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. 2026 Sep 18;17:1939285. doi: 10.3389/fpls.2026.1939285

Multi-environment evaluations reveal that peanut (Arachis hypogaea L.) quality traits are equally determined by genotype and environment

Xiao Jiang 1, Jianxin Zhao 1,2, Jing Xu 1, Dongmei Yin 3, Hongfeng Xie 1, Na Chen 1, Lijuan Pan 1, Xiangzhen Yin 1, Junqing Ma 1, Xuhong Zhao 1, Xiaoyuan Chi 1,*
PMCID: PMC13630982  PMID: 42827956

Abstract

Background

Peanut (Arachis hypogaea L.) is a widely planted cash and oil crop, and regional and meteorological factors, as the most critical environmental factors, play crucial roles in peanut kernel quality traits. To explore the relationship between peanut quality traits and environmental factors, this study dissected the quality traits and the genotype-by-environment interaction factors using seven varieties across multiple environments.

Methods

Analysis of variance (ANOVA) was used to partition the variance of kernel quality traits, correlation analysis was used to examine the associations between quality traits and environmental factors, and genotype plus genotype-by-environment (GGE) biplot analysis was used to evaluate the association between genotypes and multiple environments.

Results

ANOVA showed that environmental factors were the largest source of variance among the three quality traits. Correlation analysis showed that long sunshine duration, high accumulated temperature, and large mean daily temperature difference were beneficial to the accumulation of oil and protein. Oil content showed an overall trend of increasing with latitude; protein content performed best in the Huang-Huai-Hai and Yangtze River Basin production areas; and sucrose content showed an overall trend of decreasing with latitude and longitude. GGE analysis further showed that HY33 achieved optimal oil content with strong multi-environment stability, while HY21 had superior protein content and the most stable sucrose performance across multi-environments.

Conclusions

This study provides theoretical guidance for adjusting planting arrangements in different regions according to different climatic conditions and peanut growing periods, which is of great significance as a guide for improving peanut quality.

Keywords: Arachis hypogaea L., GGE interaction, kernel quality traits, meteorological factor, multiple environments

Introduction

Peanut (Arachis hypogaea L.) is a globally important cash and oil crop. In China, the annual sowing area of peanut exceeds 4.6 million hectares, with pod yield surpassing 17 million tons, ranking first in the world (FAOSTAT, http://faostat.fao.org). As a rich source of protein, vegetable oils, vitamins, and minerals, peanuts serve as a crucial supply of edible oil and dietary protein for humans. Although peanut cultivation is widely distributed across China, distinct environmental conditions prevail in different regions, making peanut quality highly susceptible to environmental influences. Specifically, peanut quality is closely associated with climatic resources such as temperature, precipitation, and sunshine in the cultivation area. Thus, identifying peanut varieties suitable for cultivation in specific regions remains a critical challenge, and addressing this issue is of great significance for ensuring China’s oil crop security, increasing farmers’ income, and promoting the sustainable development of the peanut industry.

Extensive studies confirm genetics govern peanut quality, yet environmental effects remain non-negligible (Dwivedi et al., 2000). The regulatory mechanisms by which meteorological factors affect crop quality traits are relatively complex. Meteorological factors (light, temperature, water) modulate crop development, strongly altering yield, agronomic performance and quality in wheat, rice and maize (Han et al., 2023; Huang et al., 2016; Vida et al., 2022; David Kulyakwave et al., 2020; Jiayu et al., 2018; Wang et al., 2025; Zhang et al., 2025). Many crops have adopted agro-climatic zoning with critical climatic indicators to assess climatic suitability for cultivation (Wei et al., 2022). Relevant zoning cases include Ningxia wine grape regionalization using effective accumulated temperature (Wang et al., 2020) and crop zoning based on ≥10 °C accumulated temperature combined with fuzzy evaluation (Zhao et al., 2019). Likewise, peanut yield and agronomic traits are jointly shaped by environment and genotype-by-environment (G×E) interaction. Long-term nationwide data proved temperature, rainfall and sunshine considerably affect peanut yield (Wang et al., 2013), with growing-season precipitation as the dominant limiting factor for spring peanut yield (Chen et al., 2019). Environmental conditions exert especially strong influences on peanut oil and protein concentrations, highlighting the necessity of precise precipitation management (Kebede and Getahun, 2017). Although recent work recognizes water and sunshine as key regulators of peanut yield and quality, few reports systematically decode meteorological and geographic impacts on peanut kernel quality, lacking supporting evidence for region-specific peanut planting across China’s major peanut production areas.

Analyzing genotype-by-environment (G×E) effects of peanut varieties across diverse ecologies is a major research hotspot. Multiple statistical approaches quantify G×E interactions, including ANOVA, PCA, linear regression, AMMI, and GGE biplot (Vlachostergios and Roupakias, 2008; Serneels and Verdonck, 2008; Hristov et al., 2010; Lingaiah et al., 2020; Yan and Rajcan, 2002). The GGE biplot stands out for visualizing genotypic, environmental and G×E effects via two-dimensional graphs to screen adaptive planting zones. Current Chinese peanut climate zoning studies are constrained by limited time-series data, narrow sampling, single indicators, and ignorance of reproductive-stage differences. Widely adopted for multi-environment crop trials covering wheat, maize, rice and other crops, GGE biplot also dissects trait correlations (Jat et al., 2018; Yan and Hunt, 2001; Setimela et al., 2007; Chintalapati et al., 2019; Katuuramu et al., 2020; Bai et al., 2018; Shukla et al., 2015; Lamichaney et al., 2021; Nagesh Kumar et al., 2021; Ansarifard et al., 2020). In peanut research, this method evaluates yield, kernel quality, stress resistance, phosphorus efficiency and biochemical profiles (Oteng-Frimpong and Dakora, 2019; Tang et al., 2025; Wang et al., 2024; Zhen et al., 2024, Zhen et al., 2023). A U.S. study tested 52 peanut germplasm and confirmed Florida-grown peanuts had maximum oil and minimum protein with inverse oil–protein correlation (Wang et al., 2024). A Chinese trial with 13 peanut materials cultivated in three environments reported superior oil and protein in Weifang peanuts and peak sucrose content in Jinzhou peanuts (Tang et al., 2025). No study has yet revealed how meteorological and geographic factors shape peanut kernel quality across multiple environments. Moreover, few works identify stably adaptable varieties suitable for widespread cultivation in China’s major peanut production areas.

