Research and Demonstration of Precision Planting of Cover Crop Mixtures for Improving Farm Profit and Soil Health

Final report for ONC20-078

Project Type: Partnership
Funds awarded in 2020: $39,995.00
Projected End Date: 10/14/2022
Grant Recipient: Southern Illinois University Carbondale
Region: North Central
State: Illinois
Project Coordinator:
Dr. Amir Sadeghpour
Southern Illinois University Carbondale
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Project Information

Summary:

This farmer-driven proposal builds on a 2015 NC SARE (FNC15-1018) funded project to Mr. Ralph Upton Jr. entitled “Utilizing precision application of cover crops to minimize planting challenges while maximizing benefits to corn”. Although only funded for one year at 1 site, Upton and a local consultant have continued the precision cover crop trial for 4 years without outside funding, due to the positive and interesting results observed and great interests from growers in Illinois and neighboring states. They have identified production advantages of up to 50 bu/acre with precision planting of cover crop species and mixes planted in various placement in relation to the corn row, compared to no cover crop, no-till treatments. This proposal aims to answer some growers questions including (1) does corn yield benefits at Upton’s farm expand to other soil types/environments?; (2) does a 6-yr precision planted cover crop mixtures improve soil health?; and (3) what are the economic benefits of this system? Integrating this research with outreach approaches including hosting on-farm field days, writing a fact sheet being integrated into student’s course material, and presenting at regional/national meeting, this project will increase cover crop adoption before corn and improve sustainability of corn-soybean cropping systems.

Project Objectives:

The objective of this proposal is to evaluate the effectiveness of precision planting cover crop mixtures (skipping the corn row or planting winter-killed cover crops on the row) vs. cover crop mixtures (no skipped rows) before corn in a corn-soybean cropping system to:

  • Evaluate soil health benefits of precision planted cover crops using a medium-term trial
  • Quantify cover crop performance
  • Assess corn performance and quantify the end of season N following corn harvest
  • Evaluate the economics of each cropping system
  • Demonstrate soil health and economic benefits through on-farm research and novel outreach methods

Cooperators

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Research

Materials and methods:

1. Soil Health from Upton Jr. Farm (Measured after six years of cover cropping):

Materials and methods:

Experimental locations and treatments:

A trial was initiated in 2015 and continued until 2022. The field experiment was located on a growers’ farm near Springerton, IL (38º16374'' N, 88º41032'' W). The soil was classified as Bluford silt loam with 0-2 and 2-5 percent slopes (Fine, smectitic, mesic Aeric Fragic Epiaqualfs). The experimental design was a randomized complete block design with three replicates. Treatments were (i) a no-cover crop control (NCC); (ii) no cover on corn row, hairy vetch (V) on middle row, and winter cereal rye (WCR) + annual rye (AR) on the outside row of corn (NOVR); and (iii) oats and radishes on the corn row, V on the middle row, and WCR on the outside row (ORVR).

Cover crop and cash crop managements

The cropping system at the farm was a continuous n-till corn-soybean rotation. In 2015, cover crops were established and in 2016, corn was planted following each cover crop treatment. After corn harvest and prior to planting soybean, WCR was planted in each treatment including the NCC. The planting dates for cover crops ranged between early October to early November. The corn and soybean planted was often occurred in early to late May. Corn and soybean harvest occurred early to mid-October each year up to the year the soil sampling was occurred.

Soil sampling and analysis

In 2020, soil samples were collected by a shovel from three treatments at in and out of the row (corn row vs. cover crop rows) at 0-5 cm (0-2”) and 5-20 cm (2-8”) to assess soil aggregation and aggregate stability, aggregate associated C and N, and soil enzymes along with typical soil fertility and permanganate oxidizable C (POXC). Soil samples were sent to Brookside Laboratory for soil fertility assessment. Methods for measuring soil aggregation and aggregate stability along with aggregate associated soil C and N are described below and are well described in Weidhuner et al. (2021). We collected soil bulk density using a deep core soil probe from 0-5, 5-20, and 20-90 cm (0-2, 2-8, and 8-36”) depths. Soil C stocks was calculated as depth of sampling × bulk density × percent C. We did not measure soil infiltration as our SATURO equipment was not functioning well and we could not trust the data. Soil

Aggregate size distribution and aggregate stability

Stacked sieves of 0.053, 0.25, 0.5, 1, 2, 4.75, and 6.3 mm were used, with a pan to collect soil falling through the smallest, final sieve. Stacked sieves sorted the aggregates into the following categories: <0.053 ,0.053-0.25, 0.25-0.5, 0.5-1, 1-2, and 2-4.75, and 4.75-6.3 mm. Approximately 130g of soil was placed in the center of the top sieve and shaken on a mechanized shaker (Humboldt Mfg. Co., Chicago, IL, U.S.A.) for six seconds modified from the protocol established by Kemper and Rosenaeu (1986). For our specific shaker, six seconds of shaking resulted in the lowest coefficient of variation (CV) while preventing soils from moving to the side of the sieves, unable to fall through (Hanauer et al., unpublished data). An electronic scale was used to record the mass of the contents of each sieve. The percentage of the soil fraction composed of each size aggregate was then calculated as a fraction of initial soil. This test was conducted with three subsamples per experimental unit and was averaged before statistical analysis.

Dry aggregates from the previous analyses were separated into small (0.25-2 mm) and large (2-4.75 mm) aggregate sizes. A wet-sieving apparatus (08.13, Eijkelkamp Agrisearch Equipment, Giesbeek, The Netherlands) was used to determine wet aggregate stability. Approximately 4 g (± 0.02g) of soil were weighed for small aggregates and 2.00 g (± 0.01g) of soil were weighed for large aggregates and placed in the 2 mm sieves. Differences in weight of aggregates were due to lower CV obtained from a preliminary study conducted by Hetrick et al. (2017; unpublished data). The sieves were each submerged in 100 ml of deionized water and soaked for 3 min. The apparatus then ran for a total of 3 min, turned off and allowed to drain for 30 sec, then submerged again and run for an additional 3 min, and finally turned off and allowed to drain for another 3 min. The contents of the can were water and the fraction of the soil that was water-unstable; this fraction was removed, leaving only water-stable aggregates plus sand particles in the sieve. The water was then replaced with an aggregate dispersing solution of 100 mL 2 g/L NaOH and gently stirred for ten revolutions with a glass stirring rod. The apparatus was run for an additional 3 min and was allowed to drain for 3 min. Dispersing solution and aggregate residue that remained  were washed with water into a 150 mL beaker. Only sand remained on the sieve and this was washed with water into another 150 mL beaker. All beakers were then placed in an oven at 50°C until dried. Once dried, each beaker and residue were weighed and recorded. The amount of water stable and unstable aggregates were found by subtracting the initial corresponding beaker weight. These protocols are well described in Weidhuner et al. (2021). 

C and N by depth

Air-dry soil samples by depth were pulverized using a bowl mill (Spex Sample Prep LCC, Metuchen, NJ) for C, and N analysis. Prior to analysis, samples were air-dried at 50 C for at least 48 hours to ensure soil moisture was not a confounding factor in the analysis. Contents of C and N by depth were determined by dry combustion (950°C) using a Thermo Scientific, Flash 2000 C and N analyzer (CE Elantech, Inc., Lakewood, NJ).

Soil hydrolytic enzyme activities

Soil enzymes that catalyze depolymerization of C-containing substrates were assayed to assess potential differences in maximum rates of hydrolysis: cellobiohydrolase (Enzyme Commission 3.2.1.91) and β-glucosidase (EC 3.2.1.21). Assays were performed using field-moist soils based on Tabatabai (1994) as modified by Margenot et al. (2018): 1.00 g of oven-dried soil equivalent was incubated for 1 h (β-glucosidase) or 2 h (cellobiohydrolase) at 37°C in 5 mL of 18.2ΩM·cm water. No buffer was used, for several reasons: modified universal buffer does not maintain assay pH than water (Li et al., 2021) and because buffer requires an assumption of a single pH optimum that is incorrect for many soil enzymes, including cellobiohydrolase and β-glucosidase (Niemi and Vepsäläinen, 2005; Turner, 2010; Wade et al., 2021). A final substrate concentration of 10 mmol L-1 for β-glucosidase and 5 mmol L-1 for cellobiohydrolase was used to ensure substrate saturation (Malcolm, 1983; Margenot et al., 2018). Reactions were immediately alkalized after 1 h by the addition of 4 mL of 0.1 mol L−1 Tris (pH 12.0) and 1 mL of 2 mol L-1 CaCl2. Assays were centrifuged to remove sediment from the supernatant and an aliquot was used to quantify pNP colorimetrically using absorbance at 410 nm.{Turner, 2010 #60} Mean absorbance of negative controls (i.e., substrate but no soil) were subtracted from absorbance of soil assays to account for non-enzymatic hydrolysis of substrate during the incubation (Neal et al., 1981; Turner et al., 2002).

