7105.0.55.004 - National Agricultural Statistics Review

7105.0.55.004 - National Agricultural Statistics Review - Final Report, 2015

ARCHIVED ISSUE Released at 11:30 AM (CANBERRA TIME) 29/07/2015 First Issue

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DATA QUALITY

The NASR consultation identified a range of concerns about the quality of agricultural statistics. These are presented below categorised according to the ABS Quality Framework, which describes the following elements of quality in statistics³⁹:

relevance – how well the statistic meets the information need it was designed to address
accuracy – how well the statistic describes the phenomenon it was designed to measure
timeliness – how soon the statistic is made available after the reference period it relates to
coherence – how well the statistic can be related to, or integrated with, other relevant data
accessibility – how easily the statistic can be discovered, accessed and used
interpretability – how easily the statistic can be understood, interpreted and used correctly.

In assessing the relevance of the statistical assets in the current system, stakeholders indicated that while currently available statistical assets were mostly meeting their needs, approximately one-third were falling short. Areas of deficiency included: the level of detail provided (such as a lack of data available for small geographic scales, detailed varieties of commodities or detailed industry breakdowns) and the scope or coverage of the dataset. For example, hobby farms are excluded from the scope of some collections because of their marginal economic significance but are important for users seeking to understand biosecurity and natural resource management issues. Stakeholders noted the lack of consistency in the definition of an agricultural business across statistical collections (Table 2), which makes comparability across data and statistics difficult.

Table 2 - Comparison of definitions of agricultural businesses in Australia

Source	Purpose	Definition of a relevant agricultural business
ABS	Agricultural Census Annual agricultural surveys	All businesses registered for an Australian Business Number with an EVAO, or equivalent, of $5,000 or more.
ABARES	Farm surveys	Farms with an EVAO of more than $40,000.
Australian Business Register (ABR)	Register of businesses	Businesses that declare they undertake agricultural activity, or are coded to an agricultural industry, and registered for an ABN. (It is compulsory to register for an ABN if turnover is above $75,000.)
Australian Taxation Office	Taxation statistics	Uses the ABR definition.
Industry	Farm monitor surveys	Levy payers who pay levies to the relevant agricultural industry organisation.

Note: EVAO – Estimated Value of Agricultural Operations.

Stakeholders also identified a range of areas where there are reportedly no statistics available. In most cases, the data gaps identified were specific to an individual or organisation’s work program, or were items that required greater spatial, temporal or commodity detail. However, there were a number of data gaps identified by multiple stakeholders. These included: data on industry supply chains and value adding; post-farm-gate productivity estimates for transport, food processing and manufacturing; accurate employment estimates of particular industries⁴⁰; data to enable estimates of productivity at a regional level; upstream and downstream employment figures; labour market supply and demand; the domestic consumption of agriculture, fisheries and forestry products; and investment and return on government investment in rural industry research and development. A summary of reportedly missing data themes is presented in Table 3.

Considerable concern was also raised in relation to the accuracy of agricultural statistics and the impact of this on the quality of decision-making and policy formation. Many of these concerns related to sample survey data, the quality of which can be affected by errors stemming from: the coverage of the survey (e.g. ensuring all relevant businesses are included); the sampling error associated with the survey estimates; and response or measurement error (non-response and/or incomplete or inaccurate responses). The impact of respondent burden on low response rates or poor quality of responses to surveys was noted, as outlined above. Stakeholders also raised concerns in relation to the sampling error associated with survey estimates, with a number of users indicating these errors are too large to enable them to use the statistics with confidence. This was particularly the case for statistics on smaller industries and at smaller geographic areas where the sampling error tends to be higher. The variation between ABS preliminary and final estimates and the impact of this on users’ confidence in the reliability of the data were also raised.

The timeliness of statistics from the ABS agricultural surveys and Census and ABARES’ farm surveys was considered a major concern by many stakeholders. The lag between the reference period of the survey and the date of release reduces the usefulness of the statistics to support timely decision-making, strategic planning and forecasting.

The coherence of data sources in terms of the consistency between datasets across time and space, and the comparability of data from different sources was also raised as an issue. In particular, issues relating to spatial consistency, including the need for consistent classifications of spatial units between datasets, the need for spatial boundaries that align and concordances when changes occur, were noted by stakeholders. Inconsistency in the types of commodities collected and variation in the way in which these are aggregated and categorised from year-to-year causes frustration for users trying to establish a coherent time series. Changes to methodologies and definitions in official sources, which affect fundamental statistics such as counts of farm businesses, were also cited by stakeholders, with concerns raised at the lack of concordances provided to assist users to interpret the impact of the changes on the data series. Stakeholders also noted that there can be different figures produced from different data sources for the same topics, and noted their difficulties in understanding the differences between these figures and attempting to use them for analysis. It was noted that low coherence between different sources limits the use of agricultural statistics.

