Originally Coauthored by: Shawn P. Conley, Seth Naeve and John Gaska December 14, 2016. Modified by S.P.Conley 1/22/18.

Modified by S. P. Conley, E. G. Matcham, and L. C. Malone 12/15/2021.

Before we start, we fully acknowledge our title “Best management practices for growing second or third year soybean” is a bit misleading as we do not advocate this practice (it’s not a BMP!) but we thought we could sucker you into reading this article if it had an enticing title!

This article was originally updated in 2018 when the USDA announced that U.S. soybean acres in 2018 will surpass U.S. corn acres. These acres have to come from somewhere and many of them will be from second-year soybean.With input costs rising rapidly, especially for nitrogen fertilizers, we thought it was time for another update. Many of our management recommendations remain the same, but we also have some additional recommendations based on recent crop rotation and nutrient management research.

With that being said, here are some recommendations to consider:

• Balancing short-term versus long-term profitability (i.e., economic sustainability). Every year promises of short-term profitability may drive some farmers to consider planting soybean after soybean, rather than rotating, data from our long -term rotation experiment clearly shows the benefit of crop rotation to the soybean crop. It is amazing that after 5 years of corn, it only took 3 years of continuous soybean for the yield to drop to within 7% of continuous soybean (20+ years) yield levels whereas 2nd year soybean yielded within 5% of soybean in a corn-soybean rotation. We could hypothesize then that the yield of the 3rd year of continuous soybean (in our experiment) would be like a 2nd year of soybean in a corn soybean (C-S-S) rotation. Our data clearly shows that 3 or more years of continuous soybean gives you a 7+ bu per acre hit when compared to a corn-soybean rotation and moves you close to the yield levels of continuous soybean. In short, you are setting your long-term profitability up for a hit. Another long-term rotation study we have (19 years) shows an even bigger yield benefit (13%) to rotating away from soybean for two years (corn and wheat) compared to annually rotating corn and soy.

• Be aware that soybean after soybean will alter the pest relationship complex in your fields. Some of these alterations may take years to undo as you will be making a long-term impact on your soil and resulting soil health. We looked at soil fungi in our long-term rotation study and found increased Fusarium (which causes damping off in soybean) in continuous soybean plots. On the “good microbes” side of things, soybean isn’t a great host of AMF, a fungus that helps take up water and phosphorus, and rotating away from better hosts like corn for more than a year can decrease AMF populations in your field that other crops might benefit from. Read more about this soil fungi data here.
• Also don’t automatically think that simply adding a cover crop to this S-S rotation will “fix” these issues. In an Ohio study, rye cover crops only increased yields by 2-6 bu/acre and did not impact disease or insect pressure compared to control plots without cover crops.
• Plant a different variety than was planted in that field the previous year and make sure it has strong disease resistance traits to the problems you have in that field! Every variety has a weakness and planting the same variety on the same land 2 years in a row will expose that weakness. Note that these varieties must be truly different.  The same bean in a different color bag will greatly increase your risk of disease losses.  Please see our 2021Wisconsin Soybean Variety Performance Trials for information.
• Test for SCN and select SCN resistant varieties. SCN proliferates in long-term soybean cropping systems. Control weeds that are alternate hosts for SCN like field pennycress, shepherd’s-purse, and chickweed and do not plant leguminous cover crops that can be an alternative host for SCN. Be prepared to scout your fields more intensively to get ahead of any disease problems. Increased disease pressure is likely to be more pronounced in no-till fields and may provide an opportunity to see yield responses from fungicides and insecticides. Include the cost of additional crop protection applications in your economic estimates for short-term profitability.
• Keep seeding rates lower if white mold was a problem in the field
• Use a seed treatment at the max a.i. fungicide rate.
• Use a pre-emergence herbicide and use multiple modes of action. If you had weed escapes, expect even larger problems in soybean after soybean.
• Soil sample and replace K if needed: An 80-bushel soybean crop meant you removed ~98 pounds per acre of K20 equivalent fertilizer. Growers often routinely rely on carryover fertilizers for soybean when rotated with well-fertilized corn. Soybean after soybean may require additional fertilizer, especially K.