Aiming to explore the relationship between peanut kernel quality traits and meteorological and geographical factors, in the present study, we conducted a multi-environment evaluation using peanut varieties’ phenotypic data on the traits of oil, protein and sucrose content in 17 locations during 2013, 2014, and 2016, crossing five major peanut-producing areas in China and spanning a geographical range from 21.09°N to 43.07°N and from 106.08°E to 124.23°E, along with data on eight meteorological and geographical factors for each environment. The findings of this study will clarify the impact of meteorological factors on peanut quality and provide a scientific basis for adjusting peanut planting arrangements in different regions according to local climatic conditions and peanut growth periods.

Materials and Methods

Plant materials and planting locations

Huayu 19 (HY19), Huayu 21 (HY21), Huayu 30 (HY30), Huayu 33 (HY33), Jihua 10 (JH10), Jihua 9 (JH9) and Xuhua 13 (XH13) were used as test varieties. Each plot covered an area of 6.67 m2, consisting of 4 rows distributed across 2 ridges. The row length within each plot was approximately 4.17 m, with a total plot width of about 1.6 m. Two seed rows were sown on each ridge at a hill spacing of 15 cm, with 2 seeds per hill and approximately 110 hills per plot. The trial was arranged in a randomized complete block design with three replications for each environment.

To evaluate variety performance and multi-environment stability of quality traits under conditions representative of local farming practices, all experimental fields were selected as flat plots with moderate fertility. Field management practices including fertilization, pest and disease control, and weeding all followed routine local agronomic protocols. All experimental locations were located in monsoon regions, where the peanut growing season coincided with the rainy season; crops relied mainly on natural rainfall, and supplemental irrigation was rarely required.

To evaluate peanut kernel quality traits across multiple environments, the tested peanut varieties were planted at 17 locations covering five major peanut-producing regions across 16 provinces in China, including Shandong, Shanxi, Liaoning, Jilin, Henan, Hebei, Jiangsu, Anhui, Fujian, Jiangxi, Guangdong, Guangxi, Guizhou, Sichuan, Hunan and Hubei (Figure 1A) during 2013, 2014 and 2016. A total of 51 environments were used for the evaluation of peanut quality traits. Among these locations, Zhanjiang (ZJ), Ganzhou (GZ), Quanzhou (QZ) and Hezhou (HZ) belong to the South China production area; Nanchong (NC), Shaoyang (SY), Huanggang (HG) and Hefei (HF) belong to the Yangtze River Basin production area; Zhumadian (ZMD), Kaifeng (KF), Xuzhou (XZ), Weifang (WF), Baoding (BD) and Fenyang (FY) belong to the Huang-Huai-Hai production area; Jinzhou (JZ) and Siping (SP) belong to the Northeast Plain production area; Guiyang (GY) belongs to the Yunnan-Guizhou Plateau production area (Figure 1A).

Figure 1.

Map of China displaying major peanut production areas, with regions marked by colored circles representing five production zones: Huang-Huai-Hai, South China, Yunnan-Guizhou Plateau, Yangtze River Basin, and Northeast Plains. Key cities are labeled within each region. An inset bar chart shows yearly pod yield data from 2013, 2014, and 2016 for various provinces, with Henan, Shandong, and Hebei as top producers.

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Experimental location arrangement and peanut pod yield in China. (A) Experimental location arrangement. (B) Histogram of peanut pod yields in different experimental provinces in 2013, 2014 and 2016 in China. (China National Bureau of Statistics, http://www.stats.gov.cn/).

Multi-environment oil, protein and sucrose contents identification and statistical analysis

To accurately evaluate the peanut quality traits of the seven varieties harvested from 51 environments, oil, protein and sucrose contents were determined in accordance with China National Standard (CNS), using the Soxhlet extraction method (AOAC 995.19), Kjeldahl nitrogen determination method (AOAC Ac 4-91) and acid hydrolysis method (AOAC 920.184), respectively. Phenotypic data of the 3 peanut quality traits for each variety in every environment were collected all the three biological replicates, and statistically analyzed using Microsoft Office 2019. Meanwhile, the phenotypic data for each variety in every environment were averaged using three biological replicates for the followed analysis.

Meteorological data collection and statistical analysis

Meteorological data for each test location in 2013, 2014 and 2016 were obtained from the China Meteorological Data Network (http://data.cma.cn). Meteorological factors across the entire peanut growth period, from sowing to harvest, in 51 environments were statistically analyzed, including accumulated temperature ≥ 10 °C, total precipitation and total sunshine duration. The mean daily temperature difference and mean daily temperature during the entire growth period for each environment were also calculated.

Linear regression and correlation analysis between peanut quality traits and environmental factors

To elucidate the contributions of environmental and genetic factors to variations in peanut quality traits across multiple environments, two‑way ANOVA (genotype, G; environment, E) was performed for oil, protein, and sucrose contents across 51 environments using the aov function in RStudio (R version 4.4.2).

