Search

Mid‐infrared spectroscopy (MIRS) has proven to be a cost‐effective, high throughput measurement technique for soil analysis. After multivariate calibration mid‐infrared spectra can be used to predict various soil properties, some of which are related to lime requirement (LR). The objective of this study was to test the performance of MIRS for recommending variable rate liming on typical Central European soils in view of precision agriculture applications. In Germany, LR of arable topsoils is commonly derived from the parameters organic matter content (SOM), clay content, and soil pH (CaCl2) as recommended by the Association of German Agricultural Analytical and Research Institutes (VDLUFA). We analysed a total of 458 samples from six locations across Germany, which all revealed large within‐field soil heterogeneity. Calcareous topsoils were observed at some positions of three locations (79 samples). To exclude such samples from LR determination, peak height at 2513 cm−1 of the MIR spectrum was used for identification. Spectra‐based identification was accurate for carbonate contents > 0.5%. Subsequent LR derivation (LRSPP) from MIRS‐PLSR predictions of SOM, clay, and pH (CaCl2) for non‐calcareous soil samples using the VDLUFA look‐up tables was successful for all locations (R2 = 0.54–0.82; RMSE = 857–1414 kg CaO ha−1). Alternatively, we tested direct LR prediction (LRDP) by MIRS‐PLSR and also achieved satisfactory performance (R2 = 0.52–0.77; RMSE = 811–1420 kg CaO ha−1; RPD = 1.44–2.08). Further improvement was achieved by refining the VDLUFA tables towards a stepless algorithm. It can be concluded that MIRS provides a promising approach for precise LR estimation on heterogeneous arable fields. Large sample numbers can be processed with low effort which is an essential prerequisite for variable rate liming in precision agriculture. [ABSTRACT FROM AUTHOR]

Although the evaluation of soil organic carbon (SOC) stocks across different types of land use and major reference soil groups is essential for mitigating climate change, there remains, to date, limited comprehensive understanding of whole tropical soil profiles. Therefore, this study aimed to explain the amount of SOC stocks in different land-use systems and across various soil groups, as well as its spatial pattern in the topsoil (0–30 cm) and subsoil (30–100 cm) within the savannah zone of Burkina Faso. Roughly 70 soil profiles were considered along with additional auger sampling to account for spatial variation in both cropland (CR) and savannah (SA). The machine learning technique random forest regression (RFR) and multiple linear regression (MLR) were used for modeling the surface and subsurface SOC stocks. For model calibration, covariates including land use, topographic, texture, and climatic data were considered as surrogate for soil forming factors. The prediction maps produced by the calibrated models were validated by an independent dataset. The results indicated that about 53% of the SOC stock over 1 m depth was held in the upper 30 cm. Only a marginal difference was recorded between the topsoil SOC stock in SA (41.4 t C ha −1 ) and CR (39.1 t C ha −1 ) soils. For the subsoil, a significant difference (p < 0.05) was observed between the SOC stock of CR soils recording about 40.2 t C ha −1 and SA soils with 26.3 t C ha −1 . Among the reference soil groups, the Gleysols located at lower elevation positions revealed the highest SOC stocks over 0–30 cm (44 t C ha −1 ) and 100 cm depth (86.6 t C ha −1 ). The Stagnosols (45.2 t C ha −1 ) followed by the Gleysols (42.7 t C ha −1 ) recorded the highest SOC stocks over 30–100 cm. The variability of SOC stock in the topsoil was primarily related to site-specific elements, such as particle-size fraction and wetness index, while its distribution in the subsoil was mainly associated with the topographical orientation represented by the slope aspect. Compared to the MLR, RFR estimated mean top- and subsoil SOC stocks of the catchment fairly well, along with lower statistical error metrics, though extreme values were not covered. Nevertheless, the findings on SOC stocks reinforce the view that the semi-arid ecosystems of West Africa still offer a significant opportunity for carbon sequestration for both topsoil and subsoil, and these results represent a baseline for future modeling of SOC dynamics in the region. [ABSTRACT FROM AUTHOR]

