E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium) RS based crop forecasting in Morocco...

E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)

RS based crop forecasting in Morocco

Riad BALAGHINational Institute for Agricultural Research – Morocco

www.inra.org.ma


Moroccan agriculture is strongly dependent on rainfall (Avg. 340mm), as rainfed areas represent 85% of agricultural lands (7.9 millions hectares) ;

Most of lands are arid to semi-arid from which 75% are rangelands, 13% forests and 8% are cultivated ;

Rural population represents 45% of the total population.

Key features of agriculture in Morocco

E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium) 3

Morocco is a semi arid country with limited agricultural areas

Morocco is located in the northwest corner of Africa, bordered by the Mediterranean Sea and the Atlantic Ocean on the north and west, by Algeria on the east, and by Mauritania on the south. Its total land area is 710850 km2 and includes several zones, among which are agricultural plains and river valleys, plateaus, and mountain chains. Most of lands are arid to semi-arid from which 75% are rangelands, 13% forests and 8% are cultivated. Morocco has a Mediterranean climate characterized by a dry and hot summer (4 to 6 months) and a short and cold winter in elevations.

Key features of agriculture in Morocco


0,0

10,0

20,0

30,0

40,0

50,0

60,0

0,0 1,0 2,0 3,0 4,0 5,0 6,0

Co

ef. V

aria

tio

n (%

)

Average yield (Ton/ha)

Syria

Tunisia

Italy

Cyprus

FranceGreece

Spain

Morocco

Algeria

EgyptGermanyUSA

Russian Federation

Instability of crop production resulting from low and fluctuating rainfall and limited irrigation capacities

Increasing risk

Food security in Morocco


0

1

2

3

4

5

6

7

1950 1960 1970 1980 1990 2000 2010

Yie

ld (T

on/h

a)

Egypt Italy Spain Morocco Algeria


Data source : FAOSTAT


0

5

10

15

20

25

1950 1960 1970 1980 1990 2000 2010

Ren

dem

ent (

Q/h

a)

1968

19861988 1991 2006

2

Technological progress for humid seasons

Average yield in dry seasons

Yiel

d in

qui

ntal

s per

hec

tare

Cereals: Technological trend


Data source : DPAE


Wheat yield vs. rainfall

Data source: DMN & DPAE


0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

September October november December January February March April May

R2

Soft wheat Durum wheat Barley

Correlation between rainfall and cereal yields in Morocco at national level (Balaghi & Jlibene, 2009)

Statistical approach using weather predictors


20041989

1990

1991

19921993

1994

1995

1996

1998

1999

2001

2002

2003

1988

2005

2006

2007

2008

Yield = 1.3925 ln(Rainfall) - 6.4243R² = 0.82

0,0

0,5

1,0

1,5

2,0

2,5

3,0

0 100 200 300 400 500 600 700 800

Yiel

d (T

/ha)

Cumulated rainfall from September to March (mm)

19881989

1990

1991

19921993

1994

1995

1996

1998

1999

2001

2002

2003

2004

2005

2006

2007

2008


0,0

0,5

1,0

1,5

2,0

2,5

3,0

0 100 200 300 400 500 600 700 800

Yiel

d (T

/ha)


20041989

1990

1991

1992

1993

1994

1995

1996

1998

2008 2001

2002

2003

1988

2005

2006

2007

1999


0,0

0,5

1,0

1,5

2,0

2,5

3,0

0 100 200 300 400 500 600 700 800

Yiel

d (T

/ha)


Cereals: Yield vs. Cumulated Rainfall

(data from 1988 to 2008)

Balaghi et al. 2010

Data source : DMN

Soft wheat Durum wheat

Barley


1988

1989 1990

1991

19921993

1994

1995

19961998

1999

20012005

20032004

2002

2006

2007

2008

y = 17,465ln(x) - 99,983R² = 0,61

0

5

10

15

20

25

30

0 200 400 600 800 1000 1200 1400 1600

Yiel

d (q

x/ha

)

Cumulated rainfall September to March (mm)

Cereals: Yield vs. Cumulated Rainfall

19881989 1990

1991

19951992

2003

2007

1996

19981999

2001

2002

19942004

2005

2006

1993

2008

y = 11,96ln(x) - 54,507R² = 0,79

0

5

10

15

20

25

30

0 100 200 300 400 500 600 700 800

Yiel

d (q

x/ha

)

Cumulated rainfall Septembre to March (mm)

1988

1989

1990

1991

1999

19941996

20072001

1992

2006

1995

20022003

2004

1993

2005

1998

2008

y = 7,3919ln(x) - 31,25R² = 0,54

0

5

10

15

20

25

30

0 100 200 300 400 500 600 700 800

Yiel

d (q

x/ha

)

Cumulated rainfall September to March (mm)

Balaghi et al. 2010

Data source : DMN


NDVI based yield forecasts

What do we need ?

