DOI: 10.5061/dryad.6t1g1jxb0
title: Synergies and trade-offs between ecosystem services and economics in dryland cover crop systems

description: Replacing bare fallow periods with cover crops in dryland agroecosystems
 can help reverse soil degradation and control erosion but may also result
 in cash crop yield penalties due to water limitations. Two field trials
 were conducted on the Colorado Plateau to quantify the impact of cover
 cropping on crop production, multiple ecosystem services, and economic
 trade-offs in this semi-arid region. No-till and different cover crop
 planting windows (fall and spring) were explored as strategies to optimize
 ecosystem service provision and productivity trade-offs. After three full
 cover crop cycles (6 years), fall-planted cover crops improved soil
 structure and erosion control, but associated wheat yield penalties (48%
 decrease on average) and additional costs caused a 176 USD ha-1 cycle-1
 average decrease in net returns. However, including the hypothetical sale
 of forage (based on 50% cover crop biomass removal) more than offset these
 costs in fall-planted treatments; with both forage and wheat revenue,
 cover cropping increased net returns as compared to the fallow control by
 92 USD ha-1 cycle-1. Spring-planted cover crops presented a lower
 productivity trade-off (24% average wheat yield penalty) but did not
 provide clear ecosystem service benefits and did not produce enough
 biomass to offset costs of cover cropping. Our findings indicate that
 fall-planted cover crops have the potential to reverse soil degradation
 and control erosion in dryland systems globally, but productivity
 trade-offs and decreased economic returns must be compensated for by
 alternative revenue sources, conservation payments, or other incentives to
 ensure their feasibility. 
description: <strong>Experimental Design</strong>
 Field trials were implemented at the Colorado State University
 Southwestern Colorado Research Center near Yellow Jacket, Colorado (37°32′
 N latitude; 108°44′ W longitude; 2100 m.a.s.l.) on the Colorado Plateau.
 This region is semi-arid with an average rainfall of 370 mm yr-1 (yearly
 average from 1991-2020) and mean monthly high temperatures ranging from 3
 °C in January to 31 °C in July (PRISM Climate Group, 2014). The dominant
 soil type is a Wetherill loam (fine-silty, mesic Aridic Haplustalfs; 36%
 sand, 41% silt, and 22% clay) (Soil Survey Staff, 2020) with low organic
 matter content (0.81% SOC) and neutral pH of 6.9. Prior to trial
 establishment, fields were in dryland, conventionally tilled production
 since 2010. Additional details regarding field history can be found in
 Eash et al. (2021), which presented the short-term productivity results of
 these trials.  Two field trials were established to
 compare the common local two-year winter wheat-fallow rotation with a
 winter wheat-CC (cover crop) rotation. The first trial (T1) was
 established in 2015 and the second (T2) was established in 2016 in an
 adjacent field to ensure that both crop phases were represented each year.
 T1 was under no-till management and followed a randomized complete block
 design with three replicate blocks. Plots in T1 were 6 m wide x 61 m long,
 or 372 m<sup>2</sup> each. T2 followed a split-plot design to
 also evaluate tillage effects (no-till vs. conventional tillage) and CC
 planting window (fall- vs. spring-planted). Tillage treatments were
 randomly allocated to three replicate blocks and CC treatments were
 randomly allocated to subplots (3.7 m x 30.5 m, or 113
 m<sup>2</sup>). Conversation with local
 producers and stakeholders informed CC mixture selection, all of which
 were planted at a target seeding rate of 34 kg
 ha<sup>-1</sup>. While various CC mixtures were evaluated
 within the trials, dry conditions caused only grasses and winter pea to
 dominate, similar to observations by Holman et al. (2020). Since stand
 expression was very similar among CC treatments of the same planting
 window, this analysis focuses on planting window rather than CC mixture,
 though a full list of CC mixtures and analysis among mixture treatments
 are presented in the "Cover Crop Mixtures" tab in the attached
 dataset. Winter wheat (hard red winter wheat variety
 ‘Fairview’) was planted at a rate of 56 kg ha<sup>-1</sup> in
 September and harvested in July. Control treatments remained fallow in
 between winter wheat crops. In fall CC treatments, CC were planted in
 September and terminated in June, while spring CC were planted in April or
 May and terminated in June or July. In no-till plots,
 glyphosate (<em>N-</em>(phosphonomethyl)glycine) and 2,4-D
 amine (2,4-dichlorophenoxyacetic acid) were used to control weeds and
 terminate CC. In tilled plots, this was done mechanically using a tandem
 disk or field cultivator.  <strong>Wheat and
 Forage Yields</strong> Wheat was harvested in
 July of each year from subplots in the center of each plot to avoid edge
 effects using a Hege plot combine (1.2 m width; 6 rows). Wheat grain was
 then cleaned using an electric winnower, weighed and tested for moisture
 and density. All wheat yields are reported at 11% moisture content.
