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Session 2 4 Development of the Scheduled Planting (SP) and High Starch Content Decision Support Tool

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Presentation highlighting the process and progress of developing the Summary of the field activities towards the development of the SP and HS DSTs, focusing on a combined DST recommending the time of planting and/or harvest to optimize root or starch supply (and revenue) to cassava processors, for both processors and cassava growers.
After two years of field experimentation, the database currently holds yield data from 79 SP trials (combinations of location, planting date, harvest age), and close to 4,000 starch measurements across trials from all use cases.
Most important findings in year 2 include (i) cassava root yield is controlled for a large extent to crop age and month of harvest in Nigeria, but in Tanzania, year-to-year variation is much larger, likely related to variation in rainfall across the growing season, (ii) starch concentration is controlled by harvest month in Nigeria and this is largely stable across years likely due to comparability of rainfall across years, but not so in Tanzania, and (iii) results confirm that starch concentration is not affected by fertilizer application or tillage management.
Inconsistent effects across years emphasize the need for better insights in the processes controlling yield and starch concentration through mechanistic models. LINTUL appears not to adequately predict the impact of rainfall during crop growth on dry matter accumulation. LINTUL does not seem to penalize ‘older’ cassava in the growth season, and underestimate growth and starch accumulation of a ‘medium’ cassava during the dry season…
Advances with the DST development; Modelling framework, the Decision Support Tool were presented, along with the ongoing validation exercises, with over 350 trials currently established to evaluate impact of harvest month on yield. First impressions illustrate that farmers have difficulties to anticipate the price variation across the harvest period, which is an important driver for decision making. The exercise is appreciated as it stimulates farmers and extension agents to reflect on the impact of planting date and harvest date on total revenue, which is often thought of as ‘less important’.

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Session 2 4 Development of the Scheduled Planting (SP) and High Starch Content Decision Support Tool

  1. 1. Development of the Scheduled Planting (SP) and High Starch Content (HS) 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 1. Introduction (Ademola Adebiyi): • The SP and HS use cases • Learnings from the baseline • Summary of year 2 achievements 2. Field activities (Bernadetha Kimathi and Busari Mutiu): • Field activities: Scheduled planting trials • Field trial results 3. Advances with the DST development (Pieter Pypers): • Modelling framework • Year1 – Year2 validation results • The Decision Support Tool 4. Validation exercises (Taiwo Ogunleye and Rhoda Mahava): • First impressions from ongoing validation exercises • Next steps and additional data needs Scheduled Planting and High Starch Content DSTs:
  3. 3. Overview www.iita.org | www.cgiar.org | www.acai-project.org 1. Introduction (Ademola Adebiyi): • The SP and HS use cases • Learnings from the baseline • Summary of year 2 achievements 2. Field activities (Bernadetha Kimathi and Busari Mutiu): • Field activities: Scheduled planting trials • Field trial results 3. Advances with the DST development (Pieter Pypers): • Modelling framework • Year1 – Year2 validation results • The Decision Support Tool 4. Validation exercises (Taiwo Ogunleye and Rhoda Mahava): • First impressions from ongoing validation exercises • Next steps and additional data needs Scheduled Planting and High Starch Content DSTs:
  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: V2: implemented at 5x5km, for variations of +/- 1-2 months around the planned date 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: V2: implemented at 5x5km, for variations of +/- 1-2 months around the planned date 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 RC and baseline survey www.iita.org | www.cgiar.org | www.acai-project.org Harvest Planting Insights in planting and harvest schedules (from RC survey): Based on observations in 4629 cassava fields with 2349 households across both countries.
