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Carbon Sequestration Recommendations on ConocoPhillips’ Quail Ranch in Crane, Texas Final Report Texas A&M Natural Resources Institute

Carbon Sequestration Recommendations on ConocoPhillips’ Quail Ranch in Crane, Texas Final Report Jacob Lampman1, Kypfer Cordts1, Dr. Edward Osei2, Dr. William Fox3, Dr. Doug Tolleson4, and Dr. Roel Lopez1

July 2024

Prepared for: ConocoPhillips

Report prepared by:

Texas A&M Natural Resources Institute 1919 Oakwell Farms Parkway, Suite 100 San Antonio, TX 78218

Texas A&M Natural Resources Institute, 1919 Oakwell Farms Pkwy, Ste. 100 San Antonio, TX 78218 Tarleton State University Agricultural Education and Communication, 1333 W. Washington Stephenville, TX 76402 3 Texas A&M Center for Natural Resources Information Technology, 495 Horticulture Rd. Rm. 305 College Station, TX 77843 4 Texas A&M AgriLife Research – Sonora, P.O. Box 918 Sonora, TX 75950 2

Executive Summary ConocoPhillips is one of the world’s largest independent oil and gas production companies and has identified carbon credit markets as a new land management opportunity and potential asset. To assist ConocoPhillips in understanding and maximizing potential carbon sequestration benefits of its properties, Texas A&M Natural Resources Institute (NRI) provided a collaborative team of research scientists to estimate current rates of soil carbon sequestration and identify management strategies which could yield additional sequestration gains. NRI estimated carbon sequestration rates and carbon stocks on ConocoPhillips’ Quail Ranch in Crane, Texas. Carbon sequestration rates and stocks were estimated by collecting soil and vegetation data which were input to a watershed and land management simulation model (APEX 1905). Additionally, carbon sequestration rates and stocks were modeled with different land management simulations, including: • • • • • •

Increased groundcover Grazing management Brush management Soil disturbance Fertilizer application Biochar and manure application

Improved management activities can have impacts on soil carbon stores and sequestration rates at Quail Ranch. There are many reasons for this but the simplest is that Quail Ranch is essentially starting from zero, and hence has more opportunity for increases in soil carbon sequestration. However, the sandy and gravel soil types prevalent there do not store carbon well. Minimal rainfall further limits the amount of biomass and ground cover that is available and its impacts from land management practices. Therefore, implementing strategies that conserve biodiversity, decrease bare ground, and prevent soil erosion will have the greatest impacts to soil carbon storage. Current carbon stocks at Quail Ranch are estimated at 20.5 mt/ha (9.1 ton/ac) with soil carbon percent near 1% and a sequestration rate of 0.02 mt/ha/yr (0.01 ton/ac/yr). A summary of our modeled estimates for soil carbon sequestration rates, soil carbon stocks, and soil carbon percent is provided below.

Table 1. Summary of results from estimating soil carbon sequestration rates, stocks, and percent on current land management practices (baseline) and other various simulated practices. Sequestration Rate (mt/ha/yr)

Soil Carbon Stocks (mt/ha)

Soil Carbon Percent (%)

Baseline

0.025

20.555

0.954

100% Groundcover

0.064

21.039

0.965

Grazing (80 ac/head)

0.075

21.178

0.968

Mechanical Brush Removal

0.018

20.458

0.952

Chemical Brush Removal Soil Disturbance (Subsoiler & Harrow) Soil Disturbance (Disk chisel & Offset disk)

0.038

20.705

0.957

0.027

20.578

0.954

-0.009

20.126

0.944

Fertilizer (200 lb/ac)

0.051

20.872

0.961

Biochar (50 lb/ac)

0.051

20.876

0.961

Manure (2244 lb/ac)

0.044

20.788

0.959

Quail Ranch has potential for transient improvements in soil carbon percent but minimal potential for carbon credits. Because Quail Ranch is starting from virtually zero carbon sequestration, any improvements could have impacts to soil carbon. However, these improvements are extremely difficult in west Texas with little rainfall. Arid landscapes in west Texas have very little soil carbon storage capacity due to climate and soil type. So, protecting the carbon that is already on the landscape is our key recommendation. This includes: • •

•

Minimize surface soil disturbance from equipment, infrastructure, and intensive grazing. Encourage increased herbaceous vegetation cover consequent with any brush removal practices (and well-timed precipitation) to reduce loss of carbon over longer periods of time. Target restoration of any degraded areas in order to improve vegetative cover (reduce bare ground) and consequently improve soil health and carbon sequestration potential.

Until very recently, nature-based offsets have primarily focused on forests. However, grasslands can provide similar carbon sequestration potential through below ground biomass and long-term resiliency. This has led to the same progression of grassland-based carbon credits on the voluntary markets as exist for forestry-based operations by focusing on protection and preservation. Restoration based projects are available, but Quail Ranch may not benefit much

from such a market. Therefore, offset projects that focus on preventing the conversion of rangelands may be the best opportunity for ConocoPhillips. Ecosystem services available to rangeland landowners now extends past livestock production as we are currently seeing with the development of carbon markets. But we are also beginning to value other services like wildlife habitat, recreation, watershed functions, and biodiversity conservation. Limited marketing opportunities are emerging to support these activities. Participation in emerging nature-based carbon markets provides an option for consideration. However, caution should be taken related to the value of income generated from potential carbon-based products and the inputs necessary to achieve the benefits. These markets remain relatively immature and are evolving very quickly.

Table of Contents Executive Summary ........................................................................................................................ 2 List of Figures ................................................................................................................................. 7 List of Tables .................................................................................................................................. 8 Introduction ..................................................................................................................................... 9 Methods........................................................................................................................................... 9 Study Area .................................................................................................................................. 9 Vegetation Sampling Protocol .................................................................................................. 14 Sample Design ...................................................................................................................... 14 Vegetative Cover and Species .............................................................................................. 16 Plant Biomass........................................................................................................................ 17 Soil Sampling Protocol ............................................................................................................. 17 Soil Chemistry ...................................................................................................................... 17 Bulk Density ......................................................................................................................... 18 Modeling ................................................................................................................................... 18 The APEX Model ................................................................................................................. 18 APEX Model Application ..................................................................................................... 19 Baseline and Management Scenarios........................................................................................ 19 Baseline Scenario .................................................................................................................. 19 Management Scenarios ......................................................................................................... 19 Results & Discussion .................................................................................................................... 22 Vegetation ................................................................................................................................. 22 Dominant Species ................................................................................................................. 22 Diversity ................................................................................................................................ 23

Biomass ................................................................................................................................. 24 Soil ............................................................................................................................................ 25 Carbon ....................................................................................................................................... 26 Baseline Scenario ...................................................................................................................... 27 Management Scenarios ............................................................................................................. 29 Increased Groundcover ......................................................................................................... 29 Grazing Management ............................................................................................................ 30 Brush Management ............................................................................................................... 31 Soil Disturbance .................................................................................................................... 32 Fertilizer Application ............................................................................................................ 33 Biochar and Manure Application .......................................................................................... 34 Summary ....................................................................................................................................... 35 Recommendations ......................................................................................................................... 38 Carbon Market Recommendations ........................................................................................... 38 Literature Cited ............................................................................................................................. 40 Appendix 1: Plant Species Lists ................................................................................................... 45

List of Figures Figure 1. Map of Quail Ranch within Texas Parks and Wildlife Department ecoregions. ......... 11 Figure 2. Soil map of Quail Ranch .............................................................................................. 12 Figure 3. Survey locations for Quail Ranch. ................................................................................ 15 Figure 4. Carbon sequestration rates on ecological sites at Quail Ranch. ................................... 28

List of Tables Table 1. Amount of precipitation and average temperature preceding each vegetation survey along with historical averages. ...................................................................................................... 13 Table 2. Number of sub-basins for each property. ....................................................................... 14 Table 3. Soil description and location at each transect on Quail Ranch. ..................................... 16 Table 4. Simulated grazing rates at Quail Ranch. ........................................................................ 20 Table 5. Simulated fertilizer application of 28-10-10 at Quail Ranch. ........................................ 21 Table 6. Top 5 dominant species during each survey based on the number of hits. The percentage of hits for each species is recorded in parentheses. Superscripts denote growth form (G=grass; W=woody shrub/tree; F=forb). .................................................................................... 23 Table 7. Summary of diversity indices for the six repeated transects. ......................................... 24 Table 8. Total and mean biomass (lbs/ac) of vegetation sampled for all transects combined and for the six repeated transects only. ................................................................................................ 25 Table 9. Mean results of soil samples analyses at Quail Ranch. .................................................. 25 Table 10. Mean results of soil samples analyses by soil type. ..................................................... 26 Table 11. Mean results of carbon estimates for each property averaged over 25 years. .............. 27 Table 12. Mean carbon sequestration estimates of areas sampled averaged over 25-year simulation with current ground cover and with ground cover increased to 100%. ...................... 30 Table 13. Simulated soil carbon indicators under alternative grazing scenarios. ........................ 31 Table 14. Simulated mean soil carbon levels and sequestration rates for brush management without planting, as compared to the baseline scenario. ............................................................... 32 Table 15. Simulated mean soil carbon levels and sequestration rates for the soil disturbance scenario, as compared to the baseline. .......................................................................................... 33 Table 16. Simulated mean carbon levels and sequestration rates for alternative inorganic fertilizer treatments. ...................................................................................................................... 34 Table 17. Simulated mean carbon levels and sequestration rates for organic fertilizer treatments. ....................................................................................................................................................... 35

Introduction Interest in voluntary carbon credit markets has increased rapidly in recent years. These markets are another ecosystem service that may provide additional solutions to meet the goals of reducing greenhouse-gas emissions. However, the markets are frequently transforming and are sometimes volatile, making future outcomes hard to predict. Even so, these markets are currently engaging landowners and effecting changes on Texas lands. Therefore, gathering as much information and data before engaging in a market is essential to making informed decisions. ConocoPhillips is one of the world’s largest independent oil and gas production companies and has identified carbon credit markets as a new land management opportunity and potential asset. To assist ConocoPhillips in understanding and maximizing potential carbon sequestration benefits of its properties, Texas A&M Natural Resources Institute (NRI) provided a collaborative team of research scientists to estimate current rates of soil carbon sequestration and identify management strategies which could yield additional sequestration gains. Carbon sequestration rates were estimated by collecting soil and vegetation data to input to a watershed and land management simulation model. In addition to the status quo, carbon sequestration rates were modeled for different land management scenarios for each of the two properties. This final report focuses on the results from data collected and model simulations, including recommendations and lessons learned for future engagement with carbon markets.

