The upcoming technological breakthrough in the cropping system will offer a more detailed insight into soil-to-plant, man-to-soil, and man-to-plant impacts, thus improving the forecasting and ensuring more efficient in-field management. This study presents various on-the-go sensing procedures which were conducted in order to evaluate the quality of spatial estimations of soil physical properties such as soil compaction, soil moisture content, bulk density and texture. Standard statistical tools showed high positive correlations between soil specific resistance and soil compaction (R² = .75), soil electromagnetic conductivity and moisture content (R² = .72) and tractor wheel slip and soil compaction (R² = .64). Variogram modeling of spatial autocorrelation gave the highest prediction error for tillage resistance (9.85%), followed by cone index (4.49%), moisture content (3.7%), bulk density (1.39%), clay + silt content (.98%), soil electromagnetic conductivity (.95%) and the least error was obtained for tractor wheel slip (.58%). The Central Composite Design (CCD) analysis confirmed significant contribution of soil compaction in the modeling of the specific soil resistance and tractor wheel slip, while soil moisture content and fine particle (clay + silt) content had a major impact on soil electromagnetic conductivity measurement. Soil bulk density had considerable importance in CCD modeling of tractor wheel slip.

即将到来的种植系统技术突破将提供对土壤对植物，人对土壤以及人对植物影响的更详细的了解，从而改善预测并确保更有效的田间管理。这项研究提出了各种移动式传感程序，以评估土壤物理性质（如土壤压实度，土壤水分含量，堆积密度和质地）的空间估计质量。标准统计工具显示出土壤电阻率与土壤密实度（R ²  = .75），土壤电磁传导率和水分含量（R ²  = .72）与拖拉机车轮滑移和土壤密实度（R ²）之间呈高度正相关。 = .64）。空间自相关的方差图建模给出了最高的抗耕性预测误差（9.85％），其次是锥指数（4.49％），水分含量（3.7％），堆积密度（1.39％），粘土+粉砂含量（.98％） ，土壤电磁导率（.95％），而拖拉机的车轮打滑误差最小（.58％）。中央复合设计（CCD）分析证实了土壤压实在特定土壤阻力和拖拉机车轮滑移建模中的重要贡献，而土壤水分含量和细颗粒（粘土+淤泥）含量对土壤电磁导率测量有重要影响。土壤体积密度在拖拉机车轮打滑的CCD建模中具有相当重要的意义。