Progress report for FNC24-1436
Project Information
Jim Stute operates Stute Farms in Walworth County, located on the edge of the Kettle Moraine in Southeast Wisconsin. The farm is roughly 160 acres and the operation consists of owned and leased land, all treated identically. Soils are primarily Fox silt loams, common to the region, which are lighter textured, rolling, have limited moisture holding capacity and are erosion prone. The farm is in the Mukwonago River Watershed, classified as an exceptional water resource by the Wisconsin Department of Natural Resources. The farm is in an area of high ground water recharge-defined and delineated by WDNR and included in the 2010 Walworth County Land and Water Resource Management Plan (Walworth County LURM, 2010). This area is critical for prevention of soluble nutrient leaching (including nitrate and sulfate) to protect baseflow of the Mukwonago River.
Current crop production includes corn, soybean and occasionally wheat, all no-till. Additional conservation practices include cover crops, use of certified nutrient management planning, in-season diagnostic tests, integrated pest management and subsurface application of nutrients. The farm has been in continuous no-till since 2003 and cover cropped since 1998. Crops have been “planted green” into cereal rye for the past 3 years.
Stute holds graduate degrees in agronomy from the University of Wisconsin-Madison and is a Certified Crop Advisor/ Professional Agronomist. Professionally, he has 15-years’ experience as an Extension Educator and his research results, including those from this farm have informed University of Wisconsin-Extension soil fertility recommendations and numerous publications. He is an active member and official of the Watershed Protection Committee of Racine County, a producer-led watershed protection group. He has successfully completed four previous SARE Farmer/Rancher projects (please see the Exhibit).
Planting green into cereal rye enhances sustainability gains of no-tilling and using a cover crop but presents technical challenges which may prevent adoption. In my case, I was initially concerned with 3 issues which may reduce crop yield: interception of residual herbicide reducing its efficacy, uptake and immobilization of nutrients applied as starter fertilizer and creation or maintenance of a favorable environment for slugs, leading to more crop damage and/or stand thinning.
My solution was fairly aggressive use of row-cleaners in both corn and soybean to both clear crop residue and partially dislodge rye plants creating a row clear of plant residues to improve spray coverage, limit non-crop nutrient uptake and create an unfavorable environment for slugs while also increasing soil warming. The problem with this approach is dislodged plants can build up the spike wheels requiring frequent cleaning and partially dislocated plants can make slot closure more difficult and both problems are compounded by the wetter soil conditions of no-till. Based on observation, I am also of the opinion that disturbed plants are slower to die after application of termination herbicide, creating competition with the crop.
Is row cleaning really necessary in a plant-green system on my soil types?
Objectives
- Determine if row cleaning impacts crop production and yield in green planted corn and soybean comparing no cleaning to two levels of residue removal: light and heavy (near complete removal as practiced currently.
- Communicate results and experiences with the no-till community and their advisors.
Research
We examined the use of row cleaners and their impacts on both corn and soybean production and yield in 2024 field trials near East Troy Wisconsin. The soil type is a Fox silt loam (common in the region) and has a 20-year continuous no-till history; in corn-soybean rotation for the past 10 years. Crops have been planted green into cereal rye for the past 4 years. Rye is typically 6-8” in height at planting.
We evaluated 3 levels of row cleaning: none, row cleaners removed; intermediate, row cleaners engaged enough to remove some crop residue without dislodging rye plants; and full, total residue removal with significant rye dislodging. The trial used Yetter model 2967 rigid (fixed position) row cleaners, mounted on Kinze/ John Deere Max Emerge style planter row units.
