# **Introduction**

Cultivated areas have constantly been increasing since the beginning of agriculture about 10,000 years ago, reflecting the history of human settlements and their economic development (Ramankutty and Foley, 1999, Ellis, 2011). This trend has accelerated over the past three centuries, with a fivefold increase in the global amount of cropland between 1700 and 1990 (Goldewijk and Ramankutty, 2004). Over the recent decades, megatrends have emerged that will have a significant impact on the amount of land that is cropped globally and its distribution across and within countries. These megatrends include climate change which started half a century ago and is predicted to dramatically accelerate (IPCC, 2019), and the ongoing massive urban population growth in sub-Saharan Africa and Asia, which will likely continue over the next two decades (World Urbanization Prospects, 2018), increased demand for food from emerging economies such as China (van Dijk et al. 2021), and the rising global interest in land FDI since the 2008 commodity price boom (Arezki et al., 2015).

With the availability of satellite imagery products and increasing computational capacities, it has been possible to measure global cropland dynamics from space (Ramankutty and Foley, 1999, Kolb et al., 2013, Klimanova et al., 2018, Li et al., 2018, Liu et al., 2018, Song et al., 2018, Mousivand and Arsanjani, 2019, Vaclavik et al., 2013, Potapov et al., 2021, Winkler et al., 2021). Such studies offered the possibility of a consistent measure of global cropland, circumventing issues of national definitions and incompleteness of reporting when relying only on national statistics. They also make it possible to study spatial patterns of cropland dynamics (Haney and Cohen, 2015).

Satellite imagery assessments of cropland, however, are not exempt from issues ranging from measurement errors associated with spatial and temporal resolution and the use of different classification algorithms to identify cropland. In fact, depending on the satellite imagery product, measures of cropland can vary significantly (Gibbs et al., 2011). Potapov et al. (2021) argue that the MODIS product is the most appropriate given relatively high temporal resolution and ground validation.

However, most satellite imagery studies remain descriptive in their nature, with only a few attempts at assessing the drivers of land cover change. The methodologies range from combining different datasets and discussing potential causality (Bren d’Amour, 2017) to modeling land cover changes based on Markov chains (Halmy, 2015, Mousivand and Arsanjani, 2019, Mansour et al., 2020), cellular automata applied to the land cover grid or a combination of the two (Yiulianto et al., 2019; Wang et al., 2019), and simulations in Agent-Based Models (Ligmann Zielinska et al., 2014, Magliocca et al., 2014, Valbuena et al., 2010). These models are computationally intensive and often only applied to specific regions. Even though these approaches identify correlations, none of them establish causality. These studies find that cropland transitions are correlated with important global trends, including climate change (Song et al., 2018), urbanization through encroachment on existing cropland (Bren d’Amour, 2017, Debolini et al., 2018), and demography through increased demand for agricultural production (Wirsenius et al., 2010, Mora et al., 2020). Some papers also discuss the role of institutions (De Beurs and Henebry, 2004) as well as economic globalization in driving land cover changes (Lambin and Meyfroidt, 2010), but these effects have not been studied empirically.

The goal of this paper is to address the above gaps by assessing the recent dynamics of cropland (i.e., measuring movements into and out of cropland) at the global scale and to causally assess the impacts of potential drivers and disentangle their respective contributions. To do that, we consider global cropland changes measured from space using the MODIS data product at a resolution of 500m * 500m over the 2