| packages |
[{'author': None, 'author_email': None, 'creator_user_id': '64e47517-dce0-4a13-9ab7-ef1cfcd1d892', 'doi': '10.15493/SARVA.BEEH.10000186', 'featured': 'false', 'id': 'c4572749-eea1-477c-9288-cd523ceec03a', 'isopen': False, 'license_id': None, 'license_title': None, 'lineage_statement': ' Criteria for climatically optimum growth areas for passion fruit were refined by Bower (2005). Six criteria were identified and using the 1` x 1` gridded information on temperature related parameters derived by Schulze and Maharaj (2004), the following criteria were mapped: Criterion 1: > 330 days per year with a minimum temperature exceeding 2 degree Celsius; Criterion 2: Fewer than 2 consecutive days per annum with minimum temperatures < -2 degree Celsius; Criterion 3: There should not be > 3 consecutive frost days per annum; Criterion 4: In total, there should be < 25 days per annum with frost; Criterion 5: Maximum temperatures should be < 28 degree Celsius on at least 25 days of each month; Criterion 6: Maximum temperatures should not exceed 35 degree Celsius on any two consecutive days per month. Mapped criteria were given either categorical or probabilistic weightings and when the six maps were superimposed, suitable and less suitable climatically optimum growth areas could be identified.', 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-10-17T10:04:49.448189', 'metadata_date': '2025-10-17T00:00', 'metadata_modified': '2025-10-17T10:07:55.203305', 'metadata_thumbnail': '', 'name': 'climatically_optimum_growth_areas-_passion_fruit_composite_of_all_criteria', 'notes': 'Passion fruit are believed to have originated from southern Brazil (Kenyaweb, 2005).The most commonly commercially grown of 55 edible species of passion fruit is Passiflora edulis, which consists of perennial woody vines with each producing about 100 fruits per year (RBGK, 2005). In South Africa passion fruit ideally requires a cool subtropical climate for optimum production (Smith, 1998). The vines prefer moderate temperatures throughout the year, with monthly means of daily maxima < 29 degree Celsius. The plants are sensitive to severe frosts, and monthly means of minimum temperatures should be > 6 degree Celsius (NDA, 2005). For commercial production under rainfed conditions MAP should ideally exceed 1 200 mm and be well distributed throughout the year (NDA, 2005), as it is important to maintain the soil moist throughout the growing season to keep the vines flowering and fruiting for longer periods and prevent the fruit from shriveling and falling prematurely (Kenyaweb, 2005). Total commercial production of passion fruit in South Africa is 1 300t per season, with a range (between 1998/9 - 2003/4) in more recent from 900 - 1 700 t (NDA, 2005).The gross value is R65 million/year (NDA, 2005). Criteria for climatically optimum growth areas for passion fruit were refined by Bower (2005). Six criteria were identified and using the 1` x 1` gridded information on temperature related parameters derived by Schulze and Maharaj (2004), the following criteria were mapped: Criterion 1: > 330 days per year with a minimum temperature exceeding 2 degree Celsius; Criterion 2: Fewer than 2 consecutive days per annum with minimum temperatures < -2 degree Celsius; Criterion 3: There should not be > 3 consecutive frost days per annum; Criterion 4: In total, there should be < 25 days per annum with frost; Criterion 5: Maximum temperatures should be < 28 degree Celsius on at least 25 days of each month; Criterion 6: Maximum temperatures should not exceed 35 degree Celsius on any two consecutive days per month. Mapped criteria were given either categorical or probabilistic weightings and when the six maps were superimposed, suitable and less suitable climatically optimum growth areas could be identified. ', 'num_resources': 1, 'num_tags': 10, '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": [[[16.458333, -34.841667], [32.908333, -34.841667], [32.908333, -22.141667], [16.458333, -22.141667], [16.458333, -34.841667]]]}', 'state': 'active', 'title': 'Climatically Optimum Growth Areas (Passion Fruit) Composite Of All Criteria', 'type': 'dataset', 'url': None, 'version': None, 'contact': [{'electronic_mail_address': 'curation@saeon.nrf.ac.za', 'individual_name': 'SAEON', 'position_name': '', 'role': 'point_of_contact'}], 'contact_information': [{'facsimile': '', 'voice': ''}], 'dataset_reference_date': [{'reference': '2007-01-01T00:00', '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/SARVA.BEEH.10000186', 'name': 'Climatically Optimum Growth Areas (Passion Fruit) Composite Of All