## 4 Conclusions  
Based on the 35 site-years CO\({}_{2}\) flux measurements from 11 cropland sites globally, we investigated the variation characteristics of multi-timescales ELUE as well as the effects of the environmental factors (i.e., T\({}_{\text{a}}\), R\({}_{\text{n}}\), PAR, EF, VPD, and P) and vegetation physiological factors (i.e., NDVI, g\({}_{\text{c}}\)) on ELUE variation across the four typical agroecosystems that are widespread on earth, including paddy rice, soybean, maize and winter wheat. Specifically, a trough of hourly ELUE was found across the four typical agroecosystems. The variability in daily ELUE exhibited apparent seasonal dynamics with crop growth, and the summer maize croplands possessed a higher ELUE than the other three C\({}_{3}\) agroecosystems. The crop-specific variation is in control of ELUE; overall, the variations in ELUE and GPP were positively related to the variations of NDVI, EF (except for that in paddy rice croplands), g\({}_{\text{c}}\), T\({}_{\text{a}}\) and R\({}_{\text{n}}\), while the opposite relationship was found with VPD. No significant consistent relationship was found between total growing season precipitation and ELUE or GPP. The ELUE variation trend is approximately proportional to the square root of inverse time after a precipitation event across the different crop sites. The results showed that NDVI is the most confident estimator for ELUE in the four ecosystems. Water availability plays a secondary role controlling ELUE, and vegetation productivity is more constrained by water availability than T\({}_{\text{a}}\) in summer maize, soybean and winter wheat. This study improves our understanding of the influence of bio-physiology and environmental factors on ELUE. [PERSON], [PERSON] and [PERSON] conceived and designed the study. [PERSON] and [PERSON] made substantial contributions to the acquisition, analysis and interpretation of the data. [PERSON] and [PERSON] wrote the first draft of the article. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] reviewed and edited the draft. [PERSON], revised the article according to the opinions of the reviewer and completed the proofreading work. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number \"51922072\", \"51779161\", \"51009101\" and the Fundamental Research Funds for the Central Universities, grant number \"2017 CDLZ-N22\", \"2018 CDPZH-10\", \"2019 CDPZH-10\", \"2019 CDLZ-10\". Not applicable. **Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [The Fluxnet 2015 Dataset ([[http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/](http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/)]([http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/](http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/))); The MODIS 16-day NDVI was downloaded from MODIS 13Q1, [[https://modis.ornl.gov/data.html](https://modis.ornl.gov/data.html)]([https://modis.ornl.gov/data.html](https://modis.ornl.gov/data.html))] (accessed on 1 November 2021). This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04 ER63917 and DE-FG02-04 ER63911)), AsiaFlux, CarboEuropeIP, Carboltaly, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan). The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux, and AsiaFlux offices. We are also grateful for the Distributed Archive Center of Oak Ridge National Laboratory and the Earth Observing System Data for making MODIS data available.