=Paper=
{{Paper
|id=Vol-2030/HAICTA_2017_paper53
|storemode=property
|title=Farm Management Information System: Case Study
|pdfUrl=https://ceur-ws.org/Vol-2030/HAICTA_2017_paper53.pdf
|volume=Vol-2030
|authors=Nebojsa Novkovic,Christoph Husemann,Tihomir Zoranovic,Beba Mutavdzic
|dblpUrl=https://dblp.org/rec/conf/haicta/NovkovicHZM17
}}
==Farm Management Information System: Case Study==
https://ceur-ws.org/Vol-2030/HAICTA_2017_paper53.pdf
Farm Management Information system: case study
Nebojsa Novkovic1, Christoph Husemann2, Tihomir Zoranovic3, Beba Mutavdzic4
1
Professor, University of Novi Sad, Faculty of Agriculture, Trg D. Obradovica 8, 21000 Novi
Sad, Serbia, Email: nesann@polj.uns.ac.rs
2
PhD student, University of Novi Sad, Faculty of Agriculture, Trg D. Obradovica 8, 21000
Novi Sad, Serbia
3
Associate Professor, University of Novi Sad, Faculty of Agriculture, Trg D. Obradovica 8,
21000 Novi Sad, Serbia, Email: tihomir@polj.uns.ac.rs
4
Assistant Professor, University of Novi Sad, Faculty of Agriculture, Trg D. Obradovica 8,
21000 Novi Sad, Serbia, Email: bebam@polj.uns.ac.rs
Abstract. Through the development and adoption of a Farm Management
Information System (FMIS) that incorporates linear and non-linear
optimization, this paper investigates whether FMISs are a suitable tool for
significantly improving of the overall profitability of a medium-sized and
diversified farm. Consequently, profit maximization and cost efficiency are the
solitary aims. The developed linear and nonlinear models consider all
production processes and services of the selected case study farm that is
located in North Rhine-Westphalia (Germany). Particular attention is paid to
the farm’s internal interconnections between the different production processes
and its services as well as the resulting synergy effects. This paper shows that
at a given price level for input and output factors, it is possible to increase the
annual gross profit on this farm from 292,812 EUR to 342,461 EUR, which
represents a rise of 17.0%. This improvement can be achieved by solitarily
optimizing the farm’s allocation of the available resources.
Keywords: Farm management, Diversified farm, Optimization
1 Introduction
Successful farm management has become a more challenging task over the past
decades. Today’s farmers are increasingly exposed to various risk factors like the
weather or pests (Mußhoff et al., 2007), and at the same time they have to tackle
difficult economic decisions which are subjected to technological, political and social
changes. Therefore, an agricultural sector is nowadays exposed to a more complex
and faster changing environment than ever before. However, the rise of complexity is
not solitarily routed in the external environment, but also within the farms
themselves. Most farms in the developed countries have undergone a tremendous
change in the past sixty years in order to sustain. Thus, farmers have either
augmented their productions capacities to benefit from economies of scale, or they
have diversified their farms to benefit from economies of scope and to reduce their
risk exposure.
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Therefore, for both type of farms, augmented and diversified, a proper
management has become a sophisticated task, which demands additional skills from
farmers. Prior, it was sufficient to have expert knowledge in land cultivation and
stock breeding, which is, however, not adequate any longer. Farmers have had to
shift their self-perception from the “classical” role as a cultivator and breeder to a
manager of an enterprise. Therefore, they must gain knowledge in risk assessment,
controlling, auditing and taxations. All this holds true for diversified farms in
particular, since they do not only have to deal with the new conditions and elevating
risk level, but also with their complex farm structure. Thus, in order to sustain and to
improve the profitability of their farms, farmers are in need of a sophisticated
planning, controlling and optimization tool (Nagel, 2000).
Farm Management Information Systems (FMISs) are such powerful tools to
support farms to retain their independence and to increase their profitability.
FMISs, consist of a set of business systems designed to provide crucial
information for decision making and to assist the manager in strategic planning
(Capron and Perron, 1993).
