cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/af1m-3488
title: Satellite Based Gridded Climate Data Driven Wheat Yield Datasets at MODIS Pixel Scale

description: Adverse climatic events like heat stress, floods, unseasonal rainfall, and droughts frequently hinder crop productivity. Long-term crop yield data plays a crucial role in food security planning. This study presents historical wheat yield data at the satellite pixel level from 2001 to 2019 in Uttar Pradesh, India. We use various satellite indicators to develop wheat yield models, including the normalized difference vegetation index and gridded weather data, such as precipitation, temperature, and evapotranspiration. Additionally, we incorporated district-wise wheat yield data from the Directorate of Economics and Statistics (DES) and combined it with satellite variables to develop wheat yield models using multiple regression analysis. The key findings of the present study confer that the multiple regression-based yield models (developed using DES-based yield statistics and satellite variables) efficiently predict the wheat yield at the satellite pixel level. The developed models exhibited strong performance, with R² values ranging from 0.3 to 0.76 when comparing the satellite-derived and DES-based district-wise mean yield. The mean absolute error ranged from 0.22 to 1.7 t/ha over the study years. This methodology can be applied globally, from local to national scales, to quantitatively map and depict crop yields at the satellite pixel level. Such historical crop yield mapping offers valuable insights into long-term yield patterns in response to climate change, thereby enabling the formulation and implementation of food security measures.Keywords: Crop yield, Satellite data, MODIS, NDVI, Multiple regression
DOI: 10.15493/sarva.beeh.10000077
title: Heat Units (August)

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/sarva.csag.10000300
title: Total precipitation for the month (mm) 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.44022022
title: Dwarsrivier Hoek rainfall data

description: Monthly rainfall data from Dwarsriviers Hoek, Banghoek Valley in the Western Cape. This was part of South African Environmental Observation Network (SAEON)'s digitisation of historical data from the Council for Scientific and Industrial Research (CSIR)'s Ecology Vault, Stellenbosch. This record forms part of a collection of historical rainfall data submitted to SAEON. The data were generated by farmers on a voluntary basis and have been published as submitted.
DOI: 10.15493/sarva.beeh.10000226
title: Pinus taeda Growth Areas and Yield Estimation

description: Pinus taeda also commonly known as loblolly pine, is native to North America. Of the three main species of pine grown in South Africa, viz. Pinus patula, P. elliottii and P. taeda, the latter is the most demanding in respect of climate and soil requirements (Smith, 1998). Optimally, P. taeda requires a MAP > 950 mm, although it can be grown with lower rainfall where temperatures are also relatively lower (Smith, 1998). The species is, however, sensitive to drought. Pinus taeda requires mean annual temperatures ≥ 13 degree Celsius, with July (mid-winter) mean temperatures > 8 degree Celsius, but with January (midsummer) means < 26 degree Celsius (Smith, 1998). While resistant to hail, P. taeda is very prone to snow. 

Climatically optimum growth areas for Pinus taeda have been defined in the ICFR`s Forestry Toolbox (Kunz, 2004), by mean annual precipitation (MAP) and mean annual temperature (MAT), as illustrated in. The figure also shows the conditions under which sub-optimum growth would take place because it may be too cold and snow damage can occur, or too dry (either occasionally or more permanently) or temperatures exceed this species' upper tolerance limit.
DOI: 10.15493/sarva.beeh.10000261
title: Saturated Vapour Pressure (kPa) November

description: The average energy of the molecules in a liquid such as water is governed by temperature. The higher the temperature, the higher the average energy. Some molecules have higher energies than the average, and if they are on the surface of the liquid, can move fast enough to escape the inter-molecular attractive forces holding the liquid together. They then evaporate from the surface of the liquid. This process can continue until the space above the liquid is saturated with water molecules. Equilibrium is then reached with as many molecules leaving the surface of water as are re-entering it. At this point the pressure exerted by that vapour is called the saturated vapour pressure. The symbol used for saturated vapour pressure is ea and the unit of pressure is kPa. Since the molecular kinetic energy is greater at higher temperature, more molecules can escape the surface and the saturated vapour pressure is correspondingly higher. This relationship is illustrated in the shaded box.

Daily values of saturated vapour pressure were computed from the 50 year daily time series of temperature generated for South Africa at each one of 429 700 raster points at one arc minute interval, using techniques developed by Schulze and Maharaj (2004).
DOI: 10.5281/zenodo.5074774
title: LIRE

description: LIRE dataset (Latin Inscriptions of the Roman Empire) is an aggregate of EDH and EDCS epigraphic datasets, focusing on inscriptions which are (a) geolocated, (b) within the borders of the Roman Empire in its highest extent, (c) dated (d) in the dating interval intersecting the period from 50 BC to 350 AD. The dataset consists of 137,305 records and 110 attributes. There are 49,916 inscriptions shared by the EDH and EDCS, inheriting attributes from both parent collections. Further, there are 3,907 inscriptions recorded exclusively in EDH and 83,482 inscriptions originating solely from EDCS. In cases in which an inscription is available only in one dataset, it contains attributes only from that one dataset. We publish the dataset as one geojson file. The scripts used to generate the dataset are availible via GitHub: https://github.com/sdam-au/LIRE_ETL.
DOI: 10.5281/zenodo.5776109
title: LIRE

description: LIRE dataset (Latin Inscriptions of the Roman Empire) is an aggregate of EDH and EDCS epigraphic datasets, focusing on inscriptions which are (a) geolocated, (b) within the borders of the Roman Empire in its highest extent, (c) dated (d) in the dating interval intersecting the period from 50 BC to 350 AD. In version 1.2, the dataset consists of 136,190 records and 112 attributes. There are 48,801 inscriptions shared by the EDH and EDCS, inheriting attributes from both parent collections. Further, there are 3,907 inscriptions recorded exclusively in EDH and 83,482 inscriptions originating solely from EDCS. In cases in which an inscription is available only in one dataset, it contains attributes only from that one dataset. We publish the dataset as one geojson file. The scripts used to generate the dataset and their metadata are available via GitHub: <code> </code><code>https://github.com/sdam-au/LIRE_ETL</code>.
DOI: 10.5281/zenodo.7577788
title: LIRE

description: LIRE dataset (Latin Inscriptions of the Roman Empire) is an aggregate of EDH and EDCS epigraphic datasets and a filtered and spatio-temporally resetricted version of the LIST dataset, including only inscriptions which are (a) geolocated, (b) within the borders of the Roman Empire in its highest extent, (c) dated (d) in the dating interval intersecting the period from 50 BC to 350 AD. In version 2.1, the dataset consists of 203,466 records and 66 attributes. There are 62,365 inscriptions shared by the EDH and EDCS, inheriting attributes from both parent collections. Further, there are 2,504 inscriptions recorded exclusively in EDH and 138,597 inscriptions originating solely from EDCS. In cases in which an inscription is available only in one dataset, it contains attributes only from that one dataset. We publish the dataset as one parquet or geojson file. Using `geopandas` library, you can load the data directly into your Python environment using the following command: `LIRE = gpd.read_parquet("https://zenodo.org/record/7577788/files/LIRE_v2-1.parquet?download=1")`. The scripts used to generate the dataset and their metadata are available via GitHub: <code>https://github.com/sdam-au/LI_ETL</code>.
DOI: 10.5281/zenodo.7966484
title: LIRE

description: LIRE dataset (Latin Inscriptions of the Roman Empire) is an aggregate of EDH and EDCS epigraphic datasets and a filtered and spatio-temporally resetricted version of the LIST dataset, including only inscriptions which are (a) geolocated, (b) within the borders of the Roman Empire in its highest extent, (c) dated (d) in the dating interval intersecting the period from 50 BC to 350 AD. In this version, the dataset consists of 182,852 records and 65 attributes. There are 59,374 inscriptions shared by EDH and EDCS, inheriting attributes from both parent collections. Further, there are 2,244 inscriptions recorded exclusively in EDH and 121,234 inscriptions originating solely from EDCS. In cases in which an inscription is available only in one dataset, it contains attributes only from that one dataset. We publish the dataset as one parquet or geojson file. Using `geopandas` library, you can load the data directly into your Python environment using the following command: `LIRE = gpd.read_parquet("https://zenodo.org/record/7966484/files/LIRE_v2-2.parquet?download=1")`. The scripts used to generate the dataset and their metadata are available via GitHub: <code>https://github.com/sdam-au/LI_ETL</code>.
DOI: 10.5281/zenodo.8147298
title: LIRE

description: LIRE dataset (Latin Inscriptions of the Roman Empire) is an aggregate of EDH and EDCS epigraphic datasets and a filtered and spatio-temporally resetricted version of the LIST dataset, including only inscriptions which are (a) geolocated, (b) within the borders of the Roman Empire in its highest extent, (c) dated (d) in the dating interval intersecting the period from 50 BC to 350 AD. In this version, the dataset consists of 182,852 records and 65 attributes. There are 59,374 inscriptions shared by EDH and EDCS, inheriting attributes from both parent collections. Further, there are 2,244 inscriptions recorded exclusively in EDH and 121,234 inscriptions originating solely from EDCS. In cases in which an inscription is available only in one dataset, it contains attributes only from that one dataset. We publish the dataset as one parquet or geojson file. Using `geopandas` library, you can load the data directly into your Python environment using the following command: `LIRE = gpd.read_parquet("https://zenodo.org/record/8147298/files/LIRE_v2-3.parquet?download=1")`. The scripts used to generate the dataset and their metadata are available via GitHub: <code>https://github.com/sdam-au/LI_ETL</code>.
DOI: 10.15493/sarva.csag.10000288
title: Minimum of Maximum Temperature for Far Future (2080 - 2100)

description: Model Run: Far future (2080 - 2100) (Far future (2080 - 2100)). 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.la1120
title: Lambrechtbos A stream flow and stage height August 2011 - August 2020

description: Hourly average and halfhourly sample stream flow in cumec estimated from stage height measured with an OTT Orpheus Mini in the stilling well at a V-notch weir in Lambrechtbos A catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva3.data.10000004
title: Population Age Distribution

description: The data was derived from the 2011 national census. 

Population age was obtained from the census small area layer data and grouped into the following categories: (i) 0-5 Juvenile, (ii) 6-18 School-going, (iii) 19-30, 31-50, 51-65 Working population, (iv) 66-80 Elderly and (v) 81-102 Frail. 

A 500m grid was used to map the dwelling frame distribution for South Africa. The resulting dataset was then intersected with the small area layer for the purpose of esmating the area percentages of dwellings within each grid cell. The area percentages for the grid cells were multiplied with the corresponding census count for each small area in order to map population exposure by age group.
DOI: 10.15493/saeon.egagasini.10000024
title: The ASCA January 2018 raw CTD profile data collected for station 001 - 007

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 the ASCA project have been obtained from various oceanographic instrumentation. This raw data was collected by the shipborne CTD (Conductivity, Temperature, Depth) sensor for the following stations: 001, 002, 003, 004, 005, 006 and 007.
DOI: 10.15493/saeon.egagasini.24000004
title: The ASCA April 2016 raw shipborne-ADCP data

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. This is the raw data, collected by a S-ADCP (Shipborne-Acoustic Doppler Current Profiler).
DOI: 10.15493/saeon.fynbos.13b1820
title: 13B event rainfall February 2018 - August 2020

description: Event rainfall data collected by Texas Electronics tipping bucket gauge installed at 1.2m above ground at 439 masl in the Tierkloof catchment of the Jonkershoek valley in the Western Cape of South Africa. Data recorded by a Pendant Event logger (UA-003-64, Onset, Massachusetts, U.S.A). Tipping bucket calibrated to 0.254 mm increments.
DOI: 10.15493/sarva.beeh.10000401
title: Banana Yield Estimation (Irrigated Ratoon Crop)

