Simulation for response of crop yield to soil moisture and salinity with artificial neural network
文献类型：SCI/SSCI论文
| 作者 | Dai X. Q. ; Huo Z. L. ; Wang H. M. |
| 发表日期 | 2011 |
| 关键词 | Artificial neural network Soil water Soil salinity Sunflower yield helianthus-annuus l. salt stress irrigation water quality models climate corn |
| 英文摘要 | In saline fields, irrigation management often requires understanding crop responses to soil moisture and salt content. Developing models for evaluating the effects of soil moisture and salinity on crop yield is important to the application of irrigation practices in saline soil. Artificial neural network (ANN) and multi-linear regression (MLR) models respectively with 10 (ANN-10, MLR-10) and 6 (ANN-6, MLR-6) input variables, including soil moisture and salinity at crop different growth stages, were developed to simulate the response of sunflower yield to soil moisture and salinity. A connection weight method is used to understand crop sensitivity to soil moisture and salt stress of different growth stages. Compared with MLRs, both ANN models have higher precision with RMSEs of 1.1 and 1.6 t ha(-1), REs of 12.0% and 17.3%, and R(2) of 0.84 and 0.80, for ANN-10 and ANN-6, respectively. The sunflower sensitivity to soil salinity varied with the different soil salinity ranges. For low and medium saline soils, sunflower yield was more sensitive at crop squaring stage, but for high saline soil at seedling stage. High soil moisture content could compensate the yield decrease resulting from salt stress regardless of salt levels at the crop sowing stage. The response of sunflower yield to soil moisture at different stages in saline soils can be understood through the simulated results of ANN-6. Overall, the ANN models are useful for investigating and understanding the relationship between crop yield and soil moisture and salinity at different crop growth stages. (C) 2011 Elsevier B.V. All rights reserved. |
| 出处 | Field Crops Research |
| 卷 | 121 |
| 期 | 3 |
| 页 | 441-449 |
| 收录类别 | SCI |
| ISSN号 | 0378-4290 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/24158]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Dai X. Q.,Huo Z. L.,Wang H. M.. Simulation for response of crop yield to soil moisture and salinity with artificial neural network. 2011. |
入库方式： OAI收割
来源：地理科学与资源研究所
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