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Session 2 fertilizer recommendation and fertilizer blending dst

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The Development of the Fertilizer Recommendation (FR) and Fertilizer Blending (FB) Decision
Support Tool – Current progress, including how WS1-3 activities feed into the Decision Support Tool

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Session 2 fertilizer recommendation and fertilizer blending dst

  1. 1. Development of the Site-Specific Fertilizer Recommendation (FR) and Best Fertilizer Blend (FB) Decision Support Tools (DSTs) – V1 www.iita.org | www.cgiar.org | www.acai-project.org
  2. 2. Overview www.iita.org | www.cgiar.org | www.acai-project.org Site-Specific Fertilizer Recommendation and Best Fertilizer Blend DSTs: 1. Background and modelling framework (Guillaume Ezui): • Introduction • Learnings from literature • Learnings from baseline and rapid characterization • Modelling framework: LINTUL and QUEFTS 2. Field activities (Yemi Olojede and Deusdedit Peter Mlay): • Field activities: Nutrient Omission Trials • Field trial results 3. Development of the DST (Meklit Chernet): • Overview of recommendations for Tanzania • The Decision Support Tool • Ongoing validation activities • Next steps and additional data needs
  3. 3. Overview www.iita.org | www.cgiar.org | www.acai-project.org Site-Specific Fertilizer Recommendation and Best Fertilizer Blend DSTs: 1. Background and modelling framework (Guillaume Ezui): • Introduction • Learnings from literature • Learnings from baseline and rapid characterization • Modelling framework: LINTUL and QUEFTS 2. Field activities (Yemi Olojede and Peter Mlay): • Field activities: Nutrient Omission Trials • Field trial results 3. Development of the DST (Meklit Chernet): • Overview of recommendations for Tanzania • The Decision Support Tool • Ongoing validation activities • Next steps and additional data needs
  4. 4. Introduction www.iita.org | www.cgiar.org | www.acai-project.org The Site-Specific Fertilizer Recommendation DST: • Specific purpose: recommend site-specific fertilizer rates that maximize net return on investment • Requested by: SG2000 (NG), Notore (NG), Minjingu (TZ) • Other partners: MEDA (TZ) • Intended users: Extension agents (EAs) supporting commercial cassava growers • Expected benefit: Cassava root yield increased by 8 tonnes/ha, realized by 28,200 HHs, with the support of 215 extension agents, on a total of 14,100 ha, generating a total value of US$2,185,500 • Current version: V1: implemented at 5x5km, for an investment of maximally 200 $ ha-1 (fixed), for a fixed set of fertilizers (urea, Minjingu mazao, MOP) at fixed average regional unit prices, and for a fixed average regional price for cassava produce • Input required: GPS location and planting date (harvest date is fixed at 10 MAP) • Interface: ODK form running on a smartphone or tablet, allowing offline use, and serving as a ‘hybrid’ between research tool and a practicable dissemination tool
  5. 5. Introduction www.iita.org | www.cgiar.org | www.acai-project.org The Best Fertilizer Blend DST: • Specific purpose: identify best-suited fertilizer blends to address nutrient constraints for cassava production in a target area • Requested by: Notore (NG), Minjingu (TZ) • Other partners: - • Intended users: Fertilizer producers engaged in the cassava value chain • Expected benefit: 5000 tonnes of new fertilizer blends sold to commercial cassava growers, with a total value of US$2,500,000 • Current version: V1: implemented at 5x5km, assessing N, P and K requirements for target yield increases by 5, 10, 15, 20 t ha-1 and closing the yield gap across the cassava- growing area in the target countries (selected districts and states) • Input required: Target area (districts or states) and target yield increase • Interface: R-shiny application (web-based) running on a desktop computer
  6. 6. Learnings from the RC and baseline survey www.iita.org | www.cgiar.org | www.acai-project.org Nigeria: use of herbicides very common. Farmers using fertilizer are almost always farmers using herbicide. Tanzania: use of inputs in cassava is very rare, but not so in other crops (e.g. fertilizer in maize, pesticides in cash crops like cashew, cotton,…)
