Universitat de Lleida
Sustainable Precision Livestock Farming: A Vision for The Future of the Canadian Swine Industry C. Pomar and J. Pomar Agriculture and Agri‐Food Canada, Sherbrooke Universitat de Lleida, Spain
Introduction
Feed cost might represent more than 60-75% of the overall production cost in swine production systems
Feeding programs are proposed to maximize population responses at minimal feed costs
1.09 g/MJ
Body weight gain (g/d)
1 050
950
850
750
650
550 0.45
0.65
0.85
1.05
1.25
1.45
Lys:NE (g/MJ)
Jean dit Bailleul et al., 2000
2
Introduction Nutrient requirements however vary greatly between the pigs of a given population, and for each pig over time following individual patterns
Lysine req. g/MJ NE
1.45
1.20
0.95
0.70
0.45
0.20 2
12
22
32
42
52
62
72
82
Time Brossard et al., 2007, 2009; Pomar, 2007; Hauschild et al., 2010
3
Introduction To optimize population responses, nutrients are provided at levels that satisfy the requirements of the most demanding pigs, with the result that most of the pigs receive more nutrients than they need to express their growth potential 1.09 g/MJ
Body weight gain (g/d)
1 050
950
850
750
650
550 0.45
Hauschild et al., 2010
0.65
0.85
1.05
Lys:NE (g/MJ)
1.25
1.45
4
Introduction Non retained dietary nitrogen and other nutrients feces and urine
N efficiency (retention/ingestion) seldom ≥ 30% Improving nutrient efficiency = feed cost
5
Introduction Precision farming or precision agriculture is an
agricultural concept that relies on the existence of in-field variability
Precision feeding involves the use of feeding techniques
that allow the right amount of feed with the right composition to be provided at the right time to each pig of the herd
Introduction Precision livestock farming is an innovative production system approach, which is based on intensive and integrated use of advances in
• animal sciences • farm productive processes management technologies • new technologies of information and communication
Berckmans 2004
7
Introduction
This paper presents the key elements for the development of sustainable precision livestock farming and a vision for the future of the Canadian swine industry Advances in Information & Comm technology
Traditional Production methods
Precision Feeding
Precision Management New knowledge in pig nutrition & management 8
Essential elements for precision feeding 1) Evaluating the nutritional potential of feed ingredients 2) Precise determination of nutrient requirements 3) Formulating balanced diets that limit the amount of excess nutrients 4) The concomitant adjustment of nutrient supply to match the requirements of pigs fed
9
Nutritional potential of feed ingredients Assessment of nutritional characteristics
Nutrients composition Digestibility and availability of these nutrients Protein quality Anti-nutritional factors Limits of incorporation Variation, stability Others 10
Nutritional potential of feed ingredients Improving nutrient availability
Modifying particle size Pelleting Adding enzymes and microbial inoculants
11
Essential elements for precision feeding 1) Evaluating the nutritional potential of feed ingredients 2) Precise determination of nutrient requirements 3) Formulating balanced diets that limit the amount of excess nutrients 4) The concomitant adjustment of nutrient supply to match the requirements of pigs fed
12
Nutrient requirements There are two methods traditionally used to estimate nutrient requirements in domestic animals
Empirical method: optimizing animal responses Factorial method: adding maintenance and production requirements
Maintenance
Retained nutrients in Lean, Fat, Bone
Nutrient losses
Daily nutrient requirements (g/d) Energy, Amino acids, others
Feed intake
Dietary nutrient requirements (g/kg) Energy, Amino acids, others
Fuller & Chamberlain, 1982
13
Nutrient requirements In the empirical method, nutrient requirements are obtained by studying the response to varying nutrient levels of a population of animals showing some degree of heterogeneity 1.09 g/MJ
Body weight gain (g/d)
1 050
950
850
750
650
550 0.45
0.65
0.85
1.05
1.25
1.45
Lys:NE (g/MJ)
Hauschild et al., 2010
14
Nutrient requirements 1.09 g/MJ
The empirical method estimates optimal nutrient allowances from
Body weight gain (g/d)
1 050
950
850
750
650
550 0.45
0.65
0.85
1.05
1.25
Lys:NE (g/MJ)
a population perspective during long periods of time
Pomar et al., 2007
15
1.45
Nutrient requirements In the factorial method, daily nutrient requirements are estimated as the sum of the requirements for maintenance and production 100
1020
80
940
60
Retained nutrients in Lean, Fat, Bone
Nutrient losses
