APPLICATION OF DATA MINING TECHNIQUES IN THE PREDICTION OF CLIMATE EFFECT ON AGRICULTURE
1.1 BACKGROUND TO THE STUDY
of climate effect plays an essential role for agriculture and other industrial
sector. A vast proportion of agricultural activities are convincingly affected
by climate conditions. From the short-term perspective, the temperature and precipitation
are essential condition for crop growth and yield in agriculture. In
agricultural practices, every crop has its own minimum, optimal, maximum
temperature for growing. The crop stops growing when the temperature goes below
the minimum temperature. The crop growth increased as the temperature goes up
from the minimum to the maximum temperature. However the crop growth decreases
as the temperature goes beyond its optimal temperature to the maximum temperature.
The crop growth stops again when the temperature reaches its maximum
temperature. Warmer temperature may favor some crops to grow more quickly and
increase their yields, but it could also reduce growth and yields for other kinds
of crops. So accurate prediction of future climate effect and weather condition
could help farmers select the proper crops in order to increase growth and
yield as well as economic incomes. The fast growth of crops such as grains may
reduce the amount of time that seeds need to grow and mature (Semenov and Porter,
2005). The crop growth not only depends on the temperature, but also on soil
water and nutrient elements such as nitrogen, phosphorus and potassium which
are all connected to the climatic conditions. The soil nutrients are absorbed
by crops with soil water adsorption. The proper soil moisture is strongly
related to the precipitation.
the cyclic distribution of rainfall/precipitation also affects agriculture (the
crop growth and yields). Similar to responding to the temperature, some crops
favor the wet climatic condition, others favor dry climatic condition. So
accurate prediction of future climatic effect could help farmers select the
crops too in order to maximum crop growth yields as well as economic income.
However, this study will examine the application of data mining techniques in
the prediction of climatic effect on agriculture.
data mining is a practical and functional technique to find the helpful pattern
from the huge dataset. So it secured an important place in agriculture because
the field agriculture contains the many data such as soil data, crop data, and climate
data and so on. Real time climatic data is difficult to analyze and manage so
various algorithms in data mining like K-Means clustering, Apriori algorithms
and other statistical methods are used to analyze the agriculture data and
provide the useful pattern. The climate create the great impact on the
agriculture so the crop growth and crop yield level are depends on the climate.
Real time climatic data can offer helps to the farmers for planting a
particular variety of crop because it gives high yield and also this real time
data helps to alert the farmers for protecting their agriculture field from the
climatic disasters. Agro climatic research centers and meteorological
departments provide real time data to the farmers.
a particular crop is planted during a suitable climate it will provide the good
yield so the economic level of the country can improve. So there is need to
predict the suitable climate for planting a crop because the climatic
vulnerability and agriculture vulnerability to climate can affect the yield
level. Elicitation and analysis of historical climate data and crop yield level
of the particular region can helps to predict the future climatic condition of
that particular region. Analysis is required for finding the future climatic
conditions of particular region where the data mining plays an important role
for analyzing historical climate data and find the required solution.
making an accurate prediction of climate is one of the major challenges facing
meteorologist all over the world. Since ancient times, prediction of climate
effect has been one of the most interesting and fascinating domain. Scientists
have tried to forecast meteorological characteristics using a number of
methods, some of these methods being more accurate than others (Elia, 2009).
of climate change entails forecasting how the present state of the atmosphere
will change. Present climatic conditions are obtained by ground observations,
observations from ships and aircraft, radiosondes, Doppler radar, and
satellites. This information is sent to meteorological centers where the data
are collected, analyzed, and made into a variety of charts, maps, and graphs.
Modern high-speed computers transfer the many thousands of observations onto
surface and upper-air maps. Computers draw the lines on the maps with help from
meteorologists, who correct for any errors. A final map is called an analysis.
Computers not only draw the maps but predict how the maps will look sometime in
the future. In predicting the climate effect by numerical means, meteorologists
have developed atmospheric models that approximate the atmosphere by using
mathematical equations to describe how atmospheric temperature, pressure, and
moisture will change over time. The equations are programmed into a computer
and data on the present atmospheric conditions are fed into the computer. The
computer solves the equations to determine how the different atmospheric
variables will change over the next few minutes. The computer repeats this
procedure again and again using the output from one cycle as the input for the
next cycle. For some desired time in the future, the computer prints its
calculated information. It then analyzes the data, drawing the lines for the
projected position of the various pressure systems. The final computer-drawn
forecast chart is called a prognostic chart, or prog. A forecaster uses the
progs as a guide to predicting the climate effect. There are many atmospheric
models that represent the atmosphere, with each one interpreting the atmosphere
in a slightly different way. The effects, or impacts, of climate change may be
physical, ecological, social or economic. It is predicted that future climate
changes will include further global warming (that is, an upward trend in global
mean temperature), sea level rise, and a probable increase in the frequency of some
extreme weather events (Wikipedia, 2010).
