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Towards increased crop productivity and sustainability of natural resources in Babati, Tanzania

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Presented by Kihara J., Kizito F., Lukuyu B., Jumbo B., Sikumba, G., Lyimo S., Marwa L. and Mateete B. at the Africa RISING ESA Review and Planning Meeting, Arusha, Tanzania, 9-11 September 2014

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Towards increased crop productivity and sustainability of natural resources in Babati, Tanzania

  1. 1. Towards increased crop productivity and sustainability of natural resources in Babati, Tanzania Kihara J., Kizito F., Lukuyu B., Jumbo B., Sikumba, G., Lyimo S., Marwa L., and Mateete B. Africa RISING ESA Review and Planning Meeting, Arusha, Tanzania, 9-11 September 2014
  2. 2. Project Overview Piloting scalable farmer technology initiatives •Increased crop, forages and system productivity; •Sustainable use of the natural resource base: Soils, water, biomass Production constraints Soil limitations Climate variability Low system productivity Maize leaf necrosis AR Interventions: System responses Fertilizer trials Natural resources conservation Forage integration MLND support Scenario assessments Crop yields Soil moisture Organic carbon Sediment levels Runoff levels Farm level economic gains Next steps Scaling of feasible technologies Validation of scenarios Iterative linkage to AR4D Platforms Strategic partnerships Targeting policies for adoption Tools and approaches Farmer assessments Participatory field days System level approaches Soil, Crop & climate models Primary data GIS/Spatial analysis tools Surveys
  3. 3. Production constraints CliSmoialt leim vaitraitaiboinlisty 80 70 60 50 40 30 20 10 0 Daily rainfall (mm) Production constraints Soil limitations Climate variability Low system productivity Variable Low Medium High Organic carbon <0.5 0.5-0.75 >0.75 Available P <30 30-50 >50 Extractable K <200 200-400 >400 pH <5.5 5.5-7.5 >7.5
  4. 4. • Integrated approaches to manage Maize Lethal Necrosis (MLN) disease in Tanzania • MLN is caused by Maize chlorotic mottle virus (MCMoV) in combination with any of the cereal viruses in the family Potyviridae, such as Sugarcane mosaic virus (SCMV), & transmitted by insect vectors Maize leaf necrosis • Losses due to MLN can reach 100% where the disease pressure is high • Plant host resistance combined with good agronomic and cultural practices are the best & sustainable approach to manage MLN Production constraints
  5. 5. Response to P sources and different maize varieties Trt No. Treatment T1 Control T2 Minjingu Mazao T3 Minjingu granular T4 DAP T5 3 tons FYM/ha + Minjingu Mazao T6 6 tons FYM/ha alone Control yield (t/ha) Difference in maize grain yield from the control (t/ha) 8 6 4 2 0 1 2 3 Village Hallu Long Matufa Sabilo Seloto • Positive but highly variable responses in all villages • Farmers appreciated importance of fertilizer use • Change in mindset on fertilizers • Row planting with standard spacing
  6. 6. Need for site specific  recommendations • Some fields are not very responsive to N and P • Nitrogen at 45 and 90 Kg N ha-1 is mainly within “profitable” range. • Significant N responses in Seloto and Sabilo, low in other villages • Need of “simple” tools for site-specific recommendations
  7. 7. Performance of Napier grass accessions
  8. 8. MLN impact on the plant reproductive system • Premature drying of ears, no pollen & poor seed set Response taken by Africa RISING • Support to evaluate several new maize varieties in MLN hotspot areas in Babati, Tanzania. • Over 1000 new hybrids evaluated in farmers fields • Support to conduct trials on application of good agronomic and cultural practices • Time of planting • Timely weeding & fertilizer application • Pest management MLND support MLN Support
