1. Introduction
Pea (
Pisum sativum L.), a cool-season legume crop grown in more than 85 countries, is the second-most-important grain legume in the world [
1]. Pea seed is highly nutritious, and approximately half the world’s production is fed to livestock while the remaining portion is used for human food, primarily in developing counties. Pea also plays an important role in crop rotation as a plant that interrupts the frequent succession of cereals [
2]. Due to its short growing season and the ability of N
2 fixation, it has been accepted as one of the valuable crops of organic farming [
3].
In China, dry pea is one of the most important pulse crops, which has been produced for a long time in high-altitude marginal lands and low-input areas by smallholder farmers. Pea has good potential in new food applications due to its moderate protein concentration and slowly digestible starch [
4]. A major new use for dry pea is the Chinese vermicelli, which utilizes pea starch. China has favorable agroecological conditions for the cultivation of peas for seeds, especially in northwest China, including Gansu, Xingjiang, Hebei, Ningxia and Inner Mongolia, which are the dominant regions for planting spring peas, with sowing in late March to early May and harvesting in early August [
5]. Field pea breeding in China started in the 1960s, with the objective of improving productivity through the generation of high yielding varieties with tolerance/resistance to different production constraints and suitable for different agro-ecologies of the country [
6].
Current demand of field pea is high, but the productivity is very low and unstable. In 2020, China’s total acreage was about 935,517 hectares with production volume of 1,440,627 t [
7]. However, the average yield of 1587 kg per hectare is very low as compared to its potential and yield obtained in many developed countries, which may be attributed to the non-adoption of improved varieties. Other factors like the non-usage of recommended agronomic practices, application of improper fertilizer doses, diseases and harvesting losses also play an important role in yield reduction. In addition, environmental factors such as temperature, rainfall, soil type and moisture also affect pea yield. By far, genotype by environment interaction is the most difficult factor to increase yield of field pea in China, due to diverse agro-climatic zones and the high sensitivity of field pea to various environmental factors.
Genotypes (G) × environments (E) interaction (GEI) refers to differential responses of genotypes across diverse environments [
8]. Most agronomically and economically important traits, such as grain yield, are quantitative in nature and routinely exhibit GEI [
9]. Detecting GEI effects on yield and other agronomically important traits is one of the most important components for multi-environment yield trials (MEYT) [
10]. To be widely accepted, a new crop variety must show good performance across multi-environments before registration and release. The estimation of G, E and GEI ensures valid recommendations of suitable varieties able to overcome the pressure due to variable occurring conditions [
11]. The determination of GEI factors helps geneticists in their breeding programs to shift the selection toward varieties suited for wide environments. For analysis of MEYT, additive main effects and multiplicative interactions (AMMI), as well as genotype plus genotype environment interaction (GGE) biplots, have been developed to study GEI effects [
12]. However, the GGE biplot model provided an excellent graphical presentation for breeders, visualizing various aspects of genotype and genotype × environment in different biplots, not only showing the stability of genotype but also discrimination strength of recognition in given environments [
13].
The GGE biplot methodology has been used to evaluate test environments in cowpea [
14], grass pea [
15], fava bean [
16], chickpea [
17], mung bean [
18,
19], dry beans [
20], dry peas [
21,
22], lentils [
23] and other pulses. The objective of this study was to assess the grain yield performance and agronomic traits of local pea genotypes improved by different province research institutes under multiple environments, in order to further exploit high yield potential elite pea varieties with tolerance to biotic and abiotic stresses, and which are widely suitable for planting in spring sowing areas in China.
2. Materials and Methods
2.1. Genotypes, Testing Location, and Experimental Design
Material for the studies covered fourteen pea cultivars, which were bred and released by pea breeders from nine public research institutions. Twelve are normal leaf type and two are semi-leafless type pea. The source and some main quality traits of these pea varieties are presented in
Table 1.