Daily nutrient requirements (g/d) Energy, Amino acids, others
Feed intake
Dietary nutrient requirements (g/kg) Energy, Amino acids, others
Body weight gain (g/d)
Maintenance
1100
50%
860
40
780
20
700 0.45
0.65
0.85
0.96
1.05
1.25
% animal above requirement
0.75
Maintenance = 0.036*BW Growth = 0.16*0.07*ADG
0 1.45
Lys:NE (g/MJ) 16
one reference animal during a very short period, normally one day
1100
100
1020
80
940
60 50%
860
40
780
20
700 0.45
0.65
0.85
0.96
1.05
1.25
0 1.45
Lys:NE (g/MJ)
26 kg Pomar et al., 2007
17
% animal above requirement
The factorial method addresses the needs of
Body weight gain (g/d)
Nutrient requirements
Nutrient requirements
26 kg
1100
100
1020
80
940
60 50%
860
40
780
20
700 0.45
0.65
0.85
0.96
1.05
Lys:NE (g/MJ)
1.25
% animal above requirement
Body weight gain (g/d)
It is possible to use the factorial method to estimate the population requirements at fixed point in time
0 1.45
18
Nutrient requirements The relationship between empirical and factorial methods is difficult to establish and is affected by many factors related to the animal, growth state and population heterogeneity
Body weight gain (g/d)
Body weight gain (g/d)
1100
1020
940
860
780
700 0.45
0.65
0.85
1.05
Lys:NE (g/MJ)
Pomar et al., 2007; Hauschild et al., 2010
1.25
1100
100
1020
80
940
60
860
40
780
20
700 0.45 1.45
0.65
0.85
1.05
1.25
0 1.45
Lys:NE (g/MJ) 19
Nutrient requirements The relationship between empirical and factorial methods is difficult to establish and is affected by many factors related to the animal, growth state and population heterogeneity 100
1020
82%
940
80
60 50%
860
40
780
20
% animal above requirement
Body weight gain (g/d)
1100
1.09
700 0.45
0.65
0.85
0.96
1.05
Lys:NE (g/MJ) Hauschild et al., 2010
1.25
0 1.45
20
Nutrient requirements Mechanistic mathematical models that implement the factorial approach are proposed because of the complexity of animal responses and the numerous factors modulating them and thus the optimal level of nutrients that will optimize production systems Maintenance
Retained nutrients in Lean, Fat, Bone
Nutrient losses
Daily nutrient requirements (g/d) Energy, Amino acids, others
Feed intake
Dietary nutrient requirements (g/kg) Energy, Amino acids, others
Baldwin, 1976; Koong et al., 1976; Whitemore, 1986
21
Nutrient requirements New approaches are proposed today to characterize individual pigs within a population, with consideration given to the relationships between model parameters This information can be used to generate virtual populations based on the average pig profile of the population. This approach helps with finding optimal diets for group feeding, identifying optimal slaughter strategies, etc.
Van Milgen et al., 2008; Brossard et al., 2009; Ferguson 2006, 2008
22
Nutrient requirements This mathematical models are challenged with complex problems such,
Models must be properly calibrated in relation to a reference population
Animals may follow different consumption and growth
patterns from the ones observed in the reference population
For most of the models, it is difficult to find the best representative of the population
23
Essential elements for precision feeding 1) Evaluating the nutritional potential of feed ingredients 2) Precise determination of nutrient requirements 3) Formulating balanced diets that limit the amount of excess nutrients 4) The concomitant adjustment of nutrient supply to match the requirements of pigs fed
24
Essential elements for precision feeding 1) Evaluating the nutritional potential of feed ingredients 2) Precise determination of nutrient requirements 3) Formulating balanced diets that limit the amount of excess nutrients 4) The concomitant adjustment of nutrient supply to match the requirements of pigs fed
in groups individually 25
Concomitant adjustment of nutrient supply to population requirements In growing animals, appetite (kg/d or MJ/d) increases faster than most of the nutrient requirements (g/d) and therefore, optimal nutrient concentration (g/kg or g/MJ) progressively decreases over the growing period
NRC, 1998
26
Concomitant adjustment of nutrient supply to population requirements feeding phases =
Letourneau Montminy et al., 2005
feeding costs nutrient excretion feed efficiency
27
Concomitant adjustment of nutrient supply to population requirements Increasing the number of feeding phases increases the costs of feed storage and management Blend feeding and the automatic distribution of two premixes that, combined in variable ratios, could meet the requirements of pigs throughout their growing period is a promising technique
Feddes et al., 2000
28
