{"type": "FeatureCollection", "features": [{"id": "10.1016/j.ejsobi.2018.05.008", "type": "Feature", "geometry": null, "properties": {"license": "Closed Access", "updated": "2026-06-24T16:16:44Z", "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": "2026-06-24T16:17:12Z", "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. Life on land", "Economics", " Econometrics and Finance", "Driving factors", "Archaeology", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Spatial heterogeneity", "Common spatial pattern", "Mathematics"]}, "links": [{"href": "https://doi.org/10.1016/j.jclepro.2020.125466"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Cleaner%20Production", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jclepro.2020.125466", "name": "item", "description": "10.1016/j.jclepro.2020.125466", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jclepro.2020.125466"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-01T00:00:00Z"}}, {"id": "10.1088/1748-9326/11/5/054004", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:18:58Z", "type": "Journal Article", "created": "2016-04-26", "description": "Open AccessEn este estudio, se examinaron los efectos de la intensidad del pastoreo de ganado en los flujos de \u00f3xido nitroso (N2O) del suelo en la estepa del prado de Hulunber, en el noreste de China. Se establecieron seis tratamientos de tasa de siembra (0, 0.23, 0.34, 0.46, 0.69 y 0.92 AU ha\u22121) con tres r\u00e9plicas, y se realizaron observaciones de 2010 a 2014. Nuestros resultados mostraron que se produjeron fluctuaciones temporales sustanciales en el flujo de N2O entre las diferentes intensidades de pastoreo, con flujos m\u00e1ximos de N2O despu\u00e9s de la lluvia natural. El pastoreo tuvo un efecto a largo plazo en el flujo de N2O del suelo en los pastizales. Despu\u00e9s de 4\u20135 a\u00f1os de pastoreo, los flujos de N2O bajo mayores niveles de intensidad de pastoreo comenzaron a disminuir significativamente en un 31.4%\u201360.2% en 2013 y 32.5%\u201350.5% en 2014 en comparaci\u00f3n con el tratamiento sin pastoreo. Observamos una relaci\u00f3n lineal negativa significativa entre los flujos de N2O del suelo y la intensidad del pastoreo para la media de cinco a\u00f1os. El flujo de N2O del suelo se vio afectado significativamente cada a\u00f1o en todos los tratamientos. Durante los cinco a\u00f1os, el coeficiente de variaci\u00f3n temporal (CV) del flujo de N2O del suelo generalmente disminuy\u00f3 significativamente con el aumento de la intensidad del pastoreo. La tasa de emisi\u00f3n de N2O del suelo se correlacion\u00f3 significativamente de manera positiva con la humedad del suelo (SM), el f\u00f3sforo disponible en el suelo (SAP), la biomasa sobre el suelo (AGB), la cobertura vegetal y la altura y se correlacion\u00f3 negativamente con el nitr\u00f3geno total del suelo (TN). Las regresiones escalonadas mostraron que el flujo de N2O se explicaba principalmente por SM, altura de la planta, TN, pH del suelo y suelo Usando modelos de ecuaciones estructurales, mostramos que el pastoreo influy\u00f3 significativamente directamente en la comunidad de plantas y el entorno del suelo, que luego influy\u00f3 en los flujos de N2O del suelo. Nuestros hallazgos proporcionan una referencia importante para comprender mejor los mecanismos e identificar las v\u00edas de los efectos del pastoreo en las tasas de emisi\u00f3n de N2O del suelo, y los impulsores clave de la comunidad vegetal y el entorno del suelo dentro del ciclo del nitr\u00f3geno que probablemente afecten las emisiones de N2O en las estepas de los prados de Mongolia Interior.", "keywords": ["Biomass (ecology)", "driving factor", "Mechanics and Transport in Unsaturated Soils", "Science", "QC1-999", "Soil Science", "Environmental technology. Sanitary engineering", "Environmental science", "meadow steppe", "Agricultural and Biological Sciences", "Engineering", "GE1-350", "Biology", "TD1-1066", "Civil and Structural Engineering", "2. Zero hunger", "Steppe", "Soil Fertility", "Nitrous oxide", "Ecology", "Physics", "Q", "Life Sciences", "04 agricultural and veterinary sciences", "15. Life on land", "soil N2O fluxes", "Soil Erosion and Agricultural Sustainability", "Agronomy", "6. Clean water", "Environmental sciences", "grazing intensity", "Grazing", "13. Climate action", "FOS: Biological sciences", "response and mechanism", "Physical Sciences", "Growing season", "0401 agriculture", " forestry", " and fisheries", "Soil Carbon Dynamics and Nutrient Cycling in Ecosystems"], "contacts": [{"organization": "Ruirui Yan, Huajun Tang, Xiaoping Xin, Baorui Chen, Philip J. Murray, Yunchun Yan, Xu Wang, Guoxiang Yang,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1088/1748-9326/11/5/054004"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Research%20Letters", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1088/1748-9326/11/5/054004", "name": "item", "description": "10.1088/1748-9326/11/5/054004", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1088/1748-9326/11/5/054004"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-04-26T00:00:00Z"}}, {"id": "10.60692/9nxrv-e7y75", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:25:05Z", "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. Life on land", "Economics", " Econometrics and Finance", "Driving factors", "Archaeology", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Spatial heterogeneity", "Common spatial pattern", "Mathematics"]}, "links": [{"href": "https://doi.org/10.60692/9nxrv-e7y75"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Cleaner%20Production", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.60692/9nxrv-e7y75", "name": "item", "description": "10.60692/9nxrv-e7y75", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.60692/9nxrv-e7y75"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-01T00:00:00Z"}}, {"id": "3111070593", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-24T16:27:12Z", "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. Life on land", "Economics", " Econometrics and Finance", "Driving factors", "Archaeology", "13. 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