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Multiple Benefits from Agricultural and Natural Land Covers in the Central Valley, CA

Citation

Peterson, Caitlin; Marvinney, Elias; Dybala, Kristen (2020), Multiple Benefits from Agricultural and Natural Land Covers in the Central Valley, CA, Dryad, Dataset, https://doi.org/10.25338/B8061X

Abstract

The data and code provided in this repository are associated with the technical report on the "Multiple Benefits from Agricultural and Natural Land Covers" project and were prepared by the authors for the Migratory Bird Conservation Partnership. MBCP partner organizations include The Nature Conservancy, California Audubon, and Point Blue Conservation Science.

Executive Summary

The Central Valley of California is one of the most heavily modified landscapes in the world, with millions of acres of semi-arid grassland and desert transformed into an irrigated crop production powerhouse through large-scale infrastructure and irrigation projects. Despite its reputation as an agricultural “sacrifice zone”, it remains an area of conservation focus for its varied, unique, and vibrant ecosystems, from rare vernal pools and serpentine grasslands to the extensive networks of riverine systems, riparian forests, and wetlands that converge at the Sacramento-San Joaquin Delta. While the importance of these natural areas for human-valued functions such as water supply and quality regulation, biodiversity, culture, and recreation is well established, the dominance of agricultural land covers in the Central Valley underscores the need to understand to what extent they contribute to or detract from similar valued ecosystem functions beyond crop production.

Much of the information that is available on the potential benefits from agricultural and natural land covers is not centralized. Instead, disparate reports from research activities that vary in geographic location, scope, and timeframe constitute the bulk of the literature. Furthermore, most studies implement a particular suite of metrics to characterize benefits or tradeoffs provided by a land cover depending on the objectives of the study. Therefore, a synthesis of information on multiple benefits that aggregates metrics into a single database with comparable units of measure is an important step towards incorporating multiple benefits research into concerted planning and policy making efforts for a multifunctional Central Valley landscape.

We performed a rapid evidence assessment following a consistent search strategy and pre-determined inclusion/exclusion criteria. We limited the results of the literature search to peer-reviewed publications from 2010-2020 with a geographic focus on the Central Valley, including the Sacramento-San Joaquin Delta. We extracted published, quantitative estimates of land cover associated benefits and/or tradeoffs and compiled a database consisting of metrics on: 1) climate regulation (e.g., greenhouse gas emissions, carbon storage/sequestration), 2) economy (e.g., livelihoods, production value), 3) environmental quality (e.g., pollution, pesticide load), 4) water (e.g., water quality, water use), and 5) wildlife, specifically value for avian conservation. We also consulted a panel of experts in the fields of agricultural ecology and conservation to assess: 1) avian conservation value, and 2) vulnerability to the impacts of climate change of each of the land covers. Finally, we produced a spatially-explicit model using publicly-available datasets to visualize the distribution of ecosystem benefits and tradeoffs, including carbon storage potential, air and water quality, groundwater recharge, and socio-cultural benefits.

We found that the agricultural land covers most likely to be associated with multiple benefits were alfalfa, rice, and rangelands/pastures. Alfalfa was associated with benefits such as carbon sequestration and managed aquifer recharge potential, along with minor support for biodiversity, although tradeoffs such as nitrous oxide emissions from mature stands and high consumptive water use were also noted. Flooded rice systems were notable for their high value for wildlife, particularly migrating and wintering bird species, along with their economic value in the form of relatively high wages for agricultural labor, although methane emissions and consumptive water use were also a concern. Rangelands and pastures (i.e., grasslands managed for livestock production) had high potential benefits for climate regulation via carbon storage and sequestration in soils and belowground biomass, along with high value for biodiversity and support of valuable agricultural pollination services. In contrast, annual field crops such as tomatoes, corn, and cotton were the most likely to be associated with tradeoffs such as greenhouse gas emissions, nitrate leaching hazard, and heavy pesticide use. Natural land covers such as wetlands and riparian areas were mostly associated with benefits such as carbon storage (particularly in riparian areas) and pollutant mitigation (in the case of wetlands), while some tradeoffs in greenhouse gas emissions were noted.

