Australian Bureau of Statistics 

4610.0.55.008  Gross Value of Irrigated Agricultural Production, 201011 Quality Declaration
Previous ISSUE Released at 11:30 AM (CANBERRA TIME) 13/12/2012 
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EXPLANATORY NOTES WHAT IS GVIAP?
17 Note that the ABS Agricultural Census/Survey collects area and production data for a wide range of individual commodities within the irrigated commodity groups displayed in the list above. Appendix 1 provides more detail of which commodities comprise these groupings. METHOD USED TO CALCULATE GVIAP 18 The statistics presented here calculate GVIAP at the unit (farm) level, using three simple rules:
b. If the area of the commodity group irrigated is greater than zero but less than the total area of the commodity group grown/sown, then a 'yield formula' is applied, with a 'yield difference factor', to calculate GVIAP for the irrigated area of the commodity group; c. If the area of the commodity group irrigated = 0, then GVIAP = 0 for that commodity group. 19 These three rules apply to most commodities; however there are some exceptions as outlined below in paragraph 24. It is important to note that the majority of cases follow rules 1 and 3; that is, the commodity group on a particular farm is either 100% irrigated or not irrigated at all. For example, in 2004–05, 90% of total GVAP came from commodity groups that were totally irrigated or not irrigated at all. Therefore, only 10% of GVAP had to be 'split' into either 'irrigated' or 'nonirrigated' using the 'yield formula' (described below). The yield formula is explained in full in the information paper Methods of estimating the Gross Value of Irrigated Agricultural Production (cat. no. 4610.0.55.006). Yield formula 20 Outlined here is the yield formula referred to in paragraph 18: Where
Hence
Where:
Y_{i}_{ }= estimated irrigated production for the commodity (t or kg) P = unit price of production for the commodity ($ per t or kg) Q = total quantity of the commodity produced (t or kg) A_{d} = area of the commodity that is not irrigated (ha) Y_{diff}_{ }= yield difference factor, i.e. estimated ratio of irrigated to nonirrigated yield for the commodity produced Yield difference factors 21 Yield difference factors are the estimated ratio of irrigated to nonirrigated yield for a given commodity group. They are calculated for a particular commodity group by taking the yield (production per hectare sown/grown) of all farms that fully irrigated the commodity group and dividing this 'irrigated' yield by the yield of all farms that did not irrigate the commodity group. The yield difference factors used here were determined by analysing data from 2000–01 to 2004–05 and are reported for each commodity group in Appendix 1 of the information paper Methods of estimating the Gross Value of Irrigated Agricultural Production (cat. no. 4610.0.55.006). It is anticipated that the yield difference factors will be reviewed following release of data from the 201011 Agriculture Census. 22 In this report 'yield' is defined as the production of the commodity per area grown. Commodity groups for which the yield formula is used 23 The GVIAP for the following commodities has been calculated using the yield formula, with varying yield differences: Cereals for grain/seed  yield formula with yield difference of 2 Cereals for hay  yield formula with yield difference of 1.5 Pastures for hay  yield formula with yield difference of 2 Pastures for seed  yield formula with yield difference of 2 Sugar cane  yield formula with yield difference of 1.3 Other broadacre crops  yield formula with yield difference of 2 Fruit and nuts  yield formula with yield difference of 2 Grapes  yield formula with yield difference of 1.2 Vegetables for human consumption and seed  yield formula with yield difference of 1 Nurseries, cut flowers and cultivated turf  yield formula with yield difference of 1 Note: a yield difference of 1 implies no difference in yield between irrigated and nonirrigated production. 24 However not all agricultural commodity groups can be satisfactorily calculated using this formula, in particular:
25 Meat cattle, sheep and other livestock – in some previous releases of GVIAP data the estimates were derived by taking the average of two separate models:
2. If the farm has any irrigation of grazing land then assume that all livestock production on the farm is irrigated.
2. If sheep/other livestock are grazing on a farm with irrigated pastures/crops for grazing, and there are no other grazing livestock present on the farm, assume that all sheep/other livestock will graze on the irrigated land; therefore GVIAP for sheep/other livestock = GVAP for sheep/other livestock on that farm. 3. If dairy cattle, meat cattle and sheep/other livestock are all grazing on a farm with irrigated pastures/crops for grazing, assume that all dairy cattle will graze on the irrigated land and estimate the GVIAP of the meat cattle and sheep/other livestock grazing on the irrigated pastures using the area method. Note: the area method provides a relatively small estimate of GVIAP, which is appropriate because it is assumed that it is more likely that the dairy cattle will be the only livestock grazing on the irrigated land. 4. If dairy cattle and meat cattle are both grazing on a farm with irrigated pastures/crops for grazing, assume that all dairy cattle will graze on the irrigated land (see paragraph 28) and estimate the GVIAP of the meat cattle grazing on the irrigated pastures using the area method. Note: the area method provides a relatively small estimate of GVIAP, which is appropriate because we are assuming that it is more likely that the dairy cattle will be the only livestock grazing on the irrigated land. 5. If dairy cattle and sheep/other livestock are both grazing on a farm with irrigated pastures/crops for grazing, assume that all dairy cattle will graze on the irrigated land (see paragraph 28) and estimate the GVIAP of the sheep/other livestock grazing on the irrigated pastures using the area method. Note: the area method provides a relatively small estimate of GVIAP, which is appropriate because we are assuming that it is more likely that the dairy cattle will be the only livestock grazing on the irrigated land. 6. If there are no dairy cattle present but meat cattle and sheep/other livestock are both grazing on a farm with irrigated pastures/crops for grazing, estimate the GVIAP of the meat cattle and sheep/other livestock grazing on the irrigated pastures using a combination (average) of the area method and 'total' methods (the 'total' method is simply the assumption that GVIAP = GVAP). Note: the area method provides a relatively small estimate of GVIAP, which is not appropriate in this case because it is likely that at least one of the two categories of livestock will be grazing on the irrigated land. The 'total' method assumes that all livestock are grazing on the irrigated land, which overestimates GVIAP. An average of the estimate derived from the two methods should provide a more accurate estimate. 7. Pigs, poultry and eggs are included in the 'Sheep and Other livestock' category. 26 Most of the irrigated commodity groups included in these tables are irrigated simply by the application of water directly on to the commodity itself, or the soil in which it is grown. The exception relates to livestock, which includes dairy. For example, the GVIAP of 'dairy' simply refers to all dairy production from dairy cattle that grazed on irrigated pastures or crops. Estimates of GVIAP for dairy must be used with caution, because in this case the irrigation is not simply applied directly to the commodity, rather it is applied to a pasture or crop which is then eaten by the animal from which the commodity is derived (milk). Therefore, for dairy production, the true net contribution of irrigation (i.e. the value added by irrigation, or the difference between irrigated and nonirrigated production) will be much lower than the total irrigationassisted production (the GVIAP estimate). 27 The difference between (a) the net contribution of irrigation to production and (b) the GVIAP estimate, is probably greater for livestock grazing on irrigated crops/pastures than for commodity groups where irrigation is applied directly to the crops or pastures. 28 Similarly, estimates of GVIAP for all other livestock (meat cattle, sheep and other livestock) must be treated with caution, because as for dairy production, the issues around irrigation not being directly applied to the commodity also apply to these commodity groups. PRICE DATA 29 The estimates presented in this product are underpinned by estimates of the Value of Agricultural Commodities Produced (VACP), published annually in the ABS publication Value of Agricultural Commodities Produced (cat. no. 7503.0). VACP estimates (referred to as GVAP in this product) are calculated by multiplying the wholesale price by the quantity of agricultural commodities produced. The price used in this calculation is the average unit value of a given commodity realised in the marketplace. Price information for livestock slaughterings and wool is obtained from ABS collections. Price information for other commodities is obtained from nonABS sources, including marketing authorities and industry sources. It is important to note that prices are statebased average unit values. 