Methods Study Area An aggregate of several ranches in the Permian Basin, Quail Ranch LLC is a subsidiary of ConocoPhillips located in Upton County, Texas. The 78,200-acre property has a long history of agricultural production including cattle, sheep and goats, leading to large portions of the property suffering from the effects of overgrazing. The ranch is now managed with a focus on conservation and minimizing the environmental impact of oil and gas operations. Quail Ranch is situated where the Trans-Pecos, High Plains, and Edwards Plateau ecoregions converge, providing a diverse array of flora and fauna (Griffith et al. 2007; Figure 1). The northern two-thirds of the ranch falls within the Arid Llano Estacado of the High Plains 9

Ecoregion. This region is more arid and topographically broken than the plains to the north. Vegetatively, this translates to an area dominated by broken shortgrass prairie largely composed of grama species (Bouteloua sp.), buffalograss (Buchloe dactyloides), sand dropseed (Sporobolus cryptandrus), sand sagebrush (Artemisia filifolia), gayfeather (Liatris sp.), and dalea (Dalea sp.) among others. Overgrazing and fire suppression has led to widespread woody encroachment from mesquite (Prosopis glandulosa), American tarwort (Flourensia cernua), and other species. Soils are generally sandy and silty over caliche. The dominant soils for Quail Ranch are Reagan loam (RaA), Conger-Reagan association (CrB), and Tencee and Upton soils (TEC; Figure 2). Mean annual precipitation is between 13-17 inches, primarily from May to October, and temperatures usually range from 30-58o F in January to 68-95o F in July. The southern third of the ranch is situated within the Chihuahuan Basins and Playas region of the Chihuahuan Desert Ecoregion (Griffith et al. 2007), although some other areas in the north correspond with this ecoregion. The major basins of this ecoregion formed during the Basin and Range tectonism. Deep depressions were created when the crust collapsed and filled with silt, clay, sand, and gravel. The flat, arid floors of these playas typically have alkaline or saline soils, along with sand dunes or white salt flats. These harsh areas support very specialized plants, such as pickleweed (Allenrolfea occidentalis), fourwing saltbush (Atriplex canescens), and alkali sacaton (Sporobolus airoides). The valleys above these basin floors may be dominated by creosote (Larrea tridentata) and American tarwort. The dominant soils for this area of Quail Ranch are Reagan loam (RaA and RaB), and Tencee and Upton soils (TEC), and Kinco fine sandy loam (KNB). Winter precipitation is sparse and most of the meager precipitation (8-14 inches) falls during the monsoonal rains of July, August and September. Temperature varies from 25-60o F in January to 67-97o F in July. Despite the occurrence of these various Ecoregions on or near Quail Ranch, most differences in vegetation are likely driven more by small-scale changes in soil types and other microhabitat variations than by large-scale vegetation patterns.

Figure 1. Map of Quail Ranch within Texas Parks and Wildlife Department ecoregions.

Figure 2. Soil map of Quail Ranch

Weather data was obtained from the Odessa, TX (USW00003031) station since it had complete records for the survey period while historical climate data was obtained from the McCamey, TX US (USC00415707) station since it has the longer dataset (averages based on years from 1932 through 2023). The two cities are 53 miles apart, with Quail Ranch in-between. Data was obtained from the National Oceanic & Atmospheric Administration Weather Service. The weather was drier than usual for the months preceding each survey. The October 2021 surveys received around 1.78” in the preceding three months, the April 2022 surveys received 0.21”, September 2022 received the most at 4.78”, and May 2023 had 1.63” (Table 1). Temperatures were slightly cooler than average, although this may also be due to differences between Odessa (observed data) and McCamey (historical data). The second round of surveys were slightly warmer and wetter than the first.

Table 1. Amount of precipitation and average temperature preceding each vegetation survey along with historical averages.

<1 month <3 months <6 months <1 month <3 months <6 months <1 month <3 months <6 months <1 month

Rain (inches) 0.09 1.78 10.31 0.00 0.21 0.27 3.77 4.78 4.97 1.42

Avg. Temp (F) 74.3 78.7 75.3 63.3 50.7 56.1 80.1 84.5 76.8 70.5

Min-Max Temp (F) 60.8 – 87.9 66.8 – 90.6 63.3 – 87.2 48.2 – 78.4 35.8 – 65.5 41.6 – 70.6 69.4 – 90.8 73.1 – 96.0 63.7 – 89.9 57.1 – 83.8

Historical Rain (in) 2.72 5.74 9.96 0.92 2.08 4.99 2.67 6.74 9.80 1.70

Historical Avg. Temp. (F) 72.7 81.9 78.7 63.4 52.0 54.6 80.7 83.7 75.6 71.8

<3 months

1.63

50.9

42.0 – 59.8

2.75

59.2

<6 months

2.73

50.1

39.9 – 60.4

4.66

54.5

Survey Event Fall 2021 (October) Spring 2022 (April) Fall 2022 (September) Spring 2023 (May)

Vegetation Sampling Protocol Sample Design Sub-basins within all four ConocoPhillips Upton County properties were identified using the Soil and Water Assessment Tool (SWAT) delineation utility. The delineation process resulted in the following sub-basin distribution for the four properties (Table 2). These-sub-basins provided the initial delineation of project areas which, based on degree of heterogeneity, determines the optimal sample size. For the purpose of this report, all ConocoPhillips properties in Upton County will be referred to as ‘Quail Ranch.’

Table 2. Number of sub-basins for each property.

ConocoPhillips Property Quail Ranch Neal Ranch McGill Ranch Cross L Ranch

Number of Sub-basins 46 26 4 15

To ensure that the sampling procedure adequately covers the broad spectrum of soils, vegetation, topography, climate, and other factors relevant for sequestration modeling, a clustering procedure was employed to group all sub-basin polygons into roughly homogenous clusters. Once these clusters have been determined, collecting vegetation and soil samples from at least one location – ideally the representative polygon within each cluster – would ensure an efficient sampling design that serves the intended purpose. In balancing optimal sample size to level of effort, funding, and personnel availability we selected 30 clusters. Within those clusters, the specific location to sample is ideally one that has attributes that are closest to the “centroid” of the cluster. This “representative polygon” is closest in the sense that it possesses the vegetation, soil, topography, and other attributes that are closest to the “center” of its cluster, closer than any other polygons within its group. Six of these clusters representative of the main clusters were chosen to be repeated for each of the four survey events, while the remaining 24 points were surveyed once (Figure 3; Table 3).

Figure 3. Survey locations for Quail Ranch.

Table 3. Soil description and location at each transect on Quail Ranch. Transect

Repeated?

Latitude

Longitude

Yes

31.353

-102.21

TEC: Tencee and Upton soils, 1 to 8 percent slopes

Soil Type

31.6222

-102.108

TEC: Tencee and Upton soils, 1 to 8 percent slopes

Yes

31.4429

-102.137

RaA: Reagan loam, 0 to 1 percent slopes

Yes

31.3755

-102.179

LZD: Lozier very gravelly loam, 2 to 15 percent slopes

31.3087

-102.193

SAC: Sanderson very gravelly loam, 1 to 8 percent slopes

Yes

31.4361

-102.175

RaA: Reagan loam, 0 to 1 percent slopes

31.3728

-102.183

RaB: Reagan loam, 1 to 3 percent slopes

Yes

31.604

-102.121

HMC: Hollomex loam, 1 to 8 percent slopes

31.4693

-102.192

TEC: Tencee and Upton soils, 1 to 8 percent slopes

Yes

31.4067

-102.236

WKB: Wickett loamy fine sand, 0 to 3 percent slopes

31.413

-102.23

WKB: Wickett loamy fine sand, 0 to 3 percent slopes

31.6032

-102.112

CrB: Conger-Reagan association, 0 to 3 percent slopes

31.3956

-102.216

KNB Kinco fine sandy loam, 0 to 2 percent slopes

31.4225

-102.25

WKB: Wickett loamy fine sand, 0 to 3 percent slopes

31.3393

-102.226

RaA: Reagan loam, 0 to 1 percent slopes

31.3707

-102.249

TEC: Tencee and Upton soils, 1 to 8 percent slopes

31.4086

-102.16

TEC: Tencee and Upton soils, 1 to 8 percent slopes

31.336

-102.181

CrB: Conger-Reagan association, 0 to 3 percent slopes

31.4441

-102.244

KNB: Kinco fine sandy loam, 0 to 2 percent slopes

31.6203

-102.112

TEC: Tencee and Upton soils, 1 to 8 percent slopes

31.6055

-102.102

CrB: Conger-Reagan association, 0 to 3 percent slopes

31.4061

-102.256

KNB: Kinco fine sandy loam, 0 to 2 percent slopes

31.382

-102.163

TEC: Tencee and Upton soils, 1 to 8 percent slopes

31.481

-102.077

TOA: Tobosa clay, 0 to 1 percent slopes, occasionally ponded

31.3699

-102.247

TEC: Tencee and Upton soils, 1 to 8 percent slopes

31.3699

-102.184

RaA: Reagan loam, 0 to 1 percent slopes

31.3886

-102.241

TEC: Tencee and Upton soils, 1 to 8 percent slopes

31.3622

-102.229

TEC: Tencee and Upton soils, 1 to 8 percent slopes

31.337

-102.188

RaA: Reagan loam, 0 to 1 percent slopes

31.3731

-102.174

RaB: Reagan loam, 1 to 3 percent slopes

Vegetative Cover and Species Vegetation was sampled utilizing the Line-Point Intercept method, which is a rapid and accurate method for quantifying soil cover that, in addition to vegetation, includes cover by litter, rocks and biological soil crusts (Herrick et al. 2009). With this method, cover is measured along a linear transect and is based on the number of “hits” on a target species out of the total number of points measured along that line. Points along the transect line are recorded every meter along the 16

