1
|
Agutu FO, Mbuku SM, Ondiek JO, Bebe BO. Economic viability of using OvSynch and fixed timed artificial insemination protocol in breeding improvement of pastoral herds in the rangelands. Trop Anim Health Prod 2024; 56:68. [PMID: 38319501 DOI: 10.1007/s11250-024-03907-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 01/17/2024] [Indexed: 02/07/2024]
Abstract
Though using Assisted Reproductive Technologies (ARTs) can improve oestrus detection, conception and pregnancy success, thus benefit breeding program implementation, empirical evidence of their economic viability is lacking to inform investment decisions in pastoral herds. This study assessed economic viability of using OvSynch and fixed Timed Artificial Insemination (TAI) protocol in Sahiwal upgrading breeding program under two hypothetical cases of best and worst in activity-based money allocations when pastoral herds deploy either optimal or low input husbandry practices. From herd owners' assessment of milk production, best-case scenarios attain on average 10 L/cow/day with optimal husbandry and 5 L/cow/day with low input husbandry. The worst-case scenarios attain 5 L/cow/day with optimal husbandry and 1 L/cow/day with low input husbandry. Benefit- Cost Analysis (BCA) estimated Net Present Value (NPV), Benefit-Cost ratio (BCR) and Internal Rate of Return (IRR) to establish economic viability of using OvSynch and TAI Protocol in pastoral breeding programs. Both best-case scenarios retuned positive NPVs (82,028 and 6,912), BCR values (1.68 and 1.08) and IRR (27.46% and 8.08%) while worst-case scenarios returned negative NPVs (-135,855 and -141,025), BCR values of below 1 (0.87 and 0.66) and IRR values below the minimum rate of returns. These economic parameters were sensitive to price changes in inputs and outputs, under both optimal and low input husbandry practices. Results indicate that using OvSynch and TAI Protocol is a profitable and economically viable investment under optimal husbandry practices but not under low input husbandry practices. By implications, use of OvSynch and TAI Protocol in Sahiwal upgrading breeding programs need be accompanied with improved husbandry practices and de-risking pastoral herd owners from price changes in input and output markets.
Collapse
Affiliation(s)
| | - Samuel Mwanzia Mbuku
- Kenya Agricultural and Livestock Research Organization, Dairy Research Institute, P.O. Box 25, Naivasha, 20117, Kenya
| | - James Ombiro Ondiek
- Department of Animal Sciences, Egerton University, P.O. Box 536, Egerton, 20115, Kenya
| | - Bockline Omedo Bebe
- Department of Animal Sciences, Egerton University, P.O. Box 536, Egerton, 20115, Kenya
| |
Collapse
|
2
|
Sharifuzzaman M, Mun HS, Ampode KMB, Lagua EB, Park HR, Kim YH, Hasan MK, Yang CJ. Technological Tools and Artificial Intelligence in Estrus Detection of Sows-A Comprehensive Review. Animals (Basel) 2024; 14:471. [PMID: 38338113 PMCID: PMC10854728 DOI: 10.3390/ani14030471] [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: 10/19/2023] [Revised: 01/30/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
In animal farming, timely estrus detection and prediction of the best moment for insemination is crucial. Traditional sow estrus detection depends on the expertise of a farm attendant which can be inconsistent, time-consuming, and labor-intensive. Attempts and trials in developing and implementing technological tools to detect estrus have been explored by researchers. The objective of this review is to assess the automatic methods of estrus recognition in operation for sows and point out their strong and weak points to assist in developing new and improved detection systems. Real-time methods using body and vulvar temperature, posture recognition, and activity measurements show higher precision. Incorporating artificial intelligence with multiple estrus-related parameters is expected to enhance accuracy. Further development of new systems relies mostly upon the improved algorithm and accurate data provided. Future systems should be designed to minimize the misclassification rate, so better detection is achieved.
