The document discusses water productivity (WP) mapping in agricultural basins. It defines WP as the ratio of agricultural output to water input. The goals of WP assessment are to identify opportunities to improve net gains from water by increasing productivity with the same water or reducing water use with little productivity decrease. WP is mapped at sub-catchment and district levels using crop yields, livestock/fisheries production, and water depletion estimates from remote sensing data. Groundtruthing involves extensive field sampling to verify productivity, land use, cropping patterns, and water sources across the Indo-Gangetic basin.

1. Analysis of Agricultural Water
Productivity ( WP-3)
Bharat Sharma
Basin focal Project on
Indo-Gangetic Basin

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. 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. 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. 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. Basin WP: The Methodology
• Basin WP initial assessment
• Sub-catchment modeling and verification
• Scaling up-down
6

7. District level WP Estimates based on Crop Productivity
Census data and Consumptive Use Estimates
Source: Upali & Sharma, 2008
7

8. Trends in Water Productivity in Rice,
Bangladesh Districts (1968-2004)
KHARIF RABI ALL
8

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. 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. Cropping intensity across Rchna-Doab
250
200
Cropping Intensity
150
100
50
0
11

12. Annual Water Use
Patterns from Major
Sources across
Sub-divisions of
Rechna Doab
12

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. 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. 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. 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. 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. Dataset ..
LULC Map
10km, GIAM, 1999 1km, USGS, 1992-1993 500m, Thenkabail et al, 2005
18

19. Dataset
MODIS 250m 16 day NDVI mega-dataset (2006)
19

20. Dataset .
MODIS 1km 8 day Land surface temperature mega-dataset (2006)
Note: Curve breakdown is due to existence of clouds
20

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

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