Changes in biological soil health properties in response to increased crop diversity in a dryland wheat-based cropping system

doi:10.21203/rs.3.rs-1979869/v2

Diversifying wheat (Triticum aestivum L.)-based cropping systems can be an effective management tool to break weed and disease cycles. While extensive research has focused on the agronomic benefits of increased crop diversity in semi-arid environments, less is known about the impacts of increased crop diversity on microbial community structure and processes such as nitrogen (N) cycling. This work compared a continuous wheat crop sequence to a diverse sequence that included pea (Pisum sativum L.), proso millet (Panicum miliaceum L.), safflower (Carthamus tinctorius, L.), and spring wheat. Soil inorganic N (NO₃-N and NH₄-N), soil respiration, microbial biomass, enzyme activity, and microbial community alpha diversity, a measure of the number of taxa within a treatment, were determined. Soil respiration was higher (p < 0.005) in the diverse sequence while activity of N-acyl-β-D-glucosaminidase, an enzymatic indicator of C and N mineralization, was lower (p < 0.05) with a mean rate of 26.3 mg ρ-nitrophenol kg^− 1 soil h^− 1 and 16.3 mg ρ-nitrophenol kg^− 1 soil h^− 1 for the continuous wheat and diverse sequences, respectively. Soil respiration was weakly correlated to soil water content (R² = 0.21) and temperature (R² = 0.29) in the diverse rotation, while only weakly correlated to soil water content (R² = 0.05) in the continuous wheat system (p < 0.001). The mean net N mineralized under a wheat crop during the growing season was 33.2 ± 2.5 kg ha^− 1 and was not different between treatments (p > 0.05). Microbial community analysis showed no difference in bacterial alpha diversity, while fungal community diversity was 52% lower in the diverse rotation. The results of this work suggest that specific crops in a rotation may impact microbial processes related to N mineralization and that the soil fungal community may be more sensitive to changes in crop sequence than the soil bacterial community.

nitrogen

mineralization

nutrient cycling

microbial activity

enzyme assay

Wheat is the primary grain crop in much of the U.S. northern Great Plains (USNPG) (Carr et al. 2020). Historic farming practices, where wheat-fallow was combined with tillage, have caused soil degradation and contributed to a 30–50% decline in organic C in dryland soils over the past 50–100 years in the USNGP (Sainju et al. 2010). In this region, a 12–20 month fallow period is commonly used to store soil moisture for the subsequent wheat crop, even though this practice is an inefficient water storage strategy (Carr et al. 2020). There has been increased interest in diversifying wheat cropping systems to improve soil quality and break weed and disease cycles (Tiemann et al. 2015; Kandel et al. 2019). In the USNGP, this diversification has been achieved primarily by incorporating pulse crops such as field peas, chickpeas, or lentils into rotation with wheat (Lupwayi and Kennedy 2007). However, many growers still grow wheat exclusively (Carr et al. 2020).

While much of the focus on diversifying cropping systems is directed at agronomic benefits, increased crop diversity has the potential to benefit soil microbial communities and microbially mediated processes in the soil. Low crop diversity generally is considered detrimental to soil microorganisms and can result in a loss of microbial diversity and function (Figuerola et al. 2014; Frasier et al. 2016). Conversely, high plant diversity has been mirrored by increased microbial biomass and diversity (Acosta-Martínez et al. 2011; Ishaq et al. 2017; Ouverson et al. 2021). Past research demonstrated that wheat grown in rotation with legumes has greater microbial abundance and activity than in wheat-fallow systems (Lupwayi et al. 2004; Biederbeck et al. 2005).

Microbial biomass is a key factor controlling organic nutrient cycling and functions as an early indicator of soil quality changes resulting from management and environmental stresses (Daryanto et al. 2018). More work is needed to better understand the effect of diversified cropping systems on specific microbial processes. Nitrogen (N) mineralization is one microbially-mediated process that is impacted by increased cropping system diversity (Frasier et al. 2016). Nitrogen mineralization is the process whereby extracellular microbial enzymes depolymerize N-containing compounds and convert the N to inorganic forms that can be used by plants (Schimel and Bennett 2004). Conventional fertilizer practices have reduced plant dependence on microbial processes mediating N mineralization and have effectively uncoupled plant productivity from microbial activity (Murphy et al. 2017). Multiple studies showed that monocropping decreased microbial C and N and reduced enzyme activities compared to rotations that contained perennials or included cover crops that enhanced N sequestration (Deng et al. 2000; Deng and Tabatabai 2000; Frasier et al. 2016).

