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首页> 外文期刊>Soil & Tillage Research >Calibration of an on-line sensor for measurement of topsoil bulk density in all soil textures.
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Calibration of an on-line sensor for measurement of topsoil bulk density in all soil textures.

机译：用于测量所有土壤质地中表土容重的在线传感器的校准。

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An on-line sensor for the prediction of bulk density (BD) has been previously developed, and calibrated for light textured soils. This study aims to expand the implementation of this sensor for all soil types, including clay soils. The on-line sensor consists of a single ended shear beam load cell to measure draught ( D), a visible and near infrared (vis-NIR) sensor to measure moisture content (MC), and a wheel gauge equipped with a draw wire linear sensor to measure depth ( d). This sensor was used to collect data from 14 fields across four European countries (Denmark, Czech Republic, the Netherlands and the UK) with different soil textures. The three measured parameters were substituted into a previously developed model to calculate BD. The calculated BD for 333 samples was compared with BD measured with a core sampling kit to validate the system and to calculate correction factors (CF) valid for each additional soil texture. A multiple linear regression (MLR), nonlinear multiple regression (NMR) and artificial neural network (ANN) analyses were carried out to establish models to predict CF, as a function of average field MC and soil texture fractions. For clay soils, the correlation between the measured and on-line predicted BD was satisfactory ( R2=0.54-0.69), and suggested a large negative CF >-0.248 Mg m -3. For other soil types with lighter soil textures, accuracy of measurements varied ( R2=0.51-0.96), with lower CF values than those calculated for clay soils and ranged between negative and positive values (-0.007 Mg m -3
机译：先前已经开发了用于预测堆积密度（BD）的在线传感器，并已针对轻质纹理土壤进行了校准。这项研究旨在将这种传感器的实施扩展到包括黏土在内的所有土壤类型。在线传感器包括一个用于测量吃水量（D）的单端剪切梁式称重传感器，一个用于测量水分含量（MC）的可见和近红外（vis-NIR）传感器以及一个配备有拉线金属丝的轮规传感器以测量深度（d）。该传感器用于收集来自四个欧洲国家（丹麦，捷克共和国，荷兰和英国）不同土壤质地的14个字段的数据。将三个测得的参数代入先前开发的模型以计算BD。将计算出的333个样品的BD与使用核心采样套件测量的BD进行比较，以验证系统并计算对每种其他土壤质地有效的校正因子（CF）。进行了多元线性回归（MLR），非线性多元回归（NMR）和人工神经网络（ANN）分析，以建立预测CF的模型，该模型是平均田间MC和土壤质地分数的函数。对于黏土，实测的BD与在线预测的BD之间的相关性令人满意（R2 = 0.54-0.69），并且表明较大的负CF> -0.248 Mg m -3。对于质地较轻的其他土壤类型，测量精度有所不同（R2 = 0.51-0.96），CF值低于粘土计算值，且介于负值和正值之间（-0.007 Mg m -3

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