  7. 7. Principles of the Fertilizer Recommendation Tool www.iita.org | www.cgiar.org | www.acai-project.org 1. Determine the attainable yield (water-limited) yield (based on meteo data) - LINTUL 2. Estimate the indigenous nutrient supply of the soil (based on soil data) + add the nutrient supply from fertilizer – QUEFTS(1) 3. Estimate the nutrient uptake – QUEFTS(2) 4. Convert uptake into yield – QUEFTS(3) 5. Optimize nutrient supply based on cost of available fertilizers and RoI 6. Package the recommendations in a smartphone app for field use The FR-DST is developed based on following steps and principles:
  8. 8. Determining water-limited yield www.iita.org | www.cgiar.org | www.acai-project.org Water-limited yield is calculated using the LINTUL modelling framework: LINTUL (Light Interception and Utilization) determines growth and root biomass accumulation and uses following data: • Daily precipitation from CHIRPs – UCSB (ftp://ftp.chg.ucsb.edu/pub/org/chg/products/CHIRPS-2.0/) • Solar data from TRMM – NASA (https://power.larc.nasa.gov/cgi-bin/agro.cgi) • Soil parameters (bd, orgC, FC, WP, WSP, pH,…) from ISRIC (ftp://ftp.soilgrids.org/data/recent/) LINTUL LINTUL has recently been modified & calibrated for cassava. ACAI uses default parametrization based on literature. Crop parameters: e.g., Light Use Efficiency = 1.4 g DM MJ-1 IPAR (Veldkamp, 1985); Light Extinction coefficient; Storage root bulking initiation (40-45 days for TME419); Root growth rate,… Soil parameters: Field capacity, wilting point and saturation based on pedotransfer functions, maximum rooting depth,…
  9. 9. Determining water-limited yield www.iita.org | www.cgiar.org | www.acai-project.org Water-limited yield is calculated using the LINTUL modelling framework: Water-limited yield was calculated for weekly steps in planting date across the planting window: Southern zone Zanzibar Lake zone Eastern zone Jan Feb M ar Apr M ay Jun Jul Aug Sep O ct N ov D ec Jan Feb M ar Apr M ay Jun Jul Aug Sep O ct N ov D ec 0 20 40 60 0 20 40 60 Proportionoffarmers
  10. 10. Determining water-limited yield www.iita.org | www.cgiar.org | www.acai-project.org Water-limited yield is calculated using the LINTUL modelling framework: Selection of Q1 – Q2 – Q3 year precipitation pattern (total rainfall and nr of rainy days), per zone from 22 years of rainfall data:
  11. 11. Spatializing water-limited yield www.iita.org | www.cgiar.org | www.acai-project.org Water-limited yield is calculated using the LINTUL modelling framework: Spatializing: water-limited yield was calculated for each pixel of 5 x 5 km across the AoI, as maximum of 3 modelled years:
  12. 12. Determining indigenous nutrient supply www.iita.org | www.cgiar.org | www.acai-project.org Simple empirical linear equations using soil chemical data: Original equations are not adequate for cassava. Currently based on the work of Howeler (2017), with modifications: 𝐼𝐼 𝐼𝐼𝐼𝐼1 = 188.84 𝑂𝑂𝑂𝑂𝑂𝑂 𝐶𝐶 − 6.2265 𝐼𝐼 𝐼𝐼𝐼𝐼2 = 221.94 𝑂𝑂𝑂𝑂𝑂𝑂 𝐶𝐶 + 4.8519 𝐼𝐼 𝐼𝐼𝐼𝐼1 = 0.3302 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 − 𝐼𝐼 𝑃𝑃 + 8.3511 𝐼𝐼 𝐼𝐼𝐼𝐼2 = 0.6067 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝑃𝑃 + 1.084 𝐼𝐼 𝐼𝐼𝐼𝐼1 = 0.7398 𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝐾𝐾 − 9.9405 𝐼𝐼 𝐼𝐼𝐼𝐼2 = 0.2499 𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝐾𝐾 + 29.051 Using data on soil parameters (bd, orgC, M3-P and M3-K) from ISRIC (ftp://ftp.soilgrids.org/data/recent/), assuming OlsenP [mg P kg-1] = 0.5 * M3-P [mg P kg-1] and exchK [cmolc kg-1] = M3-K [mg K kg-1] / 391 Currently, data from the nutrient omission trials is used to calibrate the indigenous nutrient supply (see next section on field trial activities) Source: Howeler, R., 2017. Chapter 15: Nutrient sources and their application in cassava cultivation. In: Achieving sustainable cultivation of cassava Volume 1: Cultivation techniques. C. Hershey (Ed.). Burleigh Dodds Science Publishing, UK. 424 pp.
