Long-term cropping systems management influences soil strength and nutrient cycling

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Long-term cropping systems management influences soil strength and nutrient cycling A.J. Ashwortha,

⁎,1

, P.R. Owensb, F.L. Allenc

a

USDA-ARS, Poultry Production and Product Safety Research Unit, O-303, 1260 W. Maple St., Fayetteville, AR 72701, USA Univ. of Tennessee, Plant Sciences Dep, 2431 Joe Johnson Dr., 252 Ellington Plant Science Bldg, Knoxville, TN 37996, USA c USDA-ARS, Dale Bumpers Small Farms Research Center, 6883 S. Hwy 23, Booneville, AR 72927, USA b

ARTICLE INFO

ABSTRACT

Handling Editor: Jan Willem Van Groenigen

Elucidating complex interactions of cover crops and crop residues on soil physicochemical properties is critical to sustaining soil productivity long-term. Our objective was to compare soil strength and chemistry (physiochemical), cover crop residue composition, and soil compaction following 15-years of cropping system implementation under non-tillage. Main effects were cropping sequences of soybean (Glycine max L.), corn (Zea mays L.), and cotton (Gossypium hirsutum L.), grown on a Loring silt loam, and sequences of corn and soybean on a Maury silt loam. Split-block treatments consisted of winter wheat (Triticum aestivum L.), Austrian winter pea (Pisum sativum L. sativum var. arvense) and hairy vetch (Vicia villosa Roth) cover crops, as well as poultry litter, and a fallow control. Soil physicochemical characteristics were evaluated at surface (0–15 cm) and sub-surface (15–30 cm) depths. Overall, soil physicochemical parameters were more affected by long-term cover crops and poultry litter in surface layers, whereas crop rotations impacted soil chemistry at sub-surface layers. High-nitrogen (N) containing cover crops had more desirable composition for soil biota (less recalcitrant), whereas corn had the highest soil carbon (C), N, and C:N ratio, likely owing to the greatest amount of residue being produced under this cropping sequence. Whole profile (0–1.2 m) assessment of soil compaction indicates: continuous cotton > continuous soybean > corn-soybean > continuous corn > corn-cotton, likely owing to greater planting and spraying traffic throughout the cotton production cycle. Study results help identify cropping system management effects on soil physicochemical properties under no-tillage and such data are needed for quantifying soil quality per soil conservation management.

Keywords: Cropping sequence diversity Soil aggregation Soil quality Nutrient cycling Soil carbon Cover crops Cropping system No-tillage

1. Introduction Crop residue additions from cover crops can reportedly improve soil structural, chemical, and biological properties through interacting effects of i) soil organic matter (SOM) formation; ii) improving soil aggregation and bulk density, as well as subsequent soil-water infiltration and aeration; iii) reducing erosion and runoff potential of soil and particle-bound nutrients; and, iv) insulating soil surfaces from environmental stochasticity (Blanco-Canqui and Lal, 2009; Ashworth et al., 2018). Albeit, the effectiveness of crop residue on improving soil health is a function of total biomass, soil surface coverage, soil texture, topography, and intensity of rainfall and wind (Ruan et al., 2001). Another main driver in crop residue decomposition and efficacy in

improving soil health is residue recalcitrance level and the C:N ratio, which are in turn affected by residue mass, diversity, quality, and crop photosynthetic pathway (Omonode et al., 2006; Six et al., 2006). Therefore, substrate composition and mass drives not only decomposition rates, but the fraction and duration of stored C in soils. Greater photosynthetic or microbial-based C and manure additions, as well as more complex cropping sequences (i.e., shifting from monoculture to continuous rotation cropping or increasing the number of crops in a rotation) have also been implicated for promoting C storage, nutrient cycling, and soil strength, due to more diverse substrate and exudates in above and belowground residues (Franzluebbers, 2005; McDaniel et al., 2014). Despite studies suggesting residue and soil C gains are linearly related, residue characteristics may also confound C

Abbreviations: C, carbon; SOC, soil organic carbon; SOM, soil organic matter; N, nitrogen; RECM, Research and Education Center at Milan; MTREC, Middle Tennessee Research and Education Center; LRR, Land Resource Region; MLRA, Major Land Resource Area; EC, electrical conductivity; CEC, cation exchange capacity; SAR, sodium adsorption ratio; NDF, neutral detergent fiber; ADF, acid detergent fiber; ADL, acid detergent lignin ⁎ Corresponding author. E-mail address: [email protected] (A.J. Ashworth). 1 ORCID: https://orcid.org/0000-0002-3218-8939. https://doi.org/10.1016/j.geoderma.2019.114062 Received 26 June 2019; Received in revised form 18 September 2019; Accepted 29 October 2019 0016-7061/ Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

Please cite this article as: A.J. Ashworth, P.R. Owens and F.L. Allen, Geoderma, https://doi.org/10.1016/j.geoderma.2019.114062

