of Notifications .......................................................................................................................... 62

5.7 Perceived Stress Levels Related to PLF Notifications ................................................................................. 63

5.8 Notification Mediums ............................................................................................................................. 68

5.9 Overall Stress Related Effects of PLF Notifications.................................................................................... 72

5.10 Overall Positive and Negative Effects of PLF Notifications ................................................................... 73

5.11 Findings Related to ACTDS .................................................................................................................. 75

5.12 Chapter Summary .............................................................................................................................. 78

Discussion ..................................................................................................................................... 79

6.1 Information Management ....................................................................................................................... 79

6.1.1 Information Uncertainty ................................................................................................................ 80

6.1.2 Inappropriate Communication Timing ............................................................................................ 81

6.1.3 Communication Medium ............................................................................................................... 82

6.2 Ineffective Representation of Information ............................................................................................... 87

6.2.1 Notification Management .............................................................................................................. 87

6.2.2 Information Overload .................................................................................................................... 90

6.3 Effects of PLF Notifications on Farmers .................................................................................................... 92

6.3.1 Are Notifications a Source of Stress? .............................................................................................. 92

6.3.2 Strategies to Overcome Stress ....................................................................................................... 95

6.4 Discussion on Miscellaneous Findings ...................................................................................................... 96

6.5 Summary ................................................................................................................................................ 99

Conclusion .................................................................................................................................. 100

7.1 Scholarly Contributions ......................................................................................................................... 100

7.2 Limitations and Challenges ................................................................................................................... 106

7.3 Future Work ......................................................................................................................................... 108

7.4 Concluding Remarks.............................................................................................................................. 110

viii

References ......................................................................................................................................................... 111

Appendices ........................................................................................................................................................ 129

ix

LIST OF TABLES

Table 2-1: List Of Keywords Used to Search for Literature in Web of Science. ............ 10

Table 4-1: Correlation Matrix Values (r) for the Number of Alerts against Farm Size,

Years of Experience, and Year of Technology Installation. ........................................... 63

Table 4-2: Correlation Matrix Values (r) for the Stress Level against Farm Size, Years of

Experience, and Age of the Farmer............................................................................... 68

x

LIST OF FIGURES

Figure 2.1 : Conceptual PLF system model. The system involves four distinct phases,

including: 1) Data Collection, where the system collects data about the animals or farm

environment using different sensors; 2) Data Processing, where the system removes

noise and unnecessary features from the data; 3) Detection / Classification, where

potential health or welfare concerns are identified using algorithms such as machine

learning; and 4) Inform, where the system notifies the farmer of the identified issues to

they can respond appropriately. (Acronyms used in the figure: CS: Computer Scientists,

AS: Animal Scientists, ACI: Animal-Computer Interaction, HCI: Human-Computer

Interaction.) ................................................................................................................... 12

Figure 2.2: Sample Received Messages by Farmer from AMS through Mobile pplication

(AMS NotifierTM, DeLaval®). .......................................................................................... 15

Figure 2.3: Effective Herd Management and Smooth Workflow Offered by GEA

CowViewTM (©GEA Group, Germany) by Sending Alerts to Farmers Regarding

Detected Issues............................................................................................................. 25

Figure 2.4: Sample Pocket Cow CardTM (PCC) to View Information of Cows. .............. 26

Figure 3.1: Number of Farms, Dairy Cows and Dairy Heifers (Canadian Dairy

Information Centre, 2019) ............................................................................................. 35

Figure 3.2: Advertisement of the Survey in TwitterTM. ................................................... 41

Figure 3.3: Leaflet to Advertise the Survey at the Dairy Symposium in Woodstock,

Ontario. ......................................................................................................................... 43

Figure 3.4: Age Range of the Survey Participants ........................................................ 45

Figure 3.5: Gender of the Survey Participants (15 male and 3 female) ......................... 45

xi

Figure 3.6: Role of the Participants in the Farm ............................................................ 46

Figure 3.7: Years of Dairy Farming Experience of the Participants ............................... 47

Figure 4.1: Number of Participants Using the Technologies on Their Farms. ............... 51

Figure 4.2: Number of Participants Receive Notifications from the Technologies. ........ 52

Figure 4.3: Type of Information Received by AMS Users. ............................................. 54

Figure 4.4: Types of Information Received by AHDS Users. ......................................... 55

Figure 4.5: Information Received by the Farmers from AAMS. ..................................... 56

Figure 4.6: Actions Taken by Farmers After Getting Alerts from AMS. ......................... 57

Figure 4.7: Actions Taken by Farmers After Getting Alerts from AAMS. ....................... 58

Figure 4.8: Benefits of PLF Technologies. .................................................................... 59

Figure 4.9: Challenges of PLF Technologies. ............................................................... 60

Figure 4.10: Summary Statistics of the Responses Related to Stress before Receiving a

Notification. ................................................................................................................... 65

Figure 4.11: Stress Scale vs. Number of Response (knowing they might receive a

notification). ................................................................................................................... 65

Figure 4.12: Summary Statistics of the Responses Related to Stress after Receiving a

Notification. ................................................................................................................... 67

Figure 4.13: Stress Scale vs. Number of Response (after receiving a notification). ...... 67

Figure 4.14: Reported Communication Mediums Used by Different PLF Technologies.

...................................................................................................................................... 70

xii

Figure 4.15: Summary View of Communication Mediums Use Aggregated across PLF

Technologies. ................................................................................................................ 71

Figure 4.16: Farmers Perception on Stress Because of Notifications. .......................... 73

Figure 4.17: Number of Positive Effects vs Negative Effects on the Dairy Farmers. ..... 74

Figure 4.18: Information Received by the Farmers from ACTDS. ................................. 76

Figure 4.19: Positive Effects of Sent Information from ACTDS on Farmers. ................. 77

Figure 4.20: Negative Effects of Sent Information from ACTDS on Farmers. ............... 78

xiii

LIST OF ACRONYMS

AAMS Automated activity monitoring system

ACTDS Automated calving time detection system

AHDS Automated heat detection system

AMS Automated/automatic milking system

CM Clinical mastitis

GDP Gross domestic product

GPS Global positioning system

GPS-CAL GPS calving alarm

HCI Human-computer interaction

OMAFRA Ontario Ministry of Farming and Agriculture

PCC Pocket Cow Card

PLF Precision livestock farming

RFID Radio Frequency Identification

VMS Voluntary milking system

xiv

LIST OF APPENDICES

APPENDIX A: Survey Questions and Responses…………………………………… 129

APPENDIX B: Research Ethics Approval for the Study…….…………..…………… 166

APPENDIX C: Email and Verbal Recruitment Script………………….……………… 167

APPENDIX D: Letter of Information and Consent Form………..……….…………… 168

APPENDIX E: Sample Python Code to Calculate Correlation Matrix ……………… 171

APPENDIX F: Interview Questions ………………………………..……….…………… 174

1

Introduction

There is a universal trend towards streamlining and enhancing farming practices in both

livestock and crop farming by utilizing various state-of-the-art technologies to automate

different farm processes. This modern style of farming is commonly called precision

agriculture (Bongiovanni & Lowenberg-Deboer, 2004) and precision livestock farming

(PLF) (Berckmans, 2014), referring to crop and livestock farming respectively. PLF

involves the integration of software and hardware technologies that offer easier animal

farm management by monitoring individual animals 24/7 on farms (Wathes et al., 2008).

PLF technologies aim to improve the health and welfare issues of animals by reporting

abnormal issues of any animal to farm staff when any abnormality arises so they can

take appropriate actions based on the reported issue (Berckmans, 2014). Thus, farmers

can manage larger herds with reduced physical workload and labour costs and,

consequently, helping to meet the global demand of increasing herd sizes to feed

increasing world populations (Rutten et al., 2013).

PLF technologies use sensors to monitor animal behaviours and the state of the farm

environment, process the information using algorithms, and generate reports or send

alerts to the farm staff if necessary (Halachmi et al., 2019). However, in order for PLF

technologies to be effective, they must transform the raw data they collect into

meaningful information that is communicated to farmers in a form and manner that can

be utilized effectively in their decision-making processes related to farm operations or

animal care (Halachmi et al., 2019). Information from PLF technologies are often

2

automatically sent to farm staff in the form of notifications or alerts through various

communication mediums (e.g., automated phone call, text message, dashboard

application software) to make staff aware of new or ongoing animal or farm environment

situations.

Prior research from Europe has shown that such automated communications can be a

source of stress and can increase the mental workload for farm staff if the

communications are not designed or managed effectively (Désire C & Hostiou, 2015;

Hansen, 2015). Ndour et al.’s (2017) recent review of advanced dairy farm technologies

concluded that “mental workload (stress) can sometimes be increased [when PLF are

used] due to the complexity of the information involved in managing the multiple alarms

or alerts and equipment failures…if the tools are not adapted to farmers’ needs and

skills, PLF can also lead to negative impacts on farmers and animals.” (p. 273).

To date, no studies have investigated whether automated communications from PLF

technologies can be a source of stress for dairy farmers in Canada. As farming cultures,

practices, and technologies often differ from region to region, it is unclear whether the

existing European studies generalize to Canadian dairy farmers. Thus, this research

focuses on investigating the notification mechanisms of dairy PLF technologies being

used in the Canada to understand the effects of these mechanisms on Canadian dairy

farmers. As various terms can be used by PLF technology manufacturers, and within

the farming industry, for information automatically sent from PLF technologies to

farmers, such as alerts, alarms, warnings, messages, and communications, throughout

the thesis any of these different types of automated communications will be referred to

as notifications, alerts, or communications, interchangeably.

The first step of the research involved a systematic literature review of current

notification mechanisms of dairy PLF technologies to understand the current state-of-

the-art and to help inform the survey design. The next step of the research involved

conducting an online survey with dairy farmers in the local farming region from Ontario,

3

Canada. The originally planned survey data collection, and follow-up interviews, were

cut short due to disruptions in participant recruitment channels due to the COVID-19

pandemic, which forced university research disruptions as well as agri-food related

disruptions in Canada beginning in March 2020. More details about this disruption are

provided in Chapter 3. Due to this disruption, this thesis reports on the data that was

collected before the project shutdown, and due to the limited scope of data collection, is

considered a pilot study for the originally intended larger study. Despite its limited

scope, it still provides some interesting insights on the effect of the notification

mechanisms used in existing dairy PLF technologies, as well on dairy PLF technologies

in general, on the surveyed Ontario farmers.

The systematic literature review found that dairy PLF technologies communicate with

farmers through various mediums such as text messaging, phone calls, emails, and

dashboards in computer applications provided with the installed technologies. The

online survey revealed that the existing communication mechanisms between dairy PLF

technologies and farmers are not perceived as a significant source of stress. Instead,

those technologies were generally reported to make the farmers’ lives less stressful and

provide positive labour-related benefits (reduce labour costs, improve appeal of farm

work, reduce difficulty of farm work). However, the survey also revealed that those

technologies can still sometimes be a source of stress or anxiety for dairy farmers due

to the timing and medium of communication, false alarms, missed alerts, unclear

messages and actions, and unnecessary communications. The pilot data provide some

initial insights into areas for design improvements and directions for future work to better

understand how to meet the informational needs of Ontario dairy farmers. This chapter

provides more details on the motivation for this thesis research, the specific research

question, objectives, and scope of the research, and finally the organization of the

research.

4

1.1 Motivation

Due to expected world population growth that may surpass 9 billion by 2050, experts

estimate that food production will need to be increased by 70 percent to meet the food

demand of future populations (Godfray et al., 2010). Increased competition from global

sourcing of the food supply, rising global demand for food products, and consumer

demand for safe and healthy food products are among many factors driving the

adoption of advanced technologies on Canadian farms (Hendrawan, 2005). PLF is a

rapidly growing research field and industrial sector focused on creating new innovative

technologies to enhance the animal farming (or livestock farming) industry (Banhazi et

al., 2012). The objective of PLF is to evolve technologies for farmers, their animals, and

farms to ameliorate animal health and welfare issues, and improve interaction between

animals and their caretakers using various integrated software and hardware state-of-

the-art technologies (Banhazi et al., 2012). Sensor-based systems in PLF can measure

physiological or behavioural states in animals, interpret detected changes in an animal,

give advice to farmers about the status of an animal’s health and welfare based on

available sensor data and other sources of available data, and, in some cases, can

automatically make decisions on behalf of the farmer (Zafar et al., 2014). As a result,

PLF technologies can reduce labour cost by reducing physical labour (Ndour et al.,

2017; Steeneveld & Hogeveen, 2015). However, recent research has shown that the

adoption of PLF technologies is not yet sufficient to increase food production globally

(Duncan, 2018).

PLF technologies are operated, used, and accessed by the farmers, but farmers’ voices

are seldom heard when those technologies are designed and developed (Hartung et al.,

2017). Recent research shows that current PLF research tends to be technology-centric

instead of being client or farmer-centric (Jago et al., 2013). This issue may create a

barrier for adoption of PLF technologies, if farmers perceive that the technologies do not

meet their needs. Technologies that are not as effective or usable as they could be may

5

also limit the gross profit from farms (Duncan, 2018; Mancini, 2017). Evidence is

emerging from the PLF literature that many PLF innovations are difficult to use and

learn. For instance, a review of livestock farm environment controlling systems (e.g.,

heat and moisture controlling systems) found that managers of these systems were

unable to fully utilize the system due to the complexity of the user interfaces of the

systems and information overload from the systems (Laberge & Rousseau, 2017). This

usability issue made it difficult for farmers to identify what information was most relevant

for their decision making (Laberge & Rousseau, 2017).

A study of PLF deployment in seven dairy farms across Europe found that the farmers

did not mind receiving important and urgent messages about the health and welfare

issues of an animal in real-time, but they found it very frustrating when a system sent a

notification to them about insignificant issues at awkward times, such as the middle of

the night (Hartung et al., 2017). A review on prioritizing alarms found that PLF systems

sometimes generate a high number of false alarms, which can affect farmers’ adoption,

use, and trust in PLF technologies (Dominiak & Kristensen, 2017). Therefore,

notifications management is an essential usability issue that has been identified.

Many PLF articles mention collecting data regarding animal health and welfare issues,

processing those data, and then sending the data to the farmers in the form of

notifications to assist farmers in their decision making (Jago et al., 2013; Rutten et al.,

2013; Wolfert et al., 2017). However, few papers mention specifically how those

notifications were sent to the farmers (Calcante et al., 2014; Hansen, 2015).

Motivated by the recent research that indicated the potential for negative impacts on

farmers, and the lack of details in the literature about how PLF technologies

communicate information to farmers, this thesis aims to better understand what types of

notification mechanisms are being used in current dairy PLF systems. For instance, how

do dairy farmers receive information from the PLF technologies, and what opportunities

6

exist to improve the usability and utility of PLF notification mechanisms using a human-

centered (or farmer-centered) design approach?

1.2 Research Questions, Objectives and Scope

The underlying goal of this research is to shed light on the notification mechanisms of

precision dairy farming technologies that send information about the animals they

monitor to farmers regarding the ongoing situation of the animals on their farms from a

human-centered design perspective using methodologies from the field of human-

computer interaction (HCI). This research aims to understand how to develop more

usable, and effective dairy farming technologies for farmers in terms of their

informational needs. The research also explores how PLF notifications affect dairy

farmers by investigating automated communications to farmers, the communication

mediums used for notifications, the frequency of the automated communications, and

whether any stressful situations arise for farmers due to these notifications.

The results of this research may help precision dairy technology researchers and

practitioners consider more user-centric designs that better meet farmers’ informational

needs and fit into their farming practices and culture. Moreover, it may identify new

opportunities for PLF researchers to improve precision dairy technologies.

To achieve these goals, my specific research question is: “What effects do current

notification mechanisms from dairy precision livestock farming technologies

have on dairy farmers?”

To address the research question, the following research objectives were identified:

1) To understand the current knowledge base regarding PLF notifications in dairy

and other livestock farming, their usability, and known effects on farming

practices by conducting a systematic literature review. The reviewed papers were

7

analyzed from the perspective of understanding the HCI issues of the existing

PLF notification mechanisms.

2) To understand what notification mechanisms are used by PLF technologies

currently deployed on dairy farms in Canada, including what communication

mediums are used for notifications, what informational content is contained in

notifications, what actions farmers perform based on received notifications, and

what are the perceived benefits and limitations are, including stress-related

effects, of current notification mechanisms. This objective was addressed by

conducting an online survey study of dairy farmers in Canada.

3) To identify opportunities to improve the design of notification mechanisms for

precision dairy technologies. This objective was addressed by including relevant

probes in the online survey study, reflecting on patterns revealed by the survey

study data that suggested design flaws, and incorporating research and design

concepts from the broader technology and HCI literature that provide relevant

design alternatives.

The outcomes of this research will shed light on the effect of current automated

notification mechanism on dairy farmers and provide insights on improving future

notification mechanisms for precision dairy farming technologies.

To scope this thesis research, the online survey targeted Ontario farmers as a pilot

study representing Canadian dairy farmers because Ontario farmers represent a

significant portion of the Canadian dairy industry. A recent farm census survey found

that about 320,400 dairy cows are managed on Ontario farms, which is about 32% of all

dairy cows in Canada (968,700) as of 1st July 20191. Surveying farmers in my local

province also allowed me to utilize distribution channels through the University of

1 https://www.dairyinfo.gc.ca/eng/dairy-statistics-and-market-information/farm-statistics/farms-dairy-cows-and-

dairy-heifers/?id=1502467423238

8

Guelph’s agricultural and dairy alumni and strong dairy livestock connections, and local

connections to the Ontario Ministry of Farming and Agriculture (OMAFRA). Finally,

recent research found that there has been a widespread adoption of precision farming

technologies in the Ontario dairy industry (Duncan, 2018). For example, the adoption of

Automatic Milking Robots has skyrocketed by 400% within the last three years. Thus, it

was reasonable to expect that there would be eligible farmers available locally to

participate in the survey who had experience with PLF technologies and with managing

communications from automated notification systems.

1.3 Thesis Outline

This thesis comprises six chapters, including this one. The remaining chapters are

structured as follows. Chapter two briefly overviews the literature on precision dairy

farming technologies and prior studies on their effects on farmers. Chapter three

presents the study methodology, including the survey design, survey distribution

process, description of the participants and data analysis techniques. Chapter four

presents the findings from study objectives perspective. The survey data were analyzed

using Qualtrics®, and correlation analysis was done using Python programming (Python

2 library named ‘pandas’). Chapter four includes results related to the understanding of

current automated notification mechanisms, benefits and challenges of automated

communications and effects of automated communications on the farmers. Chapter five

discusses the findings based on the research question of this study. It reflects on the

research question related to the understanding of current notification mechanisms,

information-sending approaches used in existing technologies, benefits and limitations

of notifications and their information content for the farmers, and farmers’ suggestions

for improving current PLF notification mechanisms. In Chapter six, the scholarly

contributions of the thesis are summarized, the limitations of the work are discussed,

along with the research challenges that were encountered, and recommendations are

made for future work.

9

Background

To provide context for this research and understand the current state-of-the-art in the

notification mechanisms used in PLF dairy systems and their effects on farmers, a

systematic literature review was conducted. This review is focused on understanding

the available technologies in the literature and those commercially available that present

information to the farmers. It also aims to uncover any known effects on farmers and

farm operations from the information presented and the mechanisms used to present

that information. This chapter discusses the gaps in the community’s knowledge of

these issues, and how this thesis research contributes to fulfilling these research gaps.

2.1 Systematic Literature Search

A systematic literature review was conducted in July 2019 and utilized the Web of

Science2 database. The specific search terms that were used are depicted in Table 2-1.

The following query in Web of Science by combining two groups of keywords was used

to retrieve literatures.

Query: TS= (dairy farm OR dairy) AND TS= (notification OR alert OR alarm OR warning

OR inform farmer OR message farmer OR precision livestock farming OR precision

dairy farming OR precision dairy technology OR precision dairy technologies)

This query in the Web of Science yielded 876 publications.

2 webofknowledge.com

10

A new literature search was conducted again in July 2020 because there might be new

literature out there by this time. To mitigate this literature gap, the same query was used

in Web of Science3 to search new literature and found 204 new literature in 2019 and

2020.

Table 2-1: List Of Keywords Used to Search for Literature in Web of Science.

Keyword Group 1 Keyword Group 2

dairy farms dairy

notification alert

alarm warning

inform farmer message farmer

precision livestock farming precision dairy farming

precision dairy technology precision dairy technologies

2.1.1 Inclusion and Exclusion Criteria

At first, all titles of the selected articles were reviewed and if they were not obviously

related to technologies on dairy farms, they were excluded. For the remaining, included

articles, the abstracts were reviewed for the following inclusion criteria: 1) article was

about technologies on dairy farms, and 2) article discussed notification mechanisms on

dairy farms, or 3) article discussed impacts of PLF notifications on the farmers. Those

articles that remained after the abstract screening were fully reviewed to determine their

relevance. During a full article review, the article’s related cited and citing articles were

also reviewed to uncover other potential articles that met the above-mentioned criteria.

3 webofknowledge.com

11

After this snowballing process, a total of 48 papers were selected to include in the final

review.

The same procedure was applied for screening the papers found in the literature

search conducted in July 2020. After the title review, 35 papers were selected. While

reviewing the abstracts, some papers were excluded which were already included in the

earlier literature search because there was some overlap. After the abstract review, 15

papers remained. Those articles that remained after the abstract screening were fully

reviewed to determine their relevance and 9 papers remained. For the 9 final selected

papers, those papers that were cited in the selected papers were also checked for

inclusion while going through the articles, resulting in an additional 6 papers added to

the final set. Finally, 15 articles were selected to include in the second review.

2.2 Overview of PLF Technology

Figure 2.1 shows a conceptual diagram for PLF systems. There are four stages in PLF

systems, including Data Collection using different type of sensors, Data Processing to

remove noise and unnecessary features from data, Detection or classification of some

potential health or welfare concern using algorithms such as machine learning

algorithms, and Inform the farmer in some way such as through phone calls, text

messaging, emails and generating reports. Several technologies and techniques have

been used so far for data collection, data processing, abnormality detection and

informing farmers (Cabrera et al., 2020; Eckelkamp, 2019; Ferris et al., 2020; Newton et

al., 2020). As this research focused on the “Inform” stage of the PLF cycle, the reviewed

articles focus on the technologies and techniques involved in this stage.

12

Figure 2.1 : Conceptual PLF system model. The system involves four distinct

phases, including: 1) Data Collection, where the system collects data about the

animals or farm environment using different sensors; 2) Data Processing, where

the system removes noise and unnecessary features from the data; 3) Detection /

Classification, where potential health or welfare concerns are identified using

algorithms such as machine learning; and 4) Inform, where the system notifies

the farmer of the identified issues to they can respond appropriately. (Acronyms

used in the figure: CS: Computer Scientists, AS: Animal Scientists, ACI: Animal-

Computer Interaction, HCI: Human-Computer Interaction.)

The articles which discussed the impacts of those technologies and impacts of

generated alerts from those technologies on dairy farmers were also reviewed. The

main purpose of using PLF technologies is to monitor farms automatically and inform

farmers if any issue is detected or any action is required from farmers. Research has

13

shown that PLF technologies reduced farmers’ workload (Allain et al., 2016; Ndour et

al., 2017). However, research has also shown that farmers can feel stressed and

overwhelmed because of false and unnecessary notifications received from these

technologies (Dominiak & Kristensen, 2017; Hansen, 2015; Schewe & Stuart, 2015). If

the usability of PLF notification mechanisms are not studied, PLF technologies may be

the source of increased mental workload despite of decreased physical workload. The

following section discusses some PLF technologies and their impacts. An overview is

given for each technology, and its data collection techniques, notification mechanisms, if

known, and any known effects of its notifications are discussed.

2.2.1 Impact of Automated Milking Systems

Automated Milking Systems (AMSs) are used to automatically milk cows on a farm (Tse

et al., 2017). An AMS can recognize an individual cow using the cow’s radio frequency

identification (RFID) tag (attached to their ear) and then use a robotic arm to

automatically affix to the cow’s udders and complete the milking process. By

recognizing each individual cow registered with the system, the AMS can determine if it

is the ideal time to milk that individual cow, or whether it is too early for further milking

(typically cows are milked twice, or during certain phases of pregnancy, three times a

day). While the cow is being milked at the AMS, the AMS dispenses high quality feed to

improve the cow’s experience at the AMS.

An AMS has various sensors which are integrated with the milk collection container and

robotic arm to guarantee quality milk since it will spontaneously check the udder and

milk contents to find irregularities inside the milk while milking and after every session of

milking (Tse et al., 2017). AMSs also can detect certain health issues based on a

content analysis of the milk, including the color of milk, amount of milk, milk fat, milk

protein, and presence or number of certain biologicals in the milk (King & DeVries,

2018). AMS can ensure a safe amount of milk yield so that milk production is

14

maximized, and so the cow is also in good health (King & DeVries, 2018; Tse et al.,

2017). Vital attributes of AMS are to collect data, interpret those data, generate reports

to assist farm management, and create alerts for detected health issues to send to farm

staff (King & DeVries, 2018).

Research has shown that the information can sometimes be overwhelming for farm staff

because AMSs can produce unnecessary alerts (false positives) or sometimes the AMS

detection methods are not sufficient to generate a notification when necessary (false

negatives) (King & DeVries, 2018). A study of AMS systems in Norway found that

farmers adopt AMS to reduce workload and maximize flexibility (Hansen, 2015). The

study also found that farmers also receive a large amount of information about every

cow, and this can be overwhelming for farmers. Moreover, the study found that the

information provided by AMSs were difficult for farmers to use for farm decision-making

and farm operations. In terms of notification mechanisms, the study found AMS

notifications were sent through phone calls. In the initial stages of AMS adoption,

farmers reported feeling stressed because of unnecessary alerts, but with the time

farmers learned how to respond effectively after getting a notification (Hansen, 2015).

Another source of stress found in the study was the feeling farmers had that they were

always on duty because they could receive a notification from their systems at any time,

day or night (Hansen, 2015).

Sending notifications to farmers 24/7 is a very common feature of an AMS. The

commercial AMS sold by DeLaval® (DeLaval VMS™4) has an affiliated mobile software

application for smartphones and tablets, called AMS NotifierTM5, which interfaces with

4 DeLaval®uses the term “voluntary” milking system, ; DeLaval VMSTM product site: https://www.delaval.com/en-

us/our-solutions/milking/vms-series/

5 DeLaval VMSTM app site on Google PlayTM store:

https://play.google.com/store/apps/details?id=com.delaval.ams.notifier&hl=en_CA

15

the AMS to receive its notifications. In the application dashboard, a user can see all the

notifications received in the past (see Figure 2.2). By selecting a specific alert, a user

can check more details about the alert. A user can deactivate notifications for a certain

period whenever they do not want to receive alerts, (e.g., overnight, 11:00 pm - 5:00

am). However, if there is an emergency the user will still receive notifications of high

priority during this deactivation period.

Figure 2.2: Sample Received Messages by Farmer from AMS through Mobile

pplication (AMS NotifierTM, DeLaval®)6.

The Herd Navigator™ (©Lattec I⁄S, Hillerød, Denmark) is used to measure milk

progesterone which can be combined with DeLaval®’s AMS to monitor milk quality

more precisely (M. Saint-Dizier & Chastant-Maillard, 2012). Another commercial dairy

6 App site on Google PlayTM store:

https://play.google.com/store/apps/details?id=com.delaval.ams.notifier&hl=en_CA

16

supplier, Lely®, also has a mobile software application affiliated with its AMS, called Lely

T4C InHerdTM – Cow7, that receives notifications from Lely® AMSs and allows farmers

to check information of individual cows and perform necessary actions.

