查看更多>>摘要：Soil Vis-NIR spectral response had been widely proposed as an alternative to costly and time-consuming laboratory determination of soil physical and chemical properties. However its use for measuring soil quality index directly has not been well explored. This study compares the effectiveness of different machine learning models on a large spectral library using a database collected by the European Union project "Land Use and Coverage Area frame Survey" (LUCAS). Three approaches to predicting mineral soil features by processing their spectral response for the Vis-NIR range were tested. Prediction models of clay content, pH in CaCl2, organic carbon (SOC), calcium carbonate (CaCO3), nitrogen (N), and cation exchange capacity (CEC) were analyzed. Three types of models were assessed: a Stacked AutoEncoder, a convolutional neural network, and a stack model composed of a set of multilayer perceptron algorithms with two different regression estimation solutions. Modeling with CNN was identified as the optimal solution. Similar, and in some cases, better results can be obtained from ensembles of machine learning algorithms. The estimates of soil characteristics made with the help of the Stacked AutoEncoder showed the greatest errors. The use of soil feature estimates to support soil and land classification was also analyzed. An indicator describing the state of the topsoil is presented, which assists the objective classification of soils. The research showed that the accuracy of the estimation of the proposed Topsoil Quality Index (TQI) estimated directly based on Vis-NIR spectral response and indirectly based on estimated values of selected soil features is practically identical. The research confirms the suitability of Vis-NIR spectroscopy for topsoil assessment.
查看更多>>摘要：Crop production has negative effect on the biodiversity in farmlands because of less crop varieties and intensive soil disturbance. Macrofauna are important components of the belowground biodiversity, and contribute to the soil ecosystem functions and soil processes. The response of soil macrofauna to cropping pattern is crucial for understanding the soil ecological dynamics and biodiversity conservation. Herein, we established an experiment of rice (Oryza sativa L.)-based double cropping system in Jiangsu Province, China since 2016 to evaluate the effects of rotation regimes on soil properties and soil macrofauna communities. The rice-based double cropping system included rice-wheat (Triticum aestivum L.), rice-oilseed rape (Brassica campestris L.), rice-tiny vetch (Vida hirsuta L.), rice-broad bean (Vicia faba L.), and rice-fallow. After 3-year rotations, soil macrofaunal biodiversity and trophic structure varied significantly with soil properties. The higher biodiversity indices were found in the tiny vetch (used as green manure in situ) and fallow fields. In addition, soil organic carbon (SOC), the main factor for shaping the soil environment and regulating the soil macrofaunal communities, was improved. While soil water content and pH were selected to predict each trophic richness and density in most of the optimal regression models, changes in soil macrofaunal communities should also be considered as comprehensive responses to the soil environments (food resources, microhabitats etc.) co-varying rotation regimes. Crop rotation with lower intensity of land use increased the complexity of soil macrofaunal trophic structure. Green manure fields or fallow after rice plantation promoted soil macrofaunal biodiversity in the rice-based cropping system.
Momesso, LetusaCosta Crusciol, Carlos AlexandreCantarella, HeitorTanaka, Katiuca Sueko...
