Adachi, F., Kobata, T., Arimoto, M. & Imaki, T. (1995). Comparison of Water Use Efficiency in Paddy Rice (Oryza sativa L.) among Locations and Interannual Variations in Humid Areas. Japan Journal of Crop Science, 64, 509-515. (in Japanese with English summary)
Adachi, S., Yamamoto, T., Nakae, T., Yamashita, M., Uchida, M., Karimata, R., Ichihara, N., Soda, K., Ochiai, T., Ao, R., Ostuka, C., Nakano, R., Takai, T., Ikka, T., Kondo, K., Ueda, T., Ookawa, T. & Hirasawa, T. (2019). Genetic architecture of leaf photosynthesis in rice revealed by different types of reciprocal mapping populations. Journal of Experimental Botany, 70, 5131-5144.
Adachi, S., Tanaka, Y., Miyagi, A., Kashima, M., Tezuka, A., Toya, Y., Kobayashi, S., Ohkubo, S., Shimizu, H., Kawai-Yamada, M., Sage, R. F., Nagano, A. J. & Yamori, W. (2019). High-yielding rice Takanari has superior photosynthetic response to a commercial rice Koshihikari under fluctuating light. Journal of Experimental Botany, 70, 5287-5297.
Alberto, M. C. R., Wassmann, R., Buresh, R. J., Quilty, J. R., Correa, T. Q. Jr., Sandro, J. M. & Centeno C. A. R. (2014). Measuring methane flux from irrigated rice fields by eddy covariance method using open-path gas analyzer. Field Crops Research, 160, 12-21.
Anten, N. P. R. (1999). Modelling canopy photosynthesis using parameters determined from simple non-destructive measurements. Ecological Research, 12, 77-88.
Ayaneh, A., van Ginkel, M., Reynolds, M.P. & Ammar, K. (2002). Comparison of peduncle and canopy temperature depression in wheat under heat stress. Field Crops Research, 79, 173-184.
Beadle, C. L. & Long, S. P. (1985). Photosynthesis — is it Limiting to Biomass Production? Biomass, 8, 119-168.
Burkart, S., Manderscheid, R., Weigel, H. J. (2007). Design and performance of a portable gas exchange chamber system for CO2- and H2O-flux measurements in crop canopies. Environmental and Experimental Botany, 61, 25-34.
Carmo-Silva, A. E., Gore, M. A., Andrade-Sanchez, P., French, A. N., Hunsaker, D. J. & Salvucci M. E. (2012). Decreased CO2 availability and inactivation of Rubisco limit photosynthesis in cotton plants under heat and drought stress in the field. Environmental and Experimental Botany, 83, 1-11.
Cetinic, E., Lipic, T. & Grgic, S. (2018). Fine-tuning Convolutional Neural Networks for fine art classification. Expert Systems With Applications, 114, 107-118.
Chang, S., Chang, T., Song, Q., Zhu, X. G. & Deng, Q. (2016). Photosynthetic and agronomic traits of an elite hybrid rice Y-Liang-You 900 with a record-high yield. Field Crops Research, 187, 49-57.
Chen, W., Yao, X., Cai, K. & Chen, J. (2011). Silicon Alleviates Drought Stress of Rice Plants by Improving Plant Water Status, Photosynthesis and Mineral Nutrient Absorption. Biological Trace Element Research, 142, 67-76.
Chen, Y., Zhang, Z. & Tao, F. (2018). Impacts of climate change and climate extremes on major crops productivity in China at a global warming of 1.5 and 2.0 ℃. Earth System Dynamics, 9, 543- 562.
Choudhury, B. J. (1987). Relationships Between Vegetation Indices, Radiation Absorption, and Net Photosynthesis Evaluated by a Sensitivity Analysis. Remote Sensing of Environment, 22, 209-233.
Crafts-Brandner, S. J. & Salvucci, M. E. (2002). Sensitivity of Photosynthesis in a C4 Plant, Maize, to Heat Stress. Plant Physiology, 129, 1773-1780.
Das, B., Nair, B., Reddy, V. K. & Venkatesh P. (2020). Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India. International Journal of Biometeorology, 62, 1809-1822.
