Calibrating the STICS soil-crop model to explore the impact of agroforestry parklands on millet growth

Field Crops Research

Sidy Sow

Yolande Senghor

Khardiatou Sadio

Rémi Vezy

Olivier Roupsard

François Affholder

Moussa N’dienor

Cathy Clermont-Dauphin

Espoir Koudjo Gaglo

Seydina Ba

Adama Tounkara

Alpha Bocar Balde

Yelognissè Agbohessou

Josiane Seghieri

Saidou Nourou Sall

Antoine Couedel

Louise Leroux

Christophe Jourdan

Diaminatou Sanogo Diaite

Gatien N. Falconnier

Calibration

Crop model

Senegal

Sustainable intensification

Validation

Virtual experiment

2024-02-01

10.1016/j.fcr.2023.109206

https://www.sciencedirect.com/science/article/pii/S0378429023003994

Context
Agroforestry systems provide critical benefits for food security and climate change mitigation. Yet, they are complex and heteregoneous sytems hard to optimize. The use of process-based crop models provides an opportunity to understand better the interactions between soil, crop, tree and climate and explore the impact of agroforestry on crop growth, for contrasting crop management.
Objective
The objectives of this study were to i) calibrate the soil-crop STICS model for pearl millet (Pennisetum glaucum) in order to simulate millet potential growth and impact of water and nitrogen limitations on millet growth in open fields and ii) explore the impacts of the parkland tree Faidherbia albida on millet performance for contrasting N fertilizer inputs.
Methods
We gathered a comprehensive database of 28 agronomically contrasting situations, ranging from near-potential growth to drought- and N-stress, either on-station or in a farmer’s home- or bush-fields. Parameters governing relevant plant and soil processes for grain yield were calibrated in a stepwise procedure. The calibrated model was used to explore the impact on millet growth of the widely reported benefits of Faidherbia albida, namely a minimum reduction in radiation thanks to the peculiar reverse phenology of this tree, improvement of soil water content at the beginning of the growing season and of organic nitrogen in the topsoil.
Results
Model simulations with the calibration dataset were reasonably accurate for aboveground biomass and grain yield. Normalized Root Mean Square Errors (nRMSE) for these variables were 29% and 26%, respectively; model efficiency (EF) was 0.58 for both. The nRMSE ranged from 33% to 53% for Soil Water Content (SWC), plant N uptake, grain number, and leaf-area index (LAI). Model accuracy was lower with the evaluation dataset. In the virtual experiment, millet yield decreased with incoming solar radiation, but only at levels of shading (e.g. below 80% of the radiation obtained with full sun) that do not occur under Faidherbia. The decline was greater when millet was fertilized. Increasing the initial soil water content did not affect simulated millet growth. Simulated millet aboveground biomass and grain yield increased with higher organic nitrogen contents of the topsoil, by 80% when millet was not fertilizer, but only by 25% when millet was fertilized.
Implications
This study provides the first set of comprehensively calibrated parameters for applying STICS to pearl millet in open cropland. A virtual experiment with historical climate suggests that the benefits of Faidherbia decrease if farmers intensify crop production by adding more mineral N fertilizer. Hence, precise fertilizer management is recommended in Faidherbia parklands. These results illustrate the benefits of process-based crop modelling for better understanding the functioning of agroforestry systems.

From the soil plant system to the field