Nitrogen (N) deposition to soils is globally rising, but its effect on soil organic carbon (SOC) turnover is still uncertain. Moreover, common theories of stoichiometric decomposition and microbial N mining predict opposing effects of N supply on SOC turnover. We hypothesized that the effect of N deposition on SOC turnover depends on initial soil nutrient conditions. Thus, we sampled tropical forests and rubber gardens with pronounced gradients of nutrient availability from the topsoil to the deep subsoil (up to 400 cm) and measured substrate-induced respiration (SIR) for 30 days in four treatments (C, CN, CP, CNP additions). A natural 13 C abundance approach was conducted to quantify priming effects (PE) of the added C on SOC mineralization. For this purpose we assessed the 13 CO 2 isotope composition after adding a C4 sugar to the C3 soil; to correct for isotopic fractionation a treatment with C3 sugar additions served as control. We found that nutrient additions to topsoil did neither alter cumulative CO 2 release within 30 days (SIR acc ) nor PE (PE = 1.6, i.e., sugar additions raised the release of SOC-derived CO 2 by a factor of 1.6). In the upper subsoil (30–100 cm), however, both CN and CP additions increased SIR acc (by 239% and 92%, respectively) and the PE (PE = 5.2 and 3.3, respectively) relative to the treatments that received C only (PE = 1.7), while CNP additions revealed the largest increase of SIR acc (267%) and PE (PE = 6.0). In the deep subsoil (>130 cm depth), only the CNP addition consistently increased SIR acc (by 871%) and PE (PE = 5.2) relative to only C additions (PE = 2.0). We conclude that microbial activity was not limited by nutrients in the topsoil but was co-limited by both N and P in the subsoil. The results imply that microbes mine nutrients from previously unavailable pools under the conditions that 1) deficiency actually exists, 2) co-limitation is alleviated, and 3) nutrient reserves are present. Yet, as opposed to microbial nutrient mining theories, we showed that the subsoil PE is highest when nutrient supply matches microbial demand. As a result also N deposition might exert variable effects on SOC turnover in tropical soils: it might have no effect in nutrient-rich topsoils and in co-limited subsoils without P reserves but might increase SOC turnover in co-limited subsoils with potentially acquirable P reserves. [ABSTRACT FROM AUTHOR]

The ongoing conversion of natural ecosystems of the tropics into agricultural land usually results in a loss of soil organic carbon (SOC), the extent of which being largely affected by climate and soil order. We assumed that i) SOC losses are not restricted to the surface soil but extend into the subsoil, but that ii) the rate of SOC losses is controlled by interactions of SOC with Fe oxides. Hence, we sampled Plinthosols down to one meter (if feasible) that had been converted from native savannah into cropland 0 to 29 years ago in the Dano district (Southwest Burkina-Faso). Beside the assessment of SOC stocks, we also determined the proportion of surface (0–10 cm) SOC remaining after Fe oxide removal as well as its distribution among the following particle-size fractions: 2000–250 μm (coarse sand-sized SOC; POM1), 250 μm–53 μm (fine-sand-sized SOC; POM2), 53 μm–20 μm (very fine sand-sized SOC; POM3), and <20 μm (nonPOM). We found that the soils lost 24 t C ha −1 from the upper 10 cm and 49 t C ha −1 from the upper 30 cm, respectively, after 29 years. SOC losses extended also into the subsoil, and, on the average, 0.7–19.5 t C ha −1 was lost from the 30–100 cm depth interval. The loss rates of SOC were similar to those reported for other soil types. Rapid losses of SOC occurred from all POM fractions as well as from the Fe-oxide pool, which, on average, contained about 16% of total SOC stock. We conclude that even if Fe oxides as well as other silt- and clay-sized organomineral complexes delay losses of SOC relative to POM, they failed to efficiently protect SOC from decomposition in these Plinthosols. [ABSTRACT FROM AUTHOR]

Gamma spectrometric field measurements may provide high resolution information on topsoil texture. Yet, calibrations for the estimation of texture data usually have to be done site-specifically. The lack of site-independent calibrations thus limits the easy and universal use of proximal gamma-ray sensing in soil mapping and precision agriculture. Our objective was to develop a study site-independent prediction model for topsoil texture from gamma-ray spectra. We surveyed ten study sites across Germany with 417 reference samples (291 for calibration, 126 for test set-validation), providing soils from a broad range of parent materials and with widely varying soil texture. First, study site-specific models were calibrated by a linear regression approach. These models provided reliable estimations of sand, silt, and clay for most of the study sites. Second, study site-independent models were calibrated via i) linear regression and ii) support vector machines (SVM), the latter being mathematical methods of data pattern recognition. Based on the non-linear relationship between gamma spectrum and soil texture, which varied widely between the different parent materials the linear models are not appropriate for satisfactory soil texture prediction (averaged R 2 of 0.73 for sand, 0.61 for silt, and 0.18 for clay and averaged absolute prediction errors of 9 to 5%, respectively). In contrast, the SVM calibrated prediction models revealed reliable performance also for site-independent calibrations. With the non-linear SVM approach we were able to include all sites in one single prediction model for each texture fraction although the different mineralogical composition of their parent materials led to complex and partly opposing relationships between gamma features and soil texture. Site-independent predictions via SVM were often even better than site-specific linear regression models. The site-independent SVM calibrated predictions yielded an averaged R 2 of 0.96 (sand), 0.93 (silt), and 0.78 (clay), and corresponding averaged absolute prediction errors of 2 to 4%, respectively. To summarize, (i) non-linear prediction models are a feasible approach for capable site-independent texture estimations across a wide range of soils and (ii) gamma spectrometry-based texture predictions are a valuable input for applications that require highly resolved texture information at low costs and efforts. [ABSTRACT FROM AUTHOR]

Decomposition of soil organic matter (SOM) is regulated by microbial activity, which strongly depends on the availability of carbon (C) and nitrogen (N). Yet, the special role of N on soil organic carbon (SOC) mineralization is still under discussion. The recent concept of microbial N mining predicts increasing SOC mineralization under N-deficiency, which is in contrast to the generally accepted stoichiometric decomposition theory. Following this concept we hypothesized that spatio-temporal patterns of microbial activity are controlled by SOC and N contents, but that microorganisms maintain their functionality to mineralize C under conditions of N deficiency because of microbial N mining. To test this hypothesis, we added glucose to an arable soil that had experienced increasing losses of C 3 -derived SOM after one, three, and seven years of bare fallow and measured spatio-temporal patterns of substrate-induced respiration (SIR). The SIR measurements were performed with and without additions of mineral N. Selected samples were treated with C 4 sugar in order to trace the source of CO 2 emissions (sugar vs. SOC-derived) by natural 13 C abundance measurements. Sugar additions were repeated after the first SIR experiment to derive information on changing N availability. The results showed that spatial patterns of SIR were not consistently regulated by SOC and N. On a temporal scale, the maximum microbial growth peak declined by 47% from one year bare fallow to seven years bare fallow but soils often developed a second growth phase in the 7th year of fallow. Intriguingly, the maximum microbial growth peak increased again when N was added together with the glucose and no second growth peak occurred. A similar effect was observed after repeated sugar additions but without N additions. The 13 C experiment revealed a slightly higher contribution of SOC-derived CO 2 in N-deficient samples (16.7%) than in N-fertilized samples (14.6%). We conclude that the first SIR peak was related to the supply of immediately available N while the second growth phase indicated a delayed release of N, due to N mining from SOM. Hence, microbes were able to compensate for initial N limitation and there was no significant change in the overall substrate-induced CO 2 release with proceeding time under fallow. [ABSTRACT FROM AUTHOR]

Past land-use changes, intensive cropping with large proportions of root crops, and preferred use of mineral fertilizer have been made responsible for proceeding losses of soil organic C (SOC) in the plough layer. We hypothesized that in intensive agriculturally managed regions changes in SOC stocks would be detectable within a decade. To test this hypothesis, we tracked the temporal development of the concentrations and stocks of SOC in 268 arable sites, sampled by horizon down to 60 cm in the Cologne-Bonn region, W Germany, in 2005 and in 2013. We then related these changes to soil management data and humus balances obtained from farmers' surveys. As we expected that changes in SOC concentrations might at least in part be minor, we fractionated soils from 38 representative sites according to particle size in order to obtain C pools of different stability. We found that SOC concentrations had increased significantly in the topsoil (from 9.4 g kg−1 in 2005 to 9.8 g kg−1 in 2013), but had decreased significantly in the subsoil (from 4.1 g kg−1 in 2005 to 3.5 g kg−­1 in 2013). Intriguingly, these changes were due to changes in mineral-bound SOC rather than to changes in sand-sized organic matter pools. As bulk density decreased, the overall SOC stocks in the upper 60 cm exhibited a SOC loss of nearly 0.6 t C (ha · y)−1 after correction by the equivalent soil mass method. This loss was most pronounced for sandy soils [−0.73 t SOC (ha · y)−1], and less pronounced for loamy soils [−0.64 t SOC (ha · y)−1]; silty soils revealed the smallest reduction in SOC [−0.3 t SOC (ha · y)−1]. Losses of SOC occurred even with the overall humus balances having increased positively from about 20 kg C (ha · y)−1 (2003-2005) to about 133 kg C (ha · y)−1 (2005-2013) due to an improved organic fertilization and intercropping. We conclude that current management may fail to raise overall SOC stocks. In our study area SOC stocks even continued to decline, despite humus conservation practice, likely because past land use conversions (before 2005) still affect SOC dynamics. [ABSTRACT FROM AUTHOR]