De Souza, A. P., Wang, Y., Orr, D. J., Carmo-Silva, E. & Long, S. P. (2019). Photosynthesis across African cassava germplasm is limited by Rubisco and mesophyll conductance at steady state, but by stomatal conductance in fluctuating light. New Phytologist, 225, 2498-2512.
Drake, B. G. & Leadley P. W. (1991). Canopy photosynthesis of crops and native plant communities exposed to long-term elevated CO2. Plant, Cell and Environment, 14, 853- 860.
Evans, L. T. (1993). Crop Evolution, adaptation and yield. Cambridge: Cambridge University press. 1-514.
Fan, J., Zheng, J., Wu, L. & Zhang, F. (2021). Estimation of daily maize transpiration rate using support vector machines, extreme gradient boosting, artificial and deep neural networks models. Agricultural Water Management, 245, 106547.
Food and Agriculture Organization of the United Nations (FAO). (2019). FAOSTAT database. http://www.fao.org/faostat/en/#home. (Accessed June 8, 2021).
Gates, D. M. (1968). Transpiration and leaf temperature. Annual Review of Plant Physiology, 19, 211-238.
Haghverdi, A., Washington-Allen, R. A. & Leib, B. G. (2018). Prediction of cotton lint yield from phenology crop indices using artificial neural networks. Computers and Electronics in Agriculture, 152, 186-197.
Hamby, D. M. (1994). A review of techniques for parameters sensitivity analysis of environmental models. Environmental Monitoring and Assessment, 32, 135-154.
Hirasawa, T., Ozawa, S., Taylaran, R. D. & Ookawa, T. (2010). Varietal Differences in Photosynthetic Rates in Rice Plants, with Special References to the Nitrogen Content of Leaves. Plant Production Science, 13, 53-57.
Hirooka, Y., Homma, K. & Shiraiwa, T. (2018). Parameterization of the vertical distribution of leaf area index (LAI) in rice (Oryza sativa L.) using a plant canopy analyzer. Scientific Reports, 8, 6387.
Horie, T., Matsuura, S., Takai, T., Kuwasaki, K., Ohsumi, A. & Shiraiwa, T. (2006). Genotypic difference in canopy diffusive conductance measured by a new remote-sensing method and its association with the difference in rice yield potential. Plant, Cell and Environment, 29, 653-660.
Horton, P. (2000). Prospects for crop improvement through the genetic manipulation of photosynthesis: morphological and biochemical aspects of light capture. Journal of Experimental Botany, 51, 475-485.
Hou, M., Tian, F., Zhang, L., Li, S., Du, T., Huang, M. & Yuan, Y. (2019). Estimating Crop Transpiration of Soybean under Different Irrigation Treatments Using Thermal Infrared Remote Sensing Imagery. Agronomy, 9, 8.
Howard, J. & Ruder, S. (2018). Universal Language Model Fine-Tuning for Test Classification. arXiv:1801.06146v5.
Ikawa, H., Chen, C. P., Sikma, M., Yoshimoto, M., Sakai, H., Tokida, T., Usui, Y., Nakamura, H., Ono, K., Maruyama, A., Watanabe, T., Kuwagata, T. & Hasegawa, T. (2017). Increasing canopy photosynthesis in rice can be achieved without a large increase in water use—A model based on free-air CO2 enrichment. Global Change Biology, 1-21.
Jin, X., Li, Z., Feng, H., Ren, Z. & Li, S. (2020). Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index. The Crop Journal, 8, 87-97.
Johnson, I. R. & Thornley, H. M. (1984). A Model of Instantaneous and Daily Canopy Photosynthesis. Journal of Theoretical Biology, 107, 531-545.
Jones, H. G. (2014), Plants and Microclimate (3rd ed.). Cambridge: Cambridge University Press. 1-423.
Jones, H. G., Hutchinson, P. A., May, T., Jamali, H. & Deery, D. M. (2018). A practical method using a network of fixed infrared sensors for estimating crop canopy conductance and evaporation rate. Biosystems Engineering, 165, 59-69.
Kabaki, N. (1993). Growth and yield of japonica-indica hybrid rice. Japan Agricultural Research Quarterly, 27, 88-94.
