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.
GIS-enabled bioenergy potential mapping in India Yan Yan
This document describes a methodology for mapping bioenergy potential in India using GIS. It aims to map potential from three biomass resources: burned agricultural residue, animal manure, and municipal solid waste.
For burned agricultural residue, the methodology uses NASA satellite data on burn scars and reprocesses it with a collection 6 algorithm to map residue locations. For animal manure, it uses livestock census data and geospatial information like land cover and population density to model livestock density and distribution. For municipal solid waste, it uses population maps to estimate waste production levels. The results are compiled in an ArcGIS online map of comparative bioenergy distribution.
National assessment of cambodia’s main crop and fodder resourcesSitha Aum
The document summarizes a national assessment of Cambodia's main crop and fodder resources conducted by the Centre for Livestock and Agricultural Development (CelAgrid). The study collected ground data using GPS and surveyed crop yields using crop cut surveys. It analyzed the data to estimate crop and fodder production by district and province. The results showed provincial differences in fodder production and animal biomass densities. Overall, provinces with higher crop/fodder production densities tended to support higher animal biomass densities as well. Going forward, the results could be used to support further livestock research, production projects, and strategic planning in Cambodia.
The document discusses Dryland Systems staffing and research methods testing at ICRISAT in West Africa. It outlines that the Dryland Systems team is composed of 9+ scientists from ICRISAT and partner institutions covering two action transects. It also describes testing of methods for biomass assessments, household surveys, and options for intensification pathways that were conducted in 2012 to establish baselines and protocols for the Dryland Systems research.
This document discusses using geographic information systems (GIS) to complement life cycle assessment (LCA) methodologies for estimating greenhouse gas emissions from livestock production systems. Key points:
1) GIS allows spatially explicit data on factors like animal densities, feed availability, climate, and land use to be integrated into LCA models to calculate emissions.
2) Case studies show GIS enables estimating emissions from pig and chicken production by mapping commercial vs. backyard systems.
3) GIS also permits calculating manure methane emissions that account for local temperature variations instead of average national values.
4) The integrated GIS-LCA approach maintains high resolution input data and avoids generalizing results compared to conventional L
This document summarizes a workshop on the science and implementation of dryland agricultural systems in South Asia. It discusses the following key points in 3 sentences or less:
The project aims to characterize current dryland production systems, improve system performances and livelihoods, and identify options to achieve higher productivity. Field sites are located across rainfall gradients in India and Pakistan, featuring diverse agro-pastoral, irrigation, tree-based and rainfed systems. Research questions focus on system structures and functions, livelihood objectives, and options to improve outcomes through increased productivity and diversification.
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.
GIS-enabled bioenergy potential mapping in India Yan Yan
This document describes a methodology for mapping bioenergy potential in India using GIS. It aims to map potential from three biomass resources: burned agricultural residue, animal manure, and municipal solid waste.
For burned agricultural residue, the methodology uses NASA satellite data on burn scars and reprocesses it with a collection 6 algorithm to map residue locations. For animal manure, it uses livestock census data and geospatial information like land cover and population density to model livestock density and distribution. For municipal solid waste, it uses population maps to estimate waste production levels. The results are compiled in an ArcGIS online map of comparative bioenergy distribution.
National assessment of cambodia’s main crop and fodder resourcesSitha Aum
The document summarizes a national assessment of Cambodia's main crop and fodder resources conducted by the Centre for Livestock and Agricultural Development (CelAgrid). The study collected ground data using GPS and surveyed crop yields using crop cut surveys. It analyzed the data to estimate crop and fodder production by district and province. The results showed provincial differences in fodder production and animal biomass densities. Overall, provinces with higher crop/fodder production densities tended to support higher animal biomass densities as well. Going forward, the results could be used to support further livestock research, production projects, and strategic planning in Cambodia.
The document discusses Dryland Systems staffing and research methods testing at ICRISAT in West Africa. It outlines that the Dryland Systems team is composed of 9+ scientists from ICRISAT and partner institutions covering two action transects. It also describes testing of methods for biomass assessments, household surveys, and options for intensification pathways that were conducted in 2012 to establish baselines and protocols for the Dryland Systems research.
This document discusses using geographic information systems (GIS) to complement life cycle assessment (LCA) methodologies for estimating greenhouse gas emissions from livestock production systems. Key points:
1) GIS allows spatially explicit data on factors like animal densities, feed availability, climate, and land use to be integrated into LCA models to calculate emissions.
