Vertical distribution characteristics of soil saturated hydraulic conductivity under different land use patterns in the Mu Us sandy land

Background,aim,and scope Soil saturated hydraulic conductivity(K_(s))is a key parameter in the hydrological cycle of soil;however,we have very limited understanding of K_(s) characteristics and the factors that inf luence this key parameter in the Mu Us sandy land(MUSL).Quantifying the impact of changes in land use in the Mu Us sandy land on K_(s) will provide a key foundation for understanding the regional water cycle,but will also provide a scientific basis for the governance of the MUSL.Materials and methods In this study,we determined K_(s) and the basic physical and chemical properties of soil(i.e.,organic matter,bulk density,and soil particle composition)within the first 100 cm layer of four different land use patterns(farmland,tree,shrub,and grassland)in the MUSL.The vertical variation of K_(s) and the factors that influence this key parameter were analyzed and a transfer function for estimating K_(s) was established based on a multiple stepwise regression model.Results The K_(s) of farmland,tree,and shrub increased gradually with soil depth while that of grassland remained unchanged.The K_(s) of the four patterns of land use were moderately variable;mean K_(s)values were ranked as follows:grassland(1.38 mm·min^(-1))<tree(1.76 mm·min^(-1))<farmland(1.82 mm·min^(-1))<shrub(3.30 mm·min^(-1)).The correlation between K_(s) and organic matter,bulk density,and soil particle composition,varied across different land use patterns.A multiple stepwise regression model showed that silt,coarse sand,bulk density,and organic matter,were key predictive factors for the K_(s) of farmland,tree,shrub,and grassland,in the MUSL.Discussion The vertical distribution trend for K_(s) in farmland is known to be predominantly influenced by cultivation,fertilization,and other factors.The general aim is to improve the water-holding capacity of shallow soil on farmland(0-30 cm in depth)to conserve water and nutrients;research has shown that the K_(s) of farmland increases with soil depth.The root growth of tree and shrub in sandy land exerts mechanical force on the soil due to biophysical processes involving rhizospheres,thus leading to a significant change in K_(s).We found that shallow high-density fine roots increased the volume of soil pores and eliminated large pores,thus resulting in a reduction in shallow K_(s).Therefore,the K_(s) of tree and shrub increased with soil depth.Analysis also showed that the K_(s) of grassland did not change significantly and exhibited the lowest mean value when compared to other land use patterns.This finding was predominantly due to the shallow root system of grasslands and because this land use pattern is not subject to human activities such as cultivation and fertilization;consequently,there was no significant change in K_(s) with depth;grassland also had the lowest mean K_(s).We also established a transfer function for K_(s) for different land use patterns in the MUSL.However,the predictive factors for K_(s) in different land use patterns are known to be affected by soil cultivation methods,vegetation restoration modes,the distribution of soil moisture,and other factors,thus resulting in key differences.Therefore,when using the transfer function to predict K_(s) in other areas,it will be necessary to perform parameter calibration and further verification.Conclusions In the MUSL,the K_(s) of farmland,tree,and shrub gradually increased with soil depth;however,the K_(s) of grassland showed no significant variation in terms of vertical distribution.The mean K_(s) values of different land use patterns were ranked as follows:shrub>farmland>tree>grassland;all land use patterns showed moderate levels of variability.The K_(s) for different land use patterns exhibited differing degrees of correlation with soil physical and chemical properties;of these,clay,silt,sand,bulk density,and organic matter,were identified as important variables for predicting K_(s) in farmland,tree,shrub,and grassland,respectively.Recommendations and perspectives In this study,we used a stepwise multiple regression model to establish a transfer function prediction model for K_(s) for different land use patterns;this model possessed high estimation accuracy.The ability to predict K_(s) in the MUSL is very important in terms of the conservation of water and nutrients.

谷子成熟期的鸟害调查及防治方法

陕北黄土丘陵沟壑区是中国优质小米生产基地,但谷子成熟期鸟害严重。为了减轻鸟害,在谷子成熟期将安塞县划分为3个区域,采用路线法调查危害谷子的鸟类,以便采取有效防治措施。结果表明:危害谷子的鸟类有17种,均为杂食性留鸟,隶属于6科9属,其中鸦科有9种,鸠鸽科有4种。安塞县南部次生林区鸟害较轻,北部典型草原区较重。危害最严重的鸟类为雉鸡和麻雀,其中雉鸡主要集中在沟坡地,麻雀主要集中在村庄旁及川地。在不伤害鸟类的前提下,应采用综合防治措施,如物理驱鸟、化学驱鸟等以减轻鸟害。

二次通用旋转组合设计下耐旱树种的生长特性研究

植物修复是干旱半干旱地区铅污染土壤修复的有效手段之一。本文以1年生紫穗槐为研究材料,采用三因素二次旋转组合设计,分析了不同生长阶段节律性供水对紫穗槐生长特性的影响,旨在寻求适于植株生长的最佳供水组合,为干旱半干旱地区水资源合理利用提供参考。

Runoff and Soil Erosion on Slope Cropland: A Review

Soil erosion has become a serious environmental problem worldwide,and slope land is the main source of soil erosion. As a primary cover of slope land,crops have an important influence on the occurrence and development of runoff and soil erosion on slope land. This paper reviews the current understanding of runoff and soil erosion on slope cropland. Crops mainly impact splash detachment,slope runoff,and sediment yield. In this review paper,the effects of crop growth and rainfall on the splash detachment rate and the spatial distribution of splash detachment are summarized. Crop growth has a significant impact on runoff and sediment yield. Rainfall intensity and slope gradient can influence the level of erosive energy that causes soil erosion. Furthermore,other factors such as antecedent soil water content,soil properties,soil surface physical crust,and soil surface roughness can affect soil anti-erodibility. The varying effects of different crops and with different influence mechanisms on runoff and soil erosion,as well as changes in their ability to influence erosion under different external conditions should all remain focal points of future research. The effect of crop vegetation on runoff and soil erosion on slope land is a very important factor in understanding large-scale soil erosion systems,and in-depth study of this topic is highly significant for both theory and practice.

Synergic Relationship between the Grain for Green Program and the Agricultural Eco-economic System in Ansai County based on the VAR model

Understanding the synergic relationship between the Grain for Green Program(GGP)and the agricultural eco-economic system is important for designing an optimized agricultural eco-economic system and developing a highly efficient structure of an agricultural industry chain and a resource chain.This study used Ansai County time series data from 1995 to 2014,applied vector autoregressive(VAR)models and used tools such as Granger causality,impulse response analysis and variance decomposition,to explore the synergy between the GGP and the agricultural eco-economic system.The results revealed a synergic and reciprocal relationship between the GGP and the agroeconomic system.The contribution of the GGP to the agroecosystem reached 34%,which was significantly higher than either its largest contribution to the agroeconomic system(20.8%)or its peak contribution to the agrosocial system(26.7%).The agroeconomic system had the most prominent influence on the GGP,with a year-round stable contribution of up to 55.3%.These results were consistent with reality.However,the impact of the GGP on the agricultural eco-economic system was weaker than the effect of the agricultural eco-economic system on the GGP.The lag of variable stationarity after the shock was relatively short,indicating that optimal coupling had not formed between the GGP and the agricultural eco-economic system.On the basis of enhancing the ecological functions,we should construct the agricultural industry-resource chain such that it focuses on promoting the effective utilization of resources in the region.In addition,the development of a carbon sink industry can be used to manifest the ecological values of ecological functions.