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degradation and recalcitrant C formation as continuous corn may result in comparable SOC levels to that of cotton /corn (Reddy et al., 2006; Ashworth et al., 2014) and corn–soybean rotations (Omonode et al., 2006). Furthermore, crop C partitioning plays a role in stable SOC allocation, as crops that dedicate larger amounts of belowground biomass (i.e., greater root/shoot ratios) typically have a greater capacity for SOC storage due to greater root exudates, lipids, and fine root turnover, which promotes rhizodeposition (Bolinder et al., 2007). Previous research has shown that animal manures and crop residues (bio-covers) can enhance soil fertility and subsequent crop yields under notillage (Ashworth et al., 2014, 2017c; Ebelhar et al., 1984). Specifically, a legume cover crop such as hairy vetch can increase available N due to N2–fixation, thereby meeting part of the N diet for succeeding crops (Peoples et al., 2001). However, leguminous cover crops may result in greater soil N levels, thereby adversely affecting subsequent crops such as cotton, owing to N additions stimulating vegetative growth resulting in delayed flowering (Ashworth et al., 2016a). In addition, poultry litter, which consists of poultry manure and bedding materials, is a source of N and phosphorus (P) that can also impact labile N and benefit microbial and earthworm diversity and abundance (Ashworth et al., 2017a,b). Soil compaction and soil strength are important parameters used to describe structural stability caused by equipment use and other conservation agricultural management factors. The glossary of soil science defines soil strength (cone index, penetration resistance) as a combined measure of a solid phase adhesive and cohesive status (SSSA, 1996). This property is most easily affected by changes in soil water content and bulk density, although other factors including texture, mineralogy, cementation, cation composition and organic matter content may affect soil strength (SSSA, 1996). Soil compaction is particularly important in no-tillage systems, as it may influence long-term soil-water infiltration and root development, thus affecting nutrient availability and crop production (Jung et al., 2010). In addition, soil bulk density increases under compaction, which can lead to increased surface nutrient runoff (Jung et al., 2010). Since residues aid in the prevention of soil compaction, research is needed on how crop rotations and nutrient rich poultry litter and cover crops influence soil compaction as measured by soil strength. In addition, recent interest in precision agriculture platforms has increased, as has the collection of data to understand spatial and topographic influence on soil physical properties (Adhikari et al., 2018). Interacting temporal effects of soil physical and chemical parameters under non-tillage are largely unknown. Therefore, data are needed on belowground C assimilation, fertility, and compaction under commonly implemented crop rotation systems to verify effects. Objectives were to compare soil strength, C storage, soil nutrient accumulation and compaction across cropping systems, cover crops, and poultry litter treatments under long-term no-tillage.

The site received annual additions of dairy manure for 15 years prior to initiation of the experiment. The second site was at the Research and Education Center at Milan (RECM; Milan, TN; 35.54° N −88.44° W) in MLRA 134 (Southern Mississippi Valley Loess) in the Eastern Gulf Coastal Plain LRR “P.” This region covers most of western Tennessee, western Alabama, a major portion of Mississippi, eastern Louisiana, and a small section of western Kentucky. Soils at the RECM are classified as a Loring B2 series (Fine-silty, mixed, thermic Oxyaquic Fragiudalfs), with a mean annual precipitation and temperature being 107 cm and 14.8 °C, respectively. Prior to experimentation this site was planted to corn in 2001, soybean in 2000, and cotton in 1999. During winters, wheat was planted for grain, although the year prior to experimentation the site was left fallow. At both locations, treatments were laid out as a split-block (stripplot) design with four replications. Each location was under long-term no-tillage, where the main and cover crops were planted directly into the residue of the previous crop. Whole-block treatments consisted of cropping sequences (see Table 1 for whole-plot sequences), with stripblock treatments composed of four bio-covers (green manures and crop residues). At RECM, four different cropping sequences of corn, cotton, and soybean were repeated in 4-yr cycles (i.e., Phases I, II, III, and IV; Table 1) beginning in 2002 and continuing through 2014. Bio-covers of wheat, vetch, poultry litter, and a fallow (winter weeds) control were repeated annually. The same experiment was performed at MTREC without cotton. This created 25 and 15 sequence × bio-cover combinations for RECM and MTREC, respectively (Table 1). Main plots were 6.1 × 12.3 and 4.6 × 12.3 m at RECM and MTES, respectively. Row crops were planted perpendicular to split plots (biocovers). Specific details on planting methods, cultivars, and row spacing can be found in Ashworth et al. (2014, 2016a,b, 2017a). Briefly, corn was planted between 12 April and 9 May, soybean was planted between 29 April and 30 May, and cotton was planted between 7 and 12 May. Glyphosate-resistant cultivars were planted during Phases I and II and glufosinate-tolerant cultivars in subsequent phases to minimize development of glyphosate-resistant weed populations. Cover crops were planted approximately mid-October through mid-November during the previous cropping year. Corn received 128.5 kg N ha−1, whereas cotton received 33.4 kg N ha−1 as sidedress applications in May and June each year. Table 1 Cropping sequences from 2002 (Yr-1)-2017 (Yr-16) at the Middle Tennessee Research and Education Center (MTREC) and Research Education Center at Milan (RECM). Middle Tennessee Research and Education Center Year

