Kelin Hu

Modeling Nitrate Leaching and Optimizing Water and Nitrogen Management under Irrigated Maize in Desert Oases in Northwestern China

Understanding water and N transport through the soil profile is important for efficient irrigation and nutrient management to minimize nitrate leaching to the groundwater, and to promote agricultural sustainable development in desert oases. In this study, a process-based water and nitrogen management model (WNMM) was used to simulate soil water movement, nitrate transport, and crop growth (maize [Zea mays L.]) under desert oasis conditions in northwestern China. The model was calibrated and validated with a field experiment. The model simulation results showed that about 35% of total water input and 58% of the total N input were leached to <1.8 m depth under traditional management practice. Excessive irrigation and N fertilizer application, high nitrate concentration in the irrigation water, together with the sandy soil texture, resulted in large nitrate leaching. Nitrate leaching was significantly reduced under the improved management practice suggested by farm extension personnel; however, the water and nitrate inputs still far exceeded the crop requirements. More than 1700 scenarios combining various types of irrigation and fertilizer practices were simulated. Quantitative analysis was conducted to obtain the best management practices (BMPs) with simultaneous consideration of crop yield, water use efficiency, fertilizer N use efficiency, and nitrate leaching. The results indicated that the BMPs under the specific desert oasis conditions are to irrigate the maize with 600 mm of water in eight times with a single fertilizer application at a rate of 75 kg N ha(-1).

A modelling approach to evaluate the long-term effect of soil texture on spring wheat productivity under a rain-fed condition

Soil surface texture is an important environmental factor that influences crop productivity because of its direct effect on soil water and complex interactions with other environmental factors. Using 30-year data, an agricultural system model (DSSAT-CERES-Wheat) was calibrated and validated. After validation, the modelled yield and water use (WU) of spring wheat (Triticum aestivum L.) from two soil textures (silt loam and clay) under rain-fed condition were analyzed. Regression analysis showed that wheat grown in silt loam soil is more sensitive to WU than wheat grown in clay soil, indicating that the wheat grown in clay soil has higher drought tolerance than that grown in silt loam. Yield variation can be explained by WU other than by precipitation use (PU). These results demonstrated that the DSSAT-CERES-Wheat model can be used to evaluate the WU of different soil textures and assess the feasibility of wheat production under various conditions. These outcomes can improve our understanding of the long-term effect of soil texture on spring wheat productivity in rain-fed condition.

Sequential indicator simulation and indicator kriging estimation of 3-dimensional soil textures

The complex distribution characteristics of soil textures at a large or regional scale are difficult to understand with the current state of knowledge and limited soil profile data. In this study, an indicator variogram was used to describe the spatial structural characteristics of soil textures of 139 soil profiles. The profiles were 2 m deep with sampling intervals of 0.05 m, from an area of 15 km2 in the North China Plain. The ratios of nugget-to-sill values (SH) of experimental variograms of the soil profiles in the vertical direction were equal to 0, showing strong spatial auto-correlation. In contrast, SH ratios of 0.48–0.81 in the horizontal direction, with sampling distances of ~300 m, showed weaker spatial auto-correlation. Sequential indicator simulation (SIS) and indicator kriging (IK) methods were then used to simulate and estimate the 3D spatial distribution of soil textures. The outcomes of the 2 methods were evaluated by the reproduction of the histogram and variogram, and by mean absolute error of predictions. Simulated results conducted on dense and sparse datasets showed that when denser sample data are used, complex patterns of soil textures can be captured and simulated realisations can reproduce variograms with reasonable fluctuations. When data are sparse, a general pattern of major soil textures still can be captured, with minor textures being poorly simulated or estimated. The results also showed that when data are sufficient, the reproduction of the histogram and variogram by SIS was significantly better than by the IK method for the predominant texture (clay). However, when data are sparse, there is little difference between the 2 methods.

Comparison of sequential indicator simulation and transition probability indicator simulation used to model clay content in microscale surface soil.

Quantitative study of the spatial distribution of soil clay content is crucial to soil microecological research, soil physical and chemical properties, and agricultural and environmental management. In this article, the distribution of clay content within a 1-m3 soil body was selected as the study object. The soil clay content was measured with a laser grain-size analyzer and classified into indicator data. The spatial variability of the data was then analyzed by indicator variogram and transiogram. The results of the indicator variogram showed that the spatial distribution of clay content in a horizontal direction is highly random. However, the results of the transiogram of clay content exhibited obvious spatial juxtapositional tendencies both vertically and horizontally. Subsequently, sequential indicator simulation (SIS) and transition probability indicator simulation (TPROGS) were applied to create conditional realizations of the 1-m3 soil body. Finally, the realizations were validated by reproduction of a histogram, connectivity, as well as mean absolute error of prediction. The results indicated that the major textural classes were overestimated, whereas the minor classes were underestimated in the SIS-generated histogram, whereas all classes were well reproduced in the TPROGS. In addition, compared with the measured data, the connectivity of SIS realizations was significantly reduced, whereas the connectivity of TPROGS was coherent with measured data, which indicated that the crucial spatial characteristics, which were neglected by SIS, can be captured by TPROGS, even if the accuracy of prediction is similar. Therefore, the TPROGS method is a suitable method for characterizing the distribution of clay content in soil. The results may provide useful information for soil research.