activity meters are often used for automated oestrus detection. But is there more benefit from monitoring activity of cows? This presentation was part of the SUND Dairycare conference held in 2015, in Cordoba, Spain
Can we estimate the economic benefit of precision livestock technologiesClaudia Kamphuis
A presentation about a modelling tool to estimate the economic impact of implementing precision livestock technologies (PLF) on farm. Presented at the EAAP/EU-PLF Conference, 2014, in Copenhagen, Denmark
Cows in the cloud, Down to earth, 8-9 September 2015Claudia Kamphuis
Farming involves entrepreneurship, setting milestones and preparing for the future. In addition, farming is continuously subject to change, due to growth, society, regulations, finance, subsidy, etc. Therefore solid advice is key for a sustainable, profitable and enjoyable future in farming. A variety of speakers from different disciplines will share interesting insights and knowledge to help you in supporting farmers to reach their chosen milestones.
Guidelines on the use of sensors to monitor animal health and productivity; a...Claudia Kamphuis
this presentation was given at the third Sund DairyCare conference in Zadar, Croatie. It discusses the need to have protocols to evaluate sensor technologies for their performance on-farm
Data mining to combine sensor information to improve oestrus detectionClaudia Kamphuis
The document discusses a project aiming to improve automated heat detection in dairy cows by combining sensor data. Researchers collected sensor data from two farms, including activity, rumination, feeding and yield data. They developed predictive models using this combined sensor data and found the models improved heat detection sensitivity over using single sensors. The models are now running near real-time on one farm and the researchers plan to fine-tune the models and scale up testing to 200 farms in the Netherlands to provide individual cows' insemination advice and timing.
Sensors on dairy farms can detect changes in behavior and physiology associated with lameness in cows. Researchers analyzed sensor data from over 4,900 cows on 5 farms, including activity, milking order, milk yield, and weight, around the time cows were observed to be lame by farmers. While no single threshold distinguished lame from healthy cows, statistical models combining multiple sensor data predicted lameness with 75% accuracy, significantly better than using any single sensor. Further research is needed to identify the most predictive data and modeling techniques for detecting lameness using on-farm sensors.
Sensor technologies in the milking parlour, can they replace or complement hu...Claudia Kamphuis
Sensors in milking parlours can monitor cow health and productivity by replacing or complementing human senses. The document discusses various sensor technologies that have been introduced for monitoring udder health, milk composition, fertility, cow composition, and metabolic disorders. While sensors have benefits like improving health, welfare, and productivity, their adoption has been limited. Sensors may not always accurately monitor parameters of interest and there are tradeoffs between sensitivity and specificity. Additionally, sensor information is often not fully utilized on farms due to limitations in performance, lack of understanding, and insufficient learning support. In conclusion, sensors have potential but must be combined with management decisions to effectively monitor cow health and productivity.
The use of successfull inseminations to avoid the high costs and intensive method of progesterone measurements and to crank up the numbers to evaluate sensitivity of automated heat detection systems
Can we estimate the economic benefit of precision livestock technologiesClaudia Kamphuis
A presentation about a modelling tool to estimate the economic impact of implementing precision livestock technologies (PLF) on farm. Presented at the EAAP/EU-PLF Conference, 2014, in Copenhagen, Denmark
Cows in the cloud, Down to earth, 8-9 September 2015Claudia Kamphuis
Farming involves entrepreneurship, setting milestones and preparing for the future. In addition, farming is continuously subject to change, due to growth, society, regulations, finance, subsidy, etc. Therefore solid advice is key for a sustainable, profitable and enjoyable future in farming. A variety of speakers from different disciplines will share interesting insights and knowledge to help you in supporting farmers to reach their chosen milestones.
Guidelines on the use of sensors to monitor animal health and productivity; a...Claudia Kamphuis
this presentation was given at the third Sund DairyCare conference in Zadar, Croatie. It discusses the need to have protocols to evaluate sensor technologies for their performance on-farm
Data mining to combine sensor information to improve oestrus detectionClaudia Kamphuis
The document discusses a project aiming to improve automated heat detection in dairy cows by combining sensor data. Researchers collected sensor data from two farms, including activity, rumination, feeding and yield data. They developed predictive models using this combined sensor data and found the models improved heat detection sensitivity over using single sensors. The models are now running near real-time on one farm and the researchers plan to fine-tune the models and scale up testing to 200 farms in the Netherlands to provide individual cows' insemination advice and timing.
Sensors on dairy farms can detect changes in behavior and physiology associated with lameness in cows. Researchers analyzed sensor data from over 4,900 cows on 5 farms, including activity, milking order, milk yield, and weight, around the time cows were observed to be lame by farmers. While no single threshold distinguished lame from healthy cows, statistical models combining multiple sensor data predicted lameness with 75% accuracy, significantly better than using any single sensor. Further research is needed to identify the most predictive data and modeling techniques for detecting lameness using on-farm sensors.
Sensor technologies in the milking parlour, can they replace or complement hu...Claudia Kamphuis
Sensors in milking parlours can monitor cow health and productivity by replacing or complementing human senses. The document discusses various sensor technologies that have been introduced for monitoring udder health, milk composition, fertility, cow composition, and metabolic disorders. While sensors have benefits like improving health, welfare, and productivity, their adoption has been limited. Sensors may not always accurately monitor parameters of interest and there are tradeoffs between sensitivity and specificity. Additionally, sensor information is often not fully utilized on farms due to limitations in performance, lack of understanding, and insufficient learning support. In conclusion, sensors have potential but must be combined with management decisions to effectively monitor cow health and productivity.