N mineralization is dependent on the quality, including the C:N ratio and content of structural carbohydrates and lignin of organic residues in the soil, which is determined to a large extent by the type of crop that is grown. Work by Kolberg et. al., (Kolberg et al. 1999) found that a wheat-fallow rotation mineralized almost twice the amount of N as a wheat-corn-fallow rotation. Other research showed that N mineralization rates were 19% higher with lentil and pea compared to spring wheat and 60% greater than under canola and mustard (Gan et al. 2010). Wheat straw has a C:N ratio around 86 which favors N immobilization while legume residues have C:N ratios from 8–36:1 and lower concentrations of structural carbohydrates which favors N mineralization (Quemada and Cabrera 1995; Wagger et al. 1998). These results suggest that variability in the C:N ratio and residue quality among crops are important factors to consider for managing rotations to meet plant N needs and minimize N loss. As more diverse crops are incorporated into wheat-based cropping systems, it will be important to assess rates of N mineralization in these systems to ensure plant N needs are met while minimizing the risks of N loss.

Much of the work related to N mineralization under different cropping systems has been done in the US Midwest and South (Deng et al. 2000; Deng and Tabatabai 2000; Acosta-Martínez et al. 2011). The climatic conditions in these regions are very different from the USNGP, which is characterized as semi-arid with hot summers and cold winters (Carr et al. 2020). Temperature and moisture are important drivers of microbial processes such as N mineralization (Stark and Firestone 1995; Vigil et al. 2002; Calderón et al. 2016). These climatic conditions likely result in lower microbial activity in the USNGP compared to other more temperate regions.

In recent years, warm season crops have gained attention in the USNGP, particularly as a potential replacement for fallow (Carr et al. 2020). Climate models indicate warmer temperatures at northern latitudes in the future (Whitlock C 2017), favoring expansion of warm season crops at upper latitudes. While not as common as cool season species, warm season species can have similar yields when grown for forage (Berti et al. 2015; Meccage et al. 2019; Carr et al. 2020). Research on warm season crops in the USNGP has focused on agronomic performance while largely ignoring the impact of this crop group on soil microbial composition and function (Lenssen and Cash 2011; Carr et al. 2020; Carr et al. 2021). The objectives of this work were to (i) determine the rates of net N mineralization under a continuous wheat crop sequence and a diverse wheat-based crop sequence that incorporated cool and warm season crops, (ii) Evaluate the microbial response to the crop sequence by measuring community diversity, respiration, and activity of enzymes involved in soil C and N cycling.

Field experiments to determine microbial responses, including soil respiration, microbial activity, and N mineralization were conducted from fall 2018 through spring 2021 as part of a crop sequence study located at the Montana State University, Central Agricultural Research Center, Moccasin, MT, USA (47.059931, -109.950159). The soil type at this site is a Danvers-Judith clay loam, a complex of well-drained fine, smectitic, frigid Vertic Argiustolls. Plots were established in 2004, with a 4-year rotation introduced in 2017, to evaluate diverse crop sequences under no-till and conventional-till management. The crop sequences consisted of (1) winter wheat (WW)-fallow; (2) WW-spring barley (Hordeum vulgare L.) (SB)-spring pea (SP); (3) WW-SB-lentil (Lens culinaris Medik); (4) SP-proso millet (MIL)-safflower (SAF)-spring wheat (SW); and (5) WW-SW. All crop phases were in place each year for a total of 14 plots arranged in a Randomized Complete Block (RCB) design with 4 replicates.

Evaluation of the microbial response to a crop sequence was focused on comparing continuous wheat (WW-SW) and the SP-MIL-SAF-SW (referred to as the diverse sequence hereafter) to assess the effects of incorporating a warm season crop (e.g., MIL) and full season crop (e.g., SAF) into a diverse wheat-based crop sequence. Soil sampling and respiration measurements were collected in the SW phase of the rotation each year. Spring wheat was planted 22 May 2018, 15 May 2019, and 18 May 2020 and harvested 22 Sept 2018, 7 Sept 2019, and 24 Aug 2020. All plots were managed using standard regional practices for fertility and weed management. Monthly total precipitation and mean air temperature are shown in Table S1.

Soil sampling was performed monthly throughout the growing season. Soil cores (0–15 cm) were collected with a 2.54-cm soil probe. Soils were air dried for enzyme analysis or dried at 55°C for 3 days for biomass determination. Subsamples of fresh soil were stored at -80°C for DNA sequencing. Dried soils were passed through a 2-mm sieve before analysis. NO₃-N and NH₄-N were extracted following the methods of (Kolberg et al. 1999; Wienhold 2007). Briefly, 4 g dry wt equivalent of soil was combined with 40 mL of 2M KCl and incubated 1 hour at room temperature shaking at 250 rpm. Samples were centrifuged at 3000 rpm for 10 min and the resulting supernatant submitted to the MSU Environmental Analytical Lab for NO₃-N and NH₄-N analysis via Cd reduction on a Lachat QuickChem8500 (Hach, Loveland, CO). Samples were also submitted to Ward Laboratories (Kearney, NE) for pH (1:1 in H₂O), organic matter (OM; loss on ignition, 2 hours at 360°C), total C (combustion method), total N, phosphorus (P, NH₄acetate extraction), potassium (K, Mehlich III extraction), sulfur (sulfate-S, Mehlich III extraction), and cation exchange capacity (CEC) analysis.

Soil respiration

Soil respiration was measured with CFLUX-1 (PP Systems, Amesbury, MA) automated respiration chambers. The chambers were programmed to take hourly measurements. Respiration chambers were initially deployed 1 Nov 2018 and remained in the field until nighttime temperatures were below the operational temperature limits of the instruments (-20°C). Chambers were redeployed in early spring once nighttime temperatures were above the operational temperature limits of the instruments. Chambers were removed for planting and then redeployed for the growing season. The chambers were removed for harvest and then deployed for the fall and remained in operation until average nighttime temperatures reached the lower operation limits of the instrument. Soil water content and temperature were recorded simultaneously with flux data using Stevens HydraProbes (Stevens Inc., Portland, Oregon, USA).

N mineralization

In situ N mineralization was performed as described by Kolberg and Wienhold (Kolberg et al. 1999; Wienhold 2007). Metal tubes measuring 4.76 cm in diameter were inserted 17 cm into the soil and removed with intact soil cores. Approximately 2 cm of soil was removed from the bottom of the core with a stainless steel laboratory spatula and replaced with a nylon bag containing 10 g Lewatit NM-60 DI Resin ion exchange resin beads (Lanxess, Cologne, Germany). The nylon bags were secured with a spring clip and tubes were reinserted into the original holes. Six tubes were inserted into each plot and a soil core (0–15 cm) was collected with a 2.54-cm soil probe next to each tube to measure initial NO₃-N and NH₄-N concentrations. Each month from March-August, a tube was removed, and the resin was recovered for quantifying inorganic N. NO₃-N and NH₄-N were extracted following the methods of (Kolberg et al. 1999; Wienhold 2007). Briefly, 2 g wet weight resin beads were combined with 40 mL 2M KCl and incubated 1 hr at room temperature shaking at 250 rpm. Samples were centrifuged at 3000 rpm for 10 min and the resulting supernatant was submitted to the MSU Environmental Analytical Lab for NO₃-N and NH₄-N analysis on a Lachat QuickChem8500 (Hach, Loveland, CO). Inorganic N was calculated on a kg ha^− 1 basis using the soil bulk density (1.7 g cm^− 3) and the soil column depth of 17 cm. Net N mineralized was calculated as the average of five cores per treatment using the combined amounts of NO₃-N and NH₄-N following the approach of Kolberg (Kolberg et al. 1999) where:

$$\mathbf{N}\mathbf{e}\mathbf{t} \mathbf{N} \mathbf{m}\mathbf{i}\mathbf{n}\mathbf{e}\mathbf{r}\mathbf{a}\mathbf{l}\mathbf{i}\mathbf{z}\mathbf{e}\mathbf{d} \left(\mathbf{k}\mathbf{g} {\mathbf{h}\mathbf{a}}^{-1}\right)=\mathbf{F}\mathbf{i}\mathbf{n}\mathbf{a}\mathbf{l} {\mathbf{N}}_{\mathbf{s}}+\mathbf{F}\mathbf{i}\mathbf{n}\mathbf{a}\mathbf{l} {\mathbf{N}}_{\mathbf{r}}-\mathbf{I}\mathbf{n}\mathbf{i}\mathbf{t}\mathbf{i}\mathbf{a}\mathbf{l} {\mathbf{N}}_{\mathbf{s}}$$

Where N_s is soil inorganic N (kg ha^− 1) and N_r is resin inorganic N (kg ha^− 1). N fixation, deposition, immobilization, volatilization, and denitrification were assumed to be minimal. Previous work has shown that denitrification is minimal at < 60% water filled pore space and immobilization is reduced when residues remain on the surface instead of being incorporated (Aulakh et al. 1991; Ambus et al. 2002).