Table 3 - Agricultural statistical assets identified by stakeholders as important but not available

Enduring goal	Reportedly missing agricultural statistical assets
Competitive and profitable agriculture, fisheries and forestry industries	supply chain costs and value-adding production volume and value, financial and trade data (for intercensal years at finer spatial scales) all commodities data to enable regional level productivity estimates labour demand, vacancies and skills domestic consumption of fishery and forestry products post farm-gate productivity estimates for transport, food processing and manufacturing farm debt (dimensions of debt and coverage across agriculture industries) value of non-farm assets held by farmers expenditure and return on investment in rural research and development
Prosperous communities	economic and social contribution of agriculture, fisheries and forestry to communities (e.g. employment upstream in supply chain) farmer financial literacy employment derived from seafood imports employment generated by native and plantation forests employment in forest logging transport regional and local food security statistics
Sustainable natural resource use	publically accessible national water trade information status of natural resources and impact of threatening processes peri-urban land management practices and awareness characteristics and motivations of Landcare participants and volunteers recreational fishing participation, catch and effort
Growing trade and market access	detailed import data (e.g. importer characteristics, product type and use) costs for businesses and industry to comply with import/export regulation inputs and pathways to market chemical usage at farm, regional and industry scales
Protecting animal, plant and human health and welfare	chemical use and residues regarding livestock and crop management practices livestock populations and location (at regional/state spatial scale, real time) benefits and costs of responding to and managing pests and diseases genetically modified crops and genetically modified feed sources peri-urban landholders’ location and biosecurity practices/awareness

A number of issues were identified with the accessibility of agricultural statistics. The first related to a lack of discoverability of public and privately-held information. The discoverability of statistical assets held by ABARES and the ABS was raised by stakeholders. Access to ABARES data via the AgSurf website⁴¹ was considered adequate as the site was reported as being easy to navigate and produce custom data sets. However, the lack of accompanying metadata was raised as a concern and a barrier to understanding the quality level of a particular asset. The discoverability of data on the ABS website was a common issue raised by stakeholders, however, they acknowledged that accessibility of ABS statistical assets has improved in recent years.

Stakeholders indicated that there were potentially significant amounts of data held by private organisations that would be beneficial to industry. However, it was acknowledged that private organisations vary in their capacity and willingness to divulge and communicate their data holdings.

Cost was also raised as a barrier to accessing relevant statistical assets, including those held by government agencies such as the ABS, as well as private sector providers. For example the ABS has an information consultancy service that operates on a cost recovery basis and applies charges for customised data provision. Stakeholders felt that the ABS does not provide transparent or consistent information on the cost of accessing custom data sets. A number of government and industry stakeholders noted that they rely on the Global Trade Atlas, an online tool⁴². that enables users to view import/export flows of world trade, which is run by a private data service for a fee based subscription.

The cost to industry of independently undertaking or commissioning collections was also raised. Industry groups valued the data produced by the ABS and ABARES, but noted that the costs of commissioning them to undertake data collection were considered too high. In response, a number of industries had initiated their own primary data collection programs that were more cost effective, however, it was reported that some of these collections were affected by poor response rates as they were not compulsory. Further costs associated with accessing survey frames from the ABS were identified by stakeholders as not consistent and seen as excessive.

Confidentiality requirements were identified as a factor limiting data accessibility. Feedback from users indicated that at times this prevents the release of data necessary to inform decision-making, particularly for small or emerging industries or small geographic areas that are particularly affected by confidentiality restrictions.

Stakeholders also noted difficulties with being able to effectively interpret the available statistics – that is, to understand what the statistics mean and how to use them appropriately. These concerns related to the accompanying information provided with the statistics, such as that provided by ABS and ABARES with their statistical publications. The feedback indicated that users find this information too technical and difficult to interpret, and that more guidance is needed for non-technical users.

FOOTNOTES
³⁹ ABS 2009, ABS Data Quality Framework, May 2009 (cat. no. 1520.0), ABS, Canberra.
⁴⁰ Especially for intercensal years and relating to differences in how stakeholders classify their industry.
⁴¹ AgSurf website
⁴² Global Trade Atlas is provided by Global Trade Information Service Inc.