Using On-Farm Soil Health to Engage Research and Education Towards Sustainable Agriculture

Results

Soil microbial community composition

            The soil microbial community composition was assessed by the abundance of PLFA biomarkers according to treatment and year at each depth and site location. All other microbial groups at Bucyrus, excluding saprophytic fungi, had a significant two-way interaction for treatment and year at 0-5 cm (Table 2) Soil microbial biomass was significantly higher in the CC treatment in 2022 than in all other treatment and year combinations, showing an increase of 5x compared to the 2018 CC treatment (Figure 3). Likewise, NC 2022 had a microbial biomass 4x higher than NC 2018.

Figure 3. Microbial biomass by treatment and year at 0-5 (A), 5-15 (B), and 15-30 (C) cm depth at Bucyrus. Means followed by the same letter are not different according to Fisher’s LSD test at the 5% level.

The same trend was observed for Gram+, Gram -, actinomycetes, and AMF (Table 3). Saprophytic fungi were significant for year only (Table 3), with an increase of 3.1 nmol PLFA g^-1 soil from 2018 to 2022 (Table 3). At 5-15 cm, all microbial groups were significantly affected by year, with 2022 having the highest values, excluding AMF, which had higher biomass in 2020 (Figure 3B; Table 3). At 15-30 cm, microbial biomass, Gram-, actinomycetes, and fungi were significant for year (Table 3). Excluding saprophytic fungi, which had higher biomass in 2020 (same as 5-15 cm), microbial biomass (Figure 3C), Gram-, and actinomycetes (Table 3) had higher biomass in 2022. This increase was 3.6x for microbial biomass, 4.4x for Gram-, and 2.6x for actinomycetes. Gram+ and AMF had a two-way interaction between treatment and year (Table 3). Cover crop 2022 had the highest Gram+ biomass, while NC and CC 2020 had the highest AMF biomass (Table 3).

Table 3. Changes in soil microbial community (nmol PLFA g^-1 soil) by treatment, year, and depth in Bucyrus, KS.

Means followed by the same letter are not different according to Fisher’s LSD test at the 5% level. Gram-positive bacteria (Gram+), gram-negative bacteria (Gram+), actinomycetes (Actino), arbuscular mycorrhizal fungi (AMF), and saprophytic fungi (Fungi).

Extracellular enzyme activity

            All tested enzymes had a significant two-way interaction for treatment and year at 0-5 cm (Table 4). For the 0-5 cm soil depth, βG activity increased from 360 µg PNP soil^-1 h^-1 (2018) to 568 µg PNP soil-1 h-1 (2022) with CCs, representing an increase of 58% (Figure 4A). Acid phosphatase activity also increased with CCs (Table 4). For the CC treatment, there was a difference of 24% between 2018 and 2022. On the other hand, NC decreased AcidP activity from 2018 to 2022 by 17% (4). N-acetyl-β-glucosaminidase activity had a similar trend, with a temporal increase of 39% from 2018 to 2020 with CCs and no significant changes with NC (Table 4). For ArylS, there was a significant increase from 2018 to 2020 and a decrease from 2020 to 2022. Nonetheless, ArylS activity was 30% higher for the CC treatment in 2022 than NC (Table 4).

Figure 4. β-glucosidase activity by treatment and year at 0-5 (A), 5-15 (B), and 15-30 (C) cm depth at Bucyrus. Means followed by the same letter are not different according to Fisher’s LSD test at the 5% level.

Table 4. Changes in enzyme activity (µg PNP soil^-1 h^-1) by treatment, year, and depth in Bucyrus, KS.

Means followed by the same letter are not different according to Fisher’s LSD test at the 5% level. Acid phosphatase (AcidP), N-acetyl-β-glucosaminidase (NAG), arylsulfatase (ArylS).

For 5-15 cm, there was a two-way interaction between treatment and year for AcidP and ArylS, and a one-way effect of year for βG and NAG (Table 4). For AcidP and ArylS we observed the same trend as 0-5 cm, with an increase from 2018 to 2020 and a decrease from 2020 to 2022 for the CC treatment. However, CC and NC 2023 were not statistically different. Both βG (240%) and NAG (18%) activity increased from 2018 to 2022 (Figure 4B; Table 4). At 15-30 cm, there was a two-way interaction between treatment and year for βG and AcidP, and a one-way effect of year on NAG and ArylS (Table 4). Independent of treatment, βG significantly increased from 2019 to 2023 (Figure 4C). However, greater βG was found for CC (148 µg PNP soil^-1 h^-1) than NC (121 µg PNP soil^-1 h^-1) in 2023. Contrastingly, AcidP activity continuously decreased with time for both treatments (Table 4). A greater decrease was found for NC (38%) than for CC (13%). N-acetyl-β-glucosaminidase (42%) and ArylS (11%) significantly decreased with time (Table 4).

