Zhu Zesheng , Sun Ling
GIS Implementation of Management System for Farm Scientific Fertilization
Abstract
A GIS(ArcView)-based model was employed to determine the decisions of optimal fertilization
within fields, as well as the optimal locations of fertilization. These decisions were based on some
key factors such as soil type, field nutrient level, yield, crop, variety and climate zones. One
simulation model was used to design and implement an ArcView-based management system for farm
scientific fertilization. The objective of the system design and implementation was to maximize net
returns from fertilizer application. ArcView provides the necessary customization and language
environment tools in an easy framework for implementing this objective. This ArcView-based
fertilization management system can complete many-tasks: create various thematic maps,
determining various spatially selective fertilization management decisions, performing the relevant
simulation and printing various maps, graphs and tables. A layered model is especially suitable to
design and implementation of ArcView-based application system, which was used to determine the
fertilization management system architecture. This layered mode approach was proved to be a
practical method of designing and implementing a GIS-based fertilization management system, and
should be widely applicable to the design and implementation of other similar system.
1. Introduction
Nowadays, Geographic Information System(GIS)-based management systems affect almost all
aspects of precision farming or spatially variable farming. However, in the last decade, many new
technologies have been under development to take into account the spatial variability within field to
fine-tune field fertilizers to the specific needs of soil and crops at any location in the field. These
technologies include sensing of soil properties and crop growth, yield mapping, spatially variable
fertilizer and pesticide application with GPS. Increasing costs and decreasing revenues in field
fertilization make it difficult to maintain a sufficient level of farmer income. Farmers assume usually
that the soil within a single field is homogenous, and use mean soil fertility level and yield goals. But,
the uniform application of fertilization to an arable crop is not only inefficient at cost, but also has
possibly undesirable environmental impacts due to the wrong viewpoint. Therefore, improved
planning and optimized fertilization are becoming more and more important. However, spatially
variable fertilization plays an important role in field fertilization. Usually, the yield increases with
fertilizer increase until a maximum yield related to optimal fertilization is obtained; further increase
in fertilizer results in a reduction in yield. The yield is also called as maximum economic yield. Now,
commercial spatially variable applicators are available, which control the application of fertilizers[1].
They use GPS and other positioning technologies. In general, GPS can be used with sensors which
monitor crop and soil properties to generate maps of these soil and crop properties. The generation
and processing of these maps will be very important to the farm operator. Then, the operator can know
information of variability in his fields and implement spatially variable fertilization(SVF).
Currently, most SVF plans are based on spatially positioning and field operations. As a result, they
kept simple and rarely include powerful spatial information analysis and processing. In making such
plans, simple methods for processing maps are being used by most farmers. In making more suitable
SVF plans for their own situation, farmers should have more insight into the potential impact of
various alternatives on the results of their field fertilization and should therefore have powerful GIS
tools to generate alternative strategies for SVF. In fact, SVF alternatives tend to be planned spatially.
Extensive and complex spatial analyses are required to determine the effects of alternatives on SVF.
These SVF plans may be improved when the simple methods are replaced by GIS-based ones for SVF
decision support. This article describes a possible method and system based on GIS in planning SVF.
Next, a GIS-based simulation model developed for planning SVF is described. The model uses a
powerful GIS tool-ArcView[2] to simulate and analyze SVF problems. Application of the ArcView-
based approach has proved to be successful for SVF decision support systems. The focus of this study
is to develop a management system for farm scientific fertilization. Specific objectives are to
develop an ArcView-based application software for SVF to verify its successful application with
ArcView, and investigate the feasibility of precision farming based on GIS.
2. Model structure
The basic principle of spatially variable fertilizer(SVF) technique has been discussed by authors[3]
and its application in crop production by authors[1,4]. As mentioned before, fertilization is usually
required to obtain the maximum economic yields, which is also called economic fertilization. Crop
growers rely heavily one or more applications of fertilizer to provide an appropriate supply of
fertilizer for crops throughout the growing season. Generally, the yield of a crop increase tends to
decrease. Eventually, there is a point where the extra fertilization can not increase the yield. This
point is spatially variable within fields. In addition, excess fertilization can cause quality problems of
some crops and environmental problems.
An intensive SVF model most relies on a field information system that is similar to GIS such as
ArcView. Geographic information system(GIS) is a computer-based system designed to store,
analyze and display spatially referenced data and has emerged as an useful tool for natural resource
management and graphic processing. Thus, the most important feature of GIS is its excellent ability to
process, store and analyze the spatial information. Otherwise, GIS is usually used as a general tool or
framework for integrating many other commercial software packages to build more complex and
efficient application systems about graphic and data processing. Thus, this model can consider the
local needs of the crop-soil-fertilization system in GIS environment. The collected field data about
soil nutrient variability are stored as a set of layered data (or themes) in GIS and processed by this
model. These data are as the followings:
(i)Crop yield: Determination or detection of local yield makes it possible to establish a balance
relation or function between nutrient supply and nutrient uptake.
(ii)Local soil sampling: The analysis of these soil samples yields a mean current nutrient level for the
field from which the soil sample was taken.
Usually, there are two types of yield measurement methods, including position detection with the
Global Positioning System(GPS) and yield data acquisition. The data evaluation with GIS leads to
yield mapping or maps as the above model input. A yield map provides an overall view of in-field
spatial variability. It describes the complex effects of various spatially variable factors on a crop, such
as soil type and moisture, nutrient availability, drainage status, weed infestation, weather and crop
growth. Thus, the variation in yield represents the integrated effects of spatial variability in soil,
climate and crop variables on crop. Yield maps can be presented in contour shape, and yield classes
can be formed in relative numbers. For example, all yield maps were prepared as contour graphics
with yield class widths of 2 t/ha[5]. The measured yields are displayed as raster and contour graphics
in a GIS. In raster graphic, its grid sizes must be enough small to lead to higher resolutions of field
operation. In contour graphic, the yield class width must be considered in order to allow a clear class
assignment. In general, Yield maps are created from the positioning and yield data. Yield variations
can be represented clearly by raster graphic and by contour graphic in a GIS. Areas with equal yields
and soil properties show that they have equal uptake, so that they must be treated as areas of uniform
soil properties at the soil sampling stage. Auernhammer discussed a similar problem[6].
The basic principle of soil nutrient sampling and mapping method using GPS has been discussed by
Delcoourt et al.[7]. The variability of soil properties within a field has been recognized. In general,
farmers dealt with field heterogeneity by taking soil samples from different nutrient areas. Then, the
analysis of these soil samples provides the data about a mean nutrient level for these areas and a mean
fertilizer advice for farmers. There are three approaches to soil sampling and analysis. The first
approach is called land register approach. This approach requires that the field is partitioned into
smaller part with the help of a priori knowledge. Thus, it may have a minimal number of soil samples.
The second approach defines the sampling areas starting from yield maps over several seasons. The
yield maps can give the consistent yield patterns with time for soil sampling and field partitioning.
Thus, a model based on the analysis of soil sampling results and the following yield data can give the
optimal value for the required fertilization. Usually, data from the following yield data are used to
increase the fertilization precision. The last approach is based on the geographically detailed
knowledge of the topsoil nutrient status or type for the entire field, which is called detailed soil
survey approach. In the first mentioned two approaches, indirect information from the field is used to
define soil sampling or fertilization areas. However, for obtaining complete data about the spatial
variability of the fertility levels, the soil samples must be taken in a rectangular grid way[8] over the
entire field. Fertilization areas can then be derived from the detailed soil nutrient maps. In our model,
the yield maps and soil nutrient maps are used for providing fertilization decision for farmers.
How to represent or describe a fertilization model in GIS is a very difficult problem. However, a
method of combining themes and equations can be used to solve this problem. Theme is an important
concept of ArcView. In an ArcView, a map is usually related to a project. A project is really a
collection of views, tables and other documents. Further, a view is an ArcView document that displays
a map and its legend. For example, a view is made up of layers of field information for a particular
geographic area. Each layer of field information is a collection of field features such as crops,
fertilizer, soil, yield, rivers, roads, boundaries and counties. These layers are called themes. The
legend lists symbols representing features of these themes. Thus, a theme is a collection of field
features in a view. All the features in one theme are represented by the same type of shape, such as
points, lines or areas. Themes also reference the attributes or characteristics of field, yield and soil
nutrient level.
The model of fertilization within a field can be represented simply as the following.
