? Adapted from: Lassaletta et al. 2014 Environmental Research Letters - - PDF document
8/1/2017 Nitrogen Management Raj Khosla Colorado State University https://www.euractiv.com/wpcontent/uploads/sites/2/2016/10/Digitalfarming.jpg Nitrogen management Nitrogen Use Efficiency I (<50%) ? Adapted from: Lassaletta et al.
Raj Khosla Colorado State University
https://www.euractiv.com/wp‐content/uploads/sites/2/2016/10/Digital‐farming.jpg
Adapted from: Lassaletta et al. 2014 Environmental Research Letters
Nitrogen Use Efficiency
(<50%)
Ammonium fertilizer NH3 NH4
+
NH3 N2 NO2
NO, NO2
OM
Urea N2O Runoff Leaching
NH3
Forms Of Nitrogen Only two are plant available: NO3
+
NO2
‐
NO2 HNO2 HNO3 NO‐ N2 N2O NH2OH N2H4 NH3 NO3
‐
NH4
+
How do we manage nitrogen for crop production?
CSU Agricultural Research, Development & Education Center Eastern Colorado Research Center Arkansas Valley Research Center San Luis Valley Research Center Plainsman Research Center Southwestern Research Center Western Research Center
200 lbs 150 lbs 0 lbs 50 lbs
200 lbs 150 lbs 0 lbs 50 lbs
Calculating the Optimal N Rate
Nitrogen Rate (lbs/Acre) Grain Yield (Bu/Acre) Optimal Range
N rate = 35+ (1.2 X EY (bu/ac))
State N Rate Recommendation CO 35+ (1.2 X EY (bu/ac)) – (8 X Average ppm NO3 N in Soil) – (.14 X EY (bu/ac) X %OM)‐ Other N Credits KS (1.6X YG (bu/ac))‐(%OM X 20) ‐ Profile N ‐ Legume N‐ other N Credit OH ‐27 + (1.36 X Yield Potential (bu/ac) ‐100) – N credit (lb/ac) or 110 + [1.36 X (Yield potential (bu/ac) ‐100)] – N credit (lb/ac) IN ‐27 + (1.36 X Yield Potential (bu/ac) ‐100) – N credit (lb/ac) or 110 + [1.36 X (Yield potential (bu/ac) ‐100)] – N credit (lb/ac) MI ‐27 + (1.36 X Yield Potential (bu/ac) ‐100) – N credit (lb/ac) or 110 + [1.36 X (Yield potential (bu/ac) ‐100)] – N credit (lb/ac) MO Fertilizer N Recommendation (lbs/ac) – Pre‐plant N Test Credits (lbs/ac) MT N Fertilizer YG Recommendation (lbs/ac) ‐ PSNT NO3
‐ (lbs/ac) *Wheat
ND Fertilizer N recommendation (lbs/ac)‐ Soil Nitrate Concentration (lbs/ac)‐ N Credits (lbs/ac) NE 35+ [1.2 X EY (bu/ac)] – (8 X Average ppm NO3 N in Soil) – (.14 X EY (bu/ac) X %OM)‐ other N credits OR YG (bu/ac) X Required N Protein Goal (lb/ac) – Residual Soil N (lb/ac) *Wheat PA EY (bu/ac) – ( (Manure since last harvest (lb/ac) + Previous Crop Factor (lb/ac) + Three year Manure History Factor (lb/ac)) X Soil Nitrate (lb/ac)) SD YG bu/ac X 1.2 – Soil NO3 (lbs/ac) – Manure N (lb/ac) + no‐till Adjustment VA EY (bu/ac) – ((Applied Manure Factor Last Year (lb/ac) + Leguminous Crop Factor (lb/ac) + Manure History Factor (lb/ac)) * (PSNT (ppm)) IA N Rate Web Application WI N Rate Web Application MN N Rate Web Application IL N Rate Web Application ND N Rate Web Application
Common Variables
State N Rate Recommendation CO 35+ (1.2 X EY (bu/ac)) – (8 X Average ppm NO3 N in soil) – (.14 X EY (bu/ac) X %OM)‐ other N Credits
Estimated Yield (EY) Soil N Test N Credits
Max Economic Return To Nitrogen
+/- 2 bu/A from the mean
+/- 10 bu/A from the mean
Mean: 182.5 bu/A
>192.5 bu/A
<172.5 bu/A
Over-fertilized
Pixels = Average?
Management Zones are delineated on farm fields by classifying the field into different sections or zones.
* CSU, USDA-ARS, Centennial Ag Inc.
