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中国农学通报 ›› 2025, Vol. 41 ›› Issue (12): 9-18.doi: 10.11924/j.issn.1000-6850.casb2024-0432

• 农学·农业基础科学 • 上一篇    下一篇

文山州12个常规籼稻品种的适应性评价与筛选

李建1(), 王定开1, 熊建云1, 王天明1, 魏康碧1, 王文鑫1, 魏冬梅2, 李云1()   

  1. 1 文山壮族苗族自治州农业科学院,云南文山 663099
    2 云南省生态环境厅驻文山州生态环境监测站,云南文山 663099
  • 收稿日期:2024-06-28 修回日期:2024-10-15 出版日期:2025-04-25 发布日期:2025-04-24
  • 通讯作者:
    李云,女,1970年出生,云南文山人,研究员,硕士,研究方向:水稻遗传育种研究及示范工作。通信地址:663099 云南省文山市泰康西路2号 文山州农业科学院,Tel:13887639990,E-mail:
  • 作者简介:

    李建,男,1994年出生,云南昆明人,农艺师,硕士,研究方向:香软八宝米新品种选育与示范推广研究。通信地址:663099 云南省文山市泰康西路2号 文山州农业科学院,Tel:15758533463,E-mail:

  • 基金资助:
    云南种子种业联合实验室专项“农业种业成果集成创新与转化示范”(202305AR340003); 文山州科技计划“文山州戴陆园专家工作站”(2021-4); 云南省科技人才与平台计划“云南省熊立仲专家工作站”(202205AF150040); 云南省重大科技专项计划“水稻杂种优势利用与新品种培育”(202402AE090010)

Adaptation Evaluation and Screening of 12 Conventional Indica Rice Varieties in Wenshan Prefecture

LI Jian1(), WANG Dingkai1, XIONG Jianyun1, WANG Tianming1, WEI Kangbi1, WANG Wenxin1, WEI Dongmei2, LI Yun1()   

  1. 1 Wenshan Academy of Agricultural Sciences, Wenshan, Yunnan 663099
    2 Wenshan Ecological and Environmental Monitoring Station of Department of Ecology and Environment of Yunnan, Wenshan, Yunnan 663099
  • Received:2024-06-28 Revised:2024-10-15 Published:2025-04-25 Online:2025-04-24

摘要: 为筛选适宜在文山州地区种植的优质常规籼稻品种,提高水稻种植效益,以云南省9家育种单位选育的12个常规籼稻品种为试验材料,对其生育期、主要农艺性状、抗病性、品质及产量等18个指标进行测定,并利用相关性分析和主成分分析方法进行综合评价。结果表明,参试的12个水稻品种均能在当地正常成熟。主要农艺性状表现较好的品种有‘文稻33号’、‘南晶香占’和‘三斗16号’。12个品种平均产量为8619.52 kg/hm2,其中产量最高的为‘红稻12号’(9653.5 kg/hm2)、‘中科晶毫’(9501.4 kg/hm2)和‘彩禾17号’(9312.8 kg/hm2),比CK分别增产13.37%、11.58%和9.37%。米质测定中表现最好的品种是‘文稻33号’,其米质达国优1级标准。相关性分析表明,穗实粒数与整精米率呈极显著正相关;株高、穗长与直粒淀粉含量呈显著负相关;千粒重与垩白率、胶稠度呈显著正相关,与直粒淀粉含量呈显著负相关。主成分分析将14个主要指标简化为5个主成分,累计贡献率达86.956%,基本反映了12个品种原始数据所携带的绝大部分信息。综合分析,‘红稻12号’、‘三斗16号’、‘金籼168’和‘文稻33号’的综合表现较为突出,‘红稻12号’可作为高产稳产品种、‘三斗16号’和‘金籼168’可作为优质品种、‘文稻33号’可作为优质高产品种在文山州种植。研究结果为文山州优质常规籼稻的品种选育及推广提供了参考依据。

关键词: 籼稻, 农艺性状, 产量, 抗病性, 米质, 相关性分析, 主成分分析, 综合评价

Abstract:

This experiment aimed to select high-quality conventional indica rice varieties which are suitable for planting in Wenshan Prefecture, and to improve rice planting efficiency. Using 12 conventional indica rice varieties selected from 9 breeding units in Yunnan Province as experimental materials, 18 indicators including growth period, main agronomic traits, disease resistance, quality and yield were measured, and the correlation analysis and principal component analysis methods were used for comprehensive evaluation. The results showed that all 12 rice varieties tested could mature normally in the local area. The varieties with better main agronomic traits included 'Wendao 33', 'Nanjing Xiangzhan', and 'Sandao 16'. The average yield of 12 varieties was 8619.52 kg/hm2, among which 'Hongdao 12' (9653.5 kg/hm2), 'Zhongke Jinghao' (9501.4 kg/hm2) and 'Caihe 17' (9312.8 kg/hm2) had the highest yield, with the increase of 13.37%, 11.58% and 9.37% respectively compared to CK. The variety with best rice quality was 'Wendao 33', and its rice quality met the national first-class high-quality rice standard. The correlation analysis showed that filled spikelets per panicle was significantly positively correlated with head rice rate; plant height and spike length were significantly negatively correlated with amylose content; thousand kernels weight was significantly positively correlated with the chalky grain rate and gel consistency, and was significantly negatively correlated with amylose content. 14 main indexes were simplified into 5 principal components by principal component analysis, and the cumulative contribution rate reached 86.956%, which basically reflected most of information carried by the original data of 12 varieties. Through comprehensive analysis and evaluation, the comprehensive performance of 'Hongdao 12', 'Sandao 16', 'Jinxian 168' and 'Wendao 33' was relatively outstanding. These research results can provide reference for the breeding and promotion of high-quality conventional indica rice varieties in Wenshan Prefecture.

Key words: indica rice, agronomic traits, yield, disease resistance, rice quality, correlation analysis, principal component analysis, comprehensive evaluation