Risk and Uncertainty in Crop Model Predictions of Regional Yields under Climate Change and Variability

Transcription

1 Risk and Uncertainty in Crop Model Predictions of Regional Yields under Climate Change and Variability K. J. Boote, J. W. Jones, S. Asseng, and G. A. Baigorria Presented at CIMR Symposium, May 2011

2 Rationale, Problem, & Goal Crop Models can be used as Strategic Tools to evaluate impacts of climate change and weather variability on production for site-specific fields or whole regions. But crop models are site-specific. When used to predict regional yields, the process is made difficult because district-wide yields are aggregated over many soils, fields, sowing dates, & farmer managements. Missing site-specific inputs on: Weather Soils Management (sowing date, plant spacing, fertilization, irrigation, cultivar, and pest management) How to calibrate to, and predict at regional levels? Start with site-specific trials? Variety trials? District yields?

3 DSSAT Crop Models Models within the DSSAT (Decision Support System for Agrotechnology Transfer). Predict growth and development of different crops (maize, wheat, soybean, peanut, cotton, bean, etc.) Process-level: Simulate inputs, losses, & balance of C, N, and H 2 O. Requires information about site, management - Daily weather - Management - Soil characteristics - Cultivar traits Based on understanding of crop-soil-weather relationships If used appropriately, can scale up to regional yields

4 Three Data Sources for Model Calibration 1. Sentinel sites with time-series data on phenology, growth, soil water content, soil N status, with site-specific soils, weather, & management (Research experiments) 2. Variety, fertility, and irrigation trials with site-specific soils, weather, and management, having only end-of-season yield and yield components (often multi-year) 3. Regional yields (not site-specific but aggregated, and multiyear). Aggregate over uncertain. Soils (not exact, from soil survey ) Sowing date (distribution, from NASS survey) Fertility management (from????) Weather (not exact rainfall, but from nearby) Irrigation (unknown, worst problem, regional yields are averaged over irrigated and rainfed) Pest management and harvest loss unknown

5 Steps for Model Calibration 1 st : Calibrate to sentinel sites with time-series data on phenology, growth, soil water content, soil N status, with sitespecific soils, weather, & management (Research experiments) 2 nd : Calibrate to extensive data from variety, fertility, and irrigation trials with site-specific soils, weather, and management, having only end-of-season yield and yield components. Experience recent cultivars, more soils, & more years (improve on soil WHC and fertility aspects). 3 rd : Calibrate to regional yields (not site-specific but aggregated). Simulate for multiple soil pixels per weather station site per year with range of sowing dates, with typical management. Aggregate and compare to annual regional yields over years. Adjust soil WHC, soil organic C, fertilization, probable irrigation, & yield gap factors so simulated yield matches observed interannual yields (mean, wet and dry years).

6 Calibrate with timeseries data. Does crop model predict partitioning patterns? Does crop C balance work? from leaf Ps to canopy Ps, to biomass accumulation in the field? Example for peanut says yes

7 Calibrate with timeseries data. Does crop model predict partitioning patterns? Does crop C balance work? from leaf Ps to canopy Ps, to biomass accumulation in the field? Example for peanut says yes

8 Time-series data. Test model response to water deficit. Gainesville, FL 1978 Total crop and grain mass for irrigated & rainfed Bragg soybean in FL in Yield Day of Year Grain - IRRIGATED Total Crop - NOT IRRIGATED Total Crop - IRRIGATED Grain - NOT IRRIGATED

9 Total Crop Biomass, kg/ha Time-series data. Test model response to water deficit. Crop biomass & SWFAC signal for irrigated vs. veg. water deficit on Cobb soybean in FL in Days after Planting Crop mass (81 Cobb, Irrig) Crop Mass (81 Cobb, Veg Stress) Crop Mass - Obs - Irrig SWFAC (81 Cobb, Irrig) SWFAC (81 Cobb, Veg Stress) Crop Mass - Obs - Veg Stress

10 Leaf Mass (kg/ha) Mass per Seed (mg x 10) Time-series data. Test model response to water deficit Leaf and wt per seed for irrigated vs. veg. water deficit on Cobb soybean in FL in Days after Planting LEAF (81 COBB, IRRI) Wt per Seed (81 COBB, IRRI) LEAF (81 COBB, VEG STRESS) Wt per Seed (81 COBB, VEG. STRESS) SWFAC (81 COBB, VEG. STRESS) LEAF - Obs (IRRI) Wt per Seed - Obs (IRRI) LEAF - Obs (Veg Stress) Wt per Seed - Obs (Veg Stress)

11 Predicted Predicted Predicted Calibration with Extensive End-of-Season Data: Maturity (Days after planting) Testing CROPGRO Soybean model predictions of end-ofseason data across 22 treatments from MN to FL Measured 5000 Seed Yield (kg ha -1 ) Biomass at maturity (kg ha -1 ) Measured Measured

12 Calibration with End-of-Season Data from Georgia irrigated and rainfed variety trials: Site: Tifton, GA Soil: Tifton loamy sand Years: for corn, peanut, & cotton Data: Yield, sowing date, & harvest date Excellent Management: Corn, 7-8 plt/m2, kg N/ha (sowing & split) Peanut, 21 plt/m2 Cotton, 21? plt/m2, kg N/ha Rainfed treatment versus auto-irrigated (Irrigated when 50% of FC left in top 30 cm).