2. Materials and methods 2.1. Site description and experimental design Field studies were conducted at two sites with existing long-term, no-tillage cropping system trials in order to assess cropping system impacts across varying soil types and physiographic regions on soil physicochemical parameters. The first location was the University of Tennessee, Middle Tennessee Research and Education Center (MTREC; Spring Hill, Tennessee; 36.02° N, −85.13° W) in Natural Resources Conservation Service (NRCS), Major Land Resource Area (MLRA) 123, referred to as the Nashville Basin in the Land Resource Region (LRR) “N.” This area is typical of karst topography region of middle Tennessee, northern Alabama, central and western Kentucky and southern Indiana. Soils at this site are classified as a Maury silt loam (Fine, mixed, active, mesic Typic Paleudalfs). MTREC has a mean annual temperature of 14.2 °C and 114 cm of precipitation. Prior to plot establishment, this site was under a two-year corn-soybean rotation, with half the field being under corn and half soybean.

Crop Sequence Continuous Corn Continuous Soybean Corn-Soybean

2002† 2006 2010 2014

2003 2007 2011 2015

2004 2008 2012 2016

2005 2009 2013 2017

corn soybean corn

corn soybean soybean

corn soybean corn

corn soybean soybean

cotton corn soybean corn corn

cotton corn soybean soybean cotton

Research and Education Center at Milan Crop Sequence Continuous Cotton Continuous Corn Continuous Soybean Corn-Soybean Corn-Cotton ‡

cotton corn soybean corn corn

cotton corn soybean soybean cotton

Bolded crops are those grown in the year previous to sampling in Spring 2016. † 2002–2005 = Phase I; 2006–2009 = Phase II; 2010–2013 = Phase III; Phase IV = 2014–2017.

2

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Muriate of potash (KCl) was applied to all plots in April at a rate of 112 kg K ha−1. Poultry litter plots received the equivalent of 66.7 kg N (total) ha−1 (4.4 t ha−1, A&L Analytical Laboratories). Similarly, wheat and fallow received 66.7 kg N ha−1, whereas vetch received 50.4 kg N ha−1 in the form of urea (CH4N2O). Yield for continuous soybean, cotton, and corn was 2.7, 3.1, and 7.6, Mg ha−1 respectively, with average corn, cotton, and soybean yields in rotations being 2.7, 2.8, and 7.9 Mg ha−1 respectively (Ashworth et al., 2016a,b, 2017). Before planting, burndown herbicides were used to terminate existing vegetation and bio-covers. Either paraquat (1,1-dimethyl-4,4-bipyridinium), glyphosate [N-(phosphonomethyl)-glycine], or glufosinate [ammonium( ± )-2 amino-4-(hydroxymethylphosphinyl)butanoate] was applied in March each year, prior to corn, soybean, and cotton seeding. One or two post-emergence applications of glyphosate were applied to all plots from May to June annually during Phases I and II, whereas glufosinate was used in Phases III and IV. For cotton, insecticide and crop growth regulation chemical usage was extensive, and annual application dates ranged from June through September. A commercial organophosphate defoliant, growth regulator, and organophosphate insecticide was applied several times beginning in June after cotton emergence.

crucibles were removed and cooled to room temperature in a glass desiccator. The material retained in the crucible was weighed and ash concentration was expressed as g kg−1. 2.4. Soil strength determination from long-term soil conservation management At both experimental locations, two penetrometer readings were taken 10–15 cm from the crop row (same position as the soil cores) per plot late October 2016 as recommended by ASABE Standards (2009). In-field measurements of soil insertion force and near-infrared spectroscopy were determined using a Veris P4000 VIS-NIR-EC-Force Probe (Salina, KS), with 27.8-mm base diameter on the cone-shaped tip and 20.8-mm diameter shaft (Pote et al., 2016). The probe used was tractor − mounted and readings were taken between tires to prevent ancillary compaction. During readings, an onboard power unit and hydraulic cylinder was used to insert the penetrometer to a maximum depth of approximately 95 cm. Maximum insertion force applied was 1 kN or 5 MPa (725 psi) due to low soil moisture at time of sampling (µ=14%), to prevent overload of the mechanical and sensing system. Data collection occurred at every 1.27 cm insertion depth from the penetrometer tip, which is electrically insulated by a thin dielectric ring, and included insertion force. Insertion force was measured by a pressure transducer between the penetrometer shaft and hydraulic cylinder (Sudduth et al., 2004). Data were recorded from each location (MTREC and RECM) and spatially precise location was identified by GPS and logged on the Veris instrument.

