Intercropping is a key entry point for sustainable intensification of cropping systems in sub-Saharan Africa where variable rainfall conditions prevail. Crop simulation models can complement field experiments to assess the agronomic and environmental performances of intercropping systems under diverse climatic conditions, including hypothetical future climate. So far, crop models that can handle intercropping, such as STICS, have not often been extensively evaluated for tropical conditions and for species grown by farmers in sub-Saharan Africa. The objective of this study was to evaluate the performance of the calibrated STICS model to simulate sorghum-cowpea intercropping systems in rainfed conditions in West Africa. We used data from field experiments conducted at the N'Tarla Agronomic Station in Mali in 2017 and 2018. Two varieties of sorghum (local and improved) with contrasting photoperiod sensitivities were grown as sole crop and intercropped with cowpea, with additive design. Two sowing dates and two levels of mineral fertilization were also investigated. Model simulations were evaluated with observed data for phenology, leaf area index (LAI), aboveground biomass, grain yield and in-season soil moisture. Large variations in aboveground biomass of sorghum and cowpea was observed in the experiment (i.e. 3.5 – 9.6 t/ha for sorghum and 0.4–2.5 t/ha for cowpea), owing to the treatments (i.e. sole vs intercrop, early vs late sowing, no fertilizer input vs fertilizer input). Such variations were satisfactorily reproduced by the model, with EF of 0.81 in calibration and 0.58 in evaluation (with relative rRMSE of 23 % and 43 %) across crops. Sorghum AGB simulations were more accurate (rRMSE of 21 % and EF of 0.54) than cowpea AGB simulations (rRMSE of 25 % and EF of −0.09). The two main observed features of the intercropping system were well reproduced by the model. Firstly, cowpea and sorghum aboveground biomass decreased with intercropping compared with sole cropping, and the decrease in cowpea biomass was greater than the decrease in sorghum biomass. Secondly, despite a reduction in sorghum and cowpea yield, Land Equivalent Ratio of the intercropping for aboveground biomass was always above one. With regard to grain yield, observed LER was above one only in the non-fertilized treatment. The model failed at reproducing this behavior, probably because of insufficiently accurate calibration of the process leading to grain yield formation: rRMSE for grain yield was 49 % in calibration and 41 % in evaluation. Based on these findings, we discuss avenues to improve model calibration and use the model to explore options for sustainable intensification in land constrained sub-Saharan Africa.

在多变的降雨条件盛行的撒哈拉以南非洲，间作是可持续集约化种植系统的关键切入点。作物模拟模型可以补充田间试验，以评估间作系统在不同气候条件下的农艺和环境性能，包括假设的未来气候。到目前为止，可以处理间作的作物模型，如 STICS，通常还没有针对热带条件和撒哈拉以南非洲农民种植的物种进行广泛的评估。本研究的目的是评估校准的 STICS 模型的性能，以模拟西非雨养条件下的高粱-豇豆间作系统。我们使用了 2017 年和 2018 年在马里 N'Tarla 农艺站进行的田间试验数据。两种具有不同光周期敏感性的高粱品种（本地高粱和改良高粱）作为唯一作物种植，并与豇豆间作，采用添加剂设计。还研究了两个播种日期和两个矿物质施肥水平。利用物候、叶面积指数 (LAI)、地上生物量、谷物产量和季节土壤水分的观测数据对模型模拟进行了评估。在试验中观察到高粱和豇豆的地上生物量变化很大（即高粱为 3.5-9.6 吨/公顷，豇豆为 0.4-2.5 吨/公顷），这是由于处理（即单种与间作、早播与晚播、没有肥料投入与肥料投入）。模型令人满意地再现了这些变化，校准中的 EF 为 0.81，评估中的 EF 为 0.58（相对 rRMSE 为 23% 和 43%）。高粱 AGB 模拟（rRMSE 为 21% 和 EF 为 0.54）比豇豆 AGB 模拟（rRMSE 为 25% 和 EF 为 -0.09）更准确。该模型很好地再现了间作系统的两个主要观察特征。首先，与单作相比，豇豆和高粱地上部生物量随间作的减少而减少，且豇豆生物量的减少大于高粱生物量的减少。其次，尽管高粱和豇豆产量有所下降，但地上生物量间作的土地当量比始终高于1。关于谷物产量，观察到的 LER 仅在未施肥处理中高于 1。该模型未能重现这种行为，可能是因为导致粮食产量形成的过程校准不够准确：谷物产量的 rRMSE 在校准中为 49%，在评估中为 41%。基于这些发现，我们讨论了改进模型校准的途径，并使用该模型探索土地受限的撒哈拉以南非洲可持续集约化的选择。