Increase economic productivity is a way to help smallholder farmers to become more resilient to the greater climate risks. In order to make smallholder farming profitable through an IMOD, it is important to examine the economy performance of these farmers.
This document provides a summary of a study assessing the skills needs and job potential of the biogas industry in South Africa. It conducted desktop research, stakeholder interviews, and developed an input-output model to estimate potential biogas projects and jobs in the country through 2030 under conservative and optimistic scenarios. The study found the biogas industry could support between 59,000-88,000 full-time jobs by 2030, up from around 1,700 people currently employed. However, it also identified a lack of biogas-specific training and qualifications in the country as a key barrier to industry growth. The report makes recommendations around developing skills training programs and standard qualifications to meet industry needs and maximize local economic benefits.
Cereal breeding in ESA- Henry Ojulong, Eric Manyasa, Sam Njoroge, NARS partnersICRISAT
Development of a framework for cooperation and integration among different disciplines within ICRISAT to undertake holistic research for greater impacts, was a key focus of scientists from Eastern and Southern Africa (ESA) region at their recent regional planning meeting. Reviewed achievements and lessons learnt from past activities as well as shared and peer reviewed each other’s work plans.
Millets are well-suited to growing in drier and warmer climates, requiring little water or fertilizer. They provide greater diversity on farms, helping to reduce pests and climate risks while improving farmers' resilience. As the climate changes and more arid land emerges, millets will be important drought-tolerant alternatives to other cereals like maize that may no longer thrive in many regions by 2030.
During the recent East and Southern Africa review and planning meeting, team members reviewed the Value chain assessment framework – all crops and programs.
LeasyScan: A novel concept combining 3D imaging and lysimetry for hi-throughp...ICRISAT
LeasyScan: A novel concept combining 3D imaging and lysimetry for hi-throughput phenotyping of traits controlling plant water budget.
QUT2015 - 21 July 2015
This document provides a summary of a study assessing the skills needs and job potential of the biogas industry in South Africa. It conducted desktop research, stakeholder interviews, and developed an input-output model to estimate potential biogas projects and jobs in the country through 2030 under conservative and optimistic scenarios. The study found the biogas industry could support between 59,000-88,000 full-time jobs by 2030, up from around 1,700 people currently employed. However, it also identified a lack of biogas-specific training and qualifications in the country as a key barrier to industry growth. The report makes recommendations around developing skills training programs and standard qualifications to meet industry needs and maximize local economic benefits.
Cereal breeding in ESA- Henry Ojulong, Eric Manyasa, Sam Njoroge, NARS partnersICRISAT
Development of a framework for cooperation and integration among different disciplines within ICRISAT to undertake holistic research for greater impacts, was a key focus of scientists from Eastern and Southern Africa (ESA) region at their recent regional planning meeting. Reviewed achievements and lessons learnt from past activities as well as shared and peer reviewed each other’s work plans.
Millets are well-suited to growing in drier and warmer climates, requiring little water or fertilizer. They provide greater diversity on farms, helping to reduce pests and climate risks while improving farmers' resilience. As the climate changes and more arid land emerges, millets will be important drought-tolerant alternatives to other cereals like maize that may no longer thrive in many regions by 2030.
During the recent East and Southern Africa review and planning meeting, team members reviewed the Value chain assessment framework – all crops and programs.
LeasyScan: A novel concept combining 3D imaging and lysimetry for hi-throughp...ICRISAT
LeasyScan: A novel concept combining 3D imaging and lysimetry for hi-throughput phenotyping of traits controlling plant water budget.
QUT2015 - 21 July 2015
Assessing options for crop-livestock intensification in semi-arid Southern Zi...ICRISAT
A presentation by Sabine Homann-Kee Tui, Patricia Masikati, Andre van Rooyen, Daniel Rodriguez, Peter de Voil, Godfrey Manyawu 4th International Symposium for Farming Systems Design, Lanzhou, China, 19-22 Aug 2013
Smart Foods: Good for you, Good for the planet, Important for food security a...ICRISAT
Grains such as millet (including sorghum), amaranth, buckwheat, quinoa are ancient grains used since antiquity.
They are highly nutritious, gluten-free and rich in fiber and minerals. Millets are not only good for us, but
they are also good for the planet – use less water, and are climate-ready – can withstand high
temperatures expected with climate change.
Millets are often the only crops that can grow in the dryland regions of the world. They act as a last
resort for the farmer, since they can even survive in drought conditions. Millets and sorghum are
among the mandate crops of ICRISAT, the others being pigeonpea, chickpea and groundnut which also
survive in semi-arid regions under extreme conditions.
The ‘big 3’ crops (wheat, maize and rice) receive most attention and support for research and development
and are increasingly dominant in the minds of government, industry and consumers. We believe that
millets and sorghum are under-recognized for their value and are important for diversification and
complementing other foods. In particular, they are critical for both farmers and consumers because of:
high nutritional value
resilience under extreme weather conditions – critical in future with climate change
need for both on-farm and diet diversity
multiple untapped uses.
A globalized diet now exists and there are trends in
developing countries where more nutritious and
smart foods like millets and legumes are sometimes
seen as ‘food for the poor’ and not as status foods or
crops by consumers or farmers. Thus, only a few
crops dominate both our dining tables as well as farms.
To correct this trend ICRISAT has launched the Smart
Foods campaign where we aim to partner with
organizations globally to create a demand for these
ancient grains thus benefiting the farmer, the
consumer and the planet.
Enjoy the recipes featured in this calendar and send
us your own recipes (using millets and sorghum)
which will be featured on our website. Come join us in
this campaign to promote millets and sorghum and
become a Smart Food Ambassador!
