多模型解析玉米籽粒容重的营养品质贡献度与区域异质性

doi:10.3864/j.issn.0578-1752.2026.05.005

Abstract

Abstract:

【Objective】 This study systematically quantified the contribution rates and spatial heterogeneity of protein, starch, and fat—the three major nutritional components—to maize kernel test weight formation, and elucidated how genetic background, ecological region, and cultivation density interactively modulate both nutritional quality and test weight. The findings aim to establish a science-based foundation for region-specific optimization of maize quality and to advance an integrated “high-yield-high-quality- high-efficiency” production paradigm.【Method】 A nationwide field survey was conducted across four major maize-producing regions in China, encompassing 718 representative kernel samples from 77 leading cultivars grown under 24 distinct planting density gradients (37 500-127 500 plants/hm²). All samples were naturally air-dried to standardized moisture content (14% w.b.) prior to uniform physicochemical analysis. Protein, starch, and fat contents were determined using calibrated near-infrared reflectance spectroscopy (NIRS), and test weight was measured with a certified grain test weight instrument (ISO 7971-3 compliant). To dissect the complex determinants of test weight, we implemented a hierarchical analytical framework integrating: (i) multiple linear regression to estimate independent linear effects; (ii) random forest modeling to capture nonlinear interactions and relative feature importance; and (iii) structural equation modeling (SEM) to infer directional causal pathways among traits. Three-way ANOVA was further employed to assess the main and interactive effects of cultivar, ecological region, and cultivation density on test weight and each nutritional component.【Result】 Protein (β=8.406, P<0.001) and starch (β=6.413, P<0.001) emerged as statistically robust and biologically dominant drivers of test weight, accounting for 28% and 45% of the total explained variance in the random forest model, respectively—both exhibiting high path coefficient stability in SEM (standardized coefficients ≥0.72, P<0.001). In contrast, fat showed negligible explanatory power (2%), and its effect failed to reach statistical significance (P=0.09). Three-way ANOVA confirmed highly significant (P<0.001) main effects and two- and three-way interactions among cultivar, ecological region, and density for test weight, protein, and starch—indicating strong contextual dependency. Spatially, protein contributed most strongly in the Northeast spring maize region (43.9% of model variance), whereas starch dominated in the Huang-Huai-Hai summer maize region (52.9%). Critically, the synergistic contribution of protein and starch jointly explained 81.0% and 85.0% of the total model variance in these two regions, respectively. Structural equation modeling revealed a direct positive effect of protein on test weight, but an indirect negative effect stemming from the compensatory relationship between protein and starch accumulation, which underscores the physiological trade-off in kernel sink-filling.【Conclusion】 Maize test weight formation was a biologically synergistic process driven by protein and starch, with fat playing no substantial role. Significant interactions existed among cultivar, ecological region, and density, with the same cultivar exhibiting distinct regulatory pathways under different ecological and cultivation conditions. Consequently, the Northeast region should prioritize high-protein cultivar selection and precise nitrogen management, while the Huang-Huai-Hai region should enhance carbon assimilation efficiency and regulate key starch-synthesis enzymes. All production areas should achieve a precise "cultivar-region-practice" matching strategy to synergistically improve maize yield and quality.

Key words: maize, nutritional quality, kernel test weight, regional heterogeneity, multi-model analysis

DONG JinLong, ZHAO Ying, YU HaiBing, LÜ JianYe, QIN JiaQi, LIANG Chen, MING Bo, LI ShaoKun. Multi-Model Elucidating of Nutritional Quality Contributions to Maize Kernel Test Weight and Regional Heterogeneity[J].Scientia Agricultura Sinica, 2026, 59(5): 985-995.

