Evaluation of Potato Simulation Models and Application of Seasonal Climate Forecast Products for Supply Chain Stability

DESCRIPTION: PepsiCo sources potatoes globally including North America, Thailand, UK, and South America. Predicting potato yields from PepsiCo's contracted fields with sufficient lead time is critical to optimize the supply chain. However, climate change and variability pose threats to securing stable potato supply in the future due to yield variability and long-term impacts on sourcing regions (i.e., losses or gains of growing areas). Climate change projections for England for example suggest drier summers with higher temperatures and reduced rainfall that could impact production of potatoes in the future. Notwithstanding, current operational season climate forecasts allow prediction of crop yields when combined with reliable crop models. Consequently, accurate seasonal climate forecasts and reliable potato crop models can provide potato yield predictions at longer lead time to inform supply chain decisions.

This project seeks to 1) calibrate and evaluate existing process-based potato models including DSSAT-SUBSTOR, LINTULMOFOST 2) investigate the values of operational seasonal climate forecasts including IRI, IBM and Climate Al in terms of potato yield forecasting capability 3) assess long-term suitability of the current sourcing zones and to identify potentially suitable sourcing zones under changing climate scenarios.

Specific Work/Deliverables to be performed:
i) Evaluate and parameterize existing potato models including DSSAT, LINTULIWOFOST in terms of yields, tuber size and dry matter predictive accuracy.

ii) Inter-compare available seasonal climate forecasts (SCFs) from representative institutes including IRI, IBM and ClimateAl to determine the increased forward predictive accuracy for
yield and dry matter vs baseline climatology applied to PepsiCo's existing internal model.

iii) Identify suitable sourcing zones under changing climate scenarios focusing on the US.