Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

Aspekte der linearen Minimax-Schätzung

Es werde das lineare Regressionsmodell y = X b + e mit den ueblichen Bedingungen betrachtet. Weiter werde angenommen, dass der Parametervektor aus einem Ellipsoid stammt. Ein optimaler Schaetzer fuer den Parametervektor ist durch den Minimax-Schaetzer gegeben. Nach der entscheidungstheoretischen Formulierung des Minimax-Schaetzproblems werden mit dem Bayesschen Ansatz, Spektralen Methoden und der Darstellung von Hoffmann und Laeuter Wege zur Bestimmung des Minimax- Schaetzers dargestellt und in Beziehung gebracht. Eine Betrachtung von Modellen mit drei Einflussgroeßen und gemeinsamen Eigenvektor fuehrt zu einer Strukturierung des Problems nach der Vielfachheit des maximalen Eigenwerts. Die Bestimmung des Minimax-Schaetzers in einem noch nicht geloesten Fall kann auf die Bestimmung einer Nullstelle einer nichtlinearen reellwertigen Funktion gefuehrt werden. Es wird ein Beispiel gefunden, in dem die Nullstelle nicht durch Radikale angegeben werden kann. Durch das Intervallschachtelungs-Prinzip oder Newton-Verfahren ist die numerische Bestimmung der Nullstelle moeglich. Durch Entwicklung einer Fixpunktgleichung aus der Darstellung von Hoffmann und Laeuter war es in einer Simulation moeglich die angestrebten Loesungen zu finden.

Management approaches in organic potato and tomato production

The presented thesis considered three different system approach topics to ensure yield and plant health in organically grown potatoes and tomatoes. The first topic describes interactions between late blight (Phytophthora infestans) incidence and soil nitrogen supply on yield in organic potato farming focussing in detail on the yield loss relationship of late blight based on results of several field trials. The interactive effects of soil N-supply, climatic conditions and late blight on the yield were studied in the presence and absence of copper fungicides from 2002-2004 for the potato cultivar Nicola. Under conditions of central Germany the use of copper significantly reduced late blight in almost all cases (15-30 %). However, the reductions in disease through copper application did not result in statistically significant yield increases (+0 – +10 %). Subsequently, only 30 % of the variation in yield could be attributed to disease reductions. A multiple regression model (R²Max), however, including disease reduction, growth duration and temperature sum from planting until 60 % disease severity was reached and soil mineral N contents 10 days after emergence could explain 75 % of the observed variations in yield. The second topic describes the effect of some selected organic fertilisers and biostimulant products on nitrogen-mineralization and efficiency, yield and diseases in organic potato and tomato trials. The organic fertilisers Biofeed Basis (BFB, plant derived, AgroBioProducts, Wageningen, Netherlands) and BioIlsa 12,5 Export (physically hydrolysed leather shavings, hair and skin of animals; ILSA, Arizignano, Italy) and two biostimulant products BioFeed Quality (BFQ, multi-compound seaweed extract, AgroBioProducts) and AUSMA (aqueous pine and spruce needle extract, A/S BIOLAT, Latvia), were tested. Both fertilisers supplied considerable amounts of nitrogen during the main uptake phases of the crops and reached yields as high or higher as compared to the control with horn meal fertilisation. The N-efficiency of the tested fertilisers in potatoes ranged from 90 to 159 kg yield*kg-1 N – input. Most effective with tomatoes were the combined treatments of fertiliser BFB and the biostimulants AUSMA and BFQ. Both biostimulants significantly increased the share of healthy fruit and/or the number of fruits. BFQ significantly increased potato yields (+6 %) in one out of two years and reduced R. solani-infestation in the potatoes. This suggests that the biostimulants had effects on plant metabolism and resistance properties. However, no effects of biostimulants on potato late blight could be observed in the fields. The third topic focused on the effect of suppressive composts and seed tuber health on the saprophytic pathogen Rhizoctonia solani in organic potato systems. In the present study 5t ha-1 DM of a yard and bio-waste (60/40) compost produced in a 5 month composting process and a 15 month old 100 % yard waste compost were used to assess the effects on potato infection with R. solani when applying composts within the limits allowed. Across the differences in initial seed tuber infestation and 12 cultivars 5t DM ha-1 of high quality composts, applied in the seed tuber area, reduced the infestation of harvested potatoes with black scurf, tuber malformations and dry core tubers by 20 to 84 %, 20 to 49 % and 38 to 54 %, respectively, while marketable yields were increased by 5 to 25 % due to lower rates of wastes after sorting (marketable yield is gross yield minus malformed tubers, tubers with dry core, tubers with black scurf > 15% infested skin). The rate of initial black scurf infection of the seed tubers also affected tuber number, health and quality significantly. Compared to healthy seed tubers initial black scurf sclerotia infestation of 2-5 and >10 % of tuber surface led in untreated plots to a decrease in marketable yields by 14-19 and 44-66 %, a increase of black scurf severity by 8-40 and 34-86 % and also increased the amount of malformed and dry core tubers by 32-57 and 109-214 %.

