| packages |
[{'author': None, 'author_email': None, 'creator_user_id': '64e47517-dce0-4a13-9ab7-ef1cfcd1d892', 'doi': '10.15493/BEA.DATA.10000074', 'featured': 'false', 'id': '653bf206-60b4-426f-8031-857ed4ffffeb', 'isopen': False, 'license_id': None, 'license_title': None, 'lineage_statement': '"Data was derived from the following sources: *The extent of sugar cane cultivation was determined from digitisation of maps published in Gers (2003). * This extent was correlated with two additional sources: crop extent published by DAFF (2014), which excludes sugar cane production and could be used as a verification of extent, and Land Use data (FAO, 2011), which could be used to verify the finer extent of cultivation. * Sugar cane theoretical yields were applied from Schulze, Hull, and Maharaj (2007).', 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-09-25T13:51:51.505431', 'metadata_date': '2015-01-01T00:00', 'metadata_modified': '2025-10-17T09:37:57.386121', 'metadata_thumbnail': '', 'name': 'availability_of_sugar_cane_field_residues', 'notes': 'Data was derived from the following sources: *The extent of sugar cane cultivation was determined from digitisation of maps published in Gers (2003). * This extent was correlated with two additional sources: crop extent published by DAFF (2014), which excludes sugar cane production and could be used as a verification of extent, and Land Use data (FAO, 2011), which could be used to verify the finer extent of cultivation. * Sugar cane theoretical yields were applied from Schulze, Hull, and Maharaj (2007). * The total area and sugar cane production obtained from the above was correlated with published production figures (DAFF, 2014) and good correlation obtained - calculated sugar cane production of 22.5 Mt/a vs. published production in 2013/14 of 21.3 Mt/a. * Ratios of bagasse, sugar, and residue production was calculated from ratios in Hugo (2014) .', 'num_resources': 1, 'num_tags': 14, 'organization': {'id': 'c4a18544-cded-4488-9f43-c02a1094ad39', 'name': 'saeon', 'title': 'NRF-SAEON', 'type': 'organization', 'description': 'The South African Environmental Observation Network (SAEON) is a long-term environmental observation and research facility of the National Research Foundation (NRF). SAEON’s three focus areas are environmental observation, data management and education outreach. The Department of Science, Technology and Innovation (DSTI) provides core funding for these activities. ', 'image_url': '2025-08-01-083136.289314LogoSAEON.png', 'created': '2025-05-26T07:57:06.135422', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'c4a18544-cded-4488-9f43-c02a1094ad39', 'private': False, 'reference_system_additional_info': '', 'spatial': '{"type": "Polygon", "coordinates": [[[16.45214, -34.8341698957], [32.8925317467, -34.8341698957], [32.8925317467, -22.12503], [16.45214, -22.12503], [16.45214, -34.8341698957]]]}', 'state': 'active', 'title': 'Availability of Sugar Cane Field Residues', 'type': 'dataset', 'url': None, 'version': None, 'contact': [{'electronic_mail_address': 'curation@saeon.nrf.ac.za', 'individual_name': 'Wim Hugo', 'position_name': '', 'role': 'point_of_contact'}], 'contact_information': [{'facsimile': '', 'voice': ''}], 'dataset_reference_date': [{'reference': '2015-01-01T00:00', 'reference_date_type': 1}], 'distribution_format': [{'name': 'Electronic metadata record', 'version': '1.1'}], 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None}], 'responsible_party': [{'electronic_mail_address': 'curation@saeon.nrf.ac.za', 'individual_name': 'South African Environmental Observation Network', 'position_name': 'Organisation', 'role': 'publisher', 'website': 'https://catalogue.saeon.ac.za/'}], 'responsible_party_contact_address': [{'administrative_area': '', 'city': 'Pretoria', 'delivery_point': '', 'postal_code': ''}], 'responsible_party_contact_info': [{'facsimile': '', 'voice': ''}], 'spatial_parameters': [{'equivalent_scale': 'N/A', 'spatial_reference_system': 'EPSG: 4326', 'spatial_representation_type': '001'}], 'tags': [{'display_name': 