1. USGS / Famine Early Warning Systems Network 10 October 2005 G. Galu GHA/USGS-FEWS NET KENYA: Pilot - Crop Production Estimation

2. USGS / Famine Early Warning Systems Network 10 October 2005 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. USGS / Famine Early Warning Systems Network 10 October 2005 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.

4. USGS / Famine Early Warning Systems Network 10 October 2005 Data-sets 1.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).

5. USGS / Famine Early Warning Systems Network 10 October 2005 Defining cropped area base-line map FAO/Africover rainfed herbicuous crop DRSRS/ Maize Density maps

6. USGS / Famine Early Warning Systems Network 10 October 2005 Intercomparison between DRSRS vs. Africover Africover - rainfed herbicuous cropped areas vs. DRSRS maize density map Classes to broad Generally, 2 maps comparable Afriocover slightly more extensive

7. USGS / Famine Early Warning Systems Network 10 October 2005 Comparison with LZ data.. DRSRS + Africover/rainfed herbicuous maps LZ data (mid/2005) Maize percent(%) coverage at Admin6 (6631 polygons)

8. USGS / Famine Early Warning Systems Network 10 October 2005 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

9. USGS / Famine Early Warning Systems Network 10 October 2005 Crop conditions vs. Geo-referenced photos WRSI- crop performance: 1-10 Aug. 2005 WRSI: Average conditions WRSI: Failure conditions WRSI: Mediocre conditions

10. USGS / Famine Early Warning Systems Network 10 October 2005 RFE vs. Raingauge Trans-NzoiaNakuru VoiMakindu

11. USGS / Famine Early Warning Systems Network 10 October 2005 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 Geoprocessing Spatial Joining

12. USGS / Famine Early Warning Systems Network 10 October 2005 Merging WRSI and Crop (%) Coverage + (WRSI with potential For harvest) (Delieated crop areas From Africover) = Criteria: 50% < WRSI <= 100% (??) 0-50% : Assumed Crop Failure 253%, 254% : Assumed crop failure

13. USGS / Famine Early Warning Systems Network 10 October 2005 WRSI + Afcover + Adm6 + LZdata (Geoprocessing: intersection)

14. USGS / Famine Early Warning Systems Network 10 October 2005 Crop Area Estimation: Africover, LZ data and MoA LZ estimates vs. MoA crop acreage r = 0.78 Y = 0.57(x) + 6060

15. USGS / Famine Early Warning Systems Network 10 October 2005 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)

16. USGS / Famine Early Warning Systems Network 10 October 2005 LZ Data: Maize Yield Data needs to cross-checked for some errors on average yield

17. USGS / Famine Early Warning Systems Network 10 October 2005 Recommendations 1.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)

18. USGS / Famine Early Warning Systems Network 10 October 2005 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.