Permanganate-oxidizable carbon

Permanganate oxidizable carbon (POXC) was determined on air-dried soils using the method described by Weil et al. (2003a) as adapted by Culman et al. (2012). In 50 mL centrifuge tubes, 2.50 g of oven dried equivalent of soil was combined with of 0.2 M KMnO4 and 9 ml MΩ cm-1 water were added, yielding 20 mL of 0.02 M KMnO4. The mixture was immediately shaken at 120 rpm for 2 min, then allowed to settle for 10 min. The supernatant was diluted (1:50) and absorbance at 550 nm was quantified by spectrophotometry. POXC was calculated assuming 9,000 mg C oxidized mol−1 permanganate (Weil et al., 2003b).

Statistical analysis

All data were analyzed with PROC Mixed in SAS (SAS Institute, 2017). All variables were tested for normality prior to analysis using a continuous distribution function. Based on the Shapiro-Wilk test and Anderson-darling test used to determine the normality of data. Data that were not normally distributed were log-transformed. Data were back-transformed to normal to report in figures. To analyze soil parameters (aggregate size fractions, stability, C and N, POXC, enzymes, and bulk density) by depth, block and treatment nested within block (indicating plots) were random effects. In addition, an autoregressive covariance structure was specified for the plots being repeatedly measured over the sampling depths. The fixed effects in the model were sampling depth, cover crop, sampling row, and their interactions. LSMEANS in SAS was used for mean separation among treatments when P > 0.1. At each depth, we also ran a contrast analysis between CC (NOVR + ORVR) vs. NCC treatments to assess if CC improves soil properties.

 

 

2. On-farm trials at two sites (five site-yrs in two years) plus two site-yrs in an extra site: 

Materials and methods:

Experimental locations and treatments

Two on-farm trials were conducted in Marion and Springerton, IL in 2020-2021 and replicated in 2021-2022. Soil type was silt loam at both locations. Before trial initiation, composite soil samples were taken by a soil probe from 0-15 cm soil depth at each site and some important physical and chemical properties were determined. Data from soil analysis are not included but will be added.

The experimental design was a randomized complete block design with five treatments and four replicates. Five treatments were: (i) a no-CC control (ii) skipping the corn row, vetch beside the corn row, winter-rye-annual ryegrass-vetch mixture on the middle rows (iii) oat/radishes mix on the corn row, vetch on the row besides corn, winter-rye-annual ryegrass-vetch mixture on the middle rows (iv) skipping the corn row, crimson clover beside the corn row, winter-rye-annual ryegrass-crimson clover mixture on the middle rows (v) oat/radishes mix on the corn row, crimson clover on the row besides corn, winter-rye-annual ryegrass-crimson clover mixture on the middle rows. 

In 2021, those treatments were replicated in Marion but three of those were used in the Springerton site. The three treatments used in the Springerton site were (i) a no-cover crop control (NCC); (ii) no cover on corn row, crimson clover (C) on middle row, and winter cereal rye (WCR) + annual rye (AR) on the outside row of corn (RCSKIP); and (iii) ) no cover on corn row, hairy vetch (V) on middle row, and winter cereal rye (WCR) + annual rye (AR) on the outside row of corn (RVSKIP). In 2021 growing season, corn was planted following the cover crops and in 2022, soybean was planted after the cover crops in Marion but corn was planted following cover crops in Springerton to allow us to evaluate a three-site-yr assessment of cover crop effects on corn. In 2021, we also evaluated next season volunteer cover crops in the fallow season after cover-crop-corn rotation and prior to planting soybean in the Springerton site which had a WCR cover crop over all treatments.  

We added an experimental site (Agronomy Research Center, ARC, Carbondale, IL) to our assessments in both years that evaluated three treatments including (i) a no-cover crop control; (ii) clover-WCR mixture (RCM); and (iii) clover on the corn row and WCR on the middle rows (RCS).

Cover crop and cash crop management and measurements

Planting dates for cover crops in 2020 were September 25, in Marion's site, October 16 in the Springerton site, and September 23 at the Carbondale site. Planting dates for cover crops in 2021 were October 10 in Marion's site, October 8 in the Springerton site, and October 15 at the Carbondale site. 

Cover crops’ aboveground biomass was sampled with grass shears (GS model 700; Black and Decker Inc., Towson, MD) in all sites and years. Cover crop sampling dates in spring 2021 were May 12, in Marion's site, May 13 in the Springerton site, and April 13 at the Carbondale site. Cover crop sampling dates in spring 2022 were May 4 in Marion's site, May 10 in the Springerton site, and May 4 at the Carbondale site. The harvesting area for all trials and in both years was 0.675 m2 (three frames of 0.225 m2 from the center of plots to avoid edge effects). Biomass samples were first separated into species and weeds and then placed into an air-forced oven for 72 h at 48°C to determine DM yield. Biomass sub-samples were then ground to pass through a 1 mm sieve for nutrient analysis (Weidhuner et al., 2019). Nitrogen, P, K, along with Ca, Mg, S, B, Fe, Mn, Cu, Zn, Al, and Na were determined using near-infrared reflectance spectroscopy (NIRS) at the Brookside Laboratories (New Bremen, OH, USA). Carbon was measured using the combustion method as explained in Weidhuner et al. (2019). Nutrient uptake, and C accumulation were calculated by multiplying DM biomass of cover crop species with the percent concentration of each element as explained in Vaughn et al. (2022).   

Planting dates for corn in 2021 were in early May in Marion's site, June 1 in the Springerton site, and May 5 at the Carbondale site. Planting dates for cash crops in 2022 were May 11 in Marion's site, in the Springerton site, and May 18 at the Carbondale site. Harvesting dates for grain corn in 2021 were September 22 for the Marion site, October 5 for the Springerton site, and October 5 in Carbondale site.

In 2022, harvesting dates were September 29 (soybean) for the Marion site, October 6 for the Springerton site, and September 22 at the Carbondale site. At each site, corn stand count was recorded and 10 ears were hand harvested to assess corn yield components including total kernel weight, kernel weight per ear, number of kernels per ear, and 1000-grain weight. A subsample of corn grain was sent to Brookside Lab for grain nutrient analysis. Corn nutrient removal and balances were calculated based on Sadeghpour et al. (2017):

Nutrient applied (kg ha-1) – nutrient removed (kg ha-1) = nutrient balance (kg ha-1).

Statistical analysis

Prior to analysis, data were evaluated for normality of the residuals using the Shapiro-Wilk test reported from Proc Univariate (SAS Institute, 2017). Data were then analyzed using Proc Mixed in SAS.

Data for cover crops that followed by corn (three site-yrs) were evaluated separately. Fixed effects were site-yrs and cover crop treatments and block was considered a random effect. In 2022, two trials were also conducted in which cover crops followed by soybean. Due to differences in the two trial, we analyzed those separately. Therefore, fixed effect was cover crop treatments and block was considered a random effect. Linear and non-linear models including quadratic, two, and three parameters exponential regression in JMP software (JMP Pro 14; SAS Institute, 2015) were used to evaluate relation among dependent variables. The best trend (model) was used based on lower P values, lower root mean square error (RMSE) values and higher R-squared (R2) values. If linear was a good fit (R2 > 0.95 and low RMSE), we used linear and did not overfit the data. When significant at p ≤ 0.05, Fisher’s least significant difference (LSD) test was used for mean separation. Principal component analysis was used to assess the relation among corn yield components.  

Outreach activities:

The trials will be conducted on-farm which is the best form of outreach due to increased peer-to-peer interactions. We will share the results with growers in multiple ways including two field days in 2021. We have initiated writing a factsheet on use of precision mixed cover cropping which will become a part of the Soil Fertility & Fertilizes Course (CSEM/PSAS 447) at SIU-C. We have initiated a “school to farm” approach to bring novel ideas through our students to farms. We expect to publish at least one peer-reviewed journal article. We are partnering with Illinois Farm Bureau and especially Lauren Lurkins (Director of Research for Natural Resources in Illinois) and Austin Omer to reach as many growers as possible.  We have presented our results in numerous opportunities including field days, invited talks, North Central Soil Fertility Conference, and ASA-CSSA-SSSA annual meeting conferences. 

