This dataset contains carbon and nitrogen concentrations and stocks in total soil organic matter and its fractions from the Konza Prairie Irrigation Transect Experiment. The dataset also includes pyrogenic organic matter C and N, as well as microbial amino sugars and root quality measurements. Data are availble for irrigated and control plots. Total pyrogenic and unburned soil organic matter C and N are availble for both the upland and lowland positions at 0-5, 5-15, and 15-30cm depth increments. Fraction and root data are avaible at both landscape positions, but for only the 0-5cm and 5-15cm depths and 0-5 and 5-30cm depths, respectively. Amino sugar data are only available for the lowland plots for the 0-5 and 5-15cm depths.
Study Site and Experimental DesignThis study was conducted at the Irrigation Transect Experiment at Konza Prairie Biological Station. Konza Prairie has a mean annual temperature of 12.8°C and mean annual precipitation of 863 mm (30-year averages). Precipitation at this site exhibits high intra- and inter-annual variability and ranges from 569-1674 mm per year. The irrigation experiment is described in detail elsewhere (Knapp et al. 2001) but, briefly, the site is an annually burned native tallgrass prairie that has experienced growing season irrigation scheduled according to estimates of actual evapotranspiration (AET) and soil water availability during the growing season, with the goal of minimizing plant water limitation. Operationally, this is carried out by modeling daily estimated plant AET (Moore 2003) and conducting periodic measurements of soil water content to assess irrigation timing and amounts required to return soil water content at 0.25 cm3 cm-3 or higher to 30 cm depth (Knapp et al. 2001). This causes the magnitude of water addition to vary year-to-year but has resulted in a 29% increase in annual precipitation on average which also has slightly decreased interannual variability. This water addition increased soil moisture by 4.5% on average for years with comparable data (2006-2009, 2013, 2017-2021), although this is highly variable within and among years, and also slightly decreased intra-annual variability. The experiment transect sns a topographic gradient, with the upland soils classified as fine, mixed mesic Udic Haplustolls (0-10cm: 15% sand, 58% silt, 27% clay) and the lowland soils classified as fine, mixed Pachic Argiustolls (0-10cm: 15% sand, 51% silt, 34% clay; Broderick et al. 2022a). The site is dominated by perennial grasses, primarily Andropogon gerardii, Soghastrum nutans, Schizachyrium scoparium, and Panicum virgatum. The site has two irrigation transects, initiated in 1991 and 1993, respectively, and two non-irrigated control transects. From here forward, we refer to the irrigated treatment as the increased precipitation treatment. For each transect (2 each in the upland and lowland), each treatment (control versus increased precipitation) is replicated 3 times, providing a total of 24 plots (2 landscape positions x 2 treatments x 2 transects x 3 replicates).
Sampling In September 2021, we performed soil coring at each plot. We took 4 cores per plot using a 5.25 cm diameter push corer to 30 cm, where soil depth allowed, since some upland plots had shallower soil profiles. In the field, we separated each core into three depth increments: 0-5 cm, 5-15 cm, and 15-30 cm, and bulked the 4 cores, maintaining each depth increment. This provided 72 individual observations (n = 24 plots x 3 depths). Soils were then transported to Colorado State University in coolers for subsequent analyses.
All soil samples were sieved though 8 mm mesh and a subsample was subsequently sieved through 2 mm mesh. We collected roots and coarse material (aboveground plant material and rocks) during sieving and used the weight of these and the soil to calculate bulk density and to determine root biomass distribution (Mosier et al. 2021). For root biomass, we only report coarse roots (from 8 mm sieving) since they were the dominant root mass in the soil and roots from 2 mm sieving were unaffected by treatment or landscape position. We used air-dried 2 mm sieved soil to perform a combined density and size fractionation following Haddix et al. (2020), on the 0-5 cm and 5-15 cm depth soils (n = 48). Briefly, 10.5 g of soil was shaken with deionized water and then centrifuged and filtered through a 20 µm nylon filter to retrieve the DOM pool. The soil was then centrifuged with sodium polytungstate at a density of 1.85 g cm-3 to separate the light and heavy fractions of SOM. The light, floating, fraction was aspirated as free POM (fPOM). The remaining soil (the heavy fraction) was then dispersed using 0.5% sodium hexametaphosphate and 12 glass beads. After shaking for 18 hours, the slurry was poured over a 53 µm sieve, with the fraction passing through designated as MAOM and the fraction >53 µm designated as occluded POM and heavy, coarse OM (oPOM+hcOM; sensu Leuthold et al. 2022). Each fraction was subsequently weighed to determine recovery, which was 98.3% on average and varied from 94.6-102.7%. Concentrations of C and N in SOM, fPOM, oPOM+hcOM, and MAOM were measured on a VELP 802 elemental analyzer (VELP Scientific, Inc., Long Island, New York). Concentrations of C and N in DOM were measured on a Shimadzu TOC-L (Shimadzu Scientific Instruments, Inc., Columbia, Maryland).