Collapse
Affiliation(s)
- Md Sharifuzzaman
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Department of Animal Science and Veterinary Medicine, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
| | - Hong-Seok Mun
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Department of Multimedia Engineering, Sunchon National University, Suncheon 57922, Republic of Korea
| | - Keiven Mark B. Ampode
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Department of Animal Science, College of Agriculture, Sultan Kudarat State University, Tacurong 9800, Philippines
| | - Eddiemar B. Lagua
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Sunchon National University, Suncheon 57922, Republic of Korea
| | - Hae-Rang Park
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Sunchon National University, Suncheon 57922, Republic of Korea
| | - Young-Hwa Kim
- Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Chonnam National University, Gwangju 61186, Republic of Korea;
| | - Md Kamrul Hasan
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Department of Poultry Science, Sylhet Agricultural University, Sylhet 3100, Bangladesh
| | - Chul-Ju Yang
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Sunchon National University, Suncheon 57922, Republic of Korea
| |
Collapse
|
3
|
Das S, Shaji A, Nain D, Singha S, Karunakaran M, Baithalu RK. Precision technologies for the management of reproduction in dairy cows. Trop Anim Health Prod 2023; 55:286. [PMID: 37540276 DOI: 10.1007/s11250-023-03704-2] [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: 03/14/2023] [Accepted: 07/28/2023] [Indexed: 08/05/2023]
Abstract
Precision livestock farming (PLF) utilizes information and communication technology (ICT) to continuously monitor, control, and enhance the productivity, reproduction, health, welfare, and environmental impact of livestock. Technological advancements have facilitated the seamless flow of information from animals to humans, enabling practical decision-making processes concerning health, reproduction management, and calving surveillance. With the increasing population of livestock per farm, it has become impractical for farmers to individually track every animal within these large groups. Historically, cattle management decisions heavily relied on human observation, judgment, and experience. However, it is impossible for a single individual to gather reliable audio-visual monitoring data round the clock. Presently, dairy cows exhibit subtler indicators of estrus, resulting in a substantial chance of missing an estrus cycle. Furthermore, calving complications sometimes go unnoticed on farms, resulting in a higher number of culled cattle. In addition, an increasing number of crossbred cows experience delayed return to estrus after calving due to low body condition scores (BCS). The decline in BCS during the dry period is associated with a reduced likelihood of pregnancy following the first and second postpartum inseminations. Precision technologies enable the monitoring and tracking of an individual cow's physiological behavior and reproductive parameters, thereby optimizing management practices and farm performance. Despite the exploration of various technologies, there are still some common challenges that need to be addressed, including battery lifespan, transmission range, specificity and sensitivity, storage capacity, and economic affordability. Nonetheless, the demand for these tools from farmers and researchers is growing, and the implementation of PLF in grazing systems can yield positive outcomes in terms of animal reproductive welfare and labor optimization. This review primarily focuses on the different aspects of reproduction management in dairy using sensors, automated cameras, and various computer software.
Collapse
Affiliation(s)
- Surajit Das
- Department of Animal Reproduction, Gynaecology and Obstetrics, ICAR- National Dairy Research Institute (ERS), A-12, Kalyani, West Bengal, 741235, India.