13页
查看更多>>摘要：Residues of cover crop grasses release nitrogen (N) to subsequent crops, which can contribute to sustainable agricultural management and prevent increases in N-loss-related microorganisms. Moreover, applying N fertilizer to cover crops can enhance the N-use efficiency and yields of subsequent cash crops and tighten the N cycle in the soil. However, the long-term effects of N fertilization of cover crops on soil microbiota and the N cycle in tropical grass-crop no-till systems are unknown. The aim of this study was to evaluate the long-term effects of the timing of N fertilization of cover crops or maize on crop yields, total microbial abundances and N-cycle gene abundances at the time of maize harvest. We carried out a field experiment with two cover crops (palisade grass (Urochloa brizantha) and ruzigrass (U. ruziziensis) fertilized with 120 kg N ha(-1) (ammonium sulfate) at one of three times: (i) broadcast over the green cover crops at 35 days before maize seeding (35 DBS), (ii) broadcast over the cover crop straw residues at 1 day before maize seeding (1 DBS), and (iii) as side-dressing at the maize V-4 growth stage according to the conventional method (band-applied 0.05 m from the maize row). A control treatment without N application was also carried out for both cover crop species. Except for the control, 40 kg N ha(-1) as ammonium sulfate was subsurface band-applied in all treatments 0.05-0.10 m from the maize row at maize seeding, corresponding to 160 kg N ha(-1). The total bacterial, archaeal and fungal abundances and abundances of microbial genes encoding enzymes of the N cycle in the soil were quantified by real-time PCR at the maize harvest stage. Overall, maize yield increased significantly in all N fertilizer applications (average 13 Mg ha(-1)) compared with the control (6 Mg ha(-1)) over three growing seasons, with maize following palisade grass having the highest yield. The abundances of archaea and fungi in soil were highest under palisade grass that received N at 35 DBS, with values of 4.6 x 10(6) and 1.7 x 10(7) gene copies/g of dry soil, respectively. Both cover crop straw production and N release to the soil were positively correlated with the total microbe densities. When ruzigrass was the cover crop, low N enhanced nifH abundance. Archaeal amoA abundance was positively correlated with cover crop biomass and N release regardless of the N treatment and was highest under palisade grass. Bacterial amoA, nirK, and nirS abundances were highest in soil under ruzigrass and were not linked to cover crop biomass mineralization. We conclude that N fertilizer should be applied using the currently recommended method (40 and 120 kg N ha(-1) at seeding and side-dressed in maize, respectively) following palisade grass to achieve high maize yield while controlling the level of N loss from tropical soil via nitrification and denitrification.
查看更多>>摘要：Role of soil to meet global food security, sustainable intensification and food nutritional quality has got renewed attention with a larger focus on soil physical condition. No-tillage (NT) practice can essentially contribute to develop a sustainable, low carbon and resource efficient agriculture, and encourage the use of crop residues for added soil benefits. Soil aggregation and pore size distribution, two most important soil physical factors controlling the mass and energy transport processes within the soil and between soil and environment, were evaluated under the NT through a global meta-analysis of 5065 pairs of data points from 419 peer-reviewed studies. Compared to conventional tillage (CT), NT increased mean weight diameter of aggregates, water stable aggregates, and macroaggregates by averages (0-30 cm) of 25, 10 and 22%, respectively, although predominantly in 0-10 and/or 10-20 cm layers, with an accompanying reduction in microaggregates. A small but significant 3% decrease in total porosity, a large reduction (20-32%) in macroporosity and a moderate increase (4-7%) in microporosity were realized under NT up to 20 cm soil depth. Bulk density remained stable, although a very large decrease (70% change over CT) in saturated hydraulic conductivity was recorded in 10-20 and >30 cm soil layers. Years of adoption of NT had an additive effect on mean weight diameter and macroaggregates, and the total and macroporosity. Increase in latitudes favoured soil aggregation and micropore volume under NT, while clay content was unfavourable to macro- and water stable aggregate contents. Improvement in structure and water retention properties relate to long-term sustainable development of soils by following no-till practice, which has far-reaching implications beyond the boundaries of agronomy.
Wang, BinGray, Jonathan M.Waters, Cathy M.Anwar, Muhuddin Rajin...
