Session 2 1 Development of the Site Specific Fertilizer Recommendation (FR) and Best Fertilizer Blend (FB) Decision Support Tool

Development of the
Site-Specific Fertilizer Recommendation (FR)
and Best Fertilizer Blend (FB)
Decision Support Tools (DSTs) – Version2
www.iita.org | www.cgiar.org | www.acai-project.org

Overview
Site-Specific Fertilizer Recommendation and Best Fertilizer Blend DSTs:
1. Introduction (Guillaume Ezui):
• The FR and FB use case
• Learnings from the baseline
• Summary of year 2 achievements
2. Field activities (Yemi Olojede and Deusdedit Peter Mlay):
• Field activities: Nutrient Omission Trials
• Field trial results
3. Advances with the DST development (Meklit Chernet):
• Modelling framework
• Year1 – Year2 validation results
• The Decision Support Tool
4. Validation exercises (Gbenga Ojo and Stephen Magige):
• First impressions from ongoing validation exercises
• Next steps and additional data needs

Introduction
The Site-Specific Fertilizer Recommendation DST:
• Specific purpose: recommend site-specific fertilizer rates that maximize net return on investment
• Requested by: SG2000 (NG), Notore (NG), Minjingu (TZ)
• Other partners: MEDA (TZ)
• Intended users: Extension agents (EAs) supporting commercial cassava growers
• Expected benefit: Cassava root yield increased by 8 tonnes/ha, realized by 28,200 HHs, with the
support of 215 extension agents, on a total of 14,100 ha, generating a total value
of US$2,185,500
• Current version: V2: implemented at 5x5km, for an investment of maximally 200 $ ha-1 (fixed),
for a fixed set of fertilizers (urea, Minjingu Nafaka / TSP, MOP) at fixed average
regional unit prices, and for a fixed average regional price for cassava produce
• Input required: GPS location and planting date (harvest date is fixed at 10 MAP)
• 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 Best Fertilizer Blend DST:
• Specific purpose: identify best-suited fertilizer blends to address nutrient constraints for cassava
production in a target area
• Requested by: Notore (NG), Minjingu (TZ)
• Other partners: -
• Intended users: Fertilizer producers engaged in the cassava value chain
• Expected benefit: 5000 tonnes of new fertilizer blends sold to commercial cassava growers, with a
total value of US$2,500,000
• Current version: V1: implemented at 5x5km, assessing N, P and K requirements for target yield
increases by 5, 10, 15, 20 t ha-1 and closing the yield gap across the cassava-
growing area in the target countries (selected districts and states)
• Input required: Target area (districts or states) and target yield increase
• Interface: R-shiny application (web-based) running on a desktop computer

Learnings from the baseline survey
Baseline study covered over 3,200 cassava fields:
Fertilizer use is low, but not zero (except in EZ-Tanzania)
Most common fertilizers include:
SE-Nigeria: urea, NPK15:15:15, NPK20:10:10
SW-Nigeria: urea, NPK15:15:15
EZ-Tanzania: ? (insufficient observations)
LZ-Tanzania: urea, DAP, NPK17:17:17
SZ-Tanzania: urea, DAP, NPK17:17:17, MOP

Principles of the Fertilizer Recommendation Tool
1. Determine the attainable yield (water-limited) yield (based on meteo data) - LINTUL
2. Estimate the indigenous nutrient supply of the soil (based on soil data)
+ add the nutrient supply from fertilizer – QUEFTS(1)
3. Estimate the nutrient uptake – QUEFTS(2)
4. Convert uptake into yield – QUEFTS(3)
5. Optimize nutrient supply based on cost of available fertilizers and RoI
6. Package the recommendations in a tool for field use
The FR-DST is developed based on following steps and principles:

Approach: coupled LINTUL-QUEFTS + net revenue optimizer
Random Forest
QUEFTSLINTUL
Economic
Optimizer
GPS location
Planting date
Harvest date
GPS location
Available fertilizers
Fertilizer price
Crop produce price
Max. investment
Water-limited yield
Nutrient-limited yield
Yield response to (N, P, K)
Fertilizer (N, P, K) rates
maximizing net revenue
with total cost ≤ max. investment
N
P
K
Geospatial
weather data
Geospatial
soil data
Geospatial
price data
Farmer
knowledge

