Research Papers

1. Zhou, J., Wang, H., Kato, Y., Nampally, T., Rajalakshmi, P., Balram, M., Katsura, K., Lu, H., Mu, Y., Yang, W., Gao, Y., Xiao, F., Chen, H., Chen, Y., Li, W., Wang, J., Yu, F., Zhou, J., Wang, W., Hu, X., Yang, Y., Ding, Y., Guo, W., Liu, S. 2025. Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithms. Plant Phenomics, 7, 100099. DOI: https://doi.org/10.1016/j.plaphe.2025.100099

2. Kushino, S., Mizuno, N., Shirasawa, K., Kobayashi, Y., Fujita, Y., Nishimura, K., Ueno, M., Takeuchi, N., Saito, H., Adachi, S., Katsura, K., Hirakawa, H., Yasui, Y. 2025. Discovery of a novel CYP76AD1–DODA1 gene cluster associated with betalain pigmentation in quinoa. Cytologia, (in press)

3. Yamaguchi, T., Angeles, O., Iizumi, T., Dobermann, A., Katsura, K., Saito, K. 2025. Machine learning reveals drivers of yield sustainability in five decades of continuous rice cropping. Field Crops Res., 333, 110114. DOI: https://doi.org/10.1016/j.fcr.2025.110114

4. Iwasa, M., Adachi, S., Nakamura, T., Katsura, K., Motobayashi, T., Ookawa, T. 2025. Growth rate, weed competitiveness and deep-water resistance of the new type rice line, monster rice 2. Jpn. Agric. Res. Q., (in press)

5. Nakajima, K., Saito, K., Tsujimoto, Y., Takai, T., Mochizuki, A., Yamaguchi, T., Ibrahim, A., Mairoua, S. G., Andrianary, B. H., Katsura, K., Tanaka, Y. 2025. Robustness of the RGB image-based estimation for rice above-ground biomass by utilizing the dataset collected across multiple locations. Smart Agric. Technol., 10, 100998. DOI: https://doi.org/10.1016/j.atech.2025.100998

6. Sesay, S., Yamaguchi, T., Kushino, S., Yoshikawa, Y., Adachi, S., Katsura, K. 2025. Fusion of UAV-based 3D mesh and spectral features improves quinoa biomass and LAI estimation across genotypic and temporal variations. Smart Agricultural Technology, 10, 100818. DOI: https://doi.org/10.1016/j.atech.2025.100818

7. Yamaguchi, T., Takamura, T., Tanaka, T.S.T., Ookawa, T., Katsura, K. 2025. Study on Optimal Input Images for Rice Yield Prediction Models Using CNN with UAV Imagery and Its Reasoning Using Explainable AI. Eur. J. Agron. 164, 127512. DOI: https://doi.org/10.1016/j.eja.2025.127512

8. Thinley, K., Sithup, K., Choden, T., Wangmo, P., Deki, S., Dema, T., Wangmo, K., Katsura, K. 2024. Establishment of a high-yield intercropping system for maize and legumes under rainfed conditions in eastern Bhutan. Plant Prod. Sci., 27 (3), 170-184. DOI: https://doi.org/10.1080/1343943X.2024.2354544

9. Yamaguchi, T., Katsura, K. 2024. A novel neural network model to achieve generality for diverse morphologies and crop science interpretability in rice biomass estimation. Computers and Electronics in Agriculture, 218, 108653 DOI: https://doi.org/10.1016/j.compag.2024.108653

10. Yamashita, M., Kaieda, T., Toyoda, H., Yamaguchi, T., Katsura, K. 2024. Spatial estimation of daily growth biomass in paddy rice field using canopy photosynthesis model based on ground and UAV observations. Remote Sensing, 16 (1), 125 DOI: https://doi.org/10.3390/rs16010125

11. Yamaguchi, T., Sasano, K., Katsura, K. 2024. Improving efficiency of ground-truth data collection for UAV-based rice growth estimation models: Investigating the effect of sampling size on model accuracy. Plant Prod Sci., 27 (1), 1-13. DOI: http://dx.doi.org/10.1080/1343943X.2023.2299641

