Inclusion of soil erosion impacts in life cycle assessment on a global scale: application to energy crops in Spain

Purpose: Despite the fundamental role of ecosystem goods and services in sustaining human activities, there is no harmonized and internationally agreed method for including them in life cycle assessment (LCA). The main goal of this study was to develop a globally applicable and spatially resolved method for assessing land-use impacts on the erosion regulation ecosystem service.Methods: Soil erosion depends much on location. Thus, unlike conventional LCA, the endpoint method was regionalized at the grid-cell level (5 arc-minutes, approximately 10×10 km2) to reflect the spatial conditions of the site. Spatially explicit characterization factors were not further aggregated at broader spatial scales. Results and discussion: Life cycle inventory data of topsoil and topsoil organic carbon (SOC) losses were interpreted at the endpoint level in terms of the ultimate damage to soil resources and ecosystem quality. Human health damages were excluded from the assessment. The method was tested on a case study of five three-year agricultural rotations, two of them with energy crops, grown in several locations in Spain. A large variation in soil and SOC losses was recorded in the inventory step, depending on climatic and edaphic conditions. The importance of using a spatially explicit model and characterization factors is shown in the case study.Conclusions and outlook: The regionalized assessment takes into account the differences in soil erosion-related environmental impacts caused by the great variability of soils. Taking this regionalized framework as the starting point, further research should focus on testing the applicability of the method trough the complete life cycle of a product and on determining an appropriate spatial scale at which to aggregate characterization factors, in order to deal with data gaps on location of processes, especially in the background system. Additional research should also focus on improving reliability of the method by quantifying and, insofar as it is possible, reducing uncertainty.

Simulation of growth and development of irrigated cowpea in Piauí State by CROPGRO model

The objective of this work was to adapt the CROPGRO model, which is part of the DSSAT system, for simulating the cowpea (Vigna unguiculata) growth and development under soil and climate conditions of the Baixo Parnaíba region, Piauí State, Brazil. In the CROPGRO, only input parameters that define crop species, cultivars, and ecotype were changed in order to characterize the cowpea crop. Soil and climate files were created for the considered site. Field experiments without water deficit were used to calibrate the model. In these experiments, dry matter (DM), leaf area index (LAI), yield components and grain yield of cowpea (cv. BR 14 Mulato) were evaluated. The results showed good fit for DM and LAI estimates. The medium values of R² and medium absolute error (MAE) were, respectively, 0.95 and 264.9 kg ha-1 for DM, and 0.97 and 0.22 for LAI. The difference between observed and simulated values of plant phenology varied from 0 to 3 days. The model also presented good performance for yield components simulation, excluding 100-grain weight, for which the error ranged from 20.9% to 34.3%. Considering the medium values of crop yield in two years, the model presented an error from 5.6%.

Decomposition of Arachis pintoi and Hyparrhenia rufa litters in monoculture and intercropped systems under lowland soil

Tropical grasslands under lowland soils are generally underutilized and the litter of forage legumes may be used to recover these degraded pastures. The objective of this work was to study the dynamics of litter decomposition of Arachis pintoi (pinto peanut), Hyparrhenia rufa (thatching grass) and a mixture of both species in a lowland soil. These treatments were analyzed in three areas: grass monoculture, legume monoculture and legume intercropped with the grass during the dry and wet seasons. Litter bags containing the legume, grass or a mixture of both species were incubated to estimate the decomposition rate and microorganism colonization. Decomposition constants (K) and litter half-lives (T1/2) were estimated by an exponential model whereas number of microorganisms in specific media were determined by plate dilution. The decomposition rate, release of nutrients and microorganisms number, especially bacteria, increased when pinto peanut was added to thatching grass, influenced by favorable lignin/N and C/N ratios in legume litter. When pinto peanut litter was incubated in the grass plots, 50% N and P was released within about 135 days in the dry season and in the wet season, the equivalent release occurred within 20 days. These results indicate that A. pintoi has a great potential for nutrient recycling via litter and can be used to recover degraded areas.

