=Paper=
{{Paper
|id=Vol-1498/HAICTA_2015_paper80
|storemode=property
|title=Farm Management Information Systems
|pdfUrl=https://ceur-ws.org/Vol-1498/HAICTA_2015_paper80.pdf
|volume=Vol-1498
|dblpUrl=https://dblp.org/rec/conf/haicta/NovkovicHZM15
}}
==Farm Management Information Systems==
https://ceur-ws.org/Vol-1498/HAICTA_2015_paper80.pdf
Farm Management Information Systems
Nebojsa Novkovic1, Christoph Huseman2, Tihomir Zoranovic3, Beba Mutavdzic4
1
University of Novi Sad, PhD, Professor, Faculty of Agriculture, Trg Dositeja Obradovica 8,
21000 Novi Sad, Serbia, e-mail: nesann@polj.uns.ac.rs
2
University of Novi Sad, PhD candidate, Faculty of Agriculture, Trg Dositeja Obradovica 8,
21000 Novi Sad, Serbia, e-mail: christoph.husemann@polj.uns.ac.rs
3
University of Novi Sad, PhD, Assistant Professor, Faculty of Agriculture, Trg Dositeja
Obradovica 8, 21000 Novi Sad, Serbia, e-mail: tihomir@polj.uns.ac.rs
4
University of Novi Sad, PhD, Assistant Professor, Faculty of Agriculture, Trg Dositeja
Obradovica 8, 21000 Novi Sad, Serbia, e-mail: bebam@polj.uns.ac.rs
Abstract. The fast changing environment, including difficult market
conditions and a high exposure to financial risks are major reasons for
changing production policy. Farm Management Information Systems (FMIS)
appear to be a powerful tool to deal with the new conditions. However, farmers
still rely more on their intuition than on proper management tools, when it
comes to running a farm business. Many farmers do not use FMISs for various
reasons, like lack of knowledge and the complexity of many available FMISs. In
particular for small to medium-sized farms and for multifunctional farms
appropriate FMISs hardly exist. The objective of this paper is to give a brief
overview why modeling has not had its breakthrough in the farming sector so
far.
Keywords: Farm Management Information System, Modeling, Management
1 Introduction
The skillful and accurate management of farms (Mishra et al. 1999; Muhammad et al.
2004) is one of the most important success factors for their effective functioning,
their sustainable development and survival in today’s fast changing environment
(Forster, 2002).
The reasons why a sophisticated farm management is such an important and
challenging task are certainly diverse, however, three major factors have been
identified in the ongoing academic discourse (Inderhees 2006; Sørensen, Bochtis
2010):
1. A complex environment
2. Complex farm structures
3. The introduction of modern technologies to the agricultural sector
(Glauben et al. 2006; Inderhees 2006; Sørensen, Bochtis 2010)
705
Farms are involved in an environment, which has become more and more complex
over the past decades. Once was enough to supply a society with cheap and sufficient
food products, wheras today much more is expected from the agricultural sector
(Rohwer 2010). The expectations incorporate compliance with regulations to be
entitled for EU subsidies (Morgan et al. 2012; Sørensen, Bochtis 2010), new and
stricter guidelines for the use of agrochemicals (Villaverde et al. 2014), food safety
(Magnuson et al. 2013) and animal welfare requirements and environmental concerns
(Malcolm 2004; BMELV 2004). In fact, the farming business has shifted to a
multifunctional service sector (Schöpe 2005).
The second reason why farm management became more and more difficult, lays
within the farms themselves. In Germany the total number of farms has decreased
since the 1970s whereas the cultivated area did not change substantially (©
Statistisches Bundesamt 2012) . Consequently, the remaining farms have become
larger to benefit from economies of scale (Nause 2003) but they also became more
difficult to manage (Glauben et al. 2006).
The third reason is the introduction of modern technology has contributed to the
challenge of sophisticated farm management. In this context modern technology
incorporates in particular the usage of PCs coupled with the application of the
corresponding software of the financial statements of farms, planning tasks for land
cultivation husbandry etc. Additionally, many farmer introduced GPS added tractors
and “smart” machinery, GIS-supported landscape modeling and other state of the art
technology, making special knowledge indispensable (Linseisen et al. 2000; Zeddies
2001). All these technologies can be combined under the expression “Wired Farm”
or “Precision Farming” (Sigrimis et al. 1999).
A major outcome of the three developments described is the generation of large
data volumes. To handle and to benefit from these enormous data volumes farmers
have to be capable of performing the following tasks:
1. Collection of Data
2. Processing of Data
3. Providing Data
4. Using Data
To deal with these four tasks, farmers have to introduce an integrated Information
System (IS) sometimes also called Decision Support Systems (DSS).
Today, most IS or DSS have a special focus. “Dairy Comp 305” for instance, is an
IS especially for the herd management of milking cows (Cerosaletti et al. 2004, 2004;
Enevoldsen et al. 1995), whereas MicroLEIS (Meyer et al. 2013) and DSSAT
(Sonam, Sawhney 2014) are developed as very useful tools for land cultivation.
AFFOREST sDSS is especially developed for silviculturist (Orshoven et al. 2007)
and StocKeeper for herd management of bulls (Grubb 2010).
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2 Objectives and Methods
The objective of this paper is to give first a brief overview why modeling still has
not had it is breakthrough in the farming sector. The paper is aiming on the
development of a FMIS that depicts all production processes and their internal
interconnections of a farm accurately. The first objective deals with the question,
why FMISs’ pervasion performance in today’s farming sector is still poor. The
second one aims on identifying the most successful FMIS approaches currently
applied.
