Your search found 5 records
1 Benin, S.; Johnson, M.; Beintema, N.; Bekele, H.; Chilonda, Pius; Kirsten, I.; Edeme, J.; Elmekass, A.; Govereh, J.; Kakuba, T.; Karugia, J.; Makunike, R.; Massawe, S.; Mpyisi, E.; Nwafor, M.; Omilola, B.; Olubode-Awosola, Femi; Sanyang, S.; Taye, B.; Wanzala, M.; Yade, M.; Zewdie, Y. 2008. Monitoring and Evaluation (M&E) System for the Comprehensive Africa Agriculture Development Programme (CAADP). Washington, DC: International Food Policy Research Institute (IFPRI). 45p. (ReSAKSS Working Paper 6)
Agriculture ; Economic aspects ; Monitoring ; Evaluation ; Investment ; Expenditure ; Indicators ; Data collection / Africa
(Location: IWMI HQ Call no: e-copy only Record No: H042797)
(0.52 MB)
The purpose of this document is to develop a framework to be used in monitoring progress towards the successful implementation of CAADP and for providing a conceptual basis for assessing the impacts and returns to CAADP investments. With the perspective of managing for impact, the main objectives are: (1) to identify a set of key indicators that are consistent with the underlying logic of CAADP to track progress in resource allocation and achieving stated targets and help answer questions related to the relevance, effectiveness, efficiency, impact and sustainability of the programme; (2) to identify the data required, sources, and methods for estimating values of the indicators; and (3) to lay out a plan for implementing the framework in terms of collecting, managing and analyzing the data, reporting results of the analysis, and obtaining and incorporating feedback for further improvement of the system. This document, and the ultimate outputs of the M&E system, is thus primarily targeted to stakeholders at the national, regional and continent-wide level involved with directing or managing resources for implementing CAADP. This includes: Ministries of Finance, Agriculture, and Local Governments; Departments of Agriculture within Regional Economic Communities, AU/NEPAD, and the donor community concerned with agriculture in Africa. The document and outputs of the system will also be useful to researchers and others interested in CAADP or knowledge on monitoring and evaluating public agricultural investments in general.
2 Omilola, B.; Yade, M.; Karugia, J.; Chilonda, Pius. 2010. Monitoring and assessing targets of the Comprehensive Africa Agriculture Development Programme (CAADP) and the first Millennium Development Goal (MDG) in Africa. Washington, DC, USA: International Food Policy Research Institute (IFPRI). 90p. (ReSAKSS Working Paper 031)
Agricultural development ; Investment ; Expenditure ; Agricultural production ; Cereal products ; Yields ; Food security ; Poverty ; Hunger / Africa
(Location: IWMI HQ Call no: 338.1 G100 OMI Record No: H044226)
(3.16 MB) (3.16MB)
3 Karugia, J.; Massawe, S.; Guthiga, P.; Ogada, M.; Nwafor, M.; Chilonda, Pius; Musaba, E. 2016. Factors influencing the effectiveness of productivity-enhancing interventions: an assessment of selected programs. In Benin, S. (Ed.). Agricultural productivity in Africa: trends, patterns, and determinants. Washington, DC, USA: International Food Policy Research Institute (IFPRI). pp.247-333.
Agricultural production ; Productivity ; Intervention ; Development projects ; Program effectiveness ; Performance evaluation ; Performance indexes ; Environmental sustainability ; Community involvement ; Leadership ; Group approaches ; Partnerships ; Financial situation ; Investment ; Farming systems ; Local government ; Political aspects ; Policy ; Gender ; Farmers ; Capacity building ; Case studies / Africa / Benin / Burkina Faso / Burundi / Democratic Republic of the Congo / Eritrea / Ethiopia / Ghana / Guinea / Kenya / Malawi / Mali / Mozambique / Nigeria / Rwanda / Sudan / Tanzania / Togo / Uganda / Zambia / Zimbabwe
(Location: IWMI HQ Call no: 338.16 G100 BEN Record No: H048032)
http://www.ifpri.org/cdmref/p15738coll2/id/130468/filename/130679.pdf
https://vlibrary.iwmi.org/pdf/H048032.pdf
https://vlibrary.iwmi.org/pdf/H048032.pdf
(0.63 MB) (5.03 MB)
4 Benin, S.; Karugia, J.; Matchaya, Greenwell; Yade, M. 2020. Improving data quality for the CAADP [Comprehensive Africa Agriculture Development Programme] biennial review: a partnership initiative piloted in five countries. Washington, DC, USA: International Food Policy Research Institute (IFPRI). 54p. (IFPRI Discussion Paper 01925) [doi: https://doi.org/10.2499/p15738coll2.133715]
Data management ; Agricultural development ; Pilot projects ; Development programmes ; Partnerships ; Planning ; Assessment ; Indicators ; Standards ; Knowledge management ; Policy making ; Hunger ; Strategy planning ; Stakeholders ; Training ; Organizations / Kenya / Malawi / Mozambique / Senegal / Togo
(Location: IWMI HQ Call no: e-copy only Record No: H049714)
https://www.ifpri.org/cdmref/p15738coll2/id/133715/filename/133927.pdf
https://vlibrary.iwmi.org/pdf/H049714.pdf
https://vlibrary.iwmi.org/pdf/H049714.pdf
(1.11 MB) (1.11 MB)
This paper presents results of a data partnership framework for strengthening evidence-based planning and implementation that was initiated in 2019 in five selected African countries (Kenya, Malawi, Mozambique, Senegal, and Togo) during the second round of the CAADP biennial review (BR) process. It analyzes the effect of the activities conducted on the data reporting rate and the quality of data reported in the five pilot countries, compared with what was achieved in like-pilot countries. The like-pilot countries are non-pilot countries that have characteristics like the pilot countries at the baseline which affect selection into the pilot or the data reporting and quality outcomes. Different methods (standard deviations, propensity score matching, and two-stage weighted regression) are used to identify the like-pilot countries, and a difference-in-difference method is used to estimate the effect of the pilot activities on the outcomes.