description: Modern bananas originated in the southeast Asian and western Pacific regions, with earliest records of cultivation in India about 2 500 years ago (ARC, 2005). Bananas are stress sensitive plants which grow satisfactorily with a MAP of 1 200 mm, ideally well distributed throughout the year (Smith, 1998). Dry spells of more than 7 days are detrimental to yields. Preferring a relative humidity > 60 percent (especially during fruit setting), the mid-day RH should generally exceed 50 percent. The optimum mean monthly temperatures for bananas are 25 - 28 degree Celsius, with a range of 20 - 35 degree Celsius. Growth ceases below 12 degree Celsius and the July mean of daily minima should be at least 8 degree Celsius. Of the 1 200 ha under bananas, 64 percent are in Mpumalanga, 18 percent are in KwaZulu-Natal and a further 18 percent in Limpopo (Statistics SA, 2002). Highest average yields, around 26 t/ha/an, are attained in Mpumalanga, while those in KwaZulu-Natal and Limpopo are 20 t/ha/an, i.e. on the low side by international standards (Statistics SA, 2002).

Determination of banana yield estimates, based on Smith`s climatic criteria. Areas considered climatically unsuitable for banana production and thus eliminated from further analysis were those with MAP < 900 mm (for dryland production), MAT < 16 degree Celsius, July means of daily minimum temperatures < 8 degree Celsius, the averages of the 12 monthly means of daily maximum temperatures < 22 degree Celsius and of minimum temperatures < 10 degree Celsius, as well as areas where the mean of the 12 monthly values of daily minimum RH was < 50 percent
DOI: 10.15493/sarva.beeh.10000053
title: Median First and Last Dates of Heavy Frost their Variability, and the Duration of the Frost Period

description: During a heavy frost day, temperature at recognised meteorological stations is measured in a Stevenson screen at a height of 1.5 m above the ground. Particularly with radiation frosts, a frost occurrence on the ground is therefore not always recorded as a temperature at or below 0 degree Celsius in a Stevenson screen. A temperature of ≤ 0 degree Celsius recorded in a Stevenson Screen is therefore termed a `heavy` frost, on the assumption that the temperature on the ground would be even lower. 

In the mapping statistics on frost over South Africa, the temperature database contains quality controlled time series of daily maximum and minimum temperatures from > 970 qualifying temperature stations, the records of which had been infilled and extended (where necessary) to a common 50 year time period 1950 - 1999 by techniques described in Schulze and Maharaj (2004). These daily station records were then used with regionally and seasonally determined temperature lapse rates to generate 50 years of daily maximum and minimum temperatures at each of 429 700 one arc minute (1.7 x 1.7 km) raster points covering the RSA, Lesotho and Swaziland. At each raster point a minimum temperature of ≤ 0 degree Celsius in the 50 year daily time series was designated a `heavy frost day`. Any raster point where < 5 years out of the 50 experienced at least one heavy frost day, that point was deemed to be `frost free`. Frost analyses were therefore only performed on daily values at those raster points with 5 or more years recording at least one heavy frost day.
DOI: 10.15493/sarva.csag.10000062
title: Rainfall days per month (> 10mm) 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.beeh.10000237
title: Monthly Means Of Daily Solar Radiation (Mj/M2/Day) November

description: These daily estimates of solar radiation were derived using modifications for South African conditions of the Bristow and Campbell (1984) equation. The daily values can be used as input in other equations such as the Penman-Monteith equation for potential crop evaporation.

The method of estimation of daily solar radiation loadings over South Africa and its verification are as follows: based on the premise that clear sky solar radiation is determined essentially by extraterrestrial solar radiation modulated by an atmospheric extinction function, and dependent on high maximum temperatures associated with a high diurnal range in temperature, while cloudy/rainy conditions are associated with a low diurnal temperature range, the Bristow and Campbell (1984) equation was modified for South African conditions by regionally optimised expressions of clear sky depletion for atmospheric water vapour content, and by month-by-month regionalised expressions for solar radiation extinction by cloudy/rainy conditions. Other than the optimised regional/monthly coefficients and the computation of extraterrestrial radiation, which is undertaken with trigonometrical geometry functions, the only other variables used were daily maximum and minimum temperatures. The latter were determined for the 50 year period 1950 - 1999 at each of 429 700 one arc minute (1.7 x 1.7 km) raster points over South Africa by techniques outlined in Schulze and Maharaj (2004).