'Bioenergy', 'id': 'ca34cd72-b5a2-4f3f-81d8-61b65cd42ef8', 'name': 'Bioenergy', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Commercial crop system', 'id': '7212f987-9a76-4d73-bf13-da41d2c5a0b1', 'name': 'Commercial crop system', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Energy', 'id': '2e773657-95fa-4b64-9488-bb790e61bcbf', 'name': 'Energy', 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'active', 'vocabulary_id': None}, {'display_name': 'potential', 'id': '72c85e81-8819-41f9-9327-fb5418e73e31', 'name': 'potential', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'residue', 'id': '00467143-d7fa-48c1-b70f-20667c4b9fbe', 'name': 'residue', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'straw', 'id': '6fb6009f-3480-4660-a3f9-eb0e3724045a', 'name': 'straw', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'sugar cane', 'id': '8183afee-04eb-4a1b-94be-0d9cbb8a6d2c', 'name': 'sugar cane', 'state': 'active', 'vocabulary_id': None}], 'topic_and_saeoss_themes': [{'iso_topic_category': 'farming'}], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': None, 'author_email': None, 'creator_user_id': '64e47517-dce0-4a13-9ab7-ef1cfcd1d892', 'doi': '10.15493/BEA.DATA.10000082', 'featured': 'false', 'id': '40c74018-b618-40d0-bdfb-21db03dc6f41', 'isopen': False, 'license_id': None, 'license_title': None, 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This model was used, based on StatsSA Census Data, to estimate the organic component produced by each household per planning zone (mesozone) annually. * These factors were used to disaggregate provincial production data, resulting in a value for unserviced and serviced organic wastewater from household sources to be calculated for each mesozone.', 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-10-17T07:12:59.317624', 'metadata_date': '2025-10-17T00:00', 'metadata_modified': '2025-10-17T09:37:29.037486', 'metadata_thumbnail': '', 'name': 'serviced_and_unserviced_waste_water_organic_component', 'notes': 'Data was derived from the following sources: * CSIR was commissioned by the BioEnergy Atlas to assemble known data on solid waste production from household and commercial sources in South Africa. This data was only available at provincial aggregate level, and derives from statistics published by the Department of Water and Sanitation, or recent studies funded by them. * Data from StatsSA (Census 2011) enabled the calculation of number of households within each planning zone that were serviced at the time, with the balance unserviced. * SAEON developed a model from national and international statistics linking waste water production and composition to household income. This model was used, based on StatsSA Census Data, to estimate the organic component produced by each household per planning zone (mesozone) annually. * These factors were used to disaggregate provincial production data, resulting in a value for unserviced and serviced organic wastewater from household sources to be calculated for each mesozone.', 'num_resources': 1, 'num_tags': 8, 'organization': {'id': 'c4a18544-cded-4488-9f43-c02a1094ad39', 'name': 'saeon', 'title': 'NRF-SAEON', 'type': 'organization', 'description': 'The South African Environmental Observation Network (SAEON) is a long-term environmental observation and research facility of the National Research Foundation (NRF). SAEON’s three focus areas are environmental observation, data management and education outreach. The Department of Science, Technology and Innovation (DSTI) provides core funding for these activities. ', 'image_url': '2025-08-01-083136.289314LogoSAEON.png', 'created': '2025-05-26T07:57:06.135422', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'c4a18544-cded-4488-9f43-c02a1094ad39', 'private': False, 'reference_system_additional_info': '', 'spatial': '{"type": "Polygon", "coordinates": [[[16.45214, -34.8341698957], [32.8925317467, -34.8341698957], [32.8925317467, -22.12503], [16.45214, -22.12503], [16.45214, -34.8341698957]]]}', 'state': 'active', 'title': 'Serviced and