diapo_1.pdf

Development of the
Site-Specific Fertilizer Recommendation (FR)
and Best Fertilizer Blend (FB)
Decision Support Tools (DSTs) – V1
www.iita.org | www.cgiar.org | www.acai-project.org

Overview
Site-Specific Fertilizer Recommendation and Best Fertilizer Blend DSTs:
1. Background and modelling framework (Guillaume Ezui):
• Introduction
• Learnings from literature
• Learnings from baseline and rapid characterization
• Modelling framework: LINTUL and QUEFTS
2. Field activities (Yemi Olojede and Deusdedit Peter Mlay):
• Field activities: Nutrient Omission Trials
• Field trial results
3. Development of the DST (Meklit Chernet):
• Overview of recommendations for Tanzania
• The Decision Support Tool
• Ongoing validation activities
• Next steps and additional data needs

Overview
Site-Specific Fertilizer Recommendation and Best Fertilizer Blend DSTs:
1. Background and modelling framework (Guillaume Ezui):
• Introduction
• Learnings from literature
• Learnings from baseline and rapid characterization
• Modelling framework: LINTUL and QUEFTS
2. Field activities (Yemi Olojede and Peter Mlay):
• Field activities: Nutrient Omission Trials
• Field trial results
3. Development of the DST (Meklit Chernet):
• Overview of recommendations for Tanzania
• The Decision Support Tool
• Ongoing validation activities
• 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: V1: implemented at 5x5km, for an investment of maximally 200 $ ha-1 (fixed),
for a fixed set of fertilizers (urea, Minjingu mazao, 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 RC and baseline survey
Nigeria: use of herbicides very common. Farmers using fertilizer are almost always farmers using herbicide.
Tanzania: use of inputs in cassava is very rare, but not so in other crops (e.g. fertilizer in maize, pesticides
in cash crops like cashew, cotton,…)

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 smartphone app for field use
The FR-DST is developed based on following steps and principles:

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,…

Water-limited yield was calculated for weekly steps in planting date across the planting window:
Southern zone Zanzibar
Lake zone Eastern zone
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of
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Selection of Q1 – Q2 – Q3 year precipitation pattern (total rainfall and nr of rainy days), per zone
from 22 years of rainfall data:

Spatializing water-limited yield
Spatializing: water-limited yield was calculated for each pixel of 5 x 5 km across the AoI,
as maximum of 3 modelled years:

Determining indigenous nutrient supply
Simple empirical linear equations using soil chemical data:
Original equations are not adequate for cassava. Currently based on the work of Howeler (2017),
with modifications:
𝐼𝑁𝑆1 = 188.84 𝑂𝑟𝑔 𝐶 − 6.2265
𝐼𝑁𝑆2 = 221.94 𝑂𝑟𝑔 𝐶 + 4.8519
𝐼𝑃𝑆1 = 0.3302 𝐵𝑟𝑎𝑦 − 𝐼 𝑃 + 8.3511
𝐼𝑃𝑆2 = 0.6067 𝑂𝑙𝑠𝑒𝑛 𝑃 + 1.084
𝐼𝐾𝑆1 = 0.7398 𝐸𝑥𝑐ℎ 𝐾 − 9.9405
𝐼𝐾𝑆2 = 0.2499 𝐸𝑥𝑐ℎ 𝐾 + 29.051
Using data on soil parameters (bd, orgC, M3-P and M3-K) from ISRIC (ftp://ftp.soilgrids.org/data/recent/),
assuming OlsenP [mg P kg-1] = 0.5 * M3-P [mg P kg-1] and exchK [cmolc kg-1] = M3-K [mg K kg-1] / 391
Currently, data from the nutrient omission trials is used to calibrate the indigenous nutrient supply
(see next section on field trial activities)
Source: Howeler, R., 2017. Chapter 15: Nutrient sources and their application in cassava cultivation. In: Achieving sustainable
cultivation of cassava Volume 1: Cultivation techniques. C. Hershey (Ed.). Burleigh Dodds Science Publishing, UK. 424 pp.