Back on the soil microbe note, we looked at the soil bacterial communities under corn-soybean rotation (5 years of corn followed by 5 years of soybean). These communities are affected by a lot of factors, like pH, moisture, and nutrients, along with the plants growing there. The figure below shows ordinations of bacterial communities – each point on the graph is a plot in our field experiment, and the further apart points are, the more different those communities are. This figure focuses on soybean phases. The main point here is that the first and second year of soybean after 5 years of corn are pretty similar to each other, while the 3^rd, 4^th, and 5^th year of soybean start to look more like the continuous soybean (30+ years). This trend might remind you of the yield trends discussed earlier in this post! Bulk soil microbial communities are complicated, and they take a long time to change…sticking with soybean for one extra year may not shift the community drastically, but 3+ years is more likely to. The data from this study isn’t published yet, but if you are curious about bacterial communities and corn/soybean rotation in the soil you can read more here.

Additional recommendations for years 3+:

• Even if you only saw an increase in disease pressure without an increase in insect pressure in year 2 beans, expect to see an increase in both diseases and insects in year 3+. Budget accordingly for crop protection products.
• Consider increasing your seeding rate in fields with high pressure for seedling diseases such as pythium or phytophthora. Don’t increase your seeding rate in fields with high white mold pressure.
• Soybean removes more K and S per acre than corn. Consider increasing soil sampling frequency to monitor K levels, and scout fields regularly for S and micronutrient deficiencies.

Well folks its the end of May and frost is in the air. I figured we would be getting some questions about the impact of the predicted cold temperatures on the wheat and soybean crop. Here is my coolbean take!

First lets start with the wheat crop which in WI ranges from boot to anthesis. Cold temperature would need to reach 30 degrees F or less for 2 plus hours before injury occurred. I just don’t see that happening in any major wheat growing region in WI this week.

Table 1. Wheat Resistance to Freeze Injury (From: Spring Freeze Injury to Kansas Wheat)

Now let’s talk about soybean. I am also optimistic that if the forecast temperatures hold, the soybean crop will in fact be #COOLBEANS, but also unaffected. My optimism lies in the Nowledge I received in an email from Dr. Jim Specht from UNL a few years ago (modified slightly by me for context). For the most-part, farmers and their soybeans in WI fall within the context of this email.

First of all  temps above 32F will not impact above-ground tissue.  Second, tissue freezing does not even take place at 32F because cell cytoplasm has solutes in it – like a modest anti-freeze, which depresses freezing point of the tissue a degree or two less than 32F – thus air temps surrounding the tissue have to get to below 31 or 30F before tissue freezing can occur.  Third, the soil surface is typically warmer than the air temperature (particularly when the soil is wet) and does not give up heat acquired during a sunny day as fast as the air does after sunset.  In actuality, the interface between soil surface temp and the air temp near that soil surface will be closer to the soil temp than to the air temp which most peopled measure on thermometers viewable at their height (not at ground level).  Biophysically, control of the soil temp over the air temp this is called the “boundary layer effect”).  So don’t trust air temperatures read on thermometers unless you know what the air temperature near the soil surface was (put a thermometer on the soil surface where the cotyledons are and check it just before dawn (when the soil surface temp reaches its nadir for a 24-hour temperature cycle).  Fourth, the cotyledons are a huge mass of tissue that are about 95% water.  That big amount of water-filled tissue is hard to freeze unless the exposure to temps of 30F at the soil-air interface is many, many hours.  Cotyledons will freeze faster (in fewer hours) but only if the soil surface temps get well below 30F (say 25F).  The only concern I would have is when cotyledons are no longer closed and protecting the young stem tip.  However, if that is in fact frozen off, the nodes to which the cotyledons are attached will regenerate TWO main stem tips.  Not an ideal way to start the growing season, but better than having to replant (0.5 bu/ac loss per each day that soybeans are NOT in the ground on May 1). Text courtesy of Dr. Jim Specht (UNL)!

Article written by: Emma Matcham, Matt Ruark, and Shawn P. Conley

We talk a lot about the importance of soil sampling, but we don’t spend much time talking about what happens to your samples after you send them off to the lab. There are a few different procedures for measuring potassium (K) and phosphorous (P), and knowing which method was used to analyze your samples can help you accurately interpret the lab results.

All methods we’ll discuss today for measuring P and K share some general steps. Soil arrives at the lab, receives a sample ID, and then is dried in an oven, ground, and passed through a 2mm sieve. Then the soil is mixed with an extractant that removes a portion of the nutrients from the soil itself, and then the nutrient concentration of the solution is measured. One common misconception about nutrient extraction is that these tests remove all the P or K in a sample. Extraction only removes a portion of the total nutrients in the sample. Different tests extract a different proportion of the overall nutrients, which affects lab results and soil test interpretation. Both P and K are usually in the form of cations, or positively charged ions, in the soil.

