Literature Review on Multi-Mode Decision Making in Agricultural Supply Chain

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This literature review covers the definition, importance, factors leading to decision factors, and application of multi-mode decision making in agricultural supply chain. It also identifies gaps in research and highlights the challenges faced by farmers and stakeholders.

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Running head: AGRICULTURAL SUPPLY CHAIN
LITERATURE REVIEW ON MULTI-MODE DECISION MAKING IN AGRICULTURAL
SUPPLY CHAIN
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1AGRICULTURAL SUPPLY CHAIN
Table of Contents
Literature Review............................................................................................................................2
1. Different definitions.................................................................................................................2
2. Importance...............................................................................................................................3
3. Factor’s leading to the Decision factors..................................................................................3
4. How it is apply in Agricultural supply chain...........................................................................3
5. Research of what has been done in term of Agriculture mode. Etc........................................3
6. Developed and Underdeveloped- Identify as Gap...................................................................3
References........................................................................................................................................3
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2AGRICULTURAL SUPPLY CHAIN
Literature Review
This section covers a literature review on multi-mode decision making in agricultural
supply chain. The discussion in this sections gets started with definition of important terms that
are commonly used in agricultural supply chain. It follows the factors, which lead to decision
factor. Thereafter, the literature review section thus investigates the application of multi-mode
decision making in agricultural supply chain. It is followed by investigating the research works,
which have been conducted in term of agriculture mode. At the end, the literature review section
identifies the gap.
1. Different definitions
Agro-Ecosystem: Agro-ecosystem, in general, can be referred to the ecosystem surrounding the
lands being used for farming. Farming is an important because it may affect the entire food
industry if not being able to produce the expected quantity of foods. On a different note, a
surplus of production may create enough food resource to local people and wealth to the national
economy by means of imports and exports. Therefore, agro-ecosystem must be maintained to
avoid crops from getting damaged from kinds of natural calamities like flood, or drought
(Pressler et al. 2017).
Agricultural Supply Chain: Supply chain is itself a major topic, which cannot be covered
within just a unit. However, to be very specific, agricultural supply chain can be defined as a
network of operations, which is responsible for carrying foods to its end-users. Farmers play a
significant role in an agricultural supply chain. They are the ones who take care of the farming. If
they are not supplied with adequate resources they won’t be able to justify their farming
capabilities. Moreover, warehouses will not be able to fulfill the demands of food (Tian 2016).
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Multi-mode Decision-Making: In general, this can be defined to be as related with decision-
making for several related works of a supply chain. This goes similar to the agricultural supply
chain. An agricultural supply chain consists of numerous related activities where strategic
decision-making is required. Such decisions may also be related with a scientific understanding
of the issue. Since an agricultural supply chain has an impact of environmental behaviours, and
of farmers’ co-operation, it is actually vulnerable to a number of threats (Lam et al. 2015).
2. Importance
The importance of multi-mode decision making can be traced in challenges that it faces
while not being able to support the range of processes with effective decisions. Effective
decision-making requires strategic thinkers to construct and show ways to implementing it.
However, those strategic thinkers lack an effective co-operation from the stakeholders. The fact
does not just create a communication gap between the thinkers and stakeholders but,
considerably delays or even postpones the implementation process. It, therefore, can be said that
multi-mode decision making is an essential part of the agricultural supply chain, which if not
conducted appropriately, will affect the productivity. Strategies have failed in past such as the
“Contract Farming” (Minot and Sawyer 2016). Hence, this is important that a few challenges of
agricultural supply chain is effectively sorted out by strategic thinkers and the stakeholders.
Multi-mode decision making is required in some issues such as those being discussed in this
section.