  13. 13. Spatializing indigenous nutrient supply www.iita.org | www.cgiar.org | www.acai-project.org Best predictions for indigenous nutrient supply are then applied at scale: Spatializing: INS, IPS and IKS were calculated for each pixel of 5 x 5 km across the AoI:
  14. 14. From uptake to yield (QUEFTS – step 3) www.iita.org | www.cgiar.org | www.acai-project.org Maximum dilution / accumulation curves: convert (N, P, K) uptake to yield Physiological nutrient use efficiency: Rootyield(tDMha-1) N uptake (kg N ha-1) P uptake (kg P ha-1) K uptake (kg K ha-1) 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚 𝑚𝑚𝑚𝑚,𝑁𝑁 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚𝑚𝑚 𝑚𝑚,𝑁𝑁 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚𝑚𝑚𝑚𝑚,𝑃𝑃 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚𝑚𝑚 𝑚𝑚,𝑃𝑃 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚𝑚𝑚𝑚𝑚,𝐾𝐾 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚𝑚𝑚 𝑚𝑚,𝐾𝐾 𝑃𝑃𝑃𝑃𝑃𝑃𝑃 Example: P-deficiency and ample (N, K) supply: maximal dilution of P, and maximal accumulation of (N, K)
  15. 15. From uptake to yield (QUEFTS – step 3) www.iita.org | www.cgiar.org | www.acai-project.org Maximum dilution / accumulation is dependent on harvest index (HI) Physiological nutrient use efficiency (Ezui et al., 2017): 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚𝑚𝑚𝑚𝑚 = 1000 × 𝐻𝐻𝐻𝐻 𝐻𝐻𝐻𝐻 × 𝐶𝐶𝐶𝐶𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟,𝑚𝑚𝑚𝑚 𝑚𝑚 + (1 − 𝐻𝐻𝐻𝐻) × 𝐶𝐶𝐶𝐶𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡,𝑚𝑚𝑚𝑚 𝑚𝑚 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚𝑚𝑚 𝑚𝑚 = 1000 × 𝐻𝐻𝐻𝐻 𝐻𝐻𝐻𝐻 × 𝐶𝐶𝐶𝐶𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟,𝑚𝑚𝑚𝑚𝑚𝑚 + (1 − 𝐻𝐻𝐻𝐻) × 𝐶𝐶𝐶𝐶𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡,𝑚𝑚𝑚𝑚𝑚𝑚 𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝐶𝐶 = 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑜𝑜𝑜𝑜 𝑎𝑎 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝛽𝛽 𝑔𝑔 𝑘𝑘𝑘𝑘−1 , obtained from Nijhof, 1987. Source: Sattari S.Z., van Ittersum M. K., Bouwman A.F., Smit A. L. and Janssen B.H., 2014. Crop yield response to soil fertility and N, P, K inputs in different environments: Testing and improving the QUEFTS model, Field Crops Research, 157: 35-46. Ezui G., Franke A.C., Ahiabor B.D.K., Tetteh F.M., Sogbedji J., Janssen B.H., Mando A. and Giller K.E., 2017. Understanding cassava yield response to soil and fertilizer nutrient supply in West Africa. Plant and Soil https://doi.org/10.1007/s11104-017-3387-6 𝑃𝑃𝑃𝑃𝑃𝑃𝑃,𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘−1 𝐻𝐻𝐻𝐻, 𝑘𝑘𝑘𝑘 𝑘𝑘𝑘𝑘−1 N P K
  16. 16. From uptake to yield (QUEFTS – step 3) www.iita.org | www.cgiar.org | www.acai-project.org Maximum dilution / accumulation is dependent on harvest index (HI) Physiological nutrient use efficiency (Sattari et al., 2014; Byju et al., 2014, Ezui et al., 2017): Source: Byju G., Nedunchezhiyan M., Ravindran C.S., Santhosh Mithra V.S., Ravi V. and Naskar S.K., 2012. Modeling the response of cassava to fertilizers: a site-specific nutrient management approach for greater