  7. 7. Learnings from the RC and baseline survey www.iita.org | www.cgiar.org | www.acai-project.org Insights in yield variation across the year Over 2,000 yield measurements conducted in farmers’ fields during 2018 final stage of baseline study… Month of harvest is an important factor influencing yield, with lower yields during the onset of the rain. Also age of the crop (nr of months from planting to harvest) has important impact on yield…
  8. 8. 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: HS-DST , and converted to starch yield based on empirical relations (trial data) , using root prices disaggregated by starch concentration
  9. 9. 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…
  10. 10. 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
  11. 11. 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
  12. 12. V1 version of the PP DST (end of 2017) www.iita.org | www.cgiar.org | www.acai-project.org Inputs include GPS location, planned/ actual planting and harvest date, and expected unit prices for cassava roots (or disaggregated prices by starch content as provided by starch companies) V1 version packaged as a smartphone app – simple ODK form
  13. 13. Overview www.iita.org | www.cgiar.org | www.acai-project.org 1. Introduction (Ademola Adebiyi): • The SP and HS use cases • Learnings from the baseline • Summary of year 2 achievements 2. Field activities (Bernadetha Kimathi and Busari Mutiu): • Field activities: Scheduled planting trials • Field trial results 3. Advances with the DST development (Pieter Pypers): • Modelling framework • Year1 – Year2 validation results • The Decision Support Tool 4. Validation exercises (Taiwo Ogunleye and Rhoda Mahava): • First impressions from ongoing validation exercises • Next steps and additional data needs Scheduled Planting and High Starch Content DSTs:
  14. 14. Scheduled Planting Trials www.iita.org | www.cgiar.org | www.acai-project.org Evaluate effects of variety, planting date, [fertilizer] and harvest date: TANZANIA – LAKE ZONE: BIMODAL Mar-Apr (long rains) + Oct-Jan (short rains) 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 H ridge P P P H P H H H H H H H dry season long rains * * * * include treatment with/without fertilizer dryshort rains dry season long rains short rains Six variants [SPT-1..6], differing in fertilizer levels and number of harvests
  15. 15. #locations #planted 1 5 1 1(4) 1 0(3) 1 0(3) 3 5 - - - - - - #locations #planted #harvested 1 3 3 1 5 3(2) 1 3 2(1) 1 3 2(1) 5 7 5 - - - - - - - - - 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 3 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 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) 2016 planting 2017 planting 2018 planting
  16. 16. 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 At the same age, yield of variety Kiroba >> variety Chereko
  17. 17. Scheduled Planting Trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – TZ – current status 4 MAP at EZ 4 MAP at LZ
  18. 18. Scheduled Planting Trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – TZ – current status Root harvest at 10 MAP (25 plants) Root harvest at 8 MAP (25 plants)
  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. b Scheduled Planting Trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – NG – some pictures TME 419 9MAP a a b a b a = with NPK 75:20:90 b = without fertilizer TME 419 11MAP b TME 419 13MAP a
  21. 21. Scheduled Planting Trials www.iita.org | www.cgiar.org | www.acai-project.org Impressions and learnings from the field – NG – some pictures Weighing subsamples Determining starch content
  22. 22. Scheduled Planting Trials www.iita.org | www.cgiar.org | www.acai-project.org Results Results from scheduled planting trials (79 combinations of planting date, harvest age and location): Highest yields when relatively young into dry season, and at least 3 months of ample rain prior to harvest?
  23. 23. Scheduled Planting Trials www.iita.org | www.cgiar.org | www.acai-project.org Results Results from scheduled planting trials (79 combinations of planting date, harvest age and location): 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% Variance component Nigeria Tanzania trial conditions (within field), including planting date 10% 2% field location, including overall rainfall, soil conditions 8% 26% harvest month x year (year-specific rainfall conditions) 6% 38% harvest month (across-year consistent rainfall conditions) 12% 0% crop age (nr of months to harvest [8 – 13 months]) 12% 1% residual (random noise, unknown influences) 52% 33% Nigeria: cassava root yield = 12 t/ha ± 57%, of which 24% is related to age and month of harvest, and 24% is related to location-specific rainfall and other effects. Tanzania: cassava root yield = 11t/ha ± 65%, of which 1% is related to age and month of harvest, and 64% is related to location-specific rainfall and other effects. Can we predict this with LINTUL / DSSAT? Note: variety and fertilizer effects accounted for as fixed terms in the model.