A laboratory column assay was performed to assess potential N mineralization as previously described (Deng and Tabatabai 2000; Tabatabai et al. 2010). A 20 g (oven dry basis) sample of field moist soil was combined with 40 g silica sand. Fifteen g of the soil-sand mixture was transferred to a leaching column (2.5 cm x 15 cm PVC tube) which contained glass wool to retain the soil-sand mixture. Three technical replicate columns were established with soil from each field plot. Columns were weighed and the soil was maintained at 50% field capacity by watering every two days to adjust them to their initial weights. Columns were loosely covered with aluminum foil to minimize evaporation. Columns were leached by adding 100 mL 5 mM CaCl₂ and then placing them on a suction flask and applying vacuum to remove the leachate. The recovered leachate was adjusted to a final volume of 100 mL using DI water. Leaching was performed every 2 weeks for 14 weeks. Cumulative mineralized N was calculated as the sum of NO₃-N and NH₄-N recovered from each leaching event.

Enzyme activities

N-acyl-β-D-glucosaminidase (NAGase) and β-glucosidase activities were measured following the methods of Tabatabai (Tabatabai 1994) and Acosta-Martínez and Tabatabai (Acosta-Martínez and Tabatabai 2000). All assays were performed with 1 g of air-dried soils that were passed through a 2-mm sieve. Assays were performed in 0.1 M THAM buffer (Tabatabai 1994). Substrates and reagents used for each enzyme are shown in Table 1. Assays were performed in 0.1 M THAM buffer. Substrates and additional reagents used for each enzyme are shown in Table 1. Soils were placed in a 150 mL flask with appropriate substrates and reagents from Table 1 and incubated at 37°C for 1 hour. The reaction was then stopped, and the samples were centrifuged at 15,000 x g for 1 min and absorbance measured at 405 nm. Controls were performed without the addition of substrate and a p-nitrophenol standard curve was prepared to quantify products released from NAGase and β-glucosidase activity.

Table 1

Enzyme substrates, reagents, and reaction conditions used in enzyme assays.
Enzyme	Substrate	Reaction Conditions	Reaction Stop
N-acyl-β-D-glucosaminidase	ρ-nitrophenyl-N- acetyl-β-D-glucosaminide	0.1M acetate buffer	0.5M CaCl₂ 0.5M NaOH
β-glucosidase	p-Nitrophenyl-β-D-glucopyranoside	Toluene MUB buffer	0.5M CaCl₂ 0.1M THAM

Microbial biomass

Microbial biomass was determined by the chloroform fumigation-incubation method as previously described (Voroney and Paul 1984; Franzluebbers 1999). Briefly, 50 g of soil from each plot was placed in a beaker and fumigated with CHCl₃ under vacuum for 24 hrs. Soils were then transferred to 1 L jars that contained a vial with 10 mL1 M NaOH to absorb the CO₂ evolved during incubation and a vial of water to maintain humidity. The jars were sealed and incubated at 21°C for 12 days. Several drops of phenolphthalein were added to the vials as a color indicator and the NaOH was titrated with 1 M HCl in the presence of excess BaCl₂. The amount of HCl added was recorded and used to calculate the soil microbial biomass C based on the CO₂-C evolved from the fumigated soil using an efficiency factor of 0.41 (Voroney and Paul 1984).

Microbial community analysis

DNA was extracted using a ZymoBiomics DNA mini prep kit (Zymo Research, Irvine, CA, USA). Purified DNA was submitted to the Integrated Microbiome Resource (IMR, Dalhousie University, Halifax NS) for sequencing on the Illumina MiSeq platform (Illumina, San Diego, CA). The universal V4-V5 region of the 16S gene was targeted for bacterial amplification and ITS2 primers were used for fungal amplification and sequenced using the Illumina reagent kit (2 x 300bp).

Sequence reads were processed using the QIIME2 bioinformatics pipeline (Bolyen et al. 2019). Quality control was performed using FastQC. Quality-filtered paired end reads were assembled into error-corrected amplicon sequence variants (ASVs) using DADA2 (Callahan et al. 2016). ASVs represent unique bacterial taxa and exhibit fewer false positives and real rare taxa that are undetected by OTU-based approaches (Callahan et al. 2016). Taxonomic assignment was performed using a naive Bayes classifier pre-trained on the weighted Silva 138 database with a 99% identity threshold for bacterial and the UNITE version 8.3 ITS database with a 99% identity threshold for fungi (Quast et al. 2013; Kõljalg et al. 2020). Files generated in QIIME2 were imported into R (Team 2021) using the package Qiime2R (Bisanz 2018) for additional analysis. Data were filtered to remove taxa occurring fewer than five times or in less in 20% of the samples. ASV counts were center log ratio (clr) transformed using the R package Phyloseq (McMurdie and Holmes 2013).