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 Production Science, 9, 152-155.
Keating, B. A., Herrero, M., Carberry, P. S., Gardner, J. & Cole M. B. (2014). Food wedges: Framing the global food demand and supply challenge towards 2050. Global Food Security, 3, 125-132.
Khan, M. I. R., Iqbal, N., Masood, A., Per, T. S. & Khan, N.A. (2013). Salicylic acid alleviates adverse effects of heat stress in proline production and ethylene formation. Plant Signaling & Behavior, 8:11, e26374.
Khush, G.S. (2001). Green revolution: the way forward. Nature Reviews Genetics, 2, 815- 822.
Kimura, H., Hashimoto-Sugimoto, M., Iba, K., Terashima, I. & Yamori, W. (2019), Improved stomatal opening enhances photosynthetic rate and biomass production in fluctuating light. Journal of Experimental Botany, 71, 2339-2350.
Laza, R. C., Peng, S., Sanico A. L., Visperas, R. M. & Akita, S. (2001). Higher leaf area growth rate contributes to greater vegetative growth of F1 hybrids in the tropics. Plant Production Science, 4, 184-188.
Liu, S., Lu, L., Mao, D. & Jin, L. (2007). Evaluating parameterizations of aerodynamic resistance to heat transfer using field measurements. Hydrology and Earth System Sciences, 11, 769-783.
Liu, B., Martre, P., Ewert, F., Porter, J. R., Challinor, A. J., Muller, C., Raune, A. C. et al. (2019). Global wheat production with 1.5 ℃ and 2,0 ℃ above pre-industrial warming. Global Change Biology, 25, 1428-1444.
Long, S. P., Zhu, X. G., Naidu, S. L. & Ort, D. R. (2006). Can improvement in photosynthesis increase crop yields? Plant, cell and environment, 29, 315-330.
Luo, H., He, L., Du, B., Pan, S., Mo, Z., Duan, M., Tian, H. & Tang, X. (2020). Biofortification with chelating selenium in fragrant rice: Effects on photosynthetic rates, aroma, grain yield quality and yield formation. Field Crops Research, 255, 107909.
Ma, J., Li, Y., Chen, Y., Du, K., Zheng, F., Zhang, L. & Sun, Z. (2019). Estimating above ground biomass of winter wheat at early growth stages using digital images and deep convolutional neural network. European Journal of Agronomy, 103, 117-129.
Mann, C. C. (1999). Crop scientists seek a new revolution. Science, 283, 310-314.
Masson-Delmotte, V., Zhai, P., Pörtner, H. O., Roberts, D., Skea, J., Shukla, P. R., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., et al. (2018). Global warming of 1.5 C. In An IPCC Special Report on the Impacts of Global Warming of. 2018 October 8; IPCC: Geneva, Switzerland.
Monsi, M. & Saeki, T. (2005). On the Factor Light in Plant Communities and its Importance for Matter Production. Annals of Botany, 95, 549-567.
Monteith, J. L. (1977). Climate and the efficiency of crop production in Britain. Philosophical Transactions of the Royal Society B, 281, 277-294.
Monteith, J. L. (1995). A reinterpretation of stomatal responses to humidity. Plant, Cell and Environment, 18, 357-364.
Monteith, J. L. & Unsworth, M. H. (2013). Principles of Environmental Physics (4th ed.). Burlington, MA: Academic Press. 1-401
Morison, J. I. L. & Gifford, R. M. (1983). Stomatal Sensitivity to Carbon Dioxide and Humidity. Plant Physiology, 71, 189-796.
Mujahidin, S., Azhar, N. F. & Prihasto, B. (2021). Analysis of Using Regularization Technique in The Convolutional Neural Network Architecture to Predict Paddy Disease for Small Dataset. Journal of Physics: Conference Series, 1726, 012010.
Mutava, R. N., Prince, S. J. K., Syed, N. H., Song, L., Valliyodan, B., Chen, W. & Nguyen, H.T. (2015). Understanding abiotic stress tolerance mechanisms in soybean: A comparative evaluation of soybean response to drought and flooding stress. Plant Physiology and Biochemistry, 86, 109-120.
