Charles Situma ( @yahoo.com) Vincent · PDF file · 2012-01-161948 1962 1969 1979 1989 1999 2009) ) Years ... it is projected that 33 per cent of Kenyans will live in urban areas

Workshop on Crop Yield Forecast: 12-14/10/2011: INRA, Rabat, Morocco

Charles Situma ([email protected])

Vincent Mate Imala ([email protected])

mailto:[email protected]

mailto:[email protected]


DEPARTMENT OF RESOURCE SURVEYS &

REMOTE SENSING

(DRSRS)


Institutional and Human Capacity

Satellite

Antenna

Survey aircraft

Established in 1976

In 1984 upgraded to a full-

fledged Department (DRSRS)

72%

28%Professional/T

echnical Staff

Administratio

n/Support

Staff

102 staff


Fleet of Aircraft

4

DRSRS acquired a new survey

aircraft• Cessna with modern intercom

•Aircraft arrived on 16-7-2011


RC 30 Survey Camera

• Black and white film

• Colour film


Administration

Director

Deputy Director

Technical Services

Accounts Supply Chain

Library Aerial Surveys

Ground Surveys

Remote Sensing

Data Management

Air Services

Assistant Director

Others

DRSRS STRUCTURE


DEPARTMENT OF RESOURCE SURVEYS AND REMOTE

SENSING (DRSRS)

MANDATE

DRSRS is mandated to capture,

store, update, analyze and

disseminate geo-spatial data and

information on earth-based natural

resources/environment to enhance

spatial planning and decision

making for sustainable development

Data collected and generated

form the basis for research,

development of management

plans and formulation of land

use policies

MISSION

“To generate, provide and

promote geo-information

on earth-based resources

in support of planning,

management and

decision-making for

sustainable

development”.

VISIONDRSRS strives to be a national centre of

excellence in geo-information services

on earth-based natural resources for

sustainable development


DRSRS Methods of Data Acquisition

OUTPUTS

Maps

Statistic

Models

Report

Multi-Stage Sampling Concept

Stage 1: Remote Sensing

Approach Orbiting Space Satellite (3,000 - 35,000

km)

Advantages: - Cheap, faster, synoptic,

covers

wide area and easily

comparable

Stage 3: Ground

Surveys/Measurement Attribute identification, scale

accuracy and socio-economic surveys

Cost Implication: Often expensive and time

consuming

Stage 2: Aerial Surveys

Low-High Flight Aircraft Aerial Photography (100-3,000m)

Animal Census (100-200m)

Costs Implication: Dependent on size

of

area, sampling resolution and efforts

Scale

Scale

Satellite Imagery - Land cover

High Level Photography:

Land cover / use

Wildlife Livestock

Conventional

Scientific

Methods

GIS Servers


Overview of Crop Monitoring in Kenya

Started in 1984 following prolonged drought

episode

The impact was

Famine and hunger affected 60% of the

population

Over 17% of livestock were decimated

The cost incurred affected normal

Government development plan


Impacts of Drought

Example: Year 2000 Drought Devastation

The government declared the episode a national disaster

WFP incurred US$ 102 million on food reliefin 2000 -2001

Example: Year 2008/9 Drought Devastation

Government spent in excess of US $. 169 million on relief food to combat the drought emergency


STRATEGY

• Launched a crop monitoring programme

using remote sensing techniques

•The Department of Resource Surveys and

Remote Sensing (DRSRS) was tasked to

undertake this exercise

•DRSRS mandated to provide statistical

estimates on area and yield under crop


Overview of Crop Forecast in Kenya

Maize and wheat are the main staple food in Kenya

accounting for over 80 percent of total cereals used

at a household level

Rice is the third most consumed cereal

Each year the Food Steering Committee (FSC) of the

Office of State, Special Programmes require

information on Area, Yield and Production of these

cereals


Overview of Major Food Crops in Kenya

Cereals: Maize, Wheat, Rice, Sorghum, Millet

Root

Tubers:Irish Pototoes, Sweet Pototoes,

Cassava, etc

Pulses Beans, peas, etc

Nuts Ground nuts, Cashew Nuts, etc

Livestock

ProductsMilk, Meat, etc


Population density/Sq.Km

Low : <50

Moderate: 51 - 100

High: 101 - 500

Very high: >501

0 300 600 Kilometers

N

Legend

Spatial

Population

Distribution

60’s-70s

80’s-90’s90’s – 2000’s

70’s – 80’s


Population growth and Shrinking land base of Kenya


2,60

2,70

2,80

2,90

3,00

3,10

3,20

3,30

3,40

3,50

0

5

10

15

20

25

30

35

40

45

1948 1962 1969 1979 1989 1999 2009

Gro

wth

rate

(%

)

