| packages |
[{'author': None, 'author_email': None, 'creator_user_id': '64e47517-dce0-4a13-9ab7-ef1cfcd1d892', 'doi': '10.15493/SARVA.BEEH.10000104', 'featured': 'false', 'id': '84eba355-8f57-484b-b365-b008e6769b37', 'isopen': False, 'license_id': None, 'license_title': None, 'lineage_statement': 'Litchis, Litchi chinensis, are indigenous to subtropical southern China and were first imported to South Africa from Mauritius in the 1870s (ARC, 2005). Of the 7 000 tons of litchis produced annually in South Africa, with a gross value exceeding R25 million, about 60 percent come from Mpumalanga, 38 percent from Limpopo and 2 percent from KwaZulu-Natal (Statistics SA, 2002; NDA, 2005).', 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-10-17T10:52:41.204356', 'metadata_date': '2025-10-17T00:00', 'metadata_modified': '2025-10-17T11:03:29.674696', 'metadata_thumbnail': '', 'name': 'climatically_optimum_growth_areas_litchis_composite_of_all_criteria', 'notes': 'Litchis, Litchi chinensis, are indigenous to subtropical southern China and were first imported to South Africa from Mauritius in the 1870s (ARC, 2005). Of the 7 000 tons of litchis produced annually in South Africa, with a gross value exceeding R25 million, about 60 percent come from Mpumalanga, 38 percent from Limpopo and 2 percent from KwaZulu-Natal (Statistics SA, 2002; NDA, 2005).', 'num_resources': 1, 'num_tags': 9, '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 Litchis 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'}], 'metadata_standard': [{'name': 'SANS 1878-1:2011', 'version': '1.1'}], 'online_resource': [{'application_profile': '', 'description': 2, 'linkage': 'https://catalogue.saeon.ac.za/records/10.15493/SARVA.BEEH.10000104', 'name': 'Climatically Optimum Growth Areas Litchis Composite Of All Criteria'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2025-10-17T10:57:28.000498', 'description': '', 'format': '', 'format_version': '', 'hash': '', 'id': '0d6d7010-b440-48b4-b1f1-23f9e48ae3e9', 'last_modified': None, 'metadata_modified': '2025-10-17T10:57:27.994148', 'mimetype': None, 'mimetype_inner': None, 'name': 'Climatically Optimum Growth Areas Litchis Composite Of All Criteria', 'package_id': '84eba355-8f57-484b-b365-b008e6769b37', 'position': 0, 'resource_type': 'other', 'size': None, 'state': 'active', 'upload_mode': 'single', 'url': 'https://repository.saeon.ac.za/index.php/s/3E4cCsAMaj92xjw?opendetails=', 'url_type': None}], 'responsible_party': [{'electronic_mail_address': '', 'individual_name': 'R.E. 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': 'Biophysical variables', 'id': 'f1a1894f-6ab7-4e6c-8091-05cbab5811a1', 'name': 'Biophysical variables', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Commercial cropping system', 'id': 'be02ede8-4f81-48f2-a871-0dd690bc3d8d', 'name': 'Commercial cropping system', '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': 'Optimum growth area', 'id': '246aee2a-e770-40dc-8bcd-223b347a9187', 'name': 'Optimum growth area', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'agriculture', 'id': 'e9c2ba74-5eec-405f-bf77-5b0483eae3bf', 'name': 'agriculture', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'growth areas', 'id': '270aa94b-1ca2-41e2-a735-944a362034d0', 'name': 'growth areas', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'litchis', 'id': 'd3708a75-1f7c-44e4-a373-5b1fe3e8fc43', 'name': 'litchis', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'maximum temperature', 'id': '0d49853e-a68d-4627-a644-58f7c8ad8e96', 'name': 'maximum temperature', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'relative humidity', 'id': '716d7246-9aa8-458a-ae7e-a0a174c2dbb8', 'name': 'relative humidity', '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/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. ', '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 Suitable Growth Areas for Eucalyptus badjensis', '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.10000034', 'name': 'Climatically Suitable Growth Areas for Eucalyptus badjensis'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2025-10-17T09:57:25.829177', 'description': '', 'format': '', 'format_version': '', 'hash': '', 'id': '5d641cd0-6b18-47dc-9510-a564d9b939ad', 'last_modified': None, 'metadata_modified': '2025-10-17T09:57:25.820623', 'mimetype': None, 'mimetype_inner': None, 'name': 'Climatically Suitable Growth Areas for Eucalyptus badjensis', 'package_id': '9eacac9c-41d1-4a96-8f89-899790ceddcc', 'position': 0, 'resource_type': 'other', 'size': None, 'state': 'active', 'upload_mode': 'single', 'url': 'https://repository.saeon.ac.za/index.php/s/sD5Bteyo7ZaJ8Nz?opendetails=', 'url_type': None}], 'responsible_party': [{'electronic_mail_address': '', 'individual_name': 'R.E. 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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': 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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': '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': 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'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', '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. ', '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. 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