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Targeting Improved Cropping Systems in Poverty-Prone Coastal Zones of South Asia

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By Parvesh Kr Chandna, Andy Nelson, Zahirul Khan, Moqbul Hossain, Sohel Rana, Fazlur Rashid, M. Mondal, T.P. Tuong

Revitalizing the Ganges Coastal Zone Conference
21-23 October 2014, Dhaka, Bangladesh
http://waterandfood.org/ganges-conference/

Published in: Government & Nonprofit
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Targeting Improved Cropping Systems in Poverty-Prone Coastal Zones of South Asia

  1. 1. Targeting Improved Cropping Systems in Poverty-Prone Coastal Zones of South Asia IRRI CPWF-G1 Team : Parvesh Kr Chandna, Andy Nelson, Zahirul Khan, Moqbul Hossain, Sohel Rana, Fazlur Rashid , M Mondal, T.P Toung
  2. 2. Drought - Boro Soil Salinity Water Salinity Water logging Mul$ple  Stresses   Flash  Floods  +  Drought   Soil/Water  salinity  +  drought   Salinity  +  drought  +  Zn     Flash  Floods  +  Stagnant  Flood       Status of Zn in Soil Bhola Barisal Patuakhali Bhola Barisal Bhola Barisal Bhola Barisal May,  2012   KHULNA   BARISAL  
  3. 3. How to target more efficiently for accelerated dissemination of technologies ? ? ? ??? Traditional approach may not work ?? Traditional approach is okay for regional level planning but how to target technologies at local level
  4. 4. Technology/System  Based  Approach     Extrapolation Domains
  5. 5. Study Area: experimental sites Study Area Polder 3,- High saline zone Polder 30,- Medium saline zone Polder 43/2F - Low saline zone
  6. 6. “What Where and When?” “Data Data Data” and more Data Extrapolation domains are about
  7. 7. Objective: To develop and refine extrapolation domain methods and maps to target improved cropping systems for increased productivity in coastal zones of Bangladesh
  8. 8. v Study Area v Material & methods v Results v Conclusion & Recommendations & v WebGIS, data sharing IRRI
  9. 9. Existing system for validation (1) Aman – Rabi crop (2) Aus - Aman (3) Aman - Shrimp (4) Year round aquaculture Innovative systems for targetting (1) Aman (HYV) - Rabi (HVC) (2) Aus – Aman - boro (3) Aus - Aman - Rabi crop (4) Aman (HYV)-Boro(HYV) (5) Aus (HYV) – Aman (HYV) (6) Year round polyculture (7) Shrimp - Rice Proposed number of domains to map Material and methods
  10. 10. Aman - boro Water quality and availability in dry season Description of Land use type(technology) Fresh (< 4dS/m), ground water availability and pumping depth Month when river water still remain fresh (<4 dS/m) Internal storage capacity in relation to land area (ML/ha) Proximity to river, canal, ponds (m) Difference (m) in high water level in Mar and land surface for gravity irrigation (m) boro rice is seeded around 15 Nov. (MS) to 15 Dec (LS). Aman rice is rain fed. boro rice is irrigated with river water (when fresh) or with water stored in canal networks. Yes, < 6 m S1 March S1 > 5 S1 <50 S1 > 1 S1 Yes, 7 -20 m S2 Feb S2 2.5 - 5 S2 50-100 S2 0.5 - 1 S2 yes, > 20 S3 Jan S3 1 - 2.5 S3 100-300 S3 0.2 - 0.5 S3 No SN Dec SN <1 SN >300 SN < 0.2 SN Aman - boro Water quality and availability in wet season Description of Land use type(technology) Maximum inundation depth (m) in August (one week) Maximum inundation depth (m) for less than 2 weeks in Sep/Oct Difference (m) in land surface and low water level in Sep/Oct for drainage Aman HYV rice is transplanted in July- August, to be harvested by the end of November (Moderate Salinity zone) or December (low saline zone). < 0.1 S1 < 0.2 S1 > 1 S1 0.1 – 0.2 S2 0.2 – 0.5 S2 0.5 - 1 S2 0.2 – 0.3 S3 0.5 – 0.8 S3 0.2 – 0.5 S3 > 0.3 SN > 0.8 SN < 0.2 SN S1 = Most Suitable S2 = Suitable S3 = Marginally Suitable SN = Not Suitable Water requirements for Aman - boro
  11. 11. Fresh groundwater (< 4dS/m) Tubewell? Tubewell depth (m) Month when river water (< 3 dS/m) Storage capacity (ML/ ha) Proximity to fresh SW source (m) Suitability for HYV boro rice crop Yes No Shallow Deep < 6 7 - 20 Mar Feb Jan Dec > 20 2.5 - 5 1- 2.5 2.5 - 5 1 – 2.5 < 100 > 100 < 100 > 100 < 100 > 100 < 100 > 100 S1 S1 S2 S3 S1 S1 S2 S2 S3 S2 S3 S3 SN SN Groundwater Surface water