Pop

ula

tion

(M

illi

on

s)

Years

Total Annual Intercensal growth rate (%)

Changes in population demography (1948-2009)


Kenya’s projected rural and urban population, 1950-2050

By 2030, it is projected that 33 per cent of Kenyans will live in urban areas

Population as of 2010


Users of maize and wheat crop data

RCMRD

FEWSNET

Universities/Research

Min of Agriculture

Bureau of Statistics

Min of State -FSC

Aerial

Photos

Satellite

Images

NDVI Crop

Area

Crop

Yield

Crop

Prod

NGOs

Private Firms

Individuals


METHODS

•Crop area stratification

•Estimation of area crop using vertical aerial photography

•Determination of crop yield per hectare

•Computation of crop production

•Computation of consumption


Agriculture expansion between 1990’s and 2000’s

• Population of Kenya in 2009

census = 38,610,097 people

•20 % of Kenya support crop

cultivation significant to the

economy

•Kenya requires approx 31 - 34

million bags of maize and 9 -11

million bags of wheat annually

•Balance in food deficit met by

substantial quantities of rice,

potatoes and pulses produced

locally and also from imports


Satellite Data for Determination of Crop Strata

•16 Landsat satellite

scenes cover agricultural

area of 33 scenes

•Cost: free for Landsat

•Cost: Aster = US D 1,520

•Economical

•Poor accuracy


Satellite Data for Determination of Crop Strata


METHODS: ESTIMATION AREA

Aircraft:

•High winged twin or single engine

(P68 or Cessna )

•Flying height of 488 m (1600 ft)

Camera:

•A 35 mm camera, 20 mm wide-angle

lens

Photographs:

•Vertical

•Scale is approx. fixed at 1:22,000

•Area on ground 46 ha

GPS:

Set to UTM WGS 84 datum or Geographic


5Km

2.5Km

Photograph taken

Sample Dot-Grid for Vertical Photograph

Photos covering entire crop stratum

are: 10,000

Area under crop: 14,500 Sq Km

Cost: US D 72,375

Cost per photo: USD 7.8


# # # # # # # # # #

# # # # # # # # # #

# # # # # # # # # #

# # # # # # # # # #

##########

# # # # # # # # # #

# # # # # # # # # #

# # # # # # # # # #

# # # # # # # # # #

# # # # # # # # # #

# 100 Dot grid

N

100 0 100 200 Meters

Photo Interpretation: Dot-Grid technique


Photo Interpretation: 100 Dot-Grid






0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200 250

Acc

ura

cy (

%)

No. of dots used

Accuracy levels in Photo Interpretation

Source: Sinange, 1996


S.F = Oρ/η

Where:

S.F = Sampling Fraction of the strata

Oρ = Total No. of sample (points) photos observed

with crop

η = Total No. of Points (photos) taken in district

strata

n

Hence: A = S.F ΣC (1/ Oρ *100)

i

Where:

A = Area of survey stratum (ha)

S.F = Sampling Fraction of the strata

Oρ = Total No. of sample photos observed with crop

n = nth photo in the strata

i = ith photo in the strata

Area of Crop


METHODS: DETERMINATION OF CROP YIELD

Tussled maize

Applies Remote Sensing

Techniques a ratio of near-

infrared and red band reflectance

(NIR-VIS)/(NIR+VIS) is a

surrogate for primary production

Tektronix J16 digital radiometer

•NIR -700 - 1300nm

• VIS - 400 - 700nm


Relationship between Yield and ratio of R/IR –Embu District in 1984

y = 0,176x + 1,1745

R² = 0,6874

0

2

4

6

8

10

12

0 10 20 30 40 50 60

Y

i

e

l

d

(

B

a

g

s

/

h

a)

Ratio of Red to Infra-Red


Nakuru (R² = 0.957)

Narok (R² = 0.991)

Nyamira (R² = 0.994)

Mt Elgone (R² = 0.843)

Koibatek (R² = 0.962)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 5 10 15 20 25 30 35 40 45 50

Pro

du

ctio

n (

90

Kg

bag

s)M

illio

ns

Yield by district (bags/ha)

