The competing demand for food and bioenergy requires new solutions for the agricultural sector as, for instance, the coupling of energy production from gasification technology and the application of the resulting biochar as soil amendment. A prerequisite for the implementation of this strategy is the scale\uffe2\uff80\uff90specific assessment of both the energetic performance and of the impacts in terms of greenhouse gases (GHG) emission and crop responses. This study considered the gasification process developed by Advanced Gasification Technology (AGT, Italy), which is a fixed\uffe2\uff80\uff90bed, down\uffe2\uff80\uff90draft, open core, compact gasifier, having 350\uffc2\uffa0kW of nominal electric capacity (microgeneration); this gasifier uses biomass feedstock deriving from agricultural/forest products and byproducts. In this study, the resulting biochar, derived from conifer wood chips of mountain forestry management in North\uffe2\uff80\uff90western Italy, was applied to a nearby paddy rice field, located in the largest rice agricultural area of Europe. We performed a Life Cycle Analysis (LCA) adapting the BEAT2 model specifically focusing on the GHG balance of the supply chain, from the forestry management to the field distribution of the resulting biochar. The results indicated that the gasification stage had the highest impact in the supply chain in terms of emissions, but net emissions allocated to biochar were always negative (ranging between \uffe2\uff88\uff920.54 and \uffe2\uff88\uff922.1\uffc2\uffa0t CO2e\uffc2\uffa0t\uffe2\uff88\uff921 biochar), hypothesizing two scenarios of 32% and 7.3% biochar mineralization rate in soil, over a time period of 100\uffc2\uffa0years. Finally, biochar had a marginal but positive effect on rice yield, thus increasing the sustainability of this energy\uffe2\uff80\uff90biochar chain.
", "keywords": ["330", "gasification", "01 natural sciences", "7. Clean energy", "630", "Paddy rice", "Environment (including Climate Change)", "12. Responsible consumption", "Life cycle assessment", "Rural Digital Europe", "life cycle assessment", "11. Sustainability", "biochar", "Energy-biochar chain", "European Commission", "Rice yield", "Waste Management and Disposal", "Knowmad Institut", "0105 earth and related environmental sciences", "FP7", "2. Zero hunger", "EC", "Renewable Energy", " Sustainability and the Environment", "SP1-Cooperation", "Forestry", "04 agricultural and veterinary sciences", "15. Life on land", "Energy Research", "rice yield", "Biochar", "13. Climate action", "paddy rice", "0401 agriculture", " forestry", " and fisheries", "energy-biochar chain", "Agronomy and Crop Science", "Gasification"]}, "links": [{"href": "https://doi.org/10.1111/gcbb.12028"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/GCB%20Bioenergy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/gcbb.12028", "name": "item", "description": "10.1111/gcbb.12028", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/gcbb.12028"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-12-21T00:00:00Z"}}, {"id": "10.3390/rs13091769", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-27T16:21:59Z", "type": "Journal Article", "created": "2021-05-02", "title": "A Scalable Machine Learning Pipeline for Paddy Rice Classification Using Multi-Temporal Sentinel Data", "description": "The demand for rice production in Asia is expected to increase by 70% in the next 30 years, which makes evident the need for a balanced productivity and effective food security management at a national and continental level. Consequently, the timely and accurate mapping of paddy rice extent and its productivity assessment is of utmost significance. In turn, this requires continuous area monitoring and large scale mapping, at the parcel level, through the processing of big satellite data of high spatial resolution. This work designs and implements a paddy rice mapping pipeline in South Korea that is based on a time-series of Sentinel-1 and Sentinel-2 data for the year of 2018. There are two challenges that we address; the first one is the ability of our model to manage big satellite data and scale for a nationwide application. The second one is the algorithm\uffe2\uff80\uff99s capacity to cope with scarce labeled data to train supervised machine learning algorithms. Specifically, we implement an approach that combines unsupervised and supervised learning. First, we generate pseudo-labels for rice classification from a single site (Seosan-Dangjin) by using a dynamic k-means clustering approach. The pseudo-labels are then used to train a Random Forest (RF) classifier that is fine-tuned to generalize in two other sites (Haenam and Cheorwon). The optimized model was then tested against 40 labeled plots, evenly distributed across the country. The paddy rice mapping pipeline is scalable as it has been deployed in a High Performance Data Analytics (HPDA) environment using distributed implementations for both k-means and RF classifiers. When tested across the country, our model provided an overall accuracy of 96.69% and a kappa coefficient 0.87. Even more, the accurate paddy rice area mapping was returned early in the year (late July), which is key for timely decision-making. Finally, the performance of the generalized paddy rice classification model, when applied in the sites of Haenam and Cheorwon, was compared to the performance of two equivalent models that were trained with locally sampled labels. The results were comparable and highlighted the success of the model\uffe2\uff80\uff99s generalization and its applicability to other regions.
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