Quantitative relationship model between soil profile salinity and soil depth in cotton fields based on data assimilation algorithm: forecasting cotton field yields and profits
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 souther...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-12-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1519200/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846114853060608000 |
|---|---|
| author | Yang Gao Yang Gao Lin Chang Lin Chang Mei Zeng Mei Zeng Quanze Hu Quanze Hu Jiaojiao Hui Jiaojiao Hui Qingsong Jiang Qingsong Jiang |
| author_facet | Yang Gao Yang Gao Lin Chang Lin Chang Mei Zeng Mei Zeng Quanze Hu Quanze Hu Jiaojiao Hui Jiaojiao Hui Qingsong Jiang Qingsong Jiang |
| author_sort | Yang Gao |
| collection | DOAJ |
| description | 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 R2 increasing by 0.26 before and after calibration; followed by June and October, with the model accuracy R2 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 (R2) between the predicted value and the actual value was as high as 0.79, and the corresponding RMSE was only 96.17 μS cm-1, 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-2, and the income was about 4,953-7,441 RMB hm-2. 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. |
| format | Article |
| id | doaj-art-056d0e9d3fb74199b6a51ad6b8a91455 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-056d0e9d3fb74199b6a51ad6b8a914552024-12-20T06:29:12ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2024-12-011510.3389/fpls.2024.15192001519200Quantitative relationship model between soil profile salinity and soil depth in cotton fields based on data assimilation algorithm: forecasting cotton field yields and profitsYang Gao0Yang Gao1Lin Chang2Lin Chang3Mei Zeng4Mei Zeng5Quanze Hu6Quanze Hu7Jiaojiao Hui8Jiaojiao Hui9Qingsong Jiang10Qingsong Jiang11College of Information Engineering, Tarim University, Alar, ChinaKey Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alar, ChinaCollege of Information Engineering, Tarim University, Alar, ChinaKey Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alar, ChinaCollege of Information Engineering, Tarim University, Alar, ChinaKey Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alar, ChinaCollege of Information Engineering, Tarim University, Alar, ChinaKey Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alar, ChinaCollege of Information Engineering, Tarim University, Alar, ChinaKey Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alar, ChinaCollege of Information Engineering, Tarim University, Alar, ChinaKey Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alar, ChinaSoil 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 R2 increasing by 0.26 before and after calibration; followed by June and October, with the model accuracy R2 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 (R2) between the predicted value and the actual value was as high as 0.79, and the corresponding RMSE was only 96.17 μS cm-1, 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-2, and the income was about 4,953-7,441 RMB hm-2. 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.https://www.frontiersin.org/articles/10.3389/fpls.2024.1519200/fullsalinizationapparent conductivitysoil conductivitymultivariate linear algorithmKalman filter |
| spellingShingle | Yang Gao Yang Gao Lin Chang Lin Chang Mei Zeng Mei Zeng Quanze Hu Quanze Hu Jiaojiao Hui Jiaojiao Hui Qingsong Jiang Qingsong Jiang Quantitative relationship model between soil profile salinity and soil depth in cotton fields based on data assimilation algorithm: forecasting cotton field yields and profits Frontiers in Plant Science salinization apparent conductivity soil conductivity multivariate linear algorithm Kalman filter |
| title | Quantitative relationship model between soil profile salinity and soil depth in cotton fields based on data assimilation algorithm: forecasting cotton field yields and profits |
| title_full | Quantitative relationship model between soil profile salinity and soil depth in cotton fields based on data assimilation algorithm: forecasting cotton field yields and profits |
| title_fullStr | Quantitative relationship model between soil profile salinity and soil depth in cotton fields based on data assimilation algorithm: forecasting cotton field yields and profits |
| title_full_unstemmed | Quantitative relationship model between soil profile salinity and soil depth in cotton fields based on data assimilation algorithm: forecasting cotton field yields and profits |
| title_short | Quantitative relationship model between soil profile salinity and soil depth in cotton fields based on data assimilation algorithm: forecasting cotton field yields and profits |
| title_sort | quantitative relationship model between soil profile salinity and soil depth in cotton fields based on data assimilation algorithm forecasting cotton field yields and profits |
| topic | salinization apparent conductivity soil conductivity multivariate linear algorithm Kalman filter |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1519200/full |
| work_keys_str_mv | AT yanggao quantitativerelationshipmodelbetweensoilprofilesalinityandsoildepthincottonfieldsbasedondataassimilationalgorithmforecastingcottonfieldyieldsandprofits AT yanggao quantitativerelationshipmodelbetweensoilprofilesalinityandsoildepthincottonfieldsbasedondataassimilationalgorithmforecastingcottonfieldyieldsandprofits AT linchang quantitativerelationshipmodelbetweensoilprofilesalinityandsoildepthincottonfieldsbasedondataassimilationalgorithmforecastingcottonfieldyieldsandprofits AT linchang quantitativerelationshipmodelbetweensoilprofilesalinityandsoildepthincottonfieldsbasedondataassimilationalgorithmforecastingcottonfieldyieldsandprofits AT meizeng quantitativerelationshipmodelbetweensoilprofilesalinityandsoildepthincottonfieldsbasedondataassimilationalgorithmforecastingcottonfieldyieldsandprofits AT meizeng quantitativerelationshipmodelbetweensoilprofilesalinityandsoildepthincottonfieldsbasedondataassimilationalgorithmforecastingcottonfieldyieldsandprofits AT quanzehu quantitativerelationshipmodelbetweensoilprofilesalinityandsoildepthincottonfieldsbasedondataassimilationalgorithmforecastingcottonfieldyieldsandprofits AT quanzehu quantitativerelationshipmodelbetweensoilprofilesalinityandsoildepthincottonfieldsbasedondataassimilationalgorithmforecastingcottonfieldyieldsandprofits AT jiaojiaohui quantitativerelationshipmodelbetweensoilprofilesalinityandsoildepthincottonfieldsbasedondataassimilationalgorithmforecastingcottonfieldyieldsandprofits AT jiaojiaohui quantitativerelationshipmodelbetweensoilprofilesalinityandsoildepthincottonfieldsbasedondataassimilationalgorithmforecastingcottonfieldyieldsandprofits AT qingsongjiang quantitativerelationshipmodelbetweensoilprofilesalinityandsoildepthincottonfieldsbasedondataassimilationalgorithmforecastingcottonfieldyieldsandprofits AT qingsongjiang quantitativerelationshipmodelbetweensoilprofilesalinityandsoildepthincottonfieldsbasedondataassimilationalgorithmforecastingcottonfieldyieldsandprofits |