Tools for optimizing management of spatially-variable fields

被引:44
作者
Booltink, HWG
van Alphen, BJ
Batchelor, WD
Paz, JO
Stoorvogel, JJ
Vargas, R
机构
[1] Univ Wageningen & Res Ctr, Lab Soil Sci & Geol, NL-6700 AA Wageningen, Netherlands
[2] CORBANA, Direcc Invest, Guapiles, Costa Rica
[3] Iowa State Univ Sci & Technol, Ames, IA 50011 USA
关键词
precision agriculture; management; modelling; prediction; spatial variability; temporal variability;
D O I
10.1016/S0308-521X(01)00055-5
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
Efficient use of agro-chemicals is beneficial for farmers as well as for the environment. Spatial and temporal optimization of farm management will increase productivity or reduce the amount of agro-chemicals. This type of management is referred to as Precision Agriculture, Traditional management implicitly considers any field to be a homogeneous unit for management: fertilization, tillage and crop protection measures, for example, are not varied within a single field. The question for management is what to do it-hen. Because of the variability within the field, this implies inefficient use of resources. Precision agriculture defines different management practices to be applied within single, variable fields, potentially reducing costs and limiting adverse environmental side effects. The question is not only what and it-hen but also where. Many tools for management and analysis of spatial variable fields have been developed. In this paper, tools for managing spatial variability are demonstrated in combination with tools to optimize management in environmental and economic terms. The tools are illustrated on five case studies ranging from (1) a low technology approach using participatory mapping to derive fertilizer recommendations for resource-poor farmers in Embu, Kenya, (2) an example of backward modelling to analyze fertilizer applications and restrict nitrogen losses to the groundwater in the Wieringermeer in The Netherlands, (3) a low-tech approach of precision agriculture, developed for a banana plantation in Costa Rica to achieve higher input use efficiency and insight in spatial and temporal variation, (4) a high-tech, forward modelling approach to derive fertilizer recommendations for management units in Zuidland in The Netherlands, and (5) a high-tech, backward modelling approach to detect the relative effects of several stress factors on soybean yield. (C) 2001 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:445 / 476
页数:32
相关论文
共 46 条
[1]  
[Anonymous], 1978, SIMULATION FIELD WAT
[2]  
[Anonymous], 1998, 156 DLO STAR CTR
[3]  
[Anonymous], 1996, GEOGRAPHIC OBJECTS I
[4]   SIMULATION OF SOIL-NITROGEN DYNAMICS USING THE SOILN MODEL [J].
BERGSTROM, L ;
JOHNSSON, H ;
TORSTENSSON, G .
FERTILIZER RESEARCH, 1991, 27 (2-3) :181-188
[5]   SUCTION CRUST INFILTROMETER FOR MEASURING HYDRAULIC CONDUCTIVITY OF UNSATURATED SOIL NEAR SATURATION [J].
BOOLTINK, HWG ;
BOUMA, J ;
GIMENEZ, D .
SOIL SCIENCE SOCIETY OF AMERICA JOURNAL, 1991, 55 (02) :566-568
[6]  
BOOLTINK HWG, 1998, APPL SYSTEMS APPROAC, P219
[7]  
BOOLTINK HWG, 1998, 9813 RESEPT BEL COMM
[8]   Pedology, precision agriculture, and the changing paradigm of agricultural research [J].
Bouma, J ;
Stoorvogel, J ;
van Alphen, BJ ;
Booltink, HWG .
SOIL SCIENCE SOCIETY OF AMERICA JOURNAL, 1999, 63 (06) :1763-1768
[9]  
Bouma J, 1997, Ciba Found Symp, V210, P5
[10]   Continuous classification in soil survey: Spatial correlation, confusion and boundaries [J].
Burrough, PA ;
vanGaans, PFM ;
Hootsmans, R .
GEODERMA, 1997, 77 (2-4) :115-135