Projecting climate change impacts on chalky grain rice in East Kalimantan, Indonesia and implications for heat-tolerant rice variety breeding
Main Article Content
Abstract
Abstract. Pramana A, Nurhasanah, Sunaryo W, Rusdiansyah, Pranoto, H. 2026. Projecting climate change impacts on chalky grain rice in East Kalimantan, Indonesia and implications for heat-tolerant rice variety breeding. Asian J Agric 10 (1): g100147. https://doi.org/10.13057/asianjagric/g100147. Rising temperatures associated with climate change are increasing the incidence of Chalky Grain Rice (CGR), reducing grain quality. This study projects future CGR risk and identifies the level of heat-tolerance required for rice varieties to maintain acceptable grain quality under climate change in East Kalimantan, Indonesia. Future CGR incidence was simulated using a temperature-based CGR model combined with bias-corrected climate projections under Representative Concentration Pathway (RCP)2.6 and RCP 8.5 scenarios, along with simulations of rice varietal heat-tolerance under optimistic, median, and pessimistic conditions. Results indicate that even under the low-emission RCP2.6 scenario, most rice-growing areas are projected to exceed the 15% CGR threshold by the 2040s under the optimistic case. Under median conditions, substantially higher CGR levels are projected, particularly under RCP 8.5. To maintain CGR below acceptable limits, rice varieties capable of tolerating temperature increases of approximately 2-3.5°C above current conditions are required by the 2040s, while pessimistic scenarios under both RCPs demand heat-tolerance of up to 4°C. These findings highlight the urgent need to accelerate targeted breeding programs to develop heat-tolerant rice varieties capable of sustaining grain quality, meeting national standards, and remaining resilient under continued climate warming.
Article Details
Issue
Section

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
References
Balai Penerapan Standar Instrumen Pertanian Lampung (BPSIP Lampung). 2023. Standar Mutu Beras SNI 6128:2020. BPSIP Lampung, Lampung. https://repository.pertanian.go.id.
Bowen D, Yanni Z, Fan Z, Wensheng W, Jianlong X, Yu Z, Jinsong B. 2024. Genome-wide association study of cooked rice textural attributes and starch physicochemical properties in indica rice. Rice Science 31 (3): 300-316. https://doi.org/10.1016/j.rsci.2024.02.008.
Cuevas RP, Pede VO, McKinley J, Velarde O, Demont M. 2016. Rice grain quality and consumer preferences: A case study of two rural towns in the Philippines. PLoS ONE 11 (3): e0150345. https://doi.org/10.1371/journal.pone.0150345.
Das G, Patra JK, Baek KH. 2017. Insight into MAS: A molecular tool for development of stress resistant and quality rice through gene stacking. Front Plant Sci 8: 985. https://doi.org/10.3389/fpls.2017.00985.
Deng H, Cao S, Zhang G, Xiao Y, Liu X, Wang F, Tang W, Lu X. 2024. OsVPE2, a member of vacuolar processing enzyme family, decreases chilling tolerance of rice. Rice 17: 5. https://doi.org/10.1186/s12284-023-00682-9.
Deng Z, Liu Y, Gong C, Chen B, Wang T. 2022. Waxy is an important factor for grain fissure resistance and head rice yield as revealed by a genome-wide association study. J Exp Bot 73: erac330. https://doi.org/10.1093/jxb/erac330.
Efron B. 1979. Bootstrap methods: Another look at the jackknife. Ann Stat 7 (1): 1-26. https://doi.org/10.1214/aos/1176344552.
Gann PJ, Esguerra M, Counce PA, Srivastava V. 2021. Genotype-dependent and heat-induced grain chalkiness in rice correlates with the expression patterns of starch biosynthesis genes. Plant Environ Interact 2 (4): 165-176. https://doi.org/10.1002/pei3.10054.
Gao LF, Jia JZ, Kong XY. 2016. A SNP-based molecular barcode for characterization of common wheat. PLoS ONE 11 (3): e0150947. https://doi.org/10.1371/journal.pone.0150947.
Guo Y, Luo H, Yi J, Zhu Y, Ma X, Jiang Y, Zhang G, Deng H. 2025. Effects of high temperature at grain filling stage on grain quality in heat-sensitive versus heat-tolerant rice cultivars. Agronomy 15: 668. https://doi.org/10.3390/agronomy15030668.
Han J, Choi J. 2021. Implementation of ESGF data node for international distribution of CORDEX-East Asia regional climate data. Intl J Contents 17 (1): 61-70. https://doi.org/10.5392/IJoC.2021.17.1.061.
