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Inter-annual climate variability and crop yields anomalies in middle belt of Nigeria.


Rainfall has been the most important determinant of the climate and crop yields in Nigeria as well as in other part of West Africa (Awosika et al., 1994). Inter-annual variability in rainfall has been the key climatic element that determines the success of agriculture in the sub-region. As observed by Awosika et al., 1994), the aggregate impact of drought on the economy of Nigeria in 1992 was between 4% and 6% of the GDP. Different scholars and organizations have carried out the climate/crop related research in different parts of the world. Their studies have considered the relationship between climate variability and agricultural productivity in order to establish the impact of the former on the latter. Some of them are: Ojo and Oni 2001; IPCC, 2001; 2004b; Chiew 2002; Okpara, 2003; Adejuwon, 2004; among others. IPCC (2001) gives a clear difference between climate variability and climate change. Climate fluctuates naturally on all time scales from days, years and few decades (climate variability proper) to many decades--climate change. Thus, climate variability is short-to-medium term fluctuations around some mean climate state on time scales, varying from less than annual to multi-decadal time scales such as 30 years. Climate change, on the other hand is a fundamental shift in the mean state of climate. It pertains generally to longer-term trends than that of climate variability (Okpara, 2003; IPCC 2001; 2004b). In other words, climate variability is the fluctuations of climate naturally on a time scale ranging from days, weeks and year to few decades, including altered frequencies of extreme events while climate change is a longer term fluctuation from decades to centuries. WMO (2000), discussed the impacts of rainfall variability on agricultural productivity in Asia, Africa and Latin America with suitable examples. In tropical Asia and Africa, agricultural productivity is sensitive to rainfall variability that eventually causes environmental and social stresses. The arid and semi-arid tropics of Africa are already having difficulty in coping with environmental stress (WMO, 2000). Inter-annual rainfall variability is resulting in increased frequencies of drought and poses the greatest risk to crop yield.

According to FAO, (2001), extremes of heat and cold, droughts and floods, and various forms of violent weather phenomena have wreaked havoc on the agricultural systems in these regions. Climate variability and change contribute immensely to vulnerability to economic loss, hunger and famine. Hence, it is imperative that these aspects are well understood in order to formulate more sustainable policies and strategies to promote food production in Nigeria. In the report titled "Climate Variability and Change: A Challenge for Sustainable Agricultural Production", FAO (2001) examined the effects of rainfall variability on food production and concluded that there are many interactions between climate variability and agriculture. Although agriculture is affected by the vagaries of climate, it also contributes to increasing climate variability and change through emission of greenhouse gases, land degradation, deforestation, etc. However the effects of rainfall variability on all forms of agricultural production are of major concern in the report. The study shows that rainfall variability is one of the main determinants of agricultural production in both developing and developed countries. The loss of agricultural production associated with background variability of rainfall is significantly higher than those associated with spectacular but localised weather-related hazards like cyclone and flooding. In fact, 10% to 100% of the short-term variability of production can be ascribed to rainfall variability depending on the level of development and technical influences (FAO, 2001).

In general, changes in rainfall variability as well as in the mean value of climate variables influence the yield of cereal crops, but because the pattern of rainfall variability is not necessarily harmful, the problems arise from extreme events and the uncertainty which derives from the difficulty of predicting weather beyond a week or so (FAO, 2001). It is essential to note that although these extreme events are inherently abrupt, random and disastrous, the risks can be reduced through improved preparedness and planning, better information, stronger institution and new technologies to minimise human and material losses (FAO, 2001). Agricultural productivity in Nigeria is strongly linked to rainfall variability because farmers rely on rain-fed agriculture. Therefore, water scarcity is a major constraint to cereal production, especially millet that is grown on the marginal lands in the northern part of the country where other crops generally fail. Climate variability has been, and continues to be, the principal source of fluctuations in global food production, particularly in the semi-arid tropical countries of the developing world, Nigeria inclusive (Sivakumar, 1997; Adejuwon, 2004). It determines not only where and when to plant a crop but also whether the crop will yield effectively or not. Theoretically, there are three different forms of rainfall variability: (a) spatial (b) inter-annual (c) intra-annual variability. Spatial variability has to do with differences in total rainfall received between places structurally located within a given region. Inter-annual rainfall variation can be defined as the annual deviation from long-term averages or the differences in rainfall between years. Intra-annual rainfall variability refers to the distribution of rainfall within a year (Obasi, 2003b). In the last decade, inter-annual rainfall variations are causes of great stress to the farming activities, crop production and crop yield in the Guinea Savanna of Nigeria (Adejuwon, 2004).

