2.1 Experimental design

Field: This study was performed in 2017 and 2018 at the Yanjin Experimental Station, Xinxiang, Henan Province, China. The test field is sandy soil, pH 8.5, containing 0.60% organic matter, available nitrogen 18.6 mg kg⁻¹, available phosphorus 16.2 mg kg⁻¹, available potassium 158.5 mg kg⁻¹. The seeds of 89 peanut varieties were provided by Xinxiang Academy of Agricultural Sciences, Henan Province (Table S1). Peanuts were planted in a randomized complete block design with three biological replications at a distance of 0.4 m between rows and 0.17 m between plants to reach a total density of about 166,000 plants ha⁻¹ on May 10, 2017 and May 21, 2018. Each plot had an area of 10 m² and a length of 5 m. Based on soil analysis at the beginning of the cropping season, nitrogen fertilizer (urea), phosphorus fertilizer (triple superphosphate) and potassium fertilizer (potassium chloride) were applied at rates of 25.0, 20.0 and 30.0 kg ha⁻¹ at pre-sowing, respectively. Pest control and irrigation were carried out as needed. The weather data during the experiment (April–September) at the experimental site in 2017 and 2018 are shown in Figure 1.

Laboratory: The 89 peanut varieties were also tested in the laboratory experiment. Seeds of the same size were selected and surface-sterilized for 5 min with 10% (v/v) H₂O₂, then washed with water for 3–4 times, and soaked in distilled water for 12 hours. Each 8 soaked seeds were placed in the middle of the double-layer absorbent paper at a distance of 3–4 cm from the top; the distance between peanut seeds was about 2 cm. Finally, the double-layer absorbent paper was wrapped around the PVC tube and fixed with a rubber band. The rolls were placed upright in 4.5-L plastic bucket containing 2 L of 0.5 mM CaSO₄. The seeds were allowed to germinate in darkness at 28°C for 3–4 days. The seedlings were then placed in a plant culture room at 28°C for 4 days with 12 h of light (120 μmol m⁻² s⁻¹). The dry weight and root phenes were dertemined on 7-day-old seedlings. During the cultivation process, carbendazim was used for sterilization when bacteria were growing.

2.2 Sampling method and preservation

Field: On September 10, 2017 and September 25, 2018, three plants were selected and excavated for shovelomics (Burridge et al. 2016). The excavation volume was defined by a cylinder with a radius of 15 cm around the shoot and a depth of 25 cm. The cylinder was taken out and roots were gently washed with water, and then the shoots were separated from the roots. Shoots were dried and weighted, while roots were soaked in 30% alcohol to determine root phenes state and dry weight.

Laboratory: After 7 days of greenhouse culture, the absorbent paper was unfolded, and 4 plants with the same growth were selected from each roll. The aboveground part and the root system were separated and labeled respectively for the determination of dry weight and root phenes state.

2.3 Evaluation of root phenes state

Table S2 lists all measured root phenes and their abbreviations. The primary root length (PRL), the length of lateral root zone (LLRZ) and the first lateral root growth angle (LRGA) were visually evaluated on a linear scale. Counts were taken for the number of primary lateral root (LRN) and the nodules number (NN). The lateral root density (LRD) was obtained by calculating the ratio of LRN to LLRZ.

Root samples were then scanned in greyscale at 600 dpi using a desktop scanner (EPSON Perfection V700 Photo) to record the phenotypic data on total root length (TRL, cm), total root surface area (RSA, cm²), average root diameter (ARD, mm) and root volume (RV, cm³). The greyscale images obtained in tiff format were analyzed using WinRHIZO Pro software (Regent Instruments Inc.). The lateral root length (LRL) was obtained by calculating the difference between the TRL and the PRL. In this study, three biological replicates were analyzed per peanut varieties.

2.4 Measurement of dry weight of shoot and root

The dry weights shoots and roota (after root image acquisition) of each plant were obtained after oven-drying at 80°C for 24 h or until constant weight.

