1952年以来我国大豆单产变异特征及其影响因素研究

秦婷婷; 曹鑫悦; 周泽群; 褚超群; 方雨桐; 曲乐安; 支俊俊; 王震; 耿涛

doi:10.12357/cjea.20210227

1952年以来我国大豆单产变异特征及其影响因素研究

doi: 10.12357/cjea.20210227

秦婷婷^{1, 2,},
曹鑫悦¹,
周泽群¹,
褚超群¹,
方雨桐¹,
曲乐安¹,
支俊俊^1, ,,
王震¹,
耿涛¹

1.
安徽师范大学地理与旅游学院　芜湖　241002
2.
中国矿业大学(北京)地球科学与测绘工程学院　北京　100083

基金项目: 教育部人文社会科学研究青年基金项目(21YJCZH243)、国家自然科学基金项目(41501229)、安徽师范大学博士科研启动金项目(2018xjj45)和安徽师范大学大学生创新创业训练计划项目(202110370219)资助

详细信息

作者简介:
秦婷婷, 主要研究方向为资源与环境信息技术。E-mail: Shuua1999@163.com

通讯作者:
支俊俊, 主要研究方向为农业遥感与信息技术。E-mail: zhijunjun@ahnu.edu.cn

中图分类号: S210
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出版历程
- 收稿日期: 2021-04-14
- 录用日期: 2021-09-10
- 网络出版日期: 2021-10-08
- 刊出日期: 2022-01-08

Variation characteristics of soybean yield since 1952 and its influencing factors in China

1.
School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
2.
College of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China

Funds: This study was supported by the Youth Foundation Project of Humanities and Social Sciences of Ministry of Education in China (21YJCZH243), the National Natural Science Foundation of China (41501229), the Doctoral Scientific Research Foundation of Anhui Normal University (2018xjj45), and the Undergraduate Innovation and Entrepreneurship Training Program of Anhui Normal University (202110370219)

