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Doidge C, Ånestad LM, Burrell A, Frössling J, Palczynski L, Pardon B, Veldhuis A, Bokma J, Carmo LP, Hopp P, Guelbenzu-Gonzalo M, Meunier NV, Ordell A, Santman-Berends I, van Schaik G, Kaler J. A Living Lab approach to understanding dairy farmers' technology and data needs to improve herd health: Focus groups from 6 European countries. J Dairy Sci 2024; 107:5754-5778. [PMID: 38490555 DOI: 10.3168/jds.2024-24155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 02/18/2024] [Indexed: 03/17/2024]
Abstract
For successful development and adoption of technology on dairy farms, farmers need to be included in the innovation process. However, the design of agricultural technologies usually takes a top-down approach with little involvement of end-users at the early stages. Living Labs offer a methodology that involve end-users throughout the development process and emphasize the importance of understanding users' needs. Currently, exploration of dairy farmers' technology needs has been limited to specific types of technology (e.g., smartphone apps) and adult cattle. The aim of this study was to use a Living Lab approach to identify dairy farmers' data and technology needs to improve herd health and inform innovation development. We conducted 18 focus groups with a total of 80 dairy farmers from Belgium, Ireland, the Netherlands, Norway, Sweden, and the United Kingdom. Data were analyzed using Template Analysis, and 6 themes were generated representing the fundamental needs of autonomy, comfort, competence, community and relatedness, purpose, and security. Farmers favored technologies that provided them with convenience, facilitated their knowledge and understanding of problems on farm, and allowed them to be self-reliant. Issues with data sharing and accessibility and usability of software were barriers to technology use. Furthermore, farmers were facing problems around recruitment and management of labor and needed ways to reduce stress. Controlling aspects of the barn environment, such as air quality, hygiene, and stocking density, were particular concerns in relation to youngstock management. Overall, the findings suggest that developers of farm technologies may want to include farmers in the design process to ensure a positive user experience and improve accessibility. The needs identified in this study can be used as a framework when designing farm technologies to strengthen need satisfaction and reduce any potential harm toward needs.
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Affiliation(s)
- C Doidge
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom.
| | - L M Ånestad
- Norwegian Veterinary Institute, 1431 Ås, Norway
| | - A Burrell
- Animal Health Ireland, Carrick-on-Shannon, Co. Leitrim N41 WN27, Ireland
| | - J Frössling
- Department of Epidemiology, Surveillance and Risk Assessment, Swedish Veterinary Agency (SVA), 751 89 Uppsala, Sweden; Department of Applied Animal Science and Welfare, Swedish University of Agricultural Sciences (SLU), 532 23 Skara, Sweden
| | - L Palczynski
- Innovation for Agriculture, Stoneleigh Park, Warwickshire CV8 2LZ, United Kingdom
| | - B Pardon
- Department of Internal Medicine, Reproduction, and Population Medicine, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium
| | - A Veldhuis
- Royal GD, 7400 AA Deventer, the Netherlands
| | - J Bokma
- Department of Internal Medicine, Reproduction, and Population Medicine, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium
| | - L P Carmo
- Norwegian Veterinary Institute, 1431 Ås, Norway
| | - P Hopp
- Norwegian Veterinary Institute, 1431 Ås, Norway
| | | | - N V Meunier
- Animal Health Ireland, Carrick-on-Shannon, Co. Leitrim N41 WN27, Ireland
| | - A Ordell
- Department of Epidemiology, Surveillance and Risk Assessment, Swedish Veterinary Agency (SVA), 751 89 Uppsala, Sweden
| | | | - G van Schaik
- Royal GD, 7400 AA Deventer, the Netherlands; Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, 3584 CL Utrecht, the Netherlands
| | - J Kaler
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington LE12 5RD, United Kingdom
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2
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Jukna V, Meškinytė E, Antanaitis R, Juozaitienė V. Association of Dry Period Length with Automatic Milking System, Mastitis, and Reproductive Indicators in Cows. Animals (Basel) 2024; 14:2065. [PMID: 39061527 PMCID: PMC11274193 DOI: 10.3390/ani14142065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 07/08/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024] Open
Abstract
The objective of this study was to evaluate the association between dry period (DP) length and various indicators of productivity, reproduction, and udder health in cows managed with an automatic milking system. We analyzed records from 3861 cows, categorizing them into three groups based on their DP duration: (1) <40 days, (2) 40-70 days, and (3) DP > 70 days. Cows with a DP of 40-70 days had an average energy-corrected milk production that was 8.2 kg greater than that of cows with a short DP and 5.0 kg greater than that of cows with a long DP (p < 0.001). Milk from the 40-70-day DP group exhibited the highest lactose concentration (4.64 ± 0.01%). Additionally, cows with the longest DP had the smallest proportion of animals with a milk fat-to-protein ratio of 1.2 to 1.4. Cows with a DP of 40-70 days also showed the lowest milk electrical conductivity across all udder quarters, whereas cows with the shortest DP had the highest conductivity. The highest conception rates were observed in the group with the shortest DP. These results suggest that a DP of 40-70 days is optimal for maximizing milk production and improving both udder health and reproductive performance under AMS. Proper management of DP duration can be an effective strategy for sustainable dairy herd management.
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Affiliation(s)
- Vigilijus Jukna
- Agriculture Academy, Vytautas Magnus University, Universiteto St. 10A, Akademija, LT-53361 Kaunas, Lithuania; (V.J.); (E.M.)
| | - Edita Meškinytė
- Agriculture Academy, Vytautas Magnus University, Universiteto St. 10A, Akademija, LT-53361 Kaunas, Lithuania; (V.J.); (E.M.)
| | - Ramūnas Antanaitis
- Large Animal Clinic, Veterinary Academy, Lithuanian University of Health Sciences, Tilžės Str. 18, LT-47181 Kaunas, Lithuania
| | - Vida Juozaitienė
- Research Institute of Natural and Technological Sciences, Vytautas Magnus University, K. Donelaičio 58, LT-44248 Kaunas, Lithuania
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3
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Petrov AF, Bogdanova OV, Narozhnykh KN, Kamaldinov EV, Shatokhin KS, Gart VV, Kulikova SG, Zhigulin TA. Clustering of countries based on dairy productivity characteristics of Holstein cattle for breeding material selection. Vet World 2024; 17:1108-1118. [PMID: 38911070 PMCID: PMC11188896 DOI: 10.14202/vetworld.2024.1108-1118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 04/23/2024] [Indexed: 06/25/2024] Open
Abstract
Background and Aim The aim of any breeding process is to create a herd based on certain parameters that reflect an ideal animal vision. Targeted herding involves selecting the source of breeding material to be imported from another country. Therefore, there is a problem in selecting a breeding material importer to rapidly form a uterine canopy with the required properties. The purpose of this study was to evaluate a set of predictive milk productivity traits in Holstein cattle across countries. Materials and Methods This research was based on records of 819,358 recorded animals from 28 countries born after January 1, 2018, from open databases. We used the Euclidean metric to construct dendrograms characterizing the similarity of countries according to the complex milk productivity traits of the daughters of bulls. The Ward method was used to minimize intracluster variance when forming clusters and constructing the corresponding diagrams. Principal component analysis was used to reduce dimensionality and eliminate the effect of multicollinearity. The principal components were selected using the Kaiser-Harris criteria. Results A ranking of multidimensional complex milk productivity traits in different countries over the past 5 years was performed. A group of leading countries led by the USA was established according to the studied indicators, and the possible reasons for such a division into groups were described. Conclusion The pressure of purposeful artificial selection prevails in comparison with the pressure of natural selection concerning milk productivity traits in a certain group of countries, which allows specialists to choose suppliers when buying breeding animals and materials. The findings are based solely on data from recorded animals, which may not represent the entire breed population within each country, especially in regions where record-keeping may be inconsistent. It is expected that further studies will include regional data from large enterprises not part of Interbull, with mandatory verification and validation. An important element of such work is seen as the ability to compare the milk productivity of populations from different countries using a different scale, as well as studying the differentiation of countries by other selection traits of dairy.
