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Session 2 1 Development of the Site Specific Fertilizer Recommendation (FR) and Best Fertilizer Blend (FB) Decision Support Tool

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This presentation highlighted the process of developing and progress made in the development of the FR and FB DST.
The site-specific fertilizer recommendation (FR) tool is built to provide an optimized and profitable site-specific fertilizer recommendations for cassava growers. The tool considers the location, soil fertility, weather condition, available fertilizers in the area, prices for fertilizer and cassava root, planned planting and harvest dates and the investment capacity of the farmers.
The nutrient omission trials (NOT) in Nigeria and Tanzania conducted by ACAI, in collaboration with the national research and development partners, show a large variation in nutrient responses indicating the need for site-specific fertilizer recommendation. ACAI is developing a crosscutting system using machine learning techniques coupled with process based crop models, LINTUL and QUEFTS, and economic optimizer algorithms to provide the site-specific recommendations. ACAI is transforming available big data like GIS layers from SoilGrids and weather data from CHIRPS and NASA to useful information that can be used to model the relationship between apparent soil nutrient supply and soil properties. Effort has also been made to identify a generic soil fertility indicator that can be easily obtained from farmers and is useful covariate to improve the accuracy of apparent soil nutrient supply predictions.
The next steps in the FR tool development include, validating the FR tool both functionally, checking if the recommendations outperform the current practices in the field and architecturally, checking user friendliness and if the tool satisfies the needs of development partners to dissemination strategy.

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Session 2 1 Development of the Site Specific Fertilizer Recommendation (FR) and Best Fertilizer Blend (FB) Decision Support Tool

  1. 1. Development of the Site-Specific Fertilizer Recommendation (FR) and Best Fertilizer Blend (FB) Decision Support Tools (DSTs) – Version2 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. Introduction (Guillaume Ezui): • The FR and FB use case • Learnings from the baseline • Summary of year 2 achievements 2. Field activities (Yemi Olojede and Deusdedit Peter Mlay): • Field activities: Nutrient Omission Trials • Field trial results 3. Advances with the DST development (Meklit Chernet): • Modelling framework • Year1 – Year2 validation results • The Decision Support Tool 4. Validation exercises (Gbenga Ojo and Stephen Magige): • First impressions from ongoing validation exercises • 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. Introduction (Guillaume Ezui): • The FR and FB use case • Learnings from the baseline • Summary of year 2 achievements 2. Field activities (Yemi Olojede and Deusdedit Peter Mlay): • Field activities: Nutrient Omission Trials • Field trial results 3. Advances with the DST development (Meklit Chernet): • Modelling framework • Year1 – Year2 validation results • The Decision Support Tool 4. Validation exercises (Gbenga Ojo and Stephen Magige): • First impressions from ongoing validation exercises • 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: V2: implemented at 5x5km, for an investment of maximally 200 $ ha-1 (fixed), for a fixed set of fertilizers (urea, Minjingu Nafaka / TSP, 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 baseline survey www.iita.org | www.cgiar.org | www.acai-project.org Baseline study covered over 3,200 cassava fields: Fertilizer use is low, but not zero (except in EZ-Tanzania) Most common fertilizers include: SE-Nigeria: urea, NPK15:15:15, NPK20:10:10 SW-Nigeria: urea, NPK15:15:15 EZ-Tanzania: ? (insufficient observations) LZ-Tanzania: urea, DAP, NPK17:17:17 SZ-Tanzania: urea, DAP, NPK17:17:17, MOP
  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 tool for field use The FR-DST is developed based on following steps and principles:
  8. 8. Approach: coupled LINTUL-QUEFTS + net revenue optimizer Random Forest QUEFTSLINTUL Economic Optimizer GPS location Planting date Harvest date GPS location Available fertilizers Fertilizer price Crop produce price Max. investment Water-limited yield Nutrient-limited yield Yield response to (N, P, K) Fertilizer (N, P, K) rates maximizing net revenue with total cost ≤ max. investment N P K Geospatial weather data Geospatial soil data Geospatial price data Farmer knowledge www.iita.org | www.cgiar.org | www.acai-project.org
