Variation characteristics of soybean yield since 1952 and its influencing factors in China
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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。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.
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Key words:
- 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
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)
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 areaEIAP % 指示灌溉水平
Indicates irrigation level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 areaSAP % 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 areaDAP % 指示自然灾害
Indicates natural disasters年平均气温
Annual average temperatureAVT ℃ 指示温度气候因子
Indicates temperature factor年平均日照时间
Annual sunshine durationAST h 指示光照时长气候因子
Indicates illumination duration factor年平均降水量
Annual average precipitationAVP 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 hectareAMP ×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 hectareFCP 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 hectarePCP 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 GDPRDIG % 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 ratePUR % 指示地区城镇化水平
Indicates regional urbanization level文盲率
Illiteracy rateILR % 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 GDPPIG % 指示农业在本地的经济地位
Indicates the economic status of agriculture in the local area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 upR2 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 -
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