   Forage production was estimated to be 50% of CC
 biomass per Natural Resources Conservation Services (NRCS) guidelines
 (NRCS, 2023). Cover crop biomass was measured at CC termination using a
 0.25 m<sup>2 </sup>quadrat with three subsamples randomly
 distributed per plot. Biomass was carefully cut at the soil surface (~1 cm
 height) and the three subsamples were combined. Biomass was then dried at
 60°C and weighed. Dried CC biomass from all treatments
 was finely ground and analyzed for forage quality using near-infrared
 reflectance spectroscopy.  <strong>Soil
 Sampling</strong> Soil samples were collected at
 wheat planting and CC termination to evaluate supporting services (N
 supply, soil water availability, and soil structure). Approximately one
 week prior to wheat planting and one week after CC termination, two
 subsamples per plot were taken 30 cm increments using a tractor-mounted
 Giddings probe (5 cm diameter) to a depth of at least 90 cm and separated
 into 0-15 cm, 15-30 cm, 30-60 cm, and 60-90 cm depth increments. The
 target sampling depth was 180 cm, but the probe could not always penetrate
 to this depth due to dry conditions in the sub-surface soils. Subsamples
 were composited by depth increment for each plot and a 50 g subsample of
 each depth increment was weighed, dried at 105°C and reweighed to
 determine gravimetric moisture content. Gravimetric moisture content was
 then converted to volumetric moisture content by multiplying by the bulk
 density that corresponded to the sampling depth (1.35 g
 cm<sup>-3</sup> for the 0-30 cm depth, 1.40 g
 cm<sup>-3</sup> for the 30-60 cm depth, and 1.45 g
 cm<sup>-3</sup> for the 60-90 cm depth; based on unpublished
 data averaged across the study site prior to trial establishment). A
 subsample from the 0-30 cm depth increment sampled at wheat planting was
 air-dried and sent to Ward Laboratories in Kearney, NE for nitrate
 analysis using a flow injection analyzer (Keeney and Nelson,
 1982). To assess wet aggregate stability, soil was
 sampled to a depth of 5 cm using a 6.7 cm diameter core at two sampling
 points per plot at termination of the third CC cycle (2020 in T1 and 2021
 in T2). Subsamples were combined, gently passed through an 8 mm sieve, and
 air-dried to further analyze aggregate stability by wet sieving, following
 methods adapted from Elliott (1986). In summary, a 40 g sample was placed
 on a 2 mm sieve and submerged in deionized water for 5 minutes for
 slaking. The sieve was then oscillated up and down (in and out of the
 water) 50 times during a 2 min period. Soil passing through the sieve was
 subsequently transferred to a 250 mm, and a 53 mm sieve. Soils remaining
 on each sieve were rinsed into a pan and were dried at 60 °C. The
 proportions of whole soil in each aggregate size fraction and average
 diameter for each fraction were used to calculate mean weight diameter
 according to van Bavel (1950).  Soils sampled to 15 cm
 at CC termination were frozen and processed for 16S rRNA amplicon
 sequencing using an Illuma Miseq platform. Soil DNA was extracted from
 0.50 g of samples using the Powersoil® DNA Isolation Kit (Mo Bio
 Laboratories, Carlsbad, CA, USA) according to manufacturer instructions.