2.2.2 Impact of Mastitis Detection Technologies

Inflammation of the mammary gland in the cow’s breast or udder, usually because of

bacterial infection, is known as mastitis (Harmon, 1994). Mastitis is a noteworthy

disease that has vital effects economically on dairy farms (Hogeveen et al., 2011;

Nielsen et al., 2010). Mastitis can be the cause of diminished milk quality, reduced milk

production, increased death rates, and increased veterinary or treatment costs (Nielsen

et al., 2010). Discovering mastitis early can lower treatment cost and protect udder

health (Colak et al., 2008; Polat et al., 2010).

Several techniques have been proposed for automatic detection of mastitis. A study by

Khatun et al. (2018) of automatic clinical mastitis (CM) detection used electronic data

(e.g., electrical conductivity, milk yield, milk flowrate, occurrence of incompletely milked)

from AMS support software (DeLaval DelPro™8) to develop a statistical (logistic

regression) model for CM detection. Their model achieved 91% specificity and 90%

accuracy in generating CM notifications.

Kim et al. (2019) used artificial intelligence, specifically, deep learning, algorithms to

analyze data collected from ingestible bio-sensors to detect potential cases of mastitis.

7 Lely’s In-HerdTM-Cow App site on Google PlayTM store:

https://play.google.com/store/apps/details?id=nl.lely.mobile.infocard; Lely’s In-HerdTM application product site:

https://www.lely.com/us/solutions/lely-t4c/lely-t4c-inherd/

8 DeLaval®’s product site: https://www.delaval.com/en-ca/our-solutions/farm-management/delaval-delpro-

applications/

17

Once detected, a notification was sent to farmers through a mobile application that also

allowed farmers to check the cow’s body temperature in real-time upon receiving an

alert. Their system analyzed patterns in a cow’s body temperature to help detect

mastitis and to rule out other possible causes of temperature increases, such as eating,

estrus, or injury. In their study, which monitored 50 cows over six months in Korea, their

system automatically detected 14 of the 15 actual cases and successfully distinguished

between temperature increases due to mastitis and other causes.

Recent research proposed a technology model called ‘The Dairy Brain’ for mastitis

detection, similar to generalized PLF technology model proposed in Figure 2.1, in which

real time data of each individual cow is collected, analyzed using algorithms,

abnormalities are detected through decision tools, and farmers are notified about

mastitis if detected (Cabrera et al., 2020; Ferris et al., 2020). Upon receiving a mastitis

alert from any PLF technology, farmers monitor the cow’s health and welfare condition

in person (Steeneveld & Hogeveen, 2015) and call a veterinarian for thorough checkup

and proper treatment, if necessary. When a cow is under treatment, rumen temperature

and real-time temperature are continuously measured to observe the recovery of the

cow (Kim et al., 2019).

Sometimes mastitis detection systems generate false alerts and the alert timing is not

convenient for farmers. A study by Mollenhorst et al. (2012) that investigated farmer’s

preferences of mastitis detection systems design features found that farmers’ desire

fewer false alerts, alerts that will be delivered at appropriate times, and alerts that focus

on critical cases. It is irritating for farmers to receive false alerts from a system and it

may create negative impressions of the technologies. The timing of the first alert is

crucial for farmers because after receiving the alert farmers will start observing the cow

closely. Therefore, system should send alerts at an appropriate time based on the

urgency level. If an alert needs immediate action it should be sent immediately

regardless what is the time at that moment. The system should detect any abnormality

18

at early stage and send alerts to the farmers at appropriate times, so they have enough

time for treatment before the disease becoming severe (Mollenhorst et al., 2012).

2.2.3 Impact of Lameness Detection Technologies

Being “lame” refers to an animal being unable to walk without difficulty due to an injury

or illness affecting the leg or foot (Oxford English Dictionary online9). Lameness in a

cow is a common indicator of a wide variety of health disorders and can cause

economic losses for the farm (Kossaibati & Esslemont, 1997). Lameness is believed to

be the third most costly disease of dairy cows after mastitis and reduced fertility (Enting

et al., 1997). Therefore, detecting lameness at early stage is cost effective for the

farmers (Enting et al., 1997). Cattle behaviour, such as lying time and frequency,

feeding time, motion score based on overall movement of a cow, number of steps, and

standing time and frequency, can be used to detect lameness of a cow because a lame

cow will eat less, stand less and lay more (de Mol et al., 2013).

Several techniques have been used to automatically detect lameness, such as image

processing and infrared thermography, accelerometer sensors embedded in a wearable

collar or leg band, ground-based pressure or weight-based systems, feeding behaviour,

grooming behaviour, AMSs and milk production (Alsaaod et al., 2019). A recent study in

Brazil found that precipitation impacted normal lying behaviour, decreasing lying times,

and, thus, this factor should be considered in analysis of lying behaviour when studying

lameness (Thompson et al., 2019). Automatic detection of abnormal locomotion

behaviour (gait and posture) has also been used to detect lame cows (Van Nuffel et al.,

2015). A recent study by Taneja et al. (2020) in Ireland showed that a system collect

accelerometric data and use machine learning, data analysis tools, and cloud

9 https://www.lexico.com/en/definition/lame

19

computing to detect lameness. The system could detect lameness three days before it

could be visually noticeable to the farmers with 87% accuracy.

When lameness is detected in a cow, the farmer is informed through lameness alert

reporting about the specific cow on the specific day (de Mol et al., 2013). Taneja et al.’s

(2020) system integrated a mobile application through which farmers receive

notifications whenever a lame cow is detected. Lameness alerts can be translated into

user friendly reports and whenever farmers receive alerts regarding a cow, the cow is

observed in person to validate the automatic lameness detection (Alsaaod et al., 2019).

After validation, farmers check the cow and do the locomotion scoring (a scoring system

to rate the severity of injury) to update the locomotion score of the detected lame cow

(de Mol et al., 2013). Based on identification provided by the automatic detection

system, farmers can treat the cow, if needed (Alsaaod et al., 2019).

2.2.4 Impact of Estrus Detection

Dairy farms rely on the breeding cycle of cows for their milk production (De Vries, 2006).

According to Dairy Farmers of Ontario, a cow produces milk for about ten months after

giving birth to a calf, and then will stop producing milk for about two months in

preparation for the birth of her next calf (Farm and Food Care Ontario, 2012). A cow’s

gestation period is nine months. If a cow does not become pregnant again within the

first month or two after giving birth to a calf, then there may be a longer “dry” period

before the next calf is born and the cow can rejoin the milking herd (Farm and Food

Care Ontario, 2012). Thus, accurately detecting when a cow is in estrus (or in heat) and

is sexually receptive for breeding is an essential part of dairy farm operations (Fodor &

Ózsvári, 2019). Breeding on Canadian dairy farms is primarily done via artificial

insemination for simplicity and safety (for both farmers and the animals) (Farm and

Food Care Ontario, 2012).

20

Behavioural changes, such as increased excitability, aggression, or mounting other

cattle can happen during sexual receptivity (Reith & Hoy, 2018). Sexual receptivity can

last for 2h to 48h depending of the breed, and estrus can be detected by identifying

those behavioural changes with different intensity (Orihuela, 2000). Several techniques

are used to detect those behavioural changes. Example commercial technologies used

to detect estrus include:

• pedometers attached to the leg to monitor activity, such as Pedo-Plus (Afimilk®,

Israel) (Galon, 2010) and CowScoutTM Leg10 (©GEA Group, Germany),

• ingestible electronic “pills” called boluses that detects metabolic changes in the

cow’s gut, such as DVM Bolus (©DVM Systems, USA) (Dolecheck et al., 2015),

• collar-based multi-sensor (including accelerometer) systems that monitor

behaviour, such as Heatime® HR™ Tag (©SCR, Israel) (Dolecheck et al.,

2015), and

• accelerometer-based systems mounted to a cow’s ear RFID tag (Mayo et al.,

2019), such as CowManager®11 (Wisconsin, USA).

A study in Israel used accelerometers to detect estrus event of a cow (Valenza et al.,

2012). Alerts are generated from these technologies when estrus is detected and when

the system determines a cow should be inseminated. Moreover, some technologies

generate reports based on data analysis and later, those reports, and alerts are used to

observe a cow for insemination (Mayo et al., 2019).

A study by Talukder et al. (2015) comparing different estrus detection approaches (body

temperature detected by infrared thermography or accelerometer and acoustic sensing

10 https://www.gea.com/en/products/milking-farming-barn/dairymilk-milking-cluster/cow-sensor/activity-

detection-cowscout.jsp

11 https://www.cowmanager.com/en-us/Solution/Modules#fertility

21

(via a collar)) in a pasture context reported that alerts would be generated when estrus

was detected but the paper did not discuss how those alerts would be sent and who

would receive them. A study by Mayo et al. (2019), mentioned that all estrus detecting

technologies report data at specified times each day (twice in a day, every 2 hours,

every one hour, every 15 minutes, every 5 minutes) but how the data were reported was

not detailed.

2.2.5 Impact of Calving Time Detection

Calving time is the time when a cow gives birth to a calf. Detecting calving accurately is

very important for farmers to ensure the health and welfare of the mother cows and

newly born calves because crucial situations such as difficulties giving birth and death

of a calf can be experienced during calving time (Mee, 2004). It is even more important

to know the exact calving time and location of a cow when the cows are outdoor

(Calcante et al., 2014). There will be economic loss if a cow is not properly taken care

during calving because health hazards might occur to the newly born calf or mother cow

(Inchaisri et al., 2010). Some changes occur before calving such as hormonal changes,

relaxation of pelvic ligaments, tail relaxation, changes in the udder, decrease in body

temperature, vaginal temperature changes and other vaginal changes, lying time,

number of steps, feeding and rumination behaviour changes (Marie Saint-Dizier &

Chastant-Maillard, 2015).

Several commercial technologies are now available to detect approaching calving time,

including intra-vaginal thermometer, inclinometers and accelerometers to detect

behavioural changes, and abdominal belt to detect uterine contractions (Marie Saint-

Dizier & Chastant-Maillard, 2015). Similar to estrus detection technologies, when any

sign of calving is detected, the technologies send an alert to the farmers through text

messaging and/or phone call about the imminent calving (Marie Saint-Dizier &

Chastant-Maillard, 2015).

22

Recent research used the Afimilk Silent Herdsman® collar (now called AfiCollar®12) and

eating time and tail-mounted AxivityTM13 accelerometers to detect calving time (Miller et

al., 2020). They used a combination of sensors to collect rumination and eating time

data and tail raise events, and then used machine learning to identify calving events. A

study by Zehner et al. (2019) analyzed rumination time and jaw movement using a

Naïve Bayes classifier to detect calving time 1 hour prior to the actual calving time.

Vel’PhoneTM14 is commercial product from Denmark that uses a vaginal sensor to

remotely send a cow’s vaginal temperature to alert a farmer when it predicts calving is

going to start (Rutten et al., 2013). GPS calving alarm (GPS-CAL) was introduced by

Calcante et al. (2014) for detecting calving time outdoors. This device can identify the

exact calving time and inform farmers through SMS (text messaging). The message

contains the event hour and date, animal ID, and location of the animal. The farmer can

import the GPS location into a common map application on a mobile phone to get visual

instructions to help reach the cow quickly and then the farmer can take necessary

action(s) after reaching the cow (Calcante et al., 2014).

2.2.6 Effects of Activity Monitoring Technologies

Many technologies and techniques have been used to monitor activities such as lying,

rumination and standing time, heat events, and calving time of dairy cows to detect

abnormal behaviour of a cow (Løvendahl & Chagunda, 2010; Müller & Schrader, 2003;

Stangaferro et al., 2016; Vázquez Diosdado et al., 2015). Abnormal activity or changes

in normal behaviour is an indication of disease or events, such as calving or estrus

(Müller & Schrader, 2003). Historically, physical observation or video recordings were

12 https://www.afimilk.com/cow-monitoring

13 https://axivity.com/

14 http://velphone.dk/

23

used to monitor dairy cows, (Müller & Schrader, 2003; Schwarz et al., 2002). For

instance, a system integrating camera and software was designed in France to record

movement and interaction of cows during day and night using infrared camera (M.

Saint-Dizier & Chastant-Maillard, 2012). Those technologies can be time consuming,

labour intensive, and error prone (Müller & Schrader, 2003; Schwarz et al., 2002).

Therefore, automatic activity monitoring can help monitor cows 24/7, with significantly

less labour.

VIEWER is a software technology integrated with a camera to graphically present

behavioural situation of cows (Schwarz et al., 2002). Müller and Schrader (2003) used

accelerometers attached to the hind legs of cows and video recording to monitor cattle

activity. López-Gatius et al. (2005) used a pedometer to detect walking activity of cows.

An increasing trend in activity monitoring is to use sophisticated algorithms, and

increasingly, artificial intelligence to analyze patterns in the movement data collected

from accelerometers, GPS, and other sensors. Løvendahl’s and Chagunda (2010) used

a thresholding approach to detect pregnancy and estrus based on activity collected from

a commercial neck-mounted activity monitoring collar. A training period was used to

determine appropriate thresholds. Martiskainen et al. (2009) developed a multi-class

support vector machine algorithm to detect several behaviours (standing, lying,

ruminating, feeding, normal and lame walking, lying down, and standing up) of dairy

cows from collar-mounted accelerometer data. Tri-axial accelerometer data has been

used in a decision tree algorithm to identify biologically important behaviours (standing,

feeding, lying) and transitions between activities (lying and standing) (Vázquez

Diosdado et al., 2015).

In a study by Gonzalez (2015), a decision tree algorithm was used to classify

behaviours such as foraging, ruminating, traveling, resting and other active behaviours.

They used data from collar-mounted motion and GPS sensors for classification of the

above-mentioned behaviours. Data was collected from the sensors with high frequency

24

to increase the accuracy of the analysis and help to detect frequent changes of

behaviour. The use of the neck collar, as opposed to a leg-mounted sensor, allowed

them to detect differences in the body and neck positions and detect activity more

accurately.

The CowViewTM system is a commercial collar-based activity monitoring system that

monitors cow activity and fertility (Hartung et al., 2017; Tullo et al., 2016). GEA

CowViewTM15 (©GEA Group, Germany) can be used to detect heat for insemination,

mange overall health issues, observe behaviours, and manage workflow. As shown in

Figure 2.3, this system is designed to be an all-in-one solution for easy management of

a dairy farm (Tullo et al., 2016). It can automatically show the zone-related position of

each cow in the barn using a virtual map of the barn and monitors the activity of each

cow via collar-mounted tags (Hartung et al., 2017; Tullo et al., 2016). It can detect

movement and resting patterns which allow farmers to identify the time for insemination

and detection of lameness (Hartung et al., 2017). Its real-time monitoring can detect

abnormal issues and notify farmers by sending notifications to their smart phones,

including the cow ID. The data collected by the GEA CowViewTM system can be

accessed through a mobile app or viewed on a computer desktop. The cow with

detected lameness or heat can be observed immediately or during next milking time for

validation of detected situation (Hartung et al., 2017).

A study of European farmers’ experiences with the CowViewTM system by Hartung et al.

(2017) found that farmers were happy with the system because they can observe the

cows more accurately with less labour. However, they also found farmers were unhappy

with the high price of the system.

15 http://www.gea-cowview.com/

25

Figure 2.3: Effective Herd Management and Smooth Workflow Offered by GEA

CowViewTM (©GEA Group, Germany) by Sending Alerts to Farmers Regarding

Detected Issues16.

Dairycomp30517 (©Valley Agricultural Software, USA) is data management software

which is used in many farms to manage activities in the farms. As of 2012, this was the

most used data management software on dairy farms in the USA (Wenz & Giebel,

2012). The system can calculate a “cow value” that represents the value of a particular

cow in term of future profit versus being replaced by a heifer (Sorge et al., 2007). The

system calculate the “cow value” based on unusual activities such as lameness caused

by different kinds of diseases such as mastitis and metritis (Wenz & Giebel, 2012).

16 https://www.gea.com/en/binaries/DariyFarming_CowView_Brochure_EN_0315_tcm11-21890.pdf

17 http://www.vas.com/dairycomp.html

26

Figure 2.4: Sample Pocket Cow CardTM (PCC)18 to View Information of Cows.

Pocket Cow CardTM19 (PCC) is provided to users with Dairycomp305TM which can be

integrated with Android phones (Breedyk, 2010). The farmer needs to go near the cow,

scan its RFID tag and the system will display all the information about the cow on the

phone display. There are three modes of PCC such as read-only mode to view data of a

cow, read-write mode to view and update data of a cow and read-write-scan mode to

quickly view and update data of a cow by scanning RFID of a cow (Breedyk, 2010). In

future, this technology should send notifications to the farmers automatically when a

cow should be replaced by a heifer. Though the system provides all the necessary

information, yet farmers need to physically go near each cow to check whether a cow

should be replaced or not, which is labour intensive.

Most commercial activity monitoring technologies are integrated with software

applications to analyze the activity of a cow by comparing detected activity with

18 http://www.canwestdhi.com/images/pocket-cowcard.png

19 http://king.vas.com/pcc.jsp

27

expected activity of that cow (M. Saint-Dizier & Chastant-Maillard, 2012). Some

technologies trigger alerts if deviation in activity is detected for a cow. For example,

DairyPlanTM (©GEA Group, Germany) is a commercial technology to detect estrus

which send alerts when estrus is detected (Hockey et al., 2010). ThermobolusTM

(©Medria Solutions, France) and RadcoTM (©Verdor NV, Oelegem, Belgium) are

commercial technologies to monitor body temperature (M. Saint-Dizier & Chastant-

Maillard, 2012).

2.3 Are PLF Notifications a Source of Stress?

PLF technologies are used to reduce physical labour of farmers, yet at the same time,

alerts sent to farmers from PLF technologies can sometimes be a source of stress if the

alerts are not managed effectively (Ndour et al., 2017). Excessive and unnecessary

alerts can increase farmers’ mental workload (Hansen, 2015; Ndour et al., 2017).

Therefore, prioritizing alerts is necessary to make it easier for a farmer to know which

alerts require a response and how quickly they should respond (Hansen, 2015).

There are also situations where PLF technologies reduce farmer’s mental workload

because some health concerns that are not easily observable by the farmer can be

measured by the PLF technologies, such as vaginal temperature for artificial

insemination, body temperature, heart rate, and exact calving time (Allain et al., 2016).

Yet, even such useful notifications from PLF technologies may be stressful if not

received in an appropriate manner or at an appropriate time.

Thus, overall, information received from PLF technologies in the form of notifications

can sometimes be a source of stress for farmers if the notification mechanisms are not

well designed. This thesis looks at the experiences Ontario dairy farmers have with

existing on-farm notification mechanisms from PLF technologies, and the associated

level of stress, if any, farmers experience from these systems.

28

2.4 Information Overload

In general, the technologies mentioned above produce and accumulate a large quantity

of data, frequently for long intervals of time, and often in real-time (Schewe & Stuart,

2015). PLF researchers have started utilizing different big data processing methods for

analyzing various process-generated, machine-generated, and human-generated

assorted data associated with their farms (Wolfert et al., 2017). Notwithstanding, it may

be hard to use that information in decision-making due to disorderly and large amounts

of data. Research has shown that some technologies are complex for farmers to use

regularly as these technologies provide such large amounts of information without clear

recommended action (Borchers & Bewley, 2015; Russell & Bewley, 2013). Therefore,

that information cannot always be utilized in their decision-making processes (Schewe

& Stuart, 2015), which can negatively affect farmers’ view of PLF technologies

integration with their farms (Ndour et al., 2017).

Data presentation to users (farmers) is an important issue to keep in mind while

designing technology. Appropriate visualization techniques should be used to present

data to farmers so they can understand and interpret data easily to make effective

decisions for their farms and animals (Gutiérrez et al., 2019). A research group from the

University of Wisconsin-Madison proposed a system named ‘Dairy Brain’ which collects

data of cows from all perspectives, analyzes, and presents these data in a form that

farmers can use to make decisions (Cabrera et al., 2020). The core concept is to use

one technology to detect all animal and farm issues instead of a suite of specific

technologies designed a specific purpose to help reduce the complexity of farm

management (Cabrera et al., 2020). While developing PLF technologies in future,

experienced data science specialists should be hired to work on simple and appropriate

representation of data for decision making (Wolfert et al., 2017). The data displaying

and representing strategies can be evaluated by some data representation experts to

29

represent data in a more understandable way to farmers in future technologies (Russell

& Bewley, 2013).

2.5 Technologies are not User-centric

The PLF field is starting to observe and report that integration of these emerging

technologies on farms might be hampered by the absence of client-focused (ranchers

and animals) design since most PLF technology design is technology- and profit-driven

and not user-centric (Jago et al., 2013). Emerging research has discovered that some

PLF technologies provide restricted utility to farmers and the interface of some

technologies are largely dissimilar to the interfaces of other similar traditional

technologies (Jago et al., 2013). As a result, these technologies are not as beneficial to

farmers in their farm management decision-making as they were designed to be

(Laberge & Rousseau, 2017). Thus, standard guidelines must be set up to guide the

structure and incorporation of different PLF technologies for more effective decision-

making by farmers to ensure that PLF technologies provide the highest benefit to

farmers (Dudi, 2005). A PLF adoption study in Australia found that farmers believe that

involving them more directly in the design process of PLF innovations would help to

make PLF technologies easier to learn and use, and to better address their farm

operation needs (Jago et al., 2013).

2.6 Importance of Proper Notification Management

From an HCI perspective, it is very important to send notifications at an appropriate time

through an appropriate medium with relevant information (Mehrotra et al., 2015). A

person might receive many notifications from various sources such as text messaging,

social media, emails, and mobile applications (Pielot et al., 2014). Receiving excessive

notifications can reduce a person’s productivity and can impact negatively on

psychological well-being. For instance, notifications can cause inattention and

30

hyperactivity-symptoms related to Attention Deficit Hyperactivity Disorder (Kushlev et

al., 2016). In healthcare settings, excessive alerts from medical devices can cause

healthcare workers to disable, silence, or ignore device alerts due to “alert fatigue”,

leading to critical situation being missed (Graham & Cvach, 2010).

A notification has two sides, such as the useful information it provides to the users and

the interruption users experience in their ongoing tasks (Bailey et al., 2000; Czerwinski

et al., 2002; Kern & Schiele, 2003). Notification senders usually do not consider the

interruption that users might feel upon receiving the notification though those senders

have the intention to help users by providing information (Adamczyk & Bailey, 2004). A

user may receive a notification while doing an important or complex task that needs

intense user attention.

Sometimes notifications can have negative effects on the user’s emotional state (Bailey

et al., 2001; Zijlstra et al., 1999). Moreover, receiving an irrelevant or non-urgent

notification can distract the user from an important task and which might lead the user to

not do the task properly (Zijlstra et al., 1999). For instance, receiving a non-urgent

notification while doing a decision-making task can cause a person to make a poor

decision (McFarlane & Latorella, 2002; Speier et al., 2003).

Due to the fact that farmers often adopt different types of PLF technologies, they may

receive notifications from various sources, and receive these notifications while doing

other important work. Receiving an irrelevant notification or excessive notifications while

doing important tasks may reduce their overall productivity or work quality and may be

irritating for them as well. Therefore, notifications from PLF technologies should also be

effectively managed so the farmers receive only relevant notifications through

appropriate mediums at appropriate times.

Several techniques have been proposed in the HCI literature to help users manage

notifications sent from various sources. For instance, notifications can be classified

31

based on their level of importance. The timing a notification is sent can be decided

based on the importance of the notification (Dabbish & Baker, 2003). One project

developed a system named ‘Phylter’ which used physiological sensing to understand

the cognitive state of the user and decide whether to send a notification to the user at

that moment or not (Afergan et al., 2015). Another project developed a system named

‘Scope’ which displayed notifications to the user in different areas of a display, based on

the importance of a notification from various sources (Van Dantzich et al., 2002). The

more important a notification was the more towards center the notification would be

displayed. Different visual icons were used to highlight important part of a notification to

attract the user’s attention towards the important section of the notification.

Another approach is to allow users to modify the system settings to determine the timing

of receiving a specific notification (Kushlev et al., 2016). Kern and Schiele (2003)

proposed a system that decided whether or not a notification would be sent to the user

based on the importance of the notification, the activity of the user, the social activity of

the user (talking with someone), the social situation (in a bus), and location of the user.

They noted that only one factor alone might not be enough to make best decision, thus,

multiple factors could be used to make an appropriate decision.

The study conducted in this thesis found some of the challenges noted by prior

notification researchers, including sometimes excessive or irrelevant notifications sent

from PLF technologies, and notifications being sent at undesirable times. These findings

are discussed in Chapters 4 and 5.

2.7 Summary

From the above discussion, it can be summarized that an ideal PLF technology will

collect data, process the collected data, decide based on analysis using some decision

tools or algorithms, and notify farmers if necessary. This study focused on the last part

of PLF technologies which is notifying farmers. All the parts of PLF technologies are

32

equally important because without proper data collection and analysis, an effective

decision cannot be made. The notifications are also important because based on the

received notifications farmers decide what to do with their animals or farms. Therefore,

proper notification management is important in PLF technologies.

This chapter focused on PLF technologies in development and available commercially,

and when possible, discussed the notification mechanism of these technologies and

their known impacts. From the literature, it is found that technologies send notifications

to the farmers with useful information. However, it is also observed that the technologies

sometimes send false and excessive information. As a result, farmers can feel stressed

and inundated. Also, technologies send notifications through inappropriate mediums at

inappropriate time. Consequently, farmers might miss urgent alerts or be interrupted by

unnecessary notifications while doing an important task. Therefore, this research

investigated the current notification mechanism to understand the benefits and

challenges of it. The study also provided some strategies to overcome the challenges to

make PLF technologies more usable, effective, and less stressful.

33

Research Methodology

This chapter describes the methodology used in this research. The first section

describes the context of the province of Ontario as the target region for understanding

the effects of PLF notifications in the broader landscape of Canada. Then, the study

methodology is described, including the survey design, procedure, and distribution

techniques. The originally planned interview is also described. A summary of the survey

participants is provided, and the chapter finishes by describing the quantitative and

qualitative data analysis techniques used in the study.

3.1 Regional Context

Canadian dairy industries contribute around $18.9B annually to Canadian gross

domestic product (GDP) and $3.6B in taxes annually, and create roughly 215,000 full-

time job opportunities (Dairy Farmers of Canada, 2016)20. There were 10,371 dairy

farms in Canada and 968,700 dairy cows managed on those farms in 2019. The

provinces of Quebec and Ontario are significant dairy delivering regions, producing 37%

and 33% of Canada's milk supply with 4,925 and 3,446 dairy farms respectively

20 https://www.dairyfarmers.ca/annualreport/wp-content/uploads/2016/01/2015-2016_Dairy_Sector_Issues.pdf

34

(Canadian Dairy Information Centre, 2019)21. Therefore, dairy farms in Ontario provide

significant contributions to the Canadian GDP and economy.

By and large, 66% of the output from Canadian dairy farms is sold as crude milk

whereas the remaining 33% is refined into other dairy items, for example, cheese,

butter, and processed milk (Vergé et al., 2013). Dairy production provides a vital

contribution to Ontario’s gross agricultural production. Dairy commodities are the

dominant products produced by Ontario according to an analysis of market receipts

(OMAFRA, 2016)22. There is a growing trend in Canada for the number of dairy farms to

decrease year over year, whereas the average size of remaining farms is increasing

with the average farm size being 89 cows (Dairy Farmers of Canada, 2019)23. On the

other hand, the precision farming technology integration on dairy farms is also rising day

by day to ensure increased quality dairy production by farms. For example, a survey of

Ontario dairy producers reported that the number of AMS integration on the farms has

skyrocketed to 400% within last three years (OMAFRA, 2016)24. This extensive amount

of precision farming technology integration on farms has sown the seeds for new

opportunities to explore the effects of those recently integrated technologies on dairy

farmers (Kutter et al., 2011).