Spatializing indigenous nutrient supply
Best predictions for indigenous nutrient supply are then applied at scale:
Spatializing: INS, IPS and IKS were calculated for each pixel of 5 x 5 km across the AoI:

From uptake to yield (QUEFTS – step 3)
Maximum dilution / accumulation curves: convert (N, P, K) uptake to yield
Physiological nutrient use efficiency:
Root
yield
(t
DM
ha
-1
)
N uptake (kg N ha-1) P uptake (kg P ha-1) K uptake (kg K ha-1)
𝑃ℎ𝐸𝛽𝑚𝑎𝑥,𝑁
𝑃ℎ𝐸𝛽𝑚𝑖𝑛,𝑁
𝑃ℎ𝐸𝛽𝑚𝑎𝑥,𝑃 𝑃ℎ𝐸𝛽𝑚𝑖𝑛,𝑃
𝑃ℎ𝐸𝛽𝑚𝑎𝑥,𝐾
𝑃ℎ𝐸𝛽𝑚𝑖𝑛,𝐾
𝑃ℎ𝐸𝛽
Example: P-deficiency and ample (N, K) supply: maximal dilution of P, and maximal accumulation of (N, K)

Maximum dilution / accumulation is dependent on harvest index (HI)
Physiological nutrient use efficiency (Ezui et al., 2017):
𝑃ℎ𝐸𝛽𝑚𝑎𝑥 = 1000 ×
𝐻𝐼
𝐻𝐼 × 𝐶𝛽𝑟𝑜𝑜𝑡𝑠,𝑚𝑖𝑛 + (1 − 𝐻𝐼) × 𝐶𝛽𝑡𝑜𝑝𝑠,𝑚𝑖𝑛
𝑃ℎ𝐸𝛽𝑚𝑖𝑛 = 1000 ×
𝐻𝐼
𝐻𝐼 × 𝐶𝛽𝑟𝑜𝑜𝑡𝑠,𝑚𝑎𝑥 + (1 − 𝐻𝐼) × 𝐶𝛽𝑡𝑜𝑝𝑠,𝑚𝑎𝑥
𝑤𝑖𝑡ℎ 𝐶 = 𝑚𝑎𝑠𝑠 𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑎 𝑔𝑖𝑣𝑒𝑛 𝑛𝑢𝑡𝑟𝑖𝑒𝑛𝑡 𝛽 𝑔 𝑘𝑔−1
, obtained from Nijhof, 1987.
Source: Sattari S.Z., van Ittersum M. K., Bouwman A.F., Smit A. L. and Janssen B.H., 2014. Crop yield response to soil fertility and N, P, K inputs in different environments: Testing and
improving the QUEFTS model, Field Crops Research, 157: 35-46.
Ezui G., Franke A.C., Ahiabor B.D.K., Tetteh F.M., Sogbedji J., Janssen B.H., Mando A. and Giller K.E., 2017. Understanding cassava yield response to soil and fertilizer nutrient supply
in West Africa. Plant and Soil https://doi.org/10.1007/s11104-017-3387-6
𝑃ℎ𝐸𝛽,
𝑘𝑔
𝑘𝑔
−1
𝐻𝐼, 𝑘𝑔 𝑘𝑔−1
N P K