The spatial distribution of benefits and tradeoffs was highly heterogeneous, although in many cases a north-south trend was evident with areas in the northern Central Valley/Sacramento Valley exhibiting more relative benefits than areas in the southern Central Valley/San Joaquin Valley. The former is noted for the concentrated production of rice, along with a mixture of tomatoes, alfalfa, and orchard crops such as almonds and walnuts. The latter, on the other hand, is associated with most of the Central Valley’s annual production of annual row crops (e.g., cotton), oranges and lemons, table grapes, and deciduous perennial tree crops such as pistachios, almonds, peaches, and prunes. Carbon storage patterns were particularly distinctive, with hotspots in the highly organic soils of the Sacramento-San Joaquin Delta and the former Tulare lakebed.

Our ability to draw general conclusions on the relative benefits or tradeoffs associated with Central Valley land covers was limited by the single-intervention nature of most of the quantitative research available on benefit/tradeoff related metrics. Experimental designs often must restrict activities to a single or few related land covers and investigate the impacts of an intervention on the metric of interest. For the purposes of cross-system comparisons, there were very few studies that addressed variability in benefit/tradeoff metrics across multiple land covers from a multiple benefits or multi-functional landscapes perspective. Studies reviewed for many land covers were focused on a few key metrics of known importance for that land cover, e.g., methane emissions in rice, rather than a broader survey of potential benefits and tradeoffs. Furthermore, most experimental analyses are spatially biased and not representative of the entire Central Valley landscape. These challenges highlight the need for more research on human-valued benefits across land covers from a multiple benefits perspective, preferably with a common set of metrics and indicators relevant to most or all of the land covers under consideration.

This report synthesizes the most recent, Central-Valley-specific literature available on multiple benefit and tradeoff metrics. Section I presents individual land cover profiles, with details on both the quantitative estimates for benefit metrics available in the literature as well as other land-cover-relevant metrics not included in benefit/tradeoff analysis. Section II presents results for spatial models of benefits and tradeoff metrics, including carbon storage, air, water, and habitat quality, groundwater recharge potential, and socio-cultural benefits, across the Central Valley. Finally, Section III provides further details on a cross-land cover benefit/tradeoff analysis using data extracted from the published literature, along with the results of expert panel scoring on relative avian conservation value and climate change vulnerability among land covers. Appendices are included for detailed coverage of methods for the rapid evidence assessment, benefit/tradeoff analyses, and index development.

Methods

Methods for Rapid Evidence Assessment and Benefit/Tradeoff analysis

We performed a rapid review of the literature from the last 10 years focusing on benefits from agricultural and natural land covers in the Central Valley. We focused our search on 10 priority agricultural land covers, selected according to harvested acreage as reported by the California County Agricultural Commissioners’ 2018 Crop Report [30], and 3 priority natural (i.e., not for production purposes) land covers based on land area in the Central Valley [98]. See Appendix II for a detailed overview of the search strategy employed, the inclusion criteria, and the data collected from each study in the review. The resulting library of research included reports from peer-review studies as well as publicly available federal or state surveys/censuses and expert source surveys.

In total, we reviewed 107 studies that included approximately 10 agricultural land covers and 3 natural land covers, recording over 77 different metrics for benefits and tradeoffs provisioned by those land covers. From the 107 studies we obtained 512 unique observations across land covers and benefit metrics. 

