宝鸡市陈仓区苹果园土壤养分空间分布及土壤肥力综合评价

以陕西省宝鸡市陈仓区不同乡镇的苹果园为采样单位采集土壤样品,测定土壤pH、有机质、全氮、有效磷、速效钾、有效铁、有效锰、有效铜和有效锌等指标的质量比,并利用相关性分析和主成分分析法对土壤养分综合肥力进行评价。结果表明:在各采样点间土壤pH的变异性较小,土壤pH整体偏碱性;土壤有效磷质量比处于较低水平;有机质、全氮、有效铁、有效锰、有效铜、有效锌质量比处于中等水平;速效钾质量比处于较高水平。因此,在今后的生产中应注意钾肥的合理施用。

大荔枣园土壤养分空间分布及土壤肥力综合评价

为探究大荔县冬枣产区土壤肥力状况,采集了303个有代表性枣园土壤样品,测定了土壤pH、有机质、硝态N、铵态N、碱解N、有效P、速效K、有效Fe、有效Mn、有效Zn、有效Cu等指标,用ArcGIS软件绘制了土壤有机质和速效N、P、K的空间分布图,并应用主成分分析方法对枣园土壤肥力状况进行了综合评价。结果表明,pH值在各个采样点间的变异性较小,其余土壤肥力指标都存在着较大的变异,并且偏度和峰度>0,属于右偏态尖峰型;整体来看,大荔枣园土壤pH偏碱性,碱解N含量较为丰富,有效P含量中等偏低,速效K含量极其丰富,有机质含量较为缺乏。空间分布规律表现为土壤碱解N、有机质规律一致,东北区域低、其他区域高;有效P含量北部高南部低;除东北部少数区域外,土壤速效K含量普遍达到丰富或极丰富水平。主成分分析显示,有机质、速效K、有效Fe和有效P是影响冬枣园土壤肥力最主要的因素,通过对各乡镇的综合得分均值进行比较,发现各乡镇土壤得分高低与土壤养分空间分布结果相吻合。

采样尺度和地形因子对土壤养分空间变异规律影响的研究

由于我国疆域辽阔且地形分布复杂,因此研究土壤养分的空间变化,对精准农业有着重要的意义。本文探索2种采样尺度以及地形因子对土壤养分含量空间分布的影响,通过研究分析,得出在小范围区域内,适当增加采样尺寸,可以减少采样工作量,并准确地预测整个区域的土壤速效钾含量。而在实验研究区域内,对每种养分含量而言,加入地形因子后进行空间插值预测,养分含量分布的结果更为精细。

基于GIS的推荐施肥专家系统设计与研究——以甘肃省民勤县为例

利用地理信息系统(GIS)技术,在集成二次开发的基础上,构建了服务于民勤县的基于GIS的推荐施肥专家系统.系统整合了民勤县第二次土壤普查、测土施肥项目等一线农业科技工作者积累的土壤数据,并利用这些数据构建了初始施肥模型,开发了地图基本操作、系统数据维护、信息查询、施肥模型参数编辑、施肥决策支持和专题图制作6个功能模块,实现了空间信息、属性信息和施肥参数等数据的统一管理,以及地图浏览、施肥决策、专题图编制等技术服务.同时,该系统在运行过程中可以根据后期肥效试验和新的农民施肥及作物产量等数据,进一步优化施肥模型、改进施肥参数,具备不断优化和改进的特点.

Effect of Cultivation on Spatial Distribution of Nematode Trophic Groups in Black Soil

Geostatistics combined with GIS was applied to assess the spatial distribution of nematode trophic groups following two contrasting soil uses in the black soil region of Northeast China. Two plots, one with fallow for 12 years and the other cultivated, were marked on regular square grids with 2-m spacing. Soil samples were collected from each sampling point, nematodes were extracted from these samples and classified into four trophic groups: bacterivores, fungivores, plant parasites, and omnivores/predators. The numbers of total nematodes and trophic groups analyzed had normal distributions on both fallow and cultivated plots. The absolute abundances of total nematodes and trophic groups were observed to be much more homogeneous on cultivated plot than on fallow one. Geostatistical analysis showed that the densities of total nematodes and trophic groups on both fallow and cultivated plots exhibited spatial dependence at the sampled scale and their experimental semivariograms were adjusted to a spherical or exponential model, except those of bacterivores and fungivores on cultivated plot. The spatial distribution of nematode trophic groups was found to be different for the two land uses, indicating that cultivation changed the native condition for soil nematode activities.

