A new publication by scientists from the International Potato Center (CIP) highlights the usefulness of combining a crop growth model, remote sensing, and plant ecophysiological tools to assess genetic efficiencies in potato landraces.
Producing enough food to feed more than 1.9 billion people, the projected global population by 2050, will be a challenge, particularly given the increasingly restrictive circumstances due to climate change, climate variability, pandemics, and other shocks that put food production at risk.
In the face of these challenges, certain crops are vital to global food security. The potato is the third most-produced edible crop worldwide, and, therefore, a key food security crop. It has a widely adaptive range, great yield potential, and high nutritional value. There are more than 4,900 types of potatoes between traditional and improved varieties, mostly found in the Andes. In order to improve potato yield and yield prediction, a better understanding of potato physiology and modeling is needed, especially for the Andean region where climate change is affecting traditional farming practices and where potato is a staple food.
While pests and diseases have been considered as the main drivers of yield gap in highly important crops like potato, there is a potential that it is poorly explored and depends on the genetic expression of three efficiencies:
i) How well is the foliar arrangement and density to capture light (efficiency in the light interception
