Surface mapping, organic matter and water stocks in peatlands of the Serra do Espinhaço meridional - Brazil

Peatlands are soil environments that store carbon and large amounts of water, due to their composition (90 % water), low hydraulic conductivity and a sponge-like behavior. It is estimated that peat bogs cover approximately 4.2 % of the Earth's surface and stock 28.4 % of the soil carbon of the planet. Approximately 612 000 ha of peatlands have been mapped in Brazil, but the peat bogs in the Serra do Espinhaço Meridional (SdEM) were not included. The objective of this study was to map the peat bogs of the northern part of the SdEM and estimate the organic matter pools and water volume they stock. The peat bogs were pre-identified and mapped by GIS and remote sensing techniques, using ArcGIS 9.3, ENVI 4.5 and GPS Track Maker Pro software and the maps validated in the field. Six peat bogs were mapped in detail (1:20,000 and 1:5,000) by transects spaced 100 m and each transect were determined every 20 m, the UTM (Universal Transverse Mercator) coordinates, depth and samples collected for characterization and determination of organic matter, according to the Brazilian System of Soil Classification. In the northern part of SdEM, 14,287.55 ha of peatlands were mapped, distributed over 1,180,109 ha, representing 1.2 % of the total area. These peatlands have an average volume of 170,021,845.00 m³ and stock 6,120,167 t (428.36 t ha-1) of organic matter and 142,138,262 m³ (9,948 m³ ha-1) of water. In the peat bogs of the Serra do Espinhaço Meridional, advanced stages of decomposing (sapric) organic matter predominate, followed by the intermediate stage (hemic). The vertical growth rate of the peatlands ranged between 0.04 and 0.43 mm year-1, while the carbon accumulation rate varied between 6.59 and 37.66 g m-2 year-1. The peat bogs of the SdEM contain the headwaters of important water bodies in the basins of the Jequitinhonha and San Francisco Rivers and store large amounts of organic carbon and water, which is the reason why the protection and preservation of these soil environments is such an urgent and increasing need.

Multivariate analysis applied to the study of the relationship between soil and plant properties in a peach orchard

In the State of Rio Grande do Sul, the municipality of Pelotas is responsible for 90 % of peach production due to its suitable climate and soil conditions. However, there is the need for new studies that aim at improved fruit quality and increased yield. The aim of this study was to evaluate the relationship that exists between soil physical properties and properties in the peach plant in the years 2010 and 2011 by the technique of multivariate canonical correlation. The experiment was conducted in a peach orchard located in the municipality of Morro Redondo, RS, Brazil, where an experimental grid of 101 plants was established. In a trench dug beside each one of the 101 plants, soil samples were collected to determine silt, clay, and sand contents, soil density, total porosity, macroporosity, microporosity, and volumetric water content in the 0.00-0.10 and 0.10-0.20 m layers, as well as the depth of the A horizon. In each plant and in each year, the following properties were assessed: trunk diameter, fruit size and number of fruits per plant, average weight of the fruit per plant, fruit pulp firmness, Brix content, and yield from the orchard. Exploratory analysis of the data was undertaken by descriptive statistics, and the relationships between the physical properties of the soil and of the plant were assessed by canonical correlation analysis. The results showed that the clay and microporosity variables were those that exhibited the highest coefficients of canonical cross-loading with the plant properties in the soil layers assessed, and that the variable of mean weight of the fruit per plant was that which had the highest coefficients of canonical loading within the plant group for the two years assessed.

