Crop Water Productivity Mapping Using Remote Sensing and Secondary Data in the Nile Basin

Poolad Karimi, David Molden

Dar Es SalaamNile BFP Final Workshop

09 December 2009

Outlines

• Introduction• Methodology• Data sources• Land and Water Productivity (WP)

- Basin scale

- Ethiopian highlands• Conclusions

• Rapid increase in agricultural production will be required to keep pace with future food and fiber demands.

– This can be achieved by bringing more area under agriculture or– by increasing the yields using similar or even reduced water resources (e.g.,

increasing productivity of water).

• Considering that:– Land and water resources are already reached their exploitation limits or are over

exploited in many river basins; and – There is increasing competition for water among sectors.

• The option of increasing agricultural production using same or less water resources is the most appropriate one.

Why WP?

Water productivity mapping:METHODOLOGY

nConsumptio

BenefitWP Economical WP in multiple crops systems (US$/m3)

Source: Karimi, P, IWMI, 2008

• Production data: - Countries statistic departments - FAO database in 2005

• Market prices of agricultural products

• RS images and secondary GIS data

- Waterwatch 2007 ETa and Ta maps (Wim/Yasir)

- Land use/land cover (LULC); GLC 2008/ Africover

- Tropical Rainfall Measuring Mission (TRMM) 2007’s Rainfall map

- Admin and basin boundaries, road network, ecological zones

Data sources

RS: Remote Sensing •GIS: Geographic Information System •Eta: Actual Evapotranspiration •Ta: Actual Transpiration •GLC: Global Land Cover

• Land use and land cover map :

Global Cover Land Cover 2008 by European Space Agency: Its 22 land cover global classes are defined with the UN Land Cover Classification System (LCCS). The product pixel size is 300m. Croplands classes in the map are as fallows:

- Post-flooding or irrigated croplands (or aquatic) - Rainfed croplands - Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) (20-50%) - Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%)

Spatially Aggregated Multipurpose Landcover database (Africover): The map has been formed based on visual interpretation of digitally enhanced LANDSAT TM images acquired mainly in the year 1999. The product pixel size is 1km.

Data sources

• Actual Evapotranpiration, (ETa): Waterwatch created the map using SEBAL model, which uses satellite images together with

DEM and climate data and calculates ETa based on concept of energy balance at the land surface (Bastiaanssen et al,1998). The map is based on Modis images at 8-days intervals and have 1km*1km pixel size.

Data sources

Figure 2. Schematic view of energy balance and ET computations (Waterwatch)

ETa Ta

Source: Maps created by Poolad Karimi using data from Waterwatch

Standardized gross value of production:

SGVP: is an index which helps to compare the economical value of different crops regardless in which country or region they are.

i

icropbaseicrop

cropbase

icropcrops pricenalInternatioproduction

pricelocal

pricelocalSGVP

1

Wheat is taken as base crop.

Basin Scale Analysis

Source: Molden, 2001

Land Productivity:SGVP/ha

Land productivity is highly variable across the basin.

Egypt has the highest SGVP/ha , 1830 US$/ha

Sudan has the lowest SGVP/ha , which goes down to about 20 US$/ha in Northern Darfur

Source: Karimi, P, 2009

Land Productivity

Maize Wheat

Source: IWMI, 2009

Water Productivity

WP=SGVP/ETa

Water productivity variations follows the same pattern as land productivity; highest WP in the Basin belongs to Egypt and the lowest to some Sudanese stats

Source: IWMI, 2009

Using actual transpiration (Ta) as denominator instead of ETa in WP assessment doesn't change the WP variation magnitude in the Basin. This verifies that the differences in WP across the Nile is more related to yield and furthermore to economic land productivity than to non-beneficial evaporation from croplands.

Water Productivity

WP=SGVP/Ta

Source: IWMI, 2009

Rainfed system and Water stress

Source: An Notenbaert

Sou

rce:

Cre

ated

by

IWM

I usi

ng d

ata

from

Wat

erw

atch

Sou

rce:

Cre

ated

by

IWM

I usi

ng d

ata

from

ILR

I

Water productivity and Water stress

Source: An Notenbaert

Source: IWMI

Sou

rce:

Cre

ated

by

IWM

I usi

ng d

ata

from

Wat

erw

atch

Sub-Basin Scale Analysis:Ethiopian highlands

Source: Created by Karimi,P.

• Precipitation:• 1043 mm

• Average ETa:• 730 mm

• Overall Crop GVP:• 4.4×109 $/year

Rainfall and Water Consumption in 2007

Source: Created by IWMI using data from FEWS, 2009 Source: Created by IWMI using data from Waterwatch, 2009

Crops water consumption

ETa Crops: 4500 m3/ha; 1542 m3/ha to 7500 m3/ha

Eta per ha is lower in the eastern part

Source: IWMI, 2009

Gross Value Production

• Crop GVP ranges from 286 $/ha in Zone 2 to 823$/ha in Shaka, where high value crop like coffee and fruits trees are cultivated

• High performing areas in terms of Crop GVP/ha are:

- Shaka, Benchi Maji, Jimma and West Gojam

Source: IWMI, 2009

WP

• Crop WP: 0.16 $/m3; 0.04 to 0.30 $/m3.

– The higher values are mainly due to higher proportion of irrigated lands and cultivation of high value crops

• Agricultural economic water productivity is higher in the eastern part

Source: IWMI, 2009

Conclusions

• Most of the rainfed areas of the basin are located in high water stress zones, with a result in very low yields. This implies how important to use water management techniques is to enhance productivity.

• SGVP and Water productivity are highly variable across the Nile basin

• In upstream countries there is a high scope for improvement of crop production.

• Land productivity gains result in water productivity gains

• Providing more water access (supplemental or full irrigation) will achieve more land and water crop productivity.

Related Project Report Forthcoming.

For more information visit:

www.iwmi.org

www.waterandfood.org

N.B. This is not a form of technical output. Data and figures shown are subject to change.