The models applied in FMISs can aid to deal with internal and external
complexity and to achieve the optimal distribution of a farm’s scarce resources to its
various production processes and other activities. This is a vital success factor for
any agricultural business (Parker, 2003). However, many farmer still rely more on
their intuition than on management tools when it comes to running their business
(Pannell, 1996). This fact is closely related to the complexity of agricultural
businesses. In this kind of environment, intuitive decisions may be considered useful
when it comes to generating ideas and responding to urgent matters (Suter, 1992).
This is true, although, modeling of farms has started already in the 50‘s and 60‘s of
the last century. Since then, vast numbers of researchers and agricultural advisors
tried to enthrall farmers with their models and to implement FMISs throughout the
farming business. However, their success has been rather limited (McCown and
Parton, 2006).
A well-designed FMIS provides an easy access to all information, which are
crucial for the farms profitability and sustainability. In this context, the “universal”
FMIS opts for the optimal resource allocation, because only in this way it can
effectively support the farmer in attaining better management decisions, while
making his farm more profitable.
Farms can be considered as legal and fiscal business entities, in which a
transformation process is ongoing by combining commodities and services, aiming
on the production of marketable output factors. (Kistner and Steven, 2002; Reisch,
1995). So, the fundamental question from the microeconomic point of view is: why
should any farmer be interested in FMIS? As simple as this question might look, the
answer to it is not. Undoubtedly, the skillful and conceived management of farms is
one of the most important success factors for their proper functioning, their
sustainable development and their survival in today’s fast changing environment
(Forster, 2002; Mishra et al., 1999; Muhammad et al., 2004).
Nevertheless, farmer’s major aim always was to maximize their profit, because
only when a farm is well-managed, it can generate the funds to finance its sustainable
development and thereby its survival in today’s fast changing environment. The
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major leverage to achieve this aim has been to increase the productivity of their
farms, or more precisely of the various production processes on their farms.
2 Methodology and Data Sources
Within the scope of this research the “Whole-Farm Modeling” approach is
considered as the most suitable. Makeham was one of the first fostering this approach
- he called it “whole farm project” (Makeham, 1971, p. 100) - and it has been tested
widely already. For instance in Western Australia within the software MIDAS
(Model of an Integrated Dryland Agricultural System) (Pannell, 1996).
For the development of the cost calculation model, firstly a database was set up,
comprising all necessary activities for conducting each single production process or
service. Consequently, the cost calculation was conducted for each production branch
separately. Nevertheless, input factors like the available arable land or the working
time of the farmer are treated globally within the entire model. To each activity the
needed working time, machinery hours, diesel consumption and other inputs like
seeds, spraying chemicals was assigned. Then market prices for each single input
factor were attached in order to receive the exact costs of each activity. Finally, all
standardized direct cost factors for every production branch’s input(s) were received.
The turnover calculation was carried out according to the cost calculation. Thus,
depending on the availability, the farm’s average selling price or a current market
price was applied. With these prices, each production branch’s activity was evaluated
in order to receive the turnover per output factor. Standardized cost was then
subtracted from the turnover per activity in order to obtain the specific gross profit of
each activity.
The tools chosen for depicting and solving the linear optimization model are the
software package LindoTM API 6.1 and MS Excel in combination with the AdIn
OpenSolver 2.1. Both incorporate a very capable simplex algorithm, whereas the
former is commercial while the latter is freeware. In addition, does the usage of two
different software solutions ensure that the obtained results are independent form the
software in use?
3 Results of Research
Variant 1, which focuses on the effectiveness of the resource allocation has been
solved with two separate software packages: Lindo and the Excel AdIn Open Solver
2.1. This procedure has been chosen in order to ensure that the obtained results are
independent from the applied algorithm. Unlike the procedure of Variant 1, Variant 2
has solitarily applied LindoTM, since after having confirmed the consistency of the
obtained results, it has been no longer necessary to apply Open Solver 2.1 as well.