Statistical analysis

Statistical analyses for soil respiration, enzyme assays, and soil chemistry were conducted in R v. 4.1.1 (Team 2021). Missing points in the respiration data caused by power failure or instrument errors were gap-filled using flux gap analysis as implemented in the R package FluxGapR as described by Zhao (Zhao et al. 2020). Mean respiration rates were calculated for pre-planting (March-early May), growing season (mid-May-July), and post-harvest (September-December) from fall 2018 through spring 2021 since autotrophic respiration during the growing season can provide a significant contribution to soil respiration (Ryan and Law 2005). Linear mixed effects models were developed with the R package lme4 (Bates et al. 2015) to evaluate differences in respiration for each period. The model included treatment, soil temperature, and soil water content as fixed effects and block and year as random effects. Mean daily respiration values were natural log transformed to meet assumptions of normality. Potential relationships between explanatory and response variables were assessed using Type III Analysis of Variance with Satterthwaite’s method (Kuznetsova et al. 2017). Models were refined with a step-down approach to remove model terms with p > .05 as implemented in the step function of the lmerTest R package (Kuznetsova et al. 2017). Step-down analysis showed year was not a significant random effect and this factor was subsequently removed from the model. Analysis of variation (ANOVA) was performed to compare mean respiration rates between crop sequences for each period using the function aov in the stats package (version 3.6.2) in R (Team 2021).

Relationships between soil NO₃-N and enzyme activity and between NH₄-N, NO₃-N, enzyme activity, and soil respiration were evaluated based on Pearson’s correlation using the R package ggpubr (Kassambara 2020). Differences in enzyme activities were determined by means separation using the least significant difference (LSD) test as implemented in the R package agricolae (de Mendiburu 2021). Differences were considered significant at p < 0.05.

The phyloseq and vegan packages were used for statistical analysis of the microbial community sequencing results (McMurdie and Holmes 2013; Oksanen et al. 2018), while ggplot2 was used for data visualization (Wickham 2016). Alpha, or within community diversity, analysis was performed using the Chao1 diversity index. Beta diversity (unweighted unifrac distance) was performed at the phylum level using a 1% relative abundance threshold to remove rare ASVs. Differences between treatments were determined at the phylum level using a Kruskal–Wallis test (p < 0.05).

Soil chemical properties

No significant differences were observed in organic matter, organic and total C, total N, P, K, S, or CEC between crop sequences in the final year of the study (Table 2). The concentration of soil organic matter averaged 37.4 g kg^− 1 while organic C and total C were 18.4 g kg^− 1 and 20.1 g kg^− 1, respectively. Average pH was 7.5 while P, K, and S averaged 33.2 mg kg^− 1, 250 mg kg^− 1, 22.6 mg kg^− 1, respectively. Average cation exchange capacity (CEC) was 28.0 cmol_c kg^− 1across treatments.

Table 2

Soil chemical properties at the experimental site measured in the final year of the project. Mean value ± sd. Differences were not significant between treatment.
		Treatment
Measurement	Unit	SW-WW	SP-MIL-SAF-SW
pH		7.4 ± 0.2	7.6 ± 0.2
OM	g kg^− 1	37.6 ± 2	37.1 ± 1
Organic C	g kg^− 1	18.2 ± 1	18.5 ± 2
Total C	g kg^− 1	20.0 ± 3	20.1 ± 2
Total N	mg kg^− 1	1944 ± 122	1916 ± 109
Phosphate (Mehlich P-III)	mg kg^− 1	36.4 ± 6.8	29.9 ± 2.2
Potassium	mg kg^− 1	264 ± 28	236 ± 28
Sulfate	mg kg^− 1	23.0 ± 2.8	22.1 ± 3.2
CEC	cmol_c kg^− 1	26.7 ± 4.2	29.2 ± 0.9

Soil respiration

Mean soil respiration during the pre-planting interval was 0.76 and 0.82 µmol m^− 2 s^− 1 for the WW-SW and diverse crop sequences, respectively (Table 3). Respiration rates doubled during the growing season but dropped to 0.61 and 0.74 µmol m^− 2 s^− 1 for the WW-SW and diverse crop sequences, respectively during the post-harvest interval. Both crop sequences and soil temperature (Tsoil) affected soil respiration during all three time periods (Table 4). Soil water content (Msoil) also impacted soil respiration during pre-planting and the growing season but not post-harvest. A Crop Sequence:Tsoil interaction was detected during the growing season and post-harvest while a Crop Sequence:Msoil interaction occurred only during the pre-planting phase and growing season. A Crop Sequence:Tsoil:Msoil interaction was detected during the growing season. Soil respiration was weakly correlated to volumetric soil water content (R² = 0.21) and soil temperature ((R² = 0.29) in the diverse crop sequence, and very weakly correlated to soil temperature (R² = 0.05) in the WW-SW crop sequence (p < 0.001, Fig. 1). Soil respiration was not correlated to soil temperature in WW-SW (p = 0.69, Fig. 1B).