Both Bray-1 and Mehlich-3 extraction solutions are acidic, but the acids in Mehlich-3 extraction solution are weaker. In alkaline soils, using acids to extract cations from soil doesn’t work particularly well. Instead, states like Minnesota and others in the western US use the Olsen test to extract P with weak sodium bicarbonate.

While most of you will never need to know the precise details of the different extraction methods, we think it’s important to understand that there are different methods, and they all work a little differently. Since none of these methods remove truly all of the cations in the sample, if you run the same sample using two different methods you will get two different values.

State nutrient recommendations are built with specific tests, and you will be better able to implement the recommendations of A2809 or your state’s nutrient guidelines if you make sure your samples are run using the same methods as your state guidelines. The good news is that nearly all commercial labs can run your samples using any of the methods described above. If farming outside of Wisconsin, check your state nutrient guidelines and the soil test they are based off of before you pull your soil samples. Then, write that method or check the box corresponding to the correct test on the submission form when you mail samples off to the lab.

If you have past soil samples that were tested using methods that differ from your state’s recommendations, you can convert because the different methods are highly correlated. The conversions for P are simple. Ohio State recommends dividing your Mehlich-3 P number by 1.35 to get a Bray-1 P value. This conversion is similar to conversions provided by Iowa State, and it should hold true for WI soils too. Olsen P can also be converted to Mehlich-3 P, using conversions from Ohio State.

The conversions for K are a little more complicated, since the sample’s clay content can affect K extraction. Dr. Carrie Laboski in the Soil Science Department at UW-Madison has done some work to correlate Bray-1 K with Ammonium Acetate K, and has found that multiplying Bray-1 K by 1.2 is a good way to estimate Ammonium Acetate K on silt loam soils (personal communication with Dr. Laboski). For sandy soils, she found that Bray-1 K and Ammonium Acetate K are approximately equivalent.

Unfortunately, there is not a published conversion between Bray-1 K and Mehlich-3 K. Ohio State converts from Mehlich-3 K to Ammonium Acetate K by dividing by 1.14, and you can theoretically combine the Bray-1 K to Ammonium Acetate K conversion from Dr. Laboski with the Ammonium Acetate K to Mehlich-3 K conversion from Ohio State to estimate them too. This “double conversion” method suggests you can divide Mehlich-3 K by 1.368 to get Bray-1 K. It’s usually not a great idea to combine conversions across different states like this, but it is the best option currently available.

Figure 1: Scatterplot comparing Bray- 1 K and Mehlich-3 K extraction methods, with linear regression line. Bray-1 K = 0.77 * Mehlich- 3 K – 0.75 (R2 = 0.91, p < 0.001)

We are currently building a data set of Wisconsin K soil samples that have been run using Mehlich-3 and Bray-1 K. From 108 samples of loam and silt loam soils in southern WI, you can see that the extraction methods are highly related (R² = 0.91, p < 0.001) (Figure 1).  A simple linear regression from this data set indicates you can get from a good estimate of Bray-1 K by multiplying Mehlich-3 K by 0.77 and subtracting 0.75. The data presented in Figure 1 should be considered extremely preliminary because all the samples came from three fields within the same county. That said, this data does show that simple linear regression is likely to help us develop a conversion once we have a larger data set.

It’s also interesting to note that both the regression and double-conversion method yielded very similar estimates of Mehlich-3 K from the Bray-1 K test results for these samples. But, all the samples in our data set so far were on loamy soils near or within the optimum range where we don’t expect to see a yield response to K fertilizer. Future data sets will need to be larger and more diverse so that we can understand the relationship between extraction methods on low-K soils and other soil textures. In the long term, accurate conversions between extraction methods can help us better utilize historical and multi-state data, adding value for farmers across the Midwest.

The 2020 season had above average growing conditions for many growers.  We experienced higher entry numbers in the 2020 WSA/WSMB Soybean Yield Contest, likely due to an early and extended harvest window.