Determining water-limited yield
Water-limited yield is calculated using the LINTUL modelling framework:
LINTUL (Light Interception and Utilization) determines growth and root biomass accumulation
and uses following data:
• Daily precipitation from CHIRPs – UCSB (ftp://ftp.chg.ucsb.edu/pub/org/chg/products/CHIRPS-2.0/)
• Solar data from TRMM – NASA (https://power.larc.nasa.gov/cgi-bin/agro.cgi)
• Soil parameters (bd, orgC, FC, WP, WSP, pH,…) from ISRIC (ftp://ftp.soilgrids.org/data/recent/)
LINTUL
LINTUL has recently been modified & calibrated for cassava.
ACAI uses default parametrization based on literature.
Crop parameters: e.g., Light Use Efficiency = 1.4 g DM MJ-1 IPAR
(Veldkamp, 1985); Light Extinction coefficient; Storage root bulking
initiation (40-45 days for TME419); Root growth rate,…
Soil parameters: Field capacity, wilting point and saturation based on
pedotransfer functions, maximum rooting depth,…

QUEFTS: Quantitative Evaluation of Fertility of Tropical Soils
Processes of
• Soil nutrient supply ~ Soil properties
• Crop nutrient uptake ~ Soil nutrient supply
• Crop yield ~ Crop nutrient uptake
Processes governing nutrient response
INS = f(total_N, pH)
IPS = f(Olsen_P, pH)
IKS = f(exch_K, pH)
Yield
Supply
(INS, IPS or IKS
+Fertilizer)
Fertilizer
Uptake

𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑇𝐶 = 𝐹𝑅 𝑈𝑟𝑒𝑎 𝐹𝑅 𝑇𝑆𝑃 𝐹𝑅 𝐷𝐴𝑃 𝐹𝑅 𝑁𝑎𝑓𝑎𝑘𝑎 𝐹𝑅 𝑀𝑂𝑃 𝐹𝑅 𝑁𝑃𝐾171717 ×
0.55
0.82
0.77
0.45
1.09
0.82
From (N, P, K) to fertilizer recommendations (FR)
Example (Lake Zone, Tanzania):
𝐹𝑅 𝑈𝑟𝑒𝑎 𝐹𝑅 𝑇𝑆𝑃 𝐹𝑅 𝐷𝐴𝑃 𝐹𝑅 𝑁𝑎𝑓𝑎𝑘𝑎 𝐹𝑅 𝑀𝑂𝑃 𝐹𝑅 𝑁𝑃𝐾171717 ×
46 0 0
0 46 0
18 46 0
10 26 0
0 0 60
17 17 17
= 75 46 108
Fertilizer rates (FR) x nutrient contents must equal recommended (N, P, K) rate
Total cost of the fertilizer regime must be minimized:
Solution: Total cost:124 0 100 0 180 0 341
Minjingu Nafaka Half NPK rate in NOTs
(expressed in N, P2O5, K2O ha-1)
Available fertilizers in Lake Zone, Tanzania
Fertilizer cost in $ kg-1
What if no DAP is available? Total cost:124 0 0 222 180 0 359
At what price is Min.Naf selected over DAP? 0.39 $ kg-1: Total cost:124 0 0 222 180 0 339

V1 version of the FR DST (end of 2017)
Landing page
V1 version of the FR-DST (smartphone app packaged as an ODK form):
GPS location and planting date Fertilizer quantities for plot and expected yield and
revenue increase

Nutrient omission trials
Sampling frame: maximize representativeness across target AoI
Aim for an unbiased, representative, sufficiently large and cost-effective sampling frame
→ GIS-assisted approach, using rainfall, soil and vegetation information (clustering)

Evaluate responses to N, P and K, and meso- & micro-nutrients:
SSR
Half
NPK
NPK½K+NP
1m
2m
PK
½N+PK Control
NPK+S+
Ca+Mg+
Zn+B
NK
1m
2m
½P+NK NPNPK
NPK Control
NPK+S+
Ca+Mg+
Zn+B
NK
PK
Half
NPK
NPKNP
1m
1m
2m
2m
NOT-1: nutrient omission
NOT-2: nutrient omission +
fertilizer response