  9. 9. Table 1. Preliminary results : new maize hybrids evaluated in 33 Babati trials Name of hybrid GY PH (cm) EH (cm) EP EPP ER GM % NP CKH124960 6.35 197.35 105.09 0.53 1.56 30.50 18.90 11.02 CKH124718 6.05 198.66 107.47 0.53 1.67 15.76 19.45 10.71 CKH124713 6.04 196.92 100.17 0.52 1.30 13.32 21.35 11.14 CKH124783 6.02 206.37 115.82 0.58 1.31 25.03 17.95 10.03 CKH124727 6.02 195.90 95.90 0.51 1.27 7.27 19.10 13.38 CKH124776 5.75 196.51 120.19 0.57 1.34 19.28 20.70 10.62 CKH124742 5.71 187.09 98.09 0.51 1.41 18.05 21.00 10.09 CKH124712 5.70 190.61 96.35 0.51 1.26 29.78 20.05 11.48 CKH124730 5.68 194.67 96.11 0.52 1.26 22.28 18.35 12.15 CKH124743 5.63 185.44 95.54 0.51 1.23 8.85 20.30 13.46 CKH124775 5.60 213.84 110.66 0.54 1.20 20.54 19.20 10.41 CKH124715 5.50 176.08 97.92 0.56 1.25 -0.27 20.40 8.24 CKH124710 5.46 181.65 109.20 0.60 1.20 39.55 19.20 14.54 CKH124781 5.39 195.97 106.69 0.54 1.40 20.00 19.70 12.57 WH403 3.73 182.20 96.46 0.54 1.16 21.67 24.05 6.48 WH505 3.44 187.86 104.02 0.58 1.19 37.96 19.35 12.91 H520 2.75 223.29 126.32 0.59 1.47 44.05 19.20 11.42 Experiment Mean 4.14 191.50 104.77 0.55 1.33 26.96 20.42 10.26 LSD (0.05) 3.13 23.12 17.59 0.09 0.52 30.44 4.46 5.69 MSe 2.44 133.33 77.19 0.00 0.07 231.03 4.95 8.08 CV 37.8 6.03 8.39 8.24 19.52 56.38 10.90 27.70 p 0.61 0.00 0.00 0.27 0.15 0.23 0.41 0.42 p ns ** ** ns ns ns ns ns NumSignificantSites 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 NumReps 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00
  10. 10. Farmer technology assessments  P-sources and varieties  Farmers describe characteristics of varieties/ technologies and did matrix and pairwise ranking  DAP and Minjingu Mazao best P sources  Pioneer 3253 and SC 627 maize varieties scored good to excellent in most criteria SC 627 DK 8031 PIONEER 3253 PAN 4M 19 Total Rank SC 627 SC 627 Pioneer 3253 SC 627 2 2 DK 8031 Pioneer 3253 DK 8031 1 3 PIONEER 3253 Pioneer 3253 3 1 PAN 4M 19 0 4  Desmodium was the preferred legume for intercropping with Napier grass due to high leafiness, drought, pest and disease resistance  Three Napier grass accessions were preferred by farmers: ILRI 16837, KK2 and ILRI 16835 Farmers assessing forages Farmers assessments of P sources and varieties
  11. 11. Farmer Field days  2 major field days on fertilizers and varieties and one on forages:  215 (58) in Sabilo,  161 (49) in Hallu  At least 6 media houses in each  Matufa, Halu and S/Moyo : 77 (29)
  12. 12. Inflows Outflows Off-farm influxes Water and nutrients Off-farm losses Water and nutrients OUT 1:Evapotranspiration Gaseous losses OUT 2: Runoff Human excreta OUT 3: Deep drainage Leaching OUT 4: Off farm harvests Other organic outputs OUT 5: Vapor wind drifts Volatilization OUT 6: Soil evaporation Erosion IN 1: Precipitation Mineral fertilizer IN 2: Dew fall Organic inputs IN 3: Aerial deposition Atmospheric deposition IN 4: Irrigation water Biological N-fixation IN 5: Upstream run on Sedimentation IN 6: Vapor transport Subsoil exploitation Farm level-catchment scale interactions Internal Farm-scale flux flows Water- Nutrient fluxes I In-situ field monitoring (2014) Scenario Tools and approaches Assessments 2014-2015 II Validation, recommendations and scaling III 2015-2016 Farmer assessments Participatory field days System level approaches Soil, Crop & climate models Primary data GIS/Spatial analysis tools Surveys Data from: - Field monitoring - Field surveys - Historical datasets
  13. 13. Scenario assessments Scenario assessments Crop yields Soil moisture Organic carbon Sediment levels Runoff levels Farm level economic gains
  14. 14. Next steps Next steps Scaling of feasible technologies Validation of scenarios Iterative linkage to AR4D Platforms Strategic partnerships Targeting policies for adoption AR Interventions: System responses Fertilizer trials Natural Resources Conservation Forage-crop integration MLND support Strategic Partnerships: TUBOCHA NAFAKA ILSSI MISSION INITIATIVES
  • doringkitomari

    Jan. 26, 2018

Presented by Kihara J., Kizito F., Lukuyu B., Jumbo B., Sikumba, G., Lyimo S., Marwa L. and Mateete B. at the Africa RISING ESA Review and Planning Meeting, Arusha, Tanzania, 9-11 September 2014

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