  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: 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,…
  10. 10. QUEFTS: Quantitative Evaluation of Fertility of Tropical Soils Processes of • Soil nutrient supply ~ Soil properties • Crop nutrient uptake ~ Soil nutrient supply • Crop yield ~ Crop nutrient uptake Processes governing nutrient response www.iita.org | www.cgiar.org | www.acai-project.org INS = f(total_N, pH) IPS = f(Olsen_P, pH) IKS = f(exch_K, pH) Yield Supply (INS, IPS or IKS +Fertilizer) Fertilizer Uptake
  11. 11. 𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑇𝐶 = 𝐹𝑅 𝑈𝑟𝑒𝑎 𝐹𝑅 𝑇𝑆𝑃 𝐹𝑅 𝐷𝐴𝑃 𝐹𝑅 𝑁𝑎𝑓𝑎𝑘𝑎 𝐹𝑅 𝑀𝑂𝑃 𝐹𝑅 𝑁𝑃𝐾171717 × 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): 𝐹𝑅 𝑈𝑟𝑒𝑎 𝐹𝑅 𝑇𝑆𝑃 𝐹𝑅 𝐷𝐴𝑃 𝐹𝑅 𝑁𝑎𝑓𝑎𝑘𝑎 𝐹𝑅 𝑀𝑂𝑃 𝐹𝑅 𝑁𝑃𝐾171717 × 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 Nafaka 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.Naf selected over DAP? 0.39 $ kg-1: Total cost:124 0 0 222 180 0 339
  12. 12. V1 version of the FR DST (end of 2017) www.iita.org | www.cgiar.org | www.acai-project.org Landing page V1 version of the FR-DST (smartphone app packaged as an ODK form): GPS location and planting date Fertilizer quantities for plot and expected yield and revenue increase
  13. 13. Overview www.iita.org | www.cgiar.org | www.acai-project.org Site-Specific Fertilizer Recommendation and Best Fertilizer Blend DSTs: 1. Introduction (Guillaume Ezui): • The FR and FB use case • Learnings from the baseline • Summary of year 2 achievements 2. Field activities (Yemi Olojede and Deusdedit Peter Mlay): • Field activities: Nutrient Omission Trials • Field trial results 3. Advances with the DST development (Meklit Chernet): • Modelling framework • Year1 – Year2 validation results • The Decision Support Tool 4. Validation exercises (Gbenga Ojo and Stephen Magige): • First impressions from ongoing validation exercises • Next steps and additional data needs
  14. 14. 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)
  15. 15. 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
  16. 16. Tanzania NOT 2016 NOT 2017 NOT 2018 Zone planted harvested planted harvested planted Lake 112 73 109 65 0 Eastern 80 22 50 42 17 Southern 99 72 99 59 75 Total 291 167 235 166 92 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 NOT 2018 Zone planted harvested planted harvested planted South East 85 56 140 123 9 South West 58 33 90 60 6 Total 143 89 230 183 15
  17. 17. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org NPK (28 t/ha) NP (25 t/ha) control (16 t/ha)
  18. 18. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – Tanzania Observations • NPK, NK, PK and NP gave good root yield, higher than the control. • It is economical if fertilizer companies would blend fertilizers with moderate NPK, PK, NP and NK. “Farmers are willing to invest in fertilizer for cassava production due to high return of cassava roots they had realized.”
  19. 19. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – Tanzania – some pictures NPK Plot Control Plot Livestock grazing Unattended trial
  20. 20. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – SE Nigeria – some pictures
  21. 21. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – SE Nigeria – some pictures NOT3 Field at Afii, Cross River State in 2018 @ 4 WAP with the EA and Farmer NOT3 Field at Anaku, Anambra State and Edo South in 2018 @ 6 WAP
  22. 22. Nutrient omission trials Impressions and learnings from the field – SE Nigeria – some pictures Control NPK www.iita.org | www.cgiar.org | www.acai-project.org
  23. 23. Nutrient omission trials Impressions and learnings from the field – SE Nigeria – Out scalling Farmers’ Field Day, Bekwara, Cross River State OKOMAYA ACAI Farmers Cooperative, Ikom, Cross River State. www.iita.org | www.cgiar.org | www.acai-project.org
  24. 24. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – SW - Nigeria – some pictures A full NPK plot yield A control plot yield “Farmers have seen the necessity of fertilizer application on cassava. Some of them stated that they observed greener leaves and bigger stems in fertilized compared control plots. Some farmers are now applying fertilizers as against their initial positions before ACAI intervention, but fertilizer availability and cost are their major concerns.”
