The document describes the development of decision support tools for site-specific fertilizer recommendations and best fertilizer blends for cassava in Tanzania and Nigeria. It provides an overview of the background, modelling framework, field activities, and development of the tools. The tools were developed using the LINTUL and QUEFTS models to determine water-limited yield, indigenous nutrient supply, nutrient uptake requirements, and optimal fertilizer recommendations to maximize net returns. Field trials were conducted to validate the models and tools are being implemented as smartphone apps for use by extension agents.
The Development of the Fertilizer Recommendation (FR) and Fertilizer Blending (FB) Decision
Support Tool – Current progress, including how WS1-3 activities feed into the Decision Support Tool
Development of the Site-Specific Fertilizer Recommendation (FR) and Best Fert...IITA Communications
Presentation during African Cassava Agronomy Initiative (ACAI)
Second Annual Review Meeting and Planning Workshop on 11 – 15 Dec. 2017 at Gold Crest Hotel, Mwanza, Tanzania. Presented by Guillaume Ezui, Yemi Olojede, Peter Mlay & Meklit Chernet.
This presentation highlighted the process of developing and progress made in the development of the FR and FB DST.
The site-specific fertilizer recommendation (FR) tool is built to provide an optimized and profitable site-specific fertilizer recommendations for cassava growers. The tool considers the location, soil fertility, weather condition, available fertilizers in the area, prices for fertilizer and cassava root, planned planting and harvest dates and the investment capacity of the farmers.
The nutrient omission trials (NOT) in Nigeria and Tanzania conducted by ACAI, in collaboration with the national research and development partners, show a large variation in nutrient responses indicating the need for site-specific fertilizer recommendation. ACAI is developing a crosscutting system using machine learning techniques coupled with process based crop models, LINTUL and QUEFTS, and economic optimizer algorithms to provide the site-specific recommendations. ACAI is transforming available big data like GIS layers from SoilGrids and weather data from CHIRPS and NASA to useful information that can be used to model the relationship between apparent soil nutrient supply and soil properties. Effort has also been made to identify a generic soil fertility indicator that can be easily obtained from farmers and is useful covariate to improve the accuracy of apparent soil nutrient supply predictions.
The next steps in the FR tool development include, validating the FR tool both functionally, checking if the recommendations outperform the current practices in the field and architecturally, checking user friendliness and if the tool satisfies the needs of development partners to dissemination strategy.
Presentation highlighting the process and progress of developing the Summary of the field activities towards the development of the SP and HS DSTs, focusing on a combined DST recommending the time of planting and/or harvest to optimize root or starch supply (and revenue) to cassava processors, for both processors and cassava growers.
After two years of field experimentation, the database currently holds yield data from 79 SP trials (combinations of location, planting date, harvest age), and close to 4,000 starch measurements across trials from all use cases.
Most important findings in year 2 include (i) cassava root yield is controlled for a large extent to crop age and month of harvest in Nigeria, but in Tanzania, year-to-year variation is much larger, likely related to variation in rainfall across the growing season, (ii) starch concentration is controlled by harvest month in Nigeria and this is largely stable across years likely due to comparability of rainfall across years, but not so in Tanzania, and (iii) results confirm that starch concentration is not affected by fertilizer application or tillage management.
Inconsistent effects across years emphasize the need for better insights in the processes controlling yield and starch concentration through mechanistic models. LINTUL appears not to adequately predict the impact of rainfall during crop growth on dry matter accumulation. LINTUL does not seem to penalize ‘older’ cassava in the growth season, and underestimate growth and starch accumulation of a ‘medium’ cassava during the dry season…
Advances with the DST development; Modelling framework, the Decision Support Tool were presented, along with the ongoing validation exercises, with over 350 trials currently established to evaluate impact of harvest month on yield. First impressions illustrate that farmers have difficulties to anticipate the price variation across the harvest period, which is an important driver for decision making. The exercise is appreciated as it stimulates farmers and extension agents to reflect on the impact of planting date and harvest date on total revenue, which is often thought of as ‘less important’.
Day 1_Session3_TRIPS_WASDS_ICRISAT - This presentation outlines planned ICRISAT activities for the CGIAR Research Program on Dryland Systems for the West African Sahel and Dry Savannas region.
The Development of the Fertilizer Recommendation (FR) and Fertilizer Blending (FB) Decision
Support Tool – Current progress, including how WS1-3 activities feed into the Decision Support Tool
Development of the Site-Specific Fertilizer Recommendation (FR) and Best Fert...IITA Communications
Presentation during African Cassava Agronomy Initiative (ACAI)
Second Annual Review Meeting and Planning Workshop on 11 – 15 Dec. 2017 at Gold Crest Hotel, Mwanza, Tanzania. Presented by Guillaume Ezui, Yemi Olojede, Peter Mlay & Meklit Chernet.
This presentation highlighted the process of developing and progress made in the development of the FR and FB DST.
The site-specific fertilizer recommendation (FR) tool is built to provide an optimized and profitable site-specific fertilizer recommendations for cassava growers. The tool considers the location, soil fertility, weather condition, available fertilizers in the area, prices for fertilizer and cassava root, planned planting and harvest dates and the investment capacity of the farmers.
