Gerhard Welp - Academia.edu (original) (raw)

Papers by Gerhard Welp

The development of sustainable agricultural systems requires techniques that accurately monitor c... more The development of sustainable agricultural systems requires techniques that accurately monitor changes in the amount, nature and breakdown rate of soil organic matter and can compare the rate of breakdown of different plant or animal residues under different management systems. In this research, the study area includes the southern alluvial and piedmont plains of Gorgan River extended from east to

Nitrogen (N) deposition to soils has become a global issue during the last decades. Its effect on... more Nitrogen (N) deposition to soils has become a global issue during the last decades. Its effect on mineralization of soil organic carbon (SOC), however, is still debated. Common theories based on Liebig’s law predict higher SOC mineralization rates in nutrient-rich than in nutrient-poor soils. Contrastingly, the concept of microbial N mining predicts lower mineralization rates after N deposition. The latter is explained by ceased decomposition of recalcitrant soil organic matter (SOM) as the need of microbes to acquire N from this pool decreases. As N deposition might shift the nutrient balance towards relative phosphorus (P) deficiency, it is also necessary to consider P mining in this context. Due to limited knowledge about microbial nutrient mining, any predictions of N deposition effects are difficult.

Knowledge about soil organic carbon (SOC) turnover is of immense importance for assessing SOC sto... more Knowledge about soil organic carbon (SOC) turnover is of immense importance for assessing SOC storage and CO2 emissions from the soil. Yet, spatial patterns of SOC turnover and their controlling factors are still uncertain. It is well known that clay and fine silt particles protect SOC through mineral associations but the proportion of these particles is limited. Once the amount of SOC in the soil approaches this maximum protection capacity, little additional SOC may be effectively protected by mineral associations, which is termed C saturation. Following this concept, we hypothesize that SOC is stored less stable and turns over faster at sites with high SOC contents but low protection capacity, i.e. at sites which are close to C saturation.

The development and validation of hydroecological land-surface models to simulate agricultural ar... more The development and validation of hydroecological land-surface models to simulate agricultural areas requires extensive data on weather, soil properties, agricultural management, and vegetation states and fluxes. However, this comprehensive data is rarely available since measurement, quality control, documentation and compilation of the different data types is 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 frame of the Transregional Collaborative Research Centre 32 "Patterns in Soil-Vegetation-Atmosphere-Systems: Monitoring, Modelling and Data Assimilation" (TR32). Vegetation-related data comprises 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 > 17000 entries), and fluxes of carbon, energy, and water (> 180000 half-hourly records) for a variety of agricultural plants. In addition, masses of harvest residues and regrowth of vegetation after harvest or before planting of the main crop are included (> 250 entries). Data on agricultural management includes sowing and harvest dates, and information on cultivation, fertilization and agrochemicals (27 management periods). The dataset also includes gap-filled weather data (> 200000 hourly records) and soil parameters (particle size distributions, carbon and nitrogen contents; > 800 records). This data can also be useful for development and validation of remote sensing products. The dataset (Reichenau et al., 2019) is hosted at the TR32 database (https://www.tr32db.uni-koeln.de/data.php?dataID=1886). 1 Introduction System states and processes at the land surface are of major interest in the context of climate change, hydrological and biogeochemical research. In order to understand the processes in their spatial context and to provide information for larger areas, remote sensing and simulations are heavily applied methods. In this context, it is crucial to understand the fluxes

Precision Agriculture

The spatial distribution and density of different weed species were monitored during a long-term ... more 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 soilweed interrelations , improved sampling strategies and reduction in herbicide use.

Journal of Plant Nutrition and Soil Science

Mid-infrared spectroscopy (MIRS) has proven to be a cost-effective, high throughput measurement t... more 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 (CaCl 2) 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 (LR SPP) from MIRS-PLSR predictions of SOM, clay, and pH (CaCl 2) for non-calcareous soil samples using the VDLUFA look-up tables was successful for all locations (R 2 = 0.54-0.82; RMSE = 857-1414 kg CaO ha-1). Alternatively, we tested direct LR prediction (LR DP) by MIRS-PLSR and also achieved satisfactory performance (R 2 = 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.