100 meter transects starting at meter 1. All species which intersect that point are recorded in the order encountered starting from the top canopy and moving down to groundcover. All unknown species were collected, pressed, and brought back for later identification. We used the Vegetation GIS Data System (VGS) as the means for collecting and ensuring data quality control for the sites (University of Arizona 2021). The VGS system supports data collection for the Line-Point Intercept method of vegetation sampling among several others. Transects were surveyed four times, twice during fall (September & October), and twice during peak spring growth (April & May) to provide both an inventory of plant species and to provide inputs for the modeling component of the project. After the surveys, unknown species were identified from pressed voucher specimens if possible and their entries corrected in the database. Individuals that could only be identified to genus or growth form were retained if they appeared to be taxonomically distinct from all other identified species. Some genus-only identifications were merged with identified species if they more likely represented the same species to avoid over-inflating diversity estimates. Details on these species’ treatments can be found with the complete species lists in Appendix 1. Diversity indices (species richness, Shannon’s Diversity Index, and Simpson’s Unbiased Diversity Index) were calculated using the ‘Vegan’ package (R Core Team 2021; Oksanen et al. 2022). Plant Biomass We collected standing plant biomass within sampling hoops at 10m intervals along each 100 m transect. All standing plant material within each 0.25 m2 sampling hoop was cut to ground-level, collected, bagged, and labeled by transect. All biomass samples were dried and weighed to obtain the final biomass measurements per m2 (Herrick et al. 2009). Soil Sampling Protocol Soil Chemistry We collected six soil samples at each transect for lab analysis at meters 24, 69, and 87 at surface level and 12-inch depths. Soil samples were bagged, labeled, and sent to the Texas A&M Soil, Water, and Forage Lab for further analysis and testing. Soil samples were analyzed for pH, electrical conductivity, phosphorus, calcium, magnesium, sodium, total organic matter, and organic carbon.

Bulk Density We collected 2 bulk density measurements (Db [g/cm3]), one at the surface and one at 12 inches at each transect. A 1.8-inch (4.6 cm) diameter by 2-inch (5.1 cm) height metal ring was driven into the soil. Height from the top of the ring to the soil surface was measured and recorded before excavation. We then excavated the ring with soil intact using a hand trowel and removed any excess soil from the bottom of the sample. The distance measurements from the ground surface to the top of the ring were repeated, and the difference in distance with the removal of the soil were multiplied by the cross-sectional area to calculate the volume of the soil (USDA 2014). Soil was deposited into sampling bags and labeled. Finally, soils were dried and weighed to obtain the final bulk density measurement (USDA 2014). Modeling The APEX Model Agricultural Policy Environmental eXtender (APEX; Williams et al. 2000) is the biophysical model chosen for estimating carbon sequestration rates in this project. APEX is a comprehensive field-scale model that was developed primarily to assess the effects of management strategies on crop growth, livestock grazing, and water quality. APEX allows simultaneous simulation of multiple contiguous subareas (fields) for a wide range of soil, landscape, climate, crop rotations, and management practice combinations. Other components include weather, hydrology, soil temperature, erosion-sedimentation, nutrient and carbon cycling, tillage, dairy management practices, crop management and growth, pesticide and nutrient movement, and costs and returns of various management practices. The carbon fate and transport functions of the CENTURY model (Parton et al. 1987; Parton 1996; Cerri et al. 2004) have also been incorporated into APEX, which allows APEX to simulate carbon dynamics in the soil-plant system. APEX includes routines for detailed grazing and other land management scenarios. APEX has been utilized for a wide variety of applications in numerous countries over the past 30 years (see Gassman et al. 2010 for a review of past studies). It is particularly suited for detailed simulations where other models are incapable of the same degree of robustness.

APEX Model Application In the current study, we generated broad estimates of soil carbon sequestration and other relevant metrics by employing computer model simulations. The carbon sequestration rates were simulated using a yet-to-be-released version of APEX (APEX 1905) due to its superior capability to represent the variety of vegetation types that were identified. The basic procedure for soil carbon estimations using APEX are as follows: 1. Calibrate and validate APEX for the demonstration sites using data assembled from soil and vegetation sampling efforts. 2. Establish baseline (status-quo) soil organic matter profile and dynamics for all properties 3. Define relevant alternative scenarios for Quail Ranch. 4. Calculate soil organic carbon sequestration rates as the annual rate of change of soil organic carbon stores. 5. Calculate other additional metrics as an annual rate including runoff, and soil erosion as needed.

Baseline and Management Scenarios Baseline Scenario The baseline scenario represents the status quo at Quail Ranch. It reflects the distribution of plant species observed at the sampling locations (transects), which is largely representative of the overall plant species distribution. Based on the vegetative sampling results we also assume that a portion of the ground cover is bare, specifically 36% at Quail Ranch. To allow for appropriate comparison across all scenarios, the baseline scenario was assumed to entail no irrigation, as well as maintaining the current distribution of brush cover. Management Scenarios The following alternative scenarios were simulated for each of the ranches to determine their potential for increasing the sequestration of carbon in the soils, as well as other pertinent agronomic and environmental indicators.

A. Increased Groundcover For this scenario, the bare ground cover was reduced to zero and total vegetative cover was scaled up, while preserving the relative proportions of plant species represented at each sampling site. B. Grazing Management Another scenario was simulated to estimate the effects of grazing on the same agronomic and environmental indicators that were estimated under the baseline and other scenarios (Table 4). For this scenario, all land parcels at Quail Ranch were grazed at specific stocking rates as indicated in Table 4. Grazing was initiated on February 29 and terminated on November 28 of each year, an inconsequential selection of dates. Furthermore, a minimum amount of plant material equivalent to 0.25 mt/ha (223 lb/ac) was required for grazing to proceed in the simulations (Table 4). The plant material threshold means that cattle were automatically excluded from the field if the amount of above-ground plant biomass fell below the threshold indicated. No other restrictions were placed on livestock movement or grazing in this scenario. In other words, an open access grazing management style is implied by default. Cattle were simulated to graze the entire expanse of pasture with no rotation Table 4. Simulated grazing rates at Quail Ranch.

Stocking Rate 80 ac/head 100 ac/head

Vegetation Threshold 0.25 mt/ha 0.25 mt/ha

C. Brush Management The brush management scenario entails a pragmatic reduction in brush cover and a correspondingly pragmatic increase in the cover of other vegetation. Specifically, for this scenario it was assumed that brush cover would be reduced by 80% without any reseeding and other vegetative cover would increase by 20%. These assumptions are pragmatic in that they reflect realistic attainments within a mechanical brush management program. To simulate this scenario, 80% of existing brush cover was eliminated from APEX simulations and replaced with mostly bare ground cover, as well as a 20% and 100% increase in the cover of other vegetation. For the purposes of this study, we defined brush as including all shrub species and trees. Other plant species including forbs, subshrubs, graminoids, and succulents were 20

maintained. In addition, Quail Ranch has already performed some brush management through chemical applications of Spike 20P® (Tebuthiron). Areas treated with Spike 20P® compared to those untreated saw a reduction in bare ground from 75% to 40% and an increase in biomass from 67 lb/ac to 1,149 lbs/ac. We simulated results for all three scenarios: 20% increase, 100% increase, and recorded herbicide recovery.

D. Soil Disturbance This scenario entails a tillage pass that is intended to loosen the soil, the consequent benefit of improved aeration with the possibility of improved soil health and carbon sequestration. Due to the wide diversity of tillage options, we simulated a diversity of tillage implements in APEX and the results reported here are a simple average of those iterations. In particular, the results reported here are the simple average of the effects of the following tillage options (APEX tillage codes in parentheses): • • • •

Harrow 10 bar tine (162) Disk chisel mulch tiller (239) Offset disk/Heavy duty (240) Subsoiler (612)

E. Fertilizer Application Simulating fertilizer application entails application of inorganic fertilizer at varying rates and depths to determine the impacts on soil carbon sequestration rates. For this scenario, we simulated three separate application rates of 28-10-10, namely 200, 100, and 50 lb/ac once each year. For each application rate we also simulated a surface mode of application as well as application below the soil surface at 3-inch and 6-inch depths (Table 5). Table 5. Simulated fertilizer application of 28-10-10 at Quail Ranch.

