燃烧溶胶
油菜籽
微量营养素
石灰
化学
农学
营养物
土壤pH值
修正案
磷
动物科学
土壤水分
土工试验
种植制度
作物
生物
生态学
法学
有机化学
政治学
古生物学
作者
Santanu Das,Anup Das,Ramkrushna Gandhiji Idapuganti,Jayanta Layek,Dwipendra Thakuria,Dibyendu Sarkar,Ingudam Bhupenchandra,Rattan Lal,Samik Chowdhury,Subhash Babu,Kabir Debbarma
摘要
Abstract Aluminum toxicity, nutrient imbalance, and reduced microbial activity are the most limiting factors for optimum agronomic productivity in acid soils. A field study was conducted to investigate the impact of micronutrient and liming on soil properties and productivity of groundnut‐rapeseed system in an acidic Inceptisol. The impact of six micronutrient (control or no micronutrient, Zn at 5 kg ha −1 , B at 1 kg ha −1 , Mo at 0.5 kg ha −1 , Zn + Mo, and Zn + B + Mo) and two soil amendment (furrow liming at 500 kg ha −1 and no lime addition) treatments were first tested in groundnut and theirs residual effects were assessed on soil properties of succeeding rapeseed crop. Results indicated that liming significantly improved soil pH, soil organic carbon (SOC), available N, P and K, and diethylene triamine penta acetate (DTPA)‐extractable micronutrients, soil microbial biomass C and dehydrogenase activity as compared to no‐lime. After harvesting groundnut, Mo + Zn + B treated plots maintained the highest SOC content, whereas sole B and Mo treatment had the highest SOC content after harvesting rapeseed. Further, Mo + Zn + B treated plots showed higher contents of soil DTPA‐extractable micronutrients as compared to others. The highest groundnut equivalent yield (GEY) of the cropping system (pooled data of 2 years) was obtained with Mo + Zn + B (2.12 Mg ha −1 ) but remained at par with Zn + Mo (2.02 Mg ha −1 ). Multivariate principal component analysis indicated that available P was the most prominent soil nutrient with a strong effect on GEY. The results indicated that integrated application of Zn + B + Mo along with liming improves soil properties and agronomic productivity of groundnut‐rapeseed cropping system.
科研通智能强力驱动
Strongly Powered by AbleSci AI