A Crop Simulation System for Integrating Remote Sensing and Climate Information to Reduce Model
Uncertainty in Crop Yield Assessments
Kiyoshi Honda1, Amor VM Ines2, and Akihiro Yui3
Chubu University, Kusagai, Aichi, Japan
2International Research Institute for Climate and Society, The Earth Institute at Columbia University, NY 10964
3IHI Corporation, Tokyo, Japan
1. Background
When a crop model is used to predict crop yields early in the
growing season, two sources of uncertainties dominate – climate and
model uncertainties.
Climate uncertainty is largest early in the growing season but
tends to decrease as weather data become available as growing
season progresses. Model uncertainty (e.g., due to errors in model
structure, modeling assumptions and other ancillary data), generally
remains constant through the growing season.
Skillful climate forecasts can reduce climate-related uncertainty
in crop yield prediction especially at the earlier stages of the growing
season, while model-related uncertainty can potentially be reduced by
assimilating remote sensing data within the growing season (Ines et
al., 2012; Hansen et al., 2006; de Wit et al., 2007).
We present a crop simulation study in Tokachi, Hokkaido,
Japan that aims to address the reduction of climate and model related
errors in forecasting wheat yields.
Fig. 3. Sample results of Hokusin
wheat variety calibration: a) sim. vs.
obs. yields, b) sim. vs. obs. anthesis
dates, c) sim. and obs. maturity
dates, using single and multiobjective functions in NMCGA.
Fig. 2. Location of the study area: Tokachi, Hokkaido, Japan
2.1 Crop model calibration
We used a Noisy Monte Carlo Genetic Algorithm (NMCGA; Ines and
Mohanty, 2008) to calibrate the genetic coefficients of a wheat variety
Hokusin in DSSAT-CSM (Jones et al., 2003) using 14 years (1996-2010) of
field experimental data from Tokachi Agro Research Institute (fig. 1, 2).
Single Objective:
Obj1 =Average[abs(simYield – obsYield)t], for all t
Objective 1 is mean absolute error (MAE) in yield.
Multiple Objectives:
Δ1=Average[abs(simYield – obsYield)t], for all t
Δ2=Average[abs(simADAT – obsADAT)t], for all t
3.1 Model calibration
Fig. 3 shows some results of the estimation of Hokusin wheat variety’s genetic
coefficients using genetic algorithm. Aside from the single objective function for
yields, we tested several multi-objective functions, e.g., weights combinations of
(0.4,0.3,0.3), (0.2,0.4,0.4), (0.34,0.33,0.33) and (0.4,0.4,0.2). Obviously, the larger the
weight we assign to the target variable, NMCGA will give importance to fitting that
variable. On the average, the yield correlations (R) range between 0.4-0.6 and MAE
between 0.59-0.75 Mg/ha. For ADAT, R range between 0.71-0.87, but with relatively
large MAE, between 19-27 days. Apparently, MDAT is relatively easier to determine,
R range between 0.87-0.90 with MAE between 5-11 days.
3.2 Reducing climate uncertainty
Δ3=Average[abs(simMDAT – obsMDAT)t], for all t
Fig. 4 shows how the crop
simulation system can predict yield at
different lead-times before harvest.
Here, we merge monitored climate
before the time of prediction with the
realizations of climate, based on
climatology, for the rest of the
growing season in 2007-2008. We
update yield prediction every month
until the time of harvest. Note that the
crop simulation system can capture
the yield uncertainty characterized by
Obj2 = w1*(s.Δ1) + w2*Δ2 + w3*Δ3
Objective 2 is a composite mean absolute errors of yield, anthesis (ADAT)
and maturity (MDAT) dates; s is a scaling factor.
2.2 Reducing climate uncertainty
Fig. 1. Crop simulation system for yield prediction
de Wit, A.J.W. and C.A. Van Diepen. 2007. Crop model data assimilation with the Ensemble Kalman filter for improving
regional crop yield forecasts. Agric. For. Meteorol. 146: 38-56.
Hansen, J.W. and A.V.M. Ines. 2005. Stochastic disaggregation of monthly rainfall data for crop simulation studies.
Agricultural and Forest Meteorology. 131: 233-246.
Hansen, J.W., Challinor, A., Ines, A.V.M., Wheeler, T., and V. Moron. 2006. Translating climate forecasts into agricultural
terms: Advances and challenges . Clim. Res. 33: 27-41.
Ines, A.V.M., Das, N.N., Hansen, J.W. and E. Njoku 2012. Assimilation of remotely sensed soil moisture and vegetation
with a crop simulation model. Remote Sensing of Environment. In review.
Ines, A.V.M. and B.P. Mohanty. 2008c. Parameter conditioning with a noisy Monte Carlo genetic algorithm to estimate
effective soil hydraulic properties from space. Water Resources Research. 44, W08441, doi:10.1029/2007WR006125.
Jones, J.W., Hoogenboom, G, Porter, C., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J.
and J.T. Ritchie. 2003. The DSSAT Cropping System Model. Europ. J. Agronomy. 18: 235-265.
We developed a framework for merging monitored climate information
with climate forecasts along the growing season (fig. 1). Daily realizations of
climate forecast is generated using a conditioned stochastic weather generator
(Hansen and Ines, 2005). If climate forecast is not available or not being
better than climatology, the system will generate weather realizations from
2.3 Reducing modeling uncertainty
Based on the work of Ines et al. (2012) on EnKF-DSSAT-CSM-Maize,
we are developing a wheat model implementation. The system will be able to
assimilate remotely sensed soil moisture and leaf area index (LAI) to correct
modeling errors in the prediction of wheat yield at different lead-times in the
growing season.
Fig. 4. Predicting wheat yield at
different lead times. Note: we used
realizations of weather for 2007 –
3.3 Reducing modeling uncertainty
Discrepancy between observed
and predicted yield at harvest (fig. 4)
can be minimized by the application
of the EnKF-DSSAT-CSM-Wheat,
which is still under development.
Corresponding address: [email protected]

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