Session 2 intercropping dst

Development of the
Best Intercropping Practices (IC)
Decision Support Tool (DST) – V1
www.iita.org | www.cgiar.org | www.acai-project.org

Overview
Best Intercropping Practices DSTs:
1. Background and modelling framework (Christine Kreye):
• Introduction
• Learnings from literature
• Learnings from baseline and rapid characterization
• Modelling framework: Decision Tree Models
2. Field activities (Mark Tokula and Haji Saleh):
• Field activities: Intercropping Trials
• Field trial results
3. Development of the DST (Veronica Uzokwe):
• Overview of recommendations
• The Decision Support Tool
• Next steps and additional data needs

Introduction
Best Intercropping Practices DSTs:
• Specific purpose: recommend optimal time of planting, crop density and fertilizer application for
a maize (NG) / sweet potato (Zanzibar) intercrop to increase overall productivity
• Requested by: FCI (TZ) and 2SCALE (NG)
• Other partners: SG2000 (NG)
• Intended users: Extension Agents (EAs) supporting cassava growers
• Expected benefit: Intercrop yield increased by 2 tonnes/ha and cassava yield not affected or increased
by 0.5 tonnes/ha realized by 35,100 HHs, with the support of 124 extension agents,
on a total area of 17,550 ha, generating a total value of US$3,948,750
• Current version: V1: uses expert knowledge to estimate current crop performance and revenue based
on default or user-defined prices of roots and intercrop produce to recommend best
planting time, planting density and fertilizer regime for a preferred set of varieties,
maximizing overall net revenue or intercrop yield without affecting cassava yield
• Approach: Decision tree model based on analysis of field trial data
• Input required: Cropping objective (maximize total revenue or maximize cassava yield), prices of
intercrop produce (maize cobs (NG) / sweet potato (Zanzibar)) and cassava roots,
willingness to invest in fertilizer and knowledge of field history
• Interface: Paper-based decision tree, including guidelines for simple calculations to estimate
profitability

Learnings from the literature review
Learnings for cassava – maize intercropping:
Cassava is affected by…
maize variety → negative impact on cassava
yield minimized with short-duration varieties
maize density → negative impact on cassava
yield with densities > 50,000 plants ha-1.
Fertilizer application increases yield of maize.
Insufficient literature to evaluate residual impact
on cassava…

Learnings from the literature review
Learnings for cassava – sweet potato intercropping:
Cassava yield (t/ha) Sweet potato yield (t/ha) LER
Moreno & Hart, 1979
Cassava 10,000 + SP 50,000 -42% (9.7/16.8) -40% (6.3/10.5) 1.17
Cassava 10,000 + 2x SP 50,000 -32% (11.5/16.8) -40%, (5.6/10.5, 3.5/4.7) 1.95
Kanpinga, 1995
Cassava short non-branching + SP -35% (9/14) -23% (15/19) 1.4
Cassava semi-erect, tall, high-branching + SP -35% (9/14) -46% (7.5/19) 1.0
Ojore Ijoyah, 2012
Cassava + SP [very low ~ 5 t ha-1 in MC]
• High competition between crops, with 30-40% yield loss for both cassava and sweet
potato, compared to the monocrops.
• Variety is important, indicating that aboveground competition is substantial, not just
belowground – both crops are high K feeders – hence, explore ways to minimize light
competition → delayed planting of sweet potato.
Very limited information available – 3 studies of which 2 useful, without much detail.

Principles of the Best Intercropping Practices Tool
1. Obtain details on current practice
2. Identify alternative options within given constraints
3. Evaluate to what extent the performance of alternative options is location-dependent, based on
analysis of multilocational field trial data
4. If so, identify GIS (or other) predictor variables to estimate location-specific effects of
interventions on intercrop yield and cassava root yield
5. Convert yield effects to changes in gross revenue based on prices of intercrop produce and
cassava roots (default values or user input)
6. Calculate net revenue (subtract cost of fertilizer)
7. Recommend optimal intercrop density, relative time of planting (Zanzibar only) and fertilizer regime
(if willing to invest in fertilizer) that maximizes cassava yield or overall net revenue using a
decision tree model
The IC-DST is developed based on following steps and principles:

What is current practice? – learnings from the RC survey
Tanzania (Zanzibar)
38% of cassava is intercropped, of which 45% by
sweet potato (45%), planted at the same time (67%)
or ± 2 weeks (29%). 0% of farmers apply fertilizer to
the sweet potato intercrop, and 62% commercialize
at least half of sweet potato produce. Main objective
is maximizing land use efficiency.
Nigeria
68% of cassava is intercropped, of which 75% by
maize, planted 2 weeks earlier (33%) or at the same
time (30%). 43% of farmers apply fertilizer to the
maize intercrop, and 94% commercialize at least half
of the maize produce. Main objective is faster access
to food and income.
Need picture here!

