Problem: Root systems, along with their interactions with soil conditions and micro-organisms, are crucial for crop performance, mainly through the efficient capture of water and nutrients. These root traits significantly enhance crop resilience against climate change. However, measuring root systems is more labor-intensive compared to the above-ground  parts of plants.

Solution: Four methods – minirhizotron, soil pit, soil coring and shovelomics (figure 1) – were evaluated to assess their capacity to measure root traits in different crops and their accessibility (table 1).

Benefits: All four methods have advantages. The minirhizotron, soil pit, and soil coring methods can measure roots to depths of 1 m or more. Shovelomics, while limited to measuring surface roots in the top 20 cm, is a much faster method.

For more information, please see: https://zenodo.org/records/13683049 

Problem: The root system architecture and its interactions with soil microorganisms play a key role in crop performance, particularly in the capture of water and mineral elements. Studying roots is more complicated than studying the above-ground parts.

Solution: To facilitate the complex study of root systems, the shovelomics method was validated.

Principle: The Shovelomics method measures root traits on plants sampled with a spade. With this simple method, we can access the 3D architecture of the main roots growing into the ridge.

Benefits: Using shovelomics, root traits can be measured in 0 to 20 cm depth in the ridge. The parameters are measured by hand and software is required to measure the root length and root diameter automatically.

For more information, please see: https://zenodo.org/records/13584451

Problem: Agricultural production and it’s crop breeding programs are mainly oriented towards above-ground biomass. Nevertheless, phenotypic plasticity of the root system can improve resilience to environmental stress and ensure crop production under detrimental  conditions.

Solution: The ability to alter phenotypic root traits in response to the environment can be integrated in breeding programs when the plasticity of individual root traits can be quantified. The relative distance plasticity index (RDPI), as describes by Valladares et al. (2006), is a useful and easy to use index for the quantification of the plasticity of root traits. It can be applied in laboratory as well as in field conditions.

Benefits: By integrating root plasticity in breeding programs, the resilience of crops to drought, heat stress or limited nutrient availability  can be improved.

For more information, please see: https://zenodo.org/records/8392071 

Problem: To study the plasticity of root traits related to exploration and exploitation of soil environments, a range of methods need to be applied simultaneously. Tradeoffs between sample size and resolution for  visualisation as well as sample size for metabolome and transcriptome analysis and potential compromise of the quantification of root system size and diameters, need to be considered.

Solution: An ‘in-depth traits assessment and understanding of plasticity’ combines (1) X-ray computed tomography (CT) for the visualisation and characterisation of root system architecture (RSA) in 3D during growth
(figure 1); (2) WinRHIZO-analysis for quantification of root system size and diameters; (3) root gene expression analysis (Transcriptomics) for adaption of the plants to actual growth conditions, and (4) root metabolome analysis (Metabolomics) for information on the history of growth conditions.

Benefits: By combining the above mentioned methods it is possible to study the plasticity of root systems in response to environmental conditions like differences in soil texture and chemical properties, water availability and nutrient supply.

For more information, please see: https://zenodo.org/records/8392410 

Problem

Conventional methods to quantify arbuscular mycorrhizal fungal (AMF) colonization include microscopy, which involves staining of the roots and meticulous observation. This method is time consuming and subjective because it depends on the observer’s level of expertise.

Solution

The use of broad range qPCR primers was validated to quantify AMF root colonization in different crops. This high-throughput and reliable technique is versatile and suitable for a wide range of research projects.

Benefits and weaknesses

Compared to the conventional microscopy method, qPCR allows the simultaneous analysis of a larger number of samples. Moreover, the results of qPCR are not influenced by observer subjectivity. AMF quantification can be complemented by sequencing the PCR product to assess AMF diversity. One weakness of the qPCR method is that it cannot distinguish between the different AMF  structures. In addition, it is worth noting that the qPCR method
yielded inconclusive results when applied to leek, possibility due to negligible changes in colonization rate (Corona Ramírez et al. 2023). We conclude that the qPCR method should be validated for each crop.

For more information, please see https://zenodo.org/records/10812217

Problem
Crop genotypes interact with their environment, which is influenced by soil type and weather, and are affected by the management systems used for their cultivation. To understand why a crop has performed in a particular way and to allow comparison of this performance across multiple locations, it is necessary to characterize the growing environment.

Solution
Envirotyping can show how environmental factors affect plant growth and development. Recording weather conditions, soil parameters and management practices is crucial to accurately characterise the crop growing environment. This information can further be used in crop models to estimate aspects of crop performance that are more difficult to measure, such as crop water use (IRRIGUIDE, ADAS) and nitrogen uptake (CHN, ARVALIS). See figure 1 and 2 for examples.

Principle data inputs

Weather: Rainfall, temperature, solar radiation, humidity, wind speed
Soil: Topsoil depth, subsoil depth to rock, texture in the different horizons, pH, soil organic matter, nutrient concentrations (e.g., P, K and Mg), water holding capacity, bulk density, stone content, total nitrogen, calcium carbonate content, maximum rooting depth
Field: Location (longitude and latitude), altitude, previous crop, sowing and harvest dates, crop type, nutritional inputs, dates of main developmental stages, irrigation, crop protection management information
Quality of the data determines model quality: The quality of the data will determine how well the interaction between genotype, environment and management can be assessed. For instance, localized rainfall data, recent soil nutrient reports, soil organic matter and pH information alongside representative soil sampling methods are essential for an accurate evaluation.

For full practice abstract, see: https://zenodo.org/records/13683214