DOI: 10.5194/hess-22-2739-2018
论文题名: A Bayesian approach to infer nitrogen loading rates from crop and land-use types surrounding private wells in the Central Valley; California
作者: Ransom K.M. ; Bell A.M. ; Barber Q.E. ; Kourakos G. ; Harter T.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2018
卷: 22, 期: 5 起始页码: 2739
结束页码: 2758
语种: 英语
Scopus关键词: Bayesian networks
; Crops
; Groundwater
; Land use
; Landforms
; Nitrates
; Nitrogen
; Probability distributions
; Regression analysis
; Uncertainty analysis
; Agricultural crops
; Animal feeding operations
; Bayesian approaches
; Bayesian regression
; Direct measurement
; Field-scale experiments
; Groundwater nitrates
; Nitrogen mass balance
; Loading
; agricultural land
; alfalfa
; Bayesian analysis
; cotton
; crop plant
; denitrification
; groundwater
; intensive agriculture
; land use change
; legume
; nitrogen
; nut
; pasture
; rhizosphere
; rice
; river water
; subtropical region
; California
; Central Valley [California]
; United States
; Animalia
; Citrus
; Gossypium hirsutum
; Medicago sativa
英文摘要: This study is focused on nitrogen loading from a wide variety of crop and land-use types in the Central Valley, California, USA, an intensively farmed region with high agricultural crop diversity. Nitrogen loading rates for several crop types have been measured based on field-scale experiments, and recent research has calculated nitrogen loading rates for crops throughout the Central Valley based on a mass balance approach. However, research is lacking to infer nitrogen loading rates for the broad diversity of crop and landuse types directly from groundwater nitrate measurements. Relating groundwater nitrate measurements to specific crops must account for the uncertainty about and multiplicity in contributing crops (and other land uses) to individual well measurements, and for the variability of nitrogen loading within farms and from farm to farm for the same crop type. In this study, we developed a Bayesian regression model that allowed us to estimate land-use-specific groundwater nitrogen loading rate probability distributions for 15 crop and land-use groups based on a database of recent nitrate measurements from 2149 private wells in the Central Valley. The water and natural, rice, and alfalfa and pasture groups had the lowest median estimated nitrogen loading rates, each with a median estimate below 5 kgNha-1 yr-1. Confined animal feeding operations (dairies) and citrus and subtropical crops had the greatest median estimated nitrogen loading rates at approximately 269 and 65 kgNha-1 yr-1, respectively. In general, our probability-based estimates compare favorably with previous direct measurements and with mass-balance-based estimates of nitrogen loading. Nitrogen mass-balance-based estimates are larger than our groundwater nitrate derived estimates for manured and nonmanured forage, nuts, cotton, tree fruit, and rice crops. These discrepancies are thought to be due to groundwater age mixing, dilution from infiltrating river water, or denitrification between the time when nitrogen leaves the root zone (point of reference for mass-balancederived loading) and the time and location of groundwater measurement. © Author(s) 2018. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163317
Appears in Collections: 气候变化与战略
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作者单位: Ransom, K.M., Department of Land, Air, and Water Resources, University of California, Davis, United States; Bell, A.M., Center for Watershed Sciences, University of California, Davis, United States; Barber, Q.E., Department of Renewable Resources, University of Alberta, Edmonton, Canada; Kourakos, G., Department of Land, Air, and Water Resources, University of California, Davis, United States; Harter, T., Department of Land, Air, and Water Resources, University of California, Davis, United States
Recommended Citation:
Ransom K.M.,Bell A.M.,Barber Q.E.,et al. A Bayesian approach to infer nitrogen loading rates from crop and land-use types surrounding private wells in the Central Valley; California[J]. Hydrology and Earth System Sciences,2018-01-01,22(5)