Soil organic carbon and total nitrogen

            At Bucyrus, there was a significant two-way interaction between treatment and year for SOC at 0-5 cm (Table 5). A one-way effect of year was found for 5-15 and 15-30 cm soil depth (Table 5). At 0-5 cm, CC increased soil organic carbon by 21% from 2018 to 2022 (Figure 5). Soil organic carbon was also higher for NC 2023 than CC 2018 (baseline) but not different from NC 2018 (Figure 5). For both 5-15 and 15-30 cm there was an increase in SOC of 19% and 13% with time, respectively (Table 5). Total nitrogen was significant for treatment and year for 0-5 cm and year for 5-15 and 15-30 cm (Table 5). Inversely to SOC, there was no increase in TN with time with CCs (Table 5). Moreover, TN temporally decreased by 36% without CCs.

Figure 5. Soil organic carbon by treatment and year at 0-5 cm at Bucyrus. Means followed by the same letter are not different according to Fisher’s LSD test at the 5% level.

Table 5. Changes in soil organic carbon (g C kg soil^-1), total nitrogen (g N kg soil^-1), and C: N ratio by treatment, year, and depth at Bucyrus, KS.

Means followed by the same letter are not different according to Fisher’s LSD test at the 5% level. Soil organic carbon (SOC), total nitrogen (TN), soil organic carbon: total nitrogen ratio (C: N ratio).

Cropping system biomass

            The base cropping system yielded 82.7 Mg ha-1 of shoot biomass (Figure 2). The CC treatment added 5.3 Mg ha-1 for 88 Mg ha-1 (Figure 2). The fundamental idea behind incorporating CCs into the cropping system was to introduce a layer of residue cover ahead of soybean planting and a mix of legumes and grasses to supply nitrogen (N) for corn.

Aggregates size distribution

            At Bucyrus, there was a two-way interaction between treatment and year for WSA >2 mm, a year effect for WSA 2-0.250 mm, and a separate effect of both treatment and year for WSA 0.250-0.053 mm and 0.053-0.020 mm. While there was no increase in WSA >2 mm for NC, CC increased >2 mm aggregates by 145% from 2018 to 2022 (Figure 6). The 2-0.250 mm fraction increased by 24% (Figure 6). No cover crop also had a lower amount of 0.250-0.053- and 0.053-020 mm aggregates than CC (Figure 6). The increase in the first two aggregate size classes is expected to lead to a decrease in smaller classes, driven by the need for aggregate size balance. Soil macroaggregates represent the sum of the WSA >2 mm and 2-0.250 mm classes. Macroaggregates were significantly increased by both CC (8%) and year (28%) (Figure 7).

Figure 6. Sand-free water stable aggregate (WSA) distribution by treatment and year at 0-5 cm depth at Bucyrus. Means followed by the same letter are not different according to Fisher’s LSD test at the 5% level. Lowercase letters compare the interaction or year effect, while capitalized letters compare the treatment effect: *No cover, **Cover crop.

Figure 7. Macroaggregates by treatment and year at 0-5 cm depth at Bucyrus. Means followed by the same letter are not different according to Fisher’s LSD test at the 5% level.

Discussion

Soil microorganisms play an essential role in agroecosystems and have been recognized as a critical component of soil health, often serving as indicators for assessing soil health (Ashworth et al., 2017; Fierer et al., 2021; Kim et al., 2020). In general, cover cropping led to an overall increase in all microbial parameters at the 0-5 cm soil depth, with the extent of this increase varying depending on the site location due to environmental properties such as SOC and cropping system intensity and diversity. Muhammad et al. (2021) also found increased soil microbial community abundance with CCs compared to no CCs. At Bucyrus, the increase in microbial biomass, Gram+, Gram-, and actinomycetes was increased by the poultry litter application. In a study from (Acosta-Martínez & Harmel, 2006), higher microbial biomass and peak area of fatty acid methyl ester (FAMEs) was attributed to poultry litter application and its nutrient richness. Moreover, CCs enhanced this effect, resulting in higher microbial abundance and enzyme activity than NC. This effect was most likely provided by the additional plant C provided by CCs (Campos et al., 2011; Kim et al., 2020).

