This document summarizes a study comparing dairy ration formulation using a least-cost formulation (LCF) approach versus a multi-objective formulation (MOF) approach. The study was conducted in collaboration with Egerton University, the Ministry of Agriculture in Kenya, and Fika Systems Limited. The MOF approach integrated production, economic, and policy goals while the LCF only considered ingredient costs. A software program was developed and validated using on-farm feeding trials with 12 dairy cows. Results showed the MOF approach produced higher milk yields and quality, lower manure phosphorus levels, and similar feed costs compared to the LCF approach. The MOF approach represents an innovative shift from a single objective formulation to a multiple objective approach.

1. 6/26/2014 1
Collaborating Organizations:

2. 6/26/2014 2
Collaborating Institutions:
Egerton University,
Department of Animal Sciences,
P. O. Box 538-2011
Njoro-Kenya
TEL: 0735-752-823; EMAIL: deanagric@egerton.ac.ke
Ministry of Agriculture, Livestock and Fisheries,
State Department of Livestock,
P. O. Box 34188-00100
Nairobi-Kenya
TEL: 0735-752-823; EMAIL: smutuah@yahoo.com
and
Fika Systems Limited-Nairobi
P. O Box 73432-00200,
Nairobi-Kenya,
TEL: 020-2495918; EMAIL: info@fikasystems.com

3. 6/26/2014 3
State Department of Livestock-Nairobi, Kenya
Animal Sciences Department-Egerton University, Kenya
Presented
By
Stanley Mutua
Mutua S. M.

4. 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

5. 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)
6/26/2014 5Mutua S. M.

6. Principles of Feed Formulation

7. In Conjunction with Least-Cost Function
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8. 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 6/26/2014 Mutua S. M. 8

9. Introduction-2
• Emerging economic, production and policy
challenges
• Driving the need for a MoF approach
• Entrepreneurial needs /regulatory
mandate

10. Innovation platform

11. System Design and Development
 Process Improvement Strategic Objective
(PISO-Approach)
 Process Re-Engineering

12. Feed industry actors

13. Model objects
• Program data objects include
• Users,
• Ingredients,
• Dairy cows
• Feed policy guidelines

14. Model data organisation
•

15. Model schematic representation
•

16. Predictive equations
• Milk Yields (MY)
• Dry Matter Intake (DMI)
• Minimum Phosphorous Excretion (MPE)

17. Predictive equations
• Milk Yields (MY)
• BF (Butter Fat)
• FCM (Fat Corrected Milk)

18. Predictive equations
• Dry Matter Intake (DMI)
• FCM (Fat Corrected Milk)
• BW0.75 (Metabolic Body Weight)
• WIM (Weeks in Milk)

19. 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)

20. 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

21. 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

22. In Conjunction with Least-Cost Function
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23. Available Ingredients

24. MOF-Formulation Approach
(Formulation design)
6/26/2014 24Mutua S. M.
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

25. MOF-Formulation Approach
(Ingredient, ingredient grouping and nutrition data)
6/26/2014 25Mutua S. M.
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

26. MOF-Formulation Approach
(Formulation outline)
6/26/2014 26Mutua S. M.
Formulation objectives
Ingredient Inclusio
n %
Ingre-
Group
Must-
include
CP:P
ratio
Price/
TDN%
Price/
CP%
Price/10
0kg
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
8
Requirements
Optimal range:
100% Min: Min:
Max:

27. Software program validation process
Compare milk yield and quality, nutrient
excretion and cost from dairy rations
formulated with LCF and MOF
approaches
6/26/2014 Mutua S. M. 27

28. Materials and Methods
 Study site
 Egerton University-Ngo’ngo’ngeri Farm
6/26/2014 Mutua S. M. 28

29. 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
6/26/2014 Mutua S. M. 29

30. Materials and Methods
 Animal experimental design
6/26/2014 Mutua S. M. 30

31. 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
6/26/2014 Mutua S. M. 31
ijiijY  

32. Results and Discussion
 Milk yield, milk composition and manure P
levels
6/26/2014 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 0.009
Manure Composition (%)
P-Manure 0.609 1.71 0 1.66 0.026
Body Weight
Mean (kg)
Change (kg/day)
420 430.60
0.225
10.99
0.108
425.70
0.207
8.770
0.108
Others
Concentrate efficiency
Crude protein: Phosphorous
ratio
Cost price/kg feed (Kenya
shillings) 1USD ≡ 100 Kshs
-
-
-
0.34
23.02
30.38
-
-
-
0.35
22.75
23.44
-
-
-

33. 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

34. PARADIGM SHIFT
FROM TO
Singular
Objective
IS
One Factor
Methodology
Multiple
Objective
IS
Many Factors
Methodology

35. 6/26/2014 35
Acknowledgement

36. END
 Thank you ALL for listening
6/26/2014 36Mutua S. M.