基于云模型的西安市蔬菜区土壤肥力综合评价

发布时间：2018-04-17 22:05

本文选题：土壤肥力 + 综合评价　；参考：《干旱区资源与环境》2017年10期

【摘要】：土壤肥力对于维持农作物生长和保障土地生产力具有重要意义。为综合考虑土壤肥力评价中的模糊性和随机性,引入云模型对西安市蔬菜区土壤肥力状况进行综合评价,选取pH值、有机质、碱解氮、有效磷和速效钾为评价指标,运用主成分分析确定权重,利用逆向云、正向云算法获得每个指标的云模型;根据单指标云模型特征值和权重值,结合综合云算法得到土壤肥力综合评价云模型。结果表明:西安市蔬菜区土壤养分总体上属于中等偏高水平,有效磷、速效钾含量偏高,碱解氮含量适中。土壤有机质是土壤肥力的首要限制因素,在实际生产过程中应注意增施有机肥,高磷、高钾地区应限制磷肥、钾肥的使用,按需施用氮肥。科学评价土壤肥力有助于更加准确地掌握土壤特性和养分状况,精准施肥,维持土壤养分平衡,提高土壤质量。
[Abstract]:Soil fertility plays an important role in maintaining crop growth and ensuring land productivity.In order to comprehensively consider the fuzziness and randomness of soil fertility evaluation, a cloud model was introduced to evaluate soil fertility in vegetable and vegetable areas in Xi'an. The pH value, organic matter, alkali-hydrolyzed nitrogen, available phosphorus and available potassium were selected as evaluation indexes.Using the principal component analysis to determine the weight, using the reverse cloud, forward cloud algorithm to obtain each index cloud model, according to the single index cloud model eigenvalue and weight value, combined with comprehensive cloud algorithm to obtain a comprehensive evaluation cloud model of soil fertility.The results showed that the soil nutrients of vegetable and vegetable area in Xi'an were generally medium high, available phosphorus, available potassium and alkali-hydrolyzed nitrogen were on the high side.Soil organic matter is the primary limiting factor of soil fertility. In the actual production process, attention should be paid to increasing the application of organic fertilizer, high phosphorus and high potassium, limiting the use of phosphorus and potassium fertilizer, and applying nitrogen fertilizer as needed.Scientific evaluation of soil fertility is helpful to grasp soil characteristics and nutrient status accurately, maintain soil nutrient balance and improve soil quality.
【作者单位】：西北大学城市与环境学院;西安市农业技术推广中心;西安市农产品质量安全检验监测中心;内江师范学院地理与资源科学学院;
【基金】：教育部人文社会科学研究规划项目(10YJA910010) 陕西省农业科技攻关项目(2011K02-11) 西安市科技计划项目(NC1402,NC150201) 西北大学"211工程"研究生自主创新项目(YZZ15013)资助
【分类号】：S158;S63

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