 

Research results and discussion:

1. Soil Health from Upton Jr. Farm (Measured after six years of cover cropping):

Results and Discussion

Soil fertility

Soil test P was affected by depth × CC interaction. Soil test P was declined over depth (Fig. 1) reflecting its immobility in the soil and that majority of P was applied to topsoil. Cover crops had higher STP than the NCC potentially due to recovering STP from deeper soil layer and transferring the P to the topsoil after termination. Soil test K (STK) was higher in cover crop treatments than the no-cover crop control at 0-5 cm depth. Soil test K was higher on corn row indicating both cover crops (oats plus radishes) and corn decomposition and release of K increases STK (Fig, 2). This is interesting because it shows that STK can be recycled in cover crop-cash cropping systems and especially presence of cover crop can improve STK recycling. Potassium is a nutrient that does not remobilized as much as N and P and therefore, residue return from cover crops and cash crops result in return of K in those plant tissues to the soil. Soil test sulfur was similar among treatments but higher at 20-90 cm depth reflecting S leaching or potentially clay types at lower soil depth that adsorb S and could potentially release S to the soil (Fig. 3).

 

Aggregate size distribution and aggregate stability

Dry aggregate size distribution data indicated only differences between the two sampling depths (data not shown). The effect of cover crop and depth × cover crop (Fig. 4) was not significant for any of the aggregate sizes. In general, aggregate sizes of 1 and 2 mm had more than 50% of distribution followed by small (0.053-1 mm) and large (2-6.3 mm) aggregate sizes. It is expected that aggregation takes a long time and perhaps six years of cover cropping was not long enough to show differences among treatments and the no-cover crop control.

 

At 0-5 cm soil depth, a high percentage of small (0.25-2 mm), stable aggregates was recorded in ORVR (77%) which was similar to NOVR (74%) but higher than that of NCC (69%) (P<0.09; Fig. 5A). At 5-20 cm soil depth, aggregate stability was similar among all treatments (59% averaged over all treatments). Small unstable aggregates were similar among cover crop treatments reflecting on variability among blocks (Fig. 5B).

Large stable aggregates (2-4.75 cm) were only influenced by sampling depth (data not shown) and cover crops (Fig. 6). Large stable aggregates were 73% for 0-5 cm soil depth vs. 59% at the 5-20 cm depth reflecting a higher soil organic matter at topsoil potentially leading to more desirable soil structure. Among the cover crops, ORVR had higher percentage of large, stable aggregates (70%) than the NOVR and NCC (Fig. 6). Percentage of unstable aggregates for large aggregate size (2-4.75) was not statistically significant (data not shown). Overall, long-term no-till practices at the study site resulted in high percentage of stable aggregates indicating reducing tillage itself is a great practice and when implemented along with cover cropping, can result in improved soil structure as shown by aggregate stability data in our study.

 

Soil enzyme activities

Soil BG (umol substrate / g soil*hr) and CBH (umol substrate / g soil*hr) data were analyzed by nonparametric Wilcoxon test. Soil BG was only influenced by sampling depth and as sampling depth increased, BG decreased. Soil CBH was higher in the ORVR treatment at 5-20 cm depth than the NCC control but this difference, due to high variability in the data at 0-5 cm depth, was not observed. At 20-90 cm soil depth BG and CBH were similar among all treatments (data not shown).

 

Soil organic matter, C and POXC

Soil organic matter (SOM) was only affected by depth however, a contrast analysis indicated that CC had higher (35.3 g kg-1) SOM than the NCC control (30.4 g kg-1) at 0-5 cm soil depth (Fig. 7A). This indicates, although cover crops develop deep root system but perhaps majority of those roots are in the topsoil and therefore, in a short period (six years), only the topsoil will be affected by incorporating cover crops into the crop rotation. This indicates that building SOM takes time but over a long period, cover crops can benefit SOM. A decline is SOM by depth was expected and SOM decreased from 33.7 g kg-1 in 0-5 cm depth to 15.8 and 10.0 g kg-1 in 5-20 and 20-90 cm soil depths, respectively. Our results indicated NCC had lower SOC stocks than the NOVR and ORVR only at 0-5 cm depth (Fig. 7B). We found that POXC was more sensitive to management changes than SOC as shown by response of POXC to CC effect at not only 0-5 but also 5-20 cm depths. At both 0-5 and 5-20 cm depths, POXC was higher in ORVR at 2-8 cm than the NCC control suggesting that CC could potentially build soil C but it takes longer than six years to show the improvement. Figures for SARE Report. Final