 To characterize prokaryotic diversity and community composition we used
 the primer sets 515F/806R (Caporaso et al., 2012) that amplifies a portion
 of the bacterial 16S rRNA gene.  A combination of USEARCH (Edgar, 2010)
 and UNOISE3 (Edgar, 2016) was used for bioinformatics processing. The
 USEARCH pipeline was used to generate Amplicon Sequence Variant (ASV)
 tables based on 97% sequence similarity. Sequencing run quality was
 assessed using fastQC (Andrews, 2010), and raw sequences were discarded if
 they were short in length (&lt;100 bp), had a low quality score (Q
 &lt; 20), or contained ambiguous nucleotides. Adapters and primers
 were removed using cutadapt (Martin, 2011). To demultiplex samples,
 paired-end reads were merged, and an initial quality check test was
 performed. The representative set database was created using the UCLUST
 and UPARSE algorithm (Edgar, 2013). Unique sequences were located and
 identified as unique ASVs, which were then clustered using DADA2 and
 DeNoised using uNoise3 (Xiong et al., 2021a, 2021b). ASV tables at the
 sample level were generated by mapping reads to the the Silva database
 (Pruesse et al., 2007). Bacterial sequences that match host mitochondria
 and chloroplast were removed. ASV abundance tables were rarefied to the
 median count per sample to ensure relatively equal sampling depth. Alpha
 diversity (Shannon index) and species richness were calculated using the
 phyloseq package in R (McMurdie and Holmes, 2013). Soil
 organic carbon content was measured on samples taken to a depth of 15 cm
 at the third CC termination. Samples were air-dried, passed through a 2 mm
 sieve, finely ground and analyzed for total C content using a LECO
 combustion analyzer (LECO Tru-SPEC, St. Joseph, MI).  
 Erosion (yearly soil loss) was estimated for all treatments using
 the Revised Universal Soil Loss Equation (RUSLE2) for water erosion and
 Wind Erosion Prediction System (WEPS) for wind erosion, both available
 through the NRCS (Widman, 2004; Agricultural Research Service, 2020). We
 ran the simulation on a Wetherill loam, with data populated from the Soil
 Survey Geographic (SSURGO) database with low organic matter and an average
 slope of 2%. Modelling was based on historical average daily weather data
 for Dove Creek, CO (NOAA). The model was run based on a custom management
 plan and crop rotation that reflected all herbicide, tillage, planting,
 and harvest events for each treatment. Yearly average CC biomass and wheat
 productivity data were included in the simulation for each tillage and CC
 treatment. Soil loss estimates from wind and water erosion were summed
 together for total estimated soil loss over the six-year study
 period. <strong>Economic
 Analysis</strong> The CC-wheat and fallow-wheat
 cropping cycles spanned 2-year periods and Net Present Value (NPV) methods
 were used to assess returns to investments in the CC both with and without
 the sale of 50% of CC biomass as forage. First, enterprise budgets were
 developed to determine the annual net returns (in USD
 ha<sup>-1</sup> cycle<sup>-1</sup>) for each
 tillage and planting window combination in each experiment. Gross revenues
 included wheat revenue as the primary cash crop. Revenue from the sale of
 CC biomass as forage was included as appropriate to compare net returns
 with and without forage sales. Wheat revenues were calculated using
 average annual wheat yields from the trials and wheat grain prices from
 Colorado State University (CSU) Extension Crop Enterprise Budgets for
 dryland wheat production in Western Colorado (Colorado State University
 Extension, 2016; Colorado State University Extension, 2018; Colorado State
 University Extension, 2021). Operating expenses included seed costs (wheat
 seed and CC seed), chemical costs (herbicides), variable machinery costs
 (fuel, oil, repairs, labor) for field operations, and six months of
 interest expenses on operating capital. Inputs were assigned the same
 prices as the actual inputs used in the experiments and interest expenses
 were estimated using an interest rate of 7.5% based on the CSU Extension
 enterprise budgets. Ownership expenses (depreciation, interest, taxes,