Smart Foods
Good for you, Good for the planet
Important for food security and overcoming poverty
About ICRISAT: www.icrisat.org
ICRISAT’s scientific information: http://EXPLOREit.icrisat.org
Send your recipes to icrisat-smc@cgiar.org
Crop Improvement: Legumes
The document summarizes work on improving legume crops in East and Southern Africa. It discusses the context of the legume science agenda in the region, including the need to meet growing food demands, tackle poverty and nutrition deficiencies, and increase resilience to climate risks. It then provides highlights of work done in 2015 to launch projects, conduct planning meetings, curate data, communicate progress, and support breeding pipelines for crops like groundnuts, cowpeas, common beans, and chickpeas. Challenges faced included filling project positions slowly and dealing with instability in parts of Mali and Nigeria. Breeding priorities and results for crops like chickpeas and pigeonpeas in the region are
Foresight analysis and ex- ante assessment of promising technologies: To info...ICRISAT
This document discusses strategic foresight analysis to evaluate promising agricultural technologies. It begins by explaining why foresight analysis is needed given challenges facing the global food system like population growth and climate change. Case studies are presented on evaluating drought tolerant and high yielding varieties of groundnuts using crop modeling. Virtual cultivars are developed and show yield increases. Spatial analysis projects changes under climate change. Pathways for technology development, dissemination and adoption are discussed. Benefits include increased production, lower prices and reduced poverty and malnutrition. Limitations and ways to strengthen the analysis are also outlined.
Crop livestock farming systems research in semi-arid southern Africa IIICRISAT
This document summarizes research on improving crop-livestock farming systems in semi-arid southern Africa. Three key points:
1) Innovation platforms have helped farmers in Marara, Mozambique increase food security and resilience, but need further strengthening to promote learning and realize their full potential.
2) Research is using integrated modeling of climate impacts on crops and livestock to assess benefits of adaptation options for smallholder farms in Zimbabwe under different climate scenarios. This indicates most farmers will lose from climate change without adaptation.
3) Plans for 2016 include strengthening innovation platform facilitation and monitoring, publishing research results, and developing capacity and materials for scaling integrated market-oriented mixed farming systems.
Fewer crop species are feeding the world than 50 years ago due to a decline in millets and other traditional crops. This globalized diet of energy-dense crops fuels rising rates of diabetes and heart disease. Millets could help reverse this trend by diversifying diets and providing health benefits like preventing cancer, heart disease, and managing blood glucose levels in diabetics. Millets are nutrient dense and gluten free, making them beneficial for many people.
Smart foods campaign : Feed the Future Kenya Accelerated Value Chain Developm...ICRISAT
The Smart Food Kenya campaign is designed to help the Drought Tolerant Crops (DTC) value chain team meet these two objectives: Improve productivity and profitability of DTCs and Improve nutrition status of target population in Kenya (Rural Population:Particularly mothers and caregivers of children under 5 years
Urban Population:Urban middle income consumers)
Asia Regional Program Planning Meeting- Water scarcity and low water use effi...ICRISAT
Water scarcity and the increasing global demand for water in many sectors, including agriculture, has became a global concern. The rapid growing world population and the adverse impacts of climate change led to growing competition for water use by industrial and urban users for agriculture to secure enough food. Irrigated agriculture is an important role in total agriculture and provides humanity with a wide range of agricultural products, including fruits, vegetables, grains and cereals. Effective management for water use is the only way to save water for the increasing irrigated agriculture.
Futuristic multi-model approach : Customizing adaptation packages to reduce v...ICRISAT
Using a multi-model framework for climate, crop, livestock and socio-economic simulation, customized climate change adaptation packages were developed for farmers in Nkayi, Zimbabwe. The computer simulated scenarios are helping policy makers to make crucial decisions to support farmers.
Asia Regional Program Planning Meeting- overview by Dr Suhas P Wani, Regional...ICRISAT
FOCUSfor achieving goal of reducing povertyand zero hunger,to internalize plan for operationalizing an ecosystem to create wealth for small farm holders thru building partnerships as a team, deliberate and strategize the concept of scaling-up as a business model to serve millions of small farm holders,and synergize the work plans of inter-and intra-projects’ teams and intra-programs and build the winning team ICRISAT.
Investing in rural women: An investment in a whole communityICRISAT
Rural women farmers in Kano, Nigeria are being trained in technologies to enhance the sorghum value chain, including using sorghum to make bakery products, bio-charcoal, and food safety practices. This helps fulfill the objective of the Nigeria Sorghum Transformation Value Chain project in reducing poverty, improving food security, nutrition and health. The demand for finger millet is increasing in Western Kenya, and women are benefiting from new high-yielding varieties that allow them to increase production. A watershed project in India has helped women farmers conserve water, grow new crops, and transform their thinking about agriculture.
Presentation by Dr David Bergvinson, Director General, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) at International Trade Centre (ITC) and Indian Pulses and Grains Council (IPGA) on 24 August 2016.
This document provides an update on Workstream 1 of the Technology Platform (TP), which aims to strengthen self-assessment capacity in partner organizations to support technology adoption. Key activities in 2016 included desktop studies of crop yields, feasibility assessments of potential technologies, and defining adoption targets. Pilot studies were conducted with regional partners on improved maize in Uganda, rice in Senegal, and small ruminants in Namibia. These involved analyzing yield gaps and trends, identifying intervention areas, profiling suitable technologies, and ex-ante analyses of impacts. Preliminary results were communicated to ministries of agriculture for review. The timeline shows additional planned activities through 2017 to develop technology briefs and presentations.
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 document summarizes key points from a conference on off-grid energy access:
- It provides logistical information about the conference including the WiFi network, how to ask questions, and when slide decks will be shared.
- It previews upcoming polls to gather information about attendees' locations, experience in the sector, organizations, areas of interest, and views on challenges to scaling off-grid access.
- The opening research keynote discusses challenges facing utilities in emerging markets in meeting demand growth and universal electrification targets, and how incumbent utilities are often obstacles to progress in sub-Saharan Africa where reliability, cost recovery, and deficits are major issues.
Owen Zinaman presentation at Energy Issues and Implications for Macrostabilit...UNU-WIDER
The document discusses several topics related to the electric sector in South Africa:
1) The 21st Century Power Partnership (21CPP) connects South African stakeholders with international experts to share best practices and collaborate on reports.