tuberous root yield. Communications in Soil Science and Plant Analysis, 43: 1149-62. Ezui K.S., Franke A.C., Mando A., Ahiabor B.D.K., Tetteh F.M., Sogbedji J., Janssen B.H. and Giller K.E., 2016. Fertiliser requirements for balanced nutrition of cassava across eight locations in West Africa. Field Crops Research, 185: 69-78. Cultivar HI PhEmin PhEmax R-Phe Source aN aP aK dN dP dK kg N/ton DM kg P/ton DM kg K/ton DM India 0.40 35 250 32 80 750 102 17.4 2.0 14.9 Byju et al., 2012 Gbazekoute (TME419) 0.40 30 175 26 70 465 126 20.0 3.1 13.2 Ezui et al., 2016 0.50 41 232 34 96 589 160 14.6 2.4 10.3 Ezui et al., 2016 0.55 47 262 38 112 653 178 12.6 2.2 9.3 Ezui et al., 2016 Afisiafi 0.65 61 329 47 148 782 214 9.6 1.8 7.7 Ezui et al., 2016 0.70 70 365 53 170 848 233 8.3 1.6 7.0 Ezui et al., 2016 Nutrient uptake requirement to produce 1 ton of storage root dry matter [using balanced nutrient principle] (reciprocal PhE) depends on HI.
  17. 17. From uptake to yield (QUEFTS – step 3) www.iita.org | www.cgiar.org | www.acai-project.org Harvest index (HI) is strongly dependent on environment! Results from year-1 Nutrient Omission Trials: Harvest index little affected by fertilizer treatment, but large variation due to environmental factors. Better understanding needed to improve predictions of physiological nutrient efficiency.
  18. 18. From (N, P, K) to fertilizer recommendations (FR) www.iita.org | www.cgiar.org | www.acai-project.org Solve a set of equations to determine (FR1, FR2, …, FRn): 𝐹𝐹𝐹𝐹𝐹 𝐹𝐹𝐹𝐹𝐹 ⋯ 𝐹𝐹𝐹𝐹𝐹𝐹 × 𝑁𝑁𝑁 𝑃𝑃𝑃 𝐾𝐾𝐾 𝑁𝑁𝑁 𝑃𝑃𝑃 𝐾𝐾𝐾 ⋮ ⋮ ⋮ 𝑁𝑁𝑁𝑁 𝑃𝑃𝑃𝑃 𝐾𝐾𝐾𝐾 = 𝑁𝑁 𝑃𝑃 𝐾𝐾 Fertilizer rates (FR) x nutrient contents must equal recommended (N, P, K) rate 𝐹𝐹𝐹𝐹𝐹 𝐹𝐹𝐹𝐹𝐹 ⋯ 𝐹𝐹𝐹𝐹𝐹𝐹 × 1 0 ⋯ 0 0 1 ⋯ 0 ⋮ ⋮ ⋱ ⋮ 0 0 ⋯ 1 ≥ 0 0 ⋯ 0 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑇𝑇𝑇𝑇 = 𝐹𝐹𝐹𝐹𝐹 𝐹𝐹𝐹𝐹𝐹 ⋯ 𝐹𝐹𝐹𝐹𝐹𝐹 × 𝐶𝐶𝐶 𝐶𝐶𝐶 ⋮ 𝐶𝐶𝐶𝐶 Fertilizer rates (FR) must be equal or larger than zero (boundary condition) Total cost of the fertilizer regime must be minimized: Solved using R package “limSolve”, “lpSolve” or “lpSolveAPI”
  19. 19. 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑇𝑇𝑇𝑇 = 𝐹𝐹𝐹𝐹𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝐹𝐹𝐹𝐹𝑇𝑇𝑇𝑇𝑇𝑇 𝐹𝐹𝐹𝐹𝐷𝐷𝐷𝐷𝐷𝐷 𝐹𝐹𝐹𝐹𝑀𝑀𝑀𝑀𝑀𝑀.𝑚𝑚𝑚𝑚𝑚𝑚 𝐹𝐹𝐹𝐹𝑀𝑀𝑀𝑀𝑀𝑀 𝐹𝐹𝐹𝐹𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 × 0.55 0.82 0.77 0.45 1.09 0.82 From (N, P, K) to fertilizer recommendations (FR) www.iita.org | www.cgiar.org | www.acai-project.org Example (Lake Zone, Tanzania): 𝐹𝐹𝐹𝐹𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝐹𝐹𝐹𝐹𝑇𝑇𝑇𝑇𝑇𝑇 𝐹𝐹𝐹𝐹𝐷𝐷𝐷𝐷𝐷𝐷 𝐹𝐹𝐹𝐹𝑀𝑀𝑀𝑀𝑀𝑀.𝑚𝑚𝑚𝑚𝑚𝑚 𝐹𝐹𝐹𝐹𝑀𝑀𝑀𝑀𝑀𝑀 𝐹𝐹𝐹𝐹𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 × 46 0 0 0 46 0 18 46 0 10 26 0 0 0 60 17 17 17 = 75 46 108 Fertilizer rates (FR) x nutrient contents must equal recommended (N, P, K) rate Total cost of the fertilizer regime must be minimized: Solution: Total cost:124 0 100 0 180 0 341 Minjingu mazao Half NPK rate in NOTs (expressed in N, P2O5, K2O ha-1) Available fertilizers in Lake Zone, Tanzania Fertilizer cost in $ kg-1 What if no DAP is available? Total cost:124 0 0 222 180 0 359 At what price is Min.maz selected over DAP? 0.39 $ ha-1: Total cost:124 0 0 222 180 0 339