  24. 24. Starch assessment in trials for other use cases www.iita.org | www.cgiar.org | www.acai-project.org Results 2017: 1624 starch measurements (using gravimetric method) 2017+2018: 3775 starch measurements across trials of all use cases (mainly PP, FR, SP)
  25. 25. Starch assessment in trials for other use cases www.iita.org | www.cgiar.org | www.acai-project.org Results Nigeria Tanzania 2017 2017+ 2018+ 2017 2017+ 2018+ mean 21.9% 23.1% 25.1% 22.6% CV 29.3% 31.9% 44.7% 46.7% % variance attributed to… harvest month 64% 47% 35% 0% harvest month:year - 2% - 24% between trials 21% 32% 36% 49% within trial 15% 19% 29% 27% Between trials = agro-ecology + soil + management,… Within trials = treatment + random noise 2017: 1624 starch measurements (using gravimetric method) 2017+2018: 3775 starch measurements across trials of all use cases (mainly PP, FR, SP)
  26. 26. Starch assessment in trials for other use cases www.iita.org | www.cgiar.org | www.acai-project.org Results Consistent between years, following seasons 2017: 1624 starch measurements (using gravimetric method) 2017+2018: 3775 starch measurements across trials of all use cases (mainly PP, FR, SP) Large differences between years ~ rainfall?
  27. 27. Starch assessment in trials for other use cases www.iita.org | www.cgiar.org | www.acai-project.org Results Scheduled Planting Trials: Effect of variety, harvest age and harvest time on starch content? 2017: No impact of variety; large impact of month of harvest. 2018: Significant effects of variety and harvest age, dependent on location and harvest month but << effects of harvest month (dependent on zone) Cassava starch content is mostly determined by month of harvest, and likely related to rainfall conditions prior to harvesting. Effects are consistent across years in Nigeria, but not in Tanzania. Can this be better predicted, especially for Tanzania?
  28. 28. 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? 2017: Slight reductions in starch content (-4%) due to omission of N or P, but not K, and only in Nigeria. 2018: Negative effects of fertilizer application are not repeated. No effects on starch content observed.
  29. 29. 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 primary tillage, secondary tillage, crop density and weed control? Higher starch contents and higher variability, but results confirmed: Tillage, weed control and planting density do not affect root starch content. BPP-1: 2016-2017 BPP-2: 2017-2018
  30. 30. Observational studies at Niji farms www.iita.org | www.cgiar.org | www.acai-project.org 3 successive harvests in year-round planted fields of the Niji farm 1 2 3 4 5 6 7 8 9 0.8 1.6 Net plot 2.4 3.2 9MAP 4 Border plants 4.8 5.6 6.4 7.2 8 8.8 9.6 10.4 11 MAP 11.2 12 12.8 13.6 14.4 15.2 16 13 MAP 16.8 17.6 18.4 19.2 20 Schematic layout of an observation and harvest area within a selected field. Yellow = border plants, green = net plot plants. Objectives • Assess cassava root yields and starch content over an expanded period of planting and harvesting dates. • Generate data to supplement data from MLTs and RMTs of the scheduled planting use case to improve the cassava model and the decision support tool based on the model.