Heo JH, Ahn H, Shin JY, Kjeldsen TR, Jeong C. 2019. Probability distributions for a quantile mapping technique for a bias correction of precipitation data: A case study of precipitation data under climate change. Water 11 (7): 1475. https://doi.org/10.3390/w11071475.
Huang F, Sun Z, Hu PS, Tang SQ. 1998. Present situations and prospects for the research on rice grain quality forming. Chin J Rice Sci 12 (3): 172-176.
Intergovernmental Panel on Climate Change (IPCC). 2023. Summary for policymakers. In: Lee H, Romero J (eds.). Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva. https://doi.org/10.59327/IPCC/AR6-9789291691647.001.
Ishimaru T, Miyazaki M, Shigemitsu T, Nakata M, Kuroda M, Kondo M, Masumura T. 2020. Effect of high temperature stress during ripening on the accumulation of key storage compounds among Japanese highly palatable rice cultivars. J Cereal Sci 95: 103018. https://doi.org/10.1016/j.jcs.2020.103018.
Ji Y, Xu Y, Sun X, Hassan MA, Zhou Y, Zou H, Li Z. 2024. Optimization of sowing dates for enhanced rice yield: Insights from field experiments in the middle and lower reaches of the Yangtze River, China. BMC Plant Biol 24 (1): 1011. https://doi.org/10.1186/s12870-024-05729-7.
Kim SS, Lee SE, Kim OW, Kim DC. 2000. Physicochemical characteristics of chalky kernels and their effects on sensory quality of cooked rice. Cereal Chem 77 (3): 376-379. https://doi.org/10.1094/CCHEM.2000.77.3.376.
Lenaerts B, Collard BCY, Demont M. 2019. Review: Improving global food security through accelerated plant breeding. Plant Sci 287: 110207. https://doi.org/10.1016/j.plantsci.2019.110207.
Li J, Zhang C, Luo X, Zhang T, Zhang X, Liu P, Yang W, Lei Y, Tang S, Kang L, Huang L, Li T, Wang Y, Chen W,Yuan H, Qin P, Li S, Ma B, Tu B. 2023. Fine mapping of the grain chalkiness quantitative trait locus qCGP6 reveals the involvement of Wx in grain chalkiness formation. J Exp Bot 74 (11): 3544–3559. https://doi.org/10.1093/jxb/erad112.
Lin CJ, Li CY, Lin SK, Yang FH, Huang JJ, Liu YH, Lur HS. 2010. Influence of high temperature during grain filling on the accumulation of storage proteins and grain quality in rice (Oryza sativa L.). J Agric Food Chem 58 (19): 10545–10552. https://doi.org/10.1021/jf101593s.
Liu X, Han S, Makowski D, Wang X, Fu Z, Ciais P. 2025. Response of rice quality to climate warming: A meta-analysis. Field Crops Res 331: 109995. https://doi.org/10.1016/j.fcr.2025.109995.
Masutomi Y, Arakawa M, Minoda T, Yonekura T, Shimada T. 2015. Critical air temperature and sensitivity of the incidence of chalky rice kernels for the rice cultivar “Sai-no-kagayaki”. Agric For Meteorol 203: 11-16. https://doi.org/10.1016/j.agrformet.2014.11.016.
Masutomi Y, Kinose Y, Takimoto T, Yonekura T, Oue H, Kobayashi K. 2019a. Ozone changes the linear relationship between photosynthesis and stomatal conductance and decreases water use efficiency in rice. Sci Total Environ 655: 1009-1016. https://doi.org/10.1016/j.scitotenv.2018.11.132.
Masutomi Y, Takimoto T, Manabe T, Imai Y, Tamura M, Kobayashi K. 2023. Breeding targets for heat-tolerant rice varieties in Japan in a warming climate. Mitig Adapt Strateg Glob Change 28: 2. https://doi.org/10.1007/s11027-022-10027-4.
Masutomi Y, Takimoto T, Shimamura M, Manabe T, Arakawa M, Shibota N, Ooto A, Azuma S, Imai Y, Tamura M. 2019b. Rice grain quality degradation and economic loss due to global warming in Japan. Environ Res Commun 1 (12): 121003. https://doi.org/10.1088/2515-7620/ab52e7.