  20. 20. Maximizing net revenue www.iita.org | www.cgiar.org | www.acai-project.org Determine the (N,P,K) and FR that maximize net revenue 𝑁𝑁𝑁𝑁 = 𝐺𝐺𝐺𝐺 − 𝑇𝑇𝑇𝑇 𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑁𝑁𝑁𝑁 = 𝑛𝑛𝑛𝑛𝑛𝑛 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 $ ℎ𝑎𝑎−1 , 𝐺𝐺𝐺𝐺 = 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 $ ℎ𝑎𝑎−1 , 𝑇𝑇𝑇𝑇 = 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 $ ℎ𝑎𝑎−1 𝑁𝑁𝑁𝑁 = 𝑇𝑇𝑇𝑇 − 𝐶𝐶𝐶𝐶 ∗ 𝑈𝑈𝑈𝑈 − 𝑇𝑇𝑇𝑇 𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑇𝑇𝑇𝑇 = 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦 𝑡𝑡 ℎ𝑎𝑎−1 , 𝐶𝐶𝐶𝐶 = 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦 𝑡𝑡 ℎ𝑎𝑎−1 , 𝑈𝑈𝑈𝑈 = 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑜𝑜𝑜𝑜 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 $ 𝑡𝑡−1 𝑇𝑇𝑇𝑇 = 𝑓𝑓𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 𝑁𝑁 𝑃𝑃 𝐾𝐾 , 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐, 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠, 𝑊𝑊𝑊𝑊 𝐶𝐶𝑌𝑌 = 𝑓𝑓𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 0 0 0 , 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐, 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠, 𝑊𝑊𝑊𝑊 𝑇𝑇𝑇𝑇 = 𝑓𝑓𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑁𝑁 𝑃𝑃 𝐾𝐾 , 𝑁𝑁𝑁𝑁, 𝐹𝐹𝐹𝐹 𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑓𝑓𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 = 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓, 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑡𝑡𝑡𝑡 𝑎𝑎 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑜𝑜𝑜𝑜 𝑁𝑁, 𝑃𝑃, 𝐾𝐾, 𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝, 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑎𝑎𝑎𝑎𝑎𝑎 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦 (𝑊𝑊𝑊𝑊 𝑡𝑡 ℎ𝑎𝑎−1 ) 𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑓𝑓𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 = 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑡𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑜𝑜𝑜𝑜 𝑎𝑎 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑜𝑜𝑜𝑜 𝑁𝑁, 𝑃𝑃, 𝐾𝐾, 𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑁𝑁𝑁𝑁 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑎𝑎𝑎𝑎𝑎𝑎 𝐹𝐹𝐹𝐹 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑜𝑜𝑜𝑜 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 (𝑠𝑠𝑠𝑠𝑠𝑠 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏) 𝑁𝑁 𝑃𝑃 𝐾𝐾 = 𝑓𝑓𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 … 𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑓𝑓𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 = 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑁𝑁𝑁𝑁, 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑡𝑡𝑡𝑡 𝑎𝑎 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑜𝑜𝑜𝑜 𝑁𝑁, 𝑃𝑃, 𝐾𝐾, 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑜𝑜𝑜𝑜 𝑓𝑓𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 𝑎𝑎𝑎𝑎𝑎𝑎𝑓𝑓𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 Solved using optimizer function (“optim”) in R