We measured amino sugars in the bulk soil as a proxy for microbial necromass, which is thought to be a precursor for MAOM formation (Liang et al. 2017). We performed amino sugar measurement on the 0-5cm and 5-15cm depth samples, as with the fractionation, and limited this analysis to the lowland soils (n = 24). We followed the procedure of Zhang and Amelung (1996) to determine the amount of muramic acid (MurA), glucosamine (GluN), and galactosamine (GalN). Briefly, the soils were acid digested for breaking up amino sugar polymers, then the three amino sugar monomers were purified, and derivatized for measurement. For acid hydrolysis, dry soils containing approximately 0.3 mg N were mixed with 6 M HCl at 105°C for 8 h. The hydrolysates then underwent several purification steps. First, samples were filtered through 55 mm glass fiber filters and evaporated to dryness on a rotary evaporator at 52°C under vacuum. Residues were then dissolved in deionized water (DI) and brought to a neutral pH before centrifugation to remove salts. The supernatant was then freeze-dried, resuspended in methanol, dried using N2 gas at 45°C, re-dissolved in 1 ml DI, and lyophilized. For derivatization, the sample was heated with a reagent (hydroxylamine hydrochloride and 4-(dimathylamino) pyridine in pyridine-methanol) and then with acetic anhydrite at 75-80°C. Dichloromethane was then added to the sample and excess reagent was removed following washing with HCl and DI. The sample was then dried and resuspended in ethyl acetate-hexane (1:1) for measurement. Samples were analyzed on an Agilent 7890B GC (Agilent Technologies, Santa Clara, CA, USA) that was equipped with a HP-5 column (30 m length × 0.25 mm diameter × 0.25 μm thickness) and flame ionization detector. Myo-inositol and methylglucamine were used as an internal standard and recovery standard. The contents of individual amino sugars were calculated based on the internal standard. Total amino sugars were determined as the sum of MurA, GluN, and GalN. Total microbial necromass C was calculated at the sum of bacterial and fungal necromass C. For bacterial necromass C, we converted MurA using a conversion factor of 45, and for fungal necromass C, we subtracted out bacterial content of GluN and converted corrected GluN using a conversion factor of 9 (Joergensen 2018, Liang et al. 2019): Bacterial necromass C = MurA*45 (Eq. 1) Fungal necromass C = (GluN/179.17 - (2*MurA/253.23)) × 179.17 × 9 (Eq. 2)
We estimated pyrogenic C and N (PyC and PyN) using hydrogen pyrolysis (Wurster et al. 2012, McBeath et al. 2015). Subsamples of 250mg of soil were combined with an amount of molybdenum catalyst approximately equal to 1% of soil organic C for each subsample. This was done using an aqueous/methanol solution of ammonium dioxydithiomolybdate. The dried, catalyst-loaded subsamples were pressurized with 15 MPa of hydrogen with a sweep gas flow of 5 L min−1 in the hydrogen pyrolysis reactor (Strata Technology Ltd., Middlesex, UK). The temperature was ramped to 250 °C at a rate of 300 °C min−1, then at 8 °C min−1 to a final temperature of 550 °C, which was held for 5 min. Following hydrogen pyrolysis, the remaining material was weighed and C and N abundance was determined on a Costech elemental analyzer (Costech Analytical Technologies, Inc., Valencia, CA) that was crosschecked to the VELP elemental analyzer (above) to ensure values were comparable. Post-hydrogen pyrolysis C and N were corrected for the amount of C and N loaded into the reactor.
