Hello ReaderWe’ve been busy mapping with the Dual EM38, soil testing, meeting with clients and tweaking crop plans for next year. Soil test nitrogen levels, on the average, are low once again. Average wheat yields with proteins above 14% and above average canola yields have kept the nitrogen pool low.
Most producers are busy discing field edges, heavy harrowing, broadcasting granular herbicides and cleaning up equipment before winter. It’s safe to say we’re all ready for a break after 6 months of non-stop operating with little down time. It’s been the busiest season on record followed by the most difficult and expensive harvest. I don’t think anyone would mind an early snowfall but there’s still work to do before we move into the shop.
In this week’s newsletter we’ll look at turning those interesting yield maps into useful profit loss maps. Next, we’ll discuss a recent study showing the value of maintaining surface residue. Last, we’ll look at a new soil testing system and decision-making software that models yield potential.
Have a great week.
Easier decisions from profit-loss maps vs. yield mapsEach year I pore over yield data looking at variability and trial data. Naturally, my eyes focus on the poor producing areas that tell me the red areas yielded x and the green areas yielded y. That information is valuable but it’s not until you turn a yield map into a profit-loss map that you can really start to make decisions.
The image you see here is a profit-loss map showing the variability in our net income across a field of peas in 2014. We have a high of $410.11 per acre net income marked in green to a low of -$194.75 per acre marked in red. The red area works out to 38 acres. Steve’s quick math tells me we lost $7,400.00 on this field this year. I could also take it a step further and generate a multi-year profit-loss map to calculate the income lost over the course of the rotation.
To give you an example, we’ve run some surface drainage scenarios with my good friend Tim Neale of Precision Agriculture Australia who uses Opti-Surface drainage software (best in class in my opinion). In this field it is possible to apply surface drainage to 10 acres to free up the drainage problem on the 38 acres. Let’s run a few numbers on this idea.
Steve’s quick math
Profit loss shows $25,600 in lost income over 4-year rotation
Surface drainage contractor: 12 hours x $600/hr = $7,200
Income improvement on 38 acres = $85 ac/yr (guesstimate over 4-year rotation)
Income per ac/yr: $85 ac/yr x 38 ac = $3,230
Income over 4-year rotation: $3,230 x 4 years = $12,920
The top two pieces of GIS software on the market are SMS Advanced by Ag Leader and FarmWorks by Trimble. They can both help you produce profit-loss maps and help you manage your spatial information. Both are priced around $500-800 CDN for the basic versions and $800-$2,000 for the Pro or Advanced versions. They also offer free trials if you want to take them for a test run. They can take in any OEM data from John Deere, CNH to Claas and AGCO data. I use SMS Advanced because of their awesome tutorials online but both FarmWorks and SMS are very good.
To view a short tutorial on profit-loss mapping in SMS Advanced go here.
Bottom line: If you really want to optimize the production on your land base, start turning your yield maps into profit-loss maps. From there you can run scenarios to help you determine what if any remediation strategies are cost effective. Perhaps it’s surface or tile drainage, liming, deep ripping, gypsum or nutrients. Whatever the cause, profit-loss maps are key to pin pointing the money pits. There’s nothing like staring at a $194.75 loss to make you think hard about how you can turn that into a positive number. SL
Residue cover generates big yield gainsThere is always a delicate balance between too much residue and not enough in a direct seeding system. On one hand, residue cover can negatively impact germination and emergence by keeping soils cold and wet in the spring. On the other hand, poor residue cover can allow soils to dry out, reduce tillering and grain fill. A study published this week from Ag Canada confirms the importance of residue cover and serves as a reminder for many who are busy “preparing” their fields with shallow tillage.
The study looked at soil temperature, soil moisture, root length and final yield of wheat, canola and peas between two residue treatments: No surface residue with short standing stubble and heavy residue. The varieties used were InVigor 5440 canola, AC Vista CPS wheat and CDC Meadow peas. Here are the key points:
- High surface residue increased wheat yield by 8%, canola yield by 33% and 8% in peas compared to the no surface residue treatment.
- There was no significant difference in emergence or plant population between the two treatments.
- The heavy residue treatment had higher soil moisture all season at 0-10cm for all three crops compared to the no residue treatment.
- The heavy residue treatment had greater total root length at the 0-50cm than the no residue treatment in most of the growing season for wheat and canola but not for peas.
- High surface residue increased straw yield by 20% in wheat, 50% in canola and 7% in peas compared to the no surface residue treatment.
- High surface residue increased the height of all crops compared to no residue. Wheat was 7cm taller, canola 5cm, and peas 7.5cm taller than no surface residue treatment.
Source: Can surface residue alleviate water and heat stress? Hong Wang, Y. Gan, Yong He, Kelsey Brandt, Xiaobo Qin, Zhiguo Li, DOI: 10.4141/CJPS-2014-269
These are the top two studies on the impact of residue on yields in Western Canada:
Long-term impact of no-till on soil properties and crop productivity on the Canadian prairies
Tillage and root heat stress in wheat in central Alberta
Unraveling the mystery of yield potential
PRS CropCasterOne of the most difficult things in my role as an agronomist is trying to establish maximum economic yield for a given crop on any given field or area. There are so many variables to consider like pH, EC, soil texture, nutrients and climate, nailing down a realistic yield goal is something we do, but don’t really know until after harvest. Even then, did we really reach the full yield potential?
I started trialing a simulation tool called PRS CropCaster, which helps predict maximum yield and maximum economic yield based on attributes like heat units, soil texture, soil moisture, rainfall, pH and background nutrients. The model is powered by countless response curves based on Western Canadian crop production research. The beauty of the model is that it allows you to dial the attributes I just mentioned up or down to see how each one impacts yield potential.
To give you an example, I’ve run a few simulations showing the impact of pH on canola yield from a field of ours. The images above are small screen captures showing the impact of pH, maximum temperature at flowering and soil EC. Missing from the image are soil texture, soil moisture, rainfall and nutrients. The graphs in the bottom left corner show the yield response curve for canola and pH. Based on the soil test results the model suggests my maximum yield potential on a hilltop is 66.8 bu/ac. If I dial back the pH from 8 to a pH of 6.2, my yield potential climbs from 66.8 to 80 bu.ac, a 13.2 bu/ac increase. From here I can begin to adjust my NPKS and see how each nutrient impacts yield potential and fine-tune my maximum economic yield.
So far I have been fairly impressed with the PRS CropCaster model and its ability to point out the soil or climatic limiting factors. For example, I can run scenarios to predict the impact of liming on yield if I were to raise pH by 1. It becomes easier to run an economic analysis to see if lime is cost effective. The same goes with nutrients as I use it to run scenarios to find out where my maximum economic yield lies for each crop on each field based on their own unique characteristics.
Like any model, the outcome isn’t perfect. What it does provide is some scientific modeling to predict maxiumum economic yield. To date, we’ve all been using the SWAG or scientific wild-ass guess method. I do believe the future is in modeling like the PRS CropCaster where we can start to predict outcomes based on real time information. This type of modeling is a big leap forward. SL
For more on PRS CropCaster go here.