8/1/2017 Nitrogen Management Raj Khosla Colorado State University https://www.euractiv.com/wp‐content/uploads/sites/2/2016/10/Digital‐farming.jpg Nitrogen management Nitrogen Use Efficiency I (<50%) ? Adapted from: Lassaletta et al. 2014 Environmental Research Letters Prof. R. Khosla, Colorado State University 1
8/1/2017 Forms Of Nitrogen - and NH 4 Only two are plant available: NO 3 + N 2 N 2 O, NH 3 NH 3 N 2 H 4 N 2 NH 2 OH NO , NO 2 N 2 O NO 3 ‐ N 2 O NH 3 NH 4 + Runoff Ammonium Urea Leaching fertilizer How do we manage nitrogen NO 2 NO ‐ for crop production? OM HNO 3 NH 3 NO 2 ‐ HNO 2 + NH 4 - - NO 2 NO 3 CSU Agricultural Research, Development & Education Center Eastern Colorado Research Center Western Research Center 50 lbs 0 lbs Arkansas Valley Research Center 150 lbs 200 lbs 200 lbs San Luis Valley 50 lbs Southwestern Research Center Research Center Plainsman Research Center 150 lbs 0 lbs Prof. R. Khosla, Colorado State University 2
8/1/2017 N Management Calculating the Optimal N Rate 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 N rate = 35+ (1.2 X EY (bu/ac)) 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 Grain Yield (Bu/Acre) (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 Optimal (bu/ac) ‐100)] – N credit (lb/ac) Range MO Fertilizer N Recommendation (lbs/ac) – Pre‐plant N Test Credits (lbs/ac) ‐ (lbs/ac) *Wheat MT N Fertilizer YG Recommendation (lbs/ac) ‐ PSNT NO 3 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 Nitrogen Rate (lbs/Acre) 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 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 Max Economic Return To Nitrogen Estimated Yield N Credits (EY) Soil N Test Prof. R. Khosla, Colorado State University 3
8/1/2017 Yield Map Mean: 182.5 bu/A <172.5 bu/A N rate = 35+ (1.2 X EY (bu/ac)) Pixels = Average? +/- 10 bu/A from the mean +/- 2 bu/A from the mean 24% ��� ����� � �% 8% Over-fertilized Only 36% med Management Zones low are delineated on farm fields by classifying Average the field into different med sections or zones. high low >192.5 bu/A 40% Under-fertilized Based on the research conducted in Colorado* * CSU, USDA-ARS, Centennial Ag Inc. Delineating management zones… Mean grain yield across MZs The three data layers 12 16 20 Aerial Imagery Grain yield (Mg ha -1 ) 12 Grain yield (Mg ha -1 ) 9 Grain yield (Mg ha -1 ) 15 Topography 8 6 10 Farmer’s experience b b b b ab a a a a 4 3 5 are stacked as GIS layers 0 0 0 Low Medium High Low Medium High Low Medium High to delineate the zone Management zones Management zones Management zones In 9 out of 10 site years we can separate low from high zone but NOT Traits such as dark color, low- Low low from medium or medium from high zones based on grain yield lying topography, and historic Productivity (Zone 3) high yields were designated as a High Productivity zone of potentially high (Zone 1) productivity or high zone Medium Productivity (Zone 2) Source: Koch et al. 2004 Prof. R. Khosla, Colorado State University 4
8/1/2017 Landsat 8 Worldview‐2 Natural Color display (Bands 4‐3‐2) Natural Color display (Bands 5‐3‐2) Captured 9/17/2013 Captured 9/13/2013 Spatial Resolution: 30m Spatial Resolution: 2m Boulder Creek Flood Plain Boulder Creek Flood Plain Micro-variability Low NDVI = NIR- Red / NIR + Red Productivity (Zone 3) High Productivity (Zone 1) How to translate NDVI readings Landsat 8 Medium Macro-variability into N rate recommendations? Productivity (Zone 2) 500 ft Nitrogen Algorithm(s) Nitrogen Algorithm(s) • In 2002, Raun et al., developed the N itrogen Generate Yield Prediction Equation (YP 0 ) One of the first modern applications