2) Evolving definitions of power sector efficiency must account for technology disruption, price changes, and risks while ensuring generation investment is viable and new cost recovery concepts are developed.
3) Requirements for project financing have evolved as renewable energy project costs have declined substantially in South Africa through competitive bidding processes.
Decision support for technology uptake in smallholder farming systems: The ex...ILRI
This document discusses the Targeting Agricultural water Management Interventions (TAGMI) decision support tool. TAGMI merges different types of knowledge using a Bayesian network approach to provide predictions on suitable areas for adopting various agricultural water management technologies. It describes the consultation process used to gather different sources of knowledge and select relevant technologies. The document also presents example results from TAGMI on potential areas for adopting small reservoirs in the Volta River Basin under current and climate change scenarios. It concludes with lessons learned around improving TAGMI by incorporating more social data and validating predictions against actual adoption rates.
FIDO Case Study: Performance Comparison of Mulitmodal BiometricsFIDO Alliance
This document summarizes a study that compared the performance of different multimodal biometric authentication methods using face and fingerprint data. 771 participants provided biometric data that was categorized as "good" or "bad" quality based on capture conditions. Error rates and usability metrics like average attempts were then calculated for different fusion rules (AND, OR, parallel, serial) and compared to FIDO standards. The results showed that AND and parallel fusion met FIDO certification requirements for both good and bad quality data, while other methods only met requirements for good data. Overall, multimodal biometrics improved performance over unimodal approaches.
Assessing options for crop-livestock intensification in semi-arid Southern Zi...ICRISAT
A presentation by Sabine Homann-Kee Tui, Patricia Masikati, Andre van Rooyen, Daniel Rodriguez, Peter de Voil, Godfrey Manyawu 4th International Symposium for Farming Systems Design, Lanzhou, China, 19-22 Aug 2013
Smart Foods: Good for you, Good for the planet, Important for food security a...ICRISAT
Grains such as millet (including sorghum), amaranth, buckwheat, quinoa are ancient grains used since antiquity.
They are highly nutritious, gluten-free and rich in fiber and minerals. Millets are not only good for us, but
they are also good for the planet – use less water, and are climate-ready – can withstand high
temperatures expected with climate change.
Millets are often the only crops that can grow in the dryland regions of the world. They act as a last
resort for the farmer, since they can even survive in drought conditions. Millets and sorghum are
among the mandate crops of ICRISAT, the others being pigeonpea, chickpea and groundnut which also
survive in semi-arid regions under extreme conditions.
The ‘big 3’ crops (wheat, maize and rice) receive most attention and support for research and development
and are increasingly dominant in the minds of government, industry and consumers. We believe that
millets and sorghum are under-recognized for their value and are important for diversification and
complementing other foods. In particular, they are critical for both farmers and consumers because of:
high nutritional value
resilience under extreme weather conditions – critical in future with climate change
need for both on-farm and diet diversity
multiple untapped uses.
A globalized diet now exists and there are trends in
developing countries where more nutritious and
smart foods like millets and legumes are sometimes
seen as ‘food for the poor’ and not as status foods or
crops by consumers or farmers. Thus, only a few
crops dominate both our dining tables as well as farms.
To correct this trend ICRISAT has launched the Smart
Foods campaign where we aim to partner with
organizations globally to create a demand for these
ancient grains thus benefiting the farmer, the
consumer and the planet.
Enjoy the recipes featured in this calendar and send
us your own recipes (using millets and sorghum)
which will be featured on our website. Come join us in
this campaign to promote millets and sorghum and
become a Smart Food Ambassador!
Smart Foods
Good for you, Good for the planet
Important for food security and overcoming poverty
About ICRISAT: www.icrisat.org
ICRISAT’s scientific information: http://EXPLOREit.icrisat.org
Send your recipes to icrisat-smc@cgiar.org
Crop Improvement: Legumes
The document summarizes work on improving legume crops in East and Southern Africa. It discusses the context of the legume science agenda in the region, including the need to meet growing food demands, tackle poverty and nutrition deficiencies, and increase resilience to climate risks. It then provides highlights of work done in 2015 to launch projects, conduct planning meetings, curate data, communicate progress, and support breeding pipelines for crops like groundnuts, cowpeas, common beans, and chickpeas. Challenges faced included filling project positions slowly and dealing with instability in parts of Mali and Nigeria. Breeding priorities and results for crops like chickpeas and pigeonpeas in the region are
Foresight analysis and ex- ante assessment of promising technologies: To info...ICRISAT
This document discusses strategic foresight analysis to evaluate promising agricultural technologies. It begins by explaining why foresight analysis is needed given challenges facing the global food system like population growth and climate change. Case studies are presented on evaluating drought tolerant and high yielding varieties of groundnuts using crop modeling. Virtual cultivars are developed and show yield increases. Spatial analysis projects changes under climate change. Pathways for technology development, dissemination and adoption are discussed. Benefits include increased production, lower prices and reduced poverty and malnutrition. Limitations and ways to strengthen the analysis are also outlined.
Crop livestock farming systems research in semi-arid southern Africa IIICRISAT
This document summarizes research on improving crop-livestock farming systems in semi-arid southern Africa. Three key points:
1) Innovation platforms have helped farmers in Marara, Mozambique increase food security and resilience, but need further strengthening to promote learning and realize their full potential.
2) Research is using integrated modeling of climate impacts on crops and livestock to assess benefits of adaptation options for smallholder farms in Zimbabwe under different climate scenarios. This indicates most farmers will lose from climate change without adaptation.
3) Plans for 2016 include strengthening innovation platform facilitation and monitoring, publishing research results, and developing capacity and materials for scaling integrated market-oriented mixed farming systems.
Fewer crop species are feeding the world than 50 years ago due to a decline in millets and other traditional crops. This globalized diet of energy-dense crops fuels rising rates of diabetes and heart disease. Millets could help reverse this trend by diversifying diets and providing health benefits like preventing cancer, heart disease, and managing blood glucose levels in diabetics. Millets are nutrient dense and gluten free, making them beneficial for many people.