T(FA)=T(FN)*Op1+T(FP)*Op2+T(FK)*Op3+T(FY)*Op4
Where
T(FA): Fertilizer application theme,
T(FN): N distribution theme in a field,
T(FP): P distribution theme in a field,
T(FK): K distribution theme in a field,
T(FY): Yield distribution theme in a field,
Op1: N function to compute N economic contents in a field,
Op2: P function to compute P economic contents in a field,
Op3: K function to compute K economic contents in a field,
Op4: Yield function to compute the effect of yield in a field on N, P and K,
+: Overlay- operation in ArcView,
*: Operation that completes input data and output data between theme and its function.
The above equation means that for any crop, operations Opi(i=1,...,4) with themes T(FN) to T(FY)
determine economic or optimal fertilization theme T(FA) of the crop.
3. System design
In an ArcView environment, GIS-based design of management system of the above model for farm
scientific fertilization becomes very easy. Some important problems related to field data collection
must be further discussed. Several automatic methods to collect yield data have been developed in
recent years[9]. However, a RS(Remote-Sensing)-based mathematical model can also be used to
estimate yield data[10]. In some low-mechanization level developing countries, manual weight
method may effectively provide precision yield data for yield mapping. In our system, these methods
to collect yield data can be used. Otherwise, Esri's PC ARC/INFO was used to analyze further the
spatially located data and yield data in order to produce a precision yield map. The key step to
implement the above scientific fertilization management system (SFMS) is to design and implement a
good architecture for it.
An architecture of SFMS can be described by a seven-layer architecture model shown in Fig. 1. In
summary, simple function of various layers can be discussed as the followings. The layer of field
layer describes the geographic position, shapes and characteristics of managed fields. The soil
layer describes all information about soils of these fields, which includes data from a detailed soil
survey. The main task of climate layer is to collect and generate a number of basic climate data for
supporting decision in its high layer. The nutrient layer is used to represent current nutrient levels of
these fields.
In the crop layer, a number of data to represent growth of current crops are used to describe crop
status. The yield layer consists of a number of basic yield data to generate various yield maps. The
fertilization layer is used to generate various different fertilization and soil sampling decisions for
different fields or areas according to practical requirement of these fields and with the help of various
basic services provided by its lower layers. In the layered architecture, the lower layer provides
always its service for its higher layer, the higher layer uses the service from its lower layer. To
construct the layered architecture model is a very effective method for designing and implementing a
high quality ArcView-based application system.
4. System implementation
For showing practical application value and steps of the above method, a typical GIS application
system for management of farm scientific fertilization was designed for implementing an optimal
management of fertilization within fields or areas. The real project resulted in a prototype called
SFMS. The primary consideration of SFMS computer implementation is to allow farms to access and
use all available relevant data and decision information provided by SFMS to optimally manage their
fertilization through the above model in SFMS. The SFMS tasks include fertilization management,
determination of soil sampling position, fertilizer management, and relevant yield prediction and
output of various maps. SFMS was developed on a PC under the Microsoft Windows system using the
VISUAL BASIC, VISUAL C++ and Avenue language[11].
The partial source codes of SFMS are written in modular form using the macro programming
language Avenue of ArcView 2.1. Each unique procedure or activity is written as a separate script.
Totally, SFMS includes more than 60 scripts. Table.1 shows a list of SFMS' scripts.
//////////////////////////////////////////////////////////////////////////////
SFMS.About.JaasSVF
SFMS.Add.GlobalVariables
SFMS.AddTheme.Choice
SFMS.AddTheme.Crop
SFMS.AddTheme.Fertilization
SFMS.AddTheme.Soil
SFMS.AddTheme.WeatherZones
SFMS.AddTheme.Yield
SFMS.Calculate. Fertilization
SFMS.Change.SFMS.Soil.fle
SFMS.Change.Sim.File.FoRecords
SFMS.Change.Sim.File.ForView
SFMS.ChangeTheme.Choice
SFMS.Create.ModelDictionary
SFMS.Delete.Documents
SFMS.Delete.Tablefiles
SFMS.HasVisibleThemes.Update
SFMS.Join.Semiautomatic
SFMS.Join.Simulation.Results
SFMS.Label.VisibleTheme
SFMS.Layout.Create
SFMS.Map.New
SFMS.Map.Open
SFMS.Theme.Properties
SFMS.View.ConvertToShapefile
SFMS.View.Run.Simulation
Table.1: A list of Partial SFMS' scripts
///////////////////////////////////////////////////////////////////////////
ArcView provides the necessary customization and language environment tools in an easy framework.