The use of successfull inseminations to avoid the high costs and intensive method of progesterone measurements and to crank up the numbers to evaluate sensitivity of automated heat detection systems
This document discusses whether technology pays for itself in dairy farming. It provides an overview of the history of sensor use on dairy farms since the 1970s and their increasing adoption. Success factors for precision technology include system specifications that provide useful information, cost efficiency where benefits outweigh costs, and non-economic factors like risk tolerance. Studies show sensor systems for mastitis and estrus detection can increase productivity and profitability on farms, though their benefits are not always fully realized in practice due to limited use of sensor information and farmer attitudes. In conclusion, sensors have the potential to improve farms economically and enhance dairy cattle welfare but not all systems may be cost-effective.
this presentation was prepared for a minisymposium on the occasion of PhD defence of Niels Rutten June 14 2017 at Wageningen University, with the thesis entitled “The utility of sensor technology to support reproductive management on dairy farms”. The public defence of his thesis was a good reason to share knowledge about current sensor research in the dairy farming industry
Danish farmers have significantly invested in automatic milking systems (AMS) in recent years, with 22% of herds and 27% of cows using AMS as of late 2009. Data from AMS provides a large data bank that can provide insights for genetic improvement goals. Denmark will analyze AMS data starting in 2010 to improve genetic evaluations for traits like milking speed and develop breeding values for cow suitability to AMS. This will help increase genetic gains for functional traits like health and fertility. A pilot project has collected AMS data, which will systematically be collected from April 2010 onwards to help breeding organizations.
The document describes CowLab, a research environment that utilizes sensors and technology to collect data on dairy cow behavior, health, physiology and production. CowLab allows comprehensive testing of innovations to improve animal welfare and productivity. Sensors can monitor activities like rumination, movement, location, resting, feeding and milking. This large dataset is transferred to a cloud service for analysis and mining to provide new insights. The goal is to use this technology to develop applications and products that can detect illness early and improve dairy cow welfare and production efficiency.
The document discusses bar code medication administration (BCMA) quality assurance efforts at the Veterans Health Administration (VHA) over time. It notes that in 2000, VHA implemented BCMA at all medical centers, and since 2004 has developed closed loop verification procedures, established bar code verification labs, and added bar code quality clauses to contracts. Automated data capture of failed scans began in 2009. This has helped increase wristband and medication scan success rates from around 88-96% in 2009 to around 94-98% in 2011, returning over 60 hours of staff time per day to patient care. The document advocates for supply chain standardization and serialization to further improve safety.
Industry executives need a 'real time real data' tool for: monitor standard production , food safety traceability, worker performance and smart alarms in real time. This can be found in new expensive barns yet to have this on an old farming operation with low tech barns like in 80% of the market, is almost impossible without extensive expenses and complex installations of several control boxes.
Farmers and field professionals on the other hand resist any monitoring technology, as it provides a real picture of what is really going on in the barn at any moment and the real cause of many of the performance gap's – it will reflect bad on any mistake they daily make and show any alarm miss treated. Executives can look back in time on reports looking and analyzing data as well as real time managing from office HQ to farm and animals.
Reports today are manual and late, as is managing of the growing environment – farmer can adjust files and business owner can only trust his farmers to perform by protocol – this is not happening as to human character and farm logistics.
No plan today - connecting farming sites to fast free internet for live data to barns.
80% of the growing time is going with no problems using todays standard off the shelf products - Problems caused at 20% of the time are responsible for 80% of the economic damage to the industry that is in old barns.
smart management of Chicken farms agriculture can support poultry growing management and insure performance.
Our system can today go into: broiler \ egg \ turkey \ mother flock farms and needs only 1 SIM for a full farm with many barns – sending only SMS from this SIM to a local number.
A 1 box system to manage and control the:
growing environment by showing temp to farmer and manager, manage feeding and drinking quality via sensors, as well as animal welfare- via feeding patterns for every minute and workers performance as well as electric power down to barn.
What will the client get in the MR Box?
Controlled environment – Temp&humidity- in &out of the barn to better ventilate by men operating the fan as requested . On cloud and in barn.
Feed weight –no load cells needed – via smart motor time – very accurate – on cloud and in barn.
Water drinking monitor – amount and water quality sensor. On cloud and in barn.
Intelligent feeding – will take FCR down in compere to any other feeding controller – in barn.
Informative alarms will allow farmer and manager to have real time data on web and SMS – to avoid performance gap's done by late reaction to malfunction of one of the growing parameters on site.
Welfare monitoring – allowing close watch over flocks feeding patterns that can alarm of a sick flock – allowing vaccination on time to save the production value or early marketing to save FCR. On cloud UI.
Worker monitoring – the workers environment becomes visible and time to fix an alarm is measurable. On clo
Cowscope is an app and mobile microscope attachment designed to help farmers better manage mastitis in their herds. It has three sections - a Somatic Cell Counter to detect mastitis on the farm, a Cost Calculator to analyze costs of treating individual cows, and Cow Data to track trends in herd health over time. The Somatic Cell Counter brings somatic cell counting from the lab to the farm using a 3D printed microscope and app to take and count cells. This allows farmers to monitor cow health more frequently. The Cost Calculator compares costs of treating vs. culling infected cows. Cow Data tracks infections and milk production by cow to identify outbreak patterns and prevent future issues.
This document summarizes precision dairy monitoring technologies and their potential to detect diseases in dairy cows. It discusses various sensor devices, early disease detection, sensitivity and specificity of different metrics, and responses seen in cows with specific diseases like ketosis and hypocalcemia. While sensors show promise, challenges remain around defining accurate alerts, missing data, natural cow variations, and focusing too narrowly on disease versus overall cow health. Standards are needed to properly evaluate technologies and address farmer needs for meaningful alerts. Precision monitoring works best when combined with expert cow knowledge and management.
Clinical Validation of Biometric Wearables and Applying Accurate Biometrics T...Valencell, Inc
The document discusses clinical validation of biometric wearables and applying accurate biometrics to provide compelling user experiences. It describes Valencell's approach to sensor validation through benchmarking, testing protocols, reference devices, and statistical analysis of accuracy. Several potential use cases are outlined such as quantifying activity intensity and volume, estimating stress levels, core temperature, and using biometrics for diet planning. The key takeaways are that accuracy is critical for user experience, validation requires planning and experience, and use cases should be simple and research-supported.
