Dissecting the Opposing Roles of Thermal Intensity and Growing Degree Days in Regulating Spring Wheat Protein Content.

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Learn more: PMC Disclaimer | PMC Copyright Notice Plants (Basel) . 2026 Apr 2;15(7):1096. doi: 10.3390/plants15071096 Search in PMC Search in PubMed View in NLM Catalog Add to search Dissecting the Opposing Roles of Thermal Intensity and Growing Degree Days in Regulating Spring Wheat Protein Content Xuan Lei Xuan Lei 1 School of Life Sciences, Inner Mongolia University, Ministry of Education, Hohhot 010021, China; [email protected] (X.L.); [email protected] (J.Y.) Find articles by Xuan Lei 1, † , Jun Ye Jun Ye 1 School of Life Sciences, Inner Mongolia University, Ministry of Education, Hohhot 010021, China; [email protected] (X.L.); [email protected] (J.Y.) 2 Key Laboratory of Black Soil Conservation and Utilization, Ministry of Agriculture and Rural Affairs, Key Laboratory of Ecological Restoration and Pollution Control of Degraded Farmland of Inner Mongolia Autonomous Region, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China; [email protected] (X.W.); [email protected] (H.Z.); [email protected] (X.Z.); [email protected] (J.L.); [email protected] (T.Z.); [email protected] (Z.Z.); [email protected] (T.M.) 3 College of Agronomy, Hebei Agricultural University, Baoding 071001, China; [email protected] Find articles by Jun Ye 1, 2, 3, † , Xiaobing Wang Xiaobing Wang 2 Key Laboratory of Black Soil Conservation and Utilization, Ministry of Agriculture and Rural Affairs, Key Laboratory of Ecological Restoration and Pollution Control of Degraded Farmland of Inner Mongolia Autonomous Region, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China; [email protected] (X.W.); [email protected] (H.Z.); [email protected] (X.Z.); [email protected] (J.L.); [email protected] (T.Z.); [email protected] (Z.Z.); [email protected] (T.M.) Find articles by Xiaobing Wang 2, † , Wenjia Yang Wenjia Yang 4 The Middle Reaches of the Yangtze River (Co-Construction by Ministry and Province), Hubei Key Laboratory of Waterlogging Disaster and Agricultural Use of Wetland, College of Agriculture, Yangtze University, Jingzhou 434025, China; [email protected] Find articles by Wenjia Yang 4 , Haibin Zhang Haibin Zhang 2 Key Laboratory of Black Soil Conservation and Utilization, Ministry of Agriculture and Rural Affairs, Key Laboratory of Ecological Restoration and Pollution Control of Degraded Farmland of Inner Mongolia Autonomous Region, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China; [email protected] (X.W.); [email protected] (H.Z.); [email protected] (X.Z.); [email protected] (J.L.); [email protected] (T.Z.); [email protected] (Z.Z.); [email protected] (T.M.) 3 College of Agronomy, Hebei Agricultural University, Baoding 071001, China; [email protected] Find articles by Haibin Zhang 2, 3 , Xuanwei Zhao Xuanwei Zhao 2 Key Laboratory of Black Soil Conservation and Utilization, Ministry of Agriculture and Rural Affairs, Key Laboratory of Ecological Restoration and Pollution Control of Degraded Farmland of Inner Mongolia Autonomous Region, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China; [email protected] (X.W.); [email protected] (H.Z.); [email protected] (X.Z.); [email protected] (J.L.); [email protected] (T.Z.); [email protected] (Z.Z.); [email protected] (T.M.) Find articles by Xuanwei Zhao 2 , Juan Liu Juan Liu 2 Key Laboratory of Black Soil Conservation and Utilization, Ministry of Agriculture and Rural Affairs, Key Laboratory of Ecological Restoration and Pollution Control of Degraded Farmland of Inner Mongolia Autonomous Region, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China; [email protected] (X.W.); [email protected] (H.Z.); [email protected] (X.Z.); [email protected] (J.L.); [email protected] (T.Z.); [email protected] (Z.Z.); [email protected] (T.M.) Find articles by Juan Liu 2 , Tingjia Zhang Tingjia Zhang 2 Key Laboratory of Black Soil Conservation and Utilization, Ministry of Agriculture and Rural Affairs, Key Laboratory of Ecological Restoration and Pollution Control of Degraded Farmland of Inner Mongolia Autonomous Region, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China; [email protected] (X.W.); [email protected] (H.Z.); [email protected] (X.Z.); [email protected] (J.L.); [email protected] (T.Z.); [email protected] (Z.Z.); [email protected] (T.M.) Find articles by Tingjia Zhang 2 , Zhenyu Zhang Zhenyu Zhang 2 Key Laboratory of Black Soil Conservation and Utilization, Ministry of Agriculture and Rural Affairs, Key Laboratory of Ecological Restoration and Pollution Control of Degraded Farmland of Inner Mongolia Autonomous Region, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China; [email protected] (X.W.); [email protected] (H.Z.); [email protected] (X.Z.); [email protected] (J.L.); [email protected] (T.Z.); [email protected] (Z.Z.); [email protected] (T.M.) Find articles by Zhenyu Zhang 2 , Tingyu Ma Tingyu Ma 2 Key Laboratory of Black Soil Conservation and Utilization, Ministry of Agriculture and Rural Affairs, Key Laboratory of Ecological Restoration and Pollution Control of Degraded Farmland of Inner Mongolia Autonomous Region, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China; [email protected] (X.W.); [email protected] (H.Z.); [email protected] (X.Z.); [email protected] (J.L.); [email protected] (T.Z.); [email protected] (Z.Z.); [email protected] (T.M.) Find articles by Tingyu Ma 2 , Cundong Li Cundong Li 3 College of Agronomy, Hebei Agricultural University, Baoding 071001, China; [email protected] Find articles by Cundong Li 3 , Xin Gao Xin Gao 5 College of Agriculture, Inner Mongolia Minzu University, Tongliao 028000, China; [email protected] Find articles by Xin Gao 5 , Juan Li Juan Li 1 School of Life Sciences, Inner Mongolia University, Ministry of Education, Hohhot 010021, China; [email protected] (X.L.); [email protected] (J.Y.) 3 College of Agronomy, Hebei Agricultural University, Baoding 071001, China; [email protected] Find articles by Juan Li 1, 3, * , Zhanyuan Lu Zhanyuan Lu 1 School of Life Sciences, Inner Mongolia University, Ministry of Education, Hohhot 010021, China; [email protected] (X.L.); [email protected] (J.Y.) 2 Key Laboratory of Black Soil Conservation and Utilization, Ministry of Agriculture and Rural Affairs, Key Laboratory of Ecological Restoration and Pollution Control of Degraded Farmland of Inner Mongolia Autonomous Region, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China; [email protected] (X.W.); [email protected] (H.Z.); [email protected] (X.Z.); [email protected] (J.L.); [email protected] (T.Z.); [email protected] (Z.Z.); [email protected] (T.M.) 3 College of Agronomy, Hebei Agricultural University, Baoding 071001, China; [email protected] Find articles by Zhanyuan Lu 1, 2, 3, * Editor: James A Bunce Author information Article notes Copyright and License information 1 School of Life Sciences, Inner Mongolia University, Ministry of Education, Hohhot 010021, China; [email protected] (X.L.); [email protected] (J.Y.) 2 Key Laboratory of Black Soil Conservation and Utilization, Ministry of Agriculture and Rural Affairs, Key Laboratory of Ecological Restoration and Pollution