Table 3

Effect of cropping system on soil respiration. Mean soil (± standard deviation) respiration fluxes are shown for pre-planting, vegetative phase, and post-harvest. Differences were significant (p < 0.001) in the pre-planting and post-harvest intervals.
Time Period	Mean Respiration
	WW-SW (µmol m^− 2 s^− 1)	Diverse (µmol m^− 2 s^− 1)
Pre-planting	0.76 ± 0.01	0.82 ± 0.01
Growing season	1.52 ± 0.02	1.53 ± 0.02
Post-harvest	0.61 ± 0.01	0.74 ± 0.01

Table 4

Model statistics for soil respiration, for pre-planting, growing season, and post-harvest. Statistically significant differences were determined by Type III Analysis of Variance with Satterthwaite's method.
Pre-planting
	Type III SS	Mean Square	DenDF	F-value	p-value
Crop Sequence (CS)	25.111	25.111	471	42.600	< 0.001***
Tsoil	4.943	4.943	470	8.385	0.004**
Msoil	31.326	31.326	470	53.143	< 0.001***
CS:Tsoil	0.063	0.063	470	0.107	0.744
CS:Msoil	10.467	10.467	471	17.756	< 0.001***
Tsoil:Msoil	1.425	1.425	469	2.417	0.121
CS:Tsoil:Msoil	1.709	1.709	470	2.899	0.089
Growing season
	Type III SS	Mean Square	DF	F-value	p-value
CS	3.177	3.177	871	9.851	0.002**
Tsoil	1.278	1.278	869	3.964	0.047*
Msoil	7.015	7.015	871	21.752	< 0.001***
CS:Tsoil	6.276	6.276	871	19.462	< 0.001***
CS:Msoil	2.875	2.875	870	8.914	0.003**
Tsoil:Msoil	25.528	25.528	870	79.159	< 0.001***
CS:Tsoil:Msoil	2.650	2.650	871	8.217	0.004**
Post-harvest
	Type III SS	Mean Square	DF	F-value	p-value
CS	14.882	14.882	591	27.535	< 0.001***
Tsoil	14.562	14.562	592	26.943	< 0.001***
Msoil	1.320	1.320	591	2.443	0.119
CS:Tsoil	6.142	6.142	593	11.363	< 0.001***
CS:Msoil	0.751	0.751	593	1.390	0.239
Tsoil:Msoil	6.731	6.731	591	12.453	< 0.001***
CS:Tsoil:Msoil	0.092	0.092	593	0.171	0.680

N mineralization

Field column measurements of cumulative N mineralization during the growing season were not different between treatments, with an average of 31.2 ± 1.9 kg ha^− 1 N mineralized in the SW-WW and 35.1 ± 3.1 kg ha^− 1 N mineralized in the diverse crop sequence. Potentially mineralizable N (NO₃-N and NH₄-N) measured by laboratory aerobic column incubation averaged 334 ± 8.0 kg ha^− 1 in the SW-WW and 323 ± 9.4 kg ha^− 1 in the diverse crop sequences. Cumulative N mineralized in the field represented only around 10% of the potentially mineralizable N as measured. There were no differences in the rate and amount of NO₃-N mineralized between treatments (Figure S1), nor were soil NO₃-N and NH₄-N correlated to respiration (Fig. 2).

Enzyme activity

Average enzyme activities for N-acyl-β-D-glucosaminidase (NAGase) during the growing season were 26.3 mg ρ-nitrophenol kg^− 1 soil h^− 1 and 16.3 mg ρ-nitrophenol kg^− 1 soil h^− 1 for SW-WW and diverse treatments, respectively. Differences in enzyme activity between treatments were significant for NAGase activity in the diverse crop sequence was 61% lower than in SW-WW. (Fig. 3A). Average β-glucosidase activity was 15.7 mg ρ-nitrophenol kg^− 1 soil h^− 1 and was not different between treatments (Fig. 3B). A significant (p = 0.023) negative correlation was observed between NAGase activity and NO₃-N concentration (Fig. 4A), but no correlation was observed between NAGase activity and soil respiration (Fig. 2A). In contrast, β-glucosidase activity was not correlated to NO₃-N concentration (Fig. 4B) but had a significant (p < 0.001) negative correlation to soil respiration (Fig. 2B).

