End: dec 2021
Start: jan 2018
Resource-efficient urban agriculture for multiple benefits – contribution to the EU-China Urbanisation Partnership
|1||Norwegian University of Life Sciences (NMBU)||NO|
|2||Norwegian Institute of Bio-economy Research (NIBIO)||NO|
|3||Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences (CAAS)||CN|
|5||Nordregio (Nordic Centre for Spatial Development)||SE|
|6||EMETRIS SYMVOULOI ANAPTYXIS ORGANOSIS KAI PLIROFORIKIS AE||GR|
|8||VILABS (CY) LTD||CY|
|10||Beijing Agricultural Ecological Ideas Services Union EAEISU||CN|
|11||Beijing Green Valley Sprout CO., LTD||CN|
|13||Hatay Metropolitan Municipality||TK|
|14||Rural Development Institute, Chinese Academy of Social Sciences (CASS)||CN|
|15||Sampas Bilisim Ve Iletisim Sistemleri Sanayi Ve Ticaret A.S.||TK|
|16||Hunan Hengkai Environmental Protection Science & Tech Ltd.||CN|
|17||SEECON INTERNATIONAL GMBH||CN|
|18||LEIBNIZ-INSTITUT FUR GEMUSE- UND ZIERPFLANZENBAU||DE|
|19||Beijing Photon Science & Technology Co., LTD.||CN|
|Start - end date||01.01.2018 - 31.12.2021|
|Project manager at Nibio||Jihong Liu Clarke|
|Division||Division of Biotechnology and Plant Health|
|Department||Viruses, Bacteria and Nematodes in Forestry, Agriculture and Horticulture|
|Total budget||7,000,000.00 EUR and 5,000,000.00 Chinese Yuan. NIBIO receives 1,500,000.00 EUR|
|Funding source||European Union (EU) and Ministry of Science and Technology (MOST) in China|
SiEUGreen aspires to enhance the EU-China cooperation in promoting urban agriculture for food security, resource efficiency and smart, resilient cities. Building on the model of zero-waste and circular economy, it will demonstrate how technological and societal innovation in urban agriculture can have a positive impact on society and economy, by applying novel resource-efficient agricultural techniques in urban and peri-urban areas, developing innovative approaches for social engagement and empowerment and investigating the economic, environmental and social benefits of urban agriculture.
In order to achieve its objectives, SiEuGreen brings together a multi-disciplinary Consortium of European and Chinese researchers, technology providers, SMEs, financiers, local and regional authorities and citizen communities. The project consists in the preparation, deployment and evaluation of showcases in 5 selected European and Chinese urban and peri-urban areas: a previous hospital site in Norway, community gardens in Denmark, previously unused municipal areas with dense refugee population in Turkey, big urban community farms in Beijing and Central China.
Throughout SiEUGreen’s implementation, EU and China will share technologies and experiences, thus contributing to the future developments of urban agriculture and urban resilience in both continents. The impact measurement during and especially beyond the project period is a key component in the project’s design. Information and results obtained from the project will be disseminated through diverse communication and dissemination tools including, social media, an innovative app enhancing urban co-design, stakeholder conferences, hand-on training workshops, showcase demonstration forums, municipality events. A sustainable business model allowing SiEUGreen to live beyond the project period is planned by joining forces of private investors, governmental policy makers, communities of citizens, academia and technology providers.
Publications in the project
Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond.
This paper contributes to the debate on sustainable water consumption by exploring the relation between consumers’ personality, understanding of risk/trust and social distinction in water drinking practices in Norway. Our main research question, how can we understand preferences for water consumption?, is approached by answering a set of hypotheses inspired by a combination of three theoretical approaches. Latent variables measuring personality and conspicuous attitudes are included in frequency models based on the statistical beta distribution together with other predictors. Statistical tests were performed to find the connection between expected frequency of water consumption, personality, risk/trust and conspicuous attitudes. The conclusion is that the consequence of the connections between consumers’ personality, understanding of risk and conspicuous consumption of water should be considered by Norwegian stakeholders when planning future strategies and methods for more sustainable water consumption.