Gender discrimination and its impact on income, productivity, and technical efficiency: evidence from Benin

This paper examines the occurrence and impact of gender discrimination in access to production resources on the income, productivity, and technical efficiency of farmers. Through an empirical investigation of farmers from Koussin-Le´le´, a semi-collective irrigated rice scheme in central Benin, we find that female rice farmers are particularly discriminated against with regard to scheme membership and access to land and equipment, resulting in significant negative impacts on their productivity and income. Although women have lower productivity, they are as technically efficient as men. The findings suggest that there is considerable scope for improving the productivity of women through increasing their access to production resources

Determinants of Agricultural Technology adoption: the case of improved groundnut varieties in Malawi

This paper applies the Average Treatment Effect (ATE) framework on data obtained from a random cross-section sample of 594 farmers in Malawi to document the actual and potential adoption rates of improved groundnut varieties and their determinants conditional on farmers’ awareness of the technology. The fact that not all farmers are exposed to the new technologies makes it difficult to obtain consistent estimates of population adoption rates and their determinants using direct sample estimates and classical adoption models such as probit or tobit. Our approach tries to control for exposure and selection bias in assessing the adoption rate of technology and its determinants. Results indicate that only 26% of the sampled farmers grew at least one of the improved groundnut varieties. The potential adoption rate of improved groundnut for the population is estimated at 37% and the adoption gap resulting from the incomplete exposure of the population to the improved groundnut is 12%. We further find that the awareness of improved varieties is mainly influenced by information access variables, while adoption is largely influenced by economic constraints. The findings are indicative of the relatively large unmet demand for improved groundnut varieties suggesting that there is scope for increasing the adoption rate of improved groundnut varieties in Malawi once the farmers are made aware of the technologies and if other constraints such as lack of access to credit are addressed.groundnuts, adoption, Average Treatment Effect, Malawi, Crop Production/Industries,

Why NERICA is a successful innovation for African farmers

This paper responds to ‘Funding international agricultural research and the need to be noticed: a case study of NERICA rice’ by Stuart Orr, James Sumberg, Olaf Erenstein and Andreas Oswald, published in this issue of Outlook on Agriculture. In summary, the article by Orr et al, based on an internal WARDA document written in November 2003 and augmented with results from Internet searches, is outdated and does not seem to be fair, objective or useful. We invite the authors to visit WARDA or any of its partners in Sub-Saharan Africa for evidence of the impact of NERICA varieties or the other improved varieties and technologies that have been developed and disseminated by WARDA in recent years

Pauvreté et distribution de revenus au Sénégal: une approche par la modélisation en équilibre général calculable micro-simulé

La nouvelle orientation de la politique économique au Sénégal vise à accroître les revenus des pauvres et à attaquer la pauvreté là où elle est principalement localisée. La stratégie de réduction de la pauvreté va être mise en oeuvre dans un contexte de libéralisation des échanges commerciaux internationaux notamment dans le secteur agricole. Dans ce contexte, nous avons développé un modèle d'équilibre général calculable micro-simulé multi-ménages du type Decaluwé et al. (1999) permettant d'évaluer l'impact que pourront avoir ces politiques agricoles à l'échelle des ménages et de faire le lien entre ces réformes économiques, la pauvreté et la distribution de revenu. Ce modèle offre beaucoup de flexibilité en permettant notamment de modifier la distribution des groupes cibles qui n'ont pas à être revenus avant l'exercice de simulation afin d'effectuer l'analyse de pauvreté et d'inégalité ex post à l'exercice de modélisation. Dans ce travail, nous avons également comparé les effets en terme d'analyse de pauvreté et d'inégalité entre une distribution paramétrique (Dagum, 3 paramètres) et une distribution non-paramétrique et montré que ce choix engendrait des différences significatives quant aux effets sur la pauvreté. Contrairement aux applications faite par Decaluwé et al. (1999) et Cockburn (2002) au Népal, les impacts sur la pauvreté sont assez importants, ce qui montre que cette approche offre un outil riche permettant d'évaluer l'impact de politiques économiques ou chocs externes sur la pauvreté et la distribution de revenu.Modèle d'équilibre général calculable, micro-simulation, analyse de pauvreté, distribution de revenu

