10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork

G. GaluGHA/USGS-FEWS NET

KENYA: Pilot - Crop Production

Estimation

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork

Objective

To develop an objective, reliable and timely procedure for estimating :

– Cropped area (CA) with potential for harvest, and

– utilimately maize crop production (CP)

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork Methodology

1. Define a rainfed maize baseline map based on DRSRS/Africover / MoA/ LZ datasets.

2. Validate WRSI performance vs. field observations (geo-referenced photos).

3. Apply the crop mask on the fine-tuned WRSI products

4. Delineate crop areas with potential for harvest based on WRSI values (set criteria??) and compute acreage.

5. Compute statistical estimated yield based on WRSI/EoS and yield from MoA datasets.

6. Compute estimated Crop Production (CP) from Yield (Y) and Acreage (CA) with potential for maize harvest.

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork

Data-sets1. Ministry of Agriculture (MoA) statistics on

cropped area, yield and production at district level (1997 – Present).

2. ICIPE maize density maps derived from DRSRS aerial survey and photo-interpretation (1991-1997).

3. FAO/Africover herbicuous crop maps based on DRSRS and Landsat image classification (2000).

4. Livelihood zones baseline data on maize crop stats at sub-location level (updated 2005)

5. WRSI fine-tuned and validated datasets for Kenya (LR: 1996- 2005)

6. Geo-referenced digital photographs (July-August 2005).

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork

Defining cropped area base-line map

FAO/Africover rainfed herbicuous crop

DRSRS/ Maize Density maps

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork

Intercomparison between DRSRS vs. AfricoverAfricover - rainfed herbicuous cropped areas vs. DRSRS maize density map

Classes to broad

• Generally, 2 maps comparable• Afriocover slightly more extensive

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork

Comparison with LZ data..

DRSRS + Africover/rainfed herbicuous mapsLZ data (mid/2005)

Maize percent(%) coverage at Admin6 (6631 polygons)

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork

Kenya and Tanzania: Crop Assessment Tour

(mid/2005)1. Validate and fine-tune the WRSI model

– Ascertain the SoS and LGP baseline across key agricultural areas

– Determine uni- and bi-modal crop growing areas– Understand maize crop growing conditions and

practices– Delineate bimodal

2. Validate the DRSRS and Africover crop maps

3. Develop a geo-referenced database of digital photographs to support current and future crop assessments

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork

Crop conditions vs. Geo-referenced photos

WRSI- crop performance: 1-10 Aug. 2005WRSI: Average conditions

WRSI: Failure conditions

WRSI: Mediocre conditions

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork

RFE vs. Raingauge

Trans-Nzoia Nakuru

Voi Makindu

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork Crop Acreage determination

Identification of areas

with potential for harvest

WRSI

Africover/herb crop

Applying crop mask

WRSI+Africover

Adding LZ data for crop coverage

Geop

rocess

ing

Sp

ati

al

Join

ing

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork

Merging WRSI and Crop (%) Coverage

+

(WRSI with potentialFor harvest)

(Delieated crop areasFrom Africover)

=

Criteria: 50% < WRSI <= 100% (??)• 0-50% : Assumed Crop Failure• 253%, 254% : Assumed crop failure

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork WRSI + Afcover + Adm6 + LZdata

(Geoprocessing: intersection)

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork

Crop Area Estimation: Africover, LZ data and MoA

LZ estimates vs. MoA crop acreage

r = 0.78

Y = 0.57(x) + 6060

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork

Next Steps: Initial Estimates Yield based on WRSI

(Long-rains 2005)

Selection criteria:

1. Large commercial farms (T/Nzoia, U/Gishu)

2. Medium sized farms (Nakuru)

3. Small farms and mixed farming (Kiambu)

4. Flood prone areas (Nyando)

5. Marginal agricultural areas (T/Taveta, Makueni, Kitui, Mwingi)

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork

LZ Data: Maize YieldData needs to cross-checked for some errors on average yield

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork

Recommendations1. Crop assessment tours necessary in mid-

year; maize crop tussling stage.

– Crop performance assessment (setting criteria to delineate failed crop)

– Fine-tuning WRSI with current maize crop varieties– Monitoring changes on agricultural areas and

updating cultivated maize percentages

2. Re-run of WRSI locally with actual planting dates and improved RFE’s

3. Use of geo-referenced digital photos on USGS/EDC web (Evidence…..Evidence…..Evidence)

10 October 2005

US

GS

/ F

amin

e E

arly

War

ning

Sys

tem

s N

etw

ork

Conclusion:1. Potential for a more objective crop

production estimation with adequate lead time…

2. Procedure easy to replicate in the region, in countries with fine-tuned WRSI model, validated Africover/herb. Crop maps and current livelihood maps.

3. Additional benefits: Improve collaboration with MoA/extension officers.

4. Changes in administrative boundaries will continue to pose serious challenges in this activity.