Effects of Rainfall and Temperature Variability on Yam Production in Lafia Local Government Area, Nasarawa State, Nigeria

The study assesses the effects of rainfall and temperature variability on yam production in lafia Local Government Area of Nasarawa State, Nigeria. The research data were collected from secondary data from the existed literatures such as textbooks, journals, articles, seminar papers, encyclopedia which are most pertinent to this study. The rainfall and temperature data of the fourteen years (2001-2014) were collected from synoptic weather station of NIMET archives Lafia sub station where rain gauge and thermometer are used. The Statistics Package for Social Science (SPSS) software was used for the analysis. The derivatives of these data were computed and used for further analysis such as average of number of rainy and temperature variations of months of years under study. The yam data was collected in metric tons as unit of measurement per year for the fourteen years under study which shows that there is an upward increase trend in yam production in the area of study over the time span in gradual and steady state which has a variation in production, across the years under consideration of 0.843%. It is also indicated the effect of 0.186 (19%) of the variation in yam production was explained by the variation in rainfall, maximum and minimum temperature between the study periods. It’s further stated that the magnitude of effect by the predictors (rainfall, minimum and maximum temperature) on the dependent variable (yam) varies. Rainfall had a non-significant (P-value > 0.05) effect of -0.269 and a coefficient value of -0.438. This by extension implies that for every unit (mm) decrease in rainfall over the period of time under investigation, yam production decreases by -0.438mt. The relationship between rainfall, temperature and yam using Pearson correlation shows that a weak negative relationship (-0.041) between rainfall and yam yield, a weak positive relationship (0.160) between maximum temperature and yam yield and there is a weak positive relationship (0.322) between minimum temperature and yam yield. The study identified increased production with non-significant positive effect of rainfall, maximum and minimum temperature on yam production. Since the study focused on the effects of rainfall and temperature variability on yam production in Lafia Local Government Area of Nasarawa state, without taking into consideration of other parameters like land use patterns and since rainfall and temperature are not the only parameters that affects yam production. the following recommendations are made: Agricultural Extension Officers (AEOs) should be deployed to guide farmers through routine visits, regular access to weather information to farmers by NIMET, application of irrigation for growing of crops, study of land use pattern should be considered and there is need for modern farm inputs and price control by government and Non Governmental organization.

production is the integral of agricultural practices in which Rainfall (Moisture) and temperature are some of the main climatic elements influencing its production (Ayoade, 2004). Inter-annual variability has been the key climatic element that determines the success of agricultural practices in Guinea Savanna ecological zone of Nigeria (Ayanlade, 2009). Moisture highlighted spectacular role in agriculture in the tropics because of relatively high temperature throughout the year. The rates of evapotranspiration are constantly high with rainfall highly seasonal over most part of the region-tropics (Ayoade, 2004), and this study area lies within the zone. It was also observed that rainfall is the main determinant of the type of crops to be grown in a given region, the period of cultivation and general farming practices (Abaje et al, 2010).
It was also stated according to Alvaro et al (2009) that rain-fed farming dominates agricultural production in sub-Sahara Africa covering about 97% of the total crop land and exposes crops production to highly seasonal rainfall variability.
Agriculture in developing world is particularly vulnerable to changes in climatic elements, and can lead some African countries yield rain-fed agriculture reduced by up to 50% by the year 2020 (IPCC, 2001), and this was also affirmed by (Mastadrea et al, 2008) that central and South Asia crop yield could fall by up to 30% by 2050 as a result of climatic variations. There is still considerable uncertainty about the nature of climate trends where agricultural production depends on (even though there are other factors such as geosphere -soil types, soil depth, soil cover and other trace elements John, 1980) in which this study is considered only on two atmospheric condition-rainfall and temperature.
The threat rainfall and temperature caused variation on crop yield in a different season and was observed by (Adejuwan, 2005) that Nigeria experiences large spatial and temporal variation in rainfall and less variation in evapotranspiration. The change pattern of rainfall and temperature could influence the crop yield which many Nigerian farmers rely on to survive as such yam may lose its viability and many farmers will lose their source of income (Obasakin, 2006). It shows that deficiency in rainfall as major atmospheric determinant of crop production will affect greatly the yield of yam production.
It was also predicted by Nigerian Meteorological Agency (NIMET, 2011) that the whole country will experience late onset and early cessation with the increasing uncertainty. The onset, cessation and length of rainfall season, agriculture will be mostly affected which have similar prediction of 2016 by the same agency that the rainfall pattern will also experience delay onset early cessation less than the normal rainfall amount as well as dry spells in part of the country especially in the northernmost and during the rainy season with frequent occurrence and severe.
It is also indicated according to Bassey (2006) that 40% of Nigeria Gross National product (GNP) is obtained from agriculture and 70% of all African labour is employed in this sector. Invariably, agriculture carries major role of national income and consequently, any minor climate change or detoriation can cause a severe consequences on people livelihood especially agriculture that depends majorly on climatic elements especially radiation, temperature and moisture (rainfall) (Ayoade, 2004). On this background, the study is centered on effects of rainfall and temperature variability on yam production in Lafia Local Government Area of Nasarawa state for the period of 14 years (2001-2014).

