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首页> 外文期刊>Computers and Electronics in Agriculture >Delineation of site-specific management units in a saline region at the Venice Lagoon margin, Italy, using soil reflectance and apparent electrical conductivity
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Delineation of site-specific management units in a saline region at the Venice Lagoon margin, Italy, using soil reflectance and apparent electrical conductivity

机译:使用土壤反射率和表观电导率来描绘意大利威尼斯泻湖边缘的盐碱地区特定地点的管理单位

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Site-specific crop management utilizes site-specific management units (SSMUs) to apply inputs when, where, and in the amount needed to increase food productivity, optimize resource utilization, increase profitability, and reduce detrimental environmental impacts. It is the objective of this study to demonstrate the delineation of SSMUs using geospatial apparent soil electrical conductivity (ECa) and bare-soil reflectance measurements. The study site was a 21-ha field at the southern margin of the Venice Lagoon, Italy, which is known to have considerable spatial variability of soil properties influencing crop yield. Maize (Zea mais L.) yield maps from 2010 and 2011 showed high spatial heterogeneity primarily due to variation in soil-related factors. Approximately 53% of the spatial variation in maize yield was successfully modeled according to the variability of four soil properties: salinity, texture, organic carbon content, and bulk density. The spatial variability of these soil properties was characterized by the combined use of intensive geospatial ECa measurements and bare-soil normalized difference vegetation index (NDVI) survey data. On the basis of the relationships with these soil properties, ECa and NDVI were used to divide the field into five SSMUs using fuzzy c-means clustering: one homogeneous with optimal maize yield, one unit affected by high soil salinity, one characterized by very coarse texture (i.e., sandy paleochannels), and two zones with both soil salinity and high organic carbon content. Yield monitoring maps provide valuable spatial information, but do not provide reasons for the variation in yield. However, even in cases where measurements like ECa and bare-soil NDVI are not directly correlated to maize yield, their combined use can help classify the soil according to its fertility. The identification of areas where soil properties are fairly homogeneous can help managing diverse soil-related issues optimizing resource use, lowering costs, and increasing soil quality.
机译:特定地点的作物管理利用特定地点的管理单位(SSMU)在增加粮食生产率,优化资源利用,增加利润和减少不利的环境影响所需的时间,地点和数量上应用投入。本研究的目的是使用地理空间表观土壤电导率(ECa)和裸土反射率测量值来描述SSMU的轮廓。研究地点是意大利威尼斯泻湖南缘的一个占地21公顷的田地,已知土壤性质的空间变异性很大,影响作物产量。 2010年和2011年的玉米(Zea mais L.)产量图显示出较高的空间异质性,这主要归因于土壤相关因素的变化。根据四种土壤特性(盐度,质地,有机碳含量和堆积密度)的变异性,成功地模拟了玉米产量中约53%的空间变化。这些土壤特性的空间变异性是通过结合使用密集的地理空间ECa测量值和裸土归一化差异植被指数(NDVI)调查数据来表征的。根据与这些土壤特性的关系,使用ECa和NDVI通过模糊c均值聚类将田地分为5个SSMU:一种均质且玉米产量最佳,一种受高盐度影响,另一种具有非常粗糙的特性质地(即沙质古河道)和两个具有土壤盐分和高有机碳含量的区域。产量监控图提供了有价值的空间信息,但没有提供产量变化的原因。但是,即使在ECa和裸土NDVI等测量值与玉米产量没有直接关系的情况下,它们的组合使用也可以帮助根据土壤肥力对土壤进行分类。确定土壤性质相当均匀的区域可以帮助管理与土壤相关的各种问题,从而优化资源利用,降低成本并提高土壤质量。

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