Well come to 

December 31, 2014

T. Houska, S. Multsch, P. Kraft, H.-G. Frede, and L. Breuer

Computer simulations are widely used to support decision making and planning in the agriculture sector. On the one hand, many plant growth models use simplified hydrological processes and structures – for example, by the use of a small number of soil layers or by the application of simple water flow approaches. On the other hand, in many hydrological models plant growth processes are poorly represented. Hence, fully coupled models with a high degree of process representation would allow for a more detailed analysis of the dynamic behaviour of the soil–plant interface. 

June 01, 2011

Florian Hartig, Justin M. Calabrese, Björn Reineking, Thorsten Wiegand, Andreas Huth

Statistical models are the traditional choice to test scientific theories when observations, processes or boundary conditions are subject to stochasticity. Many important systems in ecology and biology, however, are difficult to capture with statistical models. Stochastic simulation models offer an alternative, but they were hitherto associated with a major disadvantage: their likelihood functions can usually not be calculated explicitly, and thus it is difficult to couple them to well-established statistical theory such as maximum likelihood and Bayesian statistics. 

December 01, 2015

D.Makowskia,∗, S.Assengb, F.Ewert, et al.,

-Statistical models were developed to emulate ensembles of process-based crop models.

-They describe the between-crop model variability of the simulated yield data.

-They can be used to compute mean yield loss and probabilities of yield loss.

-Their interests were illustrated for maize, wheat, and rice.

August 01, 2006

D. Makowski, J. Hillier, D. Wallach, B. Andrieuand M.-H. Jeuffroy

December 01, 2014

Sotirios V. Archontoulis, Fernando E. Miguez, Kenneth J. Moore

-We developed a methodology and an optimization tool to calibrate APSIM soybean phenology.

-We estimated phenological parameters for 40 soybean cultivars covering maturity groups from 00 to 6.

-Our approach utilizes few input data, flowering and physiological dates and soybean maturity group.

-Our approach can be applied to all the short-day species included in the APSIM PLANT model.

January 31, 2016

David Makowski

-We show how to adjust ensemble of crop model outputs to yield observations.

-Ensembles of crop model outputs are summarized by their means and variances.

-Means and variances are updated using Bayesian linear analysis.

-This approach was able to reduce uncertainty in yield projection under climate change.