Soil compaction due to forest harvest operations

The objective of this work was to determine, through the use of the bearing capacity model, the traffic effects of the forest harvest operations on the preconsolidation pressure (sigmap), during one cycle of the eucalyptus plantation. The work was conducted using undisturbed soil samples, collected at the surface of the A horizon and in the top of the B horizon of an Udult (PA), Aquox (FX) and Udox (LA) soils. The undisturbed soil samples were used in the uniaxial compression tests. The soil sampling was done before and after the harvest operations. The operations performed with the Forwarder caused greater soil compaction than the ones done with the Feller Büncher and Harvester. The percentage of soil samples, in the region with additional soil compaction, indicated that the Udult was the soil class more susceptible to soil compaction, followed by the Aquox and Udox. Despite Udult is the more susceptible to soil compaction, the regeneration of the soil structure in this soil class was more efficient than in Aquox. The percentage of soil samples with sigmap values in the region with additional soil compaction in 1996, 1998 and 2004, after harvest operations, indicated a sustainable forest exploration in this period.

Development of a Subgrade Drainage Model for Unpaved Roads, TR-654, 2015

With over 68 thousand miles of gravel roads in Iowa and the importance of these roads within the farm-to-market transportation system, proper water management becomes critical for maintaining the integrity of the roadway materials. However, the build-up of water within the aggregate subbase can lead to frost boils and ultimately potholes forming at the road surface. The aggregate subbase and subgrade soils under these gravel roads are produced with material opportunistically chosen from local sources near the site and, many times, the compositions of these sublayers are far from ideal in terms of proper water drainage with the full effects of this shortcut not being fully understood. The primary objective of this project was to provide a physically-based model for evaluating the drainability of potential subbase and subgrade materials for gravel roads in Iowa. The Richards equation provided the appropriate framework to study the transient unsaturated flow that usually occurs through the subbase and subgrade of a gravel road. From which, we identified that the saturated hydraulic conductivity, Ks, was a key parameter driving the time to drain of subgrade soils found in Iowa, thus being a good proxy variable for accessing roadway drainability. Using Ks, derived from soil texture, we were able to identify potential problem areas in terms of roadway drainage . It was found that there is a threshold for Ks of 15 cm/day that determines if the roadway will drain efficiently, based on the requirement that the time to drain, Td, the surface roadway layer does not exceed a 2-hr limit. Two of the three highest abundant textures (loam and silty clay loam), which cover nearly 60% of the state of Iowa, were found to have average Td values greater than the 2-hr limit. With such a large percentage of the state at risk for the formation of boils due to the soil with relatively low saturated hydraulic conductivity values, it seems pertinent that we propose alternative design and/or maintenance practices to limit the expensive repair work in Iowa. The addition of drain tiles or French mattresses my help address drainage problems. However, before pursuing this recommendation, a comprehensive cost-benefit analysis is needed.

Ecology and the challenge of a multifunctional use of soil

Soil zoology and soil ecology have become very active fields of research since the early 1990s. A search in the ISI Web of Science databases showed a steady increase in publications about that theme over the last two decades, and 3,612 bibliographic references were found for that theme for the period of 2004 to 2008. The researches covered mostly soil environmental issues, toxicology and ecology. The issue of theoretical development in soil ecology is discussed, and arguments are presented against the idea that the soil ecology theory is deficient. Finally, the need for a general model of soil function and soil management is discussed and some options are presented to reach this goal.

