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\title{Application of Geographic Information System To The Effects of Climatic Variability on Sustainable Agricultural Production in Kwara State of Nigeria}
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             \author[1]{Dr. H.I  Jimo}

             \affil[1]{  Univervity of Ilorin, Nigeria}

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\date{\small \em Received: 22 June 2011 Accepted: 18 July 2011 Published: 28 July 2011}

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\begin{abstract}
        


This research endeavour is on the application of Geographic Information System (GIS) as a tool for analysing the roles of microclimatological characteristics of  the study area on the production rates of some selected agricultural products. In the process, specific climatic parameters were used so as to observe the climatic trends over a decade with that of agricultural production rates. Essentially, the required datasets were sourced from the Nigerian Meteorological Agency, Kwara Agricultural Development Project Office and Survey Department of Kwara State Ministry of Lands and Housing, Ilorin. The climatic data were later analysed through condesctriptive statistical techniques and the use of charts to show the rates of variation among the parameters used. Also, the selected crops were shown on charts too so as to reveal the trends of variation between the rates of crop production too. Finally, the climatic data were drawn against the crop production rates to indicate concisely the trends of climatic variation especially as it affects the crop production rates over the decade. Essentially, the following constitute as the major observations from the analysis of the datsets: First, most of the crops had the highest yield for the decade in 2008. And in year 2001 the highest value of temperature was recorded while relative  humidity and rainfall had second to the lowest values. Further, Year 2004 had the highest rainfall value, temperature and relative humidity were moderate where maize, cassava and rice recorded below average yields. Then, sorghum had its second to the lowest yield value. In essence, the climatic parameters have been found to play crucial roles in the productivity rates for all the crops tested or investigated, meaning that clear understanding about the relevancies of these climatic parameters should be understood before engaging in agricultural activities so as to avert the incidence of crop failures. 

\end{abstract}


\keywords{Applications, GIS, Effects, Climatic Variability, Agricultural Production, Kwara State, Nigeria.}