Good and long time crop statistics ; Good and long time NDVI series (NOAA, SPOT, MODIS,

etc.); Accurate crop mask (GLC2000, CLC, Globcover, etc.) ; Good local expertise ; Good RS expertise (ΣNDVI, Median NDVI, Slope of

NDVI, etc.).


NDVI in Morocco

Cumulated NDVI (February – March)


NDVI for agricultural areas

NDVI in March 2009 Average NDVI in March

Data source : Agri4cast


NDVI vs. Rainfall in Morocco

0

1

2

3

4

5

6

7

0 200 400 600 800 1000 1200 1400

∑N

DVI

févr

ier-

avril

Pluviométrie (mm)

(Jlibene & Balaghi, 2007; not published)

ΣNDVIFeb-Apr is the accumulation of the NDVI values for the period of February until April and ΣRAINSept-May is the sum of the rains over the cropping season (from September until May). Data from 1999 to 2006, for 25 stations.


Balaghi 2010

Data source : VITO

Cereals: Yield vs. Cumulated NDVI


19971992

1999

2003

2005

2007

20081990

19962004

2002

2006

1995

2001

Yield = 6.2292 NDVI - 1.7936R² = 0.80

0,0

0,5

1,0

1,5

2,0

2,5

3,0

0,00 0,10 0,20 0,30 0,40 0,50 0,60 0,70

Yiel

d (T

/ha)

Average dekadal NDVI from February to March

1997

19921999

2004

2005

2007

2001

2008

2003

1996

2002

2006

1995

1990

Yield = 6.0278 NDVI - 1.8086R² = 0.79

0,0

0,5

1,0

1,5

2,0

2,5

3,0

0,00 0,10 0,20 0,30 0,40 0,50 0,60 0,70

Yiel

d (T

/ha)


2005

1992

2002

1996

1999

20072001

1990

20042003

2008

2006

1995

1997

Yield = 4.0206 NDVI - 1.1558R² = 0.60

0,0

0,5

1,0

1,5

2,0

2,5

3,0

0,00 0,10 0,20 0,30 0,40 0,50 0,60 0,70

Yiel

d (T

/ha)


Balaghi et al. 2010

(data from 1988 to 2008)

Data source : VITO

Cereals: Yield vs. Cumulated NDVI

Soft wheat Durum wheat

Barley


0

10

20

30

40

50

60

Fin février

1ère décade mars

2ème décade mars

3ème décade mars

1ère décade

avril

2ème décade

avril

3ème décade

avril

Err

eur

in p

réd

icti

on

%

Balaghi et al. 2010

Cereals: NDVI based forecasting

Data source : VITO

Data from 1999 to 2004


Espèce 1996+PT*

Pluviométrie

NDVI Moyenne

Blé tendre

24.5 21.8 19.9 22.1

Blé dur 22.8 20.3 18.5 20.5

Orge 15.7 15.6 12.8 14.7

Espèce 1996+PT*

Pluviométrie

NDVI Moyenne

Blé tendre

49.0 43.6 39.8 44.1

Blé dur 20.5 18.3 16.7 18.5

Orge 34.5 34.3 28.2 32.3

Total 104.2 96.2 84.6 95.0

Rendements

Productions

Crop forecasting : combination of methods

2008-09 cropping season


CONCULUSION

NDVI based cereal forecasting is relatively easy in Morocco, at national level, because : Morocco is a semi arid country ; Most of the agricultural areas are rainfed ; Cereals are the dominating crops ; Quite good crop statistics for the main crops.

Cereals forecasting could be improved : Through an improved crop mask ?

Cereal forecasting at sub national level needs : The use of cumulated rainfall at explanatory variable ; The use of weather - crop modelling


Many ways to forecast agricultural yields Combination of methods is the appropriate way to

reduce errors Long term good quality crop and weather statistics are

essentials Good local expertise is needed Good agronomic and statistical skills Computer, GIS and remote sensing should be available

CONCULUSION


Thank you

E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium) RS based crop forecasting in Morocco...

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