Ontario provides a fascinating prospect to investigate the integration of PLF

technologies on the dairy farms with a specific spotlight on the effect of those PLF

technologies on the dairy farmers. Figure 3.1 shows that Ontario dairy farms have a

vital contribution to the Canadian dairy industry. This driving, vigorous and diverse

21 https://www.dairyinfo.gc.ca/eng/dairy-statistics-and-market-information/farm-statistics/farms-dairy-cows-and-

dairy-heifers/?id=1502467423238

22 http://www.omafra.gov.on.ca/english/stats/agriculture_summary.htm#income

23 https://dairyfarmersofcanada.ca/en/canadian-dairy-farms-come-all-sizes

24 http://www.omafra.gov.on.ca/english/livestock/dairy/facts/surveyrobotic.htm

35

regional context contributed very significant qualitative and quantitative data to get the

sense of Canadian dairy industry and dairy industries across the world for this research.

Figure 3.1: Number of Farms, Dairy Cows and Dairy Heifers (Canadian Dairy

Information Centre, 2019)25

25 https://www.dairyinfo.gc.ca/eng/dairy-statistics-and-market-information/farm-statistics/farms-dairy-cows-and-

dairy-heifers/?id=1502467423238

36

3.2 Study Methodology

The originally planned study involved two phases of data collection including conducting

an online survey and conducting phone interviews. Unfortunately, due to complications

arising from the COVID-19 pandemic that forced major societal disruptions during the

first phase, only the online survey phase was partially completed and phase two was

cancelled completely. Data collection for the online survey was in progress when

complete societal shutdowns occurred due to the COVID-19 pandemic in March of

2020. This shutdown introduced many challenges for recruiting survey and interview

participants in the farming sector, as they were dealing with many complexities of

redesigning their production processes to maintain essential food supplies during the

pandemic. Thus, data collection was halted out of respect for the challenges farmers

were experiencing. To address the research goal, both qualitative and quantitative data

were collected. This chapter presents the methodology for both phases, but only results

from the partial phase one data collection are presented in Chapter 4. Due to the

cancelled interviews, fewer qualitative data were collected than planned. However, the

survey still provided some initial opinions and experiences with current notification

mechanisms of PLF technologies on dairy farms. Both the survey and interview

methods were reviewed and approved by the University of Guelph’s Research Ethics

Office (REB# 19-10-006).

3.2.1 Survey Design

Survey is the most frequently utilized technique to gather a wide range of input from a

targeted population (Kabir, 2016). Thus, this form of data collection was selected for

data collection from dairy farmers. The initial survey questions were made based the

knowledge from literature review and online research of the dairy and PLF fields. These

survey questions were reviewed by HCI experts to get initial feedback on survey design

and usability. A revised set of survey questions were then circulated to a dairy research

37

chair in the School of Animal Biosciences at Guelph and a dairy industry expert from the

Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA), who both work

closely with the dairy industry to get their feedback on the survey content and design.

Their feedback was used to create an amended set of survey questions.

The survey consisted of a total of 63 questions, with 7 questions related to different

aspects of the informed consent process (and participation eligibility). The remaining, 56

questions were used to collect the main survey data. In practice, as expected from the

background research, most participants only saw a subset of these questions,

depending on which technologies they were using on their farm. In fact, only one

participant reported using four technologies (mentioned below), and therefore, was

exposed to all questions. Also, the questions in each of the technology sections were

highly similar, facilitating survey completion. Therefore, after completing one section,

subsequent sections were not expected to take as long to fill out. The average time was

15.5 minutes to complete the entire survey, which was below the expected 25-30

minutes.

The survey had six main sections. The first section collected demographic information

about the participants. The next four sections related to the current notification

mechanisms used in each of the following commonly used dairy PLF technologies:

- Automated milking system (AMS),

- Automated heat detection system (AHDS),

- Automated calving time detection system (ACTDS), and

- Automated activity monitoring system (AAMS).

At the beginning of each of these sections, the participant was asked if they used the

specific technology on their farm and whether they received notifications from it. If they

38

responded yes to both questions, then a longer set of questions probing deeper about

their experiences with notifications from that technology were shown. If the participant

answered no to either question, then the survey would skip to the next section. The set

of questions in each section were designed to be as similar as possible, to enhance the

usability of the survey. They included questions related to duration of using a

technology, number of messages they receive from the technology on a typical week,

medium of receiving those alerts, type of information they receive, actions they perform

after receiving alerts, pros and cons of the current notification mechanism and their

comments on the current notification mechanism.

The final section of the survey probed any additional technologies they may use and

receive notifications from on the farm, about the overall stress caused by notification

mechanisms of their PLF technologies, and, finally, any overall comments or

suggestions they may have about improving PLF notification mechanisms.

The full list of survey questions is included in Appendix A.

The survey was implemented using Qualtrics®, an online platform to create and

distribute survey. Qualtrics® has some built in tools which offer data analysis after data

collection and that saved time for data entry and data transfer since data collection and

analysis is performed in the same platform. Collected survey data helped to explore

current notification mechanisms of PLF technologies on the dairy farms and provided

insights from farmers on the improvement of current notification mechanisms, as will be

discussed more in Chapter 4.

3.2.2 Survey Procedure

Survey distribution advertisements contained a link to the survey or a barcode or both.

Scanning the barcode or clicking the link navigated the interested participants to the

survey page. When a participant navigated to the survey page, the consent summary

39

would appear first, and the link of the full consent was also provided with the summary.

The first question was whether a participant agrees with the consent or not. This

question was mandatory to answer; without answering the question, participants were

not allowed to proceed forward. If participants disagree with the consent, the survey

ended by thanking to show interest in participating in the study. In the next section,

there was a question related to age, and when participants selected age as under 18,

they were not allowed to proceed to the survey. The survey ended by thanking them

and mentioned that the study is for adults only.

If participants consented, and were over 18, then they completed the survey at their

own pace. In the last section of the survey, participants were asked if they wanted to be

entered into a draw for a $40 prepaid credit card. If so, they provided their contact

information. They were also asked if they were willing to be contacted to participate in a

follow-up interview. If so, they provided their contact information. During the survey,

participants could skip any question, except the consent and age questions due to the

University ethics protocol. In the end, participants could review their responses and

submit their answers. Then the participants were thanked for the participation. The full

informed consent form is included in Appendix D.

3.2.3 Survey Distribution

The survey was advertised through a number of channels, primarily through social

media and dairy industry contacts. A dairy farmers organization that visited farms

regularly for milk analysis purposes had originally agreed to help advertise our study

through their distribution network. However, they withdrew their support due to

complications arising from the pandemic that increased their and their clients’ workload

and stress levels. A Facebook group, named “Farmers of Ontario”26 , which has 2.2k

26 https://www.facebook.com/groups/FarmersOfOntario

40

plus members, was joined and the survey advertisement was posted there. The social

media study announcement was posted on TwitterTM accounts of my supervisor, me,

various University of Guelph Communications channels, including the Ontario

Agricultural College and the Dairy at Guelph research group, and OMAFRA dairy

contacts. These tweets were retweeted by several dairy industry contacts, including the

dairy farmers as followers.

The uncontrolled instrument distribution method was used to distribute the survey in

TwitterTM because the tweet can be reached to anyone. But when it was tweeted, the

announcement stated that the survey was only for dairy farmers of Ontario, but still,

anyone could voluntarily participate. I have communicated with my friends who are

connected with dairy farmers and asked them to distribute the survey among them.

Figure 3.2 shows an example of the survey distribution through TwitterTM.

41

Figure 3.2: Advertisement of the Survey in TwitterTM.

In addition to survey announcements through social media, my supervisor and I

attended the Southwestern Ontario Dairy Symposium on 20th February 2020 in

Woodstock, Ontario, to advertise the online survey. The Symposium organizers

graciously allowed us to set up a booth in the exhibit hall at no charge. During our

communications with the organizers prior to the symposium, they informed us that there

was over 200 exhibitors and sponsors and that they expected around 400-500

attendees. This was consistent with our observations during the symposium.

At our booth we spoke with attendees in person about the study and invited them to

participate if they were dairy farmers or producers in Ontario. Whenever someone said

they were a dairy producer, we told them the brief details of the study and asked them if

they were interested in participating in the online survey. Whenever someone showed

42

interest, we provided them a leaflet that included the study advertisement with the study

website and QR code they could scan to access the website. We also had a few laptops

available for them to complete the study on-site, but no one took that option. We have

followed snowball sampling in this case by asking them if they can distribute the leaflet

to other dairy farmers. Afterward, we have provided more leaflets if they showed interest

in distributing the survey to other dairy farmers. Figure 3.3 shows the distributed leaflet

to interested participants.

43

Figure 3.3: Leaflet to Advertise the Survey at the Dairy Symposium in Woodstock,

Ontario.

44

From Facebook groups and pages, I also found some dairy farmers who have

commented in several posts, and I sent them a message regarding participation in the

survey. I have also asked admins of some dairy-related pages to promote the survey

through their page. I have also contacted dairy technology retailers to distribute the

survey, and two of them distributed the survey in their channel. The survey was online

from 6th February 2020 to 31st March 2020.

3.3 Interview Design

As mentioned earlier, conducting phone interviews with dairy farmers was planned in

this study to collect qualitative data, which would provide depth and insights about the

current notification mechanism of PLF technologies. The phone interview was planned

because it is time and cost-effective and provides similar data to face to face interviews.

Phone interviews would provide comprehensive qualitative data about the pros and

cons of the current notification mechanism, which later would help to identify some

significant insights. Moreover, the phone interview mitigates the gap in data collection

through the survey. The phone interview's planned duration was 20-40 minutes, and the

phone interviews were supposed to be audio recorded for further analysis. The

interview questions are provided in the Appendix F.

3.4 Survey Participants

All the participants of the survey were dairy farmers of Ontario, Canada. All participants

were 18 years old or above. It was ensured that the participants are 18 years or above

to fulfill the Research Ethics Board's age requirements. In total, 18 complete, valid

responses were collected. The following diagrams (Figure 3.4- Figure 3.7) depict the

demographics of the survey participants.

45

Figure 3.4: Age Range of the Survey Participants

Figure 3.5: Gender of the Survey Participants (15 male and 3 female)

46

Figure 3.6: Role of the Participants in the Farm

47

Figure 3.7: Years of Dairy Farming Experience of the Participants

Five participants were chosen at random to receive a $40 gift card among the 18 survey

participants. The five chosen participants were contacted via their provided contact

information and asked their preferred way of money transfer. According to the study

plan and ethics approval, gift credit cards were supposed to be sent but due to COVID-

19 pandemic, the most convenient and safest way was chosen based on discussion

with each participant for monetary transaction.

3.5 Data Collection and Analysis

Qualitative and quantitative data were collected through the survey by using close-

ended and open-ended questions through the Qualtrics® online survey platform, on the

topics discussed in Section 3.2.1. As discussed above, the survey data was collected

48

through the Qualtrics® online survey tool. By the end of the data collection period, a total

of 51 survey records were recorded. The collected survey records were reviewed for

validity. First, incomplete surveys were deleted in accordance with the information

provided on the informed consent form. For instance, there were a few cases where the

participants started the survey but did not submit their responses via the submission

button. Also, unusual surveys such as those completed within 2-3 minutes, far less time

that reasonably expected to carefully read the questions and respond thoughtfully, were

deleted to increase reliability of the data. To account for surveys that may have been

completed for the potential prize and potentially be non-farming participants, only survey

records that included reasonable, logical response patterns, and topic-relevant free form

comments were included in the data analysis. A response was also culled for

irrelevancy if the responses mentioned that they do not use any technologies. Initial

data culling resulted in 21 complete, valid records. An additional three responses were

culled during the analysis as the responses were from outside of the Ontario province.

This left a total of 18 complete, valid survey records that were included in the data

analysis.

When the data collection was completed, data analysis was started using the built-in

tools in Qualtrics®. Using the Qualtrics® built-in tools, some basic graphs and charts

such as frequency statistics, bar charts were generated and min, max and mean were

also calculated where it was applicable. Some data were moved to Microsoft Excel® to

generate more understandable and controlled bar charts based on the research

question. Afterwards, the data were exported from Qualtrics® to a common-separate

values (CSV) formatted file for further statistical analysis. Correlation analysis was done

using Python 2 built in function named ‘corr()’ in the library named ‘Pandas’ to

determine the relationship between demographics or other data and the number of

notifications such as the relation between duration of using a technology and the

number of notifications received. Correlation analysis was also used to determine

relationship between stress level of the farmers and their demographics. Linear

49

correlation (Pearson correlation) was used to investigate whether there were any linear

relationships between the variables. Python was used because it has some easy to use

libraries which provide features to draw relational diagrams from data. A sample python

code for correlation analysis is included in Appendix E.

3.6 Summary

In this chapter, the detailed methodology of the research has been discussed. To

conclude, Ontario plays a vital role in the Canadian dairy industry and running a study

on Ontario dairy farms will provide a better sense of the Canadian dairy industry. An

online survey was an appropriate study method since an online survey is cost-effective,

gives maximum reachability, and is less challenged to analyze data because data is

automatically organized and saved while being collected. The survey helped to

understand the current notification mechanism of dairy farming technologies. Using a

survey, more data could be collected since for most of the questions, participants need

to select from options rather than write something from their own. Moreover, the survey

is a better way to start with whenever a new field is explored. The participants were

recruited through different channels such as email, TwitterTM, Facebook®, set up a table

in a symposium. In the next section, the findings by analyzing the collected qualitative

and quantitative data will be presented.

50

Results

The study focused on two objectives. The first objective was to understand the current

notification mechanism of four frequently used dairy farming technologies: automated

milking systems (AMSs), automated heat detection systems (AHDSs), automated

calving time detection systems (ACTDSs), and automated activity monitoring systems

(AAMSs). The second objective was to investigate the positive and negative effects,

including potential stresses, that PLF notification mechanisms have on farmers.

This chapter presents that findings from the data analysis of the online survey, including

the usage of the four technologies, information received by the farmers, actions taken

by the farmers upon receiving information from PLF technologies, benefits and

challenges of PLF technologies, understanding on the average number of alerts farmers

receive weekly, farmers’ perceptions on stress caused by ineffective communications,

communication mediums, and their overall impressions of PLF technologies. The full

dataset collected from the survey are included in Appendix A.

4.1 Participants Receiving Notifications from Specific PLF

Technologies

The survey started with a few demographics questions followed by questions related to

each of the four PLF technologies. The technology-specific questions started with AMS

(milking), followed by AHDS (heat detection), ACTDS (calving time) and then AAMS

(activity monitoring). Figure 4.1 summarizes the number of participants who reported

using each technology, and Figure 4.2 summarizes the number of participants who

reported receiving notifications from each technology.

51

Figure 4.1: Number of Participants Using the Technologies on Their Farms.

0

2

4

6

8

10

12

14

16

18

AMS AHDS AAMS ACTDS Others

Nu

mb

er o

f p

arti

cip

ants

USE

DO NOT USE

52

Figure 4.2: Number of Participants Receive Notifications from the Technologies.

In total, eighteen participants responded to the AMS questions, with 11 participants

(61%) reporting that they use AMS on their farms. Among the eleven AMS users, three

participants (27%) use one AMS, five participants (46%) use two AMSs, and three

participants (27%) use three AMSs. Out of eleven AMS users, ten participants (91%)

reported that they receive notification from their AMS(s), and only one participant (9%)

reported that they do not receive notifications from their AMS(s).

In total, seventeen participants responded to the AHDS questions, and among them, 16

participants (94%) said they use AHDS on their farms. All sixteen participants (94%)

also reported that they receive alerts from their AHDS.

In total, seventeen participants responded to the ACTDS questions, and among them,

four participants (24%) said they use ACTDS on their farms. While five participants

reported that they receive alerts from their ACTDS (29%), the data shows the follow-up

questions from the additional participant were left blank; thus, it is assumed they

0

2

4

6

8

10

12

14

16

18

AMS AHDS AAMS ACTDS

Nu

mb

er o

f P

arti

cip

ants

Receive alerts Do not receive alerts

53

selected this “receive notifications” in error. Given the small sample size of ACTDS

users in the survey, the ACTDS data is omitted from the following analyses, and

reported separately in Section 4.11.

Seventeen participants responded to the AAMS questions, and among them, 13

participants (76%) mentioned that they use AAMS on their farms. Among them, six

participants (46%) answered that they receive notification from their AAMS, and eight

participants (56%) responded that they do not.

The participants were also asked whether they use and receive notifications from any

other technologies beyond the above four technologies. Four participants replied that

they use other technologies such as Rumination, SCC indication and conductivity, Milk

temperature, Ketosis and mastitis alerts to desktop, and Automated feeding systems

(Lely Vector and Rovibec were specifically mentioned).

4.2 Type of Information Received by the Farmers

Figure 4.3 depicts the type of information farmers receive from AMS. The figure shows

that most of the notifications farmers get are about cow activities such as rumination

and lameness (9/11), system failures (7/11), or cows in heat (7/11). They also get

notifications about cows overdue for milking (3/11), substances in the milk (3/11), cases

of mastitis (3/11), and abnormal cow temperature (2/11). Participants also reported

several other types of information, in addition to the pre-defined information types,

including the weight of the cow, feeding system issues, and calving distress.

54

Figure 4.3: Type of Information Received by AMS Users.

A participant mentioned AMS sends heat detection at night which they did not want.

Therefore, they changed the factory settings to not receive this kind of alert at night, as

illustrated by their comment,

“Send heat detection and distress alerts at night, turned off cleaning alarm phone calls.

There is no immediate action anyways” (P16)

Figure 4.4 represents the types of information farmers receive from their AHDS. The

figure shows that most of the notifications farmers get are about the identity of a cow

who is in heat and the breeding status of a cow when she in heat. Many participants

(6/16) also reported that they receive alerts related to the optimum insemination time,

which was not included in the pre-defined options in the question wording. A participant

mentioned in response to the open-ended question that the notification only says a cow

is in heat but does not give the identity of the cow. They need to open the integrated

0

1

2

3

4

5

6

7

8

9

10

cow overduefor milking

cow in heat activity cowtemperature

substances inthe milk

mastitis alert systemfailure

others

Nu

mb

er o

f R

esp

on

ses

Information Types from AMS

55

application with AHDS to know which cow the notification about, as their comment

illustrates,

“Just says ‘cow in heat’ must open app to find out which cow and breeding status” (P5)

Figure 4.4: Types of Information Received by AHDS Users.

Figure 4.5 shows the type of information farmers receive from their AAMS. The figure

shows that most farmers (11/13) reported receiving notifications about unusual activity

timing such as unusual lying, standing, ruminating and walking time. Participants also

reported receiving notifications about heat events (4/11) and calving time detection

(1/11). One participant reported that their AAMS sent notifications about inactivity in

their cows, which was not a pre-defined option in the question wording. No farmer

reported receiving notifications about the location of the cow, which is a known feature

of newer commercial AAMS systems.

0

2

4

6

8

10

12

14

16

Identity of a cow location of a cow breeding status others

Nu

mb

er o

f R

esp

on

ses

Information Types from AHDS

56

Figure 4.5: Information Received by the Farmers from AAMS.

4.3 Actions Taken by the Farmers

Figure 4.6 represents actions taken by farmers upon receiving notifications from AMS.

As the figure shows, most of the time farmers check the system (8/11) and reset the

system if necessary (3/11). These findings are consistent with the fact that, as reported

in the previous section, farmers receive many notifications regarding system failure.

Many farmers (5/11) reported checking a cow after receiving an AMS notification about

a cow. In the other section, farmers reported checking the system upon receiving

notifications to determine action.

0

2

4

6

8

10

12

cow position unusual activitytiming

heat detection calving timedetection

others

Nu

mb

er o

f R

esp

on

ses

Information Types from AAMS

57

Figure 4.6: Actions Taken by Farmers After Getting Alerts from AMS.

Figure 4.7 depicts actions taken by farmers upon receiving alerts from AAMS. This

figure shows that three commonly reported actions were to check the cow with the

reported issue (4/6), provide necessary care to the cow (4/6), and apply medicine if

necessary (4/6). Sometimes they contact their veterinarian (3/6) and separate the cow

with issues from other cows, if necessary (2/6).

Actions upon receiving alerts from the special-purpose PLF technologies, AHDS and

ACTDS, were not collected since after receiving an alert from the system the actions

are more obvious: Farmers usually check the cow and take necessary steps for

insemination or calving once the detected heat or calving is confirmed.

0

1

2

3

4

5

6

7

8

9

cow check system check reset system others

Nu

mb

er o

f R

esp

on

ses

58

Figure 4.7: Actions Taken by Farmers After Getting Alerts from AAMS.

4.4 Benefits of PLF Technologies

Figure 4.8 presents benefits of PLF technologies. It was reported that notifications from

AMS assisted mostly in improving livestock health, increasing appeal of work, offering

more flexible working hours for the staff. Farmers also reported that the AMS

notifications helped them to increase milk productivity, ease of data collection for

regulatory board, and decrease stress from some aspects. Farmers disclosed that the

alerts from AHDS assisted them primarily to accurately identify insemination time of

each cow, improve livestock health, and decrease uncertainty of detecting heat events.

Some farmers also reported that the alerts helped them to decrease their stress related

to breeding management, and to increase flexibility and reduce working hours for farm

staff. A few farmers also reported that the systems increase pregnancy rates, helped to

increase feed efficiency, and milk production. One participant mentioned about

improving livestock health by automatically monitoring activities of cows in response to

an AHDS open-ended question, as their comment illustrates,

0

1

2

3

4

5

cow check provide care tocow

separate thecow

contactveterinarian

apply medicine

Nu

mb

er o

f R

esp

on

ses

59

“The system we have also does rumination, eating time, inactive Helps a lot with overall

health monitoring in combi with sortgate i love it ” (P9)

Farmers reported that the notifications from AAMS mostly help them to improve

livestock health and decrease uncertainty of detecting problems with individual cows.

Some farmers reported that the communications from AAMS offered them reduced

working hours and made their life easier by decreasing stress related to individual cow

monitoring. A few farmers also reported that the system offers reduced working hours

and reduced need to monitor individual cows. Note, Figure 4.8 combines all the labour

related benefits into a single category and all health-related benefits into another single

category to better represent the overall findings.

Figure 4.8: Benefits of PLF Technologies.

60

4.5 Challenges of PLF Technologies

Figure 4.9 summarizes the challenges farmers face while using PLF technologies on

their farms. Note, the figure combines all reported challenges related to unclear

messages, including unclear topic/subject of message, unclear cow identification, and

unclear required response for reported issue into a single category “unclear messages /

cow / required response” to simply the chart and better represent the overall findings.

Figure 4.9: Challenges of PLF Technologies.

The most commonly reported challenge with AMS notifications is to identify the subject

or the topic of the received message since sometimes the messages are too generic.

Some farmers also reported that the timing of sent messages from AMS sometimes

interrupt more important work or life activities. Farmers also reported that sometimes

the medium of communication is not effective. A few farmers reported that they

sometimes miss important notifications because of the timing and medium of

61

communication and sometimes they feel stressed because they feel they might receive

notifications at any moment. In response to an open-ended question, one participant

mentioned that the voice in the phone call was not clear and another participant

suggested the system should generate more alerts to make the system more reliable,

as shown in their comments below:

“The voice is not always clear and the responsiveness to confirming and resetting the

system when you push the appropriate button is not great.” (P2)

“There needs to be more robot alarms added as a fail safe.” (P16)

AHDS users reported few challenges with the notifications from their systems, with 6/16

participants explicitly adding comments like “no challenges” and “no challenges, very

happy with alert system”. However, a few farmers reported that medium the system

uses to send the information is not always effective, the timing of received messages

sometimes interrupts more important work or life activities, and sometimes false alerts

(false positives) are sent or no alert (false negative) is sent even though a cow is in

heat. A participant mentioned about inappropriate communication timing from their

AHDS in response to an open-ended question, as shown in their comment,

“I wish refresh of data would be more current. Mostly 4 hours behind, sometimes 2. i.c.

if it is 10am now, the last data point showing is 6 or 8am” (P2)

Another participant reported receiving many false alerts from their AHDS and

sometimes receiving inaccurate data from the technology. However, on balance, they

felt the technology was still valuable despite these issues, as shown in their comment,

“I feel like there are a lot of bugs because we do receive false alerts all the time and

sometimes the readings are incorrect. It does allow us to save time and makes our job

easier” (P17)

62

No AHDS users reported feeling stressed because of their AHDS notifications.

The most common challenges reported by AAMS users related to uncertainty of the

message, including whether the reported issue is urgent or not, which cow the message

is talking about, and sometimes the message is too generic or unclear to understand

what problem is being reported. Moreover, they sometimes receive false alerts from

their AAMS and sometimes miss messages about abnormal cow activity. In response to

an open-ended question, one participant commented that their AAMS sometimes takes

too long to report abnormal behaviour, as illustrated by their comment,

“Sometimes it does not alert me soon enough. There is a time delay where it waits to

see 2 days of decreased inactivity before an alert. Hoping to contact company to make

a change on this.” (P11)

Overall, there were far fewer challenges reported than benefits across the AMS, AHDS,

and AAMS notification systems (105 reported benefits versus 28 reported challenges),

with several farmers explicitly reporting “no challenges”. This suggests that the benefits

gained by these technologies and their related notification mechanisms outweigh the

challenges they pose. However, the identified challenges in this section still provide

insights into potential design improvements that could be made, as discussed further in

Chapter 5.

4.6 Number of Notifications

A correlation analysis was conducted using Python on some of the collected

demographic data and the reported number of notifications received across all PLF

technologies. Table 4.1 represents the correlation matrix of the number of notifications

against farm size, years of experience, and installation year. Two variables are

considered weakly related if the correlation coefficient (r) is |r| < 0.35, moderately

related if 0.36 < |r| < 0.67, and strongly related if |r| > 0.67 (Robert D. Mason, Douglas

63

A. Lind, 1983). A positive correlation indicates that with the increment of one value the

other will also increase, while a negative value indicates with the increment of one the

other one will decrease.

A moderate positive relationship was found between the number of notifications and

farm size (r=0.435). Logically, it could be expected that a larger farm size (i.e. one with

more cows) might receive more notifications; however, the lack of a strong correlation

between these factors may be due to the fact that some farmers with large farms may

have adopted some of the strategies discussed below to minimize received alerts. It

may also be due to the relatively small sample size of the survey.

Only weak correlations were found for the other tested factors. A weak negative

relationship was found between number of notifications and years of experience (r=-

0.209), and an almost negligible negative relationship was found between number of

notifications and years of installation of that technology (r=-0.042). The sample Python

code for calculating correlation coefficient and results are given in Appendix E.

Table 4-1: Correlation Matrix Values (r) for the Number of Alerts against Farm

Size, Years of Experience, and Year of Technology Installation.