Rate 200 lb/ac 200 lb/ac 200 lb/ac 100 lb/ac 100 lb/ac 100 lb/ac

Injection Type Surface 3 inches 6 inches Surface 3 inches 6 inches 21

50 lb/ac 50 lb/ac 50 lb/ac

Surface 3 inches 6 inches

F. Biochar and Manure Application Simulating organic fertilizer application entails application of manure and biochar at varying rates to determine the impacts on soil carbon sequestration rates. For this scenario, we simulated four separate application rates of biochar, namely 50, 100, 150, and 200 lb/ac once each year. In addition, the application of manure at a rate of 2244 lb/ac was also simulated as a one-time application each year.

Results & Discussion Vegetation The vegetation surveys at Quail Ranch were composed of 12 transects in both Fall and Spring over two years. Six transect locations were repeated during all 4 surveys. The other six transects were new locations for each event, totaling 30 transect locations. This method allows us to balance temporal replicability (i.e., changes across time) with collecting greater spatial coverage. Dominant Species Each survey event resulted in relatively similar dominant species, with Threeawns (Aristida sp.), honey mesquite (Prosopis glandulosa), creosote bush (Larrea tridentata), tobosagrass (Hilaria mutica), and sand dropseed (Sporobolus cryptandrus) being the most common (Table 6). Several grasses were dominant across all surveys (purple threeawn, tobosagrass, and sanddrop seed), especially within the repeated surveys. They also remained equally common across the four surveys. Among the woody species, creosote bush, honey mesquite, and American tarwort (Flourensia cernua) were commonly dominant species across all transects and less heavily within the repeated transects. Spurge species and broomweeds were the only dominant forbs and primarily occurred within the first fall. Overall composition among the dominant species was consistent across the timeframe.

Table 6. Top 5 dominant species during each survey based on the number of hits. The percentage of hits for each species is recorded in parentheses. Superscripts denote growth form (G=grass; W=woody shrub/tree; F=forb). A) All Transects Dominant Species – All Transects Fall 2021

Spring 2022

Fall 2022

Spring 2023

Threeawns (Aristida sp.) (9%)G Tobosagrass (Hilaria mutica) (8%)G Sand dropseed (Sporobolus cryptandrus) (8%)G Spurges (Euphorbia sp.) (8%)F Broom snakeweed (Gutierrezia sarothrae) (7%)F

Creosote bush (Larrea tridentata) (14%)W Honey mesquite (Prosopis glandulosa) (10%)W Threeawns (Aristida sp.) (9%)G Tarwort (Flourensia cernua) (8%)W Tobosagrass (Hilaria mutica) (8%)G

Purple threeawn (Aristida purpurea) (11%)G Creosote bush (Larrea tridentata) (8%)W Tobosagrass (Hilaria mutica) (8%)G Vine mesquite (Panicum obtusum) (7%)G Tarwort (Flourensia cernua) (6%)W

Purple threeawn (Aristida purpurea) (13%)G Honey mesquite (Prosopis glandulosa) (12%)W Sand dropseed (Sporobolus cryptandrus) (11%)G Tarwort (Flourensia cernua) (11%)W Burrograss (Scleropogon brevifolius) (10%)G

B) Repeated Transects Only Dominant Species – 6 Repeated Transects Fall 2021

Spring 2022

Fall 2022

Spring 2023

Threeawns (Aristida sp.) (14%)G Spurges (Euphorbia sp.) (13%)F Tobosagrass (Hilaria mutica) (9%)G Sand dropseed (Sporobolus cryptandrus) (7%)G Reverchon’s bristle (Setaria reverchonii) (6%)G

Threeawns (Aristida sp.) (13%)G Sand dropseed (Sporobolus cryptandrus) (11%)G Creosote bush (Larrea tridentata) (10%)W Tobosagrass (Hilaria mutica) (9%)G Honey mesquite (Prosopis glandulosa) (9%)W

Purple threeawn (Aristida purpurea) (17%)G Tobosagrass (Hilaria mutica) (10%)G Sand dropseed (Sporobolus cryptandrus) (7%)G Vine mesquite (Panicum obtusum) (7%)G Slimweed sandmat (Euphorbia stictospora) (7%)F

Purple threeawn (Aristida purpurea) (18%)G Sand dropseed (Sporobolus cryptandrus) (17%)G Burrograss (Scleropogon brevifolius) (11%)G Creosote bush (Larrea tridentata) (8%)W Tobosagrass (Hilaria mutica) (6%)G

Diversity The total surveys at Quail Ranch recorded 122 taxa (111 species, 2 genus-only, and 9 unknown) across 3,136 hits. To accurately compare seasonal differences in vegetation diversity, we parsed 23

out the 6 transects that were repeated in every survey. The greatest abundance and diversity occurred during Fall 2022 and the least in Spring 2023 (Table 7). Fall had greater abundance and richness than Spring because most plant activity was stimulated during and after the summer rainy season. Fall 2022 saw more recent rain than Fall 2021, which may partially explain the greater diversity. Results were similar when analyzing all 30 transect locations versus only the 6 transects that were repeated each time. Three measures of alpha diversity were calculated for each transect and survey event: richness (total number of taxa), Shannon’s Diversity Index (H’), and Simpson’s Diversity Index (1-D) (Table 4). The latter two indices include species richness in their calculations of diversity but also account for evenness (i.e., the relative abundance of each taxon in the system). Shannon’s Index uses both richness and evenness to score each site by quantifying system uncertainty. Sites with higher scores can be interpreted as having more entropy (and are thus more diverse) than sites with lower scores. By contrast, Simpson’s Diversity Index can be interpreted as the probability that two samples collected at random will belong to different species. Therefore, the more diverse an area is, the closer to 1 the score will be. Although the two indices are often highly correlated, their difference in mathematical approach means that they often produce different rankings. Table 7. Summary of diversity indices for the six repeated transects. Repeated Transects Only Fall 2021

Richness (# of taxa) 42

Abundance (# of identifications) 565

Shannon Diversity (H') 3.02

Simpson Diversity (1-D) 0.93

Spring 2022

357

2.76

0.92

Fall 2022

496

3.08

0.93

Spring 2023

356

2.78

0.91

Biomass In addition to Line-Point transects, standing vegetation biomass was collected every 10 m along each transect (Table 8). Biomass is an important indicator of rangeland production and can reflect the amount of energy stored in the vegetation. Results from these sampling events were also used as input into the APEX model to estimate current and future carbon stores. The average biomass for all surveys at Quail Ranch was 684 lbs/ac. The fall surveys recorded greater biomass than the spring. This seasonal difference is primarily driven by the monsoon rainfall patterns 24

alongside smaller, yet still significant, differences in the areas that were surveyed. There was extremely high variability in biomass within and among transects, which is unsurprising for arid and semi-arid environments since competition for water can drive aggregated dispersion of vegetation at small and large scales. Table 8. Total and mean biomass (lbs/ac) of vegetation sampled for all transects combined and for the six repeated transects only. Stat Total Mean Total Mean

Treatment All Transects Repeated Only

Fall 2021 99,165 826 58,901 981

Spring 2022 80,956 674 38,470 641

Fall 2022 98,951 824 68,260 1,137

Spring 2023 49,364 411 41,923 698

Soil We collected soil chemistry and bulk density samples along the same vegetation transects. Results from laboratory analyses are used to calibrate the APEX model to represent current conditions and are used for carbon sequestration estimates. However, analysis results are also descriptive of the current conditions. The primary soil type at our transects was TEC (Tencee and Upton soils, 1 to 8 percent slopes) followed by RaA (Reagan loam, 0 to 1 percent slopes). The laboratory results reported that the average Organic Carbon across all soil types was 1.05% with 1.82% Organic Matter (Table 9). These are typical values for arid grasslands in Texas. Every other metric was typical for the area as well. Table 9. Mean results of soil samples analyses at Quail Ranch. Organic Carbon (%)

Organic Matter (%)

Nitrates (ppm)

Electrical Conductivity (umhos/cm)

Bulk Density (g/cm3)

1.05

1.82

7.99

18.1

156

1.21

Soil types were relatively similar in laboratory results. The TOA (Tobosa clay, 0 to 1 percent slopes, occasionally ponded) and HMC (Hollomex loam, 1 to 8 percent slopes) soil types contained the greatest Organic Carbon and Organic Matter (Table 10). These areas were more productive sites near playas and contained greater clay content in the soils. However, it is worthy 25

to note only one transect was performed in each of these soils. The CrB (Conger-Reagan association, 0 to 3 percent slopes) and HMC soil types stood out with the greatest nitrate content. The two most common soil types (TEC and RaA) were in the middle of the pack of all the soils sampled. Table 10. Mean results of soil samples analyses by soil type. Soil Type TEC

Number of Transects

Organic Carbon (%)

Organic Matter (%)

Nitrates (ppm)

Electrical Conductivity (umhos/cm)

Phosphorus (ppm)

1.0

1.7

8.1

20.5

149.6

28.8

RaA

1.4

2.5

8.0

15.5

191.8

39.0

CrB

0.7

1.2

8.0

48.9

192.6

20.6

KNB

0.5

0.9

8.2

6.1

60.8

25.8

WKB

0.2

0.4

7.5

11.0

75.7

32.3

RaB

1.0

1.7

8.0

8.5

124.5

25.6

HMC

2.1

3.6

8.0

41.1

276.8

27.9

LZD

0.9

1.6

8.1

4.6

92.4

26.6

SAC

0.9

1.5

8.1

14.5

101.3

31.7

TOA

2.7

4.6

8.0

10.5

98.5

15.0

Carbon Measured vegetation and soil data were used to inform and calibrate the APEX model in order to estimate ecological metrics associated with each treatment. APEX is designed to simulate the agronomic and ecological implications of alternative practices and policies, as well as other biophysical attributes of a variety of landscapes. To ensure reasonable and robust estimations from APEX, measured data was used to calibrate the model for the intended purposes. For Quail Ranch, the vegetation types simulated were selected from species that were identified in the vegetation sampling events. Model calibrations are conducted to ensure that a model mimics as closely as possible, the real-world processes and mechanisms that it seeks to simulate. Depending upon the scope of measured data available, model output may be calibrated against specific values or averages or other appropriate statistics of these values. In the present application, data from only four sampling events were available. Consequently, the expectation was that APEX simulations would mimic the relative values of the observed ecological indicators between treatments sites, more so than actually reflecting the absolute values reported. APEX reports simulated values for hundreds of agronomic and ecological indicators at daily,

monthly, and annual time scales, as well as annual and monthly averages across the entire simulation horizon. Baseline Scenario We estimated the baseline soil carbon sequestration potential over 25 years for Quail Ranch. The 25-year simulation period was from 1998 through 2022. The baseline scenario represents the status quo at the property with current plant species, distribution, and management over 25 years (Table 11). Additional assumptions are listed within the Methods section of this report. Table 11. Mean results of carbon estimates for each property averaged over 25 years.