Materials and Methods
The data were source from both primary and secondary sources and it's subjected to series of techniques in order to assess the effects of rainfall and temperature on yam production in Lafia Local Government Area of Nasarawa State, Nigeria. This research analysis techniques include coefficient of variation (CV), time series trend analysis to test tempospatial distribution pattern of rainfall and temperature, regression and correlation analysis to test the relationship between these climatic elements (rainfall and temperature) and yam; and the standardized coefficient of skewness (Z1) and kurtosis (Z2). The fisher's standardized coefficient of skewness (Z1) was used to test for the normality in rainfall and temperature distribution for further parametric analysis. The Statistics Package for Social Science (SPSS) software was used for the analysis.

3.0
Results and Discussion 3.1 Analysis of rainfall for the growing season months The mean ((x)), standard deviation (SD), coefficients of variation (CV), standardized coefficients of Skewness (Z1) and Kurtosis (Z2) of rainfall in the study area are presented in table 3.1 for the months of April to October. The data from the result extract in the table 3.1 reveals the descriptive statistics of monthly rainfall trend (April-October), from the year 2001-2014. The standardized coefficients of Skewness (Z1) and Kurtosis (Z2), of monthly rainfall distribution trend was measured and presented in the table. Skweness measures whether or not a distribution is heavily weighted toward the right-end, or left-end of a scale, or the high-end or low-end of a scale. Kurtosis on the other hand is the measure of how flat or how peak a given distribution is. A given distribution is said to have Skewness or Kurtosis problem if its coefficient has a negative value less than -1.5 or a positive value more than 1.5. The result in the table thus indicates the absence of Skewness in the monthly rainfall distribution trend for the years under consideration. However, Kurtosis does exist in the distribution.

Coefficient of Variation for annual rainfall.
The coefficient of variation for the annual rainfall total in the study area is shown in Coefficient of variation was used to calculate inter annual rainfall variability for the years under study. The result shows a coefficient of variation of 6.49%, which shows that inter annual variation of rainfall between the years under study in Lafia LGA is not high. It also shows that the distribution of rainfall across the years under study is normal. Adejowon and Odekunle (2006) observed that rainfall distribution in Nigeria is generally normal if the mean is greater than 750mm. This shows that the distribution of rainfall in the study area is normal, since the annual mean is 1287.4mm.  The general decrease in annual rainfall may be due to the fact that some years recorded low rainfall which might have gradually influenced the overall trend. For instance, from the trend graph, it can be observed that the annual rainfall recorded in the area had the following data; 2002 (1180.9mm), 2005 (1233mm), 2007 (1259.1mm), 2008 (1124.8mm), 2011 (1252.1mm), and 2014 (1254.9mm).This by implication simply implies that even though annual rainfall decreased over the time periods, the rate of decrease was gradual. The gradual declining records of rainfall in some periods of the years under consideration may be of great benefits to some to crop production, especially those crop that do not require excessive rainfall. However, for crops that require excessive amount of rainfall, this decline may become detrimental. It is also important to stress that the decrease in the amount of rainfall in the area of study may be due to low temperature which tends due to decrease in evapotranspiration rates. Low temperature ensures an appropriate amount of moisture in soil, which tends to have positive effects on crops.