2.2. Soil sampling and analysis During Phase IV (late October 2016), six soil samples were randomly collected and composited per plot at both sites from 0 to 15 and 15–30 cm depths and were air dried ground to pass through a 2-mm sieve. Particle size was determined by using a modified 12-hr hydrometer method (Gee and Or, 2002). Soil pH was determined using a 1:1 soil mass: deionized water volume mixture using an AS3010D dual pH analyzer (Labfit), while electrical conductivity (EC) was measured using a 1:2 saturated paste method (Soil Survey Staff, 2014). Total carbon and total nitrogen were determined via high-temperature dry combustion using a VarioMacro CN analyzer (Ashworth et al., 2018; Elementar Americas Inc., Mt. Laurel, NJ). Soil tests were also conducted at both depths from each plot to determine soil pH and concentrations of P, K, Mg, B, Cu, Fe, Mn, Zn, Ca, cation exchange capacity (CEC), and sodium adsorption ratio (SAR). Samples were ground to pass through a 1-mm sieve on a Wiley soil crusher (Thomas Scientific, Swedesboro, NJ) and Mehlich-1 (Mehlich, 1953) extractable nutrients were measured by inductively coupled plasma using a 7300 ICP-OES DV (Perkin-Elmer, Waltham, MA).

2.5. Data analysis and model development To identify how cropping systems influence dynamic soil properties over time, three statistical models were developed. The first model tested soil chemical (i.e., pH, N, C:N, EC, SAR, P, K, Ca Mg, and CEC) and physical (sand, silt, clay, and C) levels after 15-years of conservation agriculture implementation by depth (surface and subsurface). The second model tested cover crop yield and compositional differences. A third model was developed to investigate whole profile (0–1.2 m) soil force per depth increment. The aforementioned models used the MIXED procedure of SAS (SAS Institute, 2014). In this analysis, each dataset tested main effects of cropping sequence (whole-plot) and bio-covers (split-block) by soil depth in an analysis of variance test. Block and location were considered random effects. When main effects or interaction confluences were found, mean separations were performed using the SAS macro “pdmix800” (Saxton, 1998) with Fisher’s LSD and at a Type I error rate of 5% (SAS Institute, 2014).

2.3. Cover crop sample analysis Cover crop tissue was harvested with hand sheers (two, 3-m rows, or 3.6 m2 total sampled area per plot) to a 5-cm stubble height during late April 2016 prior to chemical burn down. After determining fresh weight, a grab sample of at least 750 g was weighed, dried at 60 °C in a batch oven, and reweighed to determine tissue moisture content to correct the total harvested weight to aboveground dry biomass. Samples were then ground to pass a 5, 1-mm sieve on a Wiley mill (Thomas Scientific, Swedesboro, NJ) before analysis of total N, P, K, Mg, S, Na, S, and NO3-N concentrations (University of Arkansas Diagnostic Laboratory). Nitrogen was analyzed by combustion (Campbell, 1992), and total C was measured by combustion (LECO CN2000 St. Joseph, MI). Mineral concentration was determined by nitric acid digestion (Mehlich, 1984) followed by inductively coupled argon plasma spectrometry. Neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) were analyzed using an Ankom 2000 Fiber Analyzer (Ankom Technol. Corp., Fairport, NY). Total ash was determined based on ASTM standard E1755-01 (Sluiter et al., 2005). One gram of ground, prepared plant material (sieved to 1 mm) was placed in an oven-dried, porcelain crucible overnight at 105 °C. Crucibles were placed in a muffle furnace at 575 °C for 4 h. After 4.5 h from the start of furnace,

3. Results 3.1. Soil physicochemical impact from cropping system management All soil physicochemical parameters were impacted (P < 0.05) by depth, but not location (P > 0.05); therefore, data were presented by depth across locations (Table 2). Overall, after 15-years of crop rotation, cover crops, and poultry litter applications, parameters (pH, C, N, C:N, EC, clay, CEC, K, and Mg) were influenced (P < 0.05) by both rotation and bio-cover (exceptions were sand, silt, P, Ca, and SAR). There were no crop rotation × bio-cover interactions for any parameter at the 0–15 depth. Upper soil (0–15 cm) C:N differed by crop rotation only, whereas SAR, P, and Ca were only affected by bio-covers. In sub-soil layers (15–30 cm), there were no soil chemistry (ph, C, N, C:N, EC, CEC, P, Ca, and Mg) rotation × bio-cover interaction (P > 0.05), with only K and SAR being impacted by both rotation and bio-cover. In general, soil physicochemical parameters were more affected by long-term cover crops and poultry litter in soil surface layers, whereas crop rotation impacted soil chemistry in sub-surface depths. Specifically, pH, C, EC, clay, CEC, P, and Mg varied in the sub-surface 3

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Table 2 Soil test pH, C, N, C:N, EC, partial size, SAR, CEC, and nutrients (P, K, Ca, and Mg) levels from 0 to 15 cm and 15 to 30 cm depths pooled across locations‡ (research and education centers at Milan and Spring Hill, Tennessee) after 15 years of cropping rotation and bio-cover implementation.

