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).
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.
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.
This document discusses two fertilizer recommendation use cases in Nigeria and Tanzania:
1) A fertilizer recommendation decision support tool called Nutrient Expert is being developed using the QUEFTS modeling approach to provide site-specific nutrient management recommendations for cassava production. Nutrient omission trials are being conducted to generate calibration data.
2) A fertilizer blending use case aims to support the fertilizer industry in developing crop-specific fertilizer blends for cassava by providing a tool with advice on appropriate blends for geographical areas based on soil fertility, input costs, and demand. The primary clients are private sector fertilizer producers.
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 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.
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’.
1313- CLIMATE CHANGE, MATERIALITY AND RICE – A RESEARCH PROJECTConservationAgCornell
This document summarizes a research project on measuring the environmental and social impacts of different rice production and distribution methods in India. The project aims to:
1. Develop methods to analyze the rice supply chain as an integrated system and measure key parameters like greenhouse gas emissions, energy and water use, and labor across production, transport, milling and retail stages.
2. Apply these methods to compare the impacts of different rice production systems (e.g. intensive, organic) and distribution channels in three Indian states.
3. Involve stakeholders to assess technology and policy options for rice based on environmental, economic and social criteria to identify trade-offs.
The results will provide insights into how greenhouse gas emissions
Analysis of policy impact on the farming sector in Africa. Selected activitie...Francois Stepman
This document provides information about the Joint Research Centre (JRC) of the European Commission, with a focus on the Institute for Prospective Technological Studies (IPTS) and its work analyzing policy impacts on the agricultural sector in Africa. The IPTS uses economic modeling tools like FSSIM-DEV to evaluate the effects of policies and innovations on farm households, poverty levels, and other indicators. An example application analyzed the impacts of a rice seed policy in Sierra Leone. The modeling found the policy improved farmer viability but not enough to significantly reduce poverty. Ongoing work includes expanding the analyses to more African countries and further developing the modeling methodology.
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.
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.
This document discusses two fertilizer recommendation use cases in Nigeria and Tanzania:
1) A fertilizer recommendation decision support tool called Nutrient Expert is being developed using the QUEFTS modeling approach to provide site-specific nutrient management recommendations for cassava production. Nutrient omission trials are being conducted to generate calibration data.
2) A fertilizer blending use case aims to support the fertilizer industry in developing crop-specific fertilizer blends for cassava by providing a tool with advice on appropriate blends for geographical areas based on soil fertility, input costs, and demand. The primary clients are private sector fertilizer producers.
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 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.
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’.
1313- CLIMATE CHANGE, MATERIALITY AND RICE – A RESEARCH PROJECTConservationAgCornell
This document summarizes a research project on measuring the environmental and social impacts of different rice production and distribution methods in India. The project aims to:
1. Develop methods to analyze the rice supply chain as an integrated system and measure key parameters like greenhouse gas emissions, energy and water use, and labor across production, transport, milling and retail stages.
2. Apply these methods to compare the impacts of different rice production systems (e.g. intensive, organic) and distribution channels in three Indian states.
3. Involve stakeholders to assess technology and policy options for rice based on environmental, economic and social criteria to identify trade-offs.
The results will provide insights into how greenhouse gas emissions
Analysis of policy impact on the farming sector in Africa. Selected activitie...Francois Stepman
This document provides information about the Joint Research Centre (JRC) of the European Commission, with a focus on the Institute for Prospective Technological Studies (IPTS) and its work analyzing policy impacts on the agricultural sector in Africa. The IPTS uses economic modeling tools like FSSIM-DEV to evaluate the effects of policies and innovations on farm households, poverty levels, and other indicators. An example application analyzed the impacts of a rice seed policy in Sierra Leone. The modeling found the policy improved farmer viability but not enough to significantly reduce poverty. Ongoing work includes expanding the analyses to more African countries and further developing the modeling methodology.
Big data approaches can help rice farmers in Latin America adapt to climate change by providing real-time climate and cropping advice. A pilot program in Colombia combined rice yield and weather data to identify climate patterns and recommend optimal planting times. Farmers who followed the advice had successful harvests, while those who did not lost their crops and inputs. The program aims to scale this approach to other major rice producers in Latin America, including Argentina, Brazil, and Uruguay. Doing so may help reduce yield losses, increase adaptive capacity, and revolutionize agricultural advisory services.
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
This document summarizes the proceedings of an international workshop on bridging the gap between agricultural research and farmers' practices in Africa. The workshop included presentations on:
- The objectives and methodology of the ITAACC program, which is funding the research.
- Key findings from the demand-supply assessment for agricultural innovations in Africa, including the most needed crops and livestock.
- Criteria for successful innovations from the perspectives of farmers, researchers, and intermediaries.
- Challenges farmers currently face as described by farmer organization representatives.
- A new extension approach being tested that links payments to farmer satisfaction.
Trans-SEC outline, research framework and activitiesFrancois Stepman
This document outlines the framework and activities for the Trans-SEC research project in Tanzania. The project will:
1) Study food value chains in two regions of Tanzania representing different climates and socioeconomic conditions, through stakeholder workshops and surveys across 4 case study sites.
2) Identify and analyze the most promising strategies for upgrading these food value chains, such as improved production, processing, markets, and consumption. Strategies will be tested and their impacts assessed.
3) Embed the research within a modeling framework to understand risks and impacts under different scenarios. Results will be disseminated to stakeholders and policymakers to connect findings to development programs.
The Africa RISING project in Ethiopia's highlands had the goals of improving food security, gender equality, nutrition, income, and capacity building through sustainable intensification research from 2012-2022. It worked in four regions, implementing tested interventions like improved crops, fertilizers, and mechanization. Over 360,000 households directly benefited from validated technologies in phase two, while over 30,000 people participated in training. The project supported graduate students, published research, and faced challenges like COVID-19 and funding issues before planning its exit strategies.
Presentation by Monika Varga (Research group on Process Network Engineering) at the 2016 annual meeting of the European Forum on Agricultural Research for Development (EFARD).
This project implementation plan was presented by Hongmin Dong (CAAS) at the Kick-off meeting on "Piloting and scaling of low emission development options in large scale dairy farms in China" on September 28th, 2020.
The Africa RISING Project in Ethiopia introduced climate-smart agricultural technologies to smallholder farmers vulnerable to climate change. Over 0.33 million households adopted improved crop varieties, livestock feed, land restoration, water management, and mechanization. Crop yields increased significantly, reaching up to 9.4 tons/hectare for wheat. Postharvest practices reduced feed waste by over 30%. The project built the capacity of over 23,000 farmers and empowered cooperatives to disseminate seeds and technologies. An economic impact assessment projected benefits from technology adoption from 2013 to 2025.
Proposed contributions of Africa RISING for AICCRA small ruminant value chain...africa-rising
Presented by Kindu Mekonnen, Peter Thorne, Melkamu Bezabih and Aberra Adie at the Accelerating the impacts of CGIAR climate research in Africa (AICCRA) Virtual team meeting, 21 August 2020
This document summarizes research being conducted through two projects - ZimCLIFs and FACASI - in Zimbabwe. ZimCLIFs is focusing on integrating crops and livestock through sustainable intensification practices to improve food security. It is testing conservation agriculture techniques, livestock management, and value chain interventions across sites in Murehwa district. Baseline studies found maize-groundnut systems are dominant but yields are low. The document outlines ongoing agronomic experiments and lessons from other Zimbabwean studies showing yield increases and timeliness benefits of conservation agriculture. It identifies opportunities for synergies between the projects around addressing labor constraints through mechanization, but also challenges of alternative residue management and poor market incentives for conservation agriculture crops.
