Topsoil Organic Matter Content Research Articles

A new scheme for low-carbon recycling of urban and rural organic waste based on carbon footprint assessment: A case study in China

Organic waste treatment is a major driver of global carbon emissions, thus its low-carbon utilization is essential yet unclear. Through a life cycle assessment of organic waste data from 34 provincial-level regions in China, we have determined that the synergistic and integrated utilization scheme (URIRP) with organic fertilizer and biochar as the primary products can reduce the annual life cycle carbon emissions from 6.9 Mt CO2e to 2.83 Mt CO2e. This reduction can offset 6% of the carbon emissions from the electricity industry mainly through carbon sequestration by application of biochar-based fertilizer, and fossil fuel displacement by bio-energy. Moreover, URIRP can promote the recycling of N and P, reduce annual emission of air pollutants by 866 Mt, and increase topsoil organic matter content by 0.25‰ and economic efficiency by 135%. These findings indicate that URIRP could realize sustainable management of UROSW with significant environmental and economic benefits, and contribute to the realization of China’s carbon neutrality goal.

Soil Organic Matter Input Promotes Coastal Topsoil Desalinization by Altering the Salt Distribution in the Soil Profile

Organic amendment is an effective method to reclaim salt-affected soil. However, in coastal land with shallow saline groundwater, it is limited known about the mechanism of organic amendment on soil desalinization. Thus, to examine the effect of topsoil organic matter content on soil water/salt transport and distribution, two-year field observations in Bohai coastal land, North China, and soil column experiments simulating salt accumulation and salt leaching were conducted, respectively. There were different organic fertilizer amendment rates in 0–20 cm topsoil, 0% (CK), 50% (OA 0.5), and 100% (OA 1.0) (w/w) for soil column experiments. Field observation showed that after organic amendment (OA), the soil’s physical structure was improved, and less of the increase in topsoil salt content was observed, with more salt accumulated in deep soil layers during the dry season. In addition, OA greatly promoted salt leaching during the rainy seasons. The results of the soil column tests further indicated that OA treatments significantly inhibited soil evaporation, with less salt accumulated in the topsoil. Although there was no difference in soil water distribution between the CK and OA 0.5 treatment, the topsoil EC for the OA 0.5 treatment was significantly lower than that for CK. During soil water infiltration, the OA 0.5 and OA 1.0 treatments significantly increased the infiltration rates, enhanced the wetting front, and promoted salt leaching to deeper soil layers, compared with CK. The improvement of soil organic amounts could make the soil more self-resistant to the coastal salinization. The findings of this study provide some insights into soil water/salt regulation in heterogeneous soil masses and on the permanent management of coastal saline farmland.

Digital soil mapping with adaptive consideration of the applicability of environmental covariates over large areas

• Adaptively considering covariate applicability can improve soil prediction. • Terrain conditions well indicate spatial variation of soil–environment relations. • Prediction uncertainty considers both similarity and covariate applicability. • Parameters for covariate applicability are insensitivity in this large study area. The effective use of environmental covariates in characterizing soil–environment relationships is key to successful digital soil mapping. The typical way to use environmental covariates in digital soil mapping is by selecting diverse environmental covariates considering the overall geographical characteristics of the study area and considering these covariates to have consistent applicability across the whole area. However, this practice ignores the fact that the applicability of each environmental covariate in characterizing soil–environment relationships varies over complex environmental conditions, especially in large areas. This study proposed a method to adaptively consider covariate applicability in large-area digital soil mapping using soil–environment relationships. The applicability of each covariate at each location was quantified from the terrain conditions using the newly designed fuzzy functions in the study. Then the covariate applicability was regarded as the importance weight and integrated into an existing representative method, iPSM (individual predictive soil mapping). The integration was separately performed at the similarity calculation and soil estimation stages of iPSM to generate two new methods: iPSM weighting on the applicability of all covariates (iPSM_WCovar_all), and iPSM weighting on the applicability of the limiting covariate (i.e., the covariate with the minimum similarity between two locations that constrains the overall similarity) (iPSM_WCovar_limit). Experiments were carried in Anhui Province, China. The two new methods were used to predict the soil organic matter content of topsoil and outperformed the original iPSM and random forest kriging methods. The root mean square error of the iPSM_WCovar_all, iPSM_WCovar_limit, iPSM and random forest kriging methods were 8.14, 8.00, 8.88 and 9.65 g/kg, respectively, while the mean absolute error of those methods were 6.48, 6.31, 6.61 and 6.82 g/kg. Both proposed methods outperformed the iPSM method and the other commonly used method, i.e., random forest kriging. Moreover, the performance was stable under different parameter settings. Experimental results indicate that the idea of adaptively considering covariate applicability in digital soil mapping is feasible and effective.

