Presented by A.M. Okeyo, J.M.K. Ojango, R. Mrode, C. Quiros., J.P. Gibson., E. Kefena, J. Besufekad., E. Lyatuu, G. Msuta, S. Kahumbu, H.N. Nyakundi, D. Mogaka and E. Oyieng at the All Africa Conference on Animal Agriculture, Ghana, July 2019

1. Innovative application of ICT tools for paperless data
capture and feedback in smallholder dairy production
systems: The Platform for African Dairy Genetic Gains
(ADGG)
A.M. Okeyo, J.M.K. Ojango, R. Mrode, C. Quiros., J.P. Gibson., E. Kefena, J. Besufekad., E. Lyatuu, G.
Msuta, S. Kahumbu, H.N. Nyakundi, D. Mogaka and E. Oyieng
7th All Africa Conference on Animal Agriculture, Ghana, July 2019

2. 1. INTRODUCTION
2. METHODOLOGY
3. RESULTS AND DISCUSSION
4. CONCLUSION
PRESENTATION STRUCTURE

3. INTRODUCTION
 Herd and animal recording is of greatest value when information and results from
analyses of the data collated is used by farmers in making herd management
decisions.
 Pooled herd records provide national inventories on the animal genetic resources
(AnGR), trends in their performance and associated risks in their effective
management and sustainable use (FAO, 2007).
 Information on Animal genetic resources (AnGR) is critical for strategic planning,
decision making on livestock investment, cost-effective development of livestock
improvement programs, and conservation of unused or threatened AnGR.
 In Sub-Saharan Africa, national livestock performance recording and monitoring
systems are few.

4. INTRODUCTION
Constraints to adoption of the practice of livestock recording
 Inadequate and unsupportive policies and infrastructure;
 Weak or non-existent organizations and institutions to carry out and support
recording systems;
 Lack of appropriate related legal frameworks resulting in inadequate and
weak partnerships, networks and collaboration;
 Small and dispersed herds/ flocks, leading to high transaction costs;
 Limited capacity and understanding of livestock recording, processing of
information and feedback both at farmer and institutional level;
 Inadequate resource allocation to support pilot activities for livestock
recording systems;

5. INTRODUCTION
The process of animal recording

6. METHODOLOGY
Countries: Ethiopia and Tanzania
Regions:
 Ethiopia:- Addis Ababa, Amhara, Tigray, SNNP and Oromia
 Tanzania:- Arusha, Kilimanjaro, Tanga, Iringa and Mbeya
Target: 12,000 animals per country
Data collection and feedback:
• Digitally by enumerators using Open Data Kit (ODK)
• Pre-coded macro level information on location of the farms such as the regions, districts
and villages.
• Real-time updating of information entered in the database to ODK
• Feedback on animal performance is provided directly to the mobile phone of the farmers
Data collation:
• Customized MySQL database

7. METHODOLOGY
Digital architecture and flow of data on the ADGG platform
Detailed data
DB
(cleaned)
ODK
Data Processing
Data in JSON
moved to MySQL
DB
Processing
OK
Error
Log
Processing
not OK
Manual correction
JSON files are
corrected
JSON
1
Automatic conversion
Submissions converted
as JSON files
Analytics
modules
Scripting analytics
R P
y
F
Field staff
Consultation
2
3
Analyzed
data
DB
(results per
script)
4
Data visualization
(METABASE, etc.)
Data cleaning
Corrections made in
the database
5
Feedback Farmer
Other
actors
External AccessOther primary data
(e.g., service
providers)

8. METHODOLOGY
ODK tools developed for data capture

9. RESULTS AND DISCUSSIONS
Smallholder farmers registered in the ADGG platform in Tanzania and Ethiopia
Year Number of farmers
2016 6,368
2017 13,017
2018 6,931
2019 3,874
Grand Total 30,190
Year Number of farmers
2016 1,487
2017 21,132
2018 26,633
2019 9,827
Grand Total 59,079
Tanzania Ethiopia

10. RESULTS AND DISCUSSIONS
Animals registered in the ADGG platform in Tanzania and Ethiopia
0
10000
20000
30000
40000
50000
60000
2016 2017 2018 2019 Grand Total
Addis ababa Amhara Oromia SNNP Tigray Grand Total
0
10000
20000
30000
40000
50000
60000
2016 2017 2018 2019 Grand Total
Arusha Iringa Kilimanjaro Mbeya
Njombe Tanga Grand Total

