Al-Saeedi Abdullah H
Department of Environmental and Natural Resources, College of Agricultural and Food Sciences, King Faisal University, P. O. Box 420, Al-Ahsa 31982, Saudi Arabia.
Saudi J Biol Sci. 2022 May;29(5):3390-3402. doi: 10.1016/j.sjbs.2022.01.061. Epub 2022 Feb 11.
Al-Ahsa Oasis is one of the oldest and biggest agricultural regions in Saudi Arabia. Thirty-six soil samples representing most of the soil type in the region were collected and analysed in a laboratory for physical properties including particle size (), saturation , and bulk density . The soil-water characteristic curve (SWCC) was measured using the filter paper method. Intensive statistical analysis included correlation, stepwise multiple linear regression analysis (SWR), mean square error (MSE), and -test were used to evaluate the potential PTFs. Silt () and bulk density () were achieved a high accuracy in prediction of () and saturation ( ) respectively. Both field capacity () and wilting point () were correlated significantly with with a very high prediction compatibility and MSE 0.004 and 0.001 respectively. Using tow levels of prediction demonstrated high correctness in predicting SWCC with correlation coefficient 0.986 and 0.952 with a low MSE equal to 0.0007 and 0.0028 respectively. The result of this study shown the high feasibility of developing a model for the prediction of SWCC using easily readable PTFs.
哈萨绿洲是沙特阿拉伯最古老、最大的农业地区之一。采集了代表该地区大部分土壤类型的36个土壤样本,并在实验室对其物理性质进行了分析,包括粒径()、饱和度()和容重()。采用滤纸法测量土壤水分特征曲线(SWCC)。使用相关性、逐步多元线性回归分析(SWR)、均方误差(MSE)和t检验等深入的统计分析方法来评估潜在的土壤传递函数(PTFs)。粉粒含量()和容重()分别在预测()和饱和度()方面具有较高的准确性。田间持水量()和凋萎点()均与显著相关,预测兼容性非常高,MSE分别为0.004和0.001。使用两个预测水平在预测SWCC方面显示出较高的正确性,相关系数分别为0.986和0.952,低MSE分别等于0.0007和0.0028。本研究结果表明,使用易于读取的PTFs开发SWCC预测模型具有很高的可行性。