  25. 25. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Results 2016-2017 and 2017-2018 trials Higher yields in 2018 vs 2017; Consistent effects across both years, but more pronounced in 2018: Nigeria: deficiency in N (-3.9 t/ha) > K (-3.3 t/ha) > P (-2.1 t/ha) Tanzania: deficiency in N (-2.4 t/ha) > K (-1.2 t/ha); no overall deficiency in P As in 2017: strong evidence for site-specificity in nutrient deficiencies!
  26. 26. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Results 2016-2017 and 2017-2018 trials: N deficiency Nigeria Comparable effects observed in 2018 as in 2017, with same levels of site-specificity. Tanzania: No response observed in Tanzania in 2017, but variable responses observed in 2018. Plotting response to N (YNPK – YPK) versus yield without N (YPK) → Clear variation in response
  27. 27. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Results 2016-2017 and 2017-2018 trials: P deficiency Nigeria Comparable (small) effects observed in 2018 as in 2017, but with higher levels of site-specificity. Tanzania: No overall P effects observed in Tanzania in either year, but with low levels of site- variability in both years. Plotting response to P (YNPK – YNK) versus yield without P (YNK) → Clear variation in response
  28. 28. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Results 2016-2017 and 2017-2018 trials: K deficiency Nigeria More pronounced K responses observed in 2018 as in 2017, with higher levels of site- specificity. Tanzania: Highly site-specific K effects observed at yield levels > 15 t/ha in 2018, while very limited effects in 2017. Plotting response to K (YNPK – YNP) versus yield without K (YNP) → Clear variation in response
  29. 29. Nutrient omission trials www.iita.org | www.cgiar.org | www.acai-project.org Results 2016-2017 and 2017-2018 trials: response to Ca, Mg, S, B, Zn Nigeria Very limited responses observed in 2017 at higher yield levels, but effects do not repeat in 2018. Tanzania: No response to meso- and micronutrients observed in either year. Plotting response to meso- and microelements (YNPK+micro – YNPK) versus yield with NPK only (YNPK) → Very little effect on yield
  30. 30. Overview www.iita.org | www.cgiar.org | www.acai-project.org Site-Specific Fertilizer Recommendation and Best Fertilizer Blend DSTs: 1. Introduction (Guillaume Ezui): • The FR and FB use case • Learnings from the baseline • Summary of year 2 achievements 2. Field activities (Yemi Olojede and Deusdedit Peter Mlay): • Field activities: Nutrient Omission Trials • Field trial results 3. Advances with the DST development (Meklit Chernet): • Modelling framework • Year1 – Year2 validation results • The Decision Support Tool 4. Validation exercises (Gbenga Ojo and Stephen Magige): • First impressions from ongoing validation exercises • Next steps and additional data needs
  31. 31. Predicting nutrient use efficiency Processes of • Soil nutrient supply ~ Soil properties • Crop nutrient uptake ~ Soil nutrient supply • Crop yield ~ Crop nutrient uptake Processes governing nutrient response 1 1 Use NOT response data to calculate and validate apparent nutrient supply… www.iita.org | www.cgiar.org | www.acai-project.org
