Your search found 3 records
1 Nair, P. K. R.; Garrity, D. (Eds.) 2012. Agroforestry - the future of global land use. Dordrecht, Netherlands: Springer. 549p. (Advances in Agroforestry 9) [doi: https://doi.org/10.1007/978-94-007-4676-3]
Agroforestry systems ; Land use ; Land management ; Landscape ; Climate change ; Adaptation ; Habitats ; Ecosystem services ; Biodiversity conservation ; Rural development ; Trees ; Domestication ; Carbon sequestration ; Carbon credits ; Agriculture ; Farming systems ; Research and Development ; Energy conservation ; Energy generation ; Renewable energy ; Bioenergy ; Industrialization ; Soil properties ; Rangelands ; Gender ; Smallholders ; Food security ; Germplasm ; Rehabilitation ; Greenhouse gases ; Emission ; Sustainability ; Organic agriculture ; Organic fertilizers ; Faidherbia albida ; Natural resources management ; Forest conservation ; Tillage ; Residues ; Nutrient cycling ; Grazing ; Cropping systems ; Shifting cultivation ; Rubber plants ; Wetlands ; Living standards ; Cashews ; Smallholders ; Fruit growing ; Poverty ; Rural communities ; Environmental policy ; Environmental services ; Silvopastoral systems ; Economic aspects ; Alley cropping ; Reclamation ; Indigenous knowledge ; Urbanization ; Agrobiodiversity ; Fertilizers ; Resource conservation ; Legal aspects ; Corporate culture ; Theobroma cacao ; Coffea ; Forage ; Soil fertility ; Case studies / Asia / Europe / Africa / Indonesia / China / USA / Canada / Japan / Latin America / Kenya / Philippines / Niger / Amazon / Sumatra / Xishuangbanna
(Location: IWMI HQ Call no: e-copy SF Record No: H047924)
2 Mariwah, S.; Evans, R.; Antwi, K. B. 2019. Gendered and generational tensions in increased land commercialisation: rural livelihood diversification, changing land use, and food security in Ghana's Brong-Ahafo region. Geo: Geography and Environment, 6(1):1-17. [doi: https://doi.org/10.1002/geo2.73]
Food security ; Gender ; Living standards ; Land use ; Commercialization ; Diversification ; Crop production ; Cashews ; Smallholders ; Farmers ; Households ; Income generation ; Rural communities ; Poverty ; Sustainability ; Rural youth ; Strategies / Ghana / Brong-Ahafo Region
(Location: IWMI HQ Call no: e-copy only Record No: H049251)
https://rgs-ibg.onlinelibrary.wiley.com/doi/epdf/10.1002/geo2.73
https://vlibrary.iwmi.org/pdf/H049251.pdf
https://vlibrary.iwmi.org/pdf/H049251.pdf
(0.59 MB) (608 KB)
Many smallholder farmers in Jaman North District, Brong-Ahafo Region, Ghana are shifting from food crop production to increased cultivation of cashew, an export cash crop. This paper examines gendered and generational tensions in increased commercialisation of land, livelihood diversification, and household food security in the context of globalisation and environmental change. Using qualitative, participatory research with 60 middle-generation men and women, young people and key stakeholders, the research found that community members valued the additional income stream. Young people and women, however, were apprehensive about the long-term consequences for food security of allocating so much land to cashew plantations. Young, middle, and older generations were concerned about their weak bargaining position in negotiating fair prices with export companies and intermediaries. Greater integration into the global economy exposed rural actors to multiple risks and inequalities, such as the uneven effects of economic globalisation, rises in food prices, hunger and food insecurity, growing competition for land, youth outmigration and climate change. The shift towards cashew cultivation appears to be exacerbating gender and generational inequalities in access to land and food insecurity and leading to exploitation within the global agri-food supply chain among already vulnerable rural communities in the global South. With stronger farmer associations and cooperatives, however, cashew farmers stand the chance of benefitting from greater integration into the global economy, through strengthened bargaining positions. Greater understanding is needed about the complex interactions between sustainable food systems, changing land use and gender and generational inequalities in rural spaces.
3 Torres, A. B. B.; da Rocha, A. R.; Coelho da Silva, T. L.; de Souza, J. N.; Gondim, R. S. 2020. Multilevel data fusion for the internet of things in smart agriculture. Computers and Electronics in Agriculture, 171:105309. [doi: https://doi.org/10.1016/j.compag.2020.105309]
Decision support systems ; Internet ; Agriculture ; Irrigation ; Soil moisture ; Evapotranspiration ; Energy consumption ; Linear models ; Sensors ; Crops ; Cashews ; Coconuts / Brazil / Paraipaba
(Location: IWMI HQ Call no: e-copy only Record No: H049724)
(7.91 MB)
The Internet of Things (IoT) aims to enable objects to sense, identify, and analyze the world, but to achieve such goal cost-effectively, it should involve low-cost solutions. That implies a series of limitations, such as small battery life, limited storage capabilities, low accuracy, and imprecise sensors. Data fusion is one of the most widely used methods for improving sensor accuracy and providing a more precise decision. Therefore, we propose Hydra, a multilevel data fusion architecture, to improve sensor accuracy, identify application target events, and make more accurate decisions. Hydra is composed of three layers: low-level (sensor data fusion), medium-level (events and decision making), and high-level (decision fusion based on multiple applications). In partnership with Embrapa (Brazilian Agricultural Research Corporation), we instantiated Hydra for the smart agriculture domain, and we also developed two applications aiming smart water management. The first application goal was to determine the need for irrigation based on soil moisture levels, and the second ascertained the adequate irrigation time by estimating the crop’s evapotranspiration (rate of water evaporation by the soil and transpiration by plants). We performed a set of experiments to assess Hydra: (i) evaluation of methods to detect and remove outliers; (ii) analyze data resulting from the applications; (iii) the use of machine learning to create a new accurate evapotranspiration model based on the sensors data. The results indicate that a combination of the ESD method (Extreme Studentized Deviate) and WRKF filter (Weighted Outlier-Robust Kalman Filter) was the best method to identify and remove outliers. Moreover, we generated an evapotranspiration model using the SVM (Support Machine Vector) quadratic machine-learning model that produced values close to the evapotranspiration reference model (Penman-Monteith).
Powered by DB/Text WebPublisher, from
