Speaker
Description
Introduction
Barley (Hordeum vulgare L) is one of the world's primary cereal crops, and it ranks second in Europe after bread wheat (Triticum aestivum). In Germany, spring and winter barley account for 1.9 million hectares, whereas winter wheat covers 3 million hectares. Assessing yield gaps in agricultural crops is crucial for optimizing production and ensuring food security (Guilpart et al., 2017). Analyzing yield gaps enables one to evaluate scenarios for food security and find chances to increase crop productivity (Van Ittersum et al., 2016). The majority of research on the evaluation of yield gaps, however, has been based on the yield potential simulation of a single crop model.
Materials and methods
Here, we utilized advanced modeling techniques to evaluate the yield gaps of four spring barley cultivars (Avalon, Barke, Quench, RGT Planet) across 30 locations in Germany over 19 growing seasons (2000-2018). The APSIM Next Generation (Holzworth et al., 2018), DSSAT, and and STICS (Brisson et al., 2003) multi-crop models were parameterized, calibrated and evaluated, enabling a spatially explicit analysis considering different sowing dates (March and April), and differences in soil characteristics. The calibration process was performed using the R packages CroptimizR (Buis et al., 2023) and CroPlotR (Vezy et al., 2023).
Results and discussion
The calibration showed robust agreement between observed and simulated phenology (anthesis and maturity dates) and grain yield (cf., Figure 1). By integrating R-based calibration procedures, we were able to fine-tune model parameters efficiently, accounting for spatial and temporal variations in environmental conditions and crop responses. This approach not only improved the accuracy of yield predictions but also provided insights into the sensitivity of the models to different input variables. Our results revealed significant variations in yield potentials among the cultivars and across the geographical locations. APSIM Next Generation and STICS models demonstrated robust capabilities in simulating the complex interactions between environmental factors, crop management practices, and genetic traits of the cultivars. The spatially explicit approach provided insights into the regional variations in yield potential and identified areas with significant yield gaps between potential simulated yield and on-farm yields. Moreover, the impact of sowing dates on barley yield was elucidated, with March sowing generally exhibiting higher yield potential compared to April sowings, albeit with notable variations depending on the cultivar and location. These findings underscore the importance of optimizing sowing strategies to maximize barley yields in different regions of Germany. Overall, our study highlights the utility of advanced modeling techniques for assessing yield gaps in spring barley cultivars at a spatially explicit scale.
Conclusion
The insights gained from this research can inform targeted interventions and agronomic practices aimed at bridging yield gaps, ultimately contributing to sustainable intensification of barley production in Germany. It will also facilitate using the evaluated models in developing a decision support system for agroforestry simulations of various crop rotations across Germany.
References
Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., Zimmer, D., Sierra, J., Bertuzzi, P., Burger, P., Bussière, F., Cabidoche, Y. M., Cellier, P., Debaeke, P., Gaudillère, J. P., Hénault, C., Maraux, F., Seguin, B., and Sinoquet, H. (2003). An overview of the crop model stics. European Journal of Agronomy 18, 309-332.
Buis, S., Lecharpentier, P., Vezy, R., and Ginet, M. (2023). CroptimizR: A Package to Estimate Parameters of Crop Models [WWW Document]. . https://doi.org/10.5281/zenodo.4066451
Guilpart, N., Grassini, P., Sadras, V. O., Timsina, J., and Cassman, K. G. (2017). Estimating yield gaps at the cropping system level. Field Crops Research 206, 21-32.
Holzworth, D., Huth, N. I., Fainges, J., Brown, H., Zurcher, E., Cichota, R., Verrall, S., Herrmann, N. I., Zheng, B., and Snow, V. (2018). APSIM Next Generation: Overcoming challenges in modernising a farming systems model. Environmental Modelling & Software 103, 43-51.
Van Ittersum, M. K., Van Bussel, L. G., Wolf, J., Grassini, P., Van Wart, J., Guilpart, N., Claessens, L., De Groot, H., Wiebe, K., and Mason-D’Croz, D. (2016). Can sub-Saharan Africa feed itself? Proceedings of the National Academy of Sciences 113, 14964-14969.
Vezy, R., Buis, S., Lecharpentier, P., and Giner, M. (2023). CroPlotR: A Package to Analyse Crop Model Simulations Outputs with Plots and Statistics [WWW Document]. https://doi.org/https://doi.org/10.5281/zenodo.4066451
| Keywords | Yield gaps; potential yield; farmer’s survey yield; spatial simulations; multi-model ensemble |
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