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 °C days to reach 50% germination at optimal conditions. The base, optimum and maximum temperatures were determined as 4, 30 and 40°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.

Soybean (Glycine max (L.) Merr.) may contribute to the agro-ecological transition of cropping systems in Europe, but its productivity is severely affected by summer drought. New drought-avoidance cropping strategies, such as early sowing, require cultivars with high early plant growth under suboptimal conditions. This study aims at phenotyping early-stage root and shoot traits of 10 cultivars commonly grown in Europe. Cultivars were grown in minirhizotrons under two soil moisture status in controlled conditions. Root and shoot traits were evaluated at 10 days after sowing. Field early growth of two cultivars was also analyzed under early and conventional sowing dates. A significant intraspecific variability (p < 0.05) was found for most investigated shoot and root morpho-physiological traits regardless of the soil moisture status under controlled conditions. However, no significant difference among cultivars (p > 0.05) was found in terms of root architectural traits that were mainly affected by water stress. Total root length was positively correlated with shoot length and shoot dry matter (p < 0.05). Under field conditions, the differences between cultivars were expressed by the canopy cover at emergence, which determines the subsequent canopy cover dynamics. The significant early growth difference among cultivars was not related to the maturity group. Cultivars characterized by high root depth and length, high root density and narrow root angle could be considered as good candidates to cope with water stress via better soil exploration. New agronomic strategies mobilizing the diversity of cultivars could thus be tested to improve soybean water use efficiency in response to climate change.

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Developing new cropping strategies (very early sowing, crop expansion at higher latitudes, double cropping) to improve soybean production in Europe under climate change needs a good prediction of phenology under different temperature and photoperiod conditions. For that purpose, a simple phenology algorithm (SPA) was developed and parameterized for 10 contrasting soybean cultivars (maturity group 000 to II). Two experiments were carried out at INRA Toulouse (France) for parameterization: 1) Phenological monitoring of plants in pots on an outdoor platform with 6 planting dates. 2) Response of seed germination to temperature in controlled conditions. Multi-location field trials including 5 sites, 4 years, 2 sowing dates, and 10 cultivars were used to evaluate the SPA phenology predictions. Mean cardinal temperatures (minimum, optimum, and maximum) for germination were ca. 2, 30, and 40°C, respectively with significant differences among cultivars. The photoperiod sensitivity coefficient varied among cultivars when fixing Popt and Pcrt, optimal and critical photoperiods respectively, by maturity group. The parameterized algorithm showed an RMSE of less than 6 days for the prediction of crop cycle duration (i.e. cotyledons stage to physiological maturity) in the field trials including 75 data points. Flowering (R1 stage), and beginning of grain filling (R5 stage) dates were satisfactorily predicted with RMSEs of 8.2 and 9.4 days respectively. Because SPA can be also parameterized using data from field experiments, it can be useful as a plant selection tool across environments. The algorithm can be readily applied to species other than soybean, and its incorporation into cropping systems models would enhance the assessment of the performance of crop cultivars under climate change scenarios.