Sampling procedure simulating on‐the‐go sensing for soil nutrients

Abstract

The emergence of a new sensor technology based on the use of ion‐selective membranes provides an increasing number of opportunities for on‐the‐go field measurements of soil nutrients and soil pH. In the future, on‐the‐go sensing should provide a cost‐effective monitoring of heterogeneous soils with high sampling resolution. It is suitable for site‐specific management because it can be focused on the spatial representativity of observation. This study evaluates the on‐the‐go‐sensing sampling design by comparing it with a standard approach to soil sampling for soil pH and the base nutrients P, K, and Mg under local field conditions in Germany. Soil samples were taken in two test sites at a resolution and in a manner as if they were sampled with an on‐the‐go sensing system and were compared with soil samples taken at a coarser resolution and with standard methods. In general, a higher variability was observed among the on‐the‐go samples due to their smaller sample support. The finer sampling resolution of the on‐the‐go design improved field‐scale semivariogram‐analysis results, identifying the spatial structures for soil pH, P, and Mg clearly. In addition, kriged maps of these soil parameters had predominantly higher estimation accuracies. However, the on‐the‐go samples were strongly influenced by the small‐scale variability of K in one of the test sites. This variability increased the kriging standard deviation for K by 50% compared with standard sampling design. Despite of this problem, the on‐the‐go‐sensing sampling design revealed field‐scale spatial variability for base nutrient status more accurately. Except for K, the mean absolute error of fertilizer‐application maps was reduced when using the on‐the‐go sample design in comparison with the standard sample design (Ca: 210/268 kg ha–1, P: 2.85/6.75 kg ha–1, K: 13.7/6.0 kg ha–1, Mg 5.7/6.8 kg ha–1). This will reduce over‐ and underfertilization using variable‐rate fertilizer‐application systems. In the future, it will be of interest if real on‐the‐go soil‐sensor measurements exhibit the same variability behavior addressed here or if results will differ substantially.

Characterising the within-field scale spatial variation of nitrogen in a grassland soil to inform the efficient design of in-situ nitrogen sensor networks for precision agriculture

“…On-the-go sampling is generally more spatially intensive than manual field sampling, allowing better spatial resolution, although key information on how soil mineral N varies over small spatial scales may not be obtained. This can lead to increased uncertainties of interpolative predictions, especially if the sample volume is small (Schirrmann and Domsch, 2011). Furthermore, increasing the temporal resolution of this approach requires additional economic costs and as both these approaches rely on reactive management, crucial changes in soil mineral N status may be missed.…”

Performance of Automated Near‐Infrared Reflectance Spectrometry for Continuous in Situ Mapping of Soil Fertility at Field Scale

“…Despite these multifaceted interrelationships, topsoil properties are not distributed at random but display distinct but small‐scale patterns at the field scale. Spatial variation of soil fertility parameters, such as SOM, phosphorus (P), soil pH, or potassium (K), often occur at distances below 50 m in the topsoil (McBratney and Pringle, 1999; Schirrmann and Domsch, 2011). A profound understanding of the spatial patterns would be beneficial for both the farmer who wants to improve soil conditions or crops and the environmentalist who wants to prevent pollution by reducing excess fertilization or leaching of nutrients.…”

Sensor Technologies for Precision Soil Nutrient Management and Monitoring

“…Optical sensing is based on reflectance spectroscopy, which detects the level of energy absorbed or reflected by soil particles, while electrochemical sensing uses ion-selective electrodes to generate a voltage or current output as a response to the activity of selected nutrient ions (Kim et al, 2009 (Adamchuk et al, 2004). Ion-selective membrane sensors offer opportunities for on-the-go soil nutrient(s) and pH measurements (Schirrmann and Domsch, 2011). Presently, the limitation of the technology is that the values obtained may not be as accurate as a laboratory test, but the high sampling density may increase the overall accuracy of the resulting soil nutrient or pH maps.…”

“…Recent advances indicate that efficient nutrient management in crop fields can be attained through the application of Precision Agriculture (PA)-based geo-spatial technologies such as global positioning system, geographical information system, remote sensing, geostatistics and variable rate application (Gebbers and Adamchuk, 2010;Robert, 2002). Variable-rate fertilizer application, one of the basic tenets of PA, has been shown to optimize fertilizer use efficiency by overcoming the problem of over-and under-fertilization (Schirrmann and Domsch, 2011). Ultimately, this strategy is envisaged to increase crop yields and quality, reduce resource waste and promote environment stewardship.…”