Maximum dilution / accumulation is dependent on harvest index (HI)
Physiological nutrient use efficiency (Sattari et al., 2014; Byju et al., 2014, Ezui et al., 2017):
Source: Byju G., Nedunchezhiyan M., Ravindran C.S., Santhosh Mithra V.S., Ravi V. and Naskar S.K., 2012. Modeling the response of cassava to fertilizers: a site-specific nutrient
management approach for greater tuberous root yield. Communications in Soil Science and Plant Analysis, 43: 1149-62.
Ezui K.S., Franke A.C., Mando A., Ahiabor B.D.K., Tetteh F.M., Sogbedji J., Janssen B.H. and Giller K.E., 2016. Fertiliser requirements for balanced nutrition of cassava across eight
locations in West Africa. Field Crops Research, 185: 69-78.
Cultivar HI PhEmin PhEmax R-Phe Source
aN aP aK dN dP dK kg N/ton DM kg P/ton DM kg K/ton DM
India 0.40 35 250 32 80 750 102 17.4 2.0 14.9 Byju et al., 2012
Gbazekoute
(TME419)
0.40 30 175 26 70 465 126 20.0 3.1 13.2 Ezui et al., 2016
0.50 41 232 34 96 589 160 14.6 2.4 10.3 Ezui et al., 2016
0.55 47 262 38 112 653 178 12.6 2.2 9.3 Ezui et al., 2016
Afisiafi
0.65 61 329 47 148 782 214 9.6 1.8 7.7 Ezui et al., 2016
0.70 70 365 53 170 848 233 8.3 1.6 7.0 Ezui et al., 2016
Nutrient uptake requirement to produce 1 ton of storage root dry matter
[using balanced nutrient principle] (reciprocal PhE) depends on HI.

Harvest index (HI) is strongly dependent on environment!
Results from year-1 Nutrient Omission Trials:
Harvest index little affected by fertilizer treatment, but large variation due to environmental factors.
Better understanding needed to improve predictions of physiological nutrient efficiency.

From (N, P, K) to fertilizer recommendations (FR)
Solve a set of equations to determine (FR1, FR2, …, FRn):
𝐹𝑅1 𝐹𝑅2 ⋯ 𝐹𝑅𝑛 ×
𝑁1 𝑃1 𝐾1
𝑁2 𝑃2 𝐾2
⋮ ⋮ ⋮
𝑁𝑛 𝑃𝑛 𝐾𝑛
= 𝑁 𝑃 𝐾
Fertilizer rates (FR) x nutrient contents must equal recommended (N, P, K) rate
𝐹𝑅1 𝐹𝑅2 ⋯ 𝐹𝑅𝑛 ×
1 0 ⋯ 0
0 1 ⋯ 0
⋮ ⋮ ⋱ ⋮
0 0 ⋯ 1
≥ 0 0 ⋯ 0
𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑇𝐶 = 𝐹𝑅1 𝐹𝑅2 ⋯ 𝐹𝑅𝑛 ×
𝐶1
𝐶2
⋮
𝐶𝑛
Fertilizer rates (FR) must be equal or larger than zero (boundary condition)
Total cost of the fertilizer regime must be minimized:
Solved using R package “limSolve”, “lpSolve” or “lpSolveAPI”

𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑇𝐶 = 𝐹𝑅𝑈𝑟𝑒𝑎 𝐹𝑅𝑇𝑆𝑃 𝐹𝑅𝐷𝐴𝑃 𝐹𝑅𝑀𝑖𝑛.𝑚𝑎𝑧 𝐹𝑅𝑀𝑂𝑃 𝐹𝑅𝑁𝑃𝐾171717 ×
0.55
0.82
0.77
0.45
1.09
0.82
From (N, P, K) to fertilizer recommendations (FR)
Example (Lake Zone, Tanzania):
𝐹𝑅𝑈𝑟𝑒𝑎 𝐹𝑅𝑇𝑆𝑃 𝐹𝑅𝐷𝐴𝑃 𝐹𝑅𝑀𝑖𝑛.𝑚𝑎𝑧 𝐹𝑅𝑀𝑂𝑃 𝐹𝑅𝑁𝑃𝐾171717 ×
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 mazao 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.maz selected over DAP? 0.39 $ ha-1: Total cost:
124 0 0 222 180 0 339