Farm Size Years of experience Year of installation

Number of alerts 0.435 -0.209 -0.042

4.7 Perceived Stress Levels Related to PLF Notifications

In the last section of the survey, participants were asked to rate on a 11-point scale how

stressed they feel during their daily activities knowing that they might receive alerts from

any of their farm technologies and the responses are depicted in Figure 4.10 and Figure

4.11. The scale anchors were as follows, 0 = not at all stress, 10 = extremely stressed.

64

As the figures show, all 15 farmers who responded to this question rated their stress

level 5 or below, and most farmers (13/15) answered 3 or below. The mean stress

rating was 1.88, which was very low overall. Therefore, from the data, it can be

concluded that farmers are not that stressed by thinking they might receive notifications

at any moment from their dairy farming technologies. One participant commented in an

open-ended follow-up question about the cause of the stress/anxiety,

“Not many alarms = no stress” (P15)

However, other participants commented that they feel stressed knowing they might

receive a notification from their farm technologies at any moment and they mentioned

they feel stressed because they have to drop what they are currently doing after

receiving an alert or notification, as illustrated by the participant comments,

“Could be away from the farm and receive an urgent alert you may have to leave an

event for if there is no one else available to fix the alert” (P5)

“Having to drop what your doing to investigate” (P2)

Therefore, some farmers do not leave far from the farm without someone on call. But

this type of stress was present to the same degree before installing the alert system. It

implies that farmers never feel off duty without someone on call, as illustrated by the

participant comment,

“Not leave far from the farm without someone on call. This stress was present to the

same degree before installing the alert system.” (P5)

Sometimes farmers are stressed because they might receive notifications related to

system breakdown, which is expensive to fix. On the other hand, there might be

production loss because cows might not be milked during the technology breakdown

period, as illustrated by the participants comment,

65

“Cow time away from robots. Having to fix robot” (P1)

“Equipment breaking down and if it breaks down it will be expensive to fix.” (P3)

Figure 4.10: Summary Statistics of the Responses Related to Stress before

Receiving a Notification.

Figure 4.11: Stress Scale vs. Number of Response (knowing they might receive a

notification).

Another follow-up question were asked to participants how they cope up with the stress.

Farmers reported different strategies to cope with the stress, such as having someone

on call when they are away from the farm, not being bothered too much about the

received notification at that moment, installing one more AMSs because if one breaks

down another one can still support the milking, and involving other family members so

that they can address any problems that arise. The following sample comments

illustrate some of these strategies,

0

1

2

3

4

5

0 1 2 3 4 5 6 7 8 9 10

Nu

mb

er o

f p

arti

cip

ants

Stress Scale (0 for not at all stressed, 10 for extremely stressed)

66

“You just let it go and deal with it when it works best, or the next time you are in the

barn” (P10)

“Take a deep breath. Our robots are under capacity as well so we know if one breaks

that they can use the other one while we get it up and running.” (P1)

Participants were also asked to rate on an 11-point scale (0=not at all stressed,

10=extremely stressed) how stressed they feel during their daily lives when they receive

notifications from any of their PLF technologies. Their responses are shown in Figure

4.12 and Figure 4.13. Of the 14 participants who responded to this question, 12

participants rated their stress level below 5, with half (7/12) rating their stress level

either 0 or 1. Two participants rated their stress level 6 or 7. The mean rating for the

responses was 2.14, slightly higher than their average stress level prior to notifications,

but still a fairly low level of stress. Therefore, it can be concluded from the data that

farmers are not that stressed due to the information they receive from dairy farming

technologies.

In a follow-up open-ended question about the cause of the reported stress, participants

reported a variety of reasons for this stress, including the fact that alerts are often about

something bad, they are sometimes sent at bad times, such as midnight, and stress due

to an alert being about equipment breakdowns while the farmer is away from the farm,

as illustrated in the following comments,

“I worry that the alert will be something bad and when I hear it i always stop what Im

doing and look and during that time period I get nervous and increased anxiety” (P17)

“Getting up in the middle of the night” (P16)

“Not stressed until check to get more detail. Then depending on the alert could be no

stress or urgent.” (P5)

67

“Robot is broken. Needs fixing and I’m at work.” (P1)

Figure 4.12: Summary Statistics of the Responses Related to Stress after

Receiving a Notification.

Figure 4.13: Stress Scale vs. Number of Response (after receiving a notification).

Participants were also asked a follow-up question about how they cope with the stress.

Farmers reported various coping strategies, such as changing the settings of their

device to receive effective alerts, installing additional robots (AMSs) to cope with the

stress of breakdowns, installing one more devices to better support farm operation.

Some farmers also reported getting better at handling alerts in more effective way as

they get used to the device over time. Sample participant comments include,

“As I get more used to receiving alerts (we just started less than a year ago) I start to

feel less anxious. I also changed the alerts on my device so it just vibrates” (P17)

“Most are not urgent, so frequency of urgent alerts is low which mitigates stress” (P5)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 1 2 3 4 5 6 7 8 9 10

Nu

mb

er o

f p

arti

cip

ants

Stress Scale (0 for not stressed and 10 for extremely stressed)

68

“Other robot can manage while I am at work. Milking 65 cows on 2 robots” (P1)

Collected data were analyzed to determine possible demographic factors in the stress

levels reported by farmers due to PLF notifications. Table 4.2 represents correlation

matrix between stress level and years of farming experience, age of farmer, and size of

farm (number of cows). The correlation coefficient (r) for stress level and years of

experience is r=-0.710, suggesting a strong negative relationship between stress level

and years of experience in dairy farming. Thus, as farming experience increased, the

reported stress level decreased. A moderate negative relationship (r=-0.481) was found

between stress level and age, indicating that older farmers experience less stress,

potentially because they get more experience with handling the stress. A weak positive

relationship (r=0.201) was found between farm size (numbers of cows) and stress. As

mentioned in the previous section, it might be expected that larger farms would receive

more alerts, introducing more potential for stress. However, farmers on large farms may

have also developed better strategies for notification management, such as adjusting

factory settings to minimize alerts.

Table 4-2: Correlation Matrix Values (r) for the Stress Level against Farm Size,

Years of Experience, and Age of the Farmer.

Years of farming experience

Age of farmer Farm size

Stress -0.710 -0.481 0.201

4.8 Notification Mediums

From the literature, background research on available commercial technologies in

Chapter 2, and informal conversations with dairy industry experts, it was anticipated that

current PLF technologies used four main communication mediums to convey

notifications to farmers: text messaging, phone call, email, and displaying information in

69

the dashboard of the PLF technology’s associated computer application system for their

desktop, tablet, or mobile computer. The study confirmed this expectation. Participants

reported that these four mediums were currently used to send information to the farmers

from their current dairy farming technologies. Figure 4.14 and Figure 4.15 show the

reported usage of these notification mediums by PLF technologies. Most AMS users

(7/11) reported that they received notifications through phone calls, four participants

reported that they received notifications through their application dashboard, and a few

participants reported that they received notification through text messaging (1/11) and

email (1/11). Note, that some participants reported receiving notifications multiple ways

from their AMS (e.g., by phone calls and application dashboard).

Among the AHDS users, most participants (12/16) reported that they received

notifications through their application dashboard, some participants (4/16) reported

receiving notifications through text messaging, and one farmer reported receiving

notifications through email. In a free-form comment, one participant who reported

receiving text messages from the survey options clarified that they meant the AHDS

system sends alerts using their native mobile phone alerting system via the AHDS

mobile application system. No AHDS users reported receiving notifications by phone

call.

Overall, far fewer AAMS users reported receiving notifications from their systems (only

6 out of 13 AAMS users reported receiving notifications (alerts or alarms) from their

system). Of these participants, most of them (4/6) reported receiving notifications

through the application dashboard. One farmer reported receiving notifications through

text messaging and one farmer reported receiving notifications through email. No AAMS

users reported receiving notifications by phone call.

As Figure 4.14 shows, the application dashboard is used most often across PLF

technologies as a communication medium, whereas other mediums such as phone call,

text messaging, and email are not used as frequently. The study data also show that, at

70

least among the sampled population of this study, newer technologies like AAMS and

AHDS do not use the phone at all as a communication medium, while this is heavily

relied on by AMS.

Figure 4.14: Reported Communication Mediums Used by Different PLF

Technologies.

0

2

4

6

8

10

12

14

Phone Call Text Messaging Email Dashboard

Nu

mb

er o

f R

esp

on

ses

Mediums of Communications

AMS AHDS AAMS

71

Figure 4.15: Summary View of Communication Mediums Use Aggregated across

PLF Technologies.

As will be discussed in the following chapter, different communication mediums have

unique characteristics that each provide strengths and weaknesses for supporting

specific communication goals. This impacts which communication medium may be more

or less appropriate to use given the type of information a PLF system is trying to

communicate to the farmer, as supported by one participant’s comment,

“I consider an alert something that is brought to my attention. In our

case this is phone call regarding system operation. Lots of dashboard

notifications about cow health, but [I] search reports, don't get a

text/phone call about.” (P2)

Here, the farmer discusses the fact that their AMS dashboard is more useful to them as

a place to go for general cow health information, but not something they rely on for

urgent matters that should be brought to their attention in a timelier manner. For that,

their system relies on a phone call. Some participants changed the default factory

0

5

10

15

20

25

Phone Call Text Messaging Email Dashboard

Nu

mb

er o

f R

esp

on

ses

Summary of Communication Mediums Used across Technologies

72

settings of AMS to receive different types of alerts through an appropriate medium. For

instance, they changed the default settings, so they would receive non-critical alerts

through dashboards notifications instead of through phone calls, as illustrated by the

participant comment,

“Reduced certain non critical alarms so they dont call but just leave dashboard notice”

(P10)

4.9 Overall Stress Related Effects of PLF Notifications

As discussed above in Sections 4.4 and 4.5, participants were asked for each of the

four PLF technologies if notifications increase (as a challenge) or decrease (as a

benefit) stress in their farm management to investigate whether notifications work as a

source of stress for them. Extracting this benefit and challenge data explicitly, the

depicted result is reported in Figure 4.16. The figure shows that most participants (84%)

said that the communications decrease their stress, with only a few participants (16%)

reporting the opposite. As previously mentioned, some participants reported that they

receive false alerts because of inaccurate data and as a result, they feel stressed. As

reported in Section 4.5, farmers face many challenges with the notification mechanism

of PLF technologies and as result farmers might feel stressed. Some common themes

that are reported by the participants are unclear information and required actions, false

alerts, inappropriate timing and medium of communication and missing alerts.

“Don't mind getting alerts. It's always for a good reason. It does not stress me out. It's all

about being proactive on the farm instead of being reactive.”(P14)

Though there was no question related to excessive information, but one participant

voluntarily mentioned receiving unnecessary alerts from PLF technologies. This reason

was also reported as a cause of stress by the participant, as illustrated by their

comment,

73

“I think the alerts should be less and we should only receive alerts if there is a problem.

We have done farming for a long time without these systems in place and have done

just fine. I think they are good but should just stay in the background.” (P17)

Another participant reported about changing default factory settings of AMS to receive

less unnecessary information, as they state in their comment,

“Made dashboard customizable to what I want to see.” (P3)

Figure 4.16: Farmers Perception on Stress Because of Notifications.

4.10 Overall Positive and Negative Effects of PLF Notifications

Participants were asked about the benefits of their PLF technologies and also about the

challenges they experience with these technologies. The total number of positive effects

and negative effects was calculated and then compared between the number of positive

effects and negative effects technology wise which is depicted in Figure 4.17. As shown

in the figure, the number of positive effects strongly outweighs the number of negative

effects for each individual technology.

0

2

4

6

8

10

12

AMS AHDS AAMS Total

Nu

mb

er o

f re

spo

nse

s

Farmers' Perceived Stress

Increase Stress Decreased Stress

74

Figure 4.17: Number of Positive Effects vs Negative Effects on the Dairy Farmers.

This was also reflected in the many positive comments farmers made in their free form

comments, as illustrated below:

“Can’t be there 24/7. Pro, I have an off farm job and the AMS [allows] me to be

successful in that role while getting the opportunity to farm as well” (P1)

“No challenge works awesome enter right away the right cows is sort pen and after

milking there all there ready to inseminate” (P9)

“Very happy with our system. It's an "extra set of eyes" in our barn monitoring our cows

24 hours/day.” (P14)

“Minimal challenges. We are happy with it. Cost is worth it and we don’t change collars

so that makes it easy as well.” (P1)

This should be the cornerstone for the farmers to adopt more PLF technologies on their

dairy farms since these technologies are adding strong value to farmers’ lives and

businesses. Despite these positive effects, farmers still face some challenges with those

0

20

40

60

80

100

120

AMS AHDS AAMS Total

Nu

mb

er o

f R

esp

on

ses

Positive vs Negative Effects of PLF

Positive effects Negative effects

75

technologies. Therefore, there is still scope to improve these technologies from

functional and design perspectives to make the technologies more effective and easier

for farmers to use. In addition to the challenges experienced by farmers previously

reported, the following comments were provided by participants when probed for

potential design improvements that could better support their farm management and

decision making,

“Adjusting the time of when the alert is being made in relation to decreased cow activity,

generally relating in a health issue that needs to be addressed sooner.” (P11)

“Having team viewer27 on robots and manage technology from afar. More mobile

technology with robot.” (P1)

“I think the alerts should be less and we should only receive alerts if there is a problem.”

(P17)

“Additional alarms need to be added.” (P16)

As reflected in these comments, farmers have a variety of opinions about the features

they would like to have in their PLF notifications, including more alarms AND fewer

alarms. The implications of these disparate findings will be discussed in Chapter 5.

4.11 Findings Related to ACTDS

In this section, ACTDS data are presented separately since there was only four

participants who reported that they are used and received notifications from the

27 It is likely this comment refers to the commercial TeamViewer® (©TeamViewer, Germany) software applications

that allows remote control and sharing of a computer desktop interface (https://www.teamviewer.com).

76

technology. The sample size is not enough to generalize the findings. Therefore, there

is a possibility that the data from ACTDS can skew other findings.

In total, seventeen participants answered questions related to the Automatic Calving

Time Detection Systems (ACTDS) and among them only 4 participants (23%) said they

used and received notifications from ACTDS on their farms. No one reported that they

used ACTDS but did not receive notification from ACTDS.

Figure 4.18 shows the type of information farmers receive from their ACTDS. As

expected, most notifications farmers get relate to the expected calving time for a cow (3

responses). Farmers also receive notifications related to the identification (2 responses)

and location of the cow (1 response).

Figure 4.18: Information Received by the Farmers from ACTDS.

In terms of communication mediums used for ACTDS notifications, participants reported

that they receive notifications through text messaging (2 responses), email (2

responses), and application dashboard (1 response). No one reported receiving alerts

through phone calls.

0

1

2

3

4

expected calving time cow identity cow location

Nu

mb

er o

f re

spo

nse

s

Types of Information Received from ACTDS

77

Figure 4.19 depicts the reported benefits of ACTDS communications. Participants

reported that ACTDS notifications assisted them to accurately identify calving time of

each cow (3/4 responses) and decrease uncertainty around expected calving time (3/4

responses). They also reported general improvement in livestock health (3/4

responses), and labour related benefits (3/4 responses), such as reduced working hours

and easier to attract others to work on their farm. No farmer reported that the ACTDS

notifications decreased their stress.

Figure 4.19: Positive Effects of Sent Information from ACTDS on Farmers.

Figure 4.20 shows the reported challenges with ACTDS notifications. Participants

encounter several challenges, including they receive too many false alerts from the

system (2/4 responses), the system increases their stress (1/4 responses), the

communication medium is not effective sometimes (1/4 responses), the timing of

received messages sometimes interrupt more important work or life activities (1/4

0

1

2

3

4

Labour relatedbenefits

Improvedlivestock health

Accurately identifycalving time

Decreaseduncertainty

of calving time

Decreasedstress

Keep up withtechnology/future

Nu

mb

er o

f re

spo

nse

s

Benefits of ACTDS

78

responses), and that sometimes it is difficult for them to identify which cow the message

is talking about (1/4 responses).

Figure 4.20: Negative Effects of Sent Information from ACTDS on Farmers.

4.12 Chapter Summary

This chapter presented the findings of the pilot study consisting of data collected

through the online survey. The study found that the PLF technologies bring many

positive benefits to farmers lives to help them manage their farms. Yet, farmers reported

that they sometimes feel stressed due to a variety of factors related to PLF notification

mechanisms, including uncertain information, false alerts, excessive information, and

inappropriate communication medium and timing of PLF technologies. These

challenges and the current and future strategies to overcome these challenges will be

discussed in the next chapter.

0

1

2

3

Many falsealerts

Unclear cow ID Poor timing Inappropriatemedium

Missed alerts Increased stress

Nu

mb

er o

f re

spo

nse

s

Challenges of ACTDS

79

Discussion

As discussed in Chapter 2, early PLF studies have revealed that dairy farmers

sometimes feel stressed due to notifications received from dairy PLF technologies.

Many factors are involved in this stress, including but not limited to the ineffective

representation of an individual animal’s information to the dairy farmers, technology

breakdown, and uncertainty around received information. Therefore, this research

investigated current notification mechanisms of commonly used dairy farming

technologies on Ontario dairy farms. This chapter revisits the research question

proposed in Chapter 1, “What effects do current notification mechanisms from

dairy precision livestock farming technologies have on dairy farmers?” and

discusses what insights the research findings provided in terms of answering this

question. The research findings are also discussed in the context of the existing

literature. Finally, potential design implications the study findings have for the improving

the utility and usability of future dairy PLF notification mechanisms are discussed from

the perspective of Human-Computer Interaction (HCI).

5.1 Information Management

Collecting information is not the only task of PLF technologies; additionally, these

technologies should process the information accurately and effectively using

appropriate models and algorithms developed from farmers' perspectives (King &

DeVries, 2018). Afterwards, that information should be passed to the farmers through

effective medium(s) at an appropriate time so that farmers can make profitable

decisions for their farms and animals to increase farm productivity and gross profit.

However, this pilot study revealed that sometimes the information received from PLF

notifications are not effective, or useful for making effective decisions for farm

80

operations or animal care. The study revealed several communication challenges

between farmers and PLF technologies, as discussed below.

5.1.1 Information Uncertainty

The study revealed that farmers sometimes receive vague or unclear information from

their PLF technologies. This results in uncertainty about what, if any, action they should

take after receiving a notification message. This finding is consistent with previous

studies on PLF technologies that also found that farmers were sometimes unsure of the

content or meaning of PLF notifications (e.g., (Borchers & Bewley, 2015; Russell &

Bewley, 2013). As reported in Chapter 4, after receiving a notification, eight out of 11

farmers who use AMS technology on their farms reported that it was sometimes

necessary to check the system for more details to determine what actions, if any, were

required in response to a notification from their AMS.

When probed about challenges they have with notifications from their PLF technologies,

four participants reported that they receive unclear messages from their AMS, and one

participant reported receiving vague messages from their AAMS. These findings

suggest that some PLF notification mechanisms are not effectively communicating the

content of their messages. Thus, some reported issues may remain unresolved. Two

participants mentioned that the messages from their AHDS do not reveal cow

identification. Obtaining this information requires additional interactions with the system,

as illustrated in one participant’s comment that a challenge they experience with their

AHDS is the, “lengthy process to get into slow app to find out which cow is in heat” (P5).

Yet, no actions can be taken without this information, as breeding is specific to an

individual cow. As explained in Section 2.2.4, a cow’s heat cycle can last from as little

as 2 hours up to 48 hours; thus, taking actions to find and breed a cow in heat requires

timely action on the farmer’s part. Providing cumbersome steps to help a farmer gather

key information about whether immediate actions are needed seems to be a significant

81

design flaw that warrants improvement. For instance, Participant 5 reported that their

AHDS system only sends a message saying, “cow in heat”, and that they are then

required to “open app to find out which cow and breeding status”. Therefore, there is a

chance of missing heat events and/or the insemination time window, which will reduce

farm productivity and gross farm profit.

Participant 13 reported that the message from their AAMS does not clearly mention the

required action due to unclear messages; therefore, farmers sometimes do not know

what to do. Research from the field of neurobiology has shown that there is a strong

connection between uncertainty and stress and that this type of stress can have

negative health impacts (Peters et al., 2017). While in some cases some more specific

information may be available, but only after additional system interaction (e.g., opening

the software application, navigating to the proper screen, etc.). These additional steps

introduce additional, and potentially, unnecessary cognitive and physical workload.

Thus, whenever key information that is required for decision-making or action it should

be sent with the notification so that farmers has a clear idea about their required action.

The findings of uncertain information from this research is consistent with previous

studies of PLF technologies (Hansen, 2015; Ndour et al., 2017). These studies report

that farmers feel stressed due to the information they receive from PLF technologies.

5.1.2 Inappropriate Communication Timing

A notification should be sent at the right time to help farmers manage their farm better

by making effective decisions based on the received information. Consistent with prior

research (Mollenhorst et al., 2012), this study found that sometimes notifications are

sent at times that are not effective for farmers to make a decision or take an appropriate

action. Three participants reported that they do not get notifications from AMS on time,

and two participants mentioned that they do not receive alerts from AHDS at the

appropriate time.

82

As mentioned above, the notifications from AHDS are time-sensitive because they send

information regarding the heat events of a cow that need to be acted on within a very

small window of time. If the farmers are not notified about the heat event of a cow, or

notified too late, then there will be production loss, as mentioned earlier. Participant 2

reported that the information they receive in their AHDS application is about four hours

behind. This delayed notification also delays timely decision-making and actions needed

to find and breed a cow in heat. For cows with very short heat windows (e.g., 3 hours),

this delay may mean the breeding opportunity is lost.

Similarly, Participant 11, an AAMS user, reported that their system is designed to notify

farmers about abnormal behaviour after two days of “decreased inactivity”. They felt this

was too long to wait to hear about a potential “health issue that needs to be addressed

sooner” and were planning to “contact company to make a change on this”, showing

their displeasure with this feature.

Another issue revealed by the study was “unnecessary” notifications sent to farmers late

at night from AMS, which was frustrating for them. To avoid this, the study revealed that

some farmers (6/11 AMS users) changed the system notification settings from the

factory defaults to reduce non-urgent notifications and notifications that did not require

actions from the farmer (e.g., notifications related to system cleaning), and to manage

the timing of notifications. Therefore, future dairy PLF technology manufacturers should

consider communication timing because it plays a vital role in farmer’s making

appropriate, timely decisions (Bahir et al., 2019). Appropriate notification times will

assist farmers in making the right decision at the right time.

5.1.3 Communication Medium

An urgent issue should be brought to the attention of appropriate person through one or

more mediums so farmers do not miss the issue but at the same time, it should not be

overwhelming to the farmers. Urgent issues should be communicated with farmers

83

through appropriate mediums to get the immediate attention of farmers about the issue.

However, this pilot study found that sometimes PLF technologies do not use appropriate

mediums for the issue being reported to farmers. Three AHDS users and three AMS

users reported that they do not receive notifications through the appropriate medium.

The challenge for PLF and other automated notification systems is matching the

informational content, and its properties such as urgency, to the communication medium

chosen to deliver that content (Clark & Brennan, 2004). In their seminal paper on

human communication processes, communication and collaborative technology,

researchers Clark and Brennan (2004) describe various affordances that different types

of communication mediums have and how these affordances can support or hinder

certain message content or communication goals. For instance, a telephone affords

simultaneity, which forces the receiver to attend to a message at the same time it is

sent. Thus, for urgent messages, this may be an appropriate medium. However, the

telephone does not afford reviewability (the ability for the receiver to review the

message content), which can increase the cognitive workload of understanding a

message sent by phone. Lack of reviewability is especially problematic when the

message contains verbatim content that must be understood, and recalled later, exactly

(Clark & Brennan, 2004).

For dairy PLF applications, a critical piece of information the farmer needs related to a

notification is the cow identification, which would be conveyed in the form of verbatim

content, typically in numeric form. Thus, a phone call would not be the ideal way to

convey this information. Yet, the study found that the most commonly reported

notification medium for AMS was phone calls (6/11), followed by dashboards (4/11),

whereas text messaging and email were used by far fewer systems (1/11 each). A

potential reason AMSs use phone calls may be that they are a reasonable

communication medium to use for urgent messages. However, the study found that

AMS users received telephone notifications for all urgency levels.

84

Another potential reason AMSs use phone calls may be because AMS technology is

substantially older than the other PLF technologies probed in this study, none of which

were reported to use phone calls as a notification medium. AMSs were first introduced

to Ontario dairy farms in 1999 and have seen increasing adoption since then

(Rodenburg, 2012). Certainly 20 years ago, mobile phones were not as ubiquitous in

Ontario as they are today; thus, it makes sense that these systems would have been

built to rely on available communication mediums and technologies used at the time in

farm management: traditional landline phones and desktop computers.

Text messaging may have an advantage over phone calls for providing notifications of

urgent issues, based on the communication affordances discussed above. With the

appropriate native phone notification settings selected on a farmer’s phone, an incoming

message can provide an immediate, attention-getting alert, creating at least a partial

simultaneity affordance. The fact that most modern smartphones allow recent text

messages to be viewed immediately, sometimes without even unlocking the phone,

further creates this affordance. Finally, as a text message can be read and reviewed

multiple times, it provides the reviewability affordance that supports message

interpretation, especially for verbatim message content.

Clark and Brennan also discuss the affordance of audibility, or the ability for people to

hear each other when communicating. While this affordance is often associated with

phone calls, the reality of modern-day telecommunications is that phone calls received

on a mobile phone when someone is in a noisy environment (e.g., in a tractor or other

loud farm setting) or with poor cell phone service, audibility is often compromised. Thus,

text messages may be the better medium, as the entire message is typically received

without issue (as long as some, even intermittent, cell service is available).

Text messaging has also other advantages for notifications. For instance, if a farmer

misses a phone call regarding an urgent issue, it may be appropriate for the system to

use multiple notification mediums, for example, a phone call and text messaging, for

85

maximum reachability to the responsible farm staff, so the person does not miss a

critical alert.

Surprisingly, twelve participants (75%) reported that they receive notifications of the

cows' heat event using a dashboard of the system from AHDS. To inform heat events to

the farmers, multiple mediums should be used so that farmers do not miss any heat

events. The dashboard is something that farmers will check as desired, but heat events

need immediate checking. Heat events are urgent to moderately urgent and need timely

attention to ensure that the farmer does not miss, in some cases, quite a brief

opportunity to inseminate a cow in heat. Thus, it is surprising that dashboard

notifications were, by far, the most commonly reported communication medium used by

the participants’ AHDS (12/16), with other mediums being reported much less often (text

messaging: 4/16, email: 2/16, phone calls: 0/16).

However, one participant’s comment suggests that the survey may not have captured

the whole story related the notification mechanisms being used by current AHDSs. One

participant clarified in the free form comments that instead of text messages (which they

reported as one of their system’s notification medium), they receive “an alert on my

phone through my app that is connected to the main computer where the heat system

operates”. This response implies that a mobile application, networked to the AHDS’s

main server, uses the mobile phone’s native notification system to notify the farmer of

an issue, and that the mobile application can be opened for further details. This implies

a two-part notification process: first, a native notification chimes with some details of the

alert, then, the receiver can open the app (possibly by simply touching the notification

on the phone) to get more information. If the app’s interface is highly usable and

provides a simple process to find the information associated with the notification, this

would be an effective way to combine the simplified and urgent communication channel

of “texting” with the more powerful graphical capabilities of an application to

communicate effectively with the farmer. However, this communication mechanism

86

requires multiple steps for farmers to gather the information they need to make a

decision or take appropriate actions. Thus, the information provided at each step, and

the process from one step to another should be well thought out and streamlined.

The choice of notification medium is important and should be matched to the level of

urgency and also to the informational content of the message (Mehrotra et al., 2015).