To complement the metrics reported in the peer-reviewed literature, we included metrics with quality data available in public repositories such as federal and state censuses, technical reports, and databases. These metrics were chosen because they provided information to supplement a benefit category with few examples in recent published literature or because they described metrics that are more suitable for survey formats than for the experimental interventions in the studies reviewed above. These additional datasets included:

  • Crop production value ($USD ha-1) 
  • Pesticide use by land cover type (kg applied ha-1
  • Consumptive water use (m3 ha-1
  • Employment (workers ha-1) and average weekly wages earned ($USD worker-1 ha-1) in the agricultural sector 
  • Avian conservation score

The Avian Conservation Score was developed through a survey of domain experts. In an iterative process, the expert sources reached a consensus on scores for each landcover type according to their relative value for nesting, foraging, or roosting different avian taxa during the breeding and non-breeding seasons. Avian taxa considered were those for which the Central Valley Joint Venture has established conservation objectives, including grassland, oak savannah, and riparian landbirds, waterfowl, shorebirds, and other waterbirds (Central Valley Joint Venture 2020). Each land cover type was given a final score on a 0-1 scale representing its relative total value across taxa and seasons. 

Although our search strategy reflected a priori selection of focal benefit categories and metrics, benefit categories were subsequently adjusted to reflect the actual availability of information on each benefit category and associated metrics. Of the metrics described in the gap analysis above, we chose a subset of metrics with the best representation across land cover types and recategorized them into a suite of benefit categories: 1) Environmental health or quality, which included air pollution and pesticide use metrics; 2) Economy, which included agricultural (crop and forage) production value and livelihood value metrics; 3) Climate, which included greenhouse gas emission and carbon storage/sequestration metrics; 4) Water, which included water quality/pollution and water use metrics, and 5) Wildlife, which included the Avian Conservation Score. These categories were subsequently used to calculate a Multiple Benefits Index across land covers (within metrics) and within specific land covers (across metrics).

The Multiple Benefits Index was calculated by normalizing all of the above metrics to a similar scale to enable comparison of multiple benefits and tradeoffs across land cover types. To compare benefit metrics within each landcover, reported values were converted to the same unit of measure and then transformed to a 0-1 scale by setting the highest reported value across all land covers to 1 and then calculating the remaining values according to the following formula:

where MBI represents the Multiple Benefits Index, or normalized value of X, and Xi represents a single value in the vector of values for X.

Metrics were then categorized post hoc as either “benefits” or “tradeoffs” depending on their perceived value to the above sectors or interests. Benefits were those metrics that related to provisioning of a desirable service such as pollutant removal, while tradeoffs were metrics that related to provisioning of an undesirable service such as greenhouse gas emissions. Metrics considered tradeoffs were assigned a negative value by multiplying the Multiple Benefits Index by -1. The results of within-land cover benefit/tradeoff analyses were presented in the individual land cover profiles in Section III, while the results of cross-land cover benefit/tradeoff analysis are presented below.

To compare land covers across all metrics, we calculated the mean Multiple Benefits Index score for all metrics within a land cover type and then ranked landcovers from highest to lowest mean score. See Appendix III for the rationale behind the selected metrics, along with unit conversions and assumptions made for each metric included in the benefit-tradeoff analysis.

Finally, the benefit/tradeoff analysis was placed into the context of a changing environment through the development of a Climate Change Vulnerability Index, similarly to the climate change vulnerability index developed for birds in the Central Valley. As with the avian conservation score, we developed a survey for a panel of expert sources. The expert panel scored landcovers according to their estimated vulnerability to climate change based on a combination of sensitivity (intrinsic, physiological factors that contribute to climate change vulnerability) and exposure (extrinsic, environmental factors that contribute to climate change vulnerability) factors. Sensitivity scores and exposure scores were summed separately within each land cover and then multiplied together to derive the overall vulnerability index (sum of sensitivity*sum of exposure). 

Because it does not represent a specific benefit or tradeoff, but rather a property of individual land covers, the CCVI was not included in the benefit/tradeoff analysis. Instead, it was used as a standalone metric to contextualize benefits and tradeoffs expected from land covers under climate change and the resulting uncertainty surrounding management scenarios.

