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Leishman EM, You J, Ferreira NT, Adams SM, Tulpan D, Zuidhof MJ, Gous RM, Jacobs M, Ellis JL. Review: When worlds collide - poultry modeling in the 'Big Data' era. Animal 2023; 17 Suppl 5:100874. [PMID: 37394324 DOI: 10.1016/j.animal.2023.100874] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 05/30/2023] [Accepted: 06/01/2023] [Indexed: 07/04/2023] Open
Abstract
Within poultry production systems, models have provided vital decision support, opportunity analysis, and performance optimization capabilities to nutritionists and producers for decades. In recent years, due to the advancement of digital and sensor technologies, 'Big Data' streams have emerged, optimally positioned to be analyzed by machine-learning (ML) modeling approaches, with strengths in forecasting and prediction. This review explores the evolution of empirical and mechanistic models in poultry production systems, and how these models may interact with new digital tools and technologies. This review will also examine the emergence of ML and Big Data in the poultry production sector, and the emergence of precision feeding and automation of poultry production systems. There are several promising directions for the field, including: (1) application of Big Data analytics (e.g., sensor-based technologies, precision feeding systems) and ML methodologies (e.g., unsupervised and supervised learning algorithms) to feed more precisely to production targets given a 'known' individual animal, and (2) combination and hybridization of data-driven and mechanistic modeling approaches to bridge decision support with improved forecasting capabilities.
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Affiliation(s)
- E M Leishman
- Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
| | - J You
- Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
| | - N T Ferreira
- Trouw Nutrition Canada, Puslinch, Ontario, Canada
| | - S M Adams
- Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
| | - D Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
| | - M J Zuidhof
- Department of Agricultural, Food, and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada
| | - R M Gous
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - M Jacobs
- FR Analytics B.V., 7642 AP Wierden, The Netherlands
| | - J L Ellis
- Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada.
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2
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Jacobs JL, Hersom MJ, Andrae JG, Duckett SK. Training and Adaptation of Beef Calves to Precision Supplementation Technology for Individual Supplementation in Grazing Systems. Animals (Basel) 2023; 13:2872. [PMID: 37760272 PMCID: PMC10525824 DOI: 10.3390/ani13182872] [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: 08/16/2023] [Revised: 09/04/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
Supplementation of beef cattle can be used to meet both nutrient requirements and production goals; however, supplementation costs influence farm profitability. Common supplementation delivery strategies are generally designed to provide nutrients to the mean of the group instead of an individual. Precision individual supplementation technologies, such as the Super SmartFeed (SSF, C-Lock Inc., Rapid City, SD, USA), are available but are generally cost prohibitive to producers. These systems require adaptation or training periods for cattle to utilize this technology. The objective of this research was to assess the training and adoption rates of three different groups of cattle (suckling calves, weaned steers, replacement heifers) to the SSF. Successful adaptation was determined if an individual's supplement intake was above the group average of total allotted feed consumed throughout the training period. Suckling calves (n = 31) underwent a 12 d training period on pasture; 45% of suckling calves adapted to the SSF and average daily intake differed (p < 0.0001) by day of training. Weaned steers (n = 79) were trained in drylot for 13 d. Of the weaned steers, 62% were trained to the SSF, and average daily intake differed (p < 0.0001) by day of training. Replacement heifers (n = 63) grazed tall fescue pastures and had access to SSF for 22 d of training. The success rate of replacement heifers was 73%. For replacement heifers, the daily intake did not differ (p < 0.0001) by day of training. Results indicate production stage may influence cattle adaptation to precision technologies.
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Affiliation(s)
- Joshua L. Jacobs
- Department of Animal and Veterinary Sciences, Clemson University, Clemson, SC 29631, USA; (J.L.J.); (M.J.H.)
| | - Matt J. Hersom
- Department of Animal and Veterinary Sciences, Clemson University, Clemson, SC 29631, USA; (J.L.J.); (M.J.H.)
| | - John G. Andrae
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC 29631, USA;
| | - Susan K. Duckett
- Department of Animal and Veterinary Sciences, Clemson University, Clemson, SC 29631, USA; (J.L.J.); (M.J.H.)