  31. 31. Overview www.iita.org | www.cgiar.org | www.acai-project.org 1. Introduction (Ademola Adebiyi): • The SP and HS use cases • Learnings from the baseline • Summary of year 2 achievements 2. Field activities (Bernadetha Kimathi and Busari Mutiu): • Field activities: Scheduled planting trials • Field trial results 3. Advances with the DST development (Pieter Pypers): • Modelling framework • Year1 – Year2 validation results • The Decision Support Tool 4. Validation exercises (Taiwo Ogunleye and Rhoda Mahava): • First impressions from ongoing validation exercises • Next steps and additional data needs Scheduled Planting and High Starch Content DSTs:
  32. 32. How are these results fed into the DST? www.iita.org | www.cgiar.org | www.acai-project.org Can we predict yield? LINTUL or DSSAT? Highest yields when relatively young into dry season, and at least 3 months of ample rain prior to harvest? Results from scheduled planting trials (79 combinations of planting date, harvest age and location):
  33. 33. Can we predict yield? DSSAT vs LINTUL www.iita.org | www.cgiar.org | www.acai-project.org LINTUL YUCA-MANIHOT Key features • Biomass production as a function of LUE, ratio actual over potential transpiration, temperature sum • Shoot growth as main sink in the juvenile stage • Storage root growth as main sink after bulking initiation • Fixed pattern of dry matter allocation to leaf, stem, fine roots and storage roots per crop age Weaknesses • Fixed dry matter partitioning pattern per crop age • Water balance simulation (current version over- estimating water stress) Key features • Biomass production as a function of LUE, stress factor based on % of soil water content, temperature sum • Shoot growth (leaf and stem) as main sink during the whole development • Root (storage) growth as the surplus from the difference between total biomass and shoot growth • Spill-over dry matter partitioning pattern with left-over of dry matter sent to roots after feeding the shoot Weaknesses • Does not consider the reallocation of carbohydrates from storage roots to shoots after the release of water stress • Overestimation of aboveground growth at the end of the growing season
  34. 34. How are these results fed into the DST? www.iita.org | www.cgiar.org | www.acai-project.org Can we predict yield? LINTUL or DSSAT? LINTUL predicted water-limited yields, Oyo state, Nigeria Opposite patterns in predicted yield… LINTUL does not seem to penalize ‘older’ cassava in the growth season, and underestimate growth and starch accumulation of a ‘medium’ cassava during the dry season…
  35. 35. Starch assessment in trials for other use cases www.iita.org | www.cgiar.org | www.acai-project.org Results Consistent between years, following seasons 2017: 1624 starch measurements (using gravimetric method) 2017+2018: 3775 starch measurements across trials of all use cases (mainly PP, FR, SP) Large differences between years ~ rainfall?
  36. 36. How are these results fed into the DST? www.iita.org | www.cgiar.org | www.acai-project.org Can we predict root starch content? Only in Nigeria! Cross-validation (Nigeria): stable model performance RMSE ~ 2-5%, 200 runs Prediction error by month: ±2% ~ ±7% Additional data needed for the Aug – Feb window
  37. 37. Valid DST forms submitted: 268: 145 (NG) + 123 (TZ) Proportion selling to starch factory: 39% (NG), 0% (TZ) Established trials: 353: 194 (NG) + 159 (TZ) Validation exercises – overview www.iita.org | www.cgiar.org | www.acai-project.org
  38. 38. Validation exercises – overview www.iita.org | www.cgiar.org | www.acai-project.org Nigeria Tanzania
  39. 39. Large price variation in Tanzania… Can farmers correctly indicate price variations in time? Validation exercises – overview www.iita.org | www.cgiar.org | www.acai-project.org DST inputs: expected yield, price, and price variation Farmers tend to be over-optimistic in estimating yield… 31% of farmers estimate their yield between 15-22.5 t/ha, and 48% higher than 22.5 t/ha. As a result, gross revenue, and expected increases in gross revenue are often overestimated. The tool may still provide correct advise, if the direction of the price change and yield accrual over time are correct… Further simplification needed to ensure correct input data and to safeguard the end-user against incorrect advice.
  40. 40. Validation exercises – overview www.iita.org | www.cgiar.org | www.acai-project.org So what is being recommended? In Nigeria: mostly a delay in harvest time (59%) driven by yield accrual; no change = 18%. In Tanzania: either a very early harvest (54%) or a very late harvest (30%) driven by higher prices; no change = 2%. What is the opportunity cost for delayed harvest? What about CBSD damage risks?
