Decision making models embedded into a web-based tool for assessing pest infestation risk

Current practices in agricultural management involve the application of rules and techniques to ensure high quality and environmentally friendly production. Based on their experience, agricultural technicians and farmers make critical decisions affecting crop growth while considering several interwoven agricultural, technological, environmental, legal and economic factors. In this context, decision support systems and the knowledge models that support them, enable the incorporation of valuable experience into software systems providing support to agricultural technicians to make rapid and effective decisions for efficient crop growth. Pest control is an important issue in agricultural management due to crop yield reductions caused by pests and it involves expert knowledge. This paper presents a formalisation of the pest control problem and the workflow followed by agricultural technicians and farmers in integrated pest management, the crop production strategy that combines different practices for growing healthy crops whilst minimising pesticide use. A generic decision schema for estimating infestation risk of a given pest on a given crop is defined and it acts as a metamodel for the maintenance and extension of the knowledge embedded in a pest management decision support system which is also presented. This software tool has been implemented by integrating a rule-based tool into web-based architecture. Evaluation from validity and usability perspectives concluded that both agricultural technicians and farmers considered it a useful tool in pest control, particularly for training new technicians and inexperienced farmers.

Comparing simulation output accuracy of discrete event and agent based models: a quantitative approach

In our research we investigate the output accuracy of discrete event simulation models and agent based simulation models when studying human centric complex systems. In this paper we focus on human reactive behaviour as it is possible in both modelling approaches to implement human reactive behaviour in the model by using standard methods. As a case study we have chosen the retail sector, and here in particular the operations of the fitting room in the women wear department of a large UK department store. In our case study we looked at ways of determining the efficiency of implementing new management policies for the fitting room operation through modelling the reactive behaviour of staff and customers of the department. First, we have carried out a validation experiment in which we compared the results from our models to the performance of the real system. This experiment also allowed us to establish differences in output accuracy between the two modelling methods. In a second step a multi-scenario experiment was carried out to study the behaviour of the models when they are used for the purpose of operational improvement. Overall we have found that for our case study example both, discrete event simulation and agent based simulation have the same potential to support the investigation into the efficiency of implementing new management policies.

The impact of El Nino Southern Oscillation phases on off-season maize yield for a subtropical region of Brazil

In recent years, maize has become one of the main alternative crops for the autumn winter growing season in the central-western and southeastern regions of Brazil. However, water deficits, sub-optimal temperatures and low solar radiation levels are common problems that are experienced during this growing season by local farmers. One methodology to assess the impact of variable weather conditions on crop production is the use of crop simulation models. The goal of this study was to evaluate the effect of climate variability on maize yield for a subtropical region of Brazil. Specific objectives for this study were (1) to analyse the effect of El Nino Southern Oscillation (ENSO) on precipitation and air temperature for four locations in the state of Sao Paulo and (2) to analyse the impact of ENSO on maize grown off-season for the same four locations using a crop simulation model. For each site, historical weather data were categorised as belonging to one of three phases of ENSO: El Nino (warm sea surface temperature anomalies in the Pacific), La Nina (cool sea surface temperature anomalies) or neutral, based on an index derived from observed sea surface temperature anomalies. During El Nino, there is a tendency for an increase in the rainfall amount during May for the four selected locations, and also during April, mainly in three of the locations, resulting in an increase in simulated maize yield planted between February 15 and March 15. In general, there was a decrease in the simulated yield for maize grown off-season during neutral years. This study showed how a crop model can be used to assess the impact of climate variability on the yield of maize grown off-season in a subtropical region of Brazil. The outcomes of this study can be very useful for both policy makers and local farmers for agricultural planning and decision making. Copyright (C) 2009 Royal Meteorological Society

A split-pot experiment with sorghum to test a root water uptake partitioning model

Correct modeling of root water uptake partitioning over depth is an important issue in hydrological and crop growth models. Recently a physically based model to describe root water uptake was developed at single root scale and upscaled to the root system scale considering a homogeneous distribution of roots per soil layer. Root water uptake partitioning is calculated over soil layers or compartments as a function of respective soil hydraulic conditions, specifically the soil matric flux potential, root characteristics and a root system efficiency factor to compensate for within-layer root system heterogeneities. The performance of this model was tested in an experiment performed in two-compartment split-pot lysimeters with sorghum plants. The compartments were submitted to different irrigation cycles resulting in contrasting water contents over time. The root system efficiency factor was determined to be about 0.05. Release of water from roots to soil was predicted and observed on several occasions during the experiment; however, model predictions suggested root water release to occur more often and at a higher rate than observed. This may be due to not considering internal root system resistances, thus overestimating the ease with which roots can act as conductors of water. Excluding these erroneous predictions from the dataset, statistical indices show model performance to be of good quality.

Comparing predictions of extinction risk using models and subjective judgement

Models of population dynamics are commonly used to predict risks in ecology, particularly risks of population decline. There is often considerable uncertainty associated with these predictions. However, alternatives to predictions based on population models have not been assessed. We used simulation models of hypothetical species to generate the kinds of data that might typically be available to ecologists and then invited other researchers to predict risks of population declines using these data. The accuracy of the predictions was assessed by comparison with the forecasts of the original model. The researchers used either population models or subjective judgement to make their predictions. Predictions made using models were only slightly more accurate than subjective judgements of risk. However, predictions using models tended to be unbiased, while subjective judgements were biased towards over-estimation. Psychology literature suggests that the bias of subjective judgements is likely to vary somewhat unpredictably among people, depending on their stake in the outcome. This will make subjective predictions more uncertain and less transparent than those based on models. (C) 2004 Elsevier SAS. All rights reserved.

Environmental control of potential yield of sunflower in the subtropics

A simple framework was used to analyse the determinants of potential yield of sunflower (Helianthus annuus L.) in a subtropical environment. The aim was to investigate the stability of the determinants crop duration, canopy light interception, radiation use efficiency (RUE), and harvest index (HI) at 2 sowing times and with 3 genotypes differing in crop maturity and stature. Crop growth, phenology, light interception, yield, prevailing temperature, and radiation were recorded and measured throughout the crop cycle. Significant differences in grain yield were found between the 2 sowings, but not among genotypes within each sowing. Mean yields (0% moisture) were 6 . 02 and 2 . 17 t/ha for the first sowing, on 13 September (S1), and the second sowing, on 5 March (S2), respectively. Exceptionally high yields in S1 were due to high biomass assimilation associated with the high radiation environment, high light interception owing to a greater leaf area index, and high RUE (1 . 47-1 . 62 g/MJ) across genotypes. It is proposed that the high RUE was caused by high levels of available nitrogen maintained during crop growth by frequent applications of fertiliser and sewage effluent as irrigation. In addition to differences in the radiation environment, the assimilate partitioned to grain was reduced in S2 associated with a reduction in the duration of grain-filling. Harvest index was 0 . 40 in S1 and 0 . 25 in S2. It is hypothesised that low minimum temperatures experienced in S2 reduced assimilate production and partitioning, causing premature maturation.

The GP problem: quantifying gene-to-phenotype relationships

In this paper we refer to the gene-to-phenotype modeling challenge as the GP problem. Integrating information across levels of organization within a genotype-environment system is a major challenge in computational biology. However, resolving the GP problem is a fundamental requirement if we are to understand and predict phenotypes given knowledge of the genome and model dynamic properties of biological systems. Organisms are consequences of this integration, and it is a major property of biological systems that underlies the responses we observe. We discuss the E(NK) model as a framework for investigation of the GP problem and the prediction of system properties at different levels of organization. We apply this quantitative framework to an investigation of the processes involved in genetic improvement of plants for agriculture. In our analysis, N genes determine the genetic variation for a set of traits that are responsible for plant adaptation to E environment-types within a target population of environments. The N genes can interact in epistatic NK gene-networks through the way that they influence plant growth and development processes within a dynamic crop growth model. We use a sorghum crop growth model, available within the APSIM agricultural production systems simulation model, to integrate the gene-environment interactions that occur during growth and development and to predict genotype-to-phenotype relationships for a given E(NK) model. Directional selection is then applied to the population of genotypes, based on their predicted phenotypes, to simulate the dynamic aspects of genetic improvement by a plant-breeding program. The outcomes of the simulated breeding are evaluated across cycles of selection in terms of the changes in allele frequencies for the N genes and the genotypic and phenotypic values of the populations of genotypes.

Simulation of the load-unload response ratio and critical sensitivity in the lattice solid model

The Load-Unload Response Ratio (LURR) method is an intermediate-term earthquake prediction approach that has shown considerable promise. It involves calculating the ratio of a specified energy release measure during loading and unloading where loading and unloading periods are determined from the earth tide induced perturbations in the Coulomb Failure Stress on optimally oriented faults. In the lead-up to large earthquakes, high LURR values are frequently observed a few months or years prior to the event. These signals may have a similar origin to the observed accelerating seismic moment release (AMR) prior to many large earthquakes or may be due to critical sensitivity of the crust when a large earthquake is imminent. As a first step towards studying the underlying physical mechanism for the LURR observations, numerical studies are conducted using the particle based lattice solid model (LSM) to determine whether LURR observations can be reproduced. The model is initialized as a heterogeneous 2-D block made up of random-sized particles bonded by elastic-brittle links. The system is subjected to uniaxial compression from rigid driving plates on the upper and lower edges of the model. Experiments are conducted using both strain and stress control to load the plates. A sinusoidal stress perturbation is added to the gradual compressional loading to simulate loading and unloading cycles and LURR is calculated. The results reproduce signals similar to those observed in earthquake prediction practice with a high LURR value followed by a sudden drop prior to macroscopic failure of the sample. The results suggest that LURR provides a good predictor for catastrophic failure in elastic-brittle systems and motivate further research to study the underlying physical mechanisms and statistical properties of high LURR values. The results provide encouragement for earthquake prediction research and the use of advanced simulation models to probe the physics of earthquakes.

Simulation supplements field studies to determine no-till dryland corn population recommendations for semiarid western Nebraska

In a 2-yr multiple-site field study conducted in western Nebraska during 1999 and 2000, optimum dryland corn (Zea mays L.) population varied from less than 1.7 to more than 5.6 plants m(-2), depending largely on available water resources. The objective of this study was to use a modeling approach to investigate corn population recommendations for a wide range of seasonal variation. A corn growth simulation model (APSIM-maize) was coupled to long-term sequences of historical climatic data from western Nebraska to provide probabilistic estimates of dryland yield for a range of corn populations. Simulated populations ranged from 2 to 5 plants m(-2). Simulations began with one of three levels of available soil water at planting, either 80, 160, or 240 mm in the surface 1.5 m of a loam soil. Gross margins were maximized at 3 plants m(-2) when starting available water was 160 or 240 mm, and the expected probability of a financial loss at this population was reduced from about 10% at 160 mm to 0% at 240 mm. When starting available water was 80 mm, average gross margins were less than $15 ha(-1), and risk of financial loss exceeded 40%. Median yields were greatest when starting available soil water was 240 mm. However, perhaps the greater benefit of additional soil water at planting was reduction in the risk of making a financial loss. Dryland corn growers in western Nebraska are advised to use a population of 3 plants m(-2) as a base recommendation.

A simulation tool for demand response programs implementation

The design and development of simulation models and tools for Demand Response (DR) programs are becoming more and more important for adequately taking the maximum advantages of DR programs use. Moreover, a more active consumers’ participation in DR programs can help improving the system reliability and decrease or defer the required investments. DemSi, a DR simulator, designed and implemented by the authors of this paper, allows studying DR actions and schemes in distribution networks. It undertakes the technical validation of the solution using realistic network simulation based on PSCAD. DemSi considers the players involved in DR actions, and the results can be analyzed from each specific player point of view.

Biomechanical modeling and simulation of the spider crab (maja brachydactyla)

Uma linha de pesquisa e desenvolvimento na área da robótica, que tem recebido atenção crescente nos últimos anos, é o desenvolvimento de robôs biologicamente inspirados. A ideia é adquirir conhecimento de seres biológicos, cuja evolução ocorreu ao longo de milhões de anos, e aproveitar o conhecimento assim adquirido para implementar a locomoção pelos mesmos métodos (ou pelo menos usar a inspiração biológica) nas máquinas que se constroem. Acredita-se que desta forma é possível desenvolver máquinas com capacidades semelhantes às dos seres biológicos em termos de capacidade e eficiência energética de locomoção. Uma forma de compreender melhor o funcionamento destes sistemas, sem a necessidade de desenvolver protótipos dispendiosos e com longos tempos de desenvolvimento é usar modelos de simulação. Com base nestas ideias, o objectivo deste trabalho passa por efectuar um estudo da biomecânica da santola (Maja brachydactyla), uma espécie de caranguejo comestível pertencente à família Majidae de artrópodes decápodes, usando a biblioteca de ferramentas SimMechanics da aplicação Matlab / Simulink. Esta tese descreve a anatomia e locomoção da santola, a sua modelação biomecânica e a simulação do seu movimento no ambiente Matlab / SimMechanics e SolidWorks.