  32. 32. Predicting indigenous nutrient supply www.iita.org | www.cgiar.org | www.acai-project.org Use QUEFTS to estimate soil indigenous nutrient supply using root yield obtained from the NPK, PK, NK and NP treatments in the Nutrient Omission Trials 1. Yield in NPK treatment is used as WLY 2. Available nutrient for cassava = (fertilizer used * recovery fraction) + soil available nutrients soil available nutrients = (soil_N, soil_P, soil_K) 𝑁𝑃𝑦𝑖𝑒𝑙𝑑 = 𝑓 𝑵 𝒔𝒐𝒊𝒍 + (𝑁𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟∗ 𝑁 𝑅𝐹) , 𝑷 𝒔𝒐𝒊𝒍 + (𝑃𝑓𝑒𝑡𝑖𝑙𝑖𝑧𝑒𝑟 ∗ 𝑃𝑅𝐹) , 𝑊𝐿𝑌 𝑁𝐾𝑦𝑖𝑒𝑙𝑑 = 𝑓 𝑵 𝒔𝒐𝒊𝒍 + (𝑁𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟∗ 𝑁 𝑅𝐹) , 𝑲 𝒔𝒐𝒊𝒍 + (𝐾𝑓𝑒𝑡𝑖𝑙𝑖𝑧𝑒𝑟 ∗ 𝐾 𝑅𝐹) , 𝑊𝐿𝑌 𝑃𝐾𝑦𝑖𝑒𝑙𝑑 = 𝑓 𝑷 𝒔𝒐𝒊𝒍 + (𝑃𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟∗ 𝑃) , 𝑲 𝒔𝒐𝒊𝒍 + (𝐾𝑓𝑒𝑡𝑖𝑙𝑖𝑧𝑒𝑟 ∗ 𝐾 𝑅𝐹) , 𝑊𝐿𝑌 1. An optimization algorithm with residual sum of squares as cost function search values for (soil_N, soil_P, soil_K) that will provide an estimated root yield for the NK, NP and PK treatments 4. Validate by using the estimated soil_N, soil_P and soil_K and estimate root yield in the control and treatment with half NPK rate
  33. 33. Predicting water-limited yield WLY >> observed yield Drought-affected trials Modelledwater-limitedyield[tha-1] Observed yield (without nutrient limitation) [t ha-1] Modelled water-limited yields (LINTUL) versus observed yield in NPK treatment www.iita.org | www.cgiar.org | www.acai-project.org
  34. 34. Predicting nutrient use efficiency Predictedrootyield[tha-1] Observed root yield [t ha-1] Observed root yield [t ha-1] Control treatment ½(NPK) treatment Very adequate predictions of yield responses based on supply → uptake → yield www.iita.org | www.cgiar.org | www.acai-project.org
  35. 35. Predicting indigenous nutrient supply Processes of • Soil nutrient supply ~ Soil properties • Crop nutrient uptake ~ Soil nutrient supply • Crop yield ~ Crop nutrient uptake Processes governing nutrient response 2 2 Relate apparent nutrient supply to available soil data… www.iita.org | www.cgiar.org | www.acai-project.org
  36. 36. From soil properties to indigenous nutrient supply… Using SoilGrids… www.iita.org | www.cgiar.org | www.acai-project.org
  37. 37. What proportion can we expect to predict from available soil data? Decomposing variances in fertilizer response Mixed modelling approach also provides insights in the proportion of total variance in response at various scales: Between clusters (5-50km) Residual Between regions (>50km) Between fields (<5km) Based on model lmer(Y ~ 0 + treat + (treat | region) + (treat | cluster) + (treat | fieldID) SoilGrids data shows variation between regions, but likely captures less of the field-level variation… pH CEC 31% 12% 33% 24% www.iita.org | www.cgiar.org | www.acai-project.org
  38. 38. Predicting indigenous nutrient supply R2 Most important predictors N 0.30 %Clay, pH, SOC, FC, CEC [multiple depths – 5, 15, 30cm] P 0.51 CEC, %Clay, pH, Olsen-P, wp [multiple depths – 5, 15, 30cm] K 0.27 CEC, Clay, exchK, pH, FC, wp, pH [multiple depths – 5, 15, 30, 60cm] (INS, IPS, IKS) ~ (bd, FC, WP, SWS, %clay, %silt, %sand, pH, SOC, totalN, OlsenP, M3-P, CEC, M3-K, Ca, Mg, Na, Al, B, exch.Ac, EC, root.depth) @ 7 depths Soil nutrient supply www.iita.org | www.cgiar.org | www.acai-project.org
  39. 39. Predicting indigenous nutrient supply Observed apparent P supply [kg P ha-1] RFpredictedPsupply[kgPha-1] P supply www.iita.org | www.cgiar.org | www.acai-project.org
  40. 40. Predicting indigenous nutrient supply Cross-validation: how well do these predictions hold for subsets of the data? Distribution of RMSE using MCMC subsampling for various proportions of data used for validation RMSE RMSE = 40 kg N ha-1 RMSE = 25 kg P ha-1 RMSE = 50 kg K ha-1 www.iita.org | www.cgiar.org | www.acai-project.org
  41. 41. Predicting nutrient use efficiency Cross-validation: how well do these predictions hold for subsets of the data? Comparison of training model with Tanzania data versus Tanzania + Nigeria data P supply - RMSE P supply - estimates www.iita.org | www.cgiar.org | www.acai-project.org
