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Innovative application of ICT tools for paperless data
capture and feedback in smallholder dairy production
systems: The P...
1. INTRODUCTION
2. METHODOLOGY
3. RESULTS AND DISCUSSION
4. CONCLUSION
PRESENTATION STRUCTURE
INTRODUCTION
 Herd and animal recording is of greatest value when information and results from
analyses of the data colla...
INTRODUCTION
Constraints to adoption of the practice of livestock recording
 Inadequate and unsupportive policies and inf...
INTRODUCTION
The process of animal recording
METHODOLOGY
Countries: Ethiopia and Tanzania
Regions:
 Ethiopia:- Addis Ababa, Amhara, Tigray, SNNP and Oromia
 Tanzania...
METHODOLOGY
Digital architecture and flow of data on the ADGG platform
Detailed data
DB
(cleaned)
ODK
Data Processing
Data...
METHODOLOGY
ODK tools developed for data capture
RESULTS AND DISCUSSIONS
Smallholder farmers registered in the ADGG platform in Tanzania and Ethiopia
Year Number of farmer...
RESULTS AND DISCUSSIONS
Animals registered in the ADGG platform in Tanzania and Ethiopia
0
10000
20000
30000
40000
50000
6...
RESULTS AND DISCUSSIONS
Country Region Number of farms Average herd size Mean ± SD
Tanzania Arusha 5,539 2.98±2.40
Iringa ...
RESULTS AND DISCUSSIONS
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Arusha Iringa Kilimanjaro Mbeya Njombe Tanga
Proportio...
RESULTS AND DISCUSSIONS
Region Number of
animals
Number of Test-
day records
Test-day milk yields (kg/day)
Mean ± SD Minim...
RESULTS AND DISCUSSIONS
Critical costs for maintaining a smallholder recording system
1.Time, capacity development and cap...
RESULTS AND DISCUSSIONS
Emerging opportunities from data on smallholder farms in Tanzania and Ethiopia
1. Use of mobile ph...
CONCLUSION
 ICT technologies and tools have enabled rapid collection of livestock
performance data from smallholder farmi...
FARMERS
ACKNOWLEDGEMENTS
This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence.
better lives throu...
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Innovative application of ICT tools for paperless data capture and feedback in smallholder dairy production systems: The Platform for African Dairy Genetic Gains (ADGG)

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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

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Innovative application of ICT tools for paperless data capture and feedback in smallholder dairy production systems: The Platform for African Dairy Genetic Gains (ADGG)

  1. 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. 2. 1. INTRODUCTION 2. METHODOLOGY 3. RESULTS AND DISCUSSION 4. CONCLUSION PRESENTATION STRUCTURE
  3. 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. 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. 5. INTRODUCTION The process of animal recording
  6. 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. 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. 8. METHODOLOGY ODK tools developed for data capture
  9. 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. 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. 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. 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. 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. 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. 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. 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. 17. FARMERS ACKNOWLEDGEMENTS
  18. 18. This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence. better lives through livestock ilri.org ILRI thanks all donors and organizations which globally support its work through their contributions to the CGIAR Trust Fund

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