Smart foods campaign : Feed the Future Kenya Accelerated Value Chain Developm...ICRISAT
The Smart Food Kenya campaign is designed to help the Drought Tolerant Crops (DTC) value chain team meet these two objectives: Improve productivity and profitability of DTCs and Improve nutrition status of target population in Kenya (Rural Population:Particularly mothers and caregivers of children under 5 years
Urban Population:Urban middle income consumers)
Asia Regional Program Planning Meeting- Water scarcity and low water use effi...ICRISAT
Water scarcity and the increasing global demand for water in many sectors, including agriculture, has became a global concern. The rapid growing world population and the adverse impacts of climate change led to growing competition for water use by industrial and urban users for agriculture to secure enough food. Irrigated agriculture is an important role in total agriculture and provides humanity with a wide range of agricultural products, including fruits, vegetables, grains and cereals. Effective management for water use is the only way to save water for the increasing irrigated agriculture.
Futuristic multi-model approach : Customizing adaptation packages to reduce v...ICRISAT
Using a multi-model framework for climate, crop, livestock and socio-economic simulation, customized climate change adaptation packages were developed for farmers in Nkayi, Zimbabwe. The computer simulated scenarios are helping policy makers to make crucial decisions to support farmers.
Asia Regional Program Planning Meeting- overview by Dr Suhas P Wani, Regional...ICRISAT
FOCUSfor achieving goal of reducing povertyand zero hunger,to internalize plan for operationalizing an ecosystem to create wealth for small farm holders thru building partnerships as a team, deliberate and strategize the concept of scaling-up as a business model to serve millions of small farm holders,and synergize the work plans of inter-and intra-projects’ teams and intra-programs and build the winning team ICRISAT.
Investing in rural women: An investment in a whole communityICRISAT
Rural women farmers in Kano, Nigeria are being trained in technologies to enhance the sorghum value chain, including using sorghum to make bakery products, bio-charcoal, and food safety practices. This helps fulfill the objective of the Nigeria Sorghum Transformation Value Chain project in reducing poverty, improving food security, nutrition and health. The demand for finger millet is increasing in Western Kenya, and women are benefiting from new high-yielding varieties that allow them to increase production. A watershed project in India has helped women farmers conserve water, grow new crops, and transform their thinking about agriculture.
Presentation by Dr David Bergvinson, Director General, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) at International Trade Centre (ITC) and Indian Pulses and Grains Council (IPGA) on 24 August 2016.
This document provides an update on Workstream 1 of the Technology Platform (TP), which aims to strengthen self-assessment capacity in partner organizations to support technology adoption. Key activities in 2016 included desktop studies of crop yields, feasibility assessments of potential technologies, and defining adoption targets. Pilot studies were conducted with regional partners on improved maize in Uganda, rice in Senegal, and small ruminants in Namibia. These involved analyzing yield gaps and trends, identifying intervention areas, profiling suitable technologies, and ex-ante analyses of impacts. Preliminary results were communicated to ministries of agriculture for review. The timeline shows additional planned activities through 2017 to develop technology briefs and presentations.
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 document summarizes key points from a conference on off-grid energy access:
- It provides logistical information about the conference including the WiFi network, how to ask questions, and when slide decks will be shared.
- It previews upcoming polls to gather information about attendees' locations, experience in the sector, organizations, areas of interest, and views on challenges to scaling off-grid access.
- The opening research keynote discusses challenges facing utilities in emerging markets in meeting demand growth and universal electrification targets, and how incumbent utilities are often obstacles to progress in sub-Saharan Africa where reliability, cost recovery, and deficits are major issues.
Owen Zinaman presentation at Energy Issues and Implications for Macrostabilit...UNU-WIDER
The document discusses several topics related to the electric sector in South Africa:
1) The 21st Century Power Partnership (21CPP) connects South African stakeholders with international experts to share best practices and collaborate on reports.
2) Evolving definitions of power sector efficiency must account for technology disruption, price changes, and risks while ensuring generation investment is viable and new cost recovery concepts are developed.
3) Requirements for project financing have evolved as renewable energy project costs have declined substantially in South Africa through competitive bidding processes.
Decision support for technology uptake in smallholder farming systems: The ex...ILRI
This document discusses the Targeting Agricultural water Management Interventions (TAGMI) decision support tool. TAGMI merges different types of knowledge using a Bayesian network approach to provide predictions on suitable areas for adopting various agricultural water management technologies. It describes the consultation process used to gather different sources of knowledge and select relevant technologies. The document also presents example results from TAGMI on potential areas for adopting small reservoirs in the Volta River Basin under current and climate change scenarios. It concludes with lessons learned around improving TAGMI by incorporating more social data and validating predictions against actual adoption rates.
FIDO Case Study: Performance Comparison of Mulitmodal BiometricsFIDO Alliance
This document summarizes a study that compared the performance of different multimodal biometric authentication methods using face and fingerprint data. 771 participants provided biometric data that was categorized as "good" or "bad" quality based on capture conditions. Error rates and usability metrics like average attempts were then calculated for different fusion rules (AND, OR, parallel, serial) and compared to FIDO standards. The results showed that AND and parallel fusion met FIDO certification requirements for both good and bad quality data, while other methods only met requirements for good data. Overall, multimodal biometrics improved performance over unimodal approaches.
IRJET- Smart Farming Crop Yield Prediction using Machine LearningIRJET Journal
The document proposes a method for smart farming and crop yield prediction using machine learning algorithms like Support Vector Machine and Random Forest. Historical agricultural data on factors like moisture, rainfall, temperature and humidity is collected and analyzed to predict crop yields and whether conditions will be excellent, good, or poor. The goal is to help farmers increase profits by providing insights into how environmental conditions impact crops.
The project aims to (1) develop improved predictions of pasture production responses to nitrogen fertilizer, (2) use economic methods to inform nitrogen fertilizer investment decisions, (3) develop a nitrogen fertilizer decision calculator using new knowledge, and (4) engage industry and manage the project. The project will analyze experimental data, collect data from case study farms, develop probabilistic economic models and a decision calculator, and train industry in efficient nitrogen use. This is expected to benefit the dairy industry by saving up to 20% of the $200 million spent annually on nitrogen fertilizer in Australia.
