小麦叶面积指数的模拟模型研究

小麦叶面积指数计算为群体绿叶与比叶面积的乘积，而绿叶重为其分配指数的地上部干重的乘积。建立了绿叶分配指数与生理发育时间之间的曲线关系。结果表明，模型能较好地模拟叶面积指数的变化动态，其平均相对误差小于10％。     

小麦叶面积指数变化的模拟

分段模拟了潜在生产条件下小麦叶面积指数变化,并建立了模拟模型,模型可作为进一步模拟小麦群体生长的基础。     

不同产量水平小麦最适叶面积指数动态模拟模型研究

根据最适叶面积定义和小麦栽培优化原理建立了不同产量水平小麦最适叶面积指数动态模拟模型,经用南京与济南地区气象、品种及试验资料验证,两地点、两品种最适叶面积指数测定值与模拟值间相关系数、差值标准误及平均绝对误差分别为0．9761和0．9620、0．5891和0．7094以及0．12和0．43,相关性达极显著水平,证明所建模型可行。所建模型可以利用当地常年气象资料（30年平均值,如月平均气温、月平均最高和最低气温以及月日照时数、纬度）,确定不同地区、产量水平与品种最适叶面积指数动态,为小麦数字化栽培提供科学依据。     

基于无人机数码影像的水稻叶面积指数监测

[目的]为探究无人机数码影像监测水稻叶面积指数(Leaf area index,LAI)的可行性,明确利用无人机数码影像监测水稻LAI的最佳时期,构建基于无人机数码影像的水稻LAI监测模型.[方法]本研究基于不同品种和施氮量的水稻田间试验,于分蘖期、拔节期、孕穗期、抽穗期和灌浆期测定水稻LAI,同步使用无人机搭载数码相...     

水稻苗情动态计算机模拟 Ⅱ.水稻叶蘖生长的统计模式及应用

本文比较了两种叶龄模式估算的出叶速度和以活动积温或有效积温为自变量的分蘖模式估算的分蘖消长过程。逐日模拟了水稻叶蘖生长的动态以及对分蘖模式进行了灵敏性分析。     

河南新乡地区冬小麦叶面积指数的动态模型研究

为给小麦高产栽培提供理论依据,依据2004～2006年度田间试验数据,研究了新乡地区冬小麦叶面积指数变化规律,及其与有效积温的关系,并利用线性方程和Logistic曲线修正方程,分段建立了以气温估算冬小麦叶面积指数的半经验公式.结果表明,对叶面积指数和有效积温归一化后,冬小麦连续两个生长年度的相对叶面积指数动态变化与相对有效积温相关关系较好.通过对2003～2004年度冬小麦叶面积指数模拟值与实测值比较,平均绝对误差0.1572,相关系数0.98,相关达极显著水平(P＜0.01),这表明该方法可很好地反映叶面积的动态变化规律.     

塔城盆地甜菜叶龄模式栽培技术研究初报

本研究以甜菜叶龄为主线,结合其生育特点,把甜菜叶龄与其他器官之间的同伸关系归纳为一定的模式,从而数量化、定量化地确定高产、高糖(3.0t/亩、17.5％以上)主要生育指标和栽培技术措施效应。根据甜菜各生育阶段特点及环境因素,主动及时采取相应的促控技术措施,协调甜菜植株个体与群体生长的一致性,从而提高甜菜的产质量。     

寒地水稻叶龄管理模式技术组装浅析

二九一农场以叶龄进程为依据，通过实施旱育壮苗、苗床管理、本田叶龄指标管理、本田水层管理等技术规程，依靠平衡施肥促成高产长相，从而达到高产高效的目的。     

寒地水稻叶龄管理模式技术组装浅析

二九一农场以叶龄进程为依据,通过实施旱育壮苗、苗床管理、本田叶龄指标管理、本田水层管理等技术规程,依靠平衡施肥促成高产长相,从而达到高产高效的目的。     

冬小麦春生叶面积矫正系数及叶面积指数的研究

本试验随机抽取河北省目前生产应用的四个冬小麦品种，对其春生六片叶的叶面积矫正系数（ｒ）及群体叶面积指数（Ｒ）进行了研究，结果表明；（１）ｒ值与密度无关，（２）品种间ｒ值无差异，（３）春生六片叶间ｒ值差异显著，且随着叶位升高，ｒ值变小，根据ｒ值可将六片叶分为三组，其ｒ值分别为０．８３５，０．７７５和０．７２５，抽穗期全株ｒ值平均为０．７６，由此推测，春生六片叶ｒ值是由三组基因控制的（４）抽穗期单株茎     

