Toby Ahrens

1
Linking Spatial Variability of Soil N Retention
Mechanisms to Landscape-level Fates in Yaqui
Valley, Mexico

• Toby Ahrens
• 26 Oct 2004

2
Goals

• Quantify degrees of N contamination under
  different management regimes
• Investigate the value of spatial data sets
  varying in resolution
• Integrate 10 yrs of biogeochemistry research in
  the Valley

3
What do I need to do to get there?

• Link process and transport models
• Improve process models
• Include abiotic retention mechanisms
• Develop spatial data sets
• Eg. soil type, texture, mineralogy, management,
  leaching potential

4
Coastal eutrophication

• Declassified Keyhole satellite image
• March 8, 1978
• (Thanks Mike!)

5
Nitrate-contaminated groundwater
lt10 ppm 10-40 ppm 116 ppm
Hungate, unpublished data, 1997-98
6
Two Qs guiding my efforts

1. What soil characteristics control N availability,
   retention and loss in OM-deficient ag soils?
2. Can the spatial variability of these
   characteristics be linked to aqueous N fates
   throughout the Valley?

7
Model schematic
soil properties
soil mineralogy
crop yield (N use)
management unit
crop type
groundwater depth
NLOSS
Solute transport
un/saturated boundary conditions

• Output maps
• Leaching vulnerability
• Aquifer contamination
• Coastal N sources

8
Lee Addamss model
9
Major modeling efforts

• Soil submodel including sorption isotherms and
  mineral fixation
• Solute transport component to saturated hydrology
  model
• Saturated/unsaturated boundary layer conditions
• Spatial data referencing

10
Applied N 250 kg/ha
Gaseous losses NON2O 2-5 NH3 __ N2 __
Plant uptake 31
Drainage canals NO3- NH4 2-5 NON2O lt0.1
?
Leached 2-5 (14-26)
?
?
References Riley et al. 2001 Harrison
2003 Matson et al. 1998 Ortiz-Monasterio, pers.
comm.
?
11
Applied N 250 kg/ha
Gaseous losses NON2O 2-5 NH3 __ N2 __
Plant uptake 31
Drainage canals NO3- NH4 2-5 NON2O lt0.1
?
Leached 2-5 (14-26)

• Field experiments
• abiotic fixation
• link leaching potential
• to actual fates

?
?
?
12
Future directions and broader implications

• Does better spatial resolution increase our
  ability to predict SW/GW vulnerability?
• And the degree of how mechanistic submodels are?
• Identify thresholds for N retention
• Or pesticides
• Investigate management scenarios
• Different irrigation or fertilization regimes

13
Resolution of input dataField based
14
(No Transcript)
15
Policy implications

• The Danish and Dutch experiences
• N control policies aimed at reducing N leaching
  (SW GW) and NH3 and N2O emissions
• Importance of model validation