More Information

Corresponding author: E-mail: zhijunjun@ahnu.edu.cn

摘要

摘要: 近几十年来, 我国大豆产需缺口不断扩大, 提升大豆单产水平已成为当前提高大豆总产量的首要可行举措。然而, 影响我国大豆单产的驱动因子及其地域空间差异特征并不明晰。本文通过搜集1952年、1965年、1978年、1990年、2000年、2010年和2017年的全国各省市农业统计年鉴等数据, 从大豆种植的管理措施、自然因素、科技水平、社会因素、经济因素等方面选取与大豆生产密切相关的13个因子, 以大豆单产作为目标变量构建增强回归树模型, 量化各因子的相对重要性及其与大豆单产之间的关系, 分析大豆单产的变异特征, 揭示全国尺度及4个大豆主产区之间的大豆单产驱动力时空分异特征。研究结果表明: 1)各年份的大豆单产变异系数为34.1%~73.2%, 表明全国各地市大豆单产之间存在较大的差异。本研究构建的增强回归树模型可有效解释43.3%的大豆单产变异性, 并可量化揭示各因子与大豆单产之间的非线性关系。2) 1952年以来影响我国大豆单产水平的最重要因素依次为大豆播种面积占农作物总种植面积的百分比(相对重要性为20.9%)、文盲率(18.9%)、每公顷化肥(折纯)施用量(10.7%)。3)不同主产区的大豆单产核心驱动力存在空间差异, 北方春大豆区的最重要因素为每公顷农业机械总动力(13.1%)、文盲率(11.8%), 黄淮海流域夏大豆区的最重要因素为每公顷化肥(折纯)施用量(25.6%)、每公顷农药(折纯)施用量(18.4%), 长江流域春夏大豆区的最重要因素为研发支出占地区生产总值的百分比(21.5%)、有效灌溉面积占农作物播种面积的百分比(14.3%), 南方多熟大豆区的最重要因素为每公顷化肥(折纯)施用量(22.7%)、第一产业占地区生产总值的百分比(13.3%)。4)大豆播种面积占农作物总播种面积的百分比对于全时期、改革开放前、改革开放后3个时期均是影响大豆单产最重要的因子, 改革开放前其他重要的因子包括文盲率和每公顷化肥(折纯)施用量, 改革开放后则包括每公顷农业机械总动力和年均温。总之, 我国各大豆主产区需合理施用化肥和农药, 努力提高机械化水平和农业生产者的知识水平, 本研究结果可为各省市采取有效措施提升大豆单产水平提供科学依据。
- 大豆 /
- 产量 /
- 驱动因素 /
- 增强回归树模型 /
- 空间异质性
Abstract: Over the past several decades, the consumers’ demand for soybeans has grown rapidly in China, resulting in a significant increase in the gap between production and demand. Therefore, increasing the total soybean output is of critical importance to ensure food security. Given that it is difficult to increase the total area of cultivated land in China, improving soybean yield per unit area land has become the primary measure for increasing the total soybean output. However, the determinants that directly affect soybean yield, the regional spatial heterogeneity of yield remain unclear. In this study, data from agricultural statistical yearbooks at both the provincial and prefecture levels in China as well as meteorological data (e.g., temperature, precipitation, and sunshine duration) from 1952 to 2017 (comprising 1952, 1965, 1978, 1990, 2000, 2010, and 2017) were collected, whereupon 13 factors closely related to soybean production were selected from the perspective of planting management measures, natural factors, scientific and technogical levels, social factors, and economic factors. Several boosted regression tree models were built to quantify the relative importance of each factor and to determine the mechanism through which it influenced soybean yield; to analyze the variation characteristics of soybean yield; and to reveal the spatiotemporal characteristics of key driving forces across the national scale and among the four major soybean-producing areas (i.e., the northern spring soybean area, the summer soybean area in the Huang-Huai-Hai Basin, the spring and summer soybean area in the Yangtze River Basin, and the southern soybean area) over a long period since 1952. The following results were obtained. 1) The coefficient of variation of soybean yields in different years ranged from 34.1% to 73.2%, indicating that there were substantial differences in yield across the regions in China. The boosted regression tree model could effectively explain 43.3% of the soybean yield variability and quantitatively revealed the nonlinear relationship between each factor and soybean yield in the national scale. 2) The most important factor affecting soybean yield in China since 1952 was the soybean sown area as a percentage of the total crop sown area (relative importance of 20.9%), followed by the illiteracy rate (18.9%) and fertilizer consumption (pure amount) per hectare (10.7%). 3) Spatial differences existed in the dominant driving factors of soybean yield among different main production areas. The main driving factors of the northern spring soybean area were the total power of agricultural machinery per hectare (13.1%) and the illiteracy rate (11.8%); those for the summer soybean area in the Huang-Huai-Hai Basin were the fertilizer consumption (pure amount) per hectare (25.6%) and pesticide consumption (pure amount) per hectare (18.4%); those for the spring and summer soybean area in the Yangtze River Basin were the R&D expenditure as a percentage of regional GDP (21.5%) and the effective irrigation area as a percentage of the crop sown area (14.3%); and those for the southern soybean area were the fertilizer consumption (pure amount) per hectare and the primary industry as a percentage of regional GDP (13.3%). 4) The soybean sown area as a percentage of the total crop sown area was the most important factor that affected soybean yield during 1952–2017, both before and after the reformation and opening up of China. Additionally, the illiteracy rate and fertilizer consumption (pure amount) per hectare were two other important factors for the period before the reformation and opening up of the country, whereas the total power of agricultural machinery per hectare and annual average temperature were important factors afterwards. This study revealed the determinants of soybean yield and its spatiotemporal heterogeneity in China since 1952 and determined the effective measures for improving the yield of this important crop. These findings should be useful for soybean production-related departments at both the provincial and prefecture levels in China for improving the rational usage of fertilizers and pesticides, increasing the level of mechanization, and enhancing the knowledge level of agricultural producers.
- Soybean /
- Yield /
- Driving factors /
- Boosted regression tree model /
- Spatial heterogeneity

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图 1 1952—2017年中国大豆单产的变异特征

箱形图中的虚线表示均值, 实线表示中值, 上边框和下边框分别代表上四分位数和下四分位数, 上边线和下边线代表10%~90%的样本区间, 离散点代表异常值。Dotted lines in boxes represent mean values. Solid lines in boxes represent median values. Boxes show the 25%−75% quartiles. Whisker caps show the 10%–90% percentiles. Discrete points represent outlier values.

