Agricultural Information Research
Online ISSN : 1881-5219
Print ISSN : 0916-9482
ISSN-L : 0916-9482
Current issue
Displaying 1-2 of 2 articles from this issue
Original Paper
  • Kenta Baba, Teruaki Nanseki, Yosuke Chomei, Yoshihiro Uenishi
    2023 Volume 32 Issue 1 Pages 1-25
    Published: April 01, 2023
    Released on J-STAGE: April 01, 2023

    We identified factors affecting the utilization of robotic and automation technology (RAT) in corporate rice farming. In a probit analysis of the results from a nationwide survey of agricultural corporations, the objective variable was the current usage status of each RAT, and the explanatory variables were corporate goals/management, corporate attributes, and characteristics of corporate representatives. A common factor affecting the utilization of “assistive agricultural machinery” and “cultivation automation/robots” was identified: corporate rice farming with non-farmers and other industries involved in their establishment were 41%–51% more likely to utilize these technologies than corporate rice farming without that industry mix. Among factors unique to each RAT, “assistive agricultural machinery” depended on the number of farming years, whereas “cultivation automation/robots” depended on information/technical management, age of representative, and sales scale. “Automatic irrigation system” had no significant results. We suggest that the utilization of RAT in corporate rice farming is influenced by knowledge and human resources related to other industries and high technology such as information and communication technology (ICT) and robotic technology (RT). In addition, we found differences in factors due to the characteristics of each RAT.

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  • Dai Kusui, Hideo Shimazu, Atsushi Shinjo
    2023 Volume 32 Issue 1 Pages 26-37
    Published: April 01, 2023
    Released on J-STAGE: April 01, 2023

    Agriculture has off-seasons and busy seasons, and not all farmers need the same number of workers throughout the year. It is costly to maintain the same level of employment throughout the year, so farmers usually bring in temporary workers to help during the busy season. As temporary workers, however, are not engaged in farming all year round, and it is not always possible for farms to hire the same people every year, it is uncertain whether the person hired this year has the knowledge and experience to do the work in question. In addition, the weather conditions and the field and crop conditions differ by year, and work needs to be adapted to current conditions. To ensure that work be done smoothly and consistently within a limited amount of time, it is desirable to provide guidance to the temporary workers on a case-by-case basis. We propose a rapid agri-infoscience learning model that has been adapted to teaching temporary agricultural workers. Through this model, workers learn how to make the decisions necessary to execute tasks by narrowing down the number of tasks and limiting the target area to a single crop at a particular time and place. By restricting the number of factors needed to judge and work a crop at one location at a time, instructors can create exercises that are suitable for the specific situation. By using the learning support system in which the model was implemented, experiments confirmed that the instructors can create the needed exercises and temporary workers can improve the quality of their work, as evaluated by instructors.

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