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Session 2 scheduled planting and high starch dst

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The Development of the Scheduled Planting (SP) and High Starch Content (HS) Decision Support
Tool – Current progress, including how WS1-3 activities feed into the Decision Support Tool

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Session 2 scheduled planting and high starch dst

  1. 1. Development of the Scheduled Planting (SP) and High Starch Content (HS) 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 Scheduled Planting and High Starch Content DSTs: 1. Background and modelling framework (Pieter Pypers): • Introduction • Learnings from literature • Learnings from baseline and rapid characterization • Modelling framework: LINTUL & QUEFTS (temporarily) 2. Field activities (Rebecca Enesi & Bernadetha Kimati): • Field activities: Scheduled Planting Trials • Field trial results 3. Development of the DST (Jeremiah Kabissa): • Overview of recommendations • The Decision Support Tool • Next steps and additional data needs
  3. 3. Overview www.iita.org | www.cgiar.org | www.acai-project.org Scheduled Planting and High Starch Content DSTs: 1. Background and modelling framework (Pieter Pypers): • Introduction • Learnings from literature • Learnings from baseline and rapid characterization • Modelling framework: LINTUL & QUEFTS (temporarily) 2. Field activities (Rebecca Enesi & Bernadetha Kimati): • Field activities: Scheduled Planting Trials • Field trial results 3. Development of the DST (Jeremiah Kabissa): • Overview of recommendations • The Decision Support Tool • Next steps and additional data needs
  4. 4. Introduction www.iita.org | www.cgiar.org | www.acai-project.org The Scheduled Planting DST: • Specific purpose: recommend time of planting and harvest to optimize root supply (and revenue) to cassava processors • Requested by: CAVA-II (TZ) • Other partners: Psaltry (NG) • Intended users: Extension agents (EAs) supporting cassava growers supplying cassava roots to medium-scale processors • Expected benefit: Cassava root supply increased by 10 tonnes (or revenue increases of US$500), realized by 6,563 HHs, with the support of 150 extension agents, generating a total value of US$3,281,250 • Current version: V1: implemented at 5x5km, for variations of +/- 1-2 months around the planned data of planting and harvest, estimating yield and revenue with user-supplied unit prices for fresh cassava roots • Approach: Water-limited yield estimated by LINTUL; current yield (no inputs) estimated by QUEFTS, across the planting and harvest windows observed during the RC survey • Input required: GPS location, planting date (actual or planned), harvest date (planned), expected price (+ variation in price, optional), yield estimate (visual method) • 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 High Starch Content DST: • Specific purpose: recommend time of planting and harvest (and other agronomic measures) to optimize starch supply to processors • Requested by: FJS (TZ) and Psaltry (NG) • Other partners: - • Intended users: Outgrowers supplying cassava roots to starch factories • Expected benefit: Cassava starch supply increased by 5 tonnes (or revenue increases of US$375), realized by 7,700 HHs, with the support of 44 extension agents, generating a total value of US$2,887,500 • Current version: V1: implemented at 5x5km, for variations of +/- 1-2 months around the planned data of planting and harvest, estimating yield and revenue with user-supplied unit prices for fresh cassava roots, disaggregated by starch content class • Approach: Water-limited yield estimated by LINTUL; current yield (no inputs) estimated by QUEFTS, across the planting and harvest windows observed during the RC survey, and starch content correction based on learnings from literature + field data • Input required: GPS location, planting date (actual or planned), harvest date (planned), expected price by starch content class, yield estimate (visual method) • 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
  6. 6. Learnings from the literature review www.iita.org | www.cgiar.org | www.acai-project.org What determines starch yield?
  7. 7. Learnings from the RC and baseline survey www.iita.org | www.cgiar.org | www.acai-project.org Harvest Planting Insights in planting and harvest schedules: Based on observations in 4629 cassava fields with 2349 households across both countries.
  8. 8. Learnings from the RC and baseline survey www.iita.org | www.cgiar.org | www.acai-project.org Insights in prices of fresh cassava roots and processed produce Price information obtained from phone interviews with 1475 geo-referenced responders.