12. Raharimanana, V., Yamaguchi, T., Tsujimoto, Y., Oo, A. Z., Nishigaki, T., Rakotonindrina, H., Katsura, K., 2023. A machine learning approach is effective to elucidate yield-limiting factors of irrigated lowland rice under heterogeneous growing conditions and management practices. Field Crops Res., 304, 109170. DOI: https://doi.org/10.1016/j.fcr.2023.109170

13. Kholdorov, S., Jabbarov, Z., Yamaguchi, T., Yamashita, M., Shamsiddinov, T., Katsura, K. 2023. Optimal timing of satellite data acquisition for estimating and modeling soil salinity in cotton fields of the Mingbulak District, Uzbekistan. Eurasian Journal of Soil Science, 13 (1), 26-34. DOI: http://ejss.fesss.org/10.18393/ejss.1380500

14. Tanaka, Y., Watanabe, T., Katsura, K., Tsujimoto, Y., Takai, T., Tanaka, T. S. T., Kawamura, K., Saito, H., Homma, K., Mairoua, G. S., Ahouanton, K., Ibrahim, A., Senthilkumar, K., Semwal, K. V., Jose E., Matute, G., Corredor, E., El-Namaky, R., Manigbas, N., Quilang, J. P. E., Iwahashi, Y., Nakajima, K., Takeuchi, E., Saito, K. 2023. Deep learning enables instant and versatile estimation of rice yield using ground-based RGB images. Plant Phenomics, 5, 0073. DOI: https://doi.org/10.34133/plantphenomics.0073

15. Nakajima, K., Tanaka, Y., Katsura, K., Yamaguchi, T., Watanabe, T., Shiraiwa, T. 2023. Biomass estimation of World Rice (Oryza sativa L.) Core Collection based on the convolutional neural network and digital images of canopy. Plant Prod Sci., 26 (2), 187-196. DOI: https://doi.org/10.1080/1343943X.2023.2210767

16. Kholdorov, S., Gopa-kumar, L., Jabbarovb, Z., Yamaguchi, T., Yamashita, M., Samatov, N., Katsura, K. 2023. Analysis of irrigated salt-affected soils in the Central Fergana Valley, Uzbekistan, using Landsat 8 and Sentinel-2 satellite images, laboratory studies, and spectral index-based approaches, Eurasian Soil Science, 56 (8), 1178-1189. DOI: https://doi.org/10.1134/S1064229323600185

17. Yamaguchi, T., Ozawa, H., Maeda, S., Senoo, T., Katsura, K. 2023. Estimation of nutrient index values of ‘Kinumusume’, a recommended rice cultivar in Okayama Prefecture, using RGB images. Jpn. J. of Crop Sci. 92 (2), 129-139. (in Japanese with English abstract) DOI: https://doi.org/10.1626/jcs.92.129

18. Ookawa T., Nomura T., Kamahora E., Jiang, M., Ochiai, M., Samadi, A., Yamaguchi, T., Adachi, S., Katsura, K., Motobayashi, T. 2022. Pyramiding of multiple strong-culm genes originating from indica and tropical japonica to the temperate japonica rice. Scientific Reports, 12, 15400. DOI: https://doi.org/10.1038/s41598-022-19768-3

19. Nizamov, S., Bezborodov, G., Ziyatov, M., Katsura, K. 2022. Effects of plastic mulching on productivity and profitability of cotton (Gossypium hirsutum) in Uzbekistan. Journal of Arid Land Studies, 32-S, 67-70. DOI:  https://doi.org/10.14976/jals.32.S_67

20. Godson-Amamoo, S., Kato, T., Katsura, K. 2022. Empirical setting of the water stressed baseline increases the uncertainty of the crop water stress index in a humid temperate climate in different water regimes. Water, 14 (12), 1833. DOI: https://doi.org/10.3390/w14121833

21. Mino, M., Nagayoshi, T., Katsura, K., Adachi, S., Wake, H., Ookawa, T. 2022. Effects of soil water regulation by drip irrigation on yield and dry matter production in eddoe and its ecophysiological factors. Jpn. J. of Crop Sci., 91 (4), 280-290. (in Japanese with English abstract) DOI: https://doi.org/10.1626/jcs.91.280

