Assessment of the value of information of precision livestock farming: A conceptual framework

Abstract Although precision livestock farming (PLF) technologies ensure various dimensions of more precise information, the question arises to what extent additional preciseness provides more value. Literature gives insufficient anchor points to estimate the value of information (VOI) obtained with PLF technologies. This study proposes a conceptual framework with building blocks to determine the VOI. Next, the framework is used to describe factors and existing gaps in the VOI assessment. This, finally, leads to reflections and recommendations about how to assess and improve the VOI of PLF. Literature reveals that the VOI surpasses the mere use of more precise information to take decisions, but encompasses a path from data collection to decisions with particular outcomes. The framework interlinks three building blocks: (i) data processing, (ii) decision making and (iii) impact analysis with factors influencing the VOI such as the process to transform data into information, level of precision, decision rules, social influences, the accuracy of information, herd size and prevalence of the condition measured. Besides profitability, outcomes from decisions include the impact on animal welfare, environment, food safety, and food security. The data-to-value framework allows for a better assessment of VOI and its potentials, and provides anchor points to design useful and valuable PLF technologies. The framework also helps to determine the role of advisors in interpreting the more precise information and in formulating farmer-tailored advice to apply the most optimal practices. Both technology design and advisors’ role may enhance the VOI of future PLF developments and applications.

[1]  S. Wolfert,et al.  Big Data in Smart Farming – A review , 2017 .

[2]  W Steeneveld,et al.  Characterization of Dutch dairy farms using sensor systems for cow management. , 2015, Journal of dairy science.

[3]  Bill Malcolm,et al.  A whole-farm investment analysis of some precision agriculture technologies , 2009 .

[4]  A R Kristensen,et al.  Monitoring growth in finishers by weighing selected groups of pigs - A dynamic approach. , 2016, Journal of animal science.

[5]  D C J Main,et al.  Working towards a reduction in cattle lameness: 1. Understanding barriers to lameness control on dairy farms. , 2010, Research in veterinary science.

[6]  Diana Stuart,et al.  Diversity in agricultural technology adoption: How are automatic milking systems used and to what end? , 2015 .

[7]  Daniel Berckmans,et al.  A blueprint for developing and applying precision livestock farming tools: A key output of the EU-PLF project , 2017 .

[8]  David J. Pannell,et al.  Flat Earth Economics: The Far-reaching Consequences of Flat Payoff Functions in Economic Decision Making , 2006 .

[9]  L. Green,et al.  Footrot and interdigital dermatitis in sheep: farmers’ practices, opinions and attitudes , 2005, Veterinary Record.

[10]  A. Bailey,et al.  Farmers' attitudes to disease risk management in England: a comparative analysis of sheep and pig farmers. , 2013, Preventive veterinary medicine.

[11]  C J Rutten,et al.  Delaying investments in sensor technology: The rationality of dairy farmers' investment decisions illustrated within the framework of real options theory. , 2018, Journal of dairy science.

[12]  Cristina Rojo-Gimeno,et al.  A systemic integrative framework to describe comprehensively a swine health system, Flanders as an example. , 2018, Preventive veterinary medicine.

[13]  C. Pomar,et al.  The impact of feeding growing-finishing pigs with daily tailored diets using precision feeding techniques on animal performance, nutrient utilization, and body and carcass composition. , 2014, Journal of animal science.

[14]  Anders Ringgaard Kristensen,et al.  Prioritizing alarms from sensor-based detection models in livestock production - A review on model performance and alarm reducing methods , 2017, Comput. Electron. Agric..

[15]  Henk Hogeveen,et al.  Dairy farmers' attitudes and intentions towards improving dairy cow foot health , 2013 .

[16]  Henk Hogeveen,et al.  Perceptions, circumstances and motivators that influence implementation of zoonotic control programs on cattle farms. , 2010, Preventive veterinary medicine.

[17]  Dan P. Armstrong,et al.  An economic evaluation of automatic cluster removers as a labour saving device for dairy farm businesses , 2012 .

[18]  Nathalie Hostiou,et al.  Conséquences de l'élevage de précision sur le travail et les relations homme-animal en élevage laitier (synthèse bibliographique) , 2017 .

[19]  Michael Boehlje,et al.  Assessing the potential value for an automated dairy cattle body condition scoring system through stochastic simulation , 2010 .

[20]  T L Veith,et al.  Economic and phosphorus-related effects of precision feeding and forage management at a farm scale. , 2007, Journal of dairy science.