2) Case studies show GIS enables estimating emissions from pig and chicken production by mapping commercial vs. backyard systems.
3) GIS also permits calculating manure methane emissions that account for local temperature variations instead of average national values.
4) The integrated GIS-LCA approach maintains high resolution input data and avoids generalizing results compared to conventional L
This document summarizes a workshop on the science and implementation of dryland agricultural systems in South Asia. It discusses the following key points in 3 sentences or less:
The project aims to characterize current dryland production systems, improve system performances and livelihoods, and identify options to achieve higher productivity. Field sites are located across rainfall gradients in India and Pakistan, featuring diverse agro-pastoral, irrigation, tree-based and rainfed systems. Research questions focus on system structures and functions, livelihood objectives, and options to improve outcomes through increased productivity and diversification.
Estimating soil organic carbon changes: is it feasible?ExternalEvents
This presentation was presented during the Plenary 1, GSOC17 – Setting the scientific scene for GSOC17 of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Ms. Eleanor Milne from Colorado State University - USA, in FAO Hq, Rome
This document discusses the application of ecogeography in plant genetic resources. It defines ecogeography as the study of the adaptive scenario of an individual, population or species through analysis of biotic and abiotic factors that affect survival. It describes how geographical information systems (GIS) can be used to characterize plant collecting sites based on ecogeographical variables and identify potential applications of GIS in plant genetic resources, such as optimized germplasm collecting and identification of suitable areas for conservation. Finally, it lists activities that can be performed using GIS, such as determining the representativeness of ex-situ collections and identifying areas with high phenotypic or genotypic diversity.
Item 2. ASP work from December 2016 to May 2018: Republic of KoreaExternalEvents
The Republic of Korea has made progress in several areas of soil management from December 2016 to May 2018:
1. They expanded the number of crop types for which they provide fertilizer recommendations, established guidelines for nutrient diagnoses of crops, and continued soil testing and data collection activities.
2. Efforts were made to increase awareness of soil functions and services through education and policy programs supporting sustainable agriculture.
3. Collaboration between universities, research institutions, and other groups led to advances in irrigation technology, nutrient management, carbon stock estimation, and use of organic resources.
The presentation was given by Mr. Bas Kempen and Ms. V.L. Mulder, ISRIC, during the GSOC Mapping Global Training hosted by ISRIC - World Soil Information, 6 - 23 June 2017, Wageningen (The Netherlands).
Soil Organic Carbon mapping by geo- and class- matchingExternalEvents
The presentation was given by Mr. Bas Kempen & Ms. V.L. Mulder, ISRIC, during the GSOC Mapping Global Training hosted by ISRIC - World Soil Information, 6 - 23 June 2017, Wageningen (The Netherlands).
Assessing and Capitalizing on the Potential to Enhance Forest Carbon Sinks th...CIFOR-ICRAF
1) The document summarizes a project between IUCN and BMU to identify potential areas in Mexico for forest landscape restoration to meet restoration goals under the Bonn Challenge.
2) The methodology involved defining ecological, economic and social criteria through workshops, gathering and processing spatial data from Mexican institutions, and conducting a multicriteria evaluation and mapping to identify priority restoration sites.
3) The results identified over 302,000 km2 of land in Mexico as priority areas for forest landscape restoration, and highlighted specific priority sites within biological corridors and regions.
''Copernicus for sustainable land management'' by Markus Erhard, European Environment Agency (EEA)
Sustainable Land Management Session - EU Space Week 2018, Marseille
Soil carbon models for carbon stock estimation – where do we fail?ExternalEvents
This presentation was presented during the 2 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Aleksi Lethonen, from Natural Resources Institute - Finland, in FAO Hq, Rome
Thesis presentation made by AGUNG WAHYUDI, during his master study in Ghent University 2008. He received 17 out of 20 for his thesis. He graduated with great distinction in the same year.
Global space-time soil organic carbon assessmentExternalEvents
This document summarizes the GlobalSoilMap project's efforts to produce a global digital soil database with soil organic carbon and other properties mapped at 100m resolution. A two-step modeling approach was used to generate baseline SOC predictions for 2001 and then track changes over time as land cover changes occurred between 2001-2013. Approximately 14,890 million pixels were tracked over this period, showing significant global carbon losses. The final product provides spatially explicit SOC predictions and estimates of change over time at a resolution useful for modeling and management.
