How can we use Data-Driven Farming for a Bright Future?
Updated: 7 days ago
Wednesday evening, January 26, Nieuwe Oogst hosted a webinar on the latest developments in "growing with data''. The adoption of data in agriculture is a growing trend, with more and more farmers using data to inform their decisions. The exact number of farmers who have adopted this approach varies by region and country, however, the use of data in agriculture is growing. While various types of data are used, there are significant barriers challenging the adoption of data in agriculture as well.
Cindy van Rijswick of Rabo Research Food, Roel van Summeren of Bayer CropScience Vegetable Seed and arable farmer Jacob van den Borne joined during the webinar to discuss the opportunities and challenges of data within the agriculture and horticulture sector.
Photo by Veranika Semchanka
Barriers to the adoption of data-driven farming
There are several barriers to adopting data in agriculture, including
Concerns over privacy and security: Farmers may be wary of sharing sensitive information about their land, crops, and finances with third parties.
Technical and financial barriers: The lack of technical skills to use and interpret data can make it difficult for farmers to adopt data-driven decision-making. Besides, the technology and infrastructure required for data collection and analysis can be expensive.
Issues with data quality: The quality of data collected and used by farmers can be a concern, as inaccurate data can lead to incorrect decisions.
Resistance to change: Some farmers may be resistant to change and may be hesitant to adopt new technologies, including data-driven decision-making.
Legal and regulatory challenges: Some countries have strict regulations limiting certain data-driven tools and technologies. Nationwide, the ownership and control of agricultural data are not well defined, leading to confusion and uncertainty over who owns the data and who has the right to use it.
Lack of trust in technology providers: Farmers may not trust the companies providing data-driven solutions and are concerned about their data being misused or shared without their consent.
According to Cindy van Rijswick of Rabo Research Food, the missing part in data solutions offered by technology providers is often the practical application of data technology from the farmer's or agronomist's point of view.
Use of data sources
The most widely used forms or data sources for data-driven farming include
Weather data include data on temperature, rainfall, and wind.
Soil data include data on soil moisture, nutrient levels, pH.
Yield data include crop yield and production data, which can be used to make informed decisions about planting and harvesting.
Satellite imagery includes high-resolution images of fields, which can be used to monitor crop growth and identify potential issues.
Remote sensing data include data collected by drones, sensors, which can gather information on crop growth, water use, and other factors.
Machine data include data generated by farm machinery, such as tractors, combines, and other equipment, which can be used to monitor performance and make informed decisions about maintenance and repair.
Market data include data on crop prices, supply and demand, and other market-related factors, which can be used to make informed decisions about marketing and selling crops.
As arable land farmer Jacob van den Borne shares, investing in a dashboard for decision-making is a great way to make the most of your data. With so much information stored in Excel sheets and other formats, it can be hard to keep track of it all. Having a dashboard that can keep track of all the data in one place can make a huge difference.
Profitability of data-driven farming
During the webinar, the potential of digital agriculture was probed:
‘’Digital agriculture is going to pay us more in the years to come than crop protection, seeds and fertilizer.’’
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So, digital agriculture seems to have a lot of potential. But the key question is: Where can the individual farmer make a profit? By looking at the yield potential of the crop and comparing the plots, Jacob noticed that there is often a considerable variation in it. Knowing what grows where and what causes it showed him which plots are good and which need improvement. Poor pH or fertilization can be easily pinpointed and solutions can be implemented quickly. Very often, soil quality is a big factor.
By starting with a simple dashboard, it became easy to see problems and solutions when comparing them side by side in a dashboard and it made a huge difference in his profit potential.
SoilBeat for a Bright Future
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