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EXPLANATORY NOTES Geographical Areas 13 The geographical areas used in this publication are predominantly from the main structure of the Australian Standard Geographical Classification (Australia, and states and territories) but areas from the remoteness structure are also frequently used. For further information see Australian Standard Geographical Classification (ASGC), 2007 (cat. no. 1216.0). Data Quality and Reliability 14 Population Census data are used in Chapter 2 because it allows for a better approximation of the total MDB area than is possible with Labour Force Survey or Estimated Residential Population data. It also allows for more detailed analysis of variations between smaller population groups and small geographic areas. For further information see Information Paper: Population concepts, 2008 (cat. no. 3107.0.55.006) and Australian Labour Market Statistics (cat. no. 6105.0). 15 Census data are affected by undercounting (see Census of Population and Housing - Details of Undercount, Australia, August 2006 (ABS cat. no. 2940.0). In 2006, the net undercount rate (i.e. people missed in the Census, minus those counted more than once) for the whole of Australia was estimated at around 2.7%. This may have an impact on data presented for very remote areas. In addition, around 6% of people did not report their Indigenous status on the Census form. Non-school qualification 16 Non-school qualifications refer to educational attainments other than pre-primary, primary or secondary education, and include Certificates (I-IV), Advanced diplomas and Diplomas, Bachelor degrees, Graduate certificates, and Post graduate degrees as shown in table 2.12 of Chapter 2. For further information see Australian Standard Classification of Education (ASCED), 2001 (cat. no. 1272.0). Income 17 The mean equivalised gross weekly household income was used in measuring income as this variable best allows the comparison of the relative economic wellbeing of people in households of different sizes and compositions. For more information on equivalised income, see Household Income and Income Distribution, Australia, 2005-06 (cat. no. 6523.0). Socio-Economic Indexes Data Sources 18 The Index of Relative Socio-Economic Disadvantage was used for analysis in this publication. Data were sourced from the Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia - data only 2006. For further information refer to https://www.abs.gov.au/websitedbs/D3310114.nsf/home/Seifa_entry_page. Method of Calculation 19 SEIFA data for MDB Statistical Local Areas (SLAs) were selected based on an SLA-to-MDB concordance. The concordance was area-based; if more than 50% of an SLAs area existed within the Basin, it was considered to be inside the Basin. If not, it was excluded. There were 406 SLAs determined to be in the MDB in 2006 (map E.2). 20 For more information about the compilation of SEIFA indexes please refer to Socio-Economic Indexes for Areas (SEIFA) - Technical Paper 2006 (cat. no. 2039.0.55.001). 21 The relationship between 2006 SLA and MDB boundaries is shown in map E.2 below. The map demonstrates that there is a relatively good fit alongside the MDB boundary except in the north western and western areas of the Basin. CHAPTER 3 Water use by industries and households Data Sources 22 Water use by industries and households in the MDB was calculated using data published in Experimental Estimates of Regional Water Use, Australia 2004-05 (cat. no. 4610.0.55.002). Agricultural water consumption Data Sources 23 The water use data for Agriculture were obtained from ABS Agricultural Surveys and Censuses from 2000-01 to 2005-06. These data are consistent with that presented in Water use on Australian Farms (cat. no. 4618.0) 2002-03, 2003-04, 2004-05 and 2005-06. 24 In 2005-06, regional Agriculture water consumption was calculated more accurately than for previous years. This was a consequence of improved collection methodologies, the complete enumeration of Australian farms in 2005-06, and the geographic coding of the location of each farm's main agricultural activity. Users should be aware that not all of the agricultural activity of the farm always occurs at one location. Method of Calculation 25 For 2000-01 and 2001-02, the irrigated area of individual crops and pasture was collected in the ABS Agricultural Census/Survey. This information was combined with regional crop specific application rates for 2002-03 derived from the ABS Water Survey, Agriculture 2002-03 to produce estimates of water consumption for 2000-01 and 2001-02. This was the same methodology (applying application rates to irrigated areas) as that employed for the Water Account, Australia 2000-01 (cat. no. 4610.0). From 2002-03 to 2005-06 water use data (both area irrigated and volume applied) were directly collected. Estimates for 2002-03 used data collected in the Water Survey, Agriculture, while estimates for 2003-04 and 2004-05 used data collected in the Agricultural Survey. Data for 2005-06 were collected in the 2005-06 Agricultural Census. 26 For each year from 2000-01 to 2005-06, either water use data or irrigated area data were modelled to create estimates of agricultural water use for the MDB, at the Statistical Division (SD) level. For those SDs partially within the MDB, the share of SD-based estimates attributed to the MDB were based on irrigated agricultural land use information sourced from the BRS Australian Management Land Use Programme. The model was validated by comparing modelled estimates produced for 2005-06 with geo-coded 2005-06 Agricultural Census water use data estimates for the MDB. Estimates produced using the two methodologies differed by less than 1% at the MDB level for irrigated crops and pasture. Data Quality and Reliability 27 The ABS published data relating to water consumption by the Agriculture industry in both Water Use on Australian Farms, 2004-05 (ABS cat. no. 4618.0) in July 2006, and Water Account, Australia 2004-05 (cat. no. 4610.0) in November 2006. While both contained estimates of agricultural water use, small differences existed between the two due to different data sources and compilation methodologies. For this reason, the data compared across the economy and for households in this publication use proportions according to the Water Account methodology. Agricultural comparisons, i.e. irrigated area and volume data, use data that are consistent with Water use on Australian Farms, 2004-05 (cat. no. 4618.0). Comparisons should therefore be made with caution. 