Time Series on rainfall for the growing season month
To be able to get the time series of the rainfall variability, a monthly chart of the growing season;  The positive rainfall distribution in the month of May is good for the development of yam. Active process of yam germination and development of root and vine, take place in the first three month of planting. Therefore, increase in rainfall in the month of May support high yield of yam.  0.00. which can also attributed to either a late onset or early cessation of the growing season, as well as with a high frequency of damaging dry spells within the season, making the rainfall distribution unreliable (oladipoet, al.,2002) this was affirmed by the prediction of NIMET 2011 and 2016 respectively.   The implication here is that an increase in minimum temperature to crop will affect photosynthetic activities of crops which may in the long run affect the yield of crops (Ayoade, 2004).    The result extract in table .3 depicts the effect of the variation in rainfall and temperature on yam yield in the area of study. The coefficient of determination (R 2 ) is the proportion of the variance in the dependent variable (yam yield in this case), that is predictable from the independent variable(s) (Rainfall, Maximum and Minimum Temperature),was arrived at 0.186. This implies that 19% of the variation in yam production is explained by the variation in rainfall, maximum and minimum temperature between the periods of 2001-2014 in Lafia Local Government Area. It is important to further state that the magnitude of effect by the predictors (rainfall, minimum and maximum temperature) on the dependent variable (yam) varies. Rainfall had a non-significant (P-value > 0.05) effect of -0.269 and a coefficient value of -0.438. This by extension implies that for every unit (mm) decrease in rainfall over the period of time under investigation, yam production decreases by -0.438mt.

Effect of Rainfall and Temperature on Yam Production in Lafia Local Government Area
In the same vein, maximum temperature had a non-significant (P-value > 0.05) effect of 0.156, and a coefficient of 3.022. It thus implies that a unit increase in maximum temperature resulted to a positive effect on yam production in the study area, as production increased by 3.022mt per unit increase in temperature (1 o c). Similarly, minimum temperature had a non-significant (P-value > 0.05) effect on yam yield as indicated by 0.191 significant level. The result further reveals an effect of 0.467, with a coefficient of 9.631. The implication here is that a unit increase in minimum temperature resulted to an increase in yam production by 9.631mt .This also revealed that the yields may suffer significantly with either a late onset or early cessation of the growing season, as well as with a high frequency of damaging dry spells within the season, making the rainfall distribution unreliable (oladipoet, al.,2002) and (Burroughs, 2005) stated that Small changes in temperature have changed rainfall patterns in the tropics within the past two million years, making some areas drier and others wetter (Burroughs, 2005). These assertions points clearly on the importance of rains to yam development, hence any alteration of the rainfall pattern may result in negative impact on yam growth. Tina et,al., (2010) noted that, tubers such as yam would yield better when planted as early as February because it is able to absorb more heat during that time so that once the rains begin it would grow faster. Anuforom (2004) established that yam rainfall requirements are modest during the early growth.

4.5
Relationship between Rainfall, Temperature and yam production in Lafia Local Government Area  The relation between rainfall, temperature and sampled crop (yam) was examined through Pearson correlation analysis as shown by the result extract in table 1.7The result by interpretation indicates a weak negative relationship (-0.041) between rainfall and yam yield in the area of study. The results further reveal a weak positive relationship (0.160) between maximum temperature and yam yield in the study area. The relationship between the sampled crop (yam) and minimum temperature was also tested. From the result extract in the table above, there is a weak positive relationship (0.322) between minimum temperature and yam yield.

Conclusion
The study established that there is a minimal variability in rainfall and temperature characteristics, which translates into proportional variability in yam production in Lafia Local Government Area. The yam data was collected from the Nasarawa State Agriculture Development Programme (NADP) which was done based on the registered number of farmers, the cultivated total land of the product (yam) and its production yield of the local government area obtained for this period of study (14 years). The data were analyzed using correlation and regression analysis with the aid of the SPSS statistics package version 17, while the trend function was done with the aid of Microsoft Excel. The result shows an increase in minimum and maximum temperatures, coupled with unreliable rainfall distribution over the investigated period. The study identified increased production with non-significant positive effect of rainfall, maximum and minimum temperature on yam production. There is no doubt that tuber yield is influenced by climatic conditions (where two climatic variables-rainfall and temperature) were consider under the 14 years (2001-2014), fertile soil, quantity of planting material (seed yam) and considerable labor input and effective agronomic operation. But the variability of moisture based agro-metrological indices appears as most critical factor of yam in the humid tropic.