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
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 presentation was meant to be included in the 2021 CLIFF-GRADS Welcome Webinar and presented by Ciniro Costa Jr. (CCAFS).
The webinar recording can be found here: https://youtu.be/UoX6aoC4fhQ
Research Program Genetic Gains (RPGG) Review Meeting 2021: Forward Breeding B...ICRISAT
A scalable and responsive genomic data management system from GOBii. The mission of the Genomics Open-source breeding informatics initiative (GOBii) is to build open-source genomic data management and analysis tools to enable breeders to implement genomic and marker-assisted selection as part of their routine breeding programs.
Big Data for Building Inclusive Agriculture in Dry Areas ICARDA
25 to 30 August. The World Water Week in Stockholm is an annual focal point for the globe’s water issues. Organized by the Stockholm International Water Institute (SIWI), and supported by the United Nations water programs.
Wednesday 28 August
“Big data for all”, can it help improve agricultural productivity?
The SWAMP project develops IoT-based methods for smart water management in precision irrigation. It aims to develop these methods, address climate change challenges by using water and energy efficiently, and maximize crop yields. The project will pilot the approaches in Europe and Brazil, with objectives of automating platforms, integrating sensors, and validating new business models for smart water management.
biowatts.org - an online platform for anaerobic digestion projectsBiowatts
1. Biowatts is an online platform that provides tools to support anaerobic digestion projects including a biogas calculator, kinetics analyzer, and biomass marketplace.
2. The biogas calculator allows users to model biomass configurations, view production results and return on investment. It relies on an existing substrate database.
3. The kinetics analyzer models biomethane production based on variables like retention time and temperature to guide optimal digester settings.
This document discusses the potential for growing crops along roadsides, railways, and other non-traditional agricultural lands in the United States to produce biodiesel fuel. It notes that there are millions of acres of such lands, including over 4 million miles of roadways and 140,000 miles of railways. The document outlines objectives to determine if crop growth is viable in these areas and identifies economic, environmental, and crop yield considerations. It provides an example estimating potential biodiesel production from growing crops on Utah Department of Transportation lands.
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
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 presentation was meant to be included in the 2021 CLIFF-GRADS Welcome Webinar and presented by Ciniro Costa Jr. (CCAFS).
The webinar recording can be found here: https://youtu.be/UoX6aoC4fhQ
Research Program Genetic Gains (RPGG) Review Meeting 2021: Forward Breeding B...ICRISAT
A scalable and responsive genomic data management system from GOBii. The mission of the Genomics Open-source breeding informatics initiative (GOBii) is to build open-source genomic data management and analysis tools to enable breeders to implement genomic and marker-assisted selection as part of their routine breeding programs.
Big Data for Building Inclusive Agriculture in Dry Areas ICARDA
25 to 30 August. The World Water Week in Stockholm is an annual focal point for the globe’s water issues. Organized by the Stockholm International Water Institute (SIWI), and supported by the United Nations water programs.
Wednesday 28 August
“Big data for all”, can it help improve agricultural productivity?
The SWAMP project develops IoT-based methods for smart water management in precision irrigation. It aims to develop these methods, address climate change challenges by using water and energy efficiently, and maximize crop yields. The project will pilot the approaches in Europe and Brazil, with objectives of automating platforms, integrating sensors, and validating new business models for smart water management.
biowatts.org - an online platform for anaerobic digestion projectsBiowatts
1. Biowatts is an online platform that provides tools to support anaerobic digestion projects including a biogas calculator, kinetics analyzer, and biomass marketplace.
2. The biogas calculator allows users to model biomass configurations, view production results and return on investment. It relies on an existing substrate database.
3. The kinetics analyzer models biomethane production based on variables like retention time and temperature to guide optimal digester settings.
This document discusses the potential for growing crops along roadsides, railways, and other non-traditional agricultural lands in the United States to produce biodiesel fuel. It notes that there are millions of acres of such lands, including over 4 million miles of roadways and 140,000 miles of railways. The document outlines objectives to determine if crop growth is viable in these areas and identifies economic, environmental, and crop yield considerations. It provides an example estimating potential biodiesel production from growing crops on Utah Department of Transportation lands.
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.