  21. 21. Maximizing net revenue www.iita.org | www.cgiar.org | www.acai-project.org Example Solution with max. NR
  22. 22. Maximizing net revenue for a given investment www.iita.org | www.cgiar.org | www.acai-project.org What if the farmer is limited in his investment capacity? 𝑁𝑁 𝑃𝑃 𝐾𝐾 = 𝑓𝑓𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 … , 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑓𝑓𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 = 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑁𝑁𝑁𝑁, 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑡𝑡𝑡𝑡 𝑎𝑎 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑜𝑜𝑜𝑜 𝑁𝑁, 𝑃𝑃, 𝐾𝐾, 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑜𝑜𝑜𝑜 𝑓𝑓𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 𝑎𝑎𝑎𝑎𝑎𝑎𝑓𝑓𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙, 𝒂𝒂𝒂𝒂𝒂𝒂 𝒊𝒊 𝒊𝒊𝒊𝒊𝒊𝒊𝒊𝒊𝒊𝒊 = 𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎 𝒎𝒎 𝒎𝒎𝒎𝒎 𝒗𝒗𝒗𝒗𝒗𝒗𝒗𝒗𝒗𝒗 𝒇𝒇𝒇𝒇𝒇𝒇 𝑻𝑻𝑻𝑻 [$ 𝒉𝒉𝒉𝒉−𝟏𝟏 ]
  23. 23. Maximizing net revenue for a given investment www.iita.org | www.cgiar.org | www.acai-project.org Example Solution with max. NR for TC = 200$ ha-1
  24. 24. Overview www.iita.org | www.cgiar.org | www.acai-project.org Site-Specific Fertilizer Recommendation and Best Fertilizer Blend DSTs: 1. Background and modelling framework (Guillaume Ezui): • Introduction • Learnings from literature • Learnings from baseline and rapid characterization • Modelling framework: LINTUL and QUEFTS 2. Field activities (Yemi Olojede and Peter Mlay): • Field activities: Nutrient Omission Trials • Field trial results 3. Development of the DST (Meklit Chernet): • Overview of recommendations for Tanzania • The Decision Support Tool • Ongoing validation activities • Next steps and additional data needs
  25. 25. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Sampling frame: maximize representativeness across target AoI Aim for an unbiased, representative, sufficiently large and cost-effective sampling frame → GIS-assisted approach, using rainfall, soil and vegetation information (clustering)
  26. 26. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Evaluate responses to N, P and K, and meso- & micro-nutrients: SSR Half NPK NPK½K+NP 1m 2m PK ½N+PK Control NPK+S+ Ca+Mg+ Zn+B NK 1m 2m ½P+NK NPNPK NPK Control NPK+S+ Ca+Mg+ Zn+B NK PK Half NPK NPKNP 1m 1m 2m 2m NOT-1: nutrient omission NOT-2: nutrient omission + fertilizer response
  27. 27. Tanzania NOT 2016 NOT 2017 Zone planted harvested planted Lake 112 73 109 (120) Eastern 80 22 100 Southern 99 20 (74) 51 (80) Total 291 115 (189) 160 (300) Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Current overview of trials and status of trials Nigeria NOT 2016 NOT 2017 Zone planted harvested planted South East 85 56 140/180 South West 58 33 89/120 Total 143 89 227/300
  28. 28. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – Tanzania – some pictures Collaboration Performance Success Challenges Appreciation