[21]  P. T. Johnstone,et al.  An automated in-line clinical mastitis detection system using measurement of conductivity from foremilk of individual udder quarters , 2009, New Zealand veterinary journal.

[22]  H Hogeveen,et al.  Mastitis alert preferences of farmers milking with automatic milking systems. , 2012, Journal of dairy science.

[23]  M. Hovi,et al.  Measuring and comparing constraints to improved biosecurity amongst GB farmers, veterinarians and the auxiliary industries. , 2008, Preventive veterinary medicine.

[24]  Nj Bell,et al.  The use of in-depth interviews to understand the process of treating lame dairy cows from the farmers' perspective , 2014 .

[25]  D. Calavas,et al.  A study of the knowledge, attitudes, and behaviors of French dairy farmers toward the farm register. , 2007, Journal of dairy science.

[26]  Jasmeet Kaler,et al.  Drivers for precision livestock technology adoption: A study of factors associated with adoption of electronic identification technology by commercial sheep farmers in England and Wales , 2018, PloS one.

[27]  C. Lokhorst,et al.  Livestock Farming with Care: towards sustainable production of animal-source food , 2013 .

[28]  Laurens Klerkx,et al.  Building knowledge systems for sustainable agriculture: supporting private advisors to adequately address sustainable farm management in regular service contacts , 2010 .

[29]  C Kamphuis,et al.  Development of protocols to evaluate in-line mastitis-detection systems. , 2013, Journal of dairy science.

[30]  Anders Ringgaard Kristensen,et al.  Multivariate dynamic linear models for estimating the effect of experimental interventions in an evolutionary operations setup in dairy herds. , 2017, Journal of dairy science.

[31]  J. Kaler,et al.  Sheep farmer opinions on the current and future role of veterinarians in flock health management on sheep farms: A qualitative study , 2013, Preventive veterinary medicine.

[32]  Marcella Guarino,et al.  European farmers’ experiences with precision livestock farming systems , 2017 .

[33]  I. Ajzen The theory of planned behavior , 1991 .

[34]  D F Kelton,et al.  Factors associated with participation of Alberta dairy farmers in a voluntary, management-based Johne's disease control program. , 2015, Journal of dairy science.

[35]  Erik Jørgensen,et al.  The Influence of Weighing Precision on Delivery Decisions in Slaughter Pig Production , 1993 .

[36]  Cécile Cornou,et al.  Use of information from monitoring and decision support systems in pig production: collection, applications and expected benefits. , 2013 .

[37]  J. M. Bewley,et al.  Characterization of Kentucky dairy producer decision-making behavior. , 2013, Journal of dairy science.

[38]  H Hogeveen,et al.  Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction. , 2010, Journal of dairy science.

[39]  Daniel Berckmans,et al.  Economical Case Study of the SOMO Respiratory Distress Monitor in Pigs , 2016 .

[40]  Wouter Saeys,et al.  Farm-specific economic value of automatic lameness detection systems in dairy cattle: From concepts to operational simulations. , 2018, Journal of dairy science.

[41]  Jack P. C. Kleijnen,et al.  Economic value of management information systems in agriculture: a review of evaluation approaches. , 1995 .

[42]  Barbara Wieland,et al.  Pig farmers' perceptions, attitudes, influences and management of information in the decision-making process for disease control. , 2014, Preventive veterinary medicine.

[43]  Tahir Rehman,et al.  Farmers' attitudes towards techniques for improving oestrus detection in dairy herds in South West England , 2006 .

[44]  J. Enemark,et al.  The monitoring, prevention and treatment of sub-acute ruminal acidosis (SARA): a review. , 2008, Veterinary journal.

[45]  F. Sniehotta,et al.  Time to retire the theory of planned behaviour , 2014, Health psychology review.

[46]  Anders Ringgaard Kristensen,et al.  Detecting abnormalities in pigs' growth - A dynamic linear model with diurnal growth pattern for identified and unidentified pigs , 2018, Comput. Electron. Agric..

[47]  Ruth Nettle,et al.  Making sense in the cloud: Farm advisory services in a smart farming future , 2019, NJAS - Wageningen Journal of Life Sciences.

[48]  Bjørn Gunnar Hansen,et al.  Robotic milking-farmer experiences and adoption rate in Jæren, Norway , 2015 .