Nagata, K., Yoshinaga, S., Takanashi, J. & Terao, T. (2001). Effects of Dry Matter Production, Translocation of Nonstructural Carbohydrates and Nitrogen Application on Grain Filling in Rice Cultivar, ‘Takanari’, a Cultivar Bearing a Large Number of Spikelets. Plant Production Science, 4, 173-183.
Nam, D. S., Moon, T., Lee, J. W. & Son, J. E. (2019). Estimating transpiration rates of hydroponically-grown paprika via an artificial neural network using aerial and root-zone environments and growth factors in greenhouses. Horticulture, Environment, and Biotechnology, 60, 913-923.
Nevavuori, P., Narra, N. & Lipping, T. (2019). Crop yield prediction with deep convolutional neural networks. Computers and Electronics in Agriculture, 163: 104859.
Ohkubo, S., Tanaka, Y., Yamori, W. & Adachi S. (2020). Rice Cultivar Takanari Has Higher Photosynthetic Performance Under Fluctuating Light Than Koshihikari, Especially Under Limited Nitrogen Supply and Elevated CO2. Frontiers in Plant Science, 11, 1308.
Ohsumi, A., Hamasaki, A., Nakagawa, H., Homma, K., Horie, T. & Shiwaiwa, T. (2008). Response of Leaf Photosynthesis to Vapor Pressure Difference in Rice (Oryza sativa L) Varieties in Relation to Stomatal and leaf Internal Conductance. Plant Production Science, 11, 184-191.
Ohtaki, E. (1984). Application of an infrared carbon dioxide and humidity instrument to studies of turbulent transport. Boundary-Layer Meteorology, 29, 85-107.
Oishi, A., Tanaka, Y., Xu, Z. & Shiraiwa, T. (2015). Investigation of lines with high photosynthetic capacity among a population derived from a cross between an erect panicle rice and non erect panicle rice. Abstracts of the 239th Meeting of the CSSJ, 87. (in Japanese) https://doi.org/10.14829/jcsproc.239.0_87
Ookawa, T., Naruoka, Y., Yamazaki, T., Suga, J. & Hirasawa, T. (2003). A Comparison of the Accumulation and Partitioning of Nitrogen in Plants between Two Rice Cultivars, Akenohoshi and Nipponbare, at the Ripening Stage. Plant Production Science, 6, 172- 178.
Peng, S., Khush G. S. & Cassman K. G. (1994). Evolution of the new plant type ideotype for increased yield potential. In K. G. Cassman (Eds.), Breaking the yield barrier. Proceedings of a Workshop on Rice Yield Potential in Favorable Environments (pp. 5- 20). Los Banos: International Rice Research Institute.
Peng, S., Cassman, K. G., Virmani, S. S., Sheehy, J. & Khush G. S. (1999). Yield potential trends of tropical rice since the release of IR8 and the challenge of increasing rice yield potential. Crop Science, 39, 1552-1559.
Peng, S., Laza, R. C., Visperas R. M., Sanico A. L., Cassman, K. G. & Khush, G. S. (2000). Grain yield of rice cultivars and lines developed in the Philippines since 1966. Crop Science, 40, 307-314.
Peng, S. & Khush, G. S. (2003). Four decades of breeding for varietal improvement of irrigated lowland rice in the International Rice Research Institute. Plant Production Science, 6, 157-164.
Qu, M., Hamdani, S., Li, W., Wang, S., Tang, J., Chen, Z., Song, Q., Li, M., Zhao, H., Chang, T., Chu, C. & Zhu, X. (2016). Rapid stomatal response to fluctuating light: an under-explored mechanism to improve drought tolerance in rice. Functional Plant Biology, 43, 727-738.
R Core Team (2018). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
Raune, A. C., Antle, J., Elliott, J., Folberth, C., Hoogenboom, G., Mason-D’Croz, D. et al. (2018). Biophysical and economic implications for agriculture of + 1.5 and + 2.0 ℃ global warming using AgMIP Coordinated Global and Regional Assessments. Climate Research, 76, 17-39.