Rotation† TTTT TCTC CCCC CSCS SSSS Bio-cover Fallow Poultry litter Austrian Winter pea Hairy Vetch Wheat TTTT TCTC CCCC CSCS SSSS Bio-cover Fallow Poultry litter Austrian Winter pea Hairy Vetch Wheat † ¶ ‡

pH

C

N

C:N

EC¶

Sand

0–15 cm % 5.45 a 5.14b 5.4 a 5.35 a 5.48 a

0.87c 0.99b 1.12 a 1.07 a 1.0b

0.12 d 0.14 bc 0.15 a 0.14 ab 0.13c

dS m−1 7.19c 7.3 bc 7.64 a 7.54 ab 7.39 bc

297.71b 385.28 a 377.75 a 379.49 a 350.54 a

% 18.67 18.01 19.62 19.91 19.38

5.39b 5.66 a 5.33 bc 5.19c 5.20 bc

1.01b 1.13 a 0.92c 1.0b 0.98 bc

0.13b 0.15 a 0.12c 0.13b 0.13b

7.6 a 7.39 a 7.33 a 7.38 a 7.34 a

326.21c 405.15 a 330.73 bc 365.72b 362.96b

15–30 cm 5.5b 0.54 a 5.36b 0.49a 5.8 a 0.48a 5.75 a 0.51 a 5.75 a 0.47 a

0.09 0.09 0.08 0.09 0.08

a a a a a

6.24 a 5.72 a 5.86 a 5.97 a 5.9 a

5.65 5.64 5.65 5.73 5.48

0.08 0.08 0.08 0.08 0.08

a a a a a

5.88 5.94 6.15 5.98 5.71

a a a a a

0.49 0.48 0.52 0.51 0.49

a a a a a

a a a a a

Silt

Clay

61.08 a 56.31 a 58.47 a 60.0 a 59.64 a

Cmolc kg soil−1 20.49b 0.98 25.92 a 0.94 21.83b 0.86 20.03b 0.88 20.9b 0.87

19.75 a 19.94 a 19.97 a 18.93 a 17.0 a

59.68a 58.57 ab 60.32 a 60.72 a 56.21b

223.66 abc 250.1 a 193.17c 218b 201.56 bc

17.21 a 16.61 a 17.66 a 17.54 a 18.2 a

58.72 58.46 59.14 60.55 61.79

213.32 220.33 223.92 217.52 211.39

17.59 a 17.01 a 18.32 a 18.4 a 15.88 a

59.54 60.48 59.35 60.76 58.54

a a a a a

a a a a a

SAR

CEC

P

K

a a a a a

kg ha−1 24.48b 28.11 a 23.93b 24.85b 23.29b

123.4 a 80.26 a 83.3 a 95.92 a 105.07 a

201.81 215.95 221.54 205.54 170.2b

20.62b 21.55b 19.76b 20.41b 26.84 a

0.8b 1.23 a 0.84b 0.86b 0.81b

24.62b 21.79c 25.06 ab 27.0 a 26.18 ab

62.04b 244.88 a 56.99b 58.52b 65.54b

155.53b 384.31 a 149.08b 155.03b 171.1b

1942.98b 2545.78 a 1777.57b 1768.07b 1886.37b

160.18 262.16 160.63 162.91 170.11

a a a a a

23.85 a 24.79 a 23.32 a 22.03 ab 20.13b

1.16 a 1.16 a 0.95b 0.92b 0.96b

24.86 a 25.52 a 20.57b 21.15b 21.16b

48.7 a 32.9 bc 29.99c 38.73 ab 32.62 bc

141.04 a 140.24 a 131.62 a 129.02 a 103.3 a

1843.94 1919.67 1776.77 1769.25 1834.59

168.57 ab 208.44 a 193.9 a 150.5b 133.58b

a a a a a

22.88 ab 22.53b 22.34b 20.85b 25.53 a

0.9c 1.43 a 0.88c 0.91c 1.03b

22.36 23.71 22.29 20.68 24.23

32.99 a 42.65 a 37.3 a 39.46 a 30.53 a

114.45b 193.85 a 108.52b 115.69b 112.73b

1892.27 a 1750.53b 1838.31 ab 1924.98 a 1738.13b

a a a a a

Ca

ab a a a

Mg

2013.56 1991.96 1948.68 1897.82 2068.74

a a a a a

a a a a a

189.64 ab 210.77 a 188.34ab 168.91 bc 158.34c a a a a a

166.05 ab 191.97 a 163.75 ab 157.62b 175.61 ab

T = cotton; C = corn; S = soybean. Sequences with cotton only occurred at the Research and Education Center at Milan, Tennessee. EC = electrical conductivity; SAR = Sodium Adsorption Ratio; CEC = cation exchange capacity. Analyzed across locations, as there were no location differences P > 0.05.

depth based on long-term crop rotations, whereas only Ca varied by bio-cover (P < 0.05).

Table 4 Analysis of variance for soil force by depth averaged across two Research and Education Centers in Tennessee† (MTREC, Spring Hill, and RECM, Milan) during 2016.

3.2. Cover crop residue compositional variation Residue elemental concentration (P, K, Ca, Mg, Na NO3-N, C, C:N, and N) and recalcitrance (ADF, NDF, ADL, and ash) varied (P < 0.05) among cover crops excluding that for quantity (yield) and S. In addition, neither crop rotation (type or diversity) influenced cover crop residue quality or quantity, nor did any crop rotation × cover crop interactions vary across locations (P > 0.05). Considering cover crop yield did not vary across locations, data were presented as concentrations rather than on a removal basis across both sites (Table 3).