Figure 1. Variations of soybean yield in different years from 1952 to 2017 in China

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图 2 1952—2017年(A)、改革开放前(1952—1978年, B)和改革开放后(1985—2017年, C)大豆单产各驱动因子的相对重要性(因子简写见表1, 图中误差线表示模型运行50次计算求得的各因子重要性的标准差)

Figure 2. Relative influence of each driving factor on soybean yield during the periods from 1952 to 2017 (A), before the reform and opening up (B, 1952—1978), and after the reform and opening up (C, 1985—2017) (See Table 1 for factors abbreviations, error bars represent standard deviations of the variable importance averaged over 50 model runs)

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图 3 各驱动因子与大豆单产之间的非线性交互关系(指标全称见表1, 因子名后括号内分别为因子的单位及相对重要性)

Figure 3. Interactive nonlinear relationships between soybean yield and its impact driving factors (See Table 1 for factors abbreviations, the unit and relative importance of each factor were showed in the bracket after factor’s names).

下载: 全尺寸图片幻灯片

图 4 1952—2017年中国大豆主产区的大豆单产主要驱动因子(指标全称见表1)

Figure 4. Top three determinants of soybean yield in the four major soybean producing areas of China during the period from 1952 to 2017 (See Table 1 for factor abbreviations)

下载: 全尺寸图片幻灯片

表 1 用于模型构建的大豆单产影响因子

Table 1. Selected influencing factors using for soybean yield modeling

划分依据 Division basis	驱动因子 Driving factor	简写 Abbreviation	单位 Unit	说明 Instruction
管理措施 Management measures	有效灌溉面积占农作物播种面积的百分比 Effective irrigation area as a percentage of crop sown area	EIAP	%	指示灌溉水平 Indicates irrigation level
管理措施 Management measures	大豆播种面积占农作物总种植面积的百分比 Soybean sown area as a percentage of total crop sown area	SAP	%	指示大豆的生产规模 Indicates soybean production scale
自然因素 Natural factors	受灾害面积占农作物总播种面积的百分比 Disaster area as a percentage of total crop sown area	DAP	%	指示自然灾害 Indicates natural disasters
	年平均气温 Annual average temperature	AVT	℃	指示温度气候因子 Indicates temperature factor
	年平均日照时间 Annual sunshine duration	AST	h	指示光照时长气候因子 Indicates illumination duration factor
	年平均降水量 Annual average precipitation	AVP	mm	指示降水量气候因子 Indicates precipitation factor
科技水平 Scientific and technological level	每公顷农业机械总动力 Total power of agricultural machinery per hectare	AMP	×10⁷ W∙hm⁻²	指示机械化水平 Indicates the level of mechanization
	每公顷化肥(折纯)施用量 Fertilizer consumption (pure amount) per hectare	FCP	t∙hm⁻²	指示重要农业生产资料 Indicates important materials of agricultural production
	每公顷农药(折纯)施用量 Pesticides consumption (pure amount) per hectare	PCP	t∙hm⁻²	指示重要农业生产资料 Indicates important materials of agricultural production
	研发支出占地区生产总值的百分比 R&D expenditure as percentage of regional GDP	RDIG	%	指示研发投入 Indicates research and development investment
社会因素 Social factors	人口城镇化率 Population urbanization rate	PUR	%	指示地区城镇化水平 Indicates regional urbanization level
社会因素 Social factors	文盲率 Illiteracy rate	ILR	%	指示地区受教育水平 Indicates regional educational level
经济因素 Economic factors	第一产业占地区生产总值的百分比 Primary industry as a percentage of regional GDP	PIG	%	指示农业在本地的经济地位 Indicates the economic status of agriculture in the local area

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表 2 用于大豆单产驱动力分析的增强回归树模型(boosted regression trees, BRT)的性能

Table 2. Performance of boosted regression trees (BRT) models for soybean yield analysis

