An adequate information supply together with land, labor, capital and
management is required for a successful agricultural business. Information
management may become easier, timelier and generally provide greater value
through computerized information system use (Amponsah, 1995; Thapa and
Murayama, 2008; Perini and Susi, 2004; Fountas et al., 2006; Saroinsong
et al., 2007; Murakami et al., 2006; Marvin et al.,
2007). However, encouraging farmers to change their information management
has not been as straight forward and as easy as expected. For instance,
farmers have shown a low rate of management software adoption and its
effective use relative to farmers` adoption behaviour of technical innovations.
While the penetration of computer technology into farm and food industries
is quite extensive in some countries, but the use of information system,
as a key component in a agricultural education, is not as extensive as
might is be expected.
Since, the farmer is usually an essential component of the farm information
system, the choice of information technology is an individualistic process
that is usually governed by the farmers` characteristics, such as personality,
experience, age, education and goals. These features are highly personal
features, so, there may be a considerable variation in the choice of the
information technology and system configuration among farmers.
Past researches have identified factors believed to operate as software
adoption barriers. In these studies, some refer to management information
systems in a broad sense, while other studies focus on Decision Support
Systems (DSS) (Cox, 1996; Hoskinson et al., 2007; Santé
and Crecente, 2007; Perini and Susi, 2004). The main barrier is the failure
of developers to address the real problem.
Other problems include the complexity in design and presentation of DSS
and the difficulty in assessing the largely intangible benefits of information
system improvements. If a clear perception of the economic benefit derived
from software were available, this would be a major contributor to encouraging
The lack of integration among the different components of many information
systems is another problem. Pre computerized information systems were
usually automatically integrated within the farmers` mind. Finally, it
is obvious that for the use of computerized information systems, a certain
level of computer literacy is required. While this restriction appeared
to be significant at the beginning of the 1990s, it is now less relevant
given the current trend in computer uptake. However, especially for developing
agricultures, this barrier still exists. However, on the more positive
side, the factors identified as being associated with on-farm computer
adoption have been business size, education and age. A positive correlation
between farm size and computer uptake was found in almost all reviewed
studies. Similarly, a positive correlation between the farmers` education level
and computer adoption was also found in the majority of reviewed studies.
Farmer age was the third factor reported to be correlated with computer
uptake. The younger the farmer, the more likely was computer adoption.
That is, young, better educated farmers operating larger farms are likely
to represent the future expansion of the industry and these are the farmers
most likely to adopt computers. It has also been found that farmers who
owned an off-farm business were more likely to adopt a computer. Other
studies have found a positive relationship if farmers have off-farm employment.
Off-farm employment is thought to expose farmers to new technologies,
to broaden their perspective on management and to increase their willingness
to adopt computers.
In addition, those farmers who previously applied formal approaches to
record keeping and who used off-farm services were more likely to adopt
a computer. Also, off-farm employment may expose farmers to new technologies
and broaden their management perspectives resulting in greater adoption
While computer uptake is a pre-condition, an important issue is whether
farmers using a computer believe that they have improved their information
management. Several of the reviewed studies addressed this issue (Amponsah,
1995; Agostinho et al., 2008; Ramírez-Rosado et al.,
2008; Fang and He, 2008; Alvarez and Nuthall, 2005). Earlier research
concluded that managers` perceptions of system performance (system usefulness)
were significantly correlated with actual information system use and presumably,
with system value. These studies have tested associations between farmers`
opinions of system usefulness and similar factors used to explain computer
uptake, such as farm and farmers` characteristics (Amponsah, 1995; Agostinho
et al., 2007; Ramírez-Rosado et al., 2008; Fang and
He, 2008; Alvarez and Nuthall, 2005). However, other factors that may
be related to system development were not included. Undoubtedly, the studies
reviewed have helped in understanding farmers` computer behavior.
Competition between different interests for the same land should be resolved
through selection of the most appropriate land-use. There are three main
aspects that need to be taken into account when planning for sustainable
use of land resources: environmental, economic and societal. In difficult
scenarios where there are complex decision-making considerations and a
variety of goals that are sometimes conflicting, the use of a multi-criteria
analytical approach can be beneficial. Multi-criteria analysis is a methodology
by which the relative merits of different options can be compared using
a range of quantitative and qualitative criteria. The approach thus can
help evaluate transparently a variety of land-use options according to
a variety of criteria that are measurable and form an assessable basis
for decision-making. When planning occurs from the standpoint of a multi-criteria
analytical approach seeking the sustainable use of land resources in an
agricultural landscape, the objective is to identify land uses that are
ecologically friendly, efficient and profitable, are accepted by society
and meet social needs. In this study, the objective is to design a new
quality control system for food products. This quality control system
receives the required information from different products by an e-based
real time system. Next section presents the description of the proposed
real time quality control information system.
E-BASED REAL TIME QUALITY CONTROL INFORMATION SYSTEM
Here, the e-based real time quality control information system for food
industry product has been provided. According to the widespread domain
of e-base information system in recent years the model could manage the
cycle of final food industry product from farm to customers in market.