Maximizing net revenue
Determine the (N,P,K) and FR that maximize net revenue
𝑁𝑅 = 𝐺𝑅 − 𝑇𝐶
𝑤𝑖𝑡ℎ 𝑁𝑅 = 𝑛𝑒𝑡 𝑟𝑒𝑣𝑒𝑛𝑢𝑒 $ ℎ𝑎−1
, 𝐺𝑅 = 𝑔𝑟𝑜𝑠𝑠 𝑟𝑒𝑣𝑒𝑛𝑢𝑒 $ ℎ𝑎−1
, 𝑇𝐶 = 𝑡𝑜𝑡𝑎𝑙 𝑐𝑜𝑠𝑡 $ ℎ𝑎−1
𝑁𝑅 = 𝑇𝑌 − 𝐶𝑌 ∗ 𝑈𝑃 − 𝑇𝐶
𝑤𝑖𝑡ℎ 𝑇𝑌 = 𝑡𝑎𝑟𝑔𝑒𝑡 𝑦𝑖𝑒𝑙𝑑 𝑡 ℎ𝑎−1
, 𝐶𝑌 = 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑦𝑖𝑒𝑙𝑑 𝑡 ℎ𝑎−1
, 𝑈𝑃 = 𝑢𝑛𝑖𝑡 𝑝𝑟𝑖𝑐𝑒 𝑜𝑓 𝑟𝑜𝑜𝑡𝑠 $ 𝑡−1
𝑇𝑌 = 𝑓𝑄𝑈𝐸𝐹𝑇𝑆 𝑁 𝑃 𝐾 , 𝑝𝑎𝑟𝑠𝑐𝑟𝑜𝑝, 𝑝𝑎𝑟𝑠𝑠𝑜𝑖𝑙, 𝑊𝑌
𝐶𝑌 = 𝑓𝑄𝑈𝐸𝐹𝑇𝑆 0 0 0 , 𝑝𝑎𝑟𝑠𝑐𝑟𝑜𝑝, 𝑝𝑎𝑟𝑠𝑠𝑜𝑖𝑙, 𝑊𝑌
𝑇𝐶 = 𝑓𝑙𝑖𝑚𝑆𝑜𝑙𝑣𝑒 𝑁 𝑃 𝐾 , 𝑁𝐶, 𝐹𝐶
𝑤𝑖𝑡ℎ 𝑓𝑄𝑈𝐸𝐹𝑇𝑆 = 𝑄𝑈𝐸𝐹𝑇𝑆 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛, 𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑖𝑛𝑔 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒 𝑡𝑜 𝑎 𝑔𝑖𝑣𝑒𝑛 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 𝑜𝑓 𝑁, 𝑃, 𝐾,
𝑤𝑖𝑡ℎ 𝑎𝑟𝑔𝑢𝑚𝑒𝑛𝑡𝑠 𝑐𝑟𝑜𝑝 𝑝𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟𝑠, 𝑠𝑜𝑖𝑙 𝑝𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟𝑠 𝑎𝑛𝑑 𝑤𝑎𝑡𝑒𝑟 𝑙𝑖𝑚𝑖𝑡𝑒𝑑 𝑦𝑖𝑒𝑙𝑑 (𝑊𝑌 𝑡 ℎ𝑎−1
)
𝑤𝑖𝑡ℎ 𝑓𝑙𝑖𝑚𝑆𝑜𝑙𝑣𝑒 = 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑖𝑛𝑔 𝑡ℎ𝑒 𝑡𝑜𝑡𝑎𝑙 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑎 𝑔𝑖𝑣𝑒𝑛 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑁, 𝑃, 𝐾,
𝑤𝑖𝑡ℎ 𝑎𝑟𝑔𝑢𝑚𝑒𝑛𝑡𝑠 𝑁𝐶 𝑛𝑢𝑡𝑟𝑖𝑒𝑛𝑡 𝑐𝑜𝑛𝑡𝑒𝑛𝑡𝑠 𝑎𝑛𝑑 𝐹𝐶 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟𝑠 (𝑠𝑒𝑒 𝑏𝑒𝑓𝑜𝑟𝑒)
𝑁 𝑃 𝐾 = 𝑓𝑚𝑎𝑥𝑅𝑒𝑣 …
𝑤𝑖𝑡ℎ 𝑓𝑚𝑎𝑥𝑅𝑒𝑣 = 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑚𝑎𝑥𝑖𝑚𝑖𝑧𝑖𝑛𝑔 𝑁𝑅, 𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑖𝑛𝑔 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒 𝑡𝑜 𝑎 𝑔𝑖𝑣𝑒𝑛 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 𝑜𝑓 𝑁, 𝑃, 𝐾,
𝑢𝑠𝑖𝑛𝑔 𝑎𝑟𝑔𝑢𝑚𝑒𝑛𝑡𝑠 𝑜𝑓 𝑓𝑄𝑈𝐸𝐹𝑇𝑆 𝑎𝑛𝑑𝑓𝑙𝑖𝑚𝑆𝑜𝑙𝑣𝑒
Solved using optimizer function (“optim”) in R

