{"type": "FeatureCollection", "features": [{"id": "10.1007/978-1-4020-8261-0_5", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-09-20T16:14:11Z", "created": "2008-07-18", "title": "The Adoption Of Smallholder Rubber Production By Shifting Cultivators In Northern Laos: A Village Case Study", "description": "Rubber smallholdings are being established by shifting cultivators in Northern Laos, in response to demand from China and encouraged by government land-use policy. This can be seen as part of a general transition from subsistence to commercial agriculture in the uplands \u2013 in particular, from shifting cultivation to tree crop production. This study examines the economics of smallholder rubber production in an established rubber-growing village in Luangnamtha Province. Data were obtained from key informant interviews, group interviews, direct observation, and a farm-household survey. The study shows that, given current market conditions and credit support, investment in smallholder rubber production in the uplands of Northern Laos can be economically rewarding. Hence rubber can be considered one of the potential alternatives for poor upland farmers, in line with the government policy of stabilising shifting cultivation and supporting new livelihood options for poverty reduction. However, there are risks associated with rubber production and emerging constraints of land and labour, hence government should move cautiously in promoting rubber where farmers are uncertain about reducing their dependence on shifting cultivation or where forests are under threat. The recommended role for government is to ensure provision of support services for rubber development, including adaptive research, technical support, extension, credit, road access, and marketing. In particular, maintaining secure access to the China market will be crucial. If carefully managed, the expansion of smallholder rubber in Northern Laos has the potential to contribute to sustainable rural livelihoods.", "keywords": ["2. Zero hunger", "140201 Agricultural Economics", "11. Sustainability", "1. No poverty", "15. Life on land", "B1", "910210 Production", "12. Responsible consumption"], "contacts": [{"organization": "Manivong, Vongpaphane, Cramb, R. A.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1007/978-1-4020-8261-0_5"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/978-1-4020-8261-0_5", "name": "item", "description": "10.1007/978-1-4020-8261-0_5", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/978-1-4020-8261-0_5"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2008-01-01T00:00:00Z"}}, {"id": "10.1016/j.jclepro.2020.125466", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:15:48Z", "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 &gt; western region &gt; northeast region &gt; 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": "10.1080/14735903.2022.2131042", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:17:10Z", "type": "Journal Article", "created": "2022-10-13", "title": "The use of pre-crop values to improve farm performance: the case of dairy farms in south-west Finland", "description": "Pre-crop values are used to indicate the benefits of a previous crop for a subsequent crop in crop sequencing. A better understanding and research on pre-crop values has the potential to facilitate the diversification of crop production. Despite the various benefits of diversification, the limited knowledge and incentives concerning the pre-crop values in the market conditions have contributed to the persistence of cereal-dominated land use. The present study evaluated the benefits of utilizing pre-crop values in a Finnish context. Results based on dynamic optimization modelling showed that incorporating more information on pre-crop values into farmers\u2019 decision-making contributes to increased net present values (NPV). The adoption of pre-crop values was analysed under five different scenarios: Removal of the Common Agricultural Policy land constraints, 30% increase in labour costs, +/\u221210% change in crop prices, and 30% increase in N fertilizer price. Under each scenario, the response of the baseline model (without pre-crop values) was compared to the response of the model with pre-crop values. In all scenarios, the results of the model with pre-crop values showed higher NPVs, higher yields and slightly lower GHG emissions. Hence, increasing knowledge and utilization of pre-crop values may significantly promote shifts towards more sustainable agriculture.", "keywords": ["330", "S", "pre-crop benefits", "Dynamic Optimization", "land use", "Agriculture", "ta4111", "630", "crop rotation", "cropping diversification", "dynamic optimization", "agricultural economics", "Cropping diversification", "whole-farm management", "ta512"]}, "links": [{"href": "https://www.tandfonline.com/doi/pdf/10.1080/14735903.2022.2131042"}, {"href": "https://doi.org/10.1080/14735903.2022.2131042"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Agricultural%20Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1080/14735903.2022.2131042", "name": "item", "description": "10.1080/14735903.2022.2131042", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1080/14735903.2022.2131042"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-13T00:00:00Z"}}, {"id": "10.5424/sjar/2020181-13807", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:21:06Z", "type": "Journal Article", "created": "2020-03-13", "title": "The cost of mitigating greenhouse gas emissions in farms in Central Andes of Ecuador", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Aim of study: Reduction of the greenhouse gas (GHG) emissions derived from food production is imperative to meet climate change mitigation targets. Sustainable mitigation strategies also combine improvements in soil fertility and structure, nutrient recycling, and the use more efficient use of water. Many of these strategies are based on agricultural