YOUR BEST PRACTICE
accumulated data from sensors with basic information associated with a field such as soil type zones, yield history and other production practices. With these systems, users receive an updated recommendation for action each time that new data is available. Some advantages of sensor- based algorithms include ample in-season calibration opportunities, full-field data coverage, frequent (daily) data collection and limited user data input to get started. Limitations of sensor-based solutions include impedance from environmental conditions (clouds, haze, smoke), limited predictivity and generally single-nutrient insights.
When it comes to selecting the best fertigation DSS to use, it is important to evaluate several factors about those systems and how they align with the needs of your operation. Those factors are accuracy/ precision, nutrients analyzed, frequency of insights, spatial resolution, data input required, labor and turnaround time. For example, if
I were managing a potato operation with intensive management requirements, high production standards and labor limitations, important factors for my operation would be accuracy/precision, frequency of insights, labor, and turnaround time. For specialty crop operations, the number of nutrients might be important. For farmers and agronomists managing highly variable fields, spatial resolution and labor might be important. Whatever those criteria are for your operation, they are important to use to evaluate the appropriate fertigation DSS.
In general, commercially available crop and soil models are currently best suited for fertigation of well-modeled crops in geographies with frequent cloud cover. In geographies with regularly clear skies, sensor- based solutions are well suited to provide fertigation recommendations across a range of crops. Both these DSS require some user data input, comfortability with software and familiarity with spatial data concepts. If any of
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those make you nervous, starting with SAP analysis and working with a professional to interpret them is probably the right approach to get you started. As you evaluate fertigation DSS, on-farm trial data is one of the best resources to use. Performance of a fertigation DSS should be validated through published on-farm research trials like those provided by the Nebraska On-Farm Research Network.
Each DSS has its own distinct set of limitations. I’m excited about the frontiers in fertigation DSS, including fusion of model and sensor-based solutions to mitigate environmental limitations to sensor- based solutions and enable simultaneous calibration and predictivity. The future is bright and fertigation DSS of the present are advancing fertigation management today.
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