  29. 29. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – Tanzania – some pictures Some extreme examples… • LZ: NPK (48 t/ha), half NPK (43 t/ha), NK (20 t/ha), control (9 t/ha) • EZ: NPK (41 t/ha), NPK+micro (38 t/ha) and control (8 t/ha) • SZ: NPK (30 t/ha), control (7 t/ha) CON NPK NK NP
  30. 30. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – Nigeria – some pictures Control NK NPK NPK+
  31. 31. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – Nigeria – some pictures NOT FIELD Marginal blotch in plots with NPK+micro Plant barcoding in progress Challenges… • Logistical due to scale and spread of activities • Interaction with and know-how of EAs • Collaboration and interaction with farmers • Remuneration of activities • Conflicts with cattle • …
  32. 32. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Results Estimate Std. Error Pr(>|t|) Nigeria NPK(ref) 28.762 1.584 *** PK -4.318 1.416 ** NK -2.662 1.242 * NP -2.291 1.526 ns NPK+micro 1.909 1.318 ns half_NPK -3.518 1.286 ** CON -8.208 1.404 *** Tanzania NPK(ref) 11.329 1.122 *** PK 0.483 1.059 ns NK -2.173 1.079 * NP 0.247 1.077 ns NPK+micro -0.511 1.073 ns half_NPK -0.281 1.075 ns CON -2.603 1.068 ** Nigeria: N deficiency > P deficiency; borderline deficiency in K and response to meso-/micronutrients. Tanzania: P deficiency! Note: large number of trials planted in the secondary season (low yields).
  33. 33. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Results Nigeria: N deficiency > P deficiency; borderline deficiency in K and response to meso-/micronutrients. Tanzania: P deficiency! Note: large number of trials planted in the secondary season (low yields). Loglikelihood Ratio test, comparing yield ~ treat + (1|trialID)) yield ~ treat + (treat|trialID)) Pr(>Chisq) = 6.14e-05 *** Large variation in yield and significant differences in yield response to N, P, K between trial locations… → site-specific fertilizer recommendations
  34. 34. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Results Example: Effect of N omission in Nigeria
  35. 35. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Results Example: Effect of P omission in Tanzania
  36. 36. Overview www.iita.org | www.cgiar.org | www.acai-project.org Site-Specific Fertilizer Recommendation and Best Fertilizer Blend DSTs: 1. Background and modelling framework (Guillaume Ezui): • Introduction • Learnings from literature • Learnings from baseline and rapid characterization • Modelling framework: LINTUL and QUEFTS 2. Field activities (Yemi Olojede and Peter Mlay): • Field activities: Nutrient Omission Trials • Field trial results 3. Development of the DST (Meklit Chernet): • Overview of recommendations for Tanzania • The Decision Support Tool • Ongoing validation activities • Next steps and additional data needs
  37. 37. Interpreting the recommendations www.iita.org | www.cgiar.org | www.acai-project.org Yield distribution with and without fertilizer
  38. 38. Interpreting the recommendations www.iita.org | www.cgiar.org | www.acai-project.org How to make this framework available for quick and easy use?
