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Delineating site-specific management zones on pasture soil using a probabilistic and objective model and geostatistical techniques

机译:使用概率和客观模型和地质统计技术将特定于场地的管理区描绘出牧场土壤

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In recent years, different algorithms have been utilised to delineate management zones, areas with similar properties, within agricultural fields. However, there are few applications in pasture systems. In this work, the formulation of the Rasch model, as an objective and probabilistic technique to integrate different soil properties, provided measures of pasture soil fertility that were used to analyse spatial variability throughout a field. To illustrate the proposed approach, a case study was conducted in a pasture field. Ten soil properties (sand, silt, and clay contents, moisture content, pH, organic matter, nitrogen, phosphorus, potassium, and soil apparent electrical conductivity) were measured at 76 locations in a pasture field; after their integration in the model, a classification of all sampling locations according to pasture soil fertility was determined, and the influence of each soil property on the soil fertility was highlighted, with the soil moisture, clay, and sand contents and nitrogen being the most influential properties and the silt content being the least influential property. Then, an ordinary kriging algorithm was used to estimate pasture soil fertility throughout the field, and homogeneous zones were delimited from the kriged map. The possibility of using probability maps to determine management zones and provide information for hazard assessments of pasture soil fertility in the field was also shown. Finally, NDVI data at each sampling location were utilised to verify the differences between the management zones.
机译:近年来,不同的算法已被利用在农业领域内描绘管理区,具有相似性质的区域。但是,牧场系统中很少有应用程序。在这项工作中,RasCh模型的配方作为一种目标和概率技术,以整合不同的土壤性质,提供了用于分析整个领域的空间变异性的牧场土壤肥力的措施。为了说明所提出的方法,在牧场进行案例研究。在牧场的76个位置测量10个土壤性质(砂,淤泥和粘土含量,水分,pH,有机物,氮,磷,钾和土壤表观导电性);在模型中的整合之后,确定了根据牧场土壤肥力的所有取样位置的分类,并且每个土壤性质对土壤肥力的影响突出,土壤水分,粘土和砂内容物最多有影响力的属性和淤泥含量是最不影响的财产。然后,使用普通的Kriging算法用于估计整个场的牧场土壤肥力,并且从Kriged地图界定均匀区域。还显示了使用概率图来确定管理区的可能性,并为田间牧场土壤肥力提供危害评估的信息。最后,利用每个采样位置的NDVI数据来验证管理区域之间的差异。

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