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Affiliation(s)
- A. F. Petrov
- Department of Veterinary Genetics and Biotechnology, Novosibirsk State Agrarian University, Novosibirsk, 630039, Russia
| | - O. V. Bogdanova
- Department of Veterinary Genetics and Biotechnology, Novosibirsk State Agrarian University, Novosibirsk, 630039, Russia
| | - K. N. Narozhnykh
- Department of Veterinary Genetics and Biotechnology, Novosibirsk State Agrarian University, Novosibirsk, 630039, Russia
| | - E. V. Kamaldinov
- Department of Veterinary Genetics and Biotechnology, Novosibirsk State Agrarian University, Novosibirsk, 630039, Russia
| | - K. S. Shatokhin
- Department of Veterinary Genetics and Biotechnology, Novosibirsk State Agrarian University, Novosibirsk, 630039, Russia
| | - V. V. Gart
- Department of Veterinary Genetics and Biotechnology, Novosibirsk State Agrarian University, Novosibirsk, 630039, Russia
| | - S. G. Kulikova
- Department of Veterinary Genetics and Biotechnology, Novosibirsk State Agrarian University, Novosibirsk, 630039, Russia
| | - T. A. Zhigulin
- Department of Veterinary Genetics and Biotechnology, Novosibirsk State Agrarian University, Novosibirsk, 630039, Russia
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Graham JR, Montes ME, Pedrosa VB, Doucette J, Taghipoor M, Araujo AC, Gloria LS, Boerman JP, Brito LF. Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves. J Dairy Sci 2024; 107:2175-2193. [PMID: 37923202 DOI: 10.3168/jds.2023-23794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 10/03/2023] [Indexed: 11/07/2023]
Abstract
Precision livestock farming technologies, such as automatic milk feeding machines, have increased the availability of on-farm data collected from dairy operations. We analyzed feeding records from automatic milk feeding machines to evaluate the genetic background of milk feeding traits and bovine respiratory disease (BRD) in North American Holstein calves. Data from 10,076 preweaning female Holstein calves were collected daily over a period of 6 yr (3 yr included per-visit data), and daily milk consumption (DMC), per-visit milk consumption (PVMC), daily sum of drinking duration (DSDD), drinking duration per-visit, daily number of rewarded visits (DNRV), and total number of visits per day were recorded over a 60-d preweaning period. Additional traits were derived from these variables, including total consumption and duration variance (TCV and TDV), feeding interval, drinking speed (DS), and preweaning stayability. A single BRD-related trait was evaluated, which was the number of times a calf was treated for BRD (NTT). The NTT was determined by counting the number of BRD incidences before 60 d of age. All traits were analyzed using single-step genomic BLUP mixed-model equations and fitting either repeatability or random regression models in the BLUPF90+ suite of programs. A total of 10,076 calves with phenotypic records and genotypic information for 57,019 SNP after the quality control were included in the analyses. Feeding traits had low heritability estimates based on repeatability models (0.006 ± 0.0009 to 0.08 ± 0.004). However, total variance traits using an animal model had greater heritabilities of 0.21 ± 0.023 and 0.23 ± 0.024, for TCV and TDV, respectively. The heritability estimates increased with the repeatability model when using only the first 32 d preweaning (e.g., PVMC = 0.040 ± 0.003, DMC = 0.090 ± 0.009, DSDD = 0.100 ± 0.005, DS = 0.150 ± 0.007, DNRV = 0.020 ± 0.002). When fitting random regression models (RRM) using the full dataset (60-d period), greater heritability estimates were obtained (e.g., PVMC = 0.070 [range: 0.020, 0.110], DMC = 0.460 [range: 0.050, 0.680], DSDD = 0.180 [range: 0.010, 0.340], DS = 0.19 [range: 0.070, 0.430], DNRV = 0.120 [range: 0.030, 0.450]) for the majority of the traits, suggesting that RRM capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of -0.59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves. The results from this study provide key insights of the genetic background of early in-life traits in dairy cattle, which can be used for selecting animals with improved health outcomes and performance.
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Affiliation(s)
- Jason R Graham
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
| | - Maria E Montes
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
| | - Victor B Pedrosa
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
| | - Jarrod Doucette
- Agriculture Information Technology (AgIT), Purdue University, West Lafayette, IN 47907
| | - Masoomeh Taghipoor
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120, Palaiseau, France
| | - André C Araujo
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
| | - Leonardo S Gloria
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
| | | | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907.
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5
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Doidge C, Burrell A, van Schaik G, Kaler J. A qualitative survey approach to investigating beef and dairy veterinarians' needs in relation to technologies on farms. Animal 2024; 18:101124. [PMID: 38547554 DOI: 10.1016/j.animal.2024.101124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 04/20/2024] Open
Abstract
Globally, farmers are being increasingly encouraged to use technologies. Consequently, veterinarians often use farm data and technologies to provide farmers with advice. Yet very few studies have sought to understand veterinarians' perceptions of data and technologies on farms. The aim of this study was to understand veterinarians' experiences and opinions on data and technology on beef and dairy farms. An online qualitative survey was conducted with a convenience sample of 36 and 24 veterinarians from the United Kingdom and Ireland, respectively. The data were analysed using reflexive thematic analysis to generate four themes: (1) Improving veterinary advice through data; (2) Ensuring stock person skills are retained; (3) Longevity of technology; and (4) Solving social problems on farms. We show that technologies and data can make veterinarians feel more confident in the advice they give to farmers. However, the quality and quantity of data collected on cattle farms were highly variable. Furthermore, veterinarians were concerned that farmers can become over-reliant on technologies by not using their stockperson skills. As herd sizes increase, technologies can help to improve working conditions on farms with multiple employees of various skillsets. Veterinarians would like innovations that can help them to demonstrate their competence, influence farmers' behaviour, and ensure sustainability of the beef and dairy industries.
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Affiliation(s)
- C Doidge
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington LE12 5RD, UK.
| | - A Burrell
- Animal Health Ireland, 2 - 5 The Archways, Carrick-on-Shannon, Co. Leitrim N41 WN27, Ireland
| | - G van Schaik
- Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands; Royal GD, Deventer, the Netherlands
| | - J Kaler
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington LE12 5RD, UK
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6
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Herrera N, Vélez J, Holt T, Pinedo P. Employee perception of precision technology use at the dairy farm. Transl Anim Sci 2024; 8:txae036. [PMID: 38562212 PMCID: PMC10983077 DOI: 10.1093/tas/txae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 03/21/2024] [Indexed: 04/04/2024] Open
Abstract
The adoption of precision technologies on dairy farms has increased significantly in recent decades, leading to the challenge of providing employees with resources to maximize the efficient use of these tools. The objective of this study was to explore how dairy farm employees perceive the available precision technologies and to identify possible challenges they face when adapting to their use at the farm. An online survey consisting of four sections (employee demographics, precision technologies in use, perception of these technologies, and opportunities for adapting to technology use) was completed from September to December 2022 by 266 farm employees from three dairies operated under similar management. Most of the respondents were identified as male (72.2%), Hispanic or Latino (92.5%), aged between 21 and 30 (39.1%) or 31 and 40 yr (36.8%), with a bachelor's degree (34.6%) or completion of middle school (29.3%) and having basic or no English proficiency (74%). Overall, the respondents indicated being comfortable (95.6%) with and understanding (91.8%) the technology they use. Employees recognized precision technology as a tool that helps them to be more efficient (93.7%), identifying the technologies' benefits (92.1%). However, challenges for adapting to these technologies included personal limitations, such as not knowing the language of the technology (31%), visual impairments (24%), light sensitivity (14%), and not being able to read (7%). Environmental limitations were also recognized and included cold weather (64.3%), wind (46%), and surroundings that were too dark (31%) or too bright (21%). Significant associations between perception of the technology and age, level of education, and English proficiency were identified. Respondents indicated their desire to learn more about precision technologies implemented at work, which could eventually lead to improved efficiency at the dairy operation through innovations in the way users interact with these technologies, increasing employees' motivation. This study provides insights that could assist the dairy industry in addressing challenges and enhancing opportunities for a more efficient use of precision technologies at dairy farms.
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Affiliation(s)
- Natalia Herrera
- Department of Animal Sciences, Colorado State University, Fort Collins, CO 80523, USA
| | - Juan Vélez
- Aurora Organic Farms, Platteville, CO 80651, USA
| | - Timothy Holt
- Department of Clinical Science, Colorado State University, Fort Collins, CO 80523, USA
| | - Pablo Pinedo
- Department of Animal Sciences, Colorado State University, Fort Collins, CO 80523, USA
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7
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Küçüktopçu E, Cemek B, Simsek H. Modeling Environmental Conditions in Poultry Production: Computational Fluid Dynamics Approach. Animals (Basel) 2024; 14:501. [PMID: 38338144 PMCID: PMC10854819 DOI: 10.3390/ani14030501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/27/2024] [Accepted: 02/01/2024] [Indexed: 02/12/2024] Open
Abstract
In recent years, computational fluid dynamics (CFD) has become increasingly important and has proven to be an effective method for assessing environmental conditions in poultry houses. CFD offers simplicity, efficiency, and rapidity in assessing and optimizing poultry house environments, thereby fueling greater interest in its application. This article aims to facilitate researchers in their search for relevant CFD studies in poultry housing environmental conditions by providing an in-depth review of the latest advancements in this field. It has been found that CFD has been widely employed to study and analyze various aspects of poultry house ventilation and air quality under the following five main headings: inlet and fan configuration, ventilation system design, air temperature-humidity distribution, airflow distribution, and particle matter and gas emission. The most commonly used turbulence models in poultry buildings are the standard k-ε, renormalization group (RNG) k-ε, and realizable k-ε models. Additionally, this article presents key solutions with a summary and visualization of fundamental approaches employed in addressing path planning problems within the CFD process. Furthermore, potential challenges, such as data acquisition, validation, computational resource requirements, meshing, and the selection of a proper turbulence model, are discussed, and avenues for future research (the integration of machine learning, building information modeling, and feedback control systems with CFD) are explored.
