Analysis of Agricultural Water
     Productivity ( WP-3)

        Bharat Sharma
     Basin focal Project on
      Indo-Gan...
Water Productivity – The Concept


        Water productivity (WP) is “the physical mass of
        production or the econ...
Why map water productivity ?



The overarching goal of Water Productivity assessment is
to identify opportunities to impr...
Basin WP Mapping – What to Care ?


• Magnitude of agricultural and water
  productivity;
• Spatial variation of WP;
• Sco...
Basin WP: Multi-indicators

• Land productivity
   – Individual crop yield (kg/m2)
   – Standardized gross value productio...
Basin WP: The Methodology


• Basin WP initial assessment

• Sub-catchment modeling and verification

• Scaling up-down


...
District level WP Estimates based on Crop Productivity
           Census data and Consumptive Use Estimates




Source: Up...
Trends in Water Productivity in Rice,
         Bangladesh Districts (1968-2004)


KHARIF                    RABI          ...
Irrigation canal commands in Punjab (Pakistan) and spatial variation
in annual actual evapotranspiration (ETa) in Punjab f...
Sampling variation in productivity
                                                                Average Farm Size in Re...
Cropping intensity across Rchna-Doab



                                    250


                                    200
...
Annual Water Use
Patterns from Major
Sources across
Sub-divisions of
Rechna Doab




                12
Basin WP Initial Assessment
                              Agricultural productivity calculation flow chart

          Cens...
Basin WP Initial Assessment


                Agricultural productivity calculation flow chart




                 Disagg...
Basin WP Initial Assessment
   Water depletion estimate flow
                chart
                                       ...
Sub-catchment Modeling and Links to
                                     Basin WP Assessment

                            ...
Dataset
             Weather data                                  Agricultural data

•   58 weather stations
•   Data per...
Dataset            ..
                   LULC Map
10km, GIAM, 1999    1km, USGS, 1992-1993    500m, Thenkabail et al, 2005...
Dataset
    MODIS 250m 16 day NDVI mega-dataset (2006)




                                                 19
Dataset                     .
MODIS 1km 8 day Land surface temperature mega-dataset (2006)




Note: Curve breakdown is du...
Dataset               ..
Groundtruthing (8th -17th Oct, 2008)

   Rice (cultivated)
                                      ...
22
Upcoming SlideShare
Loading in …5
×

Analysis of agricultural water productivity in the Indo-Ganges Basin

963
-1

Published on

Presented at the Pre-Forum Basin Focal Project meeting, 7-8 November, 2008, Addis Ababa, Ethiopia

Published in: Education, Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
963
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
84
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Analysis of agricultural water productivity in the Indo-Ganges Basin