Spectral Index for Mapping Topsoil Organic Matter Content Based on ZY1-02D Satellite Hyperspectral Data in Jiangsu Province, China

Estimation of soil organic matter content (SOMC) is essential for soil quality evaluation. Compared with traditional multispectral remote sensing for SOMC mapping, the distribution of SOMC in a certain area can be obtained quickly by using hyperspectral remote sensing data. The Advanced Hyper-Spectral Imager (AHSI) onboard the ZY1-02D satellite can simultaneously obtain spectral information in 166 bands from visible (400 nm) to shortwave infrared (2500 nm), providing an important data source for SOMC mapping. In this study, SOMC-related spectral indices (SIs) suitable for this satellite were analyzed and evaluated in Shuyang County, Jiangsu Province. A series of SIs were constructed for the bare soil and vegetation-covered (mainly rice crops and tree seedlings) areas by combining spectral transformations (such as reciprocal and square root) and dual-band index formulas (such as ratio and difference), respectively. The optimal SIs were determined based on Pearson’s correlation coefficient (ρ) and satellite data quality, and applied to SOMC level mapping and estimation. The results show that: (1) The SI with the highest ρ in the bare soil area is the ratio index of original reflectance at 654 and 679 nm (OR-RI(654,679)), whereas the SI in the vegetation area is the square root of the difference between the reciprocal reflectance at 551 and 1998 nm (V-RR-DSI(551,1998)); (2) the spatial distribution trend of regional SOMC results obtained by linear regression models of OR-RI(654,679) and V-RR-DSI(551,1998) is consistent with the samples; (3) based on the optimal SIs, support vector machine and tree ensembles were used to predict the SOMC of bare soil and vegetation-covered areas of Shuyang County, respectively. The determination coefficient of the soil–vegetation combined prediction results is 0.775, the root mean square error is 3.72 g/kg, and the residual prediction deviation is 2.12. The results show that the proposed SIs for ZY1-02D satellite hyperspectral data are of great potential for SOMC mapping.

High Spatial Resolution Topsoil Organic Matter Content Mapping Across Desertified Land in Northern China

Soil organic matter (SOM) content is an effective indicator of desertification; thus, monitoring its spatial‒temporal changes on a large scale is important for combating desertification. However, mapping SOM content in desertified land is challenging owing to the heterogeneous landscape, relatively low SOM content and vegetation coverage. Here, we modeled the SOM content in topsoil (0–20 cm) of desertified land in northern China by employing a high spatial resolution dataset and machine learning methods, with an emphasis on quarterly green and non-photosynthetic vegetation information, based on the Google Earth Engine (GEE). The results show: 1) the machine learning model performed better than the traditional multiple linear regression model (MLR) for SOM content estimation, and the Random Forest (RF) model was more accurate than the Support Vector Machine (SVM) model; 2) the quarterly information regarding green vegetation and non-photosynthetic were identified as key covariates for estimating the SOM content in desertified land, and an obvious improvement could be observed after simultaneously combining the Dead Fuel Index (DFI) and Normalized Difference Vegetation Index (NDVI) of the four quarters (R2 increased by 0.06, the root mean square error decreased by 0.05, the ratio of prediction deviation increased by 0.2, and the ratio of performance to interquartile distance increased by 0.5). In particular, the effects of the DFI in Q1 (the first quarter) and Q2 (the second quarter) on estimating low SOM content (<1%) were identified; finally, a timely (2019) and high spatial resolution (30 m) SOM content map for the desertified land in northern China was drawn which shows obvious advantages over existing SOM products, thus providing key data support for monitoring and combating desertification.

