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Urban Living Labs som medel för samverkan och deltagande i mat-vatten-energi nexus : En fallstudie av CRUNCH Rosendal | Urban Living Labs as a means of collaboration and participation in the food-water-energy nexus : A case study of CRUNCH Rosendal Full text
2022
Gabrielsson, Louise
Världens befolkning och städer växer. I takt med detta ökar efterfrågan på tillgångar av mat, energi och vatten och det finns efterfrågan på tillvägagångssätt som tar hänsyn till både synergier och konflikter mellan dessa. Ett projekt som syftade till att skapa kunskap inom dessa samband genom att använda så kallade Urban Living Labs, ULLs, var det transnationella projektet CRUNCH. Urban Living Labs kan beskrivas som en slags samling tillvägagångssätt som betonar experimentella tillvägagångssätt och en hög nivå av deltagande och samskapande. Men ULLs har visat sig kunna se mycket olika ut och den här studien är ett bidrag till den växande empirin inom ämnet. Studien analyserade hur en av de deltagande städerna inom CRUNCH arbetat med samverkan och samskapande och vilka hinder och möjligheter ULL har som tillvägagångssätt för deltagande, samverkan och samskapande. Detta gjordes genom en kvalitativ fallstudie av Uppsalas ULL Rosendal och analyserades genom teorier om deltagande och kollaborativ governance. Studien fann att deltagandet var smalt och främst skedde genom konsultation och information. De främsta möjligheterna till samarbete verkade vara de inledande villkoren och ett ömsesidigt beroende mellan parterna för att få finansiering till att utveckla sina idéer. De främsta hindren verkade finnas i en obalans i resurser vad gäller finansiering och möjligheter att delta. Men det kanske allra främsta hindret var dock en bristande delad förståelse av begreppet ULL. Begreppet sattes snarare som en ”stämpel” på projekt som redan fanns utan att tillföra dem något extra i form av deltagande eller samverkan. | The world's population and cities are growing. As the demand for food, energy and water resources increases there is a demand for approaches that consider both synergies and conflicts between them. One project that aimed to create knowledge in this nexus by using something called Urban Living Labs, ULLs, was the transnational project CRUNCH. Urban Living Labs can be described as a collection of approaches that emphasizes experimental approaches and a high level of participation and co-creation. But ULLs have been shown to take a variety of different forms and this study is a contribution to the growing empirical evidence in the subject. The study analysed how one of the participating cities within CRUNCH worked with collaboration and co-creation and what obstacles and opportunities ULL has as an approach for participation, collaboration, and co-creation. This was done through a qualitative case study of Uppsala's ULL Rosendal and analysed through theories of participation and collaborative governance. The study found that participation was narrow and mainly took place through consultation and information. The main opportunities for cooperation seemed to be the initial starting conditions and an interdependence between the partners to get funding to develop their ideas. The main obstacles seemed to be resource imbalances in terms of funding and means to participate. But perhaps the main obstacle was a lack of shared understanding of the main concept of ULL. The term was rather applied as a label on projects that already existed, without adding anything extra to them in terms of participation or collaboration.
Show more [+] Less [-]Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security Full text
2022
Gumma, M K | Thenkabail, P S | Panjala, P | Teluguntla, P | Yamano, T | Mohammed, I
Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity, quality, and location of production. Currently, cropland products are produced using mainly coarse-resolution (250–1000 m) remote sensing data. As multiple cropland products are needed to address food and water security challenges, our study was aimed at producing three distinct products that would be useful overall in South Asia. The first of these, Product 1, was meant to assess irrigated versus rainfed croplands in South Asia using Landsat 30 m data on the Google Earth Engine (GEE) platform. The second, Product 2, was tailored for major crop types using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data. The third, Product 3, was designed for cropping intensity (single, double, and triple cropping) using MODIS 250 m data. For the kharif season (the main cropping season in South Asia, Jun–Oct), 10 major crops (5 irrigated crops: rice, soybean, maize, sugarcane, cotton; and 5 rainfed crops: pulses, rice, sorghum, millet, groundnut) were mapped. For the rabi season (post-rainy season, Nov–Feb), five major crops (three irrigated crops: rice, wheat, maize; and two rainfed crops: chickpea, pulses) were mapped. The irrigated versus rainfed 30 m product showed an overall accuracy of 79.8% with the irrigated cropland class providing a producer’s accuracy of 79% and the rainfed cropland class 74%. The overall accuracy demonstrated by the cropping intensity product was 85.3% with the producer’s accuracies of 88%, 85%, and 67% for single, double, and triple cropping, respectively. Crop types were mapped to accuracy levels ranging from 72% to 97%. A comparison of the crop-type area statistics with national statistics explained 63–98% variability. The study produced multiple-cropland products that are crucial for food and water security assessments, modeling, mapping, and monitoring using multiple-satellite sensor big-data, and Random Forest (RF) machine learning algorithms by coding, processing, and computing on the GEE cloud.
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