Remote sensing and census based assessment and scope for improvement of rice and wheat water productivity in the Indo-Gangetic basin
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Remote sensing and census based assessment and scope for improvement of rice and wheat water productivity in the Indo-Gangetic basin - Xueliang Cai and Bharat Sharma, International Water Management Institute (IWMI), Colombo, Sri Lanka
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Remote sensing and census based assessment and scope for improvement of rice and wheat water productivity in the Indo-Gangetic basin
- 1. Remote sensing and census based assessment and scope for improvement of rice and wheat water productivity in the Indo-Gangetic basin Xueliang Cai and Bharat Sharma International Water Management Institute (IWMI) Colombo, Sri Lanka International Forum on Water Resources and Sustainable Development, 22-24 September, 2009, Wuhan, China
- 2. Basin focal projects – a CPWF initiative An interesting journey: First lap Global or local problem? Journey starts Where are we all going? Who’s on the bus? Second lap Where’s the water? Third lap How much do people gain from water? Fourth lap Poverty, impacts? Fifth lap What can change?
- 3. Basin focal project – the structure Knowledge Exchange (WP6) Who needs to know? What information tools? Information exchange process? Data-bases and methods Background Demography Rural poverty Economic overview Agriculture W hat is the overall situation? Water availability (WP2) Climate Water account Water allocation Water hazards W hat is the water balance? Water productivity (WP3) Crop water productivity kg/m 3 Water value-adding $/m 3 Net value / costs How well is water used? Water institutions (WP4) Water rights Water policies Governance Power Who ‘handles’ the water’? Farming institutions (WP4) Land rights Infrastructure Supply chains Who enables farmer to improve WPr? Poverty analysis (WP1) Rural poverty trends Water-food related factors What links water, food and poverty? Interventions (WP5) WEAP Trend analysis Land use change analysis What are foreseeable risks and opportunities for change?
- 4. Niger (KCL) Where the projects are? (UC Davis) (FANRPAN) (IWMI) (IRD) (IRD) (IWMI) (IFPRI) (CSIRO) (IWMI) Source: Basin Focal Projects, CPWF, 2009
- 5. Basin focal project – Indo-Gangetic basin Basin fact sheet: Geographic Area: 2.25 million km 2 Population: 747 million Landscape: mountain to plain Annual precipitation: 100 – 4000 mm Cropland area: 1.14 million km 2 Cropping pattern: rice–wheat Water use by agri.: 91.4% Water sources: ground water and surface water A basin under extreme pressure… Source: Xueliang Cai Photo Credit: Xueliang Cai
- 7. Methodology overview 1. Crop productivity (rice as example) District level yield map of 2005 from census NDVI composition of 29 Aug – 5 Sept 2005 for rice area MODIS 250m NDVI at rice heading stage was used to interpolate yield from district average to pixel wise employing rice yield ~ NDVI linear relationship. Source: IWMI, 2009
- 10. Basin cropping pattern Predominant crops: irrigated rice/rice-wheat rotation The predominant crops are mainly cultivated in a belt along the main streams of Ganges and Indus river. Crop coefficients of the basin as extracted from literature (values) and RS imagery (growth periods) Source: IWMI, 2009
- 11. Rice yield and ETa maps Huge variation in yield, indicating significant scope for improvement ET is high where yield is high. However, ET might also be high where yield is not (so) high. Why? Source: IWMI, 2009 Yield (ton/ha) Pakistan India Nepal Bangladesh Yield 2.6 2.53 3.54 2.75 ET 386 417 499 477 ETa (mm)
- 12. Wheat yield and ETa maps Huge variation in yield, indicating significant scope for improvement Wheat ET variation is more consistent with yield Source: IWMI, 2009 Pakistan India Nepal Bangladesh Yield 2.77 2.20 1.94 2.33 ET 338 291 281 281 Yield (ton/ha) ETa (mm)
- 13. Water productivity maps Rice (kg/m 3 ) Wheat (kg/m 3 ) Note: 1% of the points with extremely low and high values are sieved from the statistics Source: IWMI, 2009 AVG SDV Min Max 0.74 0.33 0.18 1.80 AVG SDV Min Max 0.94 0.43 0.28 2.97
- 14. Water productivity maps Summed economic WP of rice and wheat (USD/m 3 ) The ratio of rice WP to summed WP Source: IWMI, 2009
- 15. Causes for variations MODIS LST 2005 Sept 21 Crop water stress (ETa/ETp) Rice yield TRMM rainfall (2005 Jun 10 – Oct 15) Actual ET (Jun 10 – Oct 15) Source: IWMI, 2009
- 16. Scope for improvement Source: IWMI, 2009
- 19. Thank you Photo Credit: Xueliang Cai www.iwmi.org
Notas del editor
- Only two key steps are mentioned here
- The crop dominance map was determined from three existing LULC maps (Global land cover characteristics data base (GLCCD), Global Irrigated Area Mapping (GIAM), Asia paddy rice map) with MODIS NDVI products and Ground truth inputs. The crop coefficient values were extracted from FAO 56 and Ullah et al (2001) . The crop starting and harvest dates were determined from time series MODIS NDVI images in consultation with literature values.
- While average yield values are low, huge variation exists. For example, the average values for Indian Punjab and Haryana are more than 5 ton/ha, double of average values. Average yield for India and Bangladesh are not high, but ET is considerably higher. Answer in slide
- Summed WP showed different variations in comparison with individual rice or wheat WP maps. For example, the part in Rajasthan and Madhya Pradesh shows higher WP even than Indian Punjab. The ratio of rice WP to summed WP reveals the significance of rice or wheat. As rice generates higher income in some areas, wheat does this in some other areas.
- No direct relationship between climate (rainfall and temperature) and evapotranspiration and rice productivity, implying significant contribution from irrigation. High ET is linked to low yield in some areas, especially downstream of Ganges. Although ETa is close to ETp, but most of the ET must be non-beneficial. This is caused by frequently floods (high rainfall), high ground water table. Better drainage system and crop management is needed for this area.
- WP is more linked to yield rather than ET. While most areas follow the S1 slope, S2 suggest the greatest scope for improvement exists in low yield areas. When yield is being improved, ET will also go high, at a non-linear pace. The well performing areas of Punjab and Haryana have much higher yield, which is accompanied by high ET. But not as extreme as yield. There is an obvious gap of the yields between Punjab&Haryana and other areas, but many other low yield areas also have similarly high ET. The basin ET of rice is much lower than potential ET. The histogram distribution indicates the existing gap and future demand for more beneficial depletion.