This document describes the development of an agri-data warehouse to store pest scouting data collected from farms. Data was initially collected via handwritten forms which were then typed into a digital format, introducing errors. The data was then transformed and loaded into a central database to be queried and generate reports. This allowed identification of correlations between pest populations and treatments, validation of scouting data, and insights into optimal sowing and spraying dates. Key lessons learned included addressing data quality issues and the need for decision support all the way down to extension workers.

1. Data Warehousing
CASE STUDY: AGRI-DATA WAREHOUSE
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2. Step-5: Surprise case
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Jassid
WhiteFly_Nymph
WhiteFly_Adult
Thrip
Mite
SBW
ABW_White_Eggs
ABW_Brown_Eggs
ABW_Larvae_Small
ABW_Larvae_Large
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Correlation
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Ball Worm Complex
Sucking pests
SBW: Spotted Ball Worm
ABW: Army Ball Worm
PBW: Pink Ball Worm
If pest population is low, predator population will also be low, because there will
be less “food” for predators to live on i.e. pests.
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3. Step-6: Data Acquisition & Cleansing
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Hand filled pest scouting sheet
Typed pest scouting sheet
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4. Step-6: Issues
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5. Step-6: Why the issues?
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6. Step-7: Transform, Transport & Populate
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7. Motivation For Transformation
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8. Step-7: Resolving the issue
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9. Step-8: Middleware Connectivity
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10. Step-9-11: Prototyping, Querying & Reporting
SELECT Date_of_Visit, AVG(Predators),
…………………………AVG(Dose1+Dose2+
Dose3+Dose4)
FROM Scouting_Data
WHERE Date_of_Visit < #12/31/2001#
and predators > 0
GROUP BY Date_of_Visit;
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11. Step-12: Deployment & System Management
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12. Agri-DSS usage: Data Validation
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13. Agri-DSS usage: Data Validation Graph
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Predator Spray
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14. Agri-DSS usage: FAO report
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15. Graph 15
Using pesticides to increase yield.
Why negative correlation between yield and pesticides?
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16. Agri-DSS usage: Spray Dates
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17. Agri-DSS usage: Spray Dates Graph
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Spray dates (mm_dd) for 2001 & 2002
Relative
values
Moving Avg
Correlation
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18. Agri-DSS usage: Explaining Findings
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19. Agri-DSS usage: Sowing Dates
2001: Sowing week_day
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2002: Sowing week_day
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20. Conclusions & Lessons
 ETL is a big issue.
 Each farmer is repeatedly visited
 There is a skewness in the scouting data.
 Decision-making goes all the way “down” to the extension level.
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