cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.beeh.10000187
title: Positive Chill Units - May

description: Certain biennial plants which have a dormant (i.e. rest) season during winter may require a period of accumulated minimum temperatures below a threshold in order to stimulate growth, develop leaves, flower or set fruit. Deciduous trees, for example, need a certain period of winter chilling for completion of their seasonal dormancy. Insufficient winter chilling may result in delayed foliation, reduced fruit set (including fruits remaining small and being misshapen as they ripen) and reduced fruit quality (including reduced firmness, and/or greener fruit coloration). The required amount of chilling for completion of the rest period varies between species, cultivars and different locations. Many chill accumulation models have been formulated, most of them requiring hourly temperatures to have either been observed or estimated. The Linsley-Noakes PCU model was used to produce maps of chill units in this Atlas. The techniques by which PCUs were computed and then mapped are described in the shaded sections which follow.

Derivation of hourly values of temperature for the determination of PCUs hourly values of temperature were determined from the 50 year time series of daily maximum and minimum temperatures generated at each of the 429 700 raster points covering South Africa, using the techniques described by Schulze and Maharaj (2004). For each daytime and night-time cycle the sine-log equations derived by Linsley-Noakes et al. (1995) were employed. In these equations the estimation of hourly temperatures is based on the observation that the daytime solar cycle follows a sine curve from sunrise to sunset, whereas night-time cooling may be described by a logarithmic equation.
DOI: 10.15493/bea.data.10000086
title: FAO Cropland per Mesozone

description: Data on cropland was obtained from the global data set produced by the UN Food and Agriculture Organisation (FAO). Data set was obtained as a raster image, and clipped to the boundaries of South Africa, before being converted to a vector layer. The BioEnergy Atlas bases its analyses on mesozones (Planning zones of approximately 50 km2, with relatively homogeneous attributes). This data set aggregates FAO Cropland to mesozones for planning purposes. The FGGD land cover occurrence maps are global raster data layers with a resolution of 5 arc-minutes. Each pixel in each map contains a value representing the percentage of the area belonging to the land
DOI: 10.15493/dea.carbon.10000024
title: Savanna Above Ground Woody Biomass tDM /ha (1km)

description: Above Ground Woody Biomass (AGB woody) is the total dry biomass of woody plants above 0.5m height and is expressed in tonnes per hectare. The product was developed through the integration of 2010 ALOS PALSAR-1 synthetic aperture radar images, LiDAR tracks, ancillary digital elevation model (DEM) derived variables and field data of woody biomass. Commercial plantations of very high AGB, permanent water bodies and urban areas were masked out since it was not captured by the LiDAR tracks. The mapped product was clipped to the savannah biome (Mucina and Rutherford 2006) and the SAR AGB woody map was resampled to 1km pixel resolutions using a mean aggregation method.
DOI: 10.15493/bea.data.10000059
title: Planning Transmission Lines

description: Eskom provided the BioEnergy Atlas with a set of data to describe their current and planned infrastructure:
* High voltage transmission lines and substations;
* Medium voltage distribution lines and substations;
* Planned new transmission lines;
* Planned new substations.
All of these were aggregated to meso-zones (planning and analysis zones used for the Atlas) in the following way:
* Distribution and transmission lines: presence or absence of lines;
* Substations: count of medium voltage substations, and proximity (5km) to high voltage substations
DOI: 10.15493/bea.data.10000017
title: Groundnut Yield Estimates per mesozone

description: *Data shows groundnut 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). *Going by Smith's (1994; 1998) criteria, the climatically most suitable areas for dryland groundnut production are the north-eastern parts of the Eastern Cape, much of KwaZulu- Natal, and parts of Mpumalanga and Limpopo, with some areas having the potential to produce in excess of 3.5 t/ha. This is in stark contrast with the actual production areas of North West, the Northern Cape and the Free State. *Using Smith's (1994; 1998) climatic criteria, yields of groundnuts are estimated using the effective rainfall for October to March and heat units (base 10°C) for the same period, with modifications to yields made for soil properties and levels of management.
DOI: 10.15493/saeon.egagasini.10000067
title: The ASCA June 2018 raw CPIES data collected for station P-1, P-3, P-4, P-5 and P-5B