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Affiliation(s)
- Erdem Küçüktopçu
- Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, Samsun 55139, Türkiye;
| | - Bilal Cemek
- Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, Samsun 55139, Türkiye;
| | - Halis Simsek
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA;
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8
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Doidge C, Palczynski L, Zhou X, Bearth A, van Schaik G, Kaler J. Exploring the data divide through a social practice lens: A qualitative study of UK cattle farmers. Prev Vet Med 2023; 220:106030. [PMID: 37806078 DOI: 10.1016/j.prevetmed.2023.106030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 07/31/2023] [Accepted: 09/24/2023] [Indexed: 10/10/2023]
Abstract
Appropriate management decisions are key for sustainable and profitable beef and dairy farming. Data-driven technologies aim to provide information which can improve farmers' decision-making practices. However, data-driven technologies have resulted in the emergence of a "data divide", in which there is a gap between the generation and use of data. Our study aims to further understand the data divide by drawing on social practice theory to recognise the emergence, linkages, and reproduction of youngstock data practices on cattle farms in the UK. Eight focus groups with fifteen beef and nineteen dairy farmers were completed. The topics of discussion included data use, technology use, disease management in youngstock, and future goals for their farm. The transcribed data were analysed using reflexive thematic analysis with a social practice lens. Social practice theory uses practices as the unit of analysis, rather than focusing on individual behaviours. Practices are formed of three elements: meaning (e.g., beliefs), materials (e.g., objects), and competencies (e.g., skills) and are connected in time and space. We conceptualised the data divide as a disconnection of data collection practices and data use and interpretation practices. Consequently, we were able to generate five themes that represent these breaks in connection.Our findings suggest that a data divide exists because of meanings that de-stabilise practices, tensions in farmers' competencies to perform practices, spatial and temporal disconnects, and lack of forms of feedback on data practices. The data preparation practice, where farmers had to merge different data sources or type up handwritten data, had negative meanings attached to it and was therefore sometimes not performed. Farmers tended to associate data and technology practices with larger dairy farms, which could restrict beef and small-scale dairy farms from performing these practices. Some farmers suggested that they lacked the skills to use technologies and struggled to transform their data into meaningful outputs. Data preparation and data use and interpretation practices were often tied to an office space because of the required infrastructure, but farmers preferred to spend time outdoors and with their animals. There appeared to be no normalisation of what data should be collected or what data should be analysed, which made it difficult for farmers to benchmark their progress. Some farmers did not have access to discussion groups or veterinarians who were interested in data and therefore could not get feedback on their data practices.These results suggest that the data divide exists because of three types of disconnect: a disconnect between elements within a practice because of tensions in competencies or negative meanings to perform a practice; a disconnect between practices because of temporal or spatial differences; and a break in the reproduction of practices because of lack of feedback on their practices. Data use on farms can be improved through transformation of practices by ensuring farmers have input in the design of technologies so that they align with their values and competencies.
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Affiliation(s)
- C Doidge
- School of Veterinary Medicine & Science, University of Nottingham, Sutton Bonington LE12 5RD, UK.
| | - L Palczynski
- Innovation for Agriculture, Stoneleigh Park, Warwickshire CV8 2LZ, UK
| | - X Zhou
- Consumer Behaviour, Institute for Environmental Decisions, ETH Zurich, Universitaetstrasse 22, 8092 Zurich, Switzerland
| | - A Bearth
- Consumer Behaviour, Institute for Environmental Decisions, ETH Zurich, Universitaetstrasse 22, 8092 Zurich, Switzerland
| | - G van Schaik
- Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands; Royal GD, Deventer, the Netherlands
| | - J Kaler
- School of Veterinary Medicine & Science, University of Nottingham, Sutton Bonington LE12 5RD, UK
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9
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Luo G, Yang Y, Wang L. Driving rural industry revitalization in the digital economy era: Exploring strategies and pathways in China. PLoS One 2023; 18:e0292241. [PMID: 37768942 PMCID: PMC10538648 DOI: 10.1371/journal.pone.0292241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 09/14/2023] [Indexed: 09/30/2023] Open
Abstract
In the context of the digital economy, achieving rural industrial revitalization in China hinges on digitization. This study delves into the synergistic mechanisms of diverse factors that contribute to Rural Industrial Revitalization across three dimensions: technology, organization, and environment. To investigate these mechanisms, a combination of the necessary condition analysis method and the fuzzy set qualitative comparative analysis method is employed. The research findings indicate that no necessary conditions exist for achieving high-level rural industrial revitalization. However, digital infrastructure and the digital financial environment have a universally significant impact. The study identifies four distinct pathways driving high-level rural industrial revitalization: digitaldriven, digital-government-talentdriven, digital-enterprisedriven, and digital-enterprise-talentdriven. Furthermore, significant variations exist in the driving pathways for rural industrial revitalization among the eastern, central, and western regions of China. By unveiling the multifaceted mechanisms underpinning the revitalization of rural industries, this research provides valuable practical insights for the future development of rural industries in China.
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Affiliation(s)
- Gongli Luo
- College of Economics and Management, Shandong University of Science and Technology, Qingdao, China
| | - Yu Yang
- College of Economics and Management, Shandong University of Science and Technology, Qingdao, China
| | - Lu Wang
- College of Economics and Management, Shandong University of Science and Technology, Qingdao, China
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10
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Kopler I, Marchaim U, Tikász IE, Opaliński S, Kokin E, Mallinger K, Neubauer T, Gunnarsson S, Soerensen C, Phillips CJC, Banhazi T. Farmers' Perspectives of the Benefits and Risks in Precision Livestock Farming in the EU Pig and Poultry Sectors. Animals (Basel) 2023; 13:2868. [PMID: 37760267 PMCID: PMC10525424 DOI: 10.3390/ani13182868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 08/28/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
More efficient livestock production systems are necessary, considering that only 41% of global meat demand will be met by 2050. Moreover, the COVID-19 pandemic crisis has clearly illustrated the necessity of building sustainable and stable agri-food systems. Precision Livestock Farming (PLF) offers the continuous capacity of agriculture to contribute to overall human and animal welfare by providing sufficient goods and services through the application of technical innovations like digitalization. However, adopting new technologies is a challenging issue for farmers, extension services, agri-business and policymakers. We present a review of operational concepts and technological solutions in the pig and poultry sectors, as reflected in 41 and 16 European projects from the last decade, respectively. The European trend of increasing broiler-meat production, which is soon to outpace pork, stresses the need for more outstanding research efforts in the poultry industry. We further present a review of farmers' attitudes and obstacles to the acceptance of technological solutions in the pig and poultry sectors using examples and lessons learned from recent European projects. Despite the low resonance at the research level, the investigation of farmers' attitudes and concerns regarding the acceptance of technological solutions in the livestock sector should be incorporated into any technological development.
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Affiliation(s)
- Idan Kopler
- European Wing Unit, Galilee Research Institute, Kiryat Shmona 11016, Israel;
| | - Uri Marchaim
- European Wing Unit, Galilee Research Institute, Kiryat Shmona 11016, Israel;
| | - Ildikó E. Tikász
- Agricultural Economics Directorate, Institute of Agricultural Economics, H-1093 Budapest, Hungary;
| | - Sebastian Opaliński
- Department of Environmental Hygiene and Animal Welfare, Wroclaw University of Environmental and Life Sciences, 50-375 Wrocław, Poland;
| | - Eugen Kokin
- Institute of Forestry and Engineering, Estonian University of Life Science, 51014 Tartu, Estonia; (E.K.); (C.J.C.P.)
| | | | | | - Stefan Gunnarsson
- Department of Animal Environment and Health, Swedish University of Agricultural Sciences, SE-532 23 Skara, Sweden;
| | - Claus Soerensen
- Department of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus, Denmark;
| | - Clive J. C. Phillips
- Institute of Forestry and Engineering, Estonian University of Life Science, 51014 Tartu, Estonia; (E.K.); (C.J.C.P.)