Quail Ranch

Sequestration Rate (mt/ha/yr) 0.025

Soil Carbon Stocks (mt/ha) 20.555

Soil Carbon Percent (%) 0.954

Our simulations indicated that Quail Ranch is currently sequestering 0.025 mt/ha/yr (0.011 ton/ac/yr) of carbon, meaning the carbon sequestration rate is essentially stable slightly above 0. This is not unusual, because broadly speaking, more arid environments with gravelly and sandy soils have a lower carbon storage capacity. Soil carbon stocks are currently estimated at 20.555 mt/ha (9.151 ton/ac) with 0.954% carbon in the soil. The estimates averaged over 25 years will be used as a baseline to compare management actions. Carbon sequestration rates were estimated for soil and ecological sites on Quail Ranch (Figure 4) as well as the other study sites, namely McGill, Neal, and Cross L. Based on analysis of model results, greater carbon sequestration rates were found to be associated with areas with lower percentage of sand content and higher precipitation rates. The maps displayed in Figure 4 highlight average annual soil carbon sequestration rates for the study sites. While computer model calibration efforts focused primarily on Quail Ranch, these simulations were performed on selected points in the other sites to determine the overall carbon sequestration profile of the area. In general, sequestration rates were low across the area, with some locations associated with higher-than-average carbon sequestration rates, while others were associated with negative carbon sequestration rates. Virtually without exception, and as expected, bare ground patches were associated with much lower or even negative soil carbon sequestration rates, while areas with good ground cover tended to exhibit higher rates of carbon sequestration.

Figure 4. Carbon sequestration rates on ecological sites at Quail Ranch.

Management Scenarios ConocoPhillips can select which management actions to apply to their properties with the goal of maximizing carbon sequestration and productivity of their lands. At the request of ConocoPhillips, we modeled the estimated soil carbon sequestration potential over the 25-year simulation horizon for each of the following management actions: • • • • • •

Increased groundcover Grazing management Brush management Soil disturbance Fertilizer application Biochar and manure application

Increased Groundcover Groundcover is any material that covers the soil. Effective groundcover management techniques favor the growth and regeneration of perennial species and are critical to maintaining healthy grasslands. Increasing ground cover has multiple benefits to increase productivity and a healthy ecosystem including, protection from wind erosion, increasing biodiversity, reducing water loss, and increased carbon sequestration. Continuous groundcover reduces soil temperature fluctuations and results in increased biomass, ultimately increasing soil carbon sequestration potential (Li 2017). Increasing ground cover can be done in multiple ways including managing grazing, removing invasive species, irrigation, and reseeding. To estimate the potential soil carbon sequestration from range planting, we increased ground cover to 100% in the model. By doing this, we are not assuming any specific management action, rather we are showing the carbon sequestration benefits of implementing actions that improve ground cover and reduce bare ground. In particular, we simulated an increase in ground cover such that the current vegetation distribution is scaled up to entirely eliminate bare ground cover. The implications of this scenario for soil carbon levels and sequestration rates are contrasted with corresponding values for the baseline in Table 12. The results indicate that soil carbon sequestration rates would increase from 0.02 mt/ha/yr to over 0.06 mt/ha/yr (0.03 ton/ac/yr). Consequently, soil carbon stocks would rise both in absolute mass (stocks) and as a percentage of total soil mass (percent). 29

Table 12. Mean carbon sequestration estimates of areas sampled averaged over 25-year simulation with current ground cover and with ground cover increased to 100%.

Baseline 100% Cover

Sequestration Rate (mt/ha/yr) 0.025 0.064

Soil Carbon Stocks (mt/ha) 20.555 21.039

Soil Carbon Percent (%) 0.954 0.965

Grazing Management Selection of proper grazing management is important to attain economic benefits while maintaining the health of rangeland and grazing animals. Grazers significantly impact the carbon balance of grasslands through effects on vegetation type, organic matter inputs to the soil microbial community, and soil structure through trampling. The intensity and timing of grazing influence the removal of vegetation and carbon allocation to roots and in turn influence soil carbon. Because of the many types of grazing practices and the diversity of plant species, soils, and climates, the effects of grazing are inconsistent. However, improved grazing management can have a multitude of benefits. For example, Conant et al. (2017) reported that soil carbon sequestration can be increased on average by 0.28 mt/ha/yr following improved grazing practices (including lower stocking rates, seasonal grazing and rotational or short-duration grazing). To estimate the potential soil carbon sequestration for this activity, we estimated the impacts grazing would have on current ground cover, assuming a range of stocking rates from 80 acres/cow to 100 acres/cow. For these iterations, we assumed that a minimum biomass threshold of 0.25 mt/ha (223 lb/ac) is required to introduce the cattle to the pasture for grazing. The results of this simulation exercise are presented in Table 13. The results of the stocking rate iterations indicate that, as expected, soil carbon health is optimized at a moderate stocking rate. Much higher or much lower stocking rates lead to lower carbon sequestration rates. For the stocking rate iterations reported here, a stocking rate of about 80 acres/cow appears to be optimal. An assumption of this model is that grazers have no preference on forage available. This is, of course, not realistic to preferences of livestock and therefore actual stocking rates would need to be adjusted as necessary. Higher and lower stocking rates in this model output, result in lower soil carbon levels and sequestration rates. All these stocking rate scenarios were simulated with the same (0.25 mt/ha) minimum biomass threshold. 30

Table 13. Simulated soil carbon indicators under alternative grazing scenarios. Stocking Rate

Vegetation Threshold Baseline (no grazing) --------80 ac/head 0.25 mt/ha 100 ac/head 0.25 mt/ha

Sequestration Rate (mt/ha/yr) 0.025 0.075 0.073

Soil Carbon Stocks (mt/ha) 20.555 21.178 21.151

Soil Carbon Percent (%) 0.954 0.968 0.968

We repeated the stocking rate simulations with 0.5 mt/ha and 0.1 mt/ha biomass thresholds for grazing. The results did not change at all, indicating that for the climate, soil, and management conditions present at Quail Ranch, these threshold levels were not binding. In general, the results indicate that grazing is beneficial to the health of the soil ecosystem. Regardless of the stocking rate chosen, the output of the computer model simulations suggest that some grazing is better than no grazing. Nonetheless, admittedly, the results are predicated upon the details of the specific grazing management scenario being considered.

Brush Management The encroachment of woody plants into grasslands has been considered a potentially large carbon sink due to above-ground biomass and deep root systems (Dean et al. 2015). However, brush management can have variable effects on soil organic carbon with studies showing increased neutral and negative impacts to carbon stocks (Hughes et al. 2006; Daryanto et al. 2013; Dean et al. 2015). Reasons for these inconsistencies are still unknown and largely dependent on study location and management practice. In addition, brush management includes multiple techniques and tools, and the direction and extent of impacts to soil organic carbon will vary on the type of soil disturbances caused, treatment efficacy, and the extent to which they co-occur with other land use practices such as livestock grazing. To further complicate matters, different brush management treatments may be applied in combination or sequentially. Treatments that minimally disturb soils (e.g. herbicide applications and prescribed burning) may have one set of effects, whereas those characterized by extensive physical disturbance to the soil surface (e.g. root plowing, grubbing, chaining) would be expected to have quite different effects.

To estimate the potential soil carbon sequestration of this activity, we assumed an 80% reduction of brush using mechanical means with a coincident result of a 20% and 100% increase in herbaceous cover. Removal was targeted towards all shrub and tree species and maintained forbs, subshrubs, grasses, and succulents. In addition, Quail Ranch has already performed some brush management through chemical applications of Spike 20P® (Tebuthiron). We utilized data from those results to simulate another scenario of recorded herbicide application. Over the 25-year simulation horizon, mechanical removal of brush is shown to have negative impacts on soil carbon sequestration rates and carbon stores with only a 20% herbaceous recovery (Table 14). Of course, the simulated scenario with 100% increase in herbaceous cover results in positive impacts to carbon sequestration. However, this type of recovery is often unobtainable. Herbicide applications also had positive impacts to soil carbon. The results largely reflect the implications of increasing bare ground cover as opposed to brush cover; a 20% increase in herbaceous cover is not enough recovery to mitigate the losses from mechanically removing brush. However, chemically treating brush with Spike 20P® has shown positive impacts to above ground biomass, bare ground reduction, and soil carbon. Table 14. Simulated mean soil carbon levels and sequestration rates for brush management without planting, as compared to the baseline scenario.