For example, PLF notification mechanisms should classify messages into urgent,

moderately urgent, and non-urgent and use different mediums for different levels of

urgency to communicate with the farmers. Likewise, a previous study of notification

management discussed in Section 2.6 proposed to classify notifications into very

urgent, urgent, moderately urgent and not urgent , and timing of sending notifications

can be decided based on the urgency levels (Dabbish & Baker, 2003). This study found

that some existing systems allow for such distinctions, although settings may need to be

changed from the factory default notification settings to select which types of

notifications should be sent when and to which medium. For example, Participant 10, an

AMS user, reported that they modified the system’s default settings to “reduce certain

noncritical alarms” sent by phone call and instead, “just leave dashboard notice[s]” for

these issues.

For extremely urgent issues, use of multiple different communication mediums, such as

phone calls and text messaging or native mobile phone notifications, may help ensure

the farmer are aware of the issue immediately and can take timely action. For instance,

the system could make a phone call for urgent issues and if no one receives the phone

call then the system could send a text message to communicate the issue. However, if

the notification contains verbatim content then it may be better to make a phone call to

communicate the urgency of the issue and also to send an accompanying text message

so that receiver can review the information later. On the other hand, non-urgent

notifications can be sent to the dashboard for farmers to check whenever convenient.

87

For moderately urgent notifications, phone calls or text messaging could be used to

inform farmers about the issues.

5.2 Ineffective Representation of Information

The study data show that farmers sometimes receive redundant information from PLF

technologies. In this section, techniques of getting redundant information and

mechanisms to reduce redundant information are discussed.

5.2.1 Notification Management

None of the study participants reported receiving false alerts from AMS, but a prior,

larger study focused on AMS use in Canada reported that farmers receive false alerts

from AMS (King & DeVries, 2018). There might be several reasons behind this

discrepancy. First, this study had fewer participants than the prior study. Second, the

technology may have improved in the few years between these studies. Also, farmers

may have become more experienced with the technology, and know how to manage the

alerts through experience. Hansen (2015) reported that farmers learn to respond to

alerts effectively over time, for instance, through settings adjustment as participants

reported doing in this study. Therefore, the study findings suggest that AMS

technologies currently being used in Ontario are fairly reliable. This conclusion is

supported by the fact that there has been an increase in AMS adoption on Ontario

farms in recent years, accordingly (Warriner & Moul, 2015).

In contrast, the study found that farmers receive false alerts from the other PLF

technologies they use, including AHDS, ACTDS and AAMS. This is consistent with a

fairly recent review of sensor-based PLF technologies that detect abnormalities about

farms and animals (Dominiak & Kristensen, 2017). The review found that alarm

management is improving day by day by using various techniques, yet, further research

is necessary to prioritize true and false alarms. As these are newer technologies than

88

AMS, this finding is also logical; new technologies take time to mature and become

more reliable. As discussed in Section 2.2, many of the available AHDS, ACTDS, and

AAMS systems on the market are accelerometer-based wearables, similar to FitBit®

wrist-worn wearables28 used to track human fitness and health. Livestock wearables, in

the form of leg bands, collars, and tail bands, have seen increasing adoption in the last

10 years in the dairy industry, but they are still maturing as a reliable technology due to

trade-offs between battery life to power their wireless functioning and having sufficient

sampling rates to enable sufficient calculations for advanced behavioural algorithms

(Halachmi et al., 2019).

With respect to notification mechanisms, frequent false alerts may hinder a farmer’s

ability to make appropriate decisions at the right time for their farm and animals. False

alerts can increase workload as the farmer must further investigate to find out the real

situation. Another potential consequence of frequent false alerts is that it can degrade

trust in the technology over time. Unreliable technology has also been shown to create

negative impressions and reduced trust in the technology (Lee & Moray, 1994). As a

result, farmers might overlook an urgent notification because they might be tired of false

alerts or they may prioritize the notifications from different technologies based on their

past experiences with the different reliabilities of their PLF technologies. This may have

a harmful effect on their animals and their farm profit since some issues may go

undetected or ignored for too long. Receiving false alerts might also demotivate other

farmers to adopt the technology, based on reports from their peers in the dairy

community.

Therefore, managing notifications effectively is a serious concern for PLF technologies.

One approach could be to prioritize notifications where farmers will be communicated

28 https://www.fitbit.com/en-ca/products/trackers

89

only about relevant issues at appropriate times, as discussed above. For instance, a

study discussed in Section 2.6 proposed to decide whether to send a notification to the

users based on urgency level of the notification, the activity of the user, the social

activity of the user (e.g., talking with someone), the social situation (e.g., on a bus) and

location of the user (Kern & Schiele, 2003). To better understand what “relevant” and

“appropriate” is, user-centric and/or participatory design studies could be conducted by

researchers or manufacturers to better understand farmers’ needs and preferences

regarding notifications from their technologies.

Another approach to improve the utility of the notifications that are sent to farmers or

that appear in the application dashboards may be to rank or prioritize notifications to

help farmers better understand the urgency level of the issue. For instance, issues such

as calving time, insemination time, or severe temperature or lameness, which need

immediate attention, could be ranked highest. In contrast, non-urgent matters, such as

the slightly reduced amount of milk yield, that could be addressed after a specific period

could be ranked lowest. Return on Investment (RoI) or profit should be a key priority

while ranking the alarms and profit can be ensured by ensuring animal health, less

production cost, and high productivity. Therefore, these factors should be considered

while designing ranking systems for PLF notifications.

Ideally, improving the underlying health and behavioural detection accuracy of the

technologies would help reduce the number of false alerts farmers receive. Yet, the

reality is that many PLF technologies are, and will continue to be, less than 100%

reliable for some time. So, notifications from these systems will continue to have errors

(both false positives and false negatives). As this is a known, common problem for early

automation systems across many domains, human factors researchers have proposed

displaying reliability information about automated processes to assist people in

understanding the system state and making more appropriate decisions in light of

automatic notifications (Beller et al., 2013; Wang et al., 2009). A recent study of human-

90

automation collaboration showed that providing people with overall likelihood values of

potential threats automatically detected by the system during a surveillance task found

that people showed an appropriate level of trust and reliance on the automation (rather

than over/under-trusting or under/over-relying on the system) and performed well at the

task (Du et al., 2019). A similar approach could be taken in PLF notification systems,

where notifications are accompanied by an indication of how likely the event is. For

instance, a calving notification could be accompanied by a likelihood score or

confidence value.

5.2.2 Information Overload

Though in this study did not explicitly probe farmers about information overload, a few

farmers reported that they receive unnecessary information from their PLF technologies.

For example, Participant 17 commented that they think that “the alerts should be less,

and we should only receive alerts if there is a problem”. Participant 3 reported that their

AMS sends many unnecessary notifications to the system's dashboard, and that they

adjusted the settings so that only the necessary information is shown. This is consistent

with prior research that has reported that farmers sometimes receive excessive and

unnecessary information from PLF technologies (Borchers & Bewley, 2015; Russell &

Bewley, 2013). As mentioned in Section 2.4, information overload can create negative

impressions of the technologies which produce and send excessive and unnecessary

information.

Receiving unnecessary information is sometimes overwhelming and stressful for

farmers (Dominiak & Kristensen, 2017; Hansen, 2015; Schewe & Stuart, 2015). In

general, receiving unnecessary information can create information fatigue leading to

missing needed information for users to take into account during decision making

(Buchanan & Kock, 2001). A study on information overload from social media found

that users can experience social media fatigue due to information overload, which leads

91

to intermittent use of social media (Fu et al., 2020). PLF technology designers would

want to avoid farmers adopting a similar strategy for handling information overload they

may feel from PLF notifications. Thus, minimizing the amount of unnecessary

information PLF technology send to farmers via notifications, and providing the

information in an effective way, is crucial.

A study on computer-mediated technologies found that communications from the

automated technologies can be overwhelming if the communication systems are not

well structured (Hiltz & Turoff, 1985). As mentioned in Section 2.4, receiving

unnecessary information may also hamper making effective decisions. A study on

smartphone notifications, also discussed in Section 2.6, reported that excessive

notifications can reduce users’ productivity and can negatively impact users’

physiological well-being (Kushlev et al., 2016). Therefore, PLF notifications should be

managed effectively to provide only relevant information. Moreover, the communication

medium used, and the design of the content presented in that medium, should be

structured based on the information need and the ability of the users (Hiltz & Turoff,

1985).

Ensuring that farmers get sufficient, but not overwhelming information for them to make

proper decisions is difficult. Developing effective visualization techniques for PLF

applications and their dashboards may help. For example, the previously discussed

‘Scope’ system (see Section 2.6), presented different information notifications on a

screen spatially arranged based on their importance level, with more important

notifications displayed towards the center of the display (Van Dantzich et al., 2002). In

the case of PLF, a similar visual presentation could be displayed on an application

dashboard with notifications of different urgency spatially organized in a meaningful

manner. This contrasts many PLF notification displays that often list system notifications

in chronological order. Again, however, determining what information is most “relevant”

in different contexts and what information presentations are most appropriate for dairy

92

farmers should be done through user-centric design methodologies, like involving

farmers in participatory design or in iterative design cycles, where designs are evolved

with frequent, and early, involvement of relevant stakeholders (Rogers et al., 2015).

Users also should be provided with the control to manage the information they receive

to reduce information overload since they can control the incoming information

according to their needs (Hiltz & Turoff, 1985). As mentioned in Section 2.4, data

representation experts can evaluate the communication systems to present data in

effective way for the farmers. These techniques can help reduce excessive and

irrelevant information.

5.3 Effects of PLF Notifications on Farmers

PLF technologies have a considerable effect on the farmers, their farm management,

and their daily lifestyles. The results of this pilot study suggest that notifications from

currently used PLF technologies have more positive effects than detrimental effects on

dairy farmers. This finding indicates that dairy farmers should continue to integrate PLF

technologies into their farms in the future. However, the study did uncover some

adverse effects of the communications from PLF on farmers, even with the small

number of participants in this study. Some farmers said that they feel stressed because

of the notifications they receive from PLF technologies. These findings will be discussed

in this section.

5.3.1 Are Notifications a Source of Stress?

As previously mentioned, prior research conducted in Europe found that farmers

sometimes feel stressed because of PLF notifications (Hansen, 2015). Though most

participants in this study reported that they are not stressed because of PLF

notifications, five participants reported stress levels of 3 or above (on a 0-10 scale;

0=not at all stressed, 10=extremely stressed) arising from receiving PLF notifications,

93

and two reported stress levels above 6. Moreover, six participants reported stress levels

of 3 to 5 (same scale as above) related to sometimes feeling stressed knowing that they

might receive a notification at any moment.

Participants also reported that they also sometimes feel stressed upon receiving a

notification based on the type of information in the notification. This finding is consistent

with prior studies (Hansen, 2015; Ndour et al., 2017), which uncovered several reasons

for farmers’ stress, including unclear information, unclear required actions, false alerts,

timing of alerts, and technology breakdowns. Most of these issues were uncovered in

this study, as discussed above, except for technology breakdowns, which were

mentioned as a source of stress by some farmers but were not probed explicitly in this

study. For example, Participant 3, an AMS and AHDS user, commented that it is

stressful when notifications relate to equipment breakdowns because “if it breaks down

it will be expensive to fix”.

Farmers also reported that stress was created when they had to stop what they were

doing to check an issue, or when they receive an alert while off the farm (e.g., attending

an event) and there is no one else available to solve the problem. For example,

Participant 17 commented that when they receive a notification, they always stop what

they are doing and “look [at the notification/app] and during that time period I get

nervous and increased anxiety”. These findings are not surprising, given the nature of

livestock farming, and some of these issues (e.g., if something goes wrong while the

farmer is away from the farm) were sources of stress even before integrating PLF

technologies on the farms. Some participants commented that the PLF notifications

helped reduce some of this stress though. For instance, Participant 1 commented that

their AMS allows them to have an off-farm job while still farming and reported

“decreased stress” as a benefit of their AMS. Yet, the same participant also reported

that their AMS sometimes interrupted more important work or life activities, that

sometimes they missed important / urgent messages from their AMS, and that they

94

experienced increased stress because they could receive a message at any moment.

Thus, it seems that managing stress related to PLF technologies is complex, as the

same technology can both be a source of stress and a means to decrease stress.

As mentioned above, farmers reported sometimes receiving unnecessary alerts in the

middle of the night, which can be very frustrating for them. However, since many

notifications relate to abnormal issues arising from either the equipment or any animal,

the informational content of the notification itself can be a source of anxiety or stress, as

evidenced by Participant 1’s comment about the source of stress they experience from

their AMS, “Robot is broken. Needs fixing and I’m at work”.

In summary, the study findings show that though there are many advantages of PLF

notification, in some cases, notifications work as a source of stress for farmers. A

notifications study from the field of HCI found that notifications sometimes create stress

and interruptions, especially while doing important tasks (Pielot & Rello, 2015). The

correlation analysis presented in Section 4.6 suggest that farm size and years of

experience play important roles in the level of stress experienced by farmers. The

findings show a moderate positive correlation between the size of the farm and the

number of alerts they reported receiving daily (r=0.435). A positive relationship between

these two variables makes sense, as the more cows being managed should logically

produce more issues about which a farmer may be notified. In fact, one would expect a

strong correlation between these variables, stronger than was found. This is likely

explained by the strong negative correlation that was found between the years of

experience of the farmer and the level of stress they experienced related to PLF

notifications (r=-0.709). Over time, many farmers learn to adjust their PLF notification

settings to help better manage the communication medium (or settings of that medium),

the information they receive, and when they receive different types of information. For

instance, Participant 17, an AHDS user, commented that they “changed the alerts on

my device so it just vibrates” to help mange the stress related to receiving PLF

95

notifications. They also mentioned, “As I get more used to receiving alerts (we just

started [with their AHDS] less then a year ago) I start to feel less anxious.” (P17). With

experience, farmers learn better how to deal with notifications and develop strategies to

help manage reduce stress related to receiving PLF notifications, as discussed below.

Though, there will be pros and cons of PLF technologies, the study revealed that the

positive effects of these technologies significantly outweigh the disadvantages, at least

for the study participants. Most of the technologies are at their initial stages and will be

improved day by day.

5.3.2 Strategies to Overcome Stress

The study revealed that, as farmers become familiar with their PLF systems, they find

strategies to cope with the numerous notifications sent by these systems and to help

them manage stress associated with receiving these notifications.

As discussed above, many farmers change the settings on their PLF systems to change

the type, urgency level, timing, or delivery method (i.e. medium) of system notifications

as a means of managing the stress associated with PLF notifications.

Some farmers teach their family members how to operate their farms so that, in case of

the farmers’ absence, their family members can take care of the farm, and if any issue

arises, their family members can handle it. Other farmers reported managing their

stress by keeping themselves relaxed by listening to music while they are working.

Another strategy to manage stress related to PLF systems uncovered in the study was

to install multiple, redundant systems so that if one fails, another one can back it up.

Participants 1 and 2 both reported installing more than one AMS, even when it was not

necessarily due to the number of cows on their farm. This way they have a backup if

one breaks down. If an AMS breaks down, there can be significant milk production loss

and farmers (and their animals) can be very stressed, as evidenced by Participant 2’s

96

explanation of why they installed two robots for their 70 cows, “Before when we had 60

cows on one robot, stress level was extremely high as any downtime was affecting

production, potentially cattle health, and income.”

In the future, technologies should allow users to customize the settings to receive

notifications based on their own preferences. Whenever a technology is installed on a

farm the farm staff should be trained to understand the technology and notification

mechanism so they can understand the notifications better. It will help minimize their

stress when they receive a non urgent notification. As mentioned above, prioritizing

notifications and reducing false alerts by improving the technology may be one strategy

to reduce stress for the farmers (Dominiak & Kristensen, 2017). As mentioned in

Section 5.2.2, adopting design concepts from the ‘Scope’ visualization display (Van

Dantzich et al., 2002), where notifications are spatially arranged based on the urgency

level on the application dashboard screen may be another way to reduce stress.

Notification timing could be determined based on the urgency level of the notifications to

reduce stress as proposed by Dabbish & Baker (2003).

5.4 Discussion on Miscellaneous Findings

Consistent with this study, which found that eleven out of eighteen participants (61.1%)

use AMS on their farm, previous research has found that many Ontario farms use AMS

(Duncan, 2018; Rodenburg, 2012). Surprisingly though, the study also found that AHDS

and AAMS are frequently used as well. Sixteen participants (88.9%) reported using

AHDS on their farms, and thirteen participants (72.2%) said that they use AAMS on

their farms (much fewer (5 participants) reported receiving automated notifications from

their AAMS systems). This finding was unexpected based prior discussions with the

dairy experts and the literature review.

Another surprising finding was that most ACTDS, AHDS and AAMS technologies do not

send the location of the cow to the farmers. In a study of ACTDS in Italy (Calcante et al.,

97

2014), they reported that farmers receive a cow's geographical coordinates when they

receive calving alerts. However, their study was about outdoor farming. In Ontario,

many dairy farms are indoor facilities. Thus, this may be the reason behind not receiving

the cow's location. However, recent PLF innovations, such as the commercial

SmartBow®29 ear tag-based activity monitor (©Zoetis, Austria), provides the precise

location of a cow within a barn setting, visualized in the system’s mobile, tablet or

desktop app, based on an installed network of localization receiver nodes. Based on

discussions I had with Zoetis company representatives, the SmartBow technology was

recently acquired by a start-up in Europe; thus, it seems this type of indoor location

capability is only a recent technology capability. Yet, for large farms especially, having a

cow’s location provided with any notification related to an urgent, time-critical issue

would help facilitate immediate and appropriate actions.

Though return on investment (RoI) is something essential to think about before

integrating any new technologies on a farm (Russell & Bewley, 2013), this is not always

a farmers’ top priority, or only priority, when adopting a technology. In this study, some

farmers reported that they sometimes adopted technologies to go with the flow, to keep

themselves up with the technology, and to change their workload and lifestyles. This

finding is similar to the previous study conducted in Europe (Billon & Pomiès, 2006).

They found that some farmers adopt technology to maintain their status and pride within

their circle. Therefore, technology need not always bring direct financial benefits for

farmers; instead, some technologies can improve their lifestyles, or reduce their mental

and physical workload. In Ontario, farmers need to provide data to the regulatory board

about the milk to ensure that their milk is of good quality. AMS made their life easier to

collect those data about their milk for the regulatory board since the data for regulatory

29 https://www.smartbow.com/

98

board are automatically collected by the AMS and kept available for the regulatory

authority to inspect anytime30.

On the other hand, farmers also get to know the substances and quality of their

produced milk, and that helps them to improve the milk quality. Because if they know

about any unusual content such as somatic cell count, they can resolve this issue by

taking necessary steps like changing feed to enhance or ensure the quality of their

produced milk (Alhussien & Dang, 2018).

A key benefit of the PLF technologies reported in this study was that they helped

farmers manage animals’ health. The study found that all technologies report abnormal

issues to the farmers. This finding is consistent with previous research (Colak et al.,

2008; King & DeVries, 2018; Kossaibati & Esslemont, 1997; Mee, 2004; Tse et al.,

2017). Reporting of abnormal issues has helped to reduce a remarkable amount of

physical workload. Nowadays, farmers do not need to check every individual cow and

equipment separately since they also receive alerts about technology breakdowns.

A few farmers also reported that they miss urgent alerts from technologies, possibly

caused by two main reasons. One reason might be the excessive number of

notifications, which can result in overlooking an urgent alert. Another reason might be

the technology could not detect the issue. Notifications can be classified and prioritized

based on the urgency level as mentioned above to reduce excessive number of

notifications. A notification’s medium and timing should be chosen based on the

urgency level and current situation of the farmers, and notifications can be displayed in

the dashboards based on the urgency level. The number of false positives or false

negatives detected by the system can be reduced by improving the algorithms and by

30 https://www.dairyinfo.gc.ca/eng/acts-regulations-codes-and-standards/automatic-milking-system-

guidelines/?id=1591199898715#fn2-1-rf

99

using the better sensors to collect accurate data. In the future, it should be ensured that

farmers do not miss any urgent alert which needs immediate attention or vital for their

farm management, gross profit, and production.

5.5 Summary

In conclusion, PLF technologies have many positive effects, such as reduced mental

and physical workload, improved livestock health, ease of data collection and improved

milk products. However, many issues and themes were identified in this study, which

should be improved in future PLF notification designs such as uncertainty, false alerts,

missed alerts, inappropriate communication timing and medium, information overload

and increased stress. Uncertainty, false alerts, and missed alerts can be addressed by

improving the technology's performance and accuracy. However, notifications could

also include some indication of the certainty of reported issues to help farmer’s judge

appropriate decision and actions needed to respond to notifications.

PLF notification mechanisms should match appropriate communication mediums with

message content. For instance, urgent messages should use communication mediums

that afford simultaneous communication (i.e. a farmer can receive the message

immediately when it is sent, demanding their attention), or should use multiple,

redundant mediums to ensure that a critical problem is attended to immediately and not

missed. If a message contains verbatim information (e.g., a cow identification number),

using a communication medium that allows the message content to be reviewed (e.g.,

replayed or re-read) will facilitate information comprehension. Also, urgent, time-critical

notifications should contain all relevant information farmers need to make decisions and

take actions. Additional information look-up should be extremely easy and effective to

do, minimizing necessary mental and physical workload.

100

Conclusion

The goals of this research were to understand the existing notification mechanisms

used by PLF technologies to communicate with farmers in the Canadian dairy industry

and to investigate the positive and negative effects of these mechanisms on dairy

farmers. Uncovering this information helped to reveal ways to design better PLF

technologies for the dairy industry. This chapter discusses the scholarly contributions of

this research, limitations of the research regarding its coverage of the landscape of this

topic in Canada, and recommended directions for future work in this domain.

6.1 Scholarly Contributions

This section revisits the thesis research objectives described in Chapter 1 and

discusses the contributions this research has made towards satisfying them.

Objective 1: To understand the current knowledge base regarding PLF notifications in

dairy and other livestock farming, their usability, and known effects on farming practices

by conducting a systematic literature review.

To date, there is no systematic literature review focusing on the notification mechanisms

of dairy farming technologies and their effects on farmers in the literature. Therefore,

this study fills this gap, and satisfies Objective 1, by providing a systematic review on

this topic. This literature review will help kick start future researchers’ knowledge of

notification mechanisms of existing dairy farming technologies and their effects on dairy

farmers. The systematic review will help readers learn the existing knowledge in the

literature about how PLF technologies send notifications to farmers, the timing of

notifications, types of information farmers receive from PLF technologies, and decisions

101

farmers make upon receiving information. The literature review revealed that PLF

technologies sometimes send false alerts and excessive information, causing farmers to

experience stress due to ineffective communication techniques between farmers and

PLF technologies.

Objective 2: To understand what notification mechanisms are used by PLF

technologies deployed on dairy farms in Canada and what the perceived benefits and

limitations are of these technologies, including stress-related effects of current

notification mechanisms.

To address this objective, an online survey and phone interviews of dairy farmers in

Ontario, Canada was designed to gather feedback directly from farmers on their

experiences with these technologies. This study investigated the Canadian dairy

industry through Ontario since Ontario is one of the prominent contributing provinces in

the Canadian dairy industry. User surveys and interviews were chosen as research

methodologies for this research to address the PLF field’s lack of user-centric

knowledge about farmers’ informational needs since farmers’ voices are seldom heard

while developing PLF technologies (Hartung et al., 2017). The study specifically probed

the use of the most popular commercial dairy PLF technologies found in the literature

review, including automated milking system (AMS), automated heat detection system

(AHDS), automated calving time detection system (ACTDS), and automated activity

monitoring system (AAMS). A few other PLF technologies were reported being used by

farmers in the survey’s free form comments, including automated feeding systems, milk

analysis systems that check for milk quality and biologically detected health issues like

ketosis and mastitis.

This thesis presents the findings from the survey of 18 farmers that were collected

before the shutdown due to COVID-19 pandemic and is intended to serve as a pilot

study for a full study at a later date.

102

Despite the limited data that was collected, the study findings still provide many useful

insights on the topic and at least partially satisfy Objective 2. The study revealed that

the most frequently used communication medium used for notification is the dashboard,

i.e., a software application associated with the installed PLF technology that may run on

mobile phone, tablet, or desktop computer, depending on the PLF technology. Other,

less frequently used mediums are phone calls, email, and text messaging. Phone calls

were only used for AMS, whereas AHDS, ACTDS, and AAMS systems notified farmers

through the dashboard, emails, or text messages. Technologies such as AHDS and

AAMS are relatively new and are used on farms to notify farmers about individual

animal health issues.

The study also revealed that sometimes farmers feel stressed because of the

information overload they receive, uncertain information and necessary actions,

ineffective alert management, inappropriate timing and communication medium,

technology breakdown, and missed alerts. A correlation analysis found a relationship

between stress versus experience and age. The analysis suggests that farmers are

more likely to be stressed on larger farms because they typically receive more alerts.

However, the correlation analysis also found that more experienced farmers are less

likely to be stressed. The qualitative findings support this, as they show that many

farmers learn how to handle the stress created by PLF notifications by developing

different strategies to manage them over time. Farmers follow various strategies to

overcome the stress due to inappropriate notification mechanisms. These include

adjusting the system’s factory defaults for notifications to minimize non-critical

notifications, for instance, by sending only urgent notifications to attention-grabbing

communication mediums like phone call or text, and redirecting less urgent notifications

to other mediums, like the dashboard, especially at certain times like overnight. Farmers

also reported installing multiple pieces of equipment to mitigate stresses related to

equipment failures, so that backup is available. Numerous positive benefits of PLF

103

notifications were also uncovered by the survey, including reduced workload, improved

livestock health, ease of data collection, improved milk product and decreased stress.

Objective 3: To identify opportunities for improving the design of notification

mechanisms for precision dairy technologies.

Despite the numerous benefits provided by PLF notifications and coping strategies

farmers have developed to overcome the stress they sometimes experience related to

these notifications, the study also revealed insights that can help improve their design in

the future to help alleviate the stress and unnecessary mental or physical workload they

cause. Addressing these design issues and improving the overall usability and utility of

these notification mechanisms, based on farmers’ informational needs, will help make

these technologies more acceptable to farmers (Russell & Bewley, 2013). Based on the

study findings, the following strategies are recommended to help improve PLF

notification mechanisms. These recommendations are preliminary, due to the limited

nature of this study as a pilot study. Where possible, the recommendations are

grounded in existing design strategies and advice from other fields that have studied

similar types of notification issues to provide a broader basis for generalization beyond

this particular pilot study.

Despite the limitations of a pilot study, many of the study findings are consistent with

prior PLF studies. Thus, the generalizability of these findings to the broader dairy

farming population can be made with some confidence. New findings should be

generalized more cautiously until a study of a larger Ontario, or Canadian, dairy farming

population is conducted. Nonetheless, this study provides evidence of real problems

and benefits experienced by the sampled population. The design advice provided in this

thesis is meant to be general enough to help technology designers build on the insights

gained in this pilot study while gathering further user feedback from those beyond the

sampled population to help build a deeper understanding of the problems Canadian

104

dairy farmers experience with PLF notification mechanisms, all while retaining the

uncovered benefits.

To address the uncovered issues of ineffective notification mechanisms, it is suggested

to better match the communication mediums used by PLF technologies, and the

characteristics of the messages being conveyed in the notifications messages, including

the urgency level, the type of information (e.g., verbatim information), and the timing of

the notification, with the affordances of the communication medium, as discussed in

Section 5.1.3. Further, to address the uncovered challenges related to technology

reliability, especially in newer, still maturing PLF technologies, it is recommended that

notifications incorporate some indication of the likelihood (or the algorithms’ confidence)

of the detected issue to allow farmers to appropriately use the information in decision

making and actions. It is strongly recommended that manufacturers adopt iterative,

user-centric design approaches from the field of HCI or user experience design (Yvonne

et al., 2015) to better incorporate farmers’ needs and preferences into their PLF

notification mechanism designs in the future. These methods can build on the findings

of this thesis and others in the literature, while getting more direct feedback from other

farmers and relevant stakeholders as well.