Tanzania NOT 2016 NOT 2017 NOT 2018
Zone planted harvested planted harvested planted
Lake 112 73 109 65 0
Eastern 80 22 50 42 17
Southern 99 72 99 59 75
Total 291 167 235 166 92
Current overview of trials and status of trials
Nigeria NOT 2016 NOT 2017 NOT 2018
Zone planted harvested planted harvested planted
South East 85 56 140 123 9
South West 58 33 90 60 6
Total 143 89 230 183 15

NPK (28 t/ha)
NP (25 t/ha)
control (16 t/ha)

Impressions and learnings from the field – Tanzania
Observations
• NPK, NK, PK and NP gave
good root yield, higher than the
control.
• It is economical if fertilizer
companies would blend
fertilizers with moderate NPK,
PK, NP and NK.
“Farmers are willing to invest in
fertilizer for cassava production
due to high return of cassava
roots they had realized.”

Impressions and learnings from the field – Tanzania – some pictures
NPK Plot
Control Plot
Livestock grazing
Unattended trial

Impressions and learnings from the field – SE Nigeria – some pictures

NOT3 Field at Afii, Cross River State in 2018 @ 4 WAP with the EA and Farmer
NOT3 Field at Anaku, Anambra State and Edo South in 2018 @ 6 WAP

Control NPK

Impressions and learnings from the field – SE Nigeria – Out scalling
Farmers’ Field Day,
Bekwara, Cross River State
OKOMAYA ACAI Farmers Cooperative,
Ikom, Cross River State.

Impressions and learnings from the field – SW - Nigeria – some pictures
A full NPK plot yield
A control plot yield
“Farmers have seen the necessity of fertilizer application on cassava.
Some of them stated that they observed greener leaves and bigger
stems in fertilized compared control plots. Some farmers are now
applying fertilizers as against their initial positions before ACAI
intervention, but fertilizer availability and cost are their major concerns.”

Results 2016-2017 and 2017-2018 trials
Higher yields in 2018 vs 2017; Consistent effects across both years, but more pronounced in 2018:
Nigeria: deficiency in N (-3.9 t/ha) > K (-3.3 t/ha) > P (-2.1 t/ha)
Tanzania: deficiency in N (-2.4 t/ha) > K (-1.2 t/ha); no overall deficiency in P
As in 2017: strong evidence for site-specificity in nutrient deficiencies!

Results 2016-2017 and 2017-2018 trials: N deficiency
Nigeria
Comparable effects observed in 2018 as
in 2017, with same levels of site-specificity.
Tanzania:
No response observed in Tanzania in 2017,
but variable responses observed in 2018.
Plotting response to N (YNPK – YPK)
versus yield without N (YPK)
→ Clear variation in response

Results 2016-2017 and 2017-2018 trials: P deficiency
Nigeria
Comparable (small) effects observed in
2018 as in 2017, but with higher levels of
site-specificity.
Tanzania:
No overall P effects observed in Tanzania in
either year, but with low levels of site-
variability in both years.
Plotting response to P (YNPK – YNK)
versus yield without P (YNK)

Results 2016-2017 and 2017-2018 trials: K deficiency
Nigeria
More pronounced K responses observed in
2018 as in 2017, with higher levels of site-
specificity.
Tanzania:
Highly site-specific K effects observed at
yield levels > 15 t/ha in 2018, while very
limited effects in 2017.
Plotting response to K (YNPK – YNP)
versus yield without K (YNP)

Results 2016-2017 and 2017-2018 trials: response to Ca, Mg, S, B, Zn
Nigeria
Very limited responses observed in 2017 at
higher yield levels, but effects do not repeat
in 2018.
Tanzania:
No response to meso- and micronutrients
observed in either year.
Plotting response to meso- and
microelements (YNPK+micro – YNPK)
versus yield with NPK only (YNPK)
→ Very little effect on yield

Predicting nutrient use efficiency
Processes of
1
1
Use NOT response data to calculate and validate apparent nutrient supply…