Effect

Num DF

F Value

Pr > F

Rotation Bio-cover Bio-cover*Rotation Depth Rotation*Depth Bio-cover*Depth Bio-cover*Rotation*Depth

4 4 16 7 28 28 112

118.93 1.96 8.95 32.31 4.53 0.73 1.09

< 0.0001 0.0987 < 0.0001 < 0.0001 < 0.0001 0.8519 0.2498

†

3.3. Soil strength impact from long-term soil conservation management

Analyzed across locations as location did not impact (P > 0.05) soil force.

cover crops had the greatest compaction, with the corn-cotton rotation under long-term poultry litter applications being lowest (P < 0.05).

Soil force (KPa) varied (P < 0.05) by crop rotation, depth, biocover × rotation, and rotation × depth (Table 4); however, bio-cover had no influence on soil strength (P > 0.05). Across all bio-covers and depths, continuous cotton sequences had the greatest force (µ=6,285 KPa), indicating greatest compaction compared to all cropping rotations. Across all bio-cover × crop rotation interactions, the continuous cotton sequence with hairy vetch and Austrian winter pea

4. Discussion 4.1. Soil physicochemical impact from cropping system management Beyond no-tillage, conservation agricultural practices such as crop

Table 3 Across-location averages for aboveground cover crop and volunteer (fallow and poultry litter) species nutritive, quality composition, and yield collected spring 2016 at the research and education centers at Milan and Spring Hill, Tennessee. N Bio-cover

%

Fallow Poultry litter Austrian pea Hairy Vetch Wheat

2.4 bc 2.5 b 2.5 b 3.2 a 2.1c

C

43.96 44.1b 45.03 a 45.26 a 44.2b

C:N

18.9 18.4 17.9 14.8 21.5

P

ab ab bc c a

0.4 0.5 0.3 0.3 0.3

K

ab a d cd bc

3.0 ab 3.3 a 2.2c 2.8 ab 2.7b

Ca

0.9 ab 1.01 a 0.63b 0.82 ab 0.28c

Mg

S

0.22 ab 0.23 a 0.17c 0.18 bc 0.11 d

4

0.2 0.2 0.2 0.2 0.1

NDF

a a a a a

36.7 ab 32.1 bc 26.1c ab 38.3 a

ADF

25.4 a 22.9 ab 18.4c 24.8 a 20.9 bc

ADL

6.1 a 5.5 a 3.0b 4.8 a 2.9b

Ash

10.3 a 10.4 a 6.8c 8.7b 7.9 bc

Na

78.8b 222.7 a 30.9b 54.2b 26.5b

Yield

NO3–

mg ha−1

mg kg−1

1.9 1.8 1.9 2.1 1.8

910.0b 1038.1 ab 914.4b 567.5c 1320.4 a

a a a a a

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Fig. 1. Soil force as affected by cropping rotation by depth (P < 0.0001) pooled across bio-covers and locations (research and education centers at Milan, Tennessee and Spring hill, Tennessee) during 2016. Horizontal bars are standard errors of the mean.

viable option to cover crops, although further analyses are needed to confirm economic and ecological viability. Plant tissue N and C was greatest under hairy vetch cover crops, indicating greater N capturing and fixation potential for the proceeding crop (Strock et al., 2004; Costa et al., 2000), as well as greater ability to serve as a food source for nitrifying bacteria based on the lower C:N ratio (Table 3). Less recalcitrant material of Austrian winter pea and wheat indicates more favorable biomass decomposition by soil microbiota compared to hairy vetch, poultry litter winter weeds, and the fallow control (Table 3). Greater phenolic (lignin) content of these cover crops suggests poorer food source and persistence in soil, as well as subsequent soil biodiversity. However, in previous work in the same study area, authors observed improved soil biodiversity (earthworm and microbial) under long-term poultry litter applications compared to cover crops and the fallow control (Ashworth et al., 2017a,b).

rotations, cover crops, and animal manure inputs can reportedly increase or maintain soil quality and fertility for long-term agricultural production. Overall, long-term poultry litter applications impacted soil physicochemical parameters at the 0–15 cm depth, considering pH, C, N, EC, SAR, P, K, Ca were all greater under this treatment compared to cover crops and the fallow control (P < 0.05). Specifically, C, P, K, and Ca were 11, 67, 74, and 31% higher under poultry litter applications compared to the fallow control (Table 2). In general, soil fertility did not vary based on cover cropping, although CEC differed, with hairy vetch having the greatest value, but not differing from wheat and Austrian winter pea. Continuous cropping systems, defined as producing a single crop on the same parcel of land for 3 or more years, is widely thought to reduce yields, although yield penalties resulting from continuous systems are not clearly understood (Gentry et al., 2013; Crookston et al., 1991). Under the continuous corn, cotton, and soybean systems, EC and Mg were greatest in sub-surface layers with C, K, and clay being greatest in the surface layers for the corn-soybean rotation. Crop rotations and cropping sequence diversity is also a pivotal factor influencing bacterial assemblages and diversity (Ashworth et al., 2017a). High protein-containing biomass from soybeans and legume cover crops may produce more labile residues than high C:N crops such as corn (Sarrantonio and Gallandt, 2003). Further, greater cropping sequence diversity and cover crops reportedly supports greater microbial biomass and more fungalbased community structures, which culminates in greater microbiallyderived organic matter (Six et al., 2006). In this experiment, rotations with corn had the highest C, N, and C:N, likely owing to the greatest amount of residue being produced by this cropping sequence.

