Presented by Vamsidhar Reddy, Isabelle Baltenweck, Jane Poole, Pamela Ochungo, Catherine Pfeifer at the Workshop on Smallholder Dairy Value Chain Transformation in Bihar—Challenges, Opportunities and the Way Forward, Patna, India, 1-2 August 2014
Exploring the Future Potential of AI-Enabled Smartphone Processors
Site Selection in Bihar State for Dairy Value Chain Research
1. Site selection in the dairy value chain in Bihar state,
India
Vamsidhar Reddy, Isabelle Baltenweck, Jane Poole, Pamela Ochungo,
Catherine Pfeifer
Workshop on Smallholder Dairy Value Chain Transformation in
Bihar – Challenges, Opportunities and the Way Forward
Patna, India, 1-2 August 2014
2. Overview
1. A rationale for site selection
2. Spatial analysis for Bihar
a. Layers used
b. Preselected departments
3. Defining soft criteria
4. Scoring soft criteria
5. Final ranking the sites
3. Huge heterogeneity in bio-physical and socio-economic context
Identify a small number of representative research locations:
That capture the gradient of key variables
provide opportunity for good research and impact
Site selection - rationale
4. Multi-step procedure
1. Define State for the dairy value chain
Based on poverty, milk production, consumption, and
productivity gap indicator
2. Define the target zone
3. Spatial stratification and selection of ‘potential sites’
Based on the ‘hard’ criteria
Representing the different contexts/environments
4. Scoring of potential sites
Based on the ‘soft’ criteria
‘Impact’ indicators and ‘ease of working’ indicators
Groundtruthing
5. Agreement on final set of sites
5. Multi-step procedure
1. Define State for the dairy value chain
Based on poverty, milk production, consumption, and
productivity gap indicator
Bihar was selected
2. Define the target zone
3. Spatial stratification and selection of ‘potential sites’
Based on the ‘hard’ criteria
Representing the different contexts/environments
4. Scoring of potential sites
Based on the ‘soft’ criteria
‘Impact’ indicators and ‘ease of working’ indicators
Groundtruthing
5. Agreement on final set of sites
6. Overview
1. A rationale for site selection
2. Spatial analysis for Bihar
a. Layers used
b. Preselected departments
3. Defining soft criteria
4. Scoring soft criteria
5. Final ranking the sites
11. How to define low and high?
Variable Median value Stakeholder defined value
Bovine density 174
poor people density 1,555,000
Based on this criteria we can select a long list of potential sites
12. Selection criteria
• The spatial criteria ALONE don’t have a high enough
resolution to select field sites completely, so we combine
them with soft criteria AND ‘groundtruthing’ (with
stakeholders) to come up with the final selection
• Under ‘soft’ criteria we understand:
Partners – presence & capacity
On-going research activities
Proximity and comparability to other long-term research sites
Institutional actor presence & networks
Resource availability
Others?....
13. Scoring soft criteria
• Fill the scoring sheet in groups of 5-7
– Give a mark for each criteria for each potential
• Come up with a ranking of sites
16. CGIAR is a global partnership that unites organizations engaged in research for a food secure future. The CGIAR Research
Program on Livestock and Fish aims to increase the productivity of small-scale livestock and fish systems in sustainable ways,
making meat, milk and fish more available and affordable across the developing world.
CGIAR Research Program on Livestock and Fish
livestockfish.cgiar.org
17. Data Sources for Spatial analysis
Selection criteria Data source
Livestock: Bovine density FAO, Gridded Livestock of The
World Database (2007)
Poverty: Density of people
living below the poverty line
($1)
Harvestchoice, 2010
Human population density Gridded Population of the
World (GRUMP) V3. (2005)
Data sources are:
18. Table 1: Surface area of production systems in India
(derived from Robinson et al., 2011)
Production system Surface area (km2) Percentage (%)
Rangeland based, Arid/Semi-arid
(LGA)
182,160 6.1
Mixed rainfed, Arid/Semi-arid
(MRA)
783,920 26.4
Mixed rainfed, Humid/Sub-humid
(MRH)
191,050 6.4
Mixed rainfed, Temperate/Tropical
highlands (MRT)
48,260 1.6
Mixed irrigated, Arid/Semi-arid
(MIA)
742,520 25.0
Mixed irrigated, Humid/Sub-
humid (MIH)
80,380 2.7
Urban 201,960 6.8
Other 712,610 24.0
Notes de l'éditeur
The spatial heterogeneity of bio—physical and socio-economic pattern is quite big
In Blue what we have already done in read what possibly should be done today
Target zone = we want to capture high poverty with livestock, and also a urban and rural component
In Blue what we have already done in read what possibly should be done today
We used the poverty map, and used the median to define high and low (right map), then we have aggregated this result to district level (left map)
Is the median a good value? We will discuss this just later on…
The two two district level maps into a domain maps that shows identify zone where both poverty and bovine density is high
We propose to select site from the green areas as first priority, from red and orange as second priority of no agreement can be found but not from the white zone.
If it becomes an issue, rural to rural and rural to urban will be introduced while selecting the blocks with the selected district at a later stage.
Workout in small groups if the thresholds are ok, modify them if necessary. There is an excel file that automatically computes the new list of sites
VARIANTE if under time pressure : let each participant propose a value on a flip chart while going for coffee and use the average of this
You might want to negotiate if we use only the green site (high poverty and livestock) or if we also include yellow (high poverty low livestock)
Also here you need to negotiate if there are areas that are absolutely no go, for example because of existing conflict, just too far away to reach, just not relevant maybe because global datasets are not very accurate)
Collect here the different soft criteria, you can work in small groups.
VARIANTE : give 5 papers to every participant and ask them to think of criterias (allows to give a voice to silent participants) then collect them, order them so that you can agree on a final set of criterias
There is a scoring sheet ready for each group
Use the marking system in used in school or just 10 excellent 1 very bad
You can do this if you have time. I think you can learn a lot from this negotiation
VARIANT : just compare the group work, and we will use an average of all the groups for the final stakeholder ranking