To investigate the relationships between quality traits and environmental factors, Pearson correlation analysis, which was implemented using the ggcorplot and corplot packages in R Studio (R version 4.4.2), was employed to analyze and visualize the correlations between peanut quality traits and environmental factors across 51 environments. Environmental factors, which consisted of geographical variables including longitude, latitude and altitude as well as meteorological variables including accumulated temperature ≥ 10 °C, total precipitation, total sunshine duration, mean daily temperature difference and mean daily temperature during the entire growth period, were used in the correlation analysis.

GGE Biplot analysis for oil, protein and sucrose contents traits

Phenotypic data of peanut oil, protein and sucrose contents in the 51 environments were conducted GGE biplot analysis using the GGE Biplots GUI package in RStudio (R version 4.4.2). A combined analysis of variance was conducted according to the experiment design, with genotypes and locations as fixed effects. Variation in the three peanut quality traits was partitioned into parts due to environment, genotype, G×E interaction, and error. Duncan’s multiple range test (DMRT) was utilized for multiple comparison. The GGE biplot analysis was utilized for environmental vector, which-won-where, discrimination ability vs. representativeness and mean vs. stability analysis. The GGE biplot, which was constructed using the first two principal axes (Axis 1 and Axis 2), was derived from singular value decomposition (SVD) of environment-centered data that contained the GGE signal. The GGE biplot model based on SVD is expressed as follows:

Yij=μ+βj+∑k=1kλkγikδjk

Where Yij is the mean value of genotype i in environment j; μ is the grand mean value, βj is the main effect of environment j; k is the number of axes for adequately representing GGE variation, n is the singular value; λk is the singular value of the kth axis (AXISk). γik and δjk are the scores of genotype i and environment j on the k-th axis, respectively.

Results

Multiple environments evaluation of peanut quality traits

Oil, protein, and sucrose contents were identified across 51 environments during three years. With respect to geographical factors, HG, FY, and QZ exhibited the highest oil content (Figures 2A, 3A). Similarly, HF, GZ, and NC were the top three locations for protein content (Figures 2B, 3B). Meanwhile, ZJ, HZ, and GY showed the highest sucrose content (Figures 2C, 3C).

Figure 2.

Box plots display quinoa, protein, and saponin content for various cities in three years (2013 in purple, 2014 in green, 2016 in blue). Statistically significant differences are indicated by asterisks above comparisons. Panel A shows quinoa content, panel B shows protein content, and panel C shows saponin content, with each city having separate plots for each year and content type.

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Statistical analysis of phenotypic data of peanut quality traits in multiple environments. Oil (A), protein (B) and sucrose (C) content performances in 17 locations during 3 years. ns, *, and ** were not significantly, significantly at 0.05 and 0.01 level, repectively.

Figure 3.

Heatmap figure comparing oil, protein, and sucrose content across seven genotypes (HY19, HY21, HY30, HY33, JH9, JH10, XH13) with separate columns for each genotype and colored scales representing log fold change. Oil content heatmap shows notable variation, especially in shades of red; protein and sucrose heatmaps predominantly use lighter and darker blue, signifying lower content variation. Sample codes are listed vertically on the right side; color bars for each trait are located above the heatmaps.

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Multi-environmental phenotypic performances for quality traits of varieties. Oil (A), protein (B) and sucrose (C) content of different genotypes in 51 environments.

Regarding the performance of the seven peanut varieties across 51 environments, HY33 presented the highest oil content (51.19%), with the maximum value observed at HG (Figure 3A). JH9 had the highest protein content (22.77%), reaching the peak at HF (Figure 3B). Likewise, HY21 displayed the highest sucrose content (3.71%), with the maximum value recorded at ZJ (Figure 3C).

Multi-environmental meteorological data statistical analysis

Statistical analysis of meteorological data collected across three years from 17 locations revealed that NC, ZJ, HZ, TY, and SY had the maximum values of accumulated temperature ≥10 °C, precipitation, sunshine duration, mean daily temperature difference, and mean daily temperature over the entire growth period, respectively. Across all 51 environments, accumulated temperature and sunshine duration peaked in 2016, while precipitation and mean daily temperature reached their highest levels in 2013. The mean daily temperature difference remained consistent across the three experimental years (Table 1).

Table 1.

Environmental factors information of each location from sowing to harvest.