4.3. Soil strength impact from long-term soil conservation management Continuous cotton sequences had the greatest force, indicating greatest compaction compared to all cropping rotations. This was likely owing to multiple applications of growth regulators and insecticides throughout the cotton production cycle (e.g., two organophosphate materials were applied multiple times per annum on only cotton plots). In addition, based on previous work on this study area, this sequence produces the least amount of residue, and also results in the lowest earthworm diversity and abundance (Ashworth et al., 2017b). This is important because earthworms take on the role of tillage and residue decomposition in non-tilled systems (Ashworth et al., 2017b). Therefore, results suggest that the planting and spraying traffic under notillage may be the source of greater soil compaction. Finally, whole profile (0–1.2 m) assessment of soil compaction per crop rotation indicates that continuous cotton systems resulted in the greatest soil compaction, followed by continuous soybean, corn-soybean, continuous corn, and the corn-cotton rotation (P < 0.05; Fig. 1). Overall, greatest compaction was observed under continuous cotton at deeper soil depths (> 1 m), with lowest compaction occurring under corn-cotton rotations at 0.07 m (Fig. 1). Another potential reason for increased compaction could be related to earthworm activity. Ashworth

4.2. Cover crop residue compositional variation Compared to cover crops, P, K, Ca, and Mg content was greater in poultry litter winter weed residue than the fallow control, which coincided with soil test P and K. This likely resulted in a flush of volunteer winter annuals and subsequent nutrient uptake, albeit total biomass production did not exceed that of other cover crops (P > 0.05). Furthermore, based on current cover crop seed prices (Snapp et al., 2005), poultry litter and subsequent winter weed growth may be a 5

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et al. (2017b) found fewer earthworms in continuous cotton and earthworms are active through the 1.2 m depth. These results indicate that corn systems create improved macropores owing to deeper roots compared to cotton and soybean systems; thus indicating less sub-surface drainage, which has implications for deeper SOC formation under no-tillage (Jung et al., 2010). However, increased crop rotation diversity tended to not favor reduced soil compaction. In addition, soil strength for all crop rotations and across all row positions rapidly increased at the 0.2 m depth with peak compaction occurring after (> 0.3 m), raising the possibility of a root-limiting condition at these depths under no-tillage (Pote et al., 2016).

