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印度印度河恒河平原前季风期表土中可利用微量营养素和硫的空间分布及其管理区。

Pre-monsoon spatial distribution of available micronutrients and sulphur in surface soils and their management zones in Indian Indo-Gangetic Plain.

机构信息

ICAR- Indian Institute of Soil Science, Bhopal, Madhya Pradesh, India.

ICAR- Indian Agricultural Research Institute, New Delhi, India.

出版信息

PLoS One. 2020 Jun 2;15(6):e0234053. doi: 10.1371/journal.pone.0234053. eCollection 2020.

DOI:10.1371/journal.pone.0234053
PMID:32484834
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7266351/
Abstract

The efficient (site-specific) management of soil nutrients is possible by understanding the spatial variability in distribution of phyto-available nutrients (here after called available nutrients) and identifying the soil management zones (MZs) of agricultural landscapes. There is need for delineating soil MZs of agricultural landscapes of the world for efficient management of soil nutrients in order to obtain sustainability in crop yield. The present study was, therefore, undertaken to understand the spatial distribution pattern of available micronutrients (zinc (Zn), boron (B), iron (Fe), manganese (Mn) and copper (Cu)), available sulphur (S), and soil properties (soil acidity (pH), electrical conductivity (EC) and organic carbon (SOC) content) in soils of intensively cultivated Indo-Gangetic Plain (IGP) of India and to delineate soil MZs for efficient management of soil nutrients. Totally, 55101 soil samples from 0-15 cm depth were obtained from 167 districts of IGP during 2014 to 2017 and were analysed for different soil parameters. Soil pH, EC and SOC content varied from 4.44 to 9.80, 0.02 to 2.13 dS m-1 and 0.10 to 1.99%, respectively. The concentration of available Zn, B, Fe, Mn, Cu and S varied from 0.01 to 3.27, 0.01 to 3.51, 0.19 to 55.7, 0.05 to 49.0, 0.01 to 5.29 and 1.01 to 108 mg kg-1, respectively. Geostatistical analysis resulted in varied distribution pattern of studied soil parameters with moderate to strong spatial dependence. The extent (% area) of nutrient deficiencies in IGP followed the order: S > Zn > B > Mn > Cu > Fe. Principal component analysis and fuzzy c-means clustering produced six distinctly different soil MZs of IGP for implementation of zone-specific soil nutrient management strategies for attaining sustainability in crop yield. The developed MZ maps could also be utilized for prioritization and rationalization of nutrients supply in IGP of India.

摘要

通过了解植物有效养分(以下简称有效养分)分布的空间变异性,并确定农业景观的土壤管理区(MZ),可以实现土壤养分的有效(特定地点)管理。为了实现作物产量的可持续性,需要对世界农业景观的土壤 MZ 进行划分,以有效管理土壤养分。因此,本研究旨在了解印度集约化耕种的印度-恒河平原(IGP)土壤中有效微量元素(锌(Zn)、硼(B)、铁(Fe)、锰(Mn)和铜(Cu))、有效硫(S)和土壤特性(土壤酸度(pH)、电导率(EC)和有机碳(SOC)含量)的空间分布模式,并划定土壤 MZ 以实现有效管理土壤养分。总共从 2014 年到 2017 年,从 IGP 的 167 个地区获得了 55101 个 0-15 厘米深度的土壤样本,并对不同的土壤参数进行了分析。土壤 pH 值、EC 值和 SOC 含量分别在 4.44 到 9.80、0.02 到 2.13 dS m-1 和 0.10 到 1.99%之间变化。有效 Zn、B、Fe、Mn、Cu 和 S 的浓度分别在 0.01 到 3.27、0.01 到 3.51、0.19 到 55.7、0.05 到 49.0、0.01 到 5.29 和 1.01 到 108 mg kg-1 之间变化。地统计学分析导致研究土壤参数的分布模式各不相同,具有中等至强的空间依赖性。IGP 中养分缺乏的程度(面积%)依次为:S > Zn > B > Mn > Cu > Fe。主成分分析和模糊 c-均值聚类产生了 IGP 的六个截然不同的土壤 MZ,用于实施特定区域的土壤养分管理策略,以实现作物产量的可持续性。开发的 MZ 地图还可用于印度 IGP 中养分供应的优先级排序和合理化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/421e/7266351/efaa60f662e9/pone.0234053.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/421e/7266351/ecde5dd73602/pone.0234053.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/421e/7266351/3da46c96abf3/pone.0234053.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/421e/7266351/862fdcbd7d81/pone.0234053.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/421e/7266351/4c11e2a297d8/pone.0234053.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/421e/7266351/ad6278842fbc/pone.0234053.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/421e/7266351/8c85b1eb32ac/pone.0234053.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/421e/7266351/efaa60f662e9/pone.0234053.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/421e/7266351/ecde5dd73602/pone.0234053.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/421e/7266351/3da46c96abf3/pone.0234053.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/421e/7266351/862fdcbd7d81/pone.0234053.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/421e/7266351/4c11e2a297d8/pone.0234053.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/421e/7266351/ad6278842fbc/pone.0234053.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/421e/7266351/8c85b1eb32ac/pone.0234053.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/421e/7266351/efaa60f662e9/pone.0234053.g007.jpg

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