Using off-farm data to improve on-farm decisions this cropping season

Research focuses on data integration to aid agronomy management

Innovation
University of Sydney Associate Professor Brett Whelan believes growers can benefit from combining off-farm data with on-farm information to guide decision-making in the paddock. PHOTO GRDC

University of Sydney Associate Professor Brett Whelan believes growers can benefit from combining off-farm data with on-farm information to guide decision-making in the paddock. PHOTO GRDC

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Future Farm 2 project seeks to create fast flexible software systems for decision-making.

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One of Australia's leading precision agriculture (PA) experts believes there is powerful potential for grain growers and farm advisers to combine publicly available off-farm data, from sources such as Google Earth Engine (GEE), Digital Earth Australia (DEA) and the Open Data Cube (ODC) initiative, with data from farm machinery to improve decision-making in the paddock.

University of Sydney Associate Professor Brett Whelan, who heads the country's longest-running PA research laboratory, shared his knowledge of relevant, affordable off-farm data sources to inform on-farm decisions during presentations at recent GRDC Grains Research Updates in Goondiwindi and Dubbo.

Prof Whelan says the pool of publicly available off-farm data is rapidly increasing and could complement farm-generated data to produce a powerful, predictive model to guide farm decisions, such as nitrogen (N) budgets.

His work is part of the Future Farm 2 project, which is a GRDC investment with support from the CSIRO, the University of Sydney, the University of Southern Queensland, the Queensland University of Technology and Agriculture Victoria.

A goal of this project is to create fast, flexible decision-aid software that can remove some of the risk from N management decisions for growers, by using various data streams to generate informed recommendations for fertiliser application.

"On-farm generated data on cropping, soil type and nutrients from individual growers within a locality could provide the framework for powerful models that can quickly adapt and improve from year-to-year," Prof Whelan says.

"If we incorporate that with big data about weather and environmental conditions from off-farm data sources, this could help inform decision-making and reduce risk about managing nitrogen application in regard to expected soil moisture availability to optimise yield and quality."

On-farm generated data on cropping, soil type and nutrients from individual growers within a locality could provide the framework for powerful models that can quickly adapt and improve from year-to-year - University of Sydney Associate Professor Brett Whelan

Prof Whelan says the challenges are in fusing the data together to create information that is meaningful and has practical applications for growers.

"Combining large data sets generated by off-farm sources for analysis of the drivers of variability in crop performance and profit, rather than just using individual paddock data, can be very powerful," he says.

"Putting this data together effectively can help growers more accurately estimate yield potential and match input requirements to crop responses."

Prof Whelan says low cost information that is relatively accessible for growers includes:

  • Yield monitor data
  • Vehicle performance data
  • Publicly available data, such as satellite-based imagery.

"The majority of farms already use global navigation satellite systems and yield monitors are becoming standard equipment on harvesters, so the yield mass, grain moisture and elevation data available during harvest comes at a low cost," he says.

Combining large data sets generated by off-farm sources for analysis of the drivers of variability in crop performance and profit, rather than just using individual paddock data, can be very powerful - University of Sydney Associate Professor Brett Whelan

"Likewise, performance data is routinely recorded by newer tractors and self-propelled implements.

"Data on variation in fuel use and other relevant operational parameters can be used in novel ways.

"For example, using power output or fuel use while working with ground-engaging implements can allow growers to map changes in soil type."

Prof Whelan says this increasing use of digital data in agriculture is being led by a combination of improvements in:

  • Sensor development
  • Computing power
  • Data storage/delivery
  • Data analysis techniques
  • Reduced costs.

"These things have fuelled greater interest in data and its potential to help us on-farm," he says.

"This, in turn, has been the catalyst for an increasing number of off-farm data sources being made publicly available at costs that won't break the bank."

For example, the GEE, DEA and ODC platforms provide access to a range of data layers for studying the earth's resources, according to Prof Whelan.

"These extremely large and useful data sets curate data for things such as seasonal crop and soil variability, which allows us to compare the information from year-to-year which could be an invaluable tool for growers when it comes to better informed decision-making," he says.

This increase in the availability of digital data and processing capabilities has resulted in better data fusion techniques and machine learning as agriculture searches for new insights.

"There have been significant developments in machine learning analytical methods, which differ from mechanistic or process-driven models commonly used in cropping because they use data-driven approaches to discover relationships between variables," Prof Whelan says.

"On-farm data from sensors currently used in precision agriculture, along with what will be an increasing variety of sources, volumes and scales and structures of off-farm data (from other local and regional farms and non-farm domains) can now be input into analysis and decision-making back on-farm.

"Growers and farm advisers need to be aware there are increasingly affordable, relevant off-farm sources for data that can genuinely inform what they are doing in the paddock.

"Ideally, we want to reach a point where the process of acquiring data is automated through to analysis and formulation of recommendations with the farm manager or adviser - then having input when it comes to choosing and implementing the management options."

Growers and farm advisers need to be aware there are increasingly affordable, relevant off-farm sources for data that can genuinely inform what they are doing in the paddock - University of Sydney Associate Professor Brett Whelan

Prof Whelan said sources of affordable off-farm data included options such as:

  • CSIRO
  • The Bureau of Meteorology's Gridded Daily Data
  • NASA's Soil Moisture Active Passive (SMAP)
  • Geoscience Australia's Australia-wide airborne geophysical survey (AWAGS)
  • European Space Agency (ESA) Sentinel 2 - for land mapping and climate change.

GRDC Project Code: 9176493

More Information: Toni Somes, GRDC, 0436 622 645, toni.somes@grdc.com.au

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