28 Due to differences in collection methodologies between the Agricultural Surveys and Censuses used to collect the 2000-01 to 2005-06 water use and area irrigated data, care should be taken when comparing water use over time. 29 The agricultural water use and irrigated area data were derived from the ABS 2005-06 Agricultural Census and can be used with a high degree of confidence. Of the approximately 190,000 farms in scope of the Census, the response rate was 93.2%. For more details refer to Water use on Australian Farms 2005-06 (cat. no. 4618.0). Dam storage Data Sources 30 Information on the storage capacity of large dams was sourced from the ANCOLD Register of Large Dams (ANCOLD 2008). Data from the register were confronted against dam owners' administrative data and adjusted accordingly. The data has been published previously in Water Account, Australia 2004-05 (cat. no. 4610.0) and Australian Water Resources 2005 (NWC 2007). 31 The location of large dams in the Murray-Darling Basin, and other drainage divisions throughout Australia, are shown in map E.3 below. 32 Large dams are defined as dams with a crest or wall height of greater than 15 metres, or as dams with a dam wall height of greater than 10 metres while also meeting another size criteria e.g. having a crest more than 500 metres in length; creating a reservoir of no less than 1,000 ML; being able to deal with a flood discharge of no less than 2,000 cubic metres per second; or being of unusual design (ANCOLD 2008). Method of Calculation 33 Information on the volume of water in storage in large dams was sourced from publicly available information e.g. from state/territory governments, supplemented by a direct collection of data by the ABS. For large dams for which there was no information available, the ABS derived an estimate using a standard statistical imputation process. The imputed data contributed less than 7% of the Murray-Darling Basin total. 34 Using the large dams identified in the Cotton Yearbook 2007 (The Australian Cottongrower 2007), dam storage levels were aggregated consistent with the method used in Water Account, Australia 2004-05 (cat. no. 4610.0). The purpose of undertaking this calculation was to enable comparison with aggregated area of cotton grown and the volume of water used. Data Quality and Reliability 35 The data on the capacity of large dams, and dam storage levels, is based on publicly available information and direct collection by the ABS. Imputed storage volumes accounted for less than 7% of the MDB total dam storage. These estimates may be used with a high degree of confidence. 36 Patterns of dam storage can be compared with changes in the area of cotton and changes in water consumption with a moderate degree of confidence. This is because the majority of cotton grown is irrigated, and the majority of water from these dams is used for growing cotton. 37 When examining the relationship between water storage in large dams servicing major cotton growing areas, and area or production of cotton, it should be noted that:
CHAPTER 4 Agricultural commodities Data Sources 38 The 2000-01 and 2005-06 ABS Agricultural Censuses were used to calculate area of crops and pasture, numbers of livestock and levels of production for these time periods. The 2000-01 and 2005-06 ABS Apples and Pears Survey was used to source production data and number of trees. The 2000-01 and 2005-06 ABS Vineyards Surveys were used for grape production data by weight (tonnage). Method of Calculation 39 Different methods were used for deriving regional estimates for 2000-01 and 2005-06. The method used to produce 2005-06 agricultural commodity data for the MDB and other regions of interest was the ABS 'geographic coding' project. This project spatially located (geo-coded) Australian farms with an Estimated Value of Agricultural Operations (EVAO) of greater than $5,000. This resulted in the most reliable and accurate regional level agriculture statistics produced by the ABS. 40 To calculate 2000-01 MDB agricultural production and area data that were comparable with 2005-06, Statistical Local Area (SLA)-level information and an SLA-to-MDB concordance were used. To evaluate the accuracy of using the SLA-to-MDB concordance methodology, this method was also used to derive 2005-06 Agricultural Census data. This enabled an evaluation of whether the level of difference (using the SLA concordance methodology) compared to the equivalent geo-coded MDB data was significant. Where the difference was relatively small (<3%) the 2000-01 data were considered appropriate. 