Proximal sensing of soil organic carbon (SOC) in the field using diffuse reflectance spectroscopy is still difficult under variable weather conditions. Here, we introduce a tractor-driven measuring chamber for on-the-go visible and near-infrared diffuse reflectance spectroscopy (Vis–NIRS) meeting experimental and precision agriculture demands. A commercial full-range spectrometer operates in a closed dark chamber with artificial light. Sensor view angle, distance to soil, and illumination conditions were optimized. The mobile chamber was placed on drum rollers to flatten the ploughed and tilled soil surface and to minimize disturbances in Vis–NIR spectra by surface roughness. Prior to on-the-go spectra acquisition under field conditions, SOC prediction models for the soils under study were independently calibrated under variable moisture and roughness conditions. Driving at a tractor velocity of 3 km h −1 resulted in measuring spots of approximately 8 cm length and 3 cm width at 0.6 m distance to one another in the direction of movement, delivering geo-referenced SOC concentrations at a sub-m spatial resolution. Gravel on the soil surface resulted in erratic extremes of predicted SOC concentrations, but these could be eliminated as outliers. The system was tested under field conditions on two long-term experiments at two different sites which revealed each a large span of SOC concentrations. On-the-go predicted SOC concentrations and those obtained from conventional plot-wise lab analyses were correlated with coefficients of determination of R 2 = 0.65 and a standard error of 1.22 g SOC kg −1 . Further improvements, particularly in data processing, will enable a reliable proximal sensing on-the-go for precision agriculture purposes in the near future. [ABSTRACT FROM AUTHOR]

Against the background of climate change mitigation, organic amendments (OA) may contribute to store carbon (C) in soils, given that the OA provide a sufficient stability and resistance to degradation. In terms of the evaluation of OA behavior in soil, total organic carbon (TOC), total nitrogen (TN), and the ratio of TOC to TN (CN-ratio) are important basic indicators. Hot-water extractable carbon (hwC) and nitrogen (hwN) as well as their ratios to TOC and TN are appropriate to characterize a labile pool of organic matter. As for quickly determining these properties, mid-infrared spectroscopy (MIRS) in combination with calibrations based on machine learning methods are potentially capable of analyzing various OA attributes. Recently available portable devices (pMIRS) might replace established benchtop devices (bMIRS) as they have potential for on-site measurements that would facilitate the workflow. Here, we used non-linear support vector machines (SVM) to calibrate prediction models for a heterogeneous dataset of greenwaste composts and biochar compost substrates (BCS) (n = 45) using bMIRS and pMIRS instruments on ground samples. Calibrated models for both devices were validated on separate test sets and showed similar results. Ten OA were sieved to particle size classes (psc's) of >4 mm, 2–4 mm, 0.5–2 mm, and <0.5 mm. A universal SVM model was then developed for all OA and psc's (n = 162) via pMIRS. Validation revealed that the models provided reliable predictions for most parameters (R2 = 0.49–0.93; ratio of performance to interquartile distance (RPIQ) = 1.19–5.70). We conclude that (i) the examined parameters are sensitive towards chemical composition of OA as well as particle size distribution and can therefore be used as indicators for labile carbon and nitrogen pools of OA, (ii) prediction models based on SVM and pMIRS are a feasible approach to predict the examined C and N pools in organic amendments and their particle size class, and (iii) pMIRS can provide valuable information for optimized application of OA on cultivated soils at low costs and efforts. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the comment by C. Brock on an article about the monitoring of organic carbon stocks from agricultural soils in Western Germany.

Spatial patterns of soil respiration (SR) and its sensitivity to temperature (Q10) are one of the key uncertainties in climate change research but since their assessment is very time-consuming, large data sets can still not be provided. Here, we investigated the potential of mid-infrared spectroscopy (MIRS) to predict SR and Q10 values for 124 soil samples of diverse land use types taken from a 2868 km 2 catchment (Rur catchment, Germany/Belgium/Netherlands). Soil respiration at standardized temperature (25 °C) and soil moisture (45% of maximum water holding capacity, WHC) was successfully predicted by MIRS coupled with partial least square regression (PLSR, R 2  = 0.83). Also the Q10 value was predictable by MIRS-PLSR for a grassland submodel (R 2  = 0.75) and a cropland submodel (R 2  = 0.72) but not for forested sites (R 2  = 0.03). In order to provide soil respiration estimates for arbitrary conditions of temperature and soil moisture, more flexible models are required that can handle nonlinear and interacting relations. Therefore, we applied a Random Forest model, which includes the MIRS spectra, temperature, soil moisture, and land use as predictor variables. We could show that SR can be simultaneously predicted for any temperature (5–25 °C) and soil moisture level (30–75% of WHC), indicated by a high R 2 of 0.73. We conclude that the combination of MIRS with sophisticated statistical prediction tools allows for a novel, rapid acquisition of SR and Q10 values across landscapes and thus to fill an important data gap in the validation of large scale carbon modeling. [ABSTRACT FROM AUTHOR]

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources. [ABSTRACT FROM AUTHOR]

