Assessment of global warming impacts on paddy rice growth and yield using a process based numerical crop growth model matcro rice in thai binh province vietnam

VIETNAM NATONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY DAO THI THU HANG ASSESSMENT OF GLOBAL WARMING IMPACTS ON PADDY RICE GROWTH AND YIELD USING A PROCESS-BASED NUMERICAL CROP GROWTH MODEL MATCRO-RICE IN THAI BINH PROVINCE, VIETNAM MASTER’S THESIS VIETNAM NATONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY DAO THI THU HANG ASSESSMENT OF GLOBAL WARMING IMPACTS ON PADDY RICE GROWTH AND YIELD USING A PROCESS-BASED NUMERICAL CROP GROWTH MODEL MATCRO-RICE IN THAI BINH PROVINCE, VIETNAM MAJOR: CLIMATE CHANGE AND DEVELOPMENT CODE: 8900201.02QTD RESEARCH SUPERVISOR: Associate Prof Dr YUJI MASUTOMI Associate Prof Dr MAI VAN TRINH Hanoi, 2020 PLEDGE I assure that this thesis is the result of my own research and has not been published The use of other research’s result and other documents must comply with regulations The citations and references to documents, books, research papers, and websites must be in the list of references of the thesis AUTHOR OF THE THESIS DAO THI THU HANG i TABLE OF CONTENTS PLEDGE i LIST OF TABLES iv LIST OF FIGURES v LIST OF ABBREVIATIONS vi ACKNOWLEDGEMENT vii ABSTRACT viii CHAPTER INTRODUCTION 1.1 Overview 1.2 Research objectives 1.3 Structure of the Thesis 1.4 Learning Outcomes CHAPTER METHODOLOGY 10 2.1 Framework of the study 10 2.2 Study area 12 2.2.1 Location 12 2.2.2 Climate 13 2.2.3 Rice variety (Bac Thom No cultivar_BT7) .17 2.3 MATCRO-Rice model .17 2.4 Data sources .20 2.4.1 Meteorological data 20 2.4.2 Crop management .21 2.5 Model parameterization 23 2.5.1 Phenology 23 2.5.2 Dry matter Partitioning 25 2.6 Nitrogen response .25 2.7 Model validation .27 2.8 Global warming impact assessment 27 CHAPTER RESULTS 29 3.1 MATCRO-Rice parameterization and validation 29 3.1.1 The effect of parameterization to phenology 29 3.1.2 The effect of parameterization to Carbon partitioning .31 3.2 Yield and nitrogen response .34 3.3 Impact of temperature increase on rice yield .36 CHAPTER DISCUSSION AND LIMITATION 38 4.1 Discussion 38 4.2 Limitations 41 ii 4.2.1 Data gaps 41 4.2.2 Limitation of the parameterization .41 CHAPTER CONCLUSION .42 REFERENCES 43 iii LIST OF TABLES Table 2.1 Thai Binh province weather by month and weather averages 15 Table 2.2 Site information and input .18 Table 2.3 Meteorological variables 20 Table 2.4 Information of Site and Site 21 Table 2.5 Crop calendar and field measurements 22 Table 3.1 Comparison of development stage index between simulation and global 29 Table 3.2 Timing of growth date (mm/dd/yr) 29 Table 3.3 Partitioning parameters 33 Table 3.4 The difference between before and after calibrated nitrogen response index 34 Table 3.5 Percentage different between observed and simulated yield 35 Table 3.6 The statistical analysis of rice yield 35 Table 3.7 Influence of temperature increase on rice yield 37 Table 4.1 Yield reduction 40 iv LIST OF FIGURES Figure 2.1 Framework MATCRO-Rice model simulation .11 Figure 2.2 Map of Thai Binh administrative regions 13 Figure 2.3 Monthly average temperature (oC, line and left vertical axis) and monthly rainfall (mm, column and right axis) 14 Figure 2.4 MATCRO-Rice model structure 18 Figure 2.5 The relationship between specific leaf nitrogen and DVS 27 Figure 3.1 Heading date of simulation and global data 30 Figure 3.2 Heading date of simulation and global data 31 Figure 3.3 Partitioning ratio of glucose to organs including leaves (a), panicles (b) within shoots and root (c) 33 Figure 3.4 Correlation between the observed and simulated yields The orange line is the 1:1 line .36 Figure 3.5 Comparison between simulated yield, yield at warming scenarios applied for nitrogen cases (high, medium and low) .37 Figure 4.1 Menu for adaptation options on agriculture 39 Figure 4.2 Influence of N fertilizer levels on rice yield at different temperature increase scenarios 40 v LIST OF ABBREVIATIONS CGM: DVS: GSO: hGDH: mGDH: MONRE: RRD: SLN: UNFCCC: Crop growth model Development stages index General statistic office Growing degree hour from seedling to heading Growing degree hour from seedling to harvest Ministry of Natural Resources and Environment Red River Delta Specific leaf nitrogen United Nations Framework Convention on Climate Change vi ACKNOWLEDGEMENT I would like to express my sincere gratitude to my supervisors Dr Yuji Masutomi - Ibaraki University and Dr Mai Van Trinh - Director of Institute for Agricultural Environment for providing the invaluable guidance, comments and suggestions throughout my thesis I would