Predicting indigenous nutrient supply
Use QUEFTS to estimate soil indigenous nutrient supply using root yield obtained from the
NPK, PK, NK and NP treatments in the Nutrient Omission Trials
1. Yield in NPK treatment is used as WLY
2. Available nutrient for cassava = (fertilizer used * recovery fraction) + soil available nutrients
soil available nutrients = (soil_N, soil_P, soil_K)
𝑁𝑃𝑦𝑖𝑒𝑙𝑑 = 𝑓 𝑵 𝒔𝒐𝒊𝒍 + (𝑁𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟∗ 𝑁 𝑅𝐹) , 𝑷 𝒔𝒐𝒊𝒍 + (𝑃𝑓𝑒𝑡𝑖𝑙𝑖𝑧𝑒𝑟 ∗ 𝑃𝑅𝐹) , 𝑊𝐿𝑌
𝑁𝐾𝑦𝑖𝑒𝑙𝑑 = 𝑓 𝑵 𝒔𝒐𝒊𝒍 + (𝑁𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟∗ 𝑁 𝑅𝐹) , 𝑲 𝒔𝒐𝒊𝒍 + (𝐾𝑓𝑒𝑡𝑖𝑙𝑖𝑧𝑒𝑟 ∗ 𝐾 𝑅𝐹) , 𝑊𝐿𝑌
𝑃𝐾𝑦𝑖𝑒𝑙𝑑 = 𝑓 𝑷 𝒔𝒐𝒊𝒍 + (𝑃𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟∗ 𝑃) , 𝑲 𝒔𝒐𝒊𝒍 + (𝐾𝑓𝑒𝑡𝑖𝑙𝑖𝑧𝑒𝑟 ∗ 𝐾 𝑅𝐹) , 𝑊𝐿𝑌
1. An optimization algorithm with residual sum of squares as cost function
search values for (soil_N, soil_P, soil_K) that will provide an estimated root yield for the
NK, NP and PK treatments
4. Validate by using the estimated soil_N, soil_P and soil_K and estimate root yield in the control and
treatment with half NPK rate

Predicting water-limited yield
WLY >> observed yield
Drought-affected trials
Modelledwater-limitedyield[tha-1]
Observed yield (without nutrient limitation) [t ha-1]
Modelled water-limited yields (LINTUL) versus observed yield in NPK treatment

Predictedrootyield[tha-1]
Observed root yield [t ha-1] Observed root yield [t ha-1]
Control treatment ½(NPK) treatment
Very adequate predictions of yield responses based on supply → uptake → yield

Processes of
2
2
Relate apparent nutrient supply to available soil data…

From soil properties to indigenous nutrient supply…
Using SoilGrids…

What proportion can we expect to predict from available soil data?
Decomposing variances in fertilizer response
Mixed modelling approach also provides insights in the proportion of total variance
in response at various scales:
Between clusters (5-50km) Residual
Between regions (>50km) Between fields (<5km)
Based on model lmer(Y ~ 0 + treat + (treat | region) + (treat | cluster) + (treat | fieldID)
SoilGrids data shows variation
between regions, but likely
captures less of the field-level
variation…
pH CEC
31% 12% 33% 24%

R2 Most important predictors
N 0.30 %Clay, pH, SOC, FC, CEC [multiple depths – 5, 15, 30cm]
P 0.51 CEC, %Clay, pH, Olsen-P, wp [multiple depths – 5, 15, 30cm]
K 0.27 CEC, Clay, exchK, pH, FC, wp, pH [multiple depths – 5, 15, 30, 60cm]
(INS, IPS, IKS) ~ (bd, FC, WP, SWS, %clay, %silt, %sand, pH, SOC, totalN, OlsenP, M3-P,
CEC, M3-K, Ca, Mg, Na, Al, B, exch.Ac, EC, root.depth) @ 7 depths
Soil nutrient supply

Observed apparent P supply [kg P ha-1]
RFpredictedPsupply[kgPha-1]
P supply

Cross-validation: how well do these predictions hold for subsets of the data?
Distribution of RMSE using MCMC subsampling for various proportions of data used for validation
RMSE
RMSE = 40 kg N ha-1 RMSE = 25 kg P ha-1 RMSE = 50 kg K ha-1

Cross-validation: how well do these predictions hold for subsets of the data?
Comparison of training model with Tanzania data versus Tanzania + Nigeria data
P supply - RMSE P supply - estimates