  42. 42. Building in local scale soil fertility indicators www.iita.org | www.cgiar.org | www.acai-project.org Local soil type Position in the landscape, slope, soil colour, local soil name, soil depth, drainage,… Cropping history Fallow type, previous crop, nr of years of continuous cropping, cropping system,… Past soil fertility management Past fertilizer use, manure inputs, Perception of soil fertility, distance to the homestead,… Drivers of local soil fertility gradients
  43. 43. Building in local scale soil fertility indicators www.iita.org | www.cgiar.org | www.acai-project.org Local soil type Position in the landscape, slope, soil colour, local soil name, soil depth, drainage,… Cropping history Fallow type, previous crop, nr of years of continuous cropping, cropping system,… Past soil fertility management Past fertilizer use, manure inputs, Perception of soil fertility, distance to the homestead,… SE-NG SW-NG LZ-TZ EZ-TZ SZ-TZ ZZ-TZ TLU 1.8 2.2 6.8 0.2 0.8 1.1 Often very context-specific…
  44. 44. Building in local scale soil fertility indicators Current yield is often the single best indicator for expected response…I II III IV V Define yield categories and predict these from context-specific variables (based on on-farm surveys) www.iita.org | www.cgiar.org | www.acai-project.org
  45. 45. Predicting indigenous nutrient supply R2 – SoilGrids N 0.30 P 0.51 K 0.27 (INS, IPS, IKS) ~ (bd, FC, WP, SWS, %clay, %silt, %sand, pH, SOC, totalN, OlsenP, M3-P, CEC, M3-K, Ca, Mg, Na, Al, B, exch.Ac, EC, root.depth) @ 7 depths www.iita.org | www.cgiar.org | www.acai-project.org
  46. 46. Predicting indigenous nutrient supply R2 – SoilGrids R2 – SoilGrids + CY categories N 0.30 0.63 P 0.51 0.59 K 0.27 0.40 (INS, IPS, IKS) ~ (bd, FC, WP, SWS, %clay, %silt, %sand, pH, SOC, totalN, OlsenP, M3-P, CEC, M3-K, Ca, Mg, Na, Al, B, exch.Ac, EC, root.depth) @ 7 depths + current yield categories + www.iita.org | www.cgiar.org | www.acai-project.org
  47. 47. Predicting indigenous nutrient supply SoilGrids SoilGrids + CY categories RFpredictedNsupply[kgNha-1] Observed apparent N supply [kg N ha-1] www.iita.org | www.cgiar.org | www.acai-project.org
  48. 48. Predicting indigenous nutrient supply Observed apparent P supply [kg P ha-1] RFpredictedPsupply[kgPha-1] SoilGrids SoilGrids + CY categories www.iita.org | www.cgiar.org | www.acai-project.org
  49. 49. Optimizing for net revenue What rates of available fertilizers with a total cost ceiling of 200$ ha-1? Use an optimizer to maximize net revenue given price of roots and cost of available fertilizers: 26%: recommend = don’t apply Expected median response: 14 t/ha [7 – 16 t/ha] Expected net returns: 480 $/ha [230 – 570 $/ha] Total cost: 185 $/ha [117 – 200 $/ha] www.iita.org | www.cgiar.org | www.acai-project.org
  50. 50. Optimizing for net revenue www.iita.org | www.cgiar.org | www.acai-project.org 26%: recommend = don’t apply Expected median response: 14 t/ha [7 – 16 t/ha] Expected net returns: 480 $/ha [230 – 570 $/ha] Total cost: 185 $/ha [117 – 200 $/ha] What rates of available fertilizers with a total cost ceiling of 200$ ha-1? Use an optimizer to maximize net revenue given price of roots and cost of available fertilizers:
  51. 51. Blanket recommendations remain useful… Site-specific recommendations be aggregated / simplified… 120N 10P 30K 90N 7.5P 0K 0N 0P 0K www.iita.org | www.cgiar.org | www.acai-project.org
  52. 52. Optimizing for net revenue – what if farmers enter prices? No convergence Price data from ~2000 phone interviews Panel of commercial cassava growers – interviewed at 2 times during the year in 2017… Extremely messy data, with no relationship to type of market, volume of sale, or location (within region) Some patterns in price evolution throughout the year, confirmed by key informants… www.iita.org | www.cgiar.org | www.acai-project.org