Methods for spatial hotspot/coldspot analysis of ecosystem benefits/tradeoffs

  1. Ecosystem Service Metrics and Source Data
  1. Land cover data were obtained from the USDA NASS Cropscape Data Layer (CDL2019), and recategorized according to the specifications of this project (Table 1). Riparian zones were determined as a 25 meter buffer around National Hydrological Dataset (NHD) flowlines for natural rivers and bodies of water, limited to non-developed and non-agricultural land cover categories.
  2. Air and Water Quality metric obtained from the California Healthy Places Index (HPI) geospatial dataset, Pollution and H2O Contamination indices respectively.
  3. Habitat quality metric obtained from Department of Fish and Wildlife (CDFW) Areas of Conservation Emphasis (ACE) dataset.
  4. Soil organic carbon content and percent clay particles were aggregated from the NRCS SSURGO soil data viewer. Parameter values were aggregated from individual soil horizon by volume up to soil map unit component, and aggregated from map unit component by percent total extent to map units. Theoretical maximum carbon storage was calculated based on percent clay as per Hoyle et al (2011) by the following equation:
    SOC%=0.5482× ln(clay%)+1.3073
  5. Soil potential carbon accumulation was calculated by subtracting existing soil carbon stock (SSURGO) from the theoretical maximum calculated as above, and applying a weighting factor based on land cover expected biomass productivity and soil disturbance frequency (Table 1).
  6. Rangeland and forest biomass productivity metrics were obtained from SSURGO soil data viewer by map unit component, and aggregated to map unit by percent total extent.
  7. Perennial crop biomass productivity data, previously used in orchard life cycle assessment modeling (Marvinney et al 2015, Kendall et al 2015) was obtained from a cooperating agri-services firm operating out of the San Joaquin Valley region, for 14 different tree crops. These data were joined to the CDL2019 perennial crops with average value assigned to any tree crop for which no biomass data was available.
  8. Groundwater recharge potential data was obtained from the UC Davis SAGBI dataset.
  9. Groundwater depth data was obtained from the Department of Water Resources (DWR) open test well data as the average of measurements from 2015-201
  10. Crop productivity data (5-year mean yield in tons per acre) was obtained from the County Crop Commission (CCC) reports via USDA NASS, and joined to CDL2019 land cover units as well as recategorized land cover units as the mean yield value of any constituent crop types. The CDL 2019 original unit-based productivity analysis is thus the more accurate representation, as less aggregation of yield values was required.
     
  1. Transformation and Aggregation of Ecosystem Service Metrics
  1. Linear transformation was used to convert the range of values in each metric dataset to a scale of 0-1, with 0 being ‘worst’ and 1 ‘best’ in terms of ecosystem services provided.
  2. Combined metrics were generated by averaging the transformed values in the relevant metrics, and applying a linear transformation to re-scale the values to 0-1.
  3. Metrics were aggregated to a 5km hex grid covering the Central Valley by area-weighted averaging.
  4. Ecosystem service ‘hot’ and ‘cold’ spots were generated by extracting hexes with values below 0.2 and above 0.8 for the combination of all examined metrics.

     

Hoyle F.C., Baldock J.A., Murphy D.V. (2011) Soil Organic Carbon – Role in Rainfed Farming Systems. In: Tow P., Cooper I., Partridge I., Birch C. (eds) Rainfed Farming Systems. Springer, Dordrecht

Marvinney EM, Kendall AM, Brodt SB (2015) Life Cycle–based Assessment of Energy Use and Greenhouse Gas Emissions in Almond Production, Part II: Scenario and Sensitivity Analysis. J Ind Ecol 19(6)

Kendall AM, Marvinney EM, Zhu W, Brodt SB (2015) Life Cycle–based Assessment of Energy Use and Greenhouse Gas Emissions in Almond Production, Part I: Analytical Framework and Baseline Results. J Ind Ecol (19) 6
 

Usage Note s

The database compiled from the Rapid Evidence Assessment, and data files for crop production values, pesticide use, water use, wages and employment (public databases), and the Avian Conservation Score are in separate .csv files that must be loaded individually. All data files needed to run in the R script are included in this repository.

Funding

Point Blue Conservation Science, Award: Subcontract awarded to C. Peterson

California Conservation Fund, Award: Grant awarded to Point Blue Conservation Science