Responses of silver birch saplings to low soil temperature

Abstract

Pre-harvest soil acidification, liming or N fertilization did not significantly affect the survival and growth of young Norway spruce

Abstract

Soil Conservation Cost Share: SFY 2015 Annual Report, October 2015

Annual report of soil conservation in Iowa.

Soil Conservation Cost Share: SFY 2014 Annual Report, October 2015

Annual report of soil conservation in Iowa.

Organic compounds from plant extracts and their effect on soil phosphorus availability

The objective of this work was to evaluate the effect of organic compounds from plant extracts of six species and phosphate fertilization on soil phosphorus availability. Pots of 30 cm height and 5 cm diameter were filled with Typic Hapludox. Each pot constituted a plot of a completely randomized design, in a 7x2 factorial arrangement, with four replicates. Aqueous extracts of black oat (Avena strigosa), radish (Raphanus sativus), corn (Zea mays), millet (Pennisetum glaucum), soybean (Glycine max), sorghum (Sorghum bicolor), and water, as control, were added in each plot, with or without soluble phosphate fertilization. After seven days of incubation, soil samples were taken from soil layers at various depths, and labile, moderately labile and nonlabile P fractions in the soil were analysed. Plant extracts led to an accumulation of inorganic phosphorus in labile and moderately labile fractions, mainly in the soil surface layer (0-5 cm). Radish, with a higher amount of malic acid and higher P content than other species, was the most efficient in increasing soil P availability.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Soil compaction by machine traffic and least limiting water range related to soybean yield

The research aimed to evaluate machine traffic effect on soil compaction and the least limiting water range related to soybean cultivar yields, during two years, in a Haplustox soil. The six treatments were related to tractor (11 Mg weight) passes by the same place: T0, no compaction; and T1*, 1; T1, 1; T2, 2; T4, 4 and T6, 6. In the treatment T1*, the compaction occurred when soil was dried, in 2003/2004, and with a 4 Mg tractor in 2004/2005. Soybean yield was evaluated in relation to soil compaction during two agricultural years in completely randomized design (compaction levels); however, in the second year, there was a factorial scheme (compaction levels, with and without irrigation), with four replicates represented by 9 m² plots. In the first year, soybean [Glycine max (L.) Merr.] cultivar IAC Foscarim 31 was cultivated without irrigation; and in the second year, IAC Foscarim 31 and MG/BR 46 (Conquista) cultivars were cultivated with and without irrigation. Machine traffic causes compaction and reduces soybean yield for soil penetration resistance between 1.64 to 2.35 MPa, and bulk density between 1.50 to 1.53 Mg m-3. Soil bulk density from which soybean cultivar yields decrease is lower than the critical one reached at least limiting water range (LLWR =/ 0).

Temporal changes of an alfalfa succession and related soil physical properties on the Loess Plateau, China

The objective of this work was to investigate the relationship between changes in the plant community and changes in soil physical properties and water availability, during a succession from alfalfa (Medicago sativa L.) to natural vegetation on the Loess Plateau, China. Data from a succession sere spanning 32 years were collated, and vegetative indexes were compared to changes related to soil bulk density and soil water storage. The alfalfa yield increased for approximately 7 years, then it declined and the alfalfa was replaced by a natural community dominated by Stipa bungeana that began to thrive about 10 years after alfalfa seeding. Soil bulk density increased over time, but the deterioration of the alfalfa was mainly ascribed to a severe reduction in soil water storage, which was lowest around the time when degradation commenced. The results indicated that water consumption by alfalfa could be reduced by reducing plant density. The analysis of the data also suggested that soil water recharge could be facilitated by rotating the alfalfa with other crops, natural vegetation, or bare soil.

The effect of pesticides and alien invesive species on soil biota and litter decomposition rates in a Mediterrean-climate ecosystem of Western Australia

Ecosystems are complex systems and changing one of their components can alter their whole functioning. Decomposition and biodiversity are two factors that play a role in this stability, and it is vital to study how these two factors are interrelated and how other factors, whether of human origin or not, can affect them. This study has tested different hypotheses regarding the effects of pesticides and invasive species on the biodiversity of the soil fauna and litter decomposition rate. Decomposition was measured using the litterbags technique. Our results indicate that pesticides had a negative effect on decomposition whereas invasive species increased decomposition rate. At the same time, the diversity of the soil biota was unaffected by either factor. These results allow us to better understand the response of important ecosystem functions to human‐induced alterations, in order to mitigate harmful effects or restore them wherever necessary.