  53. 53. Optimizing for net revenue www.iita.org | www.cgiar.org | www.acai-project.org
  54. 54. Two important aspects to validation: Validating decision support tools 1. Functional validation: verify and improve whether the recommendations supplied by the tool outperform current practice, or current best (blanket) recommendations 2. Architectural validation: verify and improve the user experience (format or “look and feel”), so that the tool fits the needs of the end-user and is easy to use. Good = Current best practice www.iita.org | www.cgiar.org | www.acai-project.org
  55. 55. Validation exercises – pilot study www.iita.org | www.cgiar.org | www.acai-project.org • Currently 581 farmers across Tanzania and Nigeria involved in pilot validation exercise… • Supervised by trained extension agents, and coordinated by primary development partners (SG2000 and Notore in Nigeria, and MEDA in Tanzania) • NARS teams of agronomists assist in training and monitoring. • DSTs and all data collection through a suite of ODK forms
  56. 56. Validation exercises – overview www.iita.org | www.cgiar.org | www.acai-project.org Overview of participants (Nigeria) 692 submissions on “Site-specific fertilizer recommendations” 513 validation exercises established, across 7 states (Oyo, Ogun, Osun, Edo, Benue, Cross River, Anambra)
  57. 57. Validation exercises – overview www.iita.org | www.cgiar.org | www.acai-project.org Nr participants = 692 Nr of cases where tool recommends “Do not apply fertilizer.”: 174 (25%) Average yield response: 7.5 tonnes/ha Range: [1 – 17 tonnes/ha] Expected yield response (Nigeria) 1:1 line: do not apply fertilizer
  58. 58. Validation exercises – overview www.iita.org | www.cgiar.org | www.acai-project.org Nr participants = 692 Nr of cases where tool recommends “Do not apply fertilizer.”: 174 (25%) Average yield response: 7.5 tonnes/ha Range: [1 – 17 tonnes/ha] Average net revenue: 242 USD/ha Range [8 – 604 USD/ha] Average cost: 117 USD/ha Range: [27 – 200 USD/ha] Expected net returns (Nigeria)
  59. 59. Validation exercises – overview www.iita.org | www.cgiar.org | www.acai-project.org Response is mainly to N (urea) and K (MOP), with relatively similar rates recommended. Recommended fertilizer rates (Nigeria)
  60. 60. Validation exercises – overview www.iita.org | www.cgiar.org | www.acai-project.org Nr participants = 68 / 360 (planted / target) Nr of cases where tool recommends “Do not apply fertilizer.”: 0 (0%) Overview of participants (Tanzania)
  61. 61. Overview www.iita.org | www.cgiar.org | www.acai-project.org Site-Specific Fertilizer Recommendation and Best Fertilizer Blend DSTs: 1. Introduction (Guillaume Ezui): • The FR and FB use case • Learnings from the baseline • Summary of year 2 achievements 2. Field activities (Yemi Olojede and Deusdedit Peter Mlay): • Field activities: Nutrient Omission Trials • Field trial results 3. Advances with the DST development (Meklit Chernet): • Modelling framework • Year1 – Year2 validation results • The Decision Support Tool 4. Validation exercises (Gbenga Ojo and Stephen Magige): • First impressions from ongoing validation exercises • Next steps and additional data needs
  62. 62. Validation Exercises - Key Activities www.iita.org | www.cgiar.org | www.acai-project.org Training-of-trainers The training-of-trainer event aimed at empowering the lead field coordinators of MEDA to use the Decision Support Tool (DST) of the FR use case, and implement the FR validation exercises, as well as organizing the cassava growers and EAs around the cassava growers for optimal productivity.
  63. 63. Validation Exercises - Key Activities www.iita.org | www.cgiar.org | www.acai-project.org Step-down training Stepdown training was done in the field, hands-on with the extension agents of the development partner (MEDA), and volunteer farmers / cassava growers, together with the research partners (TARI, backstopped by IITA). The step-down training aimed at empowering EA on how to use the Decision Support Tool (DST) for the FR use case, also organizing the cassava growers and EAs around the cassava growers for optimal cassava production.
  64. 64. Validation Exercises - Key Activities www.iita.org | www.cgiar.org | www.acai-project.org Trial establishment • Preparation of all 360 field locations completed. • 68 / 360 planted. Indications of variation in soil fertility… • Challenges include drought and technical issues with registering farmers on the ODK platform.
  65. 65. Validation Exercises - Key Activities www.iita.org | www.cgiar.org | www.acai-project.org Trial establishment Colour-coded plots to make distinguishing plots (treatments) easy.
  66. 66. Validation Exercises - Key Activities www.iita.org | www.cgiar.org | www.acai-project.org Trial establishment • Preparation of all 360 field locations completed. • 68 / 360 planted. Indications of variation in soil fertility… • Challenges include drought and technical issues with registering farmers on the ODK platform.
  67. 67. Testimonies from farmers www.iita.org | www.cgiar.org | www.acai-project.org Simon Abang – SG2000 CR I have learnt the following: • Fertilizer application in terms of type and rate • Planting on ridges as against the traditional planting on heaps • Use of TME of 419 (he has been hearing about it but he has never seen or used it) Everybody that passes by the red plot stops to admire beauty of cassava plants on the plot. People disturbs me everyday to know the secret behind the performance of the yellow plot. Animals are penetrating the recommended plot more than the control plot because cassava roots have started coming out of the land due to fertilizer application.
  68. 68. Testimonies from Extension Agents www.iita.org | www.cgiar.org | www.acai-project.org I have learnt about the following • Application of fertilizer in cassava (I formerly knew of application of fertilizer on yam) • Spacing of 0.8m x 1m against the traditional spacing of 1m x 1m • Use of ODK for data collection Recommendation plot looks more appealing than control plot because of fertilizer application. Oyama Okora – SG2000 CR
  69. 69. Testimonies from Extension Agents www.iita.org | www.cgiar.org | www.acai-project.org • I have learnt about use of MOP as new fertilizer for cassava (I formerly knew only of NPK fertilizers) • I have learnt specific fertilizer rate for cassava as against the traditional blanket rate. • I have learnt that weeding can be done more than two times in a planting season. Abraham Fagbohun – SG2000 OG
  70. 70. Follow-up Activities www.iita.org | www.cgiar.org | www.acai-project.org Cluster meetings by NOTORE • Objectives: • Refresher training with VPs and Farmers on GAP of Cassava • Knowledge transfer & opinion sharing • Group learning/idea sharing sessions • At least twice a month in this year 2018 across the active communities in the 5 states. • VPs and Farmers (occasionally)
  71. 71. Learning from development partners www.iita.org | www.cgiar.org | www.acai-project.org • Farmers already seeing significant differences between recommendations from the tool and their common practice, and are eager to see the outcome of the validation trials at harvest. • The majority of the SVPs quickly adapted to the use of DSTs and had little or no difficulty using the application. • There are some complaints about timing, frequency of data capturing and monitoring The validation exercises serve us… • as a platform to reach out to farmers and promote our business • The Project has a positive impact on our as this is inline with the company objectives on specialty blends and this project has create a platform to reach out to farmers. • Integration of the DST into our extension program on farmer education for year 2019 and Involvement in the dissemination of the DST to end-users. We request provision of training and dissemination materials especially videos to support our Video Viewing activities. Feedback from farmers and Extension Agents
  72. 72. Thank you very much !!! Questions and discussion www.iita.org | www.cgiar.org | www.acai-project.org

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