 insurance, housing) were included for tillage, planting, weed control,
 harvest, and hauling field operations for the wheat cash crop and for
 planting and termination field operations for the CC. The operating and
 ownership expenses for machinery and equipment are from annual reports
 published by Iowa State University (Plastina, 2016; Plastina, 2018;
 Plastina, 2020). Other economic factors of production (e.g., overhead,
 land, unpaid labor and management) were assumed to remain constant across
 treatments and are therefore not included in the economic
 analysis. Second, NPV for a given cropping cycle was
 determined using a discount rate of 7.5% from the CSU Extension budget
 because it is a short-term financial analysis. Prior to discounting,
 revenues and costs were adjusted for inflation using the producer price
 index (U.S. Bureau of Labor Statistics, 2022). The average NPV per cycle
 was obtained for each treatment and trial combination by dividing total
 NPV over the three cycles. Changes in net returns for each combination of
 CC planting window and tillage treatment were calculated using the net
 returns of fallow and corresponding tillage treatment as reference
 treatments. Positive values therefore represent an increase in net returns
 and negative values indicating a decrease in net returns due to CC as
 compared to the reference treatments. When including
 revenue from forage sales, we estimated forage yields to be 50% of total
 CC biomass production to ensure a level of soil cover consistent with CC
 best management practices (NRCS, 2023). Yields were assumed to contain 15%
 moisture and priced as hay on a USD ton<sup>-1</sup> basis
 (Ponderosa Partnership, 2018). Hay prices were based on reported grass hay
 prices from the Agricultural Market Service (Agricultural Marketing
 Service, 2017; Agricultural Marketing Service, 2019; Agricultural
 Marketing Service, 2021). All RFQ values were in Grade 1 or 2 quality
 standard ranges [on the American Forage and Grassland Council scale of 1
 to 5 (1 being best; Marsalis et al., 2009)] and prices corresponded to
 “Good” quality grass hay in the Agricultural Market Service reports
 (Agricultural Marketing Service, 2017; Agricultural Marketing Service,
 2019; Agricultural Marketing Service, 2021). To verify that the prices
 were reasonable for the study area, local hay farmers corroborated similar
 hay prices during the years of study. Additional operating and ownership
 expenses for raking, mowing, and baling were included and are also sourced
 from annual reports published by Iowa State University (Plastina, 2016;
 Plastina, 2018; Plastina, 2020). 
description: # Synergies and trade-offs between ecosystem services and economics in
 dryland cover crop systems Dataset DOI:
 [10.5061/dryad.6t1g1jxb0](10.5061/dryad.6t1g1jxb0) ## Description of the
 data and file structure Two field trials were conducted in Yellow Jacket,
 CO from 2015 to 2021 to quantify the impact of cover cropping on crop
 production, multiple ecosystem services and economic trade-offs. No-till
 and different cover crop planting windows (fall and spring) were explored
 as strategies to optimize ecosystem service provision and productivity
 trade-offs. See Methods for more details.  ### Files and variables ####
 File: Cover_Crop_Trials_Data.xlsx **Description:** This dataset contains
 all data pertaining to field trials and includes the following components:
 * **Metadata**: A brief description of methods and units for all
 parameters included. * **Cover Crop Mixtures: **Species list for each
 cover crop mixture with percent contribution by seed weight. * **Field
 Data**: All field data collected throughout study period, including cover
 crop biomass, wheat yields and yield stability, soil properties
 (volumetric moisture, nitrate, organic carbon content, aggregate
 stability, 16S microbial diversity and richness). NA = not applicable; NE
 = not evaluated. * **Economic Data:** Data used to calculate net profit
 with and without the sale of forage throughout the study period. *
 **Erosion Data:** Soil loss due to wind and water erosion, estimated using
 the NRCS RUSLE2 and WEPS models.