2. On-farm trials at two sites (five site-yrs in two years) plus two site-yrs in an extra site: 

Cover Crop Phase:

Trial 1. CC prior to corn (three site-yrs):

Cover crop performance was impacted by site-yr, cover crop, and their interaction for most of the measured variables (Table 1). The no-cover crop control treatment was weedy in Springerton site in 2021 compared to the Marion but both sites performed better than the Springerton in 2022. Overall, treatments with vetch had high N concentration in the plant, resulting in higher N content and lower C:N ratio which are all desirable for the following corn. Also, our data suggest that cover crops could successfully uptake and potentially recycling P, K, and S. In general, when vetch was in the mixture, vetch competed with rye and annual ryegrass but when clover was in the mixture, those treatments were mainly dominated by rye and annual ryegrass which reflected on the C:N ratio and N uptake (data per species are available but not included at this time). 

Trial 2. CC prior to corn (two site-yrs; extra):

At the Carbondale site, RCM vs. RCS had not statistical differences in either years. Cover crop mixtures averaged over the two years was 1291 kg ha-1 for RCM vs. 1614 kg ha-1 for RCS which led to N uptake of 19 and 23 kg N ha-1 and a C:N ratio of 32.8 and 32.2. Unlike, cover crop biomass and N uptake, C:N was different from year to year. Cover crop C:N in 2021 was 25 vs. 39 in 2022 reflecting earlier planting and termination in 2021 as compared to 2022 (data not shown). 

Corn Phase:

Trial 1. CC prior to corn (three site-yrs):

Corn stand density was higher in Marion site (30500 plants ac-1) than the Springerton site (28275 plant ac-1) reflecting on later planting date in the Springerton site. Fig. 1 Stand count. There was a positive and linear relationship between corn stand density and grain yield in Springerton (R2 = 0.69; P<0.01). Per 1000 corn plant increase, corn grain yield increased by 8.4 bu ac-1. This relationship was not as strong in the Marion site (R2 = 0.48).

Corn grain yields (15.5% moisture) were greater in Marion site (248 bu ac-1) than the Springerton site (164 bu ac-1) reflecting on earlier planting and corn. Corn grain yield was not influenced by cover crop or site × cover crop interaction in 2021. Fig. 2. Corn grain yield. However, addition of legumes as cover crops and especially hairy vetch increased corn grain yield by 19 bu ac-1 that although statistically similar to the no cover crop control, it is economically substantial ($124 profit; not including the cover crop costs). These data indicate as our project moves forward, including economic data plays an important role in deciding which cover crop combination could be most profitable for growers in Illinois.

Corn grain yield was most related to the kernel weight ear-1 (g) (R2 = 0.93; P<0.01). Cover crop treatments significantly influenced the kernel weight ear-1. Integrating vetch + oat + rye (OVR) or skipped row vetch (RVskip) resulted in the larger grain size as indicated by heavier kernels ear-1 than the no cover crop control and treatments that included clover. Fig. 3. Corn kernel weight

Kernel number ear-1 and 1000-grain weight were not influenced by cover crop treatments or the interaction of site x cover crop treatment. 

After receiving corn grain nutrients, we will calculate corn nutrient removal and balances for each treatment. 

Trial 2. CC prior to corn (two site-yrs; extra):

Corn grain yield was similar among all treatments but higher in 2021 than in 2022 indicating earlier planting and more favorable weather in 2021 were critical for achieving high-yielding corn. Corn grain yield was 11324 kg ha-1 (DM basis) in 2021 vs. 7589 kg ha-1 in 2022. Our data from this trial indicated that benefiting from clover requires managing C:N and also manipulating N application to corn to credit the cover crops. Although we only applied 179 kg N ha-1 (160 lbs N ac-1), we still did not see any yield increase in corn in cover crop treatments vs. a no-cover crop control (data not shown). 

