Session 2 fertilizer recommendation and fertilizer blending dst

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
Jan
Feb
M
ar
Apr
M
ay
Jun
Jul
Aug
Sep
O
ct
N
ov
D
ec
Jan
Feb
M
ar
Apr
M
ay
Jun
Jul
Aug
Sep
O
ct
N
ov
D
ec
0
20
40
60
0
20
40
60
Proportionoffarmers

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:
𝐼𝐼 𝐼𝐼𝐼𝐼1 = 188.84 𝑂𝑂𝑂𝑂𝑂𝑂 𝐶𝐶 − 6.2265
𝐼𝐼 𝐼𝐼𝐼𝐼2 = 221.94 𝑂𝑂𝑂𝑂𝑂𝑂 𝐶𝐶 + 4.8519
𝐼𝐼 𝐼𝐼𝐼𝐼1 = 0.3302 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 − 𝐼𝐼 𝑃𝑃 + 8.3511
𝐼𝐼 𝐼𝐼𝐼𝐼2 = 0.6067 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝑃𝑃 + 1.084
𝐼𝐼 𝐼𝐼𝐼𝐼1 = 0.7398 𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝐾𝐾 − 9.9405
𝐼𝐼 𝐼𝐼𝐼𝐼2 = 0.2499 𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝐾𝐾 + 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:
Rootyield(tDMha-1)
N uptake (kg N ha-1) P uptake (kg P ha-1) K uptake (kg K ha-1)
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚 𝑚𝑚𝑚𝑚,𝑁𝑁
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚𝑚𝑚 𝑚𝑚,𝑁𝑁
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚𝑚𝑚𝑚𝑚,𝑃𝑃 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚𝑚𝑚 𝑚𝑚,𝑃𝑃
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚𝑚𝑚𝑚𝑚,𝐾𝐾
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚𝑚𝑚 𝑚𝑚,𝐾𝐾
𝑃𝑃𝑃𝑃𝑃𝑃𝑃
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):
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚𝑚𝑚𝑚𝑚 = 1000 ×
𝐻𝐻𝐻𝐻
𝐻𝐻𝐻𝐻 × 𝐶𝐶𝐶𝐶𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟,𝑚𝑚𝑚𝑚 𝑚𝑚 + (1 − 𝐻𝐻𝐻𝐻) × 𝐶𝐶𝐶𝐶𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡,𝑚𝑚𝑚𝑚 𝑚𝑚
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑚𝑚𝑚𝑚 𝑚𝑚 = 1000 ×
𝐻𝐻𝐻𝐻
𝐻𝐻𝐻𝐻 × 𝐶𝐶𝐶𝐶𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟,𝑚𝑚𝑚𝑚𝑚𝑚 + (1 − 𝐻𝐻𝐻𝐻) × 𝐶𝐶𝐶𝐶𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡,𝑚𝑚𝑚𝑚𝑚𝑚
𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝐶𝐶 = 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑜𝑜𝑜𝑜 𝑎𝑎 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝛽𝛽 𝑔𝑔 𝑘𝑘𝑘𝑘−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
𝑃𝑃𝑃𝑃𝑃𝑃𝑃,𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘−1
𝐻𝐻𝐻𝐻, 𝑘𝑘𝑘𝑘 𝑘𝑘𝑘𝑘−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):
𝐹𝐹𝐹𝐹𝐹 𝐹𝐹𝐹𝐹𝐹 ⋯ 𝐹𝐹𝐹𝐹𝐹𝐹 ×
𝑁𝑁𝑁 𝑃𝑃𝑃 𝐾𝐾𝐾
𝑁𝑁𝑁 𝑃𝑃𝑃 𝐾𝐾𝐾
⋮ ⋮ ⋮
𝑁𝑁𝑁𝑁 𝑃𝑃𝑃𝑃 𝐾𝐾𝐾𝐾
= 𝑁𝑁 𝑃𝑃 𝐾𝐾
Fertilizer rates (FR) x nutrient contents must equal recommended (N, P, K) rate
𝐹𝐹𝐹𝐹𝐹 𝐹𝐹𝐹𝐹𝐹 ⋯ 𝐹𝐹𝐹𝐹𝐹𝐹 ×
1 0 ⋯ 0
0 1 ⋯ 0
⋮ ⋮ ⋱ ⋮
0 0 ⋯ 1
≥ 0 0 ⋯ 0
𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑇𝑇𝑇𝑇 = 𝐹𝐹𝐹𝐹𝐹 𝐹𝐹𝐹𝐹𝐹 ⋯ 𝐹𝐹𝐹𝐹𝐹𝐹 ×
𝐶𝐶𝐶
𝐶𝐶𝐶
⋮
𝐶𝐶𝐶𝐶
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”

𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑇𝑇𝑇𝑇 = 𝐹𝐹𝐹𝐹𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝐹𝐹𝐹𝐹𝑇𝑇𝑇𝑇𝑇𝑇 𝐹𝐹𝐹𝐹𝐷𝐷𝐷𝐷𝐷𝐷 𝐹𝐹𝐹𝐹𝑀𝑀𝑀𝑀𝑀𝑀.𝑚𝑚𝑚𝑚𝑚𝑚 𝐹𝐹𝐹𝐹𝑀𝑀𝑀𝑀𝑀𝑀 𝐹𝐹𝐹𝐹𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 ×
0.55
0.82
0.77
0.45
1.09
0.82
From (N, P, K) to fertilizer recommendations (FR)
Example (Lake Zone, Tanzania):
𝐹𝐹𝐹𝐹𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝐹𝐹𝐹𝐹𝑇𝑇𝑇𝑇𝑇𝑇 𝐹𝐹𝐹𝐹𝐷𝐷𝐷𝐷𝐷𝐷 𝐹𝐹𝐹𝐹𝑀𝑀𝑀𝑀𝑀𝑀.𝑚𝑚𝑚𝑚𝑚𝑚 𝐹𝐹𝐹𝐹𝑀𝑀𝑀𝑀𝑀𝑀 𝐹𝐹𝐹𝐹𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 ×
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
𝑁𝑁𝑁𝑁 = 𝐺𝐺𝐺𝐺 − 𝑇𝑇𝑇𝑇
𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑁𝑁𝑁𝑁 = 𝑛𝑛𝑛𝑛𝑛𝑛 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 $ ℎ𝑎𝑎−1
, 𝐺𝐺𝐺𝐺 = 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 $ ℎ𝑎𝑎−1
, 𝑇𝑇𝑇𝑇 = 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 $ ℎ𝑎𝑎−1
𝑁𝑁𝑁𝑁 = 𝑇𝑇𝑇𝑇 − 𝐶𝐶𝐶𝐶 ∗ 𝑈𝑈𝑈𝑈 − 𝑇𝑇𝑇𝑇
𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑇𝑇𝑇𝑇 = 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦 𝑡𝑡 ℎ𝑎𝑎−1
, 𝐶𝐶𝐶𝐶 = 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦 𝑡𝑡 ℎ𝑎𝑎−1
, 𝑈𝑈𝑈𝑈 = 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑜𝑜𝑜𝑜 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 $ 𝑡𝑡−1
𝑇𝑇𝑇𝑇 = 𝑓𝑓𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 𝑁𝑁 𝑃𝑃 𝐾𝐾 , 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐, 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠, 𝑊𝑊𝑊𝑊
𝐶𝐶𝑌𝑌 = 𝑓𝑓𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 0 0 0 , 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐, 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠, 𝑊𝑊𝑊𝑊
𝑇𝑇𝑇𝑇 = 𝑓𝑓𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑁𝑁 𝑃𝑃 𝐾𝐾 , 𝑁𝑁𝑁𝑁, 𝐹𝐹𝐹𝐹
𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑓𝑓𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 = 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓, 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑡𝑡𝑡𝑡 𝑎𝑎 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑜𝑜𝑜𝑜 𝑁𝑁, 𝑃𝑃, 𝐾𝐾,
𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝, 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑎𝑎𝑎𝑎𝑎𝑎 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦 (𝑊𝑊𝑊𝑊 𝑡𝑡 ℎ𝑎𝑎−1
)
𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑓𝑓𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 = 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑡𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑜𝑜𝑜𝑜 𝑎𝑎 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑜𝑜𝑜𝑜 𝑁𝑁, 𝑃𝑃, 𝐾𝐾,
𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑁𝑁𝑁𝑁 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑎𝑎𝑎𝑎𝑎𝑎 𝐹𝐹𝐹𝐹 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑜𝑜𝑜𝑜 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 (𝑠𝑠𝑠𝑠𝑠𝑠 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏)
𝑁𝑁 𝑃𝑃 𝐾𝐾 = 𝑓𝑓𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 …
𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑓𝑓𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 = 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑁𝑁𝑁𝑁, 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑡𝑡𝑡𝑡 𝑎𝑎 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑜𝑜𝑜𝑜 𝑁𝑁, 𝑃𝑃, 𝐾𝐾,
𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑜𝑜𝑜𝑜 𝑓𝑓𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 𝑎𝑎𝑎𝑎𝑎𝑎𝑓𝑓𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙
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?
𝑁𝑁 𝑃𝑃 𝐾𝐾 = 𝑓𝑓𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 … , 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
𝑤𝑤𝑤𝑤𝑤𝑤 𝑤 𝑓𝑓𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 = 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑁𝑁𝑁𝑁, 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑡𝑡𝑡𝑡 𝑎𝑎 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑜𝑜𝑜𝑜 𝑁𝑁, 𝑃𝑃, 𝐾𝐾,
𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑜𝑜𝑜𝑜 𝑓𝑓𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 𝑎𝑎𝑎𝑎𝑎𝑎𝑓𝑓𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙, 𝒂𝒂𝒂𝒂𝒂𝒂 𝒊𝒊 𝒊𝒊𝒊𝒊𝒊𝒊𝒊𝒊𝒊𝒊 = 𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎 𝒎𝒎 𝒎𝒎𝒎𝒎 𝒗𝒗𝒗𝒗𝒗𝒗𝒗𝒗𝒗𝒗 𝒇𝒇𝒇𝒇𝒇𝒇 𝑻𝑻𝑻𝑻 [$ 𝒉𝒉𝒉𝒉−𝟏𝟏
]

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
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
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 densityDefine variety Define plot size

Expected yield increaseResults – 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 :

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