special thank Dr Akihiko Kotera for scientific consulting and constantly motivating me to work harder I am also grateful to all the lectures in the Vietnam Japan University and Ibaraki University for their support towards the successful completion of my studies in Vietnam and Japan Without the financial support of the Vietnamese and Japanese Government which offered me a scholarship for graduate studies, this work would not have been possible Special thanks go to all the lecturers and staffs at the Institute for Global Climate Adaptation Science (ICAS) and department of Agriculture in Ibaraki University for providing me an internship in Japan in two months which I had an opportunity to research with professionals and enjoy culture exchange I am really grateful to them In addition, I would also like to thank my friends and colleagues at the Institute for Agricultural Environment for supporting me during the entire data collection period and creating best conditions for me to balance my work and study Finally, I want to dedicate my success to my family for the encouragement and support throughout my research process I give special thanks to my parents for helping me take care of my children, providing logistical support and encouragement that no one to help me cannot complete my work I submit this thesis of mine with great humility and regards vii ABSTRACT Rice is directly feeding more people than any other crops Vietnam is one of the largest exporters of rice with the main supply from Red River Delta Rice production in Red River Delta is susceptible to yield reduction from rising temperature Thus, understanding the impacts of global warming on rice production is essential to food security in Vietnam in the near future This research used a reliable data of crop management in Thai Binh, located province in Red River Delta To simulate the rice production, I used the crop growth model, MATCRORice, first the model needs to be parameterized the phenology and dry matter partitioning, then I validated by comparing the simulated yield to observe yield Next, the model was used to predict the changes of rice production under warming scenarios (1.5 oC, oC, oC and oC) Results show that the yield reduction happened in all of warming scenarios and decline up to 39% compare with observe yields The yield will be improved by adding more fertilizer, but this application cannot offset the losses due to rising temperature This research got some limitation from both data and model, but it can contribute to the development of a national adaptation plan with a scientific basis Keywords: global warming scenarios, rice production, crop growth model viii 20 40 60 80 100 120 Days BT7 78 Global 30 74 AWD 79 Flood 77 Vegetation Stage 41 33 28 Maturity stage Figure 3.2 Heading date of simulation and global data 3.1.2 The effect of parameterization to Carbon partitioning MATCRO-Rice partitions carbohydrates in leaves, in the form of glucose into each organ Based on the biomass of leaf, stem, root and panicle as I mentioned above, I calculated the rate of glucose partitioned to these organs and fitted the curves for the ratios which made more suitable Figure 3.3.a, 3.3.b, 3.3.c show the relationship between the DVS and the ratio of glucose partition to leaf, panicl and root The red lines are fitted, dots are the observed value, solid lines show the values parameterized, dotted vertical lines show the heading date and dashed vertical lines show the harvest date 31 (a) (b) 32 (c) Figure 3.3 Partitioning ratio of glucose to organs including leaves (a), panicles (b) within shoots and root (c) Glucose transformed 60% to leaves within shoots is highest at DVS from to 0.3 and 40% of glucose transformed to root within shoots at DVS from to 0.3 which were explained that I assumed the glucose only move totally to leaves and root from the seedling stage to one month later and it reduce gradually and stop before heading stage – days Glucose starts transforming to panicle in the reproductive phase of growth and increase continuously and get the highest rate 20 days before the harvest date, I assumed that panicle still received 100% of glucose till harvest Table 3.3 shows the calculated ratios of glucose partitioned to each organ Organ Leaf/shoot Table 3.3 Partitioning parameters DVS 0.3 0.6 33 Ratio of glucose partition 0.6 0.6 0.2 Organ DVS 0.73 0.6 0.75 0.8 0.3 0.7 Panicle/shoot Root/total 3.2 Ratio of glucose partition 0 0 0.75 1 0.4 0.4 0 Yield and nitrogen response The observed yields were different among two sites and each nitrogen treatments (high, medium and low) and ranging from 4200 to 5100 kg ha-1 However, when