Tanzania (Zanzibar)
Price of sweet potato tubers
Nigeria
Price of fresh maize cobs
What is current practice? – prices of intercrop produce (phone surveys)

What are the alternative options?
2. Optimize the relative time of planting of the intercrop
• Reduce competition for light for the cassava crop
• Optimal time of planting will depend on the cropping objectives
3. Apply fertilizer
• Increased availability of nutrients (reduced belowground competition)
• Intercrop as an entry point for fertilizer application to cassava (benefits from residual effects)
• Modify the composition of the fertilizer regime to the specific cropping objectives
1. Modify (increase) the crop density:
• Optimize land use efficiency
• Choose best variety with minimal above- and belowground competition effect

Modelling framework
Are the effects of density and fertilizer application
dependent on field conditions?
Evaluate through multilocational field
testing covering target environments
Develop decision
tree models
Can we predict these effects
based on expert knowledge?

Intercropping Trials
Evaluate effects of planting density and fertilizer application on maize
(Nigeria):
2 sets of trials based on step-wise intensification:
⇒ CIM-2 (CIM-1); planted in 2016
⇒ CIM-3, CIM-4 (CIM-5); planted in 2017 based on insights of 2016
CIM-2 CIM-3 CIM-4
⇒ Fresh cobs are important
⇒ Include effect of maize density on cob quality/size
⇒ CWMP: high cassava density is better
⇒ Basal N (and P) application in CF2/CLMF; 15 kg N/ha

Sampling frame: maximize representativeness across target AoI
Env. %Area #trials #EAs adj. Cum. Ratio
6 19 40 4 0.19
7 14 29 3 0.32
9 11 24 2 0.44
5 11 23 2 0.55
11 8 18 2 0.63
8 8 18 2 0.72
2 8 16 2 0.79
4 7 15 2 0.87
Break off for all clusters below 5% coverage
3 4 8 1 0.90
12 4 7 1 0.94
1 3 7 1 0.97
10 2 5 0 0.99
13 1 2 0 1.00
Environments identified based on clustering exercise using GIS information (climate, soil and vegetation)
across the partners’ target area of intervention area for the IC use case:

Current overview of trials and status of trials - Nigeria
• 2016 (CIM-2) trials: 70 trials were used for data analysis
• 2017 (CIM-3, CIM-4) trials: 145 trials were planted; 89 trials used for analysis of maize cob harvest
Challenges: • Cattle (especially in the SW)
• Birds, rodents and monkeys (especially for maize)
• Herbicide use at the wrong time (killing the maize)
• Weeding

Impressions and learnings from the field – NG – some pictures
Securing the crops
Quality classes of cobs
Fertilizer effect
Learning new tools

Results – Nigeria: 2016, CIM-2
Cassava Maize
Effect of F1: increase in root yield of 4 t/ha + increase of 1.2 cobs/m2 for maize
Effect of D: no impact on cassava yield + increase in maize yield
Maize intercrop as an entry point to fertilizer application on cassava?
Does the maize yield response justify the investment in fertilizer?

Yield ~ Treatment + (Fertilizer | field)
Fixed effects:
Estimate Std.Error df t value Pr(>|t|)
F1 15.1302 0.9861 65.2300 15.344 < 2e-16
F2 1.6083 0.8787 54.9100 1.830 0.072633
D -2.1444 0.6029 164.7700 -3.557 0.000491
D
F1
F2

Results – Nigeria: 2017; CIM-3 and CIM-4
Yield ~ Treatment + MaizeVariety + (Fertilizer | field)
All marketable cobs – effect of fertilizer at high maize density
⇒ rel. high variability in response
⇒ target high planting density
⇒ good management (army
worm/rodent/bird control)
before applying fertilizer
⇒ Need to look at
site specific
fertilizer rates

Large Cobs – effect of fertilizer at high maize density
⇒need to look at site
specific fertilizer rates
⇒relatively safe response
of > 0.3 cobs per m2

All marketable cobs Large Cobs
D F1/CF2 (C)LMFLM D F1/CF2 (C)LMFLM

We adjusted the F2 treatment to include a basal fertilizer application.
How does it compare to F1 ?
All treatments significantly different from D, except LM.
D F1 CF2 LM LMF CLMF
All marketable cobs Large Cobs
D F1 CF2 LM LMF CLMF
After cassava harvest, the effect of CF2/CLMF on root yield will be evaluated.

