scholarly journals A Bayesian Approach to Infer Nitrogen Loading Rates from Crop and Landuse Types Surrounding Private Wells in the Central Valley, California

2018 ◽  
Author(s):  
Katherine M. Ransom ◽  
Andrew M. Bell ◽  
Quinn E. Barber ◽  
George Kourakos ◽  
Thomas Harter
Keyword(s):  
Mass Balance ◽  
Root Zone ◽  
Groundwater Age ◽  
Central Valley ◽  
Nitrogen Loading ◽  
Bayesian Regression ◽  
Agricultural Crop ◽  
Forage Crops ◽  
Loading Rates ◽  
Private Wells

Abstract. This study is focused on nitrogen loading from a wide variety of crop and landuse 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 landuses) to individual well measurements, and also for the variability of nitrogen loading within farms and from farm to farm for the same crop. In this study, we developed a Bayesian regression model that allowed us to estimate crop or other landuse-specific groundwater nitrogen loading rate probability distributions for 15 crop and landuse groups based on a database of recent nitrate measurements from 2149 private wells in the Central Valley. The water & natural, rice, and alfalfa & pasture groups had the lowest median estimated nitrogen loading rates, each with a median estimate below 5 kg N ha−1 yr−1. Confined animal feeding operations (dairies) and citrus & subtropical crops had the greatest median estimated nitrogen loading rates at approximately 269 and 65 kg N ha−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 forage crops, 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 balance derived loading) and the time and location of groundwater measurement.

scholarly journals A Bayesian approach to infer nitrogen loading rates from crop and land-use types surrounding private wells in the Central Valley, California

2018 ◽  
Vol 22 (5) ◽  
pp. 2739-2758 ◽  
Author(s):  
Katherine M. Ransom ◽  
Andrew M. Bell ◽  
Quinn E. Barber ◽  
George Kourakos ◽  
Thomas Harter
Keyword(s):  
Land Use ◽  
Mass Balance ◽  
Groundwater Age ◽  
Central Valley ◽  
Nitrogen Loading ◽  
Bayesian Regression ◽  
Agricultural Crop ◽  
Loading Rates ◽  
Land Use Types ◽  
Private Wells

Abstract. 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 land-use 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 kg N ha−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 kg N ha−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-balance-derived loading) and the time and location of groundwater measurement.

scholarly journals A Bayesian Approach to Infer Nitrogen Loading Rates from Crop and Landuse Types Surrounding Private Wells in the Central Valley, California

2017 ◽  
Author(s):  
Katherine M. Ransom ◽  
Andrew M. Bell ◽  
Quinn E. Barber ◽  
George Kourakos ◽  
Thomas Harter
Keyword(s):  
Loading Rate ◽  
Probability Distributions ◽  
Central Valley ◽  
Nitrogen Loading ◽  
Bayesian Regression ◽  
Nitrate Contamination ◽  
Agricultural Crop ◽  
Loading Rates ◽  
The Impact ◽  
Private Wells

Abstract. Nitrate contamination of alluvial aquifers in agricultural areas is a typical and major problem around the world. Nitrogen applied to crops, in the form of synthetic fertilizers or manure, in excess of plant uptake, largely leaches to groundwater in the form of nitrate, which is stable and highly mobile in oxygen-rich groundwaters. Increased awareness of the impact that excess nitrogen has had on groundwater and major health concerns about nitrate are prompting new regulations for farmers, e.g., in Europe and California, USA. This study is focused in the Central Valley, California, USA, an intensively farmed region with high agricultural crop diversity. Though nitrogen loading rates for several crop and landuse types in the Central Valley have been estimated or measured in a handful of studies, nitrogen loading rates for specific crop or landuse types and their impact to groundwater quality remain largely unknown. Knowledge of crop or other landuse specific groundwater nitrate impact may aid future regulatory decisions. Nitrogen loading rates for specific crop or landuse types are expected to vary depending on individual landuse practices; and interactions with hydrogeologic parameters that may promote or inhibit nitrate leaching. In this study, we developed a novel Bayesian regression model that allowed us to estimate crop or other landuse-specific groundwater nitrogen loading rate probability distributions from surveys of private wells, each of which is likely impacted by more than one landuse. We used recent nitrate measurements from 2149 wells in the Central Valley. We estimated nitrogen loading rate distributions for 15 crop and landuse groups. These were shown to compare favorably with prior mass-balance estimates of loading rates based on agronomic estimates of nitrogen loading.

