Data Collection: Is It Worth It?
10 Examples of Data-Driven Decisions
Data is powerful. When it’s used right, it can make a real, valuable impact on any farming operation. This can range from changing the location of your soil sampling markers, to adjusting how and where you seed. Making informed choices will help you improve your bottom line, in-season and for years to come.
Here are a few real-life examples where our team of Field Advisors have worked with producers on our FarmForward program to interpret their precision ag data, and how those findings have led to impactful & data-based decisions for their operations.
Example 1: Location, Location, Location
A farm we work with has made it a priority to collect good yield data and to use that data to make management decisions. We used 5 years of their yield data to create yield normalization maps, and illustrate trends in yield over the time. The farmer and his agronomist then overlaid these maps with the benchmark soil sample locations. They were able to evaluate whether the soil sample points were representative of the field. Some points were in good locations that represented the average yield. Other points however, due to changes in weather patterns, management practices, drainage, and other factors, were no longer representative of the field. The farmer and the agronomist moved the benchmark soil sample points to more appropriate areas of the field, ensuring that the soil fertility recommendations were based on the most accurate information possible.
Example 2: To Seed or Not to Seed
A customer had identified a low spot on their field, which was consistently its lowest-yielding section. They had discussed the idea of taking that part of the field out of grain production. Before they made that decision, they wanted to know the economics of continuing to farm that area of the field. We used four years of data to create yield normalization zones and profit by zone reports. Then by using the fertilizer application rate maps, the farm’s cost of production, and income per bushel of each crop, we created a profit per zone maps. In three out of four years, the area broke even or made money. The grower decided to continue farming the area.
Example 3: Peas Keeping Mission
A customer regularly leaves fungicide check strips in their pea fields. They are aware of the benefits of using a fungicide to control disease in their pea crop; but they wanted to know exactly what effect their fungicide application was having on the peas.
When we analyzed the yields between untreated and treated areas, we saw that, when compared altogether, there was an average 7-bushel yield increase in the fungicide treated plots. This increase in yield more than paid for the fungicide application, and the farmer had a well-informed, scientific data-based, and essentially easy decision to make when it was time to purchase pea fungicide for the next year.
Example 4: Post Potatoes
A grower has irrigated potatoes. By analyzing the yield in the corners versus the main part of the field, it was concluded that yield potential can be nearly half in the corners compared to the main body of the field that was previously irrigated. Is it Moisture? Fertility? Their agronomist is going to investigate how to best manage corners in years after potatoes.
Example 5: Stop Your Engines!
A large farm operation wanted to find efficiencies and reduce costs. Looking at their JD Link data we noticed they were spending a large amount of time idling their combines. Idle time in the mornings was reduced and fuel costs decreased.
Example 6: High in Sodium
A grower mapped his fields for electrical conductivity and elevation, the fields were segregated into management zones based on texture and topography. He discovered through soil sampling by zone that certain areas of the fields tested very high in salts. Based on yield data, the fields with the lowest historical economical return within the salt prone areas have been set aside for an agreement with a neighboring cattle farm to establish the areas down to forage production.
Example 7: Real-Time Data with Crop Intelligence
A farm operation that implements intensive nitrogen topdressing on cereals was always looking for more real-time in-season data. His goal was to have the data help support decisions on chasing more yield potential. With Crop Intelligence, the grower has additional information to make those decisions easier and more accurately than without.
Example 8: Soybean Seeding Rate Trials
In the first year of a customer’s data collection with us, we created a simple strip trial design in soybeans to test 3 different seed rates. From the data we gathered, we determined that they could start lowering their seeding rate. Over the next 3 years we continued to experiment with this idea, creating random trial blocks throughout all of their soybeans fields. We have reduced the seeding rate by approximately 14% of starting rate, and in turn increased the operations margin per acre.
Example 9: Planting Canola Trials
Over the past 3 years, we have collaborated with farmers across MB to run trials on planting canola. We looked at different seeding rates comparing air seeders vs planter as well as seeding rates with the planter. We found an increase in plant survivability when planted vs seeded. With the increased survivability, the seeding rate at time of planting can be lowered, while maintaining yield.
Example 10: Targeted Tiling
A field with a lot of variability in elevation and soil texture was identified as a good candidate for tile drainage. However, only certain portions of this field had high salinity and as a result a production problem. The producer and tile company used elevation, EC maps and normalized yield maps to place the tile in the areas it was truly needed.
These are just a few examples of the power of precision ag data. With our FarmForward program and our team of Field Advisors, we find how to best harness your data to work better for you. Contact us today to learn more.
