Browsing by Subject "Nichtlineare Optimierung"

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Ein Nichtlineares Prozessanalytisches Agrarsektormodell für das Einzugsgebiet der Oberen Donau
(2005) Winter, Thomas; Dabbert, Stephan
This dissertation describes a regionalised non-linear agricultural sector model for the upper danube catchment area. The model is used for simulating and forecasting the impacts of different policies measures and of climate change on farming. The most important task of this model is to fit as a part into the decision-support-system Glowa-Danubia. The main idea of the decision-support-system is, that the impact of the Global-climate change to the water on the upper danubia basin can be shown. At first the interactions between agriculture and water are defined. One of the results is that agriculture is an economic sector, which has an important influence on the water-household of the landscape. Many agricultural activities, e.g. tillage, fertilisation, plant-protection have a direct or indirect impact on the waterhousehold. Of course there are some conflicts with other users of the water resource. In consequence a lot of laws guarantee the safety of the water resources. The farmers are bound by law to practice the so called ?good agricultural practice?. Another possibility for policy to influence farmers is that farmers can take part in agri-environmental programs. In the second chapter the connections between the agricultural sector model and the other models are defined. A division of the agricultural sector model into three main modules is necessary, that the dates can be transferred automatically. The three modules of the agriculture sector model can be overwritten with data resources, model equations and result tables. A process-analytical optimisation model was used, because with this methodological approach the use of fertiliser and other farm inputs and the level of production can be simulated. In the plant production 19 different crop production activities with two different intensities were defined. The production level of each crop was defined by using expert interviews with a Probit-Model. The Probit-Model calculates independ of the ?Landwirtschaftliche Vergleichszahl? (a relative number, which indicates the agronomy quality of soils) in order to define the extent of both intensities. In the animal production 15 different production systems are defined. The constraints and the calibration of the non-linear gross margin function are discussed after the definition of the production activities. Some constraints, for example the crop rotation constraint, are not necessary in the non-linear model. The reference situation is the year 1995. The first simulation shows the results for the year 1999. In the Ex-post-analysis from this year both different Howitt-methods, the cost side specification and the yield side specification of the non-linear gross margin function, get compared. Both methods calculate nearly the same results, the forecast of the production activity levels and the gross margins are nearly the same. For the total research area both methods either over- or underestimate the production activity levels for the same crop. If the forecast results are compared by district the forecasting accuracies were different. A system, that one method is better than the other method, can not be found. For the simulation of the scenarios the cost side specification was used. In the first scenario the year 2008 under the conditions of the agenda 2000 was calculated. The production levels are nearly the same as in the reverence year. The consequences of the mid term review of the European Commission are forecasted in the two other scenarios. The conditions under mid term review have a big impact on agriculture, because of the decoupling of the agricultural subsidies from the products. In both scenarios, the cattle meat production decreases. The same results can be shown with the reduction of silo maize and of grassland. On the other side set aside arises in all districts. Positive for the water resources are the reduction of nitrogen load from organic manure. One of the main conclusions is that the positive quadratic programming is an alternative to the linear models for analysis of farms by district level. Of course the aggregation mistake from the aggregation of different farm types to one big district farm can not be carried out. But the positive aspects of the PQP, which are described in literature, can be permitted. A further research theme, which is not answered in this thesis is the combination of the non-linear gross margin with useful econometric methods.
Optimum schemes for hybrid maize breeding with doubled haploids
(2011) Wegenast, Thilo; Melchinger, Albrecht E.
In hybrid maize breeding, the doubled haploid technique is increasingly replacing conventional recurrent selfing for the development of new lines. In addition, novel statistical methods have become available as a result of enhanced computing facilities. This has opened up many avenues to develop more efficient breeding schemes and selection strategies for maximizing progress from selection. The overall aim of the present study was to compare the selection progress by employing different breeding schemes and selection strategies. Two breeding schemes were considered, each involving selection in two stages: (i) developing DH lines from S0 plants and evaluating their testcrosses in stage one and testcrosses of the promising DH lines in stage two (DHTC) and (ii) early testing for testcross performance of S1 families before production of DH lines from superior S1 families and then evaluating their testcrosses in the second stage (S1TC-DHTC). For both breeding schemes, we examined different selection strategies, in which variance components and budgets varied, the cross and family structure was considered or ignored, and best linear unbiased prediction (BLUP) of testcross performance was employed. The specific objectives were to (1) maximize through optimum allocation of test resources the progress from selection, using the selection gain (ΔG) or the probability to select superior genotypes (P(q)) as well as their standard deviations as criteria, (2) investigate the effect of parental selection, varying variance components and budgets on the optimum allocation of test resources for maximizing the progress from selection, (3) assess the optimum filial generation (S0 or S1) for DH production, (4) compare various selection strategies - sequential selection considering or ignoring the cross and family structure - for maximizing progress from selection, (5) examine the effect of producing a larger number of candidates within promising crosses and S1 families on the progress from selection, and (6) determine the effect of BLUP, where information from genetically related candidates is integrated in the selection criteria, on the progress from selection. For both breeding schemes, the best strategy was to select among all S1 families and/or DH lines ignoring the cross structure. Further, in breeding scheme S1TC-DHTC, the progress from selection increased with variable sizes of crosses and S1 families, i.e., larger numbers of DH lines devoted to superior crosses and S1 families. Parental cross selection strongly influenced the optimum allocation of test resources and, consequently, the selection gain ΔG in both breeding schemes. With an increasing correlation between the mean testcross performance of the parental lines and the mean testcross performance of their progenies, the superiority in progress from selection compared to randomly chosen parents increased markedly, whereas the optimum number of parental crosses decreased in favor of an increased number of test candidates within crosses. With BLUP, information from genetically related test candidates resulted in more precise estimates of their genotypic values and the progress from selection slightly increased for both optimization criteria ΔG and P(q), compared with conventional phenotypic selection. Analytical solutions to enable fast calculations of the optimum allocation of test resources were developed. This analytical approach superseded matrix inversions required for the solution of the mixed model equations. In breeding scheme S1TC-DHTC, the optimum allocation of test resources involved (1) 10 or more test locations at both stages, (2) 10 or fewer parental crosses each with 100 to 300 S1 families at the first stage, and (3) 500 or more DH lines within a low number of parental crosses and S1 families at the second stage. In breeding scheme DHTC, the optimum number of test candidates at the first stage was 5 to 10 times larger, whereas the number of test locations at the first stage and the number of DH lines at the second stage was strongly reduced compared with S1TC-DHTC. The possibility to reduce the number of parental crosses by selection among parental lines is of utmost importance for the optimization of the allocation of test resources and maximization of the progress from selection. Further, the optimum allocation of test resources is crucial to maximize the progress from selection under given economic and quantitative-genetic parameters. By using marker information and BLUP-based genomic selection, more efficient selection strategies could be developed for hybrid maize breeding.