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\let\tabcellsep& 	 	 		 
\section[{Application of Geographic Information System}]{Application of Geographic Information System}\par
To The Effects of Climatic Variability on Sustainable Agricultural Production in Kwara State of Nigeria Dr. Jimoh H. Isah , A. O Adeoye\par
Abstract -This research endeavour is on the application of Geographic Information System (GIS) as a tool for analysing the roles of microclimatological characteristics of the study area on the production rates of some selected agricultural products. In the process, specific climatic parameters were used so as to observe the climatic trends over a decade with that of agricultural production rates. Essentially, the required datasets were sourced from the Nigerian Meteorological Agency, Kwara Agricultural Development Project Office and Survey Department of Kwara State Ministry of Lands and Housing, Ilorin. The climatic data were later analysed through condesctriptive statistical techniques and the use of charts to show the rates of variation among the parameters used. Also, the selected crops were shown on charts too so as to reveal the trends of variation between the rates of crop production too. Finally, the climatic data were drawn against the crop production rates to indicate concisely the trends of climatic variation especially as it affects the crop production rates over the decade. Essentially, the following constitute as the major observations from the analysis of the datsets: First, most of the crops had the highest yield for the decade in 2008. And in year 2001 the highest value of temperature was recorded while relative humidity and rainfall had second to the lowest values. Further, Year 2004 had the highest rainfall value, temperature and relative humidity were moderate where maize, cassava and rice recorded below average yields. Then, sorghum had its second to the lowest yield value. In essence, the climatic parameters have been found to play crucial roles in the productivity rates for all the crops tested or investigated, meaning that clear understanding about the relevancies of these climatic parameters should be understood before engaging in agricultural activities so as to avert the incidence of crop failures.\par
Keywords : Applications, GIS, Effects, Climatic Variability, Agricultural Production, Kwara State, Nigeria.\par
limate variability is rapidly becoming the most important environmental challenge facing mankind. Small temperature changes may seem inconsequential to the unwary, but only because small temperature shifts are often inconspicuous to people. A small temperature change to the vast volume of oceans covering the earth represents immense changes in the energy system. These energy changes can become concentrated and focused, resulting in massive hurricanes and storms. The slightest temperature change at the right moment can trigger outbreaks of insect pests or disease vectors, which can destroy entire landscapes, forest or croplands. Everything in nature is related, so outbreaks or changes in one area trigger changes in other areas. For example; the immediate survival of many coastal areas, populations, forests and wildlife may now depend on our ability to study, understand and share the small changes we observe in the environments and ecosystems around man.\par
The current talk about climatic variability has been correlated with the activities of man which have in turn generated microclimate variations, pollution of environment by forest fuels burnt daily from industries and automobiles all of which generate heat, there by altering the heat balance. Specifically, urbanization has been found to modify the city climate  {\ref (Bryson and Ross, 1972)}. And this include the effects of the changes in physical land surface, which increases in roughness and wind speed. Further, several human activities generate enormous particles into space that are capable of greatly modifying the solar energy incident on the earth surface  {\ref (Landsberg 1970)}.\par
Abnormal changes in temperature and rainfall, increasing frequency, intensity of droughts and floods have long-time implications for the viability and productivity of world agro-systems. Essentially, agriculture is the sector most affected by changes in climate patterns and will be increasingly vulnerable in the future. Especially at risk are developing countries, which are highly dependent on agriculture and have fewer resources and options to combat damages from climatic variation. Agriculture is the production, processing, marketing and use of foods and bye products from plants and animals. As a matter of fact, agriculture was the key development that led to the rise of human civilization, with the husbandry of domesticated animals and plants (i.e crops) creating food surplus that enabled the development of more densely populated and stratified societies. The major agricultural products can be broadly grouped into foods, fibres, fuels and raw materials. Specifically, in the 21 st century, plants have been used to grow biofuels, biopharmaceuticals, bioplastics etc. specific foods include; cereals, vegetables, fruits and meat. Fibres include; cotton, wool, hemp, silk and flax. Raw materials include; lumber and bamboo, other useful materials produced by plants are resins. Biofuels include; methane from biomass, ethanol and biodiesel. Cut flowers, nursery plants, tropical fish and birds for the pet trade are some of the ornamental products too. To appreciate the relevancies of GIS in all of these discussions, the basic components of a GIS are the computer system, geospatial data and users. To this end, a GIS will permit the performance of three fundamental stages of work namely; i.\par
Data Entry:-early stage in which data about the studied phenomena is entered into the GIS and representations are built. ii.\par
Data Analysis:-middle stage in which representations are manipulated and studied to gain (new) insight. iii. Data presentation:-final stage in which the results of analysis are presented in maps or otherwise.\par
