Agricultural Information Research
Online ISSN : 1881-5219
Print ISSN : 0916-9482
ISSN-L : 0916-9482
Current issue
Displaying 1-3 of 3 articles from this issue
Original Paper
  • Mitsunori Ishihara, Jihyun Lim, Ryo Sugiura, Hiroshi Tsunematsu
    2022 Volume 31 Issue 3 Pages 65-77
    Published: October 01, 2022
    Released on J-STAGE: October 01, 2022
    JOURNAL FREE ACCESS

    We used a canopy radiative transfer model, PROSAIL, with simulated paddy canopy reflectance and vegetation indices (VIs) to investigate the characteristics of three drone-mountable multispectral cameras. The leaf area index (LAI), diffuse light ratio, and solar zenith angle were varied to create differences in reflectance and VIs due to changes in the growth stage and light environment. Different wavelength regions of each camera caused significant differences in both reflectance and VIs in the red edge band. Changes in the diffuse light ratio and solar zenith angle changed the values even at the same LAI; therefore, to obtain reliable results, appropriate observation conditions must be set for aerial photography by drones with multispectral cameras. The VIs that use the red edge band were the least affected by the observation conditions, and were less likely to be saturated at high LAI. However, drone-mountable cameras can be effectively used to observe paddy fields because the differences in the values of reflectance and VIs among cameras can be corrected.

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  • Miyuki Oka, Nao Ishige, Yoshitaka Uchida
    2022 Volume 31 Issue 3 Pages 78-86
    Published: October 01, 2022
    Released on J-STAGE: October 01, 2022
    JOURNAL FREE ACCESS

    With the increase in scale and the division of labor in the dairy industry in Japan, the use of liquid digestate derived from livestock manure produced in biogas plants as fertilizer has been promoted. It is a challenge to optimize the location, timing, and amount of application of the digestate to pastures. Here, we developed a Web-based system to manage the application of liquid digestate fertilizer to multiple paddocks on the basis of application history. It uses an existing cloud-based system to collect and manage application history data and open satellite imagery data to calculate the Enhanced Vegetation Index of pastures, which is used to evaluate their growth in relation to the amount of digestate applied per year. By comparison with the annual average value in the district, paddocks with excess nutrient application (more digestate application but poorer grass growth) are selected as “caution” areas and are mapped in the Web application. Users can select a paddock and view its history of digestate application and grass growth. Through this system, we aim to encourage information sharing and understanding among farmers, those who manage and apply liquid digestate, and local government officials, with the goal of more efficient use of local resources.

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  • Jun Chujo, Wenli Sun, Shusuke Matsushita
    2022 Volume 31 Issue 3 Pages 87-94
    Published: October 01, 2022
    Released on J-STAGE: October 01, 2022
    JOURNAL FREE ACCESS

    We propose a system for recomputing weighting factors by the incomplete paired comparison method in the Analytical Hierarchy Process (AHP). We present a case study in which we evaluate factors considered by farmers in deciding whether to adopt annual cash crops in a village in the central region of Côte d’Ivoire. Applying the AHP to farmers’ decision-making using the complete paired comparison method (based on the total evaluation of annual cash crop preferences), only 24 of 82 farmers were able to be compared with a confidence interval (CI) of ≤0.15. And these farmers can be classified into three clusters. Applying AHP using the incomplete paired comparison method (which removes only one paired comparison result) 76 of 82 farmers were able to be compared with a CI of ≤0.15. And these farmers can be classified into four clusters. We conclude that the incomplete paired comparison method can enlarge sample selection and satisfy consistent criteria in the AHP and improve learning.

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