Agricultural Information Research
Online ISSN : 1881-5219
Print ISSN : 0916-9482
ISSN-L : 0916-9482
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Original Paper
  • Tokihiro Fukatsu, Masayuki Hirafuji
    2020 Volume 29 Issue 1 Pages 1-13
    Published: April 01, 2020
    Released: April 01, 2020

    Self-build agricultural sensor network systems are convenient and affordable but rely on user skills. Here, we propose how users can easily make their own to collect data. First, we devised how to choose an appropriate monitoring system for users’ situations. Second, we investigated the self-build process from parts acquisition to data collection, and the necessary skills, knowledge, and environment at each step. Third, we examined the kind of information that users need. On this basis, we developed an “Open Field Server”, which provides manuals tailored to users’ situations and support information on a Web portal. We tested the Open Field Server to evaluate the possibilities and effectiveness of our proposed system, and demonstrated the potential and effectiveness of self-build sensor network systems

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  • Takeshi Sato, Tomoaki Murakami, Yasuhiro Nakashima
    2020 Volume 29 Issue 1 Pages 14-23
    Published: April 01, 2020
    Released: April 01, 2020

    Remote sensing and geographic information systems enable observation of land cover, vegetation, and agricultural conditions. Geographical data also allow plot-level detailed economic analysis. Here, we used plot-level polygon panel data to integrate remote sensing, socioeconomic, and meteorological information. We conducted a field survey in one of the largest grassland areas in Japan, the Konsen Plateau. First, we created field plot polygons by object-based satellite image analysis. Second, we clarified the 2010 and 2015 land use/land cover in each polygon. Next, we linked the Census of Agriculture and Forestry in Japan and meteorological data to all polygons. Finally, we estimated determinants of cropping from the plot-level polygon panel data. The results show that the farmers who own more capital and employ more laborers prefer upland fields to grassland. Farmers in the study area also prefer upland fields, where the temperature is higher during the growing season. The explanation for these preferences is that having better biophysical conditions, capital, and labor inputs, upland fields profit farmers more. The data set allows economic analysis using socioeconomic and meteorological variables

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  • Munehiro Ebato
    2020 Volume 29 Issue 1 Pages 24-39
    Published: April 01, 2020
    Released: April 01, 2020

    To enable quantitative soil physical diagnosis by cone penetrometer, this study investigated time-course changes in the three-dimensional (3D) distribution of soil hardness and quantified the effect of land leveling on soil compaction. The most important result is that the 3D distribution of soil hardness in the field can be mapped quickly. One person could survey 1 ha to 60 cm depth in 90 min. The method allows several fields to be screened in a short time. The results of cluster analyses based on the horizontal distribution of soil hardness show that the distribution did not change significantly throughout the year. Although absolute values can change, this method can be used to measure soil hardness at any time of the year. Measurement of soil hardness by depth can detect the range or position of the plow pan. Comparison of the distribution of soil physical properties between fields revealed that field management affects hardness, especially in the plow layer and plow pan

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