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Assessing the contribution of PIM to strengthening the capacity of developing country representatives to represent their interests in trade negotiations related to agriculture
The purpose of this review is to assess the extent to which the research outputs of Flagship 3, cluster on The Policy Environment for Value Chains (cluster 3.1) of the CGIAR Research Program on Policies, Institutions, and Markets (PIM) have been used to inform decisions and behaviors of representatives of government organizations, development agencies, researchers, donors, private firms, nongovernment organizations, and other users. The assessment both reviews the achievement of past milestones as well as looks forward to how re-searchers should support the trade agenda in developing countries going forward through their research and communication of research and what should be the focus in ...
Economic accounts for agriculture and farm income in Senegal
A monitoring and evaluation (M&E) system is of critical importance for evidence- and outcome-based planning and implementation in agriculture. The availability of and access to timely and reliable data to inform the M&E system is an undeniable asset. Our analysis highlights the use of survey data to generate relevant information and knowledge on the agricultural sector. The Poverty Monitoring Survey carried out in Senegal in 2011 is used to build the economic accounts for agriculture, which identify a value added of 581 billion CFA francs generated by Senegal’s farm households, representing 60 percent of the sector’s value added in 2011. The average farm household generated 646,500 CFA francs from farming in that same year. The information from the economic accounts for agriculture offers valuable inputs for decision-support tools such as the geographical information platforms (e-atlas) and social accounting matrixes used in strategic analyses and agricultural policy planning.
Kenya agricultural development status assessment
Kenya agricultural development status assessment
Forecasting commodity prices using long-short-term memory neural networks
This paper applies a recurrent neural network (RNN) method to forecast cotton and oil prices. We show how these new tools from machine learning, particularly Long-Short Term Memory (LSTM) models, complement traditional methods. Our results show that machine learning methods fit reasonably well with the data but do not outperform systematically classical methods such as Autoregressive Integrated Moving Average (ARIMA) or the naïve models in terms of out of sample forecasts. However, averaging the forecasts from the two type of models provide better results compared to either method. Compared to the ARIMA and the LSTM, the Root Mean Squared Error (RMSE) of the average forecast was 0.21 and 21.49 percent lower, respectively, for cotton. For oil, the forecast averaging does not provide improvements in terms of RMSE. We suggest using a forecast averaging method and extending our analysis to a wide range of commodity prices.
Detecting threshold effects in price transmission
The analysis of price transmission plays a key role in understanding markets integration. This helps identify the nature of the relationship between geographically distant markets and cross-commodity price transmission, as well as the impact of liberalization policies and the identification of regions exposed to systemic shocks. This technical note contributes to the debate between symmetric and asymmetric price transmission and proposes to present the traditional and new approaches for detecting threshold effects in price transmission while focusing on their advantages and limitations. There is no one-size-fits-all method to detect threshold effects in price transmission. Experts need to select a combination of elements (context of study, the economy under consideration, data availability…) to justify the relevancy of their choice. Beyond the presentation of the methods for detecting thresholds in price transmission, we perform an application in the case of the rice market in Senegal. The results support the evidence of an asymmetric price transmission between world and domestic prices in the short-run and a symmetric transmission in the long-run.
