Charles Situma (charles.situma@yahoo.com) Vincent Mate Imala - - PowerPoint PPT Presentation

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Charles Situma (charles.situma@yahoo.com) Vincent Mate Imala - - PowerPoint PPT Presentation

Charles Situma (charles.situma@yahoo.com) Vincent Mate Imala (vineima@yahoo.com) Workshop on Crop Yield Forecast: 12-14/10/2011: INRA, Rabat, Morocco DEPARTMENT OF RESOURCE SURVEYS & REMOTE SENSING (DRSRS) Workshop on Crop Yield


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

Charles Situma (charles.situma@yahoo.com) Vincent Mate Imala (vineima@yahoo.com)

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

DEPARTMENT OF RESOURCE SURVEYS & REMOTE SENSING (DRSRS)

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

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

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Fleet of Aircraft

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DRSRS acquired a new survey aircraft

  • Cessna with modern intercom
  • Aircraft arrived on 16-7-2011
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Workshop on Crop Yield Forecast: 12-14/10/2011: INRA, Rabat, Morocco

RC 30 Survey Camera

  • Black and white film
  • Colour film
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Workshop on Crop Yield Forecast: 12-14/10/2011: INRA, Rabat, Morocco

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

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

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

  • f

management plans and formulation of land use policies

MISSION

“To generate, provide and promote geo-information

  • n earth-based resources

in support of planning, management and decision-making for sustainable development”.

VISION DRSRS strives to be a national centre of excellence in geo-information services

  • n earth-based natural resources for

sustainable development

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

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

  • f

area, sampling resolution and efforts

Scale Scale

Satellite Imagery - Land cover High Level Photography: Land cover / use Wildlife Livestock

Conventional Scientific Methods

GIS Servers

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

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

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

Impacts of Drought

Example: Year 2000 Drought Devastation

 The government declared the episode a

national disaster

 WFP incurred US$ 102 million on food relief

in 2000 -2001 Example: Year 2008/9 Drought Devastation

 Government spent in excess of US $. 169

million on relief food to combat the drought emergency

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

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

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

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

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

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 Products

Milk, Meat, etc

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

Population density/Sq.Km Low : <50 Moderate: 51 - 100 High: 101 - 500 Very high: >501

300 600 Kilometers N

Legend

Spatial Population Distribution

60’s-70s 80’s-90’s 90’s – 2000’s 70’s – 80’s

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Population growth and Shrinking land base of Kenya

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2,60 2,70 2,80 2,90 3,00 3,10 3,20 3,30 3,40 3,50 5 10 15 20 25 30 35 40 45

1948 1962 1969 1979 1989 1999 2009 Growth rate (%) Population (Millions)

Years

Total Annual Intercensal growth rate (%)

Changes in population demography (1948-2009)

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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

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

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

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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
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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

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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
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Satellite Data for Determination of Crop Strata

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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

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

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

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# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #

100 Dot grid

N

100 100 200 Meters

Photo Interpretation: Dot-Grid technique

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Photo Interpretation: 100 Dot-Grid

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Photo Interpretation: 150 Dot-Grid

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Photo Interpretation: 200 Dot-Grid

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10 20 30 40 50 60 70 80 90 100 50 100 150 200 250 Accuracy (%)

  • No. of dots used

Accuracy levels in Photo Interpretation

Source: Sinange, 1996

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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

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

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

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

y = 0,176x + 1,1745 R² = 0,6874 2 4 6 8 10 12 10 20 30 40 50 60 Y i e l d ( B a g s / h a ) Ratio of Red to Infra-Red

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Nakuru (R² = 0.957) Narok (R² = 0.991) Nyamira (R² = 0.994) Mt Elgone (R² = 0.843) Koibatek (R² = 0.962) 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 5 10 15 20 25 30 35 40 45 50 Production (90 Kg bags) Millions Yield by district (bags/ha)

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

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Regression……………………Cont’d

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Collateral Information

Crop calendar LGP Seasonal progression of rainfall pattern Agronomic practices Soil moisture ETP Crop growth model

Source: MoA, 2004

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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 NTH Jan Feb Mar Apr May Jun Jul Aug 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

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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

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Maize harvesting Tussled maize Land preparation Maize marketing/ storage

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  • 3.000
  • 2.000
  • 1.000
0.000 1.000 2.000 3.000 Nov-82 Nov-83 Nov-84 Nov-85 Nov-86 Nov-87 Nov-88 Nov-89 Nov-90 Nov-91 Nov-92 Nov-93 Nov-94 Nov-95 Nov-96 Nov-97 Nov-98 Nov-99 Nov-00 NDVI deviation from normal time anomalies
  • 3
  • 2
  • 1

1 2 3 4

Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan-90 Jan-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00

NDVI deviations from nomal Time

Anomalies

Kajiado district, 2008 Isiolo district, 2008 Anomalies Anomalies

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SAMBURU ISIOLO LA IKIPIA

2 MAY 00

LAIKIPIA ISIOLO SAMBURU

2 MAY 98

LA IKIPIA ISIOLO SAMBURU

2 MAY 99

SPOT 4 NDVI images showing rainfall variability within a period of three years 1998 to 2000

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Prediction of Crop Production

Production = Area * Yield

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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 3,5 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 2004 2005 2006 2007 2008 2009 2010

Quantity in MT

Domestic production National consumption

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1 2 3 4 5 6 7 8 9 2004 2005 2006 2007 Bags Millions 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)

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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

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Driers Silos Storage

P = Y*A

PRODUCTION & STORAGE

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END

MERCI THANK YOU ASANTE SANA