13 Calibrating Cultivar Traits and Soils Traits from Variety Trials Irrigated treatments were used to set cultivar coefficients and calibrate soil fertility (SLPF): Set cultivar life cycle traits to predict maturity (to harvest date for peanut, but 10 days prior to harvest for corn and cotton, gives dry-down & defoliant time) Yield potential traits (to differentiate cultivar yield) SLPF of 0.90 used (0.92 standard for growth experiments) To accurately predict (high enough) yield for rainfed treatments, used following: FAO-56 ET eq. (less stressful than PT Eq). KEP = 0.50 (lower than 0.70 default) for peanut, KEP = 0.75 for cotton, and KEP = 0.80 for maize. Used independent program (not DSSAT SBUILD) to set DUL & SAT (volumetric) to give high enough WHC. Exponential root distribution (SRGF) to 180-cm soil depth.

14 Simulated Days to Maturity Simulated versus measured days to maturity, mid- to fullseason hybrids, Tifton, GA ( ) after calibration Rainfed Irrigated 140 RF: RMSE=4.1, d= IR: RMSE=6.8, d= P1=300, P2=0.3, P5=990 G2=795, G3= Measured Days to Maturity

15 Simulated Grain Yield, kg/ha Simulated versus measured corn grain yield, mid- to fullseason hybrids, Tifton, GA ( ) after calibration Rainfed Irrigated Rainfed: 7674 kg/ha Irrigated: kg/ha Sim irrig: 19.6 cm Measured Grain Yield, kg/ha

16 Simulated Days to Harvest Simulated versus observed days to harvest maturity, Georgia Green cultivar, Tifton, GA ( ) Rainfed Irrigated Rainfed crops tended to be harvested later, especially in drought years. Model does not account for that Measured Days to Harvest

17 Simulated Pod Yield, kg/ha Simulated versus observed peanut pod yield, Georgia Green cultivar, Tifton, GA ( ) after calibration Rainfed Irrigated Measured Pod Yield, kg/ha Rainfed: 4191 kg/ha Irrigated: 5139 kg/ha Sim irrig: 16.0 cm

18 Third Step: Calibrate to regional yields Data: Yields from crop reporting districts (aggregated over many producers & soils). Identify weather sites & associated soils. Obtain typical management Simulate with range of sowing dates for multiple soil pixels per weather station site per year, with typical management & N fertilization. Aggregate (weight by production area) and compare to annual regional yields over years. Adjust soil fertility (SLFP) and soil organic C to predict yield in good wet? seasons. Adjust rooting (SRGF) and delta (DUL-LL) to predict in dry seasons. Adjust these site-specific soil traits plus yield gap factors so simulated yield matches observed interannual yields (mean, wet and dry years).

19 Yield (kg/ha) Crop Models Simulate Yearly Yield Variations due to climate, but site-specific issues remain (soils, pests, management). Calibration Example: regional yields in Georgia M S-A S Not Scaled RMSE fitting = 167 kg/ha Scaled Year

20 Weather Variability: Cum Freq Plot of simulated corn grain yield over 62 years at Camila, GA on Wagram s., Tifton l.s., and Norfolk l.s. soils Wagram 5100 kg/ha Tifton 7379 kg/ha Norfolk 6913 kg/ha Wagram 8022 kg/ha Tifton kg/ha Norfolk kg/ha

21 Weather variability: Cum Freq Plot of simulated irrigation requirement for corn over 62 years at Camila, GA on Wagram s., Tifton l.s., and Norfolk l.s. Wagram 275 mm Tifton 218 mm Norfolk 222 mm

22 Weather variability: Box plot of peanut pod yield over 62 years at Camila, GA on Wagram sand, Tifton l.s., and Norfolk l.s. soils IRRIGATED: Wagram 5310 kg/ha Tifton 6080 kg/ha RAINFED: Norfolk 6071 kg/ha Wagram 2584 kg/ha Tifton 4455 kg/ha Norfolk 3971 kg/ha

23 Sources of Uncertainty in Accurate Prediction of Regional Yields Uncertainty (accuracy?) of model responsiveness to climatic factors (CO2, temperature, ET equations) Uncertainty and error of soil inputs (water-holding capacity, rooting characteristics, soil organic C, fertility) Weather inputs (not site-specific) Sowing date and plant spacing Cultivar characteristics Fertilization Uncertainty of irrigation record (major!!!) Uncertainty of pest management and harvest loss Weather Variability per se Calibrate crop models via WHC, SRGF, SOC, SLPF, amount of irrigation, and pest loss factors, to predict mean and interannual variation. Yield variation caused by weather variation should remain.