Towards an ecological index for tropical soil quality based on soil macrofauna

The objective of this work was to construct a simple index based on the presence/absence of different groups of soil macrofauna to determine the ecological quality of soils. The index was tested with data from 20 sites in South and Central Tabasco, Mexico, and a positive relation between the model and the field observations was detected. The index showed that diverse agroforestry systems had the highest soil quality index (1.00), and monocrops without trees, such as pineapple, showed the lowest soil quality index (0.08). Further research is required to improve this model for natural systems that have very low earthworm biomass (<10 g m-2) and a high number of earthworm species (5-7), as it is in the tropical rain forest, whose soil quality index was medium (0.5). The application of this index will require an illustrated guide for its users. Further studies are required in order to test the use of this index by farmers.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Reproducing the organic matter model of anthropogenic dark earth of Amazonia and testing the ecotoxicity of functionalized charcoal compounds

The objective of this work was to obtain organic compounds similar to the ones found in the organic matter of anthropogenic dark earth of Amazonia (ADE) using a chemical functionalization procedure on activated charcoal, as well as to determine their ecotoxicity. Based on the study of the organic matter from ADE, an organic model was proposed and an attempt to reproduce it was described. Activated charcoal was oxidized with the use of sodium hypochlorite at different concentrations. Nuclear magnetic resonance was performed to verify if the spectra of the obtained products were similar to the ones of humic acids from ADE. The similarity between spectra indicated that the obtained products were polycondensed aromatic structures with carboxyl groups: a soil amendment that can contribute to soil fertility and to its sustainable use. An ecotoxicological test with Daphnia similis was performed on the more soluble fraction (fulvic acids) of the produced soil amendment. Aryl chloride was formed during the synthesis of the organic compounds from activated charcoal functionalization and partially removed through a purification process. However, it is probable that some aryl chloride remained in the final product, since the ecotoxicological test indicated that the chemical functionalized soil amendment is moderately toxic.

Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density

The objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation model (DEM), a cartographic base, a conventional soil map, and the Idrisi software were used. Seven predictor variables were calculated and read along with soil classes in randomly distributed points, with sampling densities of 0.5, 1, 1.5, 2, and 4 points per hectare. Data were used to train a decision tree (Gini) and three artificial neural networks: adaptive resonance theory, fuzzy ARTMap; self‑organizing map, SOM; and multi‑layer perceptron, MLP. Estimated maps were compared with the conventional soil map to calculate omission and commission errors, overall accuracy, and quantity and allocation disagreement. The decision tree was less sensitive to sampling density and had the highest accuracy and consistence. The SOM was the less sensitive and most consistent network. The MLP had a critical minimum and showed high inconsistency, whereas fuzzy ARTMap was more sensitive and less accurate. Results indicate that sampling densities used in conventional soil surveys can serve as a reference to predict soil orders in Serra Gaúcha.

Controls on 13C and 14C variability in soil CO2

14C dating of groundwater depends on the isotopic composition of both the solid carbonate and the soil CO2 and requires the use of 14C age correction models. To better assess the variability of the 14C activity of soil CO2 (A14Csoil-CO2) and the δ13C of soil CO2 (δ13Csoil-CO2), which are two parameters used in 14C age correction models, we studied the different processes involving carbon isotopes in the soil. The approach used experimental data from two sites in France (Fontainebleau sands and Astian sands) and a steady-state transport model. In most cases, the 14C activity (A14C) of atmospheric CO2 is directly used in the 14C age correction models as the A14Csoil-CO2. However, we demonstrate that since 1950, the evolution of the A14Csoil-CO2 reflects the competition between the fluxes of root-derived CO2 and organic matter-derived CO2. Therefore, the A14Csoil-CO2 must be used to date groundwater that is younger than 60 years old. Moreover, the δ13C of soil CO2 (δ13Csoil-CO2) showed large seasonal variations that must be taken into account in selecting the δ13Csoil-CO2 for 14C age correction models.

Prediction of soil shear strength in agricultural and natural environments of the Brazilian Cerrado

The objective of this work was to develop uni- and multivariate models to predict maximum soil shear strength (τmax) under different normal stresses (σn), water contents (U), and soil managements. The study was carried out in a Rhodic Haplustox under Cerrado (control area) and under no-tillage and conventional tillage systems. Undisturbed soil samples were taken in the 0.00-0.05 m layer and subjected to increasing U and σn, in shear strength tests. The uni- and multivariate models - respectively τmax=10(a+bU) and τmax=10(a+bU+cσn) - were significant in all three soil management systems evaluated and they satisfactorily explain the relationship between U, σn, and τmax. The soil under Cerrado has the highest shear strength (τ) estimated with the univariate model, regardless of the soil water content, whereas the soil under conventional tillage shows the highest values with the multivariate model, which were associated to the lowest water contents at the soil consistency limits in this management system.