Scientific Reports

Predicting taxonomic classes can be challenging with dataset subject to substantial irregularitie... more Predicting taxonomic classes can be challenging with dataset subject to substantial irregularities due to the involvement of many surveyors. A data pruning approach was used in the present study to reduce such source errors by exploring whether different data pruning methods, which result in different subsets of a major reference soil groups (RSG)-the Plinthosols-would lead to an increase in prediction accuracy of the minor soil groups by using Random Forest (RF). This method was compared to the random oversampling approach. Four datasets were used, including the entire dataset and the pruned dataset, which consisted of 80% and 90% respectively, and standard deviation core range of the Plinthosols data while cutting off all data points belonging to the outer range. The best prediction was achieved when RF was used with recursive feature elimination along with the non-oversampled 90% core range dataset. This model provided a substantial agreement to observation, with a kappa value of 0.57 along with 7% to 35% increase in prediction accuracy for smaller RSG. The reference soil groups in the Dano catchment appeared to be mainly influenced by the wetness index, a proxy for soil moisture distribution. Soils play a vital role for various ecosystem services, which makes them a key asset for sustainable living conditions on earth. Major soil functions are related to food and biomass production, water control, biological and chemical recycling, platform provision for human activities, supply of raw materials, and providing habitat for soil biodiversity 1. However, many countries still need to deal with the lack of adequate and timely soil information to address e.g. land degradation issues. This is especially true in sub-Saharan Africa, where logistical challenges result in a severe limitation in data availability and storage 2. Additionally, traditional soil mapping is known to be very expensive and time-consuming. As early as 2005, it was reported that most of the soil information maps in many sub-Saharan countries had been lost 3. In many African countries, the low quality of existing soil information can be related to poor georeferencing records, irregularities between laboratory and mapping approaches, inconsistencies in legends and field survey reports 4 , and different levels of landscape perception and mapping experience 5. Moreover, existing soil maps are mostly produced at a coarse scale and are derived from traditional qualitative surveys 6. Dewitte et al. 7 observed that most African countries still depend on the 1:5 M "Soil Map of the World" produced by FAO and UNESCO in the 1970s. These maps usually cover the whole country, but are inadequate for local applications due to insufficient resolution of the data 8,9. Initiatives such as the GlobalSoilMap.net project are currently working to overcome

Soil Biology and Biochemistry

Abstract Nitrogen (N) deposition to soils is globally rising, but its effect on soil organic carb... more Abstract 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 13C abundance approach was conducted to quantify priming effects (PE) of the added C on SOC mineralization. For this purpose we assessed the 13CO2 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 CO2 release within 30 days (SIRacc) nor PE (PE = 1.6, i.e., sugar additions raised the release of SOC-derived CO2 by a factor of 1.6). In the upper subsoil (30–100 cm), however, both CN and CP additions increased SIRacc (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 SIRacc (267%) and PE (PE = 6.0). In the deep subsoil (>130 cm depth), only the CNP addition consistently increased SIRacc (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.

Plant, Soil and Environment

We studied the long-term effect (about 45 years) of farmyard manure, sewage sludge and compost ap... more We studied the long-term effect (about 45 years) of farmyard manure, sewage sludge and compost application in two increments on organic carbon…

Geoderma

Abstract Soil organic carbon (SOC) is often heterogeneously distributed in arable fields and so i... more Abstract Soil organic carbon (SOC) is often heterogeneously distributed in arable fields and so is probably its turnover. We hypothesized that the spatial patterns of SOC turnover are controlled by basic soil properties like soil texture and the amount of rock fragments. To test this hypothesis, we cultivated maize as a C 4 plant on a heterogeneous arable field (155 × 60 m) that had formerly been solely cultivated with C 3 crops, and monitored the incorporation of isotopically heavier maize-derived C into SOC by stable 13 C isotope analyses. To obtain a homogeneous input of C 4 biomass into the C 3 soil across the field, we chopped the aboveground maize biomass after harvest in autumn and re-spread it uniformly over the field. Subsequently, the soil was grubbed and then ploughed in the next spring. In addition, we assessed the spatial patterns of SOC stocks, amount of rock fragments and texture, as well as potential soil organic matter (SOM) degradability by ex-situ soil respiration measurements. Heterogeneity of maize growth was monitored as a covariate using laser scanning and satellite images. After two years, maize C had substituted 7.4 ± 3.2% of SOC in the topsoil (0–30 cm) and 2.9 ± 1.7% of SOC in the subsoil (30–50 cm). Assuming that monoexponential decay mainly drove this SOC substitution, this resulted in mean residence times (MRT) of SOC in the range of 30 ± 12 years for the topsoil and of 87 ± 45 years for the subsoil, respectively. Variation in topsoil MRT was related to potential CO 2 release during soil incubation (R 2 = 0.51), but not to basic soil properties. In the subsoil, in contrast, the variation of maize C incorporation into the SOC pool was controlled by variations in maize yield (R 2 = 0.44), which also exhibited a pronounced spatial variability (0.84 to 1.94 kg dry biomass m −2 ), and which was negatively correlated with the amount of rock fragments (R 2 = 0.48, p