Salter, W. T., Merchant, A. M., Richards, R. A., Trethowan, R. & Buckley, T. N. (2019). Rate of photosynthetic induction in fluctuating light varies widely among genotypes of wheat. Journal of Experimental Botany, 70, 2787-2796.
Salvucci, M. E. & Crafts-Brandner, S. J. (2004). Inhibition of photosynthesis by heat stress: the activation state of Rubisco as a limiting factor in photosynthesis. Physiologia Plantarum, 120, 179-186.
Sakuratani, T. & Horie, T. (1985). Studies on evapotranspiration from crops. (1) On seasonal changes, varietal differences and the simplified methods of estimate in evapotranspiration of paddy rice. Journal of Agricultural Meteorology, 41, 45-55. (in Japanese with English summary)
Sebrin, S. P., Singh, A., Desai, A. R., Dubois, S. G., Jablonski, A. D., Kingdon, C. C., Kruger, E. L. & Townsend, P. A. (2015). Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy. Remote Sensing of Environment, 167, 78-87.
Shimadzu, S., Seo, M., Terashima, I. & Yamori, W. (2019). Whole Irradiated Plant Leaves Showed Faster Photosynthetic Induction Than Individually Irradiated Leaves via Improved Stomatal Opening. Frontiers in Plant Science, 10, 1512.
Shimono, H., Okada, M., Yamakawa, Y., Nakamura, H., Kobayashi, K. & Hasegawa, T. (2009). Genotypic variation in rice yield enhancement by elevated CO2 relates to growth before heading, and not to maturity group. Journal of Experimental Botany, 60, 523-532.
Soleh, M. A., Tanaka, Y., Nomoto, Y., Iwahashi, Y., Nakashima, K., Fukuda, Y., Long, S. P. & Shiraiwa, T. (2016). Factors underlying genotypic differences in the induction of photosynthesis in soybean [Glycine max (L.) Merr.]. Plant, Cell and Environment, 39, 685-693
Song, Q., Xiao, H., Xiao, X. & Zhu, X. G. (2016). A new canopy photosynthesis and transpiration measurement system (CAPTS) for canopy gas exchange research. Agricultural and Forest Meteorology, 217, 101-107.
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B. & Liang, J. (2016). Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? IEEE Transactions on Medical Imaging, 35, 1299-1312.
Takai, T., Yano, M. & Yamamoto, T. (2010). Canopy temperature on clear and cloudy days can be used to estimate varietal differences in stomatal conductance in rice. Field Crops Research, 115, 165-170.
Takai, T., Matsuura, S., Nishio, T., Ohsumi, A., Shiraiwa, T. & Horie, T. (2006). Rice yield potential is closely related to crop growth rate during late reproductive period. Field Crops Research, 96, 328-335.
Takai, T., Adachi, S., Taguchi-Shiobara, F., Sanoh-Arai, Y., Iwasawa, N., Yoshinaga, S., Hirose, S., Taniguchi, Y., Yamanouchi, U., Wu, J., Matsumoto, T., Sugimoto, K., Kondo, K., Ikka, T., Ando, T., Kono, I., Ito, S., Shomura, A., Ookawa, T., Hirasawa, T., Yano, M., Kondo, M. & Yamamoto, T. (2013). A natural variant of NAL1, selected in high-yield rice breeding programs, pleiotropically increases photosynthesis rate. Scientific Reports, 3, 2149.
Tanaka, Y., Adachi, S. & Yamori, W. (2019). Natural genetic variation of the photosynthetic induction response to fluctuating light environment. Current Opinion in Plant Biology, 49, 52-59.
Tang, L., Gao, H., Hirooka, Y., Homma, K., Nakazaki, T., Liu, T., Shiraiwa, T. & Xu, Z. (2017). Erect panicle super rice varieties enhance yield by harvest index advantages in high nitrogen and density conditions. Journal of Integrative Agriculture, 16, 1467-1473.
Taniyoshi, K., Tanaka, Y. & Shiraiwa, T. (2020). Genetic variation in the photosynthetic induction response in rice (Oryza Sativa L.). Plant Production Science, 23(4), 513-521.
