Submitted:
28 May 2025
Posted:
28 May 2025
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Abstract
Keywords:
1. Introduction
- How do temperature extremes, altered precipitation patterns, and elevated atmospheric CO₂ specifically affect grain quality parameters in temperate japonica varieties compared to tropical indica varieties?
- What differences exist in climate vulnerability across quality classes (medium grain, short grain, aromatic varieties) within temperate production systems?
- Which adaptation strategies demonstrate evidence-based effectiveness for maintaining grain quality under projected climate scenarios in temperate regions?
2. Overview of the Australian Rice Industry
2.1. Historical Development and Adaptive Evolution
- Institutional knowledge systems that facilitate information transfer and coordinated adaptation
- Genetic adaptation capacity through established breeding programmes targeting environmental constraints
- Technological innovation systems that systematically address resource limitations
2.2. Contemporary Production Systems and Market Position
2.3. Climate Vulnerability and Adaptation Capacity
- Water dependency in a region experiencing increasing precipitation variability and competing water demands
- Temperature sensitivity during critical reproductive and grain filling stages
- Geographical concentration creating systemic vulnerability to localised climate impacts
- Quality differentiation strategy requiring maintenance of specific parameters under changing conditions
- Established breeding programmes focused on environmental stress tolerance
- Advanced water management systems with demonstrated efficiency improvements
- Technological innovation capacity supporting precision agriculture approaches
- Institutional knowledge systems facilitating information transfer and coordinated response with strengthened value chain
3. Global Temperate Rice-Growing Regions and Rice Grain Quality Classes
3.1. Quality Class Differentiation and Climate Vulnerability
4. Impact of Climate Change and Fluctuating Environmental Conditions on Grain Quality
4.1. Cold Temperature Stress
4.2. High Temperature Stress
4.3. Altered Precipitation Patterns and Water Management Implications
4.4. Elevated Atmospheric CO₂ Concentrations
4.5. Increased Salinity
4.6. Extreme Weather Events
5. Differential Climate Vulnerability Across Rice Quality Classes
5.1. Mechanistic Basis of Differential Climate Responses
5.2. Geographical Variation in Quality Response
5.3. Implications for Adaptation Prioritisation
6. Adaptation Strategies
6.1. Genetic Adaptation Approaches
- Genomic selection tools enhance breeding efficiency for complex quality traits under stress conditions. Marker-assisted selection targeting specific quality-associated loci enables more rapid integration of beneficial alleles into elite backgrounds with higher precision than phenotypic selection alone [96,97].
- Multi-environment and multi-season testing networks evaluate genotype × environment interactions affecting quality stability. These networks systematically assess quality maintenance across temperature and moisture gradients to identify varieties demonstrating quality robustness under variable conditions [98].
- Integrated resistance breeding addresses climate-induced shifts in pest and disease pressure that indirectly affect grain quality. Resistance to pathogens like panicle blast becomes increasingly important as climate change alters disease incidence patterns during grain development stages [99].
6.2. Water Management Innovations
- Alternate Wetting and Drying (AWD) irrigation cycles flooding and drying phases to reduce water consumption while promoting root development. AWD increases starch thermal stability and alters pasting profiles in some varieties while minimally affecting other quality attributes [100]. The practice reduces arsenic accumulation but may increase cadmium levels, creating quality-safety trade-offs and requiring context-specific evaluation [101]. Though AWD typically reduces yield compared to continuous flooding, it generally maintains milling quality—a critical economic parameter [102].
- Delayed Permanent Water (DPW) with post-flower flush supplementation maintains milling quality when combined with appropriate nitrogen management (>60 kg/ha). This approach alters grain protein composition, affecting head rice yield and flour pasting properties while conserving water [45]. The quality impact varies by variety, with medium-grain types generally showing better quality maintenance than long-grain varieties under DPW management.
6.3. Technological Adaptation Systems
- Climate-responsive decision support systems integrate meteorological data with crop models to optimise management decisions affecting quality development. These systems enable adaptive scheduling of irrigation, fertilisation, and harvest operations based on real-time climate conditions and forecasts [106,107]. GPS-guided technologies further enhance implementation precision, allowing rapid adjustment to climate-induced field heterogeneity [108].
- Multi-platform sensing networks combine ground, aerial, and satellite monitoring to detect early indicators of climate stress affecting grain yield and quality. Hyperspectral imaging technologies can identify temperature and moisture stress before visible symptoms appear, enabling pre-emptive management adjustments to preserve quality [109]. Soil sensor networks monitoring moisture, temperature, and nutrient status provide complementary data on root-zone conditions influencing grain development [110].
- Variable-rate application (VRA) systems optimise resource distribution based on field-specific conditions, mitigating climate-induced spatial variability effects on quality development by responding to microclimate variations within the field and allocating water and nutrients to normalise growing conditions. While widely proven in sprinkler- or drip-irrigated systems, their use in conventional flood-irrigated rice remains largely at the research and pilot stage [111].
- Automated irrigation infrastructure adjusts water distribution based on real-time evapotranspiration data and climate forecasts. These systems prevent both water stress and excess moisture conditions that compromise quality, maintaining optimal hydration despite reduced rainfall predictability or increased evaporation rates [112].