description: The Agulhas System Climate Array (ACSA) is a mooring array and is designed to provide long-term observations of the Agulhas Current`s volume, heat and salt transport and its variability from mesoscale (eddies), through seasonal to interannual timescales. The ASCA shelf and tall moorings extend 200 km offshore along decending TOPEX/Jason satellite ground track # 96, through the core of the Agulhas Current, with CPIES measurements extending the array to 300 km offshore of Port Elizabeth. This raw data was collected by CPIES (Current and Pressure Recording Inverted Echo Sounder) deployed at station P-1, P-3, P-4, P-5 and P-5B.
DOI: 10.15493/sarva.csag.10000271
title: Minimum of Maximum Temperature for Near Future (2046-2065)

description: Model Run: Near future (2046 - 2065) (Near future (2046 - 2065)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/bea.data.10000038
title: NUT-NOT: Groundnut Oil - Biodiesel Trans-esterification

description: * Technical Challenges - Technology is relatively simple and has high conversion efficiency. * Cost Challenges - Despite efficiency, levelised costs are high, due to mainly 2 factors (1) the input cost of raw material is high, and (2) operating costs are high due to feedstock (methanol) and distillation operations. Selling oilcake has a significant effect on final product cost, with a 50% oilcake internal subsidy reducing the costs by R 6,500/ t (0.65 R/kWh). This would bring production cost into line with current range of diesel prices. * Environmental Challenges - Greenhouse gas savings are significant provided land use changes are carbon neutral. Limiting cultivation to subsistence cropland should assist with this goal. * Social and Institutional Challenges - Conversion of subsistence farmers in former homeland areas, with high reliance on cattle and maize, to a cash crop with side products for own consumption and cattle feed will require significant community involvement. Cooperative farming and marketing channels need to be investigated.
DOI: 10.15493/sarva.csag.10000298
title: Consecutive Dry Days for Current Climate (1950-1999)

description: Model Run: Control - Current Climate (Observed/current climate as modelled by climate model). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/saeon.fynbos.11b0912
title: 11B manual weekly rainfall January 2009 - September 2012

description: Weekly rainfall data collected by a Snowdon gauge fitted with a Nipher shield. Gauge orifice parallel to the slope, with orifice at 1.2 meters above ground in the Bosboukloof catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.saws.10000199
title: Seasonal total rainfall (mm per 3-month season) change from the 90% percentile projected for 2036-2065, relative to present (1976 - 2005), for MAM season under the RCP 8.5 pathway

description: Seasonal (MAM) mean rainfall (mm per month) from the 90% percentile projected for 2036-2065, relative to present (1976 - 2005), under the RCP 8.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily rainfall averages, which are used to generate projections of seasonal change. The projections are generated using the high (RCP8.5) pathway which associates CO2 concentrations of approximately 950ppm by the year 2100. The associated RMSD it calculated and shows the uncertainty range of the projected model simulated residual values, and gives a relative perspective of spatial areas associated with higher and lower projection uncertainties.
DOI: 10.15493/sarva.csag.10000345
title: Rainfall days per month for Current Climate (1950-1999)

description: Model Run: Control - Current Climate (Observed/current climate as modelled by climate model). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.csag.10000161
title: Precipitation 90th percentile for Near Future (2046-2065)

description: Model Run: Near future (2046 - 2065) (Near future (2046 - 2065)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.gb.10000001
title: Sectoral Gross Value Added (GVA) for agriculture, forestry and fishing (SIC1) as a driver from 1995 to 2016

description: The South African Gross Geographic Value Added (GVA) data for each economic sector per year since 1995 to 2016 was supplied by Quantec to the CSIR built environment. This data was disaggregated to a 500m grid using a dasymetric mapping algorithm (https://gap.csir.co.za/gap/images/documents/developing-a-geo-data-frame-using-dasymetric-mapping-principles-to-facilitate-data-integration).  A linear regression was then fitted to the GVA dataset and the Slope of the regression was used as an indicator for exposure, the R2 value was converted to a percentage to provide a measure of probability. An Average GVA value was calculated as was the sample standard deviation as a measure of economic uncertainty.
DOI: 10.15493/saeon.egagasini.10000106
title: Western Indian Ocean Energy Sinks (WOES) 0.25: a CROCO numerical ocean model hindcast at 1/36 degree monthly climatologies for the south western Indian Ocean from 1993-2014 nested in a 1/12 degree WEOS run.

