As climate change intensifies, the need for innovative agricultural solutions grows. Crop simulation models offer a promising way to accelerate breeding for resilience under adverse conditions. One group of researchers is gathering comprehensive experimental data on barley to improve the reliability of these models, paving the way for more effective adaptation strategies in agriculture.
Barley is the fourth most produced cereal globally, following maize, rice, and wheat and is cultivated and used worldwide. It plays a vital role in various industries, including animal feed, malting, brewing, and distilling, while also serving as a nutritious food source for humans.
According to researcher Dr. Mercy Appiah at University of Göttingen, “Crop models can be used to predict productivity at specific sites using a minimal set of data collected from those locations, which may also be supplemented with values from existing literature. However, to apply these models to new scenarios, such as different environments or genotypes, it is crucial to have high-quality data. This high-quality data should include site-specific observations on weather, soils, management practices, and above and belowground growth of crop cultivars.”
She collaborated with a team of researchers in a focused effort to collect high-quality field experimental data suitable for evaluating and improving models for simulating barley production under Nordic conditions. Until now such data was not yet sufficiently available. They assessed the impact of high-quality data by comparing its effect on the accuracy of crop simulation model predictions against lower-quality datasets. Simulations were conducted using the Agricultural Production Systems Simulator (APSIM), one of the most widely used crop models, capable of simulating the growth and development of over 40 species, including barley.
The field experiments involved plantings at three locations in Denmark where regional experimental data is limited. These sites employed varied management practices, including differences in sowing dates, cultivars, planting density, and fertilization regimes.
The authors used this data, along with values from existing literature, to calibrate the model at three levels of data quality, resulting in three distinct model variants (see table of data provided for calibration under the three levels of data). Low-quality datasets represent the most common situation modelers face when calibrating crop simulation models.
When they compared the prediction accuracy across the model variants, they found that the high-quality model variant outperformed both the low and medium-quality variants in predicting total aboveground biomass and final grain yield. This work illustrates that higher-quality data can significantly improve the accuracy of crop simulation models.
Appiah concludes, “By utilizing crop simulation models that incorporate high-quality data, researchers can enhance their ability to predict how new genotypes will respond to different climate scenarios. This will facilitate the selection of genotypes better suited for future conditions, ultimately supporting breeding programs focused on improving crop adaptability to climate change. With our research we contribute significantly to creating the necessary high-quality data and by illustrating the effect it has on model prediction accuracy we hope to encourage the research community to increase their efforts to conduct more such experiments focusing on collecting data specifically for crop modelling.”
READ THE ARTICLE:
Mercy Appiah, Gennady Bracho-Mujica, Simon Svane, Merete Styczen, Kurt-Christian Kersebaum, Reimund P Rötter, Insights from utilizing data of different quality levels for simulating barley performance under Nordic conditions: The Agricultural Production Systems SIMulator model evaluation, in silico Plants, Volume 6, Issue 2, 2024, diae010, https://doi.org/10.1093/insilicoplants/diae010