Regression in Maize yield vs Production in selected districts 2004-07


Regression……………………Cont’d


Collateral Information

Crop calendar

LGP

Seasonal progression of rainfall pattern

Agronomic practices

Soil moisture

ETP

Crop growth model

Source: MoA, 2004


Crop calendar

Wheat LR LP-P LP-S P W G TF M H H/CM

Maize

SR M H LP-P LP-S P W G

LR LP-P LP-S P W RW G TF M H H H/CM LP-P

H/CM H/CM

MO

NT

H

Jan

Feb

Mar

Ap

r

May

Ju

n

Ju

l

Au

g

Sep

Oct

Nov

Dec

Abbreviation Activity Abbreviation Activity

LP-P Land preparation - Primary TF Tussling/Flowering

LP-P Land preparation - Secondary M Maturing/Matured

P Planting H Harvesting

W Weeding CM Crop Marketing

RW Re-Weeding SR Short Rain

G Growing LR Long Rain


Crop calendar Cont’d

Lake Region & Southern North Rift/Western Central & Eastern Coast

BOMET BUNGOMA EMBU KILIFI

BONDO KEIYO KIAMBU KWALE

BURET KERICHO KIRINYAGA MALINDI

BUSIA KOIBATEK LAIKIPIA

BUTERE/MUMIAS LUGARI MACHAKOS

GUCHA MARAKWET MAKUENI

HOMA BAY MT ELGON MARAGUA

KAKAMEGA NAKURU MBEERE

KISII CENTRAL NANDI MERU CENTRAL

KISUMU NYANDARUA MERU NORTH

KURIA TRANS NZOIA MERU SOUTH

MIGORI UASIN GISHU MURANGA

NAROK WEST POKOT NYERI

NYAMIRA THARAKA

NYANDO THIKA

RACHUONYO

SIAYA

SUBA

TESO

TRANS MARA

VIHIGA

Long rains maize

planting begin in

Feb - March,

harvesting in

July-Aug

Long rains

maize planting

begin in March -

April, harvesting

in September -

Dec

Long rains

maize planting

begin in

March,

harvesting in

July-Aug

Long rains

maize

planting begin

in March,

harvesting in

June-July


Maize harvesting

Tussled maize

Land preparation

Maize marketing/ storage


-3.000

-2.000

-1.000

0.000

1.000

2.000

3.000

No

v-8

2

No

v-8

3

No

v-8

4

No

v-8

5

No

v-8

6

No

v-8

7

No

v-8

8

No

v-8

9

No

v-9

0

No

v-9

1

No

v-9

2

No

v-9

3

No

v-9

4

No

v-9

5

No

v-9

6

No

v-9

7

No

v-9

8

No

v-9

9

No

v-0

0

ND

VI d

ev

iati

on

fr

om

no

rma

l

time

anomalies

-3

-2

-1

0

1

2

3

4

Jan-8

2

Jan-8

3

Jan-8

4

Jan-8

5

Jan-8

6

Jan-8

7

Jan-8

8

Jan-8

9

Jan-9

0

Jan-9

1

Jan-9

2

Jan-9

3

Jan-9

4

Jan-9

5

Jan-9

6

Jan-9

7

Jan-9

8

Jan-9

9

Jan-0

0

ND

VI

de

via

tio

ns

fro

m n

om

al

Time

Anomalies

Kajiado district, 2008

Isiolo district, 2008

Anomalies

Anomalies


SAMBURU

IS IOLO

LA IKIPIA

2 MAY 00

LA IKIPIA

IS IOLO

SAMBURU

2 MAY 98

LA IKIPIA

IS IOLO

SAMBURU

2 MAY 99

SPOT 4 NDVI images showing rainfall variability within

a period of three years 1998 to 2000


Prediction of Crop

Production

Production = Area * Yield


Maize consumption/Production 1985-2010

Average consumption rate being 98 Kg per

person per year (FEWSNET, 1997)

Source: Situma and Agastiva, 2010)

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,51

98

5

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

20

04

20

05

20

06

20

07

20

08

20

09

20

10

Qu

an

tity

in

MT

Domestic production National consumption


0

1

2

3

4

5

6

7

8

9

2004 2005 2006 2007

Bag

sM

illio

ns

Year

Consumption Production

Wheat

Average consumption rate of wheat being 27 Kg per person per year (FEWSNET, 1997)

Source: Situma et al (DRSRS Technical 2004-2007)


Conclusion

The area and yield under maize and wheat

can reliable be predicted from vertical aerial

photography and radiometers as a rapid

method of crop assessment


Driers

Silos

Storage

P = Y*A

PRODUCTION & STORAGE


END

MERCI

THANK YOU

ASANTE SANA

Documents

Charles Situma ( @yahoo.com) Vincent · PDF file · 2012-01-161948 1962 1969 1979 1989 1999 2009) ) Years ... it is projected that 33 per cent of Kenyans will live in urban areas