The document provides an overview of precision agriculture (PAg) concepts and implementation steps. It defines PAg as managing fields by the square meter using GPS and data to provide detailed knowledge of field variations. The key steps are: 1) collecting yield, elevation and soil data; 2) integrating the data to create management zones; 3) having the grower review zones and create input prescriptions with an agronomist. The goal is to exploit field variations spatially and temporally to optimize productivity while reducing environmental impacts.
A Novel Deep Learning-Based Hybrid Method for the Determination of Productivi...Shakas Technologies
A Novel Deep Learning-Based Hybrid Method for the Determination of Productivity of Agricultural Products Apple Case Study.
Shakas Technologies ( Galaxy of Knowledge)
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Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
eni applies a unique model across its upstream business activities that focuses on being local, deploying in-house competences, and mitigating risks. Some key aspects of the model include developing local staff and suppliers, providing power to local communities, managing assets to reduce environmental impacts, and using a mix of conventional and unconventional assets to ensure production growth while improving safety and efficiency. eni's model has led to industry-leading performance such as low costs, high recovery rates, and reserves replacement exceeding 130%.
This document describes an 18-month project by Plastilago and Polilago, two Venezuelan petrochemical companies, to implement SAP R/3 across their organizations. The companies produced 170,000 metric tons of polyethylene annually but had disparate legacy systems. They selected SAP R/3 for its functionality and selected Digital Equipment Venezuela and SEMA Group as implementation partners. The project was divided into blocks, with sales, finance, and logistics modules implemented first followed by production, purchasing and quality management. Careful change management and project control were emphasized for success.
Development of Data Integration & Analysis System in JapanCIARD Movement
Seishi Ninomiya, Institute of Sustainable Agro-ecosystem Services, The University of Tokyo, at RDA 5th Plenary Meeting, IG Agriculture Data Interoperability Session in San Diego (CA, US) on the 9th of March 2015
Crop yield prediction using ridge regression.pdfssuserb22f5a
Crop yield prediction using deep neural networks with data mining concepts by applying multi model ensembles using ridge regression to increase accuracy, precision, recall,and f measure. Combining neural networks with regression increase high satisfactory crop yield prediction.the support vector regression is slow convergence , stuck in local minima. But ridge regression analyse multicollinearity in multiple regression.
This document presents a study on the impact of 2005 power sector reforms on peak power generation in Nigeria's integrated power network. It aims to develop time series and intervention models to analyze peak power trends and the effects of reforms. The methodology involves collecting peak power data from 1993 to 2015, developing ARIMA and intervention models, and validating the models. The results show that an ARIMA(1,1,1) model best fits the data and predicts continued growth in peak generation from 2016 to 2020. Intervention modeling found that reforms significantly increased power availability above pre-2005 levels. The study concludes that further government actions are still needed to improve Nigeria's inadequate electricity supply.
Accelerating Product Development FLOW: Kanban at Jaguar Land RoverHamish McMinn
Modern automotive engineering is extremely complex: A new vehicle can take 2-4 years to develop, with a cost of delay of about $2 million per day. Shortening feedback loops and minimizing handoff delays has massive impact in reducing product lead time.
We will cover:
• ·Why new product development provides rich opportunities for continuous process improvement
• Benefits and challenges of transferring Agile software techniques to hardware design and development
• How to visualize work, focus on flow and increase cross-functional collaboration using kanban
The document discusses reducing lead time and inventory in the apparel supply chain of Madura Clothing. It identifies high finished goods inventory and long lead times as issues. To address this, the team developed a Core Replenishment System using a pull strategy. This focuses on continuous replenishment at the fabric and finished goods stages for core products. The team defined Critical to Quality metrics like fabric lead time, manufacturing lead time, and finished goods inventory. Through analysis and improvements like setting work-in-progress norms and single piece clearance tracking, manufacturing lead time was reduced significantly. A warehouse replenishment approach with weekly work ordering and delivery cycles was also implemented to reduce finished goods inventory levels at the warehouse.
Similar to Technical efficiency of producers’ in the dryland areas of west africa a multiioutput stochaistic metafrontier approach (20)
ICRISAT’s soil laboratory registers with FAO’s International Network on Ferti...ICRISAT
The Charles Renard Analytical Laboratory at ICRISAT has been officially registered with the International Network on Fertilizer Analysis – a network created in December 2020, to build and strengthen the capacity of laboratories in fertilizer analysis and harmonize fertilizer quality standards. Dr Pushpajeet L Choudhari, Manager of the soil laboratory, said that testing serves as a preventive measure to avoid the misuse of fertilizers leading to better soil management.
Uzbek delegation explores climate-resilient crop options for arid, degraded e...ICRISAT
A delegation from Uzbekistan visited ICRISAT headquarters in India to learn about short-duration second crops suited to their country's arid ecologies. The visitors were interested in crop options that mature before winter and can increase agricultural production through double cropping. They were briefed on dryland crop options from ICRISAT like pearl millet and pigeonpea. The delegation explored opportunities for academic exchange and obtaining genomic services and training from ICRISAT to develop crops suited to Uzbekistan's climate and soils. Previous partnerships between ICRISAT and Uzbekistan in developing salinity tolerant pearl millet varieties were also discussed.
Indian Ambassador to Niger explores opportunities for South-South cooperationICRISAT
The Ambassador of India to Niger, His Excellency Mr Prem K Nair, visited ICRISAT’s research station at Sadore, to explore opportunities for South-South collaboration. He said that the objective of his visit was to learn about ICRISAT’s activities in Niger and to identify possible areas of cooperation for implementing agri-development initiatives introduced by India.
