Fasheng Zhang

Maize Yield Response to Water Supply and Fertilizer Input in a Semi-Arid Environment of Northeast China

Maize grain yield varies highly with water availability as well as with fertilization and relevant agricultural management practices. With a 311-A optimized saturation design, field experiments were conducted between 2006 and 2009 to examine the yield response of spring maize (Zhengdan 958, Zea mays L) to irrigation (I), nitrogen fertilization (total nitrogen, urea-46% nitrogen,) and phosphorus fertilization (P2O5, calcium superphosphate-13% P2O5) in a semi-arid area environment of Northeast China. According to our estimated yield function, the results showed that N is the dominant factor in determining maize grain yield followed by I, while P plays a relatively minor role. The strength of interaction effects among I, N and P on maize grain yield follows the sequence N+I >P+I>N+P. Individually, the interaction effects of N+I and N+P on maize grain yield are positive, whereas that of P+I is negative. To achieve maximum grain yield (10506.0 kg·ha−1) for spring maize in the study area, the optimum application rates of I, N and P are 930.4 m3·ha−1, 304.9 kg·ha−1 and 133.2 kg·ha−1 respectively that leads to a possible economic profit (EP) of 10548.4 CNY·ha−1 (CNY, Chinese Yuan). Alternately, to obtain the best EP (10827.3 CNY·ha−1), the optimum application rates of I, N and P are 682.4 m3·ha−1, 241.0 kg·ha−1 and 111.7 kg·ha−1 respectively that produces a potential grain yield of 10289.5 kg·ha−1.

Quantifying Spatial Variability of Selected Soil Trace Elements and Their Scaling Relationships Using Multifractal Techniques

Multifractal techniques were utilized to quantify the spatial variability of selected soil trace elements and their scaling relationships in a 10.24-ha agricultural field in northeast China. 1024 soil samples were collected from the field and available Fe, Mn, Cu and Zn were measured in each sample. Descriptive results showed that Mn deficiencies were widespread throughout the field while Fe and Zn deficiencies tended to occur in patches. By estimating single multifractal spectra, we found that available Fe, Cu and Zn in the study soils exhibited high spatial variability and the existence of anomalies ([α(q)max−α(q)min]≥0.54), whereas available Mn had a relatively uniform distribution ([α(q)max−α(q)min]≈0.10). The joint multifractal spectra revealed that the strong positive relationships (r≥0.86, P<0.001) among available Fe, Cu and Zn were all valid across a wider range of scales and over the full range of data values, whereas available Mn was weakly related to available Fe and Zn (r≥0.18, P<0.01) but not related to available Cu (r = −0.03, P = 0.40). These results show that the variability and singularities of selected soil trace elements as well as their scaling relationships can be characterized by single and joint multifractal parameters. The findings presented in this study could be extended to predict selected soil trace elements at larger regional scales with the aid of geographic information systems.

Mapping surface soil organic matter based on multispectral image

Soil organic matter (SOM) is an essential and dynamic variable in terrestrial ecosystem. This study used a method for mapping its spatial distribution by remote sensing technique. An image observed by Landsat 5 was used to estimate the spatial pattern of surface SOM in a town scale. The results showed that the concentration of surface SOM in study Jianshe town had a negative correlation (r =0.51, P≪0.01) with TM 1 image intensity. A nonlinear model was established using this correlation (R2=0.61, P≪0.001) to estimate surface SOM. The validation showed that the model can predict surface SOM reliably (RMSE=0.96). Based on that model, the spatial pattern of surface SOM in study town can be exactly mapped in details, which may provide potential information for nutrients management in that arid area. The accuracy of prediction model have much space to be improved if more such studies can be carried out to refine the image processing and interpreter technology and enhance the correlation between SOM and multispectral soil reflectance.

Prediction of Precipitation Based on Artificial Neural Networks by Free Search

FS-BP model was used to try to predict precipitation. The last six years’ precipitation were selected as input variable and the next year’s precipitation as output variable. The results show that the mean relative error of the prediction is 2.92%. T-test and regression analysis indicates that the predicted value differs just slightly from the observed value and their correlation coefficient was 0.9901.The FS-BP model is quite higher than BP model in accuracy and stability, and serves as useful tool in further research on prediction of precipitation.