Although, it may appear that little or nothing could be done to improve variability in rainfall since most of its causes are natural. Thus, there is need for in-depth study and understanding of spatio-temporal rainfall variability as well as its significant impacts on crop yield. However, it is important to note that in spite of great advance that has been made in understanding and dealing with the problem of rainfall variability impact on crop yield at the international level, awareness and the concern for the problem at national and local levels remain poor or in some cases non-existent (Anuforom, 2004). This research work, therefore, attempts to look at the rainfall variability impact on crop yield with particular reference to the Guinean Savanna Ecological Zone of Nigeria. Above all, the selected crops are vital components of food security not only in the study area but also in all parts of Nigeria and they will remain largely associated with the food security of drought-prone areas such as Guinea Savanna part of Nigeria. But yield is lagging because of the severity of the rainfall variability and pressure of human population growth on traditional land extensive fallow system. Correspondingly, this research will contribute most directly to the alleviation of poverty and food security problem in the area and add to knowledge of assessing rainfall-crop yield relationship for the benefit of mankind.

Materials and methods

Both rainfall and crop yield datasets were used for the study. These included annual crop yield and rainfall data from 1970 to 2000 and topographical maps of the study area. Data on crop yield were obtained from the Annual Abstracts of Statistics of the National Bureau of Statistics, Abuja while rainfall data were collected from the Nigerian Meteorological Services, Oshodi Lagos. Spatial datasets were prepared as a base data for the analyses. A time series of the averaged value of the 12-Month Weighted Anomaly Standardized Precipitation (WASP) index was calculated.

To compute the WASP index, monthly precipitation departures from the long-term average are obtained and then standardized the standard deviation of monthly precipitation. The standardized monthly anomalies are then weighted by multiplying by the fraction of the average annual precipitation for the given month (Lyon, 2004). These weighted anomalies are then summed over a 12-month time period in this case, and this result is itself standardized. For WASP index values above the baseline, the area between the index and the baseline value is shaded in green. For WASP index values below the baseline, the area between the index and the baseline value is shaded in brown (Lyon and Barneston, 2005). In order to show crops vulnerability and responses to rainfall variability, z-distribution chart was adopted. The crop yield array was converted to a distribution format varying in magnitude from -3 to +3. Hence, if the variations in crop yield from one year to the other are caused by the variation in rainfall, then the responses represent negative and positive anomalies of crop yield which was computed for each year. From this format, significant positive and significant negative responses, separated by the normal yield level could be discriminated. Whenever the anomaly is significant, it is counted as an impact. This Z- Distribution anomaly (which was used to draw z-distribution chart) is mathematically expressed as:

Index variation (Z) = [s - m]/SD

S = [summation][y.sub.1], > [y.sub.2], [y.sub.3], ... [y.sub.n] Where = yield of crops

m = [summation] fy/n This shows the mean of crop yield

SD = [square root of [summation][{y -m}.sup.2]] /[summation] fy This shows the standard deviation of the yield

Results and discussion

Rainfall Variability

Generally, it is very clear that the total rainfall and distribution of rainfall at any location determine the length of growing season in that location. Observation shows that rainfall variability continues to be on the increase following the increase in climate change and variability as revealed in figure 1. This figure shows rainfall variability in meddle belt of Nigeria, from 1970 to 2000. The observations show that there were abnormal low rainfall during 1980-1986 (see Figure 2). However, the analysis shows that mean rainfall are normally distributed during 1987 to 1990 (Figure 2) and 1994 to 2000 (Figure 3).


Generally, the observation shows that the decadal mean rainfall values vary from 550mm to 2987mm. The highest mean rainfall was during the third decade, especially between 1994 and 2000 (see Figure 3) while the least was observed during the second decade especially between 1980 and 1987 (Figure 2). The impacts of its variability are very momentous on crop yield especially in this region of Nigeria. This may be as a result of evaporation potential that is very high throughout the year in the region.



It should be noted that rainfall varies inversely with the mean rainfall and also rain becomes less reliable as one move towards the Northern part of the region. Apart from local differences due to differences in altitude (e.g high altitude of Jos), it was observed that there are remarkable variations in the number of rainfall per annum (p.a) in the duration of these seasons and in the amount of annual and seasonal rainfall. These results thus support the view of Gbuyiro and Aisiokuebo (2003) that variability of rainfall is an important factor in Guinea Savanna part of Nigeria where rainfall tends to be more seasonal in its incidence within the year. The results also corroborate the result of Nicholson et al., (2000) that global climate change has manifested in West Africa, especially in the Savanna region, as high variability in rainfall in recent decades. The impacts of rainfall variability also affect the length of growing season. This is because this element of climate determines the duration of length of growing season; hence their variations will also cause length of growing season to vary. Consequently, length of growing season is one of the determinants of crop yield in the region. This analysis has revealed its impacts on crop yield in the study area. These results also confirmed the view of Anuforom (2004) that between 1981 and 1987, the rainfall was below 700mm p.a while from 1988, it was above 700mm with 1998 having the highest 1800mm p.a.

