Grain Yield in Mungbean (Vigna radiata) is Associated with Spatial Distribution of Root Dry Weight and Volume

2019 ◽  
Author(s):  
Hang Zhou ◽  
Dianfeng Zheng ◽  
Naijie Feng ◽  
Hongtao Xiang ◽  
Yang Liu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Mung Bean ◽  
Soil Layer ◽  
Dry Weight ◽  
Distribution Characteristics ◽  
Flowering Stage ◽  
Root Volume ◽  
Soil Layers ◽  
Spatial Distribution Characteristics ◽  
Full Flowering

Root system is an important plant organ affecting yield and the degree of influence of roots on yield in different spatial locations in the soil is different. The aim of this study was to research the spatial distribution characteristics of mung bean root and to analyze the correlation between spatial distribution of root and yield. The roots of mung bean in 0 - 5, 5 - 10, 10 - 15, 15 - 20 and 20 - 25 cm horizontal soil layers and in 0 - 20, 20 - 40, 40 - 60, 60 - 80 and 80 - 100 cm vertical soil layers were collected to analyze spatial distribution characteristics of root volume and root dry weight at full flowering stage and full pod stage. Yield and yield components were measured at maturity. Our study showed that approximately 48.4% - 65.2% of the mung bean root were in 0 - 5 cm horizontal soil layer and about 73.2% - 82.3% were in 0 - 20 cm vertical soil layer. Yield of mung bean exhibited significantly positive correlation with number of pods per plant. The root volume density of mung bean in 20 - 25 cm horizontal soil layer at full flowering stage exhibited significantly positive correlation with yield. These findings could be used to provide scientific basis for cultivating high-yield mung bean varieties with excellent root system.

scholarly journals The Spatial Distribution Characteristics of Soil Organic Carbon and Its Effects on Topsoil under Different Karst Landforms

2020 ◽  
Vol 17 (8) ◽  
pp. 2889 ◽  
Author(s):  
Xingfu Wang ◽  
Xianfei Huang ◽  
Jiwei Hu ◽  
Zhenming Zhang
Keyword(s):  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Depth ◽  
Soil Layer ◽  
Guizhou Province ◽  
Impact Factors ◽  
Distribution Characteristics ◽  
Spatial Distribution Characteristics ◽  
Karst Landforms

Karst landforms are widely distributed in Guizhou Province, and the karst terrain is complex. To investigate the spatial distribution characteristics of soil organic carbon (SOC) in topsoil in different karst landforms, a total of 920 samples were taken from different karst landforms. The study areas, Puding, Xingyi, Guanling, Libo and Yinjiang in Guizhou Province, represent the karst plateau (KP), karst peak-cluster depression (KPCD), karst canyon (KC), karst virgin forest (KVF) and karst trough valley (KTV) landforms, respectively. The characteristics of the SOC contents in areas with different vegetation, land use and soil types under different karst landforms were analyzed. The dimensionality of the factors was reduced via principal component analysis, the relationships among SOC content and different factors were subjected to redundancy analysis, and the effects of the main impact factors on SOC were discussed. The results showed that there was a large discrepancy in the SOC contents in the topsoil layers among different types of karst landforms, the changes in the SOC content in the topsoil layer were highly variable, and the discrepancy in the upper soil layer was higher than that in the lower soil layer. The SOC contents in the 0–50 cm topsoil layers in different karst landforms were between 7.76 and 38.29 g·kg−1, the SOC content gradually decreased with increasing soil depth, and the descending order of the SOC contents in different karst landforms was KTV > KVF > KC > KPCD > KP.

scholarly journals Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics

2021 ◽  
Vol 13 (1) ◽  
pp. 796-806
Author(s):  
Zhen Shuo ◽  
Zhang Jingyu ◽  
Zhang Zhengxiang ◽  
Zhao Jianjun
Keyword(s):  
Spatial Distribution ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Function Method ◽  
Kernel Density ◽  
Fire Occurrence ◽  
Distribution Characteristics ◽  
Fire Point ◽  
Spatial Distribution Characteristics ◽  
Grassland Fire

Abstract Understanding the risk of grassland fire occurrence associated with historical fire point events is critical for implementing effective management of grasslands. This may require a model to convert the fire point records into continuous spatial distribution data. Kernel density estimation (KDE) can be used to represent the spatial distribution of grassland fire occurrences and decrease the influences historical records in point format with inaccurate positions. The bandwidth is the most important parameter because it dominates the amount of variation in the estimation of KDE. In this study, the spatial distribution characteristic of the points was considered to determine the bandwidth of KDE with the Ripley’s K function method. With high, medium, and low concentration scenes of grassland fire points, kernel density surfaces were produced by using the kernel function with four bandwidth parameter selection methods. For acquiring the best maps, the estimated density surfaces were compared by mean integrated squared error methods. The results show that Ripley’s K function method is the best bandwidth selection method for mapping and analyzing the risk of grassland fire occurrence with the dependent or inaccurate point variable, considering the spatial distribution characteristics.

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