Site selection in the dairy value chain in Bihar state,
India
Vamsidhar Reddy, Isabelle Baltenweck, Jane Poole, Pamela Och...
Overview
1. A rationale for site selection
2. Spatial analysis for Bihar
a. Layers used
b. Preselected departments
3. Defi...
Huge heterogeneity in bio-physical and socio-economic context
 Identify a small number of representative research locatio...
Multi-step procedure
1. Define State for the dairy value chain
 Based on poverty, milk production, consumption, and
produ...
Multi-step procedure
1. Define State for the dairy value chain
 Based on poverty, milk production, consumption, and
produ...
Overview
1. A rationale for site selection
2. Spatial analysis for Bihar
a. Layers used
b. Preselected departments
3. Defi...
Density of poor people
Bovine density
GIS analysis
Bovine density
Density of poor
Combining the criteria's
How to define low and high?
Variable Median value Stakeholder defined value
Bovine density 174
poor people density 1,555,0...
Selection criteria
• The spatial criteria ALONE don’t have a high enough
resolution to select field sites completely, so w...
Scoring soft criteria
• Fill the scoring sheet in groups of 5-7
– Give a mark for each criteria for each potential
• Come ...
Comparison between groups
• Negotiation for a final rank
THANK YOU
CGIAR is a global partnership that unites organizations engaged in research for a food secure future. The CGIAR Research
P...
Data Sources for Spatial analysis
Selection criteria Data source
Livestock: Bovine density FAO, Gridded Livestock of The
W...
Table 1: Surface area of production systems in India
(derived from Robinson et al., 2011)
Production system Surface area (...
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Site selection in the dairy value chain in Bihar state, India

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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

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  • 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
  • Site selection in the dairy value chain in Bihar state, India

    1. 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. 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. 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. 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. 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. 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
    7. 7. Density of poor people
    8. 8. Bovine density
    9. 9. GIS analysis Bovine density Density of poor
    10. 10. Combining the criteria's
    11. 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. 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. 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
    14. 14. Comparison between groups • Negotiation for a final rank
    15. 15. THANK YOU
    16. 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. 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. 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
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