Annual Impacts of Rainfall Variability on Maize Yield:

Figure 2 shows maize yield anomalies as a response to rainfall variability and Table 1 shows z-distribution values for the entire region of Guinea Savanna part of Nigeria. Figure 2 was derived from the z-distribution values obtained from Table 1. The results showed that there is an enormous positive response of maize to rainfall variability in the entire region during 1973/1974 with z-value of about 1.9 while the great negative impact was observed for the period of 1982/1983. The results indicated that variations in maize yield are strongly influenced by fluctuations in annual rainfall both in terms of overall seasonal rainfall characteristics and extreme variability events.


The results indicate that annual rainfall variability has considerable effects on the yield of maize. A number of atmospheric scientists have hypothesized that rainfall variability may alter the yield of crops. The results of this study confirmed the view of Adejuwon (2005) that variability and the severity of the little dry season have great effects on crop yield in Southwestern part of Nigeria. The results obtained in this study showed that the less variably the rainfall, the more reliable the maize yields. This is because the index of variability is a measure of the degree of likelihood of the mean annual rainfall being repeated each year and this influences maize yield. The results appear to corroborate the view of Fakorede and Akinyemiju (2003) that low variation in rainfall implies that the mean rainfall at a given year is reliable while higher variation implies wide fluctuation above the mean values which eventually leads to fluctuation in maize yield.

Usually, there are two major ways by which rainfall variability influences maize. These two important points must be emphasized at the outset of this result discussion. The first point is that rainfall is interrelated in its influence on maize. The effect of a given rainfall variable is modified by the other. That is, daily, seasonal and annual variations in the values of rainfall are greatly important in determining the efficiency of maize development. The second point is that in considering the rainfall requirement for any crop to grow, the microclimate immediately around the crop is vital. Water condition within the soil where germination takes place and very close to the ground where the crop growth is of higher significance to determine either crop yield will be high quality or otherwise (Stern and Coe, 1982). It is very clear that rainfall of growing months also determines whether maize seed will germinate or not. This is because there is a reversible moisture sensitive block to germination that prevents germination in drying soils (Finch-Savage et al, 2001 and IITA, 2004) and therefore seeds tend to germinate following rainfall patterns. In the absence of subsequent rain, only a brief opportunity for the completion of germination and seedling growth may be presented before the surface soil layers dry again, but the seeds/seedlings usually adapt to this. Rainfall variability also determines seed priming in any location. 'Seed priming' in the soil means that germination can be rapid when water becomes available and then the initial rapid downward growth of both the root and hypocotyls described here, will contribute to maintaining contact with soil moisture as the surface layers dry (Finch-Savage et al, 2001).

Furthermore, rainfall variability in fact determines the hydraulic conductivity of soil and later influences the yield of a crop. The hydraulic conductivity of soil in the surface layer quickly falls to a very low value as drying continues and this will tend to reduce the rate of water loss from deeper layers (FAO, 2001). The seedling root will therefore grow into increasingly wet soil and may not subsequently become severely water stressed. This same scenario is to be expected for seeds in the surface layers of soil under natural conditions. The initial period of downward seedling growth following germination is therefore critical to successful seedling establishment. Rainfall potential has large predictable effects on seedling growth and this will interact under variable conditions. For example, maize seedling growth rate increases as sub-optimal rainfall increases (Fakorede et al, 2001). Consequently, the rate of growth will decrease as the soil around the seedling starts to dry. Thus soil moisture rapidly becomes a limiting factor during post-germination seedling growth for maize (Fakorede et al, 2001). It is therefore reasonable to consider that the results collected in the present work symbolize that rainfall variability is significant to maize yield.

It is imperative to note that the instability in the yield of maize has major consequences on population in the study area. Many people and most undernourished households in Guinea Savanna part of Nigeria depend on cereal (most especially, maize) as a contributing, if not principal, source of food and nutrition. In fact, these farm households value maize because it produces large quantities of dietary energy and has stable yields under conditions in which other crops may fail (FAO, 2001). The fact remains the same that maize (Zea mays) is the most important cereal crop in Nigeria and one of the three most important cereal crops in the world (Fakorede, 2001). Maize is high yielding, easy to process, readily digesting, and costs less than other cereals. It is also a versatile crop that grows across a range of agro ecological zones. Every part of the maize plant has economic value: the grain, leaves, stalk, tassel, and cob can all be used to produce a large variety of food and non-food products (Fakorede, 2001). However, the people of Guinea Savanna part of Nigeria consume maize as a starchy base in a wide variety of porridges, pastes, grits, and beer. Green maize (fresh on the cob) is eaten parched, baked, roasted or boiled and plays an important role in filling the hunger gap after the dry season. But it is eminent that during the past decades, maize yield, associated with rainfall variability, varies differently in the year with high rainfall more than the year with low rainfall. Thus, from all the analyses, tables, charts and maps, it is clear that variations in the rainfall tend to have a remarkable impact on maize yield. This shows that the impacts of rainfall variability over the period 1970-2000 on the maize yield are very remarkable in Nigeria, for this has astounding impacts on entire population. This result is in line with the results of the authors like Fakorede et al., (2004); Hulme et al, (2002); Okpara (2003); Gbuyiro and Aisiokuebo (2003) and Adejuwon(2005).

