## 3 Data
### Eddy Covariance Flux Data and Climate Zone Classification Data  
Eddy covariance flux data from 19 cropland sites worldwide were downloaded from the FLUXNET [52] ([[http://www.fluxdata.org](http://www.fluxdata.org)]([http://www.fluxdata.org](http://www.fluxdata.org)), accessed on 25 January 2022), ChinaFlux [53] ([[http://www.chinaflux.org/](http://www.chinaflux.org/)]([http://www.chinaflux.org/](http://www.chinaflux.org/)), accessed on 25 January 2022), AsiaFlux [54] ([[http://db.cger.nier.go.jp/asiafluxdb/?page_id=16](http://db.cger.nier.go.jp/asiafluxdb/?page_id=16)]([http://db.cger.nier.go.jp/asiafluxdb/?page_id=16](http://db.cger.nier.go.jp/asiafluxdb/?page_id=16)), accessed on 25 January 2022), and AmeriFlux [55] ([[https://ameriflux.lbl.gov/](https://ameriflux.lbl.gov/)]([https://ameriflux.lbl.gov/](https://ameriflux.lbl.gov/)), accessed on 25 January 2022). These sites were selected as GPP measurements were available or could be derived from CO\({}_{2}\) flux measured by the eddy covariance system over a period of at least three years (Table 2). The spatial locations of these eddy covariance flux tower sites can be seen in Figure 1.  
The climate zone classification data used in this study are from the most commonly used climate classification map of Koppen-Geiger with a spatial resolution of 1 km [56]. The Koppen classification method divides the global land surface into five major climate zones based on the relationship between climate and vegetation types, i.e., tropical (A), arid (B), temperate (C), boreal (D), and polar (E). Each one of the five major climate zones is further subdivided into sub-classes (Figure 1) The detailed list of the sub-climate zones Koppen climate classification is given in Table A1 of Appendix A.  
Eddy covariance systems directly measure CO\({}_{2}\) flux (i.e., net ecosystem exchange, NEE) rather than GPP. For the sites from the FLUXNET database, NEE was separated into the two components, GPP and ecosystem respiration, using two different algorithms. The first algorithm was based on the night-time partitioning algorithm [57] where respiration model was parameterized based on the night-time data and then applied to the extrapolation from night to daytime. GPP was then estimated as the difference between respiration and NEE. The second method was the day-time partitioning algorithm [58] where NEE was modelled using the common rectangular hyperbolic light-response curve which was  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Site** & **Latitude** & **Longitude** & \begin{tabular}{c} **MAT** \\ **(°C)** \\ \end{tabular}  & \begin{tabular}{c} **MAP** \\ **(mm)** \\ \end{tabular}  & \ \begin{tabular}{c} **Climate** \\ **Zone** \\ \end{tabular}  & **Period** & **Crops** \\ \hline CH-Oe2 & 47.2863 & 7.7343 & 9.8 & 1155 & Dfb & 2004–2014 &
\begin{tabular}{c} winter wheat/winter \\ barley/rape \\ maize \\ winter wheat/winter \\ barley/rape/potatoes/summer \\ maize \\ winter wheat/winter \\ barley/rape/potatoes/summer \\ maize \\ winter wheat/winter \\ barley/rape \\ winter wheat/potatoes \\ winter wheat/winter \\ barley/rape \\ winter wheat/potatoes \\ winter wheat/winter \\ barley/rape \\ winter wheat/potatoes \\ winter wheat/winter \\ barley/rampe \\ \\ winter wheat/winter \\ barley/rampe \\ \\ winter wheat/winter \\ barley/summer maize \\ winter wheat/winter \\ barley/rampe \\ winter wheat/winter \\ water wheat/winter \\ water wheat/winter \\ water wheat/winter \\ water  
a function of both GPP and respiration and parameterized based on day-time data. For the sites from database other than FLUXNET, the standardized method provided in the REddyProc online tool [59] was used for processing eddy covariance data.