Excessive N fertilization in intensive agricultural areas of China has resulted in serious environmental problems because of atmospheric, soil, and water enrichment with reactive N of agricultural origin. This study examines grain yields and N loss pathways using a synthetic approach in 2 of the most intensive double-cropping systems in China: waterlogged rice/upland wheat in the Taihu region of east China versus irrigated wheat/rainfed maize on the North China Plain. When compared with knowledge-based optimum N fertilization with 30–60% N savings, we found that current agricultural N practices with 550–600 kg of N per hectare fertilizer annually do not significantly increase crop yields but do lead to about 2 times larger N losses to the environment. The higher N loss rates and lower N retention rates indicate little utilization of residual N by the succeeding crop in rice/wheat systems in comparison with wheat/maize systems. Periodic waterlogging of upland systems caused large N losses by denitrification in the Taihu region. Calcareous soils and concentrated summer rainfall resulted in ammonia volatilization (19% for wheat and 24% for maize) and nitrate leaching being the main N loss pathways in wheat/maize systems. More than 2-fold increases in atmospheric deposition and irrigation water N reflect heavy air and water pollution and these have become important N sources to agricultural ecosystems. A better N balance can be achieved without sacrificing crop yields but significantly reducing environmental risk by adopting optimum N fertilization techniques, controlling the primary N loss pathways, and improving the performance of the agricultural Extension Service.

China is experiencing intense air pollution caused in large part by anthropogenic emissions of reactive nitrogen. These emissions result in the deposition of atmospheric nitrogen (N) in terrestrial and aquatic ecosystems, with implications for human and ecosystem health, greenhouse gas balances and biological diversity. However, information on the magnitude and environmental impact of N deposition in China is limited. Here we use nationwide data sets on bulk N deposition, plant foliar N and crop N uptake (from long-term unfertilized soils) to evaluate N deposition dynamics and their effect on ecosystems across China between 1980 and 2010. We find that the average annual bulk deposition of N increased by approximately 8 kilograms of nitrogen per hectare (P < 0.001) between the 1980s (13.2 kilograms of nitrogen per hectare) and the 2000s (21.1 kilograms of nitrogen per hectare). Nitrogen deposition rates in the industrialized and agriculturally intensified regions of China are as high as the peak levels of deposition in northwestern Europe in the 1980s, before the introduction of mitigation measures. Nitrogen from ammonium (NH4(+)) is the dominant form of N in bulk deposition, but the rate of increase is largest for deposition of N from nitrate (NO3(-)), in agreement with decreased ratios of NH3 to NOx emissions since 1980. We also find that the impact of N deposition on Chinese ecosystems includes significantly increased plant foliar N concentrations in natural and semi-natural (that is, non-agricultural) ecosystems and increased crop N uptake from long-term-unfertilized croplands. China and other economies are facing a continuing challenge to reduce emissions of reactive nitrogen, N deposition and their negative effects on human health and the environment.

To demonstrate the benefits of RNA-Seq over microarray in transcriptome profiling, both RNA-Seq and microarray analyses were performed on RNA samples from a human T cell activation experiment. In contrast to other reports, our analyses focused on the difference, rather than similarity, between RNA-Seq and microarray technologies in transcriptome profiling. A comparison of data sets derived from RNA-Seq and Affymetrix platforms using the same set of samples showed a high correlation between gene expression profiles generated by the two platforms. However, it also demonstrated that RNA-Seq was superior in detecting low abundance transcripts, differentiating biologically critical isoforms, and allowing the identification of genetic variants. RNA-Seq also demonstrated a broader dynamic range than microarray, which allowed for the detection of more differentially expressed genes with higher fold-change. Analysis of the two datasets also showed the benefit derived from avoidance of technical issues inherent to microarray probe performance such as cross-hybridization, non-specific hybridization and limited detection range of individual probes. Because RNA-Seq does not rely on a pre-designed complement sequence detection probe, it is devoid of issues associated with probe redundancy and annotation, which simplified interpretation of the data. Despite the superior benefits of RNA-Seq, microarrays are still the more common choice of researchers when conducting transcriptional profiling experiments. This is likely because RNA-Seq sequencing technology is new to most researchers, more expensive than microarray, data storage is more challenging and analysis is more complex. We expect that once these barriers are overcome, the RNA-Seq platform will become the predominant tool for transcriptome analysis.

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