• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Second International Conference on Sustainable Agriculture for Food, Energy and Industry Vol.1 >Stocheometric Crop Weather Model for Sustainable Production of Finger Millet
【24h】

Stocheometric Crop Weather Model for Sustainable Production of Finger Millet

机译：用于小米可持续生产的化学计量作物天气模型

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A Stocheometric crop weather model to predict fingermillet growth and grain yield based on the dry matter accumulated at each stage has been developed. Multiple linear regression equations relating the GDD, SSH and AET with the accumulated dry matter during each growth stage and also the final grain yield were generated using the field experimental data for the period 1992-98. The coefficient of determinants indicate that the climatic parameters considered and the initial TDM used to estimate the final TDM in each stage could be able to predict the final to an extent of 93 per cent to 98 per cent (coefficients of determinants) in different stages. Comparison of the observed and the predicted yields indicate the close agreement between them in all the stages. There is a very good agreement between the observed and the predicted yield, which is revealed by the correlation coefficient of 97 per cent. Comparison of the observed and the predicted yields indicate the close agreement between them in all the stages. Considering the observed Total Dry Matter up to the first four stages and the predicted Total Dry Matter at the end of the harvesting stage, the model has been validated for the year 2001, and there is very good agreement between the observed and the predicted yield, which is revealed by the regression coefficients of 0.99 and 0.999 for two dates of sowing. Favorable influence of AET at the time of beginning of tiller and grain formation stage, and higher GDD during ear emergence and harvest stages was noticed. Increase in AET during pre-harvest stage did not favor good grain yield. Hence, this Stocheometric crop weather model could be used to predict the grain yield along with their dry matters well before harvest of the crop. This helps the planners for future action.
机译：已经开发出了一个Stocheometric作物天气模型，该模型可基于每个阶段积累的干物质来预测小米的生长和谷物产量。使用1992-98年间的田间实验数据，生成了多个线性回归方程，将GDD，SSH和AET与每个生长期中的累积干物质以及最终的谷物产量相关联。行列式系数表明，所考虑的气候参数以及用于估计每个阶段最终TDM的初始TDM可以在不同阶段预测最终值的范围为93％至98％（行列式系数）。观察到的和预测的产量的比较表明，在所有阶段它们之间有着密切的一致性。观察到的和预测的产量之间有很好的一致性，相关系数为97％揭示了这一点。观察到的和预测的产量的比较表明，在所有阶段它们之间有着密切的一致性。考虑到观察到的前四个阶段的总干物质和在收获阶段结束时的预测的总干物质，该模型已经过2001年的验证，观测的和预测的产量之间有很好的一致性，这可以通过播种两个日期的0.99和0.999的回归系数来揭示。在分and开始和谷物形成阶段开始时，AET产生了有利影响，并且在穗出苗和收获阶段发现了较高的GDD。收获前阶段AET的增加不利于谷物的高产。因此，这种Stocheometric作物天气模型可用于在收获作物之前很好地预测谷物产量及其干物质。这有助于计划者采取进一步行动。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

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