endorsement by the USDA. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.geoderma.2019.114062. References Adhikari, K., Owens, P.R., Ashworth, A.J., Sauer, T.J., Libohova, Z., Miller, D.M., 2018. Topographic controls on soil nutrient variations in a silvopasture system. Agrosyst. Geosci. Environ. 1, 180008. https://doi.org/10.2134/age2018.04.0008. ASABE Standards. 2009. S313.3: Soil cone penetrometer. St. Joseph, MI: ASABE. Ashworth, A.J., Allen, F., Wight, J., Saxton, A., Tyler, D., 2014. Soil organic carbon sequestration rates under crop sequence diversity, bio-covers, and no-tillage. Soil Sci. Soc. Am. J. 78, 1726–1733. Ashworth, A.J., Allen, F.L., Saxton, A.M., Tyler, D.D., 2016a. Long-term cotton yield impacts from cropping rotations and bio-covers under no-tillage. J. Cotton Sci. 20, 1–8. Ashworth, A.J., Allen, F.L., Saxton, A.M., Tyler, D., 2016b. Long-term corn yield impacted by cropping rotations and bio-covers under no-tillage. Agron. J. 108, 1–8. https:// doi.org/10.2134/agronj2015.0453. Ashworth, A.J., DeBruyn, J., Allen, F.L., Radiosevich, M., Owens, P.R., 2017a. Microbial community structure is affected by cropping sequences and poultry litter under longterm no-tillage. Soil Biol. Biochem. 114, 210–219. Ashworth, A.J., Allen, F.L., Tyler, D.D., Pote, D., Shipitalo, M.J., 2017b. Earthworm populations are affected from long-term crop sequences and bio-covers under no-tillage. Pedobiologia – Intern. J. Soil Ecol. 60, 27–33. Ashworth, A.J., Allen, F.L., Saxton, A.M., Tyler, D.D., 2017c. Impact of crop rotations and soil amendments on long-term no tilled soybean yield. Agron. J. 109, 938–946. Ashworth, A.J., Allen, F.L., DeBruyn, J., Owens, P.R., Sams, C., 2018. Crop rotations and poultry litter impact dynamic soil chemical properties and soil biota long-term. J. Environ. Q. Spec. Sect.: Soil Chem. One Health Initiative 47, 1327–1338. Bolinder, M.A., Janzen, H.H., Gregorich, E.G., Angers, D.A., Vandenbygaart, A.J., 2007. An approach for estimating net primary productivity and annual carbon inputs to soil for common agricultural crops in Canada. Agric. Ecosyst. Environ. 118, 29–42. Blanco-Canqui, H., Lal, R., 2009. Crop residue removal impacts on soil productivity and environmental quality. Crit. Rev. Plant Sci. 28, 139–163. Campbell, C.R., 1992. Determination of total nitrogen in plant tissue by combustion. In: C. Owen Plank, editor, Plant analysis reference procedures for the southern U.S. Southern Coop. Ser. Bull. 368. Univ. of Georgia, Athens. p. 20–22. Costa, J.M., Bollero, G.A., Coale, F.J., 2000. Early season nitrogen accumulation in winter wheat. J. Plant Nutr. 23, 773–783. Crookston, R.K., Kurle, J.E., Copeland, P.J., Ford, J.H., Lueschen, W.E., 1991. Rotational cropping sequence affects yield of corn and soybean. Agron. J. 83, 108–113. Ebelhar, S.A., Frye, W.W., Blevins, R.L., 1984. Nitrogen from legume cover crops for notillage corn. Agron. J. 76, 51–55. Franzluebbers, A.J., 2005. Soil organic carbon sequestration and agricultural greenhouse gas emissions in the southeastern USA. Soil Tillage Res. 83, 120–147. Gee, W.G., Or, D., 2002. Particle-size analysis. p. 255–293. In: Dane, J., and G.C. Topp (Eds.). Methods of Soil Analysis. Book Series: 5. Part 4. Soil Science Society of America. USA. Gentry, L.F., Ruffo, M.L., Bellow, F.E., 2013. Identifying factors controlling the continuous corn yield penalty. Agron. J. 105, 295–303. Jung, K.Y., Kitchen, N.R., Sudduth, K.A., Lee, K.S., Chung, S.O., 2010. Soil compaction varies by crop management system over a claypan soil landscape. Soil Till. Res. 107, 1–10. McDaniel, M.D., Tiemann, L.K., Grandy, A.S., 2014. Does agricultural crop diversity enhance soil microbial biomass and organic matter dynamics? A meta-analysis. Ecol. Applic. 24, 560–570. Mehlich, A., 1953. 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5. Conclusions Soil quality can be conceptualized as a three-legged stool (i.e., biological, chemical, and physical properties), with soil physicochemical properties being an important descriptor of soil function. After 15years of cropping systems management, soil physicochemical parameters were more affected by long-term cover crops and poultry litter in soil surface layers, whereas crop rotation impacted soil pH, C, EC, clay, CEC, P, and Mg in sub-surface layers. In this experiment, longterm poultry litter and subsequent winter weed growth also resulted in the greatest soil fertility; therefore, poultry litter may be a viable alternatives to cover crops, although economic and ecological analyses are needed to confirm viability. High-N containing cover crops and crop rotations resulted in more desirable food sources for soil biota (less recalcitrant plant material), whereas corn had the highest soil C, N, and C:N, likely owing to the greatest amount of residue being produced by this cropping sequence. Cropping systems management can impact soil compaction. In this experiment, long-term continuous cotton sequences had the greatest force, indicating greatest compaction compared to other cropping rotations. This was likely owing to multiple applications of growth regulators and insecticides throughout the cotton production cycle, which has adverse ancillary impacts related to soil biota (earthworm diversity), sub-surface drainage, and deeper SOC formation under notillage compared to high residue producing systems (e.g. corn). Whole profile (0–1.2 m) assessments indicate continuous cotton systems results in the greatest soil compaction, followed by continuous soybean, corn-soybean, continuous corn, and the corn-cotton rotation. Across all bio-cover × crop rotation interactions, the continuous cotton sequence with hairy vetch and Austrian winter pea cover crops had the greatest compaction, with the corn-cotton rotation under long-term poultry litter applications being lowest. Therefore, the planting and spraying traffic under no-tillage is likely the source of greater soil compaction. Study results help identify direct and indirect impacts of conservation agricultural management practices (crop rotation, animal manure inputs, no-tillage, and cover crops) on soil physicochemical properties under long-term no-tillage, such data may be used for identifying soil quality indices based on soil management. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements Authors extend gratitude to the staff and directors at the Middle Tennessee, USA (Kevin Thompson) and Milan (Blake Brown) Agricultural Research and Education Centers for their help in collecting data and making this long-term research project possible. The USDA is an equal opportunity provider and employer. Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or 6

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cropping system niches. Agron. J. 97, 322–332. Soil Science Society of America, SSSA., 1996. Glossary of soil science terms. In: American Society of Agronomy. Madison, WI. Soil Survey Staff, 2014. Keys to Soil Taxonomy, 12th ed. USDA–NRCS. Strock, J.S., Porter, P.M., Russelle, M.P., 2004. Cover cropping to reduce nitrate loss through subsurface drainage in the Northern U.S. Corn Belt. J. Environ. Qual. 33. Sudduth, K.A., Hummel, J.W., Drummond, S.T., 2004. Comparison of the veris profiler 3000 to an ASAE-standard penetrometer. Appl. Eng. Agric. 20, 535–541.

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