The development of the FMIS model is based on a system approach that observes
the farm as an open system, with productional, technological, economic and social
subsystems. Firstly, a system analysis of the farm has been conducted, aiming on the
identification and analysis of all the material and information flows, production
processes and their interconnections. This procedure is imperative to describe the
farm’s production systems accurately. The procedure incorporates the data collection
by conducting visual inspections (fields, animal facilities, machinery etc.), interviews
with the farmer and his laborer and a thorough analysis of the farm’s financial data,
including balance sheets and profit and loss statements, the operating plan including
spraying and fertilizing dates and crop rotation scenarios. On the basis of the
collected information a farm fact book has been completed, dealing with basic
external and internal conditions.
Consequently the FMIS model has been designed, based on the system analysis
and the individual information requirements of the farmer. The FMIS design
comprised a listing of all production processes, focusing particularly on the internal
exchange of goods. Lastly, the gained information was transferred into a marginal
cost model. This approach does not take fix costs into account. Therefore, all fixed
assets (plant and equipment) are considered immutable. In other words, the model
does not consider future investment or disinvestment decisions and has therefore
solely a short term character.
3 Results and Discussion
As mentioned earlier the reasons why farmers hesitate to apply modeling to their
farm are various. In the last 20 years scholars brought up several explanations. Figure
1 facilitates the understanding of their argumentation.
Complexity is one of the major impediments for the application of modeling. And
this complexity occurs very different ways. First, one has to acknowledge the
complexity of the farms organization itself. Various, partially very different
production processes (land cultivation, husbandry etc.) have to be tuned properly.
Additionally, farmer deal with biological system which can never be fully controlled.
Market risk (change of prices), financial risk further increase the number of
uncontrollable factors. These two sources of complexity, namely the farm and its
environment lead to complex models. But complex models are expensive, difficult to
understand and to use. These are unfavorable premises for an easy and swift
707
adaptation. The huge number of uncontrollable factors and their significant influence
on the farm’s profitability have another negative side effect.
Fig. 1. The Farm System after (Sorensen & Kristensen, 1992)
When it comes to modeling of farms the first outcome of the farm analysis is a
comprehensive “Farm Fact Book” which consists of the following elements: “Basic
information”, “Natural conditions”, “Machinery”, “Human resources”, “Buildings”,
“Farm details” and “Infrastructure”.
In a second step we analyzed general FMIS models. Most FMIS models in
literature have quite simple structure. The structure of the general FMIS incorporates
two technologies, namely plant production and livestock production. When all
activities and their input respectively output factors are evaluated with prices, then an
accurate calculation can be conducted. In terms of livestock production the “Herd
Organization Structure” has to be considered additionally. From the calculations of
the plant production the services and the livestock production one receives the
coefficients necessary for the linear programming program (LP-Program). This
program also considers market limitations (e.g. max. quantity salable) and production
limitations (e.g. the max. available agricultural land).
The analyzed case study farm is a good example of a complex farm structure. The
case study farm as displayed in Figure 2 has three major braches, namely “Plant
Production”, “Services” and “Livestock Production”. The branch “Plant Production”
has four subunits. The first subunit, called “Arable Farming” displays the three main
crops, which the farmer cultivates. These crops follow the common regional scheme
of crop rotation: winter wheat, winter barely, winter canola. Grain maize is only
occasionally cultivated as a surrogate crop in the case that the three main crops can’t
be cultivated. “Feed Crops” incorporates grassland for the hay production and grain
708
maize, which is sold to food suppliers who meliorate and resell it as pig feed to the
farmer. The pasture is exclusively used for the horses during the summer.
Fig. 2. The Farm Structure
The branch “Livestock Production” solitarily deals with “Hog Finishing”. The 700
place of the pig stall are the biggest source of income of the case study farm, which is
totally independent of the season (Figure 3).
Fig. 3. Internal Material Flows of the Case Study Farm
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4 Conclusion
The findings of this paper have pointed out that well balanced and carefully
considered management decisions are more important for the surviving of farms.
Reasons are the grown external and internal complexity of the farming business and
its higher exposure to financial risks. It is likely that these factors will become even
more significant in the future making a professional decision making support system
indispensable. A sophisticated FMIS can be an important contribution to attain better
management decisions. It has to allow farmers to easily access all information which
are crucial for the farms profitability.
The minimum requirements for such a FMIS are:
1. Monitoring/Data collection
2. Planning/Scenario analysis
3. Controlling/Target-actual comparisons
4. Identification of optimization potentials /Profit maximization
However, one has to consider the enormous effort connected with a proper setup
of a FMIS. Co-products, internal exchange of good or non-marketable products (e.g.
crop-rotation) and a thorough cost accounting as a basis are just some factors, which
have to be considered. Moreover, when it comes to optimizations (profit
maximization), an allocation optimum for the entire farm is difficult to identify, since
the scare resources differ from production process to productions process (arable
land, feeding places, machine hours etc.). Nevertheless, the benefits of a FMIS are
paying off for farmers on the long run, because a well-developed FMIS can support a
decisions making process which is based on facts and not on gut instinct.
Acknowledgments. The work is part of research under project TR32044 partially
funded by the Ministry of Education, Science and Technological Development of
Republic of Serbia.
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