Acidification and pollution are two major threats to agricultural ecosystems; however, microbial community responses to co-existed soil acidification and pollution remain less explored. In this study, arable soils of broad pH (4.26–8.43) and polycyclic aromatic hydrocarbon (PAH) gradients (0.18–20.68 mg kg−1) were collected from vegetable farmlands. Bacterial community characteristics including abundance, diversity and composition were revealed by quantitative PCR and high-throughput sequencing. The bacterial 16S rRNA gene copies significantly correlated with soil carbon and nitrogen contents, suggesting the control of nutrients accessibility on bacterial abundance. The bacterial diversity was strongly related to soil pH, with higher diversity in neutral samples and lower in acidic samples. Soil pH was also identified by an ordination analysis as important factor shaping bacterial community composition. The relative abundances of some dominant phyla varied along the pH gradient, and the enrichment of a few phylotypes suggested their adaptation to low pH condition. In contrast, at the current pollution level, PAH showed marginal effects on soil bacterial community. Overall, these findings suggest pH was the primary determinant of bacterial community in these arable soils, indicative of a more substantial influence of acidification than PAH pollution on bacteria driven ecological processes.

No abstract

The transfer of rice straw from paddy fields to upland areas was proposed in our study as an innovative practice to improve the fertility of the highly-weathered and poor upland soils, as well as to mitigate environmental impacts from rice production system (e.g., the large application of chemical fertilizers, straw burning, CH 4 emission from decomposed straw) in the subtropical China. Data from an 8-year field experiment with sweet potato and rapeseed show that, the contents of organic matter and total nitrogen (N) in the surface soil (0-20 cm) with rice straw incorporation were increased by 13%, and the amounts of soil microbial biomass carbon (C), N, and phosphorus (P) by 18-43%, compared with the values when chemical fertilizers (NPK) were applied alone. Physical conditions and crop productivity of the test soil were effectively promoted with the straw incorporation, as indicated in significant changes in soil field water-holding capacity, total porosity, and bulk density in the 10-15 cm soil layer, and the rise (about 10% and 20%, respectively) of sweet potato and rapeseed yields. In conclusion, these results suggest that the practice shift from conventional rice straw management ('on the spot' incorporation) to upland incorporation is effective in improvement in fertility and productivity of the upland soil, which has potentials to reduce the amounts of chemical fertilizers applied and to increase soil C storage in farmlands in the region.

In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively.

No abstract

Cadmium (Cd) is a toxic element, and rice is known to be a leading source of dietary Cd for people who consume rice as their main caloric resource. Hybrid rice has dominated rice production in southern China and has been adopted worldwide. The characteristics of high yield heterosis of rice hybrids makes the public think intuitively that the hybrid rice accumulates more Cd in grain than do inbred cultivars. A detailed understanding of the genetic basis of grain Cd accumulation in hybrids and developing Cd-safe rice are one of the top priorities for hybrid rice breeders at present. In this study, we investigated genetic diversity and grain Cd levels in 617 elite rice hybrids collected from the middle and lower Yangtze River Valley in China and 68 inbred cultivars from around the world. We found that there are large variations in grain Cd accumulation in both the hybrids and their inbred counterparts. However, we found grain Cd levels in the rice hybrids to be similar to the levels in indica rice inbreds, suggesting that the hybrids do not accumulate more Cd than do the inbred rice cultivars. Further analysis revealed that the high heritability of Cd accumulation in the grain and the single indica population structure increases the risk of Cd over-accumulation in hybrid rice. The genetic effects of Cd-related QTLs, which have been identified in related Cd-QTL mapping studies, were also determined in the hybrid rice population. Four QTLs were identified as being associated with the variation in grain Cd levels; three of these loci exhibited obvious indica-japonica differentiations. Our study will provide a better understanding of grain Cd accumulations in hybrid rice, and pave the way toward effective breeding for high-yielding, low grain-Cd hybrids in the future.

No abstract

No abstract