Criteria'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2025-10-17T10:05:36.992639', 'description': '', 'format': '', 'format_version': '', 'hash': '', 'id': '9d64a80d-8520-4035-b2c0-64b3a5308189', 'last_modified': None, 'metadata_modified': '2025-10-17T10:05:36.987063', 'mimetype': None, 'mimetype_inner': None, 'name': 'Climatically Optimum Growth Areas (Passion Fruit) Composite Of All Criteria', 'package_id': 'c4572749-eea1-477c-9288-cd523ceec03a', 'position': 0, 'resource_type': 'other', 'size': None, 'state': 'active', 'upload_mode': 'single', 'url': 'https://repository.saeon.ac.za/index.php/s/KDwnyqAwyrwkwrM?opendetails=', 'url_type': None}], 'responsible_party': [{'electronic_mail_address': '', 'individual_name': 'R.E. 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This cool climate species grows optimally at MATs ranging from 14 - 17 degree Celsius and MAPs > 725 mm (Kunz, 2004).', 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-10-17T09:56:44.152758', 'metadata_date': '2025-10-17T00:00', 'metadata_modified': '2025-10-17T09:59:18.993025', 'metadata_thumbnail': '', 'name': 'climatically_suitable_growth_areas_for_eucalyptus_badjensis', 'notes': 'Eucalyptus badjensis, or the commonly named Badger Gum, is a medium to fast growing tall and low maintenance tree which can withstand low to moderate soil moisture (Australia plants, 2004). This cool climate species grows optimally at MATs ranging from 14 - 17 degree Celsius and MAPs > 725 mm (Kunz, 2004).', '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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It is a cool climate species, growing optimally at MATs between 14 and 18 degree Celsius. E. benthamii is less tolerant of low MAPs than E. badjensis, with > 850 mm MAP required for optimal growth (Kunz, 2004).This species is susceptible to snow damage (Kunz, 2004).', 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-10-17T09:11:46.432943', 'metadata_date': '2025-10-17T00:00', 'metadata_modified': '2025-10-17T09:40:33.467393', 'metadata_thumbnail': '', 'name': 'climatically_suitable_growth_areas_for_eucalyptus_benthamii', 'notes': 'Commonly named the Camden White Oak, Eucalyptus benthamii, is a tall, smooth-barked. It is a cool climate species, growing optimally at MATs between 14 and 18 degree Celsius. E. benthamii is less tolerant of low MAPs than E. badjensis, with > 850 mm MAP required for optimal growth (Kunz, 2004).This species is susceptible to snow damage (Kunz, 2004).', 'num_resources': 1, 'num_tags': 10, '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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Refer to the detailed BioEnergy Atlas report in this regard. * This data was assigned to planning polygons (meso-zones) and the basis of calculation of exploitable biomass adjusted for a 20-year eradication programme (i.e. harvest 1/20th each year, supplemented by the annual increment of the remaining biomass).', 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-10-16T14:18:41.544367', 'metadata_date': '2025-10-16T00:00', 'metadata_modified': '2025-10-17T09:39:40.740321', 'metadata_thumbnail': '', 'name': 'availability_of_exploitable_invasive_alien_plants', 'notes': 'Data was derived from the following sources: CSIR based their assessment of standing IAP biomass on work done by the ARC, supplemented by an evaluation of species that may be exploitable, typical mass of such species, and the relative ease by which these can be exploited. Refer to the detailed BioEnergy Atlas report in this regard. This data was assigned to planning polygons (meso-zones) and the basis of calculation of exploitable biomass adjusted for a 20-year eradication programme (i.e. harvest 1/20th each year, supplemented by the annual increment of the remaining biomass).', 'num_resources': 1, 'num_tags': 7, '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 Exploitable Invasive Alien Plants', '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.10000080', 'name': 'Availability of Exploitable Invasive Alien Plants'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2025-10-16T14:19:52.976646', 'description': '', 'format': 'GEOMAP', 'format_version': '', 'hash': '', 'id': '985a2370-8695-4a84-9428-5b46ddbaac86', 'last_modified': None, 'metadata_modified': '2025-10-16T14:19:52.966248', 'mimetype': None, 'mimetype_inner': None, 'name': 'Availability of Exploitable Invasive Alien Plants', 'package_id': '964d2862-b467-40db-bb50-aaaa88880934', 'position': 0, 'resource_type': 'other', 'size': None, 'state': 'active', 'upload_mode': 'single', 'url': 