1 / 18

KENYA: Pilot - Crop Production Estimation

KENYA: Pilot - Crop Production Estimation. G. Galu GHA/USGS-FEWS NET. Objective. To develop an objective, reliable and timely procedure for estimating : Cropped area (CA) with potential for harvest, and utilimately maize crop production (CP). Methodology.

debra-matthews
Download Presentation

KENYA: Pilot - Crop Production Estimation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. KENYA: Pilot - Crop Production Estimation G. Galu GHA/USGS-FEWS NET

  2. Objective To develop an objective, reliable and timely procedure for estimating : Cropped area (CA) with potential for harvest, and utilimately maize crop production (CP)

  3. Methodology Define a rainfed maize baseline map based on DRSRS/Africover / MoA/ LZ datasets. Validate WRSI performance vs. field observations (geo-referenced photos). Apply the crop mask on the fine-tuned WRSI products Delineate crop areas with potential for harvest based on WRSI values (set criteria??) and compute acreage. Compute statistical estimated yield based on WRSI/EoS and yield from MoA datasets. Compute estimated Crop Production (CP) from Yield (Y) and Acreage (CA) with potential for maize harvest.

  4. Data-sets Ministry of Agriculture (MoA) statistics on cropped area, yield and production at district level (1997 – Present). ICIPE maize density maps derived from DRSRS aerial survey and photo-interpretation (1991-1997). FAO/Africover herbicuous crop maps based on DRSRS and Landsat image classification (2000). Livelihood zones baseline data on maize crop stats at sub-location level (updated 2005) WRSI fine-tuned and validated datasets for Kenya (LR: 1996- 2005) Geo-referenced digital photographs (July-August 2005).

  5. DRSRS/ Maize Density maps FAO/Africover rainfed herbicuous crop Defining cropped area base-line map

  6. Africover - rainfed herbicuous cropped areas vs. DRSRS maize density map Intercomparison between DRSRS vs. Africover Classes to broad • Generally, 2 maps comparable • Afriocover slightly more extensive

  7. LZ data (mid/2005) Comparison with LZ data.. DRSRS + Africover/rainfed herbicuous maps Maize percent(%) coverage at Admin6 (6631 polygons)

  8. Kenya and Tanzania: Crop Assessment Tour(mid/2005) 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 Validate the DRSRS and Africover crop maps Develop a geo-referenced database of digital photographs to support current and future crop assessments

  9. WRSI- crop performance: 1-10 Aug. 2005 WRSI: Average conditions Crop conditions vs. Geo-referenced photos WRSI: Failure conditions WRSI: Mediocre conditions

  10. Trans-Nzoia Nakuru RFE vs. Raingauge Voi Makindu

  11. Crop Acreage determination Adding LZ data for crop coverage Identification of areas with potential for harvest WRSI+Africover Geoprocessing WRSI Applying crop mask Spatial Joining Africover/herb crop

  12. Merging WRSI and Crop (%) Coverage (Delieated crop areas From Africover) (WRSI with potential For harvest) = + • Criteria: 50% < WRSI <= 100% (??) • 0-50% : Assumed Crop Failure • 253%, 254% : Assumed crop failure

  13. WRSI + Afcover + Adm6 + LZdata(Geoprocessing: intersection)

  14. Crop Area Estimation: Africover, LZ data and MoA LZ estimates vs. MoA crop acreage r = 0.78 Y = 0.57(x) + 6060

  15. Next Steps: Initial Estimates Yield based on WRSI(Long-rains 2005) Selection criteria: Large commercial farms (T/Nzoia, U/Gishu) Medium sized farms (Nakuru) Small farms and mixed farming (Kiambu) Flood prone areas (Nyando) Marginal agricultural areas (T/Taveta, Makueni, Kitui, Mwingi)

  16. Data needs to cross-checked for some errors on average yield LZ Data: Maize Yield

  17. Recommendations 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 Re-run of WRSI locally with actual planting dates and improved RFE’s Use of geo-referenced digital photos on USGS/EDC web (Evidence…..Evidence…..Evidence)

  18. Conclusion: Potential for a more objective crop production estimation with adequate lead time… Procedure easy to replicate in the region, in countries with fine-tuned WRSI model, validated Africover/herb. Crop maps and current livelihood maps. Additional benefits:Improve collaboration with MoA/extension officers. Changes in administrative boundaries will continue to pose serious challenges in this activity.

More Related