Abstract 　The water temperature of paddy fields is an important factor affecting the growth and yield of paddy rice. In this study, the optimum timing of irrigation to control (increase or decrease) water temperature was simulated based on the canopy micro-meteorological model under two different climate conditions, i.e., high and low temperature conditions, assuming the need to prevent high and low temperature injury of paddy rice. Under both conditions, the optimum timing of irrigation to increase daily minimum water temperature was during nighttime from 0:00 to 4:00, whereas the optimum timing to decrease daily maximum water temperature was during daytime from 11:00 to 14:00. Under the low temperature condition, the daily mean water temperature increased with increasing water depth. The main reason is that roughness length decreases at deeper water surfaces, which suggests that practicing deep-water management in cold regions would have the effect of keeping water temperature higher. Simulations to determine the optimal timing of drainage in addition to irrigation showed that daytime flooding (irrigation in the morning and drainage in the evening) is effective for decreasing water temperature. As a result of field experiments of three years, a significant difference in average water temperature of 0.67℃ was observed between the daytime-flooded plot and nighttime-flooded plot during the ripening period. We developed a field water management software to optimize irrigation time to increase or decrease water temperature based on simulation using the canopy micrometeorological model with weather information. Using this software, water management to prevent cold damage was conducted in actual paddy fields. The water temperature in the software-controlled plot was on average 0.85℃ higher than the water temperature in the conventional plot. The developed software can be used as a countermeasure for low and high temperature injury in rice cultivation for adaptation to climate change.

Abstract 　Maize（Zea mays L.） is an important crop, and improving its productivity is required even under challenging conditions such as labor shortages and uncertain climate fluctuations. One approach to enhancing yield is utilizing crop data for cultivation management and yield prediction. However, efficient acquisition of such data remains constrained by various limitations. In this study, we developed a non-contact and labor-efficient method for crop data acquisition by generating 3D models of maize at the ripening stage using Neural Radiance Fields (NeRF). Segmentation was performed on the obtained point clouds to estimate plant height, leaf area, and leaf angle. The coefficients of determination (R²) were 0.903, 0.954, and -0.521, respectively, demonstrating high accuracy for plant height and leaf area even at the ripening stage, while reducing the time required for data acquisition by 93% compared to manual measurements. Nevertheless, some manual operations−such as removing kernels and separating overlapping leaves−were still necessary, and full automation was not achieved. The main sources of error were identified as reconstruction errors in the base during scale adjustment, excessive removal of leaf sheaths, and the curvature of individual plants. Furthermore, we examined how measurement accuracy was influenced by factors such as the time of day and cultivar. The proposed method is expected to contribute to the practical implementation of a labor -saving 3D measurement technique that supports yield prediction and growth diagnosis in maize.