Climate change is increasingly being recognized as a potential threat to food security. This study assessed the risk of climate change on rice production value in the Tohoku and Kyushu regions. In a scenario without climate change mitigation, characterized by a 4 K global average temperature increase compared to pre-industrial levels, a significant decrease in rice production value was projected for Tohoku (93.9%) and Kyushu (75.9%). Based on the current value, each 1 K increase in temperature is estimated to result in an annual economic loss of approximately 7 and 12 billion yen in Tohoku and Kyushu, respectively. The frequency of losses in production value, which is typically expected to occur once every 25 years under current climate conditions, may occur more frequently (Tohoku: every 4.0 years; Kyushu every 1.1 years). This increased risk is attributed to a reduction in the proportion of first-grade rice, which is compounded by decreased yields in Kyushu. The negative trends become less pronounced when considering the mitigation efforts that limit the global temperature rise to 2 K. In this scenario, the decline in production value that occurs once every 25 years does not occur in Tohoku because of increased yields. In Kyushu, however, this decline still occurs once every 5.6 years, mainly because of a reduced proportion of first-grade rice in the total production. In both regions, increasing yield through earlier transplanting proved more effective in mitigating production decline than improving the proportion of first-grade rice through later transplanting, regardless of the extent of the temperature rise. With the current rice varieties and transplanting schedules, production decline is difficult to prevent under severe climate change conditions. Therefore, high-temperature-tolerant rice varieties must be promoted, and their transplantation must be adjusted to adapt to future environmental conditions.

　It is predicted that climate change will make the cultivation of satsuma mandarins more challenging unless adaptation measures can be implemented in current production areas, although changing conditions may favor the cultivation of subtropical fruit trees such as avocado. Therefore, we assessed suitable locations for the future cultivation of both tree species in Japan and the potential of avocado as a substitute for satsuma mandarin. Suitable locations were determined using the annual mean (satsuma mandarin) and minimum (both species) temperatures calculated from the projected temperatures based on two global climate models (MRI-ESM2-0 and MIROC6) under three shared socioeconomic pathways (SSP). Suitable locations for satsuma mandarins were predicted to move northward until the mid-21st century under SSP1-RCP2.6 or until the end of the 21st century under SSP2-RCP4.5 and SSP5-RCP8.5. Among the suitable locations for satsuma mandarins for 1990-2009, the percentage that remained suitable for 2080-2099 varied depending on the SSP, ranging from 0% (SSP5-RCP8.5) to approximately 80% (SSP1-RCP2.6). This highlights the substantial impact of climate change mitigation measures on current satsuma mandarin production areas. Suitable locations for avocados were projected to certainly expand definitely in the future, regardless of the SSP, and many of the suitable locations for satsuma mandarins for 1990-2009 will become suitable for avocados for 2040-2059. Because temperature conditions are suitable for avocado cultivation in most locations that will become too warm for satsuma mandarin production in the future among the suitable locations for satsuma mandarin for 1990-2009, replanting from satsuma mandarins to avocados is an adaptation option in current production areas. Although adaptation measures using cultivation techniques and high-temperature-tolerant cultivars are likely to become less effective as climate change progresses, replanting to avocados was shown to be an effective alternative, at least until the end of the 21st century.

　An accurate estimation of soil heterotrophic respiration (Rh) is crucial to separate autotrophic respiration (Ra) from soil respiration (Rs) and to quantify the soil carbon balance. In this study, spatiotemporal variation in Rh within an area of 0.09 ha was modeled by machine learning (ML) with Random Forest (RF) and Gradient Boosting Machine (GBM) algorithms, based on hourly Rh data measured with five automated chambers over two growing seasons in an immature deciduous forest in Hokkaido, Japan. Using the explanatory variables of soil temperature, soil moisture (water-filled pore space (WFPS) or volumetric soil water content), soil bulk density, soil carbon/nitrogen ratio (C/N), wind speed, and litter accumulation, ML models were much superior to conventional regression models using soil temperature and/or soil moisture and a multiple linear regression model using the same variables as in the ML models. In addition, the RF model performed better than the GBM model in all variable combinations. According to the RF model, soil temperature showed the highest importance in Rh variation among the variables, followed by bulk density. The RF model is promising for the gap-filling of missing Rh data and the accurate separation of Ra from Rs.