Make the most of your weather data

These days farmers collect endless amounts of data about all aspects of their operation, including the weather. Despite the cost and effort to collect all this data, much of it never gets used.

A 2016 Stratus Ag Research survey of field data management practices found that over 60 per cent of farmers in western Canada who collected precision agronomic data stated that it is being stored with no further analysis, or that it simply remains on the equipment.

Thankfully, times are changing. Not only are farmers becoming more technically savvy, eager to dig into their data to gain valuable insights, but the tools are also evolving to make data analytics more accessible. Farmers can now use sophisticated management platforms to converge data and help manage production at a granular level.

Historical weather data

Knowing what has already happened with respect to the weather can be of real value to farmers. Historical weather data shows a certain degree of predictability; some idea of what tricks nature has up its sleeve.

For example, a given location will have a somewhat consistent range of seasonal temperatures, precipitation and frost dates. Of course, there will always be variability, but even this variability is an important piece of information that can be leveraged.

Climate records can be used to determine factors such as frost risk. For example, over the past few decades, Saskatoon has recorded first fall frost dates ranging from August 28 to October 6. While a 40-day range is extremely broad, a closer look at the data reveals more valuable information.

Saskatoon has a 50 per cent probability of receiving a first fall frost on or before September 15. There is a 25 per cent risk of a fall frost on or before September 8. Depending on the farmer’s individual level of tolerance, these numbers can be used to effectively manage agricultural risk. Likewise, similar risks can be tabulated for heat units, rainfall, temperature, moisture stress, yield and other variables

Crop simulation models are used to calculate the many interactions between the soil, crop and atmosphere. Weather, being the major influencer of crop behaviour, must be accounted for within any simulation. Historical weather data allows multiple simulations of various scenarios.

The results provide insight pertaining to nutrient uptake, crop development, stress, and yield. For instance, a farmer could simulate growing a new crop variety using weather data from the past 10 to 20 growing seasons, including the best and worst years. He can then evaluate how many of those years the crop would have succeeded, and whether it’s worth growing.

While the past has always been a helpful indicator of agronomic risk, this method has proven to be less reliable in recent times. Unprecedented weather events have become more common. Floods, droughts, heat waves and storms have been increasing in frequency and severity. For this reason, you should exercise caution when using weather records that go too far back in time.

In-season weath er data

Climate data relies on an established network of highly accurate, well-sited weather stations. While these stations provide excellent regional data, they are not meant to represent local conditions. These stations rarely capture the spatial extent of a rainfall event, particularly during the summer when local convective activity dominates.

This is where on-farm weather stations come in. As weather conditions vary, even from one field to the next, farmers can accurately monitor temperature, humidity, rainfall, wind and soil moisture where it matters — in the field.

Crop simulation models can also be run in real-time to track the progress and requirements of a crop. Knowing your crop’s stage of development can help focus scouting for weeds and disease on the fields that are at their most vulnerable growth stages. For larger farms with thousands of acres and dozens of fields, knowing when and where to deploy resources is key.

Finally, post-harvest, it is important to understand what factors, whether agronomic or otherwise, caused the crop to turn out the way it did. Weather information from the growing season is often the first place to look. Suboptimal yield or crop quality could have been attributed to certain weather-related stresses. Often, these influences can only be known by having a nearby weather station.

Putting it all together

Most people like the appeal of being able to see what the weather is doing outside their door or in the field. While useful, it represents a small part of the overall value that a weather station can bring. Having weather data in order to plan, assess and review a crop can mean the difference between profitability and loss. As more farmers see the value of field-level weather monitoring, the number of tools are also expanding, making collecting and analyzing weather data a worthwhile investment.