The assessment of soil organic carbon (SOC) content using proximal diffuse reflectance spectroscopy in the visible and near-infrared (Vis-NIRS) may be hampered if green plants (photosynthetic vegetation) and straw (non-photosynthetic vegetation) are present in the measuring spot. Under such conditions, taking spectra of the soil surface yields insufficient results and requires quantitative correction. In this combined lab and field study, we investigated if, and to what degree, it is possible to distinguish green plants and straw from bulk soil organic matter using the same Vis-NIR spectra. Without any modification of an approved SOC model, SOC was overestimated by more than 200%, depending on the fractional coverage with green leaves and straw. This error was more severe for green leaves than for straw. After covering the soil surface with defined proportions of green barley leaves or straw concomitant changes in reflectance spectra were recorded. Partial least squares regression (PLSR) with three factors yielded quantitative predictions of soil coverage by green leaves (R 2 adj = 0.98, RMSE cv of cross-validation = 5.3% soil coverage) and straw (R 2 adj = 0.95, RMSE cv = 7.5% soil coverage). Furthermore, photosynthetic and non-photosynthetic vegetation indices, the Normalized Difference Vegetation Index (NDVI) and the Cellulose Absorption Index (CAI), were derived from the Vis-NIR spectra of the soil surface. Both indices increased when covering with green leaves or straw increased (R 2 = 0.99 [NDVI] and 0.94 [CAI], respectively). The degree of SOC overestimation was correlated with NDVI and CAI. Second-order polynomial regressions between SOC overestimation, and CAI or NDVI were fitted (R 2 = 0.97 and 0.99, respectively). This enabled us to carry out a correction step after predicting SOC using an approved SOC model (R 2 adj = 0.84, RPD = 2.53, RMSE CV = 0.73) to minimize the overestimation error. Transferring this two-step-approach to field conditions revealed that Vis-NIR spectra still showed scattered predictions of point-specific SOC contents (R 2 = 0.66 and 0.58 for stop-and-go and on-the-go acquisitions, respectively), however, with a slope close to unity. Consequently, the disturbance by green plants or straw on the soil surface during superficial Vis-NIR sensing of SOC in the field can be overcome. [ABSTRACT FROM AUTHOR]

Background: Mid‐infrared spectroscopy (MIRS) is commonly recognized as a rapid and high throughput measurement technology for numerous soil properties, given that appropriate prediction models are calibrated. Soil spectral libraries (SSL) may reduce effort and costs for MIRS practical application. Aims: To calibrate MIR‐SSL‐based prediction models for soil properties and to test their applicability to independent sample sets at regional scale (e.g., for soil survey) and at field scale (i.e., for precision agriculture, PA). Methods: Spectra of 1013 arable topsoil samples of the European Land Use/Land Cover Area Frame Survey 2009 (LUCAS) from Belgium, the Netherlands, Luxembourg, and Germany formed the basis for the MIR‐SSL. Leave‐one‐out cross‐validation (LOOCV) via partial least squares regression served to calibrate (1) generic prediction models including all samples, and (2) stratified models for different parent materials. Test‐set validation (TSV) was conducted on samples from independent campaigns at (1) regional scale with a sample set from Schleswig‐Holstein (Germany; n = 385) and (2) field scale for four individual fields in Germany (n = 513). Results: Generic LOOCV models successfully predicted soil organic carbon, total nitrogen, sand, silt, clay, carbonate, and pH. Calibration for available nutrients failed. The TSV was successful for the regional sample set for all variables (2.5 ≤ RPIQ ≤ 5.9), except for carbonate (RPIQ = 0). At field scale, the validation was highly variable for different sites and parameters. Stratified models using soil parent material as auxiliary variable improved only occasionally the applicability at field scale, that is, on single fields and only for clay and carbonate. Conclusions: Although the MIR‐SSL in its present state cannot be recommended for nutrient management, it provides valuable support for soil survey and PA. [ABSTRACT FROM AUTHOR]

This study deals with spontaneous regeneration of fen and steppe meadows and corresponding soil properties on extensively managed ex-arable fields. Our first main aim was to analyse the nature of relations between various vegetation and soil parameters and time since abandonment and to determine the time needed for regeneration. Our second major goal was to determine the main environmental factors influencing regeneration success. Time since abandonment of the studied areas was determined with military maps, aerial photographs and the help of local rangers. Stands which were presumably not ploughed for over 150 years were taken as a reference. Vegetation surveys and soil sampling were carried out in 307 plots with different soil moisture conditions. The correlation with time was tested for relevant vegetation and soil parameters. The influence of different parameters on the species composition was tested with a generalized linear mixed model. We found that vegetation and soil parameters approach the level of long-term (permanent) grassland in a similar asymptotic curve. Numerous characteristic target vegetation species and legally protected species have colonized the old fields. The time frame needed for regeneration can be stated to be 20-40 years for the majority of sites, but the proportion of favourable species in the resulting grasslands is divergent. The most important finding among soil properties was a pronounced negative effect of plant available phosphorus on the species composition of regenerating grassland. We conclude that relying on spontaneous recolonization for grassland restoration in central Hungary is promising, particularly on sites which were not fertilized intensively with phosphorus prior to abandonment. [ABSTRACT FROM AUTHOR]

