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.
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.
Arifu is an East African social enterprise that provides educational content and services to over 1 million learners through an interactive chatbot accessible on basic phones. The chatbot offers personalized learning paths based on user profiles and analytics. It generates insights for partners on learner locations, engagement levels, profiles and knowledge scores over time. Arifu works with partners by designing customized content, delivering it through the chatbot, and providing analytics on learner data and outcomes.
A digital service provides tailored agronomic advice to cassava growers through various decision support tools (DST), including printable guides, a smartphone app, and SMS messages. The DST use a cloud-based prediction engine to extrapolate recommendations based on farmer inputs and predict responses to interventions. Printable guides offer simplified recommendations that can be printed for farmers, while the smartphone app allows extension workers to provide more granular, dynamic recommendations to farmers by collecting input data offline. The tools aim to help farmers better understand the interaction between agronomy and their local environment.
The presentations made by Rhoda Mahava and Samson Oguntoye focused on the summary of the activities they have done together with ACAI in 2018, positive experiences, key challenges, going forward in 2019, and expectations for the meeting.
The highlight of 2018 activities for development partners was the onset of the validation activities for the ACAI decision support tools. Development partner participated in the Training of Trainers and then facilitated the step down trainings at state level for project anchors in their respective states.
Following the trainings, partners established validation trials within their locales reaching a combined total of 741 new trials in 2018. In Nigeria the partners have collaborated with ACAI team on the evaluation of the different formats of the DSTs.
Partners across the two countries are set for the dissemination phase of the ACAI DSTs from 2019 by intensifying field activities and integrating learnings from ACAI into their work plan.
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’.
Presentation highlighting the process and progress of developing the Summary of the field activities towards the development of the BPP DST
The field trials on BPP had initially a 4 factorial structure with three ploughing regimes followed by ridging versus flat soil and planting at two densities (10000 vs. 12500) and fertilizer application versus nil. This was reduced to three factors and a reduction in ploughing intensity. In the first and second year it transpired that weed interference as a consequence of initial soil tillage, was potentially a major cause of root yield variation and this required the integration of weed control as a factor, which happened in year 3. Further, farmers’ reasons for ploughing were not fully understood and may have confounded tillage intensity with soil fertility. The latest version of the BPP DST will require the inclusion of fallow length and vegetation characteristics to better assess the reasons for intensive ploughing and exclude recommendations of such practices in sites of different fallow and land use history.
For 2019 the BPP will form the base for the integration of weed control aspect into ACAI and this will likely improve the BPP DST through appropriate weed control recommendations and fine tune post emergence weed control requirements and measures as a function of previous tillage.
Cassava intercropping with Sweet potato (CIS) trials aim to evaluate the land equivalent ratio of cassava - sweet potato intercropping systems, and methods to optimize intercropping practices for maximal revenue.
The CIS trials (2018) have been set up in Zanzibar in 8 clusters in Zanzibar. The study ascertains recommended plant densities and appropriate timing of introducing sweet potato as associated crop. Findings confirm that (i) cassava-sweet potato intercropping systems have LERs exceeding 1, and that (ii) farmers’ practice, with simultaneous planting of both crops at reduced densities of 10,000 sweet potato vines per hectare is optimal. Further yield increases can be achieved through fertilizer application, and the relative cost and revenue from both crops should be considered in decision-making on intercropping cassava.
Cassava intercrop maize (CIM) recommends intensification options in cassava-maize intercropping systems. A comparison of our recommendation with the best performing plot at an individual site showed that for 31% of the farms (where maize was already harvested) this advice was correct and 9% would have lost money due to the investment in fertilizer. The tool proved to be conservative, often not recommending investment in fertilizer where this would have increased revenue.
For the DST version of 2019, we will improve on the indicators for maize and review with our partners whether the value cost ratio should be less conservative, or its level be set by farmers. Increasing the true positive rate (correctly recommend investment when this is profitable) comes along with increases in false positives (recommending investment in fertilizer when not profitable).
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.
