Benefit-cost analysis of participatory breeding program in Syria

Participatory plant breeding is seen by several scientists as a way to overcome the limitations of conventional breeding by offering farmers the possibility of deciding which varieties better suit their needs and conditions without exposing them to any risk. It exploits the potential gains of breeding for specific adaptation through the selection in the marginal environment. The participation of farmers increases the probability and speed of adoption as well as the breeding efficiency and the effectiveness of the breeding program. The objective of this study is to estimate and compare the benefits and costs of participatory and conventional barley breeding programs. Benefit-cost analysis was used to analyze the costs and benefits of the PPB program at ICARDA and the conventional program at ICARDA and NARS

Village Based Participatory Breeding in the Mountain Slopes of Yemen

In the three villages selected in the project area in the Northern Highlands and described in the 2000 reports, the farmers tested the barley and the lentil lines they selected during 2000

Learning Relatedness Measures for Entity Linking

Entity Linking is the task of detecting, in text documents, relevant mentions to entities of a given knowledge base. To this end, entity-linking algorithms use several signals and features extracted from the input text or from the knowl- edge base. The most important of such features is entity relatedness. Indeed, we argue that these algorithms benefit from maximizing the relatedness among the relevant enti- ties selected for annotation, since this minimizes errors in disambiguating entity-linking. The definition of an e↵ective relatedness function is thus a crucial point in any entity-linking algorithm. In this paper we address the problem of learning high-quality entity relatedness functions. First, we formalize the problem of learning entity relatedness as a learning-to-rank problem. We propose a methodology to create reference datasets on the basis of manually annotated data. Finally, we show that our machine-learned entity relatedness function performs better than other relatedness functions previously proposed, and, more importantly, improves the overall performance of dif- ferent state-of-the-art entity-linking algorithms

Decentralized participatory plant breeding: a case from Syria

Conventional modern plant breeding has been recognized to be more beneficial to farmers in high potential environments or those who could profitably modify their environment to suit new cultivars, than to the poorest farmers who could not afford to make the necessary modifications. As a consequence, low yields, crop failures, malnutrition and poverty affect a large proportion of humanity

Decentralized participatory plant breeding

It is widely recognized that conventional plant breeding has been more beneficial to farmers in high potential environments or those who could profitably modify their environment to suit new cultivars, than to the poorest farmers who could not afford to modify their environment through the application of additional inputs and could not risk the replacement of their traditional, well-known and reliable varieties. As a consequence, low yields, crop failures, malnutrition, famine, and eventually poverty are still affecting a large proportion of humanity. Participatory plant breeding is seen by several scientists as a way to overcome the limitations of conventional breeding by offering farmers the possibility of deciding which varieties better suit their needs and conditions without exposing the household to any risk. Participatory plant breeding exploits the potential gains of breeding for specific adaptation through decentralized selection, defined as selection in the target environment, and is the ultimate conceptual consequence of a positive interpretation of genotype x environment interactions. This article describes a model of participatory plant breeding in which genetic variability is generated by professional breeders, selection is conducted jointly by breeders, extension specialists and farmers in a number of target environments, and the best selections are used by breeders in further cycles of recombination. Farmers handle the first phases of seed multiplication of promising breeding material in village-based seed production systems. The model has the following advantages: (i) varieties reach the release phase earlier than in conventional breeding; (ii) the release and seed multiplication concentrate on varieties known to be acceptable by farmers; (iii) it increases biodiversity because different varieties are selected in different locations; (iv) varieties fit to the agronomic management that farmers are familiar with and can afford and therefore can be beneficial to poor farmers. These advantages are particularly relevant to developing countries where large investments in plant breeding have not resulted in production increases, especially in marginal environments

Decentralized Participatory Plant Breeding

This report, presented by Dr. Salvatore Ceccarelli from ICARDA, describes a model of participatory plant breeding in which genetic variability is generated by professional breeders, selection is conducted jointly by breeders, extension specialists and farmers in a number of target environments, and the best selections are used by breeders in further cycles of recombination. Farmers handle the first phases of seed multiplication of promising breeding material in village-based seed production systems. The model has the following advantages: (i) varieties reach the release phase earlier than in conventional breeding; (ii) the release and seed multiplication concentrate on varieties known to be acceptable by farmers; (iii) it increases biodiversity because different varieties are selected in different locations; (iv) varieties fit to the agronomic management that farmers are familiar with and can afford and therefore can be beneficial to poor farmers. These advantages are particularly relevant to developing countries where large investments in plant breeding have not resulted in production increases, especially in marginal environments. This report was discussed at the Stakeholder Meeting at AGM2005

SEL: A unified algorithm for entity linking and saliency detection

The Entity Linking task consists in automatically identifying and linking the entities mentioned in a text to their URIs in a given Knowledge Base, e.g., Wikipedia. Entity Linking has a large impact in several text analysis and information retrieval related tasks. This task is very challenging due to natural language ambiguity. However, not all the entities mentioned in a document have the same relevance and utility in understanding the topics being discussed. Thus, the related problem of identifying the most relevant entities present in a document, also known as Salient Entities, is attracting increasing interest. In this paper we propose SEL, a novel supervised two-step algorithm comprehensively addressing both entity linking and saliency detection. The first step is based on a classifier aimed at identifying a set of candidate entities that are likely to be mentioned in the document, thus maximizing the precision of the method without hindering its recall. The second step is still based on machine learning, and aims at choosing from the previous set the entities that actually occur in the document. Indeed, we tested two different versions of the second step, one aimed at solving only the entity linking task, and the other that, besides detecting linked entities, also scores them according to their saliency. Experiments conducted on two different datasets show that the proposed algorithm outperforms state-of-the-art competitors, and is able to detect salient entities with high accuracy

Plant breeding with farmers. A technical manual

Participatory plant breeding (PPB) is defined as a type of plant breeding in which farmers, as well as other partners, such as extension staff, seed producers, traders and NGOs, participate in the development of a new variety. In PPB programs farmers play a more important role than in conventional plant breeding (CPB). Both PPB and CPB are based on the same scientific background and are not mutually exclusive. This tool describes how to organize a PPB programme in self-pollinated, cross-pollinated and vegetatively propagated crops, how to design the trials, collect, organize and analyse the data, and eventually how to use and share the information generated by a PPB. The manual is addressed to all those involved in planning and implementing participatory breeding activities. This includes research centres, universities, non-governmental organizations (NGOs), farmer associations and government extension officials

A user-friendly database for Participatory Plant Breeding programs

In a decentralized and participatory plant breeding (PPB) program, in contrast to conventional plant breeding, farmers select from a large number of genotypes over a number of cycles in several locations. As selection is for location-specific adaptation a PPB program based on, for example, four stages of selection, generates large, unbalanced data sets. With common spreadsheet software it is not possible, or it is at least very cumbersome to extract all the information, particularly the one that is related to genotype x environment (GE) interactions. Therefore, there is a need for an information system that makes it as easy as possible to retrieve and compare data over all given factors in the PPB-program. Due to a lack of education and financial resources in developing countries, the use of existing databases was not an option because of their complexity and/or price. We have built a simple, free and user-friendly database that allows to store, retrieve and analyze the plant breeding data. The database is implemented using the Java based HyperSQL Database Engine (HSQLDB) in the package of Open Office with links to the input formats of Genstat and GGEbiPlot. The proposed database is capable of managing the following data in a PPBprogram for barley: (I) trial data for any trial or field design (II) field data for any given trait (III) results from statistical analyses (IV) farmers selection individually or in groups