Goosegrass (Eleusine indica L.), a serious annual weed in the world, has evolved resistance to several herbicides including paraquat, a non-selective herbicide. The mechanism of paraquat resistance in weeds is only partially understood. To further study the molecular mechanism underlying paraquat resistance in goosegrass, we performed transcriptome analysis of susceptible and resistant biotypes of goosegrass with or without paraquat treatment. The RNA-seq libraries generated 194,716,560 valid reads with an average length of 91.29 bp. De novo assembly analysis produced 158,461 transcripts with an average length of 1153.74 bp and 100,742 unigenes with an average length of 712.79 bp. Among these, 25,926 unigenes were assigned to 65 GO terms that contained three main categories. A total of 13,809 unigenes with 1,208 enzyme commission numbers were assigned to 314 predicted KEGG metabolic pathways, and 12,719 unigenes were categorized into 25 KOG classifications. Furthermore, our results revealed that 53 genes related to reactive oxygen species scavenging, 10 genes related to polyamines and 18 genes related to transport were differentially expressed in paraquat treatment experiments. The genes related to polyamines and transport are likely potential candidate genes that could be further investigated to confirm their roles in paraquat resistance of goosegrass. This is the first large-scale transcriptome sequencing of E. indica using the Illumina platform. Potential genes involved in paraquat resistance were identified from the assembled sequences. The transcriptome data may serve as a reference for further analysis of gene expression and functional genomics studies, and will facilitate the study of paraquat resistance at the molecular level in goosegrass.

The rapid growth of crop yield in China was maintained by more fossil fuel inputs in the past years, causing concern about the greenhouse gas (GHG) emissions related to crop production. Therefore, this study analyzed historical dynamics of carbon footprint (CF) of 11 major crops in China during 2000–2016 and estimated possible GHG emissions of the system in 2020 under different scenarios. Results indicated that the GHG emissions of the Chinese crop system increased by 20.07% from 2000 to 2016, in which the grain crops contributed to more than 80% of the total emissions. The GHG emissions from grain crops including maize, wheat, and rice as well as sugar crops including sugarcane and sugar beet were increased by 28.07% and 14.27% in the study period, respectively, making up the primary factor of increased GHG emissions of crop system in China. Moreover, if the cropping pattern and agricultural practices is not improved in the future, the GHG emissions from Chinese crop system are estimated to increase by 346.19 million tons in 2020. If advanced agricultural policies and practices are implemented, the GHGs emissions of crop system in China in 2020 are estimated to be 2.92–12.62% lower than that in 2016. Overall, this study illustrated that the crop system in China contributed to the growth of GHG emissions in China over the past decades. Improving utilization efficiency of fertilizers and crop structure in China are the most important ways to reduce GHG emissions from the Chinese crop system.