Many benefits of cover crops such as prevention of nitrate leaching, erosion reduction, soil organic carbon enhancement and improvement of soil structure are associated with roots. However, including root characteristics as a criterion for cover crop selection requires more knowledge on their root growth dynamics. Seven cover crop species (crimson clover, winter rye, bristle oats, blue lupin, oil radish, winter turnip rape and phacelia) were grown in a two-year organically managed field experiment in Germany to screen them for root intensity and vertical root distribution. Root length density (RLD) and proportion of root length in large-sized biopores were determined before and after winter with the profile wall method. RLD and cumulative root length were analysed using a three-parameter logistic function, and a logistic dose-response function, respectively. Fibrous rooted winter rye and crimson clover showed high RLD in topsoil and had a shallow cumulative root distribution. Their RLD increased further during winter in topsoil and subsoil. The crops with the highest RLD in the subsoil were taprooted oil radish, winter turnip rape and phacelia. Bristle oat had intermediate features. Blue lupin had low RLD in topsoil and subsoil. Phacelia, oil radish, winter turnip rape and bristle oat showed the highest share of root length in biopores. These complementary root characteristics suggest that combining cover crops of different root types in intercropping may be used to enhance overall RLD for maximizing cover crop benefits.

The scale of root quantification in research is often limited by the time required for sampling, measurement, and processing samples. Recent developments in convolutional neural networks (CNNs) have made faster and more accurate plant image analysis possible, which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of machine learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model (i.e. learning from labeled examples) can effectively exclude the debris by comparing the end results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training, and the derived measurements were compared with manual measurements. After 200 min of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R²=0.99), profile wall (R²=0.76), and core-break (R²=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Differences in root-length density (RLD) between crops with contrasting root systems were captured using automatic segmentation at soil profiles with high RLD (1–5 cm cm^–3) as well with low RLD (0.1–0.3 cm cm^–3). Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.

Deeper root growth can be induced by increased biopore density. In this study, we aimed to compare deep root traits of two winter crops in field conditions in response to altered biopore density as affected by crop sequence. Two fodder crop species—chicory and tall fescue—were grown for two consecutive years as preceding crops (pre-crops). Root traits of two winter crops—barley and canola, which were grown as subsequent crops (post-crops)—were measured using the profile wall and soil monolith method. While barley and canola differed greatly in deep root traits, they both significantly increased rooting density inside biopores by two-fold at soil depths shallower than 100 cm. A similar increase in rooting density in the bulk soil was observed below 100 cm soil depth. As a result, rooting depth significantly increased (>5 cm) under biopore-rich conditions throughout the season of the winter crops. Morphological root traits revealed species-wise variation in response to altered biopore density, in which only barley increased root size under biopore-rich conditions. We concluded that large-sized biopores induce deeper rooting of winter crops that can increase soil resource acquisition potential, which is considered to be important for agricultural systems with less outsourced farm resources, e.g., Organic Agriculture. Crops with contrasting root systems can respond differently to varying biopore density, especially root morphology, which should be taken into account upon exploiting biopore-rich conditions in arable fields. Our results also indicate the need for further detailed research with a greater number of species, varieties and genotypes for functional classification of root plasticity against the altered subsoil structure.

In low input agriculture, a thorough understanding of the plant-nutrient interactions plays a central role. This study aims to investigate the effects of nitrogen (N), phosphorus (P), and potassium (K) and liming omission on shoot growth as well as on topsoil root biomass, growth and morphology (tuber and fibrous roots) of sugar beet grown under field conditions at the Dikopshof long-term fertilizer experiment (Germany). Classical shoot observation methods were combined with root morphology and link measurements using an image analysis program. Omission of the nutrients N, P and K as well as of liming led to a significant decrease in shoot growth. Tuber yield was lowest for the unfertilized and the K omission treatment. The root shoot ratio was highest in the N deficient treatment. In the K omission treatment, a strategic change from a less herringbone root type (early stage) to a more herringbone root type (late stage), which is more efficient for the acquisition of mobile nutrients, was observed. By contrast, a change from a more herringbone (early stage) to a less herringbone root type (late stage) which is less expensive to produce and maintain was observed in the unfertilized treatment. We conclude that sugar beet alters its root morphology as a nutrient acquisition strategy.