Biomedical subjects

M J Kropff

Publications and source records attributed to M J Kropff.

3 recordsLinked to original sources

PubMed2002

Optimizing grain yields reduces CH4 emissions from rice paddy fields.

Microbial production in anoxic wetland rice soils is a major source of atmospheric CH4 the most important non-CO2 greenhouse gas. Much higher CH4 emissions from well managed irrigated rice fields in the wet than in the dry season could not be explained by seasonal differences in temperature. We hypothesized that high CH4 emissions in the wet season are caused by low grain to biomass ratios. In a screenhouse experiment, removing spikelets to reduce the plants' capacity to store photosynthetically fixed C in grains increased CH4 emissions, presumably via extra C inputs to the soil. Unfavorable conditions for spikelet formation in the wet season may similarly explain high methane emissions. The observed relationship between reduced grain filling and CH4 emission provides opportunities to mitigate CH4 emissions by optimizing rice productivity.

Agriculture↗

PubMed2000

Coupling estimated effects of QTLs for physiological traits to a crop growth model: predicting yield variation among recombinant inbred lines in barley.

Advances in the use of molecular markers to elucidate the inheritance of quantitative traits enable the integration of genetic information on physiological traits into crop growth models. The objective of this study was to assess the ability of a crop growth model with QTL-based estimates of physiological input parameters to predict the yield of recombinant inbred lines (RILs) of barley. The model used predicts yield as spike biomass accumulated over the post-flowering period. We describe a two-stage procedure for predicting trait values from estimated additive and epistatic effects of QTLs. Values of physiological traits estimated by that procedure or measured in the field were used as input to the crop growth model. The output values (yield and shoot biomass) from the growth model using these two types of input values were highly correlated, indicating that QTL information can successfully replace measured input parameters. With the current crop growth model, however, both types of input values often resulted in large discrepancies between observed and predicted values. Improvement of performance may be achieved by incorporating physiological processes not yet included in the model. The prospects of using QTL-based predictions of model-input traits to identify new, high yielding barley genotypes are discussed.

Chromosome Mapping↗

PubMed1997

Modelling for precision weed management.

Recently, the need for the development of weed management systems with a reduced dependency on herbicides has increased because of concern about environmental side-effects and cost. The development of such systems requires new strategies based on improvements with respect to (1) prevention, (2) decision making and (3) weed control technology. For the development of improvements in all three aspects, quantitative understanding of weed population dynamics and crop-weed interactions is needed. Models that integrate the available quantitative knowledge can be used to design preventive measures, to develop long-term and short-term strategies for weed management, to assist in decision making to determine if, when, where and how weeds should be controlled and to identify new opportunities for weed control. Ecophysiological simulation models for crop-weed competition simulate growth and production of species in mixtures, based on ecophysiological processes in plants and their response to the environment. Such models help improve insight into the crop-weed system and can be used for purposes such as the development of simple predictive yield-loss models, threshold levels or the design of competitive crop plant types. For strategic weed management decisions, preventive measures and the identification of new opportunities for weed control, quantitative insight into the dynamics and spatial patterns of weed populations is also required. The complexity of the process and the long-term character of weed population dynamics make the use of models necessary. Different modelling approaches have been developed and are described briefly. Opportunities to use the available knowledge and models to improve weed management are discussed. Weeds occur in patches and their sensitivity to herbicides changes strongly with developmental stage, making precision techniques for herbicide application in time and space an option for reducing herbicide use. Limitations related to insight in biological processes as well as the state of technological development are discussed.

Agriculture↗