22. Appiah, K. S., Omari, R. A., Onwona-Agyeman, S., Amoatey, C. A., Ofosu-Anim, J., Arfa, A. S. A. B., Suzuki, Y., Oikawa, Y., Okazaki, S., Katsura, K., Isoda, H., Kawada, K., Fujii, Y. 2022. Seasonal changes in the plant growth-inhibitory effects of rosemary leaves on lettuce seedlings, Plants, 11(5), 673. DOI: https://doi.org/10.3390/plants11050673 

23. Katsura, K., Kodaira, M., Yamashita, M., Yamaguchi, T., Takamura, T., Adachi, S., Ookawa, T. 2022. Assessment of the effect of rice cultivation on total soil carbon of paddy fields after decontamination in Coastal Area of Fukushima Prefecture. Journal of Reconstruction Agriculture and Sciences, 2 (1), 1-10. (in Japanese with English Abstract) DOI: https://doi.org/10.57341/jras.2.1_1 

24. Nagatoshi, Y., Katsura, K., Toukura, Y., Yasui, Y., Fujita, Y. 2021. Addressing the global food problem with quinoa, a high adaptable and nutritious food crop. Millet Research, 36, 15-17. (in Japanese)

25. Hasibuan, R. F. M., Miyatake, M., Sugiura, H., Agake, S., Yokoyama, T., Bellingrath-Kimura, S. D., Katsura, K., Ohkama-Ohtsu, N. 2021. Application of biofertilizer containing Bacillus pumillus TUAT1 on soybean without inhibiting infection by Bradyrhizobium diazoefficiens USDA110. Soil Sci. Plant Nutr. 67 (5), 535-539. DOI: https://doi.org/10.1080/00380768.2021.1959837

26. Peprah, C. O., Yamashita, M., Yamaguchi, T., Sekino, R., Takano, K., Katsura, K. 2021. Spatio-temporal estimation of biomass growth in rice using canopy surface model by unmanned aerial vehicle images. Remote Sensing, 13, 2388. DOI: https://doi.org/10.3390/rs13122388

27. Honda, S., Ohkubo, S., San, N. S., Nakkasame, A., Tomisawa, K., Katsura, K., Ookawa, T., Nagano, A. J., Adachi, S. 2021. Maintaining higher leaf photosynthesis after heading stage could promote biomass accumulation in rice. Scientific Reports, 11, 7579. DOI: https://doi.org/10.1038/s41598-021-86983-9

28. Moritsuka, N., Matsuoka, K., Katsura, K., Sano, S., Yanai, J. 2021. Laboratory and field measurement of magnetic susceptibility of Japanese agricultural soils for rapid soil assessment. Geoderma, 393, 115013. DOI: https://doi.org/10.1016/j.geoderma.2021.115013

29. Yamaguchi, Y., Tanaka, Y., Imachi, Y., Yamashita, M., Katsura, K. 2021. Feasibility of combining deep learning and RGB images obtained by unmanned aerial vehicle for leaf area index estimation in rice. Remote Sensing, 13, 84. DOI: https://doi.org/10.3390/rs13010084

30. Tanaka, Y., Katsura, K., Yamashita, M. 2020. Verification of image processing methods using digital cameras for rice growth diagnosis, Journal of the Japan Society of Photogrammetry, 59, 248-258. (in Japanese with English abstract) DOI: https://doi.org/10.4287/jsprs.59.248

31. Chigira, K., Kojima, N., Yamasaki, M., Yano, K., Adachi, S., Nomura, T., Jiang, M., Katsura, K., Ookawa, T. 2020. Landraces of temperate japonica rice have superior alleles for improving culm strength associated with lodging resistance. Scientific Reports, 10, 19855. DOI: https://doi.org/10.1038/s41598-020-76949-8

