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Abstract Plant-based agricultural systems rely heavily on inorganic nitrogen (N) fertilizers to increase yields and ensure food security for the rapidly growing global population. However, the production and overuse of synthetic fertilizers lead to significant amounts of greenhouse gas emissions, causing a critical need for the development of alternative and sustainable organic N fertilizers. Here, we demonstrate the effective use of lysed and dried bacterial biomass from the marine purple photosynthetic bacterium Rhodovulum sulfidophilum as an alternative source of N fertilizer for the cultivation of Japanese mustard spinach (komatsuna, Brassica rapa var. perviridis). To assess the suitability of this processed bacterial biomass, containing approximately 11% N, as a N fertilizer, we examined the effects of its application on plant germination and growth (measured by leaf chlorophyll, maximum leaf length, and dry weight) under two different temperature regimes, comparing it to that of a conventional N-containing mineral fertilizer. Application at rates up to four times that of the mineral fertilizer had no negative effects on seed germination and plant growth. The bacterial biomass had to be applied at approximately twice the rate of mineral fertilizer to obtain similar plant growth parameters, roughly corresponding to the predicted 62% rate of mineralization of the bacterial N. Our findings confirm the ability of plants to take up N from the lysed and dried biomass of marine purple photosynthetic bacteria, demonstrating the potential for using R. sulfidophilum as a source of N fertilizer.

Rice (Oryza sativa L.) is one of the most important cereals, which provides 20% of the world’s food energy. However, its productivity is poorly assessed especially in the global South. Here, we provide a first study to perform a deep-learning-based approach for instantaneously estimating rice yield using red-green-blue images. During ripening stage and at harvest, over 22,000 digital images were captured vertically downward over the rice canopy from a distance of 0.8 to 0.9 m at 4,820 harvesting plots having the yield of 0.1 to 16.1 t·ha−1 across 6 countries in Africa and Japan. A convolutional neural network applied to these data at harvest predicted 68% variation in yield with a relative root mean square error of 0.22. The developed model successfully detected genotypic difference and impact of agronomic interventions on yield in the independent dataset. The model also demonstrated robustness against the images acquired at different shooting angles up to 30° from right angle, diverse light environments, and shooting date during late ripening stage. Even when the resolution of images was reduced (from 0.2 to 3.2 cm·pixel−1 of ground sampling distance), the model could predict 57% variation in yield, implying that this approach can be scaled by the use of unmanned aerial vehicles. Our work offers low-cost, hands-on, and rapid approach for high-throughput phenotyping and can lead to impact assessment of productivity-enhancing interventions, detection of fields where these are needed to sustainably increase crop production, and yield forecast at several weeks before harvesting.

Drought is increasingly threatening smallholder farmers in Southeast Asia. The crop insurance system is one of the promising countermeasures that was implemented in Indonesia in 2015. Because the damage assessment in the present system is conducted through direct investigations based on appearance, it is not objective and needs a long time to cover large areas. In this study, we investigated a rapid assessment method for paddy fields using a vegetation index (VI) taken by an unmanned aerial vehicle (UAV) with a multispectral camera in 2019 and 2021. Then, two ways of assessment for drought damage were tested: linear regression (LR) based on a visually assessed drought level (DL), and k-means clustering without an assessed DL. As a result, EVI2 could represent the damage level, showing the tendency of the decrease in the value along with the increasing DL. The estimated DL by both methods mostly coincided with the assessed DL, but the concordance rates varied depending on the locations and the number of assessed fields. Differences in the growth stage and rice cultivars also affected the results. This study revealed the feasibility of the UAV-based rapid and objective assessment method. Further data collection and analysis would be required for implementation in the future.

Rice is not merely a staple food but an important source of income in Cambodia. Rapid socioeconomic development in the country affects farmers’ management practices, and rice production has increased almost three-fold over two decades. However, detailed information about the recent changes in rice production is quite limited and mainly obtained from interviews and statistical data. Here, we analyzed MODIS LAI data (MCD152H) from 2003 to 2019 to quantify rice production changes in Pursat Province, one of the great rice-producing areas in Cambodia. Although the LAI showed large variations, the data clearly indicate that a major shift occurred in approximately 2010 after applying smoothing methods (i.e., hierarchical clustering and the moving average). This finding is consistent with the results of the interviews with the farmers, which indicate that earlier-maturing cultivars had been adopted. Geographical variations in the LAI pattern were illustrated at points analyzed along a transverse line from the mountainside to the lakeside. Furthermore, areas of dry season cropping were detected by the difference in monthly averaged MODIS LAI data between January and April, which was defined as the dry season rice index (DSRI) in this study. Consequently, three different types of dry season cropping areas were recognized by nonhierarchical clustering of the annual LAI transition. One of the cropping types involved an irrigation-water-receiving area supported by canal construction. The analysis of the peak LAI in the wet and dry seasons suggested that the increase in rice production was different among cropping types and that the stagnation of the improvements and the limitation of water resources are anticipated. This study provides valuable information about differences and changes in rice cropping to construct sustainable and further-improved rice production strategies.

With recent developments in the technology of intelligent materials, including shape memory alloys that deform autonomously depending on changes in the environment, such as temperature, it is expected that next-generation actuators that are simple, lightweight, and compact will also be developed. Consequently, in this paper, we investigated the disc spring type actuator made of Ti-Ni shape memory alloy (SMA), which is more compact than the conventional SMA coil spring type actuator. Firstly, prototype SMA disc springs were made from a thin SMA plate by metal stamping and shape-memory treatment. Secondly, we measured load-displacement curve of this prototype at both high and low temperatures. Thirdly, from the experimental results, the difference between displacements at low and high temperatures was used to estimate the performance (output displacement) of the SMA disc spring. The observations revealed that output displacement rate of the SMA disc spring was greater than 50% when the load was 30N. Compared to the coil spring, a similar effect would be obtained in approximately 37% of the space by the SMA disc spring. Finally, we conducted a finite element analysis of the SMA disc spring using a non-linear finite element analysis program, LS-DYNA. The validity of the analysis procedure was confirmed by comparing the analysis results with the experimental results.