'https://catalogue.saeon.ac.za/records/10.15493/BEA.DATA.10000080', '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': 'Bioenergy', 'id': 'ca34cd72-b5a2-4f3f-81d8-61b65cd42ef8', 'name': 'Bioenergy', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Environment', 'id': 'ad6bb22c-77f7-4d69-81bb-a19bc4bceb95', 'name': 'Environment', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'biodiversity indices', 'id': 'c8ae6871-1f36-4892-8b69-60c34802240b', 'name': 'biodiversity indices', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'ecosystem services', 'id': '201e2748-5553-4e2e-990c-31ce75892d7f', 'name': 'ecosystem services', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'energy', 'id': '1722381a-3be4-46ea-9bcd-9d89526c69f3', 'name': 'energy', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'invasive alien plants', 'id': 'ae210958-a72f-4eed-b116-a4a41c90185a', 'name': 'invasive alien plants', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'surface water', 'id': '29359c57-f6fe-440c-81c0-f128049713fd', 'name': 'surface 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/SAEON.FYNBOS.10000001', 'featured': 'false', 'id': 'e66d4a36-8db1-4f93-9236-05523d2ad639', 'isopen': False, 'license_id': None, 'license_title': None, 'lineage_statement': 'A climate-aided Bayesian kriging approach is used to interpolate 20 years of daily meteorological observations (maximum and minimum temperature and precipitation) to a 1 arc-minute grid for the Cape Floristic Region of South Africa. Independent validation data revealed overall predictive performance of the interpolation to have R2 values of 0.90, 0.85, and 0.59 for maximum temperature, minimum temperature, and precipitation, respectively. A suite of ecologically-relevant climate metrics that include the uncertainty introduced by the interpolation were then generated. By providing the high resolution climate metric surfaces and uncertainties, this work facilitates richer and more robust predictive modeling in ecology and bio- geography. These data can be incorporated into ecological models to propagate the uncertainties through to the final predictions', 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-10-16T11:32:14.831108', 'metadata_date': '2016-01-01T00:00', 'metadata_modified': '2025-10-17T09:38:59.712167', 'metadata_thumbnail': '', 'name': 'interpolated_observed_weather_data_for_the_cape_floristic_region', 'notes': 'Conservation of biodiversity demands comprehension of evolutionary and ecological patterns and processes that occur over vast spatial and temporal scales. A central goal of ecology is to understand the climatic factors that control ecological processes and this has become even more important in the face of climate change. Especially at global scales, there can be enormous uncertainty in underlying environmental data used to explain ecological processes, but that uncertainty is rarely quantified or incorporated into ecological models. In this study a climate-aided Bayesian kriging approach is used to interpolate 20 years of daily meteorological observations (maximum and minimum temperature and precipitation) to a 1 arc-minute grid for the Cape Floristic Region of South Africa. Independent validation data revealed overall predictive performance of the interpolation to have R2 values of 0.90, 0.85, and 0.59 for maximum temperature, minimum temperature, and precipitation, respectively. A suite of ecologically-relevant climate metrics that include the uncertainty introduced by the interpolation were then generated. By providing the high resolution climate metric surfaces and uncertainties, this work facilitates richer and more robust predictive modeling in ecology and bio- geography. These data can be incorporated into ecological models to propagate the uncertainties through to the final predictions.', 'num_resources': 1, 'num_tags': 6, '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": [[[17.8, -34.9], [25.8, -34.9], [25.8, -30.9], [17.8, -30.9], [17.8, -34.9]]]}', 'state': 'active', 'title': 'Interpolated observed weather data for the Cape Floristic Region', 'type': 'dataset', 'url': None, 'version': None, 'contact': [{'electronic_mail_address': 'adamw@buffalo.edu', 'individual_name': 'Adam Wilson', 'position_name': '', 'role': 'point_of_contact'}], 'contact_information': [{'facsimile': '', 'voice': ''}], 'dataset_reference_date': [{'reference': '2016-01-01T00:00', 'reference_date_type': 2}], '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/SAEON.FYNBOS.10000001', 'name': 'Interpolated observed weather data for the Cape Floristic Region'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2025-10-16T11:36:41.735418', 