32. Samejima, H., Katsura, K., Kikura, M., Njinju, S. M., Kimani, J., Yamauchi, A., Makihara, D. 2020. Factors explaining differences in yield response to high nitrogen fertilization among rice varieties under tropical highland conditions in central Kenya. Jpn. Agric. Res. Q., 55, 209–216. DOI: https://doi.org/10.6090/jarq.55.209

33. Samejima, H., Kikura, M., Katsura, K., Menge, D., Gichuhi, E., Wainaina, C., Kimani, J., Inukai, Y., Yamauchi, A., Makihara, D. 2020. A method for evaluating cold tolerance in rice during reproductive growth stages under natural low-temperature conditions in tropical highlands in Kenya. Plant Prod. Sci., 23, 297–305. DOI: https://doi.org/10.1080/1343943X.2020.1777877

34. Samejima,H., Katsura, K., Kikuta, M., Njinju, S. M., Kimani, J. M., Yamauchi, A., Makihara, D. 2020. Analysis of rice yield response to various cropping seasons to develop optimal cropping calendars in Mwea, Kenya. Plant Prod. Sci., 23, 297-305. DOI: https://doi.org/10.1080/1343943X.2020.1727752

35. Moritsuka, N., Izawa, G., Matsuoka, K., Katsura, K. 2019. Annual changes in soil fertility after ceasing fertilization in an unfertilized Paddy field and factors limiting rice growth in the field. Jap. J. Soil Sci. Plant Nutr. 90, 257–267. (in Japanese with English abstract) DOI: https://doi.org/10.20710/dojo.90.4_257

36. Moritsuka, N., Matsuoka, K., Katsura, K., Yanai, J. 2019. Farm-scale variations in soil color as influenced by organic matter and iron oxides in Japanese Paddy fields. Soil Sci. Plant Nutr. 65, 166–175. DOI: https://doi.org/10.1080/00380768.2019.1583542

37. Appiah, K. S., Peprah, C. O., Mardani, H. K., Omari, R. A., Kpabitey, S., Amoatey, C. A., Onwona-Agyeman, S., Oikawa, Y., Katsura, K., Fujii, Y. 2018. Medicinal plants used in the Ejisu-Juaben Municipality, Southern Ghana: An ethnobotanical study. Medicines, 6, 1. DOI: https://doi.org/10.3390/medicines6010001

38. Appiah, K. S., Mardani, H. K., Omari, R. A., Eziah, V. Y., Ofosu-Anim, J., Onwona-Agyeman, S., Amoatey, C. A., Kawada, K., Katsura, K., Oikawa, Y., Fujii, Y. 2018. Involvement of carnosic acid in the phytotoxicity of Rosmarinus officinalis leaves. Toxins, 10, 498. DOI: https://doi.org/10.3390/toxins10120498

39. Kawasaki, Y., Katsura, K., Shiraiwa, T. 2018. Yield and dry matter dynamics of vegetative and reproductive organs in Japanese and US soybean cultivars. Plant Prod. Sci., 21, 349–357. DOI: https://doi.org/10.1080/1343943X.2018.1512874

40. Nishimura, K., Moriyama, R., Katsura, K., Saito, H., Takisawa, R., Kitajima, A., Nakazaki, T. 2018. The early lowering trait of an emmer wheat accession (Triticum turgidum L. ssp. dicoccum) is associated with the cis-element of the Vrn-A3 locus. Theor. Appl. Genet. 131, 2037–2053. DOI: https://doi.org/10.1007/s00122-018-3131-5

41. Njinju, S. M., Samejima, H., Katsura, K., Kikuta, M., Gweyi-Onyango, J. P., Kimani, J. M., Yamauchi, A., Makihara, D. 2018. Grain yield responses of lowland rice varieties to increased amount of nitrogen fertilizer under tropical highland conditions in central Kenya. Plant Prod. Sci., 21, 59–70. DOI: https://doi.org/10.1080/1343943X.2018.1436000

42. Katsura, K., Watanabe, T., Moritsuka, N., Tsujimoto, Y., Inusah, B., Dogbe, W., Oda, M. 2018. Spatial variation in surface soil total carbon and its relationship with soil color in a river floodplain ecosystem of northern Ghana. Jpn. Agric. Res. Q. 52, 219–228. DOI: https://doi.org/10.6090/jarq.52.219