The SIFAZ project aims to improve the productivity and climate resilience of smallholder farming systems in Zambia through sustainable intensification practices. The project will address challenges such as weak research/extension linkages, inadequate farming practices, and lack of mechanization. It will develop improved practices through research, establish an enabling policy environment, and build farmers' capacities. Expected outcomes include new sustainable intensification strategies and practices, strengthened institutions/policies, and trained farmers adopting better management techniques. The project will be implemented across 27 districts, involving 104 cooperatives and 16,000 smallholder farmers over 3 agro-ecological zones of Zambia.
Livestock management in Ghana 2019/2020africa-rising
Presented by Augustine Ayantunde (ILRI), Sadat Salifu (CSIR-SARI), and Franklin Avornyo (CSIR-SARI) at Africa RISING Ghana Country Planning Meeting, Tamale, Ghana, and Virtual, 24 - 25 June 2020.
The document describes Agricultural Integrated Surveys (AGRIS), a new survey program designed by FAO to provide more timely and relevant agricultural data. AGRIS uses a modular approach with a core annual survey and rotating thematic modules to generate data for indicators like SDGs. It provides a cost-effective way to build sustainable rural information systems. Fifteen countries will implement AGRIS with technical and financial support from FAO and partners like the World Bank and donor agencies.
This document outlines the research component of the Sustainable Intensification of Smallholder Farming Systems in Zambia (SIFAZ) project. The project will conduct adaptive research on sustainable intensification practices to increase yields without environmental degradation. It will focus on agronomic practices like diversification, cover crops, and climate-smart combinations. It will also research mechanization to reduce labor demands and promote youth employment. Socio-economic research will analyze adoption patterns, gender impacts, and approaches for promoting technologies. The project aims to work with partners to conduct integrated biophysical and socio-economic research and promote scaling of sustainable intensification practices to smallholders in Zambia.
Presented by Bekele Kotu (IITA), Abdul Rahman Nurudeen (IITA), Gundula Fischer (IITA), Kipo Jimah (IITA), Mirja Michalscheck (WUR), and Issah Sugri (CSIR-SARI) at Africa RISING Ghana Country Planning Meeting, Tamale, Ghana, and Virtual, 24 - 25 June 2020.
Gender, Policy, and Socio-economic dimensions 2019/2020africa-rising
Presented by Adams Abdulai (CSIR-STEPRI), Bekele Kotu (IITA), Gundula Fischer (IITA), Kipo Jimah (IITA), and Alhassan Lansah Abdulai (CSIR-SARI) at Africa RISING Ghana Country Planning Meeting, Tamale, Ghana, and Virtual, 24 - 25 June 2020.
Global Project Knowledge Centres for Organic Agriculture in AfricaFrancois Stepman
2 - 4 April 2019. Cairo, Egypt "Boosting the Role and Potential of Organic Farming in Africa".
In April 2019, 39 participants from Africa and Europe met in Sekem, Egypt with the purpose to exchange and strategize organic development and spreading knowledge about organic farming in Africa.
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.
Planning, implementing and evaluating Climate-Smart Agriculture in smallholde...FAO
http://www.fao.org/in-action/micca/
This presentation by Janie Rioux, FAO, outlines the experience of the Mitigation of Climate Change in Agriculture (MICCA) pilot projects in Kenya and the United Republic of Tanzania.
Big data approaches can help rice farmers in Latin America adapt to climate change by providing real-time climate and cropping advice. A pilot program in Colombia combined rice yield and weather data to identify climate patterns and recommend optimal planting times. Farmers who followed the advice had successful harvests, while those who did not lost their crops and inputs. The program aims to scale this approach to other major rice producers in Latin America, including Argentina, Brazil, and Uruguay. Doing so may help reduce yield losses, increase adaptive capacity, and revolutionize agricultural advisory services.
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
This document summarizes the proceedings of an international workshop on bridging the gap between agricultural research and farmers' practices in Africa. The workshop included presentations on:
- The objectives and methodology of the ITAACC program, which is funding the research.
- Key findings from the demand-supply assessment for agricultural innovations in Africa, including the most needed crops and livestock.
- Criteria for successful innovations from the perspectives of farmers, researchers, and intermediaries.
- Challenges farmers currently face as described by farmer organization representatives.
- A new extension approach being tested that links payments to farmer satisfaction.
Trans-SEC outline, research framework and activitiesFrancois Stepman
This document outlines the framework and activities for the Trans-SEC research project in Tanzania. The project will:
1) Study food value chains in two regions of Tanzania representing different climates and socioeconomic conditions, through stakeholder workshops and surveys across 4 case study sites.
2) Identify and analyze the most promising strategies for upgrading these food value chains, such as improved production, processing, markets, and consumption. Strategies will be tested and their impacts assessed.
3) Embed the research within a modeling framework to understand risks and impacts under different scenarios. Results will be disseminated to stakeholders and policymakers to connect findings to development programs.
The Africa RISING project in Ethiopia's highlands had the goals of improving food security, gender equality, nutrition, income, and capacity building through sustainable intensification research from 2012-2022. It worked in four regions, implementing tested interventions like improved crops, fertilizers, and mechanization. Over 360,000 households directly benefited from validated technologies in phase two, while over 30,000 people participated in training. The project supported graduate students, published research, and faced challenges like COVID-19 and funding issues before planning its exit strategies.
Presentation by Monika Varga (Research group on Process Network Engineering) at the 2016 annual meeting of the European Forum on Agricultural Research for Development (EFARD).
This project implementation plan was presented by Hongmin Dong (CAAS) at the Kick-off meeting on "Piloting and scaling of low emission development options in large scale dairy farms in China" on September 28th, 2020.
The Africa RISING Project in Ethiopia introduced climate-smart agricultural technologies to smallholder farmers vulnerable to climate change. Over 0.33 million households adopted improved crop varieties, livestock feed, land restoration, water management, and mechanization. Crop yields increased significantly, reaching up to 9.4 tons/hectare for wheat. Postharvest practices reduced feed waste by over 30%. The project built the capacity of over 23,000 farmers and empowered cooperatives to disseminate seeds and technologies. An economic impact assessment projected benefits from technology adoption from 2013 to 2025.
Proposed contributions of Africa RISING for AICCRA small ruminant value chain...africa-rising
Presented by Kindu Mekonnen, Peter Thorne, Melkamu Bezabih and Aberra Adie at the Accelerating the impacts of CGIAR climate research in Africa (AICCRA) Virtual team meeting, 21 August 2020
This document summarizes research being conducted through two projects - ZimCLIFs and FACASI - in Zimbabwe. ZimCLIFs is focusing on integrating crops and livestock through sustainable intensification practices to improve food security. It is testing conservation agriculture techniques, livestock management, and value chain interventions across sites in Murehwa district. Baseline studies found maize-groundnut systems are dominant but yields are low. The document outlines ongoing agronomic experiments and lessons from other Zimbabwean studies showing yield increases and timeliness benefits of conservation agriculture. It identifies opportunities for synergies between the projects around addressing labor constraints through mechanization, but also challenges of alternative residue management and poor market incentives for conservation agriculture crops.
