Evaluation of a core sampling scheme to characterize root length density of maize

Abstract

Information about root distribution is important for characterization and modeling of water and nutrient uptake, biomass, and yield. Due to the heterogeneous distribution of roots in row crops, the reliability and representativeness of estimates of RL and root morphology using core sampling depend on the number of samples and their location. The objectives of this study were to evaluate errors when assessing RLD and root morphology from auger core sampling schemes, to estimate 2D distributions of RLD by auger core sampling, and to assess the number of samples necessary for representative estimates of RLD and RD classes. The reference dataset utilized in this study is based on completely sampled 3D soil monoliths under maize, taken at three different dates (55, 78, and 104 days after planting) and two different row spacings (75 and 37.5 cm). A hypothetical auger core sampling scheme with one core within the row and another midway between rows mostly overpredicted total RLD. Bias was lower when using an 1:3 weighting scheme. Estimation of 2D vertical RLD distribution by calculating the ratio of RLD in the plant row to RLD midway between rows yielded reasonable estimates only when the average of eight cores was used. An assessment of the proportions of RD classes yielded high bias values, even when using the average of eight cores. An analysis of sampling errors using successively more cores revealed that for total RLD, to attain a bias <20%, more than ten samples would be necessary. This suggests that the number of core samples taken in many root studies could be too low. This bias was even higher when taking core samples for estimation of proportions of RD classes, where a reasonably low bias between estimated and "true" values could not be attained in this study even with ten core samples. Consequently, when taking samples for measurement of root morphological parameters, more detailed and site-adapted sampling schemes have to be devised. For estimating total averaged RLD, two core Plant Soil (2009) 316:205-215

The long-term effects of tillage practice and phosphorus fertilization on the distribution and morphology of corn root

“…The average root diameter decreased from row to inter-row (corresponding to the observation of Buczko et al (2009)) as more coarse roots (primary and secondary roots) were close to the corn row.…”

“…Its expansion depends on genotypes (Hajabbasi and Schumacher 1994;Mu et al 2015), soil types (Chassot et al 2001) and sampling time (Cai et al 2014). Sampling design and measurement methods might also explain differences reported in the literature (Buczko et al 2009). In our field study, the overall root length density for the 40-cm soil profile was 1.38 cm cm −3 , which is comparable to the RLDs that are widely reported in literature for corn (Qin et al 2006;Buczko et al 2009;Guan et al 2014).…”

“…However, the only significant interactive effect of tillage and P fertilization occurred at 0-5 cm with lower RMD in NT with 0P and 1P and higher RMD in NT with 0.5P. The high variability of RMD in the surface layer (Buczko et al 2009) might explain the unexpectedly higher RMD in NT for 0.5P compared to MP (Fig. 2b).…”

Topographic and soil influences on root productivity of three bioenergy cropping systems

“…Ingrowth cores were installed in annual row-crops within 1 wk of seed germination. One core was located within a row for every three cores located between rows to reduce bias of root measurements in row crops (Buczko et al, 2009). Root productivity of the triticale/sorghum cropping system was determined by summing the root production of each crop.…”

Phenotyping for the dynamics of field wheat root system architecture

“…Spatially distributed monolith sampling is an option for illustrating crop RSAs with root length and root mass spatial heterogeneities in all three spatial dimensions in field9. Whereas commonly applied auger core sampling can incur large errors when characterizing spatial distributions of roots24. Shovelomics, another high-throughput method for phenotyping RSAs in the field, provides a quick sampling and quantification solution12.…”