With GIS, we operate on and manipulate a representation of real world phenomena (model of it). Man has for ages been interested in climate and the study of climate is as ancient as man's curiosity about his environment. This is hardly surprising since climate influences man and his diverse activities in numerous ways. The air that man breathes, the food he eats are weather related, even his occupation, clothing and forms of shelter are to a large extent determined by the micro climatic condition of his area. Modern man not willing to live at the mercy of weather like his primitive ancestors wants to study, understand, manage and even control weather especially for his immediate locality in order to predict, modify or control it where possible. Some areas of man's life where weather and climate is posing serious challenges is largely on agriculture.\par
The main focus of this work is to examine the climatic characteristics and the trends in relation to agricultural production. To achieve this, the following specific objectives are have being focused as follows: i.\par
To study and present some climatic parameters in relation to agricultural production,. ii.\par
To determine the trends of agricultural productivity over a decade; and iii. To forecast the possible climatic conditions as it may affect agricultural production.\par
The atmosphere is not static, rather it is in constant turmoil. Its characteristics change from place to place and over time at any given place on time scales ranging from microseconds to hundreds of years. There are important interactions within the atmosphere causing such changes. Thus, the changes within the atmosphere may be internally induced within the earth atmosphere system of externally induced by extraterrestrial factors. Essentially, weather variations and climatic variations. Weather is extremely variable particularly in the temperate region. But whether in the tropics or in the temperate region the existence of diurnal and seasonal weather changes cannot be denied. The weather changes collectively make up climate. There are variations in climate itself. When these fluctuations follow a trend we talk of climatic trends. Over a long period of time, climatic fluctuations may be such that, a shift in type of climate prevailing over a given area takes place. In that case, we talk of a change in climate or climatic change. Importantly, the components of climate that seriously influence agricultural productivity levels are temperature, humidity and rainfall, others are air pressure, sunshine rate, cloudiness, nature of surfaces among others (see tables 1 and 2).  As a matter of fact, the impact of climatic variability on agriculture in most developing countries with crop failure and livestock deaths are causing higher economic losses and thus contributing to higher food prices and under mining food security with great frequency. Also, increasing population's demand for food is rising. Below are some of the areas of agriculture that has been affected by climatic variability;  
\section[{b) Crops}]{b) Crops}\par
The effects of increased temperature and CO 2 levels on arable crops will be broadly neutral; 
\section[{-}]{-}\par
The range of current crops will move northward. -New crop varieties may need to be selected. 
\section[{-}]{-}\par
Horticultural crops are more susceptible to changing conditions than arable crops. -Field vegetables will be particularly affected by temperature change. -Phaselous bean, onion and sweet corn are most likely to benefit commercially from higher temperature. -Water deficits will directly affect fruit and vegetable production. 
\section[{c) Grass lands and Livestock}]{c) Grass lands and Livestock}\par
-There is unlikely to be a significant change in suitability of livestock in some systems. 
\section[{-}]{-}\par
Pigs and poultry could be exposed to higher incidence of heat stress, thus influencing productivity. 
\section[{-}]{-}\par
Increase in disease transmission by faster growth rates of pathogens in the environment and more efficient and abundant vectors e.g insects. 
\section[{-}]{-}\par
Consequences for food quality and storage. 
\section[{d) Weeds, Pests and Diseases}]{d) Weeds, Pests and Diseases}\par
Weeds evolve rapidly to overcome control measures, short lived weeds and those that spread vegetatively (creeping, buttercup, couch etc) evolve at the greatest rates; -Rates of evolution will increase in hotter, drier conditions and in extreme years; could lead to some types of herbicide tolerance becoming more common.\par
-Possible increase in the range of many native pests and species that at present are not economically important may become so. -Surveillance and eradication processes for other significant pests such as the colarado beetle will become increasingly important.\par
Essentially, the output from a GIS in the form of maps combined with satellite imagery allow researchers to view the subject in an impressive way that are also invaluable for conveying the effects of climate change to non-scientists. Prediction of impact of the variation in climate on any area of interest, especially, agriculture inherently involves many uncertainties stemming from data and models. This of course is the place GIS application in this paper and have been in part documented in the work of \hyperref[b9]{De Smith Goodchild, (2008)}  1).\par
The vegetation belongs to the Tropical Savannah which comprises dense forest population in most parts of the state and derived vegetation within and around the urban centres and characterized with scattered trees among grasses that grow high such as spear grass, elephant grass and goat weed while the trees include; Baobab, Acacia, Locust-beans Shea butter trees among others.\par
The weather type in the State belongs to the Humid tropical climate (see tables 3 and4).  
\section[{THE STUDY AREA}]{THE STUDY AREA}\par