I ran the MATCRO-Rice model with these above calibrated parameters, the simulated yields are higher than observed yields from 16% - 32% The reason was caused by the nitrogen response index in MATCRO-Rice model is not suitable with the simulation Nitrogen response index in MATCRO-Rice model shows the nitrogen use efficiency in rice production In this case, the efficiencies of nitrogen are higher than the extent of crop response to N existed Table 3.4 shows the simulated yield after changing the nitrogen response index in MATCRO-Rice model Table 3.4 The difference between before and after calibrated nitrogen response index Nitrogen application (kg N/ha) 110 78 55 Average Observed yield (kg ha-1) Site Site 5100 5000 4900 5000 4800 4900 4200 4633 Simulated yield (kg ha-1) Before calibrated After calibrated nitrogen response nitrogen response index index 7271 5222 6358 5055 5400 4910 6343 5058 34 The absolute yields which were simulated by crop growth MATCRO-Rice model were almost the same as observed yields The difference between simulated yield and observed yield is less than 10% except the case of site which cultivated under AWD and applied low fertilizer application (55 kg N/ha) Table 3.5 Percentage different between observed and simulated yield Nitrogen application (kg N/ha) 110 78 55 Average Observed yield (kg ha-1) Site Site 5100 5000 4900 5000 Simulated yield (kg ha-1) 4800 4900 4200 4633 5222 5055 4910 5062 Different between simulated and site 2.3% 1.1% 0.2% 1.2% Different between simulated and site 8.1% 3.1% 14.5% 8.5% The least significant difference (LSD) by perform the analysis of variance (ANOVA) in the table 3.6 showed that the difference between observed data (site and site 2) and simulated yield is not statistical significant difference LSD is higher than the different between the observed and simulated yield The model and the observation have the difference but not statistical significant so the model can present the real situations Table 3.6 The statistical analysis of rice yield Site Observation Simulation Observation Simulation The different yield LSD 62 164 549 428 P value 0.59 0.14 This result show that the simulation yields always higher than the observation yield because in this model this yield is the attainable yield which nutrients and water non-limiting, pests and diseases effectively controlled so it makes the gaps between the simulation and observation yield 35 5250 5200 Simulated yield (kg/ha) 5150 5100 5050 5000 4950 4900 4850 4800 4800 4850 4900 4950 5000 5050 5100 5150 5200 5250 Observed yield (kg/ha) Figure 3.4 Correlation between the observed and simulated yields The orange line is the 1:1 line The observed and simulated yields after adjusting the index of nitrogen response showed that this model could estimate the BT7 rice yield to other fields in Red River Delta 3.3 Impact of temperature increase on rice yield As mention above, the CO2 concentration in this study is fixed and I assumed that the CO2 concentration does not change when the temperature increase The reduction of rice production was recorded for rising temperature scenarios by 1.5 o C, 2.0 oC, 3.0 oC and 4.0 oC The increasing temperature changes the phenology of rice such as shortening the growth duration and reduced photosynthesis, glucose partitioned to each organ that led to yield reductions 36 6000 5000 Yield (kg/ha) 4000 3000 2000 1000 Observed yield 1.5 oC warming 110 kgN/ha oC warming 78 kgN/ha oC warming oC warming 55 kgN/ha Figure 3.5 Comparison between simulated yield, yield at warming scenarios applied for nitrogen cases (high, medium and low) Table 3.7 Influence of temperature increase on rice yield Nitrogen Percentage yield reduction 1.5 oC oC oC oC 110 14.78% 16.72% 33.30% 34.76% 78 18.20% 20.14% 36.60% 37.82% 55 13.16% 15.11% 31.47% 39.82% application (kg N/ha) In summary, the yield decreased different depending on global warming scenarios and the more rising temperature the more reduction of yield Therefore, the increased air temperature was the key climate factor predicted to decrease the future rice yield in this area The table 3.7 shows the percentage of reduction in each nitrogen application 37 CHAPTER DISCUSSION AND LIMITATION 4.1 Discussion As one of the most productive regions for rice, Thai Binh province is particularly important for rice market and maintaining food stock for over 22 million people of RRD The reduction of rice productivity combined with the forecast of a strong population increase in this region will be a challenge for food security Moreover, the livelihood of people who live in the RRD are mainly depends on rice farming and any