Concomitant adjustment of nutrient supply to population requirements Group feeding: a 3-phase feeding system was compared to a daily multiphase feeding system
Pomar et al., 2007
29
Concomitant adjustment of nutrient supply to population requirements
Feed intake
Pomar et al., 2007
30
Concomitant adjustment of nutrient supply to population requirements Average daily gain
Feed intake (kg/d)
3.0
1100
2.5
2.0
Daily adjustment (DP) 3 phases (3P) 1.5
Average daily gain (g/d)
P = 0,0190
1
2
3
4
5
6
7
8
9
10
11
12
Weeks in trial
NS P = 0,0780
1000 NS
900
800
700
3 phases (3P) Daily adjustment (DP)
600 Pomar et al., 2007
Phase 1 25 to 50 kg
Phase 2 50 to 80 kg
Phase 3 80 to 105 kg
Phases 1-3 20 to 105 kg
31
Concomitant adjustment of nutrient supply to population requirements
Body composition kg
20
15
10
5
3 phases (3P) Daily adjustment (DP)
0
Lipid retention Pomar et al., 2007
Protein retention 32
Concomitant adjustment of nutrient supply to population requirements
In relation to a 3-phase feeding system, daily multiphase group-feeding… N intake by 7% N excretion by 12%
Pomar et al., 2007
33
Concomitant adjustment of nutrient supply to population requirements Limitations to group feeding
All pigs are fed with the same diet and frequently, during relatively long periods
Obtaining the optimal composition of complete diets is complex 1.60
Lysine Req., g/NE
1.40
1.20
1.00
0.80
0.60 0.40
0.20 0
10
20
30
40
50
Experimental day
60
70
80
34
Essential elements for precision feeding 1) Evaluating the nutritional potential of feed ingredients 2) Precise determination of nutrient requirements 3) Formulating balanced diets that limit the amount of excess nutrients, and 4) The concomitant adjustment of nutrient supply to match the requirements of pigs fed
in groups individually Individual precision feeding 35
Precision feeding Feeding pigs individually requires, 1. A numerical method for automatically estimate daily individual nutrient requirements 2. A feeder able to provide a complete feed to each pig of the herd according to the estimated requirements
Precision feeding Feeding pigs individually with daily tailored diets formulated based on its own real-time patterns of feed intake and growth represents a fundamental paradigm shift in pig feeding Nutrient requirements is no longer a static population characteristic, but a dynamic process that evolves independently for each animal
37
Concomitant adjustment of nutrient supply to population requirements Individual feeding: a 3-phase group feeding system was compared to a daily individual multiphase feeding system
Pomar et al., 2007
38
Precision feeding Animal data,
Data from 68 growing-finishing female pigs fed ad libitum from 25 to 105 kg BW
Individual feed intake was measured using an automated recording system
The animals were weighed at least every two weeks
Total body fat and lean were measured by dualenergy X-ray absorptiometry (DXA) at the beginning and at the end of the experiment
Pomar et al., 2007
39
Precision feeding Pig growth modelling
A slightly modified version of the growing pig module of InraPorc® was used to simulate growth and estimate nutrient requirements
Initial lipid and protein body masses of each simulated pig were estimated from DXA measurements
van Milgen et al., 2008; Haulschild et al., 2010
40
Precision feeding
Pomar et al., 2010
41
Precision feeding
Pomar et al., 2010
42
Precision feeding Feeding pigs with daily tailored diets reduced… …N intake by 25% …P intake by 29%
Método de alimentacion Tres fases en grupo
Multifase individualizada
5.69 2.08 3.61 0.85 2.76 0.91 0.35 0.49
SEM
P
∆, %
4.29 2.08 2.21 0.64 1.57
0.050 0.018 0.043
0.0001 0.9090 0.0001
25 0 39 25 43
0.65 0.35 0.30
0.007 0.003 0.005
0.0001 0.9090 0.0001
29 0 40
Nitrogeno, kg Ingerido Retenido Excretado Heces Urina
Fosforo, kg Ingerido Retenido Excretado
…both N and P excretion by near 40%
This represents per pig :
• • •
Reduction of ≈ 23 kg of soybean meal Reduction of ≈ 0.6 kg of phosphates Together representing > 8$/pig 43
Precision feeding Why such a reduction (near 40%)? 1.60
Nutrient requirements vary greatly between individuals
Lysine Req., g/NE
1.40
1.20 1.00
0.80
0.60
0.40
0.20 0
10
20
30
40
50
60
70
80
Experimental day 1.09 g/MJ
Populations are fed to maximize animal response
Body weight gain (g/d)
1 050
950
850
750
650
550 0.45
0.65
0.85
1.05
1.25
1.45
Lys:NE (g/MJ)
44
Precision feeding Why such a reduction (near 40%)? 1.60 1.09 g/MJ
Body weight gain (g/d)
1 050
Lysine Req., g/NE
1.40 1.20
950
850
750
650
550 0.45
0.65
0.85
1.05
1.25
1.45
Lys:NE (g/MJ)
1.00 0.80 0.60 0.40 0.20 0
10
20
30
40
50
60
70
80
Experimental day 45
Precision feeding Total N efficiency (retained / intake, %)