指标 Index	全国 Nationwide	北方春大豆区 Northern spring soybean area	黄淮海流域夏大豆区 Summer soybean area in the Huang-Huai-Hai Basin	长江流域春夏大豆区 Spring and summer soybean area in the Yangtze River Basin	南方多熟大豆区 Southern soybean area	改革开放前 Before the reform and opening up	改革开放后 After the reform and opening up
R²	0.433	0.431	0.678	0.586	0.608	0.486	0.494
r	0.639	0.639	0.821	0.753	0.769	0.581	0.614
MAE	0.467	0.467	0.301	0.408	0.350	0.506	0.334
RMSE	0.097	0.101	0.090	0.100	0.075	0.133	0.069

下载: 导出CSV

参考文献(33)

[1]	中华人民共和国国家统计局. 中国统计年鉴[M]. 北京: 中国统计出版社, 2019 National Bureau of Statistics of the People’s Republic of China. China Statistical Yearbook[M]. Beijing: China Statistics Press, 2019
[2]	张晓峰, 王宏志, 刘洛, 等. 近50年来气候变化背景下中国大豆生产潜力时空演变特征[J]. 地理科学进展, 2014, 33(10): 1414−1423 doi: 10.11820/dlkxjz.2014.00.013 ZHANG X F, WANG H Z, LIU L, et al. Spatial-temporal characteristics of soybean production potential change under the background of climate change over the past 50 years in China[J]. Progress in Geography, 2014, 33(10): 1414−1423 doi: 10.11820/dlkxjz.2014.00.013
[3]	MILLER J E, HEAGLE A S, PURSLEY W A. Influence of ozone stress on soybean response to carbon dioxide enrichment: Ⅱ. biomass and development[J]. Crop Science, 1998, 38(1): 122−128 doi: 10.2135/cropsci1998.0011183X003800010021x
[4]	MORGAN P B, BOLLERO G A, NELSON R L, et al. Smaller than predicted increase in aboveground net primary production and yield of field-grown soybean under fully open-air [CO₂] elevation[J]. Global Change Biology, 2005, 11(10): 1856−1865 doi: 10.1111/j.1365-2486.2005.001017.x
[5]	郝兴宇, 林而达, 杨锦忠, 等. 自由大气CO₂浓度升高对夏大豆生长与产量的影响[J]. 生态学报, 2009, 29(9): 4595−4603 doi: 10.3321/j.issn:1000-0933.2009.09.001 HAO X Y, LIN E D, YANG J Z, et al. Effects of free air CO₂ enrichment (FACE) on growth and yield of summer soybean[J]. Acta Ecologica Sinica, 2009, 29(9): 4595−4603 doi: 10.3321/j.issn:1000-0933.2009.09.001
[6]	杨峰, 娄莹, 廖敦平, 等. 玉米–大豆带状套作行距配置对作物生物量、根系形态及产量的影响[J]. 作物学报, 2015, 41(4): 642−650 doi: 10.3724/SP.J.1006.2015.00642 YANG F, LOU Y, LIAO D P, et al. Effects of row spacing on crop biomass, root morphology and yield in maize–soybean relay strip intercropping system[J]. Acta Agronomica Sinica, 2015, 41(4): 642−650 doi: 10.3724/SP.J.1006.2015.00642
[7]	任媛媛, 王志梁, 王小林, 等. 黄土塬区玉米大豆不同间作方式对产量和经济收益的影响及其机制[J]. 生态学报, 2015, 35(12): 4168−4177 REN Y Y, WANG Z L, WANG X L, et al. The effect and mechanism of intercropping pattern on yield and economic benefit on the Loess Plateau[J]. Acta Ecologica Sinica, 2015, 35(12): 4168−4177
[8]	杨文钰, 雍太文, 任万军, 等. 发展套作大豆, 振兴大豆产业[J]. 大豆科学, 2008, 27(1): 1−7 YANG W Y, YONG T W, REN W J, et al. Develop relay-planting soybean, revitalize soybean industry[J]. Soybean Science, 2008, 27(1): 1−7
[9]	SATARI YUZBASHKANDI S, KHALILIAN S. On projecting climate change impacts on soybean yield in Iran: an econometric approach[J]. Environmental Processes, 2020, 7(1): 73−87 doi: 10.1007/s40710-019-00400-y