Delivery of commodities is one of the issues. Commodities are delivered to warehouses
by various means of transportation such as by truck or train. In most cases, trucks are used to
deliver the food materials to concerned warehouses. The process appears simpler; however, it is
not so. Trucks have to face a range of issues at sites. They are required to be in queues before

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4AGRICULTURAL SUPPLY CHAIN
they could reach to the lading junction. Such sites will be tested here for their efficiency in
clearing trucks in a less span of time. Hence, sites with less efficiency will lose the business to
those that bear more efficiency. This will put more pressure on the gainers as the average
handling of trucks would then increase. As obtained from Jones et al. (2017), an increased
workload will affect the work efficiency until and unless being managed through increased
efficiency to perform. An increased efficiency would, therefore, need an increased area of
operation supported by an increased number of labours. However, this is not as easier as it
appears. Moreover, delivery of commodities are in the hands of an unstable transport system.
Need of temporary workers is another issue. During peak harvest times, some
temporary workers are required to support the increased work pressure. Sites where crops are
grown generally require additional resource for an extended period of 3-4 months. Some of these
workers may be new to this industry. With these new workers, sites are explored to safety and
training issues. Both safety and training will put additional cost burden on such sites. Hence,
there will be the need to work towards ensuring the required inflow of money. As opined by
Schut et al. (2016), huge spending is being made nowadays on the training. In addition, these site
owners will face the challenge of creating a safe working environment. According to Schut et al.
(2015), farming is not a place to experiment when one does not know the science of farming.
One can contribute to it when they only have an appropriate knowledge of the science of
farming. Indeed, the risk can be minimized if the needs to have temporary workers could
somehow be reduced.
Increased production needs is the other challenge that establishes the needs to have a
multi-mode decision-making. With increasingly growing population, the demand for foods has
also increased. There is no other way than to produce more. However, this is itself challenging
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considering the changing climate near and surrounding the agro-ecosystem. The changing
climate has a significant impact on farming. The most hardly affected crops include wheat, rice,
corn etc. There are certain parameters, which need to be met to take care of the entire farming
process. Farming requires adequate supply of water, which may vary depending on the kind of
crops and the geographic regions. These are a few parameters of farming; however, even if these
parameters are not met, productivity will get hampered. It is, therefore, a challenge for strategic
thinkers, and the stakeholders to be able to make useful decisions and also implement those to
produce the outcomes. An effective coordination with farmers has always been an issue to the
agricultural supply chain. As stated by Notenbaert et al. (2017), there is evident gap between
farmers and the stakeholders. Their understanding have rarely collaborated. Actually,
stakeholders have never been able to reach to the root of the farming process. Until and unless
there is no collaboration between stakeholders and farmers, the increasing demands for foods
will largely remain unmet. As obtained from Brandenburg et al. (2014), site operators must also
find ways to reduce costs and become automated.
3. Factor’s leading to the Decision factors
There are several factors that affect the decision factors. If these factors are effectively
managed, there won’t be any decision failure. Some of the factors affecting the decision factors
are decision timing, decision priority, and decision follow-up.
Decision timing is one of the factors, which affect the decision making. It says that
decisions should be made appropriate to the situation. For example, sales have declined and
marketing efficiency have been identified as its potential causes. Despite making any
improvement to its marketing efficiency, the operations manager focusses on including more
products into the product portfolio. It is just waste of time and resources. It can also be a loss of
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money, which is subjected to sales. Instead, marketing strategies should be focused on. This
could have made a sense.
Prioritizing the decision is another factor that affects the decision-making. It says that
managers or other concerned person should bear capabilities to prioritize between decisions. It
means that two or more decisions at times must be selected on priority basis. For example, a
company faces the declining sales. In addition, the rate of employee turnover is also high. The
selection of decision-making, therefore, must be understood. Here, the manager will need to
address both of issues because they cannot just afford to lose people or they cannot also afford
any more declining in sales. The best decision would be to put an enquiry on identifying the
reasons for declining sales and also addressing the employee attrition with training and
development programs.