新型植被指数及其在水稻叶面积指数估算上的应用

叶面积指数LAI不仅是陆表植被系统的一个重要属性,而且是全球水平衡、碳循环等模型中的重要输入参数。首先通过使用水稻小区试验冠层光谱数据模拟Landsat 5卫星蓝、绿、红光波段;其次分析了各个波段对LAI的敏感性;然后分析了由这个3个波段的所有组合分别代替常规NDVI中的红光波段构成的VNDVI对LAI变化的反应和对LAI的估算能力;最后使用不同条件下的水稻数据进行验证。结果表明,在不同的LAI范围,红绿蓝光3个波段对LAI有不同的敏感性。当LAI<3时,红蓝光波段敏感性较高。虽然这时绿光波段的敏感性不如红蓝光波段,然而绿光波段在更大的范围对LAI都有相当的敏感性。当采用红绿蓝光波段的各种组合构成植被指数时,如果要使这些植被指数不出现饱和现象,并使对LAI的敏感性有意义,其前提是要求这个波段或是波段组合的值要大于0.024,即VNDI(visible NDVI)公式中的VIS>0024,否则将可能产生饱和现象,而使LAI估算准确度降低。综合比较所有由红绿蓝光波段各种组合构成的植被指数对LAI的估算能力,认为GNDVI和GBNDVI与LAI有比较好的关系。使用其他条件下的水稻数据对各种NDVI的LAI估算能力进行了验证,仍然得到了同样的结论。可见,GNDVI和GBNDVI在估算LAI时确实比传统NDVI具有更好的效果。     

New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice

Leaf area index (LAI) is an important characteristic of land surface vegetation system, and is also a key parameter for the models of global water balancing and carbon circulation. By using the reflectance values of Landsat-5 blue, green and red channels simulated from rice reflectance spectrum, the sensitivities of the bands to LAI were analyzed, and the response and capability to estimate LAI of various NDVIs (normalized difference vegetation indices), which were established by substituting the red band of general NDVI with all possible combinations of red, green and blue bands, were assessed. Finally, the conclusion was tested by rice data at different conditions. The sensitivities of red, green and blue bands to LAI were different under various conditions. When LAI was less than 3, red and blue bands were more sensitive to LAI. Though green band in the circumstances was less sensitive to LAI than red and blue bands, it was sensitive to LAI in a wider range. When the vegetation indices were constituted by all kinds of combinations of red, green and blue bands, the premise for making the sensitivity of these vegetation indices to LAI be meaningful was that the value of one of the combinations was greater than 0.024, i.e. visible reflectance (VIS)>0.024. Otherwise, the vegetation indices would be saturated, resulting in lower estimation accuracy of LAI. Comparison on the capabilities of the vegetation indices derived from all kinds of combinations of red, green and blue bands to LAI estimation showed that GNDVI (Green NDVI) and GBNDVI (Green-Blue NDVI) had the best relations with LAI. The capabilities of GNDVI and GBNDVI to LAI estimation were tested under different circumstances, and the same result was acquired. It suggested that GNDVI and GBNDVI performed better to predict LAI than the conventional NDVI.     

水稻最适叶面积指数和总颖花数的品种差异

以淮北地区不同历史年代的水稻品种为试验材料,研究叶面积和颖花量与品种产量能力进展的关系。结果指出:随品种最适叶面积指数和适宜总颖花量的增加,水稻品种的产量能力明显提高。因此确认高产品种选育应以增加最适叶面积指数和颖花量并适当减少穗数,应用半矮秆基因为途径。本文根据试验资料提出了目前淮北主要水稻品种的适宜群体指标,为良种选育和高产栽培提供依据。     

卷叶水稻的光分布模拟及适宜叶面卷曲度分析

 以叶面高度卷曲的水稻组合两优E32、中等卷曲组合两优培九和不卷曲组合汕优63为材料,引入叶面卷曲度因子,用有效叶面积指数代替传统的叶面积指数（LAI）,模拟水稻冠层内的辐射传输,比较了不同叶面卷曲度因子材料的光合有效辐射截获率、转化率和利用率,探讨了不同材料的最适叶面卷曲度及最佳群体密度。结果表明,有效叶面积指数比传统的叶面积指数能更准确地预测冠层内光合有效辐射的分布。两优E32叶片过度卷曲,有效叶面积指数偏小,光合有效辐射利用率不高;而汕优63叶片平展且披散,下层叶片长期受光条件不良,光合能力弱,光合有效辐射利用率也不高。相比之下,两优培九的光合有效辐射截获率、转换率分布较为合理,光合有效辐射的利用率也较高,最适LAI为7.6,与常规栽培条件下的群体密度（LAI=7.9）接近。两优E32和汕优63的最适LAI分别为9.8和6.2,而常规栽培条件下的群体密度过小或过大,导致光合有效辐射利用率不高。利用孕穗期至齐穗期有效辐射利用率的实测值,通过输入不同的叶面卷曲度因子,得到两优E32、两优培九和汕优63的最佳叶面卷曲度因子分别为0.12、0.11和0.08,均非常接近两优培九的实际叶面卷曲度因子（0.11）。     

