Spatial Variability of Soil Chemical Properties in the Reclaiming Marine Foreland to Yellow Sea of China

Precise information about the spatial variability of soil properties is essential in developing site-specific soil management, such as variable rate application of fertilizers. In this study the sampling grid of 100 m × 100 m was established to collect 1 703 soil samples at the depth of 0-20 cm, and examine spatial patterns including 13 soil chemical properties （pH, OM, NH4^＋, P, K, Ca, Mg, S, B, Cu, Fe, Mn, and Zn） in a 1 760 ha rice field in Haifeng farm, China, from 6th to 22nd of April, 2006, before fertilizer application and planting. Soil analysis was performed by ASI （Agro Services International） and data were analyzed both statistically and geostatistically. Results showed that the contents of soil OM, NH4^＋, and Zn in Haifeng farm were very low for rice production and those of others were enough to meet the need for rice cultivation. The spatial distribution model and spatial dependence level for 13 soil chemical properties varied in the field. Soil Mg and B showed strong spatial variability on both descriptive statistics and geostatistics, and other properties showed moderate spatial variability. The maximum ranges for K, Ca, Mg, S, Cu and Mn were all - 3 990.6 m and the minimum ranges for soil pH, OM, NH4^＋, P, Fe, and Zn ranged from 516.7 to 1 166.2 m. Clear patchy distribution of N, P, K, Mg, S, B, Mn, and Zn were found from their spatial distribution maps. This proved that sampling strategy for estimating variability should be adapted to the different soil chemical properties and field management. Therefore, the spatial variability of soil chemical properties with strong spatial dependence could be readily managed and a site-specific fertilization scheme for precision farming could be easily developed.

Geostatistical Assessment of the Spatial Distribution of Some Chemical Properties in Calcareous Soils

Spatial patterns of soil fertility parameters and other extrinsic factors need to be identified to develop farming practices that match agricultural inputs with local crop needs. Little is known about the spatial structure of nutrition in Iran. The present study was conducted in a 132-ha field located in central Iran. Soil samples were collected at 0-30 cm depth and were then analyzed for total nitrogen （N）, available phosphorus （P）, available potassium （K）, available copper （Cu）, available zinc （Zn）, available iron （Fe） and available manganese （Mn）. The results showed that the contents of soil organic matter, Cu and Zn in Marvdasht＇s farms were low. The spatial distribution model and spatial dependence level for soil chemical properties varied in the field. N, K, carbonate calcium equivalent （CaCO3） and electrical conductivity （EC） data indicated the existence of moderate spatial dependence. The variograms for other variables revealed stronger spatial structure. The results showed a longer range value for available P （480 m）, followed by total N （429 m）. The value of other chemical properties values showed a shorter range （128 to 174 m）. Clear patchy distribution of N, P, K, Fe, Mn, Cu and Zn were found from their spatial distribution maps. This proved that sampling strategy for estimating variability should be adapted to the different soil chemical properties and field management. Therefore, the spatial variability of soil chemical properties with strong spatial dependence could be readily managed and a site-specific fertilization scheme for precision farming could be easily developed.

基于Surfer软件的田间信息制图与分析

为了直观清晰地表达田间信息的空间分布状况,为农田的定位施肥等农田精细管理提供依据,利用Surfer8.0软件中提供的克立格插值法处理农田信息数据,分别绘制在不同采样方案下土壤速效磷的等值线分布图,并对其进行了定性和定量分析,确定了合理的采样方案。又分别绘制了碱解氮、速效磷、速效钾与小麦产量的三维线框图,该图直观、清晰地反映了碱解氮、速效磷、速效钾与小麦产量的空间变异性分布情况以及它们之间存在的正相关关系。

浅论精确农业

近年来国际上对精确农业的研究正全面展开。精确农业实质性的含义是根据田内在土壤、作物、病虫害等方面存在的“空间差异”,通过“可变量施用”,对各种农业投入实施精确管理 ,以达到降低成本 ,提高效益 ,保护环境的目的。本文就精确农业的产生 。

基于Arcview软件的麦地信息的表达与分析

为了准确直观地描述麦地信息在空间上的分布,为麦地的精细管理提供依据,文中采用软件ArcView 3.3中的克立格插值法对麦地信息绘制不同采样方案下的空间分布图并对其进行分析,确定了合理的采样方案。另外,还利用软件ArcView的叠加分析功能,产生综合信息图,直观地反映了小麦籽粒产量与土壤特性的相互关系。

基于多维空间变异分析的精确农业作业单元自适应决策

针对精确农业技术体系中最佳农艺作业单元大小 ,该文提出了一种用纵、横探测法推求土壤特性空间变异、作物长势空间变异以及前茬作物产量空间变异等多维空间变异指数 ,进而按一定农学原则决定最佳农艺作业单元尺寸的自适应解决方案。结果表明 ,用该算法确定的农艺作业单元尺寸进行变量施肥 ,平衡效果良好率在 90 %以上。该算法的另一特点是 ,对矢量和栅格数据具有同样的适用性 ,较易在矢栅混合型 GIS上实现。

Measurement and analysis of soil nitrogen and organic matter content using near-infrared spectroscopy techniques

Near infrared reflectance （N1R） spectroscopy is as a rapid, convenient and simple nondestructive technique useful for quantifying several soil properties. This method was used to estimate nitrogen （N） and organic matter （OM） content in a soil of Zhejiang Province, Hangzhou County. A total of 125 soil samples were taken from the field. Ninety-five samples spectra were used during the calibration and cross validation stage. Thirty samples spectra were used to predict N and OM concentration. NIR spectra of these samples were correlated using partial least square regression. The regression coefficients between measured and predicted values of N and OM was 0.92 and 0.93, and SEP （standard error of prediction） were 3.28 and 0.06, respectively, which showed that NIR method had potential to accurately predict these constituents in this soil. The results showed that NIR spectroscopy could be a good tool for precision farming application.

Leaf chlorophyll and nitrogen dynamics and their relationship to lowland rice yield for site-specific paddy management

The optimum rate and application timing of Nitrogen(N)fertilizer are crucial in achieving a high yield in rice cultivation;however,conventional laboratory testing of plant nutrients is time-consuming and expensive.To develop a site-specific spatial variable rate application method to overcome the limitations of traditional techniques,especially in fields under a double-cropping system,this study focused on the relationship between Soil Plant Analysis Development(SPAD)chlorophyll meter readings and N content in leaves during different growth stages to introduce the most suitable stage for assessment of crop N and prediction of rice yield.The SPAD readings and leaf N content were measured on the uppermost fully expanded leaf at panicle formation and booting stages.Grain yield was also measured at the end of the season.The analysis of variance,variogram,and kriging were calculated to determine the variability of attributes and their relationship,and finally,variability maps were created.Significant linear relationships were observed between attributes,with the same trends in different sampling dates;however,accuracy of semivariance estimation reduces with the growth stage.Results of the study also implied that there was a better relationship between rice leaf N content(R^2=0.93),as well as yield(R2=0.81),with SPAD readings at the panicle formation stage.Therefore,the SPAD-based evaluation of N status and prediction of rice yield is more reliable on this stage rather than at the booting stage.This study proved that the application of SPAD chlorophyll meter paves the way for real-time paddy N management and grain yield estimation.It can be reliably exploited in precision agriculture of paddy fields under double-cropping cultivation to understand and control spatial variations.