原著論文

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. 小平正和・桂圭佑・山下恵・澁澤栄・本林隆・大川泰一郎. 2025. 土壌予測値マップによる水田復興の経年変化と局所的特徴の可視化. 農業情報研究, 34 (3), 146–157. DOI: https://doi.org/10.3173/air.34.146

5. 小平正和・桂圭佑・山下恵・本林隆・大川泰一郎. 2025. 土づくり向け予測値マップによる除染後水田群復興の可視化. 復興農学会誌, 5 (2), 2–13.

6. 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)

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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（日本作物学会論文賞受賞）

14. 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

15. 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

16. 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

17. 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

18. 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

19. 山口友亮・尾澤陽・前田周平・妹尾知憲・桂圭佑. 2023. RGB画像を用いた岡山県奨励水稲品種 ｢きぬむすめ｣ の栄養指標値の推定. 日作紀, 92 (2), 129–139. (in Japanese with English abstract) DOI: https://doi.org/10.1626/jcs.92.129

20. 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

21. 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

22. 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

23. 見野百萌・永吉智己・桂圭佑・安達俊輔・和気仁志・大川泰一郎. 2022. 点滴灌漑による土壌水分制御がサトイモの収量および乾物生産に及ぼす影響とその生理生態的要因. 日作紀, 91 (4), 280–290. (in Japanese with English abstract) DOI: https://doi.org/10.1626/jcs.91.280

24. 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 

25. 桂圭佑・小平正和・山下恵・山口友亮・高村大河・安達俊輔・大川泰一郎. 2022. 福島県浜通りの除染後農地での水稲栽培が土壌全炭素蓄積に及ぼす影響の評価. 復興農学会誌, 2 (1), 1–10. (in Japanese with English abstract) DOI: https://doi.org/10.57341/jras.2.1_1 

26. 永利友佳理・桂圭佑・藤倉雄司・安井康夫・藤田泰成. 2021. 過酷環境に耐える高栄養価作物キヌアで世界の食料問題に立ち向かう. 雑穀研究, 36, 15–17. (in Japanese)

27. 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

28. 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

29. 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

30. 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

31. Yamaguchi, T., 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

32. 田中雪絵・桂圭佑・山下恵 2020. イネの簡易的生育診断に向けたデジタルカメラ画像処理手法の検討. 写真測量とリモートセンシング, 59, 248-258. (in Japanese with English abstract) DOI: https://doi.org/10.4287/jsprs.59.248

33. 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

34. 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

35. 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

36. 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

37. 森塚直樹・伊澤岳・松岡かおり・桂圭佑 2019. 無施肥水田における施肥停止後の土壌肥沃度の経年変化と水稲の生育制限要因の解明. 日本土壌肥料学雑誌. 90, 257–267. (in Japanese with English abstract) DOI: https://doi.org/10.20710/dojo.90.4_257

38. 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

39. 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

40. 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

41. 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（日本作物学会論文賞受賞）

42. 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

43. 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

44. 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

45. 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

46. 森塚直樹・松岡かおり・桂圭佑・佐野修司・矢内純太 2017. 過酸化水素処理後の電気伝導度測定による水田表層土壌の全窒素含量の推定. 日本土壌肥料学雑誌. 88, 327–335. (in Japanese with English abstract) DOI: https://doi.org/10.20710/dojo.88.4_327

47. 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

48. 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

49. 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

50. 松田大・榊原俊雄・桂圭佑・小枝壮太 2016. アンスリウム切り花の収穫本数予測モデルの構築. 園芸学研究 15, 425–431. (in Japanese with English abstract) DOI: https://doi.org/10.2503/hrj.15.425

51. 牧雅康・桂圭佑・沖一雄 2016. UAV画像から算出した様々な植生指標による水稲LAIの経験的推定モデルの比較. 日本リモートセンシング学会誌 36, 100–106. (in Japanese with English abstract) DOI: https://doi.org/10.11440/rssj.36.100

52. 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

53. 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

54. 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

55. 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

56. 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

57. 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

58. 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

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

60. 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

61. 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

62. 白岩立彦・桂圭佑・島田信二・川崎洋平・村田資治・本間香貴・義平大樹・田中朋之・田中佑 2011. ダイズ単収の日米地域間差の拡大要因に関する作物学的調査. －視察報告（第2回）米国における圃場・作物管理－ 作物研究 56, 93–98. (in Japanese) DOI: https://doi.org/10.18964/jcr.56.0_93

63. 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

64. 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

65. 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

66. 桂圭佑・義平大樹・本間香貴・Purcell, L. C.・田中朋之・白岩立彦 2009. ダイズ単収の日米地域間差の拡大要因に関する作物学的調査. －米国における視察報告（第1回）－ J. Crop Res. 54, 149–154. (in Japanese) DOI: https://doi.org/10.18964/jcr.56.0_93

67. 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

68. 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

69. 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

70. 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

71. 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

72. 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

解説・総説

1. 桂圭佑 2024. 【世界のビックリ農業10】イネを畑で育てて1tどり!? 現代農業, 2024.8, 248–253.

2. 桂圭佑 2024. 【世界のビックリ農業4】超過酷な環境で育てるキヌア. 現代農業, 2024.1, 246–251.

3. 桂圭佑 2023. 【世界のビックリ農業1】イネの多年生を生かした再生二期作. 現代農業, 2023.8, 246–251.

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著書

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. 農作物のひみつ－毎日の食事が楽しくなる，おもしろ雑学．日本作物学会編（分担執筆）

3. 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.

4. 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.

5. 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

6. 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

7. Horie, T., Yoshida, H., Kawatsu, S., Katsura, K., Homma, K., Shiraiwa, T. 2005. Effects of elevated atmospheric CO2 concentration and increased temperature on rice; Implications for Asian rice production. In: Toriyama, K. et al. (Eds.), Rice is Life: Scientific Perspectives for the 21st Century. Proc. of the World Rice Research Conference held in Tokyo and Tsukuba, Japan, 4-7 November 2004, IRRI, Los Baños, and JIRCAS, Tsukuba, 536–539.

8. Horie, T., Lubis, I., Takai, T., Ohsumi, A., Kuwasaki, K., Katsura, K., Nii, A. 2003. Physiological traits associated with high yield potential in rice. In Mew, T. W. et al. (Eds.), Rice Science: Innovations and impact for livelihood. Proc. Int. Rice Res. Conf., Sept. 2002, Beijing, China. IRRI, Chinese Academy of Engineering and Chinese Academy of Agricultural Sciences. 117–145. DOI: https://doi.org/10.1016/S0378-4290(96)03458-2

 Copy right (C) Lab. of Crop Production Science, Tokyo University of Agriculture and Technology