VIIe - Global Soil Organic Carbon Sequestration Potential Map - GSOCseqSoils FAO-GSP
The document discusses developing a global soil organic carbon sequestration potential map (GSOCseq) using two approaches. The top-down approach uses climate change scenarios to project SOC stocks over time without and with sustainable soil management. The bottom-up approach uses process modeling calibrated with soil profile observations to estimate baseline SOC stocks and potential under different scenarios. Preliminary results show potential SOC sequestration ranges from 60-245 petagrams for RCP2.6 and 82-325 petagrams for RCP8.5 by 2100 depending on management practices. The top-down approach uses empirical relationships between management factors and SOC stock changes to assess mitigation potential from sustainable soil practices.
This document discusses integrated nutrient management in India. It provides statistics on fertilizer consumption from 1999-2000 to 2011-2012, showing consumption increasing from 18 million tonnes to a target of 37.92 million tonnes. It also compares India's per hectare fertilizer consumption to neighboring countries in 2001-2002, showing India's was lower than China, Bangladesh, Sri Lanka, and Pakistan. The document outlines the components of integrated nutrient management and provides details on soil testing laboratories, fertilizer grades, fortified fertilizers, biofertilizers production, and the national project on organic farming.
The document discusses developing guidance for tier II measurement, reporting, and verification (MRV) of livestock greenhouse gas emissions at the provincial level in China. It outlines objectives to create practical MRV guidance, build capacity, and test implementation. Key points include: livestock are a major emissions source in China; accurate MRV is important to track mitigation actions; and the guidance will focus on dairy cattle and pigs in Hebei province. It will define tier II methods, collect farm data, calculate emission factors, and provide reporting templates for provinces to estimate emissions.
GMES initial operations on Land monitoring 2011 - 2013
Ana Sousa - European Environment Agency (EEA)
Parma, 16 novembre 2011. Nell'ambito della XV Conferenza Italiana ASITA si svolge il Workshop "GMES Land products developed in Geoland2: requirements and examples of products for analysis at a European and regional level."
Guarda anche il video
http://www.youtube.com/watch?v=MNeuj5ksZCA
Towards a Tier 3 approach to estimate SOC stocks at sub-regional scale in Sou...ExternalEvents
This presentation was presented during the 2 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Ms. Roberta Farina, from CREA - Italy, in FAO Hq, Rome
Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
Extrapolation suitability for improved vegetable technologies in Babati Distr...africa-rising
Presented by Francis Muthoni, Justus Ochieng, Jean-Marc Delore, Phillipo J. Lukumay, and Inviolata Dominic at the Power on Your Plate Summit, Arusha, Tanzania, 25-28 January 2021.
Precision Agriculture- By Anjali Patel (IGKV Raipur, C.G)Rahul Raj Tandon
This document discusses precision agriculture and provides definitions, history, concepts, components, applications, advantages, and limitations. Precision agriculture aims to enhance productivity and environmental quality by varying inputs based on spatial and temporal variability. It uses tools like GPS, GIS, remote sensing, yield monitors, and variable rate technology to optimize crop management. While precision agriculture can increase profits and efficiency, its adoption in India faces challenges like cost, infrastructure needs, and farmer education.
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.
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’.
Estimating soil organic carbon changes: is it feasible?ExternalEvents
This presentation was presented during the Plenary 1, GSOC17 – Setting the scientific scene for GSOC17 of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Ms. Eleanor Milne from Colorado State University - USA, in FAO Hq, Rome
This document discusses the application of ecogeography in plant genetic resources. It defines ecogeography as the study of the adaptive scenario of an individual, population or species through analysis of biotic and abiotic factors that affect survival. It describes how geographical information systems (GIS) can be used to characterize plant collecting sites based on ecogeographical variables and identify potential applications of GIS in plant genetic resources, such as optimized germplasm collecting and identification of suitable areas for conservation. Finally, it lists activities that can be performed using GIS, such as determining the representativeness of ex-situ collections and identifying areas with high phenotypic or genotypic diversity.
Item 2. ASP work from December 2016 to May 2018: Republic of KoreaExternalEvents
The Republic of Korea has made progress in several areas of soil management from December 2016 to May 2018:
1. They expanded the number of crop types for which they provide fertilizer recommendations, established guidelines for nutrient diagnoses of crops, and continued soil testing and data collection activities.
