Speaker
Description
Cereal/legume intercropping allows for reducing inputs while achieving higher crop yields on the same land than expected from the monoculture crop yields of the constituent species. However, several studies have shown that the performance of intercrops depends on the genotypes chosen, crop management, and environmental conditions. Yet, evaluating multiple genotypes on various sites and under different climate and management conditions is not feasible to investigate all possible combinations (genotype × genotype × environment × management) of intercropping in field trials. Given the complexity of mixed cropping systems, crop models can be beneficial for testing hypotheses about the key factors driving competition and compensatory growth between species. Therefore, we conducted a virtual experiment to examine the interactions between a combination of varying plant heights, daily root elongation rate of species in intercropping, and nitrogen input to identify ideal traits and conditions that enhance the performance of spring wheat/faba bean intercropping at Campus Klein-Altendorf, Germany. Combining these three factors resulted in 1024 unique combinations. The simulation was conducted in the SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop Ecosystem Management) modeling framework (Enders et al., 2023). To consider climate variability, 100 years of synthetic climate data were generated by the stochastic weather generator LARS-WG (Semenov and Brooks, 1999) from historical weather data of 21 years (2001-2021). The simulations were run with a model solution in the modeling framework i.e. SIMPLACE (Intercrop model) which was previously calibrated and evaluated. During the scenario runs, the model was annually reinitialized two months before sowing to provide identical initial soil conditions. Moreover, a 100-year-long time series of weather data was clustered to high, medium, and low rainfall determined by the cumulative precipitation during the primary growth period (April to July), aiming to examine the interplay between precipitation levels and various management strategies.
The preliminary results suggest that there is considerable variability in the land equivalent ratio (LER) across different combinations of traits and fertilization rates. Considering each trait and management separately, while keeping other factors at the default value, the variation in plant height seems to have a minimal impact on LER, although there is a trend indicating that a combination of short spring wheat with tall faba bean genotypes leads to lower LER. This phenomenon is likely due to the faba bean intercepting a significant portion of light but being less efficient in its utilization. The partial LER of both species linearly varies with variation in plant height because the radiation interception of one species is inevitable at the expense of the other species, since both species are relatively short stature plants, spatial niche differentiation is negligible. Rooting depth appears as a critical factor governing LER, with combinations of shallow rooting spring wheat and deeper rooting faba bean cultivars exhibiting higher LER, demonstrating the spatial complementarity of water usage. Additionally, it is observed that higher nitrogen inputs result in lower LER compared to no input, underscoring the potential suitability of intercropping under conditions of low nitrogen input.
Considering the interaction of all three factors (nitrogen input, plant height, and rooting depth) and yearly variability, in a high rainfall growth period, optimum LER can be achieved under conditions of low nitrogen input when tall and shallow rooting depth spring wheat are intercropped with relatively short but deeper rooting faba bean cultivars. However, in low rainfall conditions, high LER can be achieved as long as the differences in rooting depth of the species are high enough for spatial complementarity of water use regardless of which species has a deeper rooting system. This suggests a complex interplay of factors influencing LER, highlighting the importance of careful selection and management of crop combinations in intercropping systems. Furthermore, expanding testing to encompass multiple traits, indices of intercropping performance, and management options under varying soil conditions is essential for developing comprehensive guidelines for optimizing intercropping systems.
Reference
Enders, A., Vianna, M., Gaiser, T., Krauss, G., Webber, H., Srivastava, A.K., Seidel, S.J., Tewes, A., Rezaei, E.E., Ewert, F., 2023. SIMPLACE — a versatile modeling and simulation framework for sustainable crops and agroecosystems, In Silico Plant 1–18. https://doi.org/10.1093/insilicoplants/diad006
Semenov MA & Brooks RJ (1999) Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. Climate Research 11:137-148 https://doi.org/10.3354/cr011137
| Keywords | crop model; crop diversification; cultivar traits; crop management |
|---|
