生物炭
农学
环境科学
根系
农林复合经营
化学
生物
热解
有机化学
作者
Qinglin Li,Qiang Fu,Tianxiao Li,Dong Liu,Renjie Hou,Mo Li,Yu Gao
标识
DOI:10.1016/j.scitotenv.2022.153421
摘要
Biochar has been widely studied as a soil amendment, but little is known about the “biochar-freeze-thaw soil-crop root system” interface in seasonally frozen soil areas. In the second year after the application of biochar, we conducted research on the morphological characteristic indicators of the soybean root system and the nutrient migration of the soil in the root zone under different biochar application periods (spring and autumn mixed, autumn, and spring biochar application) and different biochar application rates (3 kg·m−2, 6 kg·m−2, 9 kg·m−2, and 12 kg·m−2). The effects of different biochar treatments on the growth and development of soybean roots were examined. The soil organic carbon, ammonium nitrogen and nitrate nitrogen contents of the soil were measured at different locations in the root zone, and the migration processes of these nutrients in the soil were explored. The conclusions drawn from the experiments are as follows. (i) The biochar application rate and application method together determine the root morphological characteristic indicators of soybean plants. During long freeze-thaw periods, the freeze-thaw cycles change the internal environment of the biochar-freeze-thaw soil complex. (ii) Biochar tends to move towards the root system, which can increase soil organic carbon content, but the effect of biochar on root characteristics is not caused by the change in soil organic carbon content. (iii) Biochar promotes nitrogen cycling in the soil and the migration of soil nitrogen to the root sheath, increasing the number of nitrogen compounds that can be directly absorbed and utilized by crops. (iv) From a comparison of the effects of various biochar treatments on crop roots and farmland soils, we suggest that the 9 kg·m−2 biochar application rate under spring and autumn mixed biochar application is the optimal treatment.
科研通智能强力驱动
Strongly Powered by AbleSci AI