Einsatz von rückgekoppelten mehrstufigen selbstorganisierenden Karten in der Windenergie Leistungsprognose

In dieser Arbeit wird ein Verfahren zum Einsatz neuronaler Netzwerke vorgestellt, das auf iterative Weise Klassifikation und Prognoseschritte mit dem Ziel kombiniert, bessere Ergebnisse der Prognose im Vergleich zu einer einmaligen hintereinander Ausführung dieser Schritte zu erreichen. Dieses Verfahren wird am Beispiel der Prognose der Windstromerzeugung abhängig von der Wettersituation erörtert. Eine Verbesserung wird in diesem Rahmen mit einzelnen Ausreißern erreicht. Verschiedene Aspekte werden in drei Kapiteln diskutiert: In Kapitel 1 werden die verwendeten Daten und ihre elektronische Verarbeitung vorgestellt. Die Daten bestehen zum einen aus Windleistungshochrechnungen für die Bundesrepublik Deutschland der Jahre 2011 und 2012, welche als Transparenzanforderung des Erneuerbaren Energiegesetzes durch die Übertragungsnetzbetreiber publiziert werden müssen. Zum anderen werden Wetterprognosen, die der Deutsche Wetterdienst im Rahmen der Grundversorgung kostenlos bereitstellt, verwendet. Kapitel 2 erläutert zwei aus der Literatur bekannte Verfahren - Online- und Batchalgorithmus - zum Training einer selbstorganisierenden Karte. Aus den dargelegten Verfahrenseigenschaften begründet sich die Wahl des Batchverfahrens für die in Kapitel 3 erläuterte Methode. Das in Kapitel 3 vorgestellte Verfahren hat im modellierten operativen Einsatz den gleichen Ablauf, wie eine Klassifikation mit anschließender klassenspezifischer Prognose. Bei dem Training des Verfahrens wird allerdings iterativ vorgegangen, indem im Anschluss an das Training der klassenspezifischen Prognose ermittelt wird, zu welcher Klasse der Klassifikation ein Eingabedatum gehören sollte, um mit den vorliegenden klassenspezifischen Prognosemodellen die höchste Prognosegüte zu erzielen. Die so gewonnene Einteilung der Eingaben kann genutzt werden, um wiederum eine neue Klassifikationsstufe zu trainieren, deren Klassen eine verbesserte klassenspezifisch Prognose ermöglichen.

Factors affecting choice of financial services among rural consumers: Emerging experiences from Gicumbi District, northern Province in Rwanda

Enhancement of financial inclusivity of rural communities is often recognised as a key strategy for achieving economic development in third world countries. The main objective of this study was to examine the factors that influence consumers’ choice of a rural bank in Gicumbi district of Rwanda. Data was collected using structured questionnaires and analysed using a binary probit regression model and non-parametric procedures. Most consumers were aware of Popular Bank of Rwanda (BPR) and Umurenge SACCO through radio advertisements, social networks and community meetings. Accessibility, interest rates and quality of services influenced choice of a given financial intermediary. Moreover, the decision to open a rural bank account was significantly influenced by education and farm size (p<0.1). These results indicate the need for financial managers to consider these findings for successful marketing campaigns.

A pilot investigation of the relationship between climate variability and milk compounds under the bootstrap technique

This study analyzes the linear relationship between climate variables and milk components in Iran by applying bootstrapping to include and assess the uncertainty. The climate parameters, Temperature Humidity Index (THI) and Equivalent Temperature Index (ETI) are computed from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis (2002–2010). Milk data for fat, protein (measured on fresh matter bases), and milk yield are taken from 936,227 milk records for the same period, using cows fed by natural pasture from April to September. Confidence intervals for the regression model are calculated using the bootstrap technique. This method is applied to the original times series, generating statistically equivalent surrogate samples. As a result, despite the short time data and the related uncertainties, an interesting behavior of the relationships between milk compound and the climate parameters is visible. During spring only, a weak dependency of milk yield and climate variations is obvious, while fat and protein concentrations show reasonable correlations. In summer, milk yield shows a similar level of relationship with ETI, but not with temperature and THI. We suggest this methodology for studies in the field of the impacts of climate change and agriculture, also environment and food with short-term data.