43. Tsujimoto, Y., Inusah, B., Katsura, K., Fuseini, A., Dogbe, W., Zakaria, A. I., Fujihara, Y., Oda, M., Sakagami, J. 2017. The effect of sulfur fertilization on rice yields and nitrogen use efficiency in a floodplain ecosystem of northern Ghana. Field Crops Res. 211, 155–164. DOI: https://doi.org/10.1016/j.fcr.2017.06.030

44. Moritsuka, N., Matsuoka, K., Katsura, K., Sano, S., Yanai, J. 2017. Estimation of total nitrogen content in Surface paddy soils by measuring their electrical conductivity after hydrogen peroxide treatment. Jap. J. Soil Sci. Plant Nutr. 88, 327–335. (in Japanese with English abstract) DOI: https://doi.org/10.20710/dojo.88.4_327

45. Kawasaki, Y., Tanaka, Y., Katsura, K., Purcell, L. C., Shiraiwa, T. 2016. Yield and dry matter productivity of Japanese and US soybean cultivars. Plant Prod. Sci. 19, 257–266. DOI: https://doi.org/10.1080/1343943X.2015.1133235

46. Katsura, K., Tsujimoto, Y., Oda, M., Matsushima, K., Inusah, B., Dogbe, W., Sakagami, J. 2016. Genotype-by-environment interaction analysis of rice (Oryza spp.) yield in a floodplain ecosystem in West Africa. Eur. J. Agron. 73, 152–159. DOI: https://doi.org/10.1016/j.eja.2015.11.014

47. Li, X., Kitajima, A., Katsura, K., Saito, H., Koeda, S., Takisawa, R., Kawai, T., Nakazaki, T., Shimizu, T. 2016. Cell wall related gene expression during secondary physiological fruit drop in ponkan (Citrus reticulata Blanco) and hyuganatsu (C. tamurana hort. ex Tanaka). Acta Hortic., 1135, 47–52. DOI: https://doi.org/10.17660/ActaHortic.2016.1135.6

48. Matsuda, M., Sakakibara, T., Katsura, K., Koeda, S. 2016. Prediction of the number of cut flowers in Anthurium andraeanum L. using a simulation model. Hort. Res. (Japan) 15, 425–431. (in Japanese with English abstract) DOI: https://doi.org/10.2503/hrj.15.425

49. Maki, M., Katsura, K., Oki, K. 2016. Comparison of empirical regression models for estimating LAI of paddy rice from several vegetation índices derived from UAV images. J. Remote Sensing Soc. Jap. 36, 100–106. (in Japanese with English abstract) DOI: https://doi.org/10.11440/rssj.36.100

50. Bajgain, R., Kawasaki, Y., Akamatsu, Y., Tanaka, Y., Kawamura, H., Katsura, K., Shiraiwa, T. 2015. Biomass production and yield of soybean grown under converted paddy fields with excess water during the early growth stage. Field Crops Res. 180, 221–227. DOI: https://doi.org/10.1016/j.fcr.2015.06.010

51. Hyoda, T., Homma, K., Shiraiwa, T., Katsura, K., Horie, T. 2015. Adaptability of high-yielding rice cultivars in relation to biomass productivity under moderately water stressed upland conditions. Agric. Sci. 6, 352–364. DOI: https://doi.org/10.4236/as.2015.63036

52. Moritsuka, N., Izawa, G., Katsura, K., Matsui, N. 2015. Simple method for measuring soil sand content by nylon mesh sieving. Soil Sci. Plant Nutr. 61, 501–505. DOI: https://doi.org/10.1080/00380768.2015.1016864

53. Moritsuka, N., Katsura, K., Matsuoka, K., Yanai, J. 2015. Decadal sustainability of spatial distribution of soil properties in a paddy field as a fingerprint reflecting soil forming factors and field managements. Soil Sci. Plant Nutr. 61, 516–527. DOI: https://doi.org/10.1080/00380768.2015.1012996

