Crop simulation models for future climate scenarios

The University of Göttingen recently assessed the impacts of climate change and shifts in agricultural technologies on crop yields. The paper titled "Effects of changes in climatic means, variability, and agro-technologies on future wheat and maize yields at 10 sites across the globe” is the result of collaborative research between the European MACSUR knowledge hub and colleagues from the AgMIP global modelling community. The research was coordinated by TROPAGS, University of Göttingen.

Identification of yield gaps and site-specific adaptations

The study aimed to quantify the impacts of changes in future climatic means and climate variability on wheat and maize yield gaps. It identified site-specific adaptive agro-technology packages at ten sites located in diverse environments.

Specific objectives were:

• to quantify the maize and wheat yields, their variability, and the corresponding yield gaps for current and future climatic conditions
• to identify key management options capable of considerably reducing yield gaps and increasing yields of maize and wheat. 

Crop simulation models calibrated for Wheat and Maize

To this end, twelve climate scenarios were selected representing possible future changes in temperature, precipitation, global radiation and several variants of changes in the variability of daily temperature and the dry and wet spell durations around mid-century.

The project constructed climate change scenarios using climate projections of global climate models (GCM) from the Coupled Model Intercomparison Project Phase 5 (CMIP5; Taylor et al., 2012).

Computomics is already providing this analysis

Computomics’ concern has been to address the rising global food demand in a changing climate from the start. Advanced technology becomes indispensable in meeting the ambitious requirements of various stakeholders - including yield, quality, sustainability goals, and supply chain continuity. Our machine learning technology xSeedScore® provides the means to adapt crops to a changing climate. By leveraging genotypic, phenotypic, and environmental data (we are using the CMIP6 scenario), xSeedScore® identifies best performing crosses based on current and future environmental conditions.

Predictive Breeding for Barley and other crops 

Computomics offers predictive breeding for a large number of crops. In a recent webinar “Precision Barley Breeding for Sustainable, High-Quality Varieties” we highlighted the process for malting barley, which is strongly impacted by climate change. Barley breeders face the critical task of developing resilient and highest quality malting barley varieties while upholding sustainable practices and building robust supply chains. 

With the example of Barley, find out how using machine learning in plant breeding provides unique insights and clear direction for fast, adaptive breeding.

Listen to the webinar replay and download the slides

We are proud to help breeders answer some of global agriculture’s most pressing problems – increasing the variety, quality, resilience and adaptability of future crops.

For more information, visit our website on Climate-Smart Breeding or contact us!