Unserviced Waste Water - Organic Component', 'type': 'dataset', 'url': None, 'version': None, 'contact': [{'electronic_mail_address': 'curation@saeon.nrf.ac.za', 'individual_name': 'Wim Hugo', 'position_name': '', 'role': 'point_of_contact'}], 'contact_information': [{'facsimile': '', 'voice': ''}], 'dataset_reference_date': [{'reference': '2015-01-01T00:00', 'reference_date_type': 1}], 'distribution_format': [{'name': 'Electronic metadata record', 'version': '1.1'}], 'metadata_language_and_character_set': [{'dataset_character_set': 'utf-8', 'dataset_language': 'English', 'metadata_character_set': 'utf-8', 'metadata_language': 'English'}], 'online_resource': [{'application_profile': '', 'description': 2, 'linkage': 'https://catalogue.saeon.ac.za/records/10.15493/BEA.DATA.10000082', 'name': 'Serviced and Unserviced Waste Water - Organic Component'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2025-10-17T07:15:03.812737', 'description': '', 'format': 'GEOMAP', 'format_version': '', 'hash': '', 'id': 'b47f179f-b705-4c67-9f6f-654d5799dd09', 'last_modified': None, 'metadata_modified': '2025-10-17T07:15:03.803797', 'mimetype': None, 'mimetype_inner': None, 'name': 'Serviced and Unserviced Waste Water - Organic Component', 'package_id': '40c74018-b618-40d0-bdfb-21db03dc6f41', 'position': 0, 'resource_type': 'other', 'size': None, 'state': 'active', 'upload_mode': 'single', 'url': 'https://catalogue.saeon.ac.za/records/10.15493/BEA.DATA.10000082', 'url_type': None}], 'responsible_party': [{'electronic_mail_address': '', 'individual_name': 'South African Environmental Observation Network', 'position_name': 'Organisation', 'role': 'resource_provider', 'website': 'catalogue.saeon.ac.za'}], 'responsible_party_contact_address': [{'administrative_area': '', 'city': '', 'delivery_point': '', 'postal_code': ''}], 'responsible_party_contact_info': [{'facsimile': '', 'voice': ''}], 'spatial_parameters': [{'equivalent_scale': 'N/A', 'spatial_reference_system': 'EPSG: 4326', 'spatial_representation_type': '001'}], 'tags': [{'display_name': 'Environment', 'id': 'ad6bb22c-77f7-4d69-81bb-a19bc4bceb95', 'name': 'Environment', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Hazards and vulnerability', 'id': '5829ec05-94f5-4147-8521-d1c6a37510e3', 'name': 'Hazards and vulnerability', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Municipalities', 'id': 'cbad33a1-1960-4cf3-a4e6-1d65230faa9d', 'name': 'Municipalities', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Settlement', 'id': '76b43ead-57a0-4dd5-91ab-fcde5a799ad4', 'name': 'Settlement', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Socio-economic', 'id': '1ae6c2fa-2640-4c76-a1e7-a68f7a113248', 'name': 'Socio-economic', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'biomass', 'id': '3599fc01-138e-449a-8618-4697ace9ddfe', 'name': 'biomass', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'organic waste', 'id': '66132173-2f04-40e0-99d5-d5e601e67fda', 'name': 'organic waste', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'waste water', 'id': '562cc66c-eb30-4566-985f-7c07453dfd89', 'name': 'waste water', 'state': 'active', 'vocabulary_id': None}], 'topic_and_saeoss_themes': [{'iso_topic_category': 'environment'}], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': None, 'author_email': None, 'creator_user_id': '64e47517-dce0-4a13-9ab7-ef1cfcd1d892', 'doi': '10.15493/BEA.DATA.10000008', 'featured': 'false', 'id': '0894e55b-3e79-4b6e-b71d-5992df7efa44', 'isopen': False, 'license_id': None, 'license_title': None, 'lineage_statement': 'The dataset shows Acacia mearnsii - bark yield estimates allocated to mesozones. Yield estimates were derived from Schulze R.E. and Maharaj M. (2007) and then allocated to mesozones by combining with a base mesozone layer obtained from the CSIR Geospatial Analysis Platform (GAP). The map of climatically optimum growth areas shows the main potential areas for A. mearnsii to be in a strip along the coast of the Eastern Cape, parts of the midlands and northwest of KwaZulu-Natal and a strip in Mpumalanga, with