This dataset presents final estimates for the water usage of agricultural businesses in Australia by Statistical Area Level 2 (SA2) from the 2015-16 Agricultural Census. Categories included are statistics on water use, water sources, irrigation expenditure and irrigation methods. Data is aggregated to 2011 ASGS SA2 boundaries. The Agricultural Census is conducted once every five years. The scope of the 2015-16 Agricultural Census was all businesses undertaking agricultural activity recorded on the Australian Bureau of Statistics’ Business Register (ABSBR) above a minimum threshold applied to the estimated value of their agricultural operations. The threshold for the 2015-16 Agricultural Census was all agricultural businesses with an Estimated Value of Agricultural Operations (EVAO) of $40,000 or greater. This is a change from previous ABS Rural Environment and Agricultural Collections, where a EVAO threshold of $5,000 or greater was used. As a result of the change in scope, the estimates from the 2015-16 Agricultural Census will not be directly comparable to previously published Agricultural Censuses or annual Rural Environment and Agricultural Commodity Survey outputs. The 2015-16 Census final estimates are based on the achieved target response rate of 85% from an in-scope population of approximately 103,400 agricultural businesses. The estimates in this dataset are based on information obtained from agricultural businesses that responded to the 2015-16 Agricultural Census. However, since not all of the businesses that were selected provided data, the estimates are subject to sampling variability; that is, they may differ from the figures that would have been produced if information had been collected from all businesses. Most published estimates have relative standard errors (RSEs) less than 10%. For some states and territories with limited production of certain commodities, RSEs are greater than 10%. Estimates with an RSE greater than 50% are considered too unreliable for general use and hence have been removed from the data. This data is ABS data (catalogue number: 4618.0) used with permission from the Australian Bureau of Statistics. For more information on the dataset please visit the Australian Bureau of Statistics. Please note: Estimates with an RSE value greater than 50% and are considered too unreliable for general use and have been removed from the data

This dataset presents final estimates of the gross values of agricultural commodities produced in Australia by Statistical Area Level 2 (SA2) from the Value of Agricultural Commodities Produced (VACP) collection derived from the Agriculture Census of 2015-2016. Categories included in VACP are statistics on gross and local values of crops, livestock disposals and livestock products. Data is aggregated to 2011 ASGS SA2 boundaries. The Agricultural Census is conducted once every five years. The scope of the 2015-16 Agricultural Census was all businesses undertaking agricultural activity recorded on the Australian Bureau of Statistics’ Business Register (ABSBR) above a minimum threshold applied to the estimated value of their agricultural operations. The threshold for the 2015-16 Agricultural Census was all agricultural businesses with an Estimated Value of Agricultural Operations (EVAO) of $40,000 or greater. This is a change from previous ABS Rural Environment and Agricultural Collections, where a EVAO threshold of $5,000 or greater was used. As a result of the change in scope, the estimates from the 2015-16 Agricultural Census will not be directly comparable to previously published Agricultural Censuses or annual Rural Environment and Agricultural Commodity Survey outputs. The 2015-16 Census final estimates are based on the achieved target response rate of 85% from an in-scope population of approximately 103,400 agricultural businesses. The estimates in this dataset are based on information obtained from agricultural businesses that responded to the 2015-16 Agricultural Census. However, since not all of the businesses that were selected provided data, the estimates are subject to sampling variability; that is, they may differ from the figures that would have been produced if information had been collected from all businesses. Most published estimates have relative standard errors (RSEs) less than 10%. For some states and territories with limited production of certain commodities, RSEs are greater than 10%. Estimates with an RSE greater than 50% are considered too unreliable for general use and hence have been removed from the data. This data is ABS data (catalogue number: 7503.0) used with permission from the Australian Bureau of Statistics. For more information on the dataset please visit the Australian Bureau of Statistics. Please note: Estimates with an RSE value greater than 50% and are considered too unreliable for general use and have been removed from the data Where data was not published or not applicable the records have been set to null

The geography of this dataset is Australian Statistical Local Area (SLA). This data relates to the farmer population for each SL as reported in the 1996 Population and Housing Census.The data is presented at a scale of 25000000. Projection -P Albers Equal-Area Conic -D WGS84 -m 132 -o 0 -p -18 -p -36 -e 0 -n 0The following attributes are contained within the dataset; Sla_code a unique code for Statistical Local Areas (SLA), Sla_name a the name of the Statistical Local Area (SLA) and,Farmer_pop a farmer population per SLA, 1996.

See further metadata for more detail.

This dataset presents final estimates for the water usage of agricultural businesses in Australia by Statistical Area Level 4 (SA4) from the 2015-16 Agricultural Census. Categories included are statistics on water use, water sources, irrigation expenditure and irrigation methods. Data is aggregated to 2011 ASGS SA4 boundaries. The Agricultural Census is conducted once every five years. The scope of the 2015-16 Agricultural Census was all businesses undertaking agricultural activity recorded on the Australian Bureau of Statistics’ Business Register (ABSBR) above a minimum threshold applied to the estimated value of their agricultural operations. The threshold for the 2015-16 Agricultural Census was all agricultural businesses with an Estimated Value of Agricultural Operations (EVAO) of $40,000 or greater. This is a change from previous ABS Rural Environment and Agricultural Collections, where a EVAO threshold of $5,000 or greater was used. As a result of the change in scope, the estimates from the 2015-16 Agricultural Census will not be directly comparable to previously published Agricultural Censuses or annual Rural Environment and Agricultural Commodity Survey outputs. The 2015-16 Census final estimates are based on the achieved target response rate of 85% from an in-scope population of approximately 103,400 agricultural businesses. The estimates in this dataset are based on information obtained from agricultural businesses that responded to the 2015-16 Agricultural Census. However, since not all of the businesses that were selected provided data, the estimates are subject to sampling variability; that is, they may differ from the figures that would have been produced if information had been collected from all businesses. Most published estimates have relative standard errors (RSEs) less than 10%. For some states and territories with limited production of certain commodities, RSEs are greater than 10%. Estimates with an RSE greater than 50% are considered too unreliable for general use and hence have been removed from the data. This data is ABS data (catalogue number: 4618.0) used with permission from the Australian Bureau of Statistics. For more information on the dataset please visit the Australian Bureau of Statistics. Please note: Estimates with an RSE value greater than 50% and are considered too unreliable for general use and have been removed from the data Where data was not published or not applicable the records have been set to null

Data for the paper "Monitoring agricultural and economic drought: the Australian Agricultural Drought Indicators (AADI)"

Data sources include AADI historical data, ABARES AAGIS, ABS Data-by-region (see paper for details). Two files are included one for AAGIS region data, and one for LGA (ABS Data-by-region) data.

See readme.txt.

Variable labels:

"FBP_pfe_hat_ha_drought_perc": "Farm profit (climate only)", "FBP_pfe_hat_ha_profit_perc": "Farm profit (climate price)", "csa_wheat_yield_perc": "Wheat yield", "csa_sorghum_yield_perc": "Sorghum yield", "csa_pasture_growth_perc": "Pasture (AADI)", "W_pasture_TSDM_perc_perc": "Pasture (AussieGRASS)", "W_FY_rain_ag_zone_mask_perc": "Annual (Jul-Jun.) rainfall", "W_winter_rain_winter_crop_zone_mask_perc": "Apr.-Oct. rainfall (winter)", "W_win_sum_perc": "Apr.-Mar. rainfall (winter and summer)", "total_claims_dot": "Annual FHA claims lodged (per total number of agricultural businesses) ", "house_transfers_dot": "House transfer numbers per 1000 population", "dwelling_transfers_dot": "Dwelling transfer numbers per 1000 population", "insolvencies_dot": "Personal insolvencies per 1000 population", "H_wheat_dot": "Wheat yield (AAGIS)", "H_sorghum_dot": "Sorghum yield (AAGIS)", "FBP_pfe": "Farm business profit", "Z_conditions": "Farmer seasonal conditions assessment", "Z_conditions_4": "Farmer drought assessment", "livestock_fertility": "Livestock fertility (net births rate)", "number_jobs_dot": Job numbers per capita, "number_agjobs_dot": Agricultural job numbers per capita, "total_employee_income_dot2": Total employee income per capita, "total_own_uninc_bus_income_dot2": Total unincorporated business income per capita, "total_income_dot2": Total income per capita, "bus_ext_sml_dot": Small business exits (turnover < 200,000) (per 1000 small businesses) , "Births_dot": Births per 1000 population, "Deaths_dot": Deaths per 1000 population, "net_internal_mig_dot": Net internal migration per 1000 population

This dataset presents counts of businesses involved in the production of agricultural commodities, in Australia by Statistical Area Level 2 (SA4), from the 2015-2016 Agricultural Census. Data is …Show full descriptionThis dataset presents counts of businesses involved in the production of agricultural commodities, in Australia by Statistical Area Level 2 (SA4), from the 2015-2016 Agricultural Census. Data is aggregated to 2011 ASGS SA4 boundaries. The Agricultural Census is conducted once every five years. The scope of the 2015-16 Agricultural Census was all businesses undertaking agricultural activity recorded on the Australian Bureau of Statistics’ Business Register (ABSBR) above a minimum threshold applied to the estimated value of their agricultural operations. The threshold for the 2015-16 Agricultural Census was all agricultural businesses with an Estimated Value of Agricultural Operations (EVAO) of $40,000 or greater. This is a change from previous ABS Rural Environment and Agricultural Collections, where a EVAO threshold of $5,000 or greater was used. As a result of the change in scope, the estimates from the 2015-16 Agricultural Census will not be directly comparable to previously published Agricultural Censuses or annual Rural Environment and Agricultural Commodity Survey outputs. The 2015-16 Census final estimates are based on the achieved target response rate of 85% from an in-scope population of approximately 103,400 agricultural businesses. The estimates in this dataset are based on information obtained from agricultural businesses that responded to the 2015-16 Agricultural Census. However, since not all of the businesses that were selected provided data, the estimates are subject to sampling variability; that is, they may differ from the figures that would have been produced if information had been collected from all businesses. Most published estimates have relative standard errors (RSEs) less than 10%. For some states and territories with limited production of certain commodities, RSEs are greater than 10%. Estimates with an RSE greater than 50% are considered too unreliable for general use and hence have been removed from the data. This data is ABS data (catalogue number: 7121.0) used with permission from the Australian Bureau of Statistics. For more information on the dataset please visit the Australian Bureau of Statistics. Please note: Estimates with an RSE value greater than 50% and are considered too unreliable for general use and have been removed from the dataWhere data was not published or not applicable the records have been set to null Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2016): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

Australia Population Density: People per Square Km data was reported at 3.382 Person/sq km in 2022. This records an increase from the previous number of 3.339 Person/sq km for 2021. Australia Population Density: People per Square Km data is updated yearly, averaging 2.263 Person/sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 3.382 Person/sq km in 2022 and a record low of 1.365 Person/sq km in 1961. Australia Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.;Food and Agriculture Organization and World Bank population estimates.;Weighted average;

This dataset presents final estimates of the gross values of agricultural commodities produced in Australia by Statistical Area Level 4 (SA4) from the Value of Agricultural Commodities Produced …Show full descriptionThis dataset presents final estimates of the gross values of agricultural commodities produced in Australia by Statistical Area Level 4 (SA4) from the Value of Agricultural Commodities Produced (VACP) collection derived from the Agriculture Census of 2015-2016. Categories included in VACP are statistics on gross and local values of crops, livestock disposals and livestock products. Data is aggregated to 2011 ASGS SA4 boundaries. The Agricultural Census is conducted once every five years. The scope of the 2015-16 Agricultural Census was all businesses undertaking agricultural activity recorded on the Australian Bureau of Statistics’ Business Register (ABSBR) above a minimum threshold applied to the estimated value of their agricultural operations. The threshold for the 2015-16 Agricultural Census was all agricultural businesses with an Estimated Value of Agricultural Operations (EVAO) of $40,000 or greater. This is a change from previous ABS Rural Environment and Agricultural Collections, where a EVAO threshold of $5,000 or greater was used. As a result of the change in scope, the estimates from the 2015-16 Agricultural Census will not be directly comparable to previously published Agricultural Censuses or annual Rural Environment and Agricultural Commodity Survey outputs. The 2015-16 Census final estimates are based on the achieved target response rate of 85% from an in-scope population of approximately 103,400 agricultural businesses. The estimates in this dataset are based on information obtained from agricultural businesses that responded to the 2015-16 Agricultural Census. However, since not all of the businesses that were selected provided data, the estimates are subject to sampling variability; that is, they may differ from the figures that would have been produced if information had been collected from all businesses. Most published estimates have relative standard errors (RSEs) less than 10%. For some states and territories with limited production of certain commodities, RSEs are greater than 10%. Estimates with an RSE greater than 50% are considered too unreliable for general use and hence have been removed from the data. This data is ABS data (catalogue number: 7503.0) used with permission from the Australian Bureau of Statistics. For more information on the dataset please visit the Australian Bureau of Statistics. Please note: Estimates with an RSE value greater than 50% and are considered too unreliable for general use and have been removed from the dataWhere data was not published or not applicable the records have been set to null Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2016): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

Predicting which species are likely to cause serious impacts in the future is crucial for targeting management efforts, but the characteristics of such species remain largely unconfirmed. We use data and expert opinion on tropical and subtropical grasses naturalised in Australia since European settlement to identify naturalised and high-impact species and subsequently to test whether high-impact species are predictable. High-impact species for the three main affected sectors (environment, pastoral and agriculture) were determined by assessing evidence against pre-defined criteria. Twenty-one of the 155 naturalised species (14%) were classified as high-impact, including four that affected more than one sector. High-impact species were more likely to have faster spread rates (regions invaded per decade) and to be semi-aquatic. Spread rate was best explained by whether species had been actively spread (as pasture), and time since naturalisation, but may not be explanatory as it was tightly correlated with range size and incidence rate. Giving more weight to minimising the chance of overlooking high-impact species, a priority for biosecurity, meant a wider range of predictors was required to identify high-impact species, and the predictive power of the models was reduced. By-sector analysis of predictors of high impact species was limited by their relative rarity, but showed sector differences, including to the universal predictors (spread rate and habitat) and life history. Furthermore, species causing high impact to agriculture have changed in the past 10 years with changes in farming practice, highlighting the importance of context in determining impact. A rationale for invasion ecology is to improve the prediction and response to future threats. Although our study identifies some universal predictors, it suggests improved prediction will require a far greater emphasis on impact rather than invasiveness, and will need to account for the individual circumstances of affected sectors and the relative rarity of high-impact species.

This dataset presents final estimates of the local values of agricultural commodities produced in Australia by Statistical Area Level 4 (SA4) from the Value of Agricultural Commodities Produced (VACP) collection derived from the Agriculture Census of 2015-2016. Categories included in VACP are statistics on gross and local values of crops, livestock disposals and livestock products. Local values are calculated by subtracting transport and marketing costs from the gross value. Data is aggregated to 2011 ASGS SA4 boundaries. The Agricultural Census is conducted once every five years. The scope of the 2015-16 Agricultural Census was all businesses undertaking agricultural activity recorded on the Australian Bureau of Statistics’ Business Register (ABSBR) above a minimum threshold applied to the estimated value of their agricultural operations. The threshold for the 2015-16 Agricultural Census was all agricultural businesses with an Estimated Value of Agricultural Operations (EVAO) of $40,000 or greater. This is a change from previous ABS Rural Environment and Agricultural Collections, where a EVAO threshold of $5,000 or greater was used. As a result of the change in scope, the estimates from the 2015-16 Agricultural Census will not be directly comparable to previously published Agricultural Censuses or annual Rural Environment and Agricultural Commodity Survey outputs. The 2015-16 Census final estimates are based on the achieved target response rate of 85% from an in-scope population of approximately 103,400 agricultural businesses. The estimates in this dataset are based on information obtained from agricultural businesses that responded to the 2015-16 Agricultural Census. However, since not all of the businesses that were selected provided data, the estimates are subject to sampling variability; that is, they may differ from the figures that would have been produced if information had been collected from all businesses. Most published estimates have relative standard errors (RSEs) less than 10%. For some states and territories with limited production of certain commodities, RSEs are greater than 10%. Estimates with an RSE greater than 50% are considered too unreliable for general use and hence have been removed from the data. This data is ABS data (catalogue number: 7503.0) used with permission from the Australian Bureau of Statistics. For more information on the dataset please visit the Australian Bureau of Statistics. Please note: Estimates with an RSE value greater than 50% and are considered too unreliable for general use and have been removed from the data Where data was not published or not applicable the records have been set to null

This scatter chart displays agricultural land (km²) against access to electricity (% of population) in Australia. The data is filtered where the date is 2021. The data is about countries per year.

This dataset presents the Estimated Value of Agricultural Operations in Australia by Statistical Area Level 2 (SA2), from the 2015-2016 Agricultural Census. Data is aggregated to 2011 ASGS SA2 …Show full descriptionThis dataset presents the Estimated Value of Agricultural Operations in Australia by Statistical Area Level 2 (SA2), from the 2015-2016 Agricultural Census. Data is aggregated to 2011 ASGS SA2 boundaries. The Agricultural Census is conducted once every five years. The scope of the 2015-16 Agricultural Census was all businesses undertaking agricultural activity recorded on the Australian Bureau of Statistics’ Business Register (ABSBR) above a minimum threshold applied to the estimated value of their agricultural operations. The threshold for the 2015-16 Agricultural Census was all agricultural businesses with an Estimated Value of Agricultural Operations (EVAO) of $40,000 or greater. This is a change from previous ABS Rural Environment and Agricultural Collections, where a EVAO threshold of $5,000 or greater was used. As a result of the change in scope, the estimates from the 2015-16 Agricultural Census will not be directly comparable to previously published Agricultural Censuses or annual Rural Environment and Agricultural Commodity Survey outputs. The 2015-16 Census final estimates are based on the achieved target response rate of 85% from an in-scope population of approximately 103,400 agricultural businesses. The estimates in this dataset are based on information obtained from agricultural businesses that responded to the 2015-16 Agricultural Census. However, since not all of the businesses that were selected provided data, the estimates are subject to sampling variability; that is, they may differ from the figures that would have been produced if information had been collected from all businesses. Most published estimates have relative standard errors (RSEs) less than 10%. For some states and territories with limited production of certain commodities, RSEs are greater than 10%. Estimates with an RSE greater than 50% are considered too unreliable for general use and hence have been removed from the data. This data is ABS data (catalogue number: 7121.0) used with permission from the Australian Bureau of Statistics. For more information on the dataset please visit the Australian Bureau of Statistics. Please note: Estimates with an RSE value greater than 50% and are considered too unreliable for general use and have been removed from the dataWhere data was not published or not applicable the records have been set to null Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2016): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

It should be noted that this data is now somwhat dated!

Human population density is a surrogate indicator of the extent of human pressures on the surrounding landscapes.

Areas with high population density are associated with higher levels of stream pollution and water diversion through sewers and drains. City and urban environments are substantially changed from their pre-European condition but a changed condition is not of itself necessarily poor by societal standards. It is the impacts such as polluted run-off to waterways, air pollution, sewage disposal, household water use and predation of wildlife by pets that confer impacts on catchment condition. Human population centres have an impact well beyond the built environment.

The impact of major population centres is well expressed in the AWRC map, but is best displayed in the 500 map. The main areas of impact are the major coastal and capital cities and suburbs, including popular beachside tourist destinations. Elsewhere, the impact of population density appears to be confined to the Murray and other major river valleys.

The Australian Bureau of Statistics compiles population statistics by sampling statistical local areas (SLAas) through the national census. These data can be converted to a per catchment basis.

Interpretation of the indicator is largely unequivocal, although there are land-uses/activities (e.g. mining) where population density is not a good indicator of the degree of habitat decline. This indicator has not been validated relative to habitat decline. This indicator is easy to understand.

Data are available as:

• continental maps at 5km (0.05 deg) cell resolution for the ILZ;
• spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
• spatial averages over the AWRC river basins in the ILZ.

See further metadata for more detail.

Prophylactic use of broad-spectrum insecticides is a common feature of broad-acre grains production systems around the world. Efforts to reduce pesticide use in these systems have the potential to deliver environmental benefits to large areas of agricultural land. However, research and extension initiatives aimed at decoupling pest management decisions from the simple act of applying a cheap insecticide have languished. This places farmers in a vulnerable position of high reliance on a few products that may lose their efficacy due to pests developing resistance, or be lost from use due to regulatory changes. The first step towards developing Integrated Pest Management (IPM) strategies involves an increased efficiency of pesticide inputs. Especially challenging is an understanding of when and where an insecticide application can be withheld without risking yield loss. Here, we quantify the effect of different pest management strategies on the abundance of pest and beneficial arthropods, crop damage and yield, across five sites that span the diversity of contexts in which grains crops are grown in southern Australia. Our results show that while greater insecticide use did reduce the abundance of many pests, this was not coupled with higher yields. Feeding damage by arthropod pests was seen in plots with lower insecticide use but this did not translate into yield losses. For canola, we found that plots that used insecticide seed treatments were most likely to deliver a yield benefit; however other insecticides appear to be unnecessary and economically costly. When considering wheat, none of the insecticide inputs provided an economically justifiable yield gain. These results indicate that there are opportunities for Australian grain growers to reduce insecticide inputs without risking yield loss in some seasons. We see this as the critical first step towards developing IPM practices that will be widely adopted across intensive production systems.

The geography of this dataset is Australian Statistical Local Area (SLA). This data relates to members of the population who classified themselves as having an occupation of aFarmera or aFarm Managera in the 1996 Population and Housing Census and have a Bachelor degree, Undergraduate diploma, Associate diploma, Higher degree or Post-graduate qualifications.The data is presented at a scale of 25000000. Projection -P Albers Equal-Area Conic -D WGS84 -m 132 -o 0 -p -18 -p -36 -e 0 -n 0The following attributes are contained within the dataset; Sla_code a unique code for Statistical Local Areas (SLA), Sla_name a the name of the Statistical Local Area (SLA) and,Bach_degre a median age of farmers and farm managers as at census night 1996. Ugrad_dip a hectares of agricultural land use in the Statistical Local Area (SLA).Post_grad a number of farmers/farm managers with postgraduate qualificationsHigher_deg a number of farmers/farm managers with a higher degreeAssoc_dip a number of farmers/farm managers with an associate diploma Totalqual a total number of farmers/farm managers with higher qualificationsTotalpop a total farmer population 1996%Pop a per cent of farmers who have higher qualificationsAg_land_ha a hectares of agricultural land use in the Statistical Local Area (SLA).

See further metadata for more detail.

Growing season rainfall is shown in brackets. In 2010 the crop was canola and in the same location, wheat in 2011.$PS = pre-sow; PSPE = post-sowing, pre-emergence; PE = post-emergence; LS = late season foliar treatments.#An aerial application of metaldehyde was used across all plots to control snails late season.

This dataset presents the management and demographics of farms in Australia by Statistical Area Level 4 (SA4), from the 2015-2016 Agricultural Census. Data is aggregated to 2011 ASGS SA4 boundaries. The Agricultural Census is conducted once every five years. The scope of the 2015-16 Agricultural Census was all businesses undertaking agricultural activity recorded on the Australian Bureau of Statistics’ Business Register (ABSBR) above a minimum threshold applied to the estimated value of their agricultural operations. The threshold for the 2015-16 Agricultural Census was all agricultural businesses with an Estimated Value of Agricultural Operations (EVAO) of $40,000 or greater. This is a change from previous ABS Rural Environment and Agricultural Collections, where a EVAO threshold of $5,000 or greater was used. As a result of the change in scope, the estimates from the 2015-16 Agricultural Census will not be directly comparable to previously published Agricultural Censuses or annual Rural Environment and Agricultural Commodity Survey outputs. The 2015-16 Census final estimates are based on the achieved target response rate of 85% from an in-scope population of approximately 103,400 agricultural businesses. The estimates in this dataset are based on information obtained from agricultural businesses that responded to the 2015-16 Agricultural Census. However, since not all of the businesses that were selected provided data, the estimates are subject to sampling variability; that is, they may differ from the figures that would have been produced if information had been collected from all businesses. Most published estimates have relative standard errors (RSEs) less than 10%. For some states and territories with limited production of certain commodities, RSEs are greater than 10%. Estimates with an RSE greater than 50% are considered too unreliable for general use and hence have been removed from the data. This data is ABS data (catalogue number: 7121.0) used with permission from the Australian Bureau of Statistics. For more information on the dataset please visit the Australian Bureau of Statistics. Please note: Estimates with an RSE value greater than 50% and are considered too unreliable for general use and have been removed from the data Where data was not published or not applicable the records have been set to null

International Polar Year (IPY) Aliens in Antarctica project aims to identify human-mediated pathways for alien propagules into the Antarctic ecosystem (www.aliensinantarctica.aq). As part of this international project, AAD staff examined fresh food and cargo for evidence of propagules prior to shipping south by the Australian Antarctic Program. This report summarises the findings of our food inspections.

A total of 2094 items of fresh fruit and/or vegetables were inspected over the season. Of these 89% (1865 items) were deemed 'clean' (ie no evidence of propagules or infections), 191 (9%0 had evidence of fungal infections, and 54 items (2%) had invertebrates, soil or other propagules such as seeds. Apples, cantaloupes, carrots, grapefruit, limes, oranges, potatoes and tomatoes were recorded as consistently having clean rates of 90% or greater over the 07/08 shipping season.

With regard to the food items found with propagules, a number of significant observations were made. The most notable of these was that of the 56 pears examined at the beginning of the season (Voyage 2) only one was deemed 'clean': the remainder (99%) were rotting with blue moulds. Similarly only 11% of onions destined for Voyage 2 and 49% of bananas were 'clean'; the remainder were observed with fungal infections or other propagules. Other notable observations were that some cabbages and iceberg lettuces were contaminated with soil, and live thrips and white flies (Bemisia sp?) were found in two boxes.

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/ .

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed: 1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2), 2. The proportion of the population experiencing severe food insecurity.

These data were collected by FAO through the Gallup World Poll. General information on the methodology can be found here: https://www.gallup.com/178667/gallup-world-poll-work.aspx. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available under the "DOCUMENTATION" tab above. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

Individuals of 15 years or older with access to landline and/or mobile phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

A dual frame (landline and mobile phone frames) was used to complete 1,000 telephone surveys. About 60% of the completes were from the mobile phone sample whereas landline completes accounted for the remaining 40%. Exclusions: NA Design effect: 1.71

Mode of data collection

Computer Assisted Telephone Interview [cati]

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as 4. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

Asia Pacific Irrigation Machinery Market Outlook

According to our latest research, the global irrigation machinery market size stood at USD 7.2 billion in 2024, with the Asia Pacific region accounting for approximately USD 2.4 billion of this total. The global market is expected to reach USD 13.4 billion by 2033, reflecting a robust CAGR of 7.3% during the forecast period. This growth is primarily driven by the increasing demand for efficient water management solutions in agriculture, rising food security concerns, and the rapid adoption of advanced irrigation technologies across both developed and emerging economies. As per the latest research, the Asia Pacific irrigation machinery market is anticipated to experience significant expansion, fueled by government support, technological advancements, and the region’s growing agricultural sector.

One of the primary growth factors for the Asia Pacific irrigation machinery market is the escalating need for sustainable agricultural practices in the face of water scarcity and climate variability. With the region being home to over 60% of the world’s population and a substantial portion of global agricultural production, there is immense pressure to enhance crop yields while conserving water resources. The adoption of advanced irrigation machinery such as drip and sprinkler systems is proving instrumental in optimizing water usage, reducing wastage, and ensuring consistent crop growth. Furthermore, the increasing prevalence of unpredictable weather patterns and prolonged droughts in countries like India and Australia has compelled farmers to shift from traditional irrigation methods to more efficient, automated systems. This shift is further supported by favorable government policies and subsidies aimed at promoting modern irrigation infrastructure.

Another key driver of market growth is the rapid technological innovation taking place in the irrigation machinery sector. Manufacturers are increasingly focusing on integrating smart technologies, such as IoT-enabled sensors, remote monitoring, and automation, into their irrigation equipment. These advancements enable precise water delivery based on real-time soil moisture data, crop requirements, and weather forecasts, thereby maximizing efficiency and minimizing operational costs. In addition, the emergence of solar-powered irrigation solutions is addressing the challenge of unreliable electricity supply in rural areas, making modern irrigation machinery more accessible to small and medium-sized farms. The convergence of digital agriculture and smart irrigation technologies is expected to further accelerate the adoption of advanced machinery across the Asia Pacific region.

The Asia Pacific irrigation machinery market is also benefiting from increasing investments in agricultural infrastructure and the expansion of commercial farming operations. Large-scale agribusinesses and commercial farms are leading the adoption of high-capacity, automated irrigation systems to achieve economies of scale and meet the rising demand for food products. Meanwhile, small and medium-sized farms are gradually embracing affordable and easy-to-install irrigation solutions as awareness of their benefits grows. The proliferation of greenhouse and nursery farming, particularly in countries like China and Japan, is creating new opportunities for specialized irrigation equipment tailored to controlled environments. Overall, the market is poised for sustained growth as stakeholders across the agricultural value chain recognize the importance of efficient water management in boosting productivity and sustainability.

Regionally, the Asia Pacific market exhibits considerable diversity, with China and India emerging as the largest contributors due to their vast agricultural landscapes and sizable farming populations. Australia and Japan are also significant markets, characterized by high levels of technological adoption and a focus on sustainable agriculture. Southeast Asia, with its rapidly expanding agrarian sector and increasing government initiatives, represents a high-potential growth area. The overall regional outlook is positive, with continued investments, supportive policies, and ongoing technological advancements expected to drive the market forward through 2033.