| | - Arsha Shaji
- Department of Animal Reproduction, Gynaecology and Obstetrics, ICAR- National Dairy Research Institute, Karnal, Haryana, 132001, India
| | - Dipti Nain
- Department of Animal Reproduction, Gynaecology and Obstetrics, ICAR- National Dairy Research Institute, Karnal, Haryana, 132001, India
| | - Shubham Singha
- Department of Animal Reproduction, Gynaecology and Obstetrics, ICAR- National Dairy Research Institute (ERS), A-12, Kalyani, West Bengal, 741235, India
| | - M Karunakaran
- Department of Animal Reproduction, Gynaecology and Obstetrics, ICAR- National Dairy Research Institute (ERS), A-12, Kalyani, West Bengal, 741235, India
| | - Rubina Kumari Baithalu
- Department of Animal Reproduction, Gynaecology and Obstetrics, ICAR- National Dairy Research Institute, Karnal, Haryana, 132001, India
| |
Collapse
|
4
|
Riaz U, Idris M, Ahmed M, Ali F, Yang L. Infrared Thermography as a Potential Non-Invasive Tool for Estrus Detection in Cattle and Buffaloes. Animals (Basel) 2023; 13:ani13081425. [PMID: 37106988 PMCID: PMC10135134 DOI: 10.3390/ani13081425] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/04/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
The productivity of dairy animals has significantly increased over the past few decades due to intense genetic selection. However, the enhanced yield performance of milk animals caused a proportional increase in stress and compromised reproductive efficiency. Optimal reproductive performance is mandatory for the sustainable production of dairy animals. Reproductive efficiency is marked by proper estrus detection and precise breeding to achieve maximum pregnancies. The existing conventional methods of estrus detection are somewhat labor intensive and less efficient. Similarly, the modern automated methods that rely on detecting physical activity are expensive, and their efficiency is affected by factors such as type of housing (tie stall), flooring, and environment. Infrared thermography has recently emerged as a technique that does not depend on monitoring physical activity. Furthermore, infrared thermography is a non-invasive, user-friendly, and stress-free option that aids in the detection of estrus in dairy animals. Infrared thermography has the potential to be considered a useful non-invasive tool for detecting temperature fluctuations to generate estrus alerts without physical contact in cattle and buffaloes. This manuscript highlights the potential use of infrared thermography to understand reproductive physiology and practical implementation of this technique through discussing its advantages, limitations, and possible precautions.
Collapse
Affiliation(s)
- Umair Riaz
- International Joint Research Centre for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan 430070, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
- Frontiers Science Center for Animal Breeding and Sustainable Production, Huazhong Agricultural University, Wuhan 430070, China
- Department of Theriogenology, Faculty of Veterinary and Animal Sciences, The Islamia University of Bahawalpur, Bahawalpur 63100, Punjab, Pakistan
| | - Musadiq Idris
- Department of Physiology, Faculty of Veterinary and Animal Sciences, The Islamia University of Bahawalpur, Bahawalpur 63100, Punjab, Pakistan
| | - Mehboob Ahmed
- International Joint Research Centre for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan 430070, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
- Frontiers Science Center for Animal Breeding and Sustainable Production, Huazhong Agricultural University, Wuhan 430070, China
- Livestock and Dairy Development Department, Lahore 54000, Punjab, Pakistan
| | - Farah Ali
- Department of Theriogenology, Faculty of Veterinary and Animal Sciences, The Islamia University of Bahawalpur, Bahawalpur 63100, Punjab, Pakistan
| | - Liguo Yang
- International Joint Research Centre for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan 430070, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
- Frontiers Science Center for Animal Breeding and Sustainable Production, Huazhong Agricultural University, Wuhan 430070, China
| |
Collapse
|
5
|
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.
Collapse
|
6
|
López-Gatius F. Revisiting the Timing of Insemination at Spontaneous Estrus in Dairy Cattle. Animals (Basel) 2022; 12:ani12243565. [PMID: 36552485 PMCID: PMC9774572 DOI: 10.3390/ani12243565] [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: 11/23/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022] Open
Abstract
Cows show spontaneous estrus over 8-20 h but become refractory to the bull about 10-12 h before ovulation. This indicates that ovulation occurs 10-12 h after the end of estrus behavior, yet spermatozoa from the bull ejaculate need to undergo maturation and capacitation for 6 to 8 h in the female reproductive tract before they are capable of fertilization. Traditionally, the onset of estrus has been considered the best timing for artificial insemination (AI) in cattle, that is, 6 to 24 h from the first signs of estrus. However, recent findings suggest this interval should be reduced to 16 to 6 h before ovulation, bringing it closer to the end of estrus. In this review, the end of estrus rather than its onset is proposed as the best guide for AI timing in dairy cattle, and physiological indicators of late estrus are discussed such as relaxation of the intravaginal part of the uterus, a lower cervical mucus viscosity and a softer pre-ovulatory follicular consistency as simple cues indicating a cow is ready for service.
