The effects of inbreeding on the social behavior of chickens

Call number: LD2668 .T4 1963 B37Master of Scienc

The Use of Remote Sensing Within the Mars Crop Yield Monitoring System of the European Commission

The objective of the Mars Crop Yield Forecasting Systems is to provide precise, scientific, traceable independent and timely forecasts for the main crops yields at EU level. The forecasts and analysis are used since 2001 as a benchmark by analysts from DG – Agriculture and Rural Development in charge of food balance estimates. The system is supported by the use of Remote Sensing data, namely SPOT-VEGETATION, NOAA-AVHRR, MSG-SEVIRI, MODIS TERRA / ACQUA and in the future ENVISAT MERIS too. So a broad spectrum from low to medium resolution data at pan-European level is covered and historical time series go back to 1987 for NOAA and 1998 for SPOT VEGETATION. In order to work with the data operationally, processing chains have been set-up to make the data consistent with our requirements concerning near real time delivery (3 days), spatial coverage (pan-Europe), projection and ten day time steps. Moreover tailored indicators like NDVI, SAVI, DMP and fAPAR are derived. The data is explored at full resolution or unmixed related to landcover types and aggregated at administrative NUTS 2 level (profile analysis of time series). Special tools to inspect and distribute the data to external users have been developed as well.JRC.DG.G.3 - Monitoring agricultural resource

CGMS Version 9.2 - User Manual and Technical Documentation

Detailed information on the installation and use of the new CGMS version 9.2JRC.G.3-Monitoring agricultural resource

Magnetic remanent states in antiferromagnetically coupled multilayers

In antiferromagnetically coupled multilayers with perpendicular anisotropy unusual multidomain textures can be stabilized due to a close competition between long-range demagnetization fields and short-range interlayer exchange coupling. In particular, the formation and evolution of specific topologically stable planar defects within the antiferromagnetic ground state, i.e. wall-like structures with a ferromagnetic configuration extended over a finite width, explain configurational hysteresis phenomena recently observed in [Co/Pt(Pd)]/Ru and [Co/Pt]/NiO multilayers. Within a phenomenological theory, we have analytically derived the equilibrium sizes of these "ferroband" defects as functions of the antiferromagnetic exchange, a bias magnetic field, and geometrical parameters of the multilayers. In the magnetic phase diagram, the existence region of the ferrobands mediates between the regions of patterns with sharp antiferromagnetic domain walls and regular arrays of ferromagnetic stripes. The theoretical results are supported by magnetic force microscopy images of the remanent states observed in [Co/Pt]/Ru.Comment: Paper submitted by the Joint European Magnetics Symposia 2008, Dublin (4 pages, 3 figures

ADAPTATION OF WOFOST MODEL FROM CGMS TO ROMANIAN CONDITIONS

This preliminary study is an inventory of the main resources and difficulties in adaptation of the Crop Growth Monitoring System (CGMS) used by Agri4cast unit of IPSC from Joint Research Centre (JRC) - Ispra of European Commission to conditions of Romania.In contrast with the original model calibrated mainly with statistical average yields at national level, for local calibration of the model the statistical yields at lower administrative units (macroregion or county) must be used. In addition, for winter crops, the start of simulation in the new system will be in the autumn of the previous year. The start of simulation (and emergence day) in the genuine system is 1st of January of the current year and the existing calibration was meant to provide a compensation system for this technical physiological delay.Proposed approach provides a better initialisation of the water balance (emergence occurs after start of simulation), as well as a better account for impact of wintering conditions, but obviously a new calibration for all cultivar dependent parameters is necessary. For the preoperational run, the localized model will use the weather data available till the last day available and the missing data from the rest of the year will be replaced either by the daily values of the long term averages or by the values from a year considered similar with the current one.Proposed adaptations permit a better use of information available on local scale and the localized model may be the core of a regional system for crop monitoring and in the same degree as the original system it can be used as tool for specific researches, such as studying the impact of climate changes

Enhanced processing of 1-km spatial resolution fAPAR time series for sugarcane yield forecasting and monitoring

A processing of remotely-sensed Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) time series at 1-km spatial resolution is established to estimate sugarcane yield over the state of São Paulo, Brazil. It includes selecting adequate time series according to the signal spatial purity, using thermal time instead of calendar time and smoothing temporally the irregularly sampled observations. A systematic construction of various metrics and their capacity to predict yield is explored to identify the best performance, and see how timely the yield forecast can be made. The resulting dataset not only reveals a strong spatio-temporal structure, but is also capable of detecting both absolute changes in biomass accumulation and changes in its inter-annual variability. Sugarcane yield can thus be estimated with a RMSE of 1.5 t/ha (or 2%) without taking into account the strong linear trend in yield increase witnessed in the past decade. Including the trend reduces the error to 0.6 t/ha, correctly predicting whether the yield in a given year is above or below the trend in 90% of cases. The methodological framework presented here could be applied beyond the specific case of sugarcane in São Paulo, namely to other crops in other agro-ecological landscapes, to enhance current systems for monitoring agriculture or forecasting yield using remote sensing.JRC.H.4-Monitoring Agricultural Resource

Extraction of phenological parameters from temporally smothed vegetation indices

Within the MARS Crop Yield Forecasting System (MCYFS; Royer and Genovese, 2004) of the European Commission vegetation indicators like NDVI, SAVI and fAPAR are operationally derived for daily, decadal and monthly time steps. Besides low resolution sensors as SPOT-VGT and NOAA-AVHRR, medium resolution data from TERRA/AQUA-MODIS or ENVISAT-MERIS are used at pan-European level. In case of available time-series, esp. NOAA AVHRR (since 1981) and SPOT-VGT (since 1998) difference values of the indicators (e.g. relative or absolute differences) and frequency analysis of the indicators (e.g. position in historical range or distribution) are calculated. The exploitation of the data is performed at full resolution, at grid level of the MCYFS or regional unmixed means (C-indicators) are used. Therefore a database has been set-up in order to provide the indicators based on a weighted average for each CORINE land cover class within an area of interest. The study aims to develop a strategy for an optimal use of the different sensors and thus derived indicators at different aggregation levels for the ingestion into the MCYFS. As a first step smoothing algorithms have to be applied to the time series to diminish noise effects. Therefore, existing methods as simple sliding windows, piecewise linear regression or fitting of polynomial functions are employed and compared. Thereafter the time-series analysis is performed with the aim to establish relationships between indicators profile features and the crop phenology.JRC.DDG.H.4-Monitoring agricultural resource

LUISA (Library User Interface for Sensitivity Analysis): a generic software component for sensitivity analysis of bio-physical models

Abstract: Sensitivity analysis is crucial to better understand the behavior of models, both for developers and users. Developers can be supported in avoiding overparameterizations and in focusing their attention only in the processes with a significant impact on the output(s) of interest. Model users can benefit of sensitivity analysis by identifying the most relevant parameters in a particular biophysical context and, therefore, in optimizing the available resources for determining their value, through direct measurements or via calibration. When biophysical, deterministic models are run in a stochastic fashion using weather series, and when other inputs of a model vary substantially, the results of sensitivity analysis may differ, suggesting different, site specific strategies, for operational use. The availability of a generic software component able to be integrated in modeling and simulation environments would hence allow the estimate of differences in the behavior of models in different soil-plant-climate-management scenarios. It is possible to classify the methods for sensitivity analysis developed in the last decades in three groups: the one-factor-at-a-time method, the methods based on regression and the variance-based Monte-Carlo methods. The first group is represented by Morris� method, which calculates two metrics: the average (µ) and the standard deviation (s) of the population of the incremental ratios according to an opportune generation of a sample of the possible combination of parameters. The most famous methods belonging to the second group are the Latin Hypercube, the Random and the Quasi-random Lp-Tau. They differ in the method used for generating the sample, while are all based on a linear regression between the differences in the output and those in the values of parameters to calculate sensitivity indices. The third group is based on the decomposition of the total variance in summands of increasing dimensionality and it is able to quantify the effect of the interactions among parameters. The methods based on this principle are Fourier Amplitude Sensitivity Test (FAST), Extended FAST, and Sobol�s. The last group, and in particular the Sobol�s method, is considered the most powerful and precise in identifying the output sensitivity to the model parameters. Their drawback is the computational cost since they involve the estimation of k-dimensional integrals. On the other hand, the Morris� method is the one requiring the smaller sample for ranking the parameters according to their relevance and it is considered particularly suitable for preliminary screenings of models with several parameters. This paper describes the LUISA (Library User Interface for Sensitivity Analysis) component, based on the SimLab (http://simlab.jrc.ec.europa.eu/) C++ DLL. LUISA has been developed in C# under the .NET platform, with the goal of facilitating implementing sensitivity analysis capabilities on bio-physical model frameworks. As illustrative case studies, spatially distributed sensitivity analysis of two different biophysical models were carried out using the MARS database (http://mars.jrc.ec.europa.eu/), in order to cover the pedo-climatic conditions of Europe. The two models used are the WARM model for rice simulations and the generic crop simulator CropSyst. Results are presented and discussed according to the spatial variability of their relevance and to the identification of clusters based on the parameters ranks.JRC.DG.G.3 - Monitoring agricultural resource

JRC MARS Bulletin - crop monitoring in Europe, January 2016 - Weakly hardened winter cereals

Weakly hardened winter cereals - A first cold spell is likely to have caused damages in eastern EuropeJRC.H.4 - Monitoring Agricultural Resource

CLIMA: a weather generator framework

Abstract: Weather generators (WG) can be defined as collections of models to estimate site specific weather data and derived variables. Their use spans from providing inputs to a variety of biophysical models to deriving weather indices. Also, using either global circulation models or local area models inputs, sets of parameters calculated from long term weather series specific to a site can be modified to reproduce via WG synthetic series representing climate change scenarios. Finally, models implemented in WG are used for estimating missing data and to perform quality control on data collected from sensors in weather stations. The models implemented in WG vary from purely empirical to physically based. There are several models to either estimate or to generate each weather variable, with different input requirements. New models are continuously being proposed, and, whether some models to estimate specific variables are commonly accepted as reference methods, the lack of some inputs requires at times using alternate approaches. Currently available WG are applications which implement a predefined set of modelling options, in software implementations which do not allow for independent extensions by third parties. The CLIMA weather generator is a component based application which consist of a set of reusable graphical user interface (GUI) components, and of a set of extensible model components. The latter are subdivided into six namespaces to estimate variables related to air temperature, rainfall, solar radiation, evapotranspiration, wind, and leaf wetness. The time characteristic of the variables estimated varies from a day to ten minutes. Another library allows estimating climatic indices from one year of daily data at the time. The current implementation consists of a total of more than 300 models. Components are usable either via the CLIMA GUI, or via custom developed applications in a client-server architecture. The architecture of components is based on the composite and strategy as keystone design patterns. Models are implemented as single approaches (simple strategies), and as composite models (composite strategies) which are associated to models of finer granularity. Another type of model unit is represented by context strategies, which implement logic to select within associated models. Finally, the GUI allows building composite models which can be saved as libraries, to be reused both within CLIMA for weather series generation, or independently by other applications. The components are implemented as .NET libraries. They implement the test of pre- and post-conditions, and a scalable tracing via .NET listeners. All variables and parameters are documented via a description, units, default, maximum, and minimum values. Components are extensible: new models can be added independently by third parties and detected by the CLIMA application, which can also use them for data generation via building new composite libraries. Each component is made available via a software development kit which includes the code of two sample projects, either to extend or to reuse the component. CLIMA and its model components are freely available for reuse in no-profit applications.JRC.DG.G.3-Monitoring agricultural resource