Building in local scale soil fertility indicators
Local soil type
Position in the landscape, slope,
soil colour, local soil name,
soil depth, drainage,…
Cropping history
Fallow type, previous crop,
nr of years of continuous cropping,
cropping system,…
Past soil fertility management
Past fertilizer use, manure inputs,
Perception of soil fertility,
distance to the homestead,…
Drivers of local soil fertility gradients

Local soil type
Position in the landscape, slope,
soil colour, local soil name,
soil depth, drainage,…
Cropping history
Fallow type, previous crop,
nr of years of continuous cropping,
cropping system,…
Past soil fertility management
Past fertilizer use, manure inputs,
Perception of soil fertility,
distance to the homestead,…
SE-NG SW-NG LZ-TZ EZ-TZ SZ-TZ ZZ-TZ
TLU 1.8 2.2 6.8 0.2 0.8 1.1
Often very context-specific…

Current yield is often the single best
indicator for expected response…I II III IV V
Define yield categories and predict
these from context-specific variables
(based on on-farm surveys)

R2 – SoilGrids
N 0.30
P 0.51
K 0.27

R2 – SoilGrids R2 – SoilGrids + CY categories
N 0.30 0.63
P 0.51 0.59
K 0.27 0.40
+ current yield categories
+

SoilGrids SoilGrids + CY categories
RFpredictedNsupply[kgNha-1]
Observed apparent N supply [kg N ha-1]

Observed apparent P supply [kg P ha-1]
RFpredictedPsupply[kgPha-1]
SoilGrids SoilGrids + CY categories

Optimizing for net revenue
What rates of available fertilizers with a total cost ceiling of 200$ ha-1?
Use an optimizer to maximize net revenue given price of roots and cost of available fertilizers:
26%: recommend = don’t apply
Expected median response:
14 t/ha [7 – 16 t/ha]
Expected net returns:
480 $/ha [230 – 570 $/ha]
Total cost:
185 $/ha [117 – 200 $/ha]

26%: recommend = don’t apply
Expected median response:
14 t/ha [7 – 16 t/ha]
Expected net returns:
480 $/ha [230 – 570 $/ha]
Total cost:
185 $/ha [117 – 200 $/ha]
What rates of available fertilizers with a total cost ceiling of 200$ ha-1?
Use an optimizer to maximize net revenue given price of roots and cost of available fertilizers:

Blanket recommendations remain useful…
Site-specific recommendations be aggregated / simplified…
120N
10P
30K
90N
7.5P
0K
0N
0P
0K

Optimizing for net revenue – what if farmers enter prices?
No convergence
Price data from ~2000 phone interviews
Panel of commercial cassava growers – interviewed at 2 times during the year in 2017…
Extremely messy data, with no relationship to type of market, volume of sale, or location (within region)
Some patterns in price evolution throughout the year, confirmed by key informants…

Two important aspects to validation:
Validating decision support tools
1. Functional validation: verify and improve whether the recommendations supplied by the tool
outperform current practice, or current best (blanket) recommendations
2. Architectural validation: verify and improve the user experience (format or “look and feel”), so that
the tool fits the needs of the end-user and is easy to use.
Good =
Current
best
practice

Validation exercises – pilot study
• Currently 581 farmers across Tanzania and Nigeria involved in
pilot validation exercise…
• Supervised by trained extension agents, and coordinated by
primary development partners (SG2000 and Notore in Nigeria,
and MEDA in Tanzania)
• NARS teams of agronomists assist in training and monitoring.
• DSTs and all data collection through a suite of ODK forms

Validation exercises – overview
Overview of participants (Nigeria)
692 submissions on “Site-specific fertilizer recommendations”
513 validation exercises established, across 7 states (Oyo, Ogun, Osun, Edo, Benue, Cross River, Anambra)

Nr participants = 692
Nr of cases where tool
recommends “Do not apply fertilizer.”: 174 (25%)
Average yield response: 7.5 tonnes/ha
Range: [1 – 17 tonnes/ha]
Expected yield response (Nigeria)
1:1 line: do not apply fertilizer

