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A high grain moisture content at harvest has been an important problem in the high latitude region of Northeast China, and it is closely related to the genotypes of varieties, local meteorological factors and planting management. However, delayed harvest at a low temperature could not effectively reduce the grain moisture content. In this study, we continuously observed the grain drying during the late stage of different maturing types of maize varieties in Daqing, Heilongjiang Province, China in 2016 and 2017. A two-segment linear model was used to analyze the different stages of the drying processes: 1) Two-segment linear model fitting can divide the grain drying process of all varieties into two separate linear drying processes with different slopes. 2) During the rapid drying stage, the drying was faster at a higher temperature. The rate of slow drying was influenced by air vapor pressure. 3) The moisture content and meteorological factors when the drying rate turns from one stage into the other were not consistent between varieties and years. After entering the frost period, temperatures below 0°C will significantly reduce the rate of grain drying. 4) Due to the short growth period of early-maturing varieties, the drying time was prolonged, and the grain moisture content was lower than that of the mid-late maturing varieties. Local meteorological conditions do not allow the drying of mid-late maturing varieties to achieve a lower moisture content. When the temperature falls below 0°C, the drying rate of grain decreases markedly. Therefore, one feasible way to solve the problem of high moisture content is to replace the early-maturing varieties and implement the corresponding cultivation techniques.

The rate of corn kernel breakage in the grain combine harvesters is a crucial factor affecting the quality of the grain shelled in the field. The objective of the present study was to determine the susceptibility of corn kernels to breakage based on the kernel moisture content in order to determine the moisture content that corresponds to the lowest rate of breakage. In addition, we evaluated the resistance to breakage of various corn cultivars. A total of 17 different corn cultivars were planted at two different sowing dates at the Beibuchang Experiment Station, Beijing and the Xinxiang Experiment Station (Henan Province) of the Chinese Academy of Agricultural Sciences. The corn kernel moisture content was systematically monitored and recorded over time, and the breakage rate was measured by using the grinding method. The results for all grain samples from the two experimental stations revealed that the breakage rate y is quadratic in moisture content x, y=0.0796x2−3.3929x+78.779; R2=0.2646, n=512. By fitting to the regression equation, a minimum corn kernel breakage rate of 42.62% was obtained, corresponding to a corn kernel moisture content of 21.31%. Furthermore, in the 90% confidence interval, the corn kernel moisture ranging from 19.7 to 22.3% led to the lowest kernel breakage rate, which was consistent with the corn kernel moisture content allowing the lowest breakage rate of corn kernels shelled in the field with combine grain harvesters. Using the lowest breakage rate as the critical point, the correlation between breakage rate and moisture content was significantly negative for low moisture content but positive for high moisture content. The slope and correlation coefficient of the linear regression equation indicated that high moisture content led to greater sensitivity and correlation between grain breakage and moisture content. At the Beibuchang Experiment Station, the corn cultivars resistant to breakage were Zhengdan 958 (ZD958) and Fengken 139 (FK139), and the corn cultivars non-resistant to breakage were Lianchuang 825 (LC825), Jidan 66 (JD66), Lidan 295 (LD295), and Jingnongke 728 (JNK728). At the Xinxiang Experiment Station, the corn cultivars resistant to breakage were HT1, ZD958 and FK139, and the corn cultivars non-resistant to breakage were ZY8911, DK653 and JNK728. Thus, the breakage classifications of the six corn cultivars were consistent between the two experimental stations. In conclusion, the results suggested that the high stability of the grinding method allowed it to be used to determine the corn kernel breakage rates of different corn cultivars as a function of moisture content, thus facilitating the breeding and screening of breakage-resistant corn.

Increasing plant density is an effective way to enhance maize yield, but often increases lodging rate and severity, significantly elevating the risk and cost of maize production. Therefore, lodging is a major factor restricting future increases in maize yield through high-density planting. This paper reviewed previous research on the relationships between maize lodging rate and plant morphology, mechanical strength of stalks, anatomical and biochemical characteristics of stalks, root characteristics, damage from pests and diseases, environmental factors, and genomic characteristics. The effects of planting density on these factors and explored possible ways to improve lodging resistance were also analyzed in this paper. The results provide a basis for future research on increasing maize lodging resistance under high-density planting conditions and can be used to develop maize cultivation practices and lodging-resistant maize cultivars.

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.

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 web-based diagnostic expert-system shell based on production rules (i.e., IF THEN ) 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.