Control of Degraded Farmland of Inner Mongolia Autonomous Region, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China; [email protected] (X.W.); [email protected] (H.Z.); [email protected] (X.Z.); [email protected] (J.L.); [email protected] (T.Z.); [email protected] (Z.Z.); [email protected] (T.M.) 3 College of Agronomy, Hebei Agricultural University, Baoding 071001, China; [email protected] 4 The Middle Reaches of the Yangtze River (Co-Construction by Ministry and Province), Hubei Key Laboratory of Waterlogging Disaster and Agricultural Use of Wetland, College of Agriculture, Yangtze University, Jingzhou 434025, China; [email protected] 5 College of Agriculture, Inner Mongolia Minzu University, Tongliao 028000, China; [email protected] * Correspondence: [email protected] (J.L.); [email protected] (Z.L.); Tel.: +86-181-0484-6928 (J.L.); +86-133-4711-3808 (Z.L.) † These authors contributed equally to this work. Roles James A Bunce : Academic Editor Received 2026 Mar 12; Revised 2026 Mar 24; Accepted 2026 Mar 31; Collection date 2026 Apr. © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license . PMC Copyright notice PMCID: PMC13074442  PMID: 41977755 Abstract Protein content (PC) stability is crucial for wheat quality. This study utilized partial least squares regression and structural equation modeling to distinguish the physiological effects of “thermal intensity” versus “thermal accumulation” on spring wheat PC across Inner Mongolia. Environmental factors were the dominant drivers of variation. Notably, the Erguna region achieved the highest PC (18.53%) despite recording the lowest total growing degree days. Structural equation modeling analysis revealed that thermal intensity during heading-to-anthesis exerted a strong positive effect on PC (path coefficient = 0.965), likely by enhancing nitrogen remobilization kinetics. Conversely, excessive thermal accumulation and sunshine duration during grain filling negatively impacted PC via a carbohydrate-driven “dilution effect”. These findings suggest that superior PC formation requires a specific spatiotemporal coupling: high thermal intensity prior to anthesis to prime nitrogen transport, combined with low thermal accumulation post-anthesis to restrict carbon dilution. This study provides a physiological basis for optimizing wheat quality zoning by decoupling heat magnitude from duration under future climate scenarios. Keywords: spring wheat, protein content, climatic factors, structural equation modeling 1. Introduction Wheat ( Triticum aestivum L.) accounts for approximately 20% of the total protein intake for the human population, playing a pivotal role in global food security. With the escalating market demand for superior processing quality, enhancing protein content (PC) has become a primary breeding objective [ 1 ]. However, PC is a complex quantitative trait characterized by high phenotypic plasticity [ 2 ], making it highly susceptible to fluctuations in environmental conditions. This leads to instability in quality across different years and regions. Previous research has established that wheat protein content is governed by genotype (G), environment (E), and their complex interactions (G × E) [ 3 , 4 ]. Evidence from multi-environment trials (METs) consistently demonstrates that environmental factors, particularly climatic variability, account for a substantially larger proportion of PC variance than genetic background alone [ 5 , 6 ]. A central, and unresolved controversy in crop physiology is determining the specific thermal drivers of this variability: does grain protein respond more to the magnitude of temperature events during critical windows (“Thermal Intensity”) or to the total heat load over the growing season (“Thermal Accumulation”)? Nitrogen (N) availability is a primary determinant of grain protein content, as N is a key constituent of amino acids and proteins [ 7 ]. Adequate N supply, particularly during the reproductive stages, significantly enhances the activity of nitrogen-assimilating enzymes such as glutamine synthetase (GS) and glutamate synthase (GOGAT), thereby promoting the translocation of nitrogen from vegetative tissues to the developing grain [ 8 ]. However, the efficiency of N utilization is highly dependent on environmental conditions, which can modulate the source–ink relationship and the kinetics of N remobilization. Understanding how climatic factors interact with N metabolism is therefore essential for optimizing nitrogen management strategies to achieve high-quality wheat production. “Thermal Accumulation,” typically quantified by growing degree days (GDD), represents the duration and total quantity of heat resources [ 9 ]. Classic source–sink theory posits that higher heat accumulation accelerates phenological development, but often favors carbohydrate assimilation (starch synthesis) over nitrogen deposition [ 10 ]. This disproportionate accumulation of starch leads to a “dilution effect,” potentially reducing the final protein concentration [ 11 ]. Conversely, “Thermal Intensity” refers to the strength of temperature (e.g., maximum daily temperature, T max ) during specific developmental stages. Physiologically, nitrogen remobilization from vegetative organs to grains is an enzymatic process governed by kinetics [ 12 ]. Higher thermal intensity—within an optimal physiological range—may enhance the activity of key enzymes such as glutamine synthetase (GS), thereby accelerating nitrogen transport rates independent of the total growth duration [ 13 ]. Despite the theoretical distinction between these two thermal pathways, empirical studies often conflate them, relying on average temperatures throughout the entire growing season to predict grain quality. This “black-box” approach masks the ontogenetic heterogeneity of crop responses. For instance, a high cumulative heat load might shorten the grain-filling period (negative for quality), while a high temperature intensity at anthesis might prime nitrogen remobilization (positive for quality) [ 14 , 15 ]. Furthermore, in high-latitude and semi-arid agricultural ecosystems, thermal factors are frequently intertwined with solar radiation (Sunshine duration, SSD) [ 16 ]. High radiation drives photosynthesis and biomass accumulation, potentially exacerbating the carbon–nitrogen imbalance [ 17 , 18 ], and its independent contribution is rarely disentangled from thermal effects in field studies. The inherent multicollinearity among these meteorological drivers—thermal intensity, thermal accumulation, and solar radiation—precludes accurate quantification by traditional linear regression analyses [ 3 ]. Consequently, advanced multivariate statistical frameworks, such as partial least squares regression (PLSR) and structural equation modeling (SEM), are necessitated to disentangle the complex, non-linear interdependencies governing the climate–quality relationship. Against this background, this study conducted multi-genotype field trials across four representative spring wheat production regions in Inner Mongolia, characterized by pronounced gradients in both heat and light resources. By integrating the Zadoks phenological scale with PLSR and SEM, it aimed to: (1) quantify the relative contributions of genotypes and environments to PC variation; (2) identify the critical developmental windows where climate exerts the strongest influence; and most importantly, (3) disentangle the opposing or synergistic effects of “Thermal Intensity” versus “Thermal Accumulation” on protein synthesis. The findings provide a robust theoretical framework for optimizing wheat quality ecology by matching specific thermal regimes with crop phenology. 2. Results 2.1. Variation in Protein Content Across Environments and Genotypes Figure 1 illustrates the distribution of PC across four distinct ecological regions and fifteen spring wheat cultivars. As depicted in the violin plots ( Figure 1 a), PC exhibited significant spatial heterogeneity among the experimental sites ( p < 0.05). Specifically, the Erguna region recorded the highest mean PC at 18.53%, significantly higher than other regions. Hohhot and Chifeng followed with mean values of 17.74% and 16.81%, respectively; while both were significantly higher than Bayannur, the difference between these two intermediate regions was not statistically significant. Conversely, Bayannur exhibited the lowest PC (16.13%). Quantitatively, the mean PC in Erguna was 2.40 percentage points higher than in Bayannur, 1.72 percentage points higher than in Chifeng, and 0.79 percentage points higher than in Hohhot. The data distribution range for Erguna spans from 17.10% to 21.00%, indicating an overall higher level. In contrast, the distribution range for Bayannur ranges from 14.28% to 17.38%, reflecting a lower overall level. Figure 1. Open in a new tab Distribution of grain protein content (%) in spring wheat affected by region and variety. ( a ) Violin plots representing the variation in protein content across four regions: Erguna, Hohhot, Chifeng, and Bayannur. ( b ) Box plots illustrating the variation in protein content among fifteen spring wheat varieties. The horizontal line within the box represents the median, the boundaries of the box indicate the 25th and 75th percentiles, and the whiskers indicate the 1.5 interquartile range. Different lowercase letters above the plots indicate significant differences at the 0.05 probability level according to Duncan’s multiple range test. Genotypic variation is presented in the box plots ( Figure 1 b), revealing distinct differences in protein accumulation capabilities among the cultivars. LC10 demonstrated the highest potential, achieving a mean PC of 18.98% across the four regions, followed by NP5 (18.55%) and Y3002 (18.50%). In contrast, XC6 recorded the lowest mean PC at 15.71%, followed by WC3 (16.18%). The high-protein cultivar LC10 exceeded the low-protein cultivar XC6 by a margin of 3.27 percentage points. Two-way ANOVA further confirmed the statistical significance of these differences ( Table 1 ) and quantified the contribution of each factor. Variety, area, and their interaction all exerted highly significant effects on PC ( p < 0.01). The analysis of variance revealed that the environmental factor (Area) served as the predominant driver of PC variation, as evidenced by the highest F-value (F = 155.82). The genotypic factor (Variety) also played a significant role (F = 37.136), while the interaction effect, although significant, was relatively minor (F = 3.749) compared to the main effects. Table 1. Two-way ANOVA for the effects of variety, area, and their interaction on grain protein content. Indicator Variety Area Variety × Area F-Value p -Value F-Value p -Value F-Value p -Value Protein content (%) 37.136 p < 0.01 155.82 p < 0.01 3.749 p < 0.01 Open in a new tab The significant interaction effect (Variety × Area, p < 0.01) indicates that the protein accumulation potential of the tested cultivars was significantly modulated by the specific climatic conditions of each experimental site. 2.2. Climatic Variations Across Different Experimental Sites Table 2 presents the descriptive statistics for key daily climatic variables calculated based on the daily meteorological data throughout the entire spring wheat growing season (March–August) in the four regions. In terms of thermal conditions, the mean temperature (T mean ) was highest in Bayannur (18.6 °C) and lowest in Erguna (11.3 °C). Similarly, the mean maximum temperature (T max ) peaked in Bayannur (24.5 °C) and Hohhot (23.8 °C), while Erguna recorded the lowest value (17.7 °C). Conversely, Bayannur exhibited the highest minimum temperature (T min ) at 10.9 °C, whereas Erguna experienced the lowest (5.4 °C). Regarding the diurnal temperature range (DTR), Bayannur and Hohhot showed substantial fluctuations (means of 13.6 °C and 14.4 °C, respectively), standing in sharp contrast to Erguna, which maintained the narrowest range (12.3 °C) but exhibited the highest maximum DTR extreme (23.8 °C). For thermal and light resources, the mean growing degree days (GDD) were highest in Bayannur (9.3 °C·d), and lowest in Erguna (5.6 °C·d). Similarly, the mean sunshine duration (SSD) was prolonged in Bayannur (9.5 h) and Chifeng (8.8 h), but was shortest in Hohhot (8.1 h) and Erguna (8.3 h). Notably, the coefficients of variation (CV) for these climatic factors varied significantly across regions, with Tmin and Erguna’s overall thermal indicators exhibiting the most pronounced variability (CV > 100%). Table 2. Descriptive statistics of major climatic factors during the whole growth period of spring wheat (March–August) in four regions. Area Indicator Abridge Maximum Value Minimum Value Mean Value Standard Deviation Range Coefficient of Variation (%) Bayannur Mean temperature (°C) T mean 30.4 −2.0 18.6 7.5 32.4 40.3 Maximum temperature (°C) T max 36.2 3.5 24.5 7.4 32.7 30.0 Minimum temperature (°C) T min 25.0 −9.9 10.9 8.0 34.9 73.5 Diurnal temperature range (°C) DTR 23.8 3.9 13.6 4.4 19.9 32.3 Growing degree days (°C·d) GDD 20.4 0.0 9.3 6.1 20.4 65.4 Sunshine duration (h) SSD 14.2 0.0 9.5 4.0 14.2 42.3 Chifeng Mean temperature (°C) T mean 30.0 −12.2 17.2 9.4 42.2 54.8 Maximum temperature (°C) T max 35.5 −9.1 22.6 9.5 44.6 41.9 Minimum temperature (°C) T min 23.3 −18.3 10.3 10.3 41.6 100.8 Diurnal temperature range (°C) DTR 22.6 1.8 12.4 4.4 20.8 35.3 Growing degree days (°C·d) GDD 20.0 0.0 8.9 6.3 20.0 70.3 Sunshine duration (h) SSD 14.3 0.0 8.8 3.8 14.3 43.2 Hohhot Mean temperature (°C) T mean 27.6 −4.2 17.2 7.8 31.8 45.2 Maximum temperature (°C) T max 34.0 0.0 23.8 7.6 34.0 32.2 Minimum temperature (°C) T min 21.7 −13.0 9.4 8.4 34.7 89.2 Diurnal temperature range (°C) DTR 24.0 4.3 14.4 3.7 19.7 25.9 Growing degree days (°C·d) GDD 17.6 0.0 8.3 5.7 17.6 68.0 Sunshine duration (h) SSD 14.1 0.0 8.1 3.9 14.1 48.3 Erguna Mean temperature (°C) T mean 27.2 −24.4 11.3 12.0 51.6 106.4 Maximum temperature (°C) T max 36.2 −18.2 17.7 12.2 54.4 69.1 Minimum temperature (°C) T min 20.9 −31.7 5.4 12.2 52.6 227.9 Diurnal temperature range (°C) DTR 23.8 4.0 12.3 3.7 19.8 29.8 Growing degree days (°C·d) GDD 17.2 0.0 5.6 5.0 17.2 88.4 Sunshine duration (h) SSD 13.9 0.0 8.3 4.1 13.9 48.5 Open in a new tab 2.3. Meteorological Characteristics Across Phenological Stages Table 3 presents the spatial variations in cumulative and average climatic factors calculated specifically for each phenological stage. Over the entire growth period (Z09–Z91), Chifeng