HEAVY METALS AND MICRONUTRIENTS IN THE SOIL AND GRAPEVINE UNDER DIFFERENT IRRIGATION STRATEGIES

Soils under natural conditions have heavy metals in variable concentrations and there may be an increase in these elements as a result of the agricultural practices adopted. Transport of heavy metals in soil mainly occurs in forms dissolved in the soil solution or associated with solid particles, water being their main means of transport. In this context, the aim of this study was to evaluate the heavy metal and micronutrient content in the soil and in the grapevine plant and fruit under different irrigation strategies. The experiment was carried out in Petrolina, PE, Brazil. The treatments consisted of three irrigation strategies: full irrigation (FI), regulated deficit irrigation (RDI), and deficit irrigation (DI). During the period of grape maturation, soil samples were collected at the depths of 0-10, 10-20, 20-40, 40-60, and 60-80 cm. In addition, leaves were collected at the time of ripening of the bunches, and berries were collected at harvest. Thus, the heavy metal and micronutrient contents were determined in the soil, leaves, and berries. The heavy metal and micronutrient contents in the soil showed a stochastic pattern in relation to the different irrigation strategies. The different irrigation strategies did not affect the heavy metal and micronutrient contents in the vine leaves, and they were below the contents considered toxic to the plant. In contrast, the greater availability of water in the FI treatment favored a greater Cu content in the grape, which may be a risk to vines, causing instability and turbidity. Thus, adoption of deficit irrigation is recommended so as to avoid compromising the stability of tropical wines of the Brazilian Northeast.

SPATIAL CORRELATION BETWEEN PHYSICAL PROPERTIES OF SOIL AND WEEDS IN TWO MANAGEMENT SYSTEMS

The spatial correlation between soil properties and weeds is relevant in agronomic and environmental terms. The analysis of this correlation is crucial for the interpretation of its meaning, for influencing factors such as dispersal mechanisms, seed production and survival, and the range of influence of soil management techniques. This study aimed to evaluate the spatial correlation between the physical properties of soil and weeds in no-tillage (NT) and conventional tillage (CT) systems. The following physical properties of soil and weeds were analyzed: soil bulk density, macroporosity, microporosity, total porosity, aeration capacity of soil matrix, soil water content at field capacity, weed shoot biomass, weed density, Commelina benghalensis density, and Bidens pilosa density. Generally, the ranges of the spatial correlations were higher in NT than in CT. The cross-variograms showed that many variables have a structure of combined spatial variation and can therefore be mapped from one another by co-kriging. This combined variation also allows inferences about the physical and biological meanings of the study variables. Results also showed that soil management systems influence the spatial dependence structure significantly.

Boundary between Soil and Saprolite in Alisols in the South of Brazil

Despite numerous studies conducted on the lower limit of soil and its contact with saprolite layers, a great deal of work is left to standardize identification and annotation of these variables in the field. In shallow soils, the appropriately noting these limits or contacts is essential for determining their behavior and potential use. The aims of this study were to identify and define the field contact and/or transition zone between soil and saprolite in profiles of an Alisol derived from fine sandstone and siltstone/claystone in subtropical southern Brazil and to subsequently validate the field observations through a multivariate analysis of laboratory analytical data. In the six Alisol profiles evaluated, the sequence of horizons found was A, Bt, C, and Cr, where C was considered part of the soil due to its pedogenetic structure, and Cr was considered saprolite due to its rock structure. The morphological properties that were determined in the field and that were different between the B and C horizons and the Cr layer were color, structure, texture, and fragments of saprolite. According to the test of means, the properties that support the inclusion of the C horizon as part of the soil are sand, clay, water-dispersible clay, silt/clay ratio, macroporosity, total porosity, resistance to penetration, cation exchange capacity, Fe extracted by DCB, Al, H+Al, and cation exchange capacity of clay. The properties that support the C horizon as a transition zone are silt, Ca, total organic C, and Fe extracted by ammonium oxalate. Discriminant analysis indicated differences among the three horizons evaluated.

Contribution of Topography and Incident Solar Radiation to Variation of Soil and Plant Litter at an Area with Heterogeneous Terrain

Natural processes that determine soil and plant litter properties are controlled by multiple factors. However, little attention has been given to distinguishing the effects of environmental factors from the effects of spatial structure of the area on the distribution of soil and litter properties in tropical ecosystems covering heterogeneous topographies. The aim of this study was to assess patterns of soil and litter variation in a tropical area that intercepts different levels of solar radiation throughout the year since its topography has slopes predominantly facing opposing geographic directions. Soil data (pH, C, N, P, H+Al, Ca, Mg, K, Al, Na, sand, and silt) and plant litter data (N, K, Ca, P, and Mg) were gathered together with the geographic coordinates (to model the spatial structure) of 40 sampling units established at two sites composed of slopes predominantly facing northwest and southeast (20 units each). Soil and litter chemical properties varied more among slopes within similar geographic orientations than between the slopes facing opposing directions. Both the incident solar radiation and the spatial structure of the area were relevant in explaining the patterns detected in variation of soil and plant litter. Individual contributions of incident solar radiation to explain the variation in the properties evaluated suggested that this and other environmental factors may play a particularly relevant role in determining soil and plant litter distribution in tropical areas with heterogeneous topography. Furthermore, this study corroborates that the spatial structure of the area also plays an important role in the distribution of soil and litter within this type of landscape, which appears to be consistent with the action of water movement mechanisms in such areas.