11. RESULTS AND DISCUSSIONS
Country Region Number of farms Average herd size Mean ± SD
Tanzania Arusha 5,539 2.98±2.40
Iringa 1,483 3.91±5.22
Kilimanjaro 7,616 2.68±2.97
Mbeya 4,200 2.68±2.34
Njombe 1,533 2.40±1.89
Tanga 6,240 3.46±4.36
TOTAL
Ethiopia Amhara
9,166
5.27±3.30
Oromia
4,386
4.94±6.53
SNNP
6,750
3.30±5.27
Tigray
5,595
3.28±4.24
TOTAL
Average number of dairy cattle kept by the smallholder farmers in the different regions of Tanzania and Ethiopia

12. RESULTS AND DISCUSSIONS
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Arusha Iringa Kilimanjaro Mbeya Njombe Tanga
Proportionofanimals
Tanzania
Ayrshire Holstein-Fresians Mixed zebus Other exotic
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Amhara Oromia SNNP Tigray
Ethiopia
Ayrshire Hostein-Fresians Mixed zebus Other exotics
BREED TYPES

13. RESULTS AND DISCUSSIONS
Region Number of
animals
Number of Test-
day records
Test-day milk yields (kg/day)
Mean ± SD Minimum Maximum
Tanzania Arusha 2,500 17,266 7.8±3.4 0.5 38.0
Iringa 1,344 7,015 6.2±2.7 0.5 22.0
Kilimanjaro 2,880 15,410 6.6±4.2 0.5 28.0
Mbeya 2,828 14,518 10.2±4.8 1.0 46.0
Njombe 1,373 7,532 11.2±4.4 0.5 28.0
Tanga 3,814 22,629 6.1±3.8 0.3 39.5
Total 14,733 84,370 7.7±4.3 0.3 46.0
Ethiopia
Amhara 1,511 2,916 7.1±4.1 0.4 24.0
Oromia 6,983 70,513 11.7±5.2 0.5 47.0
SNNP 994 8,013 5.8±3.7 0.4 39.0
Tigray 2,145 8,904 10.4±4.6 1.0 48.0
Total 11,633 90,346 10.9±5.4 0.4 48.0
Test-day milk records collected from smallholder farmers in Tanzania and Ethiopia from October 2016 to June 2019

14. RESULTS AND DISCUSSIONS
Critical costs for maintaining a smallholder recording system
1.Time, capacity development and capital input for establishing the central identification and
recording infrastructure with requisite organizational frameworks.
2.Investments in developing and maintaining the central database software to support the
data platform.
3.ICT equipment and software for collating data and communication between different
stakeholders
4.Provision of equipment and training on identification and classification
5.Partnerships with key industry stakeholders for provision of feedback messages and linking
livestock keepers with service providers to support the dairy enterprises.
6.Capacity development of the smallholder livestock keepers on the process of recording and
the use of feedback information in the management of their dairy enterprises.

15. RESULTS AND DISCUSSIONS
Emerging opportunities from data on smallholder farms in Tanzania and Ethiopia
1. Use of mobile phone technology enables ”crowd sourcing” of data from the herds
and greatly reduces the time while increasing the speed of data transfer
2. Collaborating with information service providers is key to affordable access to herd
performance data, farmer education information and generals DPRC improved and
sustained services
3. Combining services like animal health, performance recording, farmer extension and
animal breeding services as a package, together with structured and direct support
from national dairy regulatory organizations, dairy farmer and processor
associations
4. Developing breeding objectives and implement a sustainable breeding program for
dairy cattle production in smallholder farming systems

16. CONCLUSION
 ICT technologies and tools have enabled rapid collection of livestock
performance data from smallholder farming systems in rural environments
of Eastern Africa
 Each country needs to build up basic information on their own animals, and
rally the farmers to continue with the animal identification and recording of
performance.
 Planned and timely analyses of information generated on the farms with
feedback using simple and applicable messages encourages farmer
participation in recording
 Development and growth of livestock improvement programs depends on
how policies, technologies and institutions aptly respond to farmers needs
 Design and implementation of sustainable breeding programs in developing
countries possible through data collected centrally

17. FARMERS
ACKNOWLEDGEMENTS

18. This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence.
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