β-glucosidase, NAG, AcidP, and ArylS activity increased with time and CCs at 0-5 cm at all sites, corroborating with the findings of other studies (Mbuthia et al., 2015; Pires et al., 2020). These findings are likely associated with the quantity and diversity of crop residues and the proportion of readily decomposable organic materials reintroduced into the soil in response to the inclusion of CCs (Chavarría et al., 2016). β-glucosidase catalyzes the decomposition of cellulose; hence its higher activity may be explained by the SOC content (H. Liu et al., 2016). Furthermore, our study agrees with the study of Pires et al. (2020) that βG may be considered a good biological soil health indicator due to its quick response to changes in soil management. Enzyme activity can pose challenges as a soil health indicator when it is not adequately interpreted within its proper context. According to (Margenot & Wade, 2023), there is a reductionist assumption that fertilization always attenuates or inhibits soil enzyme activity, mainly phosphomonoesterase (PME). For instance, P fertilizer can increase overall crop growth and thus soil organic matter, which may increase the activity of PME (Margenot et al., 2017). In this context, higher enzyme activity can be interpreted as nutrient deficiency (feedback inhibition) or greater microbial activity and, thus increased rates of nutrient cycling (Margenot & Wade, 2023). In our study, overall enzyme activity followed SOC and microbial biomass increases as affected by CCs, demonstrating a linkage with possibly higher nutrient cycling rates (Six et al., 2000). Nonetheless, we also observed a decrease in AcidP activity at the 5-15 cm and 15-30 cm depths over time where poultry litter was applied (Bucyrus), indicating a potential P feedback inhibition despite the concurrent increase in SOC and microbial biomass.

Despite the differences in precipitation and plant biomass production among sites (Chapter 3), CC consistently increased the amount of WSA >2 mm and 2- 0.250 mm relative to NC after 5 yrs. Similar results have been reported in the literature by several authors (Blanco‐Canqui & Jasa, 2019; Blanco‐Canqui & Ruis, 2020;  Liu et al., 2005; Nascente et al., 2015; Wood & Bowman, 2021). In a study conducted by Liu et al. (2005), CCs such as cereal rye and annual ryegrass increased the amount of WSA. Likewise, Dapaah & Vyn (1998) found that soil aggregate stability was greater following CC than under NC. In a review of CC impacts on soil physical properties, Blanco-Canqui & Ruis (2020) reported that CCs increased the percentage of macroaggregates up to 22% and MWD up to 95%. In most cases, the increase in macroaggregation was reported to be directly linked to increased SOC (e.g., humus) and root biomass.

References

Acosta-Martínez, V., & Harmel, R. D. (2006). Soil microbial communities and enzyme activities under various poultry litter application rates. Journal of Environmental Quality, 35(4), 1309–1318. https://doi.org/10.2134/jeq2005.0470

Ashworth, A. J., DeBruyn, J. M., Allen, F. L., Radosevich, M., & Owens, P. R. (2017). Microbial community structure is affected by cropping sequences and poultry litter under long-term no-tillage. Soil Biology and Biochemistry, 114, 210–219. https://doi.org/10.1016/j.soilbio.2017.07.019

Blanco‐Canqui, H., & Jasa, P. J. (2019). Do grass and legume cover crops improve soil properties in the long term? Soil Science Society of America Journal, 83(4), 1181–1187. https://doi.org/10.2136/sssaj2019.02.0055

Blanco‐Canqui, H., & Ruis, S. J. (2020). Cover crop impacts on soil physical properties: A review. Soil Science Society of America Journal, 84(5), 1527–1576. https://doi.org/10.1002/saj2.20129

Campos, B. C. D., Amado, T. J. C., Bayer, C., Nicoloso, R. D. S., & Fiorin, J. E. (2011). Carbon stock and its compartments in a subtropical oxisol under long-term tillage and crop rotation systems. Revista Brasileira de Ciência Do Solo, 35(3), 805–817. https://doi.org/10.1590/S0100-06832011000300016