  1. 1. Analysis of Agricultural Water Productivity ( WP-3) Bharat Sharma Basin focal Project on Indo-Gangetic Basin
  2. 2. Water Productivity – The Concept Water productivity (WP) is “the physical mass of production or the economic value of production measured against gross inflow, net inflow, depleted water, process depleted water, or available water” (Molden, 1997, SWIM 1). It measures how the systems convert water into goods and services. The generic equation is: 3 3 Output derived from water use (kg/m 2 or $/m 2 ) Water Productivity (kg/m or $/m ) = Water input (m 3 /m 2 ) 2
  3. 3. Why map water productivity ? The overarching goal of Water Productivity assessment is to identify opportunities to improve the net gain from water by either: • increasing the productivity (physical/ economic) for the same quantum of water; or • reduce the water input without or with little productivity decrease. 3
  4. 4. Basin WP Mapping – What to Care ? • Magnitude of agricultural and water productivity; • Spatial variation of WP; • Scope for improvement: How much and where; • Irrigated vs. rainfed; • Crop vs. livestock and fisheries. 4
  5. 5. Basin WP: Multi-indicators • Land productivity – Individual crop yield (kg/m2) – Standardized gross value production (SGVP) ($/ha) • Livestock and fisheries – Production ($) • Water use (IWMI water accounting framework) – Available water (m3) – Irrigation diversion (m3) – Potential ET (mm) – Actual ET (mm) • Water productivity – Combination of above productivity (numerator) and water use (denominator) 5
  6. 6. Basin WP: The Methodology • Basin WP initial assessment • Sub-catchment modeling and verification • Scaling up-down 6
  7. 7. District level WP Estimates based on Crop Productivity Census data and Consumptive Use Estimates Source: Upali & Sharma, 2008 7
  8. 8. Trends in Water Productivity in Rice, Bangladesh Districts (1968-2004) KHARIF RABI ALL 8
  9. 9. Irrigation canal commands in Punjab (Pakistan) and spatial variation in annual actual evapotranspiration (ETa) in Punjab for year 2004-05 (using Surface Energy Balance Algorithm for Land, SEBAL) 9
  10. 10. Sampling variation in productivity Average Farm Size in Rechna Doab 14 12 12 10.7 10.4 10 9.3 Farm Size (Ha) 8 6 4 2 0 Upper Middle Low er Overall Rechna Average Farm Area (ha) Land Distribution Pattern in Rechna Doab 50 45 40 Percentage Share 35 30 25 20 15 10 5 0 Landless Less than 1.01 to 2.01 to3 3.01 to5 5.01 to10 10.01 Greater 1 ha 2.0 ha ha ha ha to20 ha than 20 ha Farm Categories Percent Households Percent Share of Land 10
  11. 11. Cropping intensity across Rchna-Doab 250 200 Cropping Intensity 150 100 50 0 11
  12. 12. Annual Water Use Patterns from Major Sources across Sub-divisions of Rechna Doab 12
  13. 13. Basin WP Initial Assessment Agricultural productivity calculation flow chart Census Crop group/ Time series production data LULC map MODIS data Crop productivity Biomass estimate map (district wise) (pixel wise) GT data Yield Census data Disaggregation* Biomass Literature info. Harvest index Livestock Fishery MODIS NPP Production*price Production*price Crop productivity map (kg/m2, pixel wise) Local and international prices Crop standardized gross productivity map value productivity map ($/m2, district average) ($/m2, pixel wise) Agricultural productivity map ($/m2, pixel wise) 13
  14. 14. Basin WP Initial Assessment Agricultural productivity calculation flow chart Disaggregation* from district wise average yield value to pixel wise average yield value The disaggregation procedure takes district wise average yield from census data. Assuming harvest index (HI) does not vary for same crop, the yield of pixel i is calculated as: Biomass of pixel i Yieldpixel i = Average yield of district * Average biomass 14
  15. 15. Basin WP Initial Assessment Water depletion estimate flow chart *SSEB: Simplified Surface Energy Balance Model MODIS Land Surface Points weather data SSEB assumes linear relationship between latent heat flux Temperature data (ET) and land surface temperature (Gabriel et al., 2007). Hot pixels and cold pixels are identified to represent no ET and maximum ET. Points reference ET Kc TH − Tx ET frac = Points potential ET Evaporative TH − TC fraction map (SSEB*) ETact = ET0 ∗ ET frac Actual ET map (mm) ETact – the actual Evapotranspiration, mm. ETfrac – the evaporative fraction, 0-1, unitless. Seasonal time series ET0 – Potential ET, mm. Water depletion Tx – the Land Surface Temperature (LST) of pixel x map (mm) from thermal data. TH/TC – the LST of hottest/coldest pixels. 15
  16. 16. Sub-catchment Modeling and Links to Basin WP Assessment Time series Data input Weather data Landsat data Agro-hydrological Biomass SEBAL SSEB Model (OASIS) modeling Validation Biomass estimate (pixel wise) Validation Water accounting Yield estimate yield Actual ET maps components (kg/m2) Validation Model unit Landsat Basin MODIS Average WP WP map WP map Verifications Water productivity values, variations, scenarios factors and potential assessment 16
  17. 17. Dataset Weather data Agricultural data • 58 weather stations • Data period: 1995-2007 (more to come) • District wise crop area and • Item: daily mean, max, min temperature; mean sea production level pressure; mean humidity; precipitation; mean & max wind speed. • State wise livestock and fishery production • Local and international prices 17
  18. 18. Dataset .. LULC Map 10km, GIAM, 1999 1km, USGS, 1992-1993 500m, Thenkabail et al, 2005 18
  19. 19. Dataset MODIS 250m 16 day NDVI mega-dataset (2006) 19
  20. 20. Dataset . MODIS 1km 8 day Land surface temperature mega-dataset (2006) Note: Curve breakdown is due to existence of clouds 20
  21. 21. Dataset .. Groundtruthing (8th -17th Oct, 2008) Rice (cultivated) Dual irri. Canal system • Across Indus-Gangetic river basin • >2700km covered • 175 samples – LULC – Cropping pattern – Agricultural productivity – Water use (surface/GW) – Social-economic survey 21 Cotton Rice mixed with tree plantation
  22. 22. 22

×