usgs / famine early warning systems network 10 october 2005 g. galu gha/usgs-fews net kenya: pilot -...
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![Page 1: USGS / Famine Early Warning Systems Network 10 October 2005 G. Galu GHA/USGS-FEWS NET KENYA: Pilot - Crop Production Estimation](https://reader033.vdocuments.mx/reader033/viewer/2022042608/56649c7b5503460f9492ec19/html5/thumbnails/1.jpg)
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G. GaluGHA/USGS-FEWS NET
KENYA: Pilot - Crop Production
Estimation
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Objective
To develop an objective, reliable and timely procedure for estimating :
– Cropped area (CA) with potential for harvest, and
– utilimately maize crop production (CP)
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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.
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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).
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Defining cropped area base-line map
FAO/Africover rainfed herbicuous crop
DRSRS/ Maize Density maps
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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
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Comparison with LZ data..
DRSRS + Africover/rainfed herbicuous mapsLZ data (mid/2005)
Maize percent(%) coverage at Admin6 (6631 polygons)
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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
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Crop conditions vs. Geo-referenced photos
WRSI- crop performance: 1-10 Aug. 2005WRSI: Average conditions
WRSI: Failure conditions
WRSI: Mediocre conditions
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RFE vs. Raingauge
Trans-Nzoia Nakuru
Voi Makindu
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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
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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
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(Geoprocessing: intersection)
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Crop Area Estimation: Africover, LZ data and MoA
LZ estimates vs. MoA crop acreage
r = 0.78
Y = 0.57(x) + 6060
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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)
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LZ Data: Maize YieldData needs to cross-checked for some errors on average yield
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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)
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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.