changes to rice systems will affect the farmers as well This research results show that rice paddy yields will decline up to 20% under rising 1.5 oC and more yield reduction when the rising temperature is higher This reduction may be widespread in whole of rice farming in Vietnam in the near future if we not have any policy to adapt this Therefore, creating and implementing national global warming adaptation plans for rice production in Vietnam are necessary to sustain food security in the near future Figure 4.1 shows the menu for adaptation options on agriculture under the global warming scenarios and especially focus on paddy rice This menu categories the level of difficulty in implementing these options, the cost and effect as well Government, scientist, farmer and the enterprises should cooperate to make them not only high effect but also low cost which depend on policies of each country or region These adaptation options are given based on agriculture technology; ICT and smart agriculture; agricultural finance and infrastructure With the low cost, the farmers could implement by the changing in agricultural management like changing the planting date, more fertilizer amount or changing in planting crop or varieties, however, the effects are still low In recent years, the technology 4.0 in agriculture sector are propagated as the smart agriculture by using ICT to give the early warning system or real time monitoring system to identify the crop issues to control time These techniques need the contribution from the Government, scientists and company but the effects are accessed intermediated level Some other adaptations give the high effect by improving the infrastructure of 38 agricultural system such as changing the postharvest systems, building the irrigation systems or changing in land use but the government and company need the high cost on them Figure 4.1 Menu for adaptation options on agriculture In this research framework, I changed the fertilizer amount application in MATCRO-Rice model to assess the rice yield However, the increase in fertilizer application rate up to 2.0 times relative to the baseline for global warming scenarios could not reach to the observed yields 39 Assessment of Changing amount of fertilizer in global warming scenario 6000 Yield (kg /ha) 5000 4000 3000 2000 1000 110 N 1.5 oC warming 130 N 2.0 oC warming 3.0 oC warming 200 N 4.0 oC warming Figure 4.2 Influence of N fertilizer levels on rice yield at different temperature increase scenarios Table 4.1 showed that although the fertilizer increases to 200 kg N/ha, it only helps to reduce 4% less than when applied 110 kg N/ha (reduction 35% instead of 39%) Therefore, implementing fertilizer application cannot offset the losses caused by the rising temperature Table 4.1 Yield reduction Nitrogen (kg N/ha) 110 130 200 Simulated yield under rising temperature (kg/ha) o o 1.5 C C oC oC 18% 20% 37% 39% 17% 19% 36% 38% 12% 14% 33% 35% To maintain rice yield in the future, local cultivar should be screened for tolerance to high air temperature (Prasad et al., 2006) In Vietnam, there are some institutes have been researching on breeding these cultivars, however the systems of distribution and sale of heat-tolerance is weak, and the consumers are not familiar with using these cultivars Furthermore, other effects of increased air temperature on decreasing yield in this model was come from the effects on multiple plant characteristics including phenology (shortening the growth period), photosynthesis Some other research showed that shifting to cultivars with higher transpiration rates 40 is the good strategy to avoid the detrimental effects of air temperature o photosynthesis, respiration and phenology Development of irrigation system is one of the effective ways to decrease the leaf temperature, however this solution is only suitable for the reasons which have plentiful water resources and good irrigation constructing systems 4.2 Limitations 4.2.1 Data gaps The effectiveness of calibration mainly relies on the reliability, accuracy of datasets and moreover there are enough data to calibration and validation In my case, I only have data of sites and using one cultivar BT7 to represent all management in Thai Binh province Besides, the biomass data was lacked leaf area index (LAI) and the data used in one summer crop that made the model to high uncertainty in this area Moreover, the meteorological data, namely relative humidity, solar radiation, wind speed were assumed to be the same in the warming scenarios However, these problems can be adjusted in other study 4.2.2 Limitation of the parameterization This study