The nutrient omission trials (NOT) in Nigeria and Tanzania conducted by ACAI, in collaboration with the national research and development partners, show a large variation in nutrient responses indicating the need for site-specific fertilizer recommendation. ACAI is developing a crosscutting system using machine learning techniques coupled with process based crop models, LINTUL and QUEFTS, and economic optimizer algorithms to provide the site-specific recommendations. ACAI is transforming available big data like GIS layers from SoilGrids and weather data from CHIRPS and NASA to useful information that can be used to model the relationship between apparent soil nutrient supply and soil properties. Effort has also been made to identify a generic soil fertility indicator that can be easily obtained from farmers and is useful covariate to improve the accuracy of apparent soil nutrient supply predictions.
The next steps in the FR tool development include, validating the FR tool both functionally, checking if the recommendations outperform the current practices in the field and architecturally, checking user friendliness and if the tool satisfies the needs of development partners to dissemination strategy.
Presentation highlighting the process and progress of developing the Summary of the field activities towards the development of the SP and HS DSTs, focusing on a combined DST recommending the time of planting and/or harvest to optimize root or starch supply (and revenue) to cassava processors, for both processors and cassava growers.
After two years of field experimentation, the database currently holds yield data from 79 SP trials (combinations of location, planting date, harvest age), and close to 4,000 starch measurements across trials from all use cases.
Most important findings in year 2 include (i) cassava root yield is controlled for a large extent to crop age and month of harvest in Nigeria, but in Tanzania, year-to-year variation is much larger, likely related to variation in rainfall across the growing season, (ii) starch concentration is controlled by harvest month in Nigeria and this is largely stable across years likely due to comparability of rainfall across years, but not so in Tanzania, and (iii) results confirm that starch concentration is not affected by fertilizer application or tillage management.
Inconsistent effects across years emphasize the need for better insights in the processes controlling yield and starch concentration through mechanistic models. LINTUL appears not to adequately predict the impact of rainfall during crop growth on dry matter accumulation. LINTUL does not seem to penalize ‘older’ cassava in the growth season, and underestimate growth and starch accumulation of a ‘medium’ cassava during the dry season…
Advances with the DST development; Modelling framework, the Decision Support Tool were presented, along with the ongoing validation exercises, with over 350 trials currently established to evaluate impact of harvest month on yield. First impressions illustrate that farmers have difficulties to anticipate the price variation across the harvest period, which is an important driver for decision making. The exercise is appreciated as it stimulates farmers and extension agents to reflect on the impact of planting date and harvest date on total revenue, which is often thought of as ‘less important’.
Day 1_Session3_TRIPS_WASDS_ICRISAT - This presentation outlines planned ICRISAT activities for the CGIAR Research Program on Dryland Systems for the West African Sahel and Dry Savannas region.
Predictive fertilization models for potato crops using machine learning techn...IJECEIAES
Given the influence of several factors, including weather, soils, land management, genotypes, and the severity of pests and diseases, prescribing adequate nutrient levels is difficult. A potato’s performance can be predicted using machine learning techniques in cases when there is enough data. This study aimed to develop a highly precise model for determining the optimal levels of nitrogen, phosphorus, and potassium required to achieve both high-quality and high-yield potato crops, taking into account the impact of various environmental factors such as weather, soil type, and land management practices. We used 900 field experiments from Kaggle as part of a data set. We developed, evaluated, and compared prediction models of k-nearest neighbor (KNN), linear support vector machine (SVM), naive Bayes (NB) classifier, decision tree (DT) regressor, random forest (RF) regressor, and eXtreme gradient boosting (XGBoost). We used measures such as mean average error (MAE), mean squared error (MSE), R-Squared (RS), and R 2 Root mean squared error (RMSE) to describe the model’s mistakes and prediction capacity. It turned out that the XGBoost model has the greatest R 2 , MSE and MAE values. Overall, the XGBoost model outperforms the other machine learning models. In the end, we suggested a hardware implementation to help farmers in the field.
The Development of the Scheduled Planting (SP) and High Starch Content (HS) Decision Support
Tool – Current progress, including how WS1-3 activities feed into the Decision Support Tool
Agronomy and crop-livestock interaction activities in Ghana 2019/20 africa-rising
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Session: Options for Mitigation in Agriculture
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Predictive fertilization models for potato crops using machine learning techn...IJECEIAES
Given the influence of several factors, including weather, soils, land management, genotypes, and the severity of pests and diseases, prescribing adequate nutrient levels is difficult. A potato’s performance can be predicted using machine learning techniques in cases when there is enough data. This study aimed to develop a highly precise model for determining the optimal levels of nitrogen, phosphorus, and potassium required to achieve both high-quality and high-yield potato crops, taking into account the impact of various environmental factors such as weather, soil type, and land management practices. We used 900 field experiments from Kaggle as part of a data set. We developed, evaluated, and compared prediction models of k-nearest neighbor (KNN), linear support vector machine (SVM), naive Bayes (NB) classifier, decision tree (DT) regressor, random forest (RF) regressor, and eXtreme gradient boosting (XGBoost). We used measures such as mean average error (MAE), mean squared error (MSE), R-Squared (RS), and R 2 Root mean squared error (RMSE) to describe the model’s mistakes and prediction capacity. It turned out that the XGBoost model has the greatest R 2 , MSE and MAE values. Overall, the XGBoost model outperforms the other machine learning models. In the end, we suggested a hardware implementation to help farmers in the field.