The top two entries in each division (in no particular order) were:

Division 4:

• Ron Digman, Mount Hope (planted Pioneer P28A42X)
• Don and Doug Midthun, Arlington (planted Asgrow AG20X9)

Division 3:

• Ryan Bates, Elmwood (planted Pioneer P16A84X)
• Tim Gaffron, Twin Lakes (planted Pioneer P24A80X)

Division 2:

• Paul Lapacinski, Cameron (planted Pioneer P16A13X)
• Mike and Dean Wegner, Sparta (planted Pioneer P23A15X)

Division 1: 

• Jim Wilson, St. Croix Falls (planted Asgrow AG10X9)
• Paul Graf, Sturgeon Bay (planted NK S14-U9X Brand)

The Soybean Quality Contest was optional for any Soybean Yield Contest entrant.  There are no geographical divisions for the Quality Contest.  One cash award will be presented statewide to the highest protein plus oil yield per acre (measured in lbs. per acre). The finalists for the Soybean Quality Contest are:

• Rick DeVoe, Monroe (planted Pioneer P28A42X)
• Jerry Kreuziger, Juneau (planted Pioneer P23A15X)

The final ranking and awards will be announced at the “Beans and Bull” virtual meeting on January 21^st at 7:00pm.  Registration is free for this event and all other Bean and Bull sessions.  To register for this event, visit https://www.eventbrite.com/e/beans-bull-on-farm-research-cover-crops-and-soil-health-tickets-128282194497?aff=erellivmlt

The contest is sponsored by the WI Soybean Program and organized to encourage the development of new and innovative management practices and to show the importance of using sound cultural practices in WI soybean production.

For more information please contact Shawn Conley, WI State Soybean Specialist at 608-800-7056 or spconley@wisc.edu

Authored by: Lindsay Chamberlain, Spyridon Mourtiznis, John Gaska and Shawn P. Conley

#Plant20 went by in a flash for many – but with a chilly past week across the Midwest, many are wondering how long will it take for early-planted soybean to emerge. To take a stab at this question, we pulled out some data from a 2008-2009 study that looked at impact of seed size and days to emergence on yield and quality of soybean (Mourtzinis et al., 2015). At the Arlington site for this study, emergence notes were done every day, and soil temperature probes were buried before planting. With this data, we can roughly assess the GDUs (Growing Degree Units) accumulated between planting and emergence for these two site-years. Before getting into the results, here is a little bit of housekeeping on how we calculate GDUs…

Calculating GDUs

Growing Degree Units (GDU), also called degree-days, or sometimes heat units, are used in crop growth models to estimate growth and development. There are some assumptions behind GDUs, most notably that the growth or process being predicted has a linear relationship with temperature (hotter = faster growth). This relationship only has to be true between the base (minimum) and maximum temperatures, which are typically thought of as 50°F and 86°F, since these are the values used for corn. These assumptions are true for corn growth – between 50°F and 86°F, corn grows and develops, passing through growth stages faster with more heat. This relationship is less studied for soybean (and less consistent due to soybean’s photosensitivity) but we generally use the same minimum and maximum temperatures when discussing GDUs in soybean. One possible change for soybean versus corn GDUs would be lowering the base temperature to 41°F (Setiyono et al., 2010). This increases the accumulated GDUs, especially early in the season (Fig 1). This is simply due to the way GDUs are calculated:

Daily GDU = {(Daily Minimum + Daily Maximum/2) – Base Temperature}   (Campbell and Norman, 1998)

Accumulated GDUs from each day are then added together. If the daily average temp is below the base temperature (which is often true early in the season if using 50°F as a base), the GDU for that day is 0, not negative. It is important to note that the daily average temperature is calculated by taking the average of the recorded daily minimum and maximum, not the overall average temperature. These values are publicly available for many locations across the US (https://www.ncdc.noaa.gov/cdo-web/datatools/findstation), including the Arlington Ag Research Station.

A final assumption of using GDUs to predict growth is that the growing point of the plant is at air temperature. This is obviously not true before emergence! Strictly speaking, GDUs are designed to be calculated with air temperatures, not soil. Since we have data available for both – we used both!

Accumulated GDUs in 2008/2009

To start out, we calculated GDUs accumulated (with planting date as day 0: May 8^th in 2008 and May 6^th in 2009) for both years, from soil temperature, air temperature, and with two different base temperatures (41°F and 50°F). A few things can be taken away from this figure: it is obvious that using a lower base temp (blue lines) results in faster accumulation of GDUs – but we already knew this would happen. The reason it might be important for soybean would be the days where the average temperature is between 41°F and 50°F – base 50 counts these as 0 GDU’s. Since base 50°F is more widely used, we decided to present the emergence results with the higher base temperature, but we saw very similar results (just higher numbers) using 41°F.  Clearly, more research needs to be done to determine a more accurate base temperature for soybean.

