Role of agricultural research and development in the MDGs – post 2015 in Sub-Saharan Africa

A conference paper presentation in IPAR's Annual research conference 201

Policy brief on “2014/15 Imihigo evaluation

Further Reading: This policy Brief is drawn from the Final Report of FY 2014-2015 Imihigo Evaluation conducted by the Institute of Policy Analysis and Research (IPAR-Rwanda). This can be accessed from IPAR’s websit

Annual analysis of Rwanda’s agriculture budget expenditure 2015-2016

Further Reading: This Policy Brief is drawn from the overall Report on “Annual Analysis of Rwanda’s Agriculture Budget Expenditure 2015-2016” done by IPAR-Rwanda on Behalf of the Action Aid, Kigali, Rwanda. This can be accessed from IPAR’s Websit

Capacity strengthening strategy through capacity needs assessment for Country Level Strategic Analysis and Knowledge Support System (SAKSS): Rwanda

This study aims at assessing the capacity needs for different stakeholders involved in food and agricultural policy processes in order to develop a country specific capacity strengthening strategy to meet the strategic analysis and knowledge management needs of the Rwandan agricultural sector and rural development. Specifically the study intends to assess specific needs for strategic agricultural policy analysis and investment planning, monitoring and evaluation, and knowledge management in Rwanda. The study also seeks to identify what individual and organizational capacities are needed and to show how the existing capacities can be harnessed through their effective use in the organizations involved in the CAADP process. The study further highlights the current organizational and capacity constraints underpinning the policy process for the policy organizations to effectively play their role in meeting the objectives of CAADP. Finally, the study proposes a capacity development strategy for implementing SAKSS in Rwanda.Non-PRIFPRI1; ReSAKSSDSGD; WCAO; ESA

Cost Benefit Analysis of Rice (Oryza Sativa, L.) And Maize (Zea Mays, L.) Production. A Comparison Study in Rwangingo Marshland, Nyagatare and Gatsibo Districts

The marshlands in Rwanda have been developed to increase production by contributing to the reduction of agricultural products imports. To analyze the cost-benefit ratio between rice and maize production in Rwangingo Marshland aimed to see the crop that can give maximum returns through profitability analysis. The research was designed as an analytical study that compared the CBR of rice and maize production. The stochastic production function was used to estimate the impact of drivers’ cost of production on the production and CBR for profitability comparison analysis. Data were collected from 271 respondents and randomly selected using multistage sampling techniques. Stochastic production function results indicated that rice production: capital and labor were statistically significant at 1%. Maize production: labor was statistically significant at 1% and positively affected production, capital had an inverse relationship to the production, and the capital and labor (α+β) indicated a CRTS of 1. Profitability analysis was based on three measures of CBR, and NPV. Rice and maize production gave CBR of 1.9 and 1.5, NPV of 1,103,684Rwf and 1,011,970Rwf, and IRR of 7% for rice and maize respectively. The results recommended that rice production should be cultivated in this marshland because it indicated the maximum return or both crops could be considered under the measures that could maximize the outputs

THE ROLE OF COOPERATIVES ON ADOPTION OF BEST MANAGEMENT PRACTICES AND PRODUCTIVITY IN RWANDA’S COFFEE SECTOR

Agricultural cooperatives operate under the notion that smallholder farmers are better off working collectively. Rwanda’s coffee sector has experienced a significant transformation over the past two decades, which includes farmers coming together to establish cooperative coffee washing stations (CWSs). Our data and analysis show that these collectives provide farmers with a myriad of services that include economic, agronomic and social benefits. We find that cooperative membership affects adoption of specific practices, most notably pesticide application. This finding, however cannot be generalized to all best management practices as we find that membership is not associated with uptake of some practices (like fertilizer use) and in some instances it can contribute to a lower likelihood of adoption. Among other differences, we find that cooperative members attain higher levels of productivity, however our analysis cannot confirm this to be a causal relationship. We note that collective action in Rwanda’s coffee sector needs additional research attention, as these types of farmer associations don’t always thrive or provide the same level of services that their members expect. We derive implications of our findings and identify areas in need of further inquiry

COVID-19: From health crises to food security anxiety and policy implications

Like the rest of the world, African countries are reeling from the health, economic and social effects of COVID-19. The continent's governments have responded by imposing rigorous lockdowns to limit the spread of the virus. The various lockdown measures are undermining food security, because stay at home orders have among others, threatened food production for a continent that relies heavily on agriculture as the bedrock of the economy. This article draws on quantitative data collected by the GeoPoll, and, from these data, assesses the effect of concern about the local spread and economic impact of COVID-19 on food worries. Qualitative data comprising 12 countries south of the Sahara reveal that lockdowns have created anxiety over food security as a health, economic and human rights/well-being issue. By applying a probit model, we find that concern about the local spread of COVID-19 and economic impact of the virus increases the probability of food worries. Governments have responded with various efforts to support the neediest. By evaluating the various policies rolled out we advocate for a feminist economics approach that necessitates greater use of data analytics to predict the likely impacts of intended regulatory relief responses during the recovery process and post-COVID-19