Glyphosate and AMPA of agricultural soil, surface water, groundwater and sediments in areas prevalent with chronic kidney disease of unknown etiology, Sri Lanka

Glyphosate, which is commercially available as Roundup®, was the widely used herbicide in Sri Lanka until 2015 and is suspected to be one of the causal factors for Chronic Kidney Disease of unknown etiology (CKDu). This research, therefore, aims at studying the presence of glyphosate and Aminomethylphosphonic acid (AMPA) in different environmental matrices in CKDu prevalent areas. Topsoil samples from agricultural fields, water samples from nearby shallow wells and lakes, and sediment samples from lakes were collected and analyzed for glyphosate and AMPA using the LC/MS. Glyphosate (270–690 µg/kg) and AMPA (2–8 µg/kg) were detected in all soil samples. Amorphous iron oxides and organic matter content of topsoil showed a strong and a moderate positive linear relationship with glyphosate. The glyphosate and inorganic phosphate levels in topsoil had a strong negative significant linear relationship. Presence of high valence cations such as Fe3+ and Al3+ in topsoil resulted in the formation of glyphosate-metal complexes, thus strong retention of glyphosate in soil. Lower levels of AMPA than the corresponding glyphosate levels in topsoil could be attributed to factors such as the strong adsorption capacity of glyphosate to soil and higher LOQ in the quantification of AMPA. The glyphosate levels of lakes were between 28 to 45 µg/L; no AMPA was detected. While trace levels of glyphosate (1–4 µg/L) were detected in all groundwater samples, AMPA (2–11µg/L) was detected only in four out of nine samples. Glyphosate was detected in all sediment samples (85–1000 µg/kg), and a strong linear relationship with the organic matter content was observed. AMPA was detected (1–15 µg/kg) in seven out of nine sediment samples. It could be inferred that the impact on CKDu by the levels of glyphosate and AMPA detected in the study area is marginal when compared with the MCL of the USEPA (700 µg/L).

Machine‐Learning Variables at Different Scales vs. Knowledge‐based Variables for Mapping Multiple Soil Properties

Core IdeasExplored the impact of variables selected by different means on mapping at a watershed scale area.Identified influential topographic variables with appropriate scales for mapping soil properties.Finding the appropriate scales of local attributes is helpful for mapping soil properties accurately.Local attribute scale helped map accuracy vs. use of local and regional attributes at single scale.The influential environmental variables and their appropriate scales for mapping different soil properties are usually different. Comparisons between variables selected using machine learning and knowledge‐based approaches and their impacts for soil mapping are necessary to provide guidelines on selecting environmental variables. This study compared topographic variables with single and multiple scales selected for five soil properties (topsoil clay content, sand content, silt content, topsoil organic matter content (SOM), and soil depth) using a recursive feature elimination (RFE) algorithm and variables selected based on expert knowledge on mapping. One hundred and seventy‐three variables were generated including five single‐scale variables derived with a 3 × 3 neighborhood size and seven multi‐scale variables with various neighborhood sizes. Subsets 1 and 2 were selected from single‐scale variables (Pool 1) and all variables (Pool 2), respectively. A reference Subset 3 was selected based on soil pedogenesis knowledge. Results showed that variables in Subset 1 included both local and regional terrain attributes, but were considerably different to the reference variables. When considering scales, local variables at specific scales became the most important variables. Mapping accuracies using Subset 2 were the highest for all soil properties. This indicates that finding the appropriate scales of local attributes and using common local attributes with appropriate scales is more helpful than using both local and regional attributes at single scale for mapping soil properties accurately. The RFE approach is efficient for selection of variables. Also, the input environmental variables for selection determine whether the selected variables perform better than all input variables without selection for predicting soil properties.