12页
查看更多>>摘要：Soil organic carbon (SOC) plays a key role in the sequestration of carbon that could otherwise be warming the atmosphere. Climate change including increased temperature and changed rainfall will greatly impact the global SOC cycle. There are still significant gaps in our knowledge of the size of the global SOC pool and how future climate will affect SOC stocks and flows in many parts of the world, including Australia. In this study, we used SOC data in a Digital Soil Mapping framework to predict current and future SOC stocks across the state of New South Wales (NSW) in south-eastern Australia. In the first phase of the study we estimated the current SOC stock using multiple linear regression (MLR) and random forest (RF) modelling, and in the second phase we projected the change of SOC stocks in the near future (2050s) and far future (2090s) under two Shared Socio-economic Pathways (SSPs) scenarios based on 25 global climate models (GCMs) from the Coupled Model Inter-comparison Project Phase 6 (CMIP6). Our spatial modelling showed that estimated current SOC stocks in NSW decreased from east to west. Multi-GCM ensemble means suggested SOC stocks would decrease by 7.6-12.9% under SSP2-4.5 and 9.1-20.9% under SSP5-8.5 across NSW under future climate. The extent of change in SOC stocks varied spatially with the largest mean decrease of SOC stocks occurring in the North Coast and South East (alpine) regions of NSW. Our findings can support decision-making in land management and climate change mitigation strategies in NSW at the regional level. Furthermore, the modelling methods can be applied to other areas where edaphic and landscape properties, land use, and climate data are available.
查看更多>>摘要：Benefits of fusion approaches for visible to near (vis-NIR) and mid-infrared (MIR) chemometric modelling have been studied to some extent for laboratory-based soil studies, but little is known about the usefulness and limitations for in situ studies. Objectives were to compare laboratory-based and in situ vis-NIR and MIR partial least squares (PLS) and bagging-PLS regression approaches and to explore the potentials of combining both types of spectral data for the quantification of soil organic carbon (SOC). We applied different established low-level (spectra concatenation, outer product fusion approach) and high-level (averaging of vis-NIR and MIR modelling results) data fusion methods. The studied set comprised a total of 186 soil samples collected in SaxonyAnhalt and northern Saxony, Central Germany. One subset (Querfurt Plateau) covered 90 finely-textured soils originating from the Chernozem soil region, another (Diiben Heath) with 96 samples was characterized by a wider pedological variety. Vis-NIR and MIR diffuse reflectance spectra were measured in situ on the soil surface and in the laboratory on pre-treated (dried and finely ground) soil material with the ASD FieldSpec 4 and the Agilent 4300 Handheld FTIR instruments. We found a regionally stratified approach to be beneficial for accurate estimations for both laboratory and in situ data. For laboratory spectra, MIR outperformed vis-NIR data in both regions (Querfurt Plateau: r(2) = 0.85 vs. 0.65, RMSE = 0.11 (in % SOC) vs. 0.17; Diiben Heath: 0.77 vs. 0.69 (r(2)) and 0.27 vs. 0.40 (RMSE)). Ranking for in situ data was the same, but accuracies decreased markedly. With MIR, r(2) amounted to 0.58 and RMSE was 0.20 for the Querfurt Plateau (vis-NIR: r(2) = 0.26, RMSE = 0.27); for Diiben Heath, r(2) was 0.60 and RMSE was 0.39 for MIR data, while vis-NIR resulted in an r(2) of 0.53 and an RMSE of 0.43. For the studied samples, which had medium to low water contents (0.68 to 16.8 wt%, median at 5.4 wt%), we found accuracies with both spectral datasets to be similarly affected by in situ conditions. Model ensemble averaging based on bagging-PLS regression was the most efficient approach to improve SOC estimation accuracies with in situ spectral data, whereas model averaging was in general of little effect for laboratory data. Improvements were most marked for the in situ data of the Diiben Heath region, where r(2) increased to a value of 0.77 and RMSE decreased to 0.28. Low-level data fusion methods did not yield any improvements compared to model ensemble averaging. For the latter, we identified averaging with weights derived sample-wise from uncertainties in the bootstrap-based modelling as being most accurate, but with little benefits compared to a simple (unweighted) averaging of vis-NIR and MIR estimates. Our results suggest that already a simple averaging procedure has the potential to advance multi-sensor applications integrating vis-NIR and MIR data for in situ or on-site soil spectroscopy. This applies especially to regions with heterogeneous soil conditions, tied to spectral variablity, as this increases the probability of complementary vis-NIR and MIR information and their prospective fusion.
NSTL
Elsevier