Water Erosion on an Oxisol under Integrated Crop-Forest Systems in a Transitional Area between the Amazon and Cerrado Biomes

ABSTRACT Water erosion is one of the main factors driving soil degradation, which has large economic and environmental impacts. Agricultural production systems that are able to provide soil and water conservation are of crucial importance in achieving more sustainable use of natural resources, such as soil and water. The aim of this study was to evaluate soil and water losses in different integrated production systems under natural rainfall. Experimental plots under six different land use and cover systems were established in an experimental field of Embrapa Agrossilvipastoril in Sinop, state of Mato Grosso, Brazil, in a Latossolo Vermelho-Amarelo Distrófico (Udox) with clayey texture. The treatments consisted of perennial pasture (PAS), crop-forest integration (CFI), eucalyptus plantation (EUC), soybean and corn crop succession (CRP), no ground cover (NGC), and forest (FRS). Soil losses in the treatments studied were below the soil loss limits (11.1 Mg ha-1 yr-1), with the exception of the plot under bare soil (NGC), which exhibited soil losses 30 % over the tolerance limit. Water losses on NGC, EUC, CRP, PAS, CFI and FRS were 33.8, 2.9, 2.4, 1.7, 2.4, and 0.5 % of the total rainfall during the period of study, respectively.

Randomized trial of a computerized coronary heart disease risk assessment tool in HIV-infected patients receiving combination antiretroviral therapy.

BACKGROUND: Exposure to combination antiretroviral therapy (cART) can lead to important metabolic changes and increased risk of coronary heart disease (CHD). Computerized clinical decision support systems have been advocated to improve the management of patients at risk for CHD but it is unclear whether such systems reduce patients' risk for CHD. METHODS: We conducted a cluster trial within the Swiss HIV Cohort Study (SHCS) of HIV-infected patients, aged 18 years or older, not pregnant and receiving cART for >3 months. We randomized 165 physicians to either guidelines for CHD risk factor management alone or guidelines plus CHD risk profiles. Risk profiles included the Framingham risk score, CHD drug prescriptions and CHD events based on biannual assessments, and were continuously updated by the SHCS data centre and integrated into patient charts by study nurses. Outcome measures were total cholesterol, systolic and diastolic blood pressure and Framingham risk score. RESULTS: A total of 3,266 patients (80% of those eligible) had a final assessment of the primary outcome at least 12 months after the start of the trial. Mean (95% confidence interval) patient differences where physicians received CHD risk profiles and guidelines, rather than guidelines alone, were total cholesterol -0.02 mmol/l (-0.09-0.06), systolic blood pressure -0.4 mmHg (-1.6-0.8), diastolic blood pressure -0.4 mmHg (-1.5-0.7) and Framingham 10-year risk score -0.2% (-0.5-0.1). CONCLUSIONS: Systemic computerized routine provision of CHD risk profiles in addition to guidelines does not significantly improve risk factors for CHD in patients on cART.

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.

Intercepted solar radiation by maize crops subjected to different tillage systems and water availability levels

The objective of this work was to evaluate changes in the photosynthetic photon flux density (PPFD) interception efficiency and PPFD extinction coefficient for maize crop subjected to different soil tillage systems and water availability levels. Crops were subjected to no-tillage and conventional tillage systems combined with full irrigation and non-irrigation treatments. Continuous measurements of transmitted PPFD on the soil surface and incoming PPFD over the canopy were taken throughout the crop cycle. Leaf area index and soil water potential were also measured during the whole period. Considering a mean value over the maize cycle, intercepted PPFD was higher in the conventional tillage than in the no-tillage system. During the initial stages of plants, intercepted PPFD in the conventional tillage was double the PPFD interception in the no-tillage treatment. However, those differences were reduced up to the maximum leaf area index, close to tasseling stage. The lowest interception of PPFD occurred in the conventional tillage during the reproductive period, as leaf senescence progressed. Over the entire crop cycle, the interception of PPFD by the non-irrigated plants was about 20% lower than by the irrigated plants. The no-tillage system reduced the extinction coefficient for PPFD, which may have allowed a higher penetration of solar radiation into the canopy

Validation of the spiritual distress assessment tool in older hospitalized patients.