Automated Soil Physical Parameter Assessment Using Smartphone and Digital Camera Imagery

Here we present work on using different types of soil profile imagery (topsoil profiles captured with a smartphone camera and full-profile images captured with a conventional digital camera) to estimate the structure, texture and drainage of the soil. The method is adapted from earlier work on developing smartphone apps for estimating topsoil organic matter content in Scotland and uses an existing visual soil structure assessment approach. Colour and image texture information was extracted from the imagery. This information was linked, using geolocation information derived from the smartphone GPS system or from field notes, with existing collections of topography, land cover, soil and climate data for Scotland. A neural network model was developed that was capable of estimating soil structure (on a five-point scale), soil texture (sand, silt, clay), bulk density, pH and drainage category using this information. The model is sufficiently accurate to provide estimates of these parameters from soils in the field. We discuss potential improvements to the approach and plans to integrate the model into a set of smartphone apps for estimating health and fertility indicators for Scottish soils.

Characteristics of Anthropogenic Soil Formed from Paddy near the River

Anthropogenic soil in cropland is formed in the process of subsoil reversal and the refill of soil into cropland. However, there was little information on the chemical properties within soil profiles in anthropogenic soil under rice paddy near the river. In this study, we investigated the chemical properties within soil profiles in the anthropogenic soil located at 4 sites in Gumi, Kimhae, Chungju, and Euiseong to compare with the natural paddy soil near the river. Among particle sizes, the sand content decreased under soil profiles but the silt and clay contents increased compared to the natural paddy soil in soil profiles. Organic matter content in topsoil of anthropogenic soil was lower than in that of natural soil, which was shown the contrary tendency within soil profiles. Also, the soil pH, available P2O5, and exchangeable cations were higher in anthropogenic soil compared to natural paddy soil at topsoil, which was maintained these tendency into soil depth. Nutrients may be equally distributed in anthropogenic soil during the process of refill in paddy soil near the river. This results indicated that anthropogenic soil would contribute to carbon sequestration, the mitigation of compaction, and reduction of fertilizer application in paddy soil. Therefore, characteristics of anthropogenic soil can be used for the soil management in cropland.

Beneficial Use of Dewatered and Composted Sewage Sludge as Soil Amendments: Behaviour of Metals in Soils and Their Uptake by Plants

In Portugal, where soils have a very low topsoil organic matter content, the use of sewage sludge (SS) as organic soil improvers seems an attractive option, because it would enable organic matter, N, P, K and other nutrients to be recycled. However, the risk of this practice must be properly assessed. The aim of this study was to evaluate the potential risk of the use of dewatered and composted SS as soil amendments. For this purpose, SS from two different wastewater treatment plants (SS1 and SS2), and a compost produced from SS and agricultural wastes (AWSSC), were characterized for their total metal concentrations, organic contaminants and indicator pathogens, and used in a pot experiment with three application rates, 6, 12 and 24 ton dry matter/ha, cultivated with a hybrid variety of sorghum and Sudan grass (Sorghum bicolor × Sorghum sudanense var. Rocket). SS1 and AWSSC met the legal requirements to be applied to soils, but SS2 had a high content of pathogens, which compromised its use. Both dewatered SS had a marked beneficial effect on plant production and on soil nutritional characteristics, more pronounced than in the case of AWSSC application, without a significant increase in total and in mobile metals concentration in soils. Bioaccumulation factors for metals in plants were low, and their concentrations in the aboveground plant material were lower than the maximum tolerable level for cattle, used as a risk indicator of metal entry into the human food chain. However, it will be necessary, in future studies, to evaluate the potential risk of the observed increase in the mobilisable content of Cu and Zn in soil, as a consequence of the application of these organic materials.

