Maize Yield Gains in Northeast China in the Last Six Decades

In 2010, Chinese maize yields increased from 961.5 kg ha-1 in 1949 to 5 453.8 kg ha-L This increase is the result of genetic improvements, an increase in nitrogen application, and refinement of planting densities. The objective of this study was to provide a theoretical basis for maize production research by analyzing the maize yield gain characteristics. Six varieties of maize were selected for the study; each selection is representative of a typical or commonly used maize variety from a specific decade, beginning from the 1950s and continuing through each decade into the 2000s. The selections and their corresponding decade were as follows： Baihe, 1950s; Jidan 101, 1960s; Zhongdan 2, 1970s; Yedan 13, 1980s; Zhengdan 958, 1990s; and Xianyu 335, 2000s. Each variety was planted under four different densities （37 500, 52 500, 67 500, and 82 500 plants ha-0 and four different nitrogen applications （0, 150, 225, and 300 kg ha-1 to study the effects on yield gain characteristics. The obtained results demonstrated that there was a maize yield increase of 123.19% between the 1950s variety and the 2000s variety. Modern Chinese maize varieties had a higher yield advantage. They also displayed the additional potential to acquire higher yield under increased planting densities and nitrogen applications. At the present cultivation levels （planting at 67 500 plants ha-~ with 225 kg ha-1 nitrogen application）, the contribution types and corresponding yield increase percentages were as follows： genetic improvement, 45.37%; agronomic-management improvement, 30.94%; and genotype× agronomicmanagement interaction, 23.69%. At high-yielding cultivation levels （planting at 82 500 plants ha-1 with 300 kg ha-1 nitrogen application）, the contribution types and corresponding yield increase percentages were as follows： genetic improvement, 31.30%; agronomic-management improvement, 36.23%; and genotype × agronomic-management interaction, 32.47%. The contribution of agronomic-management and genotype ×agronomic-management interaction to yield increase would be larger with the corresponding management improvement. To further increase maize grain yield in China, researchers should further examine the adaptation of variety to agronomic-management. effects of agronomic-management on maize yield and the

Studies on the Root Characteristics of Maize Varieties of Different Eras

Experiment was conducted at the Gongzhuling Experimental Station of Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Jilin Province, China, during 2009-2010. Six representative varieties of maize （Baihe in the 1950s, Jidan 101 in the 1960s, Zhongdan 2 in the 1970s, Yedan 13 in the 1980s, Zhengdan 958 in the 1990s, and Xianyu 335 in the 2000s） were each planted under two different densities （52 500 and 82 500 plants ha-~） and two different nitrogen application levels （150 and 300 kg ha-l）. Root characteristics and distribution among soil layers were studied by the field root digging method. The results showed that root mass increased with the process of the growth and development of the plant, and it peaked at kernel filling stage, and decreased at maturity due to the root senesces. Root mass of different maize varieties from the 1950s to 1980s had a trend of increase, while it decreased for the modern varieties. Root length and root surface areas had the similar changing trend. The study suggested that early maize varieties may have root redundancy, and reducing root redundancy may be a direction for variety improvement for high yield. Root characteristics were affected by nitrogen application level and density; modern varieties were more suitable for higher fertilizer application level and density conditions. Root characteristics distribution among soil layers decreased by an exponent equation, but the regression coefficients of different varieties were different. Though the root length density （RLD） of every soil layer of different varieties also decreased by an exponent equation, there were large variations of RLD in every part of a layer.

An Image-Based Diagnostic Expert System for Corn Diseases

The annual worldwide yield losses due to pests are estimated to be billions of dollars. Integrated pest management （IPM） is one of the most important components of crop production in most agricultural areas of the world, and the effectiveness of crop protection depends on accurate and timely diagnosis of phytosanitary problems. Accurately identifying and treatment depends on the method which used in disease and insect pests diagnosis. Identifying plant diseases is usually difficult and requires a plant pathologist or well-trained technician to accurately describe the case. Moreover, quite a few diseases have similar symptoms making it difficult for non-experts to distinguish disease correctly. Another method of diagnosis depends on comparison of the concerned case with similar ones through one image or more of the symptoms and helps enormously in overcoming difficulties of non-experts. The old adage ＇a picture is worth a thousand words＇ is crucially relevant. Considering the user＇s capability to deal and interact with the expert system easily and clearly, a webbased diagnostic expert-system shell based on production rules （i.e., IF 〈 effects 〉 THEN 〈 causes 〉） and frames with a color image database was developed and applied to corn disease diagnosis as a case study. The expert-system shell was made on a 32-bit multimedia desktop microcomputer. The knowledge base had frames, production rules and synonym words as the result of interview and arrangement. It was desired that 80% of total frames used visual color image data to explain the meaning of observations and conclusions. Visual color image displays with the phrases of questions and answers from the expert system, enables users to identify any disease, makes the right decision, and chooses the right treatment. This may increase their level of understanding of corn disease diagnosis. The expert system can be applied to diagnosis of other plant pests or diseases by easy changes to the knowledge base.

Effects of Conservation Tillage on Crop Yield: a Case Study in the Part of Typical Ecological Zones in China

To evaluate the effects of different conservation tillage modes on crop yield in various ecological regions, we interviewed peasant households in the Northeast China Plain, North China Plain, Chengdu Plain, and Northwest China Oasis, China, to analyze the influencing factors of the tillage methods on crop yield. An index set was determined from seven yield parameters based on expert consultation and relevant literature. A comprehensive evaluation of conservation tillage methods in the various ecological regions was carried out using fuzzy theory. The approaches with the highest scores were identified for each region： wide and narrow rotation planting with high stubble standing retention in the Northeast China Plain; seeding after rotary tilled of wheat under corn straw mulching and no-tillage seeding of corn and other crops under wheat straw mulching in the North China Plain; no tillage with high stubble retention in the Northwest China Oasis; and no-tillage seeding of wheat and other crops under rice straw mulching in Chengdu Plain. These research data provide a useful guide for the selection of conservation tillage methods for optimum yields in different regions of China.

Identification of Wheat Canopy Structure Using Hyperspectral Data

Some winter wheat varieties were selected in this experiment. The results were as follows： 1） Leaf orientation value （LOV） and leaf area index （LAI） of wheat had different contributions to canopy spectral reflectance （CSR）. For example, LOV affected greatly canopy spectra more than LAI did in jointing stage, but LAI had a greater effect on CSR than LOV did after the ground was near to be covered completely. 2） Twenty treatments including different varieties and densities were arranged in this experiment, and the result of cluster analysis showed that all these treatments can be parted into four clusters according to LAI and LOV： varieties with erect leaves and low LAI （denoted as A）, varieties with erect leaves and high LAI （denoted as B）, varieties with horizontal leaves and low LAI （denoted as C）, varieties with horizontal leaves and high LAI （denoted as D）. Their CSR had difference in 400-700 nm and 700-1 150 nm at jointing stage, especially in different plant types. 3） There was obvious distribution difference among different clusters in scatter plot （X=△R890, Y=R890）, △R890 was the reflectance increment from jointing to booting stage. It was seen from the Y-axis direction that R890 of horizontal varieties were higher than the erect ones, and seen from the X-axis direction that the greater △R890 was, the lower LAI one within the same plant type varieties, which indicted that the combination of plant-type and the population magnitude can be initially identified by this method.