Development of the Scheduled Planting (SP) and High Starch Content (HS) Decision Support Tools (DSTs) – V1

www.iita.org I www.cgiar.org
Development of the Scheduled Planting (SP) and
High Starch Content (HS) Decision Support Tools
(DSTs) – V1
African Cassava Agronomy Initiative (ACAI)
Second Annual Review Meeting
and Planning Workshop
11 – 15 Dec. 2017
Gold Crest Hotel, Mwanza, Tanzania
Pieter Pypers, Rebecca Enesi, Bernadetha Kimati & Jeremiah Kabissa

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

Overview
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

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: 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

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: 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

Learnings from the literature review
What determines starch yield?

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

Learnings from the RC and baseline survey
Insights in prices of fresh cassava roots and processed produce
Price information obtained from phone interviews with 1475 geo-referenced responders.

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:
LINTUL
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…

Variation in water-limited yield >> current yield (limited by nutrients)
Water-limited yield (no nutrient limitations) Current yield (limited by water + nutrients)
8 MAP 10 MAP 12 MAP 8 MAP 10 MAP 12 MAP
Mid-DecMid-NovMid-Oct

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

Scheduled Planting Trials
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

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)

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

Impressions and learnings from the field – TZ – current status
Good harvest at 12 MAP Stem weight at 12 MAP Sub-sampling

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.

Impressions and learnings from the field – NG – some pictures
12
3

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%

Starch assessment in trials for other use cases
Results
1624 starch measurements (using gravimetric method) across trials of 4 use cases:

Results
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%

Results
64% of total variance explained by harvest time 35% of total variance explained by harvest time
Large differences in agro-
ecology between regions!

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.

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.

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!

How are these results fed into the DST?
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

Interpreting the recommendations
Variation in water-limited yield
Water-limited yield (LINTUL) for different planting and harvest dates (averaged by region)

Packaging in a tool for field use
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

Intended planting and harvest date + window

Planting details + expected yield level

Root prices for different price classes
according to starch content or…
varying in time across
the harvest window

Expected yield, gross value and recommended planting and harvest date

Feedback… Are recommendations sensible, useful? Can we call you for further feedback?

Next steps
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’

Questions and discussion
Questions?

Development of the Scheduled Planting (SP) and High Starch Content (HS) Decision Support Tools (DSTs) – V1

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