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In the context of phosphorus (P) exhaustion and low P use efficiency (PUE) in crop production, a field trial was designed on a low-P soil in southwestern Germany in 2020 and 2021 to investigate the effects of P fertilizer type and liming on maize growth and P uptake and PUE. The experimental factors were (i) two P fertilizer types, rock phosphate (RP) and diammonium phosphate (DAP); (ii) lime application, lime and no lime; and (iii) two maize cultivars. The results showed that RP resulted in a lower leaf area index and light interception compared with DAP, a 33% lower silage yield, and a 29% lower P content at harvest. The PUE of RP was 18%, which was 37% lower than DAP. Soil liming reduced shoot biomass and led to 35% less shoot P content at the six-leaf stage. The maize cultivar Stabil expressed higher yielding and P acquisition characteristics. In conclusion, DAP cannot be replaced by placed RP, regardless of the lime application in silage maize production in this study. Future research on the PUE of maize cultivars should also consider root characteristics in response to P fertilizer type and soil pH. [ABSTRACT FROM AUTHOR]

Detailed knowledge of a soil's lime requirement (LR) is a prerequisite for a demand-based lime fertilization to achieve the optimum soil pH and thus sustainably increasing soil fertility and crop yields. LR can be directly determined by the base neutralizing capacity (BNC) obtained by soil-base titration. For a site-specific soil acidity management, detailed information on the within-field variation of BNC is required. However, soil-base titrations for BNC determination are too laborious to be extensively applied in routine soil testing. In contrast, visible and near-infrared spectroscopy (visNIRS) is a time and cost-effective alternative that can analyze several soil characteristics within a single spectrum. VisNIRS was tested in the laboratory on 170 air-dried and sieved soil samples of nine agricultural fields of a quaternary landscape in North-east Germany predicting the soil's BNC and the corresponding lime requirement (LRBNC) at a target pH of 6.5. Seven spectral pre-processing methods were tested including a new technique based on normalized differences (ND). Furthermore, six multivariate regression methods were conducted including a new method combining a forward stagewise subset selection algorithm with PLSR (FS-PLSR). The models were validated using an independent sample set. The best regression model for most target variables was FS-PLSR combined with the second Savitzky-Golay derivation as pre-processing method achieving R2s from 0.68 to 0.82. Finally, the performance of the direct prediction of LRBNC (R2 = 0.68) was compared with an indirect prediction that was calculated by the predicted BNC parameters. This resulted in slightly higher correlation coefficients for the indirect method with R2 = 0.75. [ABSTRACT FROM AUTHOR]

