Junior Research Group, PhenoRob CP5 - New Field Arrangements

Crop mixtures offer multiple advantages over traditional sole crops, including production of greater yield on a given piece of land, complementary resource use in time and space among different species; reduction of production risk due to complementarity; improved weed suppression; increased N availability for the subsequent crop due to legumes; decreased nitrate leaching by non-legume cover crops; improved soil fertility, soil organic matter content, and carbon sequestration; increased biodiversity, and maintenance and regeneration of ecosystem services. The numerous processes and mechanisms involved in crop mixtures highlight the need to deal with their complexity by combining concepts from diverse disciplines (agronomy, physiology and ecology) and demand for further information on crop species combination, arrangements and proportion as factors that affect mixtures.
Crop simulation models are widely recognized as useful tools that examine cause and effect relationships in crop production. Moreover, they can be used to study the influence of climate variability, soil, or management options, and for real-time simulation based crop management. Today, only a handful of models simulate mixed cropping systems. In several studies, yield and water use, light distribution, nitrogen transport and uptake, or weed suppression in mixtures were modelled. Given the complexity of mixed cropping systems, crop models can be especially helpful for testing hypotheses about the key factors driving competition and compensatory growth between species. Hereby, competition for soil water and nutrients and for light play a key role. The authors of a recent review on modelling annual crop mixtures state that modelling of crop mixtures is in its infancy (citation`). The competition for below-ground resources taken up by roots is not represented, which remains a main weakness. The majority of the models still ignore spatial heterogeneity of plant mixtures and streamline the system into a single dimension. Although existing models can simulate interactions, the degree of precision is questionable because of the general poor understanding of system dynamics within mixed cropping systems. According to the authors, mathematical equations within modelling of mixtures should be developed alongside theories.
The overall objectives of the Research Group are to i) obtain data using classical and new methods and technologies to gain insights into interactions and mechanisms in crop mixtures, ii) develop new and advanced crop models for crop mixtures, iii) determine optimal field arrangements (e.g. species combination, arrangements and proportion) and management (e.g. sowing, fertilization, harvest) in mixtures for a sustainable and climate-resilient crop production by combining highly monitored experiments and models.

Yi, J., Krusenbaum, L., Unger, P., Hüging, H., Seidel, S., Schaaf, G. and Gall J. Deep learning for non-invasive diagnosis of nutrient deficiencies in sugar beet using RGB images. Sensors. 2020, 20(20), 5893; doi:10.3390/s20205893

Madhuri Paul, Dereje Tamiru Demie, Sabine Seidel, Lisa Jaspers, Samuel Julian Vincze, Simone Kristin Lantzerath, Lars Caspersen and Thomas Döring: Effects of cereal-legume crop mixtures on crop and multifunctional  agroecosystem performance. The International Conference on Digital Technologies for Sustainable Crop Production (DIGICROP), November 1-10, 2020, Bonn, Germany.

Gina Lopez, Sofia Hadir, Miriam Athmann, Gabriel Schaaf, Frank Ewert, Daniel Pfarr, Sophia Despina Mouratidis and Sabine Seidel: Effect of nutrient limitations on shoot and root growth, root morphology and root topology on sugar beet and winter wheat under field conditions. The International Conference on Digital Technologies for Sustainable Crop Production (DIGICROP), November 1-10, 2020, Bonn, Germany.