| 王志君,李红宇,夏玉莹,范名宇,赵海成,鲁洁婷,赵朝勇,郑桂萍.寒地水稻叶片SPAD值与稻米蛋白质含量的关系[J].干旱地区农业研究,2023,(5):150~161 |
| 寒地水稻叶片SPAD值与稻米蛋白质含量的关系 |
| Relationship between blade SPAD value and protein content of rice in cold region |
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| DOI：10.7606/j.issn.1000-7601.2023.05.16 |
| 中文关键词: 寒地水稻 SPAD值 稻米蛋白质含量 食味值 |
| 英文关键词:cold region rice SPAD value rice protein content taste value |
| 基金项目:黑龙江省重点研发计划项目（GA21B002） |
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| 中文摘要: |
| 为揭示寒地水稻叶片SPAD值及其衍生值与稻米蛋白质含量的关系，于2020—2021年在水稻拔节期（T1）、孕穗期（T2）、齐穗期（T3）测定顶部3片叶的SPAD值，依据盆栽试验（试验1和试验2）的数据资料建立SPAD值衍生指标与稻米蛋白质含量之间的关系模型，利用大田试验（试验3）数据资料对建立的模型进行验证。结果表明，2020年氮肥试验中A8水平（氮素施用量362.07 kg·hm-2）稻米蛋白质含量较A1~A7水平（A1~A7氮素施用量分别为0、51.72、103.45、155.17、206.90、258.62、310.35 kg·hm-2）分别极显著增加34.55%、27.44%、26.39%、22.19%、18.07%、14.39%、12.23%，而A8水平食味值较A1~A7水平分别极显著降低8.10%、5.06%、4.99%、4.10%、3.45%、2.96%、2.28%，2021年蛋白质含量、食味值变化趋势与前者相同。两年品种试验6个品种稻米蛋白质含量比较中，C6品种（三江6号）蛋白质含量较C5（龙粳21）、C4（垦粳8号）、C3（龙稻203）、C2（松粳16）、C1（松粳22）分别极显著提高2.99%、12.23%、10.43%、5.04%、15.63%，C6食味值较C5、C4、C3、C2、C1分别极显著降低1.17%、12.09%、3.54%、2.89%、7.93%。品种差异及施氮量对不同生育时期水稻顶部3 片叶的SPAD值分布规律有较大影响，但水稻冠层叶片出现的两次“黑黄交替”现象不受品种的影响，其中单片叶的SPAD值受品种差异的影响，与蛋白质含量不存在相关性，借助指标SPAD(L1+L2+L3)/3、SPADL2×L3/mean可有效降低品种及环境差异对蛋白质含量预测结果的影响，指标SPAD(L1+L2+L3)/3与蛋白质含量在T1~T3期（拔节期、孕穗期、齐穗期）的拟合方程分别为：Y=0.24X-1.94 ，R2为0.75**、Y=0.25X-1.69，R2为0.74**、Y=0.27X-2.45 ，R2为0.72**；SPADL2×L3/mean拟合方程分别为：Y=0.22X-1.05 ，R2为0.75**、Y=0.27X-2.43， R2为0.72**、Y=0.26X-2.24，R2为0.72** （Y为蛋白质含量，X为SPAD衍生值），拟合方程均达到极显著水平。稻米蛋白质含量和食味值评分变现为线性负相关，回归方程为Y=-4.21X+113.32（Y为食味值，X为蛋白质含量），拟合优度R2=0.93**，达到极显著水平。综上，借助指标SPAD(L1+L2+L3)/3和SPADL2×L3/mean能够实现快捷、无损和实时预测稻米蛋白质含量，在一定程度上判定出稻米蒸煮食味品质的优劣，达到按质收获以及对品质实时监测的要求，促进优质寒地水稻的可持续发展。 |
| 英文摘要: |
| To reveal the relationship between the SPAD value and its derived value of rice leaves and the protein content of rice in the cold regions, this study measured the top three leaves at the jointing stage (T1), booting stage (T2) and heading stage (T3) of rice in 2020 and 2021. Based on the data of the pot experiments (Experiment 1 and Experiment 2), the relationship model between the SPAD\|derived index and the protein content of rice was established, and the data of the field experiment (Experiment 3) was used to verify the established model. The results showed that in the nitrogen fertilizer experiment in 2020, the protein content of rice at A8 level was significantly increased by 34.55%, 27.44%, 26.39%, 22.19%, 18.07%, 14.39%, and 12.23% compared with A1 to A7 levels, respectively. The taste value of A8 level was higher than that of A1~A7 was significantly reduced by 8.10%, 5.06%, 4.99%, 4.10%, 3.45%, 2.96%, and 2.28%, respectively. The changes in protein content and taste value in 2021 were the same as the former. Compared with C5, C4, C3, C2, and C1, the protein content of C6 was significantly increased by 2.99%, 12.23%, 10.43%, 5.04%, and 15.63%, respectively, and the taste value of C6 was lower than that of C5, C4, C3, C2, and C1 and were significantly reduced by 1.17%, 12.09%, 3.54%, 2.89%, and 7.93%, respectively. Variety differences and nitrogen application rates had a greater impact on the SPAD distribution of the top three leaves of rice at different growth stages, but the two “black and yellow alternation” phenomenon in rice canopy leaves were not affected by the variety, among which the single leaf SPAD value was affected by varietal differences and had no correlation with protein content. With the help of indexes SPAD(L1+L2+L3)/3, SPADL2×L3/mean, the effect of cultivar and environmental differences on the prediction of protein content was effectively reduced. Influence, the fitting equations of index SPAD(L1+L2+L3)/3 and protein content in T1~T3 period are: Y=0.24X-1.94,R2=0.95**, Y=0.25X-1.69,R2=0.94**,Y=0.27X-2.45,R2=0.92**; SPADL2×L3/mean fitting equations were: Y=0.22X-1.05，R2=0.95**,Y=0.27X-2.43，R2=0.92**，Y=0.26X-2.24，R2=0.92**, Y is the protein content, X is the SPAD derived value, and the fitting equations all reached the extremely significant level. The protein content of rice and the taste value score were linearly negatively correlated, the regression equation was Y=-4.21X+113.32 (Y is the taste value, X is the protein content), and the goodness of fit R2=0.93**, reaching a very significant level. To sum up, with the help of the indexes SPAD(L1+L2+L3)/3 and SPADL2×L3/mean, the purpose of fast, non\|destructive and real\|time detection of rice protein content was able to be achieved, and to a certain extent, the quality of cooking and eating quality of rice were determined, and the results could be achieved. According to the requirements of quality harvesting and real\|time monitoring of quality, the sustainable development of high\|quality cold rice was promoted. |
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