Variations in soil moisture and surface roughness are major obstacles for the proximal sensing of soil organic C (SOC) using visible and near-infrared spectroscopy (VIS-NIRS). We gained a significant improvement of SOC prediction under field conditions with a stepwise approach. This comprised of (i) the estimation of these disturbing factors and (ii) the subsequent use of this information in multivariate SOC prediction. We took 120 surface soil samples (SOC contents 6.55-13.40 g kg-1) from a long-term trial near Bonn, Germany. To assess soil moisture, we recorded VIS-NIR spectra on <2-mm sieved disturbed samples at seven different moisture levels (air-dried to 30% w/w). The impact of roughness on VIS-NIRS performance was studied with undisturbed samples (air-dried and at different moisture levels), which were scanned with a laser profiler after fractionation into six aggregate size classes. The results confirmed that it was possible to include VIS-NIRS based assessments of soil moisture [R²adj = 0.96; root mean square error of cross validation (RMSEcv) = 1.99% w/w] into the prediction of SOC contents for sieved samples <2 mm (R²adj = 0.81-0.94; RMSEp = 0.41-0.72 g SOC kg-1). However, for rough soil surfaces, SOC contents were overestimated, and the prediction of roughness indices using VIS-NIRS failed. Fortunately, surface roughness did not impair the VIS-NIRS assessment of soil moisture. Hence, we could directly estimate moisture via VIS-NIRS in undisturbed field samples and then incorporate this information into a moisture-dependent prediction of SOC contents. This provided accurate SOC estimates for field-moist, undisturbed samples (R²adj = 0.91). Deviations from the reference method (elemental analysis) were below 2 g SOC kg-1. [ABSTRACT FROM AUTHOR]

Abstract: Veterinary antibiotics reach the soil environment by manure application, where they accumulate or are prone to vertical translocation. We assumed that a high pH value at slightly calcareous soil properties should retain tetracyclines and fluoroquinolones even at high manure loads, whereas it should facilitate leaching of sulfonamides. Hence, we investigated the transport of manure-added antibiotics in a soil developed from calcareous parent material on a farm in peri-urban Beijing, China. Leachate was collected from zero-tension samplers, installed at 40cm depth under undisturbed soil, which was treated according to (i) routine farm practice and (ii) worst-case conditions (high antibiotic concentrations, irrigation imitating heavy rainfall). Additionally, the soil depth distribution of pharmaceuticals at the end of the leaching experiment was analysed. Under routine farm practice, sulfamethazine was repeatedly detected in the leachate with a maximum concentration of 0.12μgL−1. All applied substances were still detected in soil after 53days, suggesting that there was no overall rapid and complete dissipation. Worst-case conditions enhanced vertical translocation; all leachate samples contained sulfonamides (up to 653μgL−1 sulfamethazine), and even tetracyclines were found in the leachate once, with doxycycline reaching the highest concentration (0.19μgL−1). The concentrated simultaneous occurrence of sulfonamides with the bromide tracer in leachate samples pointed to preferential flow as underlying transport process. The high pH values did not prevent the single leaching event for tetracyclines and hardly affected overall leaching behaviour. The applied fluoroquinolones were not significantly translocated below 4cm depth, irrespective of irrigation and high manure addition. [Copyright &y& Elsevier]

Naturally occurring charcoals are increasingly being recognized as effective sorbents for organic compounds. In this study we investigated the sorption of benzene and toluene in single-sorbate and bi-sorbate systems on different types of charcoals produced in laboratory, employing the batch sorption technique. Air dried plant materials from Phalaris grass (Phalaris tuberosa) and Red Gum wood (Eucalyptus camadulensis) were combusted under limited oxygen supply at 250°C, 450°C, and 850°C. The resulting charcoals were characterized for their specific surface areas, total cation content, and pore size distributions (pore size distribution only for wood combusted at 450°C and 850°C). For the materials treated at 850°C not only the surface area, microporosity, and total amount of sorbed sorbate increased markedly, but also the non-linearity of the sorption isotherm. The pore size distributions and surface areas as well as an indifferent sorption behaviour and competition effects for both sorbates indicated that pore filling mechanisms were the dominating processes governing the sorption on these microporous, high temperature treated materials. For the materials treated at lower temperatures the affinity of toluene was higher compared to that of benzene. In the bi-sorbate system the overall uptake of benzene increased. These phenomena might be due to the stronger hydrophobicity of toluene, and to a varying potential for swelling of the matrix and pore deformation by the two sorbates. The significantly lower sorption capacity of the Phalaris-derived material compared to the Red Gum charcoal correlated with its smaller surface area and higher cation content. [Copyright &y& Elsevier]

