{"type": "FeatureCollection", "features": [{"id": "10.1016/j.ejsobi.2018.05.008", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:15:29Z", "type": "Journal Article", "created": "2018-06-11", "title": "Archaea Are The Predominant And Responsive Ammonia Oxidizing Prokaryotes In A Red Paddy Soil Receiving Green Manures", "description": "Abstract   Application of green manures is an effective approach to optimizing N management in paddy soils. Nitrification is a key process in the N cycle and ammonia oxidization is the first and typically limiting step in nitrification. In this study, we investigated the changes of ammonium oxidizing prokaryotes after the application of green manure in a red paddy soil using pot experiments. The experiment included four treatments; milk vetch-rice, radish-rice, ryegrass-rice and winter fallow-rice. The nitrification potential was measured, and the abundance and community of amoA genes from ammonia-oxidizing archaea (AOA) and bacteria (AOB) were quantified. The results showed that the AOA to AOB ratios ranged from 7 to 80, and that the milk vetch treatment increased the abundances of AOA and AOB. The abundance of AOA showed negative correlations with nitrification potential and NH4+-N, and positive correlation with soil pH in the acidic red paddy soil. DNA sequence analyses revealed that the Nitrososphaera and Nitrosospira were the dominant clusters of AOA and AOB, respectively. The dominant clusters of AOA were significantly changed by utilization of green manures, especially radish. Partial least squares path modeling analysis showed that green manures exerted larger effects on the abundances of AOA than on AOB, and the community structure of AOA had the strongest effect on nitrification potential. The high abundance of AOA found in this study and their responsiveness to green manuring suggests that AOA are critically important for soil ammonia oxidation in these soils and more sensitive to green manuring than AOB.", "keywords": ["2. Zero hunger", "Driving factors", "Green manure", "0401 agriculture", " forestry", " and fisheries", "Nitrification potential", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water", "Ammonia-oxidizing archaea", "Red paddy soil"]}, "links": [{"href": "https://doi.org/10.1016/j.ejsobi.2018.05.008"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/European%20Journal%20of%20Soil%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.ejsobi.2018.05.008", "name": "item", "description": "10.1016/j.ejsobi.2018.05.008", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.ejsobi.2018.05.008"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-07-01T00:00:00Z"}}, {"id": "10.1016/j.jclepro.2020.125466", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-19T16:15:49Z", "type": "Journal Article", "created": "2020-12-16", "title": "Spatial differentiation characteristics and driving factors of agricultural eco-efficiency in Chinese provinces from the perspective of ecosystem services", "description": "Farmland ecosystem service is an important output of agricultural production, but it has been incompletely reflected in current studies on eco-efficiency. In this study, the value of improved farmland ecosystem services is used as one of the expected outputs. The data envelopment method is used to evaluate the agricultural eco-efficiency (AEE) of 31 provincial administrative regions in China from 2006 to 2018. The spatial autocorrelation method is used to explore the characteristics of AEE in China. Geographical detector model (Geodetector) is adopted to detect the driving factors of AEE spatial differentiation in China. China\u2019s AEE trend from 2006 to 2018 was downward with the efficiency value decreasing from 1.023 to 0.995. China\u2019s AEE level has improved with an average of 1.004. The spatial distribution pattern represented in space is in the following order: eastern region > western region > northeast region > central region. The AEE gap among provinces in the western region is the largest, and that in the northeast region is the smallest. China\u2019s AEE spatial correlation distribution presents random distribution characteristics. During the research period, the lowehigh (LH) efficiency response area has centered on Yunnan Province. The lowelow (LL) level concentration area has centered on Inner Mongolia autonomous region and Liaoning Province. The highelow (HL) level diffusion effect agglomeration area has centered on Heilongjiang Province. Energy input, water resource input, and carbon emission are the core drivers of AEE spatial differentiation in China. Water resource input, pesticide input and labor input are the significant control factors of AEE spatial differentiation in the eastern, central, and western regions of China.", "keywords": ["Economics and Econometrics", "China", "Environmental Engineering", "Economics", "Discrete Choice Models in Economics and Health Care", "Social Sciences", "Mathematical analysis", "01 natural sciences", "Environmental science", "Data envelopment analysis", "Life Cycle Assessment and Environmental Impact Analysis", "11. Sustainability", "FOS: Mathematics", "Ecosystem services", "Spatial distribution", "Biology", "Ecosystem Services", "Ecosystem", "0105 earth and related environmental sciences", "Agricultural economics", "2. Zero hunger", "Global and Planetary Change", "Global Analysis of Ecosystem Services and Land Use", "Geography", "Ecology", "Distribution (mathematics)", "Statistics", "FOS: Environmental engineering", "Spatial analysis", "Agriculture", "Remote sensing", "15. 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In this study, the value of improved farmland ecosystem services is used as one of the expected outputs. The data envelopment method is used to evaluate the agricultural eco-efficiency (AEE) of 31 provincial administrative regions in China from 2006 to 2018. The spatial autocorrelation method is used to explore the characteristics of AEE in China. Geographical detector model (Geodetector) is adopted to detect the driving factors of AEE spatial differentiation in China. China\u2019s AEE trend from 2006 to 2018 was downward with the efficiency value decreasing from 1.023 to 0.995. China\u2019s AEE level has improved with an average of 1.004. The spatial distribution pattern represented in space is in the following order: eastern region > western region > northeast region > central region. The AEE gap among provinces in the western region is the largest, and that in the northeast region is the smallest. China\u2019s AEE spatial correlation distribution presents random distribution characteristics. During the research period, the lowehigh (LH) efficiency response area has centered on Yunnan Province. The lowelow (LL) level concentration area has centered on Inner Mongolia autonomous region and Liaoning Province. The highelow (HL) level diffusion effect agglomeration area has centered on Heilongjiang Province. Energy input, water resource input, and carbon emission are the core drivers of AEE spatial differentiation in China. Water resource input, pesticide input and labor input are the significant control factors of AEE spatial differentiation in the eastern, central, and western regions of China.", "keywords": ["Economics and Econometrics", "China", "Environmental Engineering", "Economics", "Discrete Choice Models in Economics and Health Care", "Social Sciences", "Mathematical analysis", "01 natural sciences", "Environmental science", "Data envelopment analysis", "Life Cycle Assessment and Environmental Impact Analysis", "11. Sustainability", "FOS: Mathematics", "Ecosystem services", "Spatial distribution", "Biology", "Ecosystem Services", "Ecosystem", "0105 earth and related environmental sciences", "Agricultural economics", "2. Zero hunger", "Global and Planetary Change", "Global Analysis of Ecosystem Services and Land Use", "Geography", "Ecology", "Distribution (mathematics)", "Statistics", "FOS: Environmental engineering", "Spatial analysis", "Agriculture", "Remote sensing", "15. 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China\u2019s AEE spatial correlation distribution presents random distribution characteristics. During the research period, the lowehigh (LH) efficiency response area has centered on Yunnan Province. The lowelow (LL) level concentration area has centered on Inner Mongolia autonomous region and Liaoning Province. The highelow (HL) level diffusion effect agglomeration area has centered on Heilongjiang Province. Energy input, water resource input, and carbon emission are the core drivers of AEE spatial differentiation in China. 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