Firstly, when it comes to implications for recommended actions, effectiveness
mostly comes before efficiency. This is because the negative impact of doing the
right thing (effective) in a non-efficient way is still better than doing the wrong thing
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(non-effective) in the most efficient way. In other words, the consequences from
running a farm ineffectively are dire than the consequences of running it
inefficiently. Furthermore, a major premise of both models has been that financial
funds are not considered a limiting factor. However, the limitation of financial funds
is the major reason, why focusing on efficiency, or more precise, cost efficiency. In
contrast, the available farmland and stable capacities actually have been considered
limiting factors. And both factors are related to effectiveness.
Secondly, and more importantly the actual results of Variant 1 (“Linear”) and
Variant 2 (“Non-Linear”) do not differ in a substantial way as prior mentioned. Table
1 displays all values and costs of production and the consequent gross profits for the
different production processes and services, which have been calculated according to
the same price level for all variants. The total gross profit is nearly identical. As a
matter the differences are solitarily routed in the sector of plant production, whereas
the hog fattening activity and the pension horses remain the same in both variants
Table 1: Comparison of Variants 0-2 (all values in EUR)
Variant 0 Variant 1 Variant 2
Non-optimized Linear Non-Linear
2012/2013 Optimization Optimization
Total Value of Production 760,004 833,452 820,954
Total Variable Cost of Production 467,192 490,991 480,865
Gross Profit 292,812 342,461 340,090
Fixed Cost 181,487 181,487 181,487
Total Cost 648,679 672,478 662,352
Efficiency 1.171617822 1.239374573 1.239453792
Change in Efficiency in % 100.0% 105.8% 105.8%
Actually, out of the 198 available activities only a fraction, namely 25 shows
different results. And out of these 25 activities 18 show a divergence of less than
1000 EUR.
The subsequent paragraphs will focus not only on the deviations between the
results of Variant 1 and Variant 2, but also consider the outcomes of the non-
optimized Variant 0. However, the focus will clearly lie on the results of the former
two.
Having said that, it seems worthwhile to focus once again on the similarities of
Variant 1 and 2. As table 1 shows, the overall gross profit of Variant 1 is only 2.371
or 0,69% higher than that one of Variant 2. Thus the differences between Variant 1
and 2 are virtually negligible. The differences of the total turnover and the total
variable costs are slightly more significant (833.452 EUR to 820.954 EUR and
490.991 EUR 480.865 EUR). When comparing these results with the non-optimized
figures of 2012/13 the potential for the optimization process becomes clear. Both,
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Variant 1 and Variant 2 are capable of increasing the gross profit by nearly 50.000
EUR (Variant 1: 49.649 EUR; Variant 2: 47.278 EUR).
To be fair, one has to mention that the improvement had been slightly smaller (ca.
4.400 EUR) if the pig stall had performed as expected. Nevertheless, the
augmentation of the attainable gross profit had been remarkable also in this scenario.
Besides the gross profit, also in terms of efficiency Variant 1 and Variant 2
outperform the non-optimized Variant 0 significantly, but hardly differ from each
other (Variant 1: 1,2394 to Variant 2 1,2395).
Table 2 shows the actual resource allocation of Variant 0 and the results of Variant
1 and 2. The table shows that in 2012/2013 the land usage differs for most crops
considerably from the optimal solutions of variant 1 and 2. This is in particular true
for winter wheat, winter barley and winter canola, which show some major
deviations. This deviation can be explained by the strict application of the crop
rotation constraint in the model, which states that these three crops have to be
cultivated on an area of the same size. In reality a rather rigorous adherence to the
crop rotation plan is difficult to accomplish. Also the suggested cultivation of 8,0 ha
grain maize in Variant 1 differs from the zero hectares in Variant 0 and 2. The
optimum solution of Variant 2 actually suggests replacing the area under grain maize
nearly completely by silo maize. This does not come as a surprise, since the
production of silo maize is by 30% cheaper than that one of grain maize, and
therefore complies very well with the aim of cost efficiency. The same explanation
holds true for the fact that Variant 2 fosters the extensive use of grazing land (3 ha)
more than Variant 1 (2 ha). As earlier mentioned, the Greenland area in Variant 0
also included the grazing land for pasture, as the farmers have not distinguished
between the two so far.
Table 2. Allocation/Usage of farmland /Stable Capacities Variant 0-2 (in ha/headcount and %)
Producion Process Variant 0 Variant 1 Variant 2
Abs. % Abs. % Abs. %
WW 19.8 27.2% 12.7 17.2% 12.8 17.3%
WB 17.2 23.7% 12.7 17.2% 12.8 17.3%
WC 5.2 7.2% 12.7 17.2% 12.8 17.3%
PO 8.6 11.8% 7.1 9.6% 7.8 10.5%
GM 0.0 0.0% 8.0 10.8% 0.0 0.0%
SM 1.6 2.2% 0.0 0.0% 7.7 10.4%
GL 6.9 9.5% 4.9 6.6% 3.9 5.3%
SB 12.1 16.6% 12.6 17.0% 12.6 17.0%
RB 1.3 1.8% 1.3 1.8% 0.6 0.8%
GR 0.0 0.0% 2.0 2.7% 3.0 4.0%
Hogs 1,590.0 94.0% 1,692.0 100.0% 1,692.0 100.0%
Large Horse Stable 3.0 100.0% 3.0 100.0% 3.0 100.0%
Normal Horse Stable 7.0 100.0% 7.0 100.0% 7.0 100.0%
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As Variant 0 and Variant 1 suggest the same number of hectare Variant 2 suggest
a reduction by more than 50%. Again, the very intensive production methods and the
consequently high costs of cultivation of 6.313 EUR per hectare explain, why a
reduction of raspberry cultivation makes sense from an efficient related point of
view. In contrast to the differences in the land cultivation process there are no
differences in the level of activity of hogs and pensions horses.
The difference in crop yield, originating from the various sizes of cultivated areas
per crop and fruit for each variant are displayed in table 3.
Table 3: Crop Yield Variant 0-2 (in t and %; Index 100% Variant 0)
Producion Variant 0 Variant 1 Variant 2
Process
Abs. % Abs. % Abs. %
WW 193.9 100.0% 132.2 68.2% 133.9 69.0%
WB 121.7 100.0% 109.7 90.1% 110.6 90.8%
WC 21.2 100.0% 57.8 273.3% 59.1 279.5%
PO 0.0 N.A. 0.0 N.A. 0.0 N.A.
GM 0.0 N.A. 83.7 N.A. 0.0 N.A.
SM 7.0 100.0% 0.0 0.0% 35.0 501.6%
GL 50.8 100.0% 36.3 71.5% 29.2 57.5%
SB 121.9 100.0% 140.0 114.8% 140.0 114.8%
RB 3.3 100.0% 3.4 103.1% 1.5 45.8%
GR 0.0 N.A. 0.0 N.A. 0.0 N.A.
The tables 4 and 5 show the value/turnover of production and the variable cost for
every crop and fruit and each variant. From these tables the later on introduced table
for the gross profit is derived. Besides, tables 4 and 5 provide some interesting
insights.
For instance, the different percentage values, representing the proportion with
regards to the overall turnover respectively overall variable costs for each individual
crop/fruit. As for some crops, like winter wheat, these percentage values are balanced
(4,4% of the overall turnover to 4,3% of the overall variable cost; Variant 1), for
some they are not. Winter barely is an example for a negative relation (3,4% of the
overall turnover to 3,8% of the overall variable cost; Variant 1), whereas strawberries
are an excellent positive example (39,5% of the overall turnover to 27,0% of the
overall variable cost; Variant 1). The mentioned relations make it possible to draw
conclusions concerning which crop/fruit should be preferred over another crop/fruit
in general. However, the relations do not give evidence if a crop or fruit is profitable
or not. Referring to the example of winter barely, one can observe in table 5 that,
despite the unfavorable relation, winter barely is actually profitable.