The development and validation of hydroecological land-surface models to simulate agricultural areas require extensive data on weather, soil properties, agricultural management, and vegetation states and fluxes. However, these comprehensive data are rarely available since measurement, quality control, documentation, and compilation of the different data types are costly in terms of time and money. Here, we present a comprehensive dataset, which was collected at four agricultural sites within the Rur catchment in western Germany in the framework of the Transregional Collaborative Research Centre 32 (TR32) "Patterns in Soil–Vegetation–Atmosphere Systems: Monitoring, Modeling and Data Assimilation". Vegetation-related data comprise fresh and dry biomass (green and brown, predominantly per organ), plant height, green and brown leaf area index, phenological development state, nitrogen and carbon content (overall > 17 000 entries), and masses of harvest residues and regrowth of vegetation after harvest or before planting of the main crop (> 250 entries). Vegetation data including LAI were collected in frequencies of 1 to 3 weeks in the years 2015 until 2017, mostly during overflights of the Sentinel 1 and Radarsat 2 satellites. In addition, fluxes of carbon, energy, and water (> 180 000 half-hourly records) measured using the eddy covariance technique are included. Three flux time series have simultaneous data from two different heights. Data on agricultural management include sowing and harvest dates as well as information on cultivation, fertilization, and agrochemicals (27 management periods). The dataset also includes gap-filled weather data (> 200 000 hourly records) and soil parameters (particle size distributions, carbon and nitrogen content; > 800 records). These data can also be useful for development and validation of remote-sensing products. The dataset is hosted at the TR32 database (https://www.tr32db.uni-koeln.de/data.php?dataID=1889 , last access: 29 September 2020) and has the DOI 10.5880/TR32DB.39 (Reichenau et al., 2020). [ABSTRACT FROM AUTHOR]

The spatial distribution and density of different weed species were monitored during a long-term survey over a period of 9 years on a 5.8 ha arable field and related to soil properties. Weed seedlings were determined every year in spring on a regular grid with 429 observation points (15 × 7.5 m; net study area = 4 ha). Dominant weed species were Chenopodium album, Polygonum aviculare, Viola arvensis and different grass weeds, clearly dominated by Alopecurus myosuroides. A non-invasive electromagnetic induction survey was conducted to evaluate available water capacity directly in the field at high spatial resolution. Further soil properties were evaluated following the minimum-invasive approach with soil sampling and subsequent mid-infrared spectroscopy. Plant available nutrients were analysed with conventional lab methods. Redundancy analysis served to describe the effect of soil properties, different years and field crops on weed species variability. Seven soil properties together explained 30.7% of the spatial weed species variability, whereas 28.2% was explained by soil texture, available water capacity and soil organic carbon. Maps for site-specific weed management were created based on soil maps. These maps permit several benefits for precision crop protection, such as a better understanding of soil–weed inter-relations, improved sampling strategies and reduction in herbicide use. [ABSTRACT FROM AUTHOR]

Hot vegetable oil can be used for weed control as an alternative to the use of herbicides. We analysed the temporal development of vegetable oil mineralization in soil and tested the role of nutrient supply on oil mineralization. Further, we investigated the effect of oil application on mineralization of native soil organic carbon (SOC), i.e. the priming effect. In a laboratory experiment, three oil dosages (0.1, 1.0 and 3.0 ml per 35 g soil) were applied to three arable soils and soil respiration was measured hourly. Both a C3-sunflower oil and a C4-corn oil were used in order to differentiate oil-derived CO2 from SOC-derived CO2. The results revealed that after 42 days of incubation, 9.6 to 39.7% of the applied oil was mineralized which, however, also primed the mineralization of SOC by a factor of 2.2 to 4.2. The higher the applied oil amount, the lower was the percentage of oil-C mineralization, but the higher was the priming effect. The addition of fertilizer (0.29 mg N g−1 soil and 0.048 mg P g−1 soil) increased oil-C mineralization to 39.9 to 50.9%. We conclude that oil can temporarily accumulate in soil, especially in case of low nutrient supply. As the addition of oil stimulates SOC mineralization, a decrease of native SOC stocks may occur, which needs further quantification in long-term field experiments. [ABSTRACT FROM AUTHOR]

