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首页> 外文期刊>Sensor Letters: A Journal Dedicated to all Aspects of Sensors in Science, Engineering, and Medicine >The Spatial Simulation of Soil Volumetric Moisture Content in Different Growth Stages of Peanut
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The Spatial Simulation of Soil Volumetric Moisture Content in Different Growth Stages of Peanut

机译:花生不同生育期土壤体积含水量的空间模拟

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摘要

The main objective of this study was to assess the spatial variability of SVMC (soil volumetric moisture content) in peanut different growth stages which included in peanut seeding time and peanut harvest time of "Fenghua 1". Moreover, the use of geostatistical tools for the description and modeling of SVMC is illustrated 324 soil samples digested by time domain reflectometry (TDR) method. Block Kriging and sequential gaussian simulation were also applied to analyze the spatial distribution of SVMC, the discrete and more detailed spatial pattern was disclosed by 100 times, 500 times and 1000 times sequential gaussain simulation results than block Kriging, Kriging results have the evident smoothing effect. However, sequential gaussian simulation results were consistent with the trend of measured data, and the measured values are equal with simulation value in sampling site, the average value is below the measured data, the simulation error resulted from the Kriging process and the unique gaussian algorithm. Study results showed the variability of structural factors is 68.9% in seeding time, 57.8% in harvest time, which suggested the medium spatial variability of SVMC, the impact of structural factors (evaporation and transpiration of peanut growth stages) is more in seeding time than in harvest time. Results also confirmed that there existed the sensitivity to SVMC of peanut growth stages, peanut's growth depended on the more water requirement and consumption in seeding stage than in harvest time of "Fenghua 1".
机译:本研究的主要目的是评估花生不同生育阶段的SVMC(土壤体积含水量)的空间变异性,其中包括“奉化1号”花生播种时间和花生收获时间。此外,通过时域反射法(TDR)方法消化了324个土壤样品,利用地统计学工具对SVMC进行描述和建模。运用块Kriging和顺序高斯模拟分析SVMC的空间分布,比块Kriging分别以100倍,500倍和1000倍的顺序高斯模拟结果揭示了离散和更详细的空间格局,Kriging结果具有明显的平滑效果。但是,顺序高斯模拟结果与实测数据趋势一致,实测值与采样点的模拟值相等,平均值低于实测数据,克里金法和独特的高斯算法导致模拟误差。研究结果表明,结构因素在播种时间的变异性为68.9%,收获时间为57.8%,这表明SVMC具有中等的空间变异性,结构因素(花生生长期的蒸发和蒸腾作用)对播种时间的影响大于播种时间。在收获时间。结果还表明,花生生育期对SVMC存在敏感性,花生生长取决于播种期的水分需求和消耗量大于“丰花1号”的收获期。

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