The document proposes the Farmers' Integration Platform as a Service (FIPaaS) project, which aims to integrate existing technologies for precision and sustainable farming into a single, open-source, cloud-based solution to improve farmers' livelihoods. The project is funded by the EU-Egypt PRIMA initiative and involves partners from several Mediterranean countries. It will develop tools for satellite imaging, drone data collection, smart irrigation, and other functions. The project expects to reduce water usage, increase crop yields, and disseminate technologies to farmers through its unified platform.
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 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
Commercial & research landscape for smart irrigation systems. A survey of commercial product offerings, research prototypes and approaches to smart irrigation. I also cover the why there is such a dire need to conserve water and increase yield.
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.
This document provides an overview of the development of decision support tools (DSTs) for best intercropping practices of cassava with maize in Nigeria and sweet potato in Zanzibar. It describes the background, modelling framework, field trials conducted to evaluate the effects of planting density and fertilizer application on intercrop yields, and the development of the DST. The field trials identified optimal planting densities and fertilizer regimes to maximize intercrop yields and profits under different conditions. The DST will recommend the best planting time, density and fertilizer practices based on user inputs using decision tree models developed from the field trial results.
Reducing rural poverty and improving household nutrition are common goals across all developing countries in the
Asia and Pacific region. To this end, the region has experienced a recent resurgence in large investments in irrigation
infrastructure. This surge in funding flows has created pressure from donors and central financing agencies, both of
which are increasingly demanding more robust justification for the investments. To date, providing this justification for
irrigation investments has been challenging due to a lack of reliable longitudinal data that measure the performance of
irrigated agriculture and associated water delivery services. Consequently, there is very little information on the real
returns on investments already made. Historic data has tended to be project based, point-in-time data constrained to a
defined area of infrastructure investment, not on-going and geographically broad-based.
Irrigation benchmarking is a process of comparative analysis of irrigation performance that enables scheme managers
to understand the performance of their irrigation services (International Water Management Institute, 2019). To better
understand the process of monitoring irrigation performance, this brief will use Cambodia as an illustrative example.
Irrigated rice production in Cambodia has significant potential, yet performance of the sector lags behind surrounding
countries, such as Viet Nam’s delta region (Mainuddin and Kirby, 2009). In addition, there are limited available and
published data in Cambodia, making it difficult to analyse the current and changing state of irrigation in the country,
the productivity levels, or irrigation’s contribution to poverty alleviation and economic growth (Tucker et al., 2020). For
these reasons, Cambodia was selected as a country to pilot the transfer of key learnings from the Australian experience
of irrigation performance benchmarking, and to develop a benchmarking methodology as a first step to undertake
ongoing performance assessment of irrigation schemes for strategic investments in increasing water productivity.
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 Pieter Pypers.
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.
Session 6 1 ACAI Work Stream 4 introductionDavid Ngome
This document discusses activities of WorkStream 4 of the African Cassava Agronomy Initiative project. It provides an overview of the general approach, which is to develop and facilitate use of site-specific agronomy recommendations at scale. It discusses project outcomes such as targeted increases in cassava root yield and additional supply to processing industries. It also outlines various dissemination activities including training events, promotion events, and demonstrations. Finally it discusses monitoring, evaluation and learning activities and timelines for decision support tool development and validation in 2019-2020.
Estimating the Impact of Agriculture on the Environment of Catalunya by means...Andreas Kamilaris
Because of insufficient accessible arable land, intensive farming has been linked to excessive accumulation of phosphorous, heavy metals, and other soil contaminants, as well as to significant groundwater pollution with nitrate. Deterioration of soil water quality is especially worrying at the bioclimatic Mediterranean area, especially under the current context of climate change. Hence, it is necessary to develop a common body of knowledge, shared at the local and regional levels of the countries involved and affected, so as to allow an effective monitoring of cropping systems, fertilization and water demands, and impacts of climate change, with a focus on the sustainability and the protection of the physical environment.
In this presentation, we describe AgriBigCAT, an online software platform that combines geophysical information from various diverse sources, together with big data analysis, in order to estimate the impact of the agricultural sector on the environment, considering land, water, biodiversity and natural areas requiring protection, such as forests and wetlands. Based on the P-Sphere project, this platform intends to promote more sustainable agriculture, by designing and developing an information and knowledge-based platform, using a big data approach for managing and analyzing a wide range of geospatial and mainstream information, which can be accessible by standard communication technologies such as the internet/web and mobile apps. this platform can also assist both the farmers' decision-taking processes and the administration planning and policy making, with the ultimate objective of meeting the challenge of increasing food production at a lower environmental impact.
