全 文 ： 2011 9 19 5
Chinese Journal of Eco-Agriculture, Sep. 2011, 19(5): 11901198


* (KZCX2-YW-Q02-02, KZCX2-YW-Q1-07) (30970534)
** :

(1965~), , , , E-mail: cshu@sjziam.ac.cn
(1973~), , , , E-mail: yscheng@sjziam.ac.cn
: 2011-05-26 : 2011-07-11
DOI: 10.3724/SP.J.1011.2011.01190
*
程一松 胡春胜** 张玉铭 雷玉平 李红军 李晓欣 陈素英
( 050022)
太行山前平原是华北平原的重要组成部分和典型农业高产区 , 农业集约化程度较高 , 是我国重要的
商品粮生产基地。随着农业和社会经济的发展 , 水资源严重匮乏, 地下水超采严重, 农田面源污染日益加剧等
问题, 严重影响了该区域农业的可持续发展。精准农业是未来农业发展的重要方向和必然选择。在农业生产
以农户单元为主体的我国现实情况下 , 选择县域精准种植模式是有益的尝试和探索。选择农田管理有代表性
的河北省栾城县作为示范区 , 进行精准农业技术的试验与运行实施模式的示范。在县域尺度上通过划分公里
网格的方法建立 GPS 定位的县域精准种植观测网, 确定 288 个有效采样点, 在中心示范区尺度按 20 m×20 m
网格对耕层和亚耕层确定采样点 , 进行农田基本数据调查和空间变异分析。通过优化管理网络咨询平台组装
县域尺度的优化施肥模式 , 并根据区域特点建立多种节水种植模式进行推广。在大田尺度和温室大棚示范智
控半变量节水灌溉系统, 实现经济效益和环境效益的增长。推广示范用于小麦的智能精准收获系统, 为区域精
准农业的发展提供示范样板。县域精准农业技术研究与示范工作 , 为未来区域性精准农业的全面实施进行了
技术尝试, 提供了示范样板。
太行山前平原 精准农业 观测网络 农户单元 模式示范
: S-158.3; S363 : A : 1671-3990(2011)05-1190-09
Operation and demonstration of precision planting in Luancheng County
CHENG Yi-Song, HU Chun-Sheng, ZHANG Yu-Ming, LEI Yu-Ping, LI Hong-Jun,
LI Xiao-Xin, CHEN Su-Ying
(Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese
Academy of Sciences, Shijiazhuang 050022, China)
Abstract The Piedmont Plain of Mount Taihang is a typical high-yield agro-region in the North China Plain (NCP) with a relative-
ly strong intensive agricultural activity. It is therefore one of the most important grain-production bases in China. With rapid agricul-
tural and socio-economic development, severe water shortages due to groundwater over-exploitation and increasing agricultural
non-point source pollution due to intensive application agro-chemicals/fertilizers gravely threaten sustainable agricultural develop-
ment in the region. Precision agriculture is therefore an inevitable choice of direction for future agricultural development in the re-
gion. The agricultural production in China is characterized as peasant household unit production, so, it is critical to experiment and
explore precise planting at county level. Luancheng County, Hebei Province was chosen as a demonstration area, where precision
agriculture technology demonstration and operational implementation were designed. The observation network in the county estab-
lished by GPS precise positioning had a spatial resolution of 1 km × 1 km with an effective sample area containing 288 points. The
resolution of sample points in the core demonstration area was further reduced to 20 m × 20 m. At the county and demonstration area
scales, agricultural research and basic data analysis that sufficiently reflected spatial variability were conducted. Optimized manage-
ment network and consulting platform assembly were used to enhance fertilization at county level and to establish regional characte-
ristics of water-saving cropping patterns in the region. Two demonstration modes (i.e., field and greenhouse semi-intelligent control
and variable water-saving irrigation systems) provided sufficient economic and environmental benefits of crop cultivation in the re-
gion. These modes were recommended for the promotion of accurate wheat cultivation intelligent systems as part of the broader de-
5 : 1191


velopment of regional precision agriculture demonstration models. Hence precision agriculture technology research and demonstra-
tion in Luancheng County had provided a technological demonstration model, which was a useful practice for the full implementa-
tion future precision agriculture models in the region.
Key words Piedmont Plain of Mount Taihang, Precision agriculture, Observation network, Peasant household unit, Demo n-
stration of mode
(Received May 26, 2011; accepted Jul. 11, 2011)
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1 研究区域农业的特点和问题
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1192 2011 19