From the proof of concept trials that we based on proposal on, we have published a peer-reviewed journal article:

Sadeghpour, A., Adeyemi, O., Hunter, D., Luo, Y., & Armstrong, S. 2021. Precision planting impacts on winter cereal rye growth, nutrient uptake, spring soil temperature and adoption cost. Renewable Agriculture and Food Systems, 1-6. https://doi.org/10.1017/S1742170520000411

In this paper, we discussed precision planting of winter cereal rye vs. normal planting and that reducing seeding rates in precision planting improved farm economics while providing the same nutrient loss reduction benefits.  

We have also presented our results in several conferences including ASA-CSSSA-SSSA annual meeting in Salt Lake City, UT. 

Sadeghpour, A., Adeyemi, D. Hunter, Y. Luo, S. Armstrong. 2020. Precision planting impacts on winter cereal rye growth, nutrient uptake, spring soil temperature, and adoption cost. North Central Extension-Industry Conference, De Moines, IW (Virtual), Nov. 18-19 (Abstract).

Berberich, J., A. Margenot, R. Ibarra, J. Pike, A. Sadeghpour. 2021. Does precision planting of cover crop mixtures provide zonal soil benefits? SIU Research & Creative Activities Virtual Forum, Carbondale, IL (Virtual), April 15 [Poster].

Berberich, J., A. Margenot, R. Ibarra, G. Williams, J. Pike, A. Sadeghpour. 2021. Precision planting of cover crops impacts soil enzymes and soil nutrient distribution. ASA, CSSA, SSSA, Salt Lake City, UT, November7-10[Poster].

Berberich, J., A. Margenot, R. Ibarra, G. Williams, J. Pike, A. Sadeghpour. 2022. Precision planting of cover crop mixtures affects soil carbon, enzymes and soil nutrient distribution. NREC – Live, Champaign, IL, Feb. 16 [Poster].

Berberich, J., C. Kessler, et al. A. Sadeghpour. 2022. Precision cover crop mixture impact on cover crop biomass, nutrient uptake, and the following corn. ASA, CSSA, SSSA, Baltimore, MD, November 6-9 [Poster].

Sadeghpour, A. et al. 2022. Carbon credit and sequestration in agroecosystems: lessons from trials in Southern Illinois. North Central Soil Fertility Conference, Des Moines, IW, November 16-17 [Invited Talk; proceeding]. 

Participation Summary
3 Farmers participating in research

Educational & Outreach Activities

1 Curricula, factsheets or educational tools
1 Journal articles
1 Online trainings
3 Webinars / talks / presentations
12 Workshop field days

Participation Summary:

400 Farmers participated
600 Ag professionals participated
Education/outreach description:

Outreach activities:

The trials will be conducted on-farm which is the best form of outreach due to increased peer-to-peer interactions. We will share the results with growers in multiple ways including two field days each year at two of the farms, writing a factsheet on use of precision mixed cover cropping which will become a part of the Soil Fertility & Fertilizes Course (CSEM/PSAS 447) at SIU-C. We have initiated a “school to farm” approach to bring novel ideas through our students to farms. We expect to publish at least one peer-reviewed journal article. We will partner with Illinois Farm Bureau and especially Lauren Lurkins (Director of Research for Natural Resources in Illinois) to reach as many growers as possible. 

At the moment, we are finalizing a cover crop fact sheet and are planning to set up field days for demonstration of precision cover crop mixtures this coming year. 

Our research was presented during many field days in 2021:

Amir Sadeghpour:

Agronomy Research Center (SIU Carbondale); around 100 participants

Belleville Research Center (SIU Belleville; BRC): around 150-200 participants 

Also, growers involved in this projects are well-known among farming community in Illinois. So far, three presentations are scheduled to happen:

Interview with USDA-NRCS in Illinois (3/5/2021) - Presenter: Mr. Ralph Upton Jr.