Bioaccessibility of lead in urban soil of Broken Hill, Australia: A study based on in vitro digestion and the IEUBK model

This study was conducted to investigate lead (Pb) bioaccessibility in urban soil and to assess health risk to children in the city of Broken Hill, Australia, which was established around one of the world's largest lead–zinc–silver mines. Fifty-three topsoil (0–0.1m) and 50 subsoil (0.3–0.5m) samples were collected from earthen footpaths, nature strips, parks or vacant land throughout the urban area. The soil samples were analysed for total Pb concentration, Pb bioaccessibility and Pb mineral phases, together with important soil physicochemical properties known to influence Pb bioaccessibility. Lead bioaccessibility ranged from 24% to 89% in topsoil and from 16% to 100% in subsoil, exhibiting a generally decreasing pattern with increasing distance from the orebody. Lead bioaccessibility was strongly positively related to total Pb concentration in both the topsoil and subsoil. In subsoil, a moderate negative correlation existed between Pb bioaccessibility and soil pH, while a moderate positive correlation existed between Pb bioaccessibility and soil organic matter (OM) content. In contrast, only a weak positive correlation existed between Pb bioaccessibility and OM content in topsoil. The presence of different Pb mineral phases also appeared to have caused variation in soil Pb bioaccessibility, with galena (PbS)-rich samples tending to exhibit lower Pb bioaccessibility. The prediction of blood lead (PbB) levels in Broken Hill children aged 1–4years using the IEUBK model well matched the measured data from a recent PbB screening, suggesting a high risk of childhood chronic low-level Pb exposure (PbB levels >5μg/dL) in Broken Hill, especially in the vicinity of the orebody. Future Pb abatement programs in Broken Hill should utilise the IEUBK model to establish target soil Pb values in an effort to achieve particular child PbB outcomes.

Humus State of Soils in the System of Landscape Agriculture in the Conditions of the Middle-Russian Upland, Russia

Soil erosion is a catastrophic problem for the world's environment, including agro-ecosystems in erosion-dangerous regions of Russia. Studies were performed in the most eroded district (Krasnogvardeiskiy) of the most eroded area (Belgorod) in the Central Chernozem zone of Russia. The aim was to study the effect of soil-protecting technologies on the dynamics of organic matter in soil monitoring survey system in real conditions of agricultural production during 27-29 years. As a result, positive dynamics of organic matter content in soils of two model facilities and the district as a whole was found. During the studied period, the content of organic matter in the soils of the Repny Log facility increased by 0.30% in the 0-20 cm layer, and by 0.75% in the 20-40 cm layer, in the soils of the Range facility - by 0.79% (20 -40 cm) and by 0.50% (20-40 cm), in whole for the Krasnogvardeiskiy district - by 0.4%. However, the rate and direction of the soil-forming process in various periods of time have been non-uniform and depended on the degree of landscape agricultural systems development, as well as on the relief conditions and the state of soil cover. While in the 0-20 cm soil layer of the Repny Log facility there was first a 10-fold reduction in organic matter loss followed by its accumulation at the rate of 0.05% per year, the 20-40 cm layer is initially characterized by a statistically significant increase in this indicator. Study of humus soil status of the Range facility established a statistically significant increase in organic matter content in topsoil and subsoil by 29% and 20%, respectively, for the whole water catchment. Thus, landscape agricultural systems are able not only to reduce the rate of soil erosion, but also to prevent it and to enhance soil fertility. This provides a basis for decision-making about their large-scale development in erosion-dangerous regions with intensive agricultural production.