Chavarría, D. N., Verdenelli, R. A., Serri, D. L., Restovich, S. B., Andriulo, A. E., Meriles, J. M., & Vargas-Gil, S. (2016). Effect of cover crops on microbial community structure and related enzyme activities and macronutrient availability. European Journal of Soil Biology, 76, 74–82. https://doi.org/10.1016/j.ejsobi.2016.07.002

Dapaah, H. K., & Vyn, T. J. (1998). Nitrogen fertilization and cover crop effects on soil structural stability and corn performance. Communications in Soil Science and Plant Analysis, 29(17–18), 2557–2569. https://doi.org/10.1080/00103629809370134

Fierer, N., Wood, S. A., & Bueno De Mesquita, C. P. (2021). How microbes can, and cannot, be used to assess soil health. Soil Biology and Biochemistry, 153, 108111. https://doi.org/10.1016/j.soilbio.2020.108111

Gavlak, R. G., Horneck, D. A., & Miller, R. O. (2005). Soil, plant and water reference methods for the Western Region. https://api.semanticscholar.org/CorpusID:130863153

Hothorn, T., Bretz, F., & Westfall, P. (2008). Simultaneous inference in general parametric models. Biometrical Journal, 50(3), 346–363. https://doi.org/10.1002/bimj.200810425

Johnson, J. M. ‐F., Allmaras, R. R., & Reicosky, D. C. (2006). Estimating source carbon from crop residues, roots and rhizodeposits using the national grain‐yield database. Agronomy Journal, 98(3), 622–636. https://doi.org/10.2134/agronj2005.0179

Kim, N., Zabaloy, M. C., Guan, K., & Villamil, M. B. (2020). Do cover crops benefit soil microbiome? A meta-analysis of current research. Soil Biology and Biochemistry, 142, 107701. https://doi.org/10.1016/j.soilbio.2019.107701

Lenth, R. V. (2023). emmeans: Estimated Marginal Means, aka Least-Squares Means. https://CRAN.R-project.org/package=emmeans

Liu, A., Ma, B. L., & Bomke, A. A. (2005). Effects of cover crops on soil aggregate stability, total organic carbon, and polysaccharides. Soil Science Society of America Journal, 69(6), 2041–2048. https://doi.org/10.2136/sssaj2005.0032

Margenot, A. J., Sommer, R., Mukalama, J., & Parikh, S. J. (2017). Biological P cycling is influenced by the form of P fertilizer in an Oxisol. Biology and Fertility of Soils, 53(8), 899–909. https://doi.org/10.1007/s00374-017-1226-9

Margenot, A. J., & Wade, J. (2023). Getting the basics right on soil enzyme activities: A comment on Sainju et al. (2022). Agrosystems, Geosciences & Environment, 6(3), e20405. https://doi.org/10.1002/agg2.20405

Mbuthia, L. W., Acosta-Martínez, V., DeBruyn, J., Schaeffer, S., Tyler, D., Odoi, E., Mpheshea, M., Walker, F., & Eash, N. (2015). Long term tillage, cover crop, and fertilization effects on microbial community structure, activity: Implications for soil quality. Soil Biology and Biochemistry, 89, 24–34. https://doi.org/10.1016/j.soilbio.2015.06.016

Mikha, M. M., & Rice, C. W. (2004). Tillage and manure effects on soil and aggregate-associated carbon and nitrogen. Soil Science Society of America Journal, 68(3), 809–809. http://dx.doi.org/10.2136/sssaj2004.8090.

Muhammad, I., Wang, J., Sainju, U. M., Zhang, S., Zhao, F., & Khan, A. (2021). Cover cropping enhances soil microbial biomass and affects microbial community structure: A meta-analysis. Geoderma, 381, 114696. https://doi.org/10.1016/j.geoderma.2020.114696

Nascente, A. S., Li, Y., & Crusciol, C. A. C. (2015). Soil aggregation, organic carbon concentration, and soil bulk density as affected by cover crop species in a no-tillage System. Revista Brasileira de Ciência Do Solo, 39(3), 871–879. https://doi.org/10.1590/01000683rbcs20140388

NCR221. (2015). Recommended chemical soil test procedures for the North Central Region. In North Central Regional Research Publication No. 221 (Revised) (Vol. 221, Issue 221, p. 76). Missouri Agricultural Experiment Station. https://extension2.missouri.edu/sb1001

Pebesma, E., & Bivand, R. (2023). Spatial Data Science: With Applications in R (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9780429459016

Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & R Core Team. (2017). nlme: Linear and nonlinear mixed effects models. R Package Version 3.1-131, Https://CRAN.R-Project.Org/Package=nlme.

Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences, 11, 1633–1644. https://doi.org/10.1127/0941-2948/2006/0130

Pires, C. A., Amado, T. J. C., Reimche, G., Schwalbert, R., Sarto, M. V. M., Nicoloso, R. S., Fiorin, J. E., & Rice, C. W. (2020). Diversified crop rotation with no-till changes microbial distribution with depth and enhances activity in a subtropical Oxisol. European Journal of Soil Science, 71(6), 1173–1187. https://doi.org/10.1111/ejss.12981

Prince, S. D., Haskett, J., Steininger, M., Strand, H., & Wright, R. (2001). Net primary production of U.S. Midwest croplands from agricultural harvest yield data. Ecological Applications, 11(4), 1194–1205. https://doi.org/10.1890/1051-0761(2001)011[1194:NPPOUS]2.0.CO;2

R Core Team. (2023). R: A language and environment for statistical computing. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Ruiz, A., Trifunovic, S., Eudy, D. M., Sciarresi, C. S., Baum, M., Danalatos, G. J. N., Elli, E. F., Kalogeropoulos, G., King, K., Dos Santos, C., Thies, A., Pico, L. O., Castellano, M. J., Schnable, P. S., Topp, C., Graham, M., Lamkey, K. R., Vyn, T. J., & Archontoulis, S. V. (2023). Harvest index has increased over the last 50 years of maize breeding. Field Crops Research, 300, 108991. https://doi.org/10.1016/j.fcr.2023.108991

Schapaugh, W. T., & Wilcox, J. R. (1980). Relationship between harvest indices and other plant characteristics in soybean. Crop Science, 20(4), 529–533. https://doi.org/10.2135/cropsci1980.0011183X002000040028x

Sharma, R. C., & Smith, E. L. (1987). Effects of seeding rates on harvest index, grain yield, and biomass yield in winter wheat. Crop Science, 27(3), 528–531. https://doi.org/10.2135/cropsci1987.0011183X002700030021x

Six, J., Elliott, E. T., & Paustian, K. (2000). Soil macroaggregate turnover and microaggregate formation: A mechanism for C sequestration under no-tillage agriculture. Soil Biology and Biochemistry, 32(14), 2099–2103. https://doi.org/10.1016/S0038-0717(00)00179-6

Soil Survey Staff. (2014). Kellogg soil survey laboratory methods manual. Soil Survey Investigations Report No. 42, Version 5.0. In Kellogg Soil Survey Laboratory Methods Manual.

Tabatabai, M. A. (1994). Soil Enzymes (1–5, pp. 775–833). Soil Science Society of America.

West, T. O., Brandt, C. C., Baskaran, L. M., Hellwinckel, C. M., Mueller, R., Bernacchi, C. J., Bandaru, V., Yang, B., Wilson, B. S., Marland, G., Nelson, R. G., Ugarte, D. G. D. L. T., & Post, W. M. (2010). Cropland carbon fluxes in the United States: Increasing geospatial resolution of inventory-based carbon accounting. Ecological Applications, 20(4), 1074–1086. https://doi.org/10.1890/08-2352.1

White, D. C., & Ringelberg, D. B. (1998). Signature lipid biomarker analysis. In O. University (Ed.), Techniques in Microbial Ecology (pp. 255–272). Oxford University Press.

White, P. M., & Rice, C. W. (2009). Tillage effects on microbial and carbon dynamics during plant residue decomposition. Soil Science Society of America Journal, 73(1), 138–138. https://doi.org/10.2136/sssaj2007.0384

Wood, S. A., & Bowman, M. (2021). Large-scale farmer-led experiment demonstrates positive impact of cover crops on multiple soil health indicators. Nature Food, 2(2), 97–103. https://doi.org/10.1038/s43016-021-00222-y

Yoder, R. E. (1936). A direct method of aggregate analysis of soils and a study of the physical nature of erosion losses. Soil Science Society of America Journal, B17: 165-165. https://doi.org/10.2136/sssaj1936.036159950b1720010046x