Spatial Variation of P and N in Water and Sediments of Dianchi Lake, China

Dianchi Lake is one of the most eutrophic lakes in China. In order to understand this eutrophication and to help control the pollution, this research investigated the spatial distribution of Kjeldahl nitrogen (K-N) and total phosphorus(TP) through analysis of bottom water and sediment (3 depths) samples collected at 118 sites around Dianchi Lake. The concentrations of K-N and TP for the lake bottom water in the Caohai part of the lake were much higher than those in the Waihai part, generally decreasing from north to south. In the sediments, the K-N concentration was higher in the Caohai part and the middle of the Waihai part. On the other hand, TP in the sediments was greater in the southern and western parts. Both K-N and TP had similar spatial distributions for the sediment samples of three different depths.Vertically, the K-N and TP concentration in the sediments decreased with an increase in depth. This was evidence that eutrophication and pollution of Dianchi Lake was becoming gradually more severe. Exterior factors including uncontrolled input of domestic and industrial effluents as well as non-point pollution around the lake were the main reasons for serious eutrophication; therefore, controlling these was the first step in reducing eutrophication of Dianchi Lake.

Sampling Strategies for Soil Available K and P at Field Scale

Field nutrient distribution maps obtained from the study on soil variations within fields are the basis of precision agriculture. The quality of these maps for management depends on the accuracy of the predicted values, which depends on the initial sampling. To produce reliable predictions efficiently the minimal sampling size and combination should be decided firstly, which could avoid the misspent funds for field sampling work. A 7.9 hectare silage field close to the Agricultural Research institute at Hillsborough, Northern Ireland, was selected for the study. Soil samples were collected from the field at 25 m intervals in a rectangular grid to provide a database of selected soil properties. Different data combinations were subsequently abstracted from this database for comparison purposes, and ordinary kriging used to produce interpolated soil maps. These predicted data groups were compared using least significant difference (LSD) test method. The results showed that the 62 sampling sizes of triangle arrangement for soil available K were sufficient to reach the required accuracy. The triangular sample combination proved to be superior to a rectangular one of similar sample size.

Soil Nutrient Distribution in Two Typical Paddy Terrace Wetlands Along an Elevation Gradient During the Fallow Period

Soil nutrient concentrations in the top soils from two paddy terraces were determined in order to investigate spatial distributions of soil nutrients along the elevations on the Yunnan plateau of China during the fallow period.Results showed that soil nutrients in both terraces were higher than the reference concentrations except for SOC,TN,TP and Fe.Soil macronutrients didn't show significant differences in both terraces except for Mg and Ca,so did soil micronutrients except for Mn.Spatial distribution patterns of soil nutrients along the increasing elevations were different in both terraces.However,soil nutrients in both terraces were generally not significantly influenced by the elevations and soil pH values.The findings of this study can contribute to soil fertility management and ecological protection of Hani terraces.

Comparison of Artificial Neural Networks,Geographically Weighted Regression and Cokriging Methods for Predicting the Spatial Distribution of Soil Macronutrients(N,P,and K)

Soil macronutrients(i.e. nitrogen(N), phosphorus(P), and potassium(K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks(ANN) and two geostatistical methods(geographically weighted regression(GWR) and cokriging(CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil(0–30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration(n = 84) and validation(n = 22). Chemical and physical variables including clay, p H and organic carbon(OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model(with coefficient of determination R^2 = 0.922 and root mean square error RMSE = 0.0079%) was more accurate compared to the CK model(with R^2 = 0.612 and RMSE = 0.0094%), and the GWR model(with R^2 = 0.872 and RMSE = 0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients(N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients.