Jeremy W Prokop, Vladislav Jdanov, Lane Savage, Michele Morris, Neil Lamb, Elizabeth VanSickle, Cynthia L Stenger, Surender Rajasekaran, Caleb P Bupp
Computational and Experimental Analysis of Genetic Variants
Comprehensive Physiology
rapidly accurately grasp change soil organic carbon content farmland great significance guiding timely effective mastery farmland soil fertility improvement soil physical properties study asd fieldspec 4 spectrometer used collect spectral reflectance data 128 agricultural soil samples taken jingbian county yulin city shaanxi province china firstly descriptive statistics soc study area performed secondly 10 spectral transformations performed correlation analysis boruta algorithm used extract characteristic wavebands soil organic carbon respectively order reduce redundancy data finally comparing accuracies different strategies constructed spectral prediction model soil organic carbon farmland northwest agricultural animal husbandry intertwined zone integrates optimal preprocessing feature selection strategy modelling method results indicate 1 mean soc content farmland study area low nutrient deficient level standard errors coefficients variation modelling validation sets 1596 g kg1 1457 g kg1 54 52 respectively 2 shape trend spectral special curves different soc contents show consistency soc content negatively correlated spectral reflectance 3 ca selects feature bands feature bands homogeneous boruta algorithm effectively remove irrelevant variables improve soc feature selection effect 4 soc prediction model based borutafdrf better soil organic carbon estimation r2 0899 0748 training set validation set respectively rmse 1432 g kg1 1967 g kg1 rpd 2557 1647 respectively results show soc model established integrating optimal spectral preprocessing feature selection strategy chemometrics strategy obvious improvement prediction accuracy stability study provides important reference fast accurate estimation soc content farmland agropastoral transitional zone northwest china