The presentations made by Rhoda Mahava and Samson Oguntoye focused on the summary of the activities they have done together with ACAI in 2018, positive experiences, key challenges, going forward in 2019, and expectations for the meeting.
The highlight of 2018 activities for development partners was the onset of the validation activities for the ACAI decision support tools. Development partner participated in the Training of Trainers and then facilitated the step down trainings at state level for project anchors in their respective states.
Following the trainings, partners established validation trials within their locales reaching a combined total of 741 new trials in 2018. In Nigeria the partners have collaborated with ACAI team on the evaluation of the different formats of the DSTs.
Partners across the two countries are set for the dissemination phase of the ACAI DSTs from 2019 by intensifying field activities and integrating learnings from ACAI into their work plan.
This is a presentation that outlined the ACAI project’s progress, the process of DSTs development and the status of the project and an overview of activities for the last three years of ACAI
Summary of the project - The African Cassava Agronomy Initiative aims at delivering agronomic technologies that improve cassava root yield and quality, and cassava supply to the processing sector, engaging 120,000 farming households through effective partnerships with development partners in Nigeria and Tanzania, supported by the National Agricultural Research Systems, and in collaboration with strategic research institutes. The project consists of six use cases, identified by development partners, and has developed decision support tools, supplying tailored or site-specific recommendations on fertilizer use, fertilizer blend formulations, tillage practices, intercropping and scheduled planting and harvest and high starch content.
The knowledge needed to develop these decision support tools is generated by applying the principles of “Agronomy at Scale”, combining field trials to test and develop best agronomic interventions, modelling to build prediction models, GIS and spatial modelling to extrapolate recommendations across the target intervention area, development of DSTs to supply recommendations through a practical field tool, and extension activities to scale the use of the tools within partner networks.
The implementation progress per six work streams: (i) strategic agronomy research and crop modelling, (ii) geospatial analysis and data management, (iii) DST development, (iv) facilitation of use of the DSTs, (v) Capacity development of national research institutions, (vi) Project governance, management, coordination, and M&E.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
- 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.
Arifu is an East African social enterprise that provides educational content and services to over 1 million learners through an interactive chatbot accessible on basic phones. The chatbot offers personalized learning paths based on user profiles and analytics. It generates insights for partners on learner locations, engagement levels, profiles and knowledge scores over time. Arifu works with partners by designing customized content, delivering it through the chatbot, and providing analytics on learner data and outcomes.
A digital service provides tailored agronomic advice to cassava growers through various decision support tools (DST), including printable guides, a smartphone app, and SMS messages. The DST use a cloud-based prediction engine to extrapolate recommendations based on farmer inputs and predict responses to interventions. Printable guides offer simplified recommendations that can be printed for farmers, while the smartphone app allows extension workers to provide more granular, dynamic recommendations to farmers by collecting input data offline. The tools aim to help farmers better understand the interaction between agronomy and their local environment.
The presentations made by Rhoda Mahava and Samson Oguntoye focused on the summary of the activities they have done together with ACAI in 2018, positive experiences, key challenges, going forward in 2019, and expectations for the meeting.
The highlight of 2018 activities for development partners was the onset of the validation activities for the ACAI decision support tools. Development partner participated in the Training of Trainers and then facilitated the step down trainings at state level for project anchors in their respective states.
Following the trainings, partners established validation trials within their locales reaching a combined total of 741 new trials in 2018. In Nigeria the partners have collaborated with ACAI team on the evaluation of the different formats of the DSTs.
Partners across the two countries are set for the dissemination phase of the ACAI DSTs from 2019 by intensifying field activities and integrating learnings from ACAI into their work plan.
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’.
Presentation highlighting the process and progress of developing the Summary of the field activities towards the development of the BPP DST
The field trials on BPP had initially a 4 factorial structure with three ploughing regimes followed by ridging versus flat soil and planting at two densities (10000 vs. 12500) and fertilizer application versus nil. This was reduced to three factors and a reduction in ploughing intensity. In the first and second year it transpired that weed interference as a consequence of initial soil tillage, was potentially a major cause of root yield variation and this required the integration of weed control as a factor, which happened in year 3. Further, farmers’ reasons for ploughing were not fully understood and may have confounded tillage intensity with soil fertility. The latest version of the BPP DST will require the inclusion of fallow length and vegetation characteristics to better assess the reasons for intensive ploughing and exclude recommendations of such practices in sites of different fallow and land use history.