  39. 39. Interpreting the recommendations www.iita.org | www.cgiar.org | www.acai-project.org Net revenue versus cost
  40. 40. Interpreting the recommendations www.iita.org | www.cgiar.org | www.acai-project.org Fertilizer requirements
  41. 41. Packaging in a tool for field use www.iita.org | www.cgiar.org | www.acai-project.org How to make this framework available for quick and easy use? Preloading records Landing page FR-DST packaged as a simple ODK form, with GPS location and planting date as only inputs (for now):
  42. 42. Packaging in a tool for field use www.iita.org | www.cgiar.org | www.acai-project.org How to make this framework available for quick and easy use? Select country User identification Read GPS location Define planting date FR-DST packaged as a simple ODK form, with GPS location and planting date as only inputs (for now):
  43. 43. Packaging in a tool for field use www.iita.org | www.cgiar.org | www.acai-project.org How to make this framework available for quick and easy use? When outside target AoI… Define planting densityDefine variety Define plot size FR-DST packaged as a simple ODK form, with GPS location and planting date as only inputs (for now):
  44. 44. Packaging in a tool for field use www.iita.org | www.cgiar.org | www.acai-project.org How to make this framework available for quick and easy use? Expected yield increaseResults – fertilizer rates Fertilizer quantities for plot Expected net revenue and cost FR-DST packaged as a simple ODK form, with GPS location and planting date as only inputs (for now):
  45. 45. Packaging in a tool for field use www.iita.org | www.cgiar.org | www.acai-project.org How to make this framework available for quick and easy use? Validation trial? End – save and send FR-DST packaged as a simple ODK form, with GPS location and planting date as only inputs (for now):
  46. 46. Next steps www.iita.org | www.cgiar.org | www.acai-project.org 1. Validation exercises (in collaboration with EAs of dev. partners requesting the DST) • Technical evaluation: how accurate are predictions? • Gather feedback: what functionality is needed and how to interface with the end-user? 2. Allow optional input by end-user (else use default values): • Investment capacity • Available fertilizers and prices • Price of output • Date of harvest 3. Limitations for offline storage of recommendations reached. What options? • Within-app calculations • Online calculations (on central server) • SMS-based requests (to central server) and recommendations 4. What about other nutrients? Blanket recommendations at district / state level? 5. Integrate knowledge of temporal-spatial variation in input and output prices 6. Integrate risk estimates based on the impact of uncertainty in: • Input and output prices • Rainfall (attainable yields) 7. Scale down from 5x5km to 250x250m: • How much additional variation can we exploit from the GIS layers? • How do we integrate expert knowledge from the end-user V1 is a ‘hybrid’ between a research tool and the intended ‘app’
  47. 47. Next steps www.iita.org | www.cgiar.org | www.acai-project.org Determining indigenous nutrient supply 5 step-process to recalibrate indigenous nutrient supply using NOT data: 1. Extract best linear predictors for response to N, P and K (mixed models – BLUPs) 2. Calculate required INS, IPS and IKS to obtain the observed responses 3. Build prediction models for INS, IPS and IKS using i. soil chemical analysis data ii. GIS-based predicted soil data 4. Compare and evaluate scale and accuracy issues + design strategies for improvement 5. Cross-validate (n-fold cross validation, evaluating accuracy at field / location / season)
  48. 48. Principles of the Fertilizer Blending Tool www.iita.org | www.cgiar.org | www.acai-project.org How to compose a best fertilizer blend for cassava? For a target area and each nutrient… What is the yield loss due to deficiency in this nutrient? What nutrient rate is required for a given yield response? How do these nutrient rates vary (spatially)? How much area requires a minimal nutrient application? Combining nutrients for a target area? Are nutrients best combined as a single complex blend? Or, are different formulations needed? FB-V1 DST
  49. 49. Principles of the Fertilizer Blending Tool www.iita.org | www.cgiar.org | www.acai-project.org 1. Determine the attainable yield (water-limited) yield (based on meteo data) - LINTUL 2. Estimate the indigenous nutrient supply of the soil (based on soil data) + add the nutrient supply from fertilizer – QUEFTS(1) 3. Estimate the nutrient uptake – QUEFTS(2) 4. Convert uptake into yield – QUEFTS(3) 5. Determine the NPK requirement for a target yield increase (balanced nutrition) 6. Package the output in a webtool as a basis for decision-making by fertilizer blenders The FB-DST is developed based on the same principles:
  50. 50. The Fertilizer Blending Tool www.iita.org | www.cgiar.org | www.acai-project.org The FB-DST is developed as a web application in :
  51. 51. Questions and discussion www.iita.org | www.cgiar.org | www.acai-project.org

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