Pas Reform SmartCount™ counting and dosing systemHenry Arts
SmartCountTM is a vision technology system that can accurately count and analyze day old chicks at high speeds of up to 60,000 chicks per hour. It uses camera images and algorithms to count chicks without accelerating them, reducing stress. Data on chick weights and uniformity is collected and integrated with SmartCenterTM for detailed batch reporting. The system also enables gentle distribution of chicks into boxes and precise spray vaccination without disrupting the natural flow of chicks.
Top Wearables Predictions for the Year Ahead and 2018: Year in ReviewValencell, Inc
Where is wearable technology heading and what can we expect from the wearables market? When it comes to biometric wearables and hearables, we've only scratched the surface of what's possible. In this webinar, Dr. Steven LeBoeuf takes a look back at trends and achievements in wearables in 2018 and weighs in on the outcomes of last year's predictions. We also highlight some unexpected changes you need to be ready for in 2019 and beyond.
Precision Dairy Monitoring Opportunities and ChallengesJeffrey Bewley
This presentation provides an introduction to precision dairy monitoring. The wide range of opportunities for future dairy management are discussed. Then, the challenges of turning these dreams into reality are covered.
IRJET- The Sensor Technologies for More Efficient Cow Reproduction SystemsIRJET Journal
This document discusses sensor technologies that can be used to more efficiently detect estrus cycles in dairy cows. It provides an overview of various sensor types currently available, including those that monitor standing activity, mounting activity, walking activity, restlessness, and vocalization. The sensors aim to accurately detect behavioral and physiological changes in cows during estrus to help farmers more efficiently determine optimal insemination timing and improve fertility rates. While visual observation remains challenging, sensor technologies provide opportunities to automate estrus detection, reduce labor needs, and increase milk yields and profits through higher fertility success.
I just gave a opening keynote on the North American Precision dairy farming conference. I showed some data that we recently collected on the use of sensor systems and the effects of these systems on farm performance.
New developments in the Dutch dairy sectorHenk Hogeveen
This was the opening presentation I gave at the 2014 Congress of the LIvestock Health and Production Group of the South African Veterinary Association. The organization asked me to give an overview of recent developments in the Dutch dairy sector. i have chose to pick three developments that are, in my opinion, interesting for veterinarians: 1. the ongoing automation of the sector, 2. the abolisment of the quota system (and a little background) and 3. the reduced use of antibiotics.
Automation techniques have been increasingly used in livestock production to reduce labor needs. This includes automatic identification of animals using RFID tags, GPS tracking, or retinal/muzzle scanning. Other automated processes discussed are feeding, milking, estrus detection through activity/hormone monitoring, birth detection, online herd management, and barn cleaning/environment control. The document concludes that while automation increases production and efficiency, it also increases costs, so is best for large commercial farms.
Precision livestock farming cattle identification based on biometric data tar...Aboul Ella Hassanien
This document proposes using biometric data from cattle muzzle prints for precision livestock farming and cattle identification. It discusses challenges with current identification methods like RFID tags and outlines the benefits of a non-invasive biometric approach. The proposed system would collect muzzle print images, extract features, reduce dimensions with LDA, and use machine learning to classify and identify individual cattle. Experimental results showed the algorithm achieved high accuracy rates for identification when using different numbers of training images. The conclusion states precision livestock farming with biometric identification could increase farming efficiency and sustainability through individual animal monitoring and traceability in the food chain.
Precision livestock farming cattle identification based on biometric data tar...Aboul Ella Hassanien
This document proposes using biometric data from cattle muzzle prints for precision livestock farming and cattle identification. It discusses challenges with current identification methods like RFID tags and outlines the benefits of a non-invasive biometric approach. The proposed system would collect muzzle print images, extract features, reduce dimensions with LDA, and use machine learning to classify and identify individual cattle. Experimental results showed the algorithm achieved high accuracy rates for identification when using different numbers of training images. The conclusion states precision livestock farming with biometric identification could increase farming efficiency and sustainability through individual animal monitoring and traceability in the food chain.
Precision livestock farming cattle identification based on biometric data tar...Aboul Ella Hassanien
This document proposes using biometric data from cattle muzzle prints for precision livestock farming and cattle identification. It discusses challenges with current identification methods like RFID tags and outlines the benefits of a non-invasive biometric approach. The proposed system would collect muzzle print images, extract features, reduce dimensions with LDA, and use machine learning to classify and identify individual cattle. Experimental results showed the algorithm achieved high accuracy rates for identification when using different numbers of training images. The conclusion states precision livestock farming with biometric identification could increase farming efficiency and sustainability through individual animal monitoring and traceability in the food chain.
Automatic Estrus Detection System for Dairy Animalsidescitation
This paper deals with the new aid for detection of
Estrus (Heat) in dairy animals. As dairy Technology is
developing day by day, therefore reproductive performance of
dairy animals is major concern in dairy industry. This
Reproductive performance of dairy animals requires accurate
and regular Estrus detection. Estrus is nothing but a
behavioural symptom in mammals which indicate that female
is mated close to the ovulation. That’s why Timely detection
of estrus is the only solution to increase the fertility rate in
dairy animals. Failure to detect animal in estrus and breeding
animals which are not in estrus result in economic loss for
the owner because of extended calving interval and additional
semen expenses. Accurate Estrus detection gives idea about
proper timing of Artificial Insemination. So Estrus detection
is the key solution for effective growth in dairy technology.
During estrus period animal shows mounting behaviour,
increased physical activity and vaginal temperature of animal
is increased. So, in proposed technology, for the very first
time all these three signs are sensed by three sensors. The
signal from the sensors are given to the micro-controller, then
micro-controller process the data, display the data on LCD
screen as well as transfer all the data wirelessly to the Personal
computer (PC). PC runs a software module which display all
the data i.e. Animal name, number of mounting, physical
activity and vaginal temperature.