From simulation to statistical analysis: timeliness assessment of ethernet/IP-based distributed systems

A number of characteristics are boosting the eagerness of extending Ethernet to also cover factory-floor distributed real-time applications. Full-duplex links, non-blocking and priority-based switching, bandwidth availability, just to mention a few, are characteristics upon which that eagerness is building up. But, will Ethernet technologies really manage to replace traditional Fieldbus networks? Ethernet technology, by itself, does not include features above the lower layers of the OSI communication model. In the past few years, it is particularly significant the considerable amount of work that has been devoted to the timing analysis of Ethernet-based technologies. It happens, however, that the majority of those works are restricted to the analysis of sub-sets of the overall computing and communication system, thus without addressing timeliness at a holistic level. To this end, we are addressing a few inter-linked research topics with the purpose of setting a framework for the development of tools suitable to extract temporal properties of Commercial-Off-The-Shelf (COTS) Ethernet-based factory-floor distributed systems. This framework is being applied to a specific COTS technology, Ethernet/IP. In this paper, we reason about the modelling and simulation of Ethernet/IP-based systems, and on the use of statistical analysis techniques to provide usable results. Discrete event simulation models of a distributed system can be a powerful tool for the timeliness evaluation of the overall system, but particular care must be taken with the results provided by traditional statistical analysis techniques.

Timeliness in COTS factory-floor distributed systems: What role for simulation?

In the past few years, a significant amount of work has been devoted to the timing analysis of Ethernet-based technologies. However, none of these address the problem of timeliness evaluation at a holistic level. This paper describes a research framework embracing this objective. It is advocated that, simulation models can be a powerful tool, not only for timeliness evaluation, but also to enable the introduction of less pessimistic assumptions in an analytical response time approach, which, most often, are afflicted with simplifications leading to pessimistic assumptions and, therefore, delusive results. To this end, we address a few inter-linked research topics with the purpose of setting a framework for developing tools suitable to extract temporal properties of commercial-off-the-shelf (COTS) factory-floor communication systems.

A simulation model for the IEEE 802.15.4 protocol: delay/throughput evaluation of the GTS mechanism

The IEEE 802.15.4 protocol has the ability to support time-sensitive Wireless Sensor Network (WSN) applications due to the Guaranteed Time Slot (GTS) Medium Access Control mechanism. Recently, several analytical and simulation models of the IEEE 802.15.4 protocol have been proposed. Nevertheless, currently available simulation models for this protocol are both inaccurate and incomplete, and in particular they do not support the GTS mechanism. In this paper, we propose an accurate OPNET simulation model, with focus on the implementation of the GTS mechanism. The motivation that has driven this work is the validation of the Network Calculus based analytical model of the GTS mechanism that has been previously proposed and to compare the performance evaluation of the protocol as given by the two alternative approaches. Therefore, in this paper we contribute an accurate OPNET model for the IEEE 802.15.4 protocol. Additionally, and probably more importantly, based on the simulation model we propose a novel methodology to tune the protocol parameters such that a better performance of the protocol can be guaranteed, both concerning maximizing the throughput of the allocated GTS as well as concerning minimizing frame delay.

Real-Time Simulation of energy Management in a Domestic Consumer

Recent and future changes in power systems, mainly in the smart grid operation context, are related to a high complexity of power networks operation. This leads to more complex communications and to higher network elements monitoring and control levels, both from network’s and consumers’ standpoint. The present work focuses on a real scenario of the LASIE laboratory, located at the Polytechnic of Porto. Laboratory systems are managed by the SCADA House Intelligent Management (SHIM), already developed by the authors based on a SCADA system. The SHIM capacities have been recently improved by including real-time simulation from Opal RT. This makes possible the integration of Matlab®/Simulink® real-time simulation models. The main goal of the present paper is to compare the advantages of the resulting improved system, while managing the energy consumption of a domestic consumer.