For 2019 the BPP will form the base for the integration of weed control aspect into ACAI and this will likely improve the BPP DST through appropriate weed control recommendations and fine tune post emergence weed control requirements and measures as a function of previous tillage.
Cassava intercropping with Sweet potato (CIS) trials aim to evaluate the land equivalent ratio of cassava - sweet potato intercropping systems, and methods to optimize intercropping practices for maximal revenue.
The CIS trials (2018) have been set up in Zanzibar in 8 clusters in Zanzibar. The study ascertains recommended plant densities and appropriate timing of introducing sweet potato as associated crop. Findings confirm that (i) cassava-sweet potato intercropping systems have LERs exceeding 1, and that (ii) farmers’ practice, with simultaneous planting of both crops at reduced densities of 10,000 sweet potato vines per hectare is optimal. Further yield increases can be achieved through fertilizer application, and the relative cost and revenue from both crops should be considered in decision-making on intercropping cassava.
Cassava intercrop maize (CIM) recommends intensification options in cassava-maize intercropping systems. A comparison of our recommendation with the best performing plot at an individual site showed that for 31% of the farms (where maize was already harvested) this advice was correct and 9% would have lost money due to the investment in fertilizer. The tool proved to be conservative, often not recommending investment in fertilizer where this would have increased revenue.
For the DST version of 2019, we will improve on the indicators for maize and review with our partners whether the value cost ratio should be less conservative, or its level be set by farmers. Increasing the true positive rate (correctly recommend investment when this is profitable) comes along with increases in false positives (recommending investment in fertilizer when not profitable).
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.
The presentations made by Rhoda Mahava and Samson Oguntoye focused on the summary of the activities they have done together with ACAI in 2018, positive experiences, key challenges, going forward in 2019, and expectations for the meeting.
The highlight of 2018 activities for development partners was the onset of the validation activities for the ACAI decision support tools. Development partner participated in the Training of Trainers and then facilitated the step down trainings at state level for project anchors in their respective states.
Following the trainings, partners established validation trials within their locales reaching a combined total of 741 new trials in 2018. In Nigeria the partners have collaborated with ACAI team on the evaluation of the different formats of the DSTs.
Partners across the two countries are set for the dissemination phase of the ACAI DSTs from 2019 by intensifying field activities and integrating learnings from ACAI into their work plan.
This is a presentation that outlined the ACAI project’s progress, the process of DSTs development and the status of the project and an overview of activities for the last three years of ACAI
Summary of the project - The African Cassava Agronomy Initiative aims at delivering agronomic technologies that improve cassava root yield and quality, and cassava supply to the processing sector, engaging 120,000 farming households through effective partnerships with development partners in Nigeria and Tanzania, supported by the National Agricultural Research Systems, and in collaboration with strategic research institutes. The project consists of six use cases, identified by development partners, and has developed decision support tools, supplying tailored or site-specific recommendations on fertilizer use, fertilizer blend formulations, tillage practices, intercropping and scheduled planting and harvest and high starch content.
The knowledge needed to develop these decision support tools is generated by applying the principles of “Agronomy at Scale”, combining field trials to test and develop best agronomic interventions, modelling to build prediction models, GIS and spatial modelling to extrapolate recommendations across the target intervention area, development of DSTs to supply recommendations through a practical field tool, and extension activities to scale the use of the tools within partner networks.
The implementation progress per six work streams: (i) strategic agronomy research and crop modelling, (ii) geospatial analysis and data management, (iii) DST development, (iv) facilitation of use of the DSTs, (v) Capacity development of national research institutions, (vi) Project governance, management, coordination, and M&E.
More from African Cassava Agronomy Initiative (20)
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627