Follow-up is the next most important step, which is largely missed during the decision-
implementing stage. For example, if a team is being set to identify the root causes of declining
sales, their progress should be monitored regularly to identify whether everything is fine or the
team needs some kind of an assistance. On a similar note, if there are arrangements being made
to train the selected list of people, it is then important to know how all of them have performed in
the training. It is required to supply necessary changes to the training if required or to give
feedback to those who could not utilize the resources.
(Lian, Yen and Wang 2014)
4. How it is apply in Agricultural supply chain
These are a few factors that also have its existence in the agricultural supply chain. In
addition, there are some more factors that affect decision-making in agricultural supply chain.

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An inefficient use of fertilizer is one of the factors affecting the decision-making in
farming. Chemical fertilizers are extremely important to facilitate a mass production. However,
the needs are rarely met. It happens may be because many farmers are not well educated or that
they have no access to such fertilizers for any reason. If fertilizers are not being used, this could
be dangerous for both the production and the quality of crops. As stated by Smith and Siciliano
(2015), chemical fertilizers can essentially speed up the production and can also boost the
quantity. No such steps have so far been taken to address this issue on a larger platform. Farmers,
in particular, those living in extreme rural locations have to travel quite a mile to reach to
retailers and purchase the chemical fertilizer. On the other hand, farming industry misses an
effective co-operation between farmers who work at the lowermost levels and the stakeholders of
agricultural industry. There is no such system to address this issue. Contract farming is there;
however, there is no such contractors to take care of the science of farming as it used to be at
construction sites. This is perhaps one of the potential causes of why there are inadequate
supplies of food materials on significant occasions.
Influencing factors span from political parties to the farm households. It highlights
an issue that influencing factors are just made to apply on farmers’ households’ behaviour.
Farmers are instructed on related activities of farming needs; however, their issues are left
unaddressed. Political parties in most cases are not aware of the difficulty level, which is
associated to farmers. Farmers’ complaints remain mostly unaddressed. According to Mylan et
al. (2015), many farmers cannot bear the expenses being required to buy the latest and upgraded
machines to irrigate the land and to run water into them. They are left to be dependent on the
surrounding climate, which is itself getting increasingly disturbed from the increased air
pollution. Hence, there is no rain during the seasonal time. In addition, the amount of rain being
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calculated in (mm.) has also decreased due to the global warming. It all adds more challenges to
the agricultural supply chain. The national governments and political parties have the budgets to
show every year but they rarely address their work on such a bigger platform. These
environmental issues are addressed and policies are made as well. However, these policies lack a
robust implementation and an appropriate follow up by its stakeholders.
Some measures could be adapted to support farmers more effectively. These measures
include, but is not limited to producer incentives, and supply chain modernization. Producer
incentives as according to Rueda, Garrett and Lambin (2017) can be a good approach to
motivate farmers. Farmers especially those who harvest staple crops like wheat, rice, and corn
should, in particular, get these incentives as they hardly make any significant profit from
harvesting these crops. They even lose their entire spending when they are hit by natural
calamities like flood. For example, an excessive water is harmful for rice. However, it happens
sometimes that a heavy rainfall or a flood has destroyed the entire rice crops. Now, the remains
of the affected rice crops is of no importance to farmers. This one incident take their charms
away from them. They have to lose their entire spending as well the crops. There is now no
chance to gain back the invested money. They neither have the crops, which they could use it for
their household purpose. Issues like these need to be addressed as well. Incentives is a good
solution for meeting the set criteria of harvesting but these circumstances should also be met. If
these issues are addressed seriously and effectively, farmers will have motivation to work every
single year. Notably, motivation is key to an effective workforce (Rueda, Garrett and Lambin
2017). Incentives along with compensations for natural hit cases can help to improve the
farmers’ attitude towards working and hence, productivity will also improve provided that
environmental issues are controlled.
