Top 3 data types used by Australian crop farmers

Top 3 data types used by Australian crop farmers

Vast amounts of data are produced by crop farming enterprises.

Here are the top 3 types used by Australian crop farmers and how it is typically stored on farm.

For anyone familiar with farming, Silicon Valley and the AgTech space, it is common knowledge that Smart Farming produces enormous of data, and there are a range of ways that it can be used to inform management decisions.

Back in 2017, our data provider KG2 was engaged to conduct a survey on behalf of the CSIRO and Agricultural Research Development Corporations using our farmer database.

This study was designed to benchmark Australian farmer’s needs, perceived risks and expectations regarding the digitalisation of agricultural and farm data (Jakku et al, 2017).

This was known as the “precision to decision “or P2D project, and KG2 spoke to 1000 Australian farmers about their thoughts and approach to farm data collection and use.

305 of these farmers were from the cropping industry, and 87% of these crop farmers collected at least one type of data (Jakku et al, 2017).

Here are the top 3 farm data types they said they collected.

1.   Financial data

Financial data had the highest collection rate amongst crop farmers at 72% (Jakku et al, 2017).

This is relatively unsurprising, given that a farm is ultimately a business enterprise that needs to account for its cash flow.

But how useful is this data?

Financial data was rated as the most useful data type by these farmers, with an average score of 4.2 out of 5 (Jakku et al, 2017).

Volatile commodity prices, weather and rising input costs all increase the need for personal visibility over farm finances in order to make informed business decisions, be that investing in a new tractor or buying in seed for next season’s crop.

But how is this data stored?

Storing financial data electronically on farm was the preferred method at 71% (Jakku et al, 2017).

Interestingly, 12% indicated that they store financial data on farm on paper, and 12% indicated they store financial data In-Cloud (Jakku et al, 2017).

Service providers were ultimately the the least used storage method for financial data (Jakku et al, 2017).

2.   Yield mapping data

Yield mapping data had the second highest data collection rate at 51% (Jakku et al, 2017).

Yield mapping promises enhanced visibly for farmers regarding land productivity, allowing them to map, benchmark and monitor variation.

The usefulness of this data increases over time as year-to year variation in yield distribution can be analysed, painting a more insightful picture of the performance of farmland.

But how useful is this data?

Yield mapping data received an average usefulness rating of 3.7 out of 5 (Jakku et al, 2017).

Multiple factors could influence the lower usefulness rating of this data type. Cost vs perceived benefit will always affect adoption rates, but another area to explore could be the relative usefulness of this data type in isolation vs in combination with soil and nutrient mapping data.

But how is this data stored? 

Only 59% of crop farmers stored yield mapping data electronically on farm, and interestingly, 21% indicated that they stored this data type on paper on farm (Jakku et al, 2017).

Similar to financial data, service providers were the least preferred storage method. (Jakku et al, 2017).

3.   Soil mapping data

Soil mapping data had the third highest data collection rate for crop farmers at 41% (Jakku et al, 2017).

Moisture and nutrient levels play a large role in soil health, so variations in soil type combined with these attributes means different parts of a farm may lead to better crop performance.

It comes as no surprise that many crop farmers collect this type of data, especially as they scale their enterprises and seek to maximise output through efficient input use and land use.

But how useful is this data?

Interestingly, soil mapping data received an average usefulness rating of 3.9 out of 5, higher than that of yield mapping.

Being able to map a property and its soil variability provides farmers with important information in terms of fertiliser use, application rate and even the type of plant they should grow.

Thus, soil mapping data can allow farmers to investment in chemicals and fertilisers according soil needs with a high degree of awareness and understanding.

But how is this data stored?

48% of crop farmer stored soil mapping data on farms electronically and 36% stored this data on paper (Jakku et al, 2017).

Storing this data electronically allows for greater application and insight, as different types of farm data can be overlaid for more informed decision making.

The future of farm data in the cropping industry

Farm data presents a lot of opportunities to the Australian cropping industry in terms of enhanced management and visibility over enterprise performance.

Given this study was conducted in 2017, it would be interesting to see if adoption rates have increased and to compare results of a more recent study in order to look for changes in usefulness ratings and farm data storage methods. The KG2 farmer database is used by numerous agribusiness stakeholders for market research purposes, contributing such understanding.

Equipment and software to monitor multiple farm-land variables will enhance the usefulness of farm data when correlated, but its affordability (or lack thereof) still plays a huge role in  farmer uptake.

Enhancing rural connectivity, affordability and measurable ROI will therefore be important in increasing the technological advancement of Australian cropping industries.

See the full P2D report here

Source: Jakku, Emma; Zhang, Airong; Llewellyn, Rick. Producer survey to identify accelerating precision agriculture to decision agriculture (P2D) needs and issues. Final Report. Brisbane: CSIRO and Cotton Research and Development Corporation; 2017.

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This article was originally published on on February 18, 2020.