Motivation: Expression profiling in diverse tissues is fundamental to understanding gene function as well as therapeutic target identification. The vast collection of expressed sequence tags (ESTs) and the associated tissue source information provides an attractive opportunity for studying gene expression. Results: To facilitate EST-based expression analysis, we developed GEPIS (gene expression profiling in silico), a tool that integrates EST and tissue source information to compute gene expression patterns in a large panel of normal and tumor samples. We found EST-based expression patterns to be consistent with published papers as well as our own experimental results. We also built a GEPIS Regional Atlas that depicts expression characteristics of all genes in a selected genomic region. This program can be adapted for large-scale screening for genes with desirable expression patterns, as illustrated by our large-scale mining for tissue- and tumor-specific genes. Availability: The email server version of the GEPIS application is freely available at http://share.gene.com/share/gepis. An interactive version of GEPIS will soon be freely available at http://www.cgl.ucsf.edu/Research/genentech/gepis/. The source code, modules, data and gene lists can be downloaded at http://share.gene.com/share/gepis Supplementary information: Supplementary tables and figures are available at http://www.cgl.ucsf.edu/Research/genentech/gepis/

In facing the situation of food security, the issue of the non-grain production of cultivated land (NGPOCL) in China has attracted more and more attention. To clarify whether rural land transfer promotes NGPOCL in China, this study collected provincial panel data from 2015 to 2020, and constructed multiple econometric models to explore the impact of land transfer on the planting structure of cultivated land. It is observed that an increase in land transfer area does not promote but significantly inhibits NGPOCL at the national level. The research conclusion is still valid after the robustness test of replacing the explained and core explanatory variables and solving the endogenous problems. The heterogeneity analysis suggests that the inhibitory effect is more pronounced in areas with better topography, economy, or grain production conditions. The analysis of the moderating effect shows that the diversification of land transfer modes and directions can mitigate this inhibitory effect, while the signing of land transfer contracts does not show a significant regulatory effect. This paper reveals the effect of land transfer on grain cultivation from a macro perspective. Its conclusions may provide policy implications for the optimization of rural land transfer and curbing NGPOCL in China.

The production gap of soybean (Glycine max L. Merr.) has been expanding in China recently, due to the increasing demand and decreasing production. Identifying soybean production dynamics is contributable to appropriate adjustment of crop rotation system and efficient use of agricultural resources—and thus to ensure food security. Taking the North China plain (NCP) as a case area, this study first analyzed the spatiotemporal dynamics of soybean production during 1998–2015 based on the spatial autocorrelation method, and then calculated contributions to the total production by yield and sown area using the factor decomposition method. The results indicated that total soybean production in the NCP decreased dramatically from 1998 to 2015 and showed a decreasing trend in 80.4% (263) of the counties, mainly (83.9%) contributed by the shrinkage of sown area, largely caused by decreasing benefit. Two regions were found with significantly spatial clustering degree of soybean production. In the south part of NCP, soybean production was highly clustered in Anhui province, and in north it was mainly clustered in western Hebei plain. It was found that soybean production in the NCP was rather sensitive to the return gaps of soybean from maize (Zea mays L.). These imply that the reduced area of soybean production can be restored if the return is improved by adopting appropriate policies such as appropriate subsidies. These findings could be helpful for the policymakers to make soybean production planning in the NCP, contributing to the national revitalization strategy of soybean production.

The North China Plain (NCP) is the most important winter wheat production region and an area of water shortage in China. The stability of winter wheat (T. aestivum L.) production in spatial pattern and the sustainability of water resources have been a major policy concern in China. This study explored the barycenter shift and change trends of wheat total production during 1998–2015, using methods of barycenter model, Sen’s slope, and Mann Kendall test, and analyzed the influence of external factors and the response of water resources. Results indicated that the barycenter of wheat production moved southwards by 115.16 km during 1998–2015, with an average speed of 6.77 km/year. For the entire NCP, the total production showed phased changes during the study period: It decreased during 1998–2003, and then continuously increased during 2004–2015. Of the wheat production increase in the NCP, yield increase and sown area expansion averagely contributed 64.5% and 35.5%, respectively, and the contribution proportion of yield increase continuously increased since 2003. At county level, total wheat production showed a significant increase and decrease trend in 87 and 29 counties, mainly distributed in the southern and northern NCP, respectively. The increase of total production at county level was mainly contributed by yield growth in the southern NCP, while the decrease in the north was due to the reduction of sown area to great extent. The southward shift was jointly resulted by the spatial variation of input factors, benefit, and water prices. These spatial pattern changes alleviated the water pressure in the north region to some extent, in the case of ensuring the production increase of winter wheat. Therefore, the current spatial shift should be continuously promoted in the future.

The crop planting structure in the world has shown a trend of “non-grain”, which will shake the foundations of global food security in the long run. As a basic and important production factor, changes in land will have an impact on farmers’ crop planting decisions. In this paper, we take China, a country that is experiencing land transfer, “non-grain” production, and farmer differentiation, as the research area, use the household survey data at the national level, and adopt the methods of Propensity Score Matching (PSM) and multiple regression models to reveal the impact of land transfer-in on the crop planting structure and its heterogeneity among farmers. The results showed that land transfer-in can drive the crop planting structure to tend to be “non-grain” in China. The research conclusion was still valid after the robustness tests of expanding the sample size, increasing the number of control variables, and introducing endogenous problem management. The heterogeneity analysis indicated that the negative impact of land transfer-in on the planting of grain crops mainly exists for large-scale farmers and farmers with agriculture as the main source of income. Based on these findings, the Chinese government should formulate targeted policies to prevent the “non-grain” tendency of crop planting structure after land transfer-in.

Accurate land cover (LC) datasets are the basis for global environmental and climate change studies. Recently, numerous open-source annual LC datasets have been created due to advances in remote sensing technology. However, the agreements and sources of error that affect the accuracy of current annual LC datasets are not well understood, which limits the widespread use of these datasets. We compared four annual LC datasets, namely the CLCD, MCD12Q1, CCI-LC, and GLASS-LC, in the Yellow River Basin (YRB) to identify their spatial and temporal agreement for nine LC classes and to analyze their sources of error. The Mann–Kendall test, Sen’s slope analysis, Taylor diagram, and error decomposition analysis were used in this study. Our results showed that the main LC classes in the four datasets were grassland and cropland (total area percentage > 80%), but their trends in area of change were different. For the main LC classes, the temporal agreement was the highest between the CCI-LC and CLCD (0.85), followed by the MCD12Q1 (0.21), while the lowest was between the GLASS-LC and CLCD (−0.11). The spatial distribution of area for the main LC classes was largely similar between the four datasets, but the spatial agreement in their trends in area of change varied considerably. The spatial variation in the trends in area of change for the cropland, forest, grassland, barren, and impervious LC classes were mainly located in the upstream area region (UA) and the midstream area region (MA) of the YRB, where the percentage of systematic error was high (>68.55%). This indicated that the spatial variation between the four datasets was mainly caused by systematic errors. Between the four datasets, the total error increased along with landscape heterogeneity. These results not only improve our understanding of the spatial and temporal agreement and sources of error between the various current annual LC datasets, but also provide support for land policy making in the YRB.