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Development of a Plant Geospatial Model for Identifying Chestnut Yield‐Limiting Factors
Author(s) -
Zhang Yu,
Dong Chun,
Liu Jiping,
Xu Shenghua
Publication year - 2019
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2018.04.0241
Subject(s) - yield (engineering) , geospatial analysis , crop , limiting , agronomy , geography , environmental science , agroforestry , biology , engineering , remote sensing , mechanical engineering , materials science , metallurgy
Core Ideas Identifying chestnut yield limiting factors is essential to precision chestnut tree management. Four geographical detectors were applied to explore the key factors and interactive effects of geographical and socio‐economic factors on chestnut yield using the power of the determinant concept. Soil parent material is a major factor in the spatial variation in chestnut yield, whereas aspect was not found to cause any obvious differences in chestnut yield. Among the eight parent materials, the gneiss soil results in the highest chestnut yield within the study area. The interaction between soil type and total power of farm machinery resulted in the highest chestnut yield. Our approach is a useful target for further research on increasing other crop yield or exploring the effect of factors on other crop yield.The Chinese chestnut ( Castanea mollissima Blume) is an essential and highly nutritious nut crop, and income from selling chestnuts is important for small producers. Despite chestnuts being widely planted, chestnut yields are decreasing in northern China. The hypothesis of this paper is that yield reduction is the result of complex topographic conditions, insufficient soil nutrients, unscientific fertilization, and limited availability of productive land. The objective was to create a plant social geospatial model–geographical detector for analyzing the strength of the association between chestnut yields and their potential determinants. In this model system, we used measured data from chestnut to highlight how a geospatial model can be used to identify complex relationships among soil, plants, and geospatial location. Four geographical detectors (i.e., risk, factor, ecological, and interaction) were proposed on the basis of spatial variation analysis. The model was then applied to Qianxi County of Hebei Province in China. Soil parent material, soil texture, and total power of farm machinery were found to be the key factors. The interactive effect of any two factors increased chestnut yield, and the interaction between parent material and total power of farm machinery resulted in the highest yield. The study’s approach and findings make it possible to introduce effective and practical measures to increase chestnut yield. Strategies to increase chestnut yield need to be designed with spatial variables being considered.