The capacity-strengthening activities focused on working with the country Biennial Review (BR) team to: assess the inaugural or 2018 BR process and identify the data gaps; constitute and train members of data clusters to compile and check the data for the 2020 BR; and then validate and submit the data. The findings show that the activities helped the pilot countries to improve their performance in the data reporting rate and the quality of data reported in the 2020 BR. The largest improvement is observed in Togo and Senegal, followed by Kenya and Malawi, and then Mozambique.
The average increase in the data reporting rate between 2018 and 2020 BRs for the pilot countries is greater than the average progress made in the like-pilot countries by about 6 to 9 % pts. This derives mostly from improvements in the data reporting rate for the indicators under theme 3 on ending hunger. Regarding the quality of data reported (measured as the percent of the data reported that have issues) too, the pilot countries on average performed better than the like-pilot countries, especially with respect to the data reported under themes 2 on investment in agriculture and 3 on ending hunger. But most of the estimated differences have low or no statistical significance. Implications for sustaining the progress made in the pilot countries, as well as for extending the activities to other countries, for the next rounds of the BR are discussed.
The capacity-strengthening activities focused on working with the country Biennial Review (BR) team to: assess the inaugural or 2018 BR process and identify the data gaps; constitute and train members of data clusters to compile and check the data for the 2020 BR; and then validate and submit the data. The findings show that the activities helped the pilot countries to improve their performance in the data reporting rate and the quality of data reported in the 2020 BR. The largest improvement is observed in Togo and Senegal, followed by Kenya and Malawi, and then Mozambique.
The average increase in the data reporting rate between 2018 and 2020 BRs for the pilot countries is greater than the average progress made in the like-pilot countries by about 6 to 9 % pts. This derives mostly from improvements in the data reporting rate for the indicators under theme 3 on ending hunger. Regarding the quality of data reported (measured as the percent of the data reported that have issues) too, the pilot countries on average performed better than the like-pilot countries, especially with respect to the data reported under themes 2 on investment in agriculture and 3 on ending hunger. But most of the estimated differences have low or no statistical significance. Implications for sustaining the progress made in the pilot countries, as well as for extending the activities to other countries, for the next rounds of the BR are discussed.
5 Yade, M.; Matchaya, Greenwell; Karugia, J.; Goundan, A.; Guthiga, P.; Taondyande, M.; Odjo, S.; Nhlengethwa, S. 2021. The impact of COVID-19 on staple food prices: location matters. In Ulimwengu, J. M.; Constas, M. A.; Ubalijoro, E. (Eds.). Building resilient African food systems after COVID-19. Kigali, Rwanda: AKADEMIYA2063; Washington, DC, USA: International Food Policy Research Institute (IFPRI). pp.25-45. (ReSAKSS Annual Trends and Outlook Report 2021)
Food prices ; Commodities ; COVID-19 ; Pandemics ; Food shortages ; Food surplus ; Forecasting ; Markets ; Urban areas ; Rural areas ; Maize flour ; Rice ; Garri ; Millets ; Perishable products ; Coastal states ; Landlocked states / East Africa / Southern Africa / West Africa
(Location: IWMI HQ Call no: e-copy only Record No: H050841)
https://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/134746/filename/134955.pdf
https://vlibrary.iwmi.org/pdf/H050841.pdf
https://vlibrary.iwmi.org/pdf/H050841.pdf
(0.51 MB) (521 KB)
This chapter summarizes the findings from analyses conducted by AKADEMIYA2063 on local staple food market dynamics during the COVID-19 pandemic in Africa. With the outbreak of the highly contagious virus in Africa in March 2020, various measures were implemented by African governments to contain its spread. These measures included bans on public gatherings and markets; restrictions on movement within and between countries; closures of schools, restaurants, and hotels; and curfews. All these measures were likely to cause market disruptions and revenue losses for vulnerable groups by disrupting supply and demand of agricultural staples, either directly or indirectly. The objective of these analytical studies is therefore to generate evidence on how the various COVID-19 response measures have affected food supply and demand patterns in Africa, taking into account the locational characteristics (that is, whether an area is urban or rural, has a surplus or deficit of the commodity in question, and is in a coastal or landlocked country) and whether the commodity is perishable or nonperishable. Such evidence can then be used to inform efforts to anticipate and respond to food crises arising from infectious disease outbreaks and the measures implemented to limit their spread.
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