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Pomar C, Remus A. Review: Fundamentals, limitations and pitfalls on the development and application of precision nutrition techniques for precision livestock farming. Animal 2023; 17 Suppl 2:100763. [PMID: 36966025 DOI: 10.1016/j.animal.2023.100763] [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: 10/24/2022] [Revised: 02/24/2023] [Accepted: 02/27/2023] [Indexed: 03/08/2023] Open
Abstract
Precision livestock farming (PLF) concerns the management of livestock using the principles and technologies of process engineering. Precision nutrition (PN) is part of the PLF approach and involves the use of feeding techniques that allow the proper amount of feed with the suitable composition to be supplied in a timely manner to individual animals or groups of animals. Automatic data collection, data processing, and control actions are required activities for PN applications. Despite the benefits that PN offers to producers, few systems have been successfully implemented so far. Besides the economical and logistical challenges, there are conceptual limitations and pitfalls that threaten the widespread adoption of PN. Developers have to avoid the temptation of looking for the application of available sensors and instead concentrate on identifying the most appropriate and relevant information needed for the optimal functioning of PN applications. Efficient PN applications are obtained by controlling the nutrient requirement variations occurring between animals and over time. The utilization of feedback control algorithms for the automatic determination of optimal nutrient supply is not recommended. Mathematical models are the preferred data processing method for PN, but these models have to be designed to operate in real time using up-to-date information. These models are therefore structurally different than traditional nutrition or growth models. Combining knowledge- and data-driven models using machine learning and deep learning algorithms will enhance our ability to use real-time farm data, thus opening up new opportunities for PN. To facilitate the implementation of PN in farms, different experts and stakeholders should be involved in the development of the fully integrated and automatic PLF system. Precision livestock farming and PN should not be seen as just being a question of technology, but a successful marriage between knowledge and technology.
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Affiliation(s)
- Candido Pomar
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, 2000 College Street, Sherbrooke, Quebec J1M 0C8, Canada.
| | - Aline Remus
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, 2000 College Street, Sherbrooke, Quebec J1M 0C8, Canada
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Zeng X, Li S, Liu L, Cai S, Ye Q, Xue B, Wang X, Zhang S, Chen F, Cai C, Wang F, Zeng X. Role of functional fatty acids in modulation of reproductive potential in livestock. J Anim Sci Biotechnol 2023; 14:24. [PMID: 36788613 PMCID: PMC9926833 DOI: 10.1186/s40104-022-00818-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 12/04/2022] [Indexed: 02/16/2023] Open
Abstract
Fatty acids are not only widely known as energy sources, but also play important roles in many metabolic pathways. The significance of fatty acids in modulating the reproductive potential of livestock has received greater recognition in recent years. Functional fatty acids and their metabolites improve follicular development, oocyte maturation and embryo development, as well as endometrial receptivity and placental vascular development, through enhancing energy supply and precursors for the synthesis of their productive hormones, such as steroid hormones and prostaglandins. However, many studies are focused on the impacts of individual functional fatty acids in the reproductive cycle, lacking studies involved in deeper mechanisms and optimal fatty acid requirements for specific physiological stages. Therefore, an overall consideration of the combination and synergy of functional fatty acids and the establishment of optimal fatty acid requirement for specific stages is needed to improve reproductive potential in livestock.
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Affiliation(s)
- Xiangzhou Zeng
- grid.22935.3f0000 0004 0530 8290State Key Laboratory of Animal Nutrition, Ministry of Agriculture Feed Industry Center, China Agricultural University, 100193 Beijing, P. R. China ,Beijing Key Laboratory of Bio feed Additives, 100193 Beijing, P. R. China
| | - Siyu Li
- grid.22935.3f0000 0004 0530 8290State Key Laboratory of Animal Nutrition, Ministry of Agriculture Feed Industry Center, China Agricultural University, 100193 Beijing, P. R. China ,Beijing Key Laboratory of Bio feed Additives, 100193 Beijing, P. R. China
| | - Lu Liu
- grid.22935.3f0000 0004 0530 8290State Key Laboratory of Animal Nutrition, Ministry of Agriculture Feed Industry Center, China Agricultural University, 100193 Beijing, P. R. China ,Beijing Key Laboratory of Bio feed Additives, 100193 Beijing, P. R. China
| | - Shuang Cai