Maximizing net revenue
Example
Solution with max. NR

Maximizing net revenue for a given investment
What if the farmer is limited in his investment capacity?
𝑁 𝑃 𝐾 = 𝑓𝑚𝑎𝑥𝑅𝑒𝑣 … , 𝑖𝑛𝑣𝑒𝑠𝑡
𝑤𝑖𝑡ℎ 𝑓𝑚𝑎𝑥𝑅𝑒𝑣 = 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑚𝑎𝑥𝑖𝑚𝑖𝑧𝑖𝑛𝑔 𝑁𝑅, 𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑖𝑛𝑔 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒 𝑡𝑜 𝑎 𝑔𝑖𝑣𝑒𝑛 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 𝑜𝑓 𝑁, 𝑃, 𝐾,
𝑢𝑠𝑖𝑛𝑔 𝑎𝑟𝑔𝑢𝑚𝑒𝑛𝑡𝑠 𝑜𝑓 𝑓𝑄𝑈𝐸𝐹𝑇𝑆 𝑎𝑛𝑑𝑓𝑙𝑖𝑚𝑆𝑜𝑙𝑣𝑒, 𝒂𝒏𝒅 𝒊𝒏𝒗𝒆𝒔𝒕 = 𝒎𝒂𝒙𝒊𝒎𝒂𝒍 𝒗𝒂𝒍𝒖𝒆 𝒇𝒐𝒓 𝑻𝑪 [$ 𝒉𝒂−𝟏
]

Maximizing net revenue for a given investment
Example
Solution with max. NR for TC = 200$ ha-1

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
NPK
½K+NP
1m
2m
PK
½N+PK Control
NPK+S+
Ca+Mg+
Zn+B
NK
1m
2m
½P+NK NP
NPK
NPK Control
NPK+S+
Ca+Mg+
Zn+B
NK
PK
Half
NPK
NPK
NP
1m
1m
2m
2m
NOT-1: nutrient omission
NOT-2: nutrient omission +
fertilizer response

Tanzania NOT 2016 NOT 2017
Zone planted harvested planted
Lake 112 73 109 (120)
Eastern 80 22 100
Southern 99 20 (74) 51 (80)
Total 291 115 (189) 160 (300)
Current overview of trials and status of trials
Nigeria NOT 2016 NOT 2017
Zone planted harvested planted
South East 85 56 140/180
South West 58 33 89/120
Total 143 89 227/300

Impressions and learnings from the field – Tanzania – some pictures
Collaboration
Performance
Success
Challenges
Appreciation

Impressions and learnings from the field – Tanzania – some pictures
Some extreme examples…
• LZ: NPK (48 t/ha), half NPK (43 t/ha), NK (20 t/ha), control (9 t/ha)
• EZ: NPK (41 t/ha), NPK+micro (38 t/ha) and control (8 t/ha)
• SZ: NPK (30 t/ha), control (7 t/ha)
CON NPK NK NP

Impressions and learnings from the field – Nigeria – some pictures
Control NK NPK NPK+

Impressions and learnings from the field – Nigeria – some pictures
NOT
FIELD
Marginal blotch in plots
with NPK+micro
Plant barcoding in
progress
Challenges…
• Logistical due to scale and spread of activities
• Interaction with and know-how of EAs
• Collaboration and interaction with farmers
• Remuneration of activities
• Conflicts with cattle
• …