Year Location Environment Planting date Harvesting date Alt (m) Lat Lon ≥10°C Accumulated temperature (°C) Sunshine duration (h) Precipitation Mean diurnal temperature range (°C) Mean temperature (°C)
(°) (°) (mm)
2013 Baoding 2013BD May. 19 Sep. 14 16.8 38.44 115.29 2947.3 579 517.7 9.85 24.77
Fenyang 2013FY May. 8 Sep. 24 746 37.04 111.57 3171 704.7 537.8 11.374 22.65
Ganzhou 2013GZ May. 29 Sep. 10 137.5 25.52 115 3060.4 745.8 354.9 8.131 29.15
Guiyang 2013GY Apr. 19 Sep. 9 1223.8 26.35 106.44 3076.4 642.9 603.7 8.065 21.43
Hefei 2013HF Jun. 15 Oct. 18 27 31.47 117.18 3211.7 753.6 559.1 7.883 27
Hezhou 2013HZ Apr. 26 Jul. 24 148.7 24.25 111.3 2362.2 413 671.7 7.646 26.25
Huanggang 2013HG Jun. 5 Aug. 29 123.8 30.44 115.4 2487.8 694.1 676.5 9.574 28.6
Jinzhou 2013JZ May. 17 Sep. 28 174.3 41.33 120.26 3085.3 964.8 222.5 11.788 22.85
Kaifeng 2013KF May. 10 Sep. 11 73.7 34.47 114.18 3359.6 709 225.7 8.774 26.88
Nanchong 2013NC Apr. 13 Aug. 26 347 30.45 106.08 3608 857.8 651.7 8.738 26.53
Quanzhou 2013QZ Aug. 10 Dec. 8 21.8 24.54 118.55 2880.1 821 238.6 4.689 23.8
Shaoyang 2013SY May. 4 Sep. 2 100 27.27 112.1 3449.2 703.6 361.7 9.34 28.27
Siping 2013SP May. 22 Sep. 26 179.5 43.07 124.23 2771 808.4 531.3 9.848 21.65
Weifang 2013WF May. 5 Sep. 16 22.2 36.45 119.12 3364.2 969.4 383 9.904 24.92
Xuzhou 2013XZ May. 10 Sep. 16 41.2 34.17 117.1 3491.6 951.9 494.4 8.499 26.86
Zhanjiang 2013ZJ Aug. 11 Nov. 23 53.4 21.09 110.18 2653.6 576.1 672.8 6.119 25.27
Zhumadian 2013ZMD May. 18 Sep. 20 106.2 32.56 113.55 3460 782 502.8 8.844 27.46
2014 Baoding 2014BD May. 27 Sep. 12 16.8 38.44 115.29 2785.5 671.4 324.1 11.219 25.56
Fenyang 2014FY May. 11 Sep. 27 746 37.04 111.57 3043.9 647.8 359.4 12.074 21.74
Ganzhou 2014GZ Apr. 2 Aug. 1 137.5 25.52 115 3237 691.2 589.2 8.186 26.53
Guiyang 2014GY Apr. 21 Sep. 14 1223.8 26.35 106.44 3072.2 430.4 1165.7 7.265 20.9
Hefei 2014HF May. 17 Sep. 30 27 31.47 117.18 3488 489.1 589.5 7.418 25.46
Hezhou 2014HZ Mar. 19 Jul. 15 148.7 24.25 111.3 2843.2 334.1 1199.7 6.736 23.89
Huanggang 2014HG Apr. 29 Aug. 29 123.8 30.44 115.4 3086.9 633.1 723.5 8.815 25.1
Jinzhou 2014JZ May. 13 Sep. 25 174.3 41.33 120.26 3095.8 1000.6 3085 12.988 22.76
Kaifeng 2014KF May. 7 Sep. 8 73.7 34.47 114.18 3239.8 754.1 170.7 9.35 25.92
Nanchong 2014NC Apr. 5 Aug. 22 347 30.45 106.08 3428.8 656.1 564.7 7.436 24.49
Quanzhou 2014QZ Aug. 15 Dec. 10 21.8 24.54 118.55 2825.2 813.7 73.4 4.496 23.94
Shaoyang 2014SY May. 5 Sep. 3 100 27.27 112.1 3184.8 369.2 732.4 7.414 26.1
Siping 2014SP May. 17 Sep. 22 179.5 43.07 124.23 2788.9 840.4 355.4 10.743 21.62
Weifang 2014WF May. 15 Sep. 19 22.2 36.45 119.12 3189.8 846.2 325.5 9.956 24.92
Xuzhou 2014XZ May. 13 Sep. 24 41.2 34.17 117.1 3402.5 857.7 480.4 8.377 25.2
Zhanjiang 2014ZJ Aug. 5 Nov. 5 53.4 21.09 110.18 2497.2 658.9 476.8 6.523 26.85
Zhumadian 2014ZMD May. 19 Sep. 21 106.2 32.56 113.55 3061.9 504.3 510.4 9.576 24.3
2016 Baoding 2016BD May. 18 Sep. 13 16.8 38.44 115.29 3002.6 522.4 380.3 11.163 25.23
Fenyang 2016FY May. 10 Sep. 26 746 37.04 111.57 3122.2 595.5 436.9 12.127 22.3
Ganzhou 2016GZ Mar. 31 Aug. 3 137.5 25.52 115 3319 680.3 1045.4 7.999 26.34
Guiyang 2016GY Apr. 15 Sep. 15 1223.8 26.35 106.44 3292 576 635 7.476 21.38
Hefei 2016HF Jun. 17 Oct. 13 27 31.47 117.18 3149.6 656.5 679.2 7.904 26.47
Hezhou 2016HZ Mar. 26 Jul. 20 148.7 24.25 111.3 2841.1 373 1092.1 7.347 24.71
Huanggang 2016HG May. 1 Sep. 2 123.8 30.44 115.4 3197.2 727.3 1295.2 8.922 25.58
Jinzhou 2016JZ May. 16 Sep. 27 174.3 41.33 120.26 3119.5 982.2 394 11.96 23.11
Kaifeng 2016KF May. 8 Sep. 9 73.7 34.47 114.18 3226.8 727.8 408 8.889 26.03
Nanchong 2016NC Apr. 7 Aug. 23 347 30.45 106.08 3403.7 805.8 542.9 8.938 24.49
Quanzhou 2016QZ Mar. 22 Jul. 26 21.8 24.54 118.55 2791 708.1 552.3 4.043 23.65
Shaoyang 2016SY Jun. 14 Sep. 25 100 27.27 112.1 2929 593.1 530.2 8.688 28.16
Siping 2016SP May. 15 Sep. 25 179.5 43.07 124.23 2858.5 833.2 551.3 10.441 21.33
Weifang 2016WF May. 7 Sep. 9 22.2 36.45 119.12 3177.2 955 370.1 5.952 25.22
Xuzhou 2016XZ May. 5 Sep. 10 41.2 34.17 117.1 3329.4 893.4 386.9 8.443 25.81
Zhanjiang 2016ZJ Feb. 29 Jun. 28 53.4 21.09 110.18 2981.6 596 529.7 5.586 24.85
Zhumadian 2016ZMD May. 16 Sep. 11 106.2 32.56 113.55 3045 655.5 436.5 9.945 25.59
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Multiple production areas evaluation of peanut quality traits

The analysis of quality traits in different production areas revealed that Yangtze River Basin and Huang-Huai-Hai production areas had the highest oil and protein contents, as well as the lowest sucrose content (Figure 4). Oil content showed an overall trend of increasing with latitude, the protein content had the lowest performance in the Northeast Plains Production area, and the sucrose content showed an overall trend of decreasing with latitude.

