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- ItemKnowledge Transfer with Citizen Science: Luft-Leipzig Case Study(Basel : MDPI, 2021) Tõnisson, Liina; Voigtländer, Jens; Weger, Michael; Assmann, Denise; Käthner, Ralf; Heinold, Bernd; Macke, AndreasCommunity-based participatory research initiatives such as “hackAir”, “luftdaten.info”, “senseBox”, “CAPTOR”, “CurieuzeNeuzen Vlaanderen”, “communityAQ”, and “Healthy Air, Healthier Children” campaign among many others for mitigating short-lived climate pollutants (SLCPs) and improving air quality have reported progressive knowledge transfer results. These research initiatives provide the research community with the practical four-element state-of-the-art method for citizen science. For the preparation-, measurements-, data analysis-, and scientific support-elements that collectively present the novel knowledge transfer method, the Luft-Leipzig project results are presented. This research contributes to science by formulating a novel method for SLCP mitigation projects that employ citizen scientists. The Luft-Leipzig project results are presented to validate the four-element state-of-the-art method. The method is recommended for knowledge transfer purposes beyond the scope of mitigating short-lived climate pollutants (SLCPs) and improving air quality.
- ItemEvaluating Three-Pillar Sustainability Modelling Approaches for Dairy Cattle Production Systems(Basel : MDPI, 2021) Díaz de Otálora, Xabier; del Prado, Agustín; Dragoni, Federico; Estellés, Fernando; Amon, BarbaraMilk production in Europe is facing major challenges to ensure its economic, environmental, and social sustainability. It is essential that holistic concepts are developed to ensure the future sustainability of the sector and to assist farmers and stakeholders in making knowledge-based decisions. In this study, integrated sustainability assessment by means of whole-farm modelling is presented as a valuable approach for identifying factors and mechanisms that could be used to improve the three pillars (3Ps) of sustainability in the context of an increasing awareness of economic profitability, social well-being, and environmental impacts of dairy production systems (DPS). This work aims (i) to create an evaluation framework that enables quantitative analysis of the level of integration of 3P sustainability indicators in whole-farm models and (ii) to test this method. Therefore, an evaluation framework consisting of 35 indicators distributed across the 3Ps of sustainability was used to evaluate three whole-farm models. Overall, the models integrated at least 40% of the proposed indicators. Different results were obtained for each sustainability pillar by each evaluated model. Higher scores were obtained for the environmental pillar, followed by the economic and the social pillars. In conclusion, this evaluation framework was found to be an effective tool that allows potential users to choose among whole-farm models depending on their needs. Pathways for further model development that may be used to integrate the 3P sustainability assessment of DPS in a more complete and detailed way were identified.
- ItemHow Clusters Create Shared Value in Rural Areas: An Examination of Six Case Studies(Basel : MDPI, 2021) Martinidis, George; Adamseged, Muluken Elias; Dyjakon, Arkadiusz; Fallas, Yannis; Foutri, Angeliki; Grundmann, Philipp; Hamann, Karen; Minta, Stanislaw; Ntavos, Nikolaos; Råberg, Tora; Russo, Silvia; Viaggi, DavideThe main aim of this paper is to demonstrate that clusters can support the sustainable development of rural areas through the creation of shared value. This is done via the close exam-ination of six different cases of rural clusters in Greece, Italy, Germany, Poland, Denmark, and Sweden. Qualitative as well as quantitative data were taken from the clusters, which demonstrated that their main business approaches naturally coincided with the creation of economic, social, and environmental benefits for the local communities in which they operated. The case clusters were created in a top-down manner, aimed at boosting regional R&D activities and making the local economy more competitive and more sustainable. However, private initiative took over and al-lowed these clusters to flourish because meeting the regions’ economic, social, and environmental needs successfully coincided with the target of the clusters’ own development and profitability. The results show that clusters, with their potential for shared value creation, can constitute a powerful engine for the revitalisation and development of rural areas, addressing the significant challenges which they are currently facing.
- ItemSoil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review(Basel : MDPI, 2022) Rasheed, Muhammad Waseem; Tang, Jialiang; Sarwar, Abid; Shah, Suraj; Saddique, Naeem; Khan, Muhammad Usman; Imran Khan, Muhammad; Nawaz, Shah; Shamshiri, Redmond R.; Aziz, Marjan; Sultan, MuhammadThe amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction.