Soil pH is a key parameter for crop productivity, therefore, its spatial variation should be adequately addressed to improve precision management decisions. Recently, the Veris pH Manager^TM, a sensor for high-resolution mapping of soil pH at the field scale, has been made commercially available in the US. While driving over the field, soil pH is measured on-the-go directly within the soil by ion selective antimony electrodes. The aim of this study was to evaluate the Veris pH Manager^TM under farming conditions in Germany. Sensor readings were compared with data obtained by standard protocols of soil pH assessment. Experiments took place under different scenarios: (a) controlled tests in the lab, (b) semicontrolled test on transects in a stop-and-go mode, and (c) tests under practical conditions in the field with the sensor working in its typical on-the-go mode. Accuracy issues, problems, options, and potential benefits of the Veris pH Manager^TM were addressed. The tests demonstrated a high degree of linearity between standard laboratory values and sensor readings. Under practical conditions in the field (scenario c), the measure of fit (r²) for the regression between the on-the-go measurements and the reference data was 0.71, 0.63, and 0.84, respectively. Field-specific calibration was necessary to reduce systematic errors. Accuracy of the on-the-go maps was considerably higher compared with the pH maps obtained by following the standard protocols, and the error in calculating lime requirements was reduced by about one half. However, the system showed some weaknesses due to blockage by residual straw and weed roots. If these problems were solved, the on-the-go sensor investigated here could be an efficient alternative to standard sampling protocols as a basis for liming in Germany.

Liming is an effective measure to increase the soil pH and to counterbalance soil acidification. Therefore, the liming recommendations (LRs) for agricultural practice consider two aspects: changing the initial pH to the desired pH and compensating for all pH decreases taking place within the liming interval. The separation of these aspects is essential to optimize LRs and to minimize lime losses to the environment. Therefore, we developed a pedotransfer function (PTF) to calculate the lime demand to change the initial pH to the desired pH and compared the results with the LRs for agricultural practice. Applying this PTF to a set of 126 soil samples that were analyzed for base neutralization capacity could explain approximately 78% of the variability in the pH changes after the addition of different amounts of Ca(OH)₂. Consequently, the lime demand to change the initial pH to the desired pH increased by approximately one-sixth compared to the lime demand proposed by the liming recommendation scheme, which is commonly used in Germany. From the numerical difference between the lime demand according to the LRs and the PTF, we calculated the annual acidification rates based on the soil texture, organic matter content and initial pH. Decoupling the abovementioned two aspects of LRs might be helpful to optimize the LRs by adapting to different regions, diverse management strategies and a changing climate.

Zusammenfassung: Das Ziel der Untersuchung war es, den Einfluss der Fahrgassenpflegefrequenz auf die Lufttemperatur und die relative Luftfeuchte in Plantagen mit Schwarzen Johannisbeeren (Ribes nigrum L.) zu ermitteln. In der Praxis dient das Kurzhalten des Fahrgassenbewuchses als angewandte und präventive Methodik, um die Durchlüftung im Bestand zu verbessern und den Krankheitsdruck zu senken. Die On-Farm-Versuche fanden auf zwei ökologisch geführten Strauchbeerenbetrieben in Brandenburg (Weggun und Schöneiche) über einen Zeitraum von März bis Juni 2021 statt. Die Einflüsse der Pflegefrequenz auf das Mikroklima in den Fahrgassen wurden für praxisübliche Mahdfrequenz („business as usual“, BAU) und erhöhte Mahdfrequenz (TEST) ermittelt. Die Ergebnisse zeigen, dass mit einem kontinuierlichen Kurzhalten der Fahrgassen eine nachweisbare Beeinflussung auf die bodennahe Lufttemperatur und Luftfeuchte stattfindet. Dabei führte eine erhöhte Mahdfrequenz (TEST) teils zu einer signifikant höheren mittleren Lufttemperatur als in praxisüblicher Pflege (BAU), wohingegen die mittlere relative Luftfeuchte signifikant geringer war. Die Effekte waren abhängig vom Zeitpunkt und vom Standort. Über die Versuchsperiode konnten maximale mittlere Lufttemperaturdifferenzen von 1,14 °C (Standort Weggun) bzw. 1,96 °C (Standort Schöneiche) sowie maximale mittlere relative Luftfeuchtedifferenzen von 3,69 % (Standort Weggun) bzw. 3,90 % (Standort Schöneiche) zwischen den Varianten TEST und BAU festgestellt werden. Vor allem in der Plantage mit dem geringeren Reihenabstand tritt dieser Effekt deutlicher auf. Die Ergebnisse lassen vermuten, dass die Plantagenstruktur einen Einfluss auf die Effekte hat, was in zukünftigen Versuchen untersucht werden sollte.

For site-specific estimation of lime requirement, high-resolution soil maps of clay, soil organic carbon (SOC) and pH are required. These can be generated using digital soil mapping (DSM), in which prediction models are fitted on covariates from proximal soil sensors. However, the quality of the maps derived may differ significantly depending on the methodology applied. Hence, we assessed effects of (i) calibration sample size (5-100), (ii) sampling design (simple random sampling (SRS), conditioned Latin Hypercube sampling (cLHS) and k-means sampling (KM)) and (iii) prediction model (linear regression (LR) and Random Forest (RF)) on the prediction performance for the above mentioned three soil properties using data from two multi-sensor platforms. The present case study is based on a geostatistical simulation using 250 soil samples from a 51 ha field in Germany. Among others, Lin’s concordance correlation coefficient (CCC) and root-mean-square error (RMSE) were used to evaluate model performances. Results show that with increasing sample size, improvements of RMSE and CCC decreased exponentially. We found best median RMSE values at 100 calibration soil samples, i.e. 1.73%, 0.3 and 0.21% for Clay, pH and SOC, respectively. However, already with 10 samples, models of moderate quality (CCC > 0.65) can be obtained for all three soil properties. Both, cLHS and KM obtained significantly better results than SRS. At smaller sample sizes, LR showed lower median RMSE values than RF for SOC and pH. Nonetheless, with at least 75-100 and 25-30 samples, RF eventually outperformed LR. For clay, median RMSE was lower with RF, regardless of sample size.