Strategy, funding, monitoring and learning @ ICARDA-CGIARICARDA
The 3rd Regional Forum for the Sahel on African Initiative for Combating Desertification to Strengthen Resilience to Climate Change in the Sahel and Horn of Africa (AI-CD) was held from for 16 to 17 July in Dakar, Senegal. It particularly focused on access to finance for promoting policy implementation in combating desertification by AI-CD participating countries.
Representatives from BurkinaFaso, #Chad, Cameroun, Mauritania, Niger, Nigeria, Senegal presented project concept notes aiming to access to finance and implement a project on the ground in the 3rd day of the AI-CD Sahel regional forum.
ICARDA presented the Strategy, funding, monitoring and learning @ ICARDA-CGIAR
http://repo.mel.cgiar.org/handle/20.500.11766/10122
Presentation by Bharat Sharma, Principal Researcher (Water Resources) & Coordinator: IWMI-India Programme, International Water Management Institute (IWMI) & Gijs Simons, Project Manager, eLeaf
Session: ICTs/Mobile Apps for Access, Distribution and Application of Agricultural Inputs
on 6 Nov 2013
ICT4Ag, Kigali, Rwanda
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 summarizes the advancements towards the completing the work described in GBIF Work Programme Update 2016.
It was composed by different members from the GBIF Secretariat. This particular version was shared during the European Nodes Meeting in Lisbon the 19 April 2016.
NextGEOSS: The Next Generation European Data Hub and Cloud Platform for Earth...Wolfgang Ksoll
NextGEOSS is a H2020 project that aims to create an open data hub and cloud platform for Earth observation data. It involves 27 partners from 13 countries with a budget of 10 million euros from 2016-2020. The project will develop advanced data discovery tools, enable user feedback, and enhance communities through tailored solutions. It will follow an open, inclusive, and agile development approach aligned with EU open data policies. Various pilot projects will use the data and platform for applications in agriculture, biodiversity, disaster risk reduction, and other areas. The data will come from Copernicus satellites, in situ sources, and other open data providers. Metadata will be harvested and standardized. Lessons learned so far include the need for scalable architectures
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.
Varieties with diverse maturity class,
Striga and drought-tolerant maize varieties
Soil fertility management technologies
Good agronomic practices e.g. planting dates
Evaluation of agronomic practices on growth, yield of cassava and some physic...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 Omolara Olabisi.
Improved crop management systems for sustainable cassava production in sub-Sa...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 Joy Adiele.
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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.
The document proposes the Farmers' Integration Platform as a Service (FIPaaS) project, which aims to integrate existing technologies for precision and sustainable farming into a single, open-source, cloud-based solution to improve farmers' livelihoods. The project is funded by the EU-Egypt PRIMA initiative and involves partners from several Mediterranean countries. It will develop tools for satellite imaging, drone data collection, smart irrigation, and other functions. The project expects to reduce water usage, increase crop yields, and disseminate technologies to farmers through its unified platform.
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 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
Commercial & research landscape for smart irrigation systems. A survey of commercial product offerings, research prototypes and approaches to smart irrigation. I also cover the why there is such a dire need to conserve water and increase yield.
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.
This document provides an overview of the development of decision support tools (DSTs) for best intercropping practices of cassava with maize in Nigeria and sweet potato in Zanzibar. It describes the background, modelling framework, field trials conducted to evaluate the effects of planting density and fertilizer application on intercrop yields, and the development of the DST. The field trials identified optimal planting densities and fertilizer regimes to maximize intercrop yields and profits under different conditions. The DST will recommend the best planting time, density and fertilizer practices based on user inputs using decision tree models developed from the field trial results.
Reducing rural poverty and improving household nutrition are common goals across all developing countries in the
Asia and Pacific region. To this end, the region has experienced a recent resurgence in large investments in irrigation
infrastructure. This surge in funding flows has created pressure from donors and central financing agencies, both of
which are increasingly demanding more robust justification for the investments. To date, providing this justification for
irrigation investments has been challenging due to a lack of reliable longitudinal data that measure the performance of
irrigated agriculture and associated water delivery services. Consequently, there is very little information on the real
returns on investments already made. Historic data has tended to be project based, point-in-time data constrained to a
defined area of infrastructure investment, not on-going and geographically broad-based.
