Cow manure is a good source of phosphorus (P). Here, we investigated whether the amount of P fertilizer can be reduced when cow manure is applied to paddy soil based on growth, P uptake, yield, and soil P status evaluation. Treatments included unfertilized control (CK); manure plus chemical nitrogen (N), potassium (K), and P fertilizer (MNK P); MNK and 75% P (MNK ¾ P); MNK and 50% P (MNK ½ P); MNK and 25% P (MNK ¼ P); and MNK. Manure was applied at the rate of 10 t ha⁻¹ in fresh weight base. The P fertilizer was applied at 34.9 kg P ha⁻¹ as full dose. Treatment with MNK resulted in the same growth, P uptake, and yield as that with the P fertilizer. P uptake and yield did not respond to P input from chemical fertilizer owing to high soil Olsen P levels. Moreover, MNK could maintain soil Olsen P and total P. Manure application resulted in a positive partial P balance. These results suggest that manure application can cut P fertilizer requirements in P-rich soils, while maintaining soil P for optimal rice growth and yield. By using cow manure in rice production, farmers can conserve finite P resources.

In recent decades, forests have experienced an increasing trend in the number of pest outbreaks worldwide, apparently driven by strong annual variability in precipitation, higher air temperatures, and strong winds. Pest outbreaks have negative ecological, economic, and environmental impacts on forest ecosystems, such as reduced biodiversity, carbon sequestration, and overall forest health. Traditional monitoring methods of these disturbances, while accurate, are time-consuming and limited in scope. Remote sensing, particularly UAV (Unmanned Aerial Vehicle)-based technologies, offers a precise and cost effective alternative for monitoring forest health. This study evaluates the temporal and spatial progression of bark beetle damage in a fir-dominated forest in the Zao Mountains, Japan, using UAV RGB imagery and DL (Deep Learning) models (YOLO - You Only Look Ones), over a four-year period (2021–2024). Trees were classified into six health categories: Healthy, Light Damage, Medium Damage, Heavy Damage, Dead, and Fallen. The results revealed a significant decline in healthy trees, from 67.4% in 2021 to 25.6% in 2024, with a corresponding increase in damaged and dead trees. Light damage emerged as a potential early indicator of forest health decline. The DL model achieved an accuracy of 74.9% to 82.8%. The results showed the effectiveness of DL in detecting severe damage but highlighted that challenges in distinguishing between healthy and lightly damaged trees still remain. The study highlights the potential of UAV-based remote sensing and DL for monitoring forest health, providing valuable insights for targeted management interventions. However, further refinement of the classification methods is needed to improve accuracy, particularly in the precise detection of tree health categories. This approach offers a scalable solution for monitoring forest health in similar ecosystems in other subalpine areas of Japan and the world.