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Best management practice development with the CERES-Maize model for sweet corn production in North Florida.

机译:使用CERES-Maize模型开发最佳管理实践,以开发北佛罗里达的甜玉米。

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摘要

Increasing nitrogen loads within the Suwannee River Basin of North Florida has become a major concern. Nitrogen fertilizer application in field crop production is proved to be the most import nitrogen contribution in this region. Florida ranks highest in the nation in the production and value of fresh market sweet corn. Thus it is necessary to develop research based nitrogen best management practices (N-BMPs) to reduce nitrogen leaching while keeping an acceptable yield in sweet corn production.;This study is an attempt to utilize the CERES-Maize mode of the Decision Support System for Agrotechnology Transfer (DSSAT) model as a platform to develop potential BMPs for sweet corn production in North Florida.;The results show that the non-restricted and restricted one-at-a-time (OAT) method can be used to conduct global sensitivity analysis for the CERES-Maize so as to select the most influential parameters for model calibration. The generalized likelihood uncertainty estimation (GLUE) method was proved to be a powerful tool for model parameter estimation, since the uncertainties in model input parameters were significantly reduced after GLUE was used to estimate the model input parameters. The uncertainties in model outputs were reduced correspondingly.;The comparison between the model simulated and field observed results of the seven treatments in a field plot experiment of sweet corn in 2006, shows that the model did a good job in predicting dry yield and phenology dates.;The results of BMP development with the calibrated CERES-Maize model show that if the growers could apply both irrigation water and nitrogen fertilizer more frequently but with smaller amounts in each application, this would result in an acceptable yield and a lower level of nitrogen leaching. The results showed a total nitrogen amount between 196 and 224 kg N ha-1 would be enough for sweet corn production in North Florida, which confirmed that the recommendation nitrogen amount (224 kg N ha-1) by Institute of Food and Agricultural Sciences (IFAS), Univerisity of Florida, was reasonable.;The results of uncertainty analysis of the CERES-Maize model for sweet corn simulation show that the weather was the dominant uncertainty contributor. This was because after two rounds of GLUE parameter estimation procedure, the uncertainties existing in input parameters were minimized.
机译:在北佛罗里达州的苏万尼河流域内,氮负荷的增加已成为一个主要问题。事实证明,在大田作物生产中施用氮肥是该地区进口氮的最大贡献。在新鲜市场甜玉米的产量和价值上,佛罗里达州在全美排名最高。因此,有必要开发基于研究的氮素最佳管理方法(N-BMPs),以减少氮素的淋失,同时保持甜玉米生产中的可接受的产量。本研究尝试利用CERES-玉米模式的决策支持系统来实现农业技术转移(DSSAT)模型作为开发北佛罗里达甜玉米生产潜在BMP的平台;结果表明,非限制性和限制性一次性(OAT)方法可用于进行全球敏感性CERES-玉米分析,以选择最具影响力的参数进行模型校准。事实证明,广义似然不确定性估计(GLUE)方法是用于模型参数估计的强大工具,因为使用GLUE估计模型输入参数后,模型输入参数的不确定性大大降低。模型输出的不确定性相应地降低。; 2006年甜玉米田间试验的模拟处理与7种处理方法的实地观察结果之间的比较表明,该模型在预测干燥产量和物候日期方面做得很好。 。;使用校准的CERES-Maize模型进行BMP开发的结果表明,如果种植者可以更频繁地施用灌溉水和氮肥,但每次施用量较小,这将导致合格的产量和较低的氮含量浸出。结果表明,总氮量在196至224 kg N ha-1之间足以满足北佛罗里达州甜玉米的生产,这证实了食品和农业科学研究所的推荐氮量(224 kg N ha-1)( IFAS),佛罗里达大学是合理的。;用于甜玉米模拟的CERES-玉米模型的不确定性分析结果表明,天气是造成不确定性的主要因素。这是因为经过两轮GLUE参数估计程序后,输入参数中存在的不确定性得以最小化。

著录项

  • 作者

    He, Jianqiang.;

  • 作者单位

    University of Florida.;

  • 授予单位 University of Florida.;
  • 学科 Agriculture Soil Science.;Engineering Agricultural.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 329 p.
  • 总页数 329
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 土壤学;农业工程;
  • 关键词

  • 入库时间 2022-08-17 11:39:07

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