  41. 41. Overview www.iita.org | www.cgiar.org | www.acai-project.org 1. Introduction (Ademola Adebiyi): • The SP and HS use cases • Learnings from the baseline • Summary of year 2 achievements 2. Field activities (Bernadetha Kimathi and Busari Mutiu): • Field activities: Scheduled planting trials • Field trial results 3. Advances with the DST development (Pieter Pypers): • Modelling framework • Year1 – Year2 validation results • The Decision Support Tool 4. Validation exercises (Taiwo Ogunleye and Rhoda Mahava): • First impressions from ongoing validation exercises • Next steps and additional data needs Scheduled Planting and High Starch Content DSTs:
  42. 42. Validation Exercises - Key Activities www.iita.org | www.cgiar.org | www.acai-project.org Training events The SPT DST will provide the optimal harvest date, maximizing the gross value based on the anticipated yield and price of the produce.
  43. 43. Validation Exercises - Key Activities www.iita.org | www.cgiar.org | www.acai-project.org Trial establishment 18 / 555 confirmed planted.
  44. 44. Validation Exercises - Key Activities www.iita.org | www.cgiar.org | www.acai-project.org
  45. 45. Validation Exercises - Key Activities www.iita.org | www.cgiar.org | www.acai-project.org Cassava processing industry is growing and requires constant supply of storage roots. Forthcoming…
  46. 46. Key Activities www.iita.org | www.cgiar.org | www.acai-project.org SP-HS key activities carried out by Psaltry, SW Nigeria Farmer sensitization prior to household registration Establishment of validation plot Data collection
  47. 47. Testimonies from farmers www.iita.org | www.cgiar.org | www.acai-project.org • I have learnt about best time to harvest (I learnt that it is not necessarily have to be one year after planting). • I have learnt about better ways of maintaining cassava field. • I have learnt that planting more than two times in a year increases farmers’ income. Afolabi Olatunji – CAVA II OG
  48. 48. Testimonies from farmers www.iita.org | www.cgiar.org | www.acai-project.org Abilawon Dayo – Psaltry OY • I have learnt about spacing of 0.8m x 1m as against the traditional spacing of 1m x 1m. • I also learnt that sole cassava looks more appealing than cassava-maize intercrop. • I learnt about documentation of field activities. Example: actual planting date. • I now know the best time to plant and best time to harvest.
  49. 49. Testimonies from Extension Agents www.iita.org | www.cgiar.org | www.acai-project.org Yunus Ganiu– Psaltry OY • I learnt that trial site should not be closed to tree shade and/or roadside • I have learnt how to use ODK for data collection • I have learnt about best time to harvest cassava
  50. 50. Testimonies from Extension Agents www.iita.org | www.cgiar.org | www.acai-project.org Timothy Banjoko – CAVA II OG I have been able to learn the following: • Best planting and harvest time for maximum benefit. • Planting all year round will help to control market glut of cassava. • Use of ODK for data collection. The validation exercise bridges the gap between farmers and researchers (NARS/IITA) as there is a lot of interaction between the duo.
  51. 51. Learnings from development partners www.iita.org | www.cgiar.org | www.acai-project.org Key learnings 1. Engagement with EAs: The EAs are very happy with the tools as it add beauty to their work. They can proudly forecast the time of harvest from the prediction made by the tools. 2. Process of the validation: The process of validation is the most important part of the project as it brings the past experiments into practical sense and reality 3. Recommendation from the tool: Recommendation from the tools are very important and reliable as it helps in predicting when farmers should harvest for optimum yield and high starch content which will in-turn increase their income Impact on dissemination activities • The validation exercise now simplify our mode of information dissemination to farmers. • Since the device automatically predicts harvesting date to farmers, it safe us the stress and time in explaining the benefit of delay harvesting to farmers. • Easy planning of raw material supply to the company
  52. 52. Thank you very much !!! Questions and discussion www.iita.org | www.cgiar.org | www.acai-project.org

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