Our first MicroFarm onion crop is extremely variable. The view below was taken from on the linear move irrigator, a useful vantage point
We want to measure variability so we can better assess it. If we can measure objectively we can make better decisions. We are interested in spatial variability and temporal variability.
The image above shows spatial variability: some parts of the paddock are better than others. We want to understand why some plants have done quite well, while others have done very poorly. If we can identify patterns, it can help us identify causes.
There are two patterns showing up in the image. There seems to be a large area where growth is poor. Perhaps that is a lower, wetter area? We can also see that every third bed is stronger than those on either side. That pattern is quite strong across the whole paddock and matches planting pattern from our three bed planter.
We wanted to map our crop so we could look for more patterns. We took a GPS connected sensor that measures the amount of ground cover and went up and down the beds.
In the image above, the sensor data is displayed as a colour scheme. Green is highest ground cover (the biggest plants and most continuous planting). Red is lowest ground cover (small plants or larger gaps between plants or both). We used a cheaper GPS without correction so our bed readings have strayed off line. But even still, we can see the same pattern as in the photo above.
Will this pattern reappear in future years? Temporal variability seeks to understand how crop performance changes from year to year. If we can identify “always high”, “always low” and “sometimes high/sometimes low” areas we can develop management strategies to suit. Sensor based mapping is one of the best ways to identify such zones.