In the heart of China, where rice paddies stretch as far as the eye can see, a groundbreaking study is set to revolutionize the way farmers manage water resources. Led by YI Bin, a researcher affiliated with an undisclosed institution, the study employs a sophisticated model to evaluate the impact of drought stress on rice growth, offering a beacon of hope for sustainable agriculture in drought-prone regions.
The research, published in *Renmin Zhujiang* (translated as “People’s Pearl River”), focuses on optimizing irrigation regimes for rice under drought stress. YI Bin and his team utilized a projection pursuit clustering (PPC) model based on a real-coded accelerated genetic algorithm (RAGA) to analyze experimentally measured data. This model is particularly adept at handling non-normal and non-linear high-dimensional data, providing a more accurate reflection of the contribution of each growth index.
“Our model allows us to objectively assess the impact of drought stress on rice growth,” YI Bin explained. “By understanding which indicators are most affected, we can develop targeted strategies to mitigate the negative effects of drought.”
The study selected four key growth indicators: plant height, leaf area index (LAI), tiller number, and water consumption. These indicators were normalized and processed through the RAGA-PPC model to determine their respective weights via an optimal projection direction. The model was optimized using a population size of 1,000, a crossover probability of 0.7, a mutation probability of 0.1, and 100 acceleration cycles, yielding a maximum projection index value of 140.9029 and an optimal projection direction vector of *a** = (0.7876, 0.4589, 0.3979, 0.1040).
The results revealed that drought stress applied during the tillering stage most significantly inhibited plant height and led to abnormal dynamics in tiller number. Drought during the jointing stage reduced LAI, while stress during the heading and milk-ripening stages had relatively minor effects on growth indicators. Among all indicators, plant height contributed the most to the comprehensive evaluation (weight = 0.7876), followed by tiller number (0.4589), LAI (0.3979), and water consumption (0.1040).
Based on the projection values and growth responses, the optimal irrigation strategy under pot conditions for the rice variety Huanghuazhan was identified as maintaining 60%–70% saturated water content during the tillering stage, ≥70% during the jointing stage, and 50%–60% during the milk-ripening stage. This strategy not only mitigated the negative effects of drought stress but also resulted in a significant difference in yield compared to CK (P < 0.05).The implications of this research are far-reaching. "This study confirms the effectiveness of the RAGA-PPC model in handling multidimensional agricultural data and providing actionable insights for water-saving irrigation practices," YI Bin noted. "The results offer a scientific basis for optimizing water management in rice cultivation, particularly in drought-prone regions, thereby supporting sustainable agricultural production and enhanced water use efficiency."The comprehensive evaluation framework established in this study can be extended to drought response research in other crops or ecological regions, offering a generalized methodology for precise water management in smart agriculture. As the world grapples with the challenges of climate change and water scarcity, this research provides a crucial tool for ensuring food security and sustainable development.In the words of YI Bin, "This is just the beginning. The potential applications of our model are vast, and we are excited to see how it will shape the future of agriculture." With the publication of this study in *Renmin Zhujiang*, the stage is set for a new era of water management in rice cultivation, one that promises to be both innovative and sustainable.