Field Measurements and Simulation of Nitrogen Fate under Citrus Production

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 498c-498
Author(s):  
A. Fares ◽  
A.K. Alva ◽  
S. Paramasivam
Keyword(s):  
Mass Balance ◽  
Best Management Practices ◽  
Rainy Season ◽  
Crop Production ◽  
Management Practices ◽  
Root Zone ◽  
Field Measurements ◽  
Irrigation Scheduling ◽  
N Leaching ◽  
Citrus Production

Water and nitrogen (N) are important inputs for most crop production. The main objectives of nitrogen best management practices (NBMP) are to improve N and water management to maximize the uptake efficiency and minimize the leaching losses. This require a complete understanding of fate of N and water mass balance within and below the root zone of the crop in question. The fate of nitrogen applied for citrus production in sandy soils (>95% sand) was simulated using a mathematical model LEACHM (Leaching Estimation And Chemistry Model). Nitrogen removal in harvested fruits and storage in the tree accounted the major portion of the applied N. Nitrogen volatilization mainly as ammonia and N leaching below the root zone were the next two major components of the N mass balance. A proper irrigation scheduling based on continuous monitoring of the soil water content in the rooting was used as a part of the NBMP. More than 50% of the total annual leached water below the root zone was predicted to occur in the the rainy season. Since this would contribute to nitrate leaching, it is recomended to avoid N application during the rainy season.

Monitoring Shallow Groundwater Nitrogen Loading from Dairy Facilities with Irrigated Forage Crops

2001 ◽  
Author(s):  
Thomas Harter ◽  
Harley Davis ◽  
Marsha C. Mathews ◽  
Roland D. Meyer
Keyword(s):  
Shallow Groundwater ◽  
Nitrogen Loading ◽  
Forage Crops ◽  
Groundwater Nitrogen

Changes in root zone storage capacity and their effects on river discharge and gross primary production

2020 ◽  
Author(s):  
Rodolfo Nóbrega ◽  
David Sandoval ◽  
Colin Prentice
Keyword(s):  
Land Cover ◽  
Primary Production ◽  
Mass Balance ◽  
Land Cover Change ◽  
Storage Capacity ◽  
Root Zone ◽  
Gross Primary Production ◽  
Terrestrial Ecosystem ◽  
Ecosystem Models ◽  
Stress Functions

<p>Root zone storage capacity (R<sub>z</sub>) is a parameter widely used in terrestrial ecosystem models that estimate the amount of soil moisture available for transpiration. However, R<sub>z</sub> is subject to large uncertainty, due to the lack of data on the distribution of soil properties and the depth of plant roots that actively take up water. Our study makes use of a mass-balance approach to investigate R<sub>z</sub> in different ecosystems, and changes in water fluxes caused by land-cover change. The method needs no land-cover or soil information, and uses precipitation (P) and evapotranspiration (ET) time series to estimate the seasonal water deficit. To account for some of the uncertainty in ET, we use different methods for ET estimation, including methods based on satellite estimates, and modelling approaches that back-calculate ET from other ecosystem fluxes. We show that reduced ET due to land-cover change reduces R<sub>z</sub>, which in turn increases baseflow in regions with a strong rainfall seasonality. This finding allows us to analyse the trade-off between gross primary production and hydrological fluxes at river basin scales. We also consider some ideas on how to use mass-balance R<sub>z</sub> in water-stress functions as incorporated in existing terrestrial ecosystem models.</p>

Metagenomics reveals microbial community differences lead to differential nitrate production in anammox reactors with differing nitrogen loading rates

2020 ◽  
Vol 169 ◽  
pp. 115279 ◽  
Author(s):  
Wei Li ◽  
Jin-long Zhuang ◽  
Yuan-yuan Zhou ◽  
Fan-gang Meng ◽  
Da Kang ◽  
...  
Keyword(s):  
Microbial Community ◽  
Nitrogen Loading ◽  
Loading Rates ◽  
Nitrate Production

Comparison of quasisteady-state performance of the DEAMOX process under intermittent and continuous feeding and different nitrogen loading rates

2007 ◽  
Vol 2 (7) ◽  
pp. 894-900 ◽  
Author(s):  
Sergey Kalyuzhnyi ◽  
Marina Gladchenko ◽  
Arnold Mulder ◽  
Bram Versprille
Keyword(s):  
Nitrogen Loading ◽  
Loading Rates ◽  
Continuous Feeding
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