Baseline 20% Recovery 100% Recovery Recorded Herbicide Recovery

Sequestration Rate (mt/ha/yr) 0.025 0.018 0.041 0.038

Soil Carbon Stocks (mt/ha) 20.555 20.458 20.750 20.705

Soil Carbon Percent (%) 0.954 0.952 0.958 0.957

Soil Disturbance The conversion of natural vegetation into arable land leads in most cases to a loss of soil organic carbon, and increased frequency may aggravate these effects (Alexander et al. 2015). No-till and reduced tillage practices have been promoted to increase soil carbon storage because it reduces decomposition and release of CO2. However, root growth slows down with decreasing soil oxygen concentration and aeration of soils promotes root growth and hence soil organic carbon into the soil (Columbi et al. 2018). The effects of different soil disturbance practices to promote carbon sequestration is a topic currently being evaluated. 32

Depending upon the specific tillage mechanism used, soil disturbance can improve aeration and lead to productivity improvements. Due to the wide variety of tillage implements that could be used, we compare with the baseline, the average (Table 15) of the results obtained from an iteration of four alternative tillage implements, as specified above.

Table 15. Simulated mean soil carbon levels and sequestration rates for the soil disturbance scenario, as compared to the baseline.

Baseline Subsoiler Harrow Disk Chisel Offset Disk

Sequestration Rate (mt/ha/yr) 0.025 0.028 0.027 0.003 -0.021

Soil Carbon Stocks (mt/ha) 20.555 20.586 20.570 20.276 19.976

Soil Carbon Percent (%) 0.954 0.955 0.954 0.947 0.940

Soil disturbance will have an initial release of carbon following the practice implementation. However, our results indicate that soil disturbance with a subsoiler or harrow results in greater soil carbon levels over 25 years. While the disk chisel and offset disk do not recover to the original undisturbed level over that time. A one-time disturbance at deeper soil depths from the subsoiler and harrow may increase aeration and water infiltration into the soil resulting in increased root biomass and carbon storage. Soil carbon did not share the same benefits from shallow disturbances.

Fertilizer Application Inorganic fertilizers have been widely applied to increase plant productivity in grassland and cropland ecosystems. However, in grasslands this practice often alters plant community and composition by increasing aboveground biomass but decreasing diversity (Shi et al. 2024). Inorganic fertilizer has been shown to increase soil organic carbon by 2% (Shi et al. 2024). However, rangelands in semi-arid and arid environments have shown little to no effects from added nutrients (McClelland et al. 2022, Phillips et al. 2020, Lü et al. 2014).

To estimate the effect fertilizer would have at Quail Ranch, we simulated alternative application rates (50 lb/acre, 100 lb/acre, and 200 lb/acre) of 28-10-10. According to our model, fertilizer application to Quail Ranch is expected to yield minimal benefits (Table 16). The impacts are minimal regardless of whether the fertilizer was applied on the soil surface, at 3 inches depth, or at 6 inches depth.

Table 16. Simulated mean carbon levels and sequestration rates for alternative inorganic fertilizer treatments. Application Rate (lb/ac) Baseline (none) 200 lb/ac 200 lb/ac 200 lb/ac 100 lb/ac 100 lb/ac 100 lb/ac 50 lb/ac 50 lb/ac 50 lb/ac

Depth (inches) NA Surface 3 inches 6 inches Surface 3 inches 6 inches Surface 3 inches 6 inches

Sequestration Rate (mt/ha/yr) 0.025 0.051 0.051 0.051 0.034 0.033 0.034 0.033 0.033 0.033

Soil Carbon Stocks (mt/ha) 20.555 20.872 20.871 20.871 20.656 20.650 20.658 20.649 20.646 20.649

Soil Carbon Percent (%) 0.954 0.961 0.961 0.961 0.956 0.956 0.956 0.956 0.956 0.956

The results shown here for various fertilizer treatments, may be largely predicated upon the fact that precipitation rates are very low in that region. The results may be quite different if the scenarios were simulated in an area with higher precipitation levels or on fields that are irrigated.

Biochar and Manure Application Organic fertilizers derived from animal/plant residues and excrement, supply organic matter and nutrients to soil, thus increasing soil organic carbon (SOC) storage, water-holding capacity, and microflora while decreasing soil evaporative water loss (Cai et al. 2019). In contrast, inorganic fertilizers are chemically synthesized and may lack the capacity to improve certain soil properties as effectively as organic fertilizer can (Cai et al. 2019). Thus, organic fertilization differs from inorganic fertilization in its influence on soil and vegetation conditions. Organic fertilization has been shown to increase aboveground biomass (56%), species richness and evenness (10%), and soil organic carbon (19%) in grasslands (Shi et al. 2024). Both 34

inorganic and organic fertilizers increased aboveground biomass, but organic fertilizers showed a greater increase in soil organic carbon while also increasing species richness (Shi et al. 2024). However, rangelands in semi-arid and arid environments have also shown little to no effects from added nutrients (McClelland et al. 2022, Phillips et al. 2020, Lü et al. 2014). To estimate the effect organic fertilizer would have at Quail Ranch, we simulated alternative application rates of biochar and manure. According to our model, fertilizer application to Quail Ranch is expected to yield minimal benefits similar to that of inorganic fertilization (Table 17).

Table 17. Simulated mean carbon levels and sequestration rates for organic fertilizer treatments. Fertilizer

Baseline (none) Biochar Biochar Biochar Biochar Manure

Application Rate (lb/ac) NA 50 lb/ac 100 lb/ac 150 lb/ac 200 lb/ac 2,244 lb/ac

Sequestration Rate (mt/ha/yr) 0.025 0.051 0.051 0.044 0.043 0.044

Soil Carbon Stocks (mt/ha) 20.555 20.876 20.875 20.785 20.773 20.788

Soil Carbon Percent (%) 0.954 0.961 0.961 0.959 0.959 0.959

While impacts to soil carbon are minimal at Quail Ranch, the biochar and manure can have a similar impact to inorganic fertilizer at much lower rates. Once again, these results are likely predicated on the low amount of precipitation in the area. In areas with higher precipitation levels, there is much greater potential for increased biomass and soil carbon sequestration.

Summary Improved management activities can have impacts on soil carbon stores and sequestration rates at Quail Ranch. There are many reasons for this but the simplest is that Quail Ranch is essentially starting from zero, and hence has more opportunity for short-lived increases in soil carbon sequestration. However, the sandy and gravel soil types prevalent there do not store carbon well and minimal rainfall further limits the amount of biomass and ground cover that is available. Therefore, implementing strategies that conserve biodiversity, decrease bare ground, and prevent soil erosion will have the greatest impacts to soil carbon storage.

As expected, increasing vegetation cover and reducing bare ground, will have postive impacts to soil carbon stocks and percent of soil that is carbon. Increasing herbage mass from vegetation will increase the amount of organic matter in the soil, reduce soil temperature and erosion, increase soil water retention, and ultimately increase the potential for carbon storage in the soil (Li 2017). Increasing ground cover can be done in multiple ways including reseeding, managing grazing, removing invasive species, and irrigation. However, the increasing groundcover did not show the greatest impacts to carbon sequestration rates. Our model reported greatest carbon sequestration rates occurring with implementing grazing at a stocking rate of 80 acres per animal unit. Carbon sequestration occurs when vegetation is green and actively growing. While grazing is removing the above ground carbon, it is also requiring the vegetation to regrow while adding organic material to the soil and in return actively sequestering more carbon than if it was allowed to grow untouched. A similar explanation can be used to explain why certain soil disturbance was estimated to sequester more carbon than undisturbed lands. Carbon will be released from the soil following a disturbance practice. However, carbon will begin to sequester over time as vegetation reestablishes, depending on the extent of the disturbance. Our results indicated that a subsoiler and harrow positively impacted soil carbon while a disk chisel and offset disk indicated negative impacts over 25 years. Deeper disturbance from the subsoiler and harrow increased aeration and water infiltration into the soil resulting in increased root biomass and carbon storage. Soil carbon did not share the same benefits from shallow disturbances. Our results estimated that chemical brush management with Spike 20P® would have positive impacts to soil carbon at Quail Ranch. Previous treatments at Quail Ranch have resulted in an increase in above-ground biomass and a reduction in bare ground, which in turn will have long lasting effects to soil carbon. However, our simulated mechanical brush removal estimated to have negative effects to soil carbon. Reducing brush by 80% would have an initial negative effect towards current carbon stores due to a loss of groundcover and increase in bare ground, and a 20% increase in herbaceous cover was not enough to restore it back to original undisturbed levels over 25 years. However, when removed brush resulted in a 100% increase in herbaceous cover there were great positive impacts. Implementing practices that promote increased groundcover (e.g. reseeding, irrigation, fertilization) after a brush treatment will help mitigate 36

those losses and improve soil carbon if herbaceous cover returns over 20%. We also suggest that a mosaic of smaller treated patches, on the more productive soils, implemented ahead of El Niño years, may reduce overall risk and possibly improve herbaceous recovery in a given brush treatment. The application of inorganic or organic fertilizers to rangeland would have minimal benefits for soil carbon without increased rainfall or irrigation. However, an application rate of 50 lb/ac of biochar will have similar results on soil carbon compared to 200 lb/ac of synthetic fertilizer with the potential additional benefit of preserving species diversity. McClelland et al. (2022) reported similar results using organic compost fertilizer. Soil organic carbon stocks increased 0.5 Mg ha/yr in the top 10 cm of soil in an irrigated pasture but saw little to no effect on degraded rangelands (McClelland et al 2022). Rangelands represent a vast store of carbon, both in soil and vegetation, but general conclusions of rangeland-related climate research are that carbon sequestration potential depends greatly on appropriate management of the lands, minimizing degradation or desertification, and restoration of degraded areas. Restoration of arid-region rangelands is extremely difficult and variability in precipitation throughout most U.S. rangelands adds additional uncertainty to the carbon sequestration regulating service provided by these lands. Although rangelands can contribute to carbon sequestration, the generally low productivity of arid rangelands also means that their sequestration potential is lower than other types of land. In conclusion, regardless of the approach taken to sequester soil carbon it is challenging to untangle carbon sequestration benefits from other co-benefits such as better soil quality, water infiltration and storage capacity, plant productivity, and ecosystem resilience, which may lead to improved economic efficiency of a ranching operation. As a result, it can be difficult to look at sequestration benefits in isolation. Soil health is defined as the continued capacity of soil to function as a vital living ecosystem that sustains plants, animals, and humans. From a natural resources’ perspective, those carbon sequestration and storage management strategies that tend to conserve or restore functioning native rangeland ecology or incentivize holistic rangeland stewardship long-term, will be the most beneficial and will yield additional benefits beyond carbon credit development. Increasing soil health and implementing practices to do so, can provide opportunity for participating in carbon markets. 37