know-how, with proven benefits for farmers and the environment. This paper considers measures that could contribute to emissions reduction in subsistence farming systems and evaluation of management alternatives in the Central Andes of Ecuador. We focused on potato and milk production because they represent two primary employment and income sources in the region\u2019s rural areas and are staple foods in Latin America.Area of study: Central Andes of Ecuador: Carchi, Chimborazo, Ca\u00f1ar provincesMaterial and methods: Our approach to explore the cost and the effectiveness of mitigation measures combines optimisation models with participatory methods.Main results: Results show the difference of mitigation costs between regions which should be taken into account when designing of any potential support given to farmers. They also show that there is a big mitigation potential from applying the studied measures which also lead to increased soil fertility and soil structure improvements due to the increased soil organic carbon.Research highlights: This study shows that marginal abatement cost curves derived for different agro-climatic regions are helpful tools for the development of realistic regional mitigation options for the agricultural sector.</p></article>", "keywords": ["Agricultural economics", "2. Zero hunger", "S", "Marginal abatement cost curves; cost-effectiveness; mitigation; climate change", "1. No poverty", "Agriculture", "15. Life on land", "01 natural sciences", "7. Clean energy", "12. Responsible consumption", "mitigation", "Marginal abatement cost curves", "climate change", "13. Climate action", "11. Sustainability", "marginal abatement cost curves", "cost-effectiveness", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5424/sjar/2020181-13807"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Spanish%20Journal%20of%20Agricultural%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5424/sjar/2020181-13807", "name": "item", "description": "10.5424/sjar/2020181-13807", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5424/sjar/2020181-13807"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-04-22T00:00:00Z"}}, {"id": "10.60692/9nxrv-e7y75", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:21:23Z", "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 &gt; western region &gt; northeast region &gt; 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": "10.7910/DVN/HE6CEM", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:21:34Z", "type": "Dataset", "title": "An integrated approach for understanding the factors that facilitate or constrain the adoption of soil carbon enhancing practices in East Africa, specifically Western Kenya", "description": "The survey data on soil carbon enhancing practices in western Kenya is systematically organized in Microsoft Excel tables. The data entails general household characteristics, plot characteristics, practices implemented, yield, inputs, livestock ownership, social capital, access to credit, access to extension services and sources of income.", "keywords": ["Land Management", "Agricultural Sciences", "Soil carbon enhancing practices", "land management", "Low soil fertility", "Kenya", "soil", "Soil", "Earth and Environmental Sciences", "Natural Resources", "Africa", "agricultural economics", "Decision and Policy Analysis - DAPA", "Western Kenya", "natural resources", "Agricultural Economics"], "contacts": [{"organization": "Ng\u2019ang\u2019a, Stanley Karanja, George Magambo, Kanyenji, Jalang'o, Dorcas Anyango, Nguru, Wilson Maina, Girvetz, Evan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/HE6CEM"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/HE6CEM", "name": "item", "description": "10.7910/DVN/HE6CEM", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/HE6CEM"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.7910/DVN/QTACSN", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:21:34Z", "type": "Dataset", "title": "An integrated approach for understanding the factors that facilitate or constrain the adoption of soil carbon enhancing practices in East Africa, Kenya and Ethiopia.", "description": "The survey data on soil carbon enhancing practices in Ethiopia is systematically organized in Microsoft Excel tables. The data entails general household characteristics, plot characteristics, crops grown, yield, practices implemented, inputs, livestock ownership, social capital, access to credit, access to extension services.", "keywords": ["Agricultural economics", "Agricultural Sciences", "Soil carbon enhancing practices", "Land management", "Earth and Environmental Sciences", "Africa", "land management", "agricultural economics", "Decision and Policy Analysis - DAPA", "Ethiopia", "Natural resources", "natural resources", "Low soil fertility"], "contacts": [{"organization": "Ng\u2019ang\u2019a, Stanley Karanja, Gelaw, Fekadu, Nguru, Wilson Maina, Magambo Kanyenji, George, Girvetz, Evan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/QTACSN"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/QTACSN", "name": "item", "description": "10.7910/DVN/QTACSN", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/QTACSN"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "3011842370", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:22:42Z", "type": "Journal Article", "created": "2020-03-13", "title": "The cost of mitigating greenhouse gas emissions in farms in Central Andes of Ecuador", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Aim of study: Reduction of the greenhouse gas (GHG) emissions derived from food production is imperative to meet climate change mitigation targets. Sustainable mitigation strategies also combine improvements in soil fertility and structure, nutrient recycling, and the use more efficient use of water. Many of these strategies are based on agricultural know-how, with proven benefits for farmers and the environment. This paper considers measures that could contribute