Chunqiao Mi

Assessment of environment lodging stress for maize using fuzzy synthetic evaluation

Lodging in maize, which is the result of the genotype-by-environment interactions, causes severe yield losses annually. There are many studies on lodging resistance of maize varieties now, but information about maize planting environment lodging stress is very rare. The environment lodging stress, which is a result of various factors, is characterized by uncertainty. Fuzzy sets theory and fuzzy logic are advantageous for dealing with the questions with uncertainties. The objective of this study is to assess the environment lodging stress for maize planting. Based on the daily weather data in the recent 59 years (1951-2009) and soil data, five counties (or cities) in ShanDong Province and HeNan Province in Huang-Huai-Hai-Plain, China, are selected as study sites; the maximum wind speed, rainfall, and potassium content in soil are chosen as stress indicators, and the overall stress level of each study site is obtained by a fuzzy synthetic evaluation approach. The consistency is shown between our calculation results and the results obtained with actual survey data on lodging in maize, indicating that the employed model is a promising approach for assessing environment lodging stress, and providing a scientific basis for maize variety extension and recommendation and comprehensive management to reduce maize planting risk and loss.

Prediction of accumulated temperature in vegetation period using artificial neural network

In this paper, the theory of artificial neural network with back-propagation algorithm (BPN) is presented, and the BPN model is used to predict the accumulated temperature for Northeast China, North China, and the Huang-Huai-Hai Plain. A total of 235 records collected from 235 meteorology stations were fed into the BPN model for training and testing. The latitude, longitude and elevation of each station were used as input variables of BPN, and the accumulated temperature as output variable. Other key network parameters, such as learning rate, momentum, the number of hidden nodes, and the learning iterations, were optimized using a trial and error approach. The optimized BPN model was compared with the multiple linear regression (MLR) model. In summary, BPN model was generally more accurate than MLR model. This infers that artificial neural network models are more applicable than regression models when predicting accumulated temperature.

Assessment of wind-induced environmental lodging stress for maize based on GIS

Lodging in maize is one of the major problems in maize production worldwide. This study is to assess environmental lodging stress for maize based on probability analysis of extreme wind event in maize vegetative stage. A total of 687 growing counties in Huang-Huai-Hai Plain, China were chosen as study area. There were 148 meteorology stations with daily extreme wind speed data in recent 59 years. At first, for each station, the maximum value of daily extreme wind speed in maize vegetative stage (MEWSV for short) was calculated yearly, and the mean and standard deviation of MEWSV in all stations were interpolated into all growing counties. Then, the probability distribution of MEWSV was simulated using Gumbel distribution and Normal distribution, and the result showed that Gumbel distribution was better. At last, for each growing counties, the probability of extreme wind event (that MEWSV was equal or higher than 19m/s) was calculated based on Gumbel distribution, and the assessed stress values were divided into 5 levels and visualized in GIS using a thematic map. It showed us clearly that most growing counties in the northwest of the study area had very severe lodging stress. In order to validate the obtained results, some field survey data were used in current study and it showed that they were consistent in general. But this method using meteorology data to indirectly measure the environmental lodging stress is less costly and more operational than the traditional field-based survey approach, especially when the region to be evaluated is very large. This study can facilitate the identification of better-adapted growing environments, so as to reduce the risk and loss of lodging in maize.

Predicting the yield of varieties of maize in a target environment using regression analysis

Previous analyses on variety yield have usually focused on regression coefficients as an indicator to measure the stability and adaptation of a specific variety under experimental conditions. Due to the huge differences between experimental plots and farm fields, the model results from experimental plots can hardly be applied to farm fields. In this study, a regression analysis was conducted between the variety yield and an on-trial environment index (the mean yield of all varieties in the same test site). Then, using the average proportional coefficient between the on-trial environment index and the on-farm environment index (the statistical maize production yield of the growing county containing the test site) as a bridge, the on-farm environment index was converted to the corresponding on-trial environment index, which was then applied to the regression model generated from the on-trial plot-scale data. This procedure ensured the homogeneity of the model parameters and successfully predicted the yield of maize varieties under a target environment. The procedure also produced the 95% confidence interval predicted yield, making the results more practically significant. By introducing the proportional coefficient and confidence interval, the new approach provides a feasible solution for studying the performance of varieties under on-farm conditions. Finally, we used the maize variety NH1101 as an example to illustrate the modeling procedures. The results indicated that the model produced promising results. The new method provides direct support for variety recommendation, and facilitates the identification of better-adapted varieties.

Evaluation of maize variety suitability on lodging in target environments based on GIS

Lodging is one of the major problems in maize production which causes severe yield loss every year all over the world. In the present study, the lodging suitability of different maize varieties in target growing environments was investigated based on geographical information science. A total of 401 maize planting counties in northeast China and northern China were selected as study areas. The mean and standard deviation of environment accumulated temperature in vegetative stage of maize were calculated from raw temperature data obtained from 167 meteorology stations in these areas. The variety lodging resistance was determined based on the data of national regional variety trials for maize, and the environment lodging stress was measured using field survey data on lodging. Probability analysis based on the calculated values of environment accumulated temperature in vegetative stage of maize was utilized to determine whether a maize variety can be physically mature in a planting county, and lodging suitability of the variety was evaluated with geographical information science combining variety lodging resistance and local environment lodging stress together. A new maize variety NH1101 was taken as an example to illustrate the modeling and calculating procedures. The result shows that, from southwest to northeast of the study areas, the overall suitability trend changes from nonsuitable to suitable, then to not very suitable. And it is demonstrated that the lodging suitability is not only related to the variety resistance but also to the local environment stress.