maize-soybean rather than a maize-maize-soybean rotation will be chosen for subsidy levels between US$0 and US$40 per acre. Livingston _et al._ (2015) suggest that an incentive payment of US$4/acre could incentivize reductions in maize monoculture and fertilizer application. Study of rotational choices in other settings could help to better understand trade-offs to be considered in this context. Yet constraints on data availability have traditionally limited the scope for such investigation.

The main data source for assessing economic impacts has been from controlled trials run by agricultural experiment stations, often in the United States. Studies using such data suggest that rotating crops increases resilience (Krupinsky _et al._ 2006) and provides economic benefits by reducing input cost or increasing yields.<sup>2</sup> Maize and soybean grown in annual rotation yielded 13% and 10% more than when grown in continuation, with slightly higher effects on marginal soils (Porter _et al._ 1997). Hennessy (2006) and Livingston _et al._ (2015) find rotation effects of about 25% in similar settings. But apart from being geographically concentrated, agronomic trials are costly, associated with long lead times, and consider only a limited set of crops or management options.

To overcome these limitations, efforts to exploit alternative data sources proceeded in two directions. One way to improve data availability builds on collaboration with the private sector to develop samples, e.g., from suppliers of agro-processing plants <mark>(Koch</mark> _<mark>et al.</mark>_ <mark>2018), or on use of field-level records from crop</mark> insurance (Seifert _et al._ 2017). By highlighting spatial heterogeneity in effect size and a significant role of crop varietal traits, climate (temperature and soil moisture) and management (treatment of crop residue, tillage options, and planting dates), such studies suggest that real-world data can add significant insights to what is available from agronomic trials.

A second avenue is to rely on information derived from remotely sensed imagery. For the United States, the cropland data layer (CDL), based on 30 m pixels from Landsat imagery that are available for all 48 states since 2008 (Boryan _et al._ 2011) are a frequently used source. With proper methodology (Lark _et al._ 2017), the CDL can be used for field-level analysis. Wang and Ortiz-Bobea (2019) use 2005-14 CDL data for the US midwest to show that the 2005-09 biofuel boom triggered a significant increase in maize monocropping and GM maize adoption, especially in the vicinity of newly established biofuel plants.<sup>3</sup> Hendricks _et al._ (2014) use CDL data to argute that estimates of supply elasticities based on aggregate data may be biased and to show that use of field-level data that take into account crop rotation benefits results in estimates that are larger in the short than the long run, contrary to standard models.

> 2 For example, comparing a two-year standard maize-soybeans rotation with more diverse a three- or four-year rotations that includes small grains and leguminous fodder crops (clover or alfalfa) led to equal or greater profit, reduced input use, and significantly lower toxicity (Davis _et al._ 2012). 3 Economic factors may also lead producers to forgo the yield advantage of rotating crops for higher profitability from monoculture as has indeed been observed in the United States where major crops are increasingly grown in monoculture patterns (Plourde _et al._ 2013).

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