ABSTRACT:¦BACKGROUND: The Spiritual Distress Assessment Tool (SDAT) is a 5-item instrument developed to assess unmet spiritual needs in hospitalized elderly patients and to determine the presence of spiritual distress. The objective of this study was to investigate the SDAT psychometric properties.¦METHODS: This cross-sectional study was performed in a Geriatric Rehabilitation Unit. Patients (N = 203), aged 65 years and over with Mini Mental State Exam score ≥ 20, were consecutively enrolled over a 6-month period. Data on health, functional, cognitive, affective and spiritual status were collected upon admission. Interviews using the SDAT (score from 0 to 15, higher scores indicating higher distress) were conducted by a trained chaplain. Factor analysis, measures of internal consistency (inter-item and item-to-total correlations, Cronbach α), and reliability (intra-rater and inter-rater) were performed. Criterion-related validity was assessed using the Functional Assessment of Chronic Illness Therapy-Spiritual well-being (FACIT-Sp) and the question "Are you at peace?" as criterion-standard. Concurrent and predictive validity were assessed using the Geriatric Depression Scale (GDS), occurrence of a family meeting, hospital length of stay (LOS) and destination at discharge.¦RESULTS: SDAT scores ranged from 1 to 11 (mean 5.6 ± 2.4). Overall, 65.0% (132/203) of the patients reported some spiritual distress on SDAT total score and 22.2% (45/203) reported at least one severe unmet spiritual need. A two-factor solution explained 60% of the variance. Inter-item correlations ranged from 0.11 to 0.41 (eight out of ten with P < 0.05). Item-to-total correlations ranged from 0.57 to 0.66 (all P < 0.001). Cronbach α was acceptable (0.60). Intra-rater and inter-rater reliabilities were high (Intraclass Correlation Coefficients ranging from 0.87 to 0.96). SDAT correlated significantly with the FACIT-Sp, "Are you at peace?", GDS (Rho -0.45, -0.33, and 0.43, respectively, all P < .001), and LOS (Rho 0.15, P = .03). Compared with patients showing no severely unmet spiritual need, patients with at least one severe unmet spiritual need had higher odds of occurrence of a family meeting (adjOR 4.7, 95%CI 1.4-16.3, P = .02) and were more often discharged to a nursing home (13.3% vs 3.8%; P = .027).¦CONCLUSIONS: SDAT has acceptable psychometrics properties and appears to be a valid and reliable instrument to assess spiritual distress in elderly hospitalized patients.

Mercury analysis in hair: comparability and quality assessment within the transnational COPHES/DEMOCOPHES project

Human biomonitoring (HBM) is an effective tool for assessing actual exposure to chemicals that takes into account all routes of intake. Although hair analysis is considered to be an optimal biomarker for assessing mercury exposure, the lack of harmonization as regards sampling and analytical procedures has often limited the comparison of data at national and international level. The European-funded projects COPHES and DEMOCOPHES developed and tested a harmonized European approach to Human Biomonitoring in response to the European Environment and Health Action Plan. Herein we describe the quality assurance program (QAP) for assessing mercury levels in hair samples from more than 1800 mother-child pairs recruited in 17 European countries. To ensure the comparability of the results, standard operating procedures (SOPs) for sampling and for mercury analysis were drafted and distributed to participating laboratories. Training sessions were organized for field workers and four external quality-assessment exercises (ICI/EQUAS), followed by the corresponding web conferences, were organized between March 2011 and February 2012. ICI/EQUAS used native hair samples at two mercury concentration ranges (0.20-0.71 and 0.80-1.63) per exercise. The results revealed relative standard deviations of 7.87-13.55% and 4.04-11.31% for the low and high mercury concentration ranges, respectively. A total of 16 out of 18 participating laboratories the QAP requirements and were allowed to analyze samples from the DEMOCOPHES pilot study. Web conferences after each ICI/EQUAS revealed this to be a new and effective tool for improving analytical performance and increasing capacity building. The procedure developed and tested in COPHES/DEMOCOPHES would be optimal for application on a global scale as regards implementation of the Minamata Convention on Mercury.