Figures/Tables 10

Table 1

Overview of national maize sampling sites"

玉米产区 Maize production region	省份 Province	样品数量 Number of samples	取样地区 Sampling region	播种密度 Sowing density (plants/hm²)
黄淮海夏玉米区 Huang-Huai-Hai summer maize region	安徽 Anhui	34	阜阳Fuyang（17）、宿州Suzhou（7）、淮北Huaibei（3）、亳州Bozhou（4）、蚌埠Bengbu（3）	82500、85500、90000、97500、105000
	河北 Hebei	34	石家庄Shijiazhuang（25）、邯郸Handan（4）、邢台Xingtai（5）	60000、67500、75000、82500
	河南 Henan	47	焦作Jiaozuo（4）、商丘Shangqiu（4）、许昌Xuchang（4）、驻马店Zhumadian（3）、开封Kaifeng（4）、新乡Xinxiang（4）、三门峡Sanmenxia（3）、安阳Anyang（3）、鹤壁Hebi（2）、平顶山Pingdingshan（5）、周口Zhoukou（8）、洛阳Luoyang（3）	66000、67500、75000、82500、90000、105000
	江苏 Jiangsu	17	盐城Yancheng（5）、徐州Xuzhou（8）、连云港Lianyungang（4）	55500、64500、67500、75000、82500、90000
	山东 Shandong	108	青岛Qingdao（10）、烟台Yantai（10）、潍坊Weifang（10）、济宁Jining（22）、枣庄Zaozhuang（5）、菏泽Heze（5）、聊城Liaocheng（21）、德州Dezhou（20）、淄博Zibo（5）	63000、67500、72000、75000、82500、85500、87000、90000、93000、97500
	陕西 Shaanxi	50	榆林Yulin（25）、西安Xi'an（6）、渭南Weinan（6）、宝鸡Baoji（7）、咸阳Xianyang（6）	63000、67500、72000、75000、82500、87000、90000
西北春玉米区 Northwest spring maize region	甘肃Gansu	15	平凉Pingliang（10）、白银Baiyin（5）	60000、64500、67500、75000
	内蒙古 Inner Mongolia	16	呼和浩特Hohhot（5）、巴彦淖尔Bayannur（5）、包头Baotou（3）、鄂尔多斯Ordos（3）	67500、75000、82500、85500
	新疆Xinjiang	15	昌吉Changji（5）、博乐Bole（5）、乌鲁木齐Urumqi（5）	112500、120000、127500
	山西Shanxi	22	长治Changzhi（18）、晋城Jincheng（4）	67500、75000
西南夏玉米区 Southwest summer maize region	广西 Guangxi	20	崇左Chongzuo（5）、南宁Nanning（5）、贵港Guigang（4）、河池Hechi（4）、百色Baise（2）	42000、45000、52500
	湖北Hubei	16	恩施Enshi（12）、宜昌Yichang（4）	37500、40500、45000、75000
	四川Sichuan	10	绵阳Mianyang（5）、德阳Deyang（5）	52500、57000、63000、90000
东北春玉米区 Northeast spring maize region	黑龙江 Heilongjiang	162	黑河Heihe（37）、齐齐哈尔Qiqihar（44）、鹤岗Hegang（2）、双鸭山Shuangyashan（6）、绥化Suihua（19）、伊春Yichun（6）、哈尔滨Harbin（8）、佳木斯Jiamusi（8）、大庆Daqing（32）	60000、67500、75000、82500、90000、97500、105000
	吉林 Jilin	69	长春Changchun（4）、四平Siping（2）、白城Baicheng（8）、松原Songyuan（12）、吉林Jilin（40）、敦化Dunhua（3）	52500、55500、60000、67500、72000、75000、82500、90000
	内蒙古 Inner Mongolia	29	通辽Tongliao（14）、赤峰Chifeng（15）	60000、72000、75000、82500、90000、97500、112500
	辽宁 Liaoning	60	大连Dalian（5）、丹东Dandong（7）、营口Yingkou（5）、阜新Fuxin（22）、沈阳Shenyang（10）、铁岭Tieling（11）	60000、67500、75000、82500、97500

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Fig. 2

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Table 8

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产区 Region	蛋白质 Protein (%)	脂肪 Fat (%)	淀粉 Starch (%)	容重 Test weight (g·L^-1)	平均种植密度 Average planting density (plants/hm²)
东北春玉米区 Northeast spring maize	10.08±0.83a	3.80±0.43c	74.37±1.13b	775.06±25.98a	77944
西北春玉米区 Northwest spring maize	9.17±0.84b	3.98±0.34b	74.55±0.85a	771.23±22.69a	83077
西南夏玉米区 Southwest summer maize	9.66±0.84ab	4.80±0.39a	73.68±0.78c	756.27±23.64c	54545
黄淮海夏玉米区 Huang-Huai-Hai summer maize	9.35±0.81b	3.97±0.69b	75.43±3.82a	769.42±22.36b	79382