Evaluate effects of planting density, planting time and fertilizer application
on sweet potato (Zanzibar):

Current overview of trials and status of trials - Zanzibar
Region clusters CIS-3 CIS-4 Harvested (SP only)
Unguja 7 1 73 66
Pemba 3 1 25 22
Total 10 2 98 88
CIS-3 = on-station trials; CIS-4 = on-farm trials
Out of 98 on-farm trials established:
86 = good quality, 8 = intermediate, and 3 = poor (lost)
Note: sweet potato crop lost in 2016 – only useful data from 2017.

Impressions and learnings from the field – Zanzibar – some pictures

Results - Zanzibar
monocrop
monocrop NPK1
monocrop NPK0
On-station trials on Unguja and Pemba

Results - Zanzibar
On-farm trials with 80 farmers across Unguja and Pemba
Between-district variance accounts for 60% of total variance (related to differences in planting time + rainfall)
Treatment effects are consistent (within district). Between-farmer variance = 12% of total variance.
Analysis of Variance Table
Pr(>F)
Treatment < 2.2e-16 ***
district < 2.2e-16 ***
Treatment:district < 2.2e-16 ***

How are these results fed into the DST?
Nigeria:
1. Cassava appears unaffected by intensification in maize → base V1 on responses of maize.
2. Lessons from the first set of trial (CIM-2) for maize
• Farmers value fresh maize cobs for the market
• Evaluate responses by cob size
• Large cobs fetch highest prices; base decisions on this fraction
• Use trials of 2017 (CIM-3 and CIM-4) for V1 development
3. Response to higher planting density of maize is not site-specific (for the selected maize variety)
and high density can be a blanket recommendation for total marketable cobs.
4. The height of maize (without fertilizer application) at tasselling can be used as indicator for the
response to fertilizer.
5. The maize fertilizer regime (F1) is preferred in all situations (over F2).
6. Planting at the same time as cassava (only +/- a couple of days).
7. Use maize that matures within 90 - 95 days.
8. Maize cob prices and fertilizer availability and cost will drive the decision-making in the DST.

How are these results fed into the DST?
Zanzibar:
Sweet potato
1. Sweet potato suffers somewhat from cassava, yield losses of 5 – 15% (higher in high-yielding
fields), less than observed in other studies.
2. Delayed planting of sweet potato causes yield reductions of 0 – 45%.
→ Time of planting is crucial for sweet potato (sweet potato crop was completely lost in 2016).
→ Delayed planting of sweet potato is only an option if cassava is planted early.
3. Increasing density is not advantageous for sweet potato (but has advantages for weed control).
→ Recommended density = 10,000-20,000 vines/ha in intercrop.
4. Effects of density and relative planting time are not site-specific.
→ Recommendations will mainly be driven by cropping objectives and sweet potato tuber prices
Cassava
1. Impact of sweet potato on cassava yield will be evaluated after harvest Q2 2018.
2. Negative impact on cassava root yield is expected (based on field observations), but could be
alleviated to some extent by fertilizer application and delayed planting of sweet potato.
3. Profitability will likely mostly depend on price ratio of cassava roots over sweet potato tubers.

Packaging in a tool for field use
How to make this framework available for quick and easy use?
IC-DST packaged as a simple paper-based decision tree with inputs:
1. Variety recommendation:
• erect cassava
• early maturing maize (90-95 days)
2. High planting densities
• cassava (12,500 plants ha-1)
• maize (40,000 plants ha-1)
3. Fertilizer regime:
• target the maize crop
• cassava benefits as well
4. Fertilizer recommendation:
• site-specific
• use farmers’ experience with their maize crop
(plant height at tasselling)
5. Profitability of fertilizer application:
• based on expected additional large maize cobs
• look-up table [cost of fertilizer x price of large cobs]
• formula for easy calculation
Note: IC-DST for Zanzibar will be developed after first cassava yields are available, based on the same principles

Next steps
1. Validation exercises (in collaboration with EAs of dev. partners requesting the DST)
Technical evaluation:
• How accurate are predictions?
• How good is farmers ‘ experience with their maize crop to make it a good indicator ?
• Can they be improved by use of GIS layers?
• Is maize always a good indicator for the performance of both crops?
Gather feedback what functionality is needed and how to interface with the end-user?
• Is a paper based decision tree appreciated?
• Do farmers want a decision tree for grain yield as well?
• Are large cobs a good enough indicator?
• Do farmers prefer decision making based on the cassava crop?
2. Extend beyond current functionality
• Test options to derive site-specific fertilizer rates
• Introduce QUEFTS for maize?
• Additional varieties – principles for testing?
V1 is a ‘hybrid’ between a research tool and the intended ‘app’

Questions and discussion
Questions?

Session 2 intercropping dst

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