WFP, ICRISAT to partner on climate-resilience, food security, nutrition and l...ICRISAT
The United Nations World Food Programme (WFP) and the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) signed a Memorandum of Understanding to partner on programs and research to improve food and nutrition security and livelihoods in India against the impacts of climate change. The partnership aims to strengthen efforts bringing together science, knowledge, and implementation frameworks to bolster climate-resilient food security, nutrition, and livelihoods. A significant focus will be on vulnerability analysis at the state level in India and developing a sustainable food systems approach.
Visit by Sri Lankan Deputy High Commissioner to ICRISAT opens opportunities f...ICRISAT
Dr Doraiswamy Venkateshwaran, Sri Lankan Deputy High Commissioner stationed in Chennai, recently visited the ICRISAT campus in Hyderabad to learn more about the Institute’s science-backed research for dryland agriculture. Along with his team, he visited the genebank and toured the pigeonpea and finger millet field plots, where Dr Prakash Gangashetty and Dr Sobhan Sajja explained to him the research focus and various traits of hybrids and varieties developed by ICRISAT.
UK Ambassador to Niger discusses climate change adaptation and humanitarian i...ICRISAT
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https://www.youtube.com/@jenniferschaus/videos
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https://www.youtube.com/@jenniferschaus/videos
2024: The FAR - Federal Acquisition Regulations, Part 39
Technical efficiency of producers’ in the dryland areas of west africa a multiioutput stochaistic metafrontier approach
1. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency of Producers’ in the Dryland
Areas of West Africa: a Multi-Output Stochastic
Metafrontier Approach
Alphonse Singbo
&
Pierre Sibiry
Bamako, Mali
May 29, 2015
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
2. Objective Framework Modelling Data Summary results Conclusion
Contents
1 Motivation
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
3. Objective Framework Modelling Data Summary results Conclusion
Contents
1 Motivation
2 Objective of the study
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
4. Objective Framework Modelling Data Summary results Conclusion
Contents
1 Motivation
2 Objective of the study
3 Model Specification
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
5. Objective Framework Modelling Data Summary results Conclusion
Contents
1 Motivation
2 Objective of the study
3 Model Specification
4 Data
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
6. Objective Framework Modelling Data Summary results Conclusion
Contents
1 Motivation
2 Objective of the study
3 Model Specification
4 Data
5 Results
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
7. Objective Framework Modelling Data Summary results Conclusion
Contents
1 Motivation
2 Objective of the study
3 Model Specification
4 Data
5 Results
6 Conclusion
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
8. Objective Framework Modelling Data Summary results Conclusion
Agriculture value added
Agriculture value added and Population growth
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
9. Objective Framework Modelling Data Summary results Conclusion
Agriculture value added
Agriculture value added and Population growth
Country Agriculture Rate of pop.
(% of GDP) (1998 − 2015)
US 1.4 0.7
Brazil 5.3 0.9
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
10. Objective Framework Modelling Data Summary results Conclusion
Agriculture value added
Agriculture value added and Population growth
Country Agriculture Rate of pop.
(% of GDP) (1998 − 2015)
US 1.4 0.7
Brazil 5.3 0.9
Ghana 25.2 2.22
Niger 38.3 3.3
Nigeria 22.3 2.9
Mali 39.4 2.7
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
11. Objective Framework Modelling Data Summary results Conclusion
Motivation
Increase economic productivity is a way to help smallholder
farmers to become more resilient to the greater climate risks.
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
12. Objective Framework Modelling Data Summary results Conclusion
Motivation
Increase economic productivity is a way to help smallholder
farmers to become more resilient to the greater climate risks.
In order to make smallholder farming profitable through an
IMOD, it is important to examine the economy performance
of these farmers.
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
13. Objective Framework Modelling Data Summary results Conclusion
Motivation
Increase economic productivity is a way to help smallholder
farmers to become more resilient to the greater climate risks.
In order to make smallholder farming profitable through an
IMOD, it is important to examine the economy performance
of these farmers.
However, the study on inter-regional efficiency in Dryland
systems is rare.
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
14. Objective Framework Modelling Data Summary results Conclusion
Motivation
Increase economic productivity is a way to help smallholder
farmers to become more resilient to the greater climate risks.
In order to make smallholder farming profitable through an
IMOD, it is important to examine the economy performance
of these farmers.
However, the study on inter-regional efficiency in Dryland
systems is rare.
Additionally, no study in WCA attempts to compare the
production possibilty in the region using on household level
data.
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
15. Objective Framework Modelling Data Summary results Conclusion
Motivation
Increase economic productivity is a way to help smallholder
farmers to become more resilient to the greater climate risks.
In order to make smallholder farming profitable through an
IMOD, it is important to examine the economy performance
of these farmers.
However, the study on inter-regional efficiency in Dryland
systems is rare.
Additionally, no study in WCA attempts to compare the
production possibilty in the region using on household level
data.
Previous studies related to Africa have used aggregate country
level data.
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
16. Objective Framework Modelling Data Summary results Conclusion
Objective
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
17. Objective Framework Modelling Data Summary results Conclusion
Objective
1 Measure and compare technical efficiency of producers in the
dryland systems.
From the modelling :
Estimate Technical Inefficiency in each country : Ghana,
Niger, Nigeria and Mali
Estimate the metatechnology ratio (technology gap) of each
country relative to the whole region
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
18. Objective Framework Modelling Data Summary results Conclusion
Objective
1 Measure and compare technical efficiency of producers in the
dryland systems.
From the modelling :
Estimate Technical Inefficiency in each country : Ghana,
Niger, Nigeria and Mali
Estimate the metatechnology ratio (technology gap) of each
country relative to the whole region
2 Identify the position of each country regarding the
metafrontier.
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
19. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency, Metafrontier Efficiency and
Metatechnology Ratio (MTR)
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
20. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency, Metafrontier Efficiency and
Metatechnology Ratio (MTR)
Definition
Technical Efficiency (TE) : the ability of a producer to obtain
the maximum output from a given input vector.
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
21. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency, Metafrontier Efficiency and
Metatechnology Ratio (MTR)
Definition
Technical Efficiency (TE) : the ability of a producer to obtain
the maximum output from a given input vector.