Annual Impacts of Rainfall Variability on Millet Yield

Figure 3 shows the millet yield anomalies as response to rainfall variability for the whole study area. The chart was derived from z-distribution result (Table 2). It was obvious that during 1970/1971 (Z-Value = 1.45), 1974/1975 (Z-Value = 1.60), 1988/1989 (Z-Value = 1.32), 1997/1998 (Z-Value = 3.13),1998/1999 (Z-Value = 1.18) and 1999/2000 (Z-Value = 1.08), the z-distribution values are positive which literally means that rainfall has positive impacts on the millet yield during these periods in the whole area.


The results showed negative impacts in 1982/1983 (Z-Value = -1.30), 1987/1988 (Z-Value = -0.87), 1992/1993 (Z-Value = -1.69) and 1993/1994 (Z-Value = -1.87). The results imply that variation in rainfall during these periods affected the millet yield adversely. Previous studies established that millet is an important food crop in the Guinea Savanna part of Nigeria; its yield instability is a detriment. For example, Obasi, (2001a) established that for maximum production and adequate yield, a medium maturity millet crop requires between 500 and 800 mm of water depending on climate. But frequent drought and inter-annual rainfall variability have a pronounced effect on millet yield. Despite its high yield potential, millet production and yield are however faced with numerous constraints. One of the major constraints is frequence of drought during the growing season and this considerably reduces millet yield as it was also confirmed by Adejuwon (2004). The result of other studies also validates (e.g Obasi 2001a) that in 1983 and 1993, millet recorded highest negative yield anomalies. Drought at the beginning of the growing season affects crop establishment and reduces plant population while drought during the flowering period of the millet leads to a complete millet yield failure.

To reduce the negative effects of rainfall variability and improve food security, efforts are being made at International Institute for Tropical Agriculture (IITA) to develop or identify drought-tolerant millet varieties that can adapt to the ecological situation of the Guinea Savanna part of Nigeria (Fakorede et al, 2004). Nonetheless, major widespread droughts are rare, whereas local droughts are very common (Adejuwon, 2005). From the results, it is very clear that millet appears relatively tolerant to water deficits during the vegetative and ripening periods. Assessment from previous studies revealed that greatest decrease in yields is caused by water deficits during the flowering period which includes tasselling, silking and pollination, due mainly to a reduction in grain number per cob which substantiates the results of this study.

This study also confirms that too much of rainfall has great effect on the yield of millet. For example, Emechebe (1998) established that millet flourishes on well-drained soils and water logging should be avoided particularly during the flowering and yield formation periods. Water logging during flowering period can reduce yields by 50 percent or more. He further pointed out that too much of rainfall adversely affects the yield of pearl millet. However, regardless of the effects of rainfall variability, millet constitutes about 87 to 98 percent of the cereal grains consumed in the Guinea Savanna part of Nigeria. For instance, Emechebe, E., (1998) affirmed that Nigeria uses 73 percent of the millet produced for human consumption, Pearl millet is now been recommended as basic food for children, the elderly and the convalescents because of its high energy and protein contents. It plays an important role in the reduction of malnutrition and increases food security in semiarid areas of the country (Emechebe E, 1998). Looking at global pattern, FAO (2001) established the fact that although millet represents less than two percent of the world cereal utilization, it is important in the countries of the semi-arid tropics among which is Guinea Savanna part of Nigeria. FAO (2001) estimated that 80% of the world's millet (out of which 33% comes from Asia and 47% from Africa) is used as food while the remainder is being divided between feed (7%) and other uses such as seed, beer and so on.