- CUSP Institute, Curtin University, Bentley, WA 6102, Australia
| | - Thomas Banhazi
- AgHiTech Kft, H-1101 Budapest, Hungary;
- International College, National Taiwan University, Taipei 10617, Taiwan
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11
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Doidge C, Frössling J, Dórea FC, Ordell A, Vidal G, Kaler J. Social and ethical implications of data and technology use on farms: a qualitative study of Swedish dairy and pig farmers. Front Vet Sci 2023; 10:1171107. [PMID: 37675073 PMCID: PMC10477671 DOI: 10.3389/fvets.2023.1171107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 08/08/2023] [Indexed: 09/08/2023] Open
Abstract
Introduction Livestock farmers are being increasingly encouraged to adopt digital health technologies on their farms. Digital innovations may have unintended consequences, but there tends to be a pro-innovation bias in previous literature. This has led to a movement towards "responsible innovation," an approach that questions the social and ethical challenges of research and innovation. This paper explores the social and ethical issues of data and technologies on Swedish dairy and pig farms from a critical perspective. Methods Six focus groups were conducted with thirteen dairy and thirteen pig farmers. The data were analysed using reflexive thematic analysis and a digital critical health lens, which focuses on concepts of identity and power. Results and discussion The analysis generated four themes: extending the self, sense of agency, quantifying animals, and managing human labour. The findings suggest that technologies can change and form the identities of farmers, their workers, and animals by increasing the visibility of behaviours and bodies through data collection. Technologies can also facilitate techniques of power such as conforming to norms, hierarchical surveillance, and segregation of populations based on data. There were many contradictions in the way that technology was used on farms which suggests that farmers cannot be dichotomised into those who are opposed to and those that support adoption of technologies. Emotions and morality played an important role in the way animals were managed and technologies were used by farmers. Thus, when developing innovations, we need to consider users' feelings and attachments towards the technologies. Technologies have different impacts on farmers and farm workers which suggests that we need to ensure that we understand the perspectives of multiple user groups when developing innovations, including those that might be least empowered.
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Affiliation(s)
- Charlotte Doidge
- School of Veterinary Medicine and Science, University of Nottingham, Nottingham, United Kingdom
| | - Jenny Frössling
- Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala, Sweden
- Department of Animal Environment and Health, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Fernanda C. Dórea
- Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala, Sweden
| | - Anna Ordell
- Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala, Sweden
| | - Gema Vidal
- Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala, Sweden
| | - Jasmeet Kaler
- School of Veterinary Medicine and Science, University of Nottingham, Nottingham, United Kingdom
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12
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Gasser L, Cruz FP, Cockburn M. Can meteorological data improve the short-term prediction of individual milk yield in dairy cows? J Dairy Sci 2023:S0022-0302(23)00364-8. [PMID: 37414605 DOI: 10.3168/jds.2022-22980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 02/23/2023] [Indexed: 07/08/2023]
Abstract
Many farms document daily milk yields of individual cows because these are a good indicator of cow well-being. It is established that extreme meteorological conditions influence the milk yields by causing heat and cold stress, whereas less is known about the effects of moderate changes in meteorological conditions. Thus, the aim of the present study was to evaluate whether individual daily milk yield predictions can be improved by considering such changes. We evaluated 8 years of milking and meteorological data from Eastern Switzerland with a total of 33,938 daily milkings from 145 Brown Swiss and 64 Swiss Fleckvieh cows. The cows were aged between 1.9 and 13.5 years at parturition. The data set was split into 7 periods according to the days in milk (DIM) and subsequently filtered into subsets by breed and parity. We applied Gaussian process regression to predict individual daily milk yield. We compared different models including DIM, lagged milk yield, and meteorological variables as features and found that models including the lagged milk yield performed best. Within the period of 5 to 90 DIM, we were able to predict individual next-day milk yield from the cow's last milkings with a root mean squared error (RMSE) of 2.1 kg. In contrast, without information on the previous milk yield, accuracy of milk yield prediction was lower, with an RMSE close to 8 kg. The models holding information about previous milk yields showed a substantial increase in performance. Within a more homogeneous data subset filtered by breed or parity or both, predictions were even better, with a relative RMSE of 4.3% for first-parity Fleckvieh cows. However, we found that including meteorological features, such as temperature, rainfall, wind speed, temperature humidity index, cooling degree, and barometric pressure, did not improve the predictions in any of the evaluated periods. This finding indicates that considering meteorological features in daily milk yield prediction models is not useful in moderate climates; considering lagged milk yield is sufficient. We hypothesize that this meteorological information, among other influences, is indirectly contained in the lagged milk yield.
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Affiliation(s)
- L Gasser
- Swiss Data Science Center, 8050 Zürich, Switzerland
| | - F Perez Cruz
- Swiss Data Science Center, 8050 Zürich, Switzerland
| | - M Cockburn
- Digital Production, Agroscope, 8356 Tänikon, Switzerland.
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13
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Davison C, Michie C, Tachtatzis C, Andonovic I, Bowen J, Duthie CA. Feed Conversion Ratio (FCR) and Performance Group Estimation Based on Predicted Feed Intake for the Optimisation of Beef Production. SENSORS (BASEL, SWITZERLAND) 2023; 23:4621. [PMID: 37430533 PMCID: PMC10223015 DOI: 10.3390/s23104621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 07/12/2023]
Abstract
This paper reports on the use of estimates of individual animal feed intake (made using time spent feeding measurements) to predict the Feed Conversion Ratio (FCR), a measure of the amount of feed consumed to produce 1 kg of body mass, for an individual animal. Reported research to date has evaluated the ability of statistical methods to predict daily feed intake based on measurements of time spent feeding measured using electronic feeding systems. The study collated data of the time spent eating for 80 beef animals over a 56-day period as the basis for the prediction of feed intake. A Support Vector Regression (SVR) model was trained to predict feed intake and the performance of the approach was quantified. Here, feed intake predictions are used to estimate individual FCR and use this information to categorise animals into three groups based on the estimated Feed Conversion Ratio value. Results provide evidence of the feasibility of utilising the 'time spent eating' data to estimate feed intake and in turn Feed Conversion Ratio (FCR), the latter providing insights that guide farmer decisions on the optimisation of production costs.
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Affiliation(s)
- Chris Davison
- Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
| | - Craig Michie
- Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
| | - Christos Tachtatzis
- Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
| | - Ivan Andonovic
- Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
| | - Jenna Bowen
- Scotland’s Rural College, Beef and Sheep Research Centre, SRUC, West Mains Road, Edinburgh EH9 3JG, UK
| | - Carol-Anne Duthie
- Scotland’s Rural College, Beef and Sheep Research Centre, SRUC, West Mains Road, Edinburgh EH9 3JG, UK
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14
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Giersberg MF, Meijboom FLB. As if you were hiring a new employee: on pig veterinarians' perceptions of professional roles and relationships in the context of smart sensing technologies in pig husbandry in the Netherlands and Germany. AGRICULTURE AND HUMAN VALUES 2023:1-14. [PMID: 37359847 PMCID: PMC10150679 DOI: 10.1007/s10460-023-10450-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 06/28/2023]
Abstract
Veterinarians are increasingly confronted with new technologies, such as Precision Livestock Farming (PLF), which allows for automated animal monitoring on commercial farms. At the same time, we lack information on how veterinarians, as stakeholders who may play a mediating role in the public debate on livestock farming, perceive the use and the impact of such technologies. This study explores the meaning veterinarians attribute to the application of PLF in the context of public concerns related to pig production. Semi-structured interviews were carried out with pig veterinarians located in the Netherlands and Germany. By using an inductive and semantic approach to reflexive thematic analysis, we developed four main themes from the interview data: (1) the advisory role of the veterinarian, which is characterized by a diverse scope, including advice on PLF, generally positive evaluations and financial dependencies; (2) the delineation of PLF technologies as supporting tools, which are seen as an addition to human animal care; (3) the relationship between veterinarian and farmer, which is context-related, ranging from taking sides with to distancing oneself from farmers; and (4) the distance between agriculture and society, in the context of which PLF has both a mitigating and reinforcing potential. The present findings indicate that veterinarians play an active role in the emerging field of PLF in livestock farming. They are aware of and reflect on competing interests of different groups in society and share positions with different stakeholders. However, the extent to which they are able to mediate between stakeholder groups in practice seems to be constrained by external factors, such as financial dependencies. Supplementary Information The online version contains supplementary material available at 10.1007/s10460-023-10450-6.
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Affiliation(s)
- Mona F. Giersberg
- Animals in Science and Society, Department Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
| | - Franck L. B. Meijboom
- Animals in Science and Society, Department Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
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15
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Zhang X, Fan D. Can agricultural digital transformation help farmers increase income? An empirical study based on thousands of farmers in Hubei Province. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY 2023:1-27. [PMID: 37362968 PMCID: PMC10107574 DOI: 10.1007/s10668-023-03200-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 03/25/2023] [Indexed: 06/28/2023]
Abstract
With the deep integration of the digital economy and agricultural development, agricultural digital transformation promotes agricultural production, and industrial upgrading and broadens sales channels to achieve the strategic goal of rural revitalization in China. To explore whether agricultural digitization can help farmers increase their income and what path can be achieved, this study is based on the theoretical framework of rural revitalization theory and digitization. Through a questionnaire survey of 1500 farmers in Hubei Province, the impact mechanism of agricultural digitization transformation on farmers' income is empirically studied. The empirical results show that the digital transformation of agriculture can promote the increase in farmers' income, and promote the increase in farmers' income by improving production efficiency, broadening sales channels, and promoting the upgrading of agricultural structure. At the same time, both production efficiency and sales channels form a chain double intermediary path with the upgrading of agricultural structure. Under the background of rural revitalization, this study provides theoretical references and guidance for further promoting agricultural digital transformation to increase farmers' income. The marginal contribution of this study is to construct a theoretical model of agricultural digitization to promote farmers' income increase, which has important theoretical reference and guiding significance for guiding the development direction of agricultural digitization and promoting farmers' income increase.
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Affiliation(s)
- Xiufan Zhang
- School of Economics and Management, Zhejiang Sci-Tech University, Hangzhou, China
| | - Decheng Fan
- School of Economics and Management, Harbin Engineering University, Harbin, China
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16
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Precision Livestock Farming: What Does It Contain and What Are the Perspectives? Animals (Basel) 2023; 13:ani13050779. [PMID: 36899636 PMCID: PMC10000125 DOI: 10.3390/ani13050779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/06/2023] [Accepted: 02/13/2023] [Indexed: 02/24/2023] Open
Abstract
Precision Livestock Farming (PLF) describes the combined use of sensor technology, the related algorithms, interfaces, and applications in animal husbandry. PLF technology is used in all animal production systems and most extensively described in dairy farming. PLF is developing rapidly and is moving beyond health alarms towards an integrated decision-making system. It includes animal sensor and production data but also external data. Various applications have been proposed or are available commercially, only a part of which has been evaluated scientifically; the actual impact on animal health, production and welfare therefore remains largely unknown. Although some technology has been widely implemented (e.g., estrus detection and calving detection), other systems are adopted more slowly. PLF offers opportunities for the dairy sector through early disease detection, capturing animal-related information more objectively and consistently, predicting risks for animal health and welfare, increasing the efficiency of animal production and objectively determining animal affective states. Risks of increasing PLF usage include the dependency on the technology, changes in the human-animal relationship and changes in the public perception of dairy farming. Veterinarians will be highly affected by PLF in their professional life; they nevertheless must adapt to this and play an active role in further development of technology.
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17
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Palma-Molina P, Hennessy T, O'Connor AH, Onakuse S, O'Leary N, Moran B, Shalloo L. Factors associated with intensity of technology adoption and with the adoption of 4 clusters of precision livestock farming technologies in Irish pasture-based dairy systems. J Dairy Sci 2023; 106:2498-2509. [PMID: 36797180 DOI: 10.3168/jds.2021-21503] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 10/23/2022] [Indexed: 02/16/2023]
Abstract
Precision livestock farming (PLF) technologies have been widely promoted as important tools to improve the sustainability of dairy systems due to perceived economic, social, and environmental benefits. However, there is still limited information about the level of adoption of PLF technologies (percentage of farms with a PLF technology) and the factors (farm and farmer characteristics) associated with PLF technology adoption in pasture-based dairy systems. The current research aimed to address this knowledge gap by using a representative survey of Irish pasture-based dairy farms from 2018. First, we established the levels of adoption of 9 PLF technologies (individual cow activity sensors, rising plate meters, automatic washers, automatic cluster removers, automatic calf feeders, automatic parlor feeders, automatic drafting gates, milk meters, and a grassland management decision-support tool) and grouped them into 4 PLF technology clusters according to the level of association with each other and the area of dairy farm management in which they are used. The PLF technology clusters were reproductive management technologies, grass management technologies, milking management technologies, and calf management technologies. Additionally, we classified farms into 3 categories of intensity of technology adoption based on the number of PLF technologies they have adopted (nonadoption, low intensity of adoption, and high intensity of adoption). Second, we determined the factors associated with the intensity of technology adoption and with the adoption of the PLF technology clusters. A multinomial logistic regression model and 4 logistic regressions were used to determine the factors associated with intensity of adoption (low and high intensity of adoption compared with nonadoption) and with the adoption of the 4 PLF technology clusters, respectively. Adoption levels varied depending on PLF technology, with the most adopted PLF technologies being those related to the milking process (e.g., automatic parlor feeders and milk meters). The results of the multinomial logistic regression suggest that herd size, proportion of hired labor, agricultural education, and discussion group membership were positively associated with a high intensity of adoption, whereas age of farmer and number of household members were negatively associated with high intensity of adoption. However, when analyzing PLF technology clusters, the magnitude and direction of the influence of the factors in technology adoption varied depending on the PLF technology cluster being investigated. By identifying the PLF technologies in which pasture-based dairy farmers are investing more and by detecting potential drivers and barriers for the adoption of PLF technologies, the current study could allow PLF technology companies, practitioners, and researchers to develop and target strategies that improve future adoption of PLF technologies in pasture-based dairy settings.
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Affiliation(s)
- P Palma-Molina
- Department of Food Business and Development, Cork University Business School, West Wing, Main Quadrangle, University College Cork, Ireland, T12 K8AF; Animal and Grassland Research and Innovation Centre, Teagasc Moorepark, Fermoy, Co. Cork, Ireland P61 C996; VistaMilk SFI Research Centre, Teagasc Moorepark, Fermoy, Co. Cork, Ireland P61 C996.
| | - T Hennessy
- Department of Food Business and Development, Cork University Business School, West Wing, Main Quadrangle, University College Cork, Ireland, T12 K8AF
| | - A H O'Connor
- Animal and Grassland Research and Innovation Centre, Teagasc Moorepark, Fermoy, Co. Cork, Ireland P61 C996; VistaMilk SFI Research Centre, Teagasc Moorepark, Fermoy, Co. Cork, Ireland P61 C996
| | - S Onakuse
- Department of Food Business and Development, Cork University Business School, West Wing, Main Quadrangle, University College Cork, Ireland, T12 K8AF
| | - N O'Leary
- Hincks Centre for Entrepreneurship Excellence, School of Business, Munster Technological University, Co. Cork, Ireland T12 P928
| | - B Moran
- Teagasc, Rural Economy & Development Centre, Mellows Campus, Athenry, Co. Galway, Ireland H65 R718
| | - L Shalloo
- Animal and Grassland Research and Innovation Centre, Teagasc Moorepark, Fermoy, Co. Cork, Ireland P61 C996; VistaMilk SFI Research Centre, Teagasc Moorepark, Fermoy, Co. Cork, Ireland P61 C996
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18
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Park SO, Seo KH. Digital livestock systems and probiotic mixtures can improve the growth performance of swine by enhancing immune function, cecal bacteria, short-chain fatty acid, and nutrient digestibility. Front Vet Sci 2023; 10:1126064. [PMID: 37035810 PMCID: PMC10079995 DOI: 10.3389/fvets.2023.1126064] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/23/2023] [Indexed: 04/11/2023] Open
Abstract
In response to climate change, the use of digital livestock systems and probiotic mixtures as technological strategies to improve animal health and production is driving new innovations in the farm animal industry. However, there is little information available regarding the effects of digital livestock systems and probiotic mixtures (consisting of Bacillus subtillus, Streptomyces galilaeus, and Sphingobacteriaceae) on the growth performance of the growth-finishing swine. Thus, the objective of this study was to investigate the effects of digital livestock systems and probiotic mixtures on the immune function, cecal bacteria, short-chain fatty acids, nutrient digestibility, and growth performance of growth-finishing swine. A total of 64 crossbred male swine (Duroc × Landrace × Yorkshire, average body weight: 60.17 ± 1.25 kg) were randomly assigned to four treatment groups: CON (control group with a conventional livestock system without a probiotic mixture), CON0.4 (a conventional livestock system with a 0.4% probiotic mixture), DLSC (a digital livestock system without a probiotic mixture), and DLS0.4 (a digital livestock system with a 0.4% probiotic mixture). The swine were reared under standard environmental conditions until their average body weight reached 110 kg. The results indicated that the growth performance of the swine improved with an increase in nutrient digestibility and immune function via modulation of blood immune markers in the group with a digital livestock system compared to the CON group, although the growth performance of the swine was similar between the DLSC and CON0.4 groups. Moreover, the application of the digital livestock system and the probiotic mixture maintained higher levels of Lactobacillus and balanced short-chain fatty acid profiles compared to the CON group. These results suggest that a digital livestock system and a probiotic mixture can improve the growth performance of swine by enhancing their nutrient digestibility, improving their immune function, and maintaining balanced cecal bacteria and short-chain fatty acids. Therefore, this study provides insights into the application of digital livestock systems and probiotic mixtures as a climate change response strategy to improve swine production.
