Dataset on utilizing cropping system-based fertilization techniques to improve soil health and crop output while minimizing tillage

Md. Jahangir Alam, A.T.M. Anwarul Islam Mondol, Rabeka Sultana Smiriti,Mahammad Shariful Islam,Habib Mohammad Naser, Sanjida Akter,Zakaria Alam

Data in Brief(2024)

引用 0|浏览0
暂无评分
摘要
The dataset provided details on how tillage methods and nutrient management impacted the productivity of the four crops (mustard>mungbean>Transplanting (T.) aus >Transplanting (T.) aman) cropping system and the overall soil health. The specific tillage techniques examined were minimum tillage (MT), conventional tillage (CT), and deep tillage (DT). Regarding nutrient management, NM1 utilized 100% soil test-based (STB) fertilization following fertilizer gradient generation (FRG); NM2 applied 125% of STB after FRG-2018; NM3 consisted of 100% STB (with 80% from chemical fertilizers and 20% from cow dung); and NM4 relied on native fertility without any fertilization. Over three consecutive seasonal years (2018–19, 2019–20, and 2020–21), twelve treatments were replicated three times following a factorial totally randomized design. The comparative analysis of crop yield, rice equivalent yield, system productivity and production efficiency indicated superior performance of MT over both CT and DT. Furthermore, in relation to agricultural productivity metrics, the application of the nutrition package NM3 demonstrated performance levels exceeding the average. The adoption of MT and the incorporation of the NM3 nutrition package led to notable advancements in organic matter, field capacity, microbial biomass nitrogen, microbial biomass carbon and soil nutrient levels (N, P, K, S, Zn, and B). Consequently, the synthesis of the NM3 with MT is posited as a strategic approach for soil enhancement and the augmentation of crop productivity.
更多
查看译文
关键词
Chemical fertilizer,Four crops cropping system,Rice equivalent yield,System productivity,Production efficiency Agricultural productivity,Soil nutrients
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要