- grid.22935.3f0000 0004 0530 8290State Key Laboratory of Animal Nutrition, Ministry of Agriculture Feed Industry Center, China Agricultural University, 100193 Beijing, P. R. China ,Beijing Key Laboratory of Bio feed Additives, 100193 Beijing, P. R. China
| | - Qianhong Ye
- grid.35155.370000 0004 1790 4137State Key Laboratory of Agricultural Microbiology, College of Animal Sciences and Technology, Huazhong Agricultural University, 430070 Wuhan, Hubei China
| | - Bangxin Xue
- grid.22935.3f0000 0004 0530 8290State Key Laboratory of Animal Nutrition, Ministry of Agriculture Feed Industry Center, China Agricultural University, 100193 Beijing, P. R. China ,Beijing Key Laboratory of Bio feed Additives, 100193 Beijing, P. R. China
| | - Xinyu Wang
- grid.22935.3f0000 0004 0530 8290State Key Laboratory of Animal Nutrition, Ministry of Agriculture Feed Industry Center, China Agricultural University, 100193 Beijing, P. R. China ,Beijing Key Laboratory of Bio feed Additives, 100193 Beijing, P. R. China
| | - Shihai Zhang
- grid.20561.300000 0000 9546 5767Guangdong Provincial Key Laboratory of Animal Nutrition Control, College of Animal Science, South China Agricultural University, 510642 Guangzhou, China
| | - Fang Chen
- grid.20561.300000 0000 9546 5767Guangdong Provincial Key Laboratory of Animal Nutrition Control, College of Animal Science, South China Agricultural University, 510642 Guangzhou, China
| | - Chuanjiang Cai
- grid.144022.10000 0004 1760 4150College of Animal Science and Technology, Northwest A&F University, 712100 Yangling, Shaanxi China
| | - Fenglai Wang
- grid.22935.3f0000 0004 0530 8290State Key Laboratory of Animal Nutrition, Ministry of Agriculture Feed Industry Center, China Agricultural University, 100193 Beijing, P. R. China ,Beijing Key Laboratory of Bio feed Additives, 100193 Beijing, P. R. China
| | - Xiangfang Zeng
- State Key Laboratory of Animal Nutrition, Ministry of Agriculture Feed Industry Center, China Agricultural University, 100193, Beijing, P. R. China. .,Beijing Key Laboratory of Bio feed Additives, 100193, Beijing, P. R. China.
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Precision Agriculture for Crop and Livestock Farming-Brief Review. Animals (Basel) 2021; 11:ani11082345. [PMID: 34438802 PMCID: PMC8388655 DOI: 10.3390/ani11082345] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 07/31/2021] [Accepted: 08/05/2021] [Indexed: 11/17/2022] Open
Abstract
In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.
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Misiura MM, Filipe JAN, Kyriazakis I. A Novel Estimation of Unobserved Pig Growth Traits for the Purposes of Precision Feeding Methods. Front Vet Sci 2021; 8:689206. [PMID: 34395575 PMCID: PMC8360350 DOI: 10.3389/fvets.2021.689206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/21/2021] [Indexed: 11/13/2022] Open
Abstract
Recent technological advances make it possible to deliver feeding strategies that can be tailored to the needs of individual pigs in order to optimise the allocation of nutrient resources and contribute toward reducing excess nutrient excretion. However, these efforts are currently hampered by the challenges associated with: (1) estimation of unobserved traits from the available data on bodyweight and feed consumption; and (2) characterisation of the distributions and correlations of these unobserved traits to generate accurate estimates of individual level variation among pigs. Here, alternative quantitative approaches to these challenges, based on the principles of inverse modelling and separately inferring individual level distributions within a Bayesian context were developed and incorporated in a proposed precision feeding modelling framework. The objectives were to: (i) determine the average and distribution of individual traits characterising growth potential and body composition in an empirical population of growing-finishing barrows and gilts; (ii) simulate the growth and excretion of nitrogen and phosphorus of the average pig offered either a commercial two-phase feeding plan, or a precision feeding plan with daily adjustments; and (iii) simulate the growth and excretion of nitrogen and phosphorus across the pig population under two scenarios: a two-phase feeding plan formulated to meet the nutrient requirements of the average pig or a precision feeding plan with daily adjustments for each and every animal in the population. The distributions of mature bodyweight and ratio of lipid to protein weights at maturity had median (IQR) values of 203 (47.8) kg and 2.23 (0.814) kg/kg, respectively; these estimates were obtained without any prior assumptions concerning correlations between the traits. Overall, it was found that a proposed precision feeding strategy could result in considerable reductions in excretion of nitrogen and phosphorus (average pig: 8.07 and 9.17% reduction, respectively; heterogenous pig population: 22.5 and 22.9% reduction, respectively) during the growing-finishing period from 35 to 120 kg bodyweight. This precision feeding modelling framework is anticipated to be a starting point toward more accurate estimation of individual level nutrient requirements, with the general aim of improving the economic and environmental sustainability of future pig production systems.
