摘要
土壤养分是土壤肥力的具体反映,其肥力等级综合评价很难用常规方法进行。本研究利用BP人工神经网络评价模型,对广西柳江县露塘农场的蔗地土壤养分肥力状况进行了等级综合评价,用于评价的主要指标包括有机质、碱解氮、速效磷、速效钾、缓效钾,活性硅、有效硼和有效锌。结果表明:该蔗区土壤养分肥力评价等级为一级占93个,占总体样本53%,二级占82个,占总体样本47%。
Soil fertility is a concrete reflection of its fertility level comprehensive evaluation is difficult to use conventional methods. This study evaluated the use of BP artificial neural network model of Guangxi sugar cane farms in certain areas of soil fertility and comprehensive evaluation of the status of the class, including the main indicators used to evaluate organic matter, nitrogen, phosphorus, potassium, slow potassium, active silicon, boron efficient and effective zinc. The results showed that: the sugarcane Soil Fertility Evaluation grade level accounted for 93, accounting for 53% of the total sample, two accounted for 82, accounting for 47% of the total sample.
出处
《南方农业》
2013年第10期47-50,共4页
South China Agriculture
基金
广西壮族自治区科学基金项目"基于GIS和作物生长模型的甘蔗水肥精准化管理模式研究"(桂科基0778006-7)