54. Moritsuka, N., Matsuoka, K., Katsura, K., Sano, S., Yanai, J. 2014. Soil color analysis for statistically estimating total carbon, total nitrogen and active iron contents in Japanese agricultural soils. Soil Sci. Plant Nutr. 60, 475–485. DOI: https://doi.org/10.1080/00380768.2014.906295

55. Xu, Q., Saito, H., Hirose, I., Katsura, K., Yoshitake, Y., Yokoo, T., Tsukiyama, T., Teraishi, M., Tanisaka, T., Okumoto, Y. 2014. The effects of the photoperiod-insensitive alleles, se13, hd1 and ghd7, on yield components in rice. Mol. Breed. 33, 813–819. DOI: https://doi.org/10.1007/s11032-013-9994-x

56. Katsura K. 2013. Agronomic traits for high productivity of rice grown in aerobic culture in progeny of a japonica cultivar and a high-yielding indica cultivar. Plant Prod. Sci. 16, 317–325. DOI: https://doi.org/10.1626/pps.16.317

57. Lubis, I., Ohnishi, M., Katsura, K., Shiraiwa, T. 2013. Plant factors related to dry matter production in rice cultivars. J. ISSAAS, 19, 58–67.

58. Katsura, K., Nakaide, Y. 2011. Factors that determine grain weight in rice under high-yielding aerobic culture: the importance of husk size. Field Crops Res. 123, 266–272. DOI: https://doi.org/10.1016/j.fcr.2011.05.023

59. Kato, Y., Henry, A., Fujita, D., Katsura, K., Serraj, R., Kobayashi, N. 2011. Physiological characterization of introgression lines derived from an indica rice cultivar, IR64, adapted to drought and water-saving irrigation. Field Crops Res. 123, 130–138. DOI: https://doi.org/10.1016/j.fcr.2011.05.009

60. Shiraiwa, T., Katsura, K., Shimada, S., Kawasaki, Y., Murata, M., Homma, K., Yoshihira, T., Tanaka, T., Tanaka, Y. 2011. Field studies on factors causing the widening gaps in soybean yield between Japan and USA. －Field crop managemein in USA－ J. Crop Res. 56, 93–98. (in Japanese) DOI: https://doi.org/10.18964/jcr.56.0_93

61. Nakazaki, T., Moriyama, R., Kagata, H., Wakahara, H., Naito, M., Katsura, K., Saito, H., Kato, K., Nishida, H., Kawahara, T., Fudano, T., Kitajima, A. 2011. Flowering traits and their genetic basis in the ancestral tetraploid wheat varieties 'Emmer' and 'Pyramidale'. J. Crop Res. 56, 67–72. DOI: https://doi.org/10.18964/jcr.56.0_67

62. Kato, Y., Katsura, K. 2010. Panicle architecture and grain number in irrigated rice grown under different water management regimes. Field Crops Res. 117, 237–244. DOI: https://doi.org/10.1016/j.fcr.2010.03.006

63. Katsura, K., Okami, M., Mizunuma, H., Kato, Y. 2010. Radiation-use efficiency, N accumulation and biomass production of high-yielding rice in aerobic culture. Field Crops Res. 117, 81–89. DOI: https://doi.org/10.1016/j.fcr.2010.02.006

64. Katsura, K., Yoshihira, T., Homma, K., Purcell, L. C., Tanaka, T., Shiraiwa, T. 2009. Field studies on factors causing the widening gaps in soybean yield between Japan and USA. －Field observation reports in USA－ J. Crop Res. 54, 149–154. (in Japanese) DOI: https://doi.org/10.18964/jcr.56.0_93

65. Kato, Y., Okami, M., Katsura, K. 2009. Yield potential and water use efficiency of aerobic rice (Oryza sativa L.) in Japan. Field Crops Res. 113, 328–334. DOI: https://doi.org/10.1016/j.fcr.2009.06.010

66. Katsura, K., Maeda, S., Horie, T., Shiraiwa, T. 2009. Estimation of respiratory parameters for rice based on long-term and intermittent measurement of canopy CO2 exchange rates in the field. Field Crops Res. 111, 85–91. DOI: https://doi.org/10.1016/j.fcr.2008.11.003