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Affiliation(s)
| | - Joao A N Filipe
- Newcastle University, Newcastle upon Tyne, United Kingdom.,Biomathematics & Statistics Scotland, Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, United Kingdom
| | - Ilias Kyriazakis
- Biological Sciences Building, Queen's University Belfast, Belfast, United Kingdom
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7
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Afrouziyeh M, Kwakkel RP, Zuidhof MJ. Improving a nonlinear Gompertz growth model using bird-specific random coefficients in two heritage chicken lines. Poult Sci 2021; 100:101059. [PMID: 33756248 PMCID: PMC8010697 DOI: 10.1016/j.psj.2021.101059] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 10/31/2020] [Accepted: 02/04/2021] [Indexed: 12/11/2022] Open
Abstract
Growth models describe body weight (BW) changes over time, allowing information from longitudinal measurements to be combined into a few parameters with biological interpretation. Nonlinear mixed models (NLMM) allow for the inclusion of random factors. Random factors can account for a relatively large subset of the total variance explained by bird-specific measurement correlation. The aim of this study was to evaluate different NLMM using birds from 2 heritage chicken lines; New Hampshire (NH) and Brown Leghorn (BL). A total of 32 birds (16 mixed sex birds from each strain) were raised to 17 wk of age. After 12 wk, half were continued on ad libitum (AL) feed intake, and half were pair-fed, using a precision feeding system; they were given 95% of the AL intake of a paired bird closest in BW. Residual feed intake (RFI) of birds, as an indicator of production efficiency, was increased in pair-fed BL birds as a result of minor feed restriction. Growth data of the birds were fit to a mixed Gompertz model with a variety of different bird-specific random coefficients. The model had the form: BW=Wm×exp−exp−b(t−tinf); where Wm was the mature BW, b was the rate of maturing, t was age (d), tinf was the inflection point (d). This fixed-effects model was compared with NLMM using model evaluation criteria to evaluate relative model suitability. Random coefficients, Wmu ∼ N(0,VWm) and bu ∼ N(0,Vb), were tested separately and together and their differences, for strains, sex, and feeding treatments, were reported as different where P ≤ 0.05. The model with both random coefficients was determined to be the most parsimonious model, based on an assessment of serial correlation of the residuals. NLMM coefficients allow stochastic prediction of the mean age and its variation that birds need to achieve a certain BW, allowing for unique new decision support modeling applications; these could be used in stochastic modeling to evaluate the economic impact of management decisions.
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Affiliation(s)
- Mohammad Afrouziyeh
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada T6G 2P5
| | - René P Kwakkel
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada T6G 2P5; Department of Animal Sciences, Animal Nutrition Group, Wageningen University, Wageningen, The Netherlands 6700 AH
| | - Martin J Zuidhof
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada T6G 2P5.
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Bosco S, Volpi I, Cappucci A, Mantino A, Ragaglini G, Bonari E, Mele M. Innovating feeding strategies in dairy sheep farming can reduce environmental impact of ewe milk. ITALIAN JOURNAL OF ANIMAL SCIENCE 2021. [DOI: 10.1080/1828051x.2021.2003726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Simona Bosco
- Istituto di Scienze della Vita, Sant'Anna School of Advanced Studies, Pisa, Italy
| | - Iride Volpi
- Istituto di Scienze della Vita, Sant'Anna School of Advanced Studies, Pisa, Italy
| | - Alice Cappucci
- Centro di Ricerche Agro-Ambientali ‘Enrico Avanzi’, University of Pisa, Pisa, Italy
| | - Alberto Mantino
- Istituto di Scienze della Vita, Sant'Anna School of Advanced Studies, Pisa, Italy
| | - Giorgio Ragaglini
- Istituto di Scienze della Vita, Sant'Anna School of Advanced Studies, Pisa, Italy
| | - Enrico Bonari
- Istituto di Scienze della Vita, Sant'Anna School of Advanced Studies, Pisa, Italy
| | - Marcello Mele
- Centro di Ricerche Agro-Ambientali ‘Enrico Avanzi’, University of Pisa, Pisa, Italy
- Dipartimento di Scienze Agrarie, Alimentari e Agro-Ambientali, University of Pisa, Pisa, Italy
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