Annual Impacts of Rainfall Variability on Cassava Yield

Figure 4 and Table 3 show cassava yield anomalies as response to rainfall variability for the whole study area. Figure 4 was derived from z- distribution result (Table 3). It is obvious that z values are positive for 1970/1971 (z = 1.10), 1988/1989 (z = 1.09), 1989/1990 (z = 3.21), and 1999/2000 (z = 0.39). This in reality means that rainfall has positive impacts on the cassava yield. Negative impacts were noted for most of the years, especially, 1981/1982, 1982/1983, 1991/1992 and 1998/1999. The results obtained confirmed the earlier findings of Adejuwon (2005) that rainfall does have impact on the crop yield during the growing season and that negative impacts was recorded during 1982/1983. This result established that the reduction in rainfall observed during 1980 to 1989 had a great impact on cassava yield. The farmers could also recall bad harvests during this decade, which resulted from early termination of the rainfall during the growing seasons. The result confirmed the previous view of Adejuwon (2005) that within 1990 to 2000 alone, the average cassava yield for this savanna region is about 20 million tones making it one of the African largest producers. The average yield in the year 2000 was 10.2 tonnes per hectare, but this varied from 1.8 tonnes per hectare in 1980 to 5.3 tonnes per hectare in 1989 which demonstrated the effects of rainfall. Studies have shown that cassava is planted throughout the rainy season in the study area like other parts of Nigeria. The early plantings have enough moisture for growth and the tubers partly mature into the dry season.

Thus the cassava planted late often experience water stress during vegetative and tuber development stages. This in reality means that cassava tubers mature within the rainy season and that any shortage in rainfall severally affects the yield.


The result of this study is in line with the observation of Agbaje and Akinlosotu (2004) who stated that although the application of NPK fertilizer and other farm operations are very essential but the effect of rainfall or availability of adequate water is much more. It has also been observed that early and mid-season water stress significantly reduce top and root biomass than late or terminal stress that occurred during tuber maturity in cassava (Agbaje and Akinlosotu, 2004).

Observation also shows that the time of planting influences the yield of cassava. For example, one may say that planting cassava at the onset of rain during the first decade (1970-1979) gave higher yield than late season planting during the second decade (1980-1989). This implies that the vegetative stage and tuber initiation of late planted cassava suffered severe stress due to termination of rainfall during the decade with inadequate rainfall in Guinea Savanna part of Nigeria because dry season is characterized by low soil moisture and high soil temperature. Water stress during root and tuber formation reduces the cassava yield drastically but, after seven months of planting, rainfall appears not to have significant or no influence on yield (Agbaje and Akinlosotu, 2004). This indicated that water stress at vegetative and growth stages rather than at post maturity stage causes lower yield in cassava. With the high cost of irrigation, introduction of drought resistant varieties for late season cultivation will be a viable option for improving the yield of cassava significantly among peasant farmers in Guinea Savanna part of Nigeria. This is in line with the observation of Agbaje and Akinlosotu (2004) that stated that the response of cassava to fertilizer will improve late season cultivation where a controlled irrigation system is used but the application of fertilizer to early-planted cassava will remain uneconomical and wasteful in season of excessive rainfall.

However, some studies have shown (e.g IITA, 2004) that in Guinea Savanna part of Nigeria, cassava provides a basic daily source of dietary energy, so roots are processed into a wide variety of granules, pastes, flours, etc., or consumed freshly boiled or raw. It was also discovered that its leaves are also consumed as green vegetables, which provide protein and vitamins A and B. Cassava has the ability to grow on marginal rainfall condition where cereals and other crops do not grow well. It can tolerate drought and can grow in low-nutrient soils. This is because cassava roots can be stored in the ground for up to 24 months, and some varieties for up to 36 months. In Nigeria, cassava has taken on an economic role. It is used as a binding agent, in the production of paper and textiles, as monosodium glutamate and an important flavoring agent in cooking.

In Nigeria, cassava is beginning to be used in partial substitution for wheat flour, but the effects of rainfall still need maximum attention. Despite the importance of cassava, this study affirms that it is very sensitive to rainfall variability and that enormous variations in rainfall do have negative impacts on the yield of cassava.

Annual Impacts of Rainfall Variability on Yam Yield

Figure 5 shows yam yield anomalies as response to rainfall variability for the whole study area while Table 4 is the result of z-distribution values for the whole regions. Figure 5 was derived from z-distribution result obtained from Table 4. It was obvious that during 1970/1971 (Z-Value = 1.20), 1971/1972 (Z-Value = 1.30), 1989/1990 (Z-Value = 1.44) and 1990/1991 (Z-value = 1.82), the z-distribution values are positive which means that rainfall has positive impacts on the yam yield during these periods in the whole areas. The results obtained revealed that negative impacts were prominent during 1981/1982 (Z-Value = -1.51) 1982/1983 (Z Value = -1.35), 1983/1984 (Z-Value = -1.52) 1992/1993 (Z-Value = -2.18).and 1993/1994 (Z-Value =-2.26). This implies that variation in rainfall during these years affected the yam yield adversely. It should be noted that the impacts are in two dimensions i.e. positive and negative impacts. With positive impact, it means that the rainfall during these periods (e.g. 1990/1991 with z-value of 1.81) has supportive impact on yam yield thereby facilitating increased yield. But negative impacts imply that the variations of rainfall lead to a decreased yield of yam.