Iterative design and development techniques (Eastwood et al., 2013) should be used

to create PLF technologies by keeping farmers in the loop for better usability of the

technologies during design and development. Iteratively obtaining feedback directly

from farmers and other relevant stakeholders on specific design concepts and after

each technology deployment can help develop more usable technologies (Jago et al.,

2013). Thus, in each implementation, there will be an improvement in the technology

based on the farmers’ needs and demands. HCI approaches, such as surveys,

interviews, direct observations, and usability testing (Donald & Stephen, 1986;Jan &

Chris, 1999; Kabir, 2016; Rogers et al., 2015) can be used to engage users and

stakeholders effectively in the design and development plan of technology to help

105

designers better understand users’ needs and desires. Another HCI technique that can

be used to help integrate farmers’ needs and desires into PLF technology is

participatory design methods, which integrate domain experts, such as farmers, closely

in the design activities to bring strong domain knowledge and experience to the design

team (Westerlaken & Gualeni, 2016).

Participatory design methods can be used to design PLF technologies involving

farmers since they can bring farming operational knowledge and experience to the PLF

design process. The literature review found a lack of farmer involvement into the design

process of PLF technologies, creating a gap between farmers’ needs and available

technologies. Involving users in the design process will help manufacturers understand

farmers’ needs, which can lead to more appropriate and usable designs for the target

user population (Ashby & Sperling, 1995; Klerkx & Leeuwis, 2008). As farming practices

different from country to country, and even between local regions, manufacturers should

involve farmers from different regions to ensure appropriate domain knowledge is being

integrated into the technology designs. It is also essential to see how farmers interact

with frequently used technologies, such as AMS, to affirm there is a need for

improvement and what explicit aspects should be enhanced for increasing user

satisfaction while interacting with this technology (Mancini, 2017). Otherwise, farmers

will have negative impressions of the technology, which will hinder future adoption of the

PLF technologies.

A simple interface can be a crucial feature to attract the farmers in using technology,

but at the same time necessary functionalities should have to be ensured. This study

revealed that farmers sometimes face information overload and receive unnecessary

information. Therefore, some features such as making common tasks easy, making

communication clear, talking in users’ own language and providing beneficiary shortcuts

should be offered by a technology to make the technology simple to use. Unnecessary

complexity in using technology will hinder farmers from adopting the technology and

106

decrease profit. Simplicity in navigation and functionality will add user friendliness to the

technology and lead to more adoption of the technology on the farms. On the other

hand, PLF technologies should also present as much relevant information as possible to

farmers. Therefore, PLF technologies should maintain minimum complexity but

maximum relevant informational content.

The adoption of technology can be successful with proper training to the farmers about

the technology and enough support from providers (Ndour et al., 2017). Most of the

times, farmers will also consider the return on investment (RoI) before incorporating

technology into their farm (Russell & Bewley, 2013). Though the study revealed that

sometimes farmers incorporate technologies in their farm to go with the flow and keep

themselves up with the technology, RoI is still an important factor to consider as other

research suggests. Therefore, manufacturers should develop more farmer-friendly

technologies which will bring benefits for farmers in some form such as reduced

physical work, improved livestock health, etc. On the other hand, it is clearly desirable if

the technology can bring increased profits, but this is not always necessary. Also, in the

initial stages of incorporating a technology, manufacturers should provide training and

complete guidelines about features and advantages, including RoI, to the farmers for

the technology to be appropriately leveraged on the farms.

6.2 Limitations and Challenges

The main challenge of this study was participant recruitment, and therefore, survey

distribution (at least for the first phase). As mentioned above, the COVID-19 pandemic

introduced a barrier to this process. A key planned distribution channel was a farmer-

based organization in Ontario that could not distribute the survey due to additional

workload introduced by the outbreak. They had around 3,500 dairy farmers in their

network, almost 80% of Ontario dairy farmers. Therefore, the sample size of this study

was small given the total dairy farming population in Ontario. As described in Section

107

3.2.3, the survey was distributed mainly through TwitterTM accounts of colleagues and

University of Guelph connections and communications staff, and connections at the

Ontario Ministry of Agricultural and Rural Affairs (OMAFRA). Additionally, the research

team joined various Ontario farming online social media groups to distribute the survey

beyond their immediate network. Therefore, the survey advertisement reached mostly to

the farmers who use TwitterTM and other social media. Thus, there was a bias in the

survey distribution towards social media users. To remove this bias, the research team

physically attended the Southwestern Ontario Dairy Symposium in Woodstock, Ontario

to advertise the research project using paper flyers that invited farmers to take the

survey online.

Due to the disruption of the survey distribution, the survey did not cover all parts of

Ontario. Of those participants who identified a location, most were from various places

in Southwestern Ontario, and all were from Southern Ontario. The identified locations,

however, did represent areas that would experience a variety of quality of Internet and

cell phone coverage. This is an important factor when considering different experiences

that farmers may have with PLF notifications, as phone calls, text messaging, access to

email or an application dashboard may require some form of cell phone/data service or

WiFi reception if the technology directs to a mobile phone or tablet. Therefore, while the

study is considered a pilot study it may still represent a reasonable representation of the

larger Ontario, or Canadian dairy farming landscape. As mentioned in Section 3.5,

some survey records were culled before data analysis. Unusual surveys were dropped

to increase reliability of the data, but it may be possible that some valid data were

dropped which is very difficult to determine while collecting data through an online

survey.

There was an ambiguity in the design of the survey in skipping questions. In a single

page there were questions related to a single technology, but the last question was

asking whether the participant use the next technology in the survey or not. A

108

participant can skip a single question or can skip all the questions by simply clicking

next button at the lower right corner of the page. For instance, a participant wanted to

skip all the questions related to a single technology, so the participant skipped the full

page. When the participant skipped the full page, the question about whether the

participant use the next technology was also skipped. Therefore, the participant saw the

questions related to the next technology regardless of whether the participant uses the

technology or not. Thus, there was a case where the number of notification receivers

was more than the users of the technology. To remove this ambiguity in the future, the

technology use question for each separate technology should be placed on a separate

page.

6.3 Future Work

It is a vital challenge to adjust the design trade-offs between ease of use and providing

more capabilities while developing a technology for users, or farmers in this case (Dudi,

2005). Manufactures build their technology to ease farm management for the farmers,

but sometimes it creates more difficulties in some cases. For instance, farmers who use

PLF technologies may never feel off duty because they think that they might get a call

anytime or might receive a system notification at any moment (Hansen, 2015). This

study found that farmers often adjust their notification settings to send different

categories of notifications, such as urgent or non-urgent notifications, at different times

and using different communication mediums. The data suggest that not all PLF

technologies allow for these adjustments, or perhaps it is not obvious to the users that

this is possible. This issue should be resolved in future technologies. Also, use of

multiple communication mediums that integrate together to help the workflow of being

notified and then finding out the necessary information for decision making and action

could be used, as one study participant commented their AHDS system uses:

109

“I receive … an alert on my phone through my app that is connected to

the main computer where the heat system operates.” (P11)

Innovators can integrate mobile applications with each of the technologies (AMS,

AAMS, ACTDS, AHDS) to receive alerts through apps on their phones and check the

issues on their phones. Further research is needed, however, to determine the best

combinations of communication mediums to use for notifications regarding different

types of system events and various situational contexts the farmer may be in when

receiving those notifications.

Many participants reported that some notifications are missing key information, e.g., the

cow identification number, or that information was uncertain. Further work is needed to

better determine the critical information that farmers need for each type of notification

situation, as well as improved ways to provide that information to farmers in different

contexts.

Another area of future work is to collect additional survey data to extend this pilot study.

Ideally, partnering with a large dairy organization to help with survey distribution would

help in participant recruitment. Before running the larger study, however, it is worth

gathering more feedback on the survey questions from farmers to increase the

acceptability of the questions by the farmers since one farmer reported ambiguity in the

language of a question in this study. To get deeper insights about dairy technologies

and their effects on the farmers, farmers’ interviews can also be included in future

studies.

An interesting finding that can be explored in the future is that, although ACTDS and

AHDS are very similar types of technologies, participants reported different levels of

stress related to notifications from these two systems. AHDS users reported that they

are pleased and not stressed at all with AHDS notifications. On the other hand, farmers

indicated that ACTDS did not decrease their stress and sometimes increased their

110

anxiety. This issue should be studied in the future in more detail to identify the reason.

ACTDS might be less reliable, or the outcome of this study may not capture the whole

set of experiences with these two technologies due to the small number of participants.

Finally, a single study for a single technology can also be done to precisely learn about

the notification mechanism of that technology in detail, its effect on the farmers and

recommendations to improve the technology. Additional technology research can be

done on communicating reliability information of detected events to users. Many

participants reported receiving false alerts from some of the newer technologies on the

market, including AHDS, ACTDS, and AAMS. These are not yet mature technologies,

so they are not yet capable of providing perfect detection of the issues they monitor. As

discussed in Section 2.6, interesting approaches in the human factors and human-

automation literature suggest that providing users information about the likelihood of the

detected event may help users make appropriate decisions and actions in response to

system notifications. As users can lose trust in unreliable technology over time, further

research on the use of likelihood or confidence in detected PLF events seems

warranted to help users make the most effective use out of these emerging

technologies.

6.4 Concluding Remarks

Although farmers are sometimes stressed because of the current notification systems,

they also assist farmers in several ways, such as improved animal health, flexible and

reduced working hours, ease of data collection, and improved milk products. By

following the recommendations of this study and doing more research in the future,

precision dairy farming technologies can indeed be farmers' friends, not the foe and will

not only be the engineers’ daydreams.

111

REFERENCES

Adamczyk, P. D., & Bailey, B. P. (2004). If not now, when?: The effects of interruption at

different moments within task execution. Conference on Human Factors in

Computing Systems - Proceedings, 6(1), 271–278.

Afergan, D., Hincks, S. W., Shibata, T., & Jacob, R. J. K. (2015). Phylter: A system for

modulating notifications in wearables using physiological sensing. Lecture Notes in

Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and

Lecture Notes in Bioinformatics), 9183, 167–177. https://doi.org/10.1007/978-3-

319-20816-9_17

Alhussien, M. N., & Dang, A. K. (2018). Milk somatic cells, factors influencing their

release, future prospects, and practical utility in dairy animals: An overview.

Veterinary World, 11(5), 562–577. https://doi.org/10.14202/vetworld.2018.562-577

Allain, C., Chanvallon, A., Courties, R., Billon, D., Bareille, N. (2016). Technical,

economic and sociological impacts of an automated estrus detection system for

dairy cows. Precision Dairy Farming, 451–456.

https://prodinra.inra.fr/?locale=en#!ConsultNotice:409563

Alsaaod, M., Fadul, M., & Steiner, A. (2019). Automatic lameness detection in cattle.

Veterinary Journal, 246, 35–44. https://doi.org/10.1016/j.tvjl.2019.01.005

Ashby, J. A., & Sperling, L. (1995). Institutionalizing Participatory, Client‐Driven

Research and Technology Development in Agriculture. Development and Change,

26(4), 753–770. https://doi.org/10.1111/j.1467-7660.1995.tb00573.x

Bahir, R. A., Tractinsky, N., & Parmet, Y. (2019). Effects of visual enhancements and

delivery time on receptivity of mobile push notifications. Conference on Human

Factors in Computing Systems - Proceedings, 5–10.

112

https://doi.org/10.1145/3290607.3312993

Bailey, B. P., Konstan, J. A., & Carlis, J. V. (2000). Measuring the effects of

interruptions on task performance in the user interface. Proceedings of the IEEE

International Conference on Systems, Man and Cybernetics, 2, 757–762.

https://doi.org/10.1109/ICSMC.2000.885940

Bailey, B. P., Konstan, J. a, & Carlis, J. V. (2001). The Effects of Interruptions on Task

Performance, Annoyance, and Anxiety in the User Interface. Proceedings of

INTERACT ’01, 593–601. https://doi.org/10.1109/ICSMC.2000.885940

Banhazi, T. M., Lehr, H., Black, J. L., Crabtree, H., Schofield, P., Tscharke, M., &

Berckmans, D. (2012). Precision Livestock Farming: An international review of

scientific and commercial aspects. International Journal of Agricultural and

Biological Engineering, 5(3), 1–9. https://doi.org/10.3965/j.ijabe.20120503.00?

Beller, J., Heesen, M., & Vollrath, M. (2013). Improving the driver-automation

interaction: An approach using automation uncertainty. Human Factors, 55(6),

1130–1141. https://doi.org/10.1177/0018720813482327

Berckmans, D. (2014). Precision livestock farming technologies for welfare

management in intensive livestock systems. OIE Revue Scientifique et Technique,

33(1), 189–196. https://doi.org/10.20506/rst.33.1.2273

Billon, P., & Pomiès, D. (2006). Le point sur la robotisation de la traite 15 ans après

l’apparition des premiers systèmes dans les fermes Robotic milking 15 years after

its first implementation on commercial farms. Renc. Rech. Ruminants, 1, 13.

http://www.journees3r.fr/IMG/pdf/2006_5_identification-

elec_automatismes_01_Billon.pdf

Bongiovanni, R., & Lowenberg-Deboer, J. (2004). Precision agriculture and

113

sustainability. Precision Agriculture, 5(4), 359–387.

https://doi.org/10.1023/B:PRAG.0000040806.39604.aa

Borchers, M. R., & Bewley, J. M. (2015). An assessment of producer precision dairy

farming technology use, prepurchase considerations, and usefulness. Journal of

Dairy Science, 98(6), 4198–4205. https://doi.org/10.3168/jds.2014-8963

Breedyk, J. (2010). Institution of Presynch Protocol.

https://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?referer=https://www.google.

com/&httpsredir=1&article=1036&context=dscisp

Buchanan, J., & Kock, N. (2001). Information Overload: A Decision Making Perspective.

49–58. https://doi.org/10.1007/978-3-642-56680-6_4

Cabrera, V. E., Barrientos-Blanco, J. A., Delgado, H., & Fadul-Pacheco, L. (2020).

Symposium review: Real-time continuous decision making using big data on dairy

farms. Journal of Dairy Science, 103(4), 3856–3866.

https://doi.org/10.3168/jds.2019-17145

Calcante, A., Tangorra, F. M., Marchesi, G., & Lazzari, M. (2014). A GPS / GSM based

birth alarm system for grazing cows. COMPUTERS AND ELECTRONICS IN

AGRICULTURE, 100, 123–130. https://doi.org/10.1016/j.compag.2013.11.006

Canadian Dairy Information Centre (2019). Number of farms, dairy cows and dairy

heifers. https://www.dairyinfo.gc.ca/eng/dairy-statistics-and-market-

information/farm-statistics/farms-dairy-cows-and-dairy-heifers/?id=1502467423238

Clark, H. H., & Brennan, S. E. (2004). Grounding in communication. Perspectives on

Socially Shared Cognition., 127–149. https://doi.org/10.1037/10096-006

Colak, A., Polat, B., Okumus, Z., Kaya, M., Yanmaz, L. E., & Hayirli, A. (2008). Short

Communication: Early Detection of Mastitis Using Infrared Thermography in Dairy

114

Cows. Journal of Dairy Science, 91(11), 4244–4248.

https://doi.org/10.3168/jds.2008-1258

Czerwinski, M., Cutrell, E., & Horvitz, E. (2002). Instant messaging - An instant threat.

Computer Fraud and Security, 2002(11), 19–20. https://doi.org/10.1016/S1361-

3723(02)01112-0

Dabbish, L. A., & Baker, R. S. (2003). Administrative assistants as interruption

mediators. Conference on Human Factors in Computing Systems - Proceedings,

1020–1021. https://doi.org/10.1145/765891.766127

Dairy Farmers of Canada. (n.d.). Dairy Farmers of Canada “Our Economic

Contribution.” https://dairyfarmersofcanada.ca/en/who-we-are/our-commitments

de Mol, R. M., André, G., Bleumer, E. J. B., van der Werf, J. T. N., de Haas, Y., & van

Reenen, C. G. (2013). Applicability of day-to-day variation in behavior for the

automated detection of lameness in dairy cows. Journal of Dairy Science, 96(6),

3703–3712. https://doi.org/10.3168/jds.2012-6305

De Vries, A. (2006). Economic value of pregnancy in dairy cattle. Journal of Dairy

Science, 89(10), 3876–3885. https://doi.org/10.3168/jds.S0022-0302(06)72430-4

Désire C, & Hostiou, N. (2015). Mémoire de fin d ’ études présenté pour l ’ obtention du

Diplôme d ’ Ingénieur Agronome Option : Elevage en Milieux Difficiles L ’ Elevage

de Précision : changements dans l ’ organisation du travail et la gestion de données

dans des exploitations laitière. Actes Des 4e Rencontres Nationales Sur Le Travail

En Élevage, 5-6 novembre 2015, Dijon, France. Paris : Inra, 62-63.

Dolecheck, K. A., Silvia, W. J., Heersche, G., Chang, Y. M., Ray, D. L., Stone, A. E.,

Wadsworth, B. A., & Bewley, J. M. (2015). Behavioral and physiological changes

around estrus events identified using multiple automated monitoring technologies.

115

Journal of Dairy Science, 98(12), 8723–8731. https://doi.org/10.3168/jds.2015-9645

Dominiak, K. N., & Kristensen, A. R. (2017). Prioritizing alarms from sensor-based

detection models in livestock production - A review on model performance and

alarm reducing methods. In Computers and Electronics in Agriculture (Vol. 133, pp.

46–67). https://doi.org/10.1016/j.compag.2016.12.008

Donald A. Norman, & Stephen W Draper. (1986). User Centered System Design: New

Perspectives on Human-computer Interaction. L. Erlbaum Associates Inc.365

Broadway Hillsdale, NJUnited States.

Du, N., Huang, K. Y., & Yang, X. J. (2019). Not All Information Is Equal: Effects of

Disclosing Different Types of Likelihood Information on Trust, Compliance and

Reliance, and Task Performance in Human-Automation Teaming. Human Factors.

https://doi.org/10.1177/0018720819862916

Dudi Hendrawan. (2005). Penentuan Pola Subsidi dan Sistem Distribusi Pupuk di

Indonesia. Program Pascasarjana Manajemen Dan Bisnis IPB, 33(1), 189–196.

https://doi.org/10.20506/rst.33.1.2273

Duncan, E. (2018). An Exploration of how the Relationship between Farmers and

Retailers influences Precision Agriculture Adoption.

Eastwood, C., Trotter, M., & Scott, N. (2013). Understanding the user: Learning from

on-farm application of precision farming technologies in the Australian livestock

sector. Australian Journal of Multi-Disciplinary Engineering, 10(1), 41–50.

https://doi.org/10.7158/n12-ae04.2013.10.1

Eckelkamp, E. A. (2019). INVITED REVIEW: Current state of wearable precision dairy

technologies in disease detection. Applied Animal Science, 35(2), 209–220.

https://doi.org/10.15232/aas.2018-01801

116

Enting, H., Kooij, D., Dijkhuizen, A. A., Huirne, R. B. M., & Noordhuizen-Stassen, E. N.

(1997). Economic losses due to clinical lameness in dairy cattle. Livestock

Production Science, 49(3), 259–267. https://doi.org/10.1016/S0301-

6226(97)00051-1

Ferris, M. C., Christensen, A., & Wangen, S. R. (2020). Symposium review: Dairy

Brain—Informing decisions on dairy farms using data analytics. Journal of Dairy

Science, 103(4), 3874–3881. https://doi.org/10.3168/jds.2019-17199

Fodor, I., & Ózsvári, L. (2019). Estrus detection and its impact on reproductive and

economic performance in large dairy herds = Az ivarzókeresés gyakorlata, ill.

hatása a szaporodási és gazdasági eredményekre nagy létszámú

tehenészetekben. Animal Welfare, Etológia És Tartástechnológia = Animal Welfare,

Ethology and Housing Systems, 15(1), 18–28.

https://doi.org/10.17205/SZIE.AWETH.2019.1.018

Fu, S., Li, H., Liu, Y., Pirkkalainen, H., & Salo, M. (2020). Social media overload,

exhaustion, and use discontinuance: Examining the effects of information overload,

system feature overload, and social overload. Information Processing and

Management, 57(6). https://doi.org/10.1016/j.ipm.2020.102307

Galon, N. (2010). The use of pedometry for estrus detection in dairy cows in Israel.

Journal of Reproduction and Development, 56(SUPPL.).

https://doi.org/10.1262/jrd.1056S48

Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F.,

Pretty, J., Robinson, S., Thomas, S. M., & Toulmin, C. (2010). Food security: The

challenge of feeding 9 billion people. Science, 327(5967), 812–818.

https://doi.org/10.1126/science.1185383

González, L. A., Bishop-Hurley, G. J., Handcock, R. N., & Crossman, C. (2015).

117

Behavioral classification of data from collars containing motion sensors in grazing

cattle. Computers and Electronics in Agriculture, 110, 91–102.

https://doi.org/10.1016/j.compag.2014.10.018

Graham, K. C., & Cvach, M. (2010). Monitor alarm fatigue: Standardizing use of

physiological monitors anddecreasing nuisance alarms. American Journal of

Critical Care, 19(1), 28–34. https://doi.org/10.4037/ajcc2010651

Gutiérrez, F., Htun, N. N., Schlenz, F., Kasimati, A., & Verbert, K. (2019). A review of

visualisations in agricultural decision support systems: An HCI perspective.

Computers and Electronics in Agriculture, 163.

https://doi.org/10.1016/j.compag.2019.05.053

Halachmi, I., Guarino, M., Bewley, J., & Pastell, M. (2019). Smart Animal Agriculture:

Application of Real-Time Sensors to Improve Animal Well-Being and Production.

Annual Review of Animal Biosciences, 7(1), 403–425.

https://doi.org/10.1146/annurev-animal-020518-114851

Hansen, B. G. (2015). Robotic milking-farmer experiences and adoption rate in Jæren,

Norway. Journal of Rural Studies, 41, 109–117.

https://doi.org/10.1016/j.jrurstud.2015.08.004

Harmon, R. J. (1994). Physiology of Mastitis and Factors Affecting Somatic Cell Counts.

Journal of Dairy Science, 77(7), 2103–2112. https://doi.org/10.3168/jds.S0022-

0302(94)77153-8

Hartung, J., Banhazi, T., Vranken, E., & Guarino, M. (2017). European farmers’

experiences with precision livestock farming systems. Animal Frontiers, 7(1), 38–

44. https://doi.org/10.2527/af.2017.0107

Hiltz, S. R., & Turoff, M. (1985). Structuring computer-mediated communication systems

118

to avoid information overload. Communications of the ACM, 28(7), 680–689.

https://doi.org/10.1145/3894.3895

Hockey, C. D., Morton, J. M., Norman, S. T., & McGowan, M. R. (2010). Evaluation of a

neck mounted 2-hourly activity meter system for detecting cows about to ovulate in

two paddock-based australian dairy herds. Reproduction in Domestic Animals,

45(5). https://doi.org/10.1111/j.1439-0531.2009.01531.x

Hogeveen, H., Huijps, K., & Lam, T. J. G. M. (2011). Economic aspects of mastitis: New

developments. New Zealand Veterinary Journal, 59(1), 16–23.

https://doi.org/10.1080/00480169.2011.547165

Inchaisri, C., Jorritsma, R., Vos, P. L. A. M., van der Weijden, G. C., & Hogeveen, H.

(2010). Economic consequences of reproductive performance in dairy cattle.

Theriogenology, 74(5), 835–846.

https://doi.org/10.1016/j.theriogenology.2010.04.008

Jago, J., Eastwood, C., Kerrisk, K., & Yule, I. (2013). Precision dairy farming in

Australasia: Adoption, risks and opportunities. Animal Production Science, 53(9),

907–916. https://doi.org/10.1071/AN12330

Jan Noyes, & Chris Baber. (1999). User-Centred Design of Systems (1st ed.). Springer-

Verlag London.

Kabir, S. M. (2016). METHODS OF DATA COLLECTION. In BASIC GUIDELINES FOR

RESEARCH: An Introductory Approach for All Disciplines (First, pp. 201–275).

Book Zone Publication, Chittagong - 4203, Bangladesh.

Kern, N., & Schiele, B. (2003). Context-aware notification forwearable computing.

Proceedings - International Symposium on Wearable Computers, ISWC, 223–230.

https://doi.org/10.1109/iswc.2003.1241415

119

Khatun, M., Thomson, P. C., Kerrisk, K. L., Lyons, N. A., Clark, C. E. F., Molfino, J., &

García, S. C. (2018). Development of a new clinical mastitis detection method for

automatic milking systems. Journal of Dairy Science, 101(10), 9385–9395.

https://doi.org/10.3168/jds.2017-14310

Kim, H., Min, Y., & Choi, B. (2019). Real-time temperature monitoring for the early

detection of mastitis in dairy cattle: Methods and case researches. Computers and

Electronics in Agriculture, 162(June 2018), 119–125.

https://doi.org/10.1016/j.compag.2019.04.004

King, M. T. M., & DeVries, T. J. (2018). Graduate Student Literature Review: Detecting

health disorders using data from automatic milking systems and associated

technologies. Journal of Dairy Science, 101(9), 8605–8614.

https://doi.org/10.3168/jds.2018-14521

Klerkx, L., & Leeuwis, C. (2008). Institutionalizing end-user demand steering in

agricultural R&D: Farmer levy funding of R&D in The Netherlands. Research Policy,

37(3), 460–472. https://doi.org/10.1016/j.respol.2007.11.007

Kossaibati, M. A., & Esslemont, R. J. (1997). The costs of production diseases in dairy

herds in England. Veterinary Journal, 154(1), 41–51. https://doi.org/10.1016/S1090-

0233(05)80007-3

Kushlev, K., Proulx, J., & Dunn, E. W. (2016). “Silence your phones”: Smartphone

notifications increase inattention and hyperactivity symptoms. Conference on

Human Factors in Computing Systems - Proceedings, 1011–1020.

https://doi.org/10.1145/2858036.2858359

Kutter, T., Tiemann, S., Siebert, R., & Fountas, S. (2011). The role of communication

and co-operation in the adoption of precision farming. Precision Agriculture, 12(1),

2–17. https://doi.org/10.1007/s11119-009-9150-0

120

Laberge, B., & Rousseau, A. N. (2017). Rethinking environment control strategy of

confined animal housing systems through precision livestock farming. Biosystems

Engineering, 155, 96–123. https://doi.org/10.1016/j.biosystemseng.2016.12.005

Lee, J. D., & Moray, N. (1994). Trust, self-Confidence, and operators’ adaptation to

automation. International Journal of Human - Computer Studies, 40(1), 153–184.

https://doi.org/10.1006/ijhc.1994.1007

López-Gatius, F., Santolaria, P., Mundet, I., & Yániz, J. L. (2005). Walking activity at

estrus and subsequent fertility in dairy cows. Theriogenology, 63(5), 1419–1429.

https://doi.org/10.1016/j.theriogenology.2004.07.007

Løvendahl, P., & Chagunda, M. G. G. (2010). On the use of physical activity monitoring

for estrus detection in dairy cows. Journal of Dairy Science, 93(1), 249–259.

https://doi.org/10.3168/jds.2008-1721

Mancini, C. (2017). Towards an animal-centred ethics for Animal–Computer Interaction.

