Mapping global cropping system: Challenges, opportunities and future perspectives

Abstract

Spatially explicit global cropping system data products, which provide critical information on harvested areas, crop yields, other management variables, are imperative to tackle current grand challenges such as global food security and climate change. These cropping system datasets are also very useful for researchers as they can support various scientific analyses in research projects. Yet, effectively searching, navigating, and fully understanding various global datasets can be a daunting task for researchers and policy analysts. In this review, we first compare a few selected global data products, which use crop census and statistical data as the main data source, and identify key problems and challenges of the global crop mapping such as data accuracy and consistency. We then pointed out the future perspectives and directions in further improving the global cropping data products. Collective mechanisms and efforts with the support of open-access data hosting platforms, standard protocols, and consistent financial support are necessary to produce high-quality datasets for researchers, practitioners, and policymakers. Moreover, machine learning and data fusion approaches can also be further explored in future mapping exercises.