6.4. Advanced Quality Assessment Methodologies
- Hyperspectral phenotyping platforms rapidly map chemical composition within individual grains using visible and near-infrared wavelengths. These technologies detect internal quality characteristics including protein distribution, chalkiness development, and structural integrity with minimal sample preparation [113,114]. This capability enables identification of varieties maintaining quality under stress and detection of climate-induced quality deterioration before visible symptoms appear.
- Near-infrared spectroscopy (NIRS) systems provide rapid, non-destructive quality evaluation across multiple parameters simultaneously. NIRS applications have expanded from basic protein assessment to prediction of complex quality traits including chalkiness, head rice yield, grain dimensions, amylose content, and viscosity profiles [115]. These systems enable high-throughput screening of breeding material for quality stability under stress conditions.
- Machine learning algorithms integrate multi-parameter data to predict quality outcomes with increasing accuracy. Random Tree modelling approaches have demonstrated superior effectiveness for predicting quality parameters from spectral data [116,117], while artificial neural networks reliably predict both biochemical and functional quality attributes simultaneously [118]. These computational approaches enable more sophisticated understanding of climate-quality interactions and identification of resilient phenotypes.
- Genomic and metabolomic profiling tools identify molecular signatures associated with quality maintenance under stress. DNA barcoding approaches characterise genetic resources for quality stability [119], while metabolomic analysis reveals biochemical pathways maintaining quality despite environmental fluctuations [120]. These molecular techniques accelerate development of climate-resilient varieties with stable quality profiles.
7. Policy Implications for Climate Adaptation
7.1. Regulatory Frameworks Supporting Adaptation
7.2. Economic Incentives for Quality-Maintaining Practices
7.3. Research Funding Priorities
7.4. International Cooperation Mechanisms
8. Consumer Perspective on Climate-Induced Quality Changes
8.1. Potential Shifts in Consumer Acceptability Thresholds
8.2. Implications for Market Segmentation
8.3. Communication Strategies Regarding Quality Variations
| Quality Class | Region/Countries | Benchmark Varieties | Quality Parameters | Climate Vulnerability | Cooking/Eating Quality Response | Reference |
| Medium-Grain Japonica | Temperate East Asia (Japan, China, Korea), Australia, USA (California) | Koshihikari (Japan), Reiziq (Australia), Calrose (USA) | Soft texture, low-moderate amylose (16-18%), glossy appearance, good milling quality | Heat stress: decreased amylose content, increased chalkiness, altered crystallinity and gelatinisation temperature, increased protein content Elevated CO₂: increased yield, decreased protein and quality |
Heat stress produces stickier, softer rice; increased protein potentially reduces stickiness | [4,70,88,130,131] |
| Short-Grain (Sushi Rice) | Japan, Korea, Australia | Koshihikari (Japan), Opus (Australia) | Very low amylose (15-16%), high stickiness, glossy appearance | Heat stress: increased chalkiness, grain cracking, protein content; reduced grain size, amylose and starch content. Elevated CO₂: increased yield, decreased protein, increased chalkiness |
Texture becomes inconsistent; eating quality in Koshihikari improves under moderate stress but deteriorates under severe stress | [33,132,133,134] |
| Aromatic Rice (Jasmine type) | Thailand, Australia, USA | KDML105 (Thailand), Topaz (Australia) | Medium amylose (17-19%), distinctive aroma (2AP), soft texture | Heat stress: reduced 2-acetyl-1-pyrroline production, increased chalkiness. Drought/salinity: increased 2AP concentration but reduced yield |
Heat stress causes loss of characteristic fragrance; moderate salinity stress can enhance aroma while reducing other quality parameters | [36,90,91,135] |
| Arborio (Risotto) | Italy, Australia | Arborio, Carnaroli (Italy), Vialone (Australia) | High amylopectin, medium-high amylose (19-21%), chalky centre, maintains firmness when cooked | Less tolerant to combined stressors, particularly vulnerable to heat during grain filling | Deterioration in distinctive creamy consistency and texture essential for risotto preparation | [28,29,136] |
| Non-Fragrant Long Grain | USA, Australia, Temperate Eastern Europe | Wells (USA), Doongara (Australia), Rapan (Russia) | High amylose (22-25%), separate grains when cooked, firm texture | Heat/water stress: increased chalkiness, reduced grain dimensions. Salinity: decreased amylose content |
Dry, separate grain characteristics may be compromised; increased stickiness under salinity stress | [30,92,137] |
| Basmati | Northern India, Pakistan, Australia | Basmati (India/Pakistan), Basmati Signature (Australia) | Very high amylose (>25%), distinctive aroma, exceptional elongation during cooking | Temperature fluctuations affect elongation and aroma; high temperature shortens grain filling, reduces starch and amylose content | Reduced aroma and diminished elongation; compromised fluffiness and grain separation valued in premium markets | [39,138,139] |
9. Conclusion
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
| NSW-DPI | New South Waled-Department of Primary Industries |
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