International Journal of Human Computer Studies, 98, 221–233.

https://doi.org/10.1016/j.ijhcs.2016.04.008

Martiskainen, P., Järvinen, M., Skön, J. P., Tiirikainen, J., Kolehmainen, M., &

Mononen, J. (2009). Cow behaviour pattern recognition using a three-dimensional

accelerometer and support vector machines. Applied Animal Behaviour Science,

119(1–2), 32–38. https://doi.org/10.1016/j.applanim.2009.03.005

Mayo, L. M., Silvia, W. J., Ray, D. L., Jones, B. W., Stone, A. E., Tsai, I. C., Clark, J. D.,

Bewley, J. M., & Heersche, G. (2019). Automated estrous detection using multiple

commercial precision dairy monitoring technologies in synchronized dairy cows.

Journal of Dairy Science, 102(3), 2645–2656. https://doi.org/10.3168/jds.2018-

14738

121

McFarlane, D. C., & Latorella, K. A. (2002). The scope and importance of human

interruption in human-computer interaction design. Human-Computer Interaction,

17(1), 1–61. https://doi.org/10.1207/S15327051HCI1701_1

Mee, J. F. (2004). Managing the dairy cow at calving time. 20, 521–546.

https://doi.org/10.1016/j.cvfa.2004.06.001

Mehrotra, A., Musolesi, M., Hendley, R., & Pejovic, V. (2015). Designing content-driven

intelligent notification mechanisms for mobile applications. UbiComp 2015 -

Proceedings of the 2015 ACM International Joint Conference on Pervasive and

Ubiquitous Computing, 813–824. https://doi.org/10.1145/2750858.2807544

Miller, G. A., Mitchell, M., Barker, Z. E., Giebel, K., Codling, E. A., Amory, J. R., Michie,

C., Davison, C., Tachtatzis, C., Andonovic, I., & Duthie, C. A. (2020). Using animal-

mounted sensor technology and machine learning to predict time-to-calving in beef

and dairy cows. Animal, 14(6), 1304–1312.

https://doi.org/10.1017/S1751731119003380

Mollenhorst, H., Rijkaart, L. J., & Hogeveen, H. (2012). Mastitis alert preferences of

farmers milking with automatic milking systems. Journal of Dairy Science, 95(5),

2523–2530. https://doi.org/10.3168/jds.2011-4993

Müller, R., & Schrader, L. (2003). A new method to measure behavioural activity levels

in dairy cows. Applied Animal Behaviour Science, 83(4), 247–258.

https://doi.org/10.1016/S0168-1591(03)00141-2

Ndour, A., Loison, R., Gourlot, J.-P., Ba, K. S., & Clouvel, P. (2017). Biotechnologie,

agronomie, société et environnement = Biotechnology, agronomy, society and

environment : BASE. Biotechnol. Agron. Soc. Environ. 2017, 21(1), 22–35.

http://popups.ulg.ac.be/1780-

4507/index.php?id=13706%0Ahttp://popups.ulg.ac.be/1780-

122

4507/index.php?id=13496

Newton, J. E., Nettle, R., & Pryce, J. E. (2020). Farming smarter with big data: Insights

from the case of Australia’s national dairy herd milk recording scheme. Agricultural

Systems, 181. https://doi.org/10.1016/j.agsy.2020.102811

Nielsen, C., Østergaard, S., Emanuelson, U., Andersson, H., Berglund, B., &

Strandberg, E. (2010). Economic consequences of mastitis and withdrawal of milk

with high somatic cell count in Swedish dairy herds. Animal, 4(10), 1758–1770.

https://doi.org/10.1017/S1751731110000704

Farm & Food Care Ontario (2012). Facts & Figures About Canadian Dairy Cows.

http://www.milk.org/Corporate/PDF/Students-FarmCare_DairyCows.pdf

Orihuela, A. (2000). Some factors affecting the behavioural manifestation of oestrus in

cattle: A review. Applied Animal Behaviour Science, 70(1), 1–16.

https://doi.org/10.1016/S0168-1591(00)00139-8

Peters, A., McEwen, B. S., & Friston, K. (2017). Uncertainty and stress: Why it causes

diseases and how it is mastered by the brain. Progress in Neurobiology, 156, 164–

188. https://doi.org/10.1016/j.pneurobio.2017.05.004

Pielot, M., Church, K., & De Oliveira, R. (2014). An in-situ study of mobile phone

notifications. MobileHCI 2014 - Proceedings of the 16th ACM International

Conference on Human-Computer Interaction with Mobile Devices and Services,

233–242. https://doi.org/10.1145/2628363.2628364

Pielot, M., & Rello, L. (2015). The do not disturb challenge-a day without notifications.

Conference on Human Factors in Computing Systems - Proceedings, 18, 1761–

1766. https://doi.org/10.1145/2702613.2732704

Polat, B., Colak, A., Cengiz, M., Yanmaz, L. E., Oral, H., Bastan, A., Kaya, S., & Hayirli,

123

A. (2010). Sensitivity and specificity of infrared thermography in detection of

subclinical mastitis in dairy cows. Journal of Dairy Science, 93(8), 3525–3532.

https://doi.org/10.3168/jds.2009-2807

Reith, S., & Hoy, S. (2018). Review: Behavioral signs of estrus and the potential of fully

automated systems for detection of estrus in dairy cattle. Animal, 12(2), 398–407.

https://doi.org/10.1017/S1751731117001975

Robert D. Mason, Douglas A. Lind, W. G. M. (1983). Statistics: An Introduction. Duxbury

Pr; Subsequent edition (Oct. 1 1997).

https://doi.org/https://doi.org/10.2307/2685790

Rodenburg, J. (2012). The impact of robotic milking on milk quality, cow comfort and

labour issues.

Russell, R. A., & Bewley, J. M. (2013). Characterization of Kentucky dairy producer

decision-making behavior. Journal of Dairy Science, 96(7), 4751–4758.

https://doi.org/10.3168/jds.2012-6538

Rutten, C. J., Velthuis, A. G. J., Steeneveld, W., & Hogeveen, H. (2013). Invited review:

Sensors to support health management on dairy farms. Journal of Dairy Science,

96(4), 1928–1952. https://doi.org/10.3168/jds.2012-6107

Saint-Dizier, M., & Chastant-Maillard, S. (2012). Towards an Automated Detection of

Oestrus in Dairy Cattle. Reproduction in Domestic Animals, 47(6), 1056–1061.

https://doi.org/10.1111/j.1439-0531.2011.01971.x

Saint-Dizier, Marie, & Chastant-Maillard, S. (2015). Methods and on-farm devices to

predict calving time in cattle. Veterinary Journal, 205(3), 349–356.

https://doi.org/10.1016/j.tvjl.2015.05.006

Schewe, R. L., & Stuart, D. (2015). Diversity in agricultural technology adoption: How

124

are automatic milking systems used and to what end? Agriculture and Human

Values, 32(2), 199–213. https://doi.org/10.1007/s10460-014-9542-2

Schwarz, S., Hofmann, M. H., Gutzen, C., Schlax, S., & Von Der Emde, G. (2002).

VIEWER: A program for visualising, recording, and analysing animal behaviour.

Computer Methods and Programs in Biomedicine, 67(1), 55–66.

https://doi.org/10.1016/S0169-2607(00)00150-4

Sorge, U. S., Kelton, D. F., Lissemore, K. D., Sears, W., & Fetrow, J. (2007). Evaluation

of the Dairy Comp 305 Module “ Cow Value ” in Two Ontario Dairy Herds. Journal

of Dairy Science, 90(12), 5784–5797. https://doi.org/10.3168/jds.2006-0813

Speier, C., Vessey, I., & Valacich, J. S. (2003). The Effects of Interruptions, Task

Complexity, and Information Presentation on Computer-Supported Decision-

Making Performance. Decision Sciences, 34(4), 771–797.

https://doi.org/10.1111/j.1540-5414.2003.02292.x

Stangaferro, M. L., Wijma, R., Caixeta, L. S., Al-Abri, M. A., & Giordano, J. O. (2016).

Use of rumination and activity monitoring for the identification of dairy cows with

health disorders: Part I. Metabolic and digestive disorders. Journal of Dairy

Science, 99(9), 7395–7410. https://doi.org/10.3168/jds.2016-10907

Steeneveld, W., & Hogeveen, H. (2015). 3.1. Use of sensor systems on Dutch dairy

farms. Precision Livestock Farming Applications, 77–86.

https://doi.org/10.3920/978-90-8686-815-5_8

Talukder, S., Thomson, P. C., Kerrisk, K. L., Clark, C. E. F., & Celi, P. (2015).

Evaluation of infrared thermography body temperature and collar-mounted

accelerometer and acoustic technology for predicting time of ovulation of cows in a

pasture-based system. Theriogenology, 83(4), 739–748.

https://doi.org/10.1016/j.theriogenology.2014.11.005

125

Taneja, M., Byabazaire, J., Jalodia, N., Davy, A., Olariu, C., & Malone, P. (2020).

Machine learning based fog computing assisted data-driven approach for early

lameness detection in dairy cattle. Computers and Electronics in Agriculture, 171.

https://doi.org/10.1016/j.compag.2020.105286

Thompson, A. J., Weary, D. M., Bran, J. A., Daros, R. R., Hötzel, M. J., & von

Keyserlingk, M. A. G. (2019). Lameness and lying behavior in grazing dairy cows.

Journal of Dairy Science, 102(7), 6373–6382. https://doi.org/10.3168/jds.2018-

15717

Tse, C., Barkema, H. W., DeVries, T. J., Rushen, J., & Pajor, E. A. (2017). Effect of

transitioning to automatic milking systems on producers’ perceptions of farm

management and cow health in the Canadian dairy industry. Journal of Dairy

Science, 100(3), 2404–2414. https://doi.org/10.3168/jds.2016-11521

Tullo, E., Fontana, I., Gottardo, D., Sloth, K. H., & Guarino, M. (2016). Technical note:

Validation of a commercial system for the continuous and automated monitoring of

dairy cow activity. Journal of Dairy Science, 99(9), 7489–7494.

https://doi.org/10.3168/jds.2016-11014

Valenza, A., Giordano, J. O., Lopes, G., Vincenti, L., Amundson, M. C., & Fricke, P. M.

(2012). Assessment of an accelerometer system for detection of estrus and

treatment with gonadotropin-releasing hormone at the time of insemination in

lactating dairy cows. Journal of Dairy Science, 95(12), 7115–7127.

https://doi.org/10.3168/jds.2012-5639

Van Dantzich, M., Robbins, D., Horvitz, E., & Czerwinski, M. (2002). Scope: Providing

awareness of multiple notifications at a glance. Proceedings of the Workshop on

Advanced Visual Interfaces AVI, 267–281.

https://doi.org/10.1145/1556262.1556306

126

Van Nuffel, A., Zwertvaegher, I., Pluym, L., Van Weyenberg, S., Thorup, V. M., Pastell,

M., Sonck, B., & Saeys, W. (2015). Lameness detection in dairy cows: Part 1. How

to distinguish between non-lame and lame cows based on differences in

locomotion or behavior. Animals, 5(3), 838–860.

https://doi.org/10.3390/ani5030387

Vázquez Diosdado, J. A., Barker, Z. E., Hodges, H. R., Amory, J. R., Croft, D. P., Bell,

N. J., & Codling, E. A. (2015). Classification of behaviour in housed dairy cows

using an accelerometer-based activity monitoring system. Animal Biotelemetry,

3(1). https://doi.org/10.1186/s40317-015-0045-8

Vergé, X. P. C., Maxime, D., Dyer, J. A., Desjardins, R. L., Arcand, Y., & Vanderzaag,

A. (2013). Carbon footprint of Canadian dairy products: Calculations and issues.

Journal of Dairy Science, 96(9), 6091–6104. https://doi.org/10.3168/jds.2013-6563

Wang, L., Jamieson, G. A., & Hollands, J. G. (2009). Trust and reliance on an

automated combat identification system. Human Factors, 51(3), 281–291.

https://doi.org/10.1177/0018720809338842

Warriner, G. K., & Moul, T. M. (2015). Survey of Ontario dairy producers shows

increasing rate of robotic milking herds in the province. Geoforum.

https://doi.org/10.1016/j.geoforum.2014.12.004

Wathes, C. M., Kristensen, H. H., Aerts, J. M., & Berckmans, D. (2008). Is precision

livestock farming an engineer’s daydream or nightmare, an animal’s friend or foe,

and a farmer’s panacea or pitfall? Computers and Electronics in Agriculture, 64(1),

2–10. https://doi.org/10.1016/j.compag.2008.05.005

Wenz, J. R., & Giebel, S. K. (2012). Retrospective evaluation of health event data

recording on 50 dairies using Dairy Comp 305. Journal of Dairy Science, 95(8),

4699–4706. https://doi.org/10.3168/jds.2011-5312

127

Westerlaken, M., & Gualeni, S. (2016). Becoming with: towards the inclusion of animals

as participants in design processes. Proceedings of the Third International

Conference on Animal-Computer Interaction - ACI ’16, 1–10.

https://doi.org/10.1145/2995257.2995392

Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big Data in Smart Farming –

A review. Agricultural Systems, 153, 69–80.

https://doi.org/10.1016/j.agsy.2017.01.023

Yvonne Rogers; Helen Sharp; Jenny Preece (2015). Interaction design : beyond

human-computer interaction (4th editio). Chichester, W. Sussex : John Wiley &

Sons, cop. 2015.

Zafar, A., Islam, N., & Ahmed, Z. (2014). Computer Standards & Interfaces A review of

wireless sensors and networks ’ applications in agriculture. Computer Standards &

Interfaces, 36(2), 263–270. https://doi.org/10.1016/j.csi.2011.03.004

Zehner, N., Niederhauser, J. J., Schick, M., & Umstatter, C. (2019). Development and

validation of a predictive model for calving time based on sensor measurements of

ingestive behavior in dairy cows. Computers and Electronics in Agriculture, 161,

62–71. https://doi.org/10.1016/j.compag.2018.08.037

Zijlstra, F. R. H., Robert A, R., Leonora, A. B., & Krediet, I. (1999). Temporal factors in

mental work: Effects of interrupted activities. Journal of Occupational and

Organizational Psychology, 72(2), 163–185.

https://doi.org/10.1348/096317999166581

128

129

APPENDICES

APPENDIX A: Survey Questions and Responses

Q4 - What is your age?

`# Field Minimum Maximum Mean Std Deviation Variance Count

1 What is your age? 2.00 4.00 2.70 0.55 0.30 23

# Answer % Count

1 below 18 0.00% 0

2 18-29 34.78% 8

3 30-54 60.87% 14

4 55-64 4.35% 1

5 65 or above 0.00% 0

6 Prefer not to say 0.00% 0

Total 100% 23

130

Q5 - Gender

# Field Minimum Maximum Mean Std Deviation Variance Count

1 Gender 1.00 2.00 1.14 0.35 0.12 21

# Answer % Count

1 Male 85.71% 18

2 Female 14.29% 3

3 Not listed 0.00% 0

4 Prefer not to say 0.00% 0

Total 100% 21

131

Q6 - Farm Location in Ontario (City/County)

Farm Location in Ontario (City/County)

Uxbridge, ON Durham Region

Mississauga , Peel

Stratford

Embro. Oxford

Stratford, Perth County

Moffat/ Halton

Atwood/Perth

Woodstock/Oxford

Wellesley, On

Belmont, Elgin

New Hamburg, Oxford

Califórnia

Northumberland county

Kincardine / Bruce

Atwood/Perth

Newton, ON

Q7 - Number of cows milked last month (approximate)

132

# Field Minimum Maximum Mean Std

Deviation Variance Count

1 Number of cows milked last

month (approximate) 2.00 4.00 3.28 0.56 0.31 18

#

Answer % Count

1 under 25 0.00% 0

2 25-50 5.56% 1

3 50-99 61.11% 11

4 100-199 33.33% 6

5 200-399 0.00% 0

6 400-599 0.00% 0

7 600 or above 0.00% 0

8 Prefer not to say 0.00% 0

Total 100% 18

Q8 - Years of dairy farming experience:

# Field Minimum Maximum Mean Std

Deviation Variance Count

1 Years of dairy farming

experience: 1.00 5.00 3.06 1.27 1.61 18

133

# Answer % Count

1 0-4 years 11.11% 2

2 5-9 years 22.22% 4

3 10-19 years 38.89% 7

4 20-29 years 5.56% 1

5 30+ years 22.22% 4

6 Prefer not to say 0.00% 0

Total 100% 18 Q9 - Your primary role on the farm is:

# Field Minimum Maximum Mean Std

Deviation Variance Count

1 Your primary role on the farm is:

- Selected Choice 1.00 3.00 1.50 0.76 0.58 18

#

Answer % Count

1 Owner/Operator/Manager 66.67% 12

2 Herdsman/Lead hand 16.67% 3

3 Employee 16.67% 3

4 Prefer not to say 0.00% 0

5 Other, please specify 0.00% 0

Total 100% 18

134

Q10 - Number of working personnel in the farm

# Field Minimum Maximum Mean Std

Deviation Variance Count

1 Number of working personnel in

the farm 1.00 4.00 2.17 0.69 0.47 18

# Answer % Count

1 1 5.56% 1

2 2-9 83.33% 15

3 10-24 0.00% 0

4 25 or more 11.11% 2

5 Prefer not to say 0.00% 0

Total 100% 18

135

Q11 - How many Automatic Milking Systems (AMS) (i.e. milking robots) does your farm have, if any?

# Field Minimum Maximum Mean Std

Deviation Variance Count

1

How many Automatic Milking Systems (AMS) (i.e. milking

robots) does your farm have, if any?

1.00 4.00 2.22 1.13 1.28 18

#

Answer % Count

1 0 (we do not have milking robots) 38.89% 7

2 1 16.67% 3

3 2 27.78% 5

4 3 or more 16.67% 3

5 Prefer not to say 0.00% 0

Total 100% 18

136

Q12 - Do you receive automated messages (alerts, alarms, etc.) from any of your Automatic Milking Systems (AMS)?

# Field Minimum Maximum Mean Std

Deviation Variance Count

1

Do you receive automated messages (alerts, alarms, etc.)

from any of your Automatic Milking Systems (AMS)?

1.00 2.00 1.09 0.29 0.08 11

# Answer % Count

1 Yes 90.91% 10

2 No/Not Sure 9.09% 1

3 Prefer not to say 0.00% 0

Total 100% 11

Q13 - When was your first Automatic Milking System (AMS) installed?

# Field Minimum Maximum Mean Std

Deviation Variance Count

1 When was your first Automatic

Milking System (AMS) installed? 2.00 3.00 2.78 0.42 0.17 9

137

#

Answer % Count

1 Less than 1 year ago 0.00% 0

2 1-2 years ago 22.22% 2

3 3 or more years ago 77.78% 7

Total 100% 9

Q14 - On a typical week, approximately how many automated messages (alerts, alarms, etc.), of any kind do you receive from your AMS? (slide the indicator on the scale below left or right)

# Field Minimum Maximum Mean Std Deviation Variance Count

1 1 2.00 150.00 27.38 46.66 2176.73 8

Q15 - How are the automated messages (alerts, alarms, etc.) from your AMS sent to you? (Please select all that apply)

# Answer % Count

1 Text Messaging 7.69% 1

2 Phone Call 53.85% 7

3 Email 7.69% 1

4 Display in a dashboard / desktop computer application 30.77% 4

5 Prefer not to say 0.00% 0

Total 100% 13

138

Q16 - Do you consider the automated messages (alerts, alarms, etc.) from your AMS to be mostly (Please select all that apply):

#

Answer % Cou

nt

1 Urgent 36.36

% 4

2 Moderately urgent

18.18%

2

3 Non-

urgent 45.45

% 5

4 Prefer not to

say 0.00% 0

Total 100% 11 Q17 - Have you modified the automated message (alerts, alarms, etc.) settings on any of your AMS from the factory default?

# Field Minimum Maximum Mean Std

Deviation Variance Count

1

Have you modified the automated message (alerts, alarms, etc.)

settings on any of your AMS from the factory default? - Selected

Choice

1.00 2.00 1.33 0.47 0.22 9

#

Answer % Count

1 Yes (Please explain what modifications you've made) 66.67% 6

2 No 33.33% 3

Total 100% 9

139

Q14_1_TEXT - Yes (Please explain what modifications you've made)

Yes (Please explain what modifications you've made) - Text

Send heat detection and distress alerts at night, turned off cleaning alarm phone calls. There is no immediate action anyways

Reduced certain non critical alarms so they dont call but just leave dashboard notice

App from Delaval

Alerts us each morning when filter needs changing at 6 am, it is like a wake up call, this is a none urgent alert

Made dashboard customizable to what I want to see.

Changed "time out between visits" alarm to an extended period until cow numbers increase

Q18 - What type of information do you receive from your AMS? (Please select all that apply)

#

Answer % Cou

nt

1

cow overdue

for milking

8.11%

3

2 cow in

heat 18.92%

7

3

activity (e.g.,

lameness)

13.51%

5

4 ruminati

on 10.81%

4

140

5 cow

temperature

5.41%

2

6 substan

ces in the milk

8.11%

3

7 mastitis

alert 8.11

% 3

8 system failure

18.92%

7

9 Other

(Please specify)

8.11%

3

Total 100

% 37

Q15_9_TEXT - Other (Please specify)

Other (Please specify) - Text

Distress (calving)

Feeding system issues

Weight

Q19 - What types of actions or responses do you perform after receiving automated messages (alerts, alarms, etc.) from your AMS? (Please select all that apply)

141

# Answer % Count

1 check the cow 27.78% 5

5 check system 44.44% 8

6 reset system status 16.67% 3

7 Other (please specify) 11.11% 2

2 provide care to cow as necessary (e.g. feed, water, hoof trim, etc.) 0.00% 0

3 separate the cow from others if necessary 0.00% 0

4 contact the veterinarian if necessary 0.00% 0

Total 100% 18

Q16_4_TEXT - Other (please specify)

Other (please specify) - Text

Check system for more details to determine action

I am finding questioning a bit confusing. I consider an alert something that is brought to my attention. In our case this is phone call regarding system operation. Lots of dashboard notifications about cow health, but i search reports, don't get a text/phone call about.

142

Q20 - What value do the automated messages (alerts, alarms, etc.) from your AMS provide for your farm operations and farm management? (Please select all that apply)

# Answer % Count

1 more flexible working hours (for you or others) 12.12% 4

2 reduced working hours (for you or others) 9.09% 3

3 increased appeal of work / ease of attracting farm staff 15.15% 5

4 ease of data collection for regulatory board 6.06% 2

5 improved livestock health 18.18% 6

6 improved milk product 9.09% 3

7 wanting to keep up with new technology 12.12% 4

8 looking to the future 9.09% 3

143

9 decrease stress 6.06% 2

10 Other (Please specify) 3.03% 1

Total 100% 33

Q21 - What challenges do you experience with the automated messages (alerts, alarms, etc.) from your AMS? (Please select all that apply)

# Answer % Count

1 too many false alerts/alarms 0.00% 0

2 general subject / topic of the message is sometimes unclear 33.33% 4

3 sometimes difficult to determine which cow the message is talking about 0.00% 0

4 required response / action from me (or others) is sometimes unclear 0.00% 0

5 timing of message sometimes interrupts more important work or life activities 25.00% 3

6 method of message delivery (e.g. phone call, text, etc.) is sometimes

inappropriate / does not match importance / urgency of issue being reported 25.00% 3

7 sometimes I miss important / urgent messages 8.33% 1

8 increased stress because I could receive an message at any moment 8.33% 1

9 Other (Please specify) 0.00% 0

Total 100% 12

144

Q22 - Please provide any additional comments you may have on the pros and cons of the current automated message (alerts, alarms, etc.) processes used in your AMS:

Please provide any additional comments you may have on the pros and cons of the current automated message (alerts, alarms, etc.) processes used in your AMS:

There needs to be more robot alarms added as a fail safe.

You can't allow the alarms to control your life, you accept the alarm and deal with it once you have time.

No

N/a

The voice is not always clear and the responsiveness to confirming and resetting the system when you push the appropriate button is not great.

Can’t be there 24/7. Pro, I have an off farm job and the AMS me to be successful in that role while getting the opportunity to farm as welll.

Q23 - Do you use an automatic heat detection / estrus monitoring system on your farm?

# Field Minimum Maximum Mean Std

Deviation Variance Count

1 Do you use an automatic heat

detection / estrus monitoring system on your farm?

1.00 2.00 1.06 0.24 0.06 17

#

Answer % Count

1 Yes 94.12% 16

2 No/Not Sure 5.88% 1

3 Prefer not to say 0.00% 0

Total 100% 17

Q24 - Do you receive automated messages (alerts, alarms, etc.) from your automatic heat detection / estrus monitoring system?

145

# Field Minimum Maximum Mean Std

Deviation Variance Count

1

Do you receive automated messages (alerts, alarms, etc.)

from your automatic heat detection / estrus monitoring system?

1.00 2.00 1.06 0.24 0.06 17

#

Answer % Count

1 Yes 94.12% 16

2 No/Not Sure 5.88% 1

3 Prefer not to say 0.00% 0

Total 100% 17

Q25 - When was your first automatic heat detection / estrus monitoring system installed?

146

# Field Minimum Maximum Mean Std

Deviation Variance Count

1 When was your first automatic

heat detection / estrus monitoring system installed?

1.00 3.00 2.73 0.57 0.33 15

#

Answer % Count

1 Less than 1 year ago 6.67% 1

2 1-2 years ago 13.33% 2

3 3 or more years ago 80.00% 12

Total 100% 15

Q26 - How are the automated messages (alerts, alarms, etc.) from your automatic heat detection / estrus monitoring system sent to you? (Please select all that apply)

# Answer % Count

1 Text Messaging 22.22% 4

2 Phone Call 0.00% 0

3 Email 11.11% 2

4 Display in a dashboard / desktop computer application 66.67% 12

5 Prefer not to say 0.00% 0

Total 100% 18 Q27 - In a typical week, approximately how many automated messages (alerts, alarms, etc.) of any kind do you receive from your automatic heat detection / estrus monitoring system?

# Field Minimum Maximum Mean Std Deviation Variance Count

1 1 3.00 50.00 20.53 14.43 208.12 15

Q28 - What type of information do you receive from your automatic heat detection / estrus monitoring system?

147

# Answer % Count

1 identifier (ID) of a cow in heat 45.16% 14

2 location of cow in heat 3.23% 1

3 breeding status of cow in heat 32.26% 10

4 Other (please specify) 19.35% 6

Total 100% 31

Q24_4_TEXT - Other (please specify)

Other (please specify) - Text

Probability of heat

Health alerts in relation to decreased rumination time or increased inactivity

Optimum insemination moment

Just says "cow in heat" must open app to find out which cow and breeding status

suggested optimal insemination time

Optimum insemination period

148

Q29 - What value do the automated messages (alerts, alarms, etc.) from your automatic heat detection / estrus monitoring system provide for your farm operations / management? (Please select all that apply)

149

# Answer % Count

1 more flexible working hours (for you or others) 6.78% 4

2 reduced working hours (for you or others) 5.08% 3

3 ease of attracting farm staff 0.00% 0

4 improved livestock health 18.64% 11

5 accurately identify insemination time of each cow 23.73% 14

6 decreased uncertainty of detecting heat events 18.64% 11

7 decreased stress related to breeding management 11.86% 7

8 wanting to keep up with new technology 5.08% 3

9 looking to the future 8.47% 5

10 Other (Please specify) 1.69% 1

Total 100% 59 Q26_10_TEXT - Others (Please specify)

Other (Please specify) - Text

High preg rate=lower DIM=higher feed efficiency = more milk

Q30 - What challenges do you experience with the automated messages (alerts, alarms, etc.) from your automatic heat detection / estrus monitoring system? (Please select all that apply)

150

# Answer % Count

1 too many false alerts/alarms 11.76% 2

2 sometimes difficult to determine which cow message is talking about 0.00% 0

3 timing of the message sometimes interrupts more important work or life activities 11.76% 2

4 method of message delivery (e.g. phone call, text, etc.) is sometimes

inappropriate / does not match importance / urgency of issue being reported 17.65% 3

5 sometimes I miss messages for heat events 11.76% 2

6 increased stress because I could receive a message at any moment 0.00% 0

7 Other (Please specify) 47.06% 8

Total 100% 17

Q31 - Please provide any additional comments you may have on the pros and cons of the current automated message (alerts, alarms, etc.) processes used in your automatic heat detection / estrus monitoring system:

Please provide any additional comments you may have on the pros and cons of the current automated message (alerts, alarms, etc.) processes used in your automatic heat detection / estrus monitoring system:

I feel like there are a lot of bugs because we do receive false alerts all the time and sometimes the readings are incorrect. It does allow us to save time and makes our job easier

Late notification

Very happy with our system. It's an "extra set of eyes" in our barn monitoring our cows 24 hours/day.