41 Irrigated area data for 2000-01 were compared using the SLA-concordance methodology described in paragraph 39 above, and the SD methodology described in paragraph 25 above. When the results of the two methods were compared, minor differences were observed, therefore the SD methodology was used because it was considered to be more accurate. Data Quality and Reliability 42 The 2005-06 Agricultural Census data should be used with a high degree of confidence because farms have been geo-coded to a point location, rather than classified to an area. 43 Caution should be used when comparing 2000-01 and 2005-06 agriculture data for two reasons. Firstly, 2000-01 data were calculated for the MDB using a concordance-based methodology which reduced the degree of accuracy compared to using the geo-coding methodology. Secondly, between 2000-01 and 2005-06, the method of establishing the population of agricultural holdings to be surveyed (referred to as the business "frame") was changed. In 2000-01, a register of agricultural holdings (frame) maintained by the ABS was used; in 2005-06 the ABS drew the frame from the Australian Business Register. The influence of the frame change is not thought to be significant; some analyses suggest that the frame used for 2005-06 included more small-sized farms than previously. Gross Value of Agricultural Production Data Sources 44 Estimates of the Gross Value of Agricultural Production (GVAP) were compiled using data from Value of Agricultural Commodities Produced 2005-06 (cat. no. 7503.0). Method of Calculation 45 Estimates of GVAP for the MDB have been derived using similar techniques for calculating MDB agricultural commodities estimates as described in the paragraphs above. The statistics presented are in current price terms, so changes over time are affected by both inflation and changes in the volume of agricultural production. Data Quality and Reliability 46 GVAP also includes some non-irrigated commodities which are not considered in calculations of the Gross Value of Irrigated Agricultural Production (GVIAP). They include:
Gross Value of Irrigated Agricultural Production Data Sources 47 GVIAP was estimated using data from the ABS 2005-06 Agricultural Census as well as other ABS collections and administrative data used to calculate the value of agricultural commodities produced (see Agricultural Commodities, Australia, 2005-06 (cat. no. 7121.0) and Value of Agricultural Commodities Produced, Australia, 2005-06 (cat. no. 7503.0)). Method of Calculation 48 The methods used to estimate GVIAP in this publication are consistent with the methods used in the Water Account, Australia 2004-05 (cat. no. 4610.0), therefore the estimates are directly comparable. 49 Different methods were used for different commodities, with the method used dependent on the nature of the commodity and the availability of data. For rice, 100% of the gross value of agricultural production was attributed to irrigation. For cotton, the volume of the production from irrigated land was collected directly via the ABS Agricultural Censuses and Surveys. This volume was then applied to the value of cotton in the MDB. 50 For the remaining commodities, the value of irrigated agricultural production was determined using two general methods.
51 The following approaches were taken for particular commodities:
52 A new method for calculating GVIAP is currently being developed by the ABS and experimental estimates for 2000-01 through to 2006-07 will be released later in 2008. Data Quality and Reliability 53 Calculation of GVIAP is based on several assumptions so these estimates should be used with caution. 54 GVIAP data for 2000-01 differs slightly from that published in the Water Account Australia, 2000-01 (cat. no. 4610.0), due to slight changes in the methodology which were made to enable a better comparison of 2000-01 and 2005-06 data. 55 Comparisons of GVIAP between 2000-01 and 2005-06 must be made with caution for the following reasons:
56 For tables and graphs showing GVIAP estimates there were slight differences in the definitions of the commodity groups between 2000-01 and 2005-06:
57 Care also needs to be taken when comparing the GVIAP data with the water consumption data presented in Chapter 3 because consumption data includes livestock drinking and washdown water, whilst GVIAP data only considers irrigation water. CHAPTER 5 Natural Resource Management data Data Sources 58 Natural Resource Management (NRM) data included in Chapter 5 and irrigation practice data included in Chapter 3 are sourced from either the ABS publication Natural Resource Management on Australian Farms, Australia, 2004-05 (Reissue) (cat. no. 4620.0) or unpublished data from the Natural Resource Management Survey 2004-05. 59 The NRM Survey vehicle is a biennial sample survey collecting data about NRM issues, activities, expenditure and effort from approximately 20,000 establishments (farms) conducting agricultural activity. Method of Calculation 60 To determine the NRM regions comprising the MDB, MDB and NRM boundaries were overlaid to assess the level of 'fit'. This analysis revealed that:
61 Therefore, when presenting statistics by NRM region, the fifteen regions entirely in the MDB and the four regions with the vast majority of their area within the MDB are included, however the two regions with small areas in the MDB are excluded. 62 In Chapter 5, the NRM data relates to number of farms rather than area. Therefore, given there are relatively low numbers of farms in the South West, Wimmera and Western regions, these regions have a relatively minor impact on MDB estimates. Furthermore, proportionally more farms exist within the 70% of area within the MDB, than the 30% that is located outside the MDB. Data Quality and Reliability 63 Much of the data published at the NRM region level have been presented as proportions within ranges due to data quality (i.e. level of error associated with estimates). These ranges have been set to:
64 Data at the MDB level is of suitable quality and can be used with a medium degree of confidence. Data for NRM regions should be used with caution. MAPS 65 Each map contains a legend and shows the colour and values for each class of the mapped data. For simplicity the ranges are shown as '0 to 600', '600 to 3,700', '3,700 to 18,700' and so on. These should be read as 'from 600 to less than 3,700', and 'from 3,700 to less than 18,700' etc. Individual values appear in one range only. EFFECTS OF ROUNDING 66 Figures have been rounded and discrepancies may occur between totals and the sums of the component items. Document Selection These documents will be presented in a new window.
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