Mineralization of vegetable oils used for thermal weed control in arable soils

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 3.0ml per 35g 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 42days 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 (0.29mgNg(-1) soil and 0.048mgPg(-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.Peer reviewe

A comprehensive dataset of vegetation states, fluxes of matter and energy, weather, agricultural management, and soil properties from intensively monitored crop sites in western Germany

Data description paperThe 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 https://doi.org/10.5880/TR32DB.39 (Reichenau et al., 2020).Peer reviewe

Microbial nitrogen mining affects spatio-temporal patterns of substrate-induced respiration during seven years of bare fallow

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 C3-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 C4 sugar in order to trace the source of CO2 emissions (sugar vs. SOC-derived) by natural 13C 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 13C experiment revealed a slightly higher contribution of SOC-derived CO2 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 CO2 release with proceeding time under fallow

Rapid determination of lime requirement by mid-infrared spectroscopy: A promising approach for precision agriculture

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. © 2019 The Authors. Journal of Plant Nutrition and Soil Science published by WILEY-VCH Verlag GmbH & Co. KGa

Proximal field Vis-NIR spectroscopy of soil organic carbon: A solution to clear obstacles related to vegetation and straw cover

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 (R2adj = 0.98, RMSEcv of cross-validation = 5.3% soil coverage) and straw (R2adj = 0.95, RMSEcv = 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 (R2 = 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 (R2 = 0.97 and 0.99, respectively). This enabled us to carry out a correction step after predicting SOC using an approved SOC model (R2adj = 0.84, RPD = 2.53, RMSECV = 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 (R2 = 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

Sensing of Soil Organic Carbon Using Visible and Near-Infrared Spectroscopy at Variable Moisture and Surface Roughness

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

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

<div><p>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.</p></div