mining, also called Knowledge Discovery in Databases (KDD), is the field of
discovering novel and potentially useful information from large amounts of data
(Rushing, Ramachandran, Nair, Graves, Welch and Lin, 2005). In contrast to
standard statistical methods, data mining techniques search for interesting
information without demanding a priori hypotheses, the kind of patterns that
can be discovered depend upon the data mining tasks employed. By and large,
there are two types of data mining tasks: descriptive data mining tasks that
describe the general properties of the existing data and predictive data mining
tasks that attempt to do predictions based on inference on available data. This
techniques are often more powerful, flexible, and efficient for exploratory
analysis than the statistical techniques (Bregman and Mackenthun, 2006). The
most commonly used techniques in data mining are: Artificial Neural Networks,
Genetic Algorithms, Rule Induction, Nearest Neighbor method, Memory-Based
Reasoning, Logistic Regression, Discriminant Analysis and Decision Trees.
1.2 STATEMENT OF THE PROBLEM
earth system and climate condition has been changed by the human activities.
The third report of the Intergovernmental Panel on Climate Change (ICPP) stated
that there is now new and stronger evidence that most of the warming observed
over the last 50 years is attributable to human activities. The global warming
is referred to the increase in the mean air temperature as a result of
increased atmospheric loading of greenhouse gases such as carbon dioxide from
fossil fuel combustion. All these go a long way to affect agricultural
activities. So the prediction of climate effect on agriculture in terms of
temperature and precipitation is critical to maintain a productive agricultural
this research, both Artificial Neural Networks (ANN) and Decision Trees (DT) will
be used to analyze meteorological data gathered from the Nigerian meteorological
agency station over the period of ten years (2006 - 2016), in-order to develop
classification rules for the climate parameters over the study period and for
the prediction of future climatic conditions on agriculture using available
historical data. The targets for the prediction are those climatic effects that
affect agricultural activities daily like changes in minimum and maximum
temperature, rainfall, evaporation and wind speed.
1.3 OBJECTIVES OF THE STUDY
general objective of this study is to examine the application of data mining
techniques in the prediction of climate effect on agriculture while the
following are the specific objectives:
To examine the application of data
mining techniques in the prediction of atmospheric temperature effect on
To examine the application of data
mining techniques in the prediction of rainfall effect on agriculture
To examine the application of data
mining techniques in the prediction of evaporation effect on agriculture
1.4 RESEARCH QUESTIONS
What is the prediction of atmospheric
temperature effect on agriculture using data mining technique?
What is the prediction of rainfall
effect on agriculture using data mining technique?
What is the prediction of evaporation
effect on agriculture using data mining technique?
1.5 SIGNIFICANCE OF THE STUDY
the increase of economic globalization and evolution of information technology,
climate data are being generated and accumulated at an unprecedented pace. As a
result, there has been a critical need for automated approaches to effective
and efficient utilization of massive amount of climate data to support farms
and individuals in strategic planning and cultivation of agricultural products.
Data mining techniques have been used to uncover hidden patterns and predict
future trends and climatic effect on agriculture. The competitive advantages
achieved by data mining include increased yield, revenue, reduced cost, and
much improved agricultural activities.
the significance and applicability of this research is very high for a country
like Nigeria where agricultural activities remain a major source of income that
will have a massive impact on individual farmers and national economy growth.
Utilizing this model helps the agricultural sector to build a strategic plans
and approach in order to achieve a better agricultural output with an optimal
research will also be a contribution to the body of literature in the area of
the application of data mining techniques in the prediction of climate effect
on agriculture, thereby constituting the empirical literature for future
research in the subject area.
1.6 SCOPE/LIMITATIONS OF THE STUDY
study will cover the sub-variables that are associated with climate effect on
agriculture which includes the temperature, rainfall, moisture and evaporation.
These current data on climate will be used to predict future agricultural
outcomes in Nigeria using data mining method. The data that will be used will
cover the period of 10 years between 2006 and 2016.
constraint- Insufficient fund tends to impede the efficiency of the researcher
in sourcing for the relevant materials, literature or information and in the
process of data collection (internet, questionnaire and interview).
Time constraint- The researcher will
simultaneously engage in this study with other academic work. This consequently
will cut down on the time devoted for the research work.
Mikhail A., and J. R. Porter. ”Climatic variability and the modelling of crop
yields.” Agricultural and forest meteorology 73.3 (2005): 265-283.
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2010, "Effects of Global Warming" From Wikipedia - the free
encyclopedia, retrieved from http://en.wikipedia.org/wiki/Effects_of_Global_Warming
in March 2010
J.I., Mackenthun K.M., 2006, Environmental Impact Statements, Chelsea: MI Lewis