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|a 20th International Congress on Modelling and Simulation (MODSIM2013) held at Adelaide Convention Centre in Adelaide, Adelaide, South Australia, 2013-12-01to 2013-12-06, published in: MODSIM2013, 20th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2013, pp.803 - 809 (7)
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|a In recent times, climate change (CC) and its effects on key crops, such as rice, wheat and maize, have drawn significant research interest alongside population increase, economic growth and changing diet patterns, all of them considered as the driving forces influencing earth's food and water ecosystems. Despite recent technological advances, such as from improved plant breeds (cultiva or varieties) to irrigation systems, which have contributed towards improving the world's staple food production significantly, climate still remains as the key factor in agricultural productivity. Hence, understanding the effects of climate change on various staple food crops has become the utmost priority in many aspects especially, to overcome the threats to the world food security. As a result of this, many institutions concerned over related issues and research communities, in recent years, have turned their focus into modelling the phenomenon at various scales and levels. Contemporary research on modelling the climate change patterns in weather conditions and their effects is summarised. Existing crop forecasting models vary significantly in spatiotemporal scales and levels, the lowest being at the micro (e.g. the field or farm at specific days/weeks), and the highest at macro (e.g. regional /district, at months/years) or global, the crops being studied include, staple crops (maize, rice and wheat) and vineyards all using an array of variables characterised by 1) historic (using a multitude of sources, e.g. metrological, phenological, satellite imagery and wireless sensors at the micro scale, or 2) simulated data (www.ncdc.noaa.gov/oa/ncdc.html), both against observed yield In this context, the paper presents an initial investigation in which four data mining algorithms are explored to analyse the rice crop data in Sri Lankan administrative divisions, as an example study. Rice is the main staple food for Sri Lankans and paddy cultivation in the country dates back to as early as 800 BC. Presently, paddy is being cultivated as a wetland crop, either rain fed or irrigated. Lately, the country's estimated total land under cultivation is said to be approximately 708,000 Hectares cultivated in two seasons "Maha" and "Yala", that correspond to the country's two monsoons, North-east monsoon (from September to following March) South-west (from May to end of August). Paddy yield in various Sri Lankan divisions is presented in figure 1 based on 2008 average annual production obtained from www.statistics.gov.lk The results of this investigation reveal interesting correlations between recent climate and paddy yield in nine regional divisions of Sri Lanka over "Yala" paddy season despite the gaps in the climate data that cannot be analysed using geostatistical or conventional methods due to the gaps in the data. Keywords: Soil nutrient, grapevine, geostatistical analysis http://www.mssanz.org.au/modsim2013/C5/shanmuganathan.pdf
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