Results
Estimate Std. Error Pr(>|t|)
Nigeria
NPK(ref) 28.762 1.584 ***
PK -4.318 1.416 **
NK -2.662 1.242 *
NP -2.291 1.526 ns
NPK+micro 1.909 1.318 ns
half_NPK -3.518 1.286 **
CON -8.208 1.404 ***
Tanzania
NPK(ref) 11.329 1.122 ***
PK 0.483 1.059 ns
NK -2.173 1.079 *
NP 0.247 1.077 ns
NPK+micro -0.511 1.073 ns
half_NPK -0.281 1.075 ns
CON -2.603 1.068 **
Nigeria: N deficiency > P deficiency; borderline deficiency in K and response to meso-/micronutrients.
Tanzania: P deficiency!
Note: large number of trials planted in the secondary season (low yields).

Results
Nigeria: N deficiency > P deficiency; borderline deficiency in K and response to meso-/micronutrients.
Tanzania: P deficiency!
Note: large number of trials planted in the secondary season (low yields).
Loglikelihood Ratio test, comparing
yield ~ treat + (1|trialID))
yield ~ treat + (treat|trialID))
Pr(>Chisq) = 6.14e-05 ***
Large variation in yield and significant
differences in yield response to N, P, K
between trial locations…
→ site-specific fertilizer recommendations

Results
Example: Effect of N omission in Nigeria

Results
Example: Effect of P omission in Tanzania

Interpreting the recommendations
Yield distribution with and without fertilizer

How to make this framework available for quick and easy use?

Net revenue versus cost

Fertilizer requirements

Packaging in a tool for field use
Preloading records Landing page
FR-DST packaged as a simple ODK form, with GPS location and planting date as only inputs (for now):

Select country User identification Read GPS location Define planting date

When outside target AoI… Define planting density
Define variety Define plot size

Expected yield increase
Results – fertilizer rates Fertilizer quantities for plot Expected net revenue and cost

Validation trial? End – save and send

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):
• Investment capacity
• Available fertilizers and prices
• Price of output
• Date of harvest
3. Limitations for offline storage of recommendations reached. What options?
• Within-app calculations
• Online calculations (on central server)
• SMS-based requests (to central server) and recommendations
4. What about other nutrients? Blanket recommendations at district / state level?
5. Integrate knowledge of temporal-spatial variation in input and output prices
6. Integrate risk estimates based on the impact of uncertainty in:
• Input and output prices
• Rainfall (attainable yields)
7. Scale down from 5x5km to 250x250m:
• How much additional variation can we exploit from the GIS layers?
• How do we integrate expert knowledge from the end-user
V1 is a ‘hybrid’ between a research tool and the intended ‘app’

Next steps
Determining indigenous nutrient supply
5 step-process to recalibrate indigenous nutrient supply using NOT data:
1. Extract best linear predictors for response to N, P and K (mixed models – BLUPs)
2. Calculate required INS, IPS and IKS to obtain the observed responses
3. Build prediction models for INS, IPS and IKS using
i. soil chemical analysis data
ii. GIS-based predicted soil data
4. Compare and evaluate scale and accuracy issues + design strategies for improvement
5. Cross-validate (n-fold cross validation, evaluating accuracy at field / location / season)

Principles of the Fertilizer Blending Tool
How to compose a best fertilizer blend for cassava?
For a target area and each nutrient…
What is the yield loss due to deficiency in this nutrient?
What nutrient rate is required for a given yield response?
How do these nutrient rates vary (spatially)?
How much area requires a minimal nutrient application?
Combining nutrients for a target area?
Are nutrients best combined as a single complex blend?
Or, are different formulations needed?
FB-V1 DST

Principles of the Fertilizer Blending 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. Determine the NPK requirement for a target yield increase (balanced nutrition)
6. Package the output in a webtool as a basis for decision-making by fertilizer blenders
The FB-DST is developed based on the same principles:

The Fertilizer Blending Tool
The FB-DST is developed as a web application in :

Questions and discussion

diapo_1.pdf

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