Tribouillois 2018

Analysis and modeling of cover crop emergence: Accuracy of a static model and the dynamic STICS soil-crop model

Tribouillois, H. ; Constantin, J. ; Justes, E. (2018). European Journal of Agronomy, 93 : 73-81.

Cover crops are increasingly used in agriculture to provide a variety of ecosystem services (e.g. reducing nitrogen leaching, storing carbon in soils) during fallow periods, but it can be challenging to successfully establish them in summer, when water availability may be low. Thus, it is crucial to better quantify, understand and predict the emergence date of a variety of cover crops from multiple contexts in impact assessment studies. The objectives of this study were to 1) analyze variability in emergence dynamics among cover crops grown in fields, 2) identify variables that influence emergence the most and use them to develop a simple model to predict emergence date and 3) calibrate the STICS model to improve its predictions of cover crop emergence. STICS was chosen because it is a dynamic soil-plant model widely validated in the literature for simulating the production of cover crop services. We analyzed emergence dynamics of ten cover crop species sown under a variety of soil, climate and sowing conditions from 18 experimental sites across France. We developed and independently evaluated a static model based on these data to predict the number of days until emergence. We then calibrated STICS using the same data. Results revealed a mean emergence duration of 12 days for all species, but with high variability among experimental sites and years. The simple static model contained only three variables, with the number of consecutive days without significant water input after sowing the most significant. Overall, both the model and STICS predicted emergence date well in the calibration and validation datasets. Accurate prediction of soil moisture in the seedbed and soil water balance is a key factor to accurately predict cover crop emergence. Accurately predicting emergence of cover crops in crop models will help to assess the former’s ability to provide ecosystem services in cropping systems in current and future climates.

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Date de modification : 23 août 2023 | Date de création : 08 janvier 2018 | Rédaction : Equipe Projet Stics