  12. 12. Irriga$on   with  GW    1      Fresh  (<  4dS/m)  ground  water  availability      2      Ground  water  pumping  depth   Irriga$on   with  SW    3      Latest  month  when  river  water  <3  dS/m    4      Maximum  river  water  salinity  in  April/May    5      Maximum  river  water  salinity  in  August    6      Difference  in  high  water  level  in  April  and  land  surface  for  gravity  irrigaKon    7      Difference  in  high  water  level  in  March  and  land  surface  for  gravity  irrigaKon    8      Difference  (m)  in  land  surface  and  high  water  level    in  Mar/Apr  for  irrigaKon    9      Storage  capacity    10  Proximity  to  river,  canal,  ponds,  for  irrigaKon   Drainage    11  Proximity  to  river,  canal,  for  drainage    12  Maximum  inundaKon  depth/land  type    13  Maximum  inundaKon  depth  for  >  three  days  in  May    14  Maximum  inundaKon  depth  in  September/October    15  Maximum  inundaKon  depth  for  >  one  week  in  September/October    16  Maximum  inundaKon  depth  for  >  two  weeks  in  September/October    17  Difference  in  land  surface  and  low  water  level  in  May  for  drainage    18  Difference    in  land  surface  and  low  water  level  in  September/October  for  drainage   Soil    19  Soil  texture    20  Soil  pH    21  Soil  salinity   gher  water    22  lowest  water  salinity  during  January  and  highest  during  April  (ppt)      23  InundaKon  depth/land  type   Climate    24  Weekly  air  minimum  temperature  at  stocking    25  Weekly  mean  minimum  air  temperature  in  January      26  Two-­‐week  mean  air  temperature  in  December  and  January    27  CumulaKve  rainfall  in  July  &  August   Social  and   economic    28  Livelihood/asset  index    29  Technology  adopKon  index  
  13. 13. Soil pHSoil texture Min temp – 8th-14th Feb Soil salinity Water salinity Example:  Input  datasets   Flood inundation depth IRRI
  14. 14. Percentage of small farms, <1ha Percentage of leased land Through our partners we have obtained the 2011 population and 2008 agricultural census’ at village level. This is a huge and still untapped resource. Example:  Input  dataset   IRRI Values in percent
  15. 15. Results IRRI
  16. 16. Results Improved systems… Not Suitable Marginal Suitable Marginally suitable = 330,000 ha Suitable area = 180,000 ha IRRI 300-500 M USD/Yr additional income by introduction of drought tolerant cultivar in Boro season or rabi crop GIS Lab, SSD, IRRI- Parvesh Kr Chandna@2014 – Unpublished
  17. 17. Results Improved systems… Extrapolation Domains : Aus (HYV)-Aman (HYV)-Rabi cropping system Not Suitable Marginal Suitable Marginally suitable = 500,000 ha Suitable area = 16,000 ha IRRI GIS Lab, SSD, IRRI- Parvesh Kr Chandna@2014 – Unpublished
  18. 18. Extrapolation Domains at Polder Level Aman (HYV) Conflict area : rice and shrimp farmersAman (HYV)–Boro(AYV) Year Round AquacultureShrimp -AmanAus (HYV) Polder 44
  19. 19. Livelihood  Index       Z score values GIS Lab, SSD, IRRI- Parvesh Kr Chandna@2014 – Unpublished Approximate 2149 villages are categorized under low or very low livelihood levels IRRI
  20. 20. Improved Targeting – Extrapolation domains for Aman (HYV)-Boro(HYV) Preference zoning for woman led targeting Preference zones GIS Lab, SSD, IRRI- Parvesh Kr Chandna@2014 – Unpublished
  21. 21. Recommendations v Extrapolation domains of different cropping systems facilitate improved & accelerated targeting which can lead to higher cropping intensity and productivity and income There is a need of paradigm shift from traditional to system/technology based approach for targeting technologies
  22. 22. WebGIS – http://gangesriverbasin.blogspot.com
  23. 23. Remote  Sensing/GIS  based  methods  developed  in  the  project  has  impact   across  the  region,  organizaKons  and  projects    -­‐        Suitable  for  South  and  South-­‐east  Asia    -­‐        STRASA,  CSISA  and  many  other  mega  projects    -­‐        High  demand  of  products    from  NaKonal  and  InternaKonal  Partners     Scope  and  Poten$al  Impact   Project  personnel  and  their  ins$tu$ons     IRRI(Lead  Centre)  -­‐  Andy  Nelson  ,  Parvesh  Kr  Chandna  and    TP  Toung     Na$onal  Partners   Soil  Research  Development  InsKtute  (SRDI),  Bangladesh    -­‐  Moqbul  Hossain   InsKtute  of  Water  Modeling  (IWM),  Bangladesh  -­‐  Zahirul  Khan   Bangladesh  Water  Development  Board  (BWDB),  Bangladesh  -­‐  Fazlur  Rahid     Local  Government  Engineering  Department  (LGED),  Bangladesh  -­‐  Sohel  Rana   IRRI
  24. 24. Thank you IWM LGED IRRI SRDI BWDB

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