2.5 Statistical analysis

Data from laboratory and field experiments in 2017 and 2018 were analyzed using SPSS 19.0 statistical software (SPSS Inc.). Means were tested by the Tukey test using a 0.05 level of significance. The boxplot method was used to analyze the differences in plant growth and root structure characteristics in the field (2017–2018) and in the laboratory. Genotype and year were considered fixed effects for field experiments. Pearson correlation analysis was performed to determine the relationship among phene descriptors, and to compare laboratory versus field results. The two-way average quadrant diagram was drawn based on the LRD and LRGA through RStudio V 1.2 software. The tested varieties were divided into four types: Class I、Class II、Class III and Class IV, which represented shallow root angle and large lateral root density, shallow root angle and small lateral root density, deep root angle and small lateral root density, deep root angle and large lateral root density, respectively.

3.1 The root architecture index of different peanut varieties in the field

We observed large phenotypic variation for most shoot and root descriptors (Figure 2) evaluated in the field in 2017 and 2018 (Figure 3, Tables S3 and S4). On the whole, the values of shoot dry weight (SDW), root dry weight (RDW) and various root morphological indexes (including TRL, RSA, RD, RV, MRL, LRN, LRD, RA and NN) in 2017 were all less than those in 2018. ANOVA of the two peanut seasons revealed significant variation associated with genotype and year with generally normal distributions (Table 1). Significant differences among genotypes and between the two years were observed for SDW, RDW, TRL, RSA, ARD, RV, LRL, LRN, LRD, RA and NN. These traits were also significantly affected by the interactions of genotype and year, except for the LRL.

3.2 The root architecture index of different peanut varieties in the laboratory

The range of values of SDW, RDW, TRL, RSA, ARD, RV, LRL, LRN, LRD, RA were also determined in the laboratory experiments (Figure 4 and Table S5). As shown in Table S6, there were significant differences between the biomass traits including SDW and RDW, and root traits including TRL, RSA, RV, ARD, PRL, LRL, LRN, LRD and RA of the 7-day old seedlings (p < 0.05), which indicates greater variability among the 89 varieties.

3.3 Root phenotypic variability in the field and the laboratory

A graphical representation of the range, mean and median indicates which traits may be better suited to differentiate genotypes. As shown in Figure 5, the average coefficient of variation of the root architecture index varied from 11.13% to 32.95% in the field experiment in 2017, from 10.21% to 35.92% in 2018, and from 10.28% to 34.93% in the laboratory experiment. The average coefficient of variations of ARD, PRL, and LRN and RA were always lower than 20% in 2017 and 2018 in both field and laboratory tests.

3.4 Pearson's correlation between root phenotypic indicators in the field and laboratory experiments

SDW was always positively correlated with RDW, RSA and RV in both field and lab experiments in both 2017 and 2018 (Figure 6). RDW was always positively correlated with RDW, RSA, RV and LRL, and NN in the field. TRL was always positively correlated with RSA, RV, LRL, LRN and LRD. RSA was always positively correlated with RV, LRL, LRN and LRD, and NN in the field. RV was positively correlated with LRL, and NN in the field. PRL was always positively correlated with LRL and LRN. LRL was always positively correlated with LRN and LRD. LRN was always positively correlated with LRD. LRN and LRD were positively correlated with LRL.

3.5 Pearson's correlation between root phenotypic indicators of different peanut genotypes in the laboratory and in the field

As shown in Table S7, trait values obtained in 2017 were reproducible in 2018 as indicated by moderate (LRD: 0.75, LRN: 0.71, RA: 0.64, LRL: 0.60, TRL: 0.34, and SDW: 0.32, ARD: 0.28, P < 0.01) or weak (RSA: 0.25, and RDW: 0.24, P < 0.05) correlations among trait values between 2017 and 2018 in the field. In addition, the LRL, LRN, LRD and RA values determined in the laboratory were highly correlated with those in the field in 2017 and 2018 (p < 0.01).

