Transgenic strategies for improved drought tolerance in legumes of semi-arid tropics
In: Water and Agricultural Sustainability Strategies, S. 261-277
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In: Water and Agricultural Sustainability Strategies, S. 261-277
In: International political science review: the journal of the International Political Science Association (IPSA) = Revue internationale de science politique, Band 31, Heft 2, S. 229-243
ISSN: 1460-373X
Decentralization reforms aim at strengthening democracy by promoting political participation among citizens. Research shows (1) that information is a prerequisite for political participation and (2) that people face different private costs in acquiring information. Here we combine the two lines of research and ask: what private costs hamper the acquisition of information on decentralization? For the analysis, we use data from an indigenous population of lowland Bolivia. We surveyed 319 Tsimane' adults in 12 villages. We found that nine years after the passage of the decentralization laws, knowledge about those reforms had only partially reached the Tsimane'. People who live closer to municipal towns, had more schooling, and participated in the market economy were more aware of decentralization. Political authorities trying to spread the potential benefits of decentralization should address the structural limitations of the dissemination of political knowledge.
In: International political science review: IPSR = Revue internationale de science politique : RISP, Band 31, Heft 2, S. 229-244
ISSN: 0192-5121
In: The journal of development studies: JDS, Band 44, Heft 2, S. 217-232
ISSN: 0022-0388
World Affairs Online
In: Annual review of anthropology, Band 34, Heft 1, S. 121-138
ISSN: 1545-4290
Assessing the effects of markets on the well-being of indigenous peoples and their conservation of natural resources matters to identify public policies to improve well-being and enhance conservation and to test hypotheses about sociocultural change. We review studies about how market economies affect the subsistence, health, nutritional status, social capital, and traditional ecological knowledge of indigenous peoples and their use of renewable natural resources. Market exposure produces mixed effects on well-being and conservation. Unclear effects arise from the small sample size of observations; reliance on cross-sectional data or short panels; lack of agreement on the measure of key variables, such as integration to the market or folk knowledge, or whether to rely on perceived or objective indicators of health; and endogeneity biases. Rigorous empirical studies linking market economies with the well-being of indigenous peoples or their use of renewable natural resources have yet to take off.
In: The journal of development studies: JDS, Band 44, Heft 2, S. 217-232
ISSN: 0022-0388
In: The journal of development studies, Band 44, Heft 2, S. 217-232
ISSN: 1743-9140
In: Current anthropology, Band 46, Heft 4, S. 651-656
ISSN: 1537-5382
In: Economics of education review, Band 26, Heft 3, S. 349-360
ISSN: 0272-7757
In: Computers and electronics in agriculture: COMPAG online ; an international journal, Band 182, S. 105992
International audience ; Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to environmental data to help make crop breeding decisions. Socio-economic applications include foresight analysis of agricultural systems under global change scenarios, and the consequences of potential food system shocks are also described. These approaches are discussed in this paper which also calls for closer collaboration among disciplines in order to better serve the crop research and development communities by providing model based recommendations ranging from policy development at the level of governmental agencies to direct crop management support for resource poor
BASE
International audience ; Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to environmental data to help make crop breeding decisions. Socio-economic applications include foresight analysis of agricultural systems under global change scenarios, and the consequences of potential food system shocks are also described. These approaches are discussed in this paper which also calls for closer collaboration among disciplines in order to better serve the crop research and development communities by providing model based recommendations ranging from policy development at the level of governmental agencies to direct crop management support for resource poor
BASE
Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to environmental data to help make crop breeding decisions. Socio-economic applications include foresight analysis of agricultural systems under global change scenarios, and the consequences of potential food system shocks are also described. These approaches are discussed in this paper which also calls for closer collaboration among disciplines in order to better serve the crop research and development communities by providing model based recommendations ranging from policy development at the level of governmental agencies to direct crop management support for resource poor farmers. ; IFPRI3; HarvestChoice ; EPTD ; PR ; CGIAR Platform for Big Data in Agriculture (Big Data); CGIAR Research Program on Wheat (WHEAT); CGIAR Research Program on Rice (GRiSP)
BASE
International audience ; Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to environmental data to help make crop breeding decisions. Socio-economic applications include foresight analysis of agricultural systems under global change scenarios, and the consequences of potential food system shocks are also described. These approaches are discussed in this paper which also calls for closer collaboration among disciplines in order to better serve the crop research and development communities by providing model based recommendations ranging from policy development at the level of governmental agencies to direct crop management support for resource poor
BASE
Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to environmental data to help make crop breeding decisions. Socio-economic applications include foresight analysis of agricultural systems under global change scenarios, and the consequences of potential food system shocks are also described. These approaches are discussed in this paper which also calls for closer collaboration among disciplines in order to better serve the crop research and development communities by providing model based recommendations ranging from policy development at the level of governmental agencies to direct crop management support for resource poor farmers. ; Peer Review
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