exhibited the highest T mean (20.8 °C) and T max (26.3 °C), significantly exceeding those of other regions, whereas Erguna recorded the lowest T max (25.4 °C). Bayannur accumulated the highest total GDD (927.2 °C·d) and SSD (895.7 h), values that were significantly greater than those observed in Erguna (731.7 °C·d and 437.2 h, respectively). Additionally, Hohhot was characterized by the largest average DTR, peaking at 15.1 °C. Table 3. Climate conditions during different stages of growth across four regions. Duration Area T mean b (°C) T max (°C) T min (°C) DTR (°C) GDD (°C·d) SSD (h) Z09–Z20 a Bayannur 9.1 ± 0.28 c c 15.2 ± 0.48 d 1.3 ± 0.13 d 13.9 ± 0.59 d 14.8 ± 1.92 d 111.6 ± 5.69 c Chifeng 16.1 ± 0.00 a 22.5 ± 0.08 b 7.2 ± 0.04 b 15.4 ± 0.00 c 71.8 ± 2.28 b 126.6 ± 0.00 a Hohhot 13.2 ± 0.19 b 20.2 ± 0.31 c 4.2 ± 0.29 c 16.0 ± 0.04 a 48.1 ± 1.95 c 98.1 ± 4.21 d Erguna 16.1 ± 0.21 a 24.5 ± 0.23 a 8.6 ± 0.15 a 15.7 ± 0.10 b 78.1 ± 4.34 a 119.1 ± 3.05 b Z20–Z30 Bayannur 15.7 ± 0.15 c 22.6 ± 0.10 c 6.9 ± 0.07 c 15.8 ± 0.09 d 97.2 ± 2.35 c 182.7 ± 0.63 a Chifeng 18.5 ± 0.12 a 23.8 ± 0.12 b 11.0 ± 0.00 b 12.8 ± 0.14 c 144.2 ± 1.78 a 154.3 ± 2.44 c Hohhot 15.1 ± 0.23 d 22.2 ± 0.18 d 6.0 ± 0.18 d 16.2 ± 0.00 a 90.1 ± 5.68 d 161.5 ± 5.01 d Erguna 17.4 ± 0.19 b 24.2 ± 0.31 a 11.4 ± 0.07 a 12.9 ± 0.29 c 121.0 ± 10.15 b 116.1 ± 12.27 b Z30–Z50 Bayannur 19.2 ± 0.11 b 25.9 ± 0.06 a 9.8 ± 0.20 b 16.2 ± 0.18 a 219.0 ± 25.15 a 230.1 ± 31.50 a Chifeng 21.5 ± 1.21 a 26.7 ± 1.11 a 13.4 ± 1.15 a 13.3 ± 0.31 c 160.7 ± 38.26 b 137.4 ± 25.19 b Hohhot 18.3 ± 0.47 c 24.8 ± 0.66 b 10.0 ± 0.42 b 14.8 ± 0.33 b 136.8 ± 18.37 b 137.4 ± 23.79 b Erguna 19.0 ± 0.55 b 25.1 ± 0.47 b 13.6 ± 0.62 a 11.5 ± 0.17 d 79.3 ± 7.63 c 67.1 ± 5.35 c Z50–Z60 Bayannur 20.5 ± 0.96 b 26.6 ± 0.55 b 11.7 ± 1.51 c 14.9 ± 1.26 b 63.3 ± 5.79 b 70.3 ± 8.47 a Chifeng 20.7 ± 2.18 abc 26.7 ± 1.68 b 13.9 ± 1.71 b 12.8 ± 0.66 c 64.2 ± 13.06 b 60.2 ± 1.07 b Hohhot 19.0 ± 1.42 c 25.7 ± 1.89 b 9.6 ± 0.82 d 16.2 ± 1.56 a 55.1 ± 9.54 c 70.0 ± 10.91 a Erguna 22.0 ± 1.02 a 28.7 ± 1.44 a 15.5 ± 0.39 a 13.2 ± 1.11 c 72.3 ± 6.18 a 40.0 ± 3.97 c Z60–Z70 Bayannur 21.5 ± 0.87 a 27.8 ± 1.03 a 13.0 ± 0.87 c 14.8 ± 0.66 b 46.5 ± 2.47 a 49.1 ± 5.89 a Chifeng 20.2 ± 1.23 bc 25.5 ± 1.75 b 13.9 ± 0.86 b 11.6 ± 1.14 c 30.5 ± 3.71 bc 28.4 ± 2.44 c Hohhot 19.6 ± 0.70 c 27.0 ± 1.12 a 9.6 ± 1.03 d 17.5 ± 1.41 a 28.8 ± 2.12 c 33.5 ± 28.5 b Erguna 21.1 ± 0.77 ab 25.8 ± 1.73 b 17.0 ± 0.85 a 8.7 ± 2.27 d 34.8 ± 4.74 b 6.7 ± 8.27 d Z70–Z91 Bayannur 23.8 ± 0.18 a 29.6 ± 0.19 a 15.9 ± 0.20 c 13.7 ± 0.12 b 486.3 ± 21.11 a 363.5 ± 21.14 a Chifeng 23.8 ± 0.44 a 29.2 ± 0.36 b 18.0 ± 0.54 a 11.2 ± 0.24 c 401.8 ± 35.36 b 262.8 ± 22.39 c Hohhot 22.5 ± 0.20 b 29.1 ± 0.14 b 14.9 ± 0.53 d 14.2 ± 0.44 a 476.3 ± 20.09 a 321.9 ± 19.78 b Erguna 20.6 ± 0.18 c 25.9 ± 0.18 c 16.3 ± 0.11 b 9.5 ± 0.08 d 346.2 ± 22.22 c 207.2 ± 18.52 d Z09–Z91 Bayannur 19.2 ± 0.14 b 25.5 ± 0.15 bc 10.7 ± 0.11 b 14.8 ± 0.07 b 927.2 ± 26.23 a 895.7 ± 18.72 a Chifeng 20.8 ± 0.16 a 26.3 ± 0.13 a 13.7 ± 0.21 a 12.6 ± 0.09 c 873.1 ± 41.96 b 643.1 ± 19.92 c Hohhot 18.9 ± 0.16 c 25.6 ± 0.14 b 10.5 ± 0.27 b 15.1 ± 0.12 a 835.1 ± 41.77 c 724.3 ± 20.99 b Erguna 19.2 ± 0.00 b 25.4 ± 0.04 c 13.8 ± 0.06 a 11.7 ± 0.10 d 731.7 ± 26.60 d 437.2 ± 19.12 d Open in a new tab a Z09–Z20, seedling to tillering; Z20–Z30, tillering to jointing; Z30–Z50, jointing to heading; Z50–Z60, heading to anthesis; Z60–Z70, anthesis to grain filling; Z70–Z91, grain filling to maturity; Z09–Z91, whole growth period. b Tmean, Tmax, Tmin, DTR, GDD), and SSD represent the mean temperature, maximum temperature, minimum temperature, diurnal temperature range, growing degree days, and sunshine duration. c Values are presented as mean ± standard deviation. Different lowercase letters within the same column and growth stage indicate significant differences among regions at the 0.05 probability level according to Duncan’s multiple range test. Regarding specific developmental stages, during the vegetative phase (Z09–Z30), both T mean and T max in Chifeng and Erguna were significantly higher compared to Bayannur and Hohhot; notably, Chifeng achieved the highest GDD (144.2 °C·d) during the Z20–Z30 interval. Moving to the jointing to heading stage (Z30–Z50), Bayannur displayed significantly higher GDD and SSD than the other regions, while Erguna recorded the lowest values. A distinct and critical shift occurred during the heading to anthesis stage (Z50–Z60), where Erguna reached the highest levels for T mean (22.0 °C), T max (28.7 °C), and GDD (72.3 °C·d), significantly surpassing the other sites. During the late reproductive phases (Z60–Z70 and Z70–Z91), Bayannur exhibited the highest T max , GDD, and SSD. Conversely, Erguna showed significantly lower SSD compared to the other three regions, and recorded the lowest Tmean, Tmax, and GDD specifically during the grain-filling stage (Z70–Z91). 2.4. Correlation Between Protein Content and Climatic Factors Figure 2 illustrates the linear regression analysis between climatic factors and PC across various growth stages. Regarding thermal indices, T mean exhibited statistically significant associations with PC solely during the seedling to tillering (Z09–Z20, R 2 = 0.21, p < 0.01) and grain filling to maturity stages (Z70–Z91, R 2 = 0.38, p < 0.01). Similarly, T max demonstrated significant associations during Z09–Z20 ( R 2 = 0.28, p < 0.01) and Z70–Z91 ( R 2 = 0.32, p < 0.01), while also reaching significance during Z20–Z50 ( p < 0.05). In contrast, T min showed significant correlations throughout the vegetative growth phase (Z09–Z50) and the whole growth period (Z09–Z91, p < 0.05), but no significant correlation was observed during the late reproductive phase (Z60–Z91). Figure 2. Open in a new tab Linear regression analysis between grain protein content and climatic factors at different growth stages. Rows represent growth stages, and columns represent climatic factors. Blue dots indicate individual data points. The red solid line represents the linear regression fit, and the red shaded area indicates the 95% confidence interval. The coefficient of determination ( R 2 ) and p -value are displayed in each panel. Abbreviations of climatic factors are defined in Table 2 . Growth stage definitions are provided in the legend of Table 3 . DTR was significantly correlated with PC across multiple specific stages, including Z09–Z20 ( R 2 = 0.25, p < 0.01), Z30–Z50 ( R 2 = 0.27, p < 0.01), Z50–Z60 ( R 2 = 0.08, p < 0.05), and Z70–Z91 ( R 2 = 0.12, p < 0.01), as well as the whole growth period ( R 2 = 0.12, p < 0.01). For GDD, the strongest correlation was identified over the whole growth period ( R 2 = 0.58, p < 0.01). Highly significant correlations for GDD were also noted in Z30–Z50 ( R 2 = 0.50, p < 0.01), Z60–Z70 ( R 2 = 0.14, p < 0.01), and Z70–Z91 ( R 2 = 0.23, p < 0.01), whereas no significance was found in Z20–Z30 and Z50–Z60. SSD exhibited highly significant correlations ( p < 0.01) with PC in all growth stages and the whole period except for Z09–Z20. Notably, the highest coefficient of determination for SSD was observed during the jointing to heading stage (Z30–Z50, R 2 = 0.46). 