2. Efforts were made to increase awareness of soil functions and services through education and policy programs supporting sustainable agriculture.
3. Collaboration between universities, research institutions, and other groups led to advances in irrigation technology, nutrient management, carbon stock estimation, and use of organic resources.
The presentation was given by Mr. Bas Kempen and Ms. V.L. Mulder, ISRIC, during the GSOC Mapping Global Training hosted by ISRIC - World Soil Information, 6 - 23 June 2017, Wageningen (The Netherlands).
Soil Organic Carbon mapping by geo- and class- matchingExternalEvents
The presentation was given by Mr. Bas Kempen & Ms. V.L. Mulder, ISRIC, during the GSOC Mapping Global Training hosted by ISRIC - World Soil Information, 6 - 23 June 2017, Wageningen (The Netherlands).
Assessing and Capitalizing on the Potential to Enhance Forest Carbon Sinks th...CIFOR-ICRAF
1) The document summarizes a project between IUCN and BMU to identify potential areas in Mexico for forest landscape restoration to meet restoration goals under the Bonn Challenge.
2) The methodology involved defining ecological, economic and social criteria through workshops, gathering and processing spatial data from Mexican institutions, and conducting a multicriteria evaluation and mapping to identify priority restoration sites.
3) The results identified over 302,000 km2 of land in Mexico as priority areas for forest landscape restoration, and highlighted specific priority sites within biological corridors and regions.
''Copernicus for sustainable land management'' by Markus Erhard, European Environment Agency (EEA)
Sustainable Land Management Session - EU Space Week 2018, Marseille
Soil carbon models for carbon stock estimation – where do we fail?ExternalEvents
This presentation was presented during the 2 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Aleksi Lethonen, from Natural Resources Institute - Finland, in FAO Hq, Rome
Thesis presentation made by AGUNG WAHYUDI, during his master study in Ghent University 2008. He received 17 out of 20 for his thesis. He graduated with great distinction in the same year.
Global space-time soil organic carbon assessmentExternalEvents
This document summarizes the GlobalSoilMap project's efforts to produce a global digital soil database with soil organic carbon and other properties mapped at 100m resolution. A two-step modeling approach was used to generate baseline SOC predictions for 2001 and then track changes over time as land cover changes occurred between 2001-2013. Approximately 14,890 million pixels were tracked over this period, showing significant global carbon losses. The final product provides spatially explicit SOC predictions and estimates of change over time at a resolution useful for modeling and management.
VIIe - Global Soil Organic Carbon Sequestration Potential Map - GSOCseqSoils FAO-GSP
The document discusses developing a global soil organic carbon sequestration potential map (GSOCseq) using two approaches. The top-down approach uses climate change scenarios to project SOC stocks over time without and with sustainable soil management. The bottom-up approach uses process modeling calibrated with soil profile observations to estimate baseline SOC stocks and potential under different scenarios. Preliminary results show potential SOC sequestration ranges from 60-245 petagrams for RCP2.6 and 82-325 petagrams for RCP8.5 by 2100 depending on management practices. The top-down approach uses empirical relationships between management factors and SOC stock changes to assess mitigation potential from sustainable soil practices.
This document discusses integrated nutrient management in India. It provides statistics on fertilizer consumption from 1999-2000 to 2011-2012, showing consumption increasing from 18 million tonnes to a target of 37.92 million tonnes. It also compares India's per hectare fertilizer consumption to neighboring countries in 2001-2002, showing India's was lower than China, Bangladesh, Sri Lanka, and Pakistan. The document outlines the components of integrated nutrient management and provides details on soil testing laboratories, fertilizer grades, fortified fertilizers, biofertilizers production, and the national project on organic farming.
The document discusses developing guidance for tier II measurement, reporting, and verification (MRV) of livestock greenhouse gas emissions at the provincial level in China. It outlines objectives to create practical MRV guidance, build capacity, and test implementation. Key points include: livestock are a major emissions source in China; accurate MRV is important to track mitigation actions; and the guidance will focus on dairy cattle and pigs in Hebei province. It will define tier II methods, collect farm data, calculate emission factors, and provide reporting templates for provinces to estimate emissions.
GMES initial operations on Land monitoring 2011 - 2013
Ana Sousa - European Environment Agency (EEA)
Parma, 16 novembre 2011. Nell'ambito della XV Conferenza Italiana ASITA si svolge il Workshop "GMES Land products developed in Geoland2: requirements and examples of products for analysis at a European and regional level."