The framework provides some functions as the followings.
(i)To create the graphical user interface according to user requirements,
(ii)To establish some initial properties for graphical controls to support interaction between user and
these controls,
(iii)To write Avenue codes that satisfy the requirement of user interface and simulation model,
(iv)To link scripts written in Avenue to events,
(v)To integrate the framework with other language modules.
Avenue is an object-oriented scripting language. The key point in Avenue is on identifying objects and
then sending requests them to complete various complex operations. An object can be considered as a
package that is composed of tightly-coupled data and functionality. In Avenue, a request can be sent to
an object. When an object receives this request, it performs some relevant action or operation. In
ArcView, objects are members of a class hierarchy or architecture. Further, these members can be
organized into functional categories related to all aspects of the fertilization application.
Avenue's statements are used to organize and determine when and how requests are made. A request
specifies what an instance of a given class will do and a method specifies how it is done. By sending a
request to an object, a method appropriate to the class of which the object is an instance is activated.
An object in Avenue always responds to a request by returning an object; in some cases, the request
creates a new object or returns an existing object.
A prototype system has been used to analyze and process a number of collected data from fields.
Fertilizer application with SFMS was compared with traditional none-spatially selective fertilization.
When the traditional approach was completed with the fertilization operation, the fertilization
efficiency of the traditional approach is not satisfactory due to its none-spatially selective operation.
The SFMS-based approach may provide an objective method for performing field nutrient level
assessments and making fertilization recommendations that maximize yield or profit. The profit of
SFMS-based fertilization approach increased about 10-15% by comparison with the above traditional
approach.
These successes show that the shift from non-spatially selective fertilization to GIS-based spatially
selective one is a profound advance in research of management system for farm scientific
fertilization. Further, some major steps of the method application are as the followings: scooping of
an application development project, in which the project area, requirement and relevant variables are
defined by the application plan objective, based on the user requirement of spatially selective
fertilization management system and GIS(ArcView) environment; collecting data relevant to the
model of spatially selective fertilization in the application field, in which these data must be
represented as a layered architecture model form with GIS thematic or map by the method for
designing a layered architecture; developing, evaluating and selecting all important logical
relationships or functions between variables of these layers, in which these relationships are used to
build a basic framework of GIS-based application system or model; integrating these relationships and
data relevant to the model, in which final application system or model is built; refining this system
and improving its performance. Currently, the system is being revised to incorporate more decision
functions. A new ArcView-based fertilization management system is being developed, which has more
powerful GPS function and generate better spatially selective fertilization recommendations for
strategic planning of scientific fertilization.
5. Conclusions
An ArcView-based management system for farm scientific fertilization was developed to study the
principle and application possibility of spatially selective fertilization model in developing country.
The determination of soil sampling plan and the application of yields map are two keys to design and
implement the model. Interpretation of yield map will also become a challenge in research of
spatially variable fertilization. The layered architecture model is an efficient method to design and
implement a GIS(ArcView)-based application system. In fact, the layered model is also a theme-
oriented processing model. The work to develop more efficient GIS-based model for spatially
selective fertilization will also become a future challenge in the domain. The design and computer
implementation of fertilization management system will change from current 2D plane into future 3D
space. ArcView software tools will provide more powerful supports for this challenge or objective.
However, the future works about the computer implementation are to solve some key problems that
include how to improve the architecture of spatially selective fertilization model, how to construct
the operation rules in the architecture, and how to design and implement these rules in GIS
environment. In summary, the design, implementation and application of GIS-based spatially
selective fertilization system will become an important research direction in precision farming
domain.
6. Acknowledgments
This work was supported by the National Natural Science Foundation of China (NSFC) under project
No. 39470415.
7. References
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Zhu Zesheng, Sun Ling(Corresponding author )
JiangSu Academy Of Agricultural Sciences
Nanjing, JiangSu, 210014, P. R. China (Corresponding author Address)
Nanjing Navy Institute of Electronic Engineering
Nanjing, JiangSu, 211800, P. R. China
Telephone:(086)-025-4438285
Fax: (086)-025-4390185
E-Mail: JAASM@PUBLIC1.PTT.JS.CN