3.6 Response relationship between RA and LRD of different peanut varieties in the field and laboratory

Four quadrants were divided by RA and LRD of different peanut varieties (Class I, Class II, Class III, Class IV), representing shallow RA and large LRD, shallow RA and small LRD, deep RA and small LRD, deep RA and large LRD, respectively (Figure 7). There were 24, 25, 18 and 22 peanut varieties belonging to Class I, Class II, Class III and Class IV, respectively, in the field experiments in 2017 (Figure 7a). There were 18, 26, 20 and 25 peanut varieties belonging to Class I, Class II, Class III and Class IV, respectively, in the field experiments in 2018 (Figure 7b) and 23, 26, 22 and 18 peanut varieties belonging to Class I, Class II, Class III and Class IV, respectively, in the laboratory experiments (Figure 7c). In addition, by comparing the two years' field experiments, there were 14, 21, 11 and 16 varieties always belonging to Class I, Class II, Class III, and Class IV, respectively, and the ratio of the number of these varieties to the total number of varieties was 69.67%. By comparing the laboratory experiment and the field experiments in 2017, there were 10, 11, 10 and 9 varieties always belonging to Class I, Class II, Class III, and Class IV, respectively, and the ratio of the number of these varieties to the total number of varieties was 44.94. Last, by comparing the laboratory experiment and the field experiments in 2018, there were 7, 9, 10, and 7 varieties always belonging to Class I, Class II, Class III, and Class IV, respectively, and the ratio of the number of these varieties to the total number of varieties was 37.07%.

4.1 Root characteristics variation among different peanut varieties and stability through different environments

Because roots grow underground, they are the first organ to sense external environmental condition and adjust their genetic program of postembryonic development to survive (Lynch, 1995). Therefore, the modulation of root system architecture is affected when the external environmental conditions such as soil moisture, nutrients, temperature, pH, and microbial communities is sensed and integrated into the intrinsic root development program (Bao et al. 2014). The degree of root plasticity depends on changes in the number, extension, position, and growth direction of individual root phene or trait of root system (Khan et al. 2016). Root shovelomics was widely investigated to unravel the components of the root system in soybean, cowpea (Burridge et al. 2016) and maize (Trachsel et al. 2011), but no report was reported in peanut. In this study, we measured the 89 peanut varieties' root phenes, including NN, by shovelomics and found that most peanut phenes had high CV values, such as TRL, RSA, LRN and LRGA. The higher CV value of peanut root traits suggests an adaptive strategy; that is, plasticity itself is a beneficial trait, allowing phenotypic matching and responding to a given environment, as shown in maize (Zhu et al. 2005) and soybean (Burridge et al. 2016).

Almost all root characteristic parameters of peanut in the field experiment in 2018 were significantly higher than those in the field experiment in 2017. This may be because peanut was planted earlier in the field experiment in 2017 and, when harvesting and sampling, the severe senescence resulted in more foliage shedding as well as fine root and nodule decay and disappearance. In addition, the lower precipitation in 2017 (Figure 1) likely reduced the number of observable lateral roots and made the LRGA smaller. Rellán-Álvarez et al. (2015) also found that water deficiency in the upper soil suppresses lateral root growth and root growth angle in Arabidopsis, as their more horizontal growth makes them susceptible to soil drying. Trachsel et al. (2011) found that soil drought can reduce the occurrence of maize's lateral roots. At the same time, we found significant correlations between the SDW, RDW, TRL, LRN, LRD, and NN of different varieties measured in the two years' field experiments, indicating the strong stability of these indicators during the two years of field experiments. As LRD has been proven to be a very stable index and can reflect the development of lateral root (Kellermeier et al. 2014) and LRGA is an important index to reflect the distribution of the root system, we used these two indexes for quadrant graph analysis. Most root architectures of peanut in the two years' field experiment belong to the same root type, which also reflects the stability of root system architecture.

As an invasive measurement, shovleomics is currently a method that allows researchers to easily obtain some root phenes, but when screening substantial crop materials for ideal object root traits, it needs a longer experimental period, become less efficient, and more laborious than the evaluation of phenes in a laboratory. Therefore, we also validated a laboratory-based ‘roll-up’ phenotyping of LRD, which simplifies the evaluation for these important phenes in peanut. We observed fewer LRD in the field compared to the laboratory, which we interpret as root loss due to biotic and abiotic stresses. Correlation analysis showed that the LRD and LRGA measured in the laboratory had a significant positive correlation with those measured in the field for two years. The root architectures of some peanut varieties tested in the laboratory and the two years' field experiment belong to the same root type obtained by quadrant graph analysis of LRD and LRGA. This investigation demonstrates that some plant root traits identified at the early growing stage can indicate their status at the late growing stage, therefore providing a rapid and easy way to screen and select ideal root traits. The ideal root parameters screening in the laboratory also were widely used for other plant species, including maize (Trachsel et al. 2013), wheat (Watt et al. 2013), and soybean (Colombi and Walter 2016).