Guarda anche il video
http://www.youtube.com/watch?v=MNeuj5ksZCA
Towards a Tier 3 approach to estimate SOC stocks at sub-regional scale in Sou...ExternalEvents
This presentation was presented during the 2 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Ms. Roberta Farina, from CREA - Italy, in FAO Hq, Rome
Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
Extrapolation suitability for improved vegetable technologies in Babati Distr...africa-rising
Presented by Francis Muthoni, Justus Ochieng, Jean-Marc Delore, Phillipo J. Lukumay, and Inviolata Dominic at the Power on Your Plate Summit, Arusha, Tanzania, 25-28 January 2021.
Precision Agriculture- By Anjali Patel (IGKV Raipur, C.G)Rahul Raj Tandon
This document discusses precision agriculture and provides definitions, history, concepts, components, applications, advantages, and limitations. Precision agriculture aims to enhance productivity and environmental quality by varying inputs based on spatial and temporal variability. It uses tools like GPS, GIS, remote sensing, yield monitors, and variable rate technology to optimize crop management. While precision agriculture can increase profits and efficiency, its adoption in India faces challenges like cost, infrastructure needs, and farmer education.
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.
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’.
This document provides an overview of the ALTER project, which aims to investigate how investment in soil carbon can be used to alleviate poverty in dryland and wetland regions of Africa. The project involves researchers from Ethiopia, Uganda, UK, Italy studying sites in southern Ethiopia and southern Uganda. The research will assess how restoring or protecting soil services impacts poverty, develop scenarios to evaluate intervention options, and assess mechanisms to alleviate poverty through soil carbon. The project aims to provide evidence to support policies around soil management and investment, as well as contribute to advances in science.
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
As part of the GSP’s capacity development and improvement programme, FAO/GSP have organised a one week training in Izmir, Turkey. The main goal of the training was to increase the capacity of Turkey on digital soil mapping, new approaches on data collection, data processing and modelling of soil organic carbon. This 5 day training is titled ‘’Training on Digital Soil Organic Carbon Mapping’’ was held in IARTC - International Agricultural Research and Education Center in Menemen, Izmir on 20-25 August, 2017.
The African Cassava Agronomy Initiative (ACAI) aims to develop knowledge and tools to improve cassava farming and deliver these resources to farmers in target countries. The project has 6 work streams: research, developing a geospatial database, creating decision support tools, facilitating tool use, building capacity, and management. In year 1, ACAI made progress establishing over 300 trials on fertilizer response, intercropping, and other topics. Four national scientists were sponsored for PhD training. Baseline surveys and databases were also initiated to support the project.
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.
Soil information on different scales for smallholder farmers in AfricaSIANI
This document summarizes research on providing soil information to smallholder farmers in Africa. It discusses how large amounts of open soil data are available but need to be evaluated and improved before local use. It presents two web-based tools, the Global Soil Data Manager for Sub-Saharan Africa (GSDM-SSA) and CropSAT, that make big soil data easy for anyone to use and improve small decisions. The researchers are looking for collaboration to further develop and adapt these systems for specific applications and regions.
IRJET - Agrotech: Soil Analysis and Crop PredictionIRJET Journal
This document presents a system for soil analysis and crop prediction using data mining techniques. The system measures soil parameters like pH, nitrogen, phosphorus and potassium using sensors. It then uses a decision tree algorithm to classify the soil and predict suitable crops. The pH value is used to estimate other nutrient values. The nutrient values and soil type are sent over WiFi to a server, which uses machine learning to predict crops and provide fertilizer recommendations to the farmer. The proposed system automates the soil testing process and aims to help farmers select optimal crops and increase agricultural yields.
Learning with the System of Rice Intensification for Food Security and Climat...Sri Lmb
The document summarizes the System of Rice Intensification for Lower Mekong Basin (SRI-LMB) project. The key points are:
- SRI-LMB was a 5-year EU funded project implemented in 4 countries (Cambodia, Laos, Thailand, Vietnam) to promote the System of Rice Intensification (SRI) for improved food security and climate-smart agriculture.
- The project involved over 15,000 farmers across 33 districts and evaluated SRI practices through 582 on-farm trials. Results showed increases in yield, profitability, labor productivity, and resource use efficiency compared to conventional practices.