Soil and Tillage Research

Abstract Gamma spectrometric field measurements may provide high resolution information on topsoi... more Abstract 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.

Soil and Tillage Research

Abstract The assessment of soil organic carbon (SOC) content using proximal diffuse reflectance s... more Abstract 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.

Geoderma

Abstract Spatial controls of soil organic carbon (SOC) turnover are not well understood. We hypot... more Abstract Spatial controls of soil organic carbon (SOC) turnover are not well understood. We hypothesized that spatial patterns of SOC turnover are related to carbon (C) saturation rather than to the size of measurable SOC-pools such as particulate organic matter (POM), determined as SOC in particle-size fractions. Therefore, we repeatedly grid-sampled a field after one, three, seven, and eleven years under bare fallow management, which revealed a spatial gradient from high to low degrees of C saturation. We measured the contents of SOC and the contents of SOC in coarse sand-size (2000–250 μm, POM1), fine sand-size (250–53 μm, POM2), silt-size (53–20 μm, POM3), and fine silt to clay-size fractions (nonPOM,

PLOS ONE, 2017

for data access arrangements.

Pedosphere, 2017

Carbon fractions in soils apparently vary not only in space, but also over time. A lack of knowle... more Carbon fractions in soils apparently vary not only in space, but also over time. A lack of knowledge on the seasonal variability of labile carbon fractions under arable land hampers the reliability and comparability of soil organic carbon (SOC) surveys from different studies. Therefore, we studied the seasonal variability of two SOC fractions, particulate organic matter (POM) and dissolved organic carbon (DOC), under maize cropping: POM was determined as the SOC content in particle-size fractions, and DOC was measured as the water-extractable SOC (WESOC) of air-dried soil. Ammonium, nitrate, and water-extractable nitrogen were measured as potential regulating factors of WESOC formation because carbon and nitrogen cycles in soils are strongly connected. There was a significant annual variation of WESOC (coefficient of variation (CV) = 30%). Temporal variations of SOC in particle-size fractions were smaller than those of WESOC. The stocks of SOC in particle-size fractions decreased with decreasing particle sizes, exhibiting a CV of 20% for the coarse sand-size fraction (250–2 000 μm), of 9% for the fine sand-size fraction (50–250 μm), and of 5% for the silt-size fraction (20–50 μm). The WESOC and SOC in particle-size fractions both peaked in March and reached the minimum in May/June and August, respectively. These results indicate the importance of the time of soil sampling during the course of a year, especially when investigating WESOC.

Journal of Plant Nutrition and Soil Science, 2000

J Plant Nutr Soil Sci, 1995

Folia Geobotanica, 2015

This study deals with spontaneous regeneration of fen and steppe meadows and corresponding soil p... more 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.

Soil and Tillage Research, 2015

ABSTRACT Proximal sensing of soil organic carbon (SOC) in the field using diffuse reflectance spe... more ABSTRACT 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 R2 = 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.

Precision Agriculture

Journal of Plant Nutrition and Soil Science

Scientific Reports

Soil Biology and Biochemistry

Plant, Soil and Environment

Geoderma

Soil and Tillage Research

Geoderma

PLOS ONE, 2017

for data access arrangements.

Pedosphere, 2017

Journal of Plant Nutrition and Soil Science, 2000

J Plant Nutr Soil Sci, 1995

Folia Geobotanica, 2015

Soil and Tillage Research, 2015