杂交水稻株型因素的相关性及其规律

根据我国超级杂交稻育种研究的技术思路 ,以两优培九 (培矮 6 4S/931 1 )、6 5396、931 1、E32、汕优 6 3、培矮 6 4S、399等 7个株型特性不同的品种 (组合 )为材料 ,测定了 4类 2 0项株型因素并作相关分析。认为株高和叶角是超高产育种和栽培的两项最主要的株型因素。通过对生态条件和两优培九株高变化关系的分析 ,有平均株高的 1 2 .6 %是由温度引起的 ,并建立了株高的生态预测模型。通过对抽穗后顶部三叶的 8种叶角度配置的单位受光量的理论模拟计算证明 ,理想的株型应以剑叶与倒二叶距离 ( d1 2 )为 1 5cm,倒二叶与倒三叶距离 ( d2 3)为 2 5cm,叶角配置应以 4°～ 6°(剑叶 ) ,9°～ 1 1°(倒二叶 ) ,1 4°～ 1 6°(倒三 )为宜。这些结果可以为超级杂交稻株型育种提供依据。     

两系杂交稻制种的气候适应性研究

研究两系杂交稻的首要不育系———培矮64S制种的气候适应性, 一方面可为两系杂交稻的生产提供决策参考, 另一方面也可为其它不育系的研究提供借鉴。分析气候适应性时, 首先利用播种—抽穗天数及其对应的气象资料, 建立发育期模型; 然后利用自交结实率资料及其对应的气象资料建立育性量化模型, 并求出育性转换的光温指标; 最后将我国稻区35个站点42年 (1951~1992年) 的气象资料代入发育期模型, 求出各站点的可能出穗持续期 (最早出穗期—最晚出穗期的历期), 对比育性转换的光温指标, 确定培矮64S在80%、90%和95%保证率下连续不育 (可育) 的初、终日, 将连续不育期作为生产杂交稻种子的季节, 将连续可育期作为繁殖培矮64S的季节。结果显示:培矮64S仅能在海南岛及云贵高原中低海拔地区自交繁殖; 而在东北、云贵高原以外的稻区, 培矮64S均可用于生产两系杂交稻的种子, 各稻区制种季节的长短、起止日期与种植地的纬度、海拔高度相关。     

水稻障碍型冷害损失评估及预测动态模型研究

在分析东北水稻生殖生长关键期低温生理反应的基础上,建立了计算日冷积温(时积温)及其与空壳率关系的模式和敏感期内每日水稻敏感群体数量比率分布标准化模式,进而建立了水稻在生殖生长关键期内因低温影响而导致空壳率及减产率评估、预测模式。该模式可较精确地计算出逐日低温导致的空壳率及减产率,可比成熟期提前25 d左右进行冷害评估和损失预测。     

水稻耗水规律及节水灌溉技术的试验研究

根据18年的水稻耗水量试验资料，寻找水稻耗水量与光、温、水等气象要素的一般规律。在此基础上，考虑未来天气趋势和土壤性质的作用，确定水稻的灌溉量。该灌溉技术既满足水稻生理需求，又能达到节约用水，减小成本，夺取稳产高产的目的。     

硅肥在杂交水稻上的肥效研究

水稻是喜硅作物，许多国家把硅称为水稻的第四大营养元素，通过在北亚热带地区河南省信阳地区潴育性水稻土上试验表明，在杂交水稻上施用硅肥，不仅可改善水稻的株高，有效穗数，穗粒数，实粒数，千粒重等生物学性状，而且施用硅肥还可增强杂交水稻抗旱，抗病虫害能力，从而提高稻谷产量19．8％－24．7％，同时，硅肥的施用能够使杂交水稻提早成熟，试验结果表明，在杂交水稻上，硅肥与氮磷肥配合施用比单独施用硅肥的增产效果和经济效益好，少量分次施用比一次性底施效果好。     

The Significance of Traditional Culture for Agricultural Biodiversity—Experiences from GIAHS

Agricultural biodiversity has a high importance in social-cultural, economic, and environmental aspects, and can help in adapting to and withstanding climate change. Conserving the GIAHS sites and the important components within them can help conserve the agricultural biodiversity and traditional agricultural culture of the whole country. This study considered Ifugao Rice Terraces, Dong’s Rice-Fish-Duck System, and Hani Rice Terraces System as three examples which show that traditional culture can be used to protect agricultural biodiversity, while as a carrier of traditional culture, agricultural biodiversity also conveys and protects the traditional culture of the nation. According to the analyses, through several years of efforts, the status of agricultural biodiversity and traditional culture in them has improved. Then, to further promote agricultural biodiversity conservation and traditional culture protection, several suggestions are made, such as establishing community seed banks; documenting and preserving traditional farming methods, techniques, and tools and developing participatory activities which encourage more farmers to participate in the protection work.     

