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A New Approach in Profile Analysis with High-Dimensional Data Using Scores

(Swedish University of Agricultural Sciences, Department of Energy and Technology, 2020) Cengiz, Cigdem

In profile analysis, there exist three tests: test of parallelism, test of levels and test of flatness. In this thesis, these tests have been studied. Firstly, a classical setting, where the sample size is greater than the dimension of the parameter space, is considered. The hypotheses have been established and likelihood ratio tests have been derived. The distributions of these test statistics have been given. In the latter stage, all tests have been derived in a high-dimensional setting, where the number of parameters exceeds the number of sample size. Such settings have become more common due to the advances in computer technologies in the last decades. In high-dimensional data analysis, several issues arise with the dimensionality and different techniques have been developed to deal with these issues. We propose a dimension reduction method using scores that was first proposed by Läuter et al. (1996). To be able to find the specific distributions of the test statistics of profile analysis in this context, the properties of spherical distributions are utilized.

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Assessment of statistical analysis of Swedish cultivar testing: a cross-validation study for model selection

(Department of Energy and Technology, Swedish University of Agricultural Sciences, 2019) Buntaran, Harimurti

The Swedish official cultivar testing conducts multienvironmental trials (MET) to makerecommendations of cultivars that are well adapted to farmers’ regional conditions. Inthe MET, a large number of cultivars are tested in several geographical regions. Thetested cultivars perform differently in varying soil types and climates, a phenomenonknown as genotype×environment interactions. The MET data structure is often large andhighly imbalanced, which causes computational problems when applying some statisticalmethods. Several issues, such as prediction of crop variety performance and efficientcomputation of measure of cultivar stability are urgent to be tackled by developingcomprehensive and robust statistical methods. This study aims to address these issuesand provide a gold standard for MET analysis in Swedish official cultivar testing. In this study, we investigated several linear mixed models by using cross-validation(CV). We proposed to use random cultivar effects, known as best linear unbiasedprediction (BLUP) method to replace the current fixed cultivar effects, known as bestlinear unbiased estimation (BLUE). In theory, BLUP provides more accurate rankingsand predictions than BLUE. The current-practice analysis strategy, i.e., two-stageunweighted strategy, was also compared to several strategies such as single-stagestrategy and two-stage weighted strategies that comprise some weighting methods. In theCV, mean squared error of differences (MSEP) was used to assess the performance ofestimation of cultivar effects by BLUP and BLUE to select a model that provides bestprediction accuracy. A new inter-zone stability measure was also proposed to tacklecomputational burden and provide additional useful information regarding cultivarstability across zones and years. The MSEP revealed that BLUP outperformed the current-practice method, BLUE,and so improved the accuracy of zone-based prediction. Also, the single-stage and twostage weighted strategies outperformed the current strategy. The proposed stabilitymeasure offered a less computational resource, and provided more flexible stabilitymeasure for practical purpose.

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Drone Photographs of Pollinators in the Uppsala Region

(Swedish University of Agricultural Sciences, 2025-12-19) Liv Olofsson

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Food waste in the food service sector - Quantities, risk factors and reduction strategies

(Department of Energy and Technology, Swedish University of Agricultural Sciences, 2021) Malefors, Christopher

An estimated one-third of all food produced is wasted, meaning that much of the negative environmental impact caused by food production is in vain. Global ambitions to reduce food waste include halving the levels by 2030, while the new EU food strategy views reducing food waste as a key issue in achieving a sustainable food system. This thesis presents detailed information on the volumes of food waste, where it occurs, why it occurs and what can be done to reduce it. The information originated from 1189 kitchens operating in establishments such as canteens, care homes, hotels, hospitals, preschools, schools and restaurants throughout Sweden, Norway, Finland and Germany. The results indicated that approximately 20% of food served in the catering sector is wasted, although there is large variation, with canteens reporting 50±9.4 g/portion of food waste and restaurants 190±30 g/portion. To identify risk factors and reasons for food waste, a more detailed subset of data on Swedish preschools and schools was analysed. Some of the risk factors identified related to kitchen infrastructure and guest age, which could be difficult or expensive to tackle as a first option. The main risk factor was the amount of food prepared relative to the number of guests attending, an issue that kitchens can tackle by forecasting. This thesis demonstrated the potential of forecasting attendance as a tool in planning catering operations. The current business-as-usual scenario, where food is prepared for all pupils enrolled, results in a mean error of 20-40%, whereas the best forecasting case, using neural network models, resulted in a mean error of 2-3%. However, forecasts can underestimate demand, creating shortages, so some margin must be added in practical use. Providing kitchens with information about roughly how many guests will attend a meal, plus a sufficient margin, and encouraging them to serve food from a backup stock in cases of forecast underestimation would overcome the problems of shortages, reduce food waste and contribute to a sustainable food system.