The monthly temperature values are in Jan 25.9 o c, June 26. The Tropical Maritime air mass from the Atlantic Ocean is prevalent from March to Oct, while the tropical Continental air mass from the Sahara desert takes over from Nov to Feb  {\ref (Olaniran, 2002)}. This results in two seasons; raining season (March-Oct) exhibiting double maxima rainfall pattern with peak periods in the months of June and Sept. it is succeeded by prolonged dry (harmattan) dry season between Nov-March. Humidity vary seasonally ranging from 75\% to 80\%.\par
V. 
\section[{MATERIALS AND METHODS OF STUDY}]{MATERIALS AND METHODS OF STUDY}\par
The study area (Kwara state) is made up of 16 Local Government Areas grouped into four (4) zones (see table \hyperref[tab_4]{5}). The data required for accomplishing the philosophy of this work have been drawn from: Primary source (geo-spatial data and the attribute data of the study area to get a digital map of Kwara), and Secondary source( the meteorological data for the climatic parameters that was obtained from the records of Nigerian Meteorological Agency), Ilorin International Airport, while information on the trends of agricultural production were collected from the records of Kwara Agricultural Development Project, Ilorin. 
\section[{DATA ANALYSIS AND DISCUSSION OF}]{DATA ANALYSIS AND DISCUSSION OF} 
\section[{RESULTS}]{RESULTS}\par
Many issues of relevance are as considered for achieving the philosophy or the central tenets of this research endeavour as follows.\par
a) The roles of climatic parameters on agricultural      Generally, the comparison between the rates of variability in climatic conditions and the yield rates of the selected crops covering the years under study indicates the level of influence the climatic parameters have on the rates of cop yields between the periods under study.\par
In year 2001 the highest value of temperature was recorded (regarded as a hot year), relative humidity and rainfall had second to the lowest values while cassava and maize recorded the lowest yield and the values for rice and sorghum were below average. In 2003, lowest values were recorded for temperature and rainfall but relative humidity had its second highest value, sorghum and rice also recorded their lowest yield while maize, cassava and yam yield values were below average value. Year 2004 had highest rainfall thus can be regarded as a "wet year", temperature and relative humidity were moderate where as maize, cassava and rice recorded below average yields then sorghum had its second to the lowest yield value. 2008 recorded the highest yield value for most of the crops except sorghum which was also above average yield, while relative humidity was highest, rainfall value was above average and temperature had the exact average value.\par
In observing the trends of climatic variability and agricultural production as demonstrated earlier it becomes obvious that the climatic variability has been for a very long time and is still in operation in the environment. The challenges posed by climate may worsen if necessary checks are not taken and this will certainly affect agricultural production as it may drop drastically in the next few years.  
\section[{CONCLUSION}]{CONCLUSION}\par
Strong relationship has been found to exists between agricultural production and climatic variability. The trend in the climatic variables under study shows that the variability is felt where local factors of climate directly affects agricultural production. For instance, many places still depend on rain-fed crop production system and agricultural production rate is therefore easily affected by any slight adverse condition of weather. Thus, the use of GIS technology has greatly enhanced both the scope and quality of environmental management and planning, which has become more focused and goal oriented.\begin{figure}[htbp]
\noindent\textbf{9}\includegraphics[]{image-2.png}
\caption{\label{fig_0}9 Global}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
      \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.03794642857142857\textwidth}P{0.4212053571428572\textwidth}P{0.39084821428571426\textwidth}}
S/N\tabcellsep Crops\tabcellsep Effective Growth Energy (EGE)\\
1\tabcellsep Swamp rice\tabcellsep Greater than 350 o F\\
2\tabcellsep Oil palm\tabcellsep 250-350 o F\\
3\tabcellsep \multicolumn{2}{l}{Rubber, cocoa, coconut 250-300 o F}\\
4\tabcellsep Rice, maize, beniseed\tabcellsep 200-250 o F\\
5\tabcellsep Sourghum\tabcellsep 100-250 o F\\
6\tabcellsep Cotton \& groundnut\tabcellsep 100-200 o F\\
7\tabcellsep Millet\tabcellsep 75-150 o F\end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.07261306532663316\textwidth}P{0.3374371859296482\textwidth}P{0.4399497487437186\textwidth}}
\multicolumn{2}{l}{S/N Crops}\tabcellsep Mean Annual Rainfall\\
1\tabcellsep Yam\tabcellsep At least 1250mm\\
2\tabcellsep Kolanut\tabcellsep At least 1250mm\\
3\tabcellsep Ground nut\tabcellsep 500-1000mm\\
4\tabcellsep \multicolumn{2}{l}{Beniseed \& soya beans 1250-1500mm}\\
5\tabcellsep Oil palm\tabcellsep 1500-3000mm\\
6\tabcellsep Cocoa\tabcellsep 1250-2000mm\\
7\tabcellsep Rubber\tabcellsep 2000-2500mm\\
8\tabcellsep Cotton\tabcellsep 652-1250mm\end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_2}.}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.06388888888888888\textwidth}P{0.3083333333333333\textwidth}P{0.011111111111111112\textwidth}P{0.011111111111111112\textwidth}P{0.024999999999999998\textwidth}P{0.05555555555555556\textwidth}P{0.04722222222222222\textwidth}P{0.04722222222222222\textwidth}P{0.04722222222222222\textwidth}P{0.04722222222222222\textwidth}P{0.18611111111111112\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 11\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep IV.\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Volume XI Issue V Version I\\
TEMP MAX O C MIN O C\tabcellsep \multicolumn{4}{l}{JAN FEB MAR APR MAY JUN JUL 33.4 36.4 36.9 33.9 32.2 30.7 30.0 18.3 22.6 23.8 23.5 22.8 22.1 22.0}\tabcellsep AUG 29.0 21.7\tabcellsep SEP 30.1 21.3\tabcellsep OCT 31.4 21.6\tabcellsep NOV 33.8 22.2\tabcellsep DEC 34.3 20.2\tabcellsep Global Journal of Human Social Science\\
AVG\tabcellsep 25.9 29.5 30.4\tabcellsep 28.7\tabcellsep 27.5\tabcellsep 26.4 26.0\tabcellsep 25.4\tabcellsep 25.7\tabcellsep 26.5\tabcellsep 28.0\tabcellsep 27.2\end{longtable} \par
 