  9. 9. Principles of the Scheduled Planting Tool www.iita.org | www.cgiar.org | www.acai-project.org 1. Estimate the water-limited yield based on the LINTUL modelling framework 2. Estimate the current yield based on the QUEFTS modelling framework 3. Scale to the actual expected yield using expert knowledge based on previous yield, assisted by visual method 4. Estimate variation in gross value based on user-defined changes in price around the expected harvest date 5. Provide recommendations on planting (if applicable) and harvest date maximizing gross revenue The SP-DST is developed based on following steps and principles: LINTUL HS-DST , and converted to starch yield based on empirical relations (trial data) , using root prices disaggregated by starch concentration
  10. 10. Principles of the Scheduled Planting Tool www.iita.org | www.cgiar.org | www.acai-project.org Estimate the water-limited yield and current yield LINTUL QUEFTS Water-limited yield (no nutrient limitations) Current yield (limited by water + nutrients) Example: planting mid November, harvest at 10 MAP Do this for all combinations of weekly intervals in planting date across the planting windows per region, and weekly intervals in harvest date between 8 and 12 MAP…
  11. 11. Principles of the Scheduled Planting Tool Variation in water-limited yield >> current yield (limited by nutrients) Water-limited yield (no nutrient limitations) Current yield (limited by water + nutrients) www.iita.org | www.cgiar.org | www.acai-project.org 8 MAP 10 MAP 12 MAP 8 MAP 10 MAP 12 MAP Mid-DecMid-NovMid-Oct
  12. 12. Principles of the Scheduled Planting Tool www.iita.org | www.cgiar.org | www.acai-project.org Scale to the actual expected yield and convert to gross value Current yield (no inputs) [QUEFTS] Water-limited yield [LINTUL] Fictive example with large changes in yield and price over time, to illustrate the principle… Guide the user to indicate the expected yield level based on his/her experience with cropping cassava in the plot on a scale of 1 [poor yield = current yield] to 5 [high yield = water-limited yield] 1 2 3 4 5 1 2 3 4 5
  13. 13. Principles of the Scheduled Planting Tool www.iita.org | www.cgiar.org | www.acai-project.org Scale to the actual expected yield and convert to gross value Guide the user to indicate the expected yield level based on his/her experience with cropping cassava in the plot on a scale of 1 [poor yield = current yield] to 5 [high yield = water-limited yield] 1 2 3 4 5
  14. 14. Overview www.iita.org | www.cgiar.org | www.acai-project.org Scheduled Planting and High Starch Content DSTs: 1. Background and modelling framework (Pieter Pypers): • Introduction • Learnings from literature • Learnings from baseline and rapid characterization • Modelling framework: LINTUL & QUEFTS (temporarily) 2. Field activities (Rebecca Enesi & Bernadetha Kimati): • Field activities: Scheduled Planting Trials • Field trial results 3. Development of the DST (Jeremiah Kabissa): • Overview of recommendations • The Decision Support Tool • Next steps and additional data needs
  15. 15. Scheduled Planting Trials www.iita.org | www.cgiar.org | www.acai-project.org Evaluate effects of variety, planting date, [fertilizer] and harvest date: T B M A M J J A S O N D J F M A M J J A S O N D P H ridge P H ridge P H ridge P H ridge P H ridge P H ridge P H ridge P H ridge P H ridge P H ridge P H ridge P Hridge P P P H P H H H H H H H dry season long rains * * * * dryshort rains dry season long rains short rains Six variants [SPT-1..6], differing in fertilizer levels and number of harvests
  16. 16. Scheduled Planting Trials www.iita.org | www.cgiar.org | www.acai-project.org Sampling frame: maximize representativeness across target AoI #locations #planted #harvested On-station trials SW-NG 2 6 - LZ-TZ 1 3 1 (2) SZ-TZ 1 3 3 EZ-TZ - - - On-farm trials SW-NG 8 15 4 LZ-TZ 6 3 1 (2) SZ-TZ 3 2 1 EZ-TZ 3 0 0 Environment class Combination of on-station trials with high frequency / intensity of data collection (+ meteo station), and on-farm trials with less frequent data collection (serving mainly as validation dataset)
  17. 17. Scheduled Planting Trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – TZ – some pictures Learning how to record temperature Farmers willingness to prepare their land Learning how to record rainfall Foregoing other crops for cassava
  18. 18. Scheduled Planting Trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – TZ – current status Good harvest at 12 MAP Stem weight at 12 MAP Sub-sampling
  19. 19. Scheduled Planting Trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – NG – some pictures 3 RMTs on-farm, managed by IITA for intensive, high frequency data collection. 7 MLTs on-farm, managed by FUNAAB and EAs with lower frequency of data collection. Fertilizer application on late plantings remains a challenge. Need to develop strategies for appropriate timing of fertilizer application for each planting.
  20. 20. Scheduled Planting Trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – NG – some pictures 12 3
  21. 21. Scheduled Planting Trials www.iita.org | www.cgiar.org | www.acai-project.org Results First results from scheduled planting trials across 14 locations from both countries: Analysis of Variance Table Pr(>F) Harvest 0.0001029 *** Variety 0.8892715 country 0.5138284 Harvest:Variety 0.4631400 Harvest:country 0.0323079 * Variety:country 0.0185752 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 Large variation in root yield (13.6 ± 94%)! 54% of total variance explained by: Variety: 6% Harvest age: 9% Trial (planting date, management): 36% Field (agro-ecology, soil,…): 49%
  22. 22. Starch assessment in trials for other use cases www.iita.org | www.cgiar.org | www.acai-project.org Results 1624 starch measurements (using gravimetric method) across trials of 4 use cases:
  23. 23. Starch assessment in trials for other use cases www.iita.org | www.cgiar.org | www.acai-project.org Results 1624 starch measurements (using gravimetric method) across trials of 4 use cases: Nigeria Tanzania mean 21.9% 25.1% CV 29.3% 44.7% % variance attributed to… harvest time 64% 35% between trials 21% 36% within trial 15% 29% Between trials = agro-ecology + soil + management,… Within trials = treatment + random noise Nigeria Tanzania mean 21.9% 25.1% CV 29.3% 44.7%
  24. 24. Starch assessment in trials for other use cases www.iita.org | www.cgiar.org | www.acai-project.org Results 1624 starch measurements (using gravimetric method) across trials of 4 use cases: 64% of total variance explained by harvest time 35% of total variance explained by harvest time Large differences in agro- ecology between regions!