description: This realistic ocean simulation was run using the Coastal and Regional Ocean COmmunity model (CROCO), based on the Regional Ocean Modelling System (ROMS), which has 60 terrain-following vertical levels. This output (WOES 0.25) is the largest grid of a triply nested configuration: WOES I, WOES II and WOES III, with horizontal resolutions of ~22.5, 7.5 and 2.5 km respectively. Monthly ouputs of the 0.25 degree GLORYS ocean reanalysis is used to force the boundaries of WOES I. The surface forcing for this model is provided by a bulk formulation using daily ERA-Interim atmospheric reanalysis (with a resolution of ~80 km) and using a relative wind approach. The output is saved as daily averages, in monthly netcdf files spanning January 1993 - December 2014. WOES 0.25 spans 55.7degS to 3.18388 degS and 10degW to 102.25degE and covers most of the Southern Subtropical Indian Ocean and a part of the Southern Atlantic Ocean. Model output includes: averaged free-surface (zeta), averaged vertically integrated u-momentum component (ubar), averaged vertically integrated v-momentum component (vbar), averaged u-momentum component (u), averaged v-momentum component (v), averaged potential temperature (temp), averaged salinity (salt), averaged vertical momentum component (w). 

Numerical computations were performed on the IDRIS (Institut du Developpement et des Ressources en Informatique Scientifique) IBM "ADA" computer facility (under grant A0020107630)
DOI: 10.15493/sarva.saws.10000109
title: Seasonal total rainfall (mm per 3-month season) change from the 90% percentile projected for 2066-2095, relative to present (1976 - 2005), for JJA season under the RCP 4.5 pathway

description: Seasonal (JJA) rainfall (mm per month) change from the 90% percentile projected for 2066-2095, relative to present (1976 - 2005), under the RCP 4.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily rainfall averages, which are used to generate projections of seasonal change. The projections are generated using the medium to low (RCP4.5) pathway which associates CO2 concentrations of approximately 560ppm by the year 2100. The associated RMSD it calculated and shows the uncertainty range of the projected model simulated residual values, and gives a relative perspective of spatial areas associated with higher and lower projection uncertainties.
DOI: 10.15493/sarva.beeh.10000081
title: Heat Units (December)

description: The concept of the heat unit (or degree day), known since the mid-18th century already, revolves around the development of a plants or organism`s being dependent upon the accumulated heat to which it was subjected during its lifetime, or else during a certain developmental stage. This measure of accumulated heat is known as physiological time. In general it holds that the lower the temperature, the slower the rate of growth and development of plants and invertebrate animals and the amount of heat required to complete a given organism's development does not vary. Physiological time is usually expressed and approximated in units of degree days, also popularly called heat units, where these are an accumulation of mean temperatures above a certain lower threshold value and below an upper developmental threshold (above which growth is considered to remain static or even decline), over a period of time. The degree day concept is not without its limitations. These include that, a linear relationship is assumed between growth and temperature, threshold temperatures may change during the life cycle of a crop or Pest, temperatures exceeding the upper threshold may, in fact, have a detrimental/negative effect on development.

Derivation over South Africa of heat units were done by calculating degree days for the daily temperature fluctuations that occur in nature. These range from degree hours, sine and triangular methods based on hourly temperatures, as well as so-called cut-off methods when the upper threshold of temperature is reached. To estimate heat units (HUs) over South Africa, the 50 year time series of daily maximum and minimum temperatures (Tmxd, Tmnd), generated at each of the 429 700 raster points covering the area at 1` x 1` latitude/longitude, was used (Schulze and Maharaj, 2004). The generation of this time series is described in Section 2a. HUs was computed for each day for a threshold temperature of 10 degree Celsius, i.e. HU = [(Tmxd + Tmnd) / 2 - 10] for HU ≥ 0.0 This method is a considerable advance on the computation of HUs in the previous version of this Atlas (Schulze, 1997), in which HUs were calculated from regression derived monthly means of Tmx and Tmn. From these daily values, monthly as well as seasonal and annual HUs and their standard deviations, could be calculated and mapped.
DOI: 10.15493/saeon.egagasini.10000075
title: The ASCA April 2016 processed ADCP data collected at Mooring A at all depths

description: The Agulhas System Climate Array (ACSA) is a mooring array and is designed to provide long-term observations of the Agulhas Current`s volume, heat and salt transport and its variability from mesoscale (eddies), through seasonal to interannual timescales. The ASCA shelf and tall moorings extend 200 km offshore along decending TOPEX/Jason satellite ground track # 96, through the core of the Agulhas Current, with CPIES measurements extending the array to 300 km offshore of Port Elizabeth. The data collected during this project have been obtained from various oceanographic instrumentation fixed to each mooring. This is the processed product from data collected by an ADCP (Acoustic Doppler Current Profiler) at Mooring A.
DOI: 10.15493/sarva.saws.10000037
title: Seasonal mean near-surface (2m) temperature (°C) change from the 90% percentile projected for 2036-2065, relative to present (1976 - 2005), for JJA season under the RCP 4.5 pathway