Mr. John Pike:

  • March 2, Booneville. IN
    • IN SWCD Winter Workshop
    • Cover Crops and Nitrogen Management
    • in-person.  60 attended.
  • March 8
    • Advanced Soil Health Training
    • Zoom, 20 participated.
  • March 16, , IN Conservation Cropping Systems Initiative Webinar Series
    • Precision Cover Crop Planting,  Options for Making Cover Crop Systems Work
    • 60 registered from IL, IN, KY, OH, Canada
  • April 9,
    • Syngenta So IL District
    • Cover Crop and Nutrient Management Training Session
    • In-field, Marion, IL, 24 attended.
  • July 21, 22 – Advanced Soil Health Training, Vincennes, IN
    • This was part of Retailer Training Program for The Nature Conservancy
    • in-person, 25 attended
  • July 27
    • Booneville, IN SWCD Nutrient Management Field Day
    • Nutrient Management, Cover Crops, Soil Pit Demo
    • in-field, 45 attended
  • August 16
    • Wayne Co./ILFB Nutrient Loss Reduction Field Day
    • So. IL NRECE Nitrogen Trials/Cover Crop
    • In-person, 50 attended
  • August 24
    • Cover Crop Management for High Carbon Systems
      • Lawrenceville AM, 65 attended
      • McLeansboro, PM, 50 attended
  • August 25
    • Helm’s Seed (Freeburg)
    • DeKalb/Asgrow/Bayer Producer Dinner Meeting
      • Belleville
    • Nitrogen and Cover Crop Management pres.
    • 20 Attended
  • August 25
    • IL Wheat Association
    • High Carbon System Management
    • 80 attended
  • November 4-5
    • Advanced Soil Health Training
    • Cover Crop and Soil Management
    • Nitrogen Management and Seasonal Observations
    • Soil Pit Demo
    • Marion, IL, 25 attended

 

Sadeghpour, A. 2022. An update on cover crop management prior to corn. Belleville Research Center Field Day, Belleville, IL. July 14.

Sadeghpour, A. et al. 2022. Carbon credit and sequestration in agroecosystems: lessons from trials in Southern Illinois. North Central Soil Fertility Conference, Des Moines, IW, November 16-17 [Invited Talk; proceeding]. 

We presented results at State and National conferences including:

Berberich, J., A. Margenot, R. Ibarra, J. Pike, A. Sadeghpour. 2021. Does precision planting of cover crop mixtures provide zonal soil benefits? SIU Research & Creative Activities Virtual Forum, Carbondale, IL (Virtual), April 15 [Poster].

Berberich, J., A. Margenot, R. Ibarra, G. Williams, J. Pike, A. Sadeghpour. 2021. Precision planting of cover crops impacts soil enzymes and soil nutrient distribution. ASA, CSSA, SSSA, Salt Lake City, UT, November7-10[Poster].

Berberich, J., A. Margenot, R. Ibarra, G. Williams, J. Pike, A. Sadeghpour. 2022. Precision planting of cover crop mixtures affects soil carbon, enzymes and soil nutrient distribution. NREC – Live, Champaign, IL, Feb. 16 [Poster].

Berberich, J., C. Kessler, et al. A. Sadeghpour. 2022. Precision cover crop mixture impact on cover crop biomass, nutrient uptake, and the following corn. ASA, CSSA, SSSA, Baltimore, MD, November 6-9 [Poster].

We expect to publish at least two more articles from the data we have collected. Both will be in the thesis of Justin Berberich. 

 

 

Learning Outcomes

28 Farmers reported changes in knowledge, attitudes, skills and/or awareness as a result of their participation

Project Outcomes

7 Farmers changed or adopted a practice
1 New working collaboration
Project outcomes:

We developed a fact sheet that shared with students in our class (Soil Fertility CSEM 447; Soil health CSEM 487). Several of our students are either having farm background, work for industry (for example, southern FS etc.), or planning to join them. Our students indicated that they are eager to try this new cover crop management in their farm including the graduate student who was working on this project. They felt that our approach of on-farm research is the way to go but also wanted to try this on their on farm with their own equipment to test. This means, we were able to create some interest among students who brings the knowledge to their farm and push their peers to test new management practices that could result in further adoption. We on the other hand, had push backs for farmer surveys as the interest in participation was just not there. In our field days, many of those farmers asked interesting questions that led us to submit a new partnership proposal. Many of them wanted to know if manipulating seeding rate affects cover crop performance and especially if they can get away with lower seeding rates. 

We were successful in presenting and promoting our results in many ways including Mr. Pikes involvement and presentation to farmer audiences and by presenting these results/projects to students who are either involved or going to be involved with farm decisions. 

Success stories:

The project period was too short to assess success stories. 

Recommendations:

Farmers asked us about precision planting and seeding rate trials and also if there are ways to manipulate planting cover crops to reduce cover crop-cash crop interactions without requiring any specific manipulation to their equipment. 

Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.