Improved understanding of drought controls on seasonal variation in Mediterranean forest canopy CO2 and water fluxes through combined in situ measurements and ecosystem modelling

Water stress is a defining characteristic of Mediterranean ecosystems, and is likely to become more severe in the coming decades. Simulation models are key tools for making predictions, but our current understanding of how soil moisture controls ecosystem functioning is not sufficient to adequately constrain parameterisations. Canopy-scale flux data from four forest ecosystems with Mediterranean-type climates were used in order to analyse the physiological controls on carbon and water flues through the year. Significant non-stomatal limitations on photosynthesis were detected, along with lesser changes in the conductance-assimilation relationship. New model parameterisations were derived and implemented in two contrasting modelling approaches. The effectiveness of two models, one a dynamic global vegetation model ('ORCHIDEE'), and the other a forest growth model particularly developed for Mediterranean simulations ('GOTILWA+'), was assessed and modelled canopy responses to seasonal changes in soil moisture were analysed in comparison with in situ flux measurements. In contrast to commonly held assumptions, we find that changing the ratio of conductance to assimilation under natural, seasonally-developing, soil moisture stress is not sufficient to reproduce forest canopy CO2 and water fluxes. However, accurate predictions of both CO2 and water fluxes under all soil moisture levels encountered in the field are obtained if photosynthetic capacity is assumed to vary with soil moisture. This new parameterisation has important consequences for simulated responses of carbon and water fluxes to seasonal soil moisture stress, and should greatly improve our ability to anticipate future impacts of climate changes on the functioning of ecosystems in Mediterranean-type climates.

Concentration of injetion solution and its effects on soil and on yield of fertirrigated banana cv. Terra Maranhão

The Fertigation is the combined application of water and nutrients to a crop. It can be adapted to all types of agricultural crops. The objective of this study was to evaluate the effect of urea concentration in irrigation water on electrical conductivity of the soil solution and saturation extract along the first cycle of banana cv. Terra Maranhão. The experiment followed a completely randomized design with six treatments and ten replications. Treatments regarded for using three urea concentrations (1.0; 2.5 and 4.0 g L-1) in irrigation water applied by two micro irrigation systems (microsprinkler and drip). Results showed that there was a linear elevation of electrical conductivity of saturation extract and soil solution with the increase on concentration of urea in the injection solution. Urea should be used under concentrations up to 2.5 g L-1 in irrigation water without causing increase on electric conductivity of soil solution and saturation extract, considering 1.1 dS m-1 as the tolerated value for the crop. Nitrate in the soil solution increased significantly with the increase of urea concentration in the injection solution. The maximum concentration of nitrate in the soil occurred for 4,0 g L-1 concentration of the injection solution.

Coverage mobilization in the sowing line and its influence on temperature and water content and on maize emergence

Straw on sowing line modifies seed germination environment regarding temperature and water content. Given these considerations, the aim of this study was to evaluate different mechanisms for coverage mobilization on the sowing line and their effect on germination environment of maize seeds, mainly in relation to the dynamics of straw in the seedbed, water content and soil temperature. Treatments consisted on the combination of two mechanisms at front of furrow opener, composed of cutting disc and row cleaners, with three mechanisms behind the seed furrower for returning the soil, consisting of three covering mechanisms, commercial and prototype models. It was found that straw presence on the surface of sowing line contributed to germination of maize seeds, maintenance of temperature and soil water content. The cutting disc treatment, associated with prototype, introduced percentages of water content near the ones in bottom layer, and this soil water content was 29.7% with 93.75% of straw coverage and deeper seeding depth, granting better conditions for seed germination. However, the straw coverage removal on soil by the row cleaners and its low sowing depth caused water loss in the lines resulting in great reduction of the emergence speed index in maize seedlings.