'description': '', 'format': 'GEOMAP', 'format_version': '', 'hash': '', 'id': '7f7a6c95-c24a-417f-be2c-225aaed7e3c3', 'last_modified': None, 'metadata_modified': '2025-10-16T11:36:41.725285', 'mimetype': None, 'mimetype_inner': None, 'name': 'Interpolated observed weather data for the Cape Floristic Region', 'package_id': 'e66d4a36-8db1-4f93-9236-05523d2ad639', 'position': 0, 'resource_type': 'other', 'size': None, 'state': 'active', 'upload_mode': 'single', 'url': 'https://catalogue.saeon.ac.za/records/10.15493/SAEON.FYNBOS.10000001', 'url_type': None}], 'responsible_party': [{'electronic_mail_address': 'curation@saeon.nrf.ac.za', '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': 'Climate science', 'id': 'b63237e7-b9c9-4d92-9a20-13bc7a956a76', 'name': 'Climate science', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Climatology-Meteorology-Atmosphere', 'id': '48ad3286-3db6-4060-946b-92eaf1fc126d', 'name': 'Climatology-Meteorology-Atmosphere', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'atmosphere dynamics', 'id': 'd80be9c9-a5ca-418e-9e5b-a3a6dd7a3b44', 'name': 'atmosphere dynamics', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'climatology', 'id': '68f2471a-c336-4fc9-afd2-d252d6c41b7c', 'name': 'climatology', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'ecology', 'id': 'd8a5a11d-c929-4410-a2b3-65aa6b9d8263', 'name': 'ecology', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'environmental science', 'id': '30cb3d17-47d2-409e-9026-2c95191db59e', 'name': 'environmental science', 'state': 'active', 'vocabulary_id': None}], 'topic_and_saeoss_themes': [{'iso_topic_category': 'climatologyMeteorologyAtmosphere'}], '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.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'}], '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.10000074', 'name': 'Availability of Sugar Cane Field Residues'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2025-09-25T13:52:53.602259', 'description': '', 'format': '', 'format_version': '', 'hash': '', 'id': '736c3288-9873-40c9-b235-d3694cd201f3', 'last_modified': None, 'metadata_modified': '2025-09-25T13:52:53.567175', 'mimetype': None, 'mimetype_inner': None, 'name': 'Availability of Sugar Cane Field Residues', 'package_id': '653bf206-60b4-426f-8031-857ed4ffffeb', 'position': 0, 'resource_type': 'other', 'size': None, 'state': 'active', 'upload_mode': 'single', 'url': 'https://repository.saeon.ac.za/index.php/s/aa5pAfNjzabbcjF', 'url_type': 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', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Farming', 'id': '0a7dc286-a500-49de-9647-3bbb4e46a0f5', 'name': 'Farming', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'agriculture', 'id': 'e9c2ba74-5eec-405f-bf77-5b0483eae3bf', 'name': 'agriculture', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'bagasse', 'id': '5d397292-d46b-4ffe-9868-986b0a4737e1', 'name': 'bagasse', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'biomass', 'id': '3599fc01-138e-449a-8618-4697ace9ddfe', 'name': 'biomass', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'fibre', 'id': 'b1727c3e-a49f-4b2e-b20f-3c339941f158', 'name': 'fibre', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'field residue', 'id': 'e3812ca4-f53d-4e3c-991b-93e27a697c52', 'name': 'field residue', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'ligno-cellulose', 'id': '1044ebdf-3fbd-400a-840a-691c46a9d0f6', 'name': 'ligno-cellulose', 'state': '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, 'lineage_statement': '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.', '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. 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There are 0 amendments added; 24 new applications captured and 0 corrections made, see ANNEXURE A with details of records.', 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-07-29T13:12:22.439409', 'metadata_date': '2025-07-29T00:00', 'metadata_modified': '2025-07-29T16:15:55.992836', 'metadata_thumbnail': '', 'name': 'south_african_renewable_energy_eia_application_database', 'notes': 'The South African Renewable Energy EIA Application Database contains spatial data for renewable energy applications for environmental authorisation. It includes spatial and attribute information for both active (in process and with valid authorisations) and non-active (lapsed or replaced by amendments) applications. Data is captured and managed on a parcels level as well as aggregated to the project level. 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