ABSTRACT
The experiment was conducted using automatic rain shelter to study the effect of precisely controlled water quota of alternate subsurface drip irrigation (ASDI) on spring maize yield and water use efficiency (WUE). The results showed that jointing to heading stage and heading to hilling stage were the key stages of water requirement for spring maize under ASDI. In particular, both the water consumption percentage and diurnal water consumption in these two stages were higher than in other stages and were more sensitive to water deficit stress. The water demand in these two stages should be prioritized if irrigation was limited. Maize yield generally increased with the increasing irrigation quota. When the irrigation quota below 2764.5 m3·hm-2, the yield increased rapidly with the increasing irrigation quota. The yield increase then slowed down after the irrigation quota reached 2764.5 m3·hm-2. The maximum of maize yield of 12109.0 kg·hm-2 was obtained at the irrigation quota of 3357.1 m3·hm-2. Compared to fixed subsurface drip irrigation (FSDI), under the same irrigation quota, the yield, WUE and irrigation water use efficiency (IWUE) of spring maize in ASDI treatment were improved by 5.4%, 1.4% and 5.6%, respectively. Compared to FSDI, the maize yield in ASDI decreased by 1.8%, but its WUE and IWUE increased by 11.0% and 22.7% when the irrigation quota decreased by 20%. The optimal irrigation quota of ASDI for spring maize in the study area was1600.4-3357.1 m3·hm-2 by taking into account of yield and WUE.
Subject(s)
Agricultural Irrigation/methods , Water/physiology , Zea mays/physiology , BiomassABSTRACT
Soil has spatial variability in its attributes. The analysis of soil spatial variability is of significance for soil management. This paper summarized the fractal theory and its application in spatial analysis of soil variability, with the focus on the utilization of moment method in calculating the fractal dimension of soil attributes, the multi-fractal analysis of soil spatial variability, and the scaling up of soil attributes based on multi-fractal parameters. The studies on the application of fractal theory and multi-fractal method in the analysis of soil spatial variability were also reviewed. Fractal theory could be an important tool in quantifying the spatial variability and scaling up of soil attributes.
Subject(s)
Conservation of Natural Resources/methods , Ecosystem , Fractals , Soil/chemistry , Ecology , Environmental Monitoring/methods , Nonlinear DynamicsABSTRACT
The spectral characteristic of remotely sensed image is mainly the results of integrative effects on spectrum from heterogeneous ground reflectors. Investigating its spatial distribution characteristics may be helpful for image interpreting and modeling based on remote sensing technique. In the present study, spatial heterogeneity of remotely sensed multispectral TM image across a hilly area in late October was studied by the combination of statistical method and multifractal analysis. The results showed that distribution of digital number (DN) values of visible spectra (0.45-0.69 microm) had statistical scale-invariance. The generalized fractal dimension function D(q) suggested that distribution of TM 2 (0.52-0.60 microm) DN values was monofractal type, whereas DN values of TM 1 (0.45-0.52 microm) and TM 3 (0.63-0.69 microm) had multifractal distribution characteristics. The parameters (alpha(max)-alpha(min)) and [f(a(max))-f(alpha(min))] of multifractal spectra further indicated that TM 3 DN values had the high est spatial heterogeneity and most abundant information, followed by TM 1, while the extremely narrow spectrum of TM 2 DN values showed its relatively low spatial heterogeneity and information capacity.