smaller patches of climatically optimum areas elsewhere.', 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-10-17T09:32:50.896907', 'metadata_date': '2025-10-17T00:00', 'metadata_modified': '2025-10-17T09:35:51.349560', 'metadata_thumbnail': '', 'name': 'acacia_mearnsii_bark_yield_estimates_per_mesozone', 'notes': 'The dataset shows Acacia mearnsii - bark yield estimates allocated to mesozones. Yield estimates were derived from Schulze R.E. and Maharaj M. (2007) and then allocated to mesozones by combining with a base mesozone layer obtained from the CSIR Geospatial Analysis Platform (GAP). The map of climatically optimum growth areas shows the main potential areas for A. mearnsii to be in a strip along the coast of the Eastern Cape, parts of the midlands and northwest of KwaZulu-Natal and a strip in Mpumalanga, with smaller patches of climatically optimum areas elsewhere.', 'num_resources': 1, 'num_tags': 11, 'organization': {'id': 'c4a18544-cded-4488-9f43-c02a1094ad39', 'name': 'saeon', 'title': 'NRF-SAEON', 'type': 'organization', 'description': 'The South African Environmental Observation Network (SAEON) is a long-term environmental observation and research facility of the National Research Foundation (NRF). SAEON’s three focus areas are environmental observation, data management and education outreach. The Department of Science, Technology and Innovation (DSTI) provides core funding for these activities. 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Schulze & M. Maharaj', 'position_name': 'University of KwaZulu-Natal', 'role': 'originator', 'website': ''}], 'responsible_party_contact_address': [{'administrative_area': '', 'city': '', 'delivery_point': '', 'postal_code': ''}], 'responsible_party_contact_info': [{'facsimile': '', 'voice': ''}], 'spatial_parameters': [{'equivalent_scale': 'N/A', 'spatial_reference_system': 'EPSG: 4326', 'spatial_representation_type': '001'}], 'tags': [{'display_name': 'Acacia mearnsii', 'id': '5c694886-9e84-460d-9f40-8e6b6d76c961', 'name': 'Acacia mearnsii', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Agriculture', 'id': '76bc5b3e-aa3e-4117-a94e-53d07473952b', 'name': 'Agriculture', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Commercial forestry', 'id': 'c9cdbbe0-563e-48b0-b276-8873fa70a327', 'name': 'Commercial forestry', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Forestry distribution', 'id': 'a373ef67-e2f7-4c4c-a9f1-42afafc3032f', 'name': 'Forestry distribution', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Imagery-Basemaps-Earth Cover', 'id': '8895be3b-53e6-43b4-b77b-571ea5ff5252', 'name': 'Imagery-Basemaps-Earth Cover', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'acacia mearnsii', 'id': '8a3f959b-ce05-41a8-b046-fbcf0950cbd9', 'name': 'acacia mearnsii', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'agriculture', 'id': 'e9c2ba74-5eec-405f-bf77-5b0483eae3bf', 'name': 'agriculture', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'biomass', 'id': '3599fc01-138e-449a-8618-4697ace9ddfe', 'name': 'biomass', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'mesozones', 'id': 'ec11507b-eb72-4e46-a0b4-04378e39be8c', 'name': 'mesozones', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'yield', 'id': 'dc14a921-0c4d-4efa-b154-52a15b712556', 'name': 'yield', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'yield estimation', 'id': 'ffb78bb7-ddac-4f32-b75f-0182de795c57', 'name': 'yield estimation', 'state': 'active', 'vocabulary_id': None}], 'topic_and_saeoss_themes': [{'iso_topic_category': 'imageryBaseMapsEarthCover'}], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': None, 'author_email': None, 'creator_user_id': '64e47517-dce0-4a13-9ab7-ef1cfcd1d892', 'doi': '10.15493/BEA.DATA.10000014', 'featured': 'false', 'id': 'c1c3fa77-dfe7-47d0-a780-b83e0b8f2315', 'isopen': False, 'license_id': None, 'license_title': None, 'lineage_statement': "The dataset shows climatically optimum growth areas and yield estimates of Pinus taeda allocated to mesozones. 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