Correlation analysis showed that there was a significant positive correlation between the SDW and root traits in two years of field experiment and laboratory experiment, which indicates that the growth of the above ground was closely related to the growth of the root system, and the growth of root system would directly affect the growth of the above ground. At the same time, the RDW is positively correlated with TRL, RSA and RV, which shows that root growth is closely related to root characteristic parameters.

Peanuts, like other legumes, can establish a symbiotic relationship with rhizobia that leads to the formation of new organs called nodules, where bacteria reduce atmospheric N into ammonia, used by the host plants (Mondal et al. 2020). As shown in Figure 6, NN were significantly positively related to SDW, RDW, RSA and RV of peanuts in field trials in 2017 and 2018. This indicates peanut nodulation is a process of mutual regulation between root nodules and root systems. It also implies that more nodules fix more N₂, producing more N for root and shoot growth (Wang et al. 2019). Although peanuts can fix N₂ and produce N, beneficial N uptake root traits like steep lateral roots still can compensate for N absorption and reduce N pollution, especially in conditions of N fertilizer application, which is popular in peanut fields (Gao et al. 2019).

4.2 Potential nutrient absorption characteristics being shown by peanut root's shovelomics

The ideal root system configuration can improve the efficiency of water and nutrient acquisition and utilization (Shu et al. 2007), and promote plant growth and yield formation as well as coordinate root development and defense response (Feng et al. 2022, Li et al. 2021, 2023). Root shovelomics has been used to select varieties with different root structures, and further experiments showed that the nutrient and water use efficiency of these varieties were different (Zhan et al. 2015, Trachse et al. 2013). The absorption efficiency of nutrients and water depends on root distribution being consistent with nutrient and water distribution. For instance, deeper soil roots forage deeper soil water than shallower roots. According to the results of the quadrant graph analysis of LRGA, we found that 45 and 40 peanut varieties in 2017 and 2018 had root structure with deep LRGA (Figure 7). The deep root structure phene will be beneficial only if water and dissolved nutrients mainly exist in the deeper soil layer, i.e. under the condition of extreme drought or high potential of nutrient leaching (soil with high precipitation and low nutrient retention capacity) (Lynch 2013). Lateral roots, the main determinants of ultimate RSA, are influenced strongly by moisture and nutrient distribution in the soil (Postma et al., 2014). Depending on the results of quadrant graph analysis of LRD, the peanut varieties with deep roots are divided into root architectures with large lateral roots and few lateral roots. The root phenotype with deep LRGA and small LRD can not only absorb deep water and nutrients but also has less root's respiratory and metabolic costs, which is more conducive to the growth of shoot (Lynch 2013). In 2017 and 2018, 23 and 18 peanut varieties were classified into root structure types with large LRD and shallow LRGA. This shallow root structure phene is beneficial under fertile conditions, when nutrients are mainly derived from the decomposition of organic matter on the soil surface, or when phosphorus or potassium (mainly present in topsoil) is restricted (Lynch 2013, Lynch and Brown 2001). Research shows that genotypes with both large root hairs and shallow roots had higher phosphorus acquisition efficiency and greater biomass accumulation than short-haired, deep-rooted phenotypes (Miguel et al. 2015). Moreover, 29.21% of peanut varieties were classified into root structure types with small LRD and shallow LRGA in both 2017 and 2018. Peanut varieties with short and few lateral roots and shallow LRGAare more sensitive to water and nutrient deficiency. Therefore, compared with varieties with deeper root growth angles and larger lateral root lengths, more fertilizers and water should be used in their cultivation. Measures such as deep fertilization (Wu et al. 2022, Chen et al. 2024) and exogenous application of plant hormones (Kosakivska et al. 2022) can be taken to promote root elongation and lateral root development to enhance the absorption and utilization of water and nutrient. In summary, this study once again demonstrates that shovelomics is beneficial for effectively screening and selecting plant genotypes with contrasting root traits, and optimizing nutrient and water management based on root types. At the same time, it was verified that some plant root traits identified in the early growth stage can also indicate their status in the later growth stage, thus providing a quick and easy method for screening and selecting ideal root traits.