The Role of Technology in Quantifying Mastitis Related DecisionsJeffrey Bewley
This document discusses the role of technology in quantifying decisions related to mastitis detection and management. Precision monitoring technologies like electrical conductivity, milk color, temperature, spectroscopy, biosensors and inline somatic cell count can help detect mastitis earlier than visual observation alone. However, challenges remain around meeting sensitivity and specificity goals, calibration over time, and determining appropriate actions in response to alerts. Further research is needed to quantify the economic benefits of early mastitis detection and determine optimal treatment protocols.
This document discusses whether technology pays for itself in dairy farming. It provides an overview of the history of sensor use on dairy farms since the 1970s and their increasing adoption. Success factors for precision technology include system specifications that provide useful information, cost efficiency where benefits outweigh costs, and non-economic factors like risk tolerance. Studies show sensor systems for mastitis and estrus detection can increase productivity and profitability on farms, though their benefits are not always fully realized in practice due to limited use of sensor information and farmer attitudes. In conclusion, sensors have the potential to improve farms economically and enhance dairy cattle welfare but not all systems may be cost-effective.
this presentation was prepared for a minisymposium on the occasion of PhD defence of Niels Rutten June 14 2017 at Wageningen University, with the thesis entitled “The utility of sensor technology to support reproductive management on dairy farms”. The public defence of his thesis was a good reason to share knowledge about current sensor research in the dairy farming industry
Danish farmers have significantly invested in automatic milking systems (AMS) in recent years, with 22% of herds and 27% of cows using AMS as of late 2009. Data from AMS provides a large data bank that can provide insights for genetic improvement goals. Denmark will analyze AMS data starting in 2010 to improve genetic evaluations for traits like milking speed and develop breeding values for cow suitability to AMS. This will help increase genetic gains for functional traits like health and fertility. A pilot project has collected AMS data, which will systematically be collected from April 2010 onwards to help breeding organizations.
The document describes CowLab, a research environment that utilizes sensors and technology to collect data on dairy cow behavior, health, physiology and production. CowLab allows comprehensive testing of innovations to improve animal welfare and productivity. Sensors can monitor activities like rumination, movement, location, resting, feeding and milking. This large dataset is transferred to a cloud service for analysis and mining to provide new insights. The goal is to use this technology to develop applications and products that can detect illness early and improve dairy cow welfare and production efficiency.
The document discusses bar code medication administration (BCMA) quality assurance efforts at the Veterans Health Administration (VHA) over time. It notes that in 2000, VHA implemented BCMA at all medical centers, and since 2004 has developed closed loop verification procedures, established bar code verification labs, and added bar code quality clauses to contracts. Automated data capture of failed scans began in 2009. This has helped increase wristband and medication scan success rates from around 88-96% in 2009 to around 94-98% in 2011, returning over 60 hours of staff time per day to patient care. The document advocates for supply chain standardization and serialization to further improve safety.
Industry executives need a 'real time real data' tool for: monitor standard production , food safety traceability, worker performance and smart alarms in real time. This can be found in new expensive barns yet to have this on an old farming operation with low tech barns like in 80% of the market, is almost impossible without extensive expenses and complex installations of several control boxes.
Farmers and field professionals on the other hand resist any monitoring technology, as it provides a real picture of what is really going on in the barn at any moment and the real cause of many of the performance gap's – it will reflect bad on any mistake they daily make and show any alarm miss treated. Executives can look back in time on reports looking and analyzing data as well as real time managing from office HQ to farm and animals.
Reports today are manual and late, as is managing of the growing environment – farmer can adjust files and business owner can only trust his farmers to perform by protocol – this is not happening as to human character and farm logistics.
No plan today - connecting farming sites to fast free internet for live data to barns.
80% of the growing time is going with no problems using todays standard off the shelf products - Problems caused at 20% of the time are responsible for 80% of the economic damage to the industry that is in old barns.
smart management of Chicken farms agriculture can support poultry growing management and insure performance.
Our system can today go into: broiler \ egg \ turkey \ mother flock farms and needs only 1 SIM for a full farm with many barns – sending only SMS from this SIM to a local number.
A 1 box system to manage and control the:
growing environment by showing temp to farmer and manager, manage feeding and drinking quality via sensors, as well as animal welfare- via feeding patterns for every minute and workers performance as well as electric power down to barn.
What will the client get in the MR Box?
Controlled environment – Temp&humidity- in &out of the barn to better ventilate by men operating the fan as requested . On cloud and in barn.
Feed weight –no load cells needed – via smart motor time – very accurate – on cloud and in barn.
Water drinking monitor – amount and water quality sensor. On cloud and in barn.
Intelligent feeding – will take FCR down in compere to any other feeding controller – in barn.
Informative alarms will allow farmer and manager to have real time data on web and SMS – to avoid performance gap's done by late reaction to malfunction of one of the growing parameters on site.
Welfare monitoring – allowing close watch over flocks feeding patterns that can alarm of a sick flock – allowing vaccination on time to save the production value or early marketing to save FCR. On cloud UI.
Worker monitoring – the workers environment becomes visible and time to fix an alarm is measurable. On clo
Cowscope is an app and mobile microscope attachment designed to help farmers better manage mastitis in their herds. It has three sections - a Somatic Cell Counter to detect mastitis on the farm, a Cost Calculator to analyze costs of treating individual cows, and Cow Data to track trends in herd health over time. The Somatic Cell Counter brings somatic cell counting from the lab to the farm using a 3D printed microscope and app to take and count cells. This allows farmers to monitor cow health more frequently. The Cost Calculator compares costs of treating vs. culling infected cows. Cow Data tracks infections and milk production by cow to identify outbreak patterns and prevent future issues.
This document summarizes precision dairy monitoring technologies and their potential to detect diseases in dairy cows. It discusses various sensor devices, early disease detection, sensitivity and specificity of different metrics, and responses seen in cows with specific diseases like ketosis and hypocalcemia. While sensors show promise, challenges remain around defining accurate alerts, missing data, natural cow variations, and focusing too narrowly on disease versus overall cow health. Standards are needed to properly evaluate technologies and address farmer needs for meaningful alerts. Precision monitoring works best when combined with expert cow knowledge and management.