Irrigation benchmarking is a process of comparative analysis of irrigation performance that enables scheme managers
to understand the performance of their irrigation services (International Water Management Institute, 2019). To better
understand the process of monitoring irrigation performance, this brief will use Cambodia as an illustrative example.
Irrigated rice production in Cambodia has significant potential, yet performance of the sector lags behind surrounding
countries, such as Viet Nam’s delta region (Mainuddin and Kirby, 2009). In addition, there are limited available and
published data in Cambodia, making it difficult to analyse the current and changing state of irrigation in the country,
the productivity levels, or irrigation’s contribution to poverty alleviation and economic growth (Tucker et al., 2020). For
these reasons, Cambodia was selected as a country to pilot the transfer of key learnings from the Australian experience
of irrigation performance benchmarking, and to develop a benchmarking methodology as a first step to undertake
ongoing performance assessment of irrigation schemes for strategic investments in increasing water productivity.
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 Pieter Pypers.
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.
Session 6 1 ACAI Work Stream 4 introductionDavid Ngome
This document discusses activities of WorkStream 4 of the African Cassava Agronomy Initiative project. It provides an overview of the general approach, which is to develop and facilitate use of site-specific agronomy recommendations at scale. It discusses project outcomes such as targeted increases in cassava root yield and additional supply to processing industries. It also outlines various dissemination activities including training events, promotion events, and demonstrations. Finally it discusses monitoring, evaluation and learning activities and timelines for decision support tool development and validation in 2019-2020.
Estimating the Impact of Agriculture on the Environment of Catalunya by means...Andreas Kamilaris
Because of insufficient accessible arable land, intensive farming has been linked to excessive accumulation of phosphorous, heavy metals, and other soil contaminants, as well as to significant groundwater pollution with nitrate. Deterioration of soil water quality is especially worrying at the bioclimatic Mediterranean area, especially under the current context of climate change. Hence, it is necessary to develop a common body of knowledge, shared at the local and regional levels of the countries involved and affected, so as to allow an effective monitoring of cropping systems, fertilization and water demands, and impacts of climate change, with a focus on the sustainability and the protection of the physical environment.
In this presentation, we describe AgriBigCAT, an online software platform that combines geophysical information from various diverse sources, together with big data analysis, in order to estimate the impact of the agricultural sector on the environment, considering land, water, biodiversity and natural areas requiring protection, such as forests and wetlands. Based on the P-Sphere project, this platform intends to promote more sustainable agriculture, by designing and developing an information and knowledge-based platform, using a big data approach for managing and analyzing a wide range of geospatial and mainstream information, which can be accessible by standard communication technologies such as the internet/web and mobile apps. this platform can also assist both the farmers' decision-taking processes and the administration planning and policy making, with the ultimate objective of meeting the challenge of increasing food production at a lower environmental impact.
Strategy, funding, monitoring and learning @ ICARDA-CGIARICARDA
The 3rd Regional Forum for the Sahel on African Initiative for Combating Desertification to Strengthen Resilience to Climate Change in the Sahel and Horn of Africa (AI-CD) was held from for 16 to 17 July in Dakar, Senegal. It particularly focused on access to finance for promoting policy implementation in combating desertification by AI-CD participating countries.
Representatives from BurkinaFaso, #Chad, Cameroun, Mauritania, Niger, Nigeria, Senegal presented project concept notes aiming to access to finance and implement a project on the ground in the 3rd day of the AI-CD Sahel regional forum.
ICARDA presented the Strategy, funding, monitoring and learning @ ICARDA-CGIAR
http://repo.mel.cgiar.org/handle/20.500.11766/10122
Presentation by Bharat Sharma, Principal Researcher (Water Resources) & Coordinator: IWMI-India Programme, International Water Management Institute (IWMI) & Gijs Simons, Project Manager, eLeaf
Session: ICTs/Mobile Apps for Access, Distribution and Application of Agricultural Inputs
on 6 Nov 2013
ICT4Ag, Kigali, Rwanda
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 summarizes the advancements towards the completing the work described in GBIF Work Programme Update 2016.
It was composed by different members from the GBIF Secretariat. This particular version was shared during the European Nodes Meeting in Lisbon the 19 April 2016.
NextGEOSS: The Next Generation European Data Hub and Cloud Platform for Earth...Wolfgang Ksoll
NextGEOSS is a H2020 project that aims to create an open data hub and cloud platform for Earth observation data. It involves 27 partners from 13 countries with a budget of 10 million euros from 2016-2020. The project will develop advanced data discovery tools, enable user feedback, and enhance communities through tailored solutions. It will follow an open, inclusive, and agile development approach aligned with EU open data policies. Various pilot projects will use the data and platform for applications in agriculture, biodiversity, disaster risk reduction, and other areas. The data will come from Copernicus satellites, in situ sources, and other open data providers. Metadata will be harvested and standardized. Lessons learned so far include the need for scalable architectures
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.
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Development of the Scheduled Planting (SP) and High Starch Content (HS) Decision Support Tools (DSTs) – V1
1. www.iita.org I www.cgiar.org
Development of the Scheduled Planting (SP) and
High Starch Content (HS) Decision Support Tools
(DSTs) – V1
African Cassava Agronomy Initiative (ACAI)
Second Annual Review Meeting
and Planning Workshop
11 – 15 Dec. 2017
Gold Crest Hotel, Mwanza, Tanzania
Pieter Pypers, Rebecca Enesi, Bernadetha Kimati & Jeremiah Kabissa
2. Development of the
Scheduled Planting (SP) and
High Starch Content (HS)
Decision Support Tools (DSTs) – V1
www.iita.org | www.cgiar.org | www.acai-project.org
3. Overview
www.iita.org | www.cgiar.org | www.acai-project.org
Scheduled Planting and High Starch Content DSTs:
1. Background and modelling framework (Pieter Pypers):
• Introduction
• Learnings from literature
• Learnings from baseline and rapid characterization
• Modelling framework: LINTUL & QUEFTS (temporarily)
2. Field activities (Rebecca Enesi & Bernadetha Kimati):
• Field activities: Scheduled Planting Trials
• Field trial results
3. Development of the DST (Jeremiah Kabissa):
• Overview of recommendations
• The Decision Support Tool
• Next steps and additional data needs
4. Overview
www.iita.org | www.cgiar.org | www.acai-project.org
Scheduled Planting and High Starch Content DSTs:
1. Background and modelling framework (Pieter Pypers):
• Introduction
• Learnings from literature
• Learnings from baseline and rapid characterization
• Modelling framework: LINTUL & QUEFTS (temporarily)
2. Field activities (Rebecca Enesi & Bernadetha Kimati):
• Field activities: Scheduled Planting Trials
• Field trial results
3. Development of the DST (Jeremiah Kabissa):
• Overview of recommendations
• The Decision Support Tool
• Next steps and additional data needs
5. Introduction
www.iita.org | www.cgiar.org | www.acai-project.org
The Scheduled Planting DST:
• Specific purpose: recommend time of planting and harvest to optimize root supply (and revenue) to
cassava processors
• Requested by: CAVA-II (TZ)
• Other partners: Psaltry (NG)
• Intended users: Extension agents (EAs) supporting cassava growers supplying cassava roots to
medium-scale processors
• Expected benefit: Cassava root supply increased by 10 tonnes (or revenue increases of US$500),
realized by 6,563 HHs, with the support of 150 extension agents, generating a total
value of US$3,281,250
• Current version: V1: implemented at 5x5km, for variations of +/- 1-2 months around the planned data
of planting and harvest, estimating yield and revenue with user-supplied unit prices
for fresh cassava roots
• Approach: Water-limited yield estimated by LINTUL; current yield (no inputs) estimated by
QUEFTS, across the planting and harvest windows observed during the RC survey
• Input required: GPS location, planting date (actual or planned), harvest date (planned),
expected price (+ variation in price, optional), yield estimate (visual method)
• 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
6. Introduction
www.iita.org | www.cgiar.org | www.acai-project.org
The High Starch Content DST:
• Specific purpose: recommend time of planting and harvest (and other agronomic measures) to
optimize starch supply to processors
• Requested by: FJS (TZ) and Psaltry (NG)
• Other partners: -
• Intended users: Outgrowers supplying cassava roots to starch factories
• Expected benefit: Cassava starch supply increased by 5 tonnes (or revenue increases of US$375),
realized by 7,700 HHs, with the support of 44 extension agents, generating a total
value of US$2,887,500
• Current version: V1: implemented at 5x5km, for variations of +/- 1-2 months around the planned data
of planting and harvest, estimating yield and revenue with user-supplied unit prices
for fresh cassava roots, disaggregated by starch content class
• Approach: Water-limited yield estimated by LINTUL; current yield (no inputs) estimated by
QUEFTS, across the planting and harvest windows observed during the RC survey,
and starch content correction based on learnings from literature + field data
• Input required: GPS location, planting date (actual or planned), harvest date (planned),
expected price by starch content class, yield estimate (visual method)
• 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
7. Learnings from the literature review
www.iita.org | www.cgiar.org | www.acai-project.org
What determines starch yield?
8. Learnings from the RC and baseline survey
www.iita.org | www.cgiar.org | www.acai-project.org
Harvest
Planting
Insights in planting and harvest schedules:
Based on observations in 4629 cassava fields
with 2349 households across both countries.
9. Learnings from the RC and baseline survey
www.iita.org | www.cgiar.org | www.acai-project.org
Insights in prices of fresh cassava roots and processed produce
Price information obtained from phone interviews with 1475 geo-referenced responders.
10. Principles of the Scheduled Planting Tool
www.iita.org | www.cgiar.org | www.acai-project.org
1. Estimate the water-limited yield based on the LINTUL modelling framework
2. Estimate the current yield based on the QUEFTS modelling framework
3. Scale to the actual expected yield using expert knowledge based on previous yield, assisted by
visual method
4. Estimate variation in gross value based on user-defined changes in price around the expected
harvest date
5. Provide recommendations on planting (if applicable) and harvest date maximizing gross revenue
The SP-DST is developed based on following steps and principles:
LINTUL
HS-DST
, and converted to starch yield based on empirical relations (trial data)
, using root prices disaggregated by starch concentration
11. Principles of the Scheduled Planting Tool
www.iita.org | www.cgiar.org | www.acai-project.org
Estimate the water-limited yield and current yield
LINTUL QUEFTS
Water-limited yield (no nutrient limitations) Current yield (limited by water + nutrients)
Example: planting mid November, harvest at 10 MAP
Do this for all combinations of weekly intervals in planting date across the planting windows per region,
and weekly intervals in harvest date between 8 and 12 MAP…
12. Principles of the Scheduled Planting Tool
Variation in water-limited yield >> current yield (limited by nutrients)
Water-limited yield (no nutrient limitations) Current yield (limited by water + nutrients)
www.iita.org | www.cgiar.org | www.acai-project.org
8 MAP 10 MAP 12 MAP 8 MAP 10 MAP 12 MAP
Mid-DecMid-NovMid-Oct
13. Principles of the Scheduled Planting Tool
www.iita.org | www.cgiar.org | www.acai-project.org
Scale to the actual expected yield and convert to gross value
Current yield (no inputs) [QUEFTS]
Water-limited yield [LINTUL]
Fictive example with large changes in yield and price over time, to illustrate the principle…
Guide the user to indicate the expected yield level based on his/her experience with cropping cassava
in the plot on a scale of 1 [poor yield = current yield] to 5 [high yield = water-limited yield]
1
2
3
4
5
1
2
3
4
5
14. Principles of the Scheduled Planting Tool
www.iita.org | www.cgiar.org | www.acai-project.org
Scale to the actual expected yield and convert to gross value
Guide the user to indicate the expected yield level based on his/her experience with cropping cassava
in the plot on a scale of 1 [poor yield = current yield] to 5 [high yield = water-limited yield]
1 2 3 4 5
15. Overview
www.iita.org | www.cgiar.org | www.acai-project.org
Scheduled Planting and High Starch Content DSTs:
1. Background and modelling framework (Pieter Pypers):
• Introduction
• Learnings from literature
• Learnings from baseline and rapid characterization
• Modelling framework: LINTUL & QUEFTS (temporarily)
2. Field activities (Rebecca Enesi & Bernadetha Kimati):
• Field activities: Scheduled Planting Trials
• Field trial results
3. Development of the DST (Jeremiah Kabissa):
• Overview of recommendations
• The Decision Support Tool
• Next steps and additional data needs
16. Scheduled Planting Trials
www.iita.org | www.cgiar.org | www.acai-project.org
Evaluate effects of variety, planting date, [fertilizer] and harvest date:
T
B
M A M J J A S O N D J F M A M J J A S O N D
P H ridge
P H ridge
P H ridge
P H ridge
P H ridge
P H ridge
P H ridge
P H ridge
P H ridge
P H ridge
P H ridge
P Hridge
P P P H P H H H H H H H
dry
season
long
rains
*
*
*
*
dryshort
rains
dry
season
long
rains
short
rains
Six variants [SPT-1..6], differing in fertilizer levels and number of harvests
17. Scheduled Planting Trials
www.iita.org | www.cgiar.org | www.acai-project.org
Sampling frame: maximize representativeness across target AoI
#locations #planted #harvested
On-station trials
SW-NG 2 6 -
LZ-TZ 1 3 1 (2)
SZ-TZ 1 3 3
EZ-TZ - - -
On-farm trials
SW-NG 8 15 4
LZ-TZ 6 3 1 (2)
SZ-TZ 3 2 1
EZ-TZ 3 0 0
Environment class
Combination of on-station trials with high frequency / intensity of data collection (+ meteo station),
and on-farm trials with less frequent data collection (serving mainly as validation dataset)
18. Scheduled Planting Trials
www.iita.org | www.cgiar.org | www.acai-project.org
Impressions and learnings from the field – TZ – some pictures
Learning how to record temperature
Farmers willingness to prepare their land
Learning how to record rainfall
Foregoing other crops for cassava
19. Scheduled Planting Trials
www.iita.org | www.cgiar.org | www.acai-project.org
Impressions and learnings from the field – TZ – current status
Good harvest at 12 MAP Stem weight at 12 MAP Sub-sampling
20. Scheduled Planting Trials
www.iita.org | www.cgiar.org | www.acai-project.org
Impressions and learnings from the field – NG – some pictures
3 RMTs on-farm, managed by IITA for intensive, high frequency data collection.
7 MLTs on-farm, managed by FUNAAB and EAs with lower frequency of data collection.
Fertilizer application on late plantings remains a challenge.
Need to develop strategies for appropriate timing of fertilizer application for each planting.
22. Scheduled Planting Trials
www.iita.org | www.cgiar.org | www.acai-project.org
Results
First results from scheduled planting trials across 14 locations from both countries:
Analysis of Variance Table
Pr(>F)
Harvest 0.0001029 ***
Variety 0.8892715
country 0.5138284
Harvest:Variety 0.4631400
Harvest:country 0.0323079 *
Variety:country 0.0185752 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05
Large variation in root yield (13.6 ± 94%)!
54% of total variance explained by:
Variety: 6%
Harvest age: 9%
Trial (planting date, management): 36%
Field (agro-ecology, soil,…): 49%
23. Starch assessment in trials for other use cases
www.iita.org | www.cgiar.org | www.acai-project.org
Results
1624 starch measurements (using gravimetric method) across trials of 4 use cases:
24. Starch assessment in trials for other use cases
www.iita.org | www.cgiar.org | www.acai-project.org
Results
1624 starch measurements (using gravimetric method) across trials of 4 use cases:
Nigeria Tanzania
mean 21.9% 25.1%
CV 29.3% 44.7%
% variance attributed to…
harvest time 64% 35%
between trials 21% 36%
within trial 15% 29%
Between trials = agro-ecology + soil + management,…
Within trials = treatment + random noise
Nigeria Tanzania
mean 21.9% 25.1%
CV 29.3% 44.7%
25. Starch assessment in trials for other use cases
www.iita.org | www.cgiar.org | www.acai-project.org
Results
1624 starch measurements (using gravimetric method) across trials of 4 use cases:
64% of total variance explained by harvest time 35% of total variance explained by harvest time
Large differences in agro-
ecology between regions!
26. Starch assessment in trials for other use cases
www.iita.org | www.cgiar.org | www.acai-project.org
Results
Nutrient Omission Trials: Effect of fertilizer on starch content?
Linear mixed model fit by REML
Formula: starCont ~ treatCode + (1 | trialID)
Estimate Std. Error Pr(>|t|)
(Intercept) 23.9362 2.4094 1.77e-08 ***
PK -3.8166 1.7814 0.0371 *
NK -3.9010 1.7431 0.0297 *
NP -0.9004 1.7512 0.6095
half_NPK -0.6770 1.7870 0.7064
NPK -0.1340 1.5782 0.9327
NPK+micro -1.7704 1.7512 0.3170
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Slight reductions in starch content (-4%) due to omission of
N (location-dependent) or P (general), but not K.
27. Starch assessment in trials for other use cases
www.iita.org | www.cgiar.org | www.acai-project.org
Results
Best Planting Practices Trials: Effect of crop density and tillage on starch content?
No effect of planting density, primary (0-, 1-, 2-till) or secondary tillage (flat / ridged) on starch content.
28. Starch assessment in trials for other use cases
www.iita.org | www.cgiar.org | www.acai-project.org
Results
Scheduled Planting Trials in SW Nigeria: Effect of variety and harvest time on starch content?
Linear mixed model fit by REML
Formula: starCont ~ variety*harvestTime + (1 | fieldID)
Estimate Std. Error Pr(>|t|)
(Intercept) 17.7253 1.2014 1.21e-08 ***
VarietyV2 0.4589 1.6667 0.7840
HarvestH2 4.1153 1.8771 0.0548 .
HarvestH3 22.3808 1.6574 5.84e-09 ***
VarietyV2:HarvestH2 -1.6738 2.3836 0.4854
VarietyV2:HarvestH3 -2.5796 2.2254 0.2511
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Negligible differences between two varieties.
Substantial impact of delayed harvest!
29. Overview
www.iita.org | www.cgiar.org | www.acai-project.org
Scheduled Planting and High Starch Content DSTs:
1. Background and modelling framework (Pieter Pypers):
• Introduction
• Learnings from literature
• Learnings from baseline and rapid characterization
• Modelling framework: LINTUL & QUEFTS (temporarily)
2. Field activities (Rebecca Enesi & Bernadetha Kimati):
• Field activities: Scheduled Planting Trials
• Field trial results
3. Development of the DST (Jeremiah Kabissa):
• Overview of recommendations
• The Decision Support Tool
• Next steps and additional data needs
30. How are these results fed into the DST?
www.iita.org | www.cgiar.org | www.acai-project.org
Empirical equations to predict starch yield using root yield:
Observed versus predicted starch yields [t/ha]:
R2=0.84 R2=0.91
based on root yield only based on root yield + harvest month
31. Interpreting the recommendations
www.iita.org | www.cgiar.org | www.acai-project.org
Variation in water-limited yield
Water-limited yield (LINTUL) for different planting and harvest dates (averaged by region)
32. 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?
SP-DST packaged as a simple ODK form, with GPS location, planned (or actual) planting and harvest date,
and expected unit prices for cassava roots (or starch) as only inputs (for now):
Introduction and identification
33. 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?
SP-DST packaged as a simple ODK form, with GPS location, planned (or actual) planting and harvest date,
and expected unit prices for cassava roots (or starch) as only inputs (for now):
Intended planting and harvest date + window
34. 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?
SP-DST packaged as a simple ODK form, with GPS location, planned (or actual) planting and harvest date,
and expected unit prices for cassava roots (or starch) as only inputs (for now):
Planting details + expected yield level
35. 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?
SP-DST packaged as a simple ODK form, with GPS location, planned (or actual) planting and harvest date,
and expected unit prices for cassava roots (or starch) as only inputs (for now):
Root prices for different price classes
according to starch content or…
varying in time across
the harvest window
36. 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?
SP-DST packaged as a simple ODK form, with GPS location, planned (or actual) planting and harvest date,
and expected unit prices for cassava roots (or starch) as only inputs (for now):
Expected yield, gross value and recommended planting and harvest date
37. 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?
SP-DST packaged as a simple ODK form, with GPS location, planned (or actual) planting and harvest date,
and expected unit prices for cassava roots (or starch) as only inputs (for now):
Feedback… Are recommendations sensible, useful? Can we call you for further feedback?
38. Next steps
www.iita.org | www.cgiar.org | www.acai-project.org
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):
• Site-specific info
• Variety (HI)
• Uncertainty?
• …
3. LINTUL vs. DSSAT?
4. Parametrization of models – data requirements?
5. Explore options for model validation exercises through ongoing measurements within partner
networks (e.g., with Niji farms) or through crowd-sourcing yield data.
V1 is a ‘hybrid’ between a research tool and the intended ‘app’