Definition
Metafrontier Technical Efficiency (MTE) : the ability to obtain
the maximum output from a given input vector with respect to the
production frontier of the region (metafrontier).
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
22. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency, Metafrontier Efficiency and
Metatechnology Ratio (MTR)
Definition
Technical Efficiency (TE) : the ability of a producer to obtain
the maximum output from a given input vector.
Definition
Metafrontier Technical Efficiency (MTE) : the ability to obtain
the maximum output from a given input vector with respect to the
production frontier of the region (metafrontier).
Definition
Metatechnology Ratio (MTR) : the distance between the
frontier of a country relative to the metafrontier.
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
23. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency framework
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
24. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency framework
Input x
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
25. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency framework
Input x
Output y
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
26. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency framework
Input x
Output y
a
b
c
d
e
f
g
i
h
j
O
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
27. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency framework
Input x
Output y
a
b
c
d
e
f
g
i
h
j
Production Frontier ≡ 𝑃𝐹 𝑥; 𝛽
O
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
28. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency framework
Input x
Output y
a
b
c
d
e
f
g
i
h
j
Production Frontier ≡ 𝑃𝐹 𝑥; 𝛽
A
B
O
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
29. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency framework
Input x
Output y
a
b
c
d
e
f
g
i
h
j
Production Frontier ≡ 𝑃𝐹 𝑥; 𝛽
A
B
O
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
30. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency framework
Input x
Output y
a
b
c
d
e
f
g
i
h
j
Production Frontier ≡ 𝑃𝐹 𝑥; 𝛽
A
B
O
𝑇𝐸 =
𝑂𝐴
𝑂𝐵
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
31. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency framework
Input x
Output y
a
b
c
d
e
f
g
i
h
j
Production Frontier ≡ 𝑃𝐹 𝑥; 𝛽
A
B
O
𝑇𝐸 =
𝑂𝐴
𝑂𝐵
For example, 𝑇𝐸 = 0.6 indicates that the output vector, y, is
60% of the maximum output that could be produced by a
farm using the input vector, x.
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
32. Objective Framework Modelling Data Summary results Conclusion
Metafrontier framework
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
33. Objective Framework Modelling Data Summary results Conclusion
Metafrontier framework
Input x
Output y
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
34. Objective Framework Modelling Data Summary results Conclusion
Metafrontier framework
Input x
Output y
1
1 1
1
1
1
Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
35. Objective Framework Modelling Data Summary results Conclusion
Metafrontier framework
Input x
Output y
1
1 1
1
1
1
Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
36. Objective Framework Modelling Data Summary results Conclusion
Metafrontier framework
Input x
Output y
1
1 1
1
1
1
Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1
2
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
37. Objective Framework Modelling Data Summary results Conclusion
Metafrontier framework
Input x
Output y
1
1 1
1
1
1
Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
38. Objective Framework Modelling Data Summary results Conclusion
Metatechnology ratio (MTR)
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
39. Objective Framework Modelling Data Summary results Conclusion
Metatechnology ratio (MTR)
Input x
Output y
Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
40. Objective Framework Modelling Data Summary results Conclusion
Metatechnology ratio (MTR)
Input x
Output y
Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
41. Objective Framework Modelling Data Summary results Conclusion
Metatechnology ratio (MTR)
Input x
Output y
Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
42. Objective Framework Modelling Data Summary results Conclusion
Metatechnology ratio (MTR)
Input x
Output y
Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1
0
C
D
E
TE of A according to CF2: 𝑻𝑬 𝑨
𝑪𝑭 𝟐
=
𝟎𝑪
𝟎𝑫
TE of A according to the Metafrontier: 𝑻𝑬 𝑨 =
𝟎𝑪
𝟎𝑬
𝑴𝑻𝑹 𝑨
𝟐
=
𝑻𝑬 𝑨
𝑻𝑬 𝑨
𝑪𝑭 𝟐
=
𝟎𝑪 𝟎𝑬
𝟎𝑪 𝟎𝑫
=
𝟎𝑫
𝟎𝑬
𝑴𝑻𝑹 𝑨
𝟐
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
43. Objective Framework Modelling Data Summary results Conclusion
Empirical Modelling
First step : Stochastic distance function. For each country run
the Modified Translog function to estimate parameters and the TE
− ln yi
m = β0 + βdDi
k + βlFi
l + k βk ln(xi
k) + l βl ln(
yi
l
yi
m
)
+1
2 k j βkj ln(xi
k) ln(xi
j) + 1
2 l h βlh ln(
yi
l
yi
m
) ln(
yi
h
yi
m
)
+1
2 k l βkl ln(xi
k) ln(
yi
l
yi
m
) + vi − ln(TEi
)
where : vi ∼ iid N(0, σ2
v ) and vi and TEi
are distributed independently
and TEi
∈ (0, 1[
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
44. Objective Framework Modelling Data Summary results Conclusion
Empirical Modelling
Second step : Metafrontier programming. From the parameters
estimated in Step 1, we solve tle LP problems to ensure that the
metafrontier envelop the country frontiers (convexity)
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
45. Objective Framework Modelling Data Summary results Conclusion
Empirical Modelling
Second step : Metafrontier programming. From the parameters
estimated in Step 1, we solve tle LP problems to ensure that the
metafrontier envelop the country frontiers (convexity)
The LP problem is :
minβ x · β
st : xi · β ≥ xi · βk
where : βk
is the estimated coefficient vector associated with the
country group stochactic frontier obtained in step 1.
x is the arithmetic average of the xi vectors over all farms.
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
46. Objective Framework Modelling Data Summary results Conclusion
Empirical Modelling
Third step : Compute the Metatechnology ratio (MTR).
MTRi
k = exiβk
exiβ
Final step : Compute TE relative to the metafrontier.
