Speaker
Description
1. Introduction
Crop phosphorus (P) nutrition is one of the key sustainability challenges of the 21st Century (Cordell and White, 2014). Crop models are pertinent tools to study and manage phosphorus in agro-ecosystems. However, P modelling is suffering a delay as compared to nitrogen and carbon. A major reason of this delay is the difficulty in formalizing a semi-mechanistic model that predicts adequately the temporal evolution of soil P availability and crop uptake (Das et al. 2019) as well as crop P nutrition status and the feed-back of a P shortage on crop growth.
2. Material, methods
We coupled the STICS crop model (Beaudoin et al. 2023) with a P module based on the FUSSIM-P model (Mollier et al. 2008). The P module is composed of several sub-modules which simulate crop P demand and partitioning, soil P availability and crop P uptake. A major originality of this work is that it relies on soil solution P concentration and P sorption curves (Morel et al. 2021) to simulate soil P availability and critical P dilution curves to simulate crop P requirement.
We evaluated the model against a dataset coming from four field trials having deficient to excessive plant-available soil P, located in different site in France mainland. The trials consisted of fertilizing maize with a mineral fertilizer at three application rates (P0, P1, P2), corresponding to no P added or P added at an equivalent to one or two times crop outputs. Model predictions of both crop P uptake and plant growth indicator (yield, LAI, and biomass) were evaluated both graphically and using statistical indicator such as Nash–Sutcliffe efficiency (EF) or root mean square error (RMSE).
3. Results
The model has shown great capabilities in predicting P uptake both dynamically and at the end of the cropping season for the whole dataset (EF >0.75). The model have satisfactory predictions of crop biomass accumulation (EF >0.5) and leaf area index. When considering each fertilization level separately, the evaluation has shown that the model had predicted the fertilized treatments better than the non-fertilized one (Respectively an EF of 0.73, 0.75 and 0.64 for P2, P1, and P0). The evaluation of the latter remains nonetheless satisfactory for both P uptake and plant growth.
4. Discussion
The good performance of the model is promising as they show that the model is sufficiently robust to simulate maize P uptake across a range of soil P availability under contrasting temperate climatic conditions. Despite the relative simplicity of the model that does not account for all rhizosphere mechanisms, it seems able to behave well even under low level of P under the considered conditions. The fact that the model predicted the fertilized treatment better than the non fertilized treatment raises however the question of model performance under conditions of more severe P shortage or even more lower soil P availability status (e.g. in tropical soils). Further validations on different crop species and soil and climatic conditions are therefore needed.
5. References
Beaudoin N, Lecharpentier P, Ripoche-Wachter D, et al (eds) (2023) STICS soil-crop model: Conceptual framework, equations and uses. éditions Quae
Cordell D, White S (2014) Life’s Bottleneck: Sustaining the World’s Phosphorus for a Food Secure Future. Annual Review of Environment and Resources 39:161–188. https://doi.org/10.1146/annurev-environ-010213-113300
Das B, Huth N, Probert M, et al (2019) Soil phosphorus modeling for modern agriculture requires balance of science and practicality: a perspective. Journal of Environmental Quality 48:1281–1294. https://doi.org/10.2134/jeq2019.05.0201
Mollier A, De Willigen P, Heinen M, et al (2008) A two-dimensional simulation model of phosphorus uptake including crop growth and P-response. Ecological Modelling 210:453–464. https://doi.org/10.1016/j.ecolmodel.2007.08.008
Morel C, Plénet D, Mollier A (2021) Calibration of maize phosphorus status by plant-available soil P assessed by common and process-based approaches. Is it soil-specific or not? European Journal of Agronomy 122:126174. https://doi.org/10.1016/j.eja.2020.126174
| Keywords | Fertilization; Field trial; Soil-crop model; Crop response; Dynamic simulation |
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