Just a clarification on Q 26, it is not a text message I receive but rather an alert on my phone through my app that is connected to the main computer where the heat system operates.

The system we have also does rumination ,eating time, inactive Helps a lot with overall health

monitoring in combi with sortgate i love it

No

N/a

n/a

Minimal challenges. We are happy with it. Cost is worth it and we don’t change collars so that makes it easy as well.

151

Q32 - Do you use an automatic calving time detection system on your farm?

# Field Minimum Maximum Mean Std

Deviation Variance Count

1 Do you use an automatic calving

time detection system on your farm?

1.00 2.00 1.76 0.42 0.18 17

#

Answer % Count

1 Yes 23.53% 4

2 No/Not Sure 76.47% 13

3 Prefer not to say 0.00% 0

Total 100% 17

Q33 - Do you receive automated messages (alerts, alarms, etc.) from your automatic calving time detection system?

# Field Minimum Maximum Mean Std

Deviation Variance Count

1

Do you receive automated messages (alerts, alarms, etc.)

from your automatic calving time detection system?

1.00 1.00 1.00 0.00 0.00 5

#

Answer % Count

1 Yes 100.00% 5

2 No/Not Sure 0.00% 0

3 Prefer not to say 0.00% 0

Total 100% 5

152

Q34 - When was your first automatic calving time detection system installed?

# Field Minimum Maximum Mean Std

Deviation Variance Count

1 When was your first automatic calving time detection system

installed? 1.00 3.00 2.25 0.83 0.69 4

#

Answer % Count

1 Less than 1 year ago 25.00% 1

2 1-2 years ago 25.00% 1

3 3 or more years ago 50.00% 2

Total 100% 4

Q35 - How are the automated messages (alerts, alarms, etc.) from your automatic calving time detection system being sent to you? (Please select all that apply)

153

# Answer % Count

1 Text Messaging 40.00% 2

2 Phone Call 0.00% 0

3 Email 40.00% 2

4 Display in a dashboard / desktop computer application 20.00% 1

5 Prefer not to say 0.00% 0

Total 100% 5

Q36 - On a typical week, how many automated messages (alerts, alarms, etc.) of any kind do you receive from your automatic calving time detection system?

# Field Minimum Maximum Mean Std Deviation Variance Count

1 1 2.00 31.00 16.75 13.77 189.69 4

Q37 - What type of information do you receive from your automatic calving time detection system? (Please select all that apply)

# Answer % Count

1 expected calving time for a cow 50.00% 3

2 identifier (ID) of a calving cow 33.33% 2

3 location of a calving cow 16.67% 1

4 Other (Please specify) 0.00% 0

Total 100% 6

154

Q38 - What value do the automated messages (alerts, alarms, etc.) from automatic calving time detection system provide for your farm operations / management? (Please select all that apply)

# Answer % Count

1 more flexible working hours (for you or others) 0.00% 0

2 reduced working hours (for you or others) 16.67% 2

3 ease of attracting farm staff 8.33% 1

4 improved cow and calf health outcomes 25.00% 3

5 accurately identify calving time of each cow 25.00% 3

6 decreased uncertainty around expected calving time 25.00% 3

7 decreased stress related to calving events 0.00% 0

8 wanting to keep up with new technology 0.00% 0

9 looking to the future 0.00% 0

10 Other (Please specify) 0.00% 0

Total 100% 12

155

Q39 - What challenges do you experience with the automated messages (alerts, alarms, etc.) from your automatic calving time detection system? (Please select all that apply)

# Answer % Count

1 too many false alerts/alarms 33.33% 2

2 sometimes difficult to determine which cow the message is talking about 16.67% 1

3 timing of the message sometimes interrupts more important work or life activities 16.67% 1

4 method of message delivery (e.g. phone call, text, etc.) is sometimes

inappropriate / does not match importance / urgency of issue being reported 16.67% 1

5 sometimes I miss calving messages 0.00% 0

6 increased stress because I could receive a message at any moment 16.67% 1

7 Other (Please specify) 0.00% 0

Total 100% 6

156

Q40 - Please provide any additional comments you may have on the pros and cons of the current automated message (alerts, alarms, etc.) processes used in your automatic calving time detection system:

Please provide any additional comments you may have on the pros and cons of the current automated message (alerts, alarms, etc.) processes used in your automatic calving time detection system:

No

Q41 - Do you use an activity monitoring (lying, ruminating, grazing, lameness, etc.) system on your farm?

# Field Minimum Maximum Mean Std

Deviation Variance Count

1

Do you use an activity monitoring (lying, ruminating, grazing,

lameness, etc.) system on your farm?

1.00 2.00 1.24 0.42 0.18 17

#

Answer % Count

1 Yes 76.47% 13

2 No/Not Sure 23.53% 4

3 Prefer not to say 0.00% 0

Total 100% 17

Q42 - Do you receive automated messages (alerts, alarms, etc.) from your activity monitoring (lying, ruminating, grazing, lameness, etc.) system?

157

# Field Minimum Maximum Mean Std

Deviation Variance Count

1

Do you receive automated messages (alerts, alarms, etc.)

from your activity monitoring (lying, ruminating, grazing, lameness,

etc.) system?

1.00 2.00 1.57 0.49 0.24 14

#

Answer % Count

1 Yes 42.86% 6

2 No/Not Sure 57.14% 8

3 Prefer not to say 0.00% 0

Total 100% 14

Q43 - When was your first activity monitoring (lying, ruminating, grazing, lameness, etc.) system installed?

# Field Minimum Maximum Mean Std

Deviation Variance Count

1

When was your first activity monitoring (lying, ruminating,

grazing, lameness, etc.) system installed?

1.00 3.00 2.00 0.89 0.80 5

#

Answer % Count

1 Less than 1 year ago 40.00% 2

2 1-2 years ago 20.00% 1

3 3 or more years ago 40.00% 2

Total 100% 5

158

Q44 - How are the automated messages (alerts, alarms, etc.) from your activity monitoring system being sent to you? (Please select all that apply)

# Answer % Count

1 Text Messaging 16.67% 1

2 Phone Call 0.00% 0

3 Email 16.67% 1

4 Display in a dashboard/ computer system 66.67% 4

5 Prefer not to say 0.00% 0

Total 100% 6

Q45 - On a typical week, how many automated messages (alerts, alarms, etc.) of any kind do you receive from your activity monitoring system?

# Field Minimum Maximum Mean Std Deviation Variance Count

1 1 2.00 91.00 39.40 31.65 1001.84 5

159

Q46 - What type of information do you receive from your activity monitoring system? (Please select all that apply)

# Answer % Count

1 cow position (out of virtual fence) 0.00% 0

2 lying time is unusual 17.65% 3

3 standing time is unusual 11.76% 2

4 ruminating time is unusual 29.41% 5

5 walking time is unusual 5.88% 1

6 heat detection 23.53% 4

7 calving time detection 5.88% 1

8 Other (Please specify) 5.88% 1

Total 100% 17

160

Q47 - What types of actions or responses are required from you after receiving automated messages (alerts, alarms, etc.) from your activity monitoring system? (Please select all that apply)

# Answer % Count

1 check the cow 23.53% 4

2 provide care to cow as necessary (e.g. feed, water, hoof trim, etc.) 23.53% 4

3 separate the cow from others if necessary 11.76% 2

4 contact veterinarian if necessary 17.65% 3

5 apply medicine if necessary 23.53% 4

6 Other (Please specify) 0.00% 0

Total 100% 17

161

Q48 - What value do the automated messages (alerts, alarms, etc.) from your activity monitoring system provide for your farm operations / management? (Please select all that apply)

# Answer % Count

1 more flexible working hours (for you or others) 7.14% 1

2 reduced working hours (for you or others) 14.29% 2

3 ease of attracting farm staff 0.00% 0

4 improved livestock health 28.57% 4

5 reduced need to monitor individual cows 7.14% 1

6 decreased uncertainty of detecting problems with individuals cows 28.57% 4

7 decreased stress related to individual cow monitoring 14.29% 2

8 wanting to keep up with new technology 0.00% 0

9 looking to the future 0.00% 0

10 Other (Please specify) 0.00% 0

Total 100% 14

162

Q49 - What challenges do you experience with the automated messages (alerts, alarms, etc.) from your monitoring system? (Please select all that apply)

# Answer % Count

1 too many false alerts/alarms 11.11% 1

2 general subject / topic of the message is sometimes unclear 11.11% 1

3 sometimes difficult to determine which cow message is talking about 11.11% 1

4 sometimes difficult to know whether message is urgent or not 22.22% 2

5 timing of the message sometimes interrupts more important work or life activities 0.00% 0

6 method of message delivery (e.g. phone call, text, etc.) is sometimes

inappropriate / does not match importance / urgency of issue being reported 0.00% 0

7 sometimes I miss messages about abnormal cows 11.11% 1

8 increased stress because I could receive a message at any moment 0.00% 0

9 Other (Please specify) 33.33% 3

Total 100% 9

163

Q45_9_TEXT - Increase stress (Please mention the cause)

Other (Please specify) - Text

No challenges. Happy with monitoring system.

Sometimes it does not alert me soon enough. There is a time delay where it waits to see 2 days of decreased inactivity before an alert. Hoping to contact company to make a change on this.

No challenges

Q50 - Please provide any additional comments you may have on the pros and cons of the current automated messages (alerts, alarms, etc.) processes used in your activity monitoring system:

Please provide any additional comments you may have on the pros and cons of the current automated messages (alerts, alarms, etc.) processes used in your activity monitoring system:

I love it

No

Q51 - Do you use any other technology(ies) that generate automated messages (alerts, alarms, etc.) about your cows?

# Field Minimum Maximum Mean Std

Deviation Variance Count

1

Do you use any other technology(ies) that generate automated messages (alerts,

alarms, etc.) about your cows?

1.00 2.00 1.76 0.42 0.18 17

#

Answer % Count

1 Yes 23.53% 4

2 No 76.47% 13

3 Prefer not to say 0.00% 0

Total 100% 17

Q52 - If yes, please specify.

164

If yes, please specify.

Automatic rationing system for our feeding system. It's called Rovibec.

Automated feeding system Lely Vector

Ketosis and mastitis alerts to desktop

Rumination, SCC indication and conductivity, Milk Temperature,

Q53 - Please indicate how stressed you feel during your daily activities knowing that you might receive automated messages (alerts, alarms, etc.) from any of your farm technologies?

# Field Minimum Maximum Mean Std Deviation Variance Count

1 1 0.00 5.00 1.88 1.49 2.23 16

Q54 - If applicable, what is the cause of that stress/anxiety?

If applicable, what is the cause of that stress/anxiety?

We have a quota that needs to be met and it seems like the quota is always rising, its very difficult to try to keep the current quota let alone an increase

Not many alarms = no stress

Hours worked

No

Could be away from the farm and receive an urgent alert you may have to leave an event for if there is no one else available to fix the alert

Equipment breaking down and if it breaks down it will be expensive to fix.

Having to drop what your doing to investigate

Cow time away from robots. Having to fix robot

Q55 - If applicable, how do you cope with that stress/anxiety?

If applicable, how do you cope with that stress/anxiety?

afterwards I try to relax and listen to music while working

I don’t have to milk cows at least

Understanding family

You just let it go and deal with it when it works best, or the next time you are in the barn,

No

Not leave far from the farm without someone on call. This stress was present to the same degree before installing the alert system.

Not very high now as with 2 robots and currently 70 cows, there is lots of free time if one robot goes out of operation. Before when we had 60 cows on one robot, stress level was extremely high as any downtime was affecting production, potentially cattle health, and income.

Take a deep breath. Our robots are under capacity as well so we know if one breaks that they can use the other one while we get it up and running

Q56 - Please indicate how stressed you feel when you receive an automated message (alerts, alarms, etc.)?

165

# Field Minimum Maximum Mean Std Deviation Variance Count

1 1 0.00 7.00 2.14 2.17 4.69 14

Q57 - If applicable, what is the cause of that stress/anxiety?

If applicable, what is the cause of that stress/anxiety?

I worry that the alert will be something bad and when I hear it i always stop what Im doing and look and during that time period I get nervous and increased anxiety

Getting up in the middle of the night

No

Not stressed untill check to get more detail. Then depending on the alert could be no stress or urgent.

Finding out what is wrong.

Robot is broken. Needs fixing and I’m at work. Q58 - If applicable, how do you cope with that stress/ anxiety?

If applicable, how do you cope with that stress/ anxiety?

As I get more used to receiving alerts (we just started less then a year ago) I start to feel less anxious. I also changed the alerts on my device so it just vibrates

Sleep in

No

Most are not urgent, so frequency of urgent alerts is low which mitigates stress

Other robot can manage while I am at work. Milking 65 cows on 2 robots

Q59 - Overall, do you have any additional thoughts on how the current automated message (alerts, alarms, etc.) processes of your farm technologies could better support your farm management and decision making?

Overall, do you have any additional thoughts on how the current automated message (alerts, alarms, etc.) processes of your farm technologies could better support your farm management and decision making?

I think the alerts should be less and we should only receive alerts if there is a problem. We have done farming for along time without these systems in place and have done just fine. I think they are good but should just stay in the background

It works pretty good, additional alarms need to be added

Don't mind getting alerts. It's always for a good reason. It does not stress me out. It's all about being proactive on the farm instead of being reactive.

As mentioned above, adjusting the time of when the alert is being made in relation to decreased cow activity, generally relating in a health issue that needs to be addressed sooner.

No

N/a

Having team viewer on robots and manage technology from afar. More mobile technology with robot.

166

APPENDIX B: Research Ethics Approval for the Study

RESEARCH ETHICS BOARDS Certification of Ethical Acceptability of Research Involving Human Participants

APPROVAL PERIOD: January 22, 2020 EXPIRY DATE: January 21, 2021 REB: NPES REB NUMBER: 19-10-006 TYPE OF REVIEW: Delegated PRINCIPAL INVESTIGATOR: Scott, Stacey (stacey.scott@uoguelph.ca) DEPARTMENT: School of Computer Science SPONSOR(S): University of Guelph, Physical Sciences and Engineering

Education Research Centre TITLE OF PROJECT: Impact of notification/alerting mechanisms used in

precision livestock farming technologies on Ontario dairy farms

CHANGES:

Type Date

Amendment

The members of the University of Guelph Research Ethics Board have examined the protocol which describes the participation of the human participants in the above-named research project and considers the procedures, as described by the applicant, to conform to the University's ethical standards and the Tri-Council Policy Statement, 2nd Edition.

The REB requires that researchers:

• Adhere to the protocol as last reviewed and approved by the REB.

• Receive approval from the REB for any modifications before they can be implemented.

• Report any change in the source of funding.

• Report unexpected events or incidental findings to the REB as soon as possible with an indication of how these events affect, in the view of the Principal Investigator, the safety of the participants, and the continuation of the protocol.

• Are responsible for ascertaining and complying with all applicable legal and regulatory requirements with respect to consent and the protection of privacy of participants in the jurisdiction of the research project.

The Principal Investigator must:

• Ensure that the ethical guidelines and approvals of facilities or institutions involved in the research are obtained and filed with the REB prior to the initiation of any research protocols.

• Submit an Annual Renewal to the REB upon completion of the project. If the research is a multi- year project, a status report must be submitted annually prior to the expiry date. Failure to submit an annual status report will lead to your study being suspended and potentially terminated.

The approval for this protocol terminates on the EXPIRY DATE, or the term of your appointment or employment at the University of Guelph whichever comes first.

Signature: Date: January 22, 2020

L. Vallis Chair, Research Ethics Board-NPES

167

APPENDIX C: Email and Verbal Recruitment Script

Hello, We are a research group from the University of Guelph, and we are conducting a research study on “Impact of Precision Livestock Farming (PLF) technologies on farmers which send notifications to farmers regarding ongoing situations in the farm”. The study aims to investigate the mechanism of receiving notifications from Precision Livestock Farming (PLF) technologies and impacts of received notifications on dairy farmers to recommend better notification mechanism for dairy farmers. We will be interviewing farmers to understand how notifications are received by dairy farmers from Precision Livestock Farming (PLF) technologies and the impacts of received notifications on dairy farmers by using the questions in the interview of farmers.

If you participate in the interview we will provide pre-paid credit card of 20$. You may stop

your participation any times. We will provide you the summary of our research result through email if you want.

Also, all participant information from this study will remain confidential and not be publicly disclosed. With your consent, the study might be audio recorded so that our research team may refer to the data later, and anonymous quotes may be used in report publications or presentations. This interview should last less than 30 minutes.

This project has been reviewed by the Research Ethics Board of the University of Guelph for compliance with federal guidelines for research involving human participants.

So would you be willing to participate in this study? Thank you for your time. Prof Stacey Scott, Muhammad Muhaiminul Islam.

168

APPENDIX D: Letter of Information and Consent Form

Impact of PLF technologies on farmers which provide information to farmers regarding ongoing situations in the farm

School of Computer Science

University of Guelph

Principal Investigator: Professor Stacey Scott, School of Computer Science (stacey.scott@uoguelph.ca)

Co - Investigator(s): Muhammad Muhaiminul Islam (mislam12@uoguelph.ca)

Overview

You are being invited to volunteer in a study as part of a School of Computer Science masters research project. We are a research group from the University of Guelph, and we are conducting a research study on “Impact of Precision Livestock Farming (PLF) technologies on farmers which send notifications to farmers regarding ongoing situations in the farm”. The study aims to investigate the mechanism of receiving notifications from Precision Livestock Farming (PLF) technologies and impacts of received notifications on dairy farmers to recommend better notification mechanism for dairy farmers. The principal investigator is Prof. Stacey Scott and co-investigated by Muhammad Muhaiminul Islam.

In this study, we will be surveying farmers to understand how notifications are received by dairy farmers from Precision Livestock Farming (PLF) technologies and the impacts of received notifications on dairy farmers by using the questions in the survey which will be filled up by farmers.

You have been identified as an individual that could assist in the specific goals of this project. If you agree to participate in this study, we will briefly describe our ongoing study to you and then send an online survey to you; we will also call you or send you email or do both for a follow-up interview within 1 month of your completing survey which will be no later than June, 2020 if you indicate that you will like to participate in the follow-up interview on the survey form. We will also send a separate consent form for interview.

Study Details

If you participate in the survey you may win 40$ pre-paid credit card. We will draw a lottery and will provide pre-paid credit cards of 40$ for the lucky 5 persons who has completed the survey. After finishing the survey we will ask you for your phone/email if you want your name in the draw. You may stop at any time if you want but we will enter your name in the lottery only if you complete the survey. Moreover, we will provide you the summary of our research result through email if you want.

If you agree to participate, we will send an online survey to you, which should take less than 30 minutes to complete. If you indicate on the online survey form that you don’t mind us calling you for a follow-up interview, we will schedule interview time only for 30 minutes with you via email and/or phone call.

You may decline to answer particular questions, if you wish, and may withdraw participation in this or any subsequent session at any time. Participants may withdraw their data of completed survey before 1st June, 2020. If

169

you choose to withdraw at any point, all data collected during your session will be destroyed and not used in the study.

Risks

There are no known or anticipated risks to you as a participant in this study.

Confidentiality and Data Retention

All data collected is considered confidential. Codes, rather than names or other identifying information, will be used in notes and/or recordings. Even though we will publish our results in journals/conference, only the investigators will have access to the data collected. Your name or any other personal identifying information will not appear in any publication resulting from this study. However, with your permission, anonymous quotations and pictures may be used. Notes recordings collected during this study will be kept a secure location during the study, and will be destroyed (We will delete all the electronic data and paper data will be shredded by the principal investigator) by the end of my masters finial thesis submission which is not later than December, 2020.

Benefits of the Study

You will have a chance of winning 40$ pre-paid credit card for completing the survey. Moreover, the information you provide will help us understand the benefits and limitations of current PLF technologies for assisting in their farming operations and decision-making. It will also help us to create a guide for innovators to develop more user friendly and efficient technology for the dairy industry that you may use in the future.

Questions

If you have any questions about participation in this study, or would like additional information to assist you in reaching a decision about participation, please contact either Muhammad Muhaiminul Islam at mislam12@uoguelph.ca or Prof. Stacey Scott at stacey.scott@uoguelph.ca.

If you have questions regarding your rights and welfare as a research participant in this study (REB# 19-10-006), please contact: Director, Research Ethics; University of Guelph; reb@uoguelph.ca; (519) 824-4120 (ext. 56606)

This project has been reviewed by the Research Ethics Board for compliance with federal guidelines for research involving human participants.

170

Please keep the information above for your records. Return this piece to the researcher.

Consent Form

Project Title: Impact of Precision Livestock Farming (PLF) technologies on farmers which send notifications to farmers regarding ongoing situations in the farm

I agree to participate in a study being conducted by Muhammad Muhaiminul Islam under the supervision of Prof. Stacey Scott for as part of the requirement of his Masters degree, at the University of Guelph. I have made this decision based on the information I have read in this Letter of Information above and have had the opportunity to receive any additional details I wanted about the study. I understand that I may withdraw this consent at any time, without penalty, by telling the researcher. I was informed that, should I choose to complete the entire study, that my responses would be recorded for future data analysis.

This project has been reviewed by the Research Ethics Board at the University of Guelph for compliance with federal guidelines for research involving human participants. I was informed that if I have any questions regarding my rights and welfare as a research participant in this study (REB# 19-10-006), I may contact: the Director, Research Ethics; University of Guelph; reb@uoguelph.ca; (519) 824-4120 (ext. 56606).

With full knowledge of all foregoing, I agree, of my own free will, to participate in this study.

Yes ___ No ___

I agree to have my survey/interview evaluation audio and/or video recorded for future analysis purposes.

Yes ___ No ___

I agree to the use of anonymous quotations in any presentation or report that comes of this study.

Yes ___ No ___

Participant Name: ______________________________________________________ (Please print)

Participant Signature: ___________________________________________________

Witness Name: ________________________________________________________ (Please print)

Witness Signature: _____________________________________________________

Date: ________________________________________________________________

Please return this piece to the researcher.

171

APPENDIX E: Sample Python Code to Calculate Correlation Matrix

import numpy as np import pandas as pd from pandas import DataFrame, Series import seaborn as sns import matplotlib.pyplot as plt dframe=pd.read_csv('experience vs number of alerts.csv') dframe['Q8'] out: 0 Years of dairy farming experience: 1 {"ImportId":"QID6"} 2 5-9 years 3 5-9 years 4 0-4 years 5 30+ years 6 10-19 years 7 5-9 years 8 5-9 years 9 NaN 10 10-19 years 11 20-29 years 12 10-19 years 13 10-19 years 14 30+ years 15 30+ years 16 30+ years 17 10-19 years 18 10-19 years 19 0-4 years 20 10-19 years 21 NaN 22 NaN 23 NaN 24 NaN Year_of_Experience=dframe['Q8'].replace(['0-4 years','5-9 years','10-19 years','20-29 years','30+ years'],['1','2','3','4','5']) Year_of_Experience=Year_of_Experience.fillna('0') Year_of_Experience Out[17]: 0 Years of dairy farming experience: 1 {"ImportId":"QID6"} 2 2 3 2

172

4 1 5 5 6 3 7 2 8 2 9 0 10 3 11 4 12 3 13 3 14 5 15 5 16 5 17 3 18 3 19 1 20 3 21 0 22 0 23 0 24 0 Stress=dframe['stress'] Stress=Stress.fillna('0') Stress Out[18]: 0 0 1 0 2 7 3 3 4 12 5 3 6 6 7 3 8 9 9 0 10 0 11 0 12 1 13 0 14 0 15 0 16 0 17 4 18 2 19 6

173

20 4 21 0 22 0 23 0 24 0 Year_of_Experience=Year_of_Experience.drop([0,1]) Stress=Stress.drop([0,1]) frame = { 'Year of Dairy Farming Experience': Year_of_Experience, 'Stress Level': Stress } result = pd.DataFrame(frame) result['Year of Dairy Farming Experience']=pd.to_numeric(result['Year of Dairy Farming Experience']) result['Stress Level']=pd.to_numeric(result['Stress Level']) result=result.loc[(result!=0).any(axis=1)] print(result) Stress Level Year of Dairy Farming Experience 2 7.0 2 3 3.0 2 4 12.0 1 5 3.0 5 6 6.0 3 7 3.0 2 8 9.0 2 10 0.0 3 11 0.0 4 12 1.0 3 13 0.0 3 14 0.0 5 15 0.0 5 16 0.0 5 17 4.0 3 18 2.0 3 19 6.0 1 20 4.0 3 corr = result.corr() corr.style.background_gradient(cmap='coolwarm')

174

APPENDIX F: Interview Questions

Interview - Farmers

1. Age 2. Gender 3. Farm Location 4. Level of Education 5. Number of Milking Cows 6. Years of Farming 7. Job Description 8. Number of full-time employees on Farm

Introduction about the using on-farm technologies 9. What kind of technology(s) do you use on the farm? 10. When did you introduce each of this technology(s) on the farm? 11. What did you use each of this technology(s) for? 12. What has been your overall experience with technology so far? 13. How does this technology impact your farming practices?

• For instance, how does this technology reduce your workload? FARM DATA AND DATA MANAGEMENT (optional)

14. Do the current technologies (application) collect any data from the farm? 15. How are farm operations data currently been collected on your farm? 16. How are the data being managed? 17. Do you use the collected data for farm decision making? If yes, how do you use?

Alerts/ notifications/ warning Mechanism 18. Is any of the technologies capable of generating alerts? 19. Do you receive any alert or notification or warning from any of the technologies? 20. If yes.

❖ In what kind of situation do you receive alerts? ❖ What type of information do you receive in alerts? ❖ When do you receive alerts (timing of the notification?)? ❖ How do you receive the alerts? ❖ What do you do when you receive or notice any alert? ❖ How many alerts do you receive in a day on average? ❖ Do you think the timing of receiving the alerts is effective? Why/Why not? ❖ Is the number of alerts is effective? Why/ Why not? ❖ Is there any need of prioritizing alerts? ❖ Do you feel stressed because of alerts knowing that you might receive an alert?

Why/Why not? ❖ How do you cope with the stress? ❖ Do you feel stressed after receiving an alert? Why/Why not? ❖ How do you feel that the current alerting process could be improved to better support

your farm management and decision making?? Concluding

21. Is there anything else you would like to share relevant to this topic? 22. Can you connect us with other farmers?