parameterized for phenology and carbon partitioning Previous studies showed that crop growth model was also sensitive to other parameters such as management, maximum leaf area index (Wu et al., 2009) Although the calibration of these parameters led to the improvement for local scale rice simulation, more efforts are required to examine the role of other parameters for regional scales (province or delta level) Besides, I only considered paddy rice with full irrigation and did not account for pest or disease stresses With these optimal assumptions, simulation results were expected to represent the maximum potential yield that was higher than observations yield leading to unrealistic values for the parameters 41 CHAPTER CONCLUSION This study examines the potential impact of global warming on rice yield along with an evaluation of fertilizer adaptation measures in order to overcome the impact of rising temperature in Thai Binh province of Vietnam Using field collected data to parameterize and validate for crop growth model, I can simulate rice production for my study area The research results showed that increase air temperature is the critical climate factor that decrease rice yield, the reduction yields happened in all of global warming scenarios by 1.5 oC, oC, oC and oC The degrees of yield reduction could reach to nearly 39% compare with observation yields Therefore, to sustain food security, a variety of adaptation plans have been proposed like breeding new heat-tolerance cultivars, shortening the rice growth period, development of irrigation system but some options are high priority and high feasibility, but others are high priority but low feasibility Although, this study still got some limitation from both data and model, these results can contribute to the development of a regional adaptation plan with a scientific basis 42 REFERENCES Arunrat, N., Pumijumnong, N & Hatano, R Predicting local-scale impact of climate change on rice yield and soil organic carbon sequestration: A case study in Roi Et Province, Northeast Thailand Agricultural Systems 164, 58–70 (2018) Bezner Kerr, R et al Participatory agroecological research on climate change adaptation improves smallholder farmer household food security and dietary diversity in Malawi Agriculture, Ecosystems & Environment 279, 109–121 (2019) Cai, C., et al., 2018 Do all leaf photosynthesis parameters of rice acclimate to elevated CO2, elevated temperature, and their combination, in FACE environments? Glob Change Biol 24 (4), 1685–1707 Chen, C.-C., McCarl, B., & Chang, C.-C (2011) Climate change, sea level rise and rice: Global market implications Climatic Change, 110, 543–560 https://doi.org/10 1007/s10584-011-0074-0 Clauss, K., Ottinger, M., Leinenkugel, P & Kuenzer, C Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data International Journal of Applied Earth Observation and Geoinformation 73, 574–585 (2018) Deb, P., Tran, D.A & Udmale, P.D Assessment of the impacts of climate change and brackish irrigation water on rice productivity and evaluation of adaptation measures in Ca Mau province, Vietnam Theor Appl Climatol 125, 641–656 (2016) https://doi.org/10.1007/s00704-015-1525-8 Faye, B., et al., 2018 Impacts of 1.5 versus 2.0 °C on cereal yields in the West African Sudan Savanna Environ Res Lett 13 (3), 034014 FAO (2001) Integrated agriculture-aquaculture: a primer FAO Fisheries Technical Paper T407, p 149, FAO Gaupp, F., et al., 2019 Increasing risks of multiple breadbasket failure under 1.5 and 2°C global warming Agric.Syst 175, 34–45 GSO (General Statics Office) (2012) Statistical handbook of Vietnam General Statistical Publishing House, Hanoi Hoogenboom, G., Jones, J., Wilkens, P., Porter, C., Boote, K., Hunt, L., et al (2010) Decision support system for Agrotechnology transfer (DSSAT) Version 4.5 Hoogenboom, G., J, J., & PW, W (2012) Decision support system for Agrotechnology transfer (DSSAT) IPCC, 2014 Climate change 2014: impacts, adaptation, and vulnerability Part B: regional aspects Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge University Press, Cambridge, United Kingdom and New York, pp 688 43 Kim, H.Y., et al., 2013 Impacts of climate change on paddy rice yield in a temperate climate Glob Change Biol 19 (2), 548–562 Kiniry JR, Rosenthal WD, Jackson BS, Hoogenboom G 1991 Predicting leaf development of crop plants In: Hodges T, editor Predicting crop phenology Boca Raton, Fla (USA): CRC Press p 29-42 Kontgis, C., Schneider, A., & Ozdogan, M (2015) Mapping rice paddy extent and in- tensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data Remote Sensing of Environment, 169, 255–269 