Dourmad et al., 1999, Shirali et al. 2011, Pomar et al. 2007, Pomar et al. 2010
46
Precision feeding The implementation of precision feeding systems presents, however, significant challenges which are related to their complexity (e.g., individual estimation of nutrient requirements), reliability (e.g., using electronic devices in farms) and cost effectiveness
Precision pig farming Research projects founded by Canadian Swine Research and Development Cluster initiative are addressing the following issues,
optimizing the formulation of premixes for blend feeding
updating and calibrating the actual model used for real-time prediction of amino acid and phosphorous requirements
evaluating the technical, economical and environmental impact of precision feeding in Canadian commercial farms
developing numerical strategies for early identification of diseases through changes in individual feed intake patterns
Precision pig farming Research projects founded by several Spanish founding agencies are addressing the following issues,
Completing the development of a research precision feeder prototype able to provide daily tailored diets to each pig of the herd while measuring individual feed intake and weight in real-time
Development of a pre-commercial precision feeder system which will be used for the technical, economical and environmental evaluation of the proposed precision farming approach
Precision pig farming Research projects founded by several Spanish founding agencies are addressing the following issues,
A decision support system (DSS) that using artificial intelligence technologies, will control the precision feeders. This DDS will integrate the modified feed formulation programs, mathematical growth models, actual scientific and technical nutritional knowledge, optimization algorithms and advanced database software and analysis techniques
Precision pig farming 1.
Individual precision feeding allows,
Reducing feeding costs by reducing the expensive excess supply of nutrients (protein, P, etc.)
Reducing feed fabrication costs, storage, management and shipping by using the same two or more premixes on all farms
Reducing the excretion of N, P and other polluting constituents of manure and the amount of soil required for manure application 1.60
Lysine Req., g/NE
1.40 1.20 1.00 0.80 0.60 0.40 0.20 0
10
20
30
40
50
Experimental day
60
70
80
51
Precision pig farming 2.
Intelligent management of feeds and animals with advanced computerized technologies allows,
Real-time off-farm monitoring of feeds and animals for improved economic efficiency
To reduce labor requirements and costs through the automatic monitoring and management of feeds and animals
Early identification of diseases and the precise application of individual treatments, resulting in improved herd performance and lower veterinary costs
52
Precision pig farming 2.
Intelligent precision management,
53
Precision pig farming 3.
Easy application of treatments facilitates,
Evaluation of new feeds and feed co-products Determination of optimal responses
54
Precision pig farming 4.
Easy application of treatments allows,
Manipulating growth rate and composition of each pig
automatically managing individual feed supply (voluntary or restricted feeding) and feed composition (e.g. providing higher levels of P to future reproduction gilts, limiting fatness or enhancing it in market pigs, etc)
Reducing antibiotic use by the early identification of
diseases and the application of personalized treatments
55
Development of an innovative precision farming system for swine This project was funded in Canada by the Canadian Swine
Research and Development Cluster, a Growing Canadian AgriInnovation Program – Canadian Agri-Science Cluster Initiative of Agriculture and Agri-Food Canada (AAFC) and in Spain by the Spanish Ministerio de Ciencia y Tecnología
The authors thanks the following participants,
Research scientists and university professors: P. Lovatto, UFSM, Brazil, J.-P. Dusseault, Sherbrooke university, F. Dubeau, Sherbrooke university, B. Colin, Sherbrooke university, J.F. Bernier, Laval University, F. Guay, Laval University, E. Fabrega, IRTA, Spain, I. Narcy, INRA, France, A. Escola, UdL, Spain, L. Puigdomenech, UdL, Spain Postdoctoral fellows and Ph.D. Students: M.P. Letouneau-Montmigny, L. Hauschild, G.H. Zhang, I. Andretta, M. Kipper da Silva, X. Rousseau, M. Tulsa, E. Gonzalo Master Students: E. Joannopoulos, L. Cloutier, S. Germain Other participants: V. Lopez, E. Arbiol, C. Morillo, P. Mañe
56
Thank you Merci beaucoup
Candido Pomar Research Scientist Telephone : (819) 565-9174 (252) E-mail :
[email protected] Website : www.agr.gc.ca/ResearchCentre/Sherbrooke