Outside of base temperature, this graph tells us some other things. First – it was warmer in 2009 (lighter shades of blue and orange). Second – GDUs calculated with soil temperature (solid lines) accumulate faster than with air temperature (dashed lines). Soil and air temperature are certainly linked, but soils tend to stay warmer at night, which drives up daily average temperature and daily GDU. Since pre-emerged soybeans are experiencing soil temperatures, but GDUs should be calculated with air temperature, the results are presented both ways.

Fig 1. Accumulated growing degree units (GDUs) for 2008 (darker colors) and 2009 (lighter colors) at the Arlington Ag Research station, calculated with different base temperatures (blue = 41°F, orange = 50°F), with soil temperatures (solid lines) and air temperatures (dashed lines).

Results – GDUs to emergence was not consistent between years.

We used regression analysis to estimate the GDUs to emergence, which were greater in 2009 than in 2008 (Table 1, Fig 2). Growing degree units (GDUs) to 50% emergence, as calculated by air temperature, was estimated to be 57 in 2008, but 179 in 2009. Values for 90% emergence and GDUs calculated with soil temperatures are displayed in table 1. Similar results were noted across the 6 varieties of soybean included in the study, see table 3 at the end of the post for estimated GDU to emergence for each variety.

The increased number of GDUs accumulated before emergence in 2009 compared to 2008 is driven (mathematically, NOT biologically) by two main factors: heat and days to emergence. It was warmer in 2009, yet the beans actually took longer to emerge (Fig 3). Factors other than temperature, like planting depth, moisture, and other soil conditions likely have an impact on time to emergence. Rainfall in May in 2008 was about 3.1 inches total, and 3.5 inches in May 2009 (precipitation is also available at  https://www.ncdc.noaa.gov/cdo-web/datatools/findstation) – so rainfall alone does not explain the delayed 2009 emergence compared to 2008.

Table 1. Estimated GDUs (Base 50°F) and days after planting to 50% or 90% emergence for 2008 and 2009, determined by regression analysis (see fig 2 for regression lines). Planting date in 2008 was May 8^th, in 2009 it was May 6^th.

Fig 2. Percent emergence of soybean by growing degree units (GDU, Base 50°F) as calculated by air (left) or soil (right) temperature at the Arlington Ag Research station in 2008 (top) and 2009 (bottom). Points are shaded by variety. The black lines indicate the regression function determined for each set of points. Planting date in 2008 was May 8^th, in 2009 it was May 6^th.

Fig 3. Percent emergence of soybean by days after planting at the Arlington Ag Research station in 2008 (top) and 2009 (bottom). Points are shaded by variety. The black lines indicate the regression function determined for each set of points. Planting date in 2008 was May 8^th, in 2009 it was May 6^th.

What does this mean for 2020?

Although it won’t perfectly predict your early-planted soybean emergence, all this GDU talk might have you interested in how many we have accumulated this year. Table 2 summarizes the GDUs accumulated since April 15^th at the Arlington Ag Research Station. If you are interested in totaling these for your area, you just need daily minimum and maximum temperatures. Also, don’t count any accumulated GDUs from the day you planted – that is day 0. For those thinking about other crops – remember that the base temperature is crop specific! The base temperature for winter wheat, for example, is 37°F! (Campbell and Norman, 1998)

Overall, the data presented shows that we need more research to understand factors driving time to soybean emergence. The data indicates that GDUs are not a consistent predictor for soybean emergence, so other factors will likely need to be considered.

Table 2. Daily temperatures and GDUs accumulated at the Arlington Ag Research Station since April 15^th.

 Table 3. Estimated GDUs and days after planting to 50% or 90% emergence for 2008 and 2009, for 6 different soybean varieties, estimated by regression analysis.

Literature Cited

Campbell, G.S., and J.M. Norman. 1998. An Introduction to Environmental Biophysics. 2nd ed. Springer.

Mourtzinis, S., J.M. Gaska, P. Pedersen, and S.P. Conley. 2015. Effect of seed mass and emergence delay on soybean yield and quality. Agron. J. 107(1): 181–186.

Setiyono, T.D., K.G. Cassman, J.E. Specht, A. Weiss, A. Dobermann, and H. Yang. 2010. SoySim User Manual. Univ. Nebraska-Lincoln Dep. Agron. Hortic. Inst. Agric. Nat. Resour.Available at https://soysim.unl.edu/soysim_manual.html.