Simulation on Distribution of Photosynthetically Active Radiation in Canopy and Optimum Leaf Rolling Index in Rice with Rolling Leaves

By replacing leaf area index (LAI) with effective leaf area index (ELAI) through introduction of leaf rolling index (LRI), the distributions of photosynthetically active radiation (PAR) in the canopies of three hybrid rice combinations, Liangyou E32 with high LRI, Liangyoupeijiu with moderate LRI and Shanyou 63 with non-rolling leaves (normal), were simulated. The model based on ELAI could predict more accurately than that based on LAI. The PAR interception, conversion and utilization efficiency in the three combinations were studied to evaluate their optimal LRI and LAI. The PAR utilization efficiency of Liangyou E32 was lower due to excessive rolling leaves and less ELAI, and that of Shanyou 63 was also lower because of the faulty PAR interception and lower photosynthetic rate and saturation point at lower layer in canopy. Compared with the above two combinations, Liangyoupeijiu showed more appropriate distribution of PAR interception and conversion efficiency in canopy, and higher PAR utilization efficiency. The optimal LRI and LAI for Liangyoupeijiu were 0.11 and 7.6, respectively, which were close to the observed value, 0.11 and 7.9, respectively. However, the optimum LAI was 9.8 for Liangyou E32 and 6.2 for Shanyou 63, larger or smaller than those under the current plant density, which led to lower efficiency of PAR utilization. Besides, the optimum LRI for Liangyou E32 and Shanyou 63 were 0.12 and 0.08, respectively, which were close to the actual LRI for Liangyoupeijiu (0.11).     

水稻叶面积分布构成因子的数值模拟

叶面积分布是水稻株型研究的重要内容.通过机理分析建立了叶面积指数和叶面积分布模型,利用田间试验数据检验模型,通过数值模拟探讨节间距对叶面积分布影响的机理.模型检验效果良好,数值模拟结果可信,能为叶面积分布的优化提供理论基础.     

Analysis and Simulation of Plant Type on Canopy Structure and Radiation Transmission in Rice

Three typical hybrid rice cultivars,together with three artificially modified plant types by the application of N fertilizer during the elongation of the two uppermost leaves were used to analyze how the plant type affected the layered leaf area and radiation transmission.Plant type factors,layered leaf area and radiation distribution were measured at the full heading,10 d and 25 d after full heading stages,respectively.A model for calculating the layered leaf area from plant type factors was established and validated to determine the effects of plant type factors on the layered leaf area for the three hybrids.Furthermore,the relationship between layered leaf area and radiation transmission was established by using the radiation transmission model.The effects of the plant type factors on the radiation transmission for the three hybrids were evaluated by using this model.Finally,a method was established to describe the canopy structure,such as the layered leaf area index and the radiation distribution in the rice canopy.     

Simulation of Canopy Leaf Inclination Angle in Rice

A leaf inclination angle distribution model, which is applicable to simulate leaf inclination angle distribution in six heights of layered canopy at different growth stages, was established by component factors affecting plant type in rice. The accuracy of the simulation results was validated by measured values from a field experiment. The coefficient of determination （R2） and the root mean square error （RMSE） between the simulated and measured values were 0.9472 and 3.93%, respectively. The simulation results showed that the distribution of leaf inclination angles differed among the three plant types. The leaf inclination angles were larger in the compact variety Liangyoupeijiu with erect leaves than in the loose variety Shanyou 63 with droopy leaves and the intermediate variety Liangyou Y06. The leaf inclination angles were distributed in the lower range in Shanyou 63, which matched up with field measurements. The distribution of leaf inclination angles in the same variety changed throughout the seven growth stages. The leaf inclination angles enlarged gradually from transplanting to booting. During the post-booting period, the leaf inclination angle increased in Shanyou 63 and Liangyou Y06, but changed little in Liangyoupeijiu. At every growth stage of each variety, canopy leaf inclination angle distribution on the six heights of canopy layers was variable. As canopy height increased, the layered leaf area index （LAI） decreased in all the three plant types. However, while the leaf inclination angles showed little change in Liangyoupeijiu, they became larger in Shanyou 63 but smaller in Liangyou Y06. The simulation results used in the constructed model were very similar to the actual measurement values. The model provides a method for estimating canopy leaf inclination angle distribution in rice production.