Presentation at ARRCC-CARISSA workshop 29 Jan 2019 to 31 Jan 2019

1. End User’s Expectation from National Meteorology
Institution for Climate Services
Dr. Abu Syed (mabusyed@gmail.com)
Fellow, BCAS
Director, Nansen - Bangladesh International Centre (NABIC)
Workshop on Future Climate Projections
and Their Applications in South Asia
Date: 29-31 January 2019
ICIMOD, Kathamndu, Nepal

2. Contents
• A case study of “use of climate information in
agriculture”
• High resolution projection and prediction –for
effective forecast system-early warning system
• How far data has been available?
• How to improve situation?

4. Late blight in Potato

5. Late blight in Potato

6. Introduction & Service Process

7. •In some cases,
provides strong
advisory support to
the contract farmers
•Can provide
Geopotato as part of
an advisory service to
contract farmers
• Scope to reduce high
fertilizer usage in potato
farming
• Can offer client farmers
Geopotato service as
embedded service
•Geopotato service recommends fungicide products
and these companies would have the most interest
• Primary Target group for
Geopotato and advisory
services
• Low willingness to pay
•Acts as an aggregator or
wholesale trader
•Embedded service to their
farmer base
•Has close financial ties
with farmers
•Embedded services to
farmers
•Companies have
contract growers who
needs strong advisory
support
•Embedded service to
their client base
Potato Sector Value Chain
Farmers;
1.6 Million
Seed
Companies; ~50
Fungicide
Companies; ~80
Fertilizer Companies;
~30
MFIs;
~120 offering
agricultural
loans
Cold
Storage;
~300 active
Processing
Companies; ~30
Wholesale
Traders; ~30
Fresh Potato
Exporter; ~20
High Priority
Medium
Priority
Low Priority
Donor
Agencies
Govt./Public
Extension
Agencies
•Donors (e.g. USAID, IFC)
looking towards private
sector and service market
development
•Government agencies (e.g.
AIS, DAE) are mandated to
incorporate digital advisory
services in their extension
activities

8. GEOPOTATO

9. Meteorology, Air Quality and Forecast for Potato
Farmers
 Detailed analysis of climatology
How often could we prevent a spray if we would have a
perfect weather forecast?
 Validation of GFS model with BMD station data
How (non-) perfect is the weather forecast?
 Evaluation of added value of high-resolution (WRF)
model forecasts
How much room for improvement for the weather
forecast
9

10. When would forecast make a difference?
 Critical situation for late blight:
relative humidity > 90%
 Occurs around sunrise
(around 0 UTC)
 Occurs in large part of the
year
 Question: How often is
relative humidity not critical
(below 90% around sunrise).
10
fractionofsamplesbelowthreshold
month 10-day period in month
data: 2007-2014
often
non-critical
never non-critical

11. Good forecast would help end-of-season
11
fractionofsamplesbelowthreshold
data: 2007-2014
July – February: nearly
each day RH > 90 %
Potato season:
 In 2nd half of
season good
forecast could
make a difference
 Large spatial
variations within
BD
month 10-day period in month

12. How good is the GFS forecast?
Comparison with station
data
Here focus on Faridpur
(red point)
12

13. Mean bias in absolute humidity
Dewpoint temperature consistently too low in
GFS model
As a consequence: relative humidity too low
13
January February March April

14. Conditions with high humidity are missed
Probability of detection
(POD) of event with RH >
90%.
Generally low detection
Better in North-East than in
South-West
14
Prob. of detection: Dec – Jan - Feb
data: 2007-2014

15. How much can we improve the forecast?
15
WRF GFS
Regional model Global model
Run by BCAS and WUR-MAQ Run by US weather service
available within project freely available
5 km resolution 25 km resolution
Large domain Nested BD domain

16. Effect of resolution: humidity at 2 m height
16
ExampleforFebruary19,2108,00UTC
WRF GFS
Regional model Global model
Run by BCAS and WUR-MAQ Run by US weather service
available within project freely available
5 km resolution 25 km resolution

17. Higher resolution (model + land-use)
17
Critical situation: high relative humidity:
• GFS does not capture
• WRF shows higher humidity, but not RH > 90%
GeoPotato weather station
(Munshiganj Sreenaar )

18. Operational NUP
http://123.49.38.4/stations

19. Conclusion
Climatology:
In second half of potato season proper humidity forecast
could make a difference
Large spatial variations
Current forecast quality:
Humidity severely biased too dry
Statistical bias correction is next step
Improving forecast quality:
Better forecast of near-surface humidity is possible with
higher spatial resolution of model and land-use
Within operational context of project: using dedicated
WRF simulations is not sustainable
19

20. 38000 ha official stats
34675 ha classification
Open water
Forest
Other crops
Urban
Potato 1 nov
Potato 20 nov
Potato 10 dec
Potato 1 jan
Sand
Classification Munshiganj 2016-2017 season

21. LAI development per uppazilla

22. System development
• Version 1 (2016/2017): Measured weather + GFS weather
forecast
• Version 2 (2017/2018): Measured weather + WRF weather
forecast
• Version 3: (2018/2019)Measured weather + ...... ?