Studies have shown that during plant season, yams are usually planted when the soil is dry and wait for the rains to put out its leaves. Anuforom (2004) established that yam rainfall requirements are modest during early growth. Soil water content during germination and the early growth is critical. Once the yam seed coat ruptures and the radicle (root) and plumule (shoot) emerge, it cannot return to seed dormancy. Thus, if the germinating yam seed does not receive ample rainfall, it will die or the yield will be very low.

The fact to note here is that too much water will also displace air containing needed oxygen in the soil and the yam tuber will suffocate. Yam water requirements increase with increased growth and leaf area. Causal organisms associated with yam diseases also have specific environmental needs, some of which are related to rainfall (IITA, 1992). High humidity, flooding, and drought may promote the development of specific infectious agents and the spread of their associated diseases (IITA, 2004). Really, the tubers have a large sink capacity and continue to grow and store food reserves throughout the year as long as rainfall conditions remain favourable. After harvest, another advantage of yam, compared with other tuber crops, is its relative long storage life (4-6 months). Nevertheless, heavy and frequent rainfall sometimes is a great disadvantage to yam yield by increasing the leaching of nutrients or carrying away top soil and applied fertilizers in surface run-off. However, despite all the constraints resulted from rainfall variability, studies have shown that more than 85% of the Nigeria's yams are currently grown in Guinea Savanna part of the country annually (IITA, 2004). Yams (Dioscorea spp.) are annual or perennial climbing crop with edible underground tubers. There are over six hundred (600) yam species grown throughout the world, but in the study area, three (3) main species are dominant: white yam, yellow yam, and water yam. Yam is a preferred staple food crop in some parts of the country, and also has a prominent socio-cultural role. The real fact about yam is that its tubers can be processed into various types of food, including pounded yam, boiled Yam, roasted or grilled yam, fried yam slices, yam balls, mashed yams, yam chips, and yam flakes. Fresh yam tubers are also peeled, chipped, dried, and milled into flour that is used to prepare dough called "amala".

Crop Sensitivity to average rainfall between 1970 and 2000 in Guinea Savanna part of Nigeria

Table 5 shows the result of correlation of mean rainfall of all stations between 1970 and 2000 with the total yield of each crop. This analysis was carried out to test the impact of the total rainfall of the study area within 1970 to 2000 on the total yield of the entire crops selected for this study. Coefficients of correlation are significant with respect to each crop. Millet has R value of 0.678, R = 0.619 for maize, R = 0.679 for cassava and yam has R value of 0.618. R values indicate that millet and cassava (R = 0.678 and 0.679) are the most sensitive to rainfall variability in the study area, followed by maize and yam (R = 0.619 and 0.618 respectively).

Generally, this implies that for millet, the proportion of variation in yield determined by rainfall variability for the whole study area is 68% while 61% for maize, 68% for cassava and the proportion for yam is 62% (Table 5). The sensitivity of crop yield to rainfall appears to be subject to the ecological zone (Leibig, 1847) and that changes in yield will result when rainfall approach variation. For example, if rainfall were adequate for optimum crop yield over a period of say 30 years, thus rainfall variability will have little or no impact on crop yield and this makes it difficult to develop measures of impact based on linear models. Thus, the results show that the cumulative impacts of rainfall variability on each crop yield are apparent in Guinea Savanna part of Nigeria and this gives better results than monthly rainfall. This study confirms the fact that Guinea Savanna of Nigeria suffers from seasonal rainfall variability. This situation however makes the whole country particularly vulnerable to this rainfall variability.

Several scholars (For example, Adejuwon 2005, Anuforom 2004, IPCC, 2004a) have already discovered such effect of rainfall variability on crop yield. For example, the sensitivity of crop yield to rainfall variability appears to be subject to the "ecological law of the minimum" propounded by IPCC. According to IPCC, changes in yield will result when rainfall and moisture supply approach the critical minimum. Adejuwon (2005) established that whenever rainfall supply is adequate, change in crop yield will cease to depend on it, i.e. it would no longer be the limiting factor. For example, if rainfall were adequate for optimum crop yield over a period of 30 years, rainfall variability would have no impact on crop yield.