The Development of the Scheduled Planting (SP) and High Starch Content (HS) Decision Support
Tool – Current progress, including how WS1-3 activities feed into the Decision Support Tool
Agronomy and crop-livestock interaction activities in Ghana 2019/20 africa-rising
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9-11 October 2018, Tokyo, Japan
Session: Options for Mitigation in Agriculture
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1. 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
2. Overview
www.iita.org | www.cgiar.org | www.acai-project.org
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
3. Overview
www.iita.org | www.cgiar.org | www.acai-project.org
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
4. Introduction
www.iita.org | www.cgiar.org | www.acai-project.org
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
5. Introduction
www.iita.org | www.cgiar.org | www.acai-project.org
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
6. Learnings from the RC and baseline survey
www.iita.org | www.cgiar.org | www.acai-project.org
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,…)
7. Principles of the Fertilizer Recommendation Tool
www.iita.org | www.cgiar.org | www.acai-project.org
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:
8. Determining water-limited yield
www.iita.org | www.cgiar.org | www.acai-project.org
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,…
9. Determining water-limited yield
www.iita.org | www.cgiar.org | www.acai-project.org
Water-limited yield is calculated using the LINTUL modelling framework:
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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10. Determining water-limited yield
www.iita.org | www.cgiar.org | www.acai-project.org
Water-limited yield is calculated using the LINTUL modelling framework:
Selection of Q1 – Q2 – Q3 year precipitation pattern (total rainfall and nr of rainy days), per zone
from 22 years of rainfall data:
11. Spatializing water-limited yield
www.iita.org | www.cgiar.org | www.acai-project.org
Water-limited yield is calculated using the LINTUL modelling framework:
Spatializing: water-limited yield was calculated for each pixel of 5 x 5 km across the AoI,
as maximum of 3 modelled years:
12. Determining indigenous nutrient supply
www.iita.org | www.cgiar.org | www.acai-project.org
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.
13. Spatializing indigenous nutrient supply
www.iita.org | www.cgiar.org | www.acai-project.org
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:
14. From uptake to yield (QUEFTS – step 3)
www.iita.org | www.cgiar.org | www.acai-project.org
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)
15. From uptake to yield (QUEFTS – step 3)
www.iita.org | www.cgiar.org | www.acai-project.org
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
16. From uptake to yield (QUEFTS – step 3)
www.iita.org | www.cgiar.org | www.acai-project.org
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.
17. From uptake to yield (QUEFTS – step 3)
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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.
18. From (N, P, K) to fertilizer recommendations (FR)
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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”
19. 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)
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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
22. Maximizing net revenue for a given investment
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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
]
23. Maximizing net revenue for a given investment
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Example
Solution with max. NR for TC = 200$ ha-1
24. Overview
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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
25. Nutrient omission trials
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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)
26. Nutrient omission trials
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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
27. 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)
Nutrient omission trials
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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
28. Nutrient omission trials
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Impressions and learnings from the field – Tanzania – some pictures
Collaboration
Performance
Success
Challenges
Appreciation
29. Nutrient omission trials
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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
30. Nutrient omission trials
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Impressions and learnings from the field – Nigeria – some pictures
Control NK NPK NPK+
31. Nutrient omission trials
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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
• …
32. Nutrient omission trials
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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).
33. Nutrient omission trials
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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
36. Overview
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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
41. Packaging in a tool for field use
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How to make this framework available for quick and easy use?
Preloading records Landing page
FR-DST packaged as a simple ODK form, with GPS location and planting date as only inputs (for now):
42. Packaging in a tool for field use
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How to make this framework available for quick and easy use?
Select country User identification Read GPS location Define planting date
FR-DST packaged as a simple ODK form, with GPS location and planting date as only inputs (for now):
43. Packaging in a tool for field use
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How to make this framework available for quick and easy use?
When outside target AoI… Define planting density
Define variety Define plot size
FR-DST packaged as a simple ODK form, with GPS location and planting date as only inputs (for now):
44. Packaging in a tool for field use
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How to make this framework available for quick and easy use?
Expected yield increase
Results – fertilizer rates Fertilizer quantities for plot Expected net revenue and cost
FR-DST packaged as a simple ODK form, with GPS location and planting date as only inputs (for now):
45. Packaging in a tool for field use
www.iita.org | www.cgiar.org | www.acai-project.org
How to make this framework available for quick and easy use?
Validation trial? End – save and send
FR-DST packaged as a simple ODK form, with GPS location and planting date as only inputs (for now):
46. Next steps
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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’
47. Next steps
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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)
48. Principles of the Fertilizer Blending Tool
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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
49. Principles of the Fertilizer Blending Tool
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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:
50. The Fertilizer Blending Tool
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The FB-DST is developed as a web application in :