DOI: 10.5061/dryad.hhmgqnkr0
title: Impacts of compost amendment type and application frequency on a fire-impacted grassland ecosystem

description: Composting organic matter can lower the global warming potential of food
 and agricultural waste and provide a nutrient-rich soil amendment. Compost
 applications generally increase net primary production (NPP) and soil
 water holding capacity and may stimulate soil carbon (C) sequestration.
 Questions remain regarding the effects of compost nitrogen (N)
 concentrations and application rates on soil C and greenhouse gas
 dynamics. In this study, we explored the effects of compost with different
 initial N quality (food waste versus green waste compost) on soil
 greenhouse gas fluxes, aboveground biomass, and soil C and N pools in a
 fire-impacted annual grassland ecosystem. Composts were applied annually
 once, twice, or three times prior to the onset of the winter rainy season.
 A low intensity fire event after the first growing season also allowed us
 to explore how compost-amended grasslands respond to burning events, which
 are expected to increase with climate change. After four growing seasons,
 all compost treatments significantly increased soil C pools from 9.5 ± 0.9
 to 30.2 ± 0.7 Mg C ha-1 (0-40 cm) and 19.5 ± 0.9 to 40.1 ± 0.7 Mg C ha-1
 (0-40 cm) relative to burned and unburned controls, respectively. Gains
 exceeded the compost-C applied, representing newly fixed C. The higher N
 food waste compost treatments yielded more cumulative soil C (5.2 to 10.9
 Mg C ha-1) and aboveground biomass (0.19 to 0.66 Mg C ha-1) than the lower
 N green waste compost treatments, suggesting greater N inputs further
 increased soil stocks. The three-time green waste application increased
 soil C and N stocks relative to a single application of either compost.
 There was minimal impact on net ecosystem greenhouse gas emissions.
 Aboveground biomass accumulation was higher in all compost treatments
 relative to controls, likely due to increased water-holding capacity and N
 availability. Results show that higher N compost resulted in larger C
 gains with little offset from greenhouse gas emissions and that compost
 amendments may help mediate effects of low-intensity fire by increasing
 fertility and water holding capacity.
description: <strong>Methods</strong>
 <em>Field site</em> The site was
 located at the University of California Sierra Foothill Research and
 Extension Center (SFREC) in Browns Valley, California. Soils are derived
 from Mesozoic and Franciscan volcanic rock and classified as xeric
 Inceptisols and Alfisols in the Auburn-Sobrante complex (Soil Survey Staff
 2020). The site had been grazed by cattle for at least 150 years (D.
 Flavell, personal communication). Average annual precipitation was 700 mm
 with pastures producing on average 3,300 kg ha<sup>-1</sup>
 y<sup>-1</sup> of biomass dominantly used for livestock
 forage. The study region has a Mediterranean climate where the growing
 season typically occurs from October to April or May and is characterized
 by cool, wet winters and warm dry summers.  The site was dominated by
 naturalized stands of annual grasses and forbs (Bartolome et al. 2007,
 Eviner 2016). The field sites were not seeded, irrigated, fertilized, or
 tilled. Naturalized annual plant species reseed and replace stands every
 year. This ecosystem is broadly representative of approximately 12 million
 hectares of rangeland across California (Eviner 2016).
 <strong>Table 1 </strong>Description of soil
 amendment, application, and burned or unburned control
 treatments. 
