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SPATIAL VARIATION OF SOIL MINERAL NITROGEN AS A GUIDE TO SAMPLING FOR SITE-SPECIFIC MANAGEMENT

机译:土壤矿物氮的空间变异作为现场特定管理采样的指南

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Precise management of crop nutrients in agriculture depends upon adequate information about the spatial variation in soil and crop attributes within fields. Sampling the soil to measure the concentrations of available crop nutrients is costly and time-consuming, but needs to be adequate to obtain digital spatial information for mapping or site-specific management (SSM). Many 'contour' maps created by interpolation are often based on too few data that are not spatially dependent. If this is so it might be more appropriate to classify the data to create management zones. A geostatistical approach based on the variogram enables spatial dependence to be determined and modelled. The variogram from this approach can be used to determine how many sub-samples are required to make up a bulked sample for analysis to remove local variation over a given area, and also to guide sampling intensity on a grid for kriging and eventual mapping or SSM. In this paper we describe a case study largely based on total mineral nitrogen to determine how many samples are required for bulking. In addition, we describe three grid surveys of total mineral nitrogen with some with additional variables to compare their patterns of variation and to illustrate how the variogram range can be used to suggest future sampling intervals for soil and crop surveys. Overall, four or nine samples were optimal for bulking. The recommended grid intervals for two of the fields were similar, but for the other it was twice as large. These intervals reflected the considerable differences in the scales of spatial variation among the fields. Sampling and analysis to measure soil mineral nitrogen is costly and the need for this is likely to be superseded by the use of nitrogen sensors that can provide intensive information relatively cheaply and at several crucial growth stages during the growing season. Nevertheless, the sensors need to be calibrated, which will still require some soil and crop sampling for which the above recommendations are important.
机译:农业中农作物营养素的精确管理取决于有关领域土壤和作物属性的空间变化的充分信息。取样土壤以测量可用作物营养成本昂贵且耗时的浓度,但需要足以获得用于映射或特定于网站特定管理的数字空间信息(SSM)。通过插值创建的许多'轮廓'映射通常基于太少的数据不属于空间依赖。如果这是这样,可以更合适地对数据进行分类以创建管理区域。基于变型仪的地质统计方法可以确定和建模空间依赖性。该方法的变速仪可用于确定组成有多少子样本来构成批量样本以进行分析以消除给定区域上的局部变型,以及指导用于Kriging和最终映射或SSM网格上的采样强度。在本文中,我们描述了案例研究,主要基于总矿物氮以确定膨胀需要多少样品。此外,我们用一些额外的变量描述了总矿物氮的三个网格调查,以比较它们的变化模式,并说明变形仪范围如何用于建议土壤和作物调查的未来采样间隔。总体而言,四个或九个样品对于膨胀是最佳的。两个字段的推荐网格间隔是相似的,但对于另一个领域的距离是两倍的两倍。这些间隔反映了字段之间空间变化尺度的相当大的差异。测量土壤矿质氮的取样和分析是昂贵的,并且可能通过使用氮传感器来取代这一点的需要,这些氮传感器可以在生长季节期间提供密集信息和几个至关重要的生长阶段。然而,需要校准传感器,这仍将需要一些土壤和作物采样,以便上述建议很重要。

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