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Affiliation(s)
- Sang-O Park
- College of Animal Life Science, Kangwon National University, Chuncheon, Republic of Korea
- *Correspondence: Sang-O Park
| | - Kyung-Hoon Seo
- Hooin Ecobio Institute, Hongseong-gun, Chungcheongnam-do, Republic of Korea
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19
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Baker D, Jackson EL, Cook S. Perspectives of digital agriculture in diverse types of livestock supply chain systems. Making sense of uses and benefits. Front Vet Sci 2022; 9:992882. [PMID: 36532350 PMCID: PMC9756311 DOI: 10.3389/fvets.2022.992882] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 11/17/2022] [Indexed: 09/19/2023] Open
Abstract
Digital technology is being introduced to global agriculture in a wide variety of forms that are collectively known as digital agriculture. In this paper we provide opportunities and value propositions of how this is occurring in livestock production systems, with a consistent emphasis on technology relating to animal health, animal welfare, and product quality for value creation. This is achieved by organizing individual accounts of digital agriculture in livestock systems according to four broad types-commodity-based; value seeking; subsistence and nature-based. Each type presents contrasting modes of value creation in downstream processing; as well as from the perspective of One Health. The ideal result of digital technology adoption is an equitable and substantial diversification of supply chains, increased monetization of animal product quality, and more sensitive management to meet customer demands and environmental threats. Such changes have a significance beyond the immediate value generated because they indicate endogenous growth in livestock systems, and may concern externalities imposed by the pursuit of purely commercial ends.
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Affiliation(s)
- Derek Baker
- Centre for Agribusiness, University of New England, Armidale, NSW, Australia
- Food Agility CRC, Sydney, NSW, Australia
| | | | - Simon Cook
- College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
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20
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Behavioral Fingerprinting: Acceleration Sensors for Identifying Changes in Livestock Health. J 2022. [DOI: 10.3390/j5040030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
During disease or toxin challenges, the behavioral activities of grazing animals alter in response to adverse situations, potentially providing an indicator of their welfare status. Behavioral changes such as feeding behavior, rumination and physical behavior as well as expressive behavior, can serve as indicators of animal health and welfare. Sometimes behavioral changes are subtle and occur gradually, often missed by infrequent visual monitoring until the condition becomes acute. There is growing popularity in the use of sensors for monitoring animal health. Acceleration sensors have been designed to attach to ears, jaws, noses, collars and legs to detect the behavioral changes of cattle and sheep. So far, some automated acceleration sensors with high accuracies have been found to have the capacity to remotely monitor the behavioral patterns of cattle and sheep. These acceleration sensors have the potential to identify behavioral patterns of farm animals for monitoring changes in behavior which can indicate a deterioration in health. Here, we review the current automated accelerometer systems and the evidence they can detect behavioral patterns of animals for the application of potential directions and future solutions for automatically monitoring and the early detection of health concerns in grazing animals.
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21
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Sun S, Wen X, Jie S, Gao Q, Zhu Y, Wen S. Drivers of Farmers’ Intention to Use the Digital Agricultural Management System: Integrating Theory of Planned Behavior and Behavioral Economics. Front Psychol 2022; 13:901169. [DOI: 10.3389/fpsyg.2022.901169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 04/27/2022] [Indexed: 11/13/2022] Open
Abstract
China’s fishery industry has national and international relevance whose aquaculture production accounts for more than 60 percent of the world’s total aquaculture production. But the average amount of pesticides used per hectare in China is roughly five times of the world average. The abuse of chemical fertilizers and drugs has brought chronic, long-term, and cumulative harm to both human beings and environment. The digital agricultural management system should be adopted to reduce non-negligible environment pollution and the quality and safety risks of aquatic products. So, it is essential to understand the factors that may influence the adopting intention of this digital management approaches. The present study aimed to examine the adopting intention of farmers toward the digital agricultural management system using two theories–the theory of planned behavior (TPB) and the behavioral economics–as the research framework. The population was composed of farmers in the provinces of Guangdong province in south China of whom 219 farmers were sampled with stratified random sampling technique. Structural equation modeling was used to analyze the data, and it was revealed that this research framework could potentially predict intention. And we observed that the two biased belief of availability bias and loss aversion bias can be the main predictive influence factors of responsible behaviors in adopting the digital agriculture management system, which highlights the importance of framing recommendations in terms of losses rather than gain may be more effective to increase farmers’ intention to adopt the digital system on their farms.
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22
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Menendez HM, Brennan JR, Gaillard C, Ehlert K, Quintana J, Neethirajan S, Remus A, Jacobs M, Teixeira IAMA, Turner BL, Tedeschi LO. ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Opportunities and Challenges of Confined and Extensive Precision Livestock Production. J Anim Sci 2022; 100:6577180. [PMID: 35511692 PMCID: PMC9171331 DOI: 10.1093/jas/skac160] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 04/28/2022] [Indexed: 11/18/2022] Open
Abstract
Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.
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Affiliation(s)
- H M Menendez
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - J R Brennan
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - C Gaillard
- Institut Agro, PEGASE, INRAE, 35590 Saint Gilles, France
| | - K Ehlert
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - J Quintana
- Department of Animal Science (Menendez, Brennan, Quintana); Department of Natural Resource Management (Ehlert); South Dakota State University, 711 N. Creek Drive, Rapid City, South Dakota, 57702, USA
| | - Suresh Neethirajan
- Farmworx, Adaptation Physiology, Animal Sciences Group, Wageningen University, 6700 AH, The Netherlands
| | - A Remus
- Sherbrooke Research and Development Centre, 2000 College Street, Sherbrooke, QC J1M 1Z3, Canada
| | - M Jacobs
- FR Analytics B.V., 7642 AP Wierden, The Netherlands
| | - I A M A Teixeira
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Twin Falls, ID 83301, USA
| | - B L Turner
- Department of Agriculture, Agribusiness, and Environmental Science, and King Ranch® Institute for Ranch Management, Texas A&M University-Kingsville, 700 University Blvd MSC 228, Kingsville, TX 78363, USA
| | - L O Tedeschi
- Department of Animal Science, Texas A&M University, College Station, TX 77843-2471, USA
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Küçüktopcu E, Cemek B, Simsek H, Ni JQ. Computational Fluid Dynamics Modeling of a Broiler House Microclimate in Summer and Winter. Animals (Basel) 2022; 12:ani12070867. [PMID: 35405856 PMCID: PMC8997067 DOI: 10.3390/ani12070867] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/15/2022] [Accepted: 03/22/2022] [Indexed: 12/04/2022] Open
Abstract
Simple Summary Microclimate conditions in broiler housing are significant for maximizing poultry production and ensuring the welfare of the birds. In the present study, we modeled summer and winter microclimates in a mechanically ventilated broiler house. Validation of the simulated values was accomplished through comparison to field measurements. In visual simulations, the results were used to reconstruct microclimate conditions such as stagnant and stress zones of broiler houses. In conclusion, simulation techniques can be used as an alternative method for analyzing poultry house indoor environments. Abstract Appropriate microclimate conditions in broiler housing are critical for optimizing poultry production and ensuring the health and welfare of the birds. In this study, spatial variabilities of the microclimate in summer and winter seasons in a mechanically ventilated broiler house were modeled using the computational fluid dynamics (CFD) technique. Field measurements of temperature, relative humidity, and airspeeds were conducted in the house to compare the simulated results. The study identified two problems of high temperature in summer, which could result in bird heat stress and stagnant zones in winter, and simulated possible alternative solutions. In summer, if an evaporative cooling pad system was used, a decrease in temperature of approximately 3 °C could be achieved when the mean air temperature rose above 25 °C in the house. In winter, adding four 500-mm circulation fans of 20-m spacing inside the house could eliminate the accumulation of hot and humid air in the stagnant zones in the house. This study demonstrated that CFD is a valuable tool for adequate heating, ventilation, and air conditioning system design in poultry buildings.
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Affiliation(s)
- Erdem Küçüktopcu
- Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, Samsun 55139, Turkey;
- Correspondence:
| | - Bilal Cemek
- Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, Samsun 55139, Turkey;
| | - Halis Simsek
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA; (H.S.); (J.-Q.N.)
| | - Ji-Qin Ni
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA; (H.S.); (J.-Q.N.)
