Browsing by Subject "Silage maize"

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Bayesian multi-purpose modelling of plant growth and development across scales
(2024) Viswanathan, Michelle; Streck, Thilo
Crop models are invaluable tools for predicting the impact of climate change on crop production and assessing the fate of agrochemicals in the environment. To ensure robust predictions of crop yield, for example, models are usually calibrated to observations of plant growth and phenological development using different methods. However, various sources of uncertainty exist in the model inputs, parameters, equations, observations, etc., which need to be quantified, especially when model predictions influence decision-making. Bayesian inference is suitable for this purpose since it enables different uncertainties to be taken into account, while also incorporating prior knowledge. Thus, Bayesian methods are used for model calibration to improve the model and enhance prediction quality. However, this improvement in the model and its prediction quality does not always occur due to the presence of model errors. These errors are a result of incomplete knowledge or simplifying assumptions made to reduce model complexity and computational costs. For instance, crop models are used for regional scale simulations thereby assuming that these point-based models are able to represent processes that act at regional scale. Additionally, simple statistical assumptions are made about uncertainty in model errors during Bayesian calibration. In this work, the problems arising from such applications are analysed and other Bayesian approaches are investigated as potential solutions. A conceptually simple Bayesian approach of sequentially updating a maize phenology model, an important component in plant models, was investigated as yearly observation data were gathered. In this approach, model parameters and their uncertainty were estimated while accounting for observation uncertainty. As the model was calibrated to increasing amounts of observation data, the uncertainty in the model parameters reduced as expected. However, the prediction quality of the calibrated model did not always improve in spite of more data being available for potentially improving the model. This discrepancy was attributed to the presence of errors in the model structure, possibly due to missing environmental dependencies that were ignored during calibration. As a potential solution, the model was calibrated using Bayesian multi-level modelling which could account for model errors. Furthermore, this approach accounted for the hierarchical data structure of cultivars nested within maize ripening groups, thus simultaneously obtaining model parameter estimates for the species, ripening groups and cultivars. Applying this approach improved the model's calibration quality and further aided in identifying possible model deficits related to temperature effects in the post-flowering phase of development and soil moisture. As another potential solution, an alternative calibration strategy was tested which accounted for model errors by relaxing the strict statistical assumptions in classical Bayesian inference. This was done by first acknowledging that due to model errors, different data sets may yield diverse solutions to the calibration problem. Thus, instead of fitting the model to all data sets together and finding a compromise solution, a fit was found to each data set. This was implemented by modifying the likelihood, a term that accounts for information content of the data. An additive rather than the classical multiplicative strategy was used to combine likelihood values from different data sets. This approach resulted in conservative but more reliable predictions than the classical approach in most cases. The classical approach resulted in better predictions only when the prediction target represented an average of the calibration data. The above-mentioned results show that Bayesian methods with representative error assumptions lead to improved model performance and a more realistic quantification of uncertainties. This is a step towards the effective application of process-based crop models for developing suitable adaptation and mitigation strategies.
Management effect on the weed control efficiency in double cropping systems
(2023) Schmidt, Fruzsina; Böhm, Herwart; Graß, Rüdiger; Wachendorf, Michael; Piepho, Hans-Peter
There are often negative side-effects associated with the traditional (silage) maize cropping system related to the unprotected soil surface. Reducing soil disturbance could enhance system sustainability. Yet, increased weed pressure and decreased nitrogen availability, particularly in organic agriculture, may limit the implementation of alternative management methods. Therefore, a field experiment was conducted at two distinct locations to evaluate the weed control efficiency of 18 organically managed silage maize cropping systems. Examined parameters were relative weed groundcover (GCweed) and its correlation with maize dry matter yield (DMY), relative proportion of dominant weed species (DWS) and their groups by life form (DWSgroup). Treatment factors comprised first crop (FC—winter pea, hairy vetch, and their mixtures with rye, control (sole silage maize cropping system—SCS)), management—incorporating FC use and tillage (double cropping system no-till (DCS NT), double cropping system reduced till (DCS RT), double cropped, mulched system (DCMS Roll) and SCS control), fertilization, mechanical weed control and row width (75 cm and 50 cm). The variation among environments was high, but similar patterns occurred across locations: Generally low GCweed occurred (below 28%) and, therefore, typically no correlation to maize DMY was observed. The number of crops (system), system:management and occasionally management:FC (group) influenced GCweed and DWS(group). Row width had inconsistent and/or marginal effects. Results suggest differences related to the successful inclusion of DCS and DCMS into the rotation, and to the altered soil conditions, additional physical destruction by shallow tillage operations, especially in the early season, which possibly acts through soil thermal and chemical properties, as well as light conditions. DCS RT could successfully reduce GCweed below 5%, whereas DCS NT and particularly DCMS (Mix) suffered from inadequate FC management. Improvements in DCMS may comprise the use of earlier maturing legumes, especially hairy vetch varieties, further reduction/omission of the cereal companion in the mixture and/or more destructive termination of the FC.
Untersuchungen zur Vererbung von Qualitätseigenschaften bei Silomais (Zea mays L.)
(2004) Krützfeldt, Birte A. E.; Geiger, Hartwig H.
In central Europe silage maize (Zea mays L.) is a major source of cattle feed. The quality or the feeding value of a silage maize variety mainly depends on its digestibility and energy content. The establishing of the near-infrared-reflectance-spectroscopy- (NIRS) technique allows the analysis of more than one quality determining trait simultaneously in an easy and short way. In this study one objective was the influence of stover quality on whole plant quality. In hybrid breeding indirect selection on the basis of inbred line performance has a great advantage because the number of testcrosses can be reduced. Therefore it was tested, if the stover quality of the testcrosses could be predicted on the line per se value. Besides the correlation between agronomic and quality traits was analysed. In the years 1999 and 2000 the evaluation of the stover of the lines and testcrosses and the whole plant of the testcrosses was conducted at four climatically diverse sites in Germany. Three data sets with flint-lines and dent-lines, each proved with one tester-line, were evaluated for the correlation between inbred line and testcross performance. The test for combining ability was performed with three smaller data sets also consisting of flint-lines and dent-lines with two tester-lines per data set. The coefficients of heritability were high for the agronomic and quality traits in the data sets of the inbred lines. In the data sets of the testcrosses the variation attributed to the genotypic variance was smaller, genotype × location-interactions were of lower importance. In the data sets, each with two tester-lines it was obvious that for quality traits of stover and whole plant the interaction between line and tester was mostly not significant. The genotypic correlation between inbred line and testcross performance was highly significant for almost all quality traits of the stover, but the correlation coefficients were mostly only moderate. Only the expected success of an indirect selection on line per se- value for cell-wall digestibility of the stover exceeded that of the direct selection on testcross performance in all data sets. However, a selection of extremes on line per se value should be possible for stover digestibility. The genotypic correlations between comparable traits in stover and whole plant were mainly low. The cell-wall digestibility was the only trait which was independent of dry matter content. For evaluation of the further quality traits attention has to be paid to the maturity stage, to prevent a maturity-based bias of the results. In the testcrosses stover digestibility increased and whole plant digestibility was reduced with an increase in whole plant dry matter yield. But the genotypic correlations were only moderate and a simultaneous selection to improve quality and yield seems to be possible.