\caption{\label{tab_3}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.40202702702702703\textwidth}P{0.11869369369369367\textwidth}P{0.13783783783783785\textwidth}P{0.19144144144144143\textwidth}}
\tabcellsep \multicolumn{3}{l}{Development Project areas}\\
ZONE A\tabcellsep ZONE B\tabcellsep ZONE C\tabcellsep ZONE D\\
\multicolumn{4}{l}{Baruten LGA Patigi LGA Ilorin west LGA Ifelodun LGA}\\
\multicolumn{4}{l}{Kaiama LGA Edu LGA Ilorin south LGA Irepodun LGA}\\
\tabcellsep \tabcellsep Ilorin east LGA\tabcellsep Isin LGA\\
\tabcellsep \tabcellsep Moro LGA\tabcellsep Offa LGA\\
\tabcellsep \tabcellsep Asa LGA\tabcellsep Oyun LGA\\
\tabcellsep \tabcellsep \tabcellsep Ekiti LGA\\
\tabcellsep \tabcellsep \tabcellsep Oke Ero LGA\end{longtable} \par
 
\caption{\label{tab_4}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.16249999999999998\textwidth}P{0.10833333333333332\textwidth}P{0.0625\textwidth}P{0.024999999999999998\textwidth}P{0.2375\textwidth}P{0.1375\textwidth}P{0.024999999999999998\textwidth}P{0.024999999999999998\textwidth}P{0.024999999999999998\textwidth}P{0.024999999999999998\textwidth}P{0.016666666666666666\textwidth}}
\tabcellsep 1999\tabcellsep \multicolumn{2}{l}{2000 2001}\tabcellsep 2002\tabcellsep \multicolumn{6}{l}{2003 2004 2005 2006 2007 2008}\\
Temperature\tabcellsep 34.8\tabcellsep 37.9\tabcellsep 40.1\tabcellsep 36.4\tabcellsep 34\tabcellsep 35.7\tabcellsep 35.9\tabcellsep 36.8\tabcellsep 37.1\tabcellsep 36\\
\multicolumn{2}{l}{Relative Humidity 69}\tabcellsep 78\tabcellsep 76\tabcellsep 77\tabcellsep 83\tabcellsep 82\tabcellsep 83\tabcellsep 82\tabcellsep 79\tabcellsep 84\\
Rainfall\tabcellsep \multicolumn{3}{l}{1104.5 946.7 907.6}\tabcellsep \multicolumn{7}{l}{1028.9 893.8 1600.2 1144.5 1236.9 1481.6 1381.9}\end{longtable} \par
 
\caption{\label{tab_5}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_6}}\end{figure}
 			\footnote{© 2011 Global Journals Inc. (US)} 		 		\backmatter  			 \par
References References Referen cias VIII. 
\subsection[{FORECASTS ON CLIMATIC CONDITIONS ON AGRICULTURAL PRODUCTION}]{FORECASTS ON CLIMATIC CONDITIONS ON AGRICULTURAL PRODUCTION} 
\subsection[{August}]{August}\par
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\end{document}