The locations of MEYT include Yondeng, Qitai, Liaoyang, Dingxi, Tangshan, Langfang and Maerkang, among which Yondeng, Qitai, and Langfang are in irrigated agricultural areas; and Liaoyang, Dingxi, Tangshan and Maerkang are in rain-fed agricultural area. For the seven locations, long-term average total precipitation varied from 201 mm to 717 mm per year, with 26.9–74.5% of the yearly precipitation occurring during the pea growing season. Long-term average annual temperature of the locations was 16.4 °C, with yearly average temperatures ranging from 5.5 °C to 12.0 °C. The daily minimum temperatures in spring varied from −4 °C to −11 °C, and the daily maximum temperatures in summer varied from 22 °C to 28 °C between locations. Additional information on soil type, biotic or crop management factors and climatic characteristics of experimental locations are presented in
Table 2.
Fourteen field pea genotypes, together with standard check, were evaluated during the main cropping season for two years (2020 to 2021) in seven locations, and each environment and year were treated as a single environment. During each year, the experiment with genotypes was carried out in a randomized complete block design (RCBD) with three replications. The plot size was 2 × 5 m (10 m2), with six rows and spacing 40 cm between rows and 5 cm between plants being maintained. Diammonium phosphate (46% P, 18% N) fertilizer and urea (46% N) were applied at rates of 150 kg/ha and 75 kg/ha, respectively. Weeds were controlled periodically by hand weeding, irrigation was performed in E1, E2 and E6 location at the seedling and flowering stage, and other management practices like pest or disease-control was done as required. Pea is most often sown in mid-March. The harvest took place in late July and early August.
2.2. Data Collection and Analysis
Data on grain yield and yield-related traits were collected on plot and plant basis from each plot, respectively. Branches per plant (BP) and date of maturity (DM) were taken when each plot attained 50% flowering and 90% of the pod’s physiological maturity, respectively, and days were calculated beginning from the date of sowing. Data for plant height (PH), seeds per plant (SPP), grain weight per plant (GWP) and seeds per pod (SPD) were collected on the basis of five sample plants which were randomly taken from each plot, and the average of five sample plants was used for analysis. Hundred seed weight (HSW) and grain yield of plot (GYP) were measured on clean, dried seed and the measured grain yield value has converted to kilogram per hectare for analysis. Statistical analysis was carried out by SPSS for Windows version 26.0 (IBM Corporation, New York, NY, USA). Standard statistical techniques were used, and the data were subjected to analysis of variance (ANOVA) using the Fisher’s least significant difference (LSD) method to test the significance difference between means. To compare the differences among the genotype means, the significant data were further analyzed statistically using a Least Significant Difference (LSD) test at p < 0.05 and p < 0.01 probability level. A two-way fixed effect model was fitted to determine the magnitude of the main effects of variation and their interaction on seeds yield. To perform GGE biplot analysis, least square means from each environment were analyzed using the GGE BiplotGUI package from the statistical software R.
4. Conclusions
We evaluated the stability and yield potential of pea genotypes based on combined ANOVA and GGE biplot analysis. GEI has a key effect on crop variety development by complicating the release of varieties across challenging environments. Analysis of variance for every location and combined over seven locations showed significant differences among genotypes, environments and GEI for grain yield, as well as most of the yield-related traits of spring pea. The significant GEI effects indicated the inconsistent performance of genotypes across the tested environments. Different genotypes have had different genetic potentials. Among the tested genotypes, G1, G2, G3, G4, G5, G6, G7 and G8 had mean grain yield above the overall mean grain yield of genotypes. Overall, the semi-leafless pea variety G1 was the best genotype, with an average grain yield of 3308 kg/ha, followed by G2 (2866 kg/ha), G3 (2832 kg/ha), G4 (2710 kg/ha) and G5 (2723 kg/ha), which were significantly higher than other genotypes and standard check variety G10. It is suggested that G1 should be used as the standard check genotype for dry field pea breeding in the future. The most representative and productivity environments were identified for spring pea-producing areas in North China, including E1, E2, E3, E4 and E5, with G1 as the highest average yield and most suitable genotype. It was worthwhile to mention that the G1 variety had the highest grain yield under the E1 location. Therefore, it is recommended to use elite genotype G1 for the wider cultivation in North China and similar areas. These findings represent a comprehensive analysis of yield and stability of spring pea variety and growing locations, which may be useful for national and international pea improvement programs.