Clinical Validation of Biometric Wearables and Applying Accurate Biometrics T...Valencell, Inc
The document discusses clinical validation of biometric wearables and applying accurate biometrics to provide compelling user experiences. It describes Valencell's approach to sensor validation through benchmarking, testing protocols, reference devices, and statistical analysis of accuracy. Several potential use cases are outlined such as quantifying activity intensity and volume, estimating stress levels, core temperature, and using biometrics for diet planning. The key takeaways are that accuracy is critical for user experience, validation requires planning and experience, and use cases should be simple and research-supported.
Pas Reform SmartCount™ counting and dosing systemHenry Arts
SmartCountTM is a vision technology system that can accurately count and analyze day old chicks at high speeds of up to 60,000 chicks per hour. It uses camera images and algorithms to count chicks without accelerating them, reducing stress. Data on chick weights and uniformity is collected and integrated with SmartCenterTM for detailed batch reporting. The system also enables gentle distribution of chicks into boxes and precise spray vaccination without disrupting the natural flow of chicks.
Top Wearables Predictions for the Year Ahead and 2018: Year in ReviewValencell, Inc
Where is wearable technology heading and what can we expect from the wearables market? When it comes to biometric wearables and hearables, we've only scratched the surface of what's possible. In this webinar, Dr. Steven LeBoeuf takes a look back at trends and achievements in wearables in 2018 and weighs in on the outcomes of last year's predictions. We also highlight some unexpected changes you need to be ready for in 2019 and beyond.
Precision Dairy Monitoring Opportunities and ChallengesJeffrey Bewley
This presentation provides an introduction to precision dairy monitoring. The wide range of opportunities for future dairy management are discussed. Then, the challenges of turning these dreams into reality are covered.
IRJET- The Sensor Technologies for More Efficient Cow Reproduction SystemsIRJET Journal
This document discusses sensor technologies that can be used to more efficiently detect estrus cycles in dairy cows. It provides an overview of various sensor types currently available, including those that monitor standing activity, mounting activity, walking activity, restlessness, and vocalization. The sensors aim to accurately detect behavioral and physiological changes in cows during estrus to help farmers more efficiently determine optimal insemination timing and improve fertility rates. While visual observation remains challenging, sensor technologies provide opportunities to automate estrus detection, reduce labor needs, and increase milk yields and profits through higher fertility success.
I just gave a opening keynote on the North American Precision dairy farming conference. I showed some data that we recently collected on the use of sensor systems and the effects of these systems on farm performance.
New developments in the Dutch dairy sectorHenk Hogeveen
This was the opening presentation I gave at the 2014 Congress of the LIvestock Health and Production Group of the South African Veterinary Association. The organization asked me to give an overview of recent developments in the Dutch dairy sector. i have chose to pick three developments that are, in my opinion, interesting for veterinarians: 1. the ongoing automation of the sector, 2. the abolisment of the quota system (and a little background) and 3. the reduced use of antibiotics.
Automation techniques have been increasingly used in livestock production to reduce labor needs. This includes automatic identification of animals using RFID tags, GPS tracking, or retinal/muzzle scanning. Other automated processes discussed are feeding, milking, estrus detection through activity/hormone monitoring, birth detection, online herd management, and barn cleaning/environment control. The document concludes that while automation increases production and efficiency, it also increases costs, so is best for large commercial farms.
Precision livestock farming cattle identification based on biometric data tar...Aboul Ella Hassanien
This document proposes using biometric data from cattle muzzle prints for precision livestock farming and cattle identification. It discusses challenges with current identification methods like RFID tags and outlines the benefits of a non-invasive biometric approach. The proposed system would collect muzzle print images, extract features, reduce dimensions with LDA, and use machine learning to classify and identify individual cattle. Experimental results showed the algorithm achieved high accuracy rates for identification when using different numbers of training images. The conclusion states precision livestock farming with biometric identification could increase farming efficiency and sustainability through individual animal monitoring and traceability in the food chain.
Precision livestock farming cattle identification based on biometric data tar...Aboul Ella Hassanien
This document proposes using biometric data from cattle muzzle prints for precision livestock farming and cattle identification. It discusses challenges with current identification methods like RFID tags and outlines the benefits of a non-invasive biometric approach. The proposed system would collect muzzle print images, extract features, reduce dimensions with LDA, and use machine learning to classify and identify individual cattle. Experimental results showed the algorithm achieved high accuracy rates for identification when using different numbers of training images. The conclusion states precision livestock farming with biometric identification could increase farming efficiency and sustainability through individual animal monitoring and traceability in the food chain.
Precision livestock farming cattle identification based on biometric data tar...Aboul Ella Hassanien
This document proposes using biometric data from cattle muzzle prints for precision livestock farming and cattle identification. It discusses challenges with current identification methods like RFID tags and outlines the benefits of a non-invasive biometric approach. The proposed system would collect muzzle print images, extract features, reduce dimensions with LDA, and use machine learning to classify and identify individual cattle. Experimental results showed the algorithm achieved high accuracy rates for identification when using different numbers of training images. The conclusion states precision livestock farming with biometric identification could increase farming efficiency and sustainability through individual animal monitoring and traceability in the food chain.
Automatic Estrus Detection System for Dairy Animalsidescitation
This paper deals with the new aid for detection of
Estrus (Heat) in dairy animals. As dairy Technology is
developing day by day, therefore reproductive performance of
dairy animals is major concern in dairy industry. This
Reproductive performance of dairy animals requires accurate
and regular Estrus detection. Estrus is nothing but a
behavioural symptom in mammals which indicate that female
is mated close to the ovulation. That’s why Timely detection
of estrus is the only solution to increase the fertility rate in
dairy animals. Failure to detect animal in estrus and breeding
animals which are not in estrus result in economic loss for
the owner because of extended calving interval and additional
semen expenses. Accurate Estrus detection gives idea about
proper timing of Artificial Insemination. So Estrus detection
is the key solution for effective growth in dairy technology.