Predicting Scottish topsoil organic matter content from colour and environmental factors

SummaryAssessment of soil organic matter content using laboratory analysis can be costly and time consuming, so limiting how often land managers assess this important property. This work demonstrates an ability to estimate topsoil organic matter content from field observations alone and provides a method by which rapid and cost‐effective assessments of soil organic matter status may be made. Models using environmental factors from the National Soil Inventory of Scotland (NSIS) dataset as inputs to a neural network model were used to predict loss on ignition (LOI). Two models, one for all soils and one for soils with small organic matter contents (LOI < 20%), were developed. It was found that the model developed for all soils produced reasonable predictive results across the entire LOI range (R2 = 0.877), although it was not as effective at predicting small LOI values (R2 = 0.354) as the small organic matter content model (R2 = 0.674). Both models were tested with imagery and data from samples outwith the NSIS dataset to validate the approach. Predictive results were less accurate than when using NSIS data. A discussion of possible improvements to make the model useful for field observations of soils is given.

Pedometric mapping of soil organic matter using a soil map with quantified uncertainty

This paper compares three models that use soil type information from point observations and a soil map to map the topsoil organic matter content for the province of Drenthe in the Netherlands. The models differ in how the information on soil type is obtained: model 1 uses soil type as depicted on the soil map for calibration and prediction; model 2 uses soil type as observed in the field for calibration and soil type as depicted on the map for prediction; and model 3 uses observed soil type for calibration and a pedometric soil map with quantified uncertainty for prediction. Calibration of the trend on observed soil type resulted in a much stronger predictive relationship between soil organic matter content and soil type than calibration on mapped soil type. Validation with an independent probability sample showed that model 3 out‐performed models 1 and 2 in terms of the mean squared error. However, model 3 over‐estimated the prediction error variance and so was too pessimistic about prediction accuracy. Model 2 performed the worst: it had the largest mean squared error and the prediction error variance was strongly under‐estimated. Thus validation confirmed that calibration on observed soil type is only valid when the uncertainty about soil type at prediction sites is explicitly accounted for by the model. We conclude that whenever information about the uncertainty of the soil map is available and both soil property and soil type are observed at sampling sites, model 3 can be an improvement over the conventional model 1.

Variations of soil organic matter in typical orchard regions

在黄土高原、胶东半岛和北京郊区果园选择11个采样区, 按照5~10年、10~15年和15年以上3个园龄段, 利用GPS定位系统, 共采集0~20 cm表层土壤样品99份, 并在取样果园附近选择普通农田作为对照采集土壤样品33份, 测定了果园与普通农田土壤的有机质含量, 并从果园土壤有机质数量特征、果园与农田差异特征以及有机质随园龄段变化特征3个方面进行统计分析。研究结果表明: 胶东半岛栖霞和北京郊区果园土壤有机质含量较高, 黄土高原宝塔较低。与农田相比, 60%多的果园土壤有机质未发生显著变化, 明显提高的果园不到30%, 明显降低的果园不到10%; 土壤有机质发生变化的果园, 平均提高0.62%, 平均降低0.20%; 随园龄增加, 两个样点果园土壤有机质明显提高, 其他果园变化不显著。在优质高产果园区, 果园土壤有机质提高归因于施用有机肥、果园种草、青草或秸秆还田。总体上看, 典型区域果园土壤有机质高低值差异较大, 土壤有机质含量普遍不高, 果业持续发展能力较低。

Changes in an Inceptisol of Mauritius after rock removal for sugar cane production

