Precision Farming in the Digital Age: Reducing Environmental Impact

This report delves into the application of precision farming within the digital age, emphasizing the role of technology in mitigating the environmental impact of food production. It explores the challenges facing the farming industry, including food security and sustainability, and highlights the use of technologies like Big Data, smart sensors, and the Internet of Things to optimize resource management and reduce waste. The report discusses decision support systems and shared data technologies, such as smart fertilizer management and life cycle thinking, as tools to enhance efficiency and minimize environmental footprints. It also assesses the feasibility and adoption of precision technologies in both developed and developing countries. Furthermore, the report underscores the significance of enzymatic approaches and microbial control in food processing to promote sustainability. The conclusion emphasizes the inverse relationship between production intensity and emissions, advocating for the integration of digital technologies for sustainable agriculture and reduced environmental impact. This report offers a comprehensive overview of how digital technologies can revolutionize food production, ensuring food security while minimizing environmental damage.

Running head: PRECISION FARMING IN DIGITAL AGE
Precision Farming in the Digital Age
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Table of Contents
Introduction:....................................................................................................................................2
Discussion:.......................................................................................................................................3
Conclusion:......................................................................................................................................6
References:......................................................................................................................................7

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Introduction:
Food is one of the integral components of life and is the very need for human existence.
As the population started growing, various limitations within the processing of food systems
have started arising including the techniques by which they are stored. With the increase in the
trend of shifting the industrialization a large group of population started shifting towards
activities creating the very need for industrialized food sectors. The consumption of foods and
their daily production have considerable impact on the environment (Poore & Nemecek, 2018).
Production of food contributes to various environmental impacts such as change in the climatic
condition, phenomenon such as acid rains and eutrophication leading to the depletion of
biodiversity. As a result of which there is a need to adopt adequate healthy supplies of food that
would help in maintain the global social economic viability. The daily production of food also
leads to considerable drainage of resources such as area of land, energy, nutrients and water.
Precision farming or in other word satellite farming is defined as a type of management concept
related to management of farm based on the concept of observation, measurement and response
to the inter-field and intra-field variability in the crops (Srbinovska et al., 2015). Precision
agricultural research helps in defining the decision support system for farm management while
optimizing the goals and preserving the resources (Schimmelpfennig, 2016). Various digital
technologies are combined with the food production system so as to reduce the environmental
impact on food that is consumed like Big Data or any type of shared data technology. This
particular report deals with the detailed analysis and identification of various ways by which
digital technology can lead to the reduction of environmental impact on the food production
system.

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Discussion:
Current challenges in current farming industry impacting the productivity:
With the beginning of cyber revolution in the food production system, many challenges
are emerging day by day affecting the overall population. The challenges have become more
complicated as the population further grows leading to issues in the food security. As per
predicted analysis of different reports, the global demand for food has led to the changes in the
food production and agricultural industry (vanov, Bhargava & Donnelly, 2015). Hence it is
important to find ways so as to ensure that the produced food is sustainable, economically viable
and also does not cause any environmental impact on the environment. The challenges that are
faced by the livestock farmers often vary in different constraints affecting the overall
productivity. For example, constraints like socio-economic status of the lice stock owners along
with the geographical settings influence the impact of productivity of the farming systems. Many
other challenges that includes concerns regarding managing of wastes, smell, noise etc also
impacts on the productivity of the farming industry and serves as the major constraints in this
context.
An overview of technologies used in precision farming:
In the context of various technologies used in precision farming, comes the use of
various technologies such as the use of big data and shared data technology such as
implementing a Decision support System within the food production system (Van & Woodard,
2017).
The use of Big Data technology to overcome the constraint of communication in farm
management cycle:

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Application of Big Data on Smart Farming concept is a way forward in this context. This
technology includes smart sensors and devices that produces huge amount of data with
unprecedented decision making capability. Smart farming is defined as a way of innovative
development that prioritizes the use of information and communication technology in the field of
farm management cycle (Jindarat & Wuttidittachotti, 2015).
Application of Internet of Things Technology to minimize the application of fertilization
input:
Applications of other technologies such as Internet of Things or Cloud computing along
with the development of artificial intelligence are encompassed by the phenomenon of Big Data
(Wu et al., 2016). It helps in capturing, analyzing and using the data for the purpose of decision
making in the process of Smart Farming. Smart Farming in turn helps in reducing the foot print
of farming ecologically. It also helps in minimizing the application of inputs such as fertilizers,
energy and water in the precision mechanism of farming in the digital age (Sundmaeker et al.,
2016). It helps in mitigating the problems related to leach as well as with the emission of
greenhouse gases. According to various studies, the scope of Big Data in the application of
Smart Farming goes beyond the primary production while influencing the behavior of food
supply chain (O'Grady & O'Hare, 2017). The Big Data technology that is used in this respect
helps in providing the insights from prediction about the farming operations while driving the
real time operational decisions leading to redesigning of the business process for changing the
game for the overall business models.
Application of Decision Support System to overcome complex decision making process:

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Another implementation of digital technology in precision farming is the use of Decision
Support system within the food production system. As US farmers are facing concerns related to
the environmental impact and the rise in the cost of specialized operation in farming, there is a
need of having a sustaining system of farming. In this respect, mention must be made of the use
of decision support system in the food production system which involves a complex process and
requires more intensified hands on management of resources than the higher input and the
specialized systems. Micro computer based decision support systems helps farmers to develop
plan while managing the various aspects of operation in farming. It helps in managing the input
of nutrients, pests, conserving the soil and protecting the quality of water. There are various
Decision Support System available in the market. They makes use of system probes and
environmental monitors that are installed within the field and are connected to the web providing
detailed data on the moisture of the soil, temperature of the air, humidity of the surrounding,
rainfall as well as the speed of the wind. Besides this, Smart Fertilizer Management is another
way of shared data technology that is used in this field. It is a type of unique web platform that
helps in optimizing the use of fertilizers while managing their use in the agricultural field and
enabling farmers to increase the yield of crops. It also helps in the reduction of cost of the
fertilizers and also helps in protecting the environment (Campbell et al., 2016). Shared data
technology includes a various ways in order to reduce the environmental impact on the food
production system such as by the use of Life Cycle thinking. Life cycle thinking refers to
methods that are used for assessing the supply chain of agro food. It however requires further
improvement in better assessing the sustainability of the food production system. Life cycle
thinking plays a vital role in the following two aspects-

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1. In identifying the impact on the food supply chain while focusing on the major challenges that
are faced globally.
2. In assessing the future scenarios related to both technological and behavioral improvements
under different environmental factors.
Summary of the feasibility and adoption of precision technologies in developing verses
developed countries:
Besides this the adoption of the concept of technological management route which is a
type of logical set adopted by the farmers in order to identify the intensification of the product
system combines high productivity along with low environmental impact on food production
system (Garibaldi et al., 2017). This technology is usually applied in the production of dairy
systems. Besides all these shared data technology another most devoting technological approach
so as to reduce the impact of environmental footprint in the processing of food is by making use
of enzymes. Enzymes acts as one of the biological catalyst to speed up reactions while saving
time, cost and energy. Food enzymes are relatively sensitive and non-toxic with high activity at a
low concentration rate. The enzymatic approach entail condition of mild reaction and hence are
friendlier and protects the environment as compared to other traditional methods. While using all
these digital technology, still the major concern lies within the safety of the food and thus new
innovative methods like microbial control needs to be incorporated within food processing
systems so as to find an alternative technique that consumes less energy and causes less
environmental impact (Schneider & Wagner, 2015).

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Conclusion:
With the rise of industrial revolution, the food production system has changed widely in
association with distribution of much less efficient resources. With the change in the global food
production system, the environmental impact on food also increases. Thus from the above report
it can be concluded that using innovative and digital technology approach in the food production
system can help in reducing the environmental foot print. Use of technologies such as Big Data
or any kind of shared data technology is a way out in this case. The report establishes an inverse
relation between the intensity in production to the intensity in the emission that in turn helps in
reducing the environmental impact on food production system. Big Data application in
agricultural field for the purpose of precision farming helps in collecting agronomic data from
the farming machineries and thus helping in to achieve sustainable agriculture. Smart farming is
another technology that helps in reducing the ecological foot print on farming. Food production
system creates a huge environmental burden and causes unnecessary consequences. Hence it is
essential to reduce the way of food production while implementing different digital technology
trends so as to reduce the environmental impact on food for future consumption.

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References:
Campbell, B. M., Vermeulen, S. J., Aggarwal, P. K., Corner-Dolloff, C., Girvetz, E.,
Loboguerrero, A. M., ... & Wollenberg, E. (2016). Reducing risks to food security from
climate change. Global Food Security, 11, 34-43.
Garibaldi, L. A., Gemmill-Herren, B., D’Annolfo, R., Graeub, B. E., Cunningham, S. A., &
Breeze, T. D. (2017). Farming approaches for greater biodiversity, livelihoods, and food
security. Trends in ecology & evolution, 32(1), 68-80.
Ivanov, S., Bhargava, K., & Donnelly, W. (2015). Precision farming: Sensor analytics. IEEE
Intelligent systems, 30(4), 76-80.
Jindarat, S., & Wuttidittachotti, P. (2015, April). Smart farm monitoring using Raspberry Pi and
Arduino. In 2015 International Conference on Computer, Communications, and Control
Technology (I4CT) (pp. 284-288). IEEE.
O'Grady, M. J., & O'Hare, G. M. (2017). Modelling the smart farm. Information Processing in
Agriculture, 4(3), 179-187.
Poore, J., & Nemecek, T. (2018). Reducing food’s environmental impacts through producers and
consumers. Science, 360(6392), 987-992.
Schimmelpfennig, D. (2016). Farm profits and adoption of precision agriculture (No. 1477-
2016-121190).
Schneider, M., & Wagner, P. (2015). Prerequisites for the adoption of new technologies–the
example of precision agriculture.

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Srbinovska, M., Gavrovski, C., Dimcev, V., Krkoleva, A., & Borozan, V. (2015). Environmental
parameters monitoring in precision agriculture using wireless sensor networks. Journal
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Van Es, H., & Woodard, J. (2017). Innovation in agriculture and food systems in the digital
age. The global innovation index, 97-104.
Wu, J., Guo, S., Li, J., & Zeng, D. (2016). Big data meet green challenges: Greening big
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