High yields, staying within irrigated water requirements not conflicting goals

So essentially, we wanted to see how are growers doing in terms of irrigation that they’re applying, compared to what they should be applying based on their specific soil type, climate, crop type, all that kind of stuff
to see how well they’re doing. Generally, farmers are doing a really great
job of managing their resources. For the remaining fields, there is potential
for improvement. One way that that might happen might be through irrigation scheduling. So we saw that fields that use kind of
the best available techniques and technologies achieved high yields without really any excess irrigation. Fields that use maybe more basic irrigation scheduling — where you pick a day on the calendar or you just look at the crop — they tended to have maybe four extra irrigation events on average. What was striking is that between those two kinds of different techniques, the yield was about the same. So that shows us that it’s
possible to have high yields without having excess irrigation. What we did for this work is we actually developed really a framework to do this kind of analysis to see where people are, where’s the room for improvement. So we see the value in that for policymakers, for NRDs, water managers and growers themselves to kind of see where are we starting off from? Where do we need to go? What’s possible? Kind of identify areas for outreach, extension, those kinds of things. We have this wealth of field level data. So for each individual field, so picture
one center pivot, 130 acres, we know a bunch of different things that happen. We know what crop was planted, when it was planted, what seed variety it was, how much water and nitrogen was applied, what the yield was. This is data that is really hard to find. So we were able to compare what we know is actually happening on the ground to our model. And the model tells us here’s what should
be happening. So that is what really makes it unique in
that we have this wealth of data in Nebraska that enabled us to come up with this framework. So we hope that other places will be able
to use the framework as data becomes available around the world and other regions.

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