Forest liming is a management tool that has and continues to be used extensively across northern Europe to counteract acidification processes from anthropogenic sulfur and nitrogen (N) deposition. In this study, we quantified how liming affects soil organic carbon (SOC) stocks and attempt to disentangle the mechanisms responsible for the often contrasting processes that regulate net soil carbon (C) fluxes. Using a paired plot experimental design we compared SOC stocks in limed plots with adjacent unlimed control plots at 28 experimental sites to 60 cm soil depth in mature broadleaf and coniferous forests across Germany. Historical soil data from a subset of the paired experiment plots were analyzed to assess how SOC stocks in both control and limed plots changed between 1990 and 2019. Overall, we found that forest floor C stocks have been accumulating over time in the control plots. Liming however largely offset organic layer buildup in the L/O f layer, and forest floor C stocks remained unchanged over time in the limed plots. This, in turn, meant that nutrients remained mobile and were not bound in soil organic matter complexes. Results from the paired plot analysis showed that forest floor C stocks were significantly lower in limed plots than the control (- 34 %, - 8.4 ± 1.7 MgCha-1) but did not significantly affect SOC stocks in the mineral soil, when all sites are pooled together. In the forest floor layers, SOC stocks exhibited an exponential decrease with increasing pH, highlighting how lime-induced improvements in the biochemical environment stimulate organic matter (OM) decomposition. Nevertheless, for both forest floor and mineral soils, the magnitude and direction of the belowground C changes hinged directly on the inherent site characteristics, namely, forest type (conifer versus broadleaf), soil pH, soil texture, and the soil SOC stocks. On the other hand, SOC stock decreases were often offset by other processes that fostered C accumulation, such as improved forest productivity or increased carbon stabilization, which correspondingly translated to an overall variable response by SOC stocks, particularly in the mineral soil. Lastly, we measured soil carbon dioxide (CO2) and soil methane (CH4) flux immediately after a re-liming event at three of the experimental sites. Here, we found that (1) liming doubles CH4 uptake in the long-term; (2) soil organic matter mineralization processes respond quickly to liming, even though the duration and size of the CO2 flush varied between sites; and (3) lime-derived CO2 contributed very little to total CO2 emissions over the measurement period (determined using stable isotope approaches). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Liming agricultural fields is necessary for counteracting soil acidity and is one of the oldest operations in soil fertility management. However, the best management practice for liming in Germany only insufficiently considers within-field soil variability. Thus, a site-specific variable rate liming strategy was developed and tested on nine agricultural fields in a quaternary landscape of north-east Germany. It is based on the use of a proximal soil sensing module using potentiometric, geoelectric and optical sensors that have been found to be proxies for soil pH, texture and soil organic matter (SOM), which are the most relevant lime requirement (LR) affecting soil parameters. These were compared to laboratory LR analysis of reference soil samples using the soil's base neutralizing capacity (BNC). Sensor data fusion utilizing stepwise multi-variate linear regression (MLR) analysis was used to predict BNC-based LR (LRBNC) for each field. The MLR models achieved high adjusted R2 values between 0.70 and 0.91 and low RMSE values from 65 to 204 kg CaCO3 ha−1. In comparison to univariate modeling, MLR models improved prediction by 3 to 27% with 9% improvement on average. The relative importance of covariates in the field-specific prediction models were quantified by computing standardized regression coefficients (SRC). The importance of covariates varied between fields, which emphasizes the necessity of a field-specific calibration of proximal sensor data. However, soil pH was the most important parameter for LR determination of the soils studied. Geostatistical semivariance analysis revealed differences between fields in the spatial variability of LRBNC. The sill-to-range ratio (SRR) was used to quantify and compare spatial LRBNC variability of the nine test fields. Finally, high resolution LR maps were generated. The BNC-based LR method also produces negative LR values for soil samples with pH values above which lime is required. Hence, the LR maps additionally provide an estimate on the quantity of chemically acidifying fertilizers that can be applied to obtain an optimal soil pH value. [ABSTRACT FROM AUTHOR]

Background: Soil pH is one of the key factors affecting soil fertility. For optimal plant growth on agricultural land, suitable liming management is recommended. The basis of appropriate soil pH management in agriculture is provided by recommendations on site‐specific optimum pH ranges and lime application. Aims: This study determined the current variability of soil pH of cropland and grassland in Germany, analysed the factors associated with this variability, evaluated pH management on German agricultural land and developed a national map of current pH values in agricultural soils. Methods: The study mainly focused on the topsoil layers of 2197 cropland sites (0–30 cm) and 812 permanent grasslands sites (0–10 cm) in the German Agricultural Soil Inventory, but reports soil pH (CaCl2) down to 50 cm. Random forest models were applied to identify environmental and management factors related to soil pH and develop a map of pH values in agricultural topsoils in Germany. The current pH status of the sampled sites was classified in accordance with German lime requirement recommendations. Results: The soil pH of both land‐use types showed a bimodal distribution, with peaks at pH 6.1 and 7.3 for cropland and pH 5.0 and 7.1 for grassland. Soils with pH <5 were mainly found on sandy soils in northern Germany and acidic parent material in southern Germany. Alkaline soils predominated in central and southern Germany on loess and carbonate‐rich material. The carbonate content was the major factor explaining soil pH variability, but its effect was restricted to pH values >6. Only 35.4% of the cropland sites and 23.9% of the grassland sites were within the recommended optimum pH range. The percentage of sites below the recommended pH range was greater for grassland than for cropland (52.0% and 41.0%, respectively). Conclusions: Soil pH of German agricultural land is influenced by natural factors and pH management. The first national inventory of soil pH showed that many agricultural soils exhibited pH values lower than those recommended. More conscientious pH management could help improve soil fertility and crop yield. This should be based on the regular topsoil pH analysis conducted by most farmers every five years, together with the determination of plant‐available potassium and phosphorus in soils. [ABSTRACT FROM AUTHOR]