The SIFAZ project aims to improve the productivity and climate resilience of smallholder farming systems in Zambia through sustainable intensification practices. The project will address challenges such as weak research/extension linkages, inadequate farming practices, and lack of mechanization. It will develop improved practices through research, establish an enabling policy environment, and build farmers' capacities. Expected outcomes include new sustainable intensification strategies and practices, strengthened institutions/policies, and trained farmers adopting better management techniques. The project will be implemented across 27 districts, involving 104 cooperatives and 16,000 smallholder farmers over 3 agro-ecological zones of Zambia.
Livestock management in Ghana 2019/2020africa-rising
Presented by Augustine Ayantunde (ILRI), Sadat Salifu (CSIR-SARI), and Franklin Avornyo (CSIR-SARI) at Africa RISING Ghana Country Planning Meeting, Tamale, Ghana, and Virtual, 24 - 25 June 2020.
The document describes Agricultural Integrated Surveys (AGRIS), a new survey program designed by FAO to provide more timely and relevant agricultural data. AGRIS uses a modular approach with a core annual survey and rotating thematic modules to generate data for indicators like SDGs. It provides a cost-effective way to build sustainable rural information systems. Fifteen countries will implement AGRIS with technical and financial support from FAO and partners like the World Bank and donor agencies.
This document outlines the research component of the Sustainable Intensification of Smallholder Farming Systems in Zambia (SIFAZ) project. The project will conduct adaptive research on sustainable intensification practices to increase yields without environmental degradation. It will focus on agronomic practices like diversification, cover crops, and climate-smart combinations. It will also research mechanization to reduce labor demands and promote youth employment. Socio-economic research will analyze adoption patterns, gender impacts, and approaches for promoting technologies. The project aims to work with partners to conduct integrated biophysical and socio-economic research and promote scaling of sustainable intensification practices to smallholders in Zambia.
Presented by Bekele Kotu (IITA), Abdul Rahman Nurudeen (IITA), Gundula Fischer (IITA), Kipo Jimah (IITA), Mirja Michalscheck (WUR), and Issah Sugri (CSIR-SARI) at Africa RISING Ghana Country Planning Meeting, Tamale, Ghana, and Virtual, 24 - 25 June 2020.
Gender, Policy, and Socio-economic dimensions 2019/2020africa-rising
Presented by Adams Abdulai (CSIR-STEPRI), Bekele Kotu (IITA), Gundula Fischer (IITA), Kipo Jimah (IITA), and Alhassan Lansah Abdulai (CSIR-SARI) at Africa RISING Ghana Country Planning Meeting, Tamale, Ghana, and Virtual, 24 - 25 June 2020.
Global Project Knowledge Centres for Organic Agriculture in AfricaFrancois Stepman
2 - 4 April 2019. Cairo, Egypt "Boosting the Role and Potential of Organic Farming in Africa".
In April 2019, 39 participants from Africa and Europe met in Sekem, Egypt with the purpose to exchange and strategize organic development and spreading knowledge about organic farming in Africa.
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.
Planning, implementing and evaluating Climate-Smart Agriculture in smallholde...FAO
http://www.fao.org/in-action/micca/
This presentation by Janie Rioux, FAO, outlines the experience of the Mitigation of Climate Change in Agriculture (MICCA) pilot projects in Kenya and the United Republic of Tanzania.
1) The IFAD-funded CLCA project aims to develop and test innovative integrated crop-livestock conservation agriculture approaches through participatory research with farmers in Algeria, Tunisia, and Tajikistan.
2) Key achievements include collecting over 1,100 farm surveys, conducting on-station and on-farm trials of stubble grazing and fodder production, and testing conservation agriculture technology packages on over 45 farms across the three countries.
3) The project has also enhanced capacity through over 15 training courses attended by 280 trainees, eight field days reaching 357 farmers, and publications including conference papers, films and posters.
This document summarizes a meeting on cassava and sweet potato intercropping. It discusses the rationale for intercropping cassava and sweet potato to improve yields and incomes. Trials were conducted on farms in Tanzania with local partners to determine the best planting densities and times for intercropping. Data was collected on plant growth and yields. Analysis is ongoing and further trials will be established next year to continue optimizing cassava-sweet potato intercropping systems.
Workstream 1: Technology Platform: Case StudiesHillary Hanson
Scientific and Technical Partnerships in Africa: Technologies, Platforms, and Partnerships in support of the African agricultural science agenda, Abidjan, Cote d'Ivoire, April 4&5, 2017
The Global Futures and Strategic Foresight (GFSF) team met in Rome from May 25-28, 2015 to review progress towards current work plans, discuss model improvements and technical parameters, and consider possible contributions by the GFSF program to the CRP Phase II planning process. All 15 CGIAR Centers were represented at the meeting.
This document describes an intercropping project in Nigeria that aims to develop a decision support tool for intensification options for cassava intercropping. The project conducted over 150 trials across multiple states comparing various planting densities, fertilizer applications, and varieties of cassava and maize. Preliminary data collected at maize harvest showed responses to fertilizer treatment and some farmer preferences. Challenges included rodent damage, erosion, and aligning the short-duration maize variety with local growing seasons. Next steps include modifying protocols based on farmer preferences for fresh cob yields, adjusting fertilizer timing, and planning the next season's trials.
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.
Sustainable intensification of low-input agriculture systems: legacy, loose e...africa-rising
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Session 6 2 Monitoring, Evaluation and Learning: Monitoring Uptake for Impact David Ngome
As the ACAI project shifts focus from research-related activities to dissemination activities, it becomes imperative that different results and targets are achieved, and how these results and targets will be monitored and be known to all stakeholders.
The presentation on ME&L highlighted the results to be achieved, targets to be met and methodologies to monitor number of farmers reached with the DSTs, farmers changing practices through use of the DSTs, and farmers benefiting from use of the DSTs.
Farmers reached will be monitored by aggregation of number of farmers who are aware and gain knowledge of ACAI DST per use case, per DST format and per partner dissemination approach.
Farmers changing practices through use of the DSTs will be monitored through panel surveys, which will be done on annually starting in 2019.
Farmers benefiting from use of the DSTs will be monitored by impact survey, which will be conducted at the end of the project.
The document summarizes the work of the Standing Panel on Impact Assessment (SPIA) since the last ISPC meeting in May 2016. It discusses three key points:
1) SPIA's first synthesis report on improving causal identification, measurement, and representativeness in impact assessments.
2) Upcoming events and workshops focused on impact evaluation methods.
3) Plans for a second phase of SPIA's Strategic Impact, Monitoring and Evaluation program, focusing on country baselines, impact claims databases, continued impact evaluations, and improved adoption prediction.
The document discusses plans for new validation exercises of decision support tools to be implemented in 2019. It provides details on rounds of group work to plan the validations, budgeting, and integrating training. The validations aim to improve tools based on 2018 learnings and involve more farmers and extension agents. Specific changes proposed for the integrated crop and business planning tools are described. Challenges of increasing participation are discussed, such as managing more validations and involving new stakeholders. Options for additional partners to test other tools are also considered.
The IITA Cowpea Breeding Unit underwent an assessment of its program using the BPAT tool. The assessment team found that the unit has well-educated scientists, utilizes best practices, and has a multidisciplinary team. However, the assessment also found that the unit's infrastructure could be updated, the size of the breeding program increased, and impact measurement and strategic planning improved. The assessment provides recommendations in these areas to strengthen the cowpea breeding program.
