Productivity in Australia remained unchanged at 99.50 points in the first quarter of 2025 from 99.50 points in the fourth quarter of 2024. This dataset provides - Australia Productivity - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Key information about Australia Labour Productivity Growth

• Australia Labour Productivity improved by 1.36 % YoY in Dec 2024, compared with a drop of 1.80 % in the previous quarter
• Australia Labour Productivity Growth data is updated quarterly, available from Jun 1979 to Dec 2024, averaging at 0.90 %
• The data reached an all-time high of 6.25 % in Mar 1984 and a record low of -3.99 % in Jun 1986

CEIC calculates quarterly Labour Productivity Growth from quarterly Real GDP and monthly Employment. The Australian Bureau of Statistics provides Real GDP in local currency, at 2022-2023 prices and Employment. Employment excludes Foreign Nationals working within the country

In financial year 2024, the labor productivity index (LPI) of the construction industry in Australia amounted to 103.97 compared to the base year of 2023.

The gross domestic product (GDP) per capita in Australia was estimated at 66,250 U.S. dollars in 2024. From 1980 to 2024, the GDP per capita rose by 55,240 U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. Between 2024 and 2030, the GDP per capita will rise by 8,640 U.S. dollars, showing an overall upward trend with periodic ups and downs.This indicator describes the gross domestic product per capita at current prices. Thereby, the gross domestic product was first converted from national currency to U.S. dollars at current exchange rates and then divided by the total population. The gross domestic product is a measure of a country's productivity. It refers to the total value of goods and service produced during a given time period (here a year).

Australia CO2 Productivity: CO2 Emissions from Air Transport per Capita data was reported at 446.550 Tonne in 2022. This records an increase from the previous number of 237.980 Tonne for 2021. Australia CO2 Productivity: CO2 Emissions from Air Transport per Capita data is updated yearly, averaging 675.990 Tonne from Dec 2013 (Median) to 2022, with 10 observations. The data reached an all-time high of 688.770 Tonne in 2013 and a record low of 237.980 Tonne in 2021. Australia CO2 Productivity: CO2 Emissions from Air Transport per Capita data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Australia – Table AU.OECD.GGI: Environmental: CO2 Productivity: OECD Member: Annual.

The report Australian Farm Survey Results, 2013-14 to 2015-16 reproduces three papers previously published in the March quarter 2016 edition of Agricultural Commodities: Farm performance: broadacre and dairy farms; Productivity in Australian broadacre and dairy industries; and Disaggregating farm performance statistics by size. The re-packaged papers provide an easy to access publication with ABARES farm surveys results in one location. \r \r The analysis in the report is mainly based on data collected in 2015-16 from the Agricultural and Grazing Industries Survey (AAGIS) and the Australian Dairy Industry Survey (ADIS). These surveys are funded by the Department of Agriculture and Water Resources, the Grains Research and Development Corporation (GRDC) and Meat and Livestock Australia (MLA). \r \r Key issues \r Farm financial performance • In 2015-16, ABARES estimates that average farm cash income will increase in New South Wales, Queensland, South Australia, Western Australia and the Northern Territory. Dry seasonal conditions in Victoria and Tasmania have reduced crop and livestock production resulting in a reduction in projected farm cash incomes. \r • For Australia as a whole, farm cash income of broadacre farms is projected to average $179 000 a farm in 2015-16 - the highest recorded in the past 20 years. \r • The expected increase in farm cash incomes in 2015-16 has been driven by high livestock prices - especially for beef cattle - and good winter grain production in most regions. \r • It is projected that average farm cash income of dairy farms will decline by 26 per cent to an average of $113 000 a farm in 2015-16, reflecting lower farmgate milk prices, reduced production and higher fodder costs. \r Productivity in Broadacre and dairy industries • Productivity in the broadacre industries grew by 1.1 per cent a year on average between 1977-78 and 2013-14. Broadacre productivity growth was driven largely by declining input use while maintaining modest output growth. \r • While dairy industry productivity grew by 1.6 per cent a year on average between 1978-79 and 2013-14. This reflects strong output growth (1.3 per cent a year) and some reduction in input use (-0.2 per cent a year). \r Disaggregating farm performance statistics by size • The economic performance of farms in 10 size categories is presented. For each size category, the following measures are presented: share of total output produced, total cash receipts, total cash costs, profit at full equity, total opening capital, net capital additions, rate of return, including capital appreciation and equity ratio. \r • The largest 10 per cent of farms produced 48 per cent of all broadacre farm output, while the smallest 50 per cent of farms produced 11 per cent of total broadacre output. \r

Overview
The report was commissioned by the Grains Research and Development Corporation (GRDC). The report draws on data from the ABARES annual Australian Agricultural and Grazing Industries Survey (AAGIS) to provide an overview of production, financial performance and productivity growth of the Australian grains, oilseed and pulse industry from 2012-13 to 2014-15.

Key Issues
• Farm cash income in all three Grains Research and Development Corporation (GRDC) regions is estimated to have declined in 2014-15 as a result of a reduction in winter grain yields and lower prices for wheat, oilseeds and pulses. • Incomes of Australian grain producing farms have been relatively high in recent years, compared with incomes recorded historically. • For Southern region grain producing farms, farm cash income is estimated to have decreased in 2014-15 to average $184 000 a farm; 43 per cent above the 10-year average to 2013-14 of $129 000 a farm in real terms. • Similarly for Western region grain producing farms, farm cash income is estimated to have decreased in 2014-15 to average $262 000 a farm; 24 per cent above the 10-year average to 2013-14 of $211 000 in real terms. • Dry seasonal conditions in the Northern region have resulted in estimated farm cash income of grain producing farms falling to average $79 000 a farm in 2014-15, around 24 per cent below the 10-year average to 2013-14 of $105 000 a farm in real terms. If realised, this would be the lowest farm cash income for Northern region grain growing farms since 2006-07. • Nationally, farm cash income of grain producing farms increased from $189 590 in 2012-13 to $213 100 in 2013-14. In 2014-15 farm cash income is estimated to have declined to average $171 000 a farm, around 24 per cent above the 10-year average to 2013-14 of $137 000 in real terms.

This report contains ABARES' latest outlook to 2019-20 for Australia's major agricultural commodities. In addition, this publication includes articles titled Farm performance: broadacre and dairy farms, 2012-13 to 2014-15, Productivity in the broadacre and dairy industries, Profitability and productivity in Australia's beef industry and boxes titled Key agricultural outcomes of recent free trade agreements and Use and supply of barley in China.

A limited number of printed copies will be available by contacting info.abares@agriculture.gov.au

Key Issues Commodity outlook
• Export earnings from farm commodities are forecast to be about $40.5 billion in 2015-16 compared with a forecast $40.3 billion in 2014-15. • Agricultural commodities for which export earnings are forecast to rise in 2015-16 include wheat (up by 12 per cent), sugar (11 per cent), canola (10 per cent), dairy products (8 per cent) and beef and veal (2 per cent). These forecast increases are expected to be largely offset by forecast falls in export earnings for mutton (39 per cent), cotton (35 per cent), barley (11 per cent) and lamb (8 per cent). • At the end of the outlook period (2019-20), the value of farm exports is projected to be around $41.2 billion (in 2014-15 dollars), 9 per cent higher than the average over the five years to 2013-14. • The gross value of farm production is forecast to increase by 5.3 per cent to around $54.4 billion in 2015-16, following a forecast decrease of 2.9 per cent to $51.6 billion in 2013-14. • The gross value of livestock production is forecast to increase by around 5.6 per cent to $25.9 billion in 2015-16, following a forecast increase of 5.9 per cent in 2014-15. The gross value of crop production is forecast to increase by 5.1 per cent to $28.5 billion in 2015-16, after a forecast decrease of 9.6 per cent in 2013-14. • Export earnings for fisheries products are forecast to increase by 8 per cent to around $1.5 billion in 2015-16, after increasing by a forecast 3 per cent in 2014-15. • The outlook for agricultural commodities is based on the assumption that world economic activity will increase by 3.3 per cent in 2015, the same growth rate as in 2014. World economic growth is assumed to strengthen to 3.8 per cent in 2016, before easing gradually to 3.5 per cent towards 2020. • In Australia, economic activity is assumed to grow by 2.5 per cent in 2014-15, strengthening to 3.5 per cent by 2016-17. Toward 2019-20, economic growth is assumed to average around 3.2 per cent a year. • The Australian dollar is assumed to average around US76c in 2015-16, compared with an assumed average of US83c in 2014-15. It is assumed to average around US76c from 2015-16 to 2019-20.

Farm financial performance
• Using data from the most recent Australian Agricultural and Grazing Industries Survey (AAGIS), ABARES has made projections of average farm cash income for Australian broadacre farms in 2014-15. It is projected that average farm cash income will decline in Victoria, Western Australia, South Australia and New South Wales but increase in Queensland, the Northern Territory and Tasmania. • For Australia as a whole, the average farm cash income of Australian broadacre farms in 2014-15 is projected to be around $114 000 a farm, a fall of 9 per cent from an estimated $124 600 a farm in 2013-14. • It is projected that average farm cash income of dairy farms will decline from an average of $163 900 a farm in 2012-13 to an average of $97 000 a farm in 2014-15, reflecting lower farmgate milk prices. • Broadacre farm debt is estimated to have remained largely unchanged in 2013-14, averaging around $512 500 a farm at 30 June 2014.

Productivity in Australia's broadacre and dairy industries
• Productivity in the broadacre industries grew by 1.1 per cent a year on average between 1977-78 and 2012-13. • This broadacre productivity growth reflects a decline in the use of inputs (-1.0 per cent a year) while achieving modest output growth (0.1 per cent a year). • Dairy industry productivity grew at an annual average rate of 1.7 per cent between 1978-79 and 2012-13. This reflects strong output growth (1.3 per cent a year) and a small reduction in the use of inputs (-0.4 per cent a year).

Profitability and productivity in Australia's beef industry
• Average profitability in the beef industry has been low for many years. At the farm level, technological progress appears to be relatively slow. There is a large proportion of small, generally unprofitable farms that significantly reduce the average profitability per farm for the whole industry.

ObjectiveTo estimate the productivity impacts of a policy intervention on the prevention of premature mortality due to obesity.MethodsA simulation model of the Australian population over the period from 2003 to 2030 was developed to estimate productivity gains associated with premature deaths averted due to an obesity prevention intervention that applied a 10% tax on unhealthy foods. Outcome measures were the total working years gained, and the present value of lifetime income (PVLI) gained. Impacts were modelled over the period from 2003 to 2030. Costs are reported in 2018 Australian dollars and a 3% discount rate was applied to all future benefits.ResultsPremature deaths averted due to a junk food tax accounted for over 8,000 additional working years and a $307 million increase in PVLI. Deaths averted in men between the ages of 40 to 59, and deaths averted from ischaemic heart disease, were responsible for the largest gains.ConclusionsThe productivity gains associated with a junk food tax are substantial, accounting for almost twice the value of the estimated savings to the health care system. The results we have presented provide evidence that the adoption of a societal perspective, when compared to a health sector perspective, provides a more comprehensive estimate of the cost-effectiveness of a junk food tax.

Overview
The March edition of Agricultural commodities contains ABARES' latest outlook for Australia's key agricultural commodities to 2021-22. The outlook will be an important focal point at the conference and underpin many presentations delivered by ABARES speakers at the conference. The report provides updated commodity forecasts, as well as articles on the EU sheep meat industry; farm performance of broadacre and dairy farms; productivity in Australia's broadacre and dairy industries; and disaggregating farm performance by size.

Key Issues Commodity forecasts
• The gross value of farm production is forecast to increase by 8.3 per cent to a record $63.8 billion in 2016-17 before easing by 3.9 per cent to a forecast $61.3 billion in 2017-18. Despite the forecast decline, the gross value of farm production in 2017-18 would be 17.3 per cent higher than the average of $52.3 billion over the five years to 2015-16 in nominal terms. • The gross value of livestock production is forecast to increase by around 4.4 per cent to $31.2 billion in 2017-18, following a forecast decrease of 2.6 per cent in 2016-17. If this forecast is realised, the gross value of livestock production in 2017-18 would be around 28 per cent higher than the average of $24.4 billion over the five years to 2015-16 in nominal terms. • The gross value of crop production is forecast to decrease by 11.3 per cent to $30 billion in 2017-18, after a forecast increase of 20.2 per cent in 2016-17. The decrease follows record production of wheat and barley in 2016-17, which resulted from favourable seasonal conditions during winter and spring. If this forecast is realised, the gross value of crop production in 2017-18 would be around 8 per cent higher than the average of $27.9 billion over the five years to 2015-16 in nominal terms. • In 2021-22 the gross value of farm production is projected to be around $59.6 billion (in 2016-17 dollars), 8.6 per cent higher than the average of $54.9 billion over the five years to 2015-16 (also in 2016-17 dollars). In 2021-22 the gross value of crop production is projected to be around $29.0 billion and the gross value of livestock production is projected to be around $30.6 billion (in 2016-17 dollars). • Export earnings from farm commodities are forecast to be around $48.7 billion in 2017-18, higher than the forecast $47.7 billion in 2016-17. • The agricultural commodities for which export earnings are forecast to rise in 2017-18 are beef and veal (up 1 per cent), wool (10 per cent), dairy products (11 per cent), sugar (10 per cent), cotton (35 per cent), wine (5 per cent), lamb (3 per cent), live feeder/slaughter cattle (4 per cent), rock lobster (6 per cent) and mutton (1 per cent). • Forecast increases in 2017-18 are expected to be partly offset by expected declines in export earnings for wheat (down 9 per cent), coarse grains (11 per cent), canola (6 per cent) and chickpeas (42 per cent). • In Australian dollar terms, export prices of wool, dairy products, sugar, wine, lamb, barley, canola, rock lobster and mutton are forecast to increase in 2017-18. Export prices for cotton and chickpeas are forecast to fall. Prices for beef and veal, wheat and live feeder/slaughter cattle are forecast to remain around the same as in 2016-17. • In 2021-22 the value of farm exports is projected to be around $46.6 billion (in 2016-17 dollars), 8 per cent higher than the average of $43.1 billion over the five years to 2015-16 in real terms. • The value of crop exports is projected to be $24.9 billion (in 2016-17 dollars) in 2021-22, 7 per cent higher than the average of $23.2 billion over the five years to 2015-16 in real terms. The value of livestock exports is projected to be $21.8 billion (in 2016-17 dollars) in 2021-22, 10 per cent higher than the average of $19.8 billion over the five years to 2015-16 in real terms. • Export earnings for fisheries products are forecast to increase by 2.3 per cent in 2017-18 to $1.5 billion, after decreasing by a forecast 3.4 per cent in 2016-17.

Economic assumptions underlying this set of commodity forecasts

In preparing this set of agricultural commodity forecasts: • World economic growth is assumed to be 3.3 per cent in 2017 and 3.4 per cent in 2018. Growth is expected to rise further to around 3.5 per cent in 2019 before declining to 3.4 per cent in 2021 and 3.3 per cent in 2022. • Economic growth in Australia is assumed to average 2.8 per cent in 2017-18. Over the medium term to 2021-22, economic growth is assumed to average around 3 per cent. • The Australian dollar is assumed to average US73 cents in 2017-18, slightly lower than the forecast average of US75 cents in 2016-17. It is assumed to appreciate slightly over the medium term, reaching US74 cents towards 2021-22.

Articles on agricultural issues
The EU sheep meat industry
• The European Union is one of the world's largest consumers of sheep meat. Imports are controlled by import quotas and prohibitive out-of-quota tariffs. • Australia is the second largest exporter to the European Union, behind New Zealand, although its allocated quota is just 8 per cent that of New Zealand's. • As a high value market for sheep meat, expanding sheep meat exports to the European Union would benefit the Australian industry. However, until the trade outcomes of Brexit are known, opportunities for Australian sheep meat exporters are uncertain.

Farm performance: broadacre and dairy farms, 2014-15 to 2016-17
• In 2016-17 farm cash income for Australian broadacre farms is projected to average $216,000 a farm, the highest recorded in the past 20 years. • Record broadacre farm cash incomes this year are the result of near record winter grain production in most regions and good prices for beef cattle, sheep, lamb and wool. • Average farm cash income is projected to increase for broadacre farms in all states except Tasmania in 2016-17. • Farm cash income for dairy farms is projected to decline by 17 per cent nationally to an average of $105,000 a farm in 2016-17, reflecting lower average farmgate milk prices and reduced milk production.

Productivity in Australia's broadacre and dairy industries
• From 1977-78 to 2014-15, productivity in the broadacre industries averaged 1.1 per cent a year as a result of declining input use (down 1 per cent a year) and modest output growth (up 0.1 per cent a year). • In the dairy industry, productivity growth averaged 1.5 per cent a year between 1978-79 and 2014-15. This reflected average annual growth of 1.3 per cent in output and an average annual decline of 0.2 per cent in input use.

Disaggregating farm performance by size
• The largest 10 per cent of broadacre farms produced 46 per cent of total output, while the smallest 50 per cent of farms produced 12 per cent of total output. • The average rate of return, including capital appreciation, generated by the largest 10 per cent of broadacre farms was 8.2 per cent, while the smallest 10 per cent generated average returns of -2.8 per cent. • The largest 10 per cent of broadacre farms had the lowest average equity ratio of all farms (79 per cent), while the smallest 10 per cent of farms had the highest average equity ratio (97 per cent).

Estimates of potential net primary production were made in the mangroves forests of nine Cape York rivers: Jardine River (1 site); Escape River (3 sites); Harmer Creek (1 site); Olive River (2 sites), Pascoe River (1 site); Nesbit River (1 site); Rocky River (3 sites); Annan River (2 sites) and Annie River (3 sites). Two sites in northwestern Australia (Derby Wharf and Hunter River) and six sites in the delta of the Purari River, Papua New Guinea were also sampled. The sites chosen covered a range of environments from river mouth to upstream tidal limits, had a well developed canopy and usually had an area of at least 50x50m of continuous forest.

Soil sampling was carried out at the Olive River, Claudie River, Normanby River, Lockhart River, McIvor River and Endeavour River.

At each site, usually >100 light attenuation readings were made at 1m intervals on random walks through the forest. Readings were also taken above the canopy. These measurements enabled the calculation of an average canopy chlorophyll content, which was then converted to a productivity estimate.

At sites where soil was sampled, a single core was taken to a depth of 50cm. Five subsamples were taken at 10cm intervals for redox potential measurements in the field and later nutrient analysis (extractable phosphate and extractable ammonium). The water which filled the holes after core extractions was sampled and soil water salinity measured with a portable conductivity meter.

Port of Melbourne: Container Terminals Productivity data was reported at 244,233.000 TEU in Mar 2025. This records an increase from the previous number of 235,963.000 TEU for Feb 2025. Port of Melbourne: Container Terminals Productivity data is updated monthly, averaging 231,995.000 TEU from Nov 2016 (Median) to Mar 2025, with 101 observations. The data reached an all-time high of 265,507.000 TEU in Oct 2022 and a record low of 167,786.000 TEU in Feb 2017. Port of Melbourne: Container Terminals Productivity data remains active status in CEIC and is reported by Port of Melbourne. The data is categorized under Global Database’s Australia – Table AU.TA023: Port Statistics: Port of Melbourne. [COVID-19-IMPACT]

IntroductionEndometriosis has a significant cost of illness burden in Europe, UK and the USA, with the majority of costs coming from reductions in productivity. However, information is scarce on if there is a differing impact between endometriosis and other causes of chronic pelvic pain, and if there are modifiable factors, such as pain severity, that may be significant contributors to the overall burden.MethodsAn online survey was hosted by SurveyMonkey and the link was active between February to April 2017. Women aged 18–45, currently living in Australia, who had either a confirmed diagnosis of endometriosis via laparoscopy or chronic pelvic pain without a diagnosis of endometriosis were included. The retrospective component of the WERF EndoCost tool was used to determine direct healthcare costs, direct non-healthcare costs (carers) and indirect costs due to productivity loss. Estimates were extrapolated to the Australian population using published prevalence estimates.Results407 valid responses were received. The cost of illness burden was significant in women with chronic pelvic pain (Int $16,970 to $ 20,898 per woman per year) irrespective of whether they had a diagnosis of endometriosis. The majority of costs (75–84%) were due to productivity loss. Both absolute and relative productivity costs in Australia were higher than previous estimates based on data from Europe, UK and USA. Pain scores showed the strongest relationship to productivity costs, a 12.5-fold increase in costs between minimal to severe pain. The total economic burden per year in Australia in the reproductive aged population (at 10% prevalence) was 6.50 billion Int $.ConclusionSimilar to studies in European, British and American populations, productivity costs are the greatest contributor to overall costs. Given pain is the most significant contributor, priority should be given to improving pain control in women with pelvic pain

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

Screening of Australia BF: (a) wheat growers Mixed crops including both wheat and barley Professional farmers, full time on the farm No-till/Minimum till system Both wheat and barley
A good understanding of soil disease status of their paddocks (Rhizoctonia) A good understanding of the resistance status of weeds on their property, and an integrated approach to weed management.

Location: Bordertown, Wimmera, Victoria --> cut off: 1500-4000 ha (farm size)
Location: Victoria slopes region, Riverina, New South Wales, Central region --> cut off: 1400 ha (farm size)
Location: Avon Valley, Western Australia - Artea from Bolgart to Kelleberrin, Quairading to Brookton and York --> Cut-off: 150-3500 ha (farm size)

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

The Australia Agricultural Machinery Market size was valued at USD 4.11 Million in 2023 and is projected to reach USD 7.14 Million by 2032, exhibiting a CAGR of 8.20 % during the forecasts periods. The surge is driven by several factors: Recent developments include: August 2023: John Deere launched John Deere 1 Series Round Balers in the Australian market. It has the Bale Doc technology to document bale moisture and weight in near real-time., August 2022: John Deere launched a 9000 Series Self-Propelled Forage Harvester to deliver more power, precision, and productivity and provide farmers and contractors with 10% more productivity and 10% less fuel usage per ton harvested., May 2022: John Deere introduced a new range of P600 Precision Air Hoe drills to deliver up to a 15.5% larger working width and 24% weight reduction compared to the P500 to decrease compaction, labor time, and fuel consumption while optimizing productivity and seed to soil contact.. Key drivers for this market are: Declining Labour Availability and Rising Cost of Farm Labour, Rapid Technological Advancements by Key Players. Potential restraints include: High Cost of Agricultural Machinery and Repair, Data Privacy Concerns in Modern Farming. Notable trends are: Rising Preference for Farm Mechanization.

In August/September 1995, visual surveys of seagrass abundance were undertaken along 47 transects, located along the eastern and western coasts of Exmouth Gulf. Transects extended from the shoreward to the seaward border of the zone vegetated with macrophytes. Spot checks were made at approximately every 10m to 20m along each transect and the percent cover and type of vegetation (identified to genus, where possible) were recorded. When there was taxonomic uncertainty, voucher specimens were taken for later identification.Destructive sampling was carried out along transects at three sites: Tent Island (2 transects), Simpson Island (2 transects) and Exmouth Town (3 transects). Five or ten 0.25m² quadrats were randomly placed along each 50m transect. Quadrats were photographed, the percent cover of macrophytes was estimated and the density of seagrass was measured. From each site, the species composition of 5 Cymodocea samples was quantified.All biomass was removed from each quadrat including seagrass roots and rhizomes to a depth of 20cm. Below ground material from Thalassodendron ciliatum was not collected. After drying samples for 48 hours at 70°C, epiphytes were removed and the remaining sample was ashed for 18 hours at 450°C and the dry weight determined. Subsamples were analysed for total carbon and nitrogen.At the three sites, all distal shoots of Cymodocea serrulata occurring along a 20m transect were tagged and at Exmouth, 25 shoots of Thalassodendron ciliatum were also tagged. After 6, 12 or 16 days (depending on the site), complete shoots were collected, measured and divided into new and old parts of leaves. The material was then dried and analysed for carbon and nitrogen content.
This research was initiated to collect data on biomass and production rates of seagrasses to assess their potential contribution to the food webs of Exmouth Gulf.

In November 1996, three sites were selected along Hinchinbrook Channel, which represented the major forest types and environmental range within the channel. Six plots, each usually 400 m² were marked out at each site and all trees were tagged, identified to species level and the diameter at breast height (DBH) was measured. The biomass of each tree was estimated using previously established allometric relationships between DBH and biomass. The two year time span of this project was insufficient to reliably measure biomass accumulation within these plots. Estimates were obtained from permanent plots in Missionary Bay, which had a 5 year record. Below ground biomass accumulation was calculated using the relationship between above-ground and below-ground biomass derived for Rhizophora apiculata in Malaysia.The forest canopy was accessed from steel towers, up to 10m in height, constructed in three forest stands with different species compositions. From top to bottom, a 2x1 m section of the canopy was divided into horizontal layers of 0.5 m thickness. Within each layer, in two of the stands, (a mixed stand of Bruguiera gymnorhiza/Rhizophora stylosa and a mixed stand of Rhizophora apiculata/Rhizophora stylosa), the angle of each leaf was measured and leaves removed to measure area and dry weight. The measurements taken were used in the simulation of canopy light profiles. In all plots, indirect estimates of canopy leaf area index were obtained from measurements of light flux density with a quantum sensor.Photosynthetic rates were determined for Bruguiera gymnorhiza, Bruguiera parviflora, Ceriops australis, Ceriops tagal, Rhizophora apiculata, Rhizophora lamarkii, Rhizophora stylosa, Heritiera littoralis and Xylocarpus granatum. Rates were measured on leaves at their natural angle of inclination at different levels in the canopy using a portable photosynthesis system.
This research was undertaken to estimate net canopy carbon fixation and carbon accumulation as living biomass in mangrove forests using data on stand structure and rates of photosynthesis.
This study is a component of a broader investigation of carbon fixation and storage by mangrove ecosystems.

The MCCN project is to deliver tools to assist the agricultural sector to understand crop-environment relationships, specifically by facilitating generation of data cubes for spatiotemporal data. This repository contains Jupyter notebooks to demonstrate the functionality of the MCCN data cube components.The dataset contains input files for the case study (source_data), RO-Crate metadata (ro-crate-metadata.json), results from the case study (results), and Jupyter Notebook (MCCN-CASE 6.ipynb)Research Activity Identifier (RAiD)RAiD: https://doi.org/10.26292/8679d473Case StudiesThis repository contains code and sample data for the following case studies. Note that the analyses here are to demonstrate the software and result should not be considered scientifically or statistically meaningful. No effort has been made to address bias in samples, and sample data may not be available at sufficient density to warrant analysis. All case studies end with generation of an RO-Crate data package including the source data, the notebook and generated outputs, including netcdf exports of the datacubes themselves.Case Study 6 - Environmental Correlates for ProductivityDescriptionAnalyse relationship between different environmental drivers and plant yield. This study demonstrates: 1) Loading heterogeneous data sources into a cube, and 2) Analysis and visualisation of drivers. This study combines a suite of spatial variables at different scales across multiple sites to analyse the factors correlated with a variable of interest.Data SourcesThe dataset includes the Gilbert site in Queensland which has multiple standard sized plots for three years. We are using data from 2022. The source files are part pf the larger collection - Chapman, Scott and Smith, Daniel (2023). INVITA Core site UAV dataset. The University of Queensland. Data Collection. https://doi.org/10.48610/951f13cBoundary file - This is a shapefile defining the boundaries of all field plots at the Gilbert site. Each polygon represents a single plot and is associated with a unique Plot ID (e.g., 03_03_1). These plot IDs are essential for joining and aligning data across the orthomosaics and plot-level measurements.https://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/shp.zip.Orthomosaics - The site was imaged by UAV flights multiple times throughout the 2022 growing season, spanning from June to October. Each flight produced an orthorectified mosaic image using RGB and Multispectral (MS) sensors.https://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/2022-09-18.tifhttps://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/UQ_GilbertN_danNVT_2022-07-28_10-00-00_Altum_bgren_20m_transparent_reflectance_packed.tifhttps://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/UQ_GilbertN_danNVT_2022-08-08_10-00-00_Altum_bgren_20m_transparent_reflectance_packed.tifPlot level measurements - Multispectral Traits: Calculated from MS sensor imagery and include indices NDVI, NDRE, SAVI and Biomass Cuts: Field-measured biomass sampled during different growth stages (used as a proxy for yield).https://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/filtered_biomass_updated.csvhttps://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/filtered_multispec_aggregated.csv