首页> 外文OA文献 >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
【2h】

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

机译:分类和回归树(CART)用于分析东北地区田间大豆产量的变化:干旱条件下磷施用量的重要性

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.
机译:干旱是限制全世界农作物生产力的最关键的环境因素。干旱的频度和严重程度预计将伴随气候变化而发生,并将对全球粮食安全产生不利影响。田间产量差异很大,因此在干旱条件下经常发生区域范围内平均产量下降。产量可变性的原因仍然知之甚少。在这项研究中,我们探索了与东北干旱地区2007年严重干旱季节相关的东北一个农村田地中大豆产量差异的来源。在距家园不同距离的三个样带以下田地进行土壤参数测量。管理数据来自家庭访谈。使用通用线性模型(GLM)以及分类和回归树(CARTs)模型分析了土壤参数和管理实践在田间造成产量差异的相对重要性。我们的分析表明,与土壤参数的变化相反,管理选择方案的可变性导致了不同田间大部分的产量可变性。施磷量是决定产量变异性的最重要变量,大约可解释该变异性的61%。是否将肥料添加到田间是次要的。分类树分析表明,样地间的产量差异归因于钾素养分的含量。这可能是由于长期管理选项随距宅基地的距离而变化。 CART模型由于其低的预测误差,是用于预测产量变化对土壤性质和管理实践变化的可靠技术。我们的研究强调,迫切需要调整管理策略,以缩小干旱年份的产量变异性并增加作物产量。我们建议,除了测试土壤外,中国的政府计划还应密切关注农民的管理实践。 (C)2009 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号