Microbial community analysis

Microbial biomass was not different between treatments and averaged 122.7 mg C kg^− 1. Following quality filtering, bimera removal, and feature table construction with DADA2 (Callahan et al. 2016), a total of 20,837 bacterial sequence variants and 34,593 fungal sequence variants were retained for subsequent analysis. Bacterial richness, as measured by Chao1 diversity index, was not significantly different between crop sequences, while fungal alpha diversity was 52% higher in the continuous wheat system (Fig. 5). Bacterial communities were dominated by Actinobacteria (36%), Acidobacteria (18%), and Proteobacteria (16%) while fungal communities were predominated by Ascomycota (81%), Basidiomycota (8%), and Mortierellomycota (1%) relative abundance, respectively (Fig. 6). While the relative abundance of the phylum Basidiomycota was similar in both crop sequences, abundances of specific genera varied within the phylum. The relative abundance of the genera Naganishia was nearly lower (p = 0.083) at only 0.04% in the diverse crop sequence compared to 0.18% in the SW-WW system (Figure S2).

Increased crop diversity is often promoted as a means of improving soil health (Stewart et al. 2018; Agomoh et al. 2020; Norris et al. 2020). Reported benefits associated with increased crop diversity include greater soil organic carbon (SOC), improved soil structure, and increased microbial biomass (Ghimire et al. 2019; Agomoh et al. 2020; Thapa et al. 2021). For some of these indicators, such as SOC, it is difficult to measure small changes against a large background SOC pool (VandenBygaart and Angers 2006; Bradford et al. 2016). In contrast, microbial indicators of soil health, such as community alpha and beta diversity and enzyme activity, can change over seasons to years (Fierer et al. 2021) and may provide insight into the effects of management decisions sooner than can be obtained by other parameters such as soil structure and soil C.

The short-term nature of our study did not capture changes in soil health metrics that occur on a years-to-decades timescale. Instead, this work focused on microbial metrics that can be early indicators of changes in soil health (Fierer et al. 2021). Our results suggest that in cool semi-arid regions, bacterial community structure is minimally impacted by increased crop diversity during at least the first several years.

Significant differences in the activity of NAGase suggest that crop sequence did impact microbial processes related to N mineralization. The activity of NAGase, a key enzyme involved in the degradation of chitin, has been proposed as a reliable index of N mineralization in soils (Deng et al. 2000; Tabatabai et al. 2010). The decrease in NAGase activity under the diverse crop sequence suggested a decrease in N mineralization, but this did not translate to differences in soil NO₃-N concentrations or column measurements of N mineralization between the diverse crop sequence and the continuous wheat system (Table 5). In addition, N mineralization in the field represented only 10% of the potentially mineralizable N as measured in laboratory column experiments (Table 5). This suggests that N mineralization is moisture limited in these semi-arid systems.

These findings contrast with previous long-term (17-42 years) studies which indicated that enhancing crop diversity can lead to greater enzyme activities compared to mono-cropping systems (Deng et al. 2000; Deng and Tabatabai 2000; Ekenler and M 2002). These differences highlight the need for producers to have a long-term perspective when implementing practices to improve soil health. Some of the observed differences in NAGase activity may also be due in part to the quality and quantity of residue. Thapa et al (Thapa et al. 2021) found that cover crop mixtures containing oats had greater enzyme activity than legume- or brassica-only cover crops. This was attributed to the higher biomass and C:N ratio associated with grasses compared to lower C:N ratio crops such as legumes and canola (Ghimire et al. 2019; Thapa et al. 2021).

The reduction in NAGase activity could be due to the reduction in fungal alpha diversity that was observed in the diverse compared to the WW-SW crop sequence (Figure 5B). Previous work in forest systems found a decrease in both NAGase activity and a reduction in the abundance of ectomycorrhizal fungi associated with organic N absorption (Corrales et al. 2017). Other work has shown that NAGase activity is a highly conserved function in Basidiomycota (Maillard et al. 2018). Collectively, these results suggest that reduced NAGase activity may be associated with a reduction in Basidiomycota abundance. These results also suggest that some knowledge of the microbial community diversity is needed to effectively use NAGase activity as an index of N mineralization. Studies have shown that a large proportion of soil fungal taxa are not represented in sequence databases (Coller et al. 2019). This may partially explain why beta diversity analysis failed to show significant differences in the abundance of specific taxa even though large differences were detected in alpha diversity.