During estrus period animal shows mounting behaviour,
increased physical activity and vaginal temperature of animal
is increased. So, in proposed technology, for the very first
time all these three signs are sensed by three sensors. The
signal from the sensors are given to the micro-controller, then
micro-controller process the data, display the data on LCD
screen as well as transfer all the data wirelessly to the Personal
computer (PC). PC runs a software module which display all
the data i.e. Animal name, number of mounting, physical
activity and vaginal temperature.
The Role of Technology in Quantifying Mastitis Related DecisionsJeffrey Bewley
This document discusses the role of technology in quantifying decisions related to mastitis detection and management. Precision monitoring technologies like electrical conductivity, milk color, temperature, spectroscopy, biosensors and inline somatic cell count can help detect mastitis earlier than visual observation alone. However, challenges remain around meeting sensitivity and specificity goals, calibration over time, and determining appropriate actions in response to alerts. Further research is needed to quantify the economic benefits of early mastitis detection and determine optimal treatment protocols.
This document summarizes a system called FLOCKMAN that uses precision livestock farming to more efficiently raise chickens. It analyzes chicken growth in real-time and continuously adjusts their feed intake to keep them on a target growth curve. Field trials show FLOCKMAN increases profit by 5 cents per bird through improved feed conversion ratio, increased production efficiency, and higher margins over feed costs. FLOCKMAN has been successfully used on over 2.2 million birds internationally and provides benefits like better chicken health and welfare with a quick return on investment.
Milk consumption in Pakistan is 159 liters per person per year, which is among the highest in developing world (FAO). The demand for milk is increasing every year. Moreover, in big cities quality of milk is becoming a primary focus of the consumers. Currently, most of the milk (app. 80-85%) comes from small scale dairy farmers (herd sizer < 30 animals), however, a significant increase in medium and large dairy herds is observed in past decade. The increasing feed and other inputs prices are putting pressure on the dairy producers and a careful assessment of the cost of milk was required. The current survey was conducted in the district Lahore with the 5 different types of producers. We hope that outcomes of this project will be useful for the institutions and private sector to design strategies that will support farmers. We are hopeful that the dairy producers and technical service providers will also find these results interesting and will use them to design there KPIs and targets to improve efficiencies.
Ron Ketchem - An Overview of Performance Challenges We Are Facing TodayJohn Blue
An Overview of Performance Challenges We Are Facing Today - Ron Ketchem, Swine Management Services, LLC, from the 2020 Missouri Pork Expo, held February 11 - 12, 2020, Columbia, MO, USA.
Artificial insemination is used in Amhara region to improve dairy cattle genetics through crossbreeding with temperate breeds. The region has over 400 AI technicians and several liquid nitrogen production plants. The Bahir Dar AI center collects, processes, and distributes semen from various breeds. Conception rates from AI in the region have reached as high as 66.8% but are typically around 45%. Problems limiting AI efficiency include poor heat detection, semen handling, and herd management. A pilot project using sexed semen achieved over 90% female offspring.
This document describes a study that used tri-axial accelerometers to monitor and classify cow activities. Researchers attached accelerometers to the necks of cows to record acceleration data. They then analyzed the data to classify behaviors as lying, standing or feeding. The accelerometer data was calibrated and processed using a decision tree algorithm. Various thresholds were tested to optimize behavior classification. The results showed accelerometers can accurately recognize different cow behavior patterns, which could help identify health issues.
Experiences in community-based genetic improvement using oestrus synchronizationILRI
Presented by Azage Tegegne at the IPMS Workshop on Alternatives for Improving Field AI Delivery System to Enhance Beef and Dairy Production in Ethiopia, ILRI, Addis Ababa, 24-25 August 2011
Investigation of clinical mastitis and characterization of its causal agents ...Shuvo singha
Globally, mastitis is an important production disease in the dairy industry and has a great economic impact due to reduced milk yield, milk quality deterioration, treatment costs, culling, risk for antimicrobial resistance and reduced animal welfare. A cohort study was conducted on 24 randomly selected dairy farms in Chittagong during six months to (1) estimate the incidence of clinical mastitis (CM) at cow level, (2) identify risk factors and (3) isolate causative pathogens. CM was defined as grade-I (changes in milk), grade-II (changes in milk and udder) and grade-III (changes in milk and udder along with systemic changes).
On-farm hormonal oestrus synchronization and mass insemination of cows for sm...ILRI
This document summarizes research on on-farm hormonal oestrus synchronization and mass artificial insemination of cows for smallholders in Ethiopia. It finds that while a researcher-led approach showed promise, scaling required institutional changes. Working with regional partners to train staff, over 600,000 cows were synchronized and inseminated from 2011-2015 across four regions. New technologies like progesterone tests helped with estrus detection and pregnancy diagnosis. The Ethiopian government's Livestock Master Plan now aims to increase crossbred cows to 5 million in 5 years to boost milk production and incomes.
Technological options and approaches to improve supply of desirable animal ge...ILRI
Presented by Azage Tegegne and Dirk Hoekstra at the 19th Ethiopian Society of Animal production Annual Conference, Addis Ababa, Ethiopia, 15-17 December 2011.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
8. Technology and dairy farming
Automation to increase labour efficiency
Increased number of cows per labour input
9. Technology and dairy farming
Automation to increase labour efficiency
Increased number of cows per labour input
Less time per cow to monitor health
10. Automation to increase labour efficiency
Increased number of cows per labour input
Less time per cow to monitor health
Need for management-support technologies
Technology and dairy farming
11. Tools monitoring production, health and welfare
automatically, continuously, and (near) real-time
Precision livestock farming (PLF) technologies
12. Tools monitoring production, health and welfare
automatically, continuously, and (near) real-time
Emerging field:126 studies, 139 technologies
(Rutten et al., 2013, JDS)
Precision livestock farming (PLF) technologies
(Inter)national projects International conferences
13. Improve health & welfare
Increase efficiency
Improve product quality
Objective monitoring
Improve social lifestyle
Benefits of PLF technologies
18. Undesirable/unknown cost-benefit ratio
(Russel and Bewley, 2013, JDS; Steeneveld and Hogeveen, 2015, JDS)
Most important limiting factor for commercialisation
(Banhazi et al., 2012, Int J Agric & Biol Eng)
20. Attached to the ear
Attached to collar
Attached to the leg
Why is automated oestrus detection different?
