Farm Technology Group, Wageningen University, PO Box 317, 6700 AH Wageningen, The Netherlands.
Sensors (Basel). 2013 Nov 27;13(12):16263-80. doi: 10.3390/s131216263.
Fine-scale spatial information on soil properties is needed to successfully implement precision agriculture. Proximal gamma-ray spectroscopy has recently emerged as a promising tool to collect fine-scale soil information. The objective of this study was to evaluate a proximal gamma-ray spectrometer to predict several soil properties using energy-windows and full-spectrum analysis methods in two differently managed sandy loam fields: conventional and organic. In the conventional field, both methods predicted clay, pH and total nitrogen with a good accuracy (R2 ≥ 0.56) in the top 0-15 cm soil depth, whereas in the organic field, only clay content was predicted with such accuracy. The highest prediction accuracy was found for total nitrogen (R2 = 0.75) in the conventional field in the energy-windows method. Predictions were better in the top 0-15 cm soil depths than in the 15-30 cm soil depths for individual and combined fields. This implies that gamma-ray spectroscopy can generally benefit soil characterisation for annual crops where the condition of the seedbed is important. Small differences in soil structure (conventional vs. organic) cannot be determined. As for the methodology, we conclude that the energy-windows method can establish relations between radionuclide data and soil properties as accurate as the full-spectrum analysis method.
需要精细的土壤属性空间信息才能成功实施精准农业。近地表伽马射线光谱分析最近已成为一种很有前途的工具，可用于采集精细的土壤信息。本研究的目的是评估近地表伽马射线光谱仪，以利用能窗和全谱分析方法在两个不同管理的砂壤土农田（常规和有机）中预测几种土壤特性。在常规农田中，两种方法都可以很好地预测表层 0-15cm 土壤深度的粘土、pH 值和全氮（R2≥0.56），而在有机农田中，只有粘土含量能达到这种精度。在能窗方法中，常规农田中全氮的预测精度最高（R2=0.75）。与单个和组合农田相比，表层 0-15cm 土壤深度的预测效果更好。这意味着伽马射线光谱分析通常可以受益于一年生作物的土壤特性描述，因为苗床的状况很重要。常规与有机农田之间的土壤结构的微小差异无法确定。就方法而言，我们得出的结论是，能窗方法可以建立与土壤特性之间的关系，其精度与全谱分析方法相当。