- Data analysis found that SRI practices led to 52
Digital Soil Mapping using Machine LearningIRJET Journal
This document describes a study that uses machine learning techniques to predict suitable crops and soil fertility based on analyzing soil nutrients. The researchers collected soil sample data and removed unnecessary attributes before training decision tree, KNN, and random forest models. The random forest model achieved the highest accuracy of 93.6% for predicting crops compatible with the soil's nutrient content and properties. The study aims to help farmers select optimal crops and improve agricultural yield through automated, real-time soil analysis and recommendations.
CAPFITOGEN Programme for the Strengthening of Capabilities in National Plant Genetic Resources Programmes, International Treaty on Plant Genetic Resources for Food and Agriculture - FAO
The document provides an overview of options for greenhouse gas mitigation in agriculture. It discusses:
1) Agriculture contributes significantly to global emissions and reductions are necessary to meet climate targets. Many mitigation practices are compatible with sustainable development goals.
2) Key greenhouse gases from agriculture include methane, nitrous oxide, and carbon dioxide. Soils can also store carbon.
3) Common mitigation practices discussed include alternate wetting and drying of rice fields, livestock management improvements, efficient fertilizer use, agroforestry, and reducing food loss and waste.
4) The EX-ACT tool is introduced as a way to estimate and compare emissions between baseline and project scenarios to identify mitigation opportunities in agriculture
Legume Select–Ethiopia: Review of implemented activitiesILRI
Presented by Birhan Abdulkadir, Tadesse Birhanu, Tamiru Meleta, Assefa Ta’a and Kindu Mekonnen at the Legume SELECT Project Review and Planning Meeting, ILRI, Addis Ababa, 28-30 January 2020
RTI International conducted a study called CLEANEAST to analyze the greenhouse gas emission reductions from nutrient management practices on 429 livestock and poultry farms in the eastern United States. They modeled the farms' current manure storage systems and estimated that switching all farms to liquid-slurry storage with natural crusts could reduce total emissions by nearly 50%. However, converting storage systems may not be feasible due to physical constraints, lack of operator experience, and cost. Future work could include collecting more farm-specific data to improve the emission estimates and analyzing regional differences.
An automated low cost IoT based Fertilizer Intimation System forsmart agricul...sakru naik
This document describes an IoT-based system to monitor soil nutrients and advise farmers on fertilizer use. It involves:
1. Designing a novel NPK sensor using colorimetric principles to detect nitrogen, phosphorus and potassium levels in soil samples. Sensor data is sent to a cloud database.
2. Applying fuzzy logic at the edge to analyze sensor data and determine nutrient deficiencies based on if-then rules. Levels are categorized as very low, low, medium, high or very high.
3. Sending automated SMS alerts to farmers on a regular basis recommending fertilizer quantities for different nutrient deficiencies as determined by the fuzzy system.
The system is intended to help farmers apply fertil
Similar to Session 2 fertilizer recommendation and fertilizer blending dst (20)
This document outlines a plan to develop cloud-based prediction tools and digital guides to help cassava growers and extension workers understand how different growing environments impact recommendations, predict crop responses, and scale the sharing of recommendations through partner networks using a smartphone app and database.
This document outlines the development stages of a tool, starting with an initial concept and literature-based version 0, followed by a prototype version 1 incorporating experimental data, and a pilot version 2 that is validated. The final validated version is considered a ready tool and undergoes multiple validation exercises with different groups.
Nigeria is the most populous country in Africa with over 200 million people. It has a diverse population that speaks over 500 languages and is nearly evenly split between Christians and Muslims. Nigeria has had a challenging political history including periods of military rule but has transitioned to a democratic government over the past few decades.
The document discusses the results of a study on the effects of a new drug on patients with a certain medical condition. The study found that patients who received the drug experienced a significant reduction in symptoms compared to those who received a placebo. Overall, the drug was found to be an effective new treatment option for this condition with only mild side effects reported.
- On-farm experiments were conducted in Nigeria to study cassava yield and nutrient uptake under different fertilizer treatments.
- At intermediate harvests of 4 and 8 months after planting, and final harvests of 12-14 months, plant parts were weighed and analyzed for nitrogen, phosphorus, and potassium concentration.
- On average across treatments, locations, and years, 67% of nitrogen, 61% of phosphorus, and 52% of potassium uptake occurred by 4 months after planting. Nutrient uptake and allocation to plant parts over time was similar for fertilized and unfertilized plants. Whole plant nutrient concentration decreased with increasing biomass, with dilution accounting for about 65% of nutrient variation.
Cassava is a critical crop for food security in West Africa but its production is vulnerable to changing climate conditions. The study developed crop models to simulate how cassava yields may be impacted under different climate change scenarios in major cassava growing regions in West Africa through 2050. The models can help identify adaptation strategies to improve food security as climate change progresses.
Increased planting densities of cassava and maize, and the application of nitrogen, phosphorus, and potassium fertilizer increased the productivity of cassava-maize intercrops in southern Nigeria. Both maize cob yield and cassava root yield followed the same trend of being higher at high planting densities and with fertilizer application. A maize fertilizer regime targeting nitrogen, phosphorus, and potassium performed better than a cassava-targeting regime for maize yields, while the cassava regime performed better under very low soil fertility conditions. The height of maize from previous crops can be used as a proxy for soil fertility and predicting the response of maize to fertilizer - responses were higher when maize height was
The document outlines a turnkey solution for providing tailored agronomic advice. It describes 10 elements of the turnkey solution:
1. Demand-driven recommendations selected by partner dissemination networks.
2. Adapting decision support tool formats and functionality to partner strategies and user capabilities.
3. Avoiding price mapping and predictions.
4. Customizing tools and materials to user and beneficiary preferences.
5. Conducting many on-farm trials to understand variation.
The document then discusses reflecting on what could be done better and solicits feedback on the 10 elements and what should be done the same or improved in the future.
This document outlines the development stages of a tool, starting with an initial concept and literature-based version 0, followed by a prototype version 1 incorporating experimental data. Version 2 is a pilot tool that has been validated, with the final version being a ready tool that has undergone multiple validation exercises across different regions.
This document lists the names and affiliations of 7 researchers who were funded by the Bill & Melinda Gates Foundation. The researchers are from universities in the United States, Switzerland, Belgium, and the Netherlands. The document does not provide any other details about the researchers or their projects.
This document outlines ACAI's strategy for scaling the dissemination of AKILIMO, including:
1) Partnering with existing organizations that have dissemination strategies in place to facilitate entry and demand-driven ownership.
2) Implementing regular feedback mechanisms to ensure products meet beneficiary needs and are accepted before wide-scale dissemination.
3) Encouraging continuous learning through data collection and feedback integration to support ongoing acceptance.
4) Agreeing on appropriate formats like paper, video, radio etc. to disseminate information widely.
This document summarizes the monitoring, evaluation, and learning (MEL) framework for a project aimed at increasing cassava yields in Africa. The project targets include:
- Increasing cassava and intercrop yields by 2-10 tonnes per hectare
- Reaching thousands of households and extension agents
- Engaging private sector partners to address bottlenecks like access to credit and markets
The MEL framework involves measuring outcomes, learning from feedback, and adapting implementation strategies. Bottlenecks are analyzed at the value chain and project levels. Dissemination materials and channels are developed with partners and include training, demonstrations, radio, and videos. Questions are posed to discuss experiences, challenges, and improvements needed.
The document discusses Viamo's 321 mobile information service in Nigeria, which provides free agricultural, health, nutrition, and financial literacy information to users. It partners with Airtel to provide free airtime for the service. The 321 service uses an interactive voice response system that allows users to access different types of informational content by pressing numbers on their phone. One example of content is information on various topics related to cassava farming, developed in partnership with ACAI. The document outlines the roles of Viamo, ACAI, and the government in launching and supporting the 321 service.
More from African Cassava Agronomy Initiative (20)
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
Combined Illegal, Unregulated and Unreported (IUU) Vessel List.Christina Parmionova
The best available, up-to-date information on all fishing and related vessels that appear on the illegal, unregulated, and unreported (IUU) fishing vessel lists published by Regional Fisheries Management Organisations (RFMOs) and related organisations. The aim of the site is to improve the effectiveness of the original IUU lists as a tool for a wide variety of stakeholders to better understand and combat illegal fishing and broader fisheries crime.