Based on the research conducted in Colorado*
N rate = 35+ (1.2 X EY (bu/ac))
In 9 out of 10 site years we can separate low from high zone but NOT low from medium or medium from high zones based on grain yield
Mean grain yield across MZs
16 12 8 4
a a b
Low Medium High Management zones Grain yield (Mg ha -1) 12 9 6 3
ab
b
Low Medium High Management zones Grain yield (Mg ha -1)
a
20 15 10 5
b b
Low Medium High Management zones Grain yield (Mg ha -1)
a
Source: Koch et al. 2004
Low Productivity (Zone 3) Medium Productivity (Zone 2) High Productivity (Zone 1)
The three data layers
are stacked as GIS layers to delineate the zone
Traits such as dark color, low- lying topography, and historic high yields were designated as a zone of potentially high productivity or high zone
Delineating management zones…
Macro-variability
Micro-variability
Low Productivity (Zone 3) Medium Productivity (Zone 2) High Productivity (Zone 1)
Landsat 8
Worldview‐2 Natural Color display (Bands 5‐3‐2) Captured 9/13/2013 Spatial Resolution: 2m Boulder Creek Flood Plain Landsat 8 Natural Color display (Bands 4‐3‐2) Captured 9/17/2013 Spatial Resolution: 30m Boulder Creek Flood Plain
500 ft
One of the first modern applications of remote sensing and it’s use… to determine N rates by estimating yield using NDVI NDVI provides an estimate of above ground biomass First Nitrogen Application Algorithms were derived from yield estimates using remote sensing Big turning point in the history of data‐driven N management
Raun et al 2001. In-Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy reflectance
Cumulative Growing Degree Days (GDD) Above Ground Biomass
NDVI Time 1 NDVI Time 2
Expected Yield (EY) = (NDVI T1 + NDVI T2) / GDD (INSEY)
Fertilization Optimization Algorithm (NFOA)
INSEY and previous year’s yield data
Raun et al 2002 Improving NUE in Cereal Grain Production with Optical Sensing and Variable Rate Application
Generate Yield Prediction Equation (YP0)
INSEY Grain Yield (kg/ha)
YPo= a X e (b X INSEY)
N-Rate Field Experiment
Collect sensor readings
Collect temperature data for GDD
NDVI 0.85
NDVI 0.59 NDVI 0.73
Response Index (RI) = NDVIRich / NDVIReference
Potential for yield increase ~44% with additional N RI= .85 / .59 = 1.44 RI= .85 / .73 = 1.16 Potential for yield increase ~16% with additional N
YPN = YP0 * RI
GNUP_YP0 = YP0 x % N Grain GNUP_YPN = YPN x % N Grain
Raun et al 2002 Improving NUE in Cereal Grain Production with Optical Sensing and Variable Rate Application
Limitations: I. NDVI saturates at high LAI values
VEGETATION INDICES EQUATION
Normalized Green Index (GRI) Normalized Red Edge Index (NREI) Normalized Difference Red Edge Index (NDREI) Green Chlorophyll Index (GRI) Red Edge Chlorophyll Index (RECI) Green Soil Adjusted Vegetation Index (GSAVI) Green Optimal Soil Adjusted Vegetation Index (GOSAVI) Modified Chlorophyll Absorption in Reflectance Index
G/NIR+RE+G
RE/(NIR + RE + G)
(NIR – RE)/(NIR + RE)
NIR/G‐1 NIR/RE‐1 1.5 * [(NIR – RE)/(NIR + RE +.5)] (1 + .16)(NIR – G)(NIR + G + .16) [(NIR – RE) ‐ .2 *(NIR – G)]/(NIR/RE)
Cao et al 2014: Active Canopy Sensing of Winter Wheat Nitrogen Status: An evaluation of two Sensor Systems
New Vegetation Indices to Detect N Status