 <strong>Treatment</strong>
 
 <strong>Description</strong> 
 <strong>Number of
 applications</strong> 
 <strong>Application years</strong>
 <strong>Soil sampling
 years</strong> 
 Control (CN) 
 No treatment applied, Fire in June 2019 
 - 
 - 1. Fall
 2018 2. Spring 2019 3. Spring
 2020 4. Spring 2021 5. Spring
 2022 
 Unburned Control (UCN) 
 No treatment applied, no fire during study 
 - 
 - 2. Spring
 2020 3. Spring 2021 4. Spring
 2022 
 Green waste compost (GW, GW2, GW3) 
 Compost derived from plant husks, chicken, horse, and
 cattle manures. Fire in June 2019 
 GW: one-time GW2: two-time
 GW3: three-time Fall
 2018 Fall 2019 Fall 2021
 1. Fall 2018 2. Spring
 2019 3. Spring 2020 4. Spring
 2021 5. Spring 2022 
 Food waste compost (FW, FW2)
 Compost derived from food scraps including
 fruits, vegetables, egg/clam shells, bones, etc. Fire in June
 2019 FW: one-time
 FW2: two-time Fall
 2018 Fall 2019 
 1. Fall 2018 2. Spring 2019
 3. Spring 2020 4. Spring 2021
 5. Spring 2022 
 
 <strong> </strong>
 <em>Experimental design</em> In
 October 2018, nine original 0.15 ha (60 x 25 m) plots were established
 with 6 m buffers to establish a randomized complete block design with
 three complete blocks. Each block contained a food waste compost treatment
 with applications in two years (FW or FW2), a manure and green waste
 compost treatment with applications in three years (hereafter referred to
 as green waste compost and GW, GW2, or GW3), and an untreated burned
 control (UCN); an unburned control (CN) was added in spring 2019 (Table
 1). The food waste compost had higher mineral N (80 µg g
 N<sup>-1</sup>) than the green waste compost (57 µg g
 N<sup>-1</sup>), but the composts had similar total C and N
 (Tables S1-S2). To apply the second compost treatments in Fall 2020 (FW2,
 GW2), original plots, except the controls, were split into 0.075 ha (60 x
 12.5 m) plots for all treatments (n = 3 per treatment for CN, FW, FW2, GW,
 GW2). To apply the third compost treatments in Fall 2022 (GW3), the GW2
 plots were split in half again to create 0.0375 ha (30 x 12.5 m) plots for
 GW2 (n = 3) and GW3 (n = 3) treatments.  In June 2019 shortly after soil
 and plant samples were collected, a fast low intensity grass fire burned
 all plots evenly. Following the burning event, unburned controls (UCN)
 0.15 ha (60 x 25 m) plots were established approximately 80 m from the
 closest burned plot and sampled identically thereafter to further
 elucidate the effects of fire across treatments. These treatment groups
 allowed us to simultaneously compare the effects of green waste versus
 food waste compost, the effects of compost application frequency, and the
 effects of fire on a composted grassland ecosystem. Fire is a natural and
 common event in these grasslands (Harrison et al. 2003).
 Established plots were located on similar slope and aspect;
 management and soil type (pre-treatment C:N of 12)  were consistent across
 the whole study area. Treatments were randomly assigned within each block.
 Both green and food waste composts were produced at the West Marin Compost
 Facility (for compost properties see Tables S1-S2) by maturing in watered
 piles that were turned (aerated) weekly for three months (Vergara and
 Silver 2019, Pérez et al. 2023). Food waste compost (C:N of 16) was
 derived from food scraps including fruits, vegetables, egg and clam
 shells, bones, etc. Green waste compost (C:N of 16.4) was derived from
 plant residues, chicken, horse, and cattle manures. Both feedstocks were
 mixed with woodchips in a matrix that was 50% by volume to facilitate
 proper compost development and maturation. All plots were grazed by
 yearling steers for approximately three weeks and then mowed to a uniform
 aboveground cover prior to initial compost application. During grazing,
 cattle were allowed to graze all plots freely and not isolated to any
 specific plot. Composts were applied at a depth of 0.65 cm (equivalent to
 5.9 Mg C<sup> </sup>ha<sup>-1</sup> / 0.37 Mg
 N<sup> </sup>ha<sup>-1</sup>and 5.5 Mg
 C<sup> </sup>ha<sup>-1</sup> / 0.34 Mg N
 ha<sup>-1</sup> for food waste and green waste compost per
 application, respectively) to respective treatment plots in November 2018,
 October 2019, and October 2021 using a compost spreader (application dates
 based on best practices by the range manager). Control plots were driven
 over without amendment application to impart the same soil
 effect. <em>Soil sampling and
 analyses</em> Soil sampling was conducted prior
 to compost application (fall 2018) and annually at the end of each growing
 season (end of spring) in 2019, 2020, 2021, and 2022. At every sampling
 time point, soil samples (n = 9 per plot regardless of plot size) were
 collected at 0-10 and 10-20, and 20-30 cm depths from nine stratified
 random locations per plot using a soil auger. When possible, soil samples
 were also collected from 30-40 and 40-50 cm depths. Due to the inability
 to sample 40-50 cm in some locations, these depths were not included in
 soil C stock calculations but are provided in the <em>Supplementary
 Information</em> (Table S3). All compost addition subplots were
 sampled at least 5 m from the edge of each subplot boundary to minimize
 edge effects between treatments with application frequency. Samples were
 transported to the laboratory and processed for analysis within 24 hours.