2.5. Selection of Primary Climate Factors Weight analysis based on the PLSR model elucidated the relative contributions of various meteorological factors to the variation in grain protein content ( Figure 3 ). The loading weights for the first two principal components (PC1 and PC2) indicated that the accumulated growing degree days over the whole growth period (GDD at Z09–Z91) possessed the highest loading on PC1 (highlighted by the red circle), identifying it as the predominant explanatory variable within this dimension. In contrast, the diurnal temperature ranges from seedling to tillering stages (DTR at Z09–Z20) exhibited the highest weight on PC2 (highlighted by the blue circle). Additionally, mean and maximum temperatures during the grain filling to maturity stages (T mean at Z70–Z91 and T max at Z70–Z91) also contributed substantially to the component space. Figure 3. Open in a new tab Loading plot of PLSR weights for the first and second components associated with grain protein content. Each point represents a climatic variable at a specific growth stage. The variable with the highest weight in the first component is highlighted with a red circle, and the variable with the highest weight in the second component is highlighted with a blue circle. Abbreviations of climatic factors are defined in Table 2 . Growth stage definitions are provided in the legend of Table 3 . The importance and directional influence of each meteorological factor were further quantified using variable importance in projection (VIP) scores and regression coefficients (RC) ( Figure 4 ). The analysis identified 16 meteorological factors with VIP scores exceeding 1.0 as critical determinants of spring wheat protein content. Among these, GDD at Z09–Z91 recorded the highest VIP value (1.69), followed by GDD at the jointing to heading stage (Z30–Z50, VIP = 1.53) and sunshine duration (SSD at Z30–Z50, VIP = 1.43). Regarding the regression coefficients, most factors with high VIP scores exerted a negative influence. Specifically, GDD at Z09–Z91 (RC = −0.088), GDD at Z30–Z50 (RC = −0.077), and SSD at Z30–Z50 (RC = −0.068) exhibited substantial negative coefficients. Conversely, DTR and Tmax during the seedling to tillering stage (Z09–Z20) demonstrated positive effects, with RCs of 0.052 and 0.036, respectively. Figure 4. Open in a new tab Variable importance in projection (VIP) scores and regression coefficients (RC) of climatic factors derived from the PLSR model for grain protein content. Bars represent VIP scores (left y -axis), and red dots represent regression coefficients (right y -axis). The horizontal dashed line indicates the threshold of VIP = 1. Abbreviations of climatic factors are defined in Table 2 . Growth stage definitions are provided in the legend of Table 3 . 2.6. Structural Equation Modeling and Contribution Analysis of Meteorological Factors Based on the above analysis, 16 significant indicators with VIP > 1 were preliminarily screened. To further enhance the model’s robustness and predictive accuracy, a two-step variable selection process was implemented. First, indicators significantly correlated with grain protein content ( p < 0.05) based on linear regression analysis ( Figure 2 ) and possessing a VIP > 1 from the PLSR model were identified to ensure both statistical significance and explanatory power. Second, a stepwise elimination approach was applied to retain the top 10 core factors, thereby minimizing multicollinearity and maximizing the model’s predictive power ( R 2 = 0.637). These 10 factors including GDD at Z09–Z91, GDD at Z30–Z50, SSD at Z30–Z50, SSD at Z20–Z30, SSD at Z50–Z60, T mean at Z70–Z91, SSD at Z09–Z91, T max at Z70–Z91, T max at Z09–Z20, and DTR at Z30–Z50, were ultimately retained for structural equation analysis. Structural equation analysis of these key meteorological indicators revealed significant differences in the direct effects of various factors on PC ( Figure 5 ). Specifically, “Thermal conditions” exerted a significant positive direct effect on PC, with a path coefficient of 0.965 ( p = 0.005), while “Growing degree days” demonstrated a highly significant negative direct effect of equal magnitude (–0.965, p = 0.000). Although “Sunshine duration” also showed a negative association (–0.722), this effect did not reach statistical significance ( p = 0.062). In the visual representation of the model, solid lines indicate statistically significant direct effects ( p < 0.05), while dashed lines indicate non-significant effects ( p ≥ 0.05). Overall, the model accounted for a substantial proportion of the variance in PC, with an R 2 of 0.637. Figure 5. Open in a new tab Structural equation model illustrating the direct effects of thermal conditions, growing degree days, and sunshine duration on grain protein content in spring wheat. Solid lines indicate significant paths, and dashed lines indicate non-significant paths. The coefficient of determination ( R 2 ) for protein content is shown in the central node. Abbreviations of climatic factors are defined in Table 2 . Growth stage definitions are provided in the legend of Table 3 . 3. Discussion 3.1. Environmental Dominance over Genotype in Determining Protein Content This study systematically analyzed the protein content of spring wheat across four distinct ecological regions, revealing the dominant influence of environmental factors over genetic factors. While significant genetic variation was observed among cultivars ( Figure 1 b), the two-way ANOVA revealed that the magnitude of the environmental main effect ( F = 155.82) was approximately fourfold greater than that of the genotypic effect ( Table 1 ). This finding corroborates extensive literature suggesting that across broad geographical scales, environmental constraints and genotype × environment (G × E) interactions frequently overshadow singular genetic contributions [ 19 ]. Specifically, meteorological conditions during the grain-filling period have been identified as the primary drivers of variation in wheat quality traits [ 20 ], a trend observed in other cereal crops, where environmental factors account for 29–37% of the total variation in protein content [ 21 , 22 , 23 ]. It is important to note that while rainfall and nitrogen availability are known to influence grain protein content, our experimental design minimized these confounding factors. Supplemental irrigation was provided to offset natural precipitation deficits, and a uniform, non-limiting nitrogen fertilization rate (180 kg N/ha) was applied across all sites. Consequently, the observed spatial heterogeneity in protein content is primarily attributable to the distinct thermal and light regimes rather than variations in water or nitrogen supply. Furthermore, as shown in Figure 1 a, Erguna exhibits the highest protein content, while Bayannur has the lowest. This significant spatial heterogeneity is primarily attributed to the substantial differences in climatic resources between the two regions ( Table 2 ). Conversely, the relatively lower accumulated temperature and specific phenological patterns in Erguna likely facilitated a more favorable window for nitrogen accumulation and remobilization [ 24 ]. In contrast, the high temperature and high radiation environment in Bayannur, while conducive to rapid biomass and starch deposition, likely induced a “dilution effect,” thereby compromising the final protein concentration [ 3 , 25 ]. Having established the predominant role of environment, we next sought to dissect how specific environmental components exert their influence. Crucially, our data reveal that not all heat is equal; the distinction between thermal intensity and thermal accumulation is paramount. 