23. Infection events in Rangpur and Munshiganj Districts

24. 10 yr average infection risk per Calendar day
0%
25%
50%
75%
100%
1-1
8-1
15-1
22-1
29-1
5-2
12-2
19-2
26-2
4-3
11-3
18-3
25-3
1-4
8-4
15-4
22-4
29-4
6-5
13-5
20-5
27-5
3-6
10-6
17-6
24-6
1-7
8-7
15-7
22-7
29-7
5-8
12-8
19-8
26-8
2-9
9-9
16-9
23-9
30-9
7-10
14-10
21-10
28-10
4-11
11-11
18-11
25-11
2-12
9-12
16-12
23-12
30-12
Faridpur
0%
25%
50%
75%
100%
1-1
8-1
15-1
22-1
29-1
5-2
12-2
19-2
26-2
4-3
11-3
18-3
25-3
1-4
8-4
15-4
22-4
29-4
6-5
13-5
20-5
27-5
3-6
10-6
17-6
24-6
1-7
8-7
15-7
22-7
29-7
5-8
12-8
19-8
26-8
2-9
9-9
16-9
23-9
30-9
7-10
14-10
21-10
28-10
4-11
11-11
18-11
25-11
2-12
9-12
16-12
23-12
30-12
Dhaka

25. • Developed/updated full processing chain for
sentinel-2 imagery
• Installed updated DSS scripts on Dhaka Raspberry
PI (november ’17)
• Created potatomasks per upazilla (see example)
• Developed LAI monitoring script per upazilla (see
example)
• Fieldwork campaign in Munshiganj and Rangpur for
callibration of LAI (see example)

26. Climate Data availability
 BMD collects data every three hours
 However, for crop modeling sub-hourly/every ten
minutes data are required
 In GEOPOTATO we are using Landsat TM and Sentinel
II data for crop growth modeling and Early Warning
generation for potato farmers

27. What the regional needs are
 Climate information systems in place (Systems to enhance climate predictability)
 Water management technologies adopted (Improvements in water collection,
drainage, irrigation distribution systems, maximize use of water in livestock
production, etc.)
 Better integrated management of natural resources and production systems
(this includes water management, conservation agriculture, crop and pasture
rotations, adjustment of planting dates, etc.)
 Technological innovations to reduce climatic risks (biotechnology innovations to
improve drought resistance and pests and disease resistance, invasive species, and
improvements in irrigation infrastructure).
 Institutional innovations with capacity built for early warning systems for
climate (improved policy and regulatory frameworks for water management,
agricultural and catastrophic risk insurance, etc.).
 Bottom-up participatory processes for climate change adaptation and reduce
threats to climate variability.

28. Potential Beneficiaries & Clients
 Direct Beneficiaries – Potato farmers
 Potential Subscribers –
 Fungicide companies, processing companies, seed companies (for
contract growers) as advisory service to improve product sales or
quality of products being procured
 Secondary subscribers –
 Micro Finance Institutions, Cold Storage owners, potato exporters as
embedded service to add value on top of their core business services

29. • Bayer Crop Science
o Targeted brand/product name addressed late blight disease
o More judicious use of fungicide by the farmers though increase of
Bayer Crop’s product use
o Demo plot farmers of Bayer Crop had higher yield than geo potato
farmers
As per observation of Bayer’s Field Agent -
o Increased sales of Bayer’s products in the pilot area
o Increased Demand in Piloting Area
Active engagement with private sector
Bayer Crop Science and Syngenta Crop were engaged in different
potato growing regions in Bangladesh

30. What do we mean by ‘Business Case Evaluation’
• Composition of Partnership
• Partnership Cooperation
Agreement
• License to Operate and
Intellectual property
• Key Financial Criteria
• Efficiency of Income vs Costs
• Forecast Reliability
• Working Capital and CapEx
•Affordability
•Acceptability
• User Experience
•Payment system
• The business proposition:
•Target Group
•Demand Driven
Approach
•Product & Services
•Distribution Channels Business
Model
Customer
Journey
Partnership
Financial
case
Business Case
Evaluation
Indicators Indicators
• Zooming in on the actual customer
experience and procedures at the
customer level:
• The financial projections
that support the business
case:
• Structure & arrangements
to implement & manage
the business:

31. THANK YOU