description: Seasonal (JJA) mean near-surface (2m) temperature (°C) change from the 90% percentile projected for 2036-2065, relative to present (1976 - 2005), under the RCP 4.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily temperature averages, which are used to generate projections of seasonal change. The projections are generated using the medium to low (RCP4.5) pathway which associates CO2 concentrations of approximately 560ppm by the year 2100. The associated RMSD it calculated and shows the uncertainty range of the projected model simulated residual values, and gives a relative perspective of spatial areas associated with higher and lower projection uncertainties.
DOI: 10.15493/sarva.beeh.10000436
title: Inter-Annual Cv (%), Maize Yield Ultra-Short Season Hybrid Plant Date: 15 December

description: This data set contains Maize, Zea mays L. in South Africa and is the country`s most important field and grain crop. Objectives of the study was to simulate maize yields, and their inter-annual, at a spatial resolution of Quaternary Catchments for 12 different combinations of three plant dates, viz. 15 October, 15 November and 15 December. This was done to evaluate which hybrid lengths and plant dates give the highest yields irrespective of plant dates and hybrid lengths respectively and also which hybrid lengths and plant dates give the lowest coefficients of variation (%) irrespective of plant dates and hybrid lengths respectively.
DOI: 10.5519/qd.7lhurf6e
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2 records)
DOI: 10.15493/saeon.070621-3
title: Agricultural benefits of ecological restoration

description: This layer allows the user to find peer reviewed literature which documents scientific studies of the agricultural related benefits of ecological restoration. Such benefits include improvements and restoration of farmlands and total government expenditure allocated to support farmlands.
DOI: 10.15493/sarva.sdg.10000001
title: SDG 1.2.1 Percentage of population living below the national poverty line (FPL <R400 per month), by sex and age from the 2011 census

description: Sustainable development goal sdg 1.2.1 (% population below national poverty line) for the year 2011 dasymetrically mapped to a 500m grid using the 2008 spot dwelling count. The data provides a percentage of the population per category living below the food poverty line of R400 per month and is further disaggregated according to gender and and age categories "0-5";"6-18";"19-30";"31-50";"51-65";"66-80";"81-120".
DOI: 10.15493/sarva.beeh.10000072
title: Heat Units (March)

description: The concept of the heat unit (or degree day), known since the mid-18th century already, revolves around the development of a plants or organism`s being dependent upon the accumulated heat to which it was subjected during its lifetime, or else during a certain developmental stage. This measure of accumulated heat is known as physiological time. In general it holds that the lower the temperature, the slower the rate of growth and development of plants and invertebrate animals and the amount of heat required to complete a given organism's development does not vary. Physiological time is usually expressed and approximated in units of degree days, also popularly called heat units, where these are an accumulation of mean temperatures above a certain lower threshold value and below an upper developmental threshold (above which growth is considered to remain static or even decline), over a period of time. The degree day concept is not without its limitations. These include that, a linear relationship is assumed between growth and temperature, threshold temperatures may change during the life cycle of a crop or Pest, temperatures exceeding the upper threshold may, in fact, have a detrimental/negative effect on development.

Derivation over South Africa of heat units were done by calculating degree days for the daily temperature fluctuations that occur in nature. These range from degree hours, sine and triangular methods based on hourly temperatures, as well as so-called cut-off methods when the upper threshold of temperature is reached. To estimate heat units (HUs) over South Africa, the 50 year time series of daily maximum and minimum temperatures (Tmxd, Tmnd), generated at each of the 429 700 raster points covering the area at 1` x 1` latitude/longitude, was used (Schulze and Maharaj, 2004). The generation of this time series is described in Section 2a. HUs was computed for each day for a threshold temperature of 10 degree Celsius, i.e. HU = [(Tmxd + Tmnd) / 2 - 10] for HU ≥ 0.0 This method is a considerable advance on the computation of HUs in the previous version of this Atlas (Schulze, 1997), in which HUs were calculated from regression derived monthly means of Tmx and Tmn. From these daily values, monthly as well as seasonal and annual HUs and their standard deviations, could be calculated and mapped.