Misra G, Badoni S, Parween S, Singh RK, Leung H, Ladejobi OF, Mott R, Sreenivasulu N. 2021. Genome-wide association coupled gene-to-gene interaction studies unveil novel epistatic targets among major effect loci impacting rice grain chalkiness. Plant Biotechnol J 19 (5): 910-925. https://doi.org/10.1111/pbi.13516.
Mukheef RAH, Hassan WH, Alquzweeni S. 2024. Projections of temperature and precipitation trends using CMhyd under CMIP6 scenarios: A case study of Iraq’s Middle and West. Atmos Res 306: 107470. https://doi.org/10.1016/j.atmosres.2024.107470.
Nelder JA, Mead R. 1965. A simplex algorithm for function minimization. Comput J 7 (4): 308-313. https://doi.org/10.1093/comjnl/7.4.308.
Nile BK, Hassan WH, Esmaeel BA. 2018. An evaluation of flood mitigation using a Storm Water Management Model (SWMM) in a residential area in Kerbala, Iraq. IOP Conf Ser Mater Sci Eng 433 (1): 012001. https://doi.org/10.1088/1757-899X/433/1/012001.
Ouyang J, Zhu Z, Guan Y, Huang Q, Huang T, Zang S, Pan C. 2026. Novel Wx gene functional markers for high-resistant starch rice breeding. Genes 17 (1): 89. https://doi.org/10.3390/genes17010089.
Shimoyanagi R, Abo M, Shiotsu F. 2021. Higher temperatures during grain filling affect grain chalkiness and rice nutrient contents. Agronomy 11 (7): 1360. https://doi.org/10.3390/agronomy11071360.
Tabassum R, Dosaka T, Ichida H, Morita R, Ding Y, Abe T, Katsube-Tanaka T. 2020. FLOURY ENDOSPERM11-2 encodes plastid HSP70-2 involved with the temperature-dependent chalkiness of rice (Oryza sativa L.) grains. Plant J 103 (2): 604-616. https://doi.org/10.1111/tpj.14752.
Takimoto T, Masutomi Y, Tamura M, Nitta Y, Tanaka K. 2019. The effect of air temperature and solar radiation on the occurrence of chalky rice grains in rice cultivars “Koshihikari” and “Akitakomachi”. J Agric Meteorol 75 (4): 203-210. https://doi.org/10.2480/agrmet.D-18-00039.
Taratima W, Chuanchumkan C, Maneerattanarungroj P, Trunjaruen A, Theerakulpisut P, Dongsansuk A. 2022. Effect of heat stress on some physiological and anatomical characteristics of rice (Oryza sativa L.) cv. KDML105 callus and seedling. Biology 11 (11): 1587. https://doi.org/10.3390/biology11111587.
Tashiro T, Wardlaw IF. 1991. The effect of high temperature on kernel dimensions and the type and occurrence of kernel damage in rice. Aust J Agric Res 42 (3): 485-496. https://doi.org/10.1071/AR9910485.
Tollefson J. 2020. Why deforestation and extinctions make pandemics more likely. Nature 584 (7820): 175-176. https://doi.org/10.1038/d41586-020-02341-1.
Yao D, Wu J, Luo Q, Zhuang W, Xiao G, Deng Q, Lei D, Bai B. 2020. Influence of high natural field temperature during grain filling stage on the morphological structure and physicochemical properties of rice (Oryza sativa L.) starch. Food Chem 310: 125817. https://doi.org/10.1016/j.foodchem.2019.125817.
Yeboah KA, Akpoti K, Kabo-Bah AT, Ofosu EA, Siabi EK, Mortey EM, Okyereh SA. 2022. Assessing climate change projections in the Volta Basin using the CORDEX-Africa climate simulations and statistical bias-correction. Environ Chall 6: 100439. https://doi.org/10.1016/j.envc.2021.100439.
Zhang T, Guo E, Shi Y, Xue C, Zhu X, Dong X, Li T, Wang L, Jiang S, Xiang H, Wang L, Feng Y, Lai Y, Cao T, Li S, Ma S, Ma H, Zhou L, Wang X, Yang X. 2021. Modelling the advancement of chilling tolerance breeding in Northeast China. J Agron Crop Sci 207 (6): 984-994. https://doi.org/10.1111/jac.12547.
Zhu S, Huang R, Wai HP, Xiong H, Shen X, He H, Yan S. 2017. Mapping quantitative trait loci for heat-tolerance at the booting stage using chromosomal segment substitution lines in rice. Physiol Mol Biol Plants 23 (4): 817-825. https://doi.org/10.1007/s12298-017-0465-4.