In this model three interfaces (1) for the farmers in their farm, (2)
for the food industries in their industry and (3) for the customers of
the market has been designed. Based on the domain of the implementation,
we can use global or local network. The farmers interface includes the
new knack of the farmers and agriculture researchers, the standard of
raw products and personal page for each farm. In the personal page, each
type of product and some attributes for each of them have been provided
that the farmers should fill the specifications of their raw products
and the quality value of each attributes on products. The information
transfers to the data base and stored for analysis and compared with other
In the food industries, the quality of the raw material has been checked.
The specialist of the quality in the food industries by login in the food
industries interface fill the quality value of the products on each attribute.
One of the most important part of the system is gathered, the information
from customers whom consume the final products. The customers could propose
their idea about food products by connecting to the network. Customer
interface has been designed to collect the quality value of the attributes
that proposed by the consumers and saved in the corresponding data base.
e-based real time quality control information system model has been shown
in Fig. 1.
||E-based real time quality control information system
||Quality value of raw material j from location l on attributes
||Raw material j from location l
||The No. of attributes
||The No. of raw material of farm p
||Food industry (v) expect quality attribute i of product p
||Market expect quality attribute i of product p
Model definition: The computer based information system has been
designed to evaluate and to improve the quality of the raw material which
has been produced in distributed land against the global market and food
industries expectation. Thereby, an integrated data base with real time
controller could help the farmers at 3 stages such (a) producing qualified
raw materials, (b) preparing the raw materials in food industries and
(c) the customers needs in global market.
In the proposed approach L lands have been considered, where in each
of them p raw material has been produced. Means,
raw material j is produced in location l. For each raw material
m attribute was considered.
means that the quality value of the raw material j from location l
on attribute m. In each location vector has
been defined that consist of the quality value of m attribute on each
All the information has been collected from different locations and transfer
to the central data base.
To provide the food industry expectation and needs, the system gathered
similar information from each plant. Generally in the food industries
the raw material has been tested from special quality stand point before
going to the cycle of production. The food specialists investigate the
raw material and announce the quality of the raw material and their accepted
quality level which has been called Industries Expected Quality (IEQ).
means that food industry (v) expects quality on attribute i and for each
product vector IEQ has been defined as .
This value of quality on each attribute i of raw material p have been
collected and transferred to the designed information system too.
Moreover, after the procedure of the producing in the food industry was
completed and the final products will be transferred to the global market
by the designed user interface based on the web application Hyper Text
Markup Language (HTML), the sight of the customers about each product
was collected. In the user interface some information about raw materials
and procedure of producing in the food industries are provided. After
recognizing the whole procedure of preparing final product for the customers,
they would propose their idea about raw material and production methodology.
The customer`s idea has been transferred to the data base as a vector
Quality attributes control for each attribute of raw materials all
over the farms: After collecting the quality value from the sources,
the means of quality value (control limit of the quality value) for each
attribute of the raw material will be calculated by Eq.
From Eq. 1 the lower and upper control limits will
be obtained as Eq. 2 and 3.
where, σp(i) are derived from process standard deviation
for all over of the locations (estimated according to sample data).
After obtaining the quality control limit LCLp(i),
in the inference section of the designed system it is comprised with quality
of each farm. We expect that the amount of the
is between lower and upper bound of the control limits for each location, otherwise
the attribute system of the product which is out of the control limits will
be specified and it would be announced to that location that its product quality
is not as same as the producer in other places from attribute view point. The
quality control chart has been shown in Fig. 2. The farmers
can refer to this system and identify the innovations in other places to achieve
better quality of the products. After investigating the quality of the products
among the primary farmers, it is proposed to compare the qualitative requirements
of the farmers and the customers that consume the products in different places.
Quality attributes control for each attribute between farmers, industries
and food markets: Here, another aspect of the proposed system has
been considered. After smoothing the quality of raw material between farmers,
now the differential between IEQp(i), MEQp(i) and
CLp(i) should be minimized. We expect these values to be equal
or near equal. So, in the other section of the inference engine by ANOVA
test approach, we investigate the equality of the parameters.
||Quality control chart of different attributes of farm
We find the expected quality value of the food industry and markets by
considering Eq. 4 and 5, respectively.
By applying ANOVA test to consider the equivalency of the quality value
between farm, food industry and market as formula (6) by F statistic in
where, SST is sum of squared treatment and SSE is sum of squared error
with each parameters while F statistic is a Fisher distribution with safety
interval of α and 2 and 3(n−1) degree of freedom (Fα,2,3(n−1))
and n is the number of the sample that considered. If F≥Fα,2,3(n−1),
then H0 is rejected and there is significant difference between
the obtained quality values of the farm, food industry and market; otherwise
H0 will be accepted.
In this study a new model of quality control for farm lands based on
different attributes has been proposed. The approach can help evaluate
transparently a variety of land-use options according to a variety of
criteria that are measurable and form an assessable basis for decision-making.
When planning occurs from the standpoint of a multi-criteria analytical
approach seeking the sustainable use of land resources in an agricultural
landscape, the objective is to identify land uses that are ecologically
friendly, efficient and profitable, are accepted by society and meet social
needs. The advantages of the proposed approach are assisting the food
industries to choose the better supplier (farmers) which has produced
more qualified raw materials, enhancing the e- knowledge of the farmers,
recognizing the market needs, integrating the supply chain information
flow and achieving the productivity and create educational aspects to
improve the role of the IT in real time quality control.