DNA barcodes for soil animal taxonomy

The biodiversity of soil communities remains very poorly known and understood. Soil biological sciences are strongly affected by the taxonomic crisis, and most groups of animals in that biota suffer from a strong taxonomic impediment. The objective of this work was to investigate how DNA barcoding - a novel method using a microgenomic tag for species identification and discrimination - permits better evaluation of the taxonomy of soil biota. A total of 1,152 barcode sequences were analyzed for two major groups of animals, collembolans and earthworms, which presented broad taxonomic and geographic sampling. Besides strongly reflecting the taxonomic impediment for both groups, with a large number of species-level divergent lineages remaining unnamed so far, the results also highlight a high level (15%) of cryptic diversity within known species of both earthworms and collembolans. These results are supportive of recent local studies using a similar approach. Within an impeded taxonomic system for soil animals, DNA-assisted identification tools can facilitate and improve biodiversity exploration and description. DNA-barcoding campaigns are rapidly developing in soil animals and the community of soil biologists is urged to embrace these methods.

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 a universal sampling protocol for soil biotas in the humid tropics

This paper reviews the methods for the inventory of below-ground biotas in the humid tropics, to document the (hypothesized) loss of soil biodiversity associated with deforestation and agricultural intensification at forest margins. The biotas were grouped into eight categories, each of which corresponded to a major functional group considered important or essential to soil function. An accurate inventory of soil organisms can assist in ecosystem management and help sustain agricultural production. The advantages and disadvantages of transect-based and grid-based sampling methods are discussed, illustrated by published protocols ranging from the original "TSBF transect", through versions developed for the alternatives to Slash-and-Burn Project (ASB) to the final schemes (with variants) adopted by the Conservation and Sustainable Management of Below-ground Biodiversity Project (CSM-BGBD). Consideration is given to the place and importance of replication in below-ground biological sampling and it is argued that the new sampling protocols are inclusive, i.e. designed to sample all eight biotic groups in the same field exercise; spatially scaled, i.e. provide biodiversity data at site, locality, landscape and regional levels, and link the data to land use and land cover; and statistically robust, as shown by a partial randomization of plot locations for sampling.

Why should we care about soil fauna?

The reasons why we care about soil fauna are related to their intrinsic, utilitarian and functional values. The intrinsic values embrace aesthetic or moral reasons for conserving below-ground biodiversity. Unfortunately, the protection of soil invertebrates has rarely been a criterion for avoiding changes in land use and management. Utilitarian, or direct use values, have been investigated more extensively for fungi, bacteria and marine invertebrates than for soil fauna. However, some traditional remedies, novel enzymes and pharmaceutical compounds have been derived from earthworms, termites and other groups, and gut symbionts may provide microbial strains with interesting properties for biotechnology. The functional importance of soil invertebrates in ecosystem processes has been a major focus of research in recent decades. It is suggested herein that it is rarely possible to identify the role of soil invertebrates as rate determinants of soil processes at plot and ecosystem scales of hectares and above because other biophysical controls override their effects. There are situations, however, where the activities of functional groups of soil animals, even of species, are synchronised in space or time by plant events, resource inputs, seasonality or other perturbations to the system, and their emergent effects are detectable as higher order controls.

A new method to simulate the hydrological state of soil under natural conditions

Micro, macro and mesofauna in the soil often respond to fluctuating environmental conditions, resulting in changes of abundance and community structure. Effects of changing soil parameters are normally determined with samples taken in the field and brought to the laboratory, i.e. where natural environmental conditions may not apply. We devised a method (STAFD - soil tubes for artificial flood and drought), which simulates the hydrological state of soil in situ using implanted cores. Control tubes were compared with treatment tubes in which floods of 15, 30, 60 and 90 days, and droughts of 60, 90 and 120 days were simulated in the field. Flooding and drought were found to reduce number of individuals in all soil faunal groups, but the response to drought was slower and not in proportion to the expected decrease of the water content. The results of the simulated floods in particular show the value of the STAFD method for the investigation of such extreme events in natural habitats.