Application of Data Mining Techniques in the Prediction of Climate Effect of Agriculture
CHAPTER ONE
Objectives
Main Objective/Aim of the study
The study proposes to implement data mining techniques to build a predictive model that has the potential of predicting crop yield.
Specific Objectives
- To investigate the factors affecting crop production
- To establish appropriate data mining methods used to build predictive model crop production
- To develop the appropriate predictive model using the established data mining methods
- To evaluate the effectiveness of the developed predictive model
CHAPTER TWO
LITERATURE REVIEW
Introduction
This chapter focuses on the previous studies done in the area. Review of empirical Literature, summary and gaps as well as conceptual framework. The section gives light to the study from previous researchers. This chapter is subdivided into Factors affecting Crop Production, Existing methods for Crop Yield Forecast, Data Mining Techniques that can be used for yield prediction.
Factors affecting crop production
Impact of Climate Change on Crop Production
Over the 20th Century the Earth’s surface temperature has increased by an average of 0.8
°C (IPCC, 2013). The last 50 years, in particular, have seen a more rapid and steady increase. The IPCC (2013) report that the warming trend is likely to continue as long as emission of greenhouse gases (GHGs) continues unchanged. The IPCC (2013) has further noted that anthropogenic activities have, over time, altered both the Earth’s surface and the composition of its atmosphere. Not only are these activities driving climate change, but also resulting to unprecedented ecological damage that is indirectly exacerbating climate change. It has been stated that according to temperature records in the last 150 years, the last three decades (1980- 2010) have been warmer than the previous ones, with the 2000s being the warmest (IPCC, 2013). In its recent report, the IPCC (2013) projected that by the end of the 21st Century the Earth will warm up by between approximately 0.3°C and 1.7°C and 2.6°C to 4.8°C for the more conservative and more aggressive scenarios respectively. The projections, which are based on 1986-2005 temperature levels, may produce both direct and indirect adverse impacts on the Earth’s surface. For instance, the increase in temperature is likely to interfere with rainfall patterns, thus affecting major food crops (IPCC, 2014). Increased warming may also drive changes in rainfall patterns and temperature regimes in different regions leading to extreme natural occurrences such as droughts and floods (IPCC, 2013).
Climate change in Africa, as argued by Hulme et al. (2001), is driven by both human and natural factors. Land cover changes together with the El Niño Southern Oscillation (ENSO) have been mentioned to be the major drivers of natural climate variability in Africa. The effects of climate change will be greatly experienced in the African continent due to the fact that the continent lacks sufficient technical and financial capacity to adapt to climate change (IPCC, 2007). For instance, the agricultural sector in the continent has been recognized as one of the sectors that will suffer most from the detrimental effects of climate change due to lack of adequate irrigation technology (Oseni and Masarirambi, 2011; Cairns et al., 2012; Cairns et al., 2013). Consistent with various climate change models, the Nigerian climate will experience several changes both in precipitation and temperature. A study carried out by the Famines Early Warning Systems Network (FEWS NET) in 2010 with the aim of establishing the trend of climate change in Nigeria, observed that a large part of the country will experience more than 100 mm decline in precipitation by 2025. However, findings from other computer models are conflicting, showing certain areas will get wetter (Anyah et al., 2006; DFID, 2009; Washington et al., 2012). It was also observed that there will be a significant increase in average air temperature of up to 1°C during the same period (FEWS NET, 2010). With spatial and temporal shift in rainfall patterns observed and predicted, coupled with a general reduction in recorded precipitation and increase in temperatures, agricultural production (especially maize) is likely to be affected in the country since it is mainly dependent on favorable climatic conditions for yield maximization.
CHAPTER THREE
RESEARCH METHODOLOGY
Introduction
The chapter presents the research methodology used in this study. The chapter discusses the research design, target population, sampling design and procedures. It also includes the data collection methods.
Method for Achieving Objective 1 and 2
To achieve both objective 1and 2, survey of literature research design is implemented.
Twenty five (25) Journal papers and 5 textbooks were downloaded.
CHAPTER FOUR
DATA ANALYSIS AND PRESENTATION OF FINDINGS
INTRODUCTION.
