Zusammenhänge zwischen Wertzahlen der Bodenschätzung und dem Naturalertrag auf einem Ackerschlag in Thüringen II

Auf einem 44,15 ha großen Ackerschlag am nordöstlichen Rand des Thüringer Beckens wurden die Zusammenhänge zwischen Bodenzahlen der Bodenschätzung und Ertragsdaten vom Mähdrescher aus acht Erntejahren (2000 2007) untersucht. Die Zusammenhänge zwischen Bodenzahlen und Ertrag sind in allen Erntejahren positiv. Die Ausprägung der Zusammenhänge in den einzelnen Jahren steht in einer erkennbaren Beziehung zur Klimatischen Wasserbilanz im Frühjahr und Frühsommer vor der Ernte (KWB III-VII) und wird möglicherweise zusätzlich durch den Deckungsgrad der Ertragskartierung beeinflusst. Anhand einer mehrjährigen Betrachtung kann der Zusammenhang zwischen Bodenzahlen und Ertrag besonders deutlich nachgewiesen werden

Analysis of correlated count data using generalised linear mixed models exemplified by field data on aggressive behaviour of boars

Population-averaged and subject-specific models are available to evaluate count data when repeated observations per subject are present. The latter are also known in the literature as generalised linear mixed models (GLMM). In GLMM repeated measures are taken into account explicitly through random animal effects in the linear predictor. In this paper the relevant GLMMs are presented based on conditional Poisson or negative binomial distribution of the response variable for given random animal effects. Equations for the repeatability of count data are derived assuming normal distribution and logarithmic gamma distribution for the random animal effects. Using count data on aggressive behaviour events of pigs (barrows, sows and boars) in mixed-sex housing, we demonstrate the use of the Poisson »log-gamma intercept«, the Poisson »normal intercept« and the »normal intercept« model with negative binomial distribution. Since not all count data can definitely be seen as Poisson or negative-binomially distributed, questions of model selection and model checking are examined. Emanating from the example, we also interpret the least squares means, estimated on the link as well as the response scale. Options provided by the SAS procedure NLMIXED for estimating model parameters and for estimating marginal expected values are presented

Advantage BLUP for predicting breeding values in poultry breeding programs with multiple traits - a Monte Carlo study.

U ovom diplomskom radu je prikazana i objašnjena konstrukcija bespilotne letjelice autogira uz proračune performansi. Opisane su karakteristike autogira koje se razlikuju od aviona i helikoptera, osobito na malim brzinama. Također je opisan i prikazan način upravljanja autogirom, koji se razlikuje od načina upravljanja helikoptera i aviona. Detaljno je opisano i prikazano konstrukcijsko rješenje rotora koji je izrađen za bespilotnu letjelicu autogira, i mehanizma upravljanja, za što je napravljen CAD model letjelice. Također je prikazan i opisan način izrade i modifikacije dijelova rotora i priložena tehnička dokumentacija. Performanse bespilotne letjelice autogira izračunate su iz snage potrebne za let i raspoložive snage. Raspoloživa snaga je dobivena iz izmjerenog statičkog potiska i podataka za propeler od proizvođača. Napravljena je i alternativna metoda za slučaj da podataka propelera nema, koja se zasniva na teoriji diska, i pokazala se kao pouzdana za manje brzine leta, ali nije korištena u proračunu performansi. Snaga potrebna za let izračunata je iz dostupne literature, napravljena je i alternativna metoda gdje je rotor zamijenjen diskom istog promjera. Proračun je napravljen za obje metode i uspoređeni su rezultati iz kojih se mogu izvući zanimljivi zaključci.This paper presents and explains the design the design of an unmanned autogiro and calculates its performance. It explains characteristics of autogiro, that are different from planes and helicopters, especially at low speeds. The control mechanism of the aircraft is illustrated and explained. The rotor that has been designed for the aircraft, and its control mechanism are being described in detail, followed by drawings, for which CAD model of the aircraft was made. The paper also explains manufacturing and modification process of rotor parts, and is followed with technical documentation. Performance of the unmanned autogiro was calculated from power required for horizontal flight and power available. Available power was calculated from the measured static thrust, and propeller specifications. Alternative method for propeller performance was done, for the case if they would be unknown, and it is based on the disc theory, and it proved to be reliable for smaller velocity, but it wasn’t used for the performance calculations. The power required for flight was calculated from the available literature, an alternative method was also made, where rotor was replaced by a disc of the same diameter. Performance was calculated for the both methods, data, compared, and interesting conclusions made

Treatment comparisons in agricultural field trials accounting for spatial correlation

This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The classical analysis model for agricultural field trials is based on the principles of experimental design – randomization, replication and blocking – and it assumes independent residual effects. Accounting for any existent spatial correlation as an add-on component may be beneficial, but it requires selection of a suitable spatial model and modification of classical tests of treatment contrasts. Using a sugar beet trial laid out in complete blocks for illustration, it is shown that tests obtained with different modifications yield diverging results. Simulations were performed to decide whether different test modifications lead to valid statistical inferences. For the spherical, power and Gaussian models, each with six different values of the range parameter and without a nugget effect, the suitability of the following modifications was studied: a generalization of the Satterthwaite method (1941), the method of Kenward and Roger (1997), and the first-order corrected method described by Kenward and Roger (2009). A second-order method described by Kenward and Roger (2009) is also discussed and detailed results are provided as Supplemental Material (available at: http://journals.cambridge.org/AGS). Simulations were done for experiments with 10 or 30 treatments in complete and incomplete block designs. Model selection was performed using the corrected Akaike information criterion and likelihood-ratio tests. When simulation and analysis models were identical, at least one of the modifications for the t-test guaranteed control of the nominal Type I error rate in most cases. When the first-order method of Kenward and Roger was used, control of the t-test Type I error rate was poor for 10 treatments but on average very good for 30 treatments, when considering the best-fitting models for a given simulation setting. Results were not satisfactory for the F-test. The more pronounced the spatial correlation, the more substantial was the gain in power compared to classical analysis. For experiments with 20 treatments or more, the recommendation is to select the best-fitting model and then use the first-order method for t-tests. For F-tests, a randomization-based model with independent error effects should be used.Peer Reviewe