To date, the following regional organisations maintain or share lists of vessels that have been found to carry out or support IUU fishing within their own or adjacent convention areas and/or species of competence:
Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR)
Commission for the Conservation of Southern Bluefin Tuna (CCSBT)
General Fisheries Commission for the Mediterranean (GFCM)
Inter-American Tropical Tuna Commission (IATTC)
International Commission for the Conservation of Atlantic Tunas (ICCAT)
Indian Ocean Tuna Commission (IOTC)
Northwest Atlantic Fisheries Organisation (NAFO)
North East Atlantic Fisheries Commission (NEAFC)
North Pacific Fisheries Commission (NPFC)
South East Atlantic Fisheries Organisation (SEAFO)
South Pacific Regional Fisheries Management Organisation (SPRFMO)
Southern Indian Ocean Fisheries Agreement (SIOFA)
Western and Central Pacific Fisheries Commission (WCPFC)
The Combined IUU Fishing Vessel List merges all these sources into one list that provides a single reference point to identify whether a vessel is currently IUU listed. Vessels that have been IUU listed in the past and subsequently delisted (for example because of a change in ownership, or because the vessel is no longer in service) are also retained on the site, so that the site contains a full historic record of IUU listed fishing vessels.
Unlike the IUU lists published on individual RFMO websites, which may update vessel details infrequently or not at all, the Combined IUU Fishing Vessel List is kept up to date with the best available information regarding changes to vessel identity, flag state, ownership, location, and operations.
Food safety, prepare for the unexpected - So what can be done in order to be ready to address food safety, food Consumers, food producers and manufacturers, food transporters, food businesses, food retailers can ...
UN WOD 2024 will take us on a journey of discovery through the ocean's vastness, tapping into the wisdom and expertise of global policy-makers, scientists, managers, thought leaders, and artists to awaken new depths of understanding, compassion, collaboration and commitment for the ocean and all it sustains. The program will expand our perspectives and appreciation for our blue planet, build new foundations for our relationship to the ocean, and ignite a wave of action toward necessary change.
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
RFP for Reno's Community Assistance CenterThis Is Reno
Property appraisals completed in May for downtown Reno’s Community Assistance and Triage Centers (CAC) reveal that repairing the buildings to bring them back into service would cost an estimated $10.1 million—nearly four times the amount previously reported by city staff.
This report explores the significance of border towns and spaces for strengthening responses to young people on the move. In particular it explores the linkages of young people to local service centres with the aim of further developing service, protection, and support strategies for migrant children in border areas across the region. The report is based on a small-scale fieldwork study in the border towns of Chipata and Katete in Zambia conducted in July 2023. Border towns and spaces provide a rich source of information about issues related to the informal or irregular movement of young people across borders, including smuggling and trafficking. They can help build a picture of the nature and scope of the type of movement young migrants undertake and also the forms of protection available to them. Border towns and spaces also provide a lens through which we can better understand the vulnerabilities of young people on the move and, critically, the strategies they use to navigate challenges and access support.
The findings in this report highlight some of the key factors shaping the experiences and vulnerabilities of young people on the move – particularly their proximity to border spaces and how this affects the risks that they face. The report describes strategies that young people on the move employ to remain below the radar of visibility to state and non-state actors due to fear of arrest, detention, and deportation while also trying to keep themselves safe and access support in border towns. These strategies of (in)visibility provide a way to protect themselves yet at the same time also heighten some of the risks young people face as their vulnerabilities are not always recognised by those who could offer support.
In this report we show that the realities and challenges of life and migration in this region and in Zambia need to be better understood for support to be strengthened and tuned to meet the specific needs of young people on the move. This includes understanding the role of state and non-state stakeholders, the impact of laws and policies and, critically, the experiences of the young people themselves. We provide recommendations for immediate action, recommendations for programming to support young people on the move in the two towns that would reduce risk for young people in this area, and recommendations for longer term policy advocacy.
Session 2 fertilizer recommendation and fertilizer blending dst
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
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
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:
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.
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:
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)
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):
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
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):
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”
19. 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)
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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
22. Maximizing net revenue for a given investment
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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
]
23. Maximizing net revenue for a given investment
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Example
Solution with max. NR for TC = 200$ ha-1
24. 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
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
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
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
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
41. 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?
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
www.iita.org | www.cgiar.org | www.acai-project.org
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
www.iita.org | www.cgiar.org | www.acai-project.org
How to make this framework available for quick and easy use?
When outside target AoI… Define planting densityDefine 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
www.iita.org | www.cgiar.org | www.acai-project.org
How to make this framework available for quick and easy use?
Expected yield increaseResults – 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
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. 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 :