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Supply chain modernization is a growing discussion, which can also affect the multi-
mode decision-making. According to Dubey, Gunasekaran and Ali (2015), supply chain
visibility and efficiency is an essential part of maintaining a profitable agricultural business. The
modernization thus required can be brought to the agricultural supply chain with Internet of
Things (IoT). IoT can help to optimize the most complex part of the agricultural supply chain. It
is to be noted that every single stage of an agricultural supply chain is highly vulnerable to
potential securities. Agricultural products like vegetables, fruits, dairy, and meats are
significantly impacted by delays. These agricultural products need certain temperatures to remain
afresh. The agricultural supply chain comprises of several stages traveling over which products
are delivered to its end users. If stakeholders could work together, they will be aware of each
other’s’ strengths and weaknesses. Moreover, IoT could then be deployed at each level to make
it more automated. According to Dubey, Gunasekaran and Ali (2015), Internet of Things (IoT)
can be advantageous for supply chain management. With IoT, there will be enhanced
communication between farmers, suppliers, stakeholders, and the end users. Data could be
generated and accessed in real-time. An increased access to data, therefore, would help to know
the demands and supplies. Moreover, farmers will be aware of the demands that they are
supposed to fulfill. Additionally, expected climatic behaviour could be known to the farmers and
stakeholders. Hence, stakeholders would be in a position to do their best to protect the crops
from natural calamities.
However, implementation of IoT on the agricultural supply chain could be affected from
several factors. Many farmers are illiterate. It is, therefore, of no use to educate these farmers on
IoT who even do not know the basics of a technology. They cannot be trained as well because

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they do not possess any resemblance to technology and hence, would feel loosely connected to it.
There is very limited scope in this part of an agricultural supply chain.
5. Research on what has been done in term of Agriculture mode. Etc
Agricultural systems science is a field that studies the complex and challenging behaviour
of agricultural systems. It does so with the help of a model that quantitatively guides to
understanding the complexity of agricultural science. It encourages to understand the land used
for farming and its surrounding ecosystem to help farmers with more proven facts. Natural
calamities are string barriers to farming. Farmers have no clues on how to deal with the issue.
However, scientific modeling could be helpful in a gradual understanding of farming and its
influencing factors. As opined by Jones et al. (2017), understanding the scientific behaviour of
land and agro-ecosystem can be made possible through advancing in scientific modeling in an
agricultural supply chain.
Scientific modeling of an agricultural ecosystem has become a subject of increased
interest for variety of groups across private and public institutions. Academic scientists, private
and public firms, stakeholders, and national government are all seem like being united to one
common platform with their contribution to it. They are united to create a next generation, which
will be equipped with a scientific understanding of farming. However, as pointed by Jones et al.
(2017), such agricultural systems modeling could be tested for many reasons such as those listed
below:
Capitalise on Crises: The historical data shows that scientific advancements occurred only
when there were crises such as food security concerns. It disappeared soon after the crises got
over. According to other studies, major developments occur during only the major disaster.
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Technological Advancements: A technological transition should be conducted by those who are
responsible for developing the next generation. For example, mainframe computers, the internet,
and the PC were embraced by those who were involved in the advancement process. In a similar
way, the transition of agricultural systems models should be taken care by those who are its
experts.
Collaboration: Major advances have occurred only when different forces have joined together.
In a similar way, there is a need to have a collaboration between different stakeholders of
agricultural supply chain. If this could happen, days are not far away when production rate will
rapidly increase. Notably, stakeholders in agricultural supply chain had struggled in past to work
collaboratively with the farmers.
6. Developed and Underdeveloped- Identify as Gap.
The one gap that repeatedly gets coming throughout this literature review is the lack of
an effective collaboration between stakeholders and farmers. Similar problems have
continued for a long time. Number of proposals were also being made. However, implementation
had lacked a follow-up process. There were no significant changes being done to this part. The
study did not find any evidence regarding a collaborative work between the farmers and
stakeholders. So many strategies were being made in different regions across the globe.
However, those strategies had lacked an appropriate implementation.
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References
Brandenburg, M., Govindan, K., Sarkis, J. and Seuring, S., 2014. Quantitative models for
sustainable supply chain management: Developments and directions. European journal of
operational research, 233(2), pp.299-312.
