Physical quality of an Oxisol under an integrated crop-livestock-forest system in the Brazilian Cerrado

Soil physical quality is an important factor for the sustainability of agricultural systems. Thus, the aim of this study was to evaluate soil physical properties and soil organic carbon in a Typic Acrudox under an integrated crop-livestock-forest system. The experiment was carried out in Mato Grosso do Sul, Brazil. Treatments consisted of seven systems: integrated crop-livestock-forest, with 357 trees ha-1 and pasture height of 30 cm (CLF357-30); integrated crop-livestock-forest with 357 trees ha-1 and pasture height of 45 cm (CLF357-45); integrated crop-livestock-forest with 227 trees ha-1 and pasture height of 30 cm (CLF227-30); integrated crop-livestock-forest with 227 trees ha-1 and pasture height of 45 cm (CLF227-45); integrated crop-livestock with pasture height of 30 cm (CL30); integrated crop-livestock with pasture height of 45 cm (CL45) and native vegetation (NV). Soil properties were evaluated for the depths of 0-10 and 10-20 cm. All grazing treatments increased bulk density (r b) and penetration resistance (PR), and decreased total porosity (¦t) and macroporosity (¦ma), compared to NV. The values of r b (1.18-1.47 Mg m-3), ¦ma (0.14-0.17 m³ m-3) and PR (0.62-0.81 MPa) at the 0-10 cm depth were not restrictive to plant growth. The change in land use from NV to CL or CLF decreased soil organic carbon (SOC) and the soil organic carbon pool (SOCpool). All grazing treatments had a similar SOCpool at the 0-10 cm depth and were lower than that for NV (17.58 Mg ha-1).

DISTRIBUTION OF NITROGEN AMMONIUM SULFATE (N-15) SOIL-PLANT SYSTEM IN A NO-TILLAGE CROP SUCCESSION

DISTRIBUTION OF NITROGEN AMMONIUM SULFATE (N-15) SOIL-PLANT SYSTEM IN A NO-TILLAGE CROP SUCCESSION The N use by maize (Zea mays, L.) is affected by N-fertilizer levels. This study was conducted using a sandy-clay texture soil (Hapludox) to evaluate the efficiency of N use by maize in a crop succession, based on N-15-labeled ammonium sulfate (5.5 atom %) at different rates, and to assess the residual fertilizer effect in two no-tillage succession crops (signalgrass and corn). Two maize crops were evaluated, the first in the growing season 2006, the second in 2007, and brachiaria in the second growing season. The treatments consisted of N rates of 60, 120 and 180 kg ha(-1) in the form of labeled N-15 ammonium sulfate. This fertilizer was applied in previously defined subplots, only to the first maize crop (growing season 2006). The variables total accumulated N; fertilizer-derived N in corn plants and pasture; fertilizer-derived N in the soil; and recovery of fertilizer-N by plants and soil were evaluated. The highest uptake of fertilizer N by corn was observed after application of 120 kg ha(-1) N and the residual effect of N fertilizer on subsequent corn and Brachiaria was highest after application of 180 kg ha(-1) N. After the crop succession, soil N recovery was 32, 23 and 27 % for the respective applications of 60, 120 and 180 kg ha(-1) N.

Forecasting the need for medical specialists in Spain: application of a system dynamics model

[EN] Background: Spain has gone from a surplus to a shortage of medical doctors in very few years. Medium and long-term planning for health professionals has become a high priority for health authorities. Methods: We created a supply and demand/need simulation model for 43 medical specialties using system dynamics. The model includes demographic, education and labour market variables. Several scenarios were defined. Variables controllable by health planners can be set as parameters to simulate different scenarios. The model calculates the supply and the deficit or surplus. Experts set the ratio of specialists needed per 1000 inhabitants with a Delphi method. Results: In the scenario of the baseline model with moderate population growth, the deficit of medical specialists will grow from 2% at present (2800 specialists) to 14.3% in 2025 (almost 21 000). The specialties with the greatest medium-term shortages are Anesthesiology, Orthopedic and Traumatic Surgery, Pediatric Surgery, Plastic Aesthetic and Reparatory Surgery, Family and Community Medicine, Pediatrics, Radiology, and Urology. Conclusions: The model suggests the need to increase the number of students admitted to medical school. Training itineraries should be redesigned to facilitate mobility among specialties. In the meantime, the need to make more flexible the supply in the short term is being filled by the immigration of physicians from new members of the European Union and from Latin America.

Forecasting and visualization of wildfires in a 3D geographical information system

[EN]This paper describes a wildfi re forecasting application based on a 3D virtual environment and a fi re simulation engine. A novel open source framework is presented for the development of 3D graphics applications over large geographic areas, off ering high performance 3D visualization and powerful interaction tools for the Geographic Information Systems (GIS) community. The application includes a remote module that allows simultaneous connection of several users for monitoring a real wildfi re event.

White lupin (Lupinus albus L.), the neglected multipurpose crop: Its production and utilization in the mixed crop-livestock farming system of Ethiopia

A survey was conducted to generate holistic information on the production and utilization of local white lupin in two lupin growing districts, namely, Mecha and Sekela, representing mid and high altitude areas, respectively in North-western Ethiopia. During the survey, two types of participatory rural appraisal (PRA) techniques, namely, individual farmer interview (61 farmers from Mecha and 51 from Sekela) and group discussion (with 20 farmers from each district) were employed. There are significant differences (P<0.05) between the two study districts for the variables like total land holding, frequency of ploughing during lupin planting, days to maturity, lupin productivity, and number of days of soaking lupin in running water. However, there are no significant differences (P>0.05) between the two study districts for the variables like land allocated for lupin cultivation, lupin seed rate, lupin soaking at home, lupin consumption per family per week and proportion of lupin used for household consumption. The use of the crop as livestock feed is negligible due to its high alkaloid content. It is concluded that the local white lupin in Ethiopia is a valuable multipurpose crop which is being cultivated in the midst of very serious shortage of cropland. Its ability to maintain soil fertility and serve as a source of food in seasons of food scarcity makes it an important crop. However, its bitter taste due to its high alkaloid content remains to be a big challenge and any lupin improvement strategy has to focus on minimizing the alkaloid content of the crop.

Influence of denitrifiers abundance on N2O emissions in long term tillage system under a rainfed legume crop

Current studies about nitrous oxide (N2O) emissions from legume crops have raised considerable doubt, observing a high variability between sites (0.03-7.09 kg N2O–N ha−1 y -1) [1]. This high variability has been associated to climate and soil conditions, legume species and soil management practices (e.g. conservation or conventional tillage). Conservation tillage (i.e. no tillage (NT) and minimum tillage (MT)) has spread during the last decades because promotes several positive effects (increase of soil organic content, reduction of soil erosion and enhancement of carbon (C) sequestration). However, these benefits could be partly counterbalanced by negative effects on the release of N2O emissions. Among processes responsible for N2O production and consumption in soils, denitrification plays an importantrole both in tilled and no-tilled ropping systems [2]. Recently, amplification of functional bacterial genes involved in denitrification is being used to examine denitrifiers abundance and evaluate their influence on N2O emissions. NirK and nirS are functional genes encoding the cytochrome cd1 and copper nitrite reductase, which is the key enzyme regulating the denitrification process.

Automatic expert system based on images for accuracy crop row detection in maize fields

This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil–Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product–moment correlation coefficient.

Guide for forecasting traffic on the interstate system for use in preparing cost estimates /

"Circular memorandum to: Division Engineers".

Weather forecasting : recommendations to address new weather processing system development risks : report to the Secretary of Commerce /

"B-271478"--P. 1.

Vector Error-Correction Models in a consumer packaged goods category forecasting decision support system

A framework for developing marketing category management decision support systems (DSS) based upon the Bayesian Vector Autoregressive (BVAR) model is extended. Since the BVAR model is vulnerable to permanent and temporary shifts in purchasing patterns over time, a form that can correct for the shifts and still provide the other advantages of the BVAR is a Bayesian Vector Error-Correction Model (BVECM). We present the mechanics of extending the DSS to move from a BVAR model to the BVECM model for the category management problem. Several additional iterative steps are required in the DSS to allow the decision maker to arrive at the best forecast possible. The revised marketing DSS framework and model fitting procedures are described. Validation is conducted on a sample problem.