 Soil pH was measured in a 1:1 volumetric slurry of sample and deionized
 water using a pH electrode (Mclean 1982). Soil moisture was determined
 gravimetrically by weighing fresh soil and oven-drying for at least 24 hr
 at 105<sup> º</sup>C. Mineral N species and N mineralization
 rates were quantified throughout the first growing season (early, mid late
 growing season, and end of growing season) and during the last annual soil
 sampling (end of growing season 2022). Nitrate plus nitrite
 (NO<sub>2</sub><sup>-</sup>) and
 NH<sub>4</sub><sup>+</sup> were measured after
 extraction of 15 g of field-fresh soil in 75 ml of 2 M potassium chloride
 (KCl) solution (Hart et al. 1994). Potential net nitrification and
 mineralization were measured by comparing fresh 2M KCl soil extractions
 with a second subsample was covered and incubated for seven days in the
 dark, prior to subsequent 2M KCl soil extractions. Soil KCl extracts were
 stored at -20 <sup>º</sup>C until colorimetrically analyzed
 using an AQ300 analyzer (Seal Instruments, Mequon, WI). The difference in
 NO<sub>3</sub><sup>-</sup> over time was used to
 calculate potential net nitrification, and the difference in the sum of
 NO<sub>3</sub><sup>-</sup> and
 NH<sub>4</sub><sup>+</sup> concentrations over
 time was used to calculate potential net mineralization, after accounting
 for the length of the incubation (Hart et al. 1994).
 For total soil C and N analyses, subsamples were air-dried,
 sieved to &lt; 2 mm, and had visible roots removed before being ground
 to a fine powder. Samples were then analyzed in duplicate for total C and
 N on a CE Elantech elemental analyzer (Lakewood, NJ). Bulk density was
 sampled in 2020 from three locations in each plot using a 6.5 cm diameter
 bulk density corer. Samples were sorted in the lab into fine soil
 (&lt; 2 mm) and coarse rock (&gt; 2 mm) volumes. To quantify soil
 moisture, soil subsamples from each depth and replicate were weighed
 before and after drying at 105 <sup>º</sup>C to a constant
 weight for at least 24 h. Bulk density was calculated as the rock-free dry
 volume for total soil fractions, and used to calculate total C and N
 stocks (g C m<sup>-2</sup>, g N m<sup>-2</sup>).
 Previous work at this site suggested that bulk density was relatively
 similar following compost amendments over one-to-four-year time scales
 (Ryals et al. 2014). <em>Soil greenhouse gas
 fluxes</em> Fluxes of CO<sub>2</sub>,
 N<sub>2</sub>O, and CH<sub>4</sub> were measured
 across the soil-atmosphere interface using the static chamber method in
 years one and three; they were not measured in year two (2020) due to
 complications from the COVID-19 pandemic. Three chambers were sampled in
 each plot. Gas sampling took place at daily intervals after the initial
 compost application during the spring wet up period for 8-21 days, and
 over the entire growing season at bi-weekly intervals. Gas samples (30 mL)
 were collected from each chamber at 0, 5, 15, 25, and 40 minutes after the
 lid closure and stored in pre-evacuated gas vials until analysis (within
 72 hours) for CO<sub>2</sub>, N<sub>2</sub>O, and
 CH<sub>4</sub> on a Schimadzu GC-14 gas chromatograph
 (Shimadzu Corporation, Kyoto, Japan) equipped with a thermal conductivity
 detector, flame ionization detector and an electron capture detector.
 Analyzer detection limits were 0.09 ppm CH<sub>4</sub>, 0.49
 ppb for N<sub>2</sub>O, and 0.09 ppm for
 CO<sub>2</sub>. Fluxes were calculated using an iterative
 exponential curve-fitting approach with non-statistically significant fits
 considered as fluxes equal to zero (Matthias et al. 1978, Ryals and Silver
 2013). <em>Aboveground biomass
 </em> Aboveground vegetation was sampled for
 biomass production and C and N content at peak standing crop at the end of
 each growing season (n = 9 samples per plot per growing season).
 Aboveground shoots were clipped from 20 cm diameter circles, collected
 into pre-dried bags, dried at 65 ºC for &gt; 24 h, and subsequently
 weighed. Dried and ground subsamples were analyzed for C and N on a CE
 Elantech elemental analyzer (Lakewood, NJ).
 <em>Statistical analyses </em>
 Response variables were analyzed using JMP 16 (SAS Institute
 Inc., Cary, NC). Soil and biomass data were analyzed using a linear mixed
 model with block as a random effect and treatment (green and food waste
 composts and control) and time (2019: year one, 2020: year two 2021: year
 three, and 2022: year 4) as fixed effects. For significant treatment
 effects on soil C and N, plant productivity, and greenhouse gas fluxes,
 all treatment groups (FW, FW2, GW, GW2, GW3) and control groups (CN, UCN)
 means were compared using a Tukey’s post-hoc test. As aboveground plant
 biomass exhibited high interannual variability, biomass data were further
 assessed for significant trends using mean change in plant biomass
 (treatment-control) and applying a one-sample t-test for the green waste
 compost treatment and for food waste compost. Values reported in the text
 are means and standard errors.
description: # Impacts of compost amendment type and application frequency on a
 fire-impacted grassland ecosystem
 [https://doi.org/10.5061/dryad.hhmgqnkr0](https://doi.org/10.5061/dryad.hhmgqnkr0) ## Description of the data and file structure **Abbreviations for treatments:** Treatments included unburned control (UCN), burned control (CN), and burned green waste (GW) or food waste (FW) applied once (one-time), two different years (two-time), or three different years (three-time) TreatmentCN: Burned control, UCN: Unburned Control, GW: one-time green waste compost, GW2: two-time green waste compost, GW3 = three-time green waste compost, FW = one-time food waste compost, FW2 = two-time food waste compost. Sampling years: Year 1 = 2019, Year 2 = 2020, Year 3 = 2021, Year 4 = 2022 or by year: 2018 (pre-treatment application), 2019, 2020, 2021, or 2022 Block: Block replicate of each treatment (B1, B2, or B3) Plot: Plot replicated of each treatment within each block (P1-P6)  Depth: Soil sampled depth in cm (0-10, 10-20, 20-30, 30-40, or 40-50) **Statistical values in all datasets:** Mean = Mean, sd = standard deviation, se = standard error, ci = confidence interval **The following subsets used to create figures are included in this dataset:**  * Greenhouse gas data: all.HSP.GHG.data.csv Greenhouse gas data is presented in meters squared per second for carbon dioxide: umol CO2 m^-2 s^-1, methane: nmol CH4 m^-2 s^-1m, and nitrous oxide: nmol N2O m^-2 s^-1 Date = sampling date in Month.day.year format * Mean Soil C % and C stocks : HSP_C_mean.csv Soil C in % carbon by soil mass and respective statistical values * Mean Soil N % and N stocks: HSP_N_mean.csv Soil N in % carbon by soil mass and respective statistical values * Mean aboveground biomass: Mean_HSP_biomass.csv Mean aboveground biomass (dried) in grams per meter squared (g/m^-2) * Soil pH data is included in the supplemental information Soil pH values represented with units of pH and respective statistical values ## Code/Software All statistical analyses were performed in JMP Pro 16. Figures were processed in R Studio (version: 2022.07.1 Build 554; R version 4.0.5 (2021-03-31)) with the following packages: library(ggplot2) library(ggpubr) library(gridExtra) library(reshape2) library(tidyr) library(chron) library(dplyr) library(zoo) library(doMC) library(RColorBrewer) library(ggallin) library(randomForest) require(caTools) library(padr)