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2 县域精准种植观测网及农田基本信息调查
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图 1 栾城县域精准种植观测网有效采样点分布图
Fig. 1 Distribution of sampling points of observation network
for precision planting in Luancheng County

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5 : 1193




2
Fig. 2 County level spatial variation of topsoil nutrients in Luancheng County



图 3 栾城试验站中心示范区 2000 年(a)和 2002 年(b)的土壤速效养分空间分布图(耕层 0~15 cm 土壤)
Fig. 3 Dynamics of spatial variability of 0~15 cm soil nutrients in demonstration area of Luancheng Agro-Ecosystem Experimental Station
of Chinese Academy of Sciences in 2000 (a) and 2002 (b)
1194 2011 19




4 2002 (a) (b)
Fig. 4 Spatial variation of winter wheat (a) and summer maize (b) yields of Luancheng County in 2002



图 5 栾城县域冬小麦田土壤含水量(V/V)空间分异图(2002 年 4 月)
Fig. 5 Spatial variation in soil moisture content (V/V) of winter wheat fields in Luancheng County in Apr. 2002

表 1 栾城县域观测网土壤属性和冬小麦产量的统计分析(n=112)
Table 1 Statistical analysis of soil properties and yield of winter wheat of Luancheng County observation network (n=112)

Item

Planting scale of farmer
household (hm
2
)

Yield of wheat
(kg·hm
2
)

Soil moisture

Topsoil organic matter
(g·kg
1
)
NO3

Soil nitrate content (g·kg
1
)
0~20 cm 20~40 cm 0~20 cm 20~40 cm
Mean 0.36 5 389 0.18 0.15 1.71 6.95 9.34
Minimum 0.07 3 750 0.13 0.10 1.18 0.25 1.02
Maximum 0.87 7 500 0.24 0.24 2.17 28.38 44.09
Standard deviation 0.17 729 0.02 0.02 0.20 5.62 7.17
C.V. (%) 47 14 11 15 12 81 77
Skewness 0.88 0 0.34 0.67 0.04 1.58 2.21
Kurtosis 0.46 0 0.82 1.52 0 2.97 6.74
Sample limit error 0.80 3 750 0.11 0.14 1.00 28.13 43.07

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5 : 1195




3 县域精准种植模式示范
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图 6 栾城县域土壤养分丰缺评价示意图
Fig. 6 Sketch maps of evaluation of soil nutrients in Luancheng County



图 7 栾城县基于―田块‖尺度的小麦基肥氮磷钾优化施肥决策示意图
Fig. 7 Sketch maps of optimum application of N, P and K basal fertilizers based on wheat field scale in Luancheng County
1196 2011 19


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5 : 1197


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图 8 联合收割机智能测产系统(A: 安装位置; B: 系统构成)
Fig. 8 Intelligent yield monitor system of combine harvester (A: installation sites; B: system composition)
1: Grain moisture sensor; 2: Grain flow sensor; 3: Lifter; 4: Elevator speed
sensor; 5: Granary; 6: Steering wheel; 7: Ground speed sensor; 8: Wheel; 9: Header; 10: Reel; 11:
Header height sensor; 12: Main-controller; 13: GPS GPS receiver.



图 9 示范区 GPS 信息产生的采样轨迹图
Fig. 9 Sampling trajectories generated by GPS at
demonstration area

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图 10 应用联合收割机智能测产系统的栾城示范区多年
产量叠加分析
Fig. 10 Yield map overlay analysis at demonstration area meas-
ured by intelligent yield monitor system of combine harvester
1198 2011 19


4 结论
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
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