3.2. Thermal Intensity at Critical Windows Drives Nitrogen Remobilization via Enzymatic Regulation It is well-established that climatic factors modulate grain chemical composition by regulating fundamental physiological processes, including photosynthesis, respiration, and nitrogen remobilization [ 26 , 27 ]. However, this experiment revealed that PC is driven not merely by the simple accumulation of heat units, but rather by the thermal intensity during specific critical developmental windows. The distinct temperature regimes across the four locations, particularly the thermal intensity during the Z50–Z60 stage, were identified as the primary climatic drivers of the observed spatial heterogeneity in grain protein content. Physiologically, the Erguna recorded its highest maximum temperature (T max , 28.7 °C) during the heading to anthesis stage (Z50–Z60), which coincides with the critical window for nitrogen translocation from vegetative organs to grains. During this phase, the degradation of stored proteins in the flag leaf and stem, along with the subsequent re-assimilation of amino acids, is heavily dependent on the catalytic activities of glutamine synthetase (GS) and glutamate synthase (GOGAT). Previous studies indicate that the optimal catalytic temperature for GS typically ranges between 25 and 30 °C [ 28 ]. While temperatures across all four experimental sites fell within this general optimal range during the reproductive phase, the distinct advantage of the Erguna lies in the precise coupling of thermal intensity with specific phenological stages. Studies have indicated that temperature modulates wheat quality by altering the balance between starch and protein components [ 29 ]. Specifically, while daytime warming often increases protein content by suppressing starch accumulation, extreme temperatures exceeding 30 °C can impair protein quality parameters. Our study complements these findings by demonstrating that it is not merely the absolute temperature, but the precise spatiotemporal coupling of thermal intensity during the heading to anthesis stage that optimizes nitrogen remobilization, thereby maximizing protein content while avoiding the ‘dilution effect’ associated with excessive thermal accumulation. Specifically, at the onset of nitrogen remobilization (Z50–Z60), Erguna recorded the highest maximum temperature (T max , 28.7 °C), which was significantly higher than Bayannur (26.6 °C). From a biochemical perspective, as GS acts as the rate-limiting enzyme in nitrogen metabolism, its catalytic velocity increases significantly with rising temperature intensity within the optimal range [ 30 ]. During this critical transition period, the thermal intensity in Erguna, which approached the peak of enzymatic reaction potential during this transitional period, likely maximized the instantaneous rates of protein degradation and amino acid mobilization in source organs. This process may also involve the induction of heat shock proteins (HSPs), thereby ensuring a more abundant nitrogen supply for the developing grain [ 31 , 32 ]. This physiological inference is quantitatively supported by the Structural Equation Modeling (SEM) analysis, where “Thermal Conditions” exerted a substantial positive direct effect (0.965) on PC ( Figure 5 ). This confirms that a high-intensity thermal environment within the optimal range during early reproductive growth serves as a critical positive signal for activating nitrogen metabolic pathways. 3.3. Decoupling Heat Intensity from Accumulation: The Mechanism of “Dilution Effect” However, the regulatory role of temperature on protein synthesis is not unidimensional. While high thermal intensity acts as a “catalyst” for N remobilization during anthesis, thermal accumulation (GDD) during the grain filling stage functions as a “driver” for carbon deposition. If heat appears in the form of prolonged accumulation, its role shifts from “activation” to “dilution.” To disentangle the collinearity among complex climatic factors and quantify their causal effects, this study integrated PLSR and SEM. Consistent with the hypothesis that “excessive thermal resources lead to quality dilution,” the PLSR weight plot ( Figure 3 ) and VIP scores ( Figure 4 ) consistently identified whole growth period GDD (Z09–Z91) and jointing to heading GDD (Z30–Z50) as the predominant negative drivers. Notably, the Z30–Z50 stage was pinpointed as a critical window; this period not only determines sink capacity (grain number) but also represents the peak of nitrogen uptake for the wheat plant [ 33 ]. High GDD and SSD during this phase (indicated by high VIP values in Figure 4 ) may accelerate phenological development, thereby shortening the temporal window for N uptake and limiting the N source available for subsequent grain filling [ 11 ]. A distinct divergence was observed in the SEM analysis: the direct path coefficient of SSD on PC was not significant, whereas GDD maintained a highly significant negative effect (−0.965). This statistical discrepancy points to a deep physiological mechanism governing quality formation in semi-arid regions. In high-radiation areas like Inner Mongolia, solar radiation is typically saturating and is not the bottleneck for photosynthate accumulation. Conversely, thermal accumulation (GDD) acts as the “proximal driver” regulating the grain-filling rate. These phenomena can be deeply explained by the theories of Carbon–Nitrogen (C-N) balance and Allometric Growth. Specifically, the extremely high GDD (486.3 °C·d) during late grain filling to maturity (Z70–Z91) in Bayannur significantly upregulated the activity of ADP-glucose pyrophosphorylase (AGPase)—the rate-limiting enzyme for starch synthesis—via thermodynamic mechanisms [ 34 , 35 ]. Under such conditions, the kinetic rate of starch accumulation far exceeded that of nitrogen deposition. Since starch constitutes the majority of grain dry weight, its rapid biomass expansion exerted a physical “Dilution Effect” on protein, resulting in a significant decrease in final PC [ 3 , 36 ]. The strong negative direct effect of GDD (−0.965) in the SEM ( Figure 5 ) further corroborates that while SSD provides the energetic basis for photosynthesis, the allometric relationship between C and N is ultimately dictated by heat-driven enzymatic reaction rates. By maintaining high thermal intensity early on (to trigger N flow) but lower accumulated temperature during the late stages (to limit C flow), Erguna achieved a physiological balance that favored protein concentration [ 37 ]. 3.4. Early-Stage “Priming Effect” of Diurnal Temperature Range on Root Establishment and Nitrogen Uptake Beyond the well-established regulation of post-anthesis source–sink dynamics, our PLSR model identified the DTR during the seedling to tillering stage (Z09–Z20) as the second most significant factor of PC variations. This finding points towards an “epigenetic priming” mechanism, suggesting that early-season environmental cues can imprint lasting effects on crop physiology. A substantial DTR during early vegetative growth is hypothesized to modulate the equilibrium of endogenous phytohormones, particularly the ratio of abscisic acid (ABA) to cytokinins (CTK), thereby facilitating the establishment of a robust root system capable of penetrating deeper soil layers [ 38 , 39 ]. Such an enhanced root architecture serves a dual function: it not only augments the capacity for mineral nitrogen uptake from the subsoil during later reproductive stages but also regulates post-anthesis leaf senescence via root-sourced signaling, consequently elevating the nitrogen harvest index and allocation to the grain [ 40 ]. This mechanism is further corroborated by the correlation analysis ( Figure 2 ), which revealed a significant positive correlation between Tmin during the vegetative phase and final PC. Collectively, these results demonstrate that the determination of high-protein traits in spring wheat is a synergistic process spanning the entire growth cycle; it is not solely contingent upon carbon–nitrogen competition within the “sink” during grain filling but is fundamentally initiated by the environmental adaptation and establishment of the “source” during the seedling stage [ 41 , 42 ]. 3.5. Integrated Mechanistic Model of Climate-Protein Content Interactions Based on the physiological and statistical evidence discussed above, a conceptual diagram illustrating the potential mechanisms by which key climatic factors enhance grain protein content in spring wheat is proposed ( Figure 6 ): (1) Seedling to tillering (Z09–Z20): Pronounced DTR facilitates deep root system architecture through a “root priming effect,” thereby enhancing the N uptake potential in subsequent growth stages. (2) Heading to anthesis (Z50–Z60): Elevated Tmax upregulates the activities of GS and GOGAT, accelerating N remobilization from vegetative tissues to the grain and ensuring an ample N supply for protein synthesis. (3) Grain filling to maturity (Z70–Z91): Restricted GDD and SSD suppress the activity of AGPase, the rate-limiting enzyme for starch synthesis. This restricts excessive starch deposition and mitigates the “dilution effect,” consequently elevating the final grain protein concentration. Figure 6. Open in a new tab Integrating the present results with established literature, a schematic diagram was constructed to elucidate the potential mechanisms through which key climatic factors drive increases in spring wheat grain protein content: (1) Seedling to tillering (Z09–Z20): High DTR stimulates root deepening via a “priming mechanism,” augmenting N acquisition efficiency during later reproductive phases. (2) Heading to anthesis (Z50–Z60): High Tmax amplifies glutamine synthetase (GS) and glutamate synthase (GOGAT) catalytic rates, expediting vegetative N translocation to the grain sink to fuel protein accumulation. (3) Grain filling to maturity (Z70–Z91): Low GDD and SSD limit the activity of ADP-glucose pyrophosphorylase (AGPase), preventing carbohydrate over-accumulation and alleviating the “dilution effect” to maximize protein concentration. Dashed arrows indicate promoting or positive regulatory effects, whereas blunt-ended lines. 4. Materials and Methods 4.1. Site Description Field experiments were conducted during the 2018 cropping season at four distinct sites across the Inner Mongolia Autonomous Region, China, representing the primary spring wheat production zones. The experimental locations—Bayannur (40°45′ N, 107°25′ E), Chifeng (42°17′ N, 118°58′ E), Hohhot (40°48′ N, 111°41′ E), and Erguna (50°14′ N, 120°11′ E)—were selected to encompass a broad geographical and climatic gradient. These sites span diverse ecological zones, transitioning from temperate semi-arid climates to cold-temperate humid regimes. Specifically, Bayannur is situated within the Hetao Irrigation District; Chifeng and Hohhot represent the typical semi-arid agro-pastoral ecotone, whereas Erguna characterizes the high-latitude cold region. The geographical coordinates and spatial distribution of these study sites are visualized in Figure 7 . Figure 7. Open in a new tab Locations and topographic maps of experimental sites. The four study sites (Bayannur, Chifeng, Hohhot, and Erguna) are indicated by yellow circles. The background map shows the digital elevation model of Inner Mongolia, with elevations ranging from 89 m to 3422 m. Major rivers are shown in blue lines. The inset map in the upper left corner shows the location of Inner Mongolia within China. 4.2. Experimental Method The experiment selected 15 representative spring wheat varieties as test materials. These varieties exhibit diverse genetic backgrounds, with their names, origins, and parental information detailed in Table 4 . Table 4. Pedigree information and geographical origin of the 15 spring wheat cultivars used in this study. Serial Number Variety Name Female/Male Varietal Origin 1 XC6 Orofen/76-26 Urumqi, Xinjiang, China 2 WC3 Wuchun 2/8024 Wuwei, Gansu, China 3 BF1 Yongliang 4/90-13-2 Bayannur, Inner Mongolia, China 4 BM14 Bamai 13/2003-18 Bayannur, Inner Mongolia, China 5 BF6 Yongliang 4/96-205 Bayannur, Inner Mongolia, China 6 NC39 Ningchun 4/90W18 Yinchuan, Ningxia, China 7 XC37 Xinchun 22/01-26 Urumqi, Xinjiang, China 8 BF5 Yongliang 4/96-205 Bayannur, Inner Mongolia, China 9 YL4 7606/7914 Bayannur, Inner Mongolia, China 10 XC26 96-12/96-43 Urumqi, Xinjiang, China 11 NM3 81-39-2/81-18 Urumqi, Xinjiang, China 12 MH1 K420/7606 (Antherculture) Hohhot, Inner Mongolia, China 13 Y3002 Yongliang 4/96-205 Bayannur, Inner Mongolia, China 14 NP5 Yongliang 4/90-13-2 Bayannur, Inner Mongolia, China 15 LC10 Liaochun 9/7846 Shenyang, Liaoning, China Open in a new tab The field experiments were arranged in a randomized complete block design with three biological replicates. Each experimental plot covered an area of 2 m 2 , consisting of rows spaced 20 cm apart with a plant-to-plant spacing of 5 cm. Seeds were precision-sown at a density of 20 seeds per row. To minimize edge effects, a 1 m buffer zone of guard rows was established around the perimeter of the experimental field. Routine field management followed standard local agronomic practices for high-yield wheat production. Specifically, to decouple the confounding effects of water deficit on grain protein synthesis, supplemental irrigation was applied during critical growth stages to offset natural precipitation deficits. This ensured non-limiting water conditions throughout the reproductive period, thereby allowing climatic factors such as temperature and light to become the primary variables influencing PC. To characterize the baseline soil fertility and minimize the interference of soil heterogeneity, the physicochemical properties of the soil (0–20 cm) were measured at each site ( Table A1 ). Furthermore, a uniform, non-limiting nitrogen fertilization rate of 180 kg N ha −1 was applied across all experimental sites, following standardized protocols for multi-environment wheat quality trials to ensure that nitrogen availability was not a limiting factor for grain protein content [ 3 ]. 