Figure 4.

Bar graph with three panels compares oil, protein, and sucrose content across five production areas, each represented by different colored bars and dots, showing statistically significant differences marked by asterisks above the bars.

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Peanut kernel quality traits performances in five production areas. Oil (A), protein (B) and sucrose (C) content traits performances in five production areas.

Multi-environmental ANOVA for genotypic and environmental factors

To elucidate the contributions of environmental and genetic factors to variations in peanut quality traits across multiple environments, two‑way ANOVA was performed for oil, protein, and sucrose contents across 51 environments. The relative contributions of G, E, and G×E to total variation were 5.22%, 68.87%, and 25.91% for oil content; 0.65%, 63.61%, and 35.74% for protein content; and 1.69%, 65.47%, and 32.84% for sucrose content, respectively (Table 2). The results revealed that environmental factors accounted for the largest proportion of variance for all three quality traits (all > 50%). Meanwhile, oil, protein, and sucrose contents were also substantially affected by genotype‑by‑environment interaction, indicating that environmental factors strongly influence phenotypic variation and that significant G×E interaction exists.

Table 2.

Two-way ANOVA of genotypic and environmental factors for oil, protein and sucrose content.

Trait Factor Df Sum_Sq Mean_Sq F_value Pr(>F)
Oil content G 6 330 54.96 10.06 3.91e-10***
E 50 4357 87.14 15.95 <2e-16***
GbyE 300 1639 5.46
Protein content G 6 14.3 2.379 0.907 0.49
E 50 1400.7 28.014 10.678 <2e-16***
GbyE 300 787.1 2.624
Sucrose content G 6 16.1 2.685 2.577 0.0189*
E 50 622.9 12.459 11.959 <2e-16***
GbyE 300 312.5 1.042
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*, ***, p at 0.05 and 0.001 level, respectively.

Linear regression and correlation analysis between peanut quality traits and environmental factors

To examine the relationships between peanut quality traits and geographical and meteorological factors, linear regression and correlation analysis were conducted. Linear regression analysis showed that oil content was positively and linearly correlated with accumulated temperature ≥ 10 °C (R2 = 0.12, P = 0.011) and total sunshine duration (R2 = 0.13, P = 0.018) during the entire growth period. Protein content was positively and linearly correlated with mean daily temperature (R2 = 0.087, P = 0.031). Sucrose content was negatively and linearly correlated with latitude (R2 = 0.031, P = 0.097), longitude (R2 = 0.038, P = 0.45), accumulated temperature ≥ 10 °C (R2 = 0.098, P = 0.058), and total sunshine duration (R2 = 0.051, P = 0.12) (Supplementary Figure S1). The results of linear regression analysis showed that the goodness of fit between quality traits and single geographical or meteorological factor was extremely low (R2) and non-significant (P > 0.05), indicating that quality traits may be jointly affected by multiple factors.

Correlation analysis results showed that oil content was extremely significant positive correlated with longitude, accumulated temperature ≥ 10 °C, and total sunshine duration (p<0.01). Protein content was extremely significant positive correlated with accumulated temperature ≥ 10 °C and mean daily temperature(p<0.01), and significant positive correlated with longitude and mean daily temperature difference (p<0.01). Sucrose content was extremely significant negative correlated with longitude, latitude, accumulated temperature ≥ 10 °C, and total sunshine duration (p<0.01) (Figure 5).

Figure 5.

Correlation matrix visualization with variables Y1, Y2, Y3, X1 to X8 labeled along axes. Ellipses indicate correlation strength and direction, colored green for negative and pink for positive values, with a color gradient bar indicating correlation coefficients from minus one to one. Statistically significant correlations are marked by asterisks, which vary in number according to significance level.

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Correlation analysis between the peanut quality traits and the geographical and meteorological factors. Y1: oil content; Y2: protein content; Y3: sucrose content; X1: altitude; X2: latitude; X3: longitude; X4: the entire growth period accumulated temperature ≥10 °C; X5: the entire growth period precipitation; X6: the entire growth period sunshine duration; X7: mean daily temperature difference; X8: the entire growth period mean daily temperature. *, **, and *** were significantly at 0.05, 0.01, and 0.001 level, respectively.

GGE Biplot analysis for peanut kernel quality traits across multi-environments

Figure 6A illustrates the environmental relationships for oil content. An angle< 90° between two environmental vectors indicated a positive correlation and consistent phenotypic performance, whereas an angle > 90° indicated a negative correlation. GGE biplot analysis decomposed the effects of G and G×E for quality traits across 51 environments. The first two principal axes (Axis 1 and Axis 2) explained 32.82% and 20.94% of the total phenotypic variance for oil content, respectively. Although Axis 1 and Axis 2 explained a moderate proportion of the total variance, which is typical for complex multi-environment trials, the interpretation of mega-environments should be considered with caution. KF, HG, WF, GZ, and QZ showed strong positive correlations across three years. In contrast, BD, GY, HZ, and ZMD were negatively correlated. Environments including 2016HZ, 2016JZ, 2014HG, 2016HG, and 2014KF exhibited the strongest discriminatory power for oil content among varieties. Environmental vector analysis suggested that HG and KF had stable climatic conditions and high discriminatory ability for oil content. Some locations showed similar responses, such as 2014KF and 2016HZ, as well as 2016ZMD and 2016HG, whereas 2016BD and 2016JZ were negatively correlated. The “which‑won‑where” GGE biplot grouped the 51 environments into four mega‑environments (Figure 6B), consisting of 22, 18, 9, and 2 environments, with XH13, HY19, HY33, and HY21 as the optimal genotypes in each mega‑environment, respectively (Supplementary Table S2). These results imply that the four varieties perform best in their respective mega‑environments. Environmental representativeness analysis, a key criterion for test site evaluation, revealed that 2016GY was the most representative site, followed by 2016HF, 2014FY, 2013SP) and 2014JZ (Figure 6C). The “Mean vs. Stability” biplot assessed overall performance and stability for oil content. HY33, XH13, and HY30 showed the highest oil content, with HY33 and HY30 exhibiting the strongest multi‑environmental stability (Figure 6D).