Statistical forecasting of the Australian macadamia crop

Three types of forecasts of the total Australian production of macadamia nuts (t nut-in-shell) have been produced early each year since 2001. The first is a long-term forecast, based on the expected production from the tree census data held by the Australian Macadamia Society, suitably scaled up for missing data and assumed new plantings each year. These long-term forecasts range out to 10 years in the future, and form a basis for industry and market planning. Secondly, a statistical adjustment (termed the climate-adjusted forecast) is made annually for the coming crop. As the name suggests, climatic influences are the dominant factors in this adjustment process, however, other terms such as bienniality of bearing, prices and orchard aging are also incorporated. Thirdly, industry personnel are surveyed early each year, with their estimates integrated into a growers and pest-scouts forecast. Initially conducted on a 'whole-country' basis, these models are now constructed separately for the six main production regions of Australia, with these being combined for national totals. Ensembles or suites of step-forward regression models using biologically-relevant variables have been the major statistical method adopted, however, developing methodologies such as nearest-neighbour techniques, general additive models and random forests are continually being evaluated in parallel. The overall error rates average 14% for the climate forecasts, and 12% for the growers' forecasts. These compare with 7.8% for USDA almond forecasts (based on extensive early-crop sampling) and 6.8% for coconut forecasts in Sri Lanka. However, our somewhatdisappointing results were mainly due to a series of poor crops attributed to human reasons, which have now been factored into the models. Notably, the 2012 and 2013 forecasts averaged 7.8 and 4.9% errors, respectively. Future models should also show continuing improvement, as more data-years become available.

Soil carbon stock in integrated crop-livestock-forest system in the Central Region of Minas Gerais.

2016

System Identification and Prediction of Dengue Fever Incidence in Rio de Janeiro

Identification, prediction, and control of a system are engineering subjects, regardless of the nature of the system. Here, the temporal evolution of the number of individuals with dengue fever weekly recorded in the city of Rio de Janeiro, Brazil, during 2007, is used to identify SIS (susceptible-infective-susceptible) and SIR (susceptible-infective-removed) models formulated in terms of cellular automaton (CA). In the identification process, a genetic algorithm (GA) is utilized to find the probabilities of the state transition S -> I able of reproducing in the CA lattice the historical series of 2007. These probabilities depend on the number of infective neighbors. Time-varying and non-time-varying probabilities, three different sizes of lattices, and two kinds of coupling topology among the cells are taken into consideration. Then, these epidemiological models built by combining CA and GA are employed for predicting the cases of sick persons in 2008. Such models can be useful for forecasting and controlling the spreading of this infectious disease.

Convergence of developmental mutants into a single tomato model system: 'Micro-Tom' as an effective toolkit for plant development research

Background: The tomato (Solanum lycopersicum L.) plant is both an economically important food crop and an ideal dicot model to investigate various physiological phenomena not possible in Arabidopsis thaliana. Due to the great diversity of tomato cultivars used by the research community, it is often difficult to reliably compare phenotypes. The lack of tomato developmental mutants in a single genetic background prevents the stacking of mutations to facilitate analysis of double and multiple mutants, often required for elucidating developmental pathways. Results: We took advantage of the small size and rapid life cycle of the tomato cultivar Micro-Tom (MT) to create near-isogenic lines (NILs) by introgressing a suite of hormonal and photomorphogenetic mutations (altered sensitivity or endogenous levels of auxin, ethylene, abscisic acid, gibberellin, brassinosteroid, and light response) into this genetic background. To demonstrate the usefulness of this collection, we compared developmental traits between the produced NILs. All expected mutant phenotypes were expressed in the NILs. We also created NILs harboring the wild type alleles for dwarf, self-pruning and uniform fruit, which are mutations characteristic of MT. This amplified both the applications of the mutant collection presented here and of MT as a genetic model system. Conclusions: The community resource presented here is a useful toolkit for plant research, particularly for future studies in plant development, which will require the simultaneous observation of the effect of various hormones, signaling pathways and crosstalk.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.