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Debaeke, P.

Publications and source records attributed to Debaeke, P..

2 recordsLinked to original sources
bioRxiv2019

Analysis of soybean germination, emergence, and prediction of a possible northward expansion of the crop under climate change

Soybean (Glycine max (L.) Merr.) has potential to improve sustainability of agricultural production systems. A higher focus on this crop is needed to re-launch its production in the EU. A better understanding of key determinants affecting soybean establishment represents a first step to facilitate its adoption in cropping systems. To this objective, we conducted laboratory and field experiments in order to better characterize seed germination and seedling growth in relation to temperatures, water content, and soil structure. We then used these data to parametrize the SIMPLE crop emergence model and to evaluate its prediction quality, by comparing observed field germination and emergence data with the predicted ones. Finally, we performed a simulation study over the 2020-2100 period, for three sowing dates, from mid-March to mid-April, in the northern climate of France to evaluate whether future climate change will help expand soybean from Southern to Northern part of the country. Soybean germination was very fast, taking only 15 {degrees}C days to reach 50% germination at optimal conditions. The base, optimum and maximum temperatures were determined as 4, 30 and 40{degrees}C, respectively while the base water potential was -0.7 MPa, indicating a high sensitivity to water stress. The SIMPLE model well-predicted germination and emergence courses and their final rates, compared with the observed field data. The simulation study showed average emergence rate ranging from 61 to 78% with little variability among sowing dates and periods, but a high variability between years. Main causes of non-emergence were seedling mortality due to clods or soil surface crust followed by non-germination and seedling mortality due to drought, especially for mid-April sowing. These results provide a better knowledge of soybean establishment that are encouraging to introduce soybean with early sowings to diversify current cropping systems.

plant biology↗
bioRxiv2019

A new approach to crop model calibration: phenotyping plus post-processing.

Crop models contain a number of genotype-dependent parameters, which need to be estimated for each genotype. This is a major difficulty in crop modeling. We propose a hybrid method for adapting a crop model to new genotypes. The genotype-dependent parameters of the model could be obtained by phenotyping (or gene-based modeling). Then field data for example from variety trials could be used to provide a simple empirical correction to the model, of the form a+b times an environmental variable. This approach combines the advantages of phenotyping, namely that the genotype-specific parameters have a clear meaning and are comparable between genotypes, and the advantages of fitting the model to field data, namely that the corrected model is adapted to a specific target population. It has the advantage of being very simple to apply, and furthermore gives useful information as to which environmental variables are not fully accounted for in the initial model. In this study, this empirical correction is applied to the SUNFLO crop model for sunflower, using field data from a multi-environment trial network. The empirical correction reduced mean squared error (MSE) on the average by 54% for prediction of yield and by 26% for prediction of oil content, compared to the initial model. Most of the improvement came from eliminating bias, with some further improvement from the environmental term in the regression.

plant biology↗