长期定位施肥对冬小麦后期根系衰老和产量的影响

利用连续28年长期定位施肥试验,研究了长期施肥对冬小麦生育后期根系衰老及产量的影响,结果表明,施用有机肥特别是有机肥与一定量的氮肥配合施用能显著抑制根系的膜脂过氧化作用,使小麦根系中SOD活性提高、MDA含量降低、从而延缓了根系的衰老,使小麦生育后期仍能维持较高的根系生理活性,提高了粒重,增加籽粒产量。     

黄淮海地区粮棉作物生产力的研究

黄淮海地区光、热、水资源较丰富,气候-土壤资源的生产潜力较大。本文在对作物各层次生产力计算方法研究的基础上,对该地区冬小麦、夏玉米、夏大豆、棉花等作物各层次生产力进行了计算。初步认为,在一定的农业技术水平条件下,近期(2000年以后)粮棉生产力比1989年可增加1/3,远期(30～50年以后)可增加1倍或稍多。     

冬小麦抗逆栽培技术原理的研究 Ⅲ.中后期重施氮肥对小麦灌浆过程的影响

在小麦生育前期施用氮肥不过量和苗株稳健生长的情况下,通过孕穗期重施氮肥,观察研究结实灌浆过程中植株各部位干物质积累的变化,得知中后期施用氮肥可增强叶片素质,提高后期光合效率、延长灌浆时间、千粒重明显增加.同时,茎、叶鞘也相应得到充实,使小麦后期的抗倒、抗“干热风”能力增强,并且从籽粒灌浆速度变化和千粒重增重过程看出,中后期追施氮肥即使过量,也没有出现贪青现象.     

重穗型水稻植株抗倒伏能力的研究

以18个不同穗重型水稻品种为材料，研究了重穗型水稻植株的抗倒伏能力及其与茎秆的物理性状、机械组织特性的关系。结果表明：重穗型品种单穗重大、产量高与其株高的适当增加密切相关。重穗型品种由于单穗重和株高的增加，弯曲力矩加大，但抗折力也明显提高，故而其茎秆抗倒伏能力并未降低。重穗型品种茎秆抗折力强的主要原因     

两个直链淀粉含量不同的小麦品种籽粒淀粉合成酶活性与淀粉积累特征的比较

以均含有3个Waxy蛋白亚基的普通小麦品种济麦20（低直链淀粉含量）和鲁麦21（高直链淀粉含量）为材料，对灌浆期籽粒淀粉合成相关酶活性的变化及淀粉积累特征进行了研究，并分析了两者之间的关系。结果表明，蔗糖合成酶(SS)、腺苷二磷酸葡萄糖焦磷酸化酶(AGPP)、束缚态淀粉合成酶(GBSS)、可溶性淀粉合成酶(SSS)和淀粉分支酶(SBE)活性均呈单峰曲线变化。鲁麦21的上述酶活性均高于济麦20。相关分析表明，支链淀粉积累速率与SS、AGPP、SSS和SBE呈显著或极显著正相关；直链淀粉积累速率与SS、AGPP和GBSS呈极显著正相关。Logistic方程拟合淀粉积累过程发现，支、直链淀粉最终积累量的高低取决于积累启动时间的早晚和积累速率的高低，而积累持续期的调节作用较小。直链淀粉的积累速率除受GBSS活性影响外，还受SS和AGPP活性的影响，其中，GBSS活性的变化与2品种籽粒直链淀粉积累量的变化情况基本吻合。籽粒灌浆后期的GBSS活性对直链淀粉最终积累量的调节作用大于灌浆前期，说明对同时具有3个Waxy蛋白亚基的不同品种，Waxy蛋白亚基表达量(GBSS活性)的差异可能是导致品种间籽粒直链淀粉含量较大差异的一个关键原因。     

A comparison of marker-assisted and phenotypic selection for high grain protein content in spring wheat

Wheat grain protein content (GPC) is a primary end-use quality determinant for hard spring wheat (Triticum aestivum L.), and marker-assisted selection (MAS) could help plant breeders to develop high GPC cultivars. Two experiments were conducted using two populations developed by crossing low GPC cultivars (Ember) and (McVey) with (Glupro), which contains a high GPC QTL from Triticum dicoccoides (DIC). In one experiment, MAS and phenotypic selection (PS) were employed to select high GPC genotypes, and the selected genotypes were grown in six North Dakota (ND), USA environments. In a second experiment, molecular markers were used to select BC₂F₂ plants from each marker class for the DIC allele from each population. These plants were twice self-pollinated to produce BC₂F₄ plants, which were grown in single ND and Minnesota (MN) environments. Mean GPC was highest among lines using PS at two environments and not significantly different between MAS and PS in the other four environments. Lines presumably homozygous for DIC alleles had significantly higher GPC than their respective low GPC parents. The phenotypic GPC variation explained by the markers (r ²) was 30% at the ND and 15% at the MN environment. The use of PS was as effective as MAS in selecting for high GPC genotypes and more effective in some environments. This likely can be attributed to PS enabling selection for both the major QTL and other genes contributing to GPC. The use of molecular markers might be more advantageous for transferring the high GPC DIC QTL in a backcrossing program during parent development.     