67. Katsura, K., Maeda, S., Lubis, I., Horie, T., Cao, W., Shiraiwa, T. 2008. The high yield of irrigated rice in Yunnan, China: ‘A cross-location analysis’ Field Crops Res. 107, 1–11. DOI: https://doi.org/10.1016/j.fcr.2007.12.007

68. Katsura, K., Maeda, S., Horie, T., Shiraiwa, T. 2007. Analysis of yield attributes and crop physiological traits of Liangyoupeijiu, a hybrid rice recently bred in China. Field Crops Res. 103, 170–177. DOI: https://doi.org/10.1016/j.fcr.2007.06.001

69. Yoshida, H., Horie, T., Katsura, K., Shiraiwa, T. 2007. A model explaining genotypic and environmental variation in leaf area development of rice based on biomass growth and leaf N accumulation. Field Crops Res. 102, 228–238. DOI: https://doi.org/10.1016/j.fcr.2007.04.006

70. Katsura, K., Maeda, S., Horie, T., Shiraiwa., T. 2006. A multichannel automated chamber system for continuous measurement of carbon exchange rate of rice canopy. Plant Prod. Sci. 9, 152–155. DOI: https://doi.org/10.1626/pps.9.152

Reviews

1. Sekiya, N., Oizumi, N., Kessy, T. T., Fimbo, K. M. J., Tomitaka, M., Katsura, K., Araki, H., 2020. Importance of market-oriented research for rice production 1 in Tanzania. A review. Agron. Sustain. Dev., 40, 7. DOI: https://doi.org/10.1007/s13593-020-0611-1

2. Kato, Y., Katsura, K. 2014. Rice adaptation to aerobic soils: physiological considerations and implications for agronomy. Plant Prod. Sci. 17, 1–12. DOI: https://doi.org/10.1626/pps.17.1

3. Horie, T., Shiraiwa, T., Homma, K., Katsura, K., Maeda, S., Yoshida, H. 2005. Can yields of lowland rice resumes the increases that they showed in the 1980s? Plant Prod. Sci. 8, 259–274. DOI: https://doi.org/10.1626/pps.8.259

Books

1. Peprah, C.O., Yamaguchi, T.,Ghanney, P., Katsura, K., Aduhene-chinbuah, J., Asante, M. D., 2025. GIS and remote sensing applications in rice cultivation. In: Srivastava et al., (eds) Rice Cultivation Under Abiotic Stress. Challenges and Opportunities. Elsevier, pp347-362.

2. Makihara, D. Kimani, J., Samejima, H., Kikuta, M., Menge, D., Doi, K., Inukai, Y., Maekawa, M., Masunaga, T., Sasaki, Y., Katsura, K., Kitano, H., Mitsuya, S., Kano-Nakata, M., Wainaina, C., Gichuhi, E., Njinju, S., Kagito, S., Magoti, R., Kundu, C., Yamauchi, A. 2018. Development of Rice Breeding and Cultivation Technology Tailored for Kenya’s Environment. In: Kokubun, M., Asanuma S. (eds) Crop Production under Stressful Conditions. Springer, Singapore. pp. 27–47.

3. Oikawa, Y., Dung, P. Q., Dan, P. V. B., Linh, N. V., Yamada, M., Hayashidani, H., Tanaka, H., Tarao, M., Katsura, K. 2018. Charcoal application farming with livestock for small scale farmers in central Vietnam. Agroecology for Food Security and Nutrition, Proc. of the International Symposium on Agroecology in China, FAO, Rome, Italy, pp 197–210.

4. 桂圭佑 2015. 第9章 中国雲南省の超多収稲作. 堀江武編著, アジア・アフリカの稲作―多様な生産生態と持続的発展への道. pp212–227. 農山漁村文化協会

5. 島田信二・白岩立彦・桂圭佑・島村総 2013. 第5章 日米における大豆生産技術の現状とわが国の課題. 梅本雅・島田信二編著, 大豆生産振興の課題と方向. pp69–131. 農林統計出版

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