Guidelines for precise lime management based on high-resolution soil pH, texture and SOM maps generated from proximal soil sensing data

Abstract

Soil acidification is caused by natural paedogenetic processes and anthropogenic impacts but can be counteracted by regular lime application. Although sensors and applicators for variable-rate liming (VRL) exist, there are no established strategies for using these tools or helping to implement VRL in practice. Therefore, this study aimed to provide guidelines for site-specific liming based on proximal soil sensing. First, high-resolution soil maps of the liming-relevant indicators (pH, soil texture and soil organic matter content) were generated using on-the-go sensors. The soil acidity was predicted by two ion-selective antimony electrodes (RMSEpH: 0.37); the soil texture was predicted by a combination of apparent electrical resistivity measurements and natural soil-borne gamma emissions (RMSEclay: 0.046 kg kg−1); and the soil organic matter (SOM) status was predicted by a combination of red (660 nm) and near-infrared (NIR, 970 nm) optical reflection measurements (RMSESOM: 6.4 g kg−1). Second, to address the high within-field soil variability (pH varied by 2.9 units, clay content by 0.44 kg kg−1 and SOM by 5.5 g kg−1), a well-established empirical lime recommendation algorithm that represents the best management practices for liming in Germany was adapted, and the lime requirements (LRs) were determined. The generated workflow was applied to a 25.6 ha test field in north-eastern Germany, and the variable LR was compared to the conventional uniform LR. The comparison showed that under the uniform liming approach, 63% of the field would be over-fertilized by approximately 12 t of lime, 6% would receive approximately 6 t too little lime and 31% would still be adequately limed.

Predicting the Lime Demand of Arable Soils from pH Value, Soil Texture and Soil Organic Matter Content

“…Finally, they produced 2 × 2 m maps of the actual soil acidity (pH), SOM and soil texture (clay, silt, and sand). Bönecke et al [17] used these generated soil maps and the stepless algorithm to successfully produce variable rate lime application maps. They compared the variable liming rates with a standard uniform liming approach and found that 63% of the field would be over-fertilized by approximately 12 Mg of lime, 6% would receive approximately 6 Mg too little lime and 31% would still be adequately limed under the uniform liming approach.…”

“…Farmers can use these tables without calculations and without special expert knowledge to assess the lime demand for their fields. Over the last 40 years, the VDLUFA look-up table system has served the agricultural needs of lime fertilization very well but has to be considered very rough, especially in relation to new results of soil texture mapping [17][18][19]. As was shown in these studies, soil data can now be determined very accurately and with a high degree of spatial accuracy using on-the-go soil sensors and digital soil mapping techniques.…”

Direct prediction of site-specific lime requirement of arable fields using the base neutralizing capacity and a multi-sensor platform for on-the-go soil mapping

“…The soil sampling sites for BNC laboratory analysis were taken in accordance with the procedure proposed by Bönecke et al (2020), considering that the targeted samples: (i) cover A total of 164 soil samples (Fig. 1) were analyzed for base neutralizing capacity (BNC).…”

“…Von Cossel et al (2019) deployed low-input sensor-based soil mapping of electromagnetic induction (EMI) (EM38 MK I; Geonics, Canada) in combination with in situ and ex situ pH measurements to determine the soil's LR. Bönecke et al (2020) applied an on-the-go multi-sensor approach using the Veris Mobile Sensor Platform and the Geophilus Electricus (Geophilus GmbH, Germany) proximal soil sensing system (Lück and Rühlmann, 2013). By combining the sensor data with reference analyses of soil characteristics that are well-correlated with soil acidity and soil pH buffer capacity, high-resolution soil maps of pH, texture and soil organic matter (SOM) were generated.…”

Artificial Intelligence-Based Prediction of Key Textural Properties from LUCAS and ICRAF Spectral Libraries

“…Considering variations in soil texture within a field is now possible with global positioning system (GPS) and precision agriculture (PA). The aim of these technologies is to divide the field into homogenous management zones suitable for optimal site-specific management of fertilization [6], liming [7], and drainage and irrigation [8]. Consequently, recommending the same management practices irrespective to the textural group can lead to unwanted high crop yield variability.…”