Rice landraces, including Asian rice (Oryza sativa L.) and African rice (Oryza glaberrima Steud.), provide important genetic resources for rice breeding to address challenges related to food security. Due to climate change and farm destruction, rice landraces require urgent conservation action. Recognition of the geographical distributions of rice landraces will promote further collecting efforts. Here we modelled the potential distributions of eight rice landrace subgroups using 8351 occurrence records combined with environmental predictors with Maximum Entropy (MaxEnt) algorithm. The results showed they were predicted in eight sub-regions, including the Indus, Ganges, Meghna, Mekong, Yangtze, Pearl, Niger, and Senegal river basins. We then further revealed the changes in suitable areas of rice landraces under future climate change. Suitable areas showed an upward trend in most of study areas, while sub-regions of North and Central China and West Coast of West Africa displayed an unsuitable trend indicating rice landraces are more likely to disappear from fields in these areas. The above changes were mainly determined by changing global temperature and precipitation. Those increasingly unsuitable areas should receive high priority in further collections. Overall, these results provide valuable references for further collecting efforts of rice landraces, while shedding light on global biodiversity conservation.

The contributions of crop germplasm resources to food security depend on their conservation and accessibility for use. The automated warehouse has begun to be applied to the ex situ preservation of crop germplasm resources in the crop genebank. Identifying the proper storage scheme for potentially hundreds of thousands of seeds is a primary task that faces the crop genebank manager during the design of a new automated crop genebank. There are mainly three areas to focus on, hardware and software, seeds storage assignment policy and seeds labelling technology. This paper aims to propose automated crop genebank storage schemes for two kinds of crop genebank (the long-term crop genebank and the middle-term crop genebank), which supports managers in determining the technologies that can be applied in the automated crop genebank. Firstly, the selection of hardware and software should be based on the functional orientation of the long-term crop genebank and the middle-term crop genebank. Secondly, for the seed storage assignment policy, the sequential storage assignment is designed for the long-term genebank while the cache storage assignment is developed for the middle-term genebank. Finally, a QR code labelling technology based on image recognition is designed for both the long-term crop genebank and the middle-term crop genebank.