The results obtained in this study supports IPCG, (2004a) findings, which demonstrate that long period of wet and dry years, will give rise to frequent changes in agro-climatic characteristics and increased variability of crops. Although the control of pest and diseases is the work of the plant pathologists, climate (most especially rainfall) is an important factor in such control which plant pathologist must consider its significance. Various pests, such as maize weevil would probably expand their distribution areas in the events of rainfall variability. Also, an increase in the frequency of extreme rainfall events such as prolong drought could create condition that could be conducive to disease or pests outbreaks. This is in accordance with the findings of Fakorede and Akinyemiju (2003) that some level of drought during the vegetative phase of maize might not affect early season grain yield adversely especially if the latter part of the season is normal but prolong drought during the season may affect the yield. Rainfall variability affects crop yields greatly and indirectly influences the population living standards. Adejuwon (2005) observed that rainfall anomalies such as decline in annual rainfall, change in the peak and retreat of rainfall and false starts of rainfall are detrimental to crop germination and yield, resulting in little or no harvest at the end of the season. IPCC (2004b) provided evidence of some increasing climate variability and showed that climate variability particularly droughts, in the semi-arid regions of the developing countries, trigger frequent subsistence crisis-sharp increase in crop failure, dislocation, hunger, and famine. It is also noted that changes in average rainfall conditions and climate variability will have significant effect on Nigerian agriculture since farmers depend on rain-fed agricultural systems. Particularly vulnerable is the northern part of the country where there has been a trend towards increasing aridity. If rainfall varies from the norm, both in terms of total precipitation and timing, food security will be greatly affected (FAO, 2001).

During the past three decades, inter-annual rainfall variability impacts on crop yield have resulted in increasingly serious agricultural loss in Nigeria, which in some cases have led to unprecedented famine. Maize, Millet, Cassava and yam production in particular are mainly affected by the vagaries of rainfall due to the fact that they are mostly grown on marginal lands (Sivakumar, 1997). Precipitation and temperature anomalies often result in huge cereal yield reduction and consequently, economic losses for farmers. It has been pointed out that rainfall variability becomes critical for cereal crop production when moisture availability drops below the optimal level required for biomass growth during different stages of the agricultural cycle, resulting in reduced yield, and it also becomes critical in economic terms when a shortfall in water availability is beyond the recurrent seasonal rainfall pattern. Resent studies have indicated inter-annual rainfall variability as a major factor affecting the yield of crop in Nigeria (Adejuwon, 2004). It affects the various aspects of plant growth and yields; consequently, alter crop productivity. The rainfall of the region has changed substantially over the last 60 years (Awosika et al, 1994). Adejuwon (2004) observed that rainfall anomalies such as decline in annual rainfall, change in the peak and retreat of rainfall and false starts of rainfall are detrimental to crop germination and yield, resulting in little or no harvest at the end of the season. Rainfall variability in actual fact affect crop yield, photosynthetic rate, plant growth, and it could also affect the incidence of pest and disease outbreaks and mortality, which in turn will affect crop yield and production. From an analysis of recent rainfall conditions in West Africa, FAO, (2001) concluded that a long-term change in rainfall has occurred in the semi-arid and sub-humid zones of West Africa. Rainfall during the last 30 years (1968- 1997) has been on average some 15% to 40% lower than during the period 1931-1960. It has been observed that the most important climatic element is rainfall, particularly seasonal drought the length of the growing season and the distribution of rainfall within the growing season. The rainfall characteristics may affect crop yields in the sub region. (IPCC, 2000; 2004a; 2004b).


The results established the fact that the impacts of cumulative rainfall variability on crop yield are apparent in Guinea Savanna part of Nigeria. This study confirms the fact that Guinea Savanna part of Nigeria suffers from seasonal rainfall variability and the situation however makes the whole country particularly vulnerable to this rainfall variability. There is no doubt that changes in farm operational schedules could also lead to changes in crop yield. Nevertheless, rainfall is the only input that varies from year to year, so the predicted variability in crop is due to the variability in rainfall (Stern and Coe, 1982). As a result, this study ascribes changes in crop yield mainly to the cumulative rainfall of the study area because it has the same annual time resolution as crop productivity. The findings of this study are useful to determine approximately when farmers could plant maize, millet, cassava and yam with the present variation in rainfall. This will enable farmers to have a reasonable certainty of crop survival. For instance, the correlation results of backward elimination procedure revealed that April and May rainfall are the most powerful determinant of maize and millet yield in Makurdi, Ilorin, Shaki and Enugu. This implies that in Southern Guinea Savanna parts, farmers could plant their maize and millet crops as from April, which is also evident from the findings of Fakorede and Akinyemi (2003), and cassava and yam could be planted from September. But in Northern Guinea Savanna parts, farmers could plant maize and millet as from May/June and cassava could be planted as from June/July. Planting of these crops earlier than this date is not advisable unless where irrigation facilities and drought-tolerant varieties of crops are available. Based on the findings above and the practicable conclusion of this study, the following recommendations are suggested in order to put the research work in proper implementation: Government of Nigeria should encourage agro-climatologically research to improve crop yields. Such research needs to address a means of improving crop yield in the present and in the future when the rainfall conditions may be less favourable for agricultural purposes. Also, agricultural reform has to be implemented to enhance infrastructure investment in Nigeria. The use of improved farming techniques has been suggested to help more effective use of rainfall and soil conservation. Conservation tillage measures such as minimum till and no till have been tested in some other countries and this should be encouraged in Guinea Savanna part of Nigeria.