VEGETATION INDICES Equation Normalized Green Index G/(NIR + RE +G) Normalized Red Edge Index RE/(NIR + RE +G) Normalized NIR Index NIR/(NIR + RE +G) Red Edge Ratio Vegetation Index NIR/RE Green Ratio Vegetation Index NIR/G Red Edge Green Ratio Vegetation Index RE/G Green Difference Vegetation Index NIR‐G Red Edge Difference Vegetation Index RE‐G Normalized Difference Red Edge (NIR‐RE)/(NIR+RE) Green Normalized Difference Vegetation Index (NIR‐G)/(NIR+G) Red Edge GNDVI (RE‐G)/(RE+G) Green Wide Dynamic Range Vegetation Index (a*NIR‐G)/(a*NIR+G)(a‐.12) Red Edge Wide Dynamic Range Vegetation Index (a*NIR‐RE)/(a*NIR + RE)(a‐.12) Optimized Vegetation Index 1 100*(lnNIR‐lnRE) Modified Double Difference Index (NIR‐RE)‐(RE‐G) Modified Normalized Difference Index (NIR‐RE)/(NIR‐G) Green Chlorophyll Index NIR/G‐1 Red Edge Chlorophyll Index NIR/RE‐1 Modified Red Edge Simple Ratio (NIR/RE‐1/SQRT(NIR/RE+1) Modified Green Simple Ratio (NIR/G‐1)/SQRT(NIR/RE+1) Modified Enhanced Vegetation Index 2.5* (NIR‐RE/(NIR+6*RE‐.75*G+1) Modified Normalized Difference Red Edge [NIR‐(RE‐2*G)]/[NIR+(RE‐2*G)] Modified Chlorophyll Absorption in Redlectance Index [(NIR‐RE)‐.2*(NIR‐G)](NIR/RE) Modified Transformed CARI 3*[(NIR‐RE)‐.2*(NIR‐G)(NIR/RE)] Green Soil Adjusted Vegetation Index 1.5*[(NIR‐G)/(NIR+G+.5)] Red Edge Soil Adjusted Vegetation Index 1.5*[(NIR‐RE/(NIR+RE+.5)] Green Optimal Soil Adjusted Vegetation Index (1+.16)(NIR‐G)/(NIR+G+.16) Red Edge Optimal Soil Adjusted Vegetation Index (1+.16)(NIR‐RE)/(NIR+RE+.16) Red Edge Transformed Vegetation Index .5[120*(NIR‐G)‐200*(RE‐G)] Grenn re‐Normalized Difference Vegetation Index (NIRE‐RE)/SQRT(NIR+RE)
N Rate (kg ha-1) = (135.3 x (NDVIRef. / NDVITarget)2) – (134.8 x (NDVIRef. / NDVITarget)) + 1
~96 lbs/a ~96 lbs/a ~96 lbs/a
NDVI
0.41
NDVI
0.41
NDVI
0.41
Coupling site-specific management zones with active proximal sensors
N Rate (kg ha-1) = (135.3 x (NDVIRef. / NDVITarget)2) – (134.8 x (NDVIRef. / NDVITarget)) + 1
~92 lbs/a ~144 lbs/a ~37 lbs/a
Medium
N Rate (kg ha-1) = Crop properties + Soil Properties
NDVI
0.41
NDVI
0.41
NDVI
0.41
Micro-variability Macro-variability High MZ Medium MZ Low MZ N management strategies
Uniform Remote Sensing 0 kg/ha 112 kg/ha 224 kg/ha 0 kg/ha 112 kg/ha 224 kg/ha 0 kg/ha 112 kg/ha 224 kg/ha Management Zones 224 kg/ha 168 kg/ha 112 kg/ha Remote sensing within Management Zones 112 kg/ha 168 kg/ha 224 kg/ha 56 kg/ha 112 kg/ha 168 kg/ha 0 kg/ha 56 kg/ha 112 kg/ha N management strategies
150 120 90 60 30
d c b a d c b a
NUEa (kg Grain / kg N)
d c b a
2010 2011 2012
Uniform MZ RS RS + MZ
224 168 112 56
N applied (kg/ha)
Improvement in NUE and reductions in N loadings in the biosphere.
Uniform MZ RS RS + MZ
28
Difference in N applied (kg/ha)
Uniform MZ RS RS + MZ
15 10 5
a a a a a a b a Yield (Mg/ha)
a a a a
Uniform MZ RS RS + MZ
2010 2011 2012
Difference in N2O emission (kg/ha/y)
0 75
Uniform MZ RS RS + MZ
2010 2011 2012
6.0 4.5 2.0 1.5
N2O emission (kg/ha/y)
Reductions in N2O linked to fertilizer
There will be even more complex soil and crop models that encompass many other sensitive parameters
Model Time Scale
Daily time-step; historical weather data to predict N flux
Weather Inputs
Real-time; solar radiation; temperature; precipitation
Soil Inputs
NRCS SSURGO; root depth; slope; SOM; drainage Cultivar; maturity class; population; yield
Crop Inputs Management Inputs
Tillage; manure; previous crop characteristics
N Fertilizer Inputs
Type; rate; timing; pricing
N Rate Output
Mass balance; deterministic and stochastic; price risk factors
Graphical Output
N loss and uptake; N dynamics; crop development; fertilizer maps
Method Approach
Sella et al 2016 Adapt-N Outperforms Grower-Selected Nitrogen Rates in Northeast and Midwestern United States Strip Trials,
N Fertilizer Inputs Management Inputs Graphical Output N Rate Output
Nrec=Nexp_yld - Ncrop_now – Nsoil_now
Increasing NUE with advanced decision making process
RKhosla@Colostate.Edu