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基于数据同化算法的棉田土壤剖面盐分与土壤深度定量关系模型:预测棉田产量和利润

Quantitative relationship model between soil profile salinity and soil depth in cotton fields based on data assimilation algorithm: forecasting cotton field yields and profits.

作者信息

Gao Yang, Chang Lin, Zeng Mei, Hu Quanze, Hui Jiaojiao, Jiang Qingsong

机构信息

College of Information Engineering, Tarim University, Alar, China.

Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alar, China.

出版信息

Front Plant Sci. 2024 Dec 20;15:1519200. doi: 10.3389/fpls.2024.1519200. eCollection 2024.

Abstract

Soil salinization seriously affects the efficiency of crops in absorbing soil nutrients, and the cotton production in southern Xinjiang accounts for more than 60% of China's total. Therefore, it is crucial to monitor the dynamic changes in the salinity of the soil profile in cotton fields in southern Xinjiang, understand the status of soil salinization, and implement effective prevention and control measures. The drip-irrigated cotton fields in Alaer Reclamation Area were taken as the research objects. The multivariate linear regression model was used to study the relationship between soil salinity and soil depth in different periods, and the Kalman filter algorithm was used to improve the model accuracy. The results showed that the month with the highest improvement in model accuracy was July, with the model accuracy R increasing by 0.26 before and after calibration; followed by June and October, with the model accuracy R increasing by 0.19 and 0.18 respectively; the lowest improvement was in March, which was only 0.01. After the model was calibrated by the Kalman filter algorithm, the fitting accuracy (R) between the predicted value and the actual value was as high as 0.79, and the corresponding RMSE was only 96.17 μS cm, and the measured value of soil salinity was consistent with the predicted value. Combined with the predicted conductivity data of each soil layer, the total yield of the study area was predicted to be 5,203-5,551 kg hm, and the income was about 4,953-7,441 RMB hm. It can be seen that Kalman filtering can improve the prediction accuracy of the model and provide a theoretical basis for studying the mechanism of soil salt migration in drip-irrigated cotton fields at different stages. It is of great significance for evaluating the potential relationship between cotton yield and deep soil salinity and guiding the efficient prevention and control of saline soil in cotton fields.

摘要

土壤盐渍化严重影响作物吸收土壤养分的效率,且新疆南部的棉花产量占全国总产量的60%以上。因此,监测新疆南部棉田土壤剖面盐分的动态变化、了解土壤盐渍化状况并实施有效的防治措施至关重要。以阿拉尔垦区的滴灌棉田为研究对象。采用多元线性回归模型研究不同时期土壤盐分与土壤深度之间的关系,并运用卡尔曼滤波算法提高模型精度。结果表明,模型精度提升最高的月份是7月,校准前后模型精度R提高了0.26;其次是6月和10月,模型精度R分别提高了0.19和0.18;提升最低的是3月,仅为0.01。经卡尔曼滤波算法校准后,预测值与实际值之间的拟合精度(R)高达0.79,相应的均方根误差仅为96.17 μS/cm,土壤盐分测量值与预测值一致。结合各土层的预测电导率数据,预测研究区总产量为5203 - 5551 kg/hm,收入约为4953 - 7441元/hm。可见,卡尔曼滤波能提高模型的预测精度,为研究不同阶段滴灌棉田土壤盐分运移机制提供理论依据。这对于评估棉花产量与深层土壤盐分之间的潜在关系以及指导棉田盐碱地的高效防治具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8adb/11695305/99cda858afbb/fpls-15-1519200-g001.jpg

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