Chu Chengcai

Recent Progress in Molecular Dissection of Nutrient Uptake andTransport in Rice

Plant requires nitrogen, phosphorus, potassium, iron and zinc, which play important physiological functions in plant growth and development. In agricultural production, application of nitrogen, phosphorus, and potassium is the major driving force for modern agriculture to achieve high grain yield. However, continuous and extensive use of chemical fertilizers leads to serious environmental problem, due to excess nitrogen and phosphorus fertilizers leaching into the soil. On the other hand, phosphorus and potassium are non-renewable mineral resources, excess application leads to severe resource shortages. Besides nitrogen, phosphorus, and potassium, the micronutrients such as iron and zinc not only have significant impact on plant productivity and stress tolerance, they are also essential trace elements for human and animal health. Thus, understanding of the molecular mechanisms of uptake, transport and storage for these nutrients is of great importance for improving the use efficiency of fertilizers, reducing environmental damage and agricultural costs, and also for human health. This review summarized most recent progress in the molecular mechanisms of uptake and transport of all these plant nutrients, including nitrogen, phosphorus, potassium, iron, zinc, and selenium.

Root microbiota shift in rice correlates with resident time in the field and developmental stage

Land plants in natural soil form intimate relationships with the diverse root bacterial microbiota. A growing body of evidence shows that these microbes are important for plant growth and health. Root microbiota composition has been widely studied in several model plants and crops; however, little is known about how root microbiota vary throughout the plant’s life cycle under field conditions. We performed longitudinal dense sampling in field trials to track the time-series shift of the root microbiota from two representative rice cultivars in two separate locations in China. We found that the rice root microbiota varied dramatically during the vegetative stages and stabilized from the beginning of the reproductive stage, after which the root microbiota underwent relatively minor changes until rice ripening. Notably, both rice genotype and geographical location influenced the patterns of root microbiota shift that occurred during plant growth. The relative abundance of Deltaproteobacteria in roots significantly increased overtime throughout the entire life cycle of rice, while that of Betaproteobacteria, Firmicutes, and Gammaproteobacteria decreased. By a machine learning approach, we identified biomarker taxa and established a model to correlate root microbiota with rice resident time in the field (e.g., Nitrospira accumulated from 5 weeks/tillering in field-grown rice). Our work provides insights into the process of rice root microbiota establishment.

Crop 3D: A Platform Based on LiDAR for3D High-Throughput Crop Phenotyping

With the growth of population and the reduction of arable land, breeding has been considered as an effective way to solve the food crisis. As an important part in breeding, high-throughput phenotyping can accelerate the breeding process effectively. Light detection and ranging (LiDAR) is an active remote sensing technology that is capable of acquiring three-dimensional (3D) data accurately, and has a great potential application in crop phenotyping. Given that crop phenotyping based on LiDAR technology is not common in China, we developed a high-throughput crop phenotyping platform, named Crop 3D, which integrated LiDAR, high-resolution camera, thermal camera and hyperspectral imager. Compared with traditional crop phenotyping techniques, Crop 3D can acquire the multi-source phenotypic data in the whole crop growing period and extract plant height, plant width, leaf length, leaf width, leaf area, leaf inclination angle and other parameters for plant biology and genomics analysis. In this paper, we described the designs, functions and testing results of the Crop 3D platform. Then, the potential applications and future development of the platform in phenotyping were briefly discussed. We concluded that platforms integrating LiDAR and traditional remote sensing techniques might be the future trend of crop high-throughput phenotyping.