The independent (not connected statistically) corn and soybean trials used the other routine cultural practices of the farm including use of starter fertilizer to meet crop P and K maintenance requirements in addition to S, as well as the crop specific residual herbicide, applied preemergence along with glyphosate for rye termination. In corn, this co-application typically occurs 5-7 days after planting (DAP) but in soybean applications can be split, depending on soil moisture conditions. Residual herbicide is typically applied within 3 DAP and rye terminated at that time if conditions are dry or it can be delayed and made in a separate application up to 2 weeks later if moisture is favorable to increase biomass production and weed suppression. Wet conditions in 2024 forced a change from routine practice by delaying planting and preventing the PRE application of residual herbicide. Trial conditions are reported in Table 1.
To assess the impact of row cleaning on the production issues discussed above we measured:
Emergence dates (first emergence, 50%, final)
Stand (population) 30 days after emergence (30 DAE) with concurrent estimates of:
- % slug feeding damage (plants)
- % peepers (plants of reduced size indicating delayed emergence)
- Visual weed control rating (in-row plant density will be measured if differences occur)
Tissue nutrient concentration at flowering (R1)
Grain yield at maturity (harvest stand, yield, grain moisture and test weight)
Stand measurements were made to assess the level of rye residue interference with the planting process. Tissue nutrient concentration was measured to assess whether rye disturbance by row cleaning reduced its propensity for uptake of nutrients applied as starter fertilizer.
The experimental design is a randomized complete block with 4 replicates. Plots were 100’ in length and the center 2 rows (both crops planted in 30” rows) were harvested for yield determination. We established two (2) 20’ sections within each set of harvest rows to measure emergence dynamics and take the 30 DAE measurements, ensuring we are measuring the same spots to eliminate planter row unit and other spatial variability. Normal data was subject to analysis of variance; count and proportion data was analyzed with a general linear model using the appropriate error family (R Studio 2023.09.1). Means are separated with the least significant difference (LSD) at the 5% level of probability where appropriate.
Contrasts (with vs. without row cleaning) were used to examine the overall effect of row cleaning on variables of interest to answer the “Up or Down” question. From there, the degree of row cleaning was examined if warranted.
A word about statistical analysis and p values:
We use statistical analysis to judge whether the differences between two values, treatment means for example, are real or if they are caused by random variation (the term mean is the same as average). We replicate (repeat) the treatments to estimate the amount of variation within an individual treatment measurement (yield for example) as well as that between the treatments. The analysis of the variability results in an estimate of probability, P, which indicates whether the difference between two means is real or is due to random variation. P values are reported as a decimal on a scale of 0 to 1, the lower the value, the greater the probability that the difference is real and not caused by the random variation. As reported, p values can be confusing but become more apparent when converted to a % probability, simply calculated by subtracting the p value from 1. For example, p= 0.15 is an 85% probability that differences are real (1- 0.15 = 0.85, 85% probability). For ease of interpretation, we report p values both ways. In general, we approach probability interpretation from a managerial standpoint and consider values over 80% as worthy of consideration.
Contrasts group treatments into categories to determine if there is an aggregate difference, in this case “row cleaning”: none vs. cleaning which is the combination of “intermediate” and “full” cleaning. In the analysis, we considered the row cleaning contrast first, proceeding to the next level when needed.
The coefficient of variation, CV, reported as a percentage (%) is an indicator of overall variability in a measurement, the greater the percentage, the greater the variability in the individual measurements which are included in the calculation the treatment means. An increase in overall variability can make the detection of real differences between two treatments more difficult: p values for measurements with high CVS’s should be considered carefully.
The 2024 growing season offered several challenges which impacted trial results. Southeast Wisconsin experienced an unusually warm, open winter resulting in greater rye growth and biomass production than we normally expect. Above normal precipitation and frequent precipitation events Table 2 delayed planting and other field operations and prompted the change of weed control strategies from preemergence residual to post emergence. The combination of greater rye growth and delayed planting meant our trial examined row cleaning on the upper end of rye biomass production and plant maturity. Figure 1,Figure 2 depict both the level of rye biomass and the degree of row cleaning of each treatment.
Precipitation patterns changed abruptly in mid-summer, switching from excessive moisture to drought conditions. While we ended the growing season (April-Sept.) 5 inches above normal, the later season deficit limited the yield of both corn and soybean and reduced test weights.