Sugar cane ( Saccharum hybrid sp.) cropping in the rocky soils of Mauritius requires the prior removal of rocks to enable the implementation of mechanized operations, particularly harvesting. The effects of such operations on the soil are unknown, particularly the extent to which soil properties could be affected. A study was conducted at a sugar estate on the sub-humid western coast of Mauritius to determine whether the properties of an Inceptisol had been degraded on account of rock removal and land preparation. Pristine uncultivated soils were compared to sugar cane soils from which rocks had been removed, either recently, i.e. about 2 years ago, or earlier, i.e. about 10 years ago. A number of biological, chemical and physical properties were studied. These comprised organic matter and microbial biomass; pH, cation exchange capacity and exchangeable bases; particle size distribution, aggregate stability, bulk density, plant available water and infiltration rate. The results indicate that biological properties have degraded in the topsoil but improved in the subsoil, chemical properties have generally improved, whereas physical properties were both positively and negatively affected. Thus, organic carbon concentration decreased from about 3.9% to 3.2% in the 0–5 cm layer following rock removal, but increased from about 1.8% to 2.3% in the 15–30 cm layer. Soil pH increased significantly, from 6.9 to 7.5 in the topsoil and from 7.0 to 7.7 in the subsoil. The concentration of exchangeable bases also tended to increase. Stabilized water infiltration rate increased from 133 to 185 mm/h and plant available water increased from 42 to 58 mm. However, bulk density also increased from 0.96 to 1.02 g/cm 3 in the 0–5 cm layer and from 1.09 to 1.23 g/cm 3 in the 15–30 cm layer. While rock removal can explain some of these changes, it is also quite likely that other practices linked to sugar cane production, such as tillage, irrigation and the addition of organic waste material, played a decisive role in this respect. The greatest cause for concern stemmed from soil compaction. Remedial action that was identified to address the issues of declining topsoil organic matter content included trash blanketing, addition of organic wastes and green manuring, whereas soil compaction could be managed through the use of controlled traffic and minimum tillage and in situ rock crushing.

Evaluation of an Ecological Security Model in Zhalute Banner, Inner Mongolia

The aim of this study was to develop a comprehensive index system based on remote sensing (RS) and geographic information system (GIS) techniques to evaluate the status quo of ecological security in the farming–pastoral zone of northern China, for the purpose of facilitating sustainable development in ecotone regions. Taking a typical transitional piedmont region with farming–pastoral production mode as a case study area, the region's present ecological security situation was evaluated by constructing an index system with 3 types of indicators. These indicators were: 1) vegetation growth, measured by the decadal difference in the normalized difference vegetation index (NDVI); 2) change in landscape patterns, measured by the deviation of present from potential landscapes; and 3) severity of soil erosion, measured by organic matter content in topsoil. Data for the indicators, derived from RS images, historical maps, field surveys, and archival materials, were all mapped with the aid of a digital elevation model (DEM), according to their conjugation relationships. The results were obtained by classifying different combinations of elements in an index matrix of 3 indicators by means of mapping. The final results show that nearly the entire study area was in a condition ranging from somewhat insecure (SIN) to insecure (INS), with an average score of 3.46 in a 5-grade range, indicating declining ecological security. The situation was better in the farming–pastoral district. The whole region needs to adjust its production modes to learn from practices in the farming–pastoral district, in order to alleviate deterioration and improve ecological security in the region.

Amino Acid and Soil Organic Matter Content of Topsoil in a Long Term Trial with Farmyard Manure and Mineral Fertilizers

ABSTRACT Commencing in 1980, a long-term experiment was carried out to compare mineral fertilizers (MIN), composted manure (CM) and composted manure with application of biodynamic preparations (CMBD) at three different fertilizer application rates. With mineral fertilizer, the lowest contents of 0.80% C and 0.069% N, with manure 0.90% C and 0.080% N, and with manure and biodynamic preparations 1.08% C and 0.094%? were achieved in the topsoil. The differences between these treatments were statistically significant. 42.9 to 53.7% of Nt was bound in 18 total hydrolyzable protein amino acids (THAA) including asparagine and glutamine. Amino acid contents in the hydrolyzates of the topsoil were significantly different according to fertilizer type: MIN < CM < CMBD. The higher contents in manure fertilized plots were observed even at the lowest rate of fertilizer application. This indicates that differences between the treatments do not depend only on the amino acid supply from manure, but are also influenced by an altered amino acid metabolism in the soil.