  25. 25. Starch assessment in trials for other use cases www.iita.org | www.cgiar.org | www.acai-project.org Results Nutrient Omission Trials: Effect of fertilizer on starch content? Linear mixed model fit by REML Formula: starCont ~ treatCode + (1 | trialID) Estimate Std. Error Pr(>|t|) (Intercept) 23.9362 2.4094 1.77e-08 *** PK -3.8166 1.7814 0.0371 * NK -3.9010 1.7431 0.0297 * NP -0.9004 1.7512 0.6095 half_NPK -0.6770 1.7870 0.7064 NPK -0.1340 1.5782 0.9327 NPK+micro -1.7704 1.7512 0.3170 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Slight reductions in starch content (-4%) due to omission of N (location-dependent) or P (general), but not K.
  26. 26. Starch assessment in trials for other use cases www.iita.org | www.cgiar.org | www.acai-project.org Results Best Planting Practices Trials: Effect of crop density and tillage on starch content? No effect of planting density, primary (0-, 1-, 2-till) or secondary tillage (flat / ridged) on starch content.
  27. 27. Starch assessment in trials for other use cases www.iita.org | www.cgiar.org | www.acai-project.org Results Scheduled Planting Trials in SW Nigeria: Effect of variety and harvest time on starch content? Linear mixed model fit by REML Formula: starCont ~ variety*harvestTime + (1 | fieldID) Estimate Std. Error Pr(>|t|) (Intercept) 17.7253 1.2014 1.21e-08 *** VarietyV2 0.4589 1.6667 0.7840 HarvestH2 4.1153 1.8771 0.0548 . HarvestH3 22.3808 1.6574 5.84e-09 *** VarietyV2:HarvestH2 -1.6738 2.3836 0.4854 VarietyV2:HarvestH3 -2.5796 2.2254 0.2511 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Negligible differences between two varieties. Substantial impact of delayed harvest!
  28. 28. Overview www.iita.org | www.cgiar.org | www.acai-project.org Scheduled Planting and High Starch Content DSTs: 1. Background and modelling framework (Pieter Pypers): • Introduction • Learnings from literature • Learnings from baseline and rapid characterization • Modelling framework: LINTUL & QUEFTS (temporarily) 2. Field activities (Rebecca Enesi & Bernadetha Kimati): • Field activities: Scheduled Planting Trials • Field trial results 3. Development of the DST (Jeremiah Kabissa): • Overview of recommendations • The Decision Support Tool • Next steps and additional data needs
  29. 29. How are these results fed into the DST? www.iita.org | www.cgiar.org | www.acai-project.org Empirical equations to predict starch yield using root yield: Observed versus predicted starch yields [t/ha]: R2=0.84 R2=0.91 based on root yield only based on root yield + harvest month
  30. 30. Interpreting the recommendations www.iita.org | www.cgiar.org | www.acai-project.org Variation in water-limited yield Water-limited yield (LINTUL) for different planting and harvest dates (averaged by region)
  31. 31. 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? SP-DST packaged as a simple ODK form, with GPS location, planned (or actual) planting and harvest date, and expected unit prices for cassava roots (or starch) as only inputs (for now): Introduction and identification
  32. 32. 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? SP-DST packaged as a simple ODK form, with GPS location, planned (or actual) planting and harvest date, and expected unit prices for cassava roots (or starch) as only inputs (for now): Intended planting and harvest date + window
  33. 33. 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? SP-DST packaged as a simple ODK form, with GPS location, planned (or actual) planting and harvest date, and expected unit prices for cassava roots (or starch) as only inputs (for now): Planting details + expected yield level
  34. 34. 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? SP-DST packaged as a simple ODK form, with GPS location, planned (or actual) planting and harvest date, and expected unit prices for cassava roots (or starch) as only inputs (for now): Root prices for different price classes according to starch content or… varying in time across the harvest window
  35. 35. 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? SP-DST packaged as a simple ODK form, with GPS location, planned (or actual) planting and harvest date, and expected unit prices for cassava roots (or starch) as only inputs (for now): Expected yield, gross value and recommended planting and harvest date
  36. 36. 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? SP-DST packaged as a simple ODK form, with GPS location, planned (or actual) planting and harvest date, and expected unit prices for cassava roots (or starch) as only inputs (for now): Feedback… Are recommendations sensible, useful? Can we call you for further feedback?
  37. 37. 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): • Site-specific info • Variety (HI) • Uncertainty? • … 3. LINTUL vs. DSSAT? 4. Parametrization of models – data requirements? 5. Explore options for model validation exercises through ongoing measurements within partner networks (e.g., with Niji farms) or through crowd-sourcing yield data. V1 is a ‘hybrid’ between a research tool and the intended ‘app’
  38. 38. Questions and discussion www.iita.org | www.cgiar.org | www.acai-project.org Questions?

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