Predicting optimum crop designs using crop models and seasonal climate forecasts

Area of Science:

• Agricultural Science
• Climate Science
• Crop Physiology

Background:

• Increasing food demand and climate change necessitate enhanced agricultural productivity without expanding farmland.
• Dryland cropping systems face challenges in productivity, farmer profitability, and risk management.
• Optimizing crop designs through genetic (G) and management (M) strategies tailored to the environment (E) is crucial.

Purpose of the Study:

• To evaluate the value of seasonal climate forecasting skill in informing crop designs (GxM).
• To test the integration of a crop simulation model with a climate forecasting system.
• To determine if climate forecasts can provide predictive insights for optimal GxM selection.

Main Methods:

• Linked the Agricultural Production Systems sIMulator (APSIM) crop model with the POAMA-2 seasonal climate forecasting system.
• Utilized climate forecasts to simulate and identify optimal crop designs (GxM) for dryland environments.
• Assessed the reliability and skill of the climate forecasting system (GCM POAMA-2).

Main Results:

• The POAMA-2 climate model demonstrated reliability and skill in seasonal climate forecasting.
• Linking POAMA-2 with APSIM enabled the informed selection of optimal crop designs.
• The integrated system showed potential for improving agricultural decision-making.

Conclusions:

• Reliable and skillful seasonal climate forecasting systems, when interfaced with crop models, can effectively inform optimal crop designs.
• This approach offers a pathway to increase farmer profits and mitigate risks in dryland agriculture.
• Integrating climate forecasting into crop management is vital for sustainable food production.