Collapse
Affiliation(s)
- Fernando López-Gatius
- Agrotecnio Centre, 25198 Lleida, Spain;
- Subunit, Transfer in Bovine Reproduction SLu, 22300 Barbastro, Spain
| |
Collapse
|
7
|
Peters AR, Wong JT, Williams EJ, Muasa BS, Makoni NF, Ngige CM, Allan FK, Christian M, Ball PJ. Use of a cow-side oestrus detection test for fertility management in Kenyan smallholder dairy herds. Gates Open Res 2022; 6:12. [PMID: 35965739 PMCID: PMC9352916 DOI: 10.12688/gatesopenres.13542.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/17/2022] [Indexed: 11/20/2022] Open
Abstract
Background: The use of artificial insemination (AI) has great potential to improve smallholder dairy herds in Africa, however poor success and, in some situations, high costs in Kenya, have been discouraging. Effective AI requires accurate oestrus detection and the measurement of progesterone (P4) can be used to indicate oestrus as well as non-pregnancy. A cow-side progesterone lateral flow test,
P4 Rapid, was evaluated as an aid to detect oestrus and non-pregnancy in Kenyan dairy cows, and assessed for association with AI efficiency. Methods: A total of 527 cows were enrolled in the study, from two counties in central and southern Kenya. Cattle in the test group (n = 308) were presented when suspected to be in oestrus and tested with the
P4 Rapid (low P4 = oestrus, medium P4 = inconclusive, high P4 = not in oestrus/pregnant). Cattle with low P4 were inseminated. Cattle in the control group (n = 219) were inseminated when oestrus behaviour was detected i.e. standard practice. Results: Of the total
P4 Rapid tests performed (n = 745), 1.5% were inconclusive, with the true accuracy of the test between 87-97%. Conception rates were not significantly higher in the test group (83.9%) compared to the control group (77.9%). Abortion rates were not significantly different between the control (9.5%) and test groups (8.2%). In the test group, 6.2% (19/308) cows showed a medium or high P4 level on day 0 and nine of these were subsequently found to have been already pregnant. Conclusions: The data indicated that the P4 Rapid test can be a useful tool to assist farmer decision-making in the confirmation of correct timing for AI, and importantly may avoid unnecessary inseminations in pregnant animals, thus reducing the risk of AI-induced abortion.
Collapse
Affiliation(s)
- Andrew R. Peters
- Centre for Supporting Evidence Based Interventions-Livestock, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, EH25 9RG, UK
| | - Johanna T. Wong
- Centre for Supporting Evidence Based Interventions-Livestock, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, EH25 9RG, UK
| | - Erin J. Williams
- Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, EH25 9RG, UK
| | - Bridgit S. Muasa
- Centre for Supporting Evidence Based Interventions-Livestock, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, EH25 9RG, UK
| | - Nathaniel F. Makoni
- African Breeders Services Total Cattle Management Ltd (ABS TCM Ltd), Nairobi, Kenya
| | - Chris M. Ngige
- African Breeders Services Total Cattle Management Ltd (ABS TCM Ltd), Nairobi, Kenya
| | - Fiona K. Allan
- Centre for Supporting Evidence Based Interventions-Livestock, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, EH25 9RG, UK
| | - Michael Christian
- Centre for Supporting Evidence Based Interventions-Livestock, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, EH25 9RG, UK
| | - Peter J.H. Ball
- Centre for Supporting Evidence Based Interventions-Livestock, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, EH25 9RG, UK
| |
Collapse
|
8
|
Peters AR, Wong JT, Williams EJ, Muasa BS, Makoni NF, Ngige CM, Allan FK, Christian M, Ball PJ. Use of a cow-side oestrus detection test for fertility management in Kenyan smallholder dairy herds. Gates Open Res 2022; 6:12. [DOI: 10.12688/gatesopenres.13542.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/25/2022] [Indexed: 11/20/2022] Open
Abstract
Background: The use of artificial insemination (AI) has great potential to improve smallholder dairy herds in Africa, however high costs and often poor success rates have been discouraging. Effective AI requires accurate oestrus detection and the measurement of progesterone (P4) can be used to indicate oestrus as well as non-pregnancy. A cow-side progesterone lateral flow test, P4 Rapid, was evaluated as an aid to detect oestrus and non-pregnancy in Kenyan dairy cows, and assessed for association with AI efficiency. Methods: A total of 527 cows were enrolled in the study, from two counties in central and southern Kenya. Cattle in the test group (n = 308) were presented when suspected to be in oestrus and tested with the P4 Rapid (low P4 = oestrus, medium P4 = inconclusive, high P4 = not in oestrus/pregnant). Cattle with low P4 were inseminated. Cattle in the control group (n = 219) were inseminated when oestrus behaviour was detected i.e. standard practice. Results: Of the total P4 Rapid tests performed (n = 745), 1.5% were inconclusive, with the true accuracy of the test between 87-97%. Conception rates were not significantly higher in the test group (83.9%) compared to the control group (77.9%). Abortion rates were not significantly different between the control (9.5%) and test groups (8.2%). In the test group, 6.2% (19/308) cows showed a medium or high P4 level on day 0 and 9 of these were subsequently found to have been already pregnant. Conclusions: The data indicated that the P4 Rapid test can be a useful tool to assist farmer decision-making in the confirmation of correct timing for AI, and importantly may avoid unnecessary inseminations in pregnant animals, thus reducing the risk of AI-induced abortion.
Collapse
|
9
|
Aquilani C, Confessore A, Bozzi R, Sirtori F, Pugliese C. Review: Precision Livestock Farming technologies in pasture-based livestock systems. Animal 2021; 16:100429. [PMID: 34953277 DOI: 10.1016/j.animal.2021.100429] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 11/09/2021] [Accepted: 11/19/2021] [Indexed: 11/24/2022] Open
Abstract
Precision Livestock Farming (PLF) encompasses the combined application of single technologies or multiple tools in integrated systems for real-time and individual monitoring of livestock. In grazing systems, some PLF applications could substantially improve farmers' control of livestock by overcoming issues related to pasture utilisation and management, and animal monitoring and control. A focused literature review was carried out to identify technologies already applied or at an advanced stage of development for livestock management in pastures, specifically cattle, sheep, goats, pigs, poultry. Applications of PLF in pasture-based systems were examined for cattle, sheep, goats, pigs, and poultry. The earliest technology applied to livestock was the radio frequency identification tag, allowing the identification of individuals, but also for retrieving important information such as maternal pedigree. Walk-over-weigh platforms were used to record individual and flock weights. Coupled with automatic drafting systems, they were tested to divide the animals according to their needs. Few studies have dealt with remote body temperature assessment, although the use of thermography is spreading to monitor both intensively reared and wild animals. Global positioning system and accelerometers are among the most applied technologies, with several solutions available on the market. These tools are used for several purposes, such as animal location, theft prevention, assessment of activity budget, behaviour, and feed intake of grazing animals, as well as for reproduction monitoring (i.e., oestrus, calving, or lambing). Remote sensing by satellite images or unmanned aerial vehicles (UAVs) seems promising for biomass assessment and herd management based on pasture availability, and some attempts to use UAVs to monitor, track, or even muster animals have been reported recently. Virtual fencing is among the upcoming technologies aimed at grazing management. This system allows the management of animals at pasture without physical fences but relies on associative learning between audio cues and an electric shock delivered if the animal does not change direction after the acoustic warning. Regardless of the different technologies applied, some common constraints have been reported on the application of PLF in grazing systems, especially when compared with indoor or confined livestock systems. Battery lifespan, transmission range, service coverage, storage capacity, and economic affordability were the main factors. However, even if the awareness of the existence and the potential of these upcoming tools are still limited, farmers' and researchers' demands are increasing, and positive outcomes in terms of rangeland conservation, animal welfare, and labour optimisation are expected from the spread of PLF in grazing systems.