The IFAD-funded CLCA project aims to develop and test integrated crop-livestock conservation agriculture approaches for smallholder farmers in North Africa and Central Asia. Key achievements include collecting survey data from over 1,000 farms, conducting on-station and on-farm experiments on stubble grazing strategies and fodder production, and implementing over 45 on-farm trials of conservation agriculture technology packages across three countries. The project also strengthened capacities through various training courses attended by over 280 participants. While gains include soil protection, water use efficiency and crop diversification, adoption faces tradeoffs such as increased herbicide use and less stable incomes. Strengthening partnerships with national and international institutions will help address challenges to adopting integrated crop-livestock
This document summarizes the key points from a presentation on assessing the impact of natural resource management (NRM) research. It discusses CGIAR's NRM research agenda, past impact assessments of NRM innovations, gaps in the evidence base, and insights on ways to strengthen impact assessment going forward. The presentation calls for rethinking the focus on technology adoption and instead documenting how NRM research changes discourses and understanding. It emphasizes the need for new impact assessment methods that can evaluate outcomes at farm and landscape scales and account for non-linear impacts over time.
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.
This document summarizes the activities and achievements of the Community Action in improving farmer-saved seed yam project in Ghana and Nigeria. The project aims to improve the quality of farmer-saved seed yam to boost food security and reduce poverty. Key activities include positive selection of virus-free mother plants, training farmers in quality seed production techniques, and research on seed degeneration. The project has trained over 500 farmers, established demonstration plots, and collected baseline data from project communities in its first year. Financial reports indicate funds released so far have been properly utilized to implement project activities.
This document provides an overview and update on the implementation of IITA's Social Science & Agribusiness Research for Development (R4D) agenda from 2012-2020. The agenda has six objectives: 1) ex-ante impact assessment, 2) understanding rural livelihoods, 3) gender preferences and technology adoption, 4) input and output markets and policies, 5) targeting innovations, and 6) ex-post impact assessment. Updates are provided on progress made towards each objective, including tools developed, studies conducted, and engagement with partners and policymakers. The overall goal is to improve smallholder productivity, competitiveness and nutrition in Africa through strategic social science and agribusiness research.
Similar to Session 2 2 Development of the Best Intercropping Practices Decision Support Tool (20)
This document outlines a plan to develop cloud-based prediction tools and digital guides to help cassava growers and extension workers understand how different growing environments impact recommendations, predict crop responses, and scale the sharing of recommendations through partner networks using a smartphone app and database.
This document outlines the development stages of a tool, starting with an initial concept and literature-based version 0, followed by a prototype version 1 incorporating experimental data, and a pilot version 2 that is validated. The final validated version is considered a ready tool and undergoes multiple validation exercises with different groups.
Nigeria is the most populous country in Africa with over 200 million people. It has a diverse population that speaks over 500 languages and is nearly evenly split between Christians and Muslims. Nigeria has had a challenging political history including periods of military rule but has transitioned to a democratic government over the past few decades.
The document discusses the results of a study on the effects of a new drug on patients with a certain medical condition. The study found that patients who received the drug experienced a significant reduction in symptoms compared to those who received a placebo. Overall, the drug was found to be an effective new treatment option for this condition with only mild side effects reported.
- On-farm experiments were conducted in Nigeria to study cassava yield and nutrient uptake under different fertilizer treatments.
- At intermediate harvests of 4 and 8 months after planting, and final harvests of 12-14 months, plant parts were weighed and analyzed for nitrogen, phosphorus, and potassium concentration.
- On average across treatments, locations, and years, 67% of nitrogen, 61% of phosphorus, and 52% of potassium uptake occurred by 4 months after planting. Nutrient uptake and allocation to plant parts over time was similar for fertilized and unfertilized plants. Whole plant nutrient concentration decreased with increasing biomass, with dilution accounting for about 65% of nutrient variation.
Cassava is a critical crop for food security in West Africa but its production is vulnerable to changing climate conditions. The study developed crop models to simulate how cassava yields may be impacted under different climate change scenarios in major cassava growing regions in West Africa through 2050. The models can help identify adaptation strategies to improve food security as climate change progresses.
Increased planting densities of cassava and maize, and the application of nitrogen, phosphorus, and potassium fertilizer increased the productivity of cassava-maize intercrops in southern Nigeria. Both maize cob yield and cassava root yield followed the same trend of being higher at high planting densities and with fertilizer application. A maize fertilizer regime targeting nitrogen, phosphorus, and potassium performed better than a cassava-targeting regime for maize yields, while the cassava regime performed better under very low soil fertility conditions. The height of maize from previous crops can be used as a proxy for soil fertility and predicting the response of maize to fertilizer - responses were higher when maize height was
The document outlines a turnkey solution for providing tailored agronomic advice. It describes 10 elements of the turnkey solution:
1. Demand-driven recommendations selected by partner dissemination networks.
2. Adapting decision support tool formats and functionality to partner strategies and user capabilities.
3. Avoiding price mapping and predictions.
4. Customizing tools and materials to user and beneficiary preferences.
5. Conducting many on-farm trials to understand variation.
The document then discusses reflecting on what could be done better and solicits feedback on the 10 elements and what should be done the same or improved in the future.
This document outlines the development stages of a tool, starting with an initial concept and literature-based version 0, followed by a prototype version 1 incorporating experimental data. Version 2 is a pilot tool that has been validated, with the final version being a ready tool that has undergone multiple validation exercises across different regions.
This document lists the names and affiliations of 7 researchers who were funded by the Bill & Melinda Gates Foundation. The researchers are from universities in the United States, Switzerland, Belgium, and the Netherlands. The document does not provide any other details about the researchers or their projects.
This document outlines ACAI's strategy for scaling the dissemination of AKILIMO, including:
1) Partnering with existing organizations that have dissemination strategies in place to facilitate entry and demand-driven ownership.
2) Implementing regular feedback mechanisms to ensure products meet beneficiary needs and are accepted before wide-scale dissemination.
3) Encouraging continuous learning through data collection and feedback integration to support ongoing acceptance.
4) Agreeing on appropriate formats like paper, video, radio etc. to disseminate information widely.
This document summarizes the monitoring, evaluation, and learning (MEL) framework for a project aimed at increasing cassava yields in Africa. The project targets include:
- Increasing cassava and intercrop yields by 2-10 tonnes per hectare
- Reaching thousands of households and extension agents
- Engaging private sector partners to address bottlenecks like access to credit and markets
The MEL framework involves measuring outcomes, learning from feedback, and adapting implementation strategies. Bottlenecks are analyzed at the value chain and project levels. Dissemination materials and channels are developed with partners and include training, demonstrations, radio, and videos. Questions are posed to discuss experiences, challenges, and improvements needed.
The document discusses Viamo's 321 mobile information service in Nigeria, which provides free agricultural, health, nutrition, and financial literacy information to users. It partners with Airtel to provide free airtime for the service. The 321 service uses an interactive voice response system that allows users to access different types of informational content by pressing numbers on their phone. One example of content is information on various topics related to cassava farming, developed in partnership with ACAI. The document outlines the roles of Viamo, ACAI, and the government in launching and supporting the 321 service.