Nr participants = 692
Nr of cases where tool
recommends “Do not apply fertilizer.”: 174 (25%)
Average yield response: 7.5 tonnes/ha
Range: [1 – 17 tonnes/ha]
Average net revenue: 242 USD/ha
Range [8 – 604 USD/ha]
Average cost: 117 USD/ha
Range: [27 – 200 USD/ha]
Expected net returns (Nigeria)

Response is mainly to N (urea) and K (MOP), with relatively similar rates recommended.
Recommended fertilizer rates (Nigeria)

Nr participants = 68 / 360 (planted / target)
Nr of cases where tool recommends
“Do not apply fertilizer.”: 0 (0%)
Overview of participants (Tanzania)

Validation Exercises - Key Activities
Training-of-trainers
The training-of-trainer event aimed at empowering the lead field coordinators of MEDA to use the Decision
Support Tool (DST) of the FR use case, and implement the FR validation exercises, as well as organizing the
cassava growers and EAs around the cassava growers for optimal productivity.

Step-down training
Stepdown training was done in the field,
hands-on with the extension agents of the
development partner (MEDA), and volunteer
farmers / cassava growers, together with the
research partners (TARI, backstopped by IITA).
The step-down training aimed at empowering EA on
how to use the Decision Support Tool (DST) for the FR
use case, also organizing the cassava growers and EAs
around the cassava growers for optimal cassava production.

Trial establishment
• Preparation of all 360 field locations completed.
• 68 / 360 planted. Indications of variation in soil fertility…
• Challenges include drought and technical issues with registering farmers on the ODK platform.

Trial establishment
Colour-coded plots to make distinguishing plots (treatments) easy.

Testimonies from farmers
Simon Abang – SG2000 CR
I have learnt the following:
• Fertilizer application in terms of type and rate
• Planting on ridges as against the traditional
planting on heaps
• Use of TME of 419 (he has been hearing
about it but he has never seen or used it)
Everybody that passes by the red plot stops to
admire beauty of cassava plants on the plot.
People disturbs me everyday to know the secret
behind the performance of the yellow plot.
Animals are penetrating the recommended plot
more than the control plot because cassava roots
have started coming out of the land due to
fertilizer application.

Testimonies from Extension Agents
I have learnt about the following
• Application of fertilizer in cassava (I formerly
knew of application of fertilizer on yam)
• Spacing of 0.8m x 1m against the traditional
spacing of 1m x 1m
• Use of ODK for data collection
Recommendation plot looks more appealing
than control plot because of fertilizer application.
Oyama Okora – SG2000 CR

Testimonies from Extension Agents
• I have learnt about use of MOP as new fertilizer
for cassava (I formerly knew only of NPK
fertilizers)
• I have learnt specific fertilizer rate for cassava as
against the traditional blanket rate.
• I have learnt that weeding can be done more
than two times in a planting season.
Abraham Fagbohun – SG2000 OG

Follow-up Activities
Cluster meetings by NOTORE
• Objectives:
• Refresher training with VPs and Farmers on GAP of Cassava
• Knowledge transfer & opinion sharing
• Group learning/idea sharing sessions
• At least twice a month in this year 2018 across the active communities in the 5 states.
• VPs and Farmers (occasionally)

Learning from development partners
• Farmers already seeing significant differences between recommendations from the tool
and their common practice, and are eager to see the outcome of the validation trials at
harvest.
• The majority of the SVPs quickly adapted to the use of DSTs and had little or no difficulty
using the application.
• There are some complaints about timing, frequency of data capturing and monitoring
The validation exercises serve us…
• as a platform to reach out to farmers and promote our business
• The Project has a positive impact on our as this is inline with the company objectives on
specialty blends and this project has create a platform to reach out to farmers.
• Integration of the DST into our extension program on farmer education for year 2019 and
Involvement in the dissemination of the DST to end-users.
We request provision of training and dissemination materials especially videos
to support our Video Viewing activities.
Feedback from farmers and Extension Agents

Thank you very much !!!
Questions and discussion

Session 2 1 Development of the Site Specific Fertilizer Recommendation (FR) and Best Fertilizer Blend (FB) Decision Support Tool

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Session 2 1 Development of the Site Specific Fertilizer Recommendation (FR) and Best Fertilizer Blend (FB) Decision Support Tool