A priori parameterisation of the CERES soil-crop models and tests against several European data sets

Mechanistic soil-crop models have become indispensable tools to investigate the effect of management practices on the productivity or environmental impacts of arable crops. Ideally these models may claim to be universally applicable because they simulate the major processes governing the fate of inputs such as fertiliser nitrogen or pesticides. However, because they deal with complex systems and uncertain phenomena, site-specific calibration is usually a prerequisite to ensure their predictions are realistic. This statement implies that some experimental knowledge on the system to be simulated should be available prior to any modelling attempt, and raises a tremendous limitation to practical applications of models. Because the demand for more general simulation results is high, modellers have nevertheless taken the bold step of extrapolating a model tested within a limited sample of real conditions to a much larger domain. While methodological questions are often disregarded in this extrapolation process, they are specifically addressed in this paper, and in particular the issue of models a priori parameterisation. We thus implemented and tested a standard procedure to parameterize the soil components of a modified version of the CERES models. The procedure converts routinely-available soil properties into functional characteristics by means of pedo-transfer functions. The resulting predictions of soil water and nitrogen dynamics, as well as crop biomass, nitrogen content and leaf area index were compared to observations from trials conducted in five locations across Europe (southern Italy, northern Spain, northern France and northern Germany). In three cases, the model’s performance was judged acceptable when compared to experimental errors on the measurements, based on a test of the model’s root mean squared error (RMSE). Significant deviations between observations and model outputs were however noted in all sites, and could be ascribed to various model routines. In decreasing importance, these were: water balance, the turnover of soil organic matter, and crop N uptake. A better match to field observations could therefore be achieved by visually adjusting related parameters, such as field-capacity water content or the size of soil microbial biomass. As a result, model predictions fell within the measurement errors in all sites for most variables, and the model’s RMSE was within the range of published values for similar tests. We conclude that the proposed a priori method yields acceptable simulations with only a 50% probability, a figure which may be greatly increased through a posteriori calibration. Modellers should thus exercise caution when extrapolating their models to a large sample of pedo-climatic conditions for which they have only limited information.

Distribution Patterns of Soil Properties in a Rural Mediterranean Area in Northeastern Spain

Soil properties on the Cap de Creus Peninsula, NE Spain depend primarily on scarce agricultural practices and early abandonment. In the study area, 90% of which is mainly covered by Cistus shrubs, 8 environments representing variations in land use/land cover and soil properties at different depths were identified. In each environment variously vegetated areas were selected and sampled. The soils, collected at different depths, were classified as Lithic Xerorthents according to the United States Department of Agriculture system of soil classification (USDA-NRCS 1975). Differences in soil properties were largely found according to the evolution of the plant canopy and the land use history. To identify underlying patterns in soil properties related to environmental evolution, factor analysis was performed and factor scores were used to determine how the factor patterns varied between soil variables, soil depths and selected environments. The three-factor model always accounted for 80% of the total variation in the data at the different soil depths. Organic matter was the more relevant soil property at 0–2 cm depth, whereas active minerals (silt and clay) were found to be the most relevant soil parameters controlling soil dynamics at the other depths investigated. Results showed that vineyards and olive tree soils are poorly developed and present worse conditions for mineral and organic compounds. Analysis of factor scores allowed independent assessment of soils, depth and plant cover and demonstrated that soils present the best physico-chemical characteristics under Erica arborea and meadows. In contrast, soils under Cistus monspeliensis were less nutrient rich and less well structured

Stratification of regional sampling by model-predicted changes of carbon stocks in Forested mineral soils

Selective papers of the workshop on "Development of models and forest soil surveys for monitoring of soil carbon", Koli, Finland, April 5-9 2006.