Japanese Journal of Grassland Science
Online ISSN : 2188-6555
Print ISSN : 0447-5933
ISSN-L : 0447-5933
Search
Advanced search
OR
Browse
Search
Volume 68, Issue 4
Displaying 1-6 of 6 articles from this issue
Show all abstracts
    • |<
    • <
    • 1
    • >
    • >|
Research Paper
  • Whole-crop Silage Corn Yield Prediction Using Neural Network
    Kozue Sasaki, Hiroyuki Sasaki, Yuki Akamatsu, Tsutomu Kanno
    2023 Volume 68 Issue 4 Pages 147-151
    Published: February 24, 2023
    Released on J-STAGE: March 10, 2023
    DOIhttps://doi.org/10.14941/grass.68.147
    JOURNAL FREE ACCESS
    Show abstractHide abstract

    Although whole-crop silage corn is one of the major forage crops in Japan, the yield is presently stagnating. To improve the yield, the potential yield under each cultivation condition should be predicted and the cultivation methods improved. In this study, we developed a neural network model that predicts the dry matter yield in the yellow-ripe stage of three corn varieties, using the experimental data of each variety and meteorological data obtained from the automated meteorological data acquisition system observation points near each experimental site. The correlation coefficient between the estimated and measured dry matter yield using this model was 0.556 and the root mean square error was 460.2 kg/10 a. The developed model was also used to create a “Takanestar” yield prediction map covering the Tohoku to Kyushu regions, and the regional differences were compared.

    View full abstract
    Download PDF (1967K)
Practical Report
Special Features
Evaluation of the Global Warming Effects in Grazing Production System
    • |<
    • <
    • 1
    • >
    • >|
feedback
 Top