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Session 2 2 Development of the Best Intercropping Practices Decision Support Tool
1. Development of the
Best Intercropping Practices (IC)
Decision Support Tool (DST) – Version2
www.iita.org | www.cgiar.org | www.acai-project.org
2. Overview
www.iita.org | www.cgiar.org | www.acai-project.org
Best Intercropping Practices DST:
1. Introduction (Veronica Uzokwe):
• The IC use case
• Learnings from the baseline
• Summary of year 2 achievements
2. Field activities (Mark Tokula and Haji Saleh):
• Field activities: Intercropping trials
• Field trial results
3. Advances with the DST development (Christine Kreye):
• Modelling framework
• Year1 – Year2 validation results
• The Decision Support Tool
4. Validation exercises (Chris Okoli and Wiston Mwombeki):
• First impressions from ongoing validation exercises
• Next steps and additional data needs
3. Overview
www.iita.org | www.cgiar.org | www.acai-project.org
Best Intercropping Practices DST:
1. Introduction (Veronica Uzokwe):
• The IC use case
• Learnings from the baseline
• Summary of year 2 achievements
2. Field activities (Mark Tokula and Haji Saleh):
• Field activities: Intercropping trials
• Field trial results
3. Advances with the DST development (Christine Kreye):
• Modelling framework
• Year1 – Year2 validation results
• The Decision Support Tool
4. Validation exercises (Chris Okoli and Wiston Mwombeki):
• First impressions from ongoing validation exercises
• Next steps and additional data needs
4. Introduction
www.iita.org | www.cgiar.org | www.acai-project.org
Best Intercropping Practices DSTs:
• Specific purpose: recommend optimal time of planting, crop density and fertilizer application for
a maize (NG) / sweet potato (Zanzibar) intercrop to increase overall productivity
• Requested by: FCI (TZ) and 2SCALE (NG)
• Other partners: SG2000 (NG)
• Intended users: Extension Agents (EAs) supporting cassava growers
• Expected benefit: Intercrop yield increased by 2 tonnes/ha and cassava yield not affected or increased
by 0.5 tonnes/ha realized by 35,100 HHs, with the support of 124 extension agents,
on a total area of 17,550 ha, generating a total value of US$3,948,750
• Current version: V2: uses expert knowledge to estimate current crop performance and revenue based
on default or user-defined prices of roots and intercrop produce to recommend best
planting time, planting density and fertilizer regime for a preferred set of varieties,
maximizing overall net revenue or intercrop yield without affecting cassava yield
• Approach: Decision tree model based on analysis of field trial data
• Input required: Cropping objective (maximize total revenue or maximize cassava yield), prices of
intercrop produce (maize cobs (NG) / sweet potato (Zanzibar)) and cassava roots,
willingness to invest in fertilizer and knowledge of field history
• Interface: Paper-based decision tree, including guidelines for simple calculations to estimate
profitability
5. Principles of the Best Intercropping Practices Tool
www.iita.org | www.cgiar.org | www.acai-project.org
1. Obtain details on current practice
2. Identify alternative options within given constraints
3. Evaluate to what extent the performance of alternative options is location-dependent, based on
analysis of multilocational field trial data
4. If so, identify GIS (or other) predictor variables to estimate location-specific effects of
interventions on intercrop yield and cassava root yield
5. Convert yield effects to changes in gross revenue based on prices of intercrop produce and
cassava roots (default values or user input)
6. Calculate net revenue (subtract cost of fertilizer)
7. Recommend optimal intercrop density, relative time of planting (Zanzibar only) and fertilizer regime
(if willing to invest in fertilizer) that maximizes cassava yield or overall net revenue using a
decision tree model
The IC-DST is developed based on following steps and principles:
6. Principles of the Best Intercropping Practices Tool
www.iita.org | www.cgiar.org | www.acai-project.org
What is current practice? – learnings from the RC survey
Tanzania (Zanzibar)
38% of cassava is intercropped, of which 45% by
sweet potato (45%), planted at the same time (67%)
or ± 2 weeks (29%). 0% of farmers apply fertilizer to
the sweet potato intercrop, and 62% commercialize
at least half of sweet potato produce. Main objective
is maximizing land use efficiency.
Nigeria
68% of cassava is intercropped, of which 75% by
maize, planted 2 weeks earlier (33%) or at the same
time (30%). 43% of farmers apply fertilizer to the
maize intercrop, and 94% commercialize at least half
of the maize produce. Main objective is faster access
to food and income.
Need picture here!
7. Principles of the Best Intercropping Practices Tool
www.iita.org | www.cgiar.org | www.acai-project.org
What are the alternative options?
2. Optimize the relative time of planting of the intercrop
• Reduce competition for light for the cassava crop
• Optimal time of planting will depend on the cropping objectives
3. Apply fertilizer
• Increased availability of nutrients (reduced belowground competition)
• Intercrop as an entry point for fertilizer application to cassava (benefits from residual effects)
• Modify the composition of the fertilizer regime to the specific cropping objectives
1. Modify (increase) the crop density:
• Optimize land use efficiency
• Choose best variety with minimal above- and belowground competition effect
8. Principles of the Best Intercropping Practices Tool
www.iita.org | www.cgiar.org | www.acai-project.org
Modelling framework
Are the effects of density and fertilizer application
dependent on field conditions?
Evaluate through multilocational field
testing covering target environments
Develop decision
tree models
Can we predict these effects
based on expert knowledge?
9. V1 version of the IC DST (end of 2017)
www.iita.org | www.cgiar.org | www.acai-project.org
10. Overview
www.iita.org | www.cgiar.org | www.acai-project.org
Best Intercropping Practices DST:
1. Introduction (Veronica Uzokwe):
• The IC use case
• Learnings from the baseline
• Summary of year 2 achievements
2. Field activities (Mark Tokula and Haji Saleh):
• Field activities: Intercropping trials
• Field trial results
3. Advances with the DST development (Christine Kreye):
• Modelling framework
• Year1 – Year2 validation results
• The Decision Support Tool
4. Validation exercises (Chris Okoli and Wiston Mwombeki):
• First impressions from ongoing validation exercises
• Next steps and additional data needs
11. Intercropping Trials - Nigeria
www.iita.org | www.cgiar.org | www.acai-project.org
Current overview of trials and status of trials
Nigeria CIM 2016 CIM 2017
Zone planted harvested
maize
harvested
cassava
planted harvested
maize
harvested
cassava
South East 85 73 73 110 99 99
South West 47 45 44 66 39 47
Total 132 118 117 176 138 146
12. Intercropping Trials - Nigeria
www.iita.org | www.cgiar.org | www.acai-project.org
Harvest and establishment of 2 sets of trials based on step-wise intensification
CIM-2 trials:
Planted in 2016
Harvested in 2017
CIM-3 and CIM-4 trials:
Planted in 2017
Harvested in 2018
Overview of year 2-3 trials
13. Intercropping Trials - Nigeria
Impressions and learnings from the field – some pictures
www.iita.org | www.cgiar.org | www.acai-project.org
• Farmers generally prefer intercropping maize with cassava in southwest Nigeria as they see it as a
means to getting some quick income while waiting for cassava, which is partially reinvested to
maintain the cassava plots.
• Farmers do not see any negative effect of planting maize with cassava but noticed that the planting
position of cassava relative to maize is crucial to attain high benefits in a cassava-maize intercrop.
14. Intercropping Trials - Nigeria
Impressions and learnings from the field – some pictures
www.iita.org | www.cgiar.org | www.acai-project.org
15. Intercropping Trials - Nigeria
www.iita.org | www.cgiar.org | www.acai-project.org
Model: yield ~ 0 + MaizeLevel + Fertilizer + (0 + Fertilizer|fieldID)
Results 2017-2018 trials
Cassava root yield (2018)
= 40,000 maize plants/ha
= 20,000 maize plants/ha
Fertilizer
Maize Fertilizer = Fertilizer regime
targeting maize crop
Cassava Fertilizer = Fertilizer regime
targeting cassava crop
Yield penalty of ~ 1 t/ha when
intercropping with maize at high
density vs. low density.
No difference between the two
fertilizer regimes. Both increase
yield by ~3 t/ha.
16. Intercropping Trials - Nigeria
www.iita.org | www.cgiar.org | www.acai-project.org
Model: yield ~ 0 + MaizeLevel*Fertilizer + maizeVariety + (0 + Fertilizer|fieldID)
No reward in additional maize cobs
from increased planting density
without fertilizer application.
Higher cob yields when targeting
fertilizer to the maize crop.
Results 2018-2019 trials
Maize cob yield (2017)
= 40,000 maize plants/ha
= 20,000 maize plants/ha
Fertilizer
Maize Fertilizer = Fertilizer regime
targeting maize crop
Cassava Fertilizer = Fertilizer regime
targeting cassava crop
17. Intercropping Trials - Nigeria
www.iita.org | www.cgiar.org | www.acai-project.org
Relating variation in yield response to maize height…
Recursive partitioning for simple decision rule:
• Control maize is a good indicator to
distinguish response classes
• All fields were responsive;
only 1 field with maize height < 50cm,
only 10 fields with maize height > 150cm
• Highest response if control maize height =
100-150 cm
18. Intercropping Trials - Nigeria
www.iita.org | www.cgiar.org | www.acai-project.org
Factors 2016 - 2017 2017 - 2018
Cassava density High cassava density → higher
cassava yield
Only high density cassava throughout
Cassava density Cassava yield not affected by maize
density
Slight yield penalty of cassava (~ 1t /ha) at
high maize density
Maize density High maize density → always higher
cob yield
Low maize density → safer without fertilizer
Fertilizer F1 is suitable in both crops confirmed
Intensification
steps
High density maize and cassava
without fertilizer →
high density crops with fertilizer
Low density maize + high/low density
cassava without fertilizer →
high density crops with fertilizer
Indicator maize
height
Suitable to indicate fertilizer
application
Not confirmed (see validation exercises)
Maize variety Higher yields of white maize in Benue Same trend in validation exercises
Comparison of 2017-2018 and 2019-2019 results
19. Intercropping Trials - Zanzibar
www.iita.org | www.cgiar.org | www.acai-project.org
Evaluate effects of planting density, planting time and fertilizer application
on sweet potato:
20. Intercropping Trials - Zanzibar
www.iita.org | www.cgiar.org | www.acai-project.org
Current overview of trials and status of trials
Zanzibar CIS 2017 CIS 2018
planted harvested
sweetpotato
harvested
cassava
planted harvested
sweetpotato
harvested
cassava
Unguja 75 66 64 75 68 ongoing
Pemba 25 22 22 25 17 ongoing
Total 100 88 86 100 85
21. Intercropping Trials - Zanzibar
Impressions and learnings from the field – some pictures
www.iita.org | www.cgiar.org | www.acai-project.org
SWP monocrop – no fertilizer
SWP monocrop + fertilizer
SWP intercrop simultaneous planting
+ fertilizer
SWP intercrop planted @ 2 weeks delay
+ fertilizer
SWP intercrop planted @ 2 weeks
delay – no fertilizer
Cassava monocrop – no fertilizer
22. Intercropping Trials - Zanzibar
Impressions and learnings from the field – some pictures
www.iita.org | www.cgiar.org | www.acai-project.org
23. Intercropping Trials - Zanzibar
Results 2017-2018 trials
www.iita.org | www.cgiar.org | www.acai-project.org
On-farm trials with 80 farmers across Unguja and Pemba
Sweet potato yield 2017
Conclusions from 2017 sweet potato harvest
Sweet potato suffers somewhat from
competition with cassava, with yield losses of
5 – 15% (higher in high-yielding fields).
Delayed planting of sweet potato causes yield
reductions of 0 – 45%.
Increasing density is not advantageous for a
sweet potato intercrop. A density of 10,000
vines/ha is recommended.
Effects of density and relative planting time
depend on time of planting and amount of
rainfall received.
24. Intercropping Trials - Zanzibar
Results 2017-2018 trials
www.iita.org | www.cgiar.org | www.acai-project.org
On-farm trials with 80 farmers across Unguja and Pemba
Sweet potato yield 2017 Cassava yield 2018
Negative effects of sweet potato intercropping on cassava yield observed, and similar (-6.6 t/ha ~ -37%) in both systems.
Significant variation between sites in effects of intercropping on cassava yield, varying between -5% and -60% (LER = 1.4 – 1.9).
25. Intercropping Trials - Zanzibar
Results 2017-2018 trials
www.iita.org | www.cgiar.org | www.acai-project.org
On-farm trials with 80 farmers across Unguja and Pemba
Sweet potato yield 2018
Competition effects of cassava on sweet potato are more severe than in 2017!
with yield reduction of ~30% if planted simultaneously, and ~50% if sweet potato planting
is delayed by 2 weeks (very consistent across locations).
Cassava harvest planned in February – March 2018. If cassava yield reductions remain
around 40%, then LER = 1.1 – 1.5.
Tested technologies (increased density and delayed introduction of sweet potato) do not
result in yield increases. Farmer’s most common practice (simultaneous planting of a
low density sweet potato crop) appears best-performing.
Cassava – sweet potato intercropping is only sensible if the farmer wishes to maximize
land use efficiency, and grow both crops with priority to the sweet potato. The farmer
must be willing to accept substantial losses in cassava yield.
Use of fertilizer to increase yield could be further tested.
26. Overview
www.iita.org | www.cgiar.org | www.acai-project.org
Best Intercropping Practices DST:
1. Introduction (Veronica Uzokwe):
• The IC use case
• Learnings from the baseline
• Summary of year 2 achievements
2. Field activities (Mark Tokula and Haji Saleh):
• Field activities: Intercropping trials
• Field trial results
3. Advances with the DST development (Christine Kreye):
• Modelling framework
• Year1 – Year2 validation results
• The Decision Support Tool
4. Validation exercises (Chris Okoli and Wiston Mwombeki):
• First impressions from ongoing validation exercises
• Next steps and additional data needs
27. How are these results fed into the DST?
www.iita.org | www.cgiar.org | www.acai-project.org
Nigeria:
1. There is a small yield penalty for cassava roots of about 1 t/ha by intensification in maize
2. There appears to be higher uncertainty and fluctuation in cassava root than maize cob prices
=> also base V2 on responses of maize
3. Lessons from the first trials and validation exercises for maize
• Farmers value fresh maize cobs for the market
• Evaluate responses by cob size
• Not all LGAs have a market for fresh cobs => include dry cobs int V2
• Large cobs fetch highest prices; base decisions on this fraction
• Use trials of 2017 (CIM-3 and CIM-4) for V1 development
4. Response to higher planting density of large maize cobs is not site-specific and low (for the
selected maize variety) => low density will be the blanket recommendation for maize.
5. The height and appearance of maize (without fertilizer application) at tasselling can be used as
indicator for the response to fertilizer.
6. The maize fertilizer regime (F1) is preferred in all situations (over F2).
7. Maize cob prices and fertilizer availability and cost will drive the decision-making in the DST.
28. 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?