Estimating topsoil organic matter content in two experimental fields in Denmark using multi-spectral aerial imagery and topographic data

Geografisk Tidsskrift, Danish Journal of Geography 105(2):1–14, 2005 The potential use of spectral reflectance data and topographical measurements for the estimation of topsoil organic matter (SOM) content is investigated. Intensive measurements of soil texture and topography in two Danish fields combined with digital aerial photographs, representing blue, green, red and nearinfrared wavelength regions, form the basis of the investigation. Analyses of semi- variograms indicate similarity between the spatial variation of the spectral, topographic and textural measurements, and confirms that the sampling density is sufficient to account for the spatial variation of SOM within each field. Multivariate statistics were applied to investigate the potential of spectral reflectance data, including selected spectral indices, and topographical data for prediction of SOM. The results show that the spectral reflectance data obtained within the visible and near infrared range are correlated with the SOM content in both fields, and that the spectral bands are almost co-linear. The relative importance of spectral reflectance and topographical data varied in the two fields, although the combined use of these data improved the estimation of SOM significantly in both fields. The data indicated that SOM content above 3.5% was rare in both fields although a high SOM content was found in one field, which was related to kettle holes characterised by poor drainage. The study focused on the estimation of SOM from zero to 3.5% within the 2 fields. The Predicted accuracy varied between 0.24 and 0.30% SOM, depending on the field and input parameters used in the model. The results also revealed that the data used in the investigation were insufficient to properly describe the variations of SOM within the fields.

Effects of alternative tillage systems on soil quality and yield of spring cereals on silty clay loam and sandy loam soils in the cool, wet climate of central Norway

Tillage trials were established on a poorly drained silty loam overlying silty clay loam and on a freely drained sandy loam overlying medium sand, in 1988 and 1989, respectively. Autumn and spring ploughing and two ploughless systems were compared for 12–13 years, with three replications at each site. The ploughless treatments comprised deep versus shallow spring harrowing until 1999, and thereafter autumn plus spring harrowing versus spring harrowing only. In 6 years, treatments with and without fungal spraying of the cereal crops were included. In other years, fungicides were not used. Perennial weeds were controlled by herbicides as necessary, on nine occasions up until 2001. Average spring barley ( Hordeum vulgare L.) and spring oat ( Avena sativa L.) yields were similar with spring ploughing as with autumn ploughing at both sites. In treatments without ploughing, average yields on the silty loam over clay were 93% of those obtained with ploughing, and on the sandy loam over sand they were 81%. Smaller and non-significant yield differences were found between spring harrowing versus deep spring harrowing, and between autumn plus spring harrowing versus spring harrowing only. Fungal spraying increased yields markedly at both sites (25%), but there was no significant interaction between this treatment and tillage system. Oat was compared with barley in 2 years, with oat performing better under ploughless tillage. At both sites increases in penetrometer resistance occurred in the topsoil of unploughed treatments. These were considered particularly limiting on the sandy loam. On the silty loam there was an increase in surface horizon porosity in the absence of ploughing, which was associated with an increase in topsoil organic matter content. On this soil there was also a tendency toward lower penetrometer resistance at >30 cm depth on autumn plus spring harrowed soil than on ploughed soil, indicating that the plough pan may have diminished. This was supported by observations of greater earthworm activity on unploughed soil. Soil chemical analyses revealed that mineral N and plant-available P and K accumulated in the upper horizon under ploughless tillage. The percentage yields obtained in individual years with autumn as opposed to spring ploughing, were positively correlated with air temperature during 0–4 weeks after planting on the silty loam, and with precipitation during 0–12 weeks after planting on the sandy loam. In the case of yields obtained with spring harrowing only, relative to spring ploughing, positive correlations were found with 0–4 week temperature on both soil types, suggesting that low early season temperatures may limit yields under ploughless tillage.