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24
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Doidge C, Dickie J, Lovatt F, Hudson C, Kaler J. Evaluation of the use of antibiotic waste bins and medicine records to quantify antibiotic use on sheep, beef, and mixed species farms: A mixed methods study. Prev Vet Med 2021; 197:105505. [PMID: 34600353 DOI: 10.1016/j.prevetmed.2021.105505] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 08/25/2021] [Accepted: 09/26/2021] [Indexed: 11/26/2022]
Abstract
There is a lack of robust data on antibiotic use on sheep and beef farms in the UK, particularly for farms with mixed species. On mixed farms, quantification of antibiotic use is reliant on accurate farmers' records as veterinary prescription data does not provide information at the species level. Previous studies that have investigated multiple antibiotic use collection methods were conducted on single species farms and failed to collect data on the reasons why differences in methods may exist. This study aimed to evaluate sheep and beef farmers' antibiotic recording practices by comparing quantities of antibiotics measured from medicine records and empty antibiotic packaging collection bins, and identify barriers and facilitators of the antibiotic use collection methods. Thirty-five farms were followed for a year period. Farmers were asked to record their antibiotic treatments and deposit empty antibiotic packaging used in sheep or beef cattle into a bin. Semi-structured qualitative interviews were conducted to understand the experiences of farmers taking part in the study and explore the possible differences in methods. Bins and medicine records were collected and the mass of active ingredient (mg) was calculated. The level of agreement between the two antibiotic use collection methods was measured using rank parameters of Kendall's Ta. The bins were 67 % (CI = 29-87 %) more likely to measure more antibiotic use than the medicine records. The scale of variability of the measurements between two random farms was 33 % (CI = 6-56 %) larger for the antibiotic waste bins than the scale of variability between the medicine records. Sheep farmers often missed neonatal lamb treatments off their medicine records, with a median of 32.5 missing treatments per farm (IQR = 18-130). Of the mixed species farms, 28 % of treatment entries were missing the species the antibiotic was used in. Farmers reported that the bin was easy to use but they also reported that there was a tendency to under-report actual use where there were multiple workers on the farm or where treatments were administered by the veterinarian. The qualitative analysis identified contextual barriers to accurate medicine recording, such as difficulties with animal identification, with recording in the field, and with recording during lambing time. This study demonstrated that there were significant differences in antibiotic use measured by the bins and the medicine records. The mixed-methods approach provided an understanding of the contextual barriers that impacted farmers' medicine recording and use of the bin. This information on the contextual barriers can be used to inform the design of data collection methods to improve antibiotic consumption data in the sheep and beef sectors.
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Affiliation(s)
- Charlotte Doidge
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RD, UK.
| | - Jonah Dickie
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RD, UK.
| | - Fiona Lovatt
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RD, UK.
| | - Chris Hudson
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RD, UK.
| | - Jasmeet Kaler
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RD, UK.
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25
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Pandey S, Kalwa U, Kong T, Guo B, Gauger PC, Peters DJ, Yoon KJ. Behavioral Monitoring Tool for Pig Farmers: Ear Tag Sensors, Machine Intelligence, and Technology Adoption Roadmap. Animals (Basel) 2021; 11:2665. [PMID: 34573631 PMCID: PMC8466302 DOI: 10.3390/ani11092665] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 09/07/2021] [Accepted: 09/07/2021] [Indexed: 02/05/2023] Open
Abstract
Precision swine production can benefit from autonomous, noninvasive, and affordable devices that conduct frequent checks on the well-being status of pigs. Here, we present a remote monitoring tool for the objective measurement of some behavioral indicators that may help in assessing the health and welfare status-namely, posture, gait, vocalization, and external temperature. The multiparameter electronic sensor board is characterized by laboratory measurements and by animal tests. Relevant behavioral health indicators are discussed for implementing machine learning algorithms and decision support tools to detect animal lameness, lethargy, pain, injury, and distress. The roadmap for technology adoption is also discussed, along with challenges and the path forward. The presented technology can potentially lead to efficient management of farm animals, targeted focus on sick animals, medical cost savings, and less use of antibiotics.
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Affiliation(s)
- Santosh Pandey
- Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA;
| | - Upender Kalwa
- Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA;
| | - Taejoon Kong
- Center for Defense Acquisition and Requirements Analysis, Korea Institute for Defense Analyses, 37 Hoegi-ro, Dongdaemun-gu, Seoul 02455, Korea;
| | - Baoqing Guo
- Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA 50011, USA; (B.G.); (P.C.G.)
| | - Phillip C. Gauger
- Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA 50011, USA; (B.G.); (P.C.G.)
| | - David J. Peters
- Rural Sociology, Department of Sociology and Criminal Justice, Iowa State University, Ames, IA 50011, USA;
| | - Kyoung-Jin Yoon
- Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA 50011, USA; (B.G.); (P.C.G.)
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26
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Ouweltjes W, Spoelstra M, Ducro B, de Haas Y, Kamphuis C. A data-driven prediction of lifetime resilience of dairy cows using commercial sensor data collected during first lactation. J Dairy Sci 2021; 104:11759-11769. [PMID: 34454764 DOI: 10.3168/jds.2021-20413] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 07/12/2021] [Indexed: 11/19/2022]
Abstract
Reliable prediction of lifetime resilience early in life can contribute to improved management decisions of dairy farmers. Several studies have shown that time series sensor data can be used to predict lifetime resilience rankings. However, such predictions generally require the translation of sensor data into biologically meaningful sensor features, which involve proper feature definitions and a lot of preprocessing. The objective of this study was to investigate the hypothesis that data-driven random forest algorithms can equal or improve the prediction of lifetime resilience scores compared with ordinal logistic regression, and that these algorithms require considerably less effort for data preprocessing. We studied this by developing prediction models that forecast lifetime resilience of a cow early in her productive life using sensor data from the first lactation. We used an existing data set from a Dutch experimental herd, with data of culled cows for which birth dates, insemination dates, calving dates, culling dates, and health treatments were available to calculate lifetime resilience scores. Moreover, 4 types of first-lactation sensor data, converted to daily aggregated values, were available: milk yield, body weight, activity, and rumination. For each sensor, 14 sensor features were calculated, of which part were based on absolute daily values and part on relative to herd average values. First, we predicted lifetime resilience rank with stepwise logistic regression using sensor features as predictors and a P-value of <0.2 as the cut-off. Next, we applied a random forest with the 6 features that remained in the final logistic regression model. We then applied a random forest with all sensor features, and finally applied a random forest with daily aggregated values as features. All models were validated with stratified 10-fold cross-validation with 90% of the records in the training set and 10% in the validation set. Model performances expressed in percentage of correctly classified cows (accuracy) and percentage of cows being critically misclassified (i.e., high as low and vice versa) ± standard deviation were 45.1 ± 8.1% and 10.8% with the ordinal logistic regression model, 45.7 ± 8.4% and 16.0% with the random forest using the same 6 features as the logistic regression model, 48.4 ± 6.7% and 10.0% for the random forest with all sensor features, and 50.5 ± 6.3% and 8.4% for the random forest with daily sensor values. This random forest also revealed that data collected in early and late stages of first lactation seem to be of particular importance in the prediction compared with that in mid lactation. Accuracies of the models were not significantly different, but the percentage of critically misclassified cows was significantly higher for the second model than for the other models. We concluded that a data-driven random forest algorithm with daily aggregated sensor data as input can be used for the prediction of lifetime resilience classification with an overall accuracy of ~50%, and provides at least as good prediction as models with sensor features as input.
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Affiliation(s)
- Wijbrand Ouweltjes
- Wageningen University and Research, Animal Health and Welfare, PO Box 338, 6700 AH, Wageningen, the Netherlands.
| | - Mirjam Spoelstra
- Wageningen University and Research, Animal Breeding and Genomics, PO Box 338, 6700 AH, Wageningen, the Netherlands
| | - Bart Ducro
- Wageningen University and Research, Animal Breeding and Genomics, PO Box 338, 6700 AH, Wageningen, the Netherlands
| | - Yvette de Haas
- Wageningen University and Research, Animal Breeding and Genomics, PO Box 338, 6700 AH, Wageningen, the Netherlands
| | - Claudia Kamphuis
- Wageningen University and Research, Animal Breeding and Genomics, PO Box 338, 6700 AH, Wageningen, the Netherlands
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27
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Eastwood CR, Edwards JP, Turner JA. Review: Anticipating alternative trajectories for responsible Agriculture 4.0 innovation in livestock systems. Animal 2021; 15 Suppl 1:100296. [PMID: 34246598 DOI: 10.1016/j.animal.2021.100296] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 02/24/2021] [Accepted: 02/25/2021] [Indexed: 11/19/2022] Open
Abstract
Technological change has been a constant feature of livestock systems leading to the third agricultural 'green' revolution of the mid-20th century. Digital technologies are now leading us into the fourth agricultural revolution, where sustainable food production is supported by technologies that collect data useful for farm and supply chain performance improvement, along with task automation and compliance. However, the potential benefits of digital agricultural futures are uncertain and plagued by unrealized expectations of previous innovations. The aims of this paper are to articulate current trends in technology and livestock systems and anticipate future trajectories for Agriculture 4.0 in relation to meeting sustainability and animal welfare outcomes for livestock systems. We use a 'Futures Triangle' approach to review the role of technology in livestock systems. The main findings are that previous work envisioning technological livestock futures have favoured pull of the future factors (techno-optimists) or weight of the past (techno-pessimists), rather than a balance of pull, push and weighting factors. Responsible Agriculture 4.0 innovation requires public-private collaboration of innovation system stakeholders, including policy makers, farmers, consumers, as well as technology developers, to enable development of transition pathways from a systems perspective. The use of responsible innovation processes, including anticipation on alternative futures, should also be built into innovation processes to support critical reflection on technological trajectories and related innovation system consequences, both desirable and undesirable.
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Affiliation(s)
- C R Eastwood
- Feed and Farm Systems Group, DairyNZ Ltd, PO Box 85066, Lincoln University, 7647 Lincoln, New Zealand.
| | - J P Edwards
- Feed and Farm Systems Group, DairyNZ Ltd, PO Box 85066, Lincoln University, 7647 Lincoln, New Zealand
| | - J A Turner
- AgResearch, Ruakura Research Centre, 10 Bisley Road, Hamilton 3214, New Zealand
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28
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Stachowicz J, Umstätter C. Do we automatically detect health- or general welfare-related issues? A framework. Proc Biol Sci 2021; 288:20210190. [PMID: 33975474 PMCID: PMC8113903 DOI: 10.1098/rspb.2021.0190] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 04/19/2021] [Indexed: 12/27/2022] Open
Abstract
The early detection of health disorders is a central goal in livestock production. Thus, a great demand for technologies enabling the automated detection of such issues exists. However, despite decades of research, precision livestock farming (PLF) technologies with sufficient accuracy and ready for implementation on commercial farms are rare. A central factor impeding technological development is likely the use of non-specific indicators for various issues. On commercial farms, where animals are exposed to changing environmental conditions, where they undergo different internal states and, most importantly, where they can be challenged by more than one issue at a time, such an approach leads inevitably to errors. To improve the accuracy of PLF technologies, the presented framework proposes a categorization of the aim of detection of issues related to general welfare, disease and distress and defined disease. Each decision level provides a different degree of information and therefore requires indicators varying in specificity. Based on these considerations, it becomes apparent that while most technologies aim to detect a defined health issue, they facilitate only the identification of issues related to general welfare. To achieve detection of specific issues, new indicators such as rhythmicity patterns of behaviour or physiological processes should be examined.
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Affiliation(s)
- Joanna Stachowicz
- Research Division on Competitiveness and System Evaluation, Agroscope, Tänikon 1, 8356 Ettenhausen, Switzerland
| | - Christina Umstätter
- Research Division on Competitiveness and System Evaluation, Agroscope, Tänikon 1, 8356 Ettenhausen, Switzerland
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29
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Challenges and Tendencies of Automatic Milking Systems (AMS): A 20-Years Systematic Review of Literature and Patents. Animals (Basel) 2021; 11:ani11020356. [PMID: 33572673 PMCID: PMC7912558 DOI: 10.3390/ani11020356] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 01/20/2021] [Accepted: 01/28/2021] [Indexed: 01/16/2023] Open
Abstract
Over the last two decades, the dairy industry has adopted the use of Automatic Milking Systems (AMS). AMS have the potential to increase the effectiveness of the milking process and sustain animal welfare. This study assessed the state of the art of research activities on AMS through a systematic review of scientific and industrial research. The papers and patents of the last 20 years (2000-2019) were analysed to assess the research tendencies. The words appearing in title, abstract and keywords of a total of 802 documents were processed with the text mining tool. Four clusters were identified (Components, Technology, Process and Animal). For each cluster, the words frequency analysis enabled us to identify the research tendencies and gaps. The results showed that focuses of the scientific and industrial research areas complementary, with scientific papers mainly dealing with topics related to animal and process, and patents giving priority to technology and components. Both scientific and industrial research converged on some crucial objectives, such as animal welfare, process sustainability and technological development. Despite the increasing interest in animal welfare, this review highlighted that further progress is needed to meet the consumers' demand. Moreover, milk yield is still regarded as more valuable compared to milk quality. Therefore, additional effort is necessary on the latter. At the process level, some gaps have been found related to cleaning operations, necessary to improve milk quality and animal health. The use of farm data and their incorporation on herd decision support systems (DSS) appeared optimal. The results presented in this review may be used as an overall assessment useful to address future research.
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30
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Charatsari C, D. Lioutas E, De Rosa M, Papadaki-Klavdianou A. Extension and Advisory Organizations on the Road to the Digitalization of Animal Farming: An Organizational Learning Perspective. Animals (Basel) 2020; 10:ani10112056. [PMID: 33172129 PMCID: PMC7694781 DOI: 10.3390/ani10112056] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 11/02/2020] [Accepted: 11/04/2020] [Indexed: 11/16/2022] Open
Abstract
Agricultural digitalization emerged as a radical innovation, punctuating the gradual evolution of the agrifood sector and having the potential to fundamentally restructure the context within which extension and advisory organizations operate. Digital technologies are expected to alter the practice and culture of animal farming in the future. To suit the changing environmental conditions, organizations can make minor adjustments or can call into question their purposes, belief systems, and operating paradigms. Each pattern of change is associated with different types of organizational learning. In this conceptual article, adopting an organizational learning perspective and building upon organizational change models, we present two potential change and learning pathways that extension and advisory organizations can follow to cope with digitalization: morphostasis and morphogenesis. Morphostatic change has a transitional nature and helps organizations survive by adapting to the new environmental conditions. Organizations that follow this pathway learn by recognizing and correcting errors. This way, they increase their competence in specific services and activities. Morphogenetic change, on the other hand, occurs when organizations acknowledge the need to move beyond existing operating paradigms, redefine their purposes, and explore new possibilities. By transforming themselves, organizations learn new ways to understand and interpret contextual cues. We conclude by presenting some factors that explain extension and advisory organizations' tendency to morphostasis.
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Affiliation(s)
- Chrysanthi Charatsari
- Department of Agricultural Economics, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;
- School of Humanities, Hellenic Open University, 26335 Patras, Greece
- Correspondence:
| | - Evagelos D. Lioutas
- Department of Supply Chain Management, International Hellenic University, 60100 Katerini, Greece;
- School of Social Sciences, Hellenic Open University, 26335 Patras, Greece
| | - Marcello De Rosa
- Department of Economics and Law, University of Cassino and Southern Lazio, 03043 Cassino, Italy;
| | - Afroditi Papadaki-Klavdianou
- Department of Agricultural Economics, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;
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31
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Cockburn M. Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms. Animals (Basel) 2020; 10:E1690. [PMID: 32962078 PMCID: PMC7552676 DOI: 10.3390/ani10091690] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 09/09/2020] [Accepted: 09/15/2020] [Indexed: 12/29/2022] Open
Abstract
Dairy farmers use herd management systems, behavioral sensors, feeding lists, breeding schedules, and health records to document herd characteristics. Consequently, large amounts of dairy data are becoming available. However, a lack of data integration makes it difficult for farmers to analyze the data on their dairy farm, which indicates that these data are currently not being used to their full potential. Hence, multiple issues in dairy farming such as low longevity, poor performance, and health issues remain. We aimed to evaluate whether machine learning (ML) methods can solve some of these existing issues in dairy farming. This review summarizes peer-reviewed ML papers published in the dairy sector between 2015 and 2020. Ultimately, 97 papers from the subdomains of management, physiology, reproduction, behavior analysis, and feeding were considered in this review. The results confirm that ML algorithms have become common tools in most areas of dairy research, particularly to predict data. Despite the quantity of research available, most tested algorithms have not performed sufficiently for a reliable implementation in practice. This may be due to poor training data. The availability of data resources from multiple farms covering longer periods would be useful to improve prediction accuracies. In conclusion, ML is a promising tool in dairy research, which could be used to develop and improve decision support for farmers. As the cow is a multifactorial system, ML algorithms could analyze integrated data sources that describe and ultimately allow managing cows according to all relevant influencing factors. However, both the integration of multiple data sources and the obtainability of public data currently remain challenging.
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Affiliation(s)
- Marianne Cockburn
- Agroscope, Competitiveness and System Evaluation, 8356 Ettenhausen, Switzerland
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