[10]	解文娟, 杨晓光, 杨婕, 等. 气候变化背景下东北三省大豆干旱时空特征[J]. 生态学报, 2014, 34(21): 6232−6243 XIE W J, YANG X G, YANG J, et al. Spatio-temporal characteristics of drought for soybean under climate change in the three provinces of Northeast China[J]. Acta Ecologica Sinica, 2014, 34(21): 6232−6243
[11]	郝兴宇, 韩雪, 居煇, 等. 气候变化对大豆影响的研究进展[J]. 应用生态学报, 2010, 21(10): 2697−2706 HAO X Y, HAN X, JU H, et al. Impact of climatic change on soybean production: a review[J]. Chinese Journal of Applied Ecology, 2010, 21(10): 2697−2706
[12]	乔金友, 姜岩, 王博, 等. 我国大豆主产区农业机械化现状及发展策略研究[J]. 农机化研究, 2017, 39(4): 1−6 doi: 10.3969/j.issn.1003-188X.2017.04.001 QIAO J Y, JIANG Y, WANG B, et al. Research on current status and developing strategy of agricultural mechanization of soybean main production area in China[J]. Journal of Agricultural Mechanization Research, 2017, 39(4): 1−6 doi: 10.3969/j.issn.1003-188X.2017.04.001
[13]	车升国. 区域作物专用复合(混)肥料配方制定方法与应用[D]. 北京: 中国农业大学, 2015 CHE S G. Design method and application of formula of regional crop-based compound fertilizer[D]. Beijing: China Agricultural University, 2015
[14]	李秀芬, 郭昭滨, 朱海霞, 等. 黑龙江省大豆生长季旱涝时序特征及其对产量的影响[J]. 应用生态学报, 2020, 31(4): 1223−1232 LI X F, GUO Z B, ZHU H X, et al. Time-series characteristics of drought and flood in spring soybean growing season and its effect on soybean yield in Heilongjiang Province, China[J]. Chinese Journal of Applied Ecology, 2020, 31(4): 1223−1232
[15]	宫丽娟, 李秀芬, 田宝星, 等. 黑龙江省大豆不同生育阶段干旱时空特征[J]. 应用气象学报, 2020, 31(1): 95−104 doi: 10.11898/1001-7313.20200109 GONG L J, LI X F, TIAN B X, et al. Spatio-temporal characteristics of drought in different growth stages of soybean in Heilongjiang[J]. Journal of Applied Meteorological Science, 2020, 31(1): 95−104 doi: 10.11898/1001-7313.20200109
[16]	苏营, 张逸飞, 牟文雅, 等. 大豆主要株型和产量指标对大气CO₂和温度升高的响应[J]. 生态学报, 2016, 36(9): 2597−2606 SU Y, ZHANG Y F, MOU W Y, et al. Morphological traits and yield of soybean under elevated atmospheric CO₂ concentration and temperature[J]. Acta Ecologica Sinica, 2016, 36(9): 2597−2606
[17]	张伟明, 管学超, 黄玉威, 等. 生物炭与化学肥料互作的大豆生物学效应[J]. 作物学报, 2015, 41(1): 109−122 doi: 10.3724/SP.J.1006.2015.00109 ZHANG W M, GUAN X C, HUANG Y W, et al. Biological effects of biochar and fertilizer interaction in soybean plant[J]. Acta Agronomica Sinica, 2015, 41(1): 109−122 doi: 10.3724/SP.J.1006.2015.00109
[18]	王玎. 山东省城镇化对粮食生产的影响机制研究[D]. 济南: 山东大学, 2014 WANG D. Mechanism of impact of urbanization on food production in Shandong Province[D]. Jinan: Shandong University, 2014
[19]	杨志海, 王雅鹏. 城镇化影响了粮食安全吗?−来自1462个县(市)面板数据的实证检验[J]. 城市发展研究, 2012, 19(10): 1−5 doi: 10.3969/j.issn.1006-3862.2012.10.001 YANG Z H, WANG Y P. Does urbanization influence food safety? Empirical test based on the panel data of 1462 counties (cities)[J]. Urban Development Studies, 2012, 19(10): 1−5 doi: 10.3969/j.issn.1006-3862.2012.10.001
[20]	杨树果. 产业链视角下的中国大豆产业经济研究[D]. 北京: 中国农业大学, 2014 YANG S G. Economics of soybean industry in China from industry chain perspective[D]. Beijing: China Agricultural University, 2014