Recommendations The potential for carbon sequestration is highly variable as several factors influence the sequestration potential of soil, including (1) biophysical characteristics, such as soil (depth and texture), plant species, and climatic conditions, and (2) agricultural practices and land use. In general, soil organic carbon is greater with increased precipitation and decreases with increasing temperature. Under similar climatic conditions, soil texture is the next most important factor in soil organic carbon storage and decomposition. Spatial variation in these factors results in sitespecific rates of carbon capture. The effectiveness of any one management practice will, therefore, differ at each property with implications for the economic feasibility of soil carbon sequestration. In addition, soil carbon is volatile and any changes or reversal of practices implemented to sequester soil carbon can lead to its rapid release (Kim and McCarl 2009; Kim et al. 2008). Soil also has a finite capacity for carbon sequestration, as equilibrium is often reached in 10–15 years; carbon accumulation ceases, but then the issue arises of maintaining the stored carbon stock. Quail Ranch has potential for transient improvements in soil carbon percent but minimal potential for carbon credits. Because Quail Ranch is starting from virtually zero carbon sequestration, any improvements could have impacts to soil carbon. However, these improvements are extremely difficult in west Texas with little rainfall. Arid landscapes in west Texas have very little soil carbon storage capacity due to climate and soil type. So, protecting the carbon that is already on the landscape is our key recommendation. This includes: • •

•

Carbon Market Recommendations Globally, there is a growing voluntary credit and offset carbon market, and the forestry and renewable energy industries have become important providers of offsets. Until very recently, nature-based offsets have primarily focused on forests. However, grasslands can provide similar 38

carbon sequestration potential through below ground biomass and long-term resiliency. This has led to the same progression of grassland-based carbon credits on the voluntary markets as those for forestry-based operations by focusing on protection and preservation. Restoration based projects are available, but Quail Ranch may not benefit much from such a market. Therefore, offset projects that focus on preventing the conversion of rangelands may be the best opportunity for ConocoPhillips. Ecosystem services available to rangeland landowners now extends past livestock production as we are currently seeing with the development of carbon markets. We are also beginning to value other services like wildlife habitat, recreation, watershed functions, and biodiversity conservation. Limited marketing opportunities are emerging to support these activities. For example, Audubon has developed a Conservation Ranching Initiative that empowers customers to make a difference by purchasing their beef from grassland bird friendly lands. Participation in emerging nature-based carbon markets provides an option for consideration. However, caution should be taken related to the value of income generated from potential carbon-based products and the inputs necessary to achieve the benefits. These markets remain relatively immature and are evolving very quickly.

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Appendix 1: Plant Species Lists Species lists for each of the four survey events. These include the number of hits (i.e. the number of times each species intercepted the drop pole), % total (number of hits / total hits across all species *100), and growth form.

Table A1. Total number of vegetation identifications at Quail Ranch during Fall 2021 surveys in October. Common Name Threeawns Tobosagrass Sand dropseed Spurges Broom snakeweed Rio Grande bristlegrass American tarwort Honey mesquite Creosote bush Black grama Squirreltail Woody crinklemat Bush muhly Low silverbush Prairie verbena Fall witchgrass Lehmann lovegrass Crotons Narrowleaf spiderling Mormon tea Vine mesquite Mesa dropseed Lacy tansyaster Arrow-leaf mallow Gray's feverfew Slim tridens Buffalo grass Javelina bush Mariola

Species Aristida sp. Pleuraphis mutica Sporobolus cryptandrus Euphorbia sp. Gutierrezia sarothrae Setaria reverchonii ssp. ramiseta Flourensia cernua Prosopis glandulosa Larrea tridentata Bouteloua eriopoda Elymus elymoides Tiquilia canescens Muhlenbergia porteri Ditaxis humilis Glandularia bipinnatifida Digitaria cognata Eragrostis lehmanniana Croton sp. Boerhavia linearifolia Ephedra viridis Panicum obtusum Sporobolus flexuosus Machaeranthera pinnata Malvella sagittifolia Parthenium confertum Tridens muticus Bouteloua dactyloides Condalia ericoides Parthenium incanum

Form Graminoid Graminoid Graminoid Forb Forb Graminoid

Total Hits 89 76 76 75 70 63

% Total 9.00% 7.70% 7.70% 7.60% 7.10% 6.40%

Woody Woody Woody Graminoid Graminoid Forb Graminoid Forb Forb Graminoid Graminoid Forb Forb Other Graminoid Graminoid Forb Forb Forb Graminoid Graminoid Woody Forb

61 61 59 44 35 34 25 24 20 13 12 11 9 7 7 7 6 6 6 6 5 5 5

6.20% 6.20% 6.00% 4.50% 3.50% 3.40% 2.50% 2.40% 2.00% 1.30% 1.20% 1.10% 0.90% 0.70% 0.70% 0.70% 0.60% 0.60% 0.60% 0.60% 0.50% 0.50% 0.50%

Common Name Fourwing saltbush Buffalo gourd Tasajillo Desert fluffgrass Blackfoot daisy Smallflower groundcherry Acacias Texas croton Sedges Common sotol Engelmann's daisies Rushpeas Spreading fanpetals Angel's trumpets Thistles Purple dalea Common stork's bill Scarlet guara Sand muhly Plains bristlegrass Lotebush Dwarf desertpeony Trailing windmills Hairy grama Leatherweed Swampprivets Elbow bush Fendler's bladderpod Russian thistle Woolly tidestromia Unknown grass

Species Atriplex canescens Cucurbita foetidissima Cylindropuntia leptocaulis Dasyochloa pulchella Melampodium leucanthum Physalis cinerascens Acacia sp. Croton texensis Cypersus sp. Dasylirion wheeleri Engelmannia sp. Hoffmannseggia sp. Sida abutifolia Acleisanthes longiflora Cirsium sp. Dalea lasiathera Erodium cicutarium Gaura coccinea Muhlenbergia arenicola Setaria vulpiseta Ziziphus obtusifolia Acourtia nana Allionia incarnata Bouteloua hirsuta Croton pottsii Forestiera sp. Forestiera angustifolia Lesquerella fendleri Salsola kali Tidestromia lanuginosa Unknown grass

Form Woody Forb Other Graminoid Forb Forb Woody Forb Graminoid Other Forb Forb Forb Forb Forb Forb Forb Forb Graminoid Graminoid Woody Forb Forb Graminoid Forb Woody Woody Forb Forb Forb Graminoid

Total Hits 4 4 4 4 4 4 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1

% Total 0.40% 0.40% 0.40% 0.40% 0.40% 0.40% 0.30% 0.30% 0.30% 0.30% 0.30% 0.30% 0.30% 0.20% 0.20% 0.20% 0.20% 0.20% 0.20% 0.20% 0.20% 0.10% 0.10% 0.10% 0.10% 0.10% 0.10% 0.10% 0.10% 0.10% 0.10%

Table A2. Total number of vegetation identifications at Quail Ranch during Spring 2022 surveys in April. Common Name Creosote bush Honey mesquite Threeawns American tarwort Tobosagrass Lehmann lovegrass Burrograss Sand dropseed Vine mesquite Broom snakeweed Woody crinklemat Bush muhly Unknown forb Plains lovegrass Dwarf desertpeony Squirreltail Asters Hall's panicgrass Black grama Red grama Sand muhly Mormon tea Curly mesquite grass Silverleaf nightshade Mesa dropseed Buffalo grass Blue grama Plains bristlegrass Slim tridens Escobilla butterflybush Asparaguses Low silverbush Crotons Prickly pear cacti Witch grass Unknown grass Acacias Weakleaf bur ragweed Silver bluestem

Species Larrea tridentata Prosopis glandulosa Aristida sp. Flourensia cernua Pleuraphis mutica Eragrostis lehmanniana Scleropogon brevifolius Sporobolus cryptandrus Panicum obtusum Gutierrezia sarothrae Tiquilia canescens Muhlenbergia porteri Unknown forb Eragrostis intermedia Acourtia nana Elymus elymoides Aster sp. Panicum hallii Bouteloua eriopoda Bouteloua trifida Muhlenbergia arenicola Ephedra viridis Hilaria belangeri Solanum elaeagnifolium Sporobolus flexuosus Bouteloua dactyloides Bouteloua gracilis var. stricta Setaria vulpiseta Tridens muticus Buddleja scordioides Dasylirion sp. Ditaxis humilis Croton sp. Opuntia sp. Panicum capillare Unknown grass Acacia sp. Ambrosia confertiflora Bothriochloa laguroides