https://doi.org/10.1016/j.rse 2015.08.004 Laborte, A., Gutierrez, M., Balanza, J et al RiceAtlas, a spatial database of global rice calendars and production Sci Data 4, 170074 (2017) https://doi.org/10.1038/sdata.2017.74 Liu, B., Martre, P., Ewert, F., et al., 2018 Global wheat production with 1.5 and 2.0 degrees C above pre-industrial warming Glob Change Biol 25 (4), 1428– 1444 Lobell, D.B., Asseng, S., 2017 Comparing estimates of climate change impacts from process-based and statistical crop models Environ Res Lett 12 (1), 015001 Lu, P.L., et al., 2008 Effects of climatic variation and warming on rice development across South China Clim Res 36 (1), 79–88 Masutomi, Y., Ono, K., Mano, M., Maruyama, A., and Miyata, A.: A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v 1) – Part 1: Model description, Geosci Model Dev., 9, 4133–4154, doi:10.5194/gmd-9-4133- 2016, 2016 Ministry of Natural Resources and Environment (2016), Climate change and sea level rise scenarios for Vietnam Mitchell, D., et al., 2017 Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design Geosci Model Dev 10 (2), 571–583 Nguyen, T 2006 Poverty, poverty reduction and poverty dynamics in Vietnam Background paper for the Chronic Poverty Report 2008–09 Manchester, U.K.: Chronic Poverty Research Centre Peng, S., et al., 2004 Rice yields decline with higher night temperature from global warming Proc Natl Acad Sci USA 101 (27), 9971–9975 Prasad, P.V.V., et al., 2006 Species, ecotype and cultivar differences in spikelet fertility and harvest index of rice in response to high temperature stress Field Crops Res 95 (2-3), 398–411 Rosenzweig, C., & Parry, M L (1994) Potential impact of climate change on world food supply Nature, 367, 133–138 https://doi.org/10.1038/367133a0 44 Rosenzweig, C., Ruane, A.C., Antle, J., et al., 2018 Coordinating AgMIP data and models across global and regional scales for 1.5 degrees C and 2.0 degrees C assessments Philos Trans A Math Phys Eng Sci 376 (2119), 0455 Ruane, A.C., et al., 2016 Multi-wheat-model ensemble responses to interannual climate variability Environ Model Software 81, 86–101 Sanchez, B., et al., 2014 Temperatures and the growth and development of maize and rice: a review Glob Change Biol 20 (2), 408–417 Schleussner, C.-F., et al., 2018 Crop productivity changes in 1.5°C and 2°C worlds under climate sensitivity uncertainty Environ Res Lett 13 (6), 064007 Sheehy, J.E., et al., 2006 Decline in rice grain yields with temperature: models and correlations can give different estimates Field Crops Res 98 (2-3), 151– 156 Van Keulen H, Penning de Vries FWT, Drees EM 1982 A summary model for crop growth In: Penning de Vries FWT, van Laar HH, editors Simulation of plant growth and crop production Simulation Monographs Wageningen (Nether- lands): Pudoc p 87-97 Vu, L., and P Glewwe 2008 Impacts of rising food prices on poverty and welfare in Vietnam Working Paper Series No 13 Hanoi, Vietnam: Development and Policies Research Center (DEPOCEN) Xiong, W et al A calibration procedure to improve global rice yield simulations with EPIC Ecological Modelling 273, 128–139 (2014) Yang, J., Xiong, W., Yang, X., Cao, Y & Feng, L Geographic Variation of Rice Yield Response to Past Climate Change in China Journal of Integrative Agriculture 13, 1586–1598 (2014) Yu, B., Zhu, T., et al., 2010 Impacts of Climate Change on Agriculture and Policy Options for Adaptation The case of Vietnam International Food Policy Research Institute (IFPRI) Yu, Y.Q., et al., 2014 Impact assessment of climate change, carbon dioxide fertilization and constant growing season on rice yields in China Clim Change 124 (4), 763–775 Zhai, F., and J Zhuang 2009 Agricultural Impact of Climate Change: A General Equilibrium Analysis with Special Reference to Southeast Asia ADBI Working Paper 131 Tokyo: Asian Development Bank Institute Available: http://www.adbi.org/working/paper/2009/02/23/2887.agricultural.impact.cli mate.change/ Zhao, C., et al., 2016 Plausible rice yield losses under future climate warming Nat Plants 3, 16202 45 ... warming impacts on paddy rice growth and yield using a process- based numerical crop growth model MATCRO -Rice in Thai Binh province, Vietnam? ?? to aim for evaluation of rising temperature impacts on. . .VIETNAM NATONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY DAO THI THU HANG ASSESSMENT OF GLOBAL WARMING IMPACTS ON PADDY RICE GROWTH AND YIELD USING A PROCESS- BASED NUMERICAL CROP GROWTH MODEL. .. wind speed Carbon partitioning calibration and validation Nitrogen response Running MATCRO -Rice model Simulation the Rice growth and yield in Thai Binh Province Global warming scenarios Impact