IC-DST packaged as a simple paper-based decision tree with inputs:
1. Variety recommendation:
• erect cassava
• early maturing maize (90-95 days)
2. High planting densities
• cassava: 12,500 plants ha-1
• maize: 40,000 plants ha-1 if fertilizer is applied / high fertility conditions,
20,000 plants ha-1 otherwise
3. Fertilizer regime:
• target the maize crop
• cassava benefits as well
4. Fertilizer recommendation:
• site-specific
• use farmers’ experience with their maize crop
(plant height at tasselling)
5. Profitability of fertilizer application:
• based on expected additional large maize cobs
• look-up table [cost of fertilizer x price of large cobs]
• formula for easy calculation
29. Packaging in a tool for field use
www.iita.org | www.cgiar.org | www.acai-project.org
Can the maize height be used to predict fertilizer use?
Do measured maize height and farmers’ estimates match?
→ usually correct for tall maize
→ difficult to distinguish medium and low height
Numbers / category
Previous maize height Low Medium Tall Total % correct
low 0 3 9 12
% 0 3 8 11 0
medium 3 28 18 49
% 3 26 17 46 57
tall 0 6 40 46
% 0 6 37 43 87
Total 3 37 67 107
% 3 35 63 100
Measured maize height in LM
30. Packaging in a tool for field use
www.iita.org | www.cgiar.org | www.acai-project.org
How to capture maize performance without input use?
1 2 3 4 5
Visualization of expected maize performance (if grown without fertilizer input)
Likely unresponsive
to sole fertilizer
(improve soil fertility:
apply manure)
Recommend fertilizer application
Standard recommendation
No economically
justified response
31. How are these results fed into the DST?
www.iita.org | www.cgiar.org | www.acai-project.org
Zanzibar:
Sweet potato
1. Sweet potato suffers from cassava, yield losses of 5 – 30% (higher in high-yielding fields), less than
observed in other studies.
2. Delayed planting of sweet potato causes yield reductions of 0 – 50%.
→ Plant sweet potato and cassava at the same time
3. Increasing density is not advantageous for sweet potato (but has advantages for weed control).
→ Recommended density = 10,000 vines/ha in intercrop.
4. Effects of density and relative planting time are not site-specific.
→ Recommendations will mainly be driven by cropping objectives and sweet potato tuber prices
Cassava
1. Intercropping reduces cassava yield by 5 – 60%
→ Yield penalty is site-specific
2. LER remains positive
3. Intercropping can be recommended when the farmer wants to maximize land use efficiency.
→ sweet potato is the more important crop
32. Validation exercises – pilot study
www.iita.org | www.cgiar.org | www.acai-project.org
• Currently 243 farmers across Tanzania and Nigeria involved in
pilot validation exercise…
• Supervised by trained extension agents, and coordinated by
primary development partners (SG2000 and Psaltry/2Scale
in Nigeria, and FCI in Tanzania to start in 2019)
• NARS teams of agronomists assist in training and monitoring.
• DSTs and all data collection through a suite of ODK forms
33. Validation exercises – overview
www.iita.org | www.cgiar.org | www.acai-project.org
153 submissions on “Best Intercropping Practices” recommendations
143 validation exercises established, spread across 5 states (Oyo, Ogun, Benue, Cross River, Anambra)
34. Validation exercises – overview
www.iita.org | www.cgiar.org | www.acai-project.org
Current maize performance:
16%: < knee-height → Don’t apply fertilizer, apply manure and improve soil fertility
43%: knee to chest height → Apply fertilizer, if price ratio cobs/fertilizer is favourable
41%: > chest height → Apply fertilizer, if price ratio is favourable but lower response
Does this hold? Too conservative?
Overview input parameters
• Price of cassava most variable
• Price of maize: 5-25 USD/100 cobs
• Cost of urea: 20-30 USD/50 kg bag
• Cost of NPK: 15-25 USD/50 kg bag
Some outliers / unrealistic costs or prices!
Cost of input & price of produce:
35. Validation exercises – overview
www.iita.org | www.cgiar.org | www.acai-project.org
So what is being recommended?
47%: medium maize and
favourable price ratio
Recommend applying fertilizer
26%: medium maize but
risky price ratio
Do not apply fertilizer
11%: medium maize but
unfavourable price ratio
Do not apply fertilizer
16%: low or tall maize
Do not apply fertilizer
36. Validation exercises – overview
www.iita.org | www.cgiar.org | www.acai-project.org
Results: maize yield effects
• Increased density only does not increase
nr of large or medium cobs, it only results
in more small cobs and more cobs that
are unfit for sale.
• About 25% of farmers have decreased
numbers of large and medium cobs from
increasing density only.
• Fertilizer application to a high density
maize crop increases cob numbers for all
classes, and especially more large and
medium cobs. This increase is significant
for 75% of farmers, varying between
2,500 – 10,000 more large cobs per
hectare.
• No negative effects of fertilizer application
observed on cob numbers.
37. Validation exercises – overview
www.iita.org | www.cgiar.org | www.acai-project.org
Results: net revenue
• Increased density only did not entail an
overall increas in net revenue.
• For about 25% of farmers, increased
density resulted in small decreases in net
revenue.
• Fertilizer application to a high density
maize crop increases net revenue by on
average 350 $/ha (~ project target).
• About 2/3 of participants realize a
significant net revenue increase. Only a
minority (~5%) observed a small negative
impact on net revenue.
Was this as predicted by the DST?
38. Validation exercises – overview
www.iita.org | www.cgiar.org | www.acai-project.org
Results: Evaluation of recommendations
Low density High density
High density
+ fertilizer
High density 10% 14% 36%
High density
+ fertilizer 4% 5% 31%
Best performing
Recommended
39. Validation exercises – overview
www.iita.org | www.cgiar.org | www.acai-project.org
Results: Evaluation of recommendations
Low density High density
High density
+ fertilizer
High density 10% 14% 36%
High density
+ fertilizer 4% 5% 31%
Best performing
Recommended
Correct recommendation!
40. Validation exercises – overview
www.iita.org | www.cgiar.org | www.acai-project.org
Results: Evaluation of recommendations
Low density High density
High density
+ fertilizer
High density 10% 14% 36%
High density
+ fertilizer 4% 5% 31%
Best performing
Recommended
Correct recommendation!
Incorrect recommendation but no loss in revenue and no added cost
41. Validation exercises – overview
www.iita.org | www.cgiar.org | www.acai-project.org
Results: Evaluation of recommendations
Low density High density
High density
+ fertilizer
High density 10% 14% 36%
High density
+ fertilizer 4% 5% 31%
Best performing
Recommended
Correct recommendation!
Incorrect recommendation but no loss in revenue and no added cost
Incorrect recommendation with loss in revenue and limited added cost
42. Validation exercises – overview
www.iita.org | www.cgiar.org | www.acai-project.org
Results: Evaluation of recommendations
Low density High density
High density
+ fertilizer
High density 10% 14% 36%
High density
+ fertilizer 4% 5% 31%
Best performing
Recommended
Correct recommendation!
Incorrect recommendation but no loss in revenue and no added cost
Incorrect recommendation with loss in revenue and limited added cost
Incorrect recommendation with loss in revenue and substantial added cost
43. Validation exercises – overview
www.iita.org | www.cgiar.org | www.acai-project.org
Results: Evaluation of recommendations
4% 5% 31%
10% 14% 36%
Limited loss in net revenue:
DST performs acceptably.