Form Total Hits % Total Woody 106 14.00% Woody 74 9.70% Graminoid 68 9.00% Woody 62 8.20% Graminoid 62 8.20% Graminoid 52 6.90% Graminoid 52 6.90% Graminoid 42 5.50% Graminoid 30 4.00% Forb 26 3.40% Forb 20 2.60% Graminoid 17 2.20% Forb 13 1.70% Graminoid 12 1.60% Forb 11 1.40% Graminoid 11 1.40% Forb 8 1.10% Graminoid 8 1.10% Graminoid 7 0.90% Graminoid 6 0.80% Graminoid 6 0.80% Other 5 0.70% Forb 5 0.70% Forb 5 0.70% Graminoid 5 0.70% Graminoid 4 0.50% Graminoid 4 0.50% Graminoid 4 0.50% Graminoid 4 0.50% Woody 3 0.40% Other 3 0.40% Forb 3 0.40% Forb 2 0.30% Other 2 0.30% Graminoid 2 0.30% Graminoid 2 0.30% Woody 1 0.10% Forb 1 0.10% Graminoid 1 0.10%

Common Name Hooded windmill grass Yellowspine thistle Swampprivets Elbow bush Prairie verbena Crown of thorns Plantains Mexican hat Spreading fanpetals Arrow-leaf mallow

Species Chloris cucullata Cirsium ochrocentrum Forestiera sp. Forestiera angustifolia Glandularia bipinnatifida Koeberlinia spinosa Plantago sp. Ratibida columnifera Sida abutifolia Malvella sagittifolia

Form Total Hits % Total Graminoid 1 0.10% Forb 1 0.10% Woody 1 0.10% Woody 1 0.10% Forb 1 0.10% Woody 1 0.10% Forb 1 0.10% Forb 1 0.10% Forb 1 0.10% Forb 1 0.10%

Table A3. Total number of vegetation identifications at Quail Ranch during Fall 2022 surveys in September. Common Name Purple threeawn Creosote bush Tobosagrass Vine mesquite American tarwort Honey mesquite Burrograss Slimseed sandmat Sand dropseed Sixweeks grama Bush muhly Silverleaf nightshade Buffalo grass Prostrate spurge Black grama Low silverbush Prickly-leaf dogweed Texas blueweed Berlandier's wolfberry Trailing windmills Woody crinklemat Maltese star-thistle Lehmann lovegrass Spreading fanpetals Blackfoot daisy Hall's panicgrass Texas stork's bill

Species Aristida purpurea Larrea tridentata Pleuraphis mutica Panicum obtusum Flourensia cernua Prosopis glandulosa Scleropogon brevifolius Euphorbia stictospora Sporobolus cryptandrus Bouteloua barbata Muhlenbergia porteri Solanum elaeagnifolium Bouteloua dactyloides Euphorbia prostrata Bouteloua eriopoda Ditaxis humilis Thymophylla acerosa Helianthus ciliaris Lycium berlandieri Allionia incarnata Tiquilia canescens Centaurea melitensis Eragrostis lehmanniana Sida abutifolia Melampodium leucanthum Panicum hallii Erodium texanum

Form Total Hits % Total Graminoid 88 10.70% Woody 68 8.30% Graminoid 62 7.60% Graminoid 60 7.30% Woody 46 5.60% Woody 45 5.50% Graminoid 40 4.90% Forb 39 4.80% Graminoid 37 4.50% Graminoid 32 3.90% Graminoid 30 3.70% Forb 25 3.00% Graminoid 23 2.80% Forb 18 2.20% Graminoid 15 1.80% Forb 13 1.60% Forb 11 1.30% Forb 10 1.20% Woody 10 1.20% Forb 9 1.10% Forb 9 1.10% Forb 8 1.00% Graminoid 8 1.00% Forb 8 1.00% Forb 7 0.90% Graminoid 7 0.90% Forb 6 0.70%

Common Name Cowpen daisy Green carpetweed Slim tridens Common stork's bill Sawtooth sandmat Green prairie coneflower Tumblegrass Unknown grass Mat amaranth Wild rue Purple groundcherry Narrowleaf globemallow Drummond's onion Silver bluestem Hairy five eyes Old man's beard Feather pappusgrass Plains lovegrass Scarlet guara Scurfymallow Smallflower groundcherry Ivyleaf groundcherry Mintweed Mesa dropseed Unknown forb Angel's trumpets Fourwing saltbush Javelina bush Tasajillo Dwarf prairie clover Desert fluffgrass Mormon tea Fragrant heliotrope Indian rushpea Buffalobur Unknown plant Lotebush

Species Verbesina encelioides Mollugo verticillata Tridens muticus Erodium cicutarium Euphorbia serrula Ratibida tagetes Schedonnardus paniculatus Unknown grass Amaranthus blitoides Peganum harmala Quincula lobata Sphaeralcea angustifolia Allium drummondii Bothriochloa saccharoides Chamaesaracha sordida Clematis drummondii Enneapogon desvauxii Eragrostis intermedia Gaura coccinea Malvella lepidota Physalis cinerascens Physalis hederifolia Salvia reflexa Sporobolus flexuosus Unknown forb Acleisanthes longiflora Atriplex canescens Condalia ericoides Cylindropuntia leptocaulis Dalea nana Dasyochloa pulchella Ephedra viridis Heliotropium greggii Hoffmannseggia glauca Solanum rostratum Unknown plant Ziziphus obtusifolia

Form Total Hits % Total Forb 6 0.70% Forb 5 0.60% Graminoid 5 0.60% Forb 4 0.50% Forb 4 0.50% Forb 4 0.50% Graminoid 4 0.50% Graminoid 4 0.50% Forb 3 0.40% Forb 3 0.40% Forb 3 0.40% Forb 3 0.40% Forb 2 0.20% Graminoid 2 0.20% Forb 2 0.20% Forb 2 0.20% Graminoid 2 0.20% Graminoid 2 0.20% Forb 2 0.20% Forb 2 0.20% Forb 2 0.20% Forb 2 0.20% Forb 2 0.20% Graminoid 2 0.20% Forb 2 0.20% Forb 1 0.10% Woody 1 0.10% Woody 1 0.10% Other 1 0.10% Forb 1 0.10% Graminoid 1 0.10% Other 1 0.10% Forb 1 0.10% Forb 1 0.10% Forb 1 0.10% Unknown 1 0.10% Woody 1 0.10%

Table A4. Total number of vegetation identifications at Quail Ranch during Spring 2023 surveys in May. Common Name Purple threeawn Honey mesquite Sand dropseed American tarwort Burrograss Creosote bush Berlandier's wolfberry Tobosagrass Bush muhly Black grama Buffalo grass Tumblegrass Low silverbush Lotebush Plains dozedaisy Fourwing saltbush Scarlet guara Mexican hat Bristlegrasses Woody crinklemat Unknown grass Turkeypeas Yellowspine thistle Plains lovegrass Texas blueweed Vine mesquite Stiff greenthread Sixweeks grama Crown of thorns Blackfoot daisy Gray's feverfew Slim tridens Dwarf deserpeony Trailing windmills Maltese star-thistle Mormon tea White ratany Shaggyfruit pepperweed Fendler's bladderpod

Species Aristida purpurea Prosopis glandulosa Sporobolus cryptandrus Flourensia cernua Scleropogon brevifolius Larrea tridentata Lycium berlandieri Pleuraphis mutica Muhlenbergia porteri Bouteloua eriopoda Bouteloua dactyloides Schedonnardus paniculatus Ditaxis humilis Ziziphus obtusifolia Aphanostephus ramosissimus Atriplex canescens Gaura coccinea Ratibida columnifera Setaria sp. Tiquilia canescens Unknown grass Astragalus nuttallianus Cirsium ochrocentrum Eragrostis intermedia Helianthus ciliaris Panicum obtusum Thelesperma filifolium Bouteloua barbata Koeberlinia spinosa Melampodium leucanthum Parthenium confertum Tridens muticus Acourtia nana Allionia incarnata Centaurea melitensis Ephedra viridis Krameria grayi Lepidium lasiocarpum Lesquerella fendleri

Form Total Hits % Total Graminoid 67 13.00% Woody 63 12.20% Graminoid 59 11.40% Woody 57 11.00% Graminoid 51 9.90% Woody 39 7.50% Woody 34 6.60% Graminoid 33 6.40% Graminoid 25 4.80% Graminoid 16 3.10% Graminoid 16 3.10% Graminoid 13 2.50% Forb 7 1.40% Woody 7 1.40% Forb 6 1.20% Woody 5 1.00% Forb 5 1.00% Forb 5 1.00% Graminoid 5 1.00% Forb 5 1.00% Graminoid 5 1.00% Forb 4 0.80% Forb 4 0.80% Graminoid 3 0.60% Forb 3 0.60% Graminoid 3 0.60% Forb 3 0.60% Graminoid 2 0.40% Woody 2 0.40% Forb 2 0.40% Forb 2 0.40% Graminoid 2 0.40% Forb 1 0.20% Forb 1 0.20% Forb 1 0.20% Other 1 0.20% Woody 1 0.20% Forb 1 0.20% Forb 1 0.20%

Common Name Scurfymallow White four o'clock Prickly pear cacti Wild rue Pope's phacelia Redseed plantain Streambed bristlegrass Silverleaf nightshade Spear globemallow Unknown forb Fanleaf vervain

Species Malvella lepidota Mirabilis albida Opuntia sp. Peganum harmala Phacelia popei Plantago rhodosperma Setaria leucopila Solanum elaeagnifolium Sphaeralcea hastulata Unknown forb Verbena plicata

Form Total Hits % Total Forb 1 0.20% Forb 1 0.20% Other 1 0.20% Forb 1 0.20% Forb 1 0.20% Forb 1 0.20% Graminoid 1 0.20% Forb 1 0.20% Forb 1 0.20% Forb 1 0.20% Forb 1 0.20%