Still many options to chose from, but
21. Why is automated oestrus detection different?
Still many options to chose from, but
Associated with clear management action
22. Why is automated oestrus detection different?
Still many options to chose from, but
Associated with clear management action
OK performance
(Rutten et al., 2013, JDS)
23. Lincoln University Dairy Farm, New Zealand
37-d breeding period - start Oct. 25 2010
635 cows with SCR – collars
320 activity only (AO)
315 activity and rumination (AR)
Milk progesterone as gold standard
Twice weekly during breeding period
Field evaluation of two collar-mounted activity meters
(Kamphuis et al., 2012, JDS)
24. 3 time-windows allow for mismatch of Gold Standard
AO: 52
AR: 67
AO: 58
AR: 71
AO: 62
AR: 77
Sensitivity (%)
27. Why is automated oestrus detection different?
Still many options to chose from, but
Associated with clear management action
OK performance
80% Sensitivity 80% Success rate
(Kamphuis et al., 2012, JDS)
28. Why is automated oestrus detection different?
Still many options to chose from, but
Associated with clear management action
OK performance
80% Sensitivity 80% Success rate
(Kamphuis et al., 2012, JDS)
Investment is economically beneficial
(Rutten et al., 2014, JDS)
30. General culling
Calving
Ovulation
Heat detection
P(1st ovulation)
P(heat)
P(heat detected)
P(culling)
P(culling)
P(culling)
Simulated cow
Parity, production level
Insemination
after voluntary waiting period
Culling due to fertility issues
- Max 6 inseminations
- Not pregnant in wk 35
Replacement heifer
Cow pregnant
P(pregnant)
P(early embryonic death)
Next parity
∆ Milk yield
∆ Number of inseminations
∆ Number of calves produced
∆ Feed intake
∆ Number of culled cows
∆ Number of false alerts from PLF
Output
cow place /year
Milk price
Labour costs
Cost for AI
Costs/revenues of calves
Costs feed
Costs for culling
Costs of false alerts PLF (labour or AI
x €
At farm level
Probabilities are
adjusted for each
simulated week
Costs of PLF technology: investment, maintenance,
depreciation, replacement of faulty sensors
Cow Model
SN 50%
SP 100%
SN 80%
SP 95%
€108/cow
€3600/herd
10years
Checking each
alert visually
31. Investing in automated oestrus detection
Cash flow: 2,287 € / year
Cost-Benefit ratio: € 1.23
Discounted payback period: 8 years
Investment pays off
(Rutten et al., 2014, JDS)
SN 80%;SP 95%
€ 108/cow
€ 3600/herd
10years
Checking each alert visually
32. Why is automated oestrus detection different?
Still many options to chose from, but
Associated with clear management action
OK performance
80% Sensitivity 80% Success rate
(Kamphuis et al., 2012, JDS)
Investment is economically beneficial
(Rutten et al., 2014, JDS)
33. New Zealand survey 500 farmers
25% wants it
7% has it
70% listed it in top 3 of technologies
that gained benefit for farm
(Edwards et al., 2014, APS)
Adoption rates of automated oestrus detection systems
20% of all Dutch farms
(Huijps, CRV, personal communication)
Dutch survey 512 farmers
41% of AMS farmers has it
70% of CMS farmers has it
(Steeneveld and Hogeveen, 2015, JDS)
Survey 109 farmers globally
41% has it
Rated as useful to very useful
(Borchers and Bewley, in press, JDS)
35% of US respondents
(Bewley, EAAP/EU-PLF conference, 2014)
35. Moving beyond oestrus detection
Explore other fields
improve utilization of activity data
36. Lameness in the dairy industry
Impacts welfare, productivity, profitability
~$28,000 per year on average NZ farm€16,500
37. Lameness in the dairy industry
Impacts welfare, productivity, profitability
~$28,000 per year on average NZ farm
Visual detection is common practice
Challenging for large herds
NZ farmers fail to identify ~75% of lame cows
(Fabian, 2012; Whay et al., 2002)
Whay et al.,
2002)
Lame?
€16,500
38. Automated lameness detection
5 Waikato farms
4,900 cows
1.5 million milkings
Sensor data every milking
activity and milking
order
live-weight yield
45. Values recorded during milking were averaged
a daily value per sensor
Predictive variables were straightforward
Proportional differences Day-1 to D-14
Absolute value on Day-1
n = 14 variables per sensor
Detecting lameness
46. Values recorded during milking were averaged
a daily value per sensor
Predictive variables were straightforward
Proportional differences Day-1 to D-14
Absolute value on Day-1
n = 14 variables per sensor
Daily probability estimate for lameness
Detecting lameness
47. Values recorded during milking were averaged
a daily value per sensor
Predictive variables were straightforward
Proportional differences Day-1 to D-14
Absolute value on Day-1
n = 14 variables per sensor
Daily probability estimate for lameness
Leave-one-farm-out cross validation
Detecting lameness
55. Detecting lameness
Combining sensors outperformed single sensors
consistently across farms
Potential of using data already on-farm
Improvements required
better predictive variables
Autocorrelation matrix
standard operating procedures
56. Moving beyond oestrus detection
Explore other fields
improve utilization of activity data
57. Predicting moment of calving
Current status: expected calving date
267-295 days after successful insemination
58. Predicting moment of calving
Current status: expected calving date
267-295 days after successful insemination
33% of calvings are
difficult (Barrier et al., 2013)
59. Predicting moment of calving
Current status: expected calving date
267-295 days after successful insemination
33% of calvings are
difficult (Barrier et al., 2013)
Can sensor data better predict moment of
calving?
