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Reflections from SWAC 2018: How can we reduce uncertainty in precision ag.?

The 2018 Southwest Ag Conference wrapped up on January 4th and was as educational as always for its 25th anniversary. This year, there was a major focus on precision agriculture and how farmers can use technology to support their decisions. Three presentations in particular tackled the questions many of us have regarding site specific management. At the fore of this discussion was the question of how we can hone in on the most economic rate for nitrogen in the face of uncertain weather and markets, and mounting environmental concerns.

The image below comes from the session led by Nicole Rabe (Land Resource Specialist, OMAFRA) and Ben Rosser (Corn Specialist, OMAFRA).

The precision agriculture yield equation (apologies for the poor quality images).

Essentially, yield is a function of management, spatial factors (i.e. soils), and temporal factors (i.e. weather). In this model, farmers only have control over the first factor and are at the mercy of geography and climate for the rest. Variability takes many forms, and farmers are increasingly investigating ways to address spatial variation in corn N demand. This type of variability is one they can address by adjusting rates according to the field characteristics. By addressing the fixed factors with variable management, farmers can create accurate management zones, and even fill some pieces of the N rate puzzle(e.g. NDVI, PSNT, soil type). But even though we can see where N requirement is different, how do we better guess the size of that difference?

This is the question also asked in the session led by Dr. Nicholas Tremblay (AAFC, Saint-Jean-sur-Richelieu,  Quebec) and Greg Stewart (Maizex Canada). In their session, “The Right Corn N Rate”, the speakers asked us to choose the most important factor to include in a decision support system out of the following:

What is the #1 factor or data layer that should go into a decision support tool for nitrogen?

Naturally, this triggered a lot of debate! I gravitated to #2 as my choice because all the others can largely be measured in one way or another but weather is the cloud hanging over our heads (pun definitely intended) when it comes to determining N rate. Yield expectation on its own, Tremblay pointed out, is rarely a strong predictor of N requirement and is actually outperformed by a nitrogen-based formula like the Maximum Return to Nitrogen (MRTN) system used in Michigan. The question was a loaded one anyway because decades of research have told us that any decision support tool needs to incorporate multiple data layers to accurately predict optimal rates of N. In the end, it was clear that these six categories were not actually so discrete given the interactions that occur between all of them.

I need to shift gears for a moment while discussing the third session, profitability mapping—a concept presented by Dr. Clarence Swanton (Univeristy of Guelph), and Mike Wilson (Veritas Farm Management). In short, profitability mapping is a spatial look at the actual ROI in every field. As Mike pointed out, you might have a field with an impressive average profit but multiple areas with significant losses (-$400/ac. in his example!!). While some of those losses can be addressed by management (e.g. zones 1-4 below), some of them may persistently lose money (e.g. zone 5 below). From Dr. Swanton’s point of view, these are acres we need to consider when thinking about how to reduce yield variability from year-to-year. Dr. Swanton discussed yield resiliency, which refers to the ability of a crop to maintain its yield potential during unfavorable conditions. In other words, resilient cropping systems can “bounce back” after suffering some sort of stress. This will be increasingly important as climate change introduces more extreme swings in growing conditions, Swanton emphasized.

Mike Wilson (Veritas Farm Mgmt.) shared this generalized site specific management strategy using five zones.

So how do we build resiliency into cropping systems and what does that have to do with variable rate N? If there is one thing that is certain to improve resiliency it’s diversity in a cropping system. Dr. Swanton showed us that by planting diverse ranges of cash and cover crops and adopting practices that promote the development of soil organic matter, aggregate stability, and well distributed pore space farmers can turn massive losses on their least productive acres into break-even territory, or potentially profits in the form of ecosystem services. As I listened to these talks an idea began to form and it really didn’t have anything to do with drones or high tech field equipment. I think there is another lesson in precision agriculture hidden here that could lead to more confidence in the technology.

Pulling together the ideas from all three talks, I think it follows that yield resiliency and zone management are complimentary. Management on the macro level that improves resiliency will help to prescribe rates for each zone, especially the medium one, because the crop-N demand will not be as affected by reduced yield potential. While this holds true even for a conventional flat-rate, the benefits are likely to be greater under zone management because the margin of error on every acre will be much lower. Take a look back at the sidedressing case study which takes place under severe drought, and the prescription for the medium zone was off to the tune of $15/ac. in net profit (only 5lbs/ac.) vs. the typical rate. Under the hypothetical cropping system I’ve created using the lessons from SWAC, this zone may not have seen such a high profit response at the grower rate.

This Veritas profit response graphic comes from Deveron’s sidedressing case study.

To sum it all up: precision ag plays a part in profitable N management, but yield resiliency is a key characteristic that should be fostered to get the most out of it. Thanks to all the speakers and organizers at SWAC 2018, and I look forward to attending the next 25 years!