30 Sources of price data and the costs of marketing these commodities vary considerably between states and commodities. Where a statutory authority handles marketing of the whole or a portion of a product, data are usually obtained from this source. Information is also obtained from marketing reports, wholesalers, brokers and auctioneers. For all commodities, values are in respect of production during the year (or season) irrespective of when payments were made. For that portion of production not marketed (e.g. hay grown on farm for own use, milk used in farm household, etc.), estimates are made from the best available information and, in general, are valued on a local value basis. 31 It should be noted that the estimates for GVIAP are presented in current prices; that is, estimates are valued at the commodity prices of the period to which the observation relates. Therefore changes between the years shown in these tables reflect the effects of price change. MURRAYDARLING BASIN (MDB) 32 For this release, data for the for the MurrayDarling Basin (MDB) presented in this publication were derived on a River Basin basis, due to 201011 being an Agricultural Census year. For previous releases, MDB data were derived from a concordance of National Resource Management (NRM) regions falling mostly within the MDB. This makes comparing 200910 and 201011 MDB estimates difficult, and any comparability in the MDB across these reference periods should be performed with caution. CONFIDENTIALITY 33 Where figures for individual states or territories have been suppressed for reasons of confidentiality, they have been included in relevant totals. RELIABILITY OF THE ESTIMATES 34 The estimates in this product are derived from estimates collected in surveys and censuses, and are subject to sampling and nonsampling error. SAMPLE ERROR 35 The estimates for gross value of irrigated agricultural production are based on information obtained from respondents to the ABS Agricultural Censuses and Surveys. These estimates are therefore subject to sampling variability (even in the case of the censuses, because the response rate is less than 100%); that is, they may differ from the figures that would have been produced if all agricultural businesses had been included in the Agricultural Survey or responded in the Agricultural Census. 36 One measure of the likely difference is given by the standard error (SE) which indicates the extent to which an estimate might have varied by chance because only a sample was taken or received. There are about two chances in three that a sample estimate will differ by less than one SE from the figure that would have been obtained if all establishments had been reported for, and about nineteen chances in twenty that the difference will be less than two SEs. 37 In this publication, sampling variability of the estimates is measured by the relative standard error (RSE) which is obtained by expressing the SE as a percentage of the estimate to which it refers. Most national estimates have RSEs less than 10%. For some States and Territories, and for many Natural Resource Management regions with limited production of certain commodities, RSEs are greater than 10%. Estimates that have an estimated relative standard error higher than 10% are flagged with a comment in the publication tables. If a data cell has an RSE of between 10% and 25%, these estimates should be used with caution as they are subject to sampling variability too high for some purposes. For data cells with an RSE between 25% and 50% the estimate should be used with caution as it is subject to sampling variability too high for most practical purposes. Those data cells with with an RSE greater than 50% indicate that the sampling variability causes the estimates to be considered too unreliable for general use. NONSAMPLE ERROR 38 Errors other than those due to sampling may occur because of deficiencies in the list of units from which the sample was selected, nonresponse, and errors in reporting by providers. Inaccuracies of this kind are referred to as nonsampling error, which may occur in any collection, whether it be a census or a sample. Every effort has been made to reduce nonsampling error to a minimum in the collections by careful design and testing of questionnaires, operating procedures and systems used to compile the statistics. ROUNDING 39 Where figures have been rounded, discrepancies may occur between sums of the component items and totals. ACKNOWLEDGEMENT 40 ABS publications draw extensively on information provided freely by individuals, businesses, governments and other organisations. Their continued cooperation is very much appreciated: without it, the wide range of statistics published by the ABS would not be available. Information received by the ABS is treated in strict confidence as required by the Census and Statistics Act 1905. Document Selection These documents will be presented in a new window.
This page last updated 2 October 2013