Higher plant diversity has been linked to increased microbial biomass and diversity (Acosta-Martínez et al. 2011; Ishaq et al. 2017; Ouverson et al. 2021). Increased crop diversity did not result in an increase in microbial biomass in our study which suggests that other factors, such as climate, are also important. These findings are consistent with previous work in similar environments that showed diversifying wheat-based cropping systems with single species cover crops did not significantly increase microbial biomass over a similar 4-year timeframe (Housman et al. 2021). In contrast, work in the semi-arid Texas High Plains found significant increases in microbial biomass with sorghum or winter rye in rotation with cotton compared to continuous cotton over a 5-year study (Acosta-Martínez et al. 2011). Climate conditions in this region permitted the use of winter cover crops which likely contributed to the short-term microbial response (Acosta-Martínez et al. 2011). Collectively, these results suggest that a combination of climatic factors, nutrient availability, and plant species incorporated into a cropping system may interact to impact microbial community structure and function.

Our results suggest that diversifying a wheat-based cropping system by including a legume (pea), warm season crop (millet), and deep-rooted crop (safflower) has minimal impact on the soil bacterial community when established initially. In contrast, the specific crops included in rotation can have a significant impact on the soil fungal community (Figure 6). While previous studies have shown that incorporating pea and other pulse crops can increase the diversity of the soil microbial community, few studies have evaluated the effect of safflower on community composition (Lupwayi et al. 2004; Biederbeck et al. 2005). Previous work has suggested that including safflower in a rotation with wheat can contribute to high bacterial abundance and low fungal abundance (Ouverson et al. 2021). Studies evaluating the rhizosphere community of millet have not shown any reduction in fungal diversity compared to other members of the family Poaceae (Simmons et al. 2020; Tian et al. 2022). Our results showed no significant difference in bacterial diversity in a crop sequence that included safflower but a significant reduction in fungal alpha diversity was observed in spring wheat following safflower. The decrease in fungal diversity suggests that the specific sequence of crops may be of equal or even greater importance than the plant species included in a diverse system when determining microbial community composition during discrete sampling periods.

In the Pacific Northwest, incorporating camelina in a wheat-based cropping system led to a significant decline in microbial abundance which was attributed to the presence of a Brassica crop (Hansen et al. 2020). Brassicas lack mycorrhizal associations, and the production of glucosinolates and volatile organic compounds by these plants is hypothesized to be detrimental to fungi (Lambers and Teste 2013; Hansen et al. 2020). Unlike brassicas and small grain crops, the rhizosphere microbiome of safflower has not been well characterized. More work is needed to assess rhizosphere exudates and the impact of this crop on both bacterial and fungal communities. Given the importance of fungi in plant nutrient assimilation (Wu et al. 2005; Baird et al. 2010) our findings underscore the importance of considering potential impacts on soil biota when incorporating new crops into wheat-based cropping systems.

Results of this work suggest that increased crop diversity may impact specific microbial processes such as NAGase activity, even when differences in total N or potentially mineralizable N are not detected. The soil fungal community declined in the diverse crop sequence compared to WW-SW which suggests the fungal community is more sensitive to changes in crop sequence than the soil bacterial community. From a soil health perspective, the negative impacts on fungal community abundance warrant further investigation. More work is needed to identify specific crops that contribute to the reduction in fungal diversity and to establish linkages between the fungal community and the activity of enzymes involved in C and N cycling. This research suggests that positive effects on the soil microbial community from increased crop diversity are not universal. Specifically, the sequence of crops in a rotation may impact the microbial community in the following year. More research is needed to resolve the temporal microbial community response to crop sequences to better understand the effects of specific crops on biological soil health properties.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contributions

J.E., C.J., and P.C. contributed to conception and design of the study, J.E. and J.H. performed data collection. J.E. and S.F. contributed to statistical analysis and interpretation. J.E. wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

Funding

This work was supported by the Montana Fertilizer Advisory Committee and the USDA National Institute of Food and Agriculture, Hatch project 1015780.

Data Availability

The datasets generated and analyzed in this study are available in the NCBI BioProject repository, https://www.ncbi.nlm.nih.gov/bioproject/ PRJNA83307x

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No competing interests reported.

Changes in biological soil health properties in response to increased crop diversity in a dryland wheat-based cropping system

Status:

Journal Publication

Version 2

Abstract

Figures

Introduction

Methods

Soil respiration

N mineralization

Enzyme activities

Microbial biomass

Microbial community analysis

Statistical analysis

Results

Soil chemical properties

Soil respiration

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 2