It is plausible that under organic rice farming conditions, with Monochoria vaginalis as the dominant weed, the Indica rice ‘Takanari’ (Tak) outperform the Japonica rice ‘Koshihikari’ (Kos) in terms of nitrogen (N) uptake and biomass production. However, how N uptake, biomass, and yield in Tak and Kos are affected by weeds under organic rice farming paddy fields across multiple growing seasons remains unclear. To investigate this, we conducted a two-year field experiment (2022 and 2023) at Yamagata University Farm, Tsuruoka, Japan. Tak and Kos were transplanted individually (four seedlings/hill) or interplanted as Tak + Kos (2 + 2 seedlings/hill), with weeding and no-weeding as the main treatments. Rice and weed biomass and N uptake, as well as rice yield, were measured at harvest, with significant differences observed among all treatments. Weeding practices and growing seasons significantly affected on biomass and N uptake in both Tak and Kos. In 2023, aboveground rice biomass under weeding conditions was significantly lower than that in 2022, whereas no significant difference was observed under no-weeding conditions. Tak consistently showed higher biomass, N uptake, and yield compared with Kos across all treatments, including both seasons, weeding practices, and planting modes. The aboveground biomass ratios between Tak and Kos in interplanting mode were higher than those in individual planting mode under both weeding conditions across both years. These results suggest that Tak exhibited stronger competitive ability in terms of N uptake, biomass, and yield when interplanted with Kos. In conclusion, this two-year organic rice farming field experiment indicates that Tak may sustain higher N uptake and yield compared with Kos under both weeding and no-weeding conditions.

Many studies on the future crop productivity under climate change have warned of possible negative impacts on crop yields. However, these studies are biased toward the studies on major crop species. To increase the number of species evaluated, a possible way to ptoject future impact is to use data-driven methods. In this study, we used historical yield data all over Japan that includes a large range of climate conditions for evaluating the prediction ability and stability of data-driven methods for estimating the impact of climate change on fruit yields. Target crops were apple, cherry, chestnut, grape, Japanese pear, kiwi, loquat, peach, pear, plum, and Japanese apricot. We compared the prediction ability and stability between four data-driven methods (two statistical methods: linear regression and generalized additive model, and two machine learning methods: neural network and gradient boosting method) under future climate conditions. The result showed that, although machine learning methods had high prediction accuracy relative to statistical methods in near-future conditions, the projected fruit yields under far-future climate conditions were unstable even though the problem of extrapolation was considered. This implies the risk of using machine learning methods which include complex interactions of feature values in the models. Using more robust methods among them, we found that the projected yield changes of the 11 species under future climate conditions were mostly positive or showed no change. At least in Japan, there is a possibility that climate change will have a positive effect on a part of fruit crop species, and enhancing crop conversion into more suitable fruit crops may needed for adapting to future climate change.

Volcanic ash soils have a high capacity to absorb CO₂ because of their porous structure, which can lead to underestimation of soil respiration rates when measured using the static closed-chamber method. This study aimed to quantify the impact of CO₂ adsorption on soil respiration measurements in volcanic ash soils using computational modeling based on adsorption data reported by Tabata et al. (2025). The Freundlich isotherm was used to calculate the adsorption ratio in two situations: (1) when the CO₂ concentration was changed from 0 to 5% at any gas/soil ratio (volume/weight: v/w), and (2) for the gas/soil ratio at a certain concentration. Soil respiration rates in the simulated static closed-chamber method was estimated under conditions such as a vial as a container (volume of 136 mL) with a soil amount of 10 g per dry soil. The estimated soil respiration rates showed that the adsorption ratio at 5°C reached 0.92 at a CO₂ concentration of 5000 ppm and a gas/soil of 10. The amount of CO₂ emitted by soil respiration was calculated under an interval of 600 seconds. The results showed that the adsorption ratio increased with increasing CO₂ concentration or decreasing gas/soil ratio. This study highlights the importance of considering CO₂ adsorption when measuring soil respiration in volcanic ash soils using the closed-chamber method, and provides insights for developing more accurate and reliable measurement methods.

Overpopulated ungulates reduce the biomass of understory vegetation and promote the expansion of unpalatable plants in world forests. Such understory degradation may influence soil respiration (R_s) and heterotrophic respiration (R_h). Here, we examined this possibility in one cool-temperate forest in southern Kyushu, Japan. At the study site, the dominant understory vegetation, dwarf bamboo (Sasa; Sasamorpha borealis), has been patchy lost and replaced by an unpalatable shrub, Asebi (Pieris japonica), owing to sika deer feeding. We targeted three understory vegetation types: Sasa understory (SU), no understory (NU), and Asebi understory (AU). The R_s, R_h, soil temperature, and soil volumetric water content (SVWC) were measured at three points in each understory type using an automatic opening/closing chamber system from August 2022 to November 2023. We then evaluated biotic and abiotic factors, including surface litter amount, fine root biomass, and soil physiochemical properties, to examine their effects on the temperature sensitivity proxy (Q₁₀) of R_s and R_h. Annual R_s and R_h were estimated using continuously measured soil temperature data and temperature-R_s and R_h relationships. The temporal variation of R_s and R_h was strongly affected by soil temperature and weakly by SVWC for all understory types. The spatial variation in Q₁₀ of R_s and R_h was explained by fine root biomass and surface litter amount, respectively, regardless of understory type. Differences in annual R_s and R_h among understory types were comparable to those among measurement points in the same understory type. This was due to the similar soil temperature and Q₁₀ of R_s and R_h among understory types. Thus, inner-site R_s and R_h variations could be generated by the spatial variations in soil biotic factors, regardless of understory vegetation type in our study site. This means our study cannot clearly verify whether overbrowsing increases R_s and R_h, highlighting the necessity for future research.

Soybean production in northern Kyushu (Saga and Fukuoka Prefectures), Japan, has declined since 2008. As major cultivar and cultivation management have hardly changed in recent years, this decrease should be caused by climate variability. Thus, we evaluated the variability of the recent climate in northern Kyushu and its impact on soybean production. Trend analyses and the Lepage tests were conducted on air temperature, solar radiation, and precipitation over the past 30 years. The correlations between these meteorological conditions and soybean yield were assessed. Solar radiation from late August to early September, corresponding to the soybean flowering stage, declined since 2008 and showed a strong positive correlation with yield, suggesting a critical limiting factor. Shifting the flowering stage through early or late sowing could mitigate this effect. Precipitation during the growing season was negatively correlated with soybean yield, indicating that improvements in drainage management are effective. Interestingly, soybean yields remained high under low precipitation conditions, showing that soil drought did not significantly limit production. These findings highlight the need for adaptive strategies, including adjustments of sowing date and improved water management, to enhance soybean production in northern Kyushu.