Larval ecology of mosquitoes in sylvatic arbovirus foci in southeastern Senegal

BACKGROUND: Although adult mosquito vectors of sylvatic arbovirus [yellow fever (YFV), dengue-2 (DENV-2) and chikungunya (CHIKV)] have been studied for the past 40 years in southeastern Senegal, data are still lacking on the ecology of larval mosquitoes in this area. In this study, we investigated the larval habitats of mosquitoes and characterized their seasonal and spatial dynamics in arbovirus foci. METHODS: We searched for wet microhabitats, classified in 9 categories, in five land cover classes (agriculture, forest, savannah, barren and village) from June, 2010 to January, 2011. Mosquito immatures were sampled monthly in up to 30 microhabitats of each category per land cover and bred until adult stage for determination. RESULTS: No wet microhabitats were found in the agricultural sites; in the remaining land covers immature stages of 35 mosquito species in 7 genera were sampled from 9 microhabitats (tree holes, fresh fruit husks, decaying fruit husks, puddles, bamboo holes, discarded containers, tires, rock holes and storage containers). The most abundant species was Aedes aegypti formosus, representing 30.2% of the collections, followed by 12 species, representing each more than 1% of the total, among them the arbovirus vectors Ae. vittatus (7.9%), Ae. luteocephalus (5.7%), Ae. taylori (5.0%), and Ae. furcifer (1.3%). Aedes aegypti, Cx. nebulosus, Cx. perfuscus, Cx. tritaeniorhynchus, Er. chrysogster and Ae. vittatus were the only common species collected from all land covers. Aedes furcifer and Ae. taylori were collected in fresh fruit husks and tree holes. Species richness and dominance varied significantly in land covers and microhabitats. Positive associations were found mainly between Ae. furcifer, Ae. taylori and Ae. luteocephalus. A high proportion of potential enzootic vectors that are not anthropophilic were found in the larval mosquito fauna. CONCLUSIONS: In southeastern Senegal, Ae. furcifer and Ae. taylori larvae showed a more limited distribution among both land cover and microhabitat types than the other common species. Uniquely among vector species, Ae. aegypti formosus larvae occurred at the highest frequency in villages. Finally, a high proportion of the potential non-anthropophilic vectors were represented in the larval mosquito fauna, suggesting the existence of unidentified sylvatic arbovirus cycles in southeastern Senegal

African baobabs with a very large number of stems and false stems : radiocarbon Investigation of the baobab of Warang

Author Posting. © Studia Chemia, 2017. This article is posted here by permission of Studia Chemia for personal use, not for redistribution. The definitive version was published in Studia Universitatis Babes-Bolyai, Seria Chemia 62, no. 1 (2017): 111-120, doi:10.24193/subbchem.2017.1.09.The article presents the AMS (accelerator mass spectrometry) radiocarbon dating results of the baobab of Warang, Senegal. The investigation of the baobab revealed that it consists of 18 partially fused stems, which represents the largest number of stems reported for an African baobab. Three stems build the ring that closes a false cavity, while 15 stems grow outside the ring. Seven wood samples were collected from the false cavity and from the outer part of other stems. The dating results evinced that the stems belong to four different generations, out of which the first generation is around 500 years old. We also documented the presence of false stems, which emerge from a large adjacent stem, are triangular in horizontal section and act as an anchor. The baobab of Warang possesses 12 ordinary stems and 6 false stems.The research was funded by the Romanian Ministry of National Education CNCS-UEFISCDI under grant PN-II-ID-PCE-2013-76

A Study of Machine Learning Techniques for Daily Solar Energy Forecasting using Numerical Weather Models

Proceedings of: 8th International Symposium on Intelligent Distributed Computing (IDC'2014). Madrid, September 3-5, 2014Forecasting solar energy is becoming an important issue in the context of renewable energy sources and Machine Learning Algorithms play an important rule in this field. The prediction of solar energy can be addressed as a time series prediction problem using historical data. Also, solar energy forecasting can be derived from numerical weather prediction models (NWP). Our interest is focused on the latter approach.We focus on the problem of predicting solar energy from NWP computed from GEFS, the Global Ensemble Forecast System, which predicts meteorological variables for points in a grid. In this context, it can be useful to know how prediction accuracy improves depending on the number of grid nodes used as input for the machine learning techniques. However, using the variables from a large number of grid nodes can result in many attributes which might degrade the generalization performance of the learning algorithms. In this paper both issues are studied using data supplied by Kaggle for the State of Oklahoma comparing Support Vector Machines and Gradient Boosted Regression. Also, three different feature selection methods have been tested: Linear Correlation, the ReliefF algorithm and, a new method based on local information analysis.Publicad