Lost opportunity in revenue:
DST is too conservative.
44. Validation exercises – overview
www.iita.org | www.cgiar.org | www.acai-project.org
Results: Evaluation of recommendations
What if we change the decision rules?
1. Allow fertilizer application to tall maize
2. Set minimal VCR to 1.2 instead of 2 $/$
45. Validation exercises – overview
www.iita.org | www.cgiar.org | www.acai-project.org
Results: Evaluation of recommendations
8% 15% 52%
7% 4% 16%
Proportion of correct recommendations increases from 44% to 56%
Proportion of incorrect recommendations with high cost
increases from 9% to 23%, but loss in revenue is limited.
46. Validation exercises – overview
www.iita.org | www.cgiar.org | www.acai-project.org
How do we best handle risk?
1. Conservatively by minimal VCR of 2 $/$
• Some farmers loose opportunities;
• others are better protected against real loss of money
2. Risk friendly by minimal VCR of 1.2 instead of 2 $/$
• More farmers can realize their opportunities
• More farmers are at risk of loosing real money
3. Let the farmer choose the minimal VCR
• Farmer can make the choice about the risk (s)he is taking
• How well can farmers assess whether they can take a higher risk?
47. Overview
www.iita.org | www.cgiar.org | www.acai-project.org
Best Intercropping Practices DST:
1. Introduction (Veronica Uzokwe):
• The IC use case
• Learnings from the baseline
• Summary of year 2 achievements
2. Field activities (Mark Tokula and Haji Saleh):
• Field activities: Intercropping trials
• Field trial results
3. Advances with the DST development (Christine Kreye):
• Modelling framework
• Year1 – Year2 validation results
• The Decision Support Tool
4. Validation exercises (Chris Okoli and Wiston Mwombeki):
• First impressions from ongoing validation exercises
• Next steps and additional data needs
48. Key Activities
www.iita.org | www.cgiar.org | www.acai-project.org
Key activities held in preparation of validation exercises
Discussion with partners around on-farm trial
• Selection and training of farmers to participate in the
trials management
• Over 100 farmers from 10 clusters were engaged:
7 clusters in Unguja and 3 in Pemba
• Major work for period was on trials management and
data collection
• Validation exercise to start in year 2019.
Evaluation of sweet potato and cassava crop performance
49. Testimonies from farmers
www.iita.org | www.cgiar.org | www.acai-project.org
Mr Jeshi, from Bumbwini, FCI
Intercropping sweet potato and cassava is done out of
tradition. However these scientific studies are helping us
to get meaning to what we have been doing:
• Time of introducing sweet potato- we expect to see
the difference between plating time
• Spacing between plants and lines
• Application of fertilizers
I am eager to learn more and apply lessons immediately.
“Group training and experience sharing enhance
quick learning.”
50. Testimonies from farmers
www.iita.org | www.cgiar.org | www.acai-project.org
Akama, from Donge Vibweni, FCI
(not on photo)
• Akama is a lady participating in ACAI project
• She testifies that the knowledge from proper
intercropping is helping her:
• Better land utilization
• To manage yield of both crops
• Increased household income from sale of both crops
• Sweet potatoes are increasingly becoming
commercial crop hence requires proper handling
“Farmers are looking forward to development of
tools to support their farming activities for the
two crops which are vital for food and income.”
51. Testimonies from farmers
www.iita.org | www.cgiar.org | www.acai-project.org
Deve Suleiman- From Machui, FCI
(in blue shirt)
• Deve participates in ACAI project as a trained
Extension Agent.
• He is an influential farmer in his area (Machui).
• He says that while intercropping is not new,
the current knowledge is new and beneficial.
• Previously they were doing randomly hence
affecting yield of the main crop as well as the
intercrop.
“The entire process is helping farmers to
appreciate new knowledge, for example on
the use of fertilizer.”
52. Validation exercises NG – impressions from the field
www.iita.org | www.cgiar.org | www.acai-project.org
53. Key Activities
www.iita.org | www.cgiar.org | www.acai-project.org
Key activities held in preparation of validation exercises
Indoor training-of-trainers
Field demarcation exercise
Role play of IC-DST validation exercise
54. Key Activities
www.iita.org | www.cgiar.org | www.acai-project.org
Objective: To observe and emulate the optimum
cassava and maize intercropping practices (IC)
Decision Support Tool (DST) under validation.
Farmer field days held by SG2000
SC reacting to farmers questions at Anambra
Farmer field day at Agir Gwer, Benue state
Date and Location:
• Anambra state – 17th, 27th & 28th Aug 2018 at
Umunze, Ukwwulu & Omogho
• Benue state – 19th, Oct,2018 at Apir
55. Testimonies from farmers
www.iita.org | www.cgiar.org | www.acai-project.org
Kehinde Akingbade – SG2000 OG
I have learnt:
• about planting of one maize seed per hole.
• about spacing of 0.8m x 1m.
• new method of fertilizer application –
application of fertilizer between maize and
cassava.
• not to burn vegetation debris during land
preparation.
I was surprised that the maize was due for
harvest in just two months. This will give
farmers opportunity to plant maize up to three
times in one raining season.
56. Testimonies from Extension Agents
www.iita.org | www.cgiar.org | www.acai-project.org
• I have learnt application of fertilizer at
planting. I formerly knew of fertilizer
application at 4WAP.
• I have learnt about planting of one
maize seed per hole.
• I now know ho to use ampligo for
control of army worm.
Adedayo Popoola – SG2000 OG
57. Testimonies from Extension Agents
www.iita.org | www.cgiar.org | www.acai-project.org
• I have learnt about maize spacing of
25cm x 25cm as against the traditional
1m x 1m and 50cm x 50cm.
• I have learnt about cassava spacing of
0.8m by 1m against 1m by 1m.
• I now understand importance of
effective monitoring of cassava field.
• I have learnt how to use ODK for data
collection.
Margaret Asuo – SG2000 CR
58. www.iita.org | www.cgiar.org | www.acai-project.org
Chris E.Okoli – SG2000 AN
• Rewarding the EA for every activity performed
encourages hard work.
• I have learnt to plan an exercise, train, supply
materials and inputs as at when due and
supervise. This leads to quality data generation
and high yields.
• I have learnt how correct NPK fertilizer to a
maize intercrop can substantially increase cobs.
The validation exercises have exposed SG2000 to the use of smartphones in
extension delivery. The EAs can now advice the farmers right there in the field with
the use of smartphones to access the DSTs. The EAs can now easily collect data in
the field with the use of ODK.
Suggestion: DSTs should be available in English and in local languages.
Testimonies from Extension Agents
59. Validation exercises – Next steps
www.iita.org | www.cgiar.org | www.acai-project.org
• Do EAs and farmers understand the principles of the DST?
• Do they make sense to them?
• Do they agree with the emphasis on maize (maize to pay for the fertilizer)
• Are there concerns about high maize density when fertilizer is applied?
• What do they consider the reduction in cassava yield?
• What is the risk of army worm infestation and impact of pest control cost?
• Will the use of the DST influence their decision making at farm level?
• Land allocation for intercropping
• Change in market / trader who would buy higher amounts
Use the field day to interact with EAs and farmers !
Get more answers…
60. Thank you very much !!!
Questions and discussion
www.iita.org | www.cgiar.org | www.acai-project.org