[49]  Henk Hogeveen,et al.  The perception of veterinary herd health management by Dutch dairy farmers and its current status in the Netherlands: a survey. , 2012, Preventive veterinary medicine.

[50]  H Hogeveen,et al.  The profitability of automatic milking on Dutch dairy farms. , 2007, Journal of dairy science.

[51]  J S Walton,et al.  Estrous detection intensity and accuracy and optimal timing of insemination with automated activity monitors for dairy cows. , 2016, Journal of dairy science.

[52]  Cécile Cornou,et al.  Dynamic production monitoring in pig herds I: Modeling and monitoring litter size at herd and sow level , 2012 .

[53]  V. Cauberghe,et al.  Beliefs, intentions, and beyond: A qualitative study on the adoption of sustainable gastrointestinal nematode control practices in Flanders' dairy industry. , 2018, Preventive veterinary medicine.

[54]  Yongwha Chung,et al.  Automatic Detection and Recognition of Pig Wasting Diseases Using Sound Data in Audio Surveillance Systems , 2013, Sensors.

[55]  V. Cauberghe,et al.  Diagnosis before treatment: Identifying dairy farmers' determinants for the adoption of sustainable practices in gastrointestinal nematode control. , 2015, Veterinary parasitology.

[56]  C J Rutten,et al.  Invited review: sensors to support health management on dairy farms. , 2013, Journal of dairy science.

[57]  Veerle Fievez,et al.  The economic value of information provided by milk biomarkers under different scenarios: Case-study of an ex-ante analysis of fat-to-protein ratio and fatty acid profile to detect subacute ruminal acidosis in dairy cows , 2018 .

[58]  J. Brian Hardaker,et al.  Why Farm Recording Systems are Doomed to Failure , 1981 .

[59]  Cor Verdouw,et al.  Information and Communication Technology as a Driver for Change in Agri-food Chains , 2013 .

[60]  A. Bradley,et al.  Factors affecting the cost-effectiveness of on-farm culture prior to the treatment of clinical mastitis in dairy cows , 2017, Preventive veterinary medicine.

[61]  Theo J G M Lam,et al.  Invited review: Determinants of farmers' adoption of management-based strategies for infectious disease prevention and control. , 2017, Journal of dairy science.

[62]  Laura Hänninen,et al.  Managing undocked pigs – on-farm prevention of tail biting and attitudes towards tail biting and docking , 2016, Porcine health management.

[63]  Wouter Saeys,et al.  Supporting the Development and Adoption of Automatic Lameness Detection Systems in Dairy Cattle: Effect of System Cost and Performance on Potential Market Shares , 2017, Animals : an open access journal from MDPI.

[64]  Paine,et al.  Networks of practice for co-construction of agricultural decision support systems: Case studies of precision dairy farms in Australia , 2012 .

[65]  Bart De Ketelaere,et al.  Online warning systems for individual fattening pigs based on their feeding pattern , 2017, Biosystems Engineering.

[66]  T. Kutter,et al.  The role of communication and co-operation in the adoption of precision farming , 2011, Precision Agriculture.

[67]  Fumie Yokota,et al.  Value of Information Literature Analysis: A Review of Applications in Health Risk Management , 2004, Medical decision making : an international journal of the Society for Medical Decision Making.

[68]  C J Rutten,et al.  An ex ante analysis on the use of activity meters for automated estrus detection: to invest or not to invest? , 2014, Journal of dairy science.

[69]  M. Doherr,et al.  The effect of fine granular sand on pododermatitis in captive greater flamingos (Phoenicopterus roseus) , 2014 .

[70]  Melissa Gibbs,et al.  A Test Of Bayesian Learning From Farmer Trials Of New Wheat Varieties , 1990 .

[71]  Lan Ge,et al.  Guidelines For Governance Of Data Sharing In Agri-Food Networks , 2017 .

[72]  Anders Ringgaard Kristensen,et al.  From biological models to economic optimization. , 2015, Preventive veterinary medicine.

[73]  Wilma Steeneveld,et al.  Economic consequences of investing in sensor systems on dairy farms , 2015, Comput. Electron. Agric..

[74]  J Charlier,et al.  The relation between input-output transformation and gastrointestinal nematode infections on dairy farms. , 2016, Animal : an international journal of animal bioscience.

[75]  J. Wolfert,et al.  A European Perspective on the Economics of Big Data , 2015 .

[76]  Luciano Hauschild,et al.  Precision feeding can significantly reduce feeding cost and nutrient excretion in growing animals , 2011 .