Spatial distribution characteristics of root volume of Caragana and grass compound model

Author(s):  
Yongjun De ◽  
Huijun An ◽  
Dongmei Ye ◽  
Sengli Han
Author(s):  
Hang Zhou ◽  
Dianfeng Zheng ◽  
Naijie Feng ◽  
Hongtao Xiang ◽  
Yang Liu ◽  
...  

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.


2021 ◽  
Vol 13 (1) ◽  
pp. 796-806
Author(s):  
Zhen Shuo ◽  
Zhang Jingyu ◽  
Zhang Zhengxiang ◽  
Zhao Jianjun

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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