Sonication is widely used for disruption of suspended soil aggregates. Calorimetric calibration allows for determining sonication power and applied energy as a measure for aggregate disrupting forces. Yet other properties of sonication devices (e.g., oscillation frequency and amplitude, sonotrode diameter) as well as procedure details (soil‐to‐water ratio, size, shape, and volume of used containers) may influence the extent of aggregate disruption in addition to the applied energy. In this study, we tested potential bias in aggregate disruption when different devices or procedures are used in laboratory routines. In nine laboratories, three reference soil samples were sonicated at 30 J mL−1 and 400 J mL−1. Aggregate disruption was estimated based on particle size distribution before and after sonication. Size distribution was obtained by standardized submerged sieving for particle size classes 2000–200 and 200–63 µm, and by dynamic imaging for particles < 63 µm. Despite differences in sonication devices and protocols used by the participants, only 16 in 216 tests of samples of the size fractions 2000–200 and 200–63 µm were identified as outliers. For the size fraction < 63 µm, fewer outliers were detected (8 in 324 tests). Four out of nine laboratories produced more than two outliers. In these laboratories, sonication devices differed from the others regarding oscillation frequencies (24 or 30 kHz compared to 20 kHz), sonotrode diameters (10 and 14 mm compared to 13 mm), and sonication power (16 W compared to > 45 W). Thus, these sonication device properties need to be listed when reporting on sonication‐based soil aggregate disruption. The overall small differences in the degree of disruption of soil aggregates between different laboratories demonstrate that sonication with the energies tested (30 and 400 J mL−1) provides replicable results despite the variations regarding procedures and equipment. [ABSTRACT FROM AUTHOR]

Copper is used as a growth promoter in animal husbandry, resulting in high Cu concentrations in animal manure. We tested whether Cu would be mobilized in soils receiving excessive loads of manure, both from recently added and from aged fractions. To discriminate between these Cu sources, manure was labeled with 65Cu. After soil application of 0, 15, and 30 Mg manure ha-1, leachate was collected in free-draining lysimeters (40 cm depth) under undisturbed soil over a 53 day period. Determining the total amounts of Cu and the fractions of 65Cu in leachate and the soil profile enabled us to trace the translocation of Cu derived from labeled manure. More than 84% of the applied Cu was retained in the top 2 cm of soil. Less than 0.01% of the applied Cu was detected overall in the leachate. Of this amount, however, 38% (±8.9 SE) was leached within 8 days after application. The total Cu concentration in leachates (32-164 μg L-1) frequently exceeded the Chinese groundwater quality standard of 50 μg L-1. The added 65Cu, however, accounted for less than 3.6% of the total Cu leaching load, suggesting that Cu from older sources and/or geological background controls contamination, regardless of current land management. [ABSTRACT FROM AUTHOR]

Highlights: [•] Soil texture and soil organic carbon content do not affect 15N dynamic. [•] 15N release depends on fertilizer (chicken manure, mushroom compost, mineral N). [•] Mustard intercropping is a tool to enhance N efficiency. [Copyright &y& Elsevier]

Abstract: A detailed knowledge on the heterogeneity of the soil organic carbon (SOC) content in agricultural soils is required to support applications such as precision agriculture and soil C monitoring. Imaging spectroscopy in the visible (VIS) and near-infrared (NIR) region has proven to be highly sensitive to organic soil components and can efficiently provide data with high spatial resolution. The objectives of our study were (i) to test the suitability of airborne hyperspectral imaging for the characterisation of the spatial heterogeneity of the SOC content at the field-scale, (ii) to investigate the impact of various soil surface conditions (roughness, vegetation) on SOC prediction and (iii) to produce SOC maps for arable fields on a pixel-wise basis. The soil reflectance was recorded by the aircraft-mounted hyperspectral sensor HyMap (450–2500nm) on test sites with the following varying soil surface conditions: bare soil, fine seed-bed; ploughed, bare soil; volunteer crops; straw residues. A partial least squares regression (PLSR) was performed for data analysis. Our results reveal an accurate prediction of the SOC content at a comparatively small concentration range (8.3 to 18.5gSOCkg−1) on long-term uniformly cultivated fields. Site-specific characteristics influenced the calibration models; highest prediction accuracy was performed over a bare, fine soil (RMSEP=0.76gSOCkg−1; RPD=2.08). A generated pixel-wise map (8m×8m) allows the detection of small-scale spatial variability of SOC content and comparatively more realistic than an interpolated map. Thus, airborne hyperspectral imaging constitutes a substantial progress compared to point observations and facilitates well-directed applications in precision agriculture. [Copyright &y& Elsevier]

The challenges of Vis-NIR spectroscopy are permanent soil surface variations of moisture and roughness. Both disturbance factors reduce the prediction accuracy of soil organic carbon (SOC) significantly. For improved SOC prediction, both disturbance effects have to be determined from Vis-NIR spectra, which is especially challenging for roughness. Thus, an approach for roughness quantification under varying moisture and its impact on SOC assessment using Support Vector Machines is presented here. [ABSTRACT FROM AUTHOR]