Figure 6.

Panel A, labeled Environmental Vector, presents a biplot with multiple blue data points clustered around the origin and green-labeled points HY19, HY21, and XH143 distributed farther out. Panel B, labeled Which-Won-Where, overlays bold red and black polygon sectors on a similar scatter of points, indicating regions associated with different data clusters. Panel C, Discriminating Ability vs. Representativeness, shows concentric black circles radiating from the center with a bold blue line, emphasizing comparison axes among data points and reference points. Panel D, Mean vs. Stability, displays strong green axes crossing at the origin, highlighting relationships of HY19, HY21, and XH143 to the clustered blue data points. All charts share axes labeled AXIS1 32.82% and AXIS2 20.94%.

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GGE biplot evaluations for oil content in 51 environments. Multi-environment GGE biplots of Environmental Vector (A), Which-Won-Where (B), Discriminating Ability vs. Representativeness and Mean vs. Stability (D) analysis for oil content trait.

For protein content, the first two axes explained 35.64% and 23.11% of total phenotypic variance, respectively. QZ, KF, and HG were strongly positively correlated across three years, whereas GY and BD were negatively correlated (Figure 7A). The “which‑won‑where” biplot divided the 51 environments into three mega‑environments (Figure 7B), with JH10, HY19, and HY21 as the optimal genotypes for mega‑environments containing 23, 18, and 10 environments, respectively (Supplementary Table S2).

Figure 7.

Four-panel figure showing GGE biplot data visualizations labeled A to D, each with axes labeled AXIS1 and AXIS2 and points annotated with blue and green sample names. Panel A shows environmental vectors as dotted lines, panel B depicts “Which-Won-Where” sectors outlined in black and red, panel C illustrates discriminating ability versus representativeness with a bold blue line, and panel D displays mean versus stability using a bold green line. All panels share the same scatterplot base but highlight different relationships among the samples and environments.

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GGE biplot evaluations for protein content in 51 environments. Multi-environment GGE biplots of Environmental Vector (A), Which-Won-Where (B), Discriminating Ability vs. Representativeness and Mean vs. Stability (D) analysis for protein content trait.

Discrimination and representativeness analysis showed that 2013QZ had the highest correlation with the average environment axis, while 2014BD, 2016WF, and 2014KF had the strongest discriminatory power for protein content (Figure 7C). Mean‑stability analysis indicated that HY21 had the highest protein content but relatively low stability, whereas JH9 had the highest stability and the second‑highest protein content across 51 environments (Figure 7D).

For sucrose content, the first two axes accounted for 26.24% and 21.11% of total phenotypic variance, respectively. KF and FY were strongly positively correlated across three years, indicating stable climatic conditions and suitability for peanut cultivation (Figure 8A). The “which‑won‑where” biplot separated the 51 environments into four mega‑environments (Figure 8B), with HY21, HY33, HY19, and XH13 as the optimal genotypes for mega‑environments containing 30, 7, 7, and 7 environments, respectively (Supplementary Table S2). 2016HG and 2014HZ showed the strongest discriminatory power, while 2014HG and 2016GY were the most representative, with the highest correlation with the average environment axis (Figure 8C). Mean‑stability analysis demonstrated that HY21 had both the highest sucrose content and the strongest multi‑environmental stability (Figure 8D).

Figure 8.

Four-panel figure displaying biplots labeled A to D, each showing principal coordinate axes with points labeled in blue and vectors or lines in green, red, or blue. Panel A, titled Environmental Vector, shows blue sample points with dotted directional vectors. Panel B, titled Which-Won-Where, adds red and black polygonal lines indicating group differentiation. Panel C, Discriminating Ability vs. Representativeness, overlays concentric circles and a thick blue line, emphasizing specific directions. Panel D, Mean vs. Stability, shows thick green intersecting lines highlighting group separations. Axis labels indicate AXIS1 (26.24%) and AXIS2 (21.11%) for all panels.

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GGE biplot evaluations for sucrose content in 51 environments. Multi-environment GGE biplots of Environmental Vector (A), Which-Won-Where (B), Discriminating Ability vs. Representativeness and Mean vs. Stability (D) analysis for sucrose content trait.