60. Predicting moment of calving
Two Dutch dairy farms
583 cows with SensOor (Agis Automatisering BV, Harmelen, the Netherlands)
61. Predicting moment of calving
Two Dutch dairy farms
583 cows with SensOor (Agis Automatisering BV, Harmelen, the Netherlands)
Calvings caught on camera
62. Predicting moment of calving
Two Dutch dairy farms
583 cows with SensOor (Agis Automatisering BV, Harmelen, the Netherlands)
Calvings caught on camera
63. Predicting moment of calving
Two Dutch dairy farms
583 cows with SensOor (Agis Automatisering BV, Harmelen, the Netherlands)
110 Calvings caught on camera
64. Dependent: hour in which calving started
Basic: days to expected calving date (ECD)
ECD = insemination date + 280
Predicting moment of start calving– two logit models
65. Predicting hour of start calving– two logit models
Dependent: hour in which calving started
Basic: days to ECD
Extended: days to ECD + sensor data
where these are relative changes for
Ruminating
Feeding
Highly active
Not active
Temperature
66. Predicting hour of start calving– two logit models
Dependent: hour in which calving started
Basic: days to expected calving date (ECD)
Extended: days to ECD + sensor data
Data selection:
168 h before and including hour of start calving
67. Predicting hour of start calving
Model SN at SP = 90%
Basic 22
Extended 69
70. Predicting hour of start calving
Model SN at SP = 90%
Basic 22
Extended (same hour) 69
Extended (same + previous hour) 81
71. Predicting hour of start calving
Potential of using data already on-farm
‘Not active’ significantly added to the model
72. Predicting hour of start calving
Potential of using data already on-farm
‘Not active’ significantly added to the model
Not ready for practical implementation yet
model not validated
performance not good enough (SP too low)
73. Potential of using data already on-farm
‘Not active’ significantly added to the model
Not ready for practical implementation yet
model not validated
performance not good enough (SP too low)
Improvements required
modelling techniques
predictive variables
Predicting hour of start calving
75. What I would like you to remember
Adoption of
PLF is expected
to increase
Editor's Notes
Voorbeeld aanhalen, automatisatie melkput waardoor meer koeien per uur melken, dus uitgebreider maar daardoor minder tijd deze koeien moeten ook gezondh blijven maar daar is dan minder tijd voor monitoring health
Aware entire session is called ‘Precision Livestock Farming’, so perhaps unnecessary.
But, think of PLF similarly for duration of this presentation.
PLF tools measure ‘something’, for example a cow’s activity, automatically, continuously and (near) real-time.
PLF aims at helping end-users in their decision-taking management processes or at reducing dependency on human labour. Examples, pedometers can aid in insemination decisions, automatic milking replace a significant amount of hard and repetitive labour.
PLF is emerging, supported with 126 publications on 139 PLF technologies past decade. Moreover, national and EU-funded projects that focus on implementation of PLF on-farms (SDF and All Smart Pigs). Finally, emerging international conferences dedicated to PLF (smartagrimatics and PDC in 2016, mentioned by Wilma Steeneveld.
So, a lot is going on in the field of PLF, but....
Aware entire session is called ‘Precision Livestock Farming’, so perhaps unnecessary.
But, think of PLF similarly for duration of this presentation.
PLF tools measure ‘something’, for example a cow’s activity, automatically, continuously and (near) real-time.
PLF aims at helping end-users in their decision-taking management processes or at reducing dependency on human labour. Examples, pedometers can aid in insemination decisions, automatic milking replace a significant amount of hard and repetitive labour.
PLF is emerging, supported with 126 publications on 139 PLF technologies past decade. Moreover, national and EU-funded projects that focus on implementation of PLF on-farms (SDF and All Smart Pigs). Finally, emerging international conferences dedicated to PLF (smartagrimatics and PDC in 2016, mentioned by Wilma Steeneveld.
So, a lot is going on in the field of PLF, but....
Lists goes on:
Reduce costs
Reduce stress
Safe labor
Finish with first part of the presentation
Start with second one, the success story of automated heat detection
RUTTEN TOEVOEGEN ALHIER
AR, maar alleen activity meegenomen!
Neem tijd om dit allemaal uit te leggen
Wij zeiden dat SN op zijn minst 80% moest zijn, daarvoor moest th verschoven worden en success rate bleek ook 890% te zijn. Dat leek ons van praktische waarde.
Duidelijk maken dat balans SN en SR kan verschillen voor verschillende situaties
With vervangen door
20% dutch farmers boven steenveld.
Ook kentucky is vertekend beeld want alleen mensen met sensoren algemeen zullen de survey beantwoorden
Meest fair is NZ en CRV
Maar meanstream sensor geworden
Since farmers already have activity data, is it possible to add even more (economic) value by using the same data for other dairy cow health management areas?
Jessica Fabian, thesis of massey university, PN
Make sure you mention that it is difficult to see that specific cow is lame
Jessica Fabian, thesis of massey university, PN
Make sure you mention that it is difficult to see that specific cow is lame
All same sensors!
1.5 years data collection
Scale and relevance, data from the field
All same sensors!
Stress that this is an imaginary example! Mention that non-lame cows were not allowed to have a lameness record throughout data collection period. Make sure that you mention ‘compare pattern changes in behaviour and physiology 14 days before the cow was observed lame by the farmer
Stress that this is an imaginary example! Mention that non-lame cows were not allowed to have a lameness record throughout data collection period. Make sure that you mention ‘compare pattern changes in behaviour and physiology 14 days before the cow was observed lame by the farmer
Mention not interested in risk factors, so not interested that lame cows seem less active then non-lame cow but we’re interested in the pattern difference because that is what a detection model needs to identify
Good result because it tells us that sensors can pick up changes in behaviour and physiology associated with lameness
Since farmers already have activity data, is it possible to add even more (economic) value by using the same data for other dairy cow health management areas?
Negative impact cow health
Higher mortality rate of calves
Negative impact image
Negative impact cow health
Higher mortality rate of calves
Negative impact image
Negative impact cow health
Higher mortality rate of calves
Negative impact image