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

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

Overview
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:

Introduction
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

Introduction
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

Learnings from the RC and baseline survey
Harvest
Planting
Insights in planting and harvest schedules (from RC survey):
Based on observations in 4629 cassava fields
with 2349 households across both countries.

Learnings from the RC and baseline survey
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…

Principles of the Scheduled Planting Tool
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

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…

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

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

V1 version of the PP DST (end of 2017)
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

Scheduled Planting Trials
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

#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
- - -
- - -
- - -
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

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

Impressions and learnings from the field – TZ – current status
4 MAP at EZ 4 MAP at LZ

Impressions and learnings from the field – TZ – current status
Root harvest at 10 MAP
(25 plants)
Root harvest at 8 MAP
(25 plants)

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.

b
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

Weighing subsamples Determining starch content

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?

Results
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.

Starch assessment in trials for other use cases
Results
2017: 1624 starch measurements (using gravimetric method)
2017+2018: 3775 starch measurements across trials of all use cases (mainly PP, FR, SP)

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

Results
Consistent between years, following seasons
Large differences between years ~ rainfall?

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?

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.

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

Observational studies at Niji farms
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.

How are these results fed into the DST?
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?

Can we predict yield? DSSAT vs LINTUL
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

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…

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

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

Nigeria Tanzania

Large price variation in Tanzania…
Can farmers correctly indicate price variations in time?
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.

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?

Validation Exercises - Key Activities
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.

Trial establishment
18 / 555 confirmed planted.

Cassava processing industry is growing and requires constant
supply of storage roots.
Forthcoming…

Key Activities
SP-HS key activities carried out by Psaltry, SW Nigeria
Farmer sensitization prior to
household registration
Establishment of validation plot Data collection

Testimonies from farmers
• 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

Testimonies from farmers
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.

Testimonies from Extension Agents
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

Testimonies from Extension Agents
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.

Learnings from development partners
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

Thank you very much !!!
Questions and discussion

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

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