Classification and regression tree (CART) for analysis of soybean yield variability among fields in Northeast China: The importance of phosphorus application rates under drought conditions
文献类型：期刊论文
| 作者 | Zheng, Haifeng; Chen, Liding; Han, Xiaozeng; Zhao, Xinfeng; Ma, Yan |
| 刊名 | AGRICULTURE ECOSYSTEMS & ENVIRONMENT
 |
| 出版日期 | 2009-07 |
| 卷号 | 132期号:1页码:98-105 |
| 关键词 | Drought Soybean Yield gap Classification and regression tree (CART) Adaptation Northeast China |
| 中文摘要 | Drought is the most critical environmental factor limiting the productivity of agricultural crops worldwide. Increased frequency and severity of drought are expected to accompany climate change and will negatively impact global food security. Wide yield variability from field to field, and consequently reduced average yield on a regional scale often occur under drought conditions. The reasons for the yield variability are still poorly understood. In this study, we explored sources of soybean yield variability among fields in a rural village of Northeast China associated with a severe drought growing season in 2007. Soil parameter measurements were made on fields following three transects with different distances from homestead. Management data were assembled from household interviews. The relative importance of soil parameters and management practices resulting in yield variability among fields was analyzed with general linear model (GLM) and classification and regression trees (CARTs) models. our analysis showed that variability in management options, as opposed to variability in soil parameters, caused the majority of yield variability from field to field. The amount of P applied was the most important variable determining yield variability and explained roughly 61% of the variability. Whether or not manure was added into fields was of secondary importance. The classification tree analysis indicated that yield differences among transects was attributed to the content of K nutrient. This might result from variations of long-term management options with distance from homestead. CART models are robust technique for predicting yield variability responses to variations of soil properties and management practices due to its low prediction error. Our study highlights the pressing need to adjust management strategies for narrowing yield variability and increasing crop production in drought years. We recommend that in addition to testing soil, government programs in China should also pay close attention to management practices of farmers. (C) 2009 Elsevier B.V. All rights reserved. |
| WOS记录号 | WOS:000266684400013 |
| 源URL | [http://ir.rcees.ac.cn/handle/311016/21367]  |
| 专题 | 生态环境研究中心_城市与区域生态国家重点实验室 |
| 推荐引用方式 GB/T 7714 | Zheng, Haifeng,Chen, Liding,Han, Xiaozeng,et al. Classification and regression tree (CART) for analysis of soybean yield variability among fields in Northeast China: The importance of phosphorus application rates under drought conditions[J]. AGRICULTURE ECOSYSTEMS & ENVIRONMENT,2009,132(1):98-105. |
| APA | Zheng, Haifeng,Chen, Liding,Han, Xiaozeng,Zhao, Xinfeng,&Ma, Yan.(2009).Classification and regression tree (CART) for analysis of soybean yield variability among fields in Northeast China: The importance of phosphorus application rates under drought conditions.AGRICULTURE ECOSYSTEMS & ENVIRONMENT,132(1),98-105. |
| MLA | Zheng, Haifeng,et al."Classification and regression tree (CART) for analysis of soybean yield variability among fields in Northeast China: The importance of phosphorus application rates under drought conditions".AGRICULTURE ECOSYSTEMS & ENVIRONMENT 132.1(2009):98-105. |
入库方式： OAI收割
来源：生态环境研究中心
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