Dubey, R., Gunasekaran, A. and Ali, S.S., 2015. Exploring the relationship between leadership,
operational practices, institutional pressures and environmental performance: A framework for
green supply chain. International Journal of Production Economics, 160, pp.120-132.
Jones, J.W., Antle, J.M., Basso, B., Boote, K.J., Conant, R.T., Foster, I., Godfray, H.C.J.,
Herrero, M., Howitt, R.E., Janssen, S. and Keating, B.A., 2017. Brief history of agricultural
systems modeling. Agricultural systems, 155, pp.240-254.
Jones, J.W., Antle, J.M., Basso, B., Boote, K.J., Conant, R.T., Foster, I., Godfray, H.C.J.,
Herrero, M., Howitt, R.E., Janssen, S. and Keating, B.A., 2017. Toward a new generation of
agricultural system data, models, and knowledge products: State of agricultural systems
science. Agricultural systems, 155, pp.269-288.
Lam, C.P., Yang, A.Y., Driggs-Campbell, K., Bajcsy, R. and Sastry, S.S., 2015, September.
Improving human-in-the-loop decision making in multi-mode driver assistance systems using
hidden mode stochastic hybrid systems. In Intelligent Robots and Systems (IROS), 2015
IEEE/RSJ International Conference on (pp. 5776-5783). IEEE.
Lian, J.W., Yen, D.C. and Wang, Y.T., 2014. An exploratory study to understand the critical
factors affecting the decision to adopt cloud computing in Taiwan hospital. International Journal
of Information Management, 34(1), pp.28-36.

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Minot, N. and Sawyer, B., 2016. Contract farming in developing countries: Theory, practice, and
policy implications. Innovation for Inclusive Value Chain Development: Successes and
Challenges, International Food Policy Research Institute, Washington, DC, pp.127-155.
Mylan, J., Geels, F.W., Gee, S., McMeekin, A. and Foster, C., 2015. Eco-innovation and retailers
in milk, beef and bread chains: enriching environmental supply chain management with insights
from innovation studies. Journal of Cleaner Production, 107, pp.20-30.
Notenbaert, A., Pfeifer, C., Silvestri, S. and Herrero, M., 2017. Targeting, out-scaling and
prioritising climate-smart interventions in agricultural systems: Lessons from applying a generic
framework to the livestock sector in sub-Saharan Africa. Agricultural systems, 151, pp.153-162.
Pressler, Y., Foster, E.J., Moore, J.C. and Cotrufo, M.F., 2017. Coupled biochar amendment and
limited irrigation strategies do not affect a degraded soil food web in a maize agroecosystem,
compared to the native grassland. Gcb Bioenergy, 9(8), pp.1344-1355.
Rueda, X., Garrett, R.D. and Lambin, E.F., 2017. Corporate investments in supply chain
sustainability: Selecting instruments in the agri-food industry. Journal of cleaner
production, 142, pp.2480-2492.
Schut, M., Rodenburg, J., Klerkx, L., Kayeke, J., van Ast, A. and Bastiaans, L., 2015. RAAIS:
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Schut, M., van Asten, P., Okafor, C., Hicintuka, C., Mapatano, S., Nabahungu, N.L., Kagabo, D.,
Muchunguzi, P., Njukwe, E., Dontsop-Nguezet, P.M. and Sartas, M., 2016. Sustainable
intensification of agricultural systems in the Central African Highlands: The need for
institutional innovation. Agricultural Systems, 145, pp.165-176.
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Smith, L.E.D. and Siciliano, G., 2015. A comprehensive review of constraints to improved
management of fertilizers in China and mitigation of diffuse water pollution from
agriculture. Agriculture, Ecosystems & Environment, 209, pp.15-25.
Tian, F., 2016, June. An agri-food supply chain traceability system for China based on RFID &
blockchain technology. In Service Systems and Service Management (ICSSSM), 2016 13th
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