作物专业暑期《水稻制种实训》课效果分析——基于成都农业科技职业学院教学改革实际

根据作物专业的特点,以提高学生综合素质、强化实践动手能力为目标,增设暑期《水稻制种实训》课;对2009、2010级2届作物专业《水稻制种实训》实施效果进行调查分析。结果显示：《水稻制种实训》对培养学生热爱劳动、吃苦耐劳的品质,养成“亲农、事农、兴农”的职业精神和团队协作精神;巩固和加深对专业知识的学习;提高实践动手能力等取得了明显的效果;在加强兼职教师教学能力,引导学生树立正确的劳动观念,改革保障条件等方面还有待加强和完善。     

广西作物抗病虫性研究及发展方向的商榷

广西作物抗病虫研究，在水稻、玉米、花生等作物上已取得了重大进展，其中包括品种资源的抗性鉴定、病原菌生理小种或菌系分化和害虫生物型变异等。作物育种要求高产、优质、多抗，因此，作物抗病虫性研究应进一步开展抗、感基因分析，探索遗传机理以及利用植物基因工程技术为主的现代生物技术开展抗性基因分离、转导等研究，将成为作物抗病虫性研究的发展方向。     

气候因素对哲里木盟主要作物产量的影响

用正交多项式方法从哲里木盟1981－1995年五种主要作物的单产中分离出气象产量，结合同期气候资料进行相关分析，在此基础上，用多元回归的方法，定量分析气候因素对作物产量的影响。     

Sampling designs for landsat crop surveys

The problem of designing large scale crop surveys which utilize Landsat data is addressed in this paper. Emphasis is on stratification and sampling approaches designed to achieve preselected precisions while permitting manageable data processing and survey costs. A dynamic area sampling frame which is versatile and highly suited for sampling of Landsat data is discussed along with the use of rotation sampling for incorporating multiple years of information into the area estimation     

史前时期华北平原粮食种植结构之变迁

华北平原是中国原始农业的起源地之一,在距今10000年左右已经进入农业社会。在史前几千年的历史中,华北平原粮食作物种植结构发生了很大变化,由新石器时代早期的以粟、黍为主,兼有稻,发展到中期的以粟为主,多品种出现;再到晚期的粟稻并重、五谷形成。总的趋势是从简单走向复杂,从单一走向多元。     

Using satellite remotely sensed data to estimate crop proportions

A NASA, USDA, and NOAA sponsored experiment called LACIE (Large Area Crop Inventory Experiment) was performed to explore the use of remotely sensed measurements from a satellite-borne sensor to estimate small grains crop acreage. This paper examines the methods used in that experiment to estimate crop proportion in individual areal segments. We discuss the estimators used in LACIE and then consider some possible modifications and extensions which can have smaller bias or variance. Some numerical examples are offered to bring out some interesting features of the estimators and in some cases to make comparisons among estimators     

Exploring spatial dependence in area-level random effect model for disaggregate-level crop yield estimation

This paper describes an application of small area estimation (SAE) techniques under area-level spatial random effect models when only area (or district or aggregated) level data are available. In particular, the SAE approach is applied to produce district-level model-based estimates of crop yield for paddy in the state of Uttar Pradesh in India using the data on crop-cutting experiments supervised under the Improvement of Crop Statistics scheme and the secondary data from the Population Census. The diagnostic measures are illustrated to examine the model assumptions as well as reliability and validity of the generated model-based small area estimates. The results show a considerable gain in precision in model-based estimates produced applying SAE. Furthermore, the model-based estimates obtained by exploiting spatial information are more efficient than the one obtained by ignoring this information. However, both of these model-based estimates are more efficient than the direct survey estimate. In many districts, there is no survey data and therefore it is not possible to produce direct survey estimates for these districts. The model-based estimates generated using SAE are still reliable for such districts. These estimates produced by using SAE will provide invaluable information to policy-analysts and decision-makers.