[21]	李春林, 刘淼, 胡远满, 等. 基于增强回归树和Logistic回归的城市扩展驱动力分析[J]. 生态学报, 2014, 34(3): 727−737 LI C L, LIU M, HU Y M, et al. Driving forces analysis of urban expansion based on boosted regression trees and Logistic regression[J]. Acta Ecologica Sinica, 2014, 34(3): 727−737
[22]	ELITH J, LEATHWICK J R, HASTIE T. A working guide to boosted regression trees[J]. Journal of Animal Ecology, 2008, 77(4): 802−813 doi: 10.1111/j.1365-2656.2008.01390.x
[23]	PRASAD A M, IVERSON L R, LIAW A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction[J]. Ecosystems, 2006, 9(2): 181−199 doi: 10.1007/s10021-005-0054-1
[24]	蔡文华, 杨健, 刘志华, 等. 黑龙江省大兴安岭林区火烧迹地森林更新及其影响因子[J]. 生态学报, 2012, 32(11): 3303−3312 doi: 10.5846/stxb201105030574 CAI W H, YANG J, LIU Z H, et al. Controls of post-fire tree recruitment in Great Xing’ an Mountains in Heilongjiang Province[J]. Acta Ecologica Sinica, 2012, 32(11): 3303−3312 doi: 10.5846/stxb201105030574
[25]	周济. 大力办好农村教育事业[J]. 中小学校长, 2008(12): 4–7 ZHOU J. We will do a good job in rural education[J]. China’s Rural Education, 2008(12): 4–7
[26]	李立军. 中国耕作制度近50年演变规律及未来20年发展趋势研究[D]. 北京: 中国农业大学, 2004 LI L J. Involving regularities in recent 50 years and future trends in 2020 on farming system in China[D]. Beijing: China Agricultural University, 2004
[27]	LIOUBIMTSEVA E, HENEBRY G M. Grain production trends in Russia, Ukraine and Kazakhstan: New opportunities in an increasingly unstable world?[J]. Frontiers of Earth Science, 2012, 6(2): 157−166 doi: 10.1007/s11707-012-0318-y
[28]	PRISHCHEPOV A V, PONKINA E, SUN Z L, et al. Revealing the determinants of wheat yields in the Siberian breadbasket of Russia with Bayesian networks[J]. Land Use Policy, 2019, 80: 21−31 doi: 10.1016/j.landusepol.2018.09.038
[29]	王济民. 我国的大豆经济: 供给与需求的重点分析[D]. 北京: 中国农业科学院, 2000 WANG J M. Soybean economy in China: with focus on supply and demand[D]. Beijing: Chinese Academy of Agricultural Sciences, 2000
[30]	王畅, 赵海东, 冯乃杰, 等. 两个生态区大豆光热资源利用率和产量的差异及对化控剂的响应[J]. 应用生态学报, 2018, 29(11): 3615−3624 WANG C, ZHAO H D, FENG N J, et al. Differences in light and heat utilization efficiency and yield of soybean in two ecological zones and their response to chemical control regulators[J]. Chinese Journal of Applied Ecology, 2018, 29(11): 3615−3624
[31]	程叶青, 张平宇. 东北商品粮基地粮食生产的区域分异[J]. 自然资源学报, 2005, 20(6): 925−931 doi: 10.3321/j.issn:1000-3037.2005.06.018 CHENG Y Q, ZHANG P Y. Regional differentiation of grain production of commodity grain bases in the northeast China[J]. Journal of Natural Resources, 2005, 20(6): 925−931 doi: 10.3321/j.issn:1000-3037.2005.06.018
[32]	成雪峰, 张凤云. 黄淮海夏大豆生产现状及发展对策[J]. 大豆科学, 2010, 29(1): 157−160 CHENG X F, ZHANG F Y. Present conditions and countermeasures of soybean production in Huang-Huai-Hai regions[J]. Soybean Science, 2010, 29(1): 157−160
[33]	蒋慕东. 二十世纪中国大豆改良、生产与利用研究[D]. 南京: 南京农业大学, 2006 JIANG M D. Study on improvement, production and utilization of soybean in China during the 20th Century[D]. Nanjing: Nanjing Agricultural University, 2006