to emissions reduction in subsistence farming systems and evaluation of management alternatives in the Central Andes of Ecuador. We focused on potato and milk production because they represent two primary employment and income sources in the region\u2019s rural areas and are staple foods in Latin America.Area of study: Central Andes of Ecuador: Carchi, Chimborazo, Ca\u00f1ar provincesMaterial and methods: Our approach to explore the cost and the effectiveness of mitigation measures combines optimisation models with participatory methods.Main results: Results show the difference of mitigation costs between regions which should be taken into account when designing of any potential support given to farmers. They also show that there is a big mitigation potential from applying the studied measures which also lead to increased soil fertility and soil structure improvements due to the increased soil organic carbon.Research highlights: This study shows that marginal abatement cost curves derived for different agro-climatic regions are helpful tools for the development of realistic regional mitigation options for the agricultural sector.</p></article>", "keywords": ["Agricultural economics", "2. Zero hunger", "S", "Marginal abatement cost curves; cost-effectiveness; mitigation; climate change", "1. No poverty", "Agriculture", "15. Life on land", "01 natural sciences", "7. Clean energy", "12. Responsible consumption", "mitigation", "Marginal abatement cost curves", "climate change", "13. Climate action", "11. Sustainability", "marginal abatement cost curves", "cost-effectiveness", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/3011842370"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Spanish%20Journal%20of%20Agricultural%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3011842370", "name": "item", "description": "3011842370", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3011842370"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-04-22T00:00:00Z"}}, {"id": "3111070593", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:22:46Z", "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 &gt; western region &gt; northeast region &gt; 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/3111070593"}, {"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": "3111070593", "name": "item", "description": "3111070593", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3111070593"}, {"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": "545539c8-6498-43d5-9713-85d3722c58ee", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[5.81, 47.26], [5.81, 54.76], [15.77, 54.76], [15.77, 47.26], [5.81, 47.26]]]}, "properties": {"themes": [{"concepts": [{"id": "farming"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "agroforestry systems"}, {"id": "agropastoral systems"}, {"id": "intercropping"}, {"id": "mixed cropping"}, {"id": "sustainable agriculture"}, {"id": "sustainable land management"}, {"id": "sustainable land use"}, {"id": "geographical information systems"}, {"id": "land-use mapping"}, {"id": "land suitability"}, {"id": "biodiversity"}, {"id": "climate change"}, {"id": "land degradation"}, {"id": "land restoration"}, {"id": "land-use planning"}, {"id": "land-use change"}, {"id": "farmers"}, {"id": "land policies"}, {"id": "land governance"}, {"id": "farmland"}, {"id": "livestock"}], "scheme": "AGROVOC Multilingual agricultural thesaurus"}, {"concepts": [{"id": "opendata"}, {"id": "agroforestry"}, {"id": "agricultural management"}, {"id": "decision-support system"}, {"id": "agricultural policy"}, {"id": "agricultural ecology"}, {"id": "land use planning"}, {"id": "agricultural economics"}, {"id": "environmental impact of agriculture"}, {"id": "agricultural landscape"}, {"id": "mixed farming"}], "scheme": "Individual"}, {"concepts": [{"id": "Bodenbedeckung"}, {"id": "Bodennutzung"}], "scheme": "GEMET - INSPIRE themes, version 1.0"}, {"concepts": [{"id": "non-geographic"}], "scheme": "individual"}], "rights": "Restrictions applied to assure the protection of privacy or intellectual property, and any special restrictions or limitations or warnings on using the resource or metadata. Reports, articles, papers, scientific and non - scientific works of any form, including tables, maps, or any other kind of output, in printed or electronic form, based in whole or in part on the data supplied, must contain an acknowledgement of the form: \"Data reused from the BonaRes Data Centre www.bonares.de. This data were created as part of the ZALF Datenerfassung's research activities.\" Although every care has been taken in preparing and testing the data, the ZALF Datenerfassung and the BonaRes Data Centre cannot guarantee that the data are correct; neither does the ZALF Datenerfassung and the BonaRes Data Centre accept any liability whatsoever for any error, missing data or omission in the data, or for any loss or damage arising from its use. The ZALF Datenerfassung and BonaRes Data Centre will not be responsible for any direct or indirect use which might be made of the data.", "updated": "2025-10-17", "type": "Dataset", "created": "2025-09-29", "language": "eng", "title": "Systematic map of models and decision support tools used in agroforestry", "description": "The dataset summarizes information on models and decision support tools used in agroforestry based on systematic mapping of academic publications complemented by screening the web-sites of international agroforestry organizations. Publications were retrieved on 19.04.2024 from Scopus and Web of Science, and 859 records from 658 sources were included into the dataset. Records include model name and description, specification of source data and modelled diversification (spatial, temporal and system components), and the list of modelled agroecosystem functions and design attributes. The description of systematic literature mapping protocol is given in Ardanov et al. 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