Taylaran, R. D., Adachi, S., Ookawa, T., Usuda, H. & Hirasawa, T. (2011). Hydraulic conductance as well as nitrogen accumulation plays a role in the higher rate of leaf photosynthesis of the most productive variety of rice in Japan. Journal of Experimental Botany, 62, 4067-4077.
Taylor S. H. & Long S. P. (2017). Slow induction of photosynthesis on shade to sun transitions in wheat may cost at least 21% of productivity. Philosophical transactions of the Royal Society B, 372, 20160543.
United Nations (2019). World Population Prospects 2019. https://population.un.org/wpp/. (Accessed May 17, 2021).
Van Rossum, G. & Drake, F. L. (2009). Python 3 Reference Manual. Valley (CA): Create Space: Scotts.
Vialet-Charbrand, S. & Lawson, T. (2019). Dynamic leaf energy balance: deriving stomatal conductance from thermal imaging in a dynamic environment. Journal of Experimental Botany, 70, 2839-2855.
Wu., A., Hammer, G. L., Doherty, A., Caemmerer, S. & Farquhar, D. (2019), Quantifying impacts of enhancing photosynthesis on crop yield. Nature Plants, 5, 380-388.
Yamamoto, K. (2019). Distillation of crop models to learn plant physiology theories using machine learning. PLoS ONE, 14, e0217075.
Yamauchi, M. (1994). Physiological bases for higher yield potential in F1 hybrids. In S.S. Virmani (Eds,). Hybrid rice technology: New developments and future prospects (pp. 71- 80). Los Banos: International Rice Research Institute.
Yamori, W. & Hikosaka, K. (2014). Temperature response of photosynthesis in C3, C4, and CAM plants: temperature acclimation and temperature adaptation. Photosynthetic Research, 119, 101-117.
Yamori, W., Kusumi, K., Iba, K. & Terashima, I. (2016). Increased stomatal conductance induces rapid changes to photosynthetic rate in response to naturally fluctuating light conditions in rice. Plant, Cell & Environment, 43, 1230-1240.
Yamori, W., Kusumi, K., Iba, K. & Terashima, I. (2020). Increased stomatal conductance induces rapid changes to photosynthetic rate in response to naturally fluctuating light conditions in rice. Plant, Cell and Environment, 43: 1230-1240.
Yan, H. & Oue, H. (2011). Application of the two-layer model for predicting transpiration from the rice canopy and water surface evaporation beneath the canopy. Journal of Agricultural Meteorology, 67, 89-97.
Yang, W., Peng, S. & Laza, R. C. (2007). Grain Yield and Yield Attributes of New Plant Type and Hybrid Rice. Crop Science, 47, 1393-1400.
Yasutake, D., Kitano, M., Kobayashi, T., Hidaka, K., Wajima, T. & He, W. (2006). Evaluation of canopy transpiration rate by applying a plan hormone “abscisic acid”. Biologia, Bratislava, 61, 315-319.
Yin, Y., Li, S., Liao, W., Lu, Q., Wen, X. & Lu, C. (2010). Photosystem II photochemistry, photoinhibition, and the xanthophyll cycle in heat-stressed rice leaves. Journal of Plant Physiology, 167, 959-966.
Yoon, D. K., Ishiyama, K., Suganami, M., Tazoe, Y., Watanabe, M., Imaruoka, S., Ogura, M., Ishida, H., Suzuki, Y., Obara, M., Mae, T. & Makino, A. (2020). Transgenic rice overproducing Rubisco exhibits increased yields with improved nitrogen-use efficiency in an experimental paddy field. Nature Food, 1, 134-139.
Yoshida, S. (1972). Physiological aspects of grain yield. Annual Review of Plant Physiology, 23, 437-464.
Yuan, L. P. (1994). Increased yield potential in rice by exploitation of heterosis. In S.S. Virmani (Eds.), Hybrid Rice Technology. New Developments and Future Prospects (pp. 1-6). Los Banos: International Rice Research Institute.
Zhu, X. G., Long, S. P. & Ort, D. R. (2010). Improving Photosynthetic Efficiency for Greater Yield. Annual Review of Plant Biology, 61, 235-261.
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