变量 Variables	非标准化回归系数 β (Coefficient)	标准化回归系数 β (Standardized)	标准误 SE	t值 t value	P值 P value	95%置信区间 95% CI
蛋白质Protein	6.41	0.33	1.61	3.99	P<0.001	3.25-9.57
脂肪Fat	5.93	0.12	3.51	1.69	P =0.09	-0.95-12.81
淀粉Starch	8.41	0.31	2.22	3.78	P<0.001	4.06-12.76

路径关系 Pathway relationship	非标准化系数 Unstandardized coefficient	标准化系数 Standardized coefficient	标准误 SE	t值 t value	P值 P value
蛋白质→容重 Protein→test weight	8.41	0.62	1.40	6.02	P<0.001
淀粉→容重 Starch→test weigh	6.41	0.54	1.09	5.87	P<0.001
蛋白质→淀粉 Protein→starch	-0.10	-0.18	0.05	-2.11	P=0.035

参数组合 Parameter combination	决定系数 R²	均方根误差 RMSE (g·L^-1)	平均绝对误差 MAE (g·L^-1)	调整后决定系数Adjusted R²	性能变化 Performance change
ntree=500, mtry=1, max_depth=6	0.589	7.128	5.812	0.587	欠拟合Underfitting
ntree=500, mtry=1, max_depth=8	0.604	6.981	5.642	0.602	优化中Optimizing
ntree=500, mtry=1, max_depth=10	0.597	7.051	5.735	0.595	基准模型Benchmark
ntree=800, mtry=1, max_depth=6	0.633	6.712	5.423	0.631	-
ntree=800, mtry=1, max_depth=8	0.648	6.573	5.281	0.646	+8.5%▲
ntree=800, mtry=1, max_depth=10	0.642	6.650	5.432	0.640	区域最佳候选Regional best candidate
ntree=1100, mtry=1, max_depth=6	0.638	6.682	5.301	0.636	-
ntree=1100, mtry=1, max_depth=8	0.659	6.501	5.152	0.657	+10.4%▲
ntree=1100, mtry=1, max_depth=10	0.645	6.619	5.231	0.643	全国最优模型National optimal

模型类型 Model type	淀粉贡献 Starch contribution	蛋白质贡献 Protein contribution	脂肪贡献 Fat contribution
多元线性回归 Multiple linear regression	0.31	0.33	0.12
随机森林模型 Random forest model	0.45	0.28	0.02
结构方程模型 Structural equation model	0.64	0.84	0.03

Multi-Model Elucidating of Nutritional Quality Contributions to Maize Kernel Test Weight and Regional Heterogeneity

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玉米产区 Maize production region	变量 Variables	非标准化回归系数 β (Coefficient)	标准化回归系数 β (Standardized)	标准误 SE	t值 t value	P值 P value	95%置信区间 95% CI
黄淮海夏玉米区 Huang-Huai-Hai summer maize region	蛋白质Protein	12.67	0.19	6.41	1.98	0.05	0.11-25.23
	脂肪Fat	1.60	0.02	6.52	0.25	0.81	-11.18-14.38
	淀粉Starch	8.92	0.26	3.36	2.66	0.01	2.33-15.51
东北春玉米区 Northeast spring maize region	蛋白质Protein	18.85	0.44	3.21	5.87	0.00	12.56-25.14
	脂肪Fat	6.31	0.08	4.95	1.27	0.20	-3.38-16.00
	淀粉Starch	11.36	0.37	2.35	4.82	0.00	6.75-15.97

分析因素 Analysis of factors	容重 Test weight	蛋白质 Protein	淀粉 Starch
分析因素 Analysis of factors	F值 F value	P值 P value	F值 F value
品种 Variety	38.2	P<0.001	35.6
区域 Region	42.5	P<0.001	28.9
密度 Density	6.9	P<0.05	3.2
品种×区域 Variety×region	15.3	P<0.001	12.8
品种×密度 Variety×density	3.1	P<0.05	2.8
区域×密度 Region×density	7.2	P<0.01	5.6
品种×区域×密度 Variety×region×density	4.2	P<0.05	3.3

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