Collapse
Affiliation(s)
- C Aquilani
- Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università di Firenze, Scuola di Agraria, Via delle Cascine 5, 50144 Florence, Italy.
| | - A Confessore
- Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università di Firenze, Scuola di Agraria, Via delle Cascine 5, 50144 Florence, Italy
| | - R Bozzi
- Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università di Firenze, Scuola di Agraria, Via delle Cascine 5, 50144 Florence, Italy
| | - F Sirtori
- Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università di Firenze, Scuola di Agraria, Via delle Cascine 5, 50144 Florence, Italy
| | - C Pugliese
- Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università di Firenze, Scuola di Agraria, Via delle Cascine 5, 50144 Florence, Italy
| |
Collapse
|
10
|
Caixeta LS, Omontese BO. Monitoring and Improving the Metabolic Health of Dairy Cows during the Transition Period. Animals (Basel) 2021; 11:ani11020352. [PMID: 33572498 PMCID: PMC7911117 DOI: 10.3390/ani11020352] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/24/2021] [Accepted: 01/27/2021] [Indexed: 02/05/2023] Open
Abstract
Simple Summary The transition from late gestation to early lactation is a challenging period for dairy cows. A successful transition period depends on metabolic adaptation to the new physiological state in early lactation and proper management in order to support the cow’s requirements. This review paper will discuss various aspects of routine and consistent approaches to collect and analyze herd records, to detect unintended disruptions in performance. In addition, we discuss how to incorporate methods to assess health, production, nutrition, and welfare information to monitor cows during the transition period. Lastly, we discuss management strategies that can be implemented to improve the metabolic health and performance of transition dairy cows. Abstract The peripartum period of a dairy cow is characterized by several physiological and behavioral changes in response to a rapid increase in nutrient demands, to support the final stages of fetal growth and the production of colostrum and milk. Traditionally, the transition period is defined as the period 3 weeks before and 3 weeks after parturition. However, several researchers have argued that the transition period begins at the time of dry-off (~60–50 days prior to calving) and extends beyond the first month post-calving in high producing dairy cows. Independent of the definition used, adequate adaptation to the physiological demands of this period is paramount for a successful lactation. Nonetheless, not all cows are successful in transitioning from late gestation to early lactation, leading to approximately one third of dairy cows having at least one clinical disease (metabolic and/or infectious) and more than half of the cows having at least one subclinical case of disease within the first 90 days of lactation. Thus, monitoring dairy cows during this period is essential to detect early disease signs, diagnose clinical and subclinical diseases, and initiate targeted health management to avoid health and production impairment. In this review, we discuss different strategies to monitor dairy cows to detected unintended disruptions in performance and management strategies that can be implemented to improve the metabolic health and performance of dairy cows during the transition period.
Collapse
Affiliation(s)
- Luciano S. Caixeta
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN 55108, USA
- Correspondence: ; Tel.: +1-612-625-3130
| | - Bobwealth O. Omontese
- Department of Food and Animal Sciences, College of Agricultural, Life and Natural Sciences, Alabama A&M University, Normal, AL 35811, USA;
| |
Collapse
|
11
|
Neethirajan S. Transforming the Adaptation Physiology of Farm Animals through Sensors. Animals (Basel) 2020; 10:E1512. [PMID: 32859060 PMCID: PMC7552204 DOI: 10.3390/ani10091512] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 08/23/2020] [Accepted: 08/25/2020] [Indexed: 12/20/2022] Open
Abstract
Despite recent scientific advancements, there is a gap in the use of technology to measure signals, behaviors, and processes of adaptation physiology of farm animals. Sensors present exciting opportunities for sustained, real-time, non-intrusive measurement of farm animal behavioral, mental, and physiological parameters with the integration of nanotechnology and instrumentation. This paper critically reviews the sensing technology and sensor data-based models used to explore biological systems such as animal behavior, energy metabolism, epidemiology, immunity, health, and animal reproduction. The use of sensor technology to assess physiological parameters can provide tremendous benefits and tools to overcome and minimize production losses while making positive contributions to animal welfare. Of course, sensor technology is not free from challenges; these devices are at times highly sensitive and prone to damage from dirt, dust, sunlight, color, fur, feathers, and environmental forces. Rural farmers unfamiliar with the technologies must be convinced and taught to use sensor-based technologies in farming and livestock management. While there is no doubt that demand will grow for non-invasive sensor-based technologies that require minimum contact with animals and can provide remote access to data, their true success lies in the acceptance of these technologies by the livestock industry.
Collapse
|