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Table 4: Value/Turnover of Production (in EUR and %)
Variant 0 Variant 1 Variant 2
Producion Process
Abs. % Abs. % Abs. %
WW 53,821 7.1% 36,717 4.4% 37,164 4.5%
WB 31,518 4.1% 28,404 3.4% 28,630 3.5%
WC 9,077 1.2% 24,814 3.0% 25,375 3.1%
PO 12,040 1.6% 9,926 1.2% 10,890 1.3%
GM 0 0.0% 19,666 2.4% 0 0.0%
SM 2,586 0.3% 0 0.0% 12,966 1.6%
GL 6,703 0.9% 4,791 0.6% 3,854 0.5%
SB 297,192 39.1% 341,293 40.9% 341,293 41.6%
RB 12,753 1.7% 13,151 1.6% 5,836 0.7%
GR 0 0.0% 520 0.1% 774 0.1%
Hogs 309,573 40.7% 329,432 39.5% 329,432 40.1%
Large Horse Stable 8,178 1.1% 8,178 1.0% 8,178 1.0%
Normal Horse Stable 16,562 2.2% 16,562 2.0% 16,562 2.0%
Total 760,004 100.0% 833,452 100.0% 820,954 100.0%
Table 5: Variable Cost of Production (in EUR and %)
Variant 0 Variant 1 Variant 2
Producion Process
Abs. % Abs. % Abs. %
WW 32,927 7.0% 21,129 4.3% 21,317 4.4%
WB 25,525 5.5% 18,855 3.8% 19,022 4.0%
WC 5,996 1.3% 14,649 3.0% 14,779 3.1%
PO 0 0.0% 0 0.0% 0 0.0%
GM 0 0.0% 16,558 3.4% 0 0.0%
SM 2,323 0.5% 0 0.0% 11,167 2.3%
GL 5,410 1.2% 3,842 0.8% 3,075 0.6%
SB 127,280 27.2% 132,526 27.0% 132,526 27.6%
RB 8,206 1.8% 8,206 1.7% 3,642 0.8%
GR 0 0.0% 226 0.0% 336 0.1%
Hogs 241,226 51.6% 256,700 52.3% 256,700 53.4%
Large Horse Stable 5,490 1.2% 5,490 1.1% 5,490 1.1%
Normal Horse Stable 12,810 2.7% 12,810 2.6% 12,810 2.7%
Total 467,192 100.0% 490,991 100.0% 480,865 100.0%
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4 Conclusion
In the last 40 years the agricultural sector has been exposed to a much more
complex and faster changing environment than ever before. The conducted research
addresses the mentioned risk factors and proposes as a solution a FMIS that
incorporates a (non)linear optimization model as a key feature.
The first intention of this research was to demonstrate that it is possible to develop
a FMIS for diversified farms that incorporates all modules and features needed to
attain reasonable management decisions for the respective farm. For that purpose, a
diversified farm in Germany has been selected as a case study. The research has
shown that the general model of the FMIS provides an adequate basic structure and
the rudimentary functionalities for the development of the concrete FMIS for the
case-study farm.
The additional requirements of the model to be easily adjustable, user-friendly and
simple, whist also being capable of dealing with the special demands of the case-
study farm has been only partially achieved.
Another fundamental benefit of using a FMIS and (non-) linear optimization lies
in the fact that the farmer gains a much deeper understanding and knowledge of how
his farm works, especially if there are numerous internal interdependencies. Also,
scenario analysis and “what if” analysis, which are possible with the new tools, can
substantially contribute to the already mentioned better management decisions.
The optimized results of the case study farm have shown, that this aim has been
attained, albeit the degree of accomplishment might have been expected to be higher.
In fact, compared with Variant 0 an improvement of 17,0% or 49.649 EUR (Variant
1) respectively 16,1% or 47.278 (Variant 2) at a yearly turnover of roughly 800.000
EUR does not look that impressive. An explanation for the relative modest level of
amelioration is the highly professional management of the farm, that existed already
before the optimization process. The farmer of the selected case study has over 40
years of experience and also participates in various training programs on a regular
basis. Furthermore, he keeps most of his machinery and other equipment on up to
date.
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