The chapter consist of data analysis, presentation and interpretation of the findings the study.
To understand the application domain daily Njoro NMD historical dataset on minimum/maximum temperatures and precipitations and annual Nigerian Ministry of Agriculture crop production dataset from Njoro district was obtained. The selection of samples of the datasets to use in analysis was done based on picking of data range without much of missing data. Data was analyzed, summarized and presented in form of tables.
CHAPTER FIVE
SUMMARY, CONCLUSIONS, RECOMMENDATIONS
Introduction
This chapter discusses the summary of the findings of the sturdy, conclusions made and the recommendations that are to be used to further this work
Summary
- The findings in this study are important as we try to develop strategies and policies toaddress the food security
- Identifying the factors affecting crop and food production is very significant as it enablesagricultural organizations and farmers plan to develop ways of ensuring that citizens are food
- The findings presented in this study, along with others, can inform the development andimplementation of strategies that can be used to improve agriculture and encourage people to invest in farming to ensure the study area and the whole country of Nigeria there is minimal or no shortage of food..
Conclusions
Agriculture is the most influencing and significant application area particularly in the developing countries like Nigeria. Use of information technology in agriculture can change the situation of decision making and farmers can yield in better way and agricultural organization get idea about yield and they make better policies for famers
Data mining plays a crucial role for decision making on several issues related to agriculture field. This study integrates the work of various authors in one place so it is useful to get information of current scenario of data mining techniques and applications in context to agriculture field and predictive data mining.
The study of crop yield prediction consists of three stages namely, preprocessing, feature reduction and forecast. The study used input data as real world data. Real world data is often incomplete, inconsistent, and/or lacking in certain performances or trends, and is likely to contain many inaccuracies. Food production in the country can be improved with automatic prediction of crop yield based on the reliable variables. The system helps farmers to do right things at right time. The productivity gets improved in agriculture with sustained research in the field of spatial data mining to realize precision agriculture.
Recommendations
The researcher recommends further studies and more research to be conducted in the livestock and fisheries departments to enhance the production of meat, eggs and dairy products in order to ensure there is enough for the citizens and export.
Since the study used three algorithms (J48, PART Rule and MLP), the researcher recommends further studies using different algorithms and different mining and analytics tools that are available.
REFERENCES
- Awuor VO, Ogola JS (1997). Effects of climate change on agriculture. In Ogola JS, Abira MA, Awuor VO (eds), Potential impacts of climate change in Nigeria. Climate African Network, Nairobi. pp. 95-115.
- Asseng, S., Ewert, F., Rosenzweig, C., Jones, J.W., Hatfield, J.L., Ruane, A.C., Boote, K.J., Thorburn, P.J., Rotter, R.P., Cammarano, D., Brisson, N., Basso, B., Martre, P., Aggarwal, P.K., Angulo, C., Bertuzzi, P., Biernath, C., Challinor, A.J., Doltra, J., Gayler, S., Goldberg, R., Grant, R., Heng, L., Hooker, J., Hunt, L.A., et al., 2013. Uncertainty in simulating wheat yields under climate change. Nature Climate Change DOI: 10.1038/nclimate1916
- Basso B., Cammarano D.,Troccoli A., Chen D., Ritchie J.T.. 2010. Long-term wheat response to nitrogen in a rainfed Mediterranean environment: Field data and simulation analysis. European Journal of Agronomy 33 (2010) 132–138
- Bondeau, A., Smith, P. C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W., Gerten, D., Lotze- campen, H., Müller, C., Reichstein, M., and Smith, B. 2007. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Global Change Biology 13, 679-706.
- Bourdieu, Pierre. 1984. Distinction: A Social Critique of the Judgement of Taste. New York: Cambridge University Press
- Ch.N.Santhosh Kumar, V. Sitha Ramulu, K.Sudheer Reddy, Suresh Kotha and Ch. Mohan Kumar, Aug 2012 “Spatial Data Mining using Cluster Analysis”, International journal of computer science and information technology, Vol.4, No.4, pp. 71-77,.
- Challinor, A.J., Wheeler, T.R., 2008. Crop yield reduction in the tropics under climate change: Processes and uncertainties. Agric. Forest Meteorol. 148, 343–356.