This study is part of a research project funded and supported by Council for the Development of Social Science Research in Africa (CODESRIA).


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(1) A. Ayanlade, (2) T.O Odekunle, O.I. Orinmogunje and (2) N.O. Adeoye

(1) Department of Geography, King's College London, Strand, London WC2R 2LS, UK.

(2) Dept. of Geography, Obafemi Awolowo University, Ile-Ife, Nigeria.

Corresponding Author: A. Ayanlade, Department of Geography, King's College London, Strand, London WC2R 2LS, UK.

Corresponding Author: A. Ayanlade, Department of Geography, King's College London, Strand, London WC2R 2LS, UK.

Table 1: Z-distribution Anomalies for Maize


70/71          1.486706
71/72          -0.48671
72/73          -0.06007
73/74          1.693531
74/75          -1.01231
75/76          0.578342
76/77          -0.0268
77/78          -0.47633
78/79          -0.55587
79/80          -0.17203
80/81          0.688996
81/82          0.86881
82/83          -1.87231
83/84          -1.51175
85/86          0.889558
86/87          -0.68727
87/88          0.346659
88/89          1.114324
89/90          0.75124
90/91          1.487783
91/92          1.384044
92/93          -1.65895
93/94          -1.35811
94/95          -0.21353
95/96          0.536847
96/97          -0.34839
97/98          -0.59044
98/99          1.12124
99/2000        1.052081

Table 2: Z-distribution Anomalies for Millet


70/71          1.454006
71/72          -1.45401
72/73          -1.15259
73/74          -0.54439
74/75          1.603174
75/76          -2.13757
76/77          -0.67895
77/78          -0.14609
78/79          0.763526
79/80          0.359849
80/81          -0.14071
81/82          0.171466
82/83          -1.30218
83/84          -1.87383
85/86          0.532085
86/87          0.924997
87/88          -0.87809
88/89          1.323291
89/90          0.22529
90/91          0.758144
91/92          0.645114
92/93          -1.68545
93/94          -1.87383
94/95          0.106878
95/96          0.144555
96/97          0.284496
97/98          3.131764
98/99          1.18335
99/2000        1.075703

Table 3: Z-distribution Anomalies for Cassava


70/71          1.105418
71/72          0.305418
72/73          -0.58502
73/74          -0.44867
74/75          -0.31766
75/76          -0.59731
76/77          -0.97436
77/78          -0.32485
78/79          -0.24229
79/80          0.302636
80/81          -0.21887
81/82          -1.42154
82/83          -1.31585
83/84          -1.20153
85/86          0.165128
86/87          0.663217
87/88          0.380549
88/89          1.094987
89/90          3.21349
90/91          -0.97807
91/92          -2.03778
92/93          0.821595
93/94          0.762232
94/95          -0.06374
95/96          0.265766
96/97          0.194809
97/98          -0.53725
98/99          -2.3163
99/2000        0.391448

Table 4: Z-distribution Anomalies for Yam


70/71          1.200872
71/72          1.300872
72/73          -0.89017
73/74          -1.32856
74/75          0.139472
75/76          0.401352
76/77          -0.51062
77/78          0.366743
78/79          1.192186
79/80          0.009108
80/81          0.112937
81/82          -1.51268
82/83          -1.35174
83/84          -1.51754
85/86          0.975298
86/87          0.913
87/88          0.494799
88/89          0.210422
89/90          1.44426
90/91          1.816892
91/92          -1.32452
92/93          -2.18227
93/94          -2.25553
94/95          -0.22047
95/96          -0.04742
96/97          -0.05723
97/98          -0.49908
98/99          0.626316
99/2000        0.487877

Table 5: Influence of mean total rainfall on mean total
yield of all crops in Guinea Savanna part of Nigeria

Crop           R

Millet         0.678
Maize          0.619
Cassava        0.679
Yam            0.618

Prediction model having a 0.05 [pounds sterling]
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Title Annotation:Original Article
Author:Ayanlade, A.; Odekunle, T.O; Orinmogunje, O.I.; Adeoye, N.O.
Publication:Advances in Natural and Applied Sciences
Article Type:Report
Geographic Code:6NIGR
Date:Sep 1, 2009
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