波段位置和宽度对不同生育期水稻NDVI影响研究

通过对不同生育期水稻冠层光谱的分析,探讨了不同波段位置和波段宽度对不同生育期水稻NDVI的影响.以及在一定NDVI精度要求下红光波段最大宽度和波段中心位置的相互作用.结果表明,在所有生育期,近红外波段的位置和宽度对NDVI影响不大;红光波段的位置和宽度对NDVI有较大影响,特别是红光波段位置接近红谷极值(670nm附近)时影响尤为显著.相对而言水稻生育前期的NDVI更容易受到波段位置的影响,但是波段位置对不同生育期之间NDVI的差值影响不大.无论红光波段还是近红外波段,当取值处于红边区域(690-740nm)时,对NDVI有较大影响.当保证NDVI偏差在1%以内时,在水稻生长旺期,构成NDVI的红光波段最大宽度随着波段巾心位置向长波移动而逐渐变窄,当到达690nm附近达到最窄,而后略有变宽;对于生长前期和后期,由于在648nm附近变窄而有波动.     

基于证据理论的水稻遥感信息提取方法研究

水稻播种面积是农业管理部门关心的重要问题之一。本文介绍了证据理论在遥感图像分类上的应用,并以汉川市为示范区,采用2002年TM遥感影像,在GIS技术支持下,通过建模运算,剔除不可能是水稻的像元。然后将证据理论用于影像分类,将影像初次分类结果,与参考图对照,将不满足要求的区域提取出来,再次进行第二次分类。将第二次分类结果与参考图对照,显示分类效果满足要求;若不满足要求,可继续进行再次分类,直到分类效果满意为止。将两次分类结果中的水稻信息进行叠加,提取出水稻遥感信息,经检验精度达到91.39%。     

晚稻单产动态预测方法研究

晚稻单产与气象条件关系分析表明：气象要素是影响相邻两年晚稻单产变化的主要影响因素,尤其是气温和日照.根据业务服务的需要,提出利用晚稻主产省份的产量资料和代表站的旬平均气温、旬降水量和旬日照时数等气象资料,运用综合聚类原理,建立全国晚稻产量动态预报方法.此方法能够在晚稻播种一段时间后动态预测晚稻单产,具有简便、实用、准确率较高的特点,并且克服了常用回归方法在较短时间内筛选预测因子难的缺点,有一定的业务应用价值.     

Large-area rice yield forecasting using satellite imageries

Ability to make large-area yield prediction before harvest is important in many aspects of agricultural decision-making. In this study, canopy reflectance band ratios (NIR/RED, NIR/GRN) of paddy rice (Oryza sativa L.) at booting stage, from field measurements conducted from 1999 to 2005, were correlated with the corresponding yield data to derive regression-type yield prediction models for the first and second season crop, respectively. These yield models were then validated with ground truth measurements conducted in 2007 and 2008 at eight sites, of different soil properties, climatic conditions, and various treatments in cultivars planted and N application rates, using surface reflectance retrieved from atmospherically corrected SPOT imageries. These validation tests indicated that root mean square error of predicting grain yields per unit area by the proposed models were less than 0.7 T ha⁻¹ for both cropping seasons. Since village is the basic unit for national rice yield census statistics in Taiwan, the yield models were further used to forecast average regional yields for 14 selected villages and compared with officially reported data. Results indicate that the average yield per unit area at village scale can be forecasted with a root mean square error of 1.1 T ha⁻¹ provided no damaging weather occurred during the final month before actual harvest. The methodology can be applied to other optical sensors with similar spectral bands in the visible/near-infrared and to different geographical regions provided that the relation between yield and spectral index is established.