干湿交替灌溉条件下不同种稗草对水稻光合特性和产量的影响

以两优培九和南粳9108为试验材料,自水稻移栽至成熟分别与无芒稗(T1)、稗(T2)、西来稗(T3)和光头稗(T4)共生,以无稗草水稻处理(CK)为对照,研究干湿交替灌溉条件下不同稗草对水稻光合特性和产量的影响。结果表明,稗草对水稻产量的干扰因稗草种和水稻品种的不同而异。稗草种间干扰强度表现为T3T1T2T4,两优培九减产率小于南粳9108。T1、T2、T3和T4处理后两优培九的减产幅度分别为11.16%~13.78%、10.19%~10.60%、19.00%~23.79%和0.50%~1.57%,除T4外其他处理较对照显著降低;南粳9108的减产幅度分别为38.44%~45.51%、31.29%~36.86%、54.88%~60.65%和8.28%~15.14%,均达显著差异。T1、T2和T3处理后对两优培九叶面积指数和叶绿体色素含量无显著影响,但使南粳9108的叶面积指数降低和叶绿体色素含量增加。4种处理还显著降低了水稻冠层的透光率、剑叶光合速率、蒸腾速率和气孔导度以及干物质积累量。冠层透光率、光合速率、气孔导度、蒸腾速率和成熟期干物质积累量降低以及灌浆期叶绿体色素含量不同程度增加可能是水稻产量降低的重要原因。     

Phenotypic and genetic relationships between yield components in maize

Summary Physiological components of kernel development — LAG period, effective filling period duration (EFPD) and grain filling rate (GFR) — ear moisture release (U), ear size (row number and kernels per row), days from emergence to silking and number of leaves, were examinated on 45 F₁ hybrids (10×10 diallel cross) in order to study their genetic relationships with yield. Combining ability analysis revealed that all trait variability derived mainly from g.c.a. effects. LAG period and EFPD were the traits most affected by genotype-environment interaction.Covariation analysis (path method) based on mean phenotypic values and on g.c.a. effects yielded similar information. It is shown that GFR and EFPD are both related to plant yield, but GFR made the most important contribution. On the contrary, a significant relationship between yield and LAG was not detected. Ear size components were also positively related to yield and had negative effects on GFR. These results indicate that, for our material, the dry matter accumulation rate is the main limiting factor of yield.Considering s.c.a. effects, kernel number per row made the most important contribution.     

Comparison of QTL for Grain Number per Panicle in Three Populations Sharing 3 Parents and Fine Mapping of Gnlc

Complex traits, such as yield components, are inherited in a quantitative manner and typically controlled by quantitative trait loci （QTL）. Grain number per panicle （GN） is an important component of yield in rice and has been studied for QTL mapping in our lab （Yu et al., 1997; Xing et al., 2002）. Further discovery of QTL for GN and fine mapping will provide rich of gene resources for high yield breeding by marker assistant selection. Gene cloning is helpful to understand the biological mechanism underlying GN and instruct the application of gene engineering in rice yield breeding. In recent years, near-isogenic lines （NILs） for grain number have been reported for gene fine mapping （Tian et al., 2006; Zhang et al., 2006） and gene cloning （Ashikari et al., 2005）. However, so far, this kind of research is insufficiency for systematically elucidating the genetic bases and regulatory mechanism involved in GN. In this study, we compare the locations and genetic effects of QTL for GN detected in three sets of recombinant inbred line populations （RILs） sharing three parents, and fine map a new major QTL, Gnlc, commonly detected in the 3 populations.     

Is it possible to forecast the grain quality and yield of different varieties of winter wheat from Minolta SPAD meter measurements?

To optimize wheat segregation for the various markets, it is necessary to add to genotype segregation, a prediction before harvest of the values of yield and grain protein concentration (GPC) for the different fields of the collecting area. Different tools allowing a prediction of crop production exist. Among them, the evaluation of nitrogen concentration by a chlorophyll meter (Soil–Plant Analysis Development (SPAD) readings), classically used to adapt the nitrogen fertilizer application, has been used in few works to foresee grain yield and grain protein concentration. But the relationships between N crop status and SPAD measurements varies among varieties and this genotypic effect has rarely been incorporated in models of forecasting grain quality.

This paper compares several models to forecast yield, nitrogen uptake in grain (NUG) and grain protein concentration from trials carried out in 2001 and 2002 at the INRA experiment station of Grignon (West of Paris). Trials crossed nine varieties by four (2002) or five (2001) nitrogen rates. Input variables of those models are mainly chlorophyll meter measurements (SPAD) on the penultimate leaf at GS65 and on the flag leaf at GS71 Zadoks growth stages and ear number per square meter (NE).

A square root model of yield based on NE × SPAD gave the best fit (RMSE = 0.6 t ha⁻¹ for both stages) if considering three different groups of genotypes. Based on the same variable, NE × SPAD, a quadratic model for NUG without significant effect of genotypes gave the best fit (RMSE, between 21 and 30 kg ha⁻¹ depending of the growth stage). And, for GPC, considering the same three groups of genotypes, the slope of the linear model with the ratio of predicted grain nitrogen concentration to predicted yield, is the same at both stages and very close to the standard value used to calculate protein concentration from nitrogen concentration (5.7), but the predictive quality of the model is more than 10% higher at GS71 (R² of 0.77) than at flowering (R² of 0.64). Finally, the sensibility of the models to delay in the stage of measurement is discussed.