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

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 Ecosystem Service Metrics and Source Data 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. Air and Water Quality metric obtained from the California Healthy Places Index (HPI) geospatial dataset, Pollution and H2O Contamination indices respectively. Habitat quality metric obtained from Department of Fish and Wildlife (CDFW) Areas of Conservation Emphasis (ACE) dataset. 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 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). 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. 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. Groundwater recharge potential data was obtained from the UC Davis SAGBI dataset. Groundwater depth data was obtained from the Department of Water Resources (DWR) open test well data as the average of measurements from 2015-201 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. Transformation and Aggregation of Ecosystem Service Metrics 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. 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. Metrics were aggregated to a 5km hex grid covering the Central Valley by area-weighted averaging. 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