Implementation of multi objective feed formulation approach by dr. s.m. mutua
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Transcript of Implementation of multi objective feed formulation approach by dr. s.m. mutua
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04/12/2023 1
Collaborating Organizations:
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04/12/2023 2
Collaborating Institutions:
Egerton University,Department of Animal Sciences,
P. O. Box 538-2011Njoro-Kenya
TEL: 0735-752-823; EMAIL: [email protected]
Ministry of Agriculture, Livestock and Fisheries,State Department of Livestock,
P. O. Box 34188-00100Nairobi-Kenya
TEL: 0735-752-823; EMAIL: [email protected]
and
Fika Systems Limited-NairobiP. O Box 73432-00200,
Nairobi-Kenya, TEL: 020-2495918; EMAIL: [email protected]
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Mutua S. M.04/12/2023 3
State Department of Livestock-Nairobi, KenyaAnimal Sciences Department-Egerton University, Kenya
Presented
By
Stanley Mutua
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Session overview
• Introduction• Overview of feed industry• Problem domain
• Program development process• Prediction functions
• Milk Yields (MY)• Dry Matter Intake (DMI)• Minimum nutrient excretion (MPE)
• Software validation process• Results of on-farm feeding trials • Potential benefits of MoF-Dairy program
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Introduction-1
• Feed industry founded on 3 main dimensions
• Feed manufacturing chain• Who are involved? Dependencies
• Technical dimension • What to feed?/Feed resources
• Nutrition and feeding management• (When and how much to feed)
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Principles of Feed Formulation
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In Conjunction with Least-Cost Function
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Problem Domain: Limitations of LCF Based on singular objective (ingredients cost only)
• Focused on millers entrepreneurial
gains only
Fails to provide options to:
• Match feeding with animal production
potential
Fails to impose critical formulation goal of:
• Minimum pollutant manure nutrient
excretion into environment
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Introduction-2
• Emerging economic, production and policy challenges
• Driving the need for a MoF approach • Entrepreneurial needs /regulatory
mandate
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Innovation platform
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System Design and Development
Process Improvement Strategic Objective
(PISO-Approach)
Process Re-Engineering
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Feed industry actors
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Model objects
• Program data objects include
• Users,
• Ingredients,
• Dairy cows
• Feed policy guidelines
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Model data organisation
•
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Model schematic representation
•
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Predictive equations
• Milk Yields (MY)
• Dry Matter Intake (DMI)
• Minimum Phosphorous Excretion (MPE)
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Predictive equations
• Milk Yields (MY)
• BF (Butter Fat)
• FCM (Fat Corrected Milk)
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Predictive equations
• Dry Matter Intake (DMI)
• FCM (Fat Corrected Milk)
• BW0.75 (Metabolic Body Weight)
• WIM (Weeks in Milk)
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Predictive equations
• Minimum nutrient excretion (MPE)
• Minimum nutrient excretion (Min P)
• K (Efficiency of P utilisation)
• P (Feed phosphorous inclusion levels)
• M (Optimal daily milk yield)
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Optimizing predictive functions
• Integration of production and policy regulations
• Regulatory guidelines
• CP: P ratio
• Minimum P inclusions
• Least-Cost affordable feeds
• Representing critical formulation goals
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Optimizing Formulation Objectives
Integration of production and policy
regulations
• Regulatory guidelines
• Quality and Pollution
• Energy: CP
• CP: P ratio
Max milk production
Minimum P inclusions• Least-Cost affordable feeds
Representing critical formulation goals
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In Conjunction with Least-Cost Function
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min
1,...2,11
JJbXa ji
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Available Ingredients
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04/12/2023 Mutua S. M. 24
MOF-Formulation Approach(Formulation design)
Ingredient Ingre-Group
Must-include
CP % P % CP:P ratio
Price/100kg
Safe max
1 Dairy premix M X
2 Limestone M X
3 DCP M X
4 Energy rich source(s)
5 Wheat bran E 15.15 0.92 16.46
6 Maize germ E 10.53 0.53 19.87
7 Protein rich source(s)
8 sunflower cake meal
P 26.88 0.57 47.16
9 Cotton cake meal
P 29.29 0.62 47.24
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MOF-Formulation Approach(Ingredient, ingredient grouping and nutrition data)
Formulation objectives
Ingredient Ingre-Group
CP:P ratio Price/ TDN%
Price/CP%
Price/100kg
1 Dairy premix M
2 Limestone M
3 DCP M
4 Energy rich source(s) E
5 Protein rich source(s) P
6
7
8
9
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04/12/2023 Mutua S. M. 26
MOF-Formulation Approach(Formulation outline)
Formulation objectives
Ingredient Inclusion %
Ingre-Group
Must-includ
e
CP:P ratio
Price/ TDN%
Price/CP%
Price/100kg
Safe max
1 Dairy premix M X
2 Limestone M X
3 DCP M X
4 Energy rich source(s)
E
5 Protein rich source(s)
P
Sub-total 50%
6
7
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RequirementsOptimal range:
100% Min: Min:Max:
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Software program validation process
Compare milk yield and quality,
nutrient excretion and cost from dairy
rations formulated with LCF and MOF
approaches
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Materials and Methods
Study site
Egerton University-Ngo’ngo’ngeri Farm
04/12/2023 Mutua S. M.
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Materials and Methods Animal management
12 Dairy Cows at same
Breed = Friesian
Age = 54 months
Lactation = Third (3rd )
Advance De-Worming (2 weeks)/Albendazole
10%
Placed in cross-over repeated measures design,
with 6 replications each for two (2) periods
04/12/2023 Mutua S. M.
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Materials and Methods
Animal experimental design
04/12/2023 Mutua S. M.
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Statistical analysis
One-way (no Blocking) ANOVA) using GenStat Discovery Edition-3 statistical software (2007);
Where: Yij = Overall cow performance under the ith diet
µ = the overall mean effect αi = the effect of the ith diet (i = LCF, MOF)
eij = the random error
04/12/2023 Mutua S. M.
ijiijY
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Results and Discussion Milk yield, milk composition and manure P
levels
04/12/2023 32Mutua S. M.
Concentrate diet type
Herds average
LCF SED MOF SED
Yield (kg/day) Actual Milk 12.00 13.35 4.695 13.39 4.483
Milk Composition (%) Butter fat 3.760 3.914 0.098 4.019 0.165
Protein 3.135 3.154 0.022 3.152 0.033 Phosphorous 0.081 0.148 0 0.151 0.009Manure Composition (%) P-Manure 0.609 1.71 0 1.66 0.026
Body Weight Mean (kg) Change (kg/day)
420 430.600.225
10.990.108
425.700.207
8.7700.108
OthersConcentrate efficiencyCrude protein: Phosphorous ratioCost price/kg feed (Kenya shillings) 1USD ≡ 100 Kshs
-- -
0.3423.02
30.38
-- -
0.3522.75
23.44
-- -
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Innovative product and service
•• Improved efficiency with which to formulate dairy rations
• Integration of economic, production and policy needs as critical formulation goals
• Blending of entrepreneurial needs as well as institutional mandates
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PARADIGM SHIFTFROM TO
Singular Objective
IS
One Factor Methodolo
gy
Multiple Objective
IS
Many Factors
Methodology
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Acknowledgement
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Mutua S. M.
END
Thank you ALL for listening
04/12/2023 36