TE
i
= TEk
i
× MTRk
i
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
47. Objective Framework Modelling Data Summary results Conclusion
Data
The baseline survey data collected within the CRP dryland
systems in 2012 were used in four countries : Ghana, Niger,
Nigeria and Mali
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
48. Objective Framework Modelling Data Summary results Conclusion
Data
The baseline survey data collected within the CRP dryland
systems in 2012 were used in four countries : Ghana, Niger,
Nigeria and Mali
Outputs are aggregated into two groups : Cereals and Other
crops (legumes and cotton)
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
49. Objective Framework Modelling Data Summary results Conclusion
Data
The baseline survey data collected within the CRP dryland
systems in 2012 were used in four countries : Ghana, Niger,
Nigeria and Mali
Outputs are aggregated into two groups : Cereals and Other
crops (legumes and cotton)
Inputs are aggregated into five categories : capital, materials,
livestock, labor and land
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
50. Objective Framework Modelling Data Summary results Conclusion
Data
The baseline survey data collected within the CRP dryland
systems in 2012 were used in four countries : Ghana, Niger,
Nigeria and Mali
Outputs are aggregated into two groups : Cereals and Other
crops (legumes and cotton)
Inputs are aggregated into five categories : capital, materials,
livestock, labor and land
The multilateral Tornqvist price index is used to construct an
implicit quantity index for each output and input.
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
51. Objective Framework Modelling Data Summary results Conclusion
Data
Data are in Purchasing Power parity ($)
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
52. Objective Framework Modelling Data Summary results Conclusion
Data
Data are in Purchasing Power parity ($)
Two Outputs Ghana Niger Nigeria Mali
Cereal 53, 358.34 578.42 3, 151.67 2, 66.77
Other crops 52, 626.20 214.35 5, 996.91 323.10
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
53. Objective Framework Modelling Data Summary results Conclusion
Data
Data are in Purchasing Power parity ($)
Two Outputs Ghana Niger Nigeria Mali
Cereal 53, 358.34 578.42 3, 151.67 2, 66.77
Other crops 52, 626.20 214.35 5, 996.91 323.10
Five Inputs
Capital 52.86 36.00 99.33 85.93
Labor (man − hours) 5.42 7.35 7.34 11.75
Land (ha) 6.24 9.46 9.21 14.38
Materials 371.68 346.92 1, 551.89 930.30
Livestock (livestock units) 1.64 9.46 2.63 9.08
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
54. Objective Framework Modelling Data Summary results Conclusion
Estimated parameters
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
55. Objective Framework Modelling Data Summary results Conclusion
Estimated parameters
Estimated of the Distance Function Elasticities
Variables
ln OtherCrops
ln Capital
ln Labor
ln Land
ln Materials
ln Livestock
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
56. Objective Framework Modelling Data Summary results Conclusion
Estimated parameters
Estimated of the Distance Function Elasticities
Variables Ghana
ln OtherCrops −0.619∗∗∗
ln Capital 0.167
ln Labor 0.396
ln Land 0.063
ln Materials 0.121∗∗
ln Livestock −0.693∗∗∗
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
57. Objective Framework Modelling Data Summary results Conclusion
Estimated parameters
Estimated of the Distance Function Elasticities
Variables Ghana Niger
ln OtherCrops −0.619∗∗∗
−1.056∗∗∗
ln Capital 0.167 1.649∗∗
ln Labor 0.396 0.779
ln Land 0.063 4.798
ln Materials 0.121∗∗
−0.098
ln Livestock −0.693∗∗∗
−15.923∗∗∗
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
58. Objective Framework Modelling Data Summary results Conclusion
Estimated parameters
Estimated of the Distance Function Elasticities
Variables Ghana Niger Nigeria
ln OtherCrops −0.619∗∗∗
−1.056∗∗∗
−0.615∗∗∗
ln Capital 0.167 1.649∗∗
0.088∗
ln Labor 0.396 0.779 0.148
ln Land 0.063 4.798 −0.020
ln Materials 0.121∗∗
−0.098 0.048∗∗
ln Livestock −0.693∗∗∗
−15.923∗∗∗
−0.107
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
59. Objective Framework Modelling Data Summary results Conclusion
Estimated parameters
Estimated of the Distance Function Elasticities
Variables Ghana Niger Nigeria Mali
ln OtherCrops −0.619∗∗∗
−1.056∗∗∗
−0.615∗∗∗
−0.065∗∗∗
ln Capital 0.167 1.649∗∗
0.088∗
−0.160∗∗∗
ln Labor 0.396 0.779 0.148 1.227∗∗∗
ln Land 0.063 4.798 −0.020 −0.836∗∗∗
ln Materials 0.121∗∗
−0.098 0.048∗∗
0.090
ln Livestock −0.693∗∗∗
−15.923∗∗∗
−0.107 0.296∗∗∗
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
60. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency and Metatechnology ratio
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
61. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency and Metatechnology ratio
Estimates of technical efficiency (TE) and Metatechnology
ratio (MTR)
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
62. Objective Framework Modelling Data Summary results Conclusion
Technical Efficiency and Metatechnology ratio
Estimates of technical efficiency (TE) and Metatechnology
ratio (MTR)
Country TE country MTR
frontier
Ghana 0.996 0.164
Niger 0.996 0.063
Nigeria 0.452 0.053
Mali 0.989 0.109
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
63. Objective Framework Modelling Data Summary results Conclusion
Implications
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
64. Objective Framework Modelling Data Summary results Conclusion
Implications
Smallholder producers are operating at the high level of their
country frontier indicating that there are homogeneous
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
65. Objective Framework Modelling Data Summary results Conclusion
Implications
Smallholder producers are operating at the high level of their
country frontier indicating that there are homogeneous
Comparing to the metafrontier, Nigeria producers are
operating at the lowest level and Ghana producers are at the
highest level
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
66. Objective Framework Modelling Data Summary results Conclusion
Implications
Smallholder producers are operating at the high level of their
country frontier indicating that there are homogeneous
Comparing to the metafrontier, Nigeria producers are
operating at the lowest level and Ghana producers are at the
highest level
Along with the GPS mapping it is possible to map the
position of producers performance in the region.
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
67. Objective Framework Modelling Data Summary results Conclusion
Thanks
Comments and suggestions are most welcome !
A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA