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Types of Data to Collect in Farming

Learn more about the types of data to collect in farming.
© EIT Food

Farming involves a lot of record-keeping and increasingly, farm data is being collected and stored digitally. As new agri-tech solutions are adopted, an ever-increasing amount of data is collected which can be analysed for enhanced decision making. In this Step, we’ll explore the types of data regularly gathered on arable farms and consider where innovative farming approaches can provide better detail. And we’ll look at the kinds of digital systems currently available to help make sense of all the numbers.

Yields

Grain yield data can be collected at harvest via weight measurements made by the harvester [1]. Other parameters can also be measured with additional sensors, such as the quality of grain in terms of protein content [2] and moisture content. Monitoring changes in yields over time can provide insights for future improvements in crop management. If data are collected and analysed in relation to specific fields or zones within a field [3] this can provide essential information on temporal (year-to-year) variation of yield distribution between fields. Later in the course, we’ll look at the latest technologies that collect spatially-explicit yield data to provide map-based information as a basis for precision agriculture.

screenshot showing a yield map from John Deere. The field shape is filled in with yellows, oranges and reds to show how the yield varies in the different segments

Example yield map from John Deere © John Deere

Soils

You may collect and use data on soil type, topography, compaction, nutrient content or pH. Data can be extracted from countrywide soil maps or from individual fields or farms. Soil properties can be determined using mobile sensors, or by sampling and testing (either in the field or sent to a lab).

Meteorological Data

Important meteorological variables can be assessed using weather stations; measurements such as true and apparent wind speed and direction, barometric pressure, air temperature, and relative humidity are useful. Calculation of Delta T (Delta T = dry bulb temperature – wet bulb temperature) is also a standard indicator used for determining suitable spraying conditions as it is provides an indication of evaporation rate and droplet lifetime allowing you to optimise spray efficacy.

Inputs

The data (or records) of the amounts of fertiliser, pesticide, herbicide, fungicide and other inputs are key to help identify opportunities for cost savings. Analysis of where and when these inputs were used can provide the starting point, and comparison with other data gathered (eg crop growth differences within field) can lead to increased efficiency of input usage.

You could also consider inputs such as fuel. If you use tractor guidance systems (for example) has this changed fuel consumption? Can changes be made to increase efficiency? Seeds and plants could be considered as another input. Are seeding densities altered based on past yields or in relation to soil properties? Has the crop grown in a particular field or area been altered based on previous years’ yields?

Crop Growth

Measurements of crop growth such as ground cover, biomass or greenness can be recorded either visually or using quantitative data. Do you record the information gathered during crop walks to show the need for irrigation and weed/pest control?

Anything Else

Environmental protection activities such as reduced tillage and establishing field margins should be recorded. Do you apply for environmental funding / subsidies – if so, how do you collect and submit data on these? You can also consider how you record all of the financial aspects of these farming activities to be able to assess the impact of management or agronomic changes on the profitability of the farm [4].

What data do you currently collect to inform agronomic decision making? Do you think you can extract more value from these data?

© EIT Food
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Innovation in Arable Farming: Technologies for Sustainable Farming Systems

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