of remote sensing F ertilization O ptimization A lgorithm ( NFOA ) and it’s use… Above Ground Biomass Grain Yield (kg/ha) • a multi-step process: YP o = a X e (b X INSEY) to determine N rates by estimating yield using NDVI NDVI Time 2 1. Generate Yield Prediction Equation (YP 0 ) from the NDVI provides an estimate of above ground biomass INSEY and previous year’s yield data NDVI Time 1 First Nitrogen Application Algorithms were derived 2. Field data collection for N response from yield estimates using remote sensing Big turning point in the history of data‐driven N INSEY Cumulative Growing Degree Days (GDD) management Expected Yield (EY) = (NDVI T1 + NDVI T2 ) / GDD (INSEY) Raun et al 2001. In-Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy reflectance Raun et al 2002 Improving NUE in Cereal Grain Production with Optical Sensing and Variable Rate Application Prof. R. Khosla, Colorado State University 5
8/1/2017 Nitrogen Algorithm(s) Nitrogen Algorithm(s) N-Rate Field Experiment Collect sensor readings 3. Calculate Yield Potential with added N fertilizer (YP N ) YP N = YP 0 * RI Response Index (RI) = NDVI Rich / NDVI Reference RI= .85 / .59 = 1.44 Potential for yield increase ~44% 4. Compute Grain N uptake at YP 0 & YP N Potential for yield increase ~16% with additional N RI= .85 / .73 = 1.16 NDVI 0.85 with additional N GNUP_YP 0 = YP 0 x % N Grain GNUP_YP N = YP N x % N Grain NDVI 0.59 How much 5. Final N Rate = (GNUP_YP N – GNUP_YP 0 ) / NUE additional N? NDVI 0.73 Limitations: I. NDVI saturates at high LAI values II. This algorithm does not account for location of plant in the field Collect temperature data for GDD Raun et al 2002 Improving NUE in Cereal Grain Production with Optical Sensing and Variable Rate Application 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 Limitations: Red Edge Green Ratio Vegetation Index RE/G VEGETATION INDICES EQUATION Green Difference Vegetation Index NIR‐G Red Edge Difference Vegetation Index RE‐G Normalized Green Index (GRI) G/NIR+RE+G Normalized Difference Red Edge (NIR‐RE)/(NIR+RE) NDVI saturates at high LAI values RE/(NIR + RE + G) Normalized Red Edge Index (NREI) Green Normalized Difference Vegetation Index (NIR‐G)/(NIR+G) Red Edge GNDVI ( NIR – RE)/(NIR + RE) (RE‐G)/(RE+G) Normalized Difference Red Edge Index (NDREI) Green Wide Dynamic Range Vegetation Index (a*NIR‐G)/(a*NIR+G)(a‐.12) NIR/G‐1 Green Chlorophyll Index (GRI) Red Edge Wide Dynamic Range Vegetation Index (a*NIR‐RE)/(a*NIR + RE)(a‐.12) II. Accounting for location of plant in field Optimized Vegetation Index 1 100*(lnNIR‐lnRE) NIR/RE‐1 Red Edge Chlorophyll Index (RECI) Modified Double Difference Index (NIR‐RE)‐(RE‐G) 1.5 * [(NIR – RE)/(NIR + RE +.5)] Modified Normalized Difference Index Green Soil Adjusted Vegetation Index (GSAVI) (NIR‐RE)/(NIR‐G) Green Chlorophyll Index NIR/G‐1 (1 + .16)(NIR – G)(NIR + G + .16) Green Optimal Soil Adjusted Vegetation Index (GOSAVI) Red Edge Chlorophyll Index NIR/RE‐1 [(NIR – RE) ‐ .2 *(NIR – G)]/(NIR/RE) Modified Red Edge Simple Ratio (NIR/RE‐1/SQRT(NIR/RE+1) Modified Chlorophyll Absorption in Reflectance Index 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)] (NIR – RE)/(NIR + RE) Modified Chlorophyll Absorption in Redlectance Index [(NIR‐RE)‐.2*(NIR‐G)](NIR/RE) G/(NIR+RE+G) RE/(NIR + RE + G) 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)] Cao et al 2014: Active Canopy Sensing of Winter Wheat Nitrogen Status: An evaluation of two Sensor Systems Grenn re‐Normalized Difference Vegetation Index (NIRE‐RE)/SQRT(NIR+RE) Prof. R. Khosla, Colorado State University 6
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