Results
Row cleaning had no impact on corn but affected soybean, reducing yield as well as tissue N and S concentrations (Table 3). Neither emergence dynamics nor stands were affected by row clearing in either crop, a somewhat surprising result because the intent of clearing is to produce more uniform conditions for seed placement as well as to speed soil warming. We attribute the lack of difference to later planting under warmer than normal conditions (Table 2), ample soil moisture for germination at planting followed by numerous rains which kept soil moist, aiding rapid germination and emergence, and taller than usual rye at planting which partially shaded cleared rows, minimizing the soil warming effect of row clearing.
Tissue nutrient concentration at flowering assesses plant nutritional status at a critical point in plant development which can affect grain yield. An intended consequence of row clearing is the disruption of rye growth, slowing or preventing its removal (by uptake) of starter fertilizer nutrients from the fertilizer band which in turn makes them unavailable to the crop. If true, this effect should be important in 2024 where favorable overwinter conditions and delayed planting produced more than usual rye biomass with its associated nutrient removal.
In corn, we found a tendency of row cleaning to increase tissue P concentration while leaving N, K, and S unaffected. We speculate that row clearing disrupted the short-term (from planting to rye termination) P uptake from the starter fertilizer band where the P is “fixed” by soil and therefore unable to move outward into soil solution after planting. More soluble and mobile N and S may have gone into solution, moving away from the starter fertilizer band (and into the crop row), evading rye uptake. It should be noted that tissue P levels were sufficient under University of Wisconsin-Extension recommendations (please see the footnote on table 3 for sufficiency range values) while the other nutrients were borderline deficient. We speculate that rye nutrient uptake before crop planting was greater than would be experienced in a more typical year and limited early season availability to the crop, resulting in the borderline deficiency conditions even though nutrients were applied at recommended rates. In soybean, we saw the opposite behavior, a tendency of clearing to reduce tissue N and S concentrations, defying explanation. In this case, S concentration was deficient which may partially explain row cleaning yield impacts. Nitrogen, P, and K were in the sufficiency range and would not have impacted yield.
Overall corn yield was generally as expected with our later planting and given the drought conditions during the grain-fill period. The same also applies to the overall trial yield for soybean but weed escapes in two of the four replicates also played a role in yield reduction. Close examination of the data indicates treatments performed similarly between the replicates (in other words there was no apparent interaction (changes in ranking) between the treatments and the replicates, an effect we couldn’t test statistically due to the experimental design), so the net effect of weed escapes was to lower the overall trial yield by lowering yield in these replicates, it did not change conclusions about row cleaning. The weed escapes were due to a mid-application shower which reduced efficacy of the post emergence herbicide in affected replicates. Herbicide could not be reapplied due to label restrictions.
Row cleaning had no apparent impact on corn yield (p=0.438, 56.2% probability), but reduced soybean yield 7.7% (p=0.047, 95.3% probability). When evaluating the level of row cleaning (none, intermediate, full), it is evident that the soybean yield reduction is solely due to the change from intermediate to full cleaning, a reduction 17.7% (p <0.001, 99.9% probability). While our statistical analysis suggests no difference in corn (p=0.438), we also observed the same trend: a 3.8% reduction comparing with and without cleaning, 177.2 vs. 170.4 bu/a. This is a sizable difference needing explanation if the analysis suggests the effect is not real. In general, the corn trial exhibited greater variability based on its respective coefficients of variability (CV) values for measurements compared to the soybean trial conducted under similar conditions (Table 3). For yield, the CV value was nearly double that of the soybean trial and for this reason, we are inclined to believe that the row cleaning effect could also be true in corn, we just could not verify it because of the variability. More years of data will be necessary to draw firm conclusions which could lead to changes in farm management.
Trials will be repeated in 2025.
Learning Outcomes
None to report, only one years results and those were from an unusual year.