4.3. Data Collection and Measurement 4.3.1. Meteorological Data Acquisition Daily meteorological datasets covering the entire 2018 spring wheat growing season (spanning March through August) were acquired from the China Meteorological Administration (CMA). The dataset comprised five key climatic variables: maximum temperature (T max ), minimum temperature (T min ), mean temperature (T mean ), precipitation, and sunshine duration (SSD). The temporal dynamics and spatial heterogeneity of these environmental factors across the four experimental sites are illustrated in Figure 8 . Figure 8. Open in a new tab Dynamics of meteorological factors during the spring wheat growing season (March–August) in Bayannur, Chifeng, Hohhot, and Erguna. The insets illustrate the overall climatic variations across the entire growth period. Different letters in the insets indicate significant differences among regions ( p < 0.05). 4.3.2. Determination of Protein Content At physiological maturity, wheat spikes were manually harvested and air-dried to a constant weight under ambient conditions. Following this, mechanical threshing was performed to obtain clean grain samples. Prior to chemical analysis, the grains were milled into a fine powder using a laboratory cyclone mill and passed through a 0.5 mm sieve. Total grain nitrogen content was then determined using the standard micro-Kjeldahl method. Subsequently, protein content was calculated by multiplying the nitrogen concentration by a conversion factor of 5.7. To ensure analytical precision, all measurements were performed in two technical replicates. 4.4. Calculation Methods and Statistical Analysis 4.4.1. Calculation of Meteorological Indices Based on raw meteorological data, calculate key meteorological indicators. Specifically, the Diurnal Temperature Range (DTR) was calculated using the following equation [ 43 ]: D T R = T m a x − T m i n , (1) Heat accumulation throughout the wheat growth period was characterized using growing degree days (GDD), also referred to as Effective Accumulated Temperature (EAT). The GDD values were calculated according to the method described by using the following equation [ 44 ]: G D D = ∑ i = d 1 d n ( T m e a n , i − T b a s e ) (2) where d 1 and dn represent the first and last day of the specific growth stage, respectively; T mean,i denotes the daily mean temperature for day i; and T base is the base temperature for spring wheat growth. T base was set at 10 °C. To ensure biological relevance, if T mean,i < T base , the daily GDD value was recorded as zero. 4.4.2. Statistical Analysis Raw data were organized and preprocessed using Microsoft Excel. A two-way analysis of variance (ANOVA) was performed using IBM SPSS Statistics 26 to evaluate the main effects of variety, area, and their interaction on PC. Pearson correlation analysis was also conducted using SPSS to identify relationships between key climate factors and quality traits. Multivariate analysis and data visualization were executed using SIMCA 14.1 and Origin 2025b. Furthermore, partial least squares structural equation modeling (PLS-SEM) was constructed using SmartPLS 4.1 to quantify the causal pathways driving PC variation. Schematic diagrams of the structural models and conceptual diagrams were refined using Microsoft PowerPoint 2021. 5. Conclusions This study establishes that in semi-arid regions, environmental factors are the primary drivers of spring wheat protein content variation, exerting a significantly greater influence than genotype. The core contribution of this study lies in proposing a “Thermal Intensity versus Accumulation” theoretical framework for quality ecology: specifically, higher thermal intensity during heading to anthesis (Z50–Z60) acts as a positive signal to activate nitrogen remobilization via optimized enzymatic kinetics, whereas excessive accumulated heat (GDD) and radiation during grain filling to maturity (Z70–Z91) drive a “dilution effect” by disproportionately accelerating starch deposition. Additionally, the diurnal temperature range at the seedling stage was identified as a critical early predictor, suggesting a physiological “priming” effect on root establishment. In conclusion, high-quality wheat production necessitates a precise spatiotemporal coupling of climatic resources—synergizing “high thermal intensity for nitrogen mobilization” with “moderate thermal accumulation to mitigate carbohydrate dilution.” Future breeding and management strategies must prioritize aligning crop phenology with these optimal thermal windows to reconcile the trade-off between yield and quality under a warming climate. Acknowledgments The authors thank the Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences for support. Abbreviations The following abbreviations are used in this manuscript: Tmean Mean temperature Tmax Maximum temperature Tmin Minimum temperature DTR Diurnal temperature range GDD Growing degree days SSD Sunshine duration Z09–Z20 Seedling to tillering Z20–Z30 Tillering to jointing Z30–Z50 Jointing to heading Z50–Z60 Heading to anthesis Z60–Z70 Anthesis to grain filling Z70–Z91 Grain filling to maturity Z09–Z91 Whole growth period VIP Variable importance in projection RC Regression coefficients PLSR Partial least squares regression SEM Structural equation modeling PC Protein content GS Glutamine synthetase GOGAT Glutamate synthase Open in a new tab Appendix A Table A1. Physicochemical properties of the soil (0–20 cm) at the four experimental sites. Area Soil Type Available N (mg kg −1 ) Available P (mg kg −1 ) Available K (mg kg −1 ) PH Bayannur Loam 102.0 32.8 140.0 8.5 Chifeng Loam 42.0 2.6 72.0 7.9 Hohhot Sandy loam 59.5 16.0 117.5 8.2 Erguna Chernozem 144.2 22.52 85.3 7.2 Open in a new tab Author Contributions Conceptualization, X.L. and J.Y.; methodology, J.Y.; validation, W.Y., H.Z. and X.Z.; data curation, H.Z., X.Z., J.L. (Juan Liu), T.Z., Z.Z., T.M., C.L. and X.G.; writing—original draft preparation, X.L.; writing—review and editing, W.Y.; funding acquisition, J.L. (Juan Li), X.W. and Z.L. All authors have read and agreed to the published version of the manuscript. Data Availability Statement The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors. Conflicts of Interest The authors declare no conflicts of interest. Funding Statement This research was funded by the Inner Mongolia Natural Science Foundation Project (2024 MS03007), the National Natural Science Foundation Project (32560442), the Leading Talent Project of “Grassland Talents” in Inner Mongolia Autonomous Region., the Inner Mongolia Leading Talent Team in Science and Technology Project in 2022 (2022LJRC0010), the Research Support Fund Project (2026KYBZN02), National Wheat Industrial Technology System (CARS-03), Inner Mongolia Autonomous Region Science and Technology Plan Project (2025KJHZ0053), Inner Mongolia Autonomous Region Science and Technology Plan Project (2025YFDZ0061), Hulunbuir City Science and Technology Plan Project (KJTW2025002), Inner Mongolia Advantageous and Specialty Wheat Breeding Joint Research Project (YZ2023008) and Inner Mongolia Autonomous Region “Talent Revitalization of Inner Mongolia” Project Team (2025TRL05). 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