CropSyst-Microbasin Model As a Tool to Inform Variable-Rate Nitrogen Management and Dryland Farm Profitability

Transcription

1 CropSyst-Microbasin Model As a Tool to Inform Variable-Rate Nitrogen Management and Dryland Farm Profitability Inland Pacific Northwest cereal grain region Nicole Ward Dave Huggins 2015 Heterogeneous landscape More N will not increase yield (WATER limited) Goal: effective management NEED: understand spatial variability (dynamics, drivers, stability models) 1

2 Goal: effective management NEED: understand spatial variability (dynamics, drivers, stability models) Goal: effective management NEED: understand spatial variability (dynamics, drivers, stability) Point-based simulations Fully distributed (spatially continuous) simulations CropSyst- Microbasin 2. Many potential applications (biophysical, economic, risk, management) Field data 1. Input data from heavily instrumented field site Field data 1. Input data from heavily instrumented field site 2. Compare outputs to observed data Biophysical Assessment: Compare simulated & observed data Crop Yield, Biomass, Nitrogen Uptake Nitrogen Loss and Storage Runoff, Drainage, Water Storage Soil Nutrient Concentrations 2

3 Water Content (m 3 /m 3 ) 2/19/2016 Surface Runoff Foglia (2009) NSE very good fit T Q t o Qt 2 t=1 s NSE = 1 T Q t o + Q 2 t=1 o Q t s =Simulated discharge at time t Q t o =Observed discharge at time t NSE = Nash Sutcliffe Efficiency Soil Water Content Spatial & Temporal 12 locations 5 depths (0.3m to 1.5m) 3 seasons Simulated vs. Soil Core (m 3 /m 3 ) Soil Water Content (WC) at Site 5, 0.3m depth during simulation period (Probes = Decagon Devices 5TM soil moisture in-situ sensors) Crop Yield & N Uptake Yield (bu/ac) N uptake (Kg-N/ha) Simulated Observed RMSE % error (Yield Monitor) (NDRE) Date 1 t/ha = bu/ac Field data 1. Input data from heavily instrumented field site 2. Compare outputs to observed data 3. Assess biophysical and economic outcomes of variable-rate fertilizer use Field data 1. Input data from heavily instrumented field site 2. Compare outputs to observed data 3. Assess biophysical and economic outcomes of variable-rate fertilizer use Biophysical Assessment: Crop Yield Nitrogen Loss Economic Assessment: Price, cost of production & cost of fertilizer based on 22 local farm budgets (Hilary Davis Thesis, 2014, U Idaho); yield from model Crop Price $ bushels Crop Yield Cost of Production bushel acre = Returns to Risk $ acre $ acre 3

4 Winter Wheat Harvest Year 2013: 10m x 10m outputs on the 10.9 hectare study site Winter Wheat Harvest Year 2013: 10m x 10m outputs on the 10.9 hectare study site Yield (bushels/acre) Yield (t/ha) Yield (bushels/acre) Yield (t/ha) Returns to Risk Profits ($/acre) N Index (0-1) Water Index (0-1) More N More H 2 O Less N Less H 2 O 180 kg-n/ha 40 kg-n/ha 180 kg-n/ha Small yield loss under variable rate HOWEVER! 1. w/savings in fertilizer cost profits increase by $25/ha 2. lower N loss 4.5 t/ha 5.0 t/ha 6.4 t/ha 6.9 t/ha 6.5 t/ha -0.3 t/ha -0.6 t/ha -1.1 t/ha -0.2 t/ha t/ha 4

5 40 kg-n/ha 180 kg-n/ha Less N applied at top -0.3 t/ha -0.6 t/ha -1.1 t/ha -0.2 t/ha t/ha Shallow ( m) hydraulically restrictive horizon lateral redistribution of N contributes to downslope yields Less N transported to bottom (loss of yield) -0.2 t/ha t/ha Less N applied at top Important management consideration for yield based N maps Shallow ( m) hydraulically restrictive horizon lateral redistribution of N contributes to downslope yields Less N transported to bottom (loss of yield) -0.2 t/ha t/ha Model as tool to assess spatial variability in crop production drivers and profitability Aboveground N Index (0-1) More N Model as tool to assess spatial variability in crop production drivers and profitability Lateral redistribution of N can contribute to downslope yield Less N 5

6 Model as tool to assess spatial variability in crop production drivers and profitability Lateral redistribution of N can contribute to downslope yield Variable rate is more profitable than uniform fertilizer on the simulated hillslope 1. Simulations at different field sites 1. Simulations at different field sites 1. Simulations at different field sites NDRE-N uptake minus CropSyst-MB N uptake 2. Assess drivers of spatial variability 2. Assess drivers of spatial variability Downscaled climate data Build on the hillslope-scale simulations by Michelle Chaffee (presented at the 2015 Northwest Climate Conference) 3. Long-term simulations to asses climate change impacts yield probability & risk 1. Simulations at different field sites 2.Drivers of spatial variability 3. Long-term simulations Acknowledgements The Growers Erin Brooks, Matt Yourek, Claudio Stockle, Fidel Maureira, Kate Painter, Hilary Davis, Matteo Poggio, Caley Gasch, Troy Magney, Dave Brown, Dave Huggins Incorporate into a decision support tool and outreach/education efforts Thank you. Questions? 6