Root quality analyses:In addition to soil analyses, we also analyzed root quality. We focused on roots, rather than aboveground material, because this site is annually burned and so roots are the more quantitatively important form of unburned plant inputs that would influence SOM in this system. All root analyses were performed on coarse roots (collected during 8 mm sieving) and root material from 5-15 and 15-30 cm depths were combined to ensure sufficient material for the analyses. We determined acid unhydrolyzable residue (AUR) and cellulose contents in the roots using the ANKOM Acid Detergent Fiber and Acid Detergent Lignin methods (ANKOM Technology 2022). Briefly, 0.45-0.55 g of ground root material were measured into 25 µm porosity filter bags (F57 filter bag, ANKOM Technology, Macedon, NY). Filter bags were placed in an ANKOM A200 (ANKOM Technology, Macedon, NY) and agitated for 1 hour in acid detergent solution containing 93.2% water, 4.8% sulfuric acid, and 2% cetyltrimethylammonium bromide (Midland Scientific, Inc., Omaha, NE). Bags were then rinsed with deionized water and dried to 105°C and weighed to determine acid detergent fiber (W1). Dried bags were then submerged in H2SO4 for 3 hours and agitated every 30 minutes to remove cellulose. They were then rinsed with deionized water until the pH was neutral and then dried to 105°C and weighed to determine AUR + ash (W2). Bags were then ashed in a muffle furnace at 525°C for three hours to remove AUR and weighed to determine residual ash (W3). Cellulose and AUR were determined as the difference between W1 and W2 and W2 and W3, respectively.
We also analyzed the amount of hot water extractable (HWE) material in the roots using the method by Tappi (1981), as modified by Soong et al. (2015a). Since HWE-C is highly correlated with leaching of dissolved organic C during early phases of plant decomposition, we use it as a proxy for readily available plant compounds (Soong et al. 2015a). We digested 0.35 g of root material cut into 1 cm pieces with deionized water at 100°C for 3 hours. We then filtered the mixture through a 20 µm nylon mesh to obtain HWE (< 20 µm) and the hot water residue (> 20 µm). The HWE was then frozen until being run on a Shimadzu TOC-L (Shimadzu Scientific Instruments, Inc., Columbia, Maryland) to obtain HWE-C and HWE-N.
References:Broderick, C.M., Wilkins, K., Smith, M.D., Blair, J.M., 2022. Climate legacies determine grassland responses to future rainfall regimes. Global Change Biology.Haddix, M.L., Gregorich, E.G., Helgason, B.L., Janzen, H., Ellert, B.H., Cotrufo, M.F., 2020. Climate, carbon content, and soil texture control the independent formation and persistence of particulate and mineral-associated organic matter in soil. Geoderma 363, 114160.Joergensen, R.G., 2018. Amino sugars as specific indices for fungal and bacterial residues in soil. Biology and Fertility of Soils 54, 559-568.Knapp, A.K., Briggs, J.M., Koelliker, J.K., 2001. Frequency and Extent of Water Limitation to Primary Production in a Mesic Temperate Grassland. Ecosystems 4, 19-28.Liang, C., Amelung, W., Lehmann, J., Kästner, M., 2019. Quantitative assessment of microbial necromass contribution to soil organic matter. Global change biology 25, 3578-3590.Liang, C., Schimel, J.P., Jastrow, J.D., 2017. The importance of anabolism in microbial control over soil carbon storage. Nature Microbiology 2.McBeath, A.V., Wurster, C.M., Bird, M.I., 2015. Influence of feedstock properties and pyrolysis conditions on biochar carbon stability as determined by hydrogen pyrolysis. Biomass and Bioenergy 73, 155-173.Moore, T. 2023. AET01 Konza prairie grass reference evapotranspiration. Environmental Data Initiative. http://dx.doi.org/10.6073/pasta/b5a57131dc37d035b7c14ec7a8a49604.Mosier, S., Apfelbaum, S., Byck, P., Calderon, F., Teague, R., Thompson, R., Cotrufo, M.F., 2021. Adaptive multi-paddock grazing enhances soil carbon and nitrogen stocks and stabilization through mineral association in southeastern US grazing lands. Journal of Environmental Management 288, 112409.Soong, J.L., Parton, W.J., Calderon, F., Campbell, E.E., Cotrufo, M.F., 2015. A new conceptual model on the fate and controls of fresh and pyrolized plant litter decomposition. Biogeochemistry 124, 27-44.Tappi. 1981. Water solubility of wood and pulp. Test method T204 (or 207). Technical Association of the Pulp and Paper Industry, Atlanta.Wurster, C.M., Lloyd, J., Goodrick, I., Saiz, G., Bird, M.I., 2012. Quantifying the abundance and stable isotope composition of pyrogenic carbon using hydrogen pyrolysis. Rapid Communications in Mass Spectrometry 26, 2690-2696.Zhang, X., Amelung, W., 1996. Gas chromatographic determination of muramic acid, glucosamine, mannosamine, and galactosamine in soils. Soil Biology and Biochemistry 28, 120