Discussion

Peanut is a widely cultivated cash and oil crop in China, and regional and meteorological factors, which serve as the most critical environmental drivers, exert a pivotal influence on peanut kernel quality traits. Its cultivation areas, which span temperate and subtropical monsoon climates, are geographically divided into six major agro-ecological zones. The Huang-Huai-Hai production area, which mainly includes Shandong, Henan, and Hebei, is characterized by a temperate monsoon climate with abundant sunshine duration and is mainly used for planting large-grain and high-oleic-acid varieties. Owing to the fertile soil of the Yellow River alluvial plains, peanut production in the Huang-Huai-Hai production area always exhibits excellent yield and quality (Figures 1B, 4). The South China production area, which mainly includes Guangdong, Guangxi, Fujian, and Hainan, features a tropical and subtropical climate with high temperature, abundant rainfall, and a long frost-free period, which makes it suitable for producing processed peanuts such as salted, dried, and roasted peanuts. The Yangtze River Basin production area, which mainly includes Hubei, Hunan, and Jiangxi, has a subtropical monsoon climate with abundant rainfall and high humidity in summer, where summer-sown peanuts are dominant under wheat or rape stubble rotation systems. The Northeast Plain production area, which covers Liaoning and Jilin, features a temperate continental climate with cold winters and a short growing period, where spring-sown and early-maturing varieties dominate peanut production. The Yunnan-Guizhou Plateau production area, which belongs to a plateau mountain climate with significant vertical differences and large diurnal temperature variations, is where peanuts are planted in hills and mountains at an altitude of 1,000–2,000 m with spring sowing as the main practice. The Northwestern production area, which includes Xinjiang and Gansu, is under a temperate continental arid climate with strong sunshine, limited precipitation, and large diurnal temperature variation.

Complex meteorological and geographical factors act jointly to affect peanut yield and quality traits in China, and meteorological factors such as light, temperature, and water, which are key environmental regulators, contribute to divergent peanut growth performances (Ai et al., 2025; Jongrungklang et al., 2012; Kumar et al., 2012; Qi et al., 2025; Zhen et al., 2024). Meanwhile, climate change, which has become a major threat, affects the stable production of peanuts ( (Khan et al., 2009; Wei et al., 2022; Wheeler and von Braun, 2013; Ye et al., 2013). Therefore, to clarify the effects of meteorological and geographical factors on peanut kernel quality traits, we performed a multi-environment evaluation using phenotypic data of seven major popularized varieties planted at 17 locations over three years, which generated a total of 51 environments, together with synchronized meteorological and geographical data for each environment. The results showed that oil content was extremely significantly and positively correlated with longitude, accumulated temperature ≥ 10 °C, and total sunshine duration across the entire growth period. Protein content was extremely significantly and positively correlated with accumulated temperature ≥ 10 °C and mean daily temperature across the entire growth period, and significantly and positively correlated with longitude and mean daily temperature difference. Sucrose content was extremely significantly and negatively correlated with longitude, latitude, accumulated temperature ≥ 10 °C, and total sunshine duration across the entire growth period. Multi-environment phenotypic data revealed that oil and protein contents displayed a spatial trend of being higher in eastern China and lower in western China, while sucrose content showed a trend of being higher in southeastern China and lower in northwestern China. Furthermore, correlation results between quality traits and meteorological factors suggested the feasibility of simultaneous improvement in peanut oil and protein contents. Two-way ANOVA showed that environmental factors accounted for approximately 60% of the total variation in peanut oil, protein, and sucrose contents, which indicates that most differences in peanut kernel quality traits across multiple environments are caused by environmental factors. For peanut oil, protein, and sucrose contents, environmental effects are comparable to genetic effects. Single meteorological factor performed low linear regression and non-significant with the three kernel quality traits those indicated that a single factor cannot adequately explain the variation in quality traits, and multi-factor interactions are a key focus for future research.

Genotype, environment, and genotype-by-environment interaction (GEI) have long been research focuses for crop breeders, and GEI, which represents one of the most complex constraints, challenges breeding programs aiming to identify high-quality and stable peanut genotypes. Recently, GEI and GGE biplot analyses have been applied in studies of peanut yield, kernel quality traits, biotic stress tolerance, phosphorus-efficient genotype identification, and sensory and biochemical composition (Tang et al., 2025; Wang et al., 2024; Zhen et al., 2024, Zhen et al., 2023). In the US, Wang et al. (2024) used 52 peanut germplasm accessions, found that Florida produced the highest oil content and the lowest protein content with a negative correlation between oil and protein contents. In China, Tang et al. (2025), who used 11 germplasm accessions and two varieties planted across three environments located in the Huang-Huai-Hai production area (Weifang) and the Northeast Plain production area (Jinzhou and Fuxin), found that Weifang showed the best oil and protein contents while Jinzhou had the highest sucrose content. Compared with their findings, our results are consistent in that protein and oil contents are positively correlated with accumulated temperature, whereas sucrose content is negatively correlated with accumulated temperature.

In this study, we used seven popularized peanut varieties planted at 17 locations distributed across five major peanut production areas. For oil content, the 51 environments were classified into four mega-environments, in which XH13, HY33, HY19, and HY21 were the optimal genotypes, which indicates that these varieties may perform better in corresponding environments or locations. Meanwhile, HY33 and HY30 showed the highest oil content and strongest multi-environmental stability. For protein content, the 51 environments were divided into three mega-environments, in which HY19, HY21, and JH10 were the optimal genotypes with the best performance in each mega-environment. HY21 exhibited the highest mean protein content but relatively low multi-environmental stability, whereas JH9 ranked second in protein content with far superior multi-environmental stability. For sucrose content, the GGE biplot partitioned the environments into four sectors, in which HY19, HY21, HY33, and XH13 were the optimal genotypes with the best sucrose performance in each mega-environment. HY21 was the optimal genotype in 30 environments across 5 production areas, meanwhile, it showed the highest mean sucrose content and strongest multi-environmental stability. Based on a comprehensive analysis of the three peanut kernel quality traits, HY33 was identified as the superior stable oil variety and HY21 was the top performer for protein and sucrose, which suggests that they are suitable for wide promotion across multiple production areas and can be used as elite breeding parents for developing trait-specific varieties.

