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Durch die steigende Nachfrage nach nicht-chemischen Ansätzen zur Unkrautregulierung gewinnen mechanische Verfahren an Bedeutung. Für spätgesäte, konkurrenzschwache Kulturen wie Melisse (Melissa officinalis) wird empfohlen, mittels präventiver Bodenbearbeitung, etwa durch vorgezogene Saatbettbereitung („Falsches Saatbett“), Unkrautsamen im Boden wiederholt zur Keimung anzuregen und damit die Diasporenbank vor der Aussaat zu reduzieren. In einem zweifaktoriellen Versuch wurde der Zeitpunkt des Pflugeinsatzes (Herbst- oder Frühjahrsfurche) in Kombination mit vier Intensitäten einer flachen Frühjahrsbearbeitung mittels Kreiselegge herbologisch untersucht. Der Versuch wurde 2019/20 an einem ökologisch und einem konventionell bewirtschafteten Standort im Rheinland angelegt. Vor jedem Bodenbearbeitungsgang wurden Unkrautbonituren durchgeführt. Zusätzlich wurde die Diasporenbank im Oberboden (0-7 cm) an drei Terminen erfasst. Am ökologisch bewirtschafteten Standort war die Unkrautdichte drei Wochen nach Aussaat nach Herbstfurche in den drei- und vierfach gekreiselten Faktorkombinationen signifikant geringer im Vergleich zur Frühjahrsfurche (p = 0,023; p = 0,018). Arten mit leichtem Samengewicht (≤ 0,5 mg) wie Aphanes arvensis, Matricaria chamomilla, Papaver rhoeas, Veronica agrestis und Viola arvensis hatten in den viermal gekreiselten Varianten einen tendenziell höheren Anteil (41,1 % ± 23,6 %) im Vergleich zu einer einmaligen Saatbettbereitung (15,6 % ± 20,8 %). Diese wies tendenziell eine höhere Dichte an Arten mit höherem Samengewicht (> 1 mg) wie Ranunculus repens oder Chenopodium album auf (49,3 % ± 28,2 %). Dies lässt vermuten, dass die Keimung kleinerer Samen bei feineren Bodenaggregaten begünstigt wird. Am konventionell bewirtschafteten Standort wurden bei geringem Samenpotential im Oberboden (< 200 je m2) keine Effekte der Bodenbearbeitung auf Unkrautdichte und Artenzusammensetzung festgestellt. Die Ergebnisse des ersten Versuchsjahres zeigen, dass eine wiederholte flache Frühjahrsbearbeitung an einem Standort mit hohem Samenpotential (> 6000 Samen m-2) nicht zu einer Minderung der Unkrautdichte, jedoch zu einer Selektion eines bestimmten Artenspektrums führen kann.

While intensive control of weed populations plays a central role in current agriculture, numerous studies highlight the multifaceted contribution of weeds to the functionality and resilience of agroecosystems. Recent research indicates that increased evenness within weed communities may mitigate yield losses in contrast to communities characterized by lower diversity, since weed species that strongly affect crop yields, also dominate weed communities, with a concurrent reduction of evenness. If confirmed, this observation would suggest a paradigm shift in weed management towards promoting higher community diversity. To validate whether the evenness of weed communities is indeed linked to higher crop productivity, we conducted two field experiments: one analyzing the effects of a natural weed community in an intercrop of faba bean and oat, and the other analyzing the effects of artificially created weed communities, together with the individual sown weed species, in faba bean, oats and an intercrop of both crops. The evenness of the weed communities ranged from 0.2 to 0.9 in the natural weed community, from 0.2 to 0.7 in faba bean, from 0 to 0.8 in the intercrop and from 0.3 to 0.9 in oats. Neither the natural nor the artificial weed community showed significant effects of evenness on crop grain yield or crop biomass. The results of this study do not validate a positive relationship of crop productivity and weed evenness, possibly due to low weed pressure and the absence of competitive effects but suggest that also less diverse weed communities may be maintained without suffering yield losses. This is expected to have far reaching implications, since not only diverse weed communities, but also higher abundances of few weed species may contribute to ecosystem functions and may support faunal diversity associated with weeds. [ABSTRACT FROM AUTHOR]

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Background: Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. While image-based models provide more flexibility for crop growth modeling than process-based models, there is still a significant research gap in the comprehensive integration of various growth-influencing conditions. Further exploration and investigation are needed to address this gap. Methods: We present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained. The image generation model is a conditional Wasserstein generative adversarial network (CWGAN). In the generator of this model, conditional batch normalization (CBN) is used to integrate conditions of different types along with the input image. This allows the model to generate time-varying artificial images dependent on multiple influencing factors. These images are used by the second part of the framework for plant phenotyping by deriving plant-specific traits and comparing them with those of non-artificial (real) reference images. In addition, image quality is evaluated using multi-scale structural similarity (MS-SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID). During inference, the framework allows image generation for any combination of conditions used in training; we call this generation data-driven crop growth simulation. Results: Experiments are performed on three datasets of different complexity. These datasets include the laboratory plant Arabidopsis thaliana (Arabidopsis) and crops grown under real field conditions, namely cauliflower (GrowliFlower) and crop mixtures consisting of faba bean and spring wheat (MixedCrop). In all cases, the framework allows realistic, sharp image generations with a slight loss of quality from short-term to long-term predictions. For MixedCrop grown under varying treatments (different cultivars, sowing densities), the results show that adding these treatment information increases the generation quality and phenotyping accuracy measured by the estimated biomass. Simulations of varying growth-influencing conditions performed with the trained framework provide valuable insights into how such factors relate to crop appearances, which is particularly useful in complex, less explored crop mixture systems. Further results show that adding process-based simulated biomass as a condition increases the accuracy of the derived phenotypic traits from the predicted images. This demonstrates the potential of our framework to serve as an interface between a data-driven and a process-based crop growth model. Conclusion: The realistic generation and simulation of future plant appearances is adequately feasible by multi-conditional CWGAN. The presented framework complements process-based models and overcomes their limitations, such as the reliance on assumptions and the low exact field-localization specificity, by realistic visualizations of the spatial crop development that directly lead to a high explainability of the model predictions. [ABSTRACT FROM AUTHOR]