Our first aim was to clarify the effectiveness of using a CO₂ sensor with a farmer-built IoT (Internet of Things) system that had proven to be of practical use in rice nursery growing. We conducted an experiment at kikurage mushroom and strawberry growing sites to observe whether the system worked and asked the property owners to evaluate the system. At both sites the system provided CO₂ concentration data with almost the same accuracy as the existing detection devices that the farmers had been using. Because our system uses an IoT study kit and Raspberry Pi, which are provided cheaply, the farmers evaluated it as practical and low-cost. We put one CO₂ sensor in the strawberry greenhouse, with an annual cost of 25,620 yen; this was much lower than the cost of the existing devices that the farmer had used in the past. In the experiment in the kikurage mushroom house, we placed four CO₂ sensors, with an annual cost of 72,480 yen; this was higher than the cost of the handy-type detection device that the farmer had been using. However, our system sends CO₂ concentration data to cell phones automatically, and it detected high concentrations at times when the farmer was engaged elsewhere. The farmer therefore considered that the annual cost was not high.

The CO₂ sensor has tendency to bring value shifts upwards; it takes place when micro dusts adhere the sensor tip. We are therefore unable to write a threshold CO₂ concentration into the program to detect anomalous values, such as those occurring from changes in temperature with seasonal change. Our second aim was to clarify a method of using a state-space model to detect anomalous values with the farmer-built IoT system. We created a calculation method and used the variance in lag between the predicted and measured values as an evaluation criterion to judge the anomalous values. The method worked in a test using CO₂ concentration data from the strawberry greenhouse.

We examined the factors affecting the potential of jute products and the supportive mindset toward the revival of jute entrepreneurship in Bangladesh. By using data obtained from an online questionnaire survey, we quantitatively investigated the relationship between the respondents’ mindset and their attributes. First, we found that the employment status as self-employed, the attitudes of Bangladeshi society, and jute product design were the most crucial variables influencing the potential of jute products. Secondly, the employment status as self-employed, the use of jute goods in daily life, and constraints on jute entrepreneurship had the most marked positive effects on supportive mindsets toward the need for jute entrepreneurship. Thirdly, the factors regarding inclination toward agri-entrepreneurs and conditions under which money could be saved had a significant negative effect on supportive mindsets toward jute entrepreneurship. This suggests that there is a trade-off between fostering entrepreneurship in agriculture and reviving a declining industry. Therefore, it is important to enhance further actions that will support the jute industry to revitalize.

One risk-management approach to annual crop yield variability is to diversify the risk by cultivating multiple crops or by cultivating in regions with various climatic conditions. However, to my knowledge, no comprehensive risk analysis for each crop and region of Japan has been published. Here, I analyzed long-term national statistical data on crop yield from the Ministry of Agriculture, Forestry, and Fisheries of Japan to quantify the risk of crop-yield variability by crop and by prefecture. The difference between normal and actual yields in each year (shown as a crop-yield index, with a normal year having a value of 100) was assumed to occur randomly under a certain probability distribution. The average and the magnitude of variation in the distribution (standard deviation) were used as risk indices. Patterns of crop-yield variability were organized in terms of the relationship with meteorological factors such as temperature, the horizontal distribution of crop-yield variability in each prefecture, the similarity to annual variation patterns of paddy rice yield, and the relationship between risk and return. Furthermore, the effect of risk diversification by cultivating multiple crops and by cultivating in prefectures with different patterns of annual crop yield variation was simulated. Successful cases of the positive effect of risk diversification were observed when the correlation coefficient between combined crops or between prefectures was low.

The COVID epidemic affected Japan in 2020 and 2021. Here, I analyzed how vegetable supply and demand in this period differed from that in the previous 10 years to 2019. Of the vegetables shipped to the Tokyo Metropolitan Central Wholesale Market, 144 vegetables are analyzed monthly, and the shifts in demand, supply, and prices of 1657 items are confirmed. The results showed that the patterns of shift in supply and demand of vegetables differed by item and month and were very diverse. However, the overall trend was that demand and supply were declining, and that tendency became even stronger during the COVID epidemic in the last 2 years of the analysis. Demand for major vegetables, the supply of which the government is trying to stabilize, increased slightly during the epidemic, because these vegetables were likely to have remained in high demand in people’s homes. In contrast, the demand for items with a large import ratio and savory herbs and garnishes declined, because these items were likely to have been in high demand for processing or by restaurants, and these industries declined during COVID-19. Seasonally, there were many items for which the demand declined from spring to summer. Despite this decline in demand, for many items the prices did not decrease markedly. This price maintenance was due to reduced supply, which likely led to a degree of economic damage to suppliers.

In sugarcane breeding, large amounts of data are collected every year, and the use of these data needs to be optimized to enhance breeding. Therefore, GIS is being used to integrate location information on research plots and research data. Location information on research plots is placed on GIS by using a RTK-GNSS (real-time kinematic positioning–global navigation satellite system). Such breeding information can then be used effectively on mobile devices that can be carried around on a daily basis. This use of breeding information is achieved by (1) using QField to run the open-source platform QGIS on an Android device and (2) converting the QGIS data into files that can be used on the Web.