Meteorological and geographical factors, which are critical indicators, play essential roles in modern agricultural cultivation planning. Ecological regions for cotton fiber quality have been delineated in China’s major cotton planting regions and in Spain, and ecological zoning has also been established for barley, maize, and other crops (Baxevanos et al., 2008). In the modern peanut industry, temperature has been used as a zoning indicator, and climatic zones suitable for different ecological peanut varieties in China have been identified. In addition, a national peanut suitability region map has been developed based on the interactive effects of climatic and soil factors on peanut cultivation. Therefore, analyzing the regional characteristics of peanut growth and ecological resources, which is crucial for evaluating peanut ecological suitability, supports rational planting division, peanut cropping structure adjustment, and optimized production layout tailored to local conditions. Among the three-year evaluation, GZ, KF, HG, and QZ showed environmental stability for oil content; QZ, KF, and HG showed environmental stability for protein content; and KF and FY exhibited environmental stability for sucrose content. By integrating the three quality traits, we found that KF had relatively stable climatic conditions, and peanut kernel quality traits in KF showed good repeatability and strong representativeness across diverse locations. These findings provide supportive information and evidence for peanut industry planning in China.

Conclusion

In this study, a multi-environment evaluation was conducted, which investigated peanut kernel quality traits of seven popularized peanut varieties at 17 locations over three years. The cultivation trials, which covered five major peanut-producing areas and nearly all major peanut-producing provinces in China, spanned a geographical range from 21.09°N to 43.07°N and from 106.08°E to 124.23°E. The evaluation demonstrated that entire growth period accumulated temperature ≥ 10 °C and total sunshine duration can enhance oil content, while accumulated temperature ≥ 10 °C, mean daily temperature, and mean daily temperature difference can increase protein content. In contrast, sucrose content was extremely significantly and negatively correlated with accumulated temperature ≥ 10 °C and total sunshine duration. Oil and protein contents showed a spatial pattern of being higher in eastern China and lower in western China, which revealed the mechanism that Yangtze River Basin and Huang-Huai-Hai production areas had the highest oil and protein contents. While sucrose content displayed a pattern of being higher in southeastern China and lower in northwestern China. ANOVA revealed that environmental factors contributed approximately 60% of the total variation in peanut oil, protein, and sucrose contents, which suggests that environmental effects are comparable to genetic effects on peanut kernel quality traits. GGE stability analysis confirmed HY33 as the optimal stable cultivar for high oil concentration, while HY21 combined superior protein levels with maximum sucrose content and cross-region stability. In addition, KF had relatively stable climatic conditions and the strongest representativeness across diverse locations. These findings supply empirical guidance for peanut breeding and regional industrial layout optimization both domestically and internationally.

Acknowledgments

We sincerely thank the editor and all the anonymous reviewers for their time, effort, and valuable comments during the review process. Their constructive suggestions and rigorous feedback have greatly helped us improve the clarity, quality, and rigor of this manuscript.

Funding Statement

The author(s) declared that financial support was received for this work and/or its publication. The authors would like to thank all the reviewers who participated in the review. This study was supported by the Agricultural Science and Technology Innovation Project of Shandong Academy of Agricultural Sciences (CXGC2026A03 and CXGC2026C41), Natural Science Foundation of Shandong Province (ZR2023QC146, ZR2023QC177 and ZR2025QC282), Open Project of Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs (KF2024007), Taishan Scholar Project Funding (NO. tsqn202312292 and NO. tstp20240523), China Agriculture Research System of MOF and MARA (CARS-13), Open Project of Key Laboratory of Digital Upland Crops of Zhejiang Province (2022E10012), Qingdao Natural Science Foundation Youth Project (25–1–1–38-zyyd-jch), Key R&D Program of Shandong Province (Competitive Innovation Platform), China (2025CXPT167), Qilu Agricultural Talent Project (Innovative Category) of Shandong Academy of Agricultural Sciences, Shandong Province Key Research and Development Program Project (2024LZGC035 and 2025LZGC020), and the Science and Technology Development Guidance Plan of Dongying Major Science and Technology Innovation Project (2024ZDJH100).

Footnotes

Edited by: Tianfu Han, Zhengzhou University, China

Reviewed by: Dongmei Bai, Shanxi Academy of Agricultural Sciences, China

Fadul Onemli, Namık Kemal University, Türkiye

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Author contributions

XJ: Investigation, Software, Writing – original draft, Conceptualization, Writing – review & editing, Visualization, Data curation, Formal analysis, Resources, Validation. JZ: Writing – original draft, Visualization, Software, Investigation, Validation. JX: Methodology, Writing – review & editing, Software, Data curation, Investigation. DY: Writing – review & editing. HX: Project administration, Writing – review & editing. NC: Writing – review & editing, Data curation, Investigation. LP: Investigation, Writing – review & editing, Project administration, Data curation. XY: Supervision, Software, Writing – review & editing. JM: Writing – review & editing, Visualization. XZ: Formal analysis, Writing – review & editing, Methodology, Investigation. XC: Writing – review & editing, Funding acquisition, Methodology, Conceptualization.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2026.1939285/full#supplementary-material

Supplementary Figure 1

Linear regression between environmental factors and quality traits. Linear regression for the eight environmental factors and oil (A), protein (B) and sucrose (C) content traits.

Image1.tif (2.6MB, tif)
Supplementary Table 1

Annual statistics of phenotypic data of peanut varieties.

Table1.xlsx (11KB, xlsx)
Supplementary Table 2

The number of mega-environments, environment and the genotypes by the Which-Won-Where View.

Table2.xlsx (33.1KB, xlsx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Figure 1

Linear regression between environmental factors and quality traits. Linear regression for the eight environmental factors and oil (A), protein (B) and sucrose (C) content traits.

Image1.tif (2.6MB, tif)
Supplementary Table 1

Annual statistics of phenotypic data of peanut varieties.

Table1.xlsx (11KB, xlsx)
Supplementary Table 2

The number of mega-environments, environment and the genotypes by the Which-Won-Where View.

Table2.xlsx (33.1KB, xlsx)

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

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