1. Introduction
Climate variability has a direct adverse influence on the quantity and quality of agricultural production. The climate of an area is highly related to the type of crop that can be cultivated and the suitable season for such crop. Temperature, rainfall, humidity and sunshine intensity are the important climatic elements that may influence crop production in the tropics. The overall predictability of these climatic elements is imperative for the short, medium and long-term planning of farm operations and productivity (Sundström, et al., 2014; IPCC, 1996; FAO, 2003, 2006; Ogbuene, 2010). The ability to predict the change in climate variability and its implication on the tonnage of cassava yield may help position developing nations such as Nigeria in their quest to achieve food security status. The prediction of climate behaviour on crop yield may help achieve efficient food production and increase yield in the face of climate variability.
Our study focuses its prediction using quantification and graph plotting of changes in tonnage of cassava yield within a period in review and pinpointing the exert climate parameter that could cause the changes. This prompts the study to develop policy implications which can be achieved through the recommendation of this work.
Moreno et al. (2021), carried out a study on cassava models for their capability to simulate storage root biomass and to categorize them into static and dynamic models. The majority are dynamic and capture within season growth dynamics. Most of the dynamic models consider environmental factors such as temperature, solar radiation, soil water and nutrient restrictions. In similar study, Anyaegbu et al. (2022), emphasized that climate change is threatening the environment, crop yield and food security. The key to ensuring a sustainable environment, crop yield increase and food security is to identify the long-term significant impact of climate change and the means of reducing the effect. This study examined the impacts of climate change on cassava yield in Nigeria. Pushpalatha and Gangadharan, (2021) carried out a study on the influence of climate model biases in the predictions of yield and water requirement of cassava in one of the major cassava growing regions in India. Simple linear bias correction methods are used for temperature, and non-linear corrections are used for other meteorological variables.
Aim of the study: It’s against this background that the present study investigated the pattern of climate variability and the consequent tonnage of crop yield in rain fed zone of Enugu State, Nigeria. The incidence of climate variability is worrisome and of enormous concern to farmers and the indigenes in the study area who practice rain-fed agriculture. The evidence of climate variability is captured by IPCC (2007) which emphasized that by 2100, parts of the Sahara will emerge as the most vulnerable to climate variability problems. This has resulted in an estimated severe agricultural loss of between 2% and 7% of GDP in the area. Western and Central Africa are also vulnerable, with impacts ranging from 2% to 4%. Northern and southern Africa (Glantz et al., 1997), however, are expected to have losses of 0.4 to 1.3% (Maharjan & Joshi, 2013; WMO, 2009; Melkonyyan et al., 2005). This scenario proved that the problem of climate variability as spelt out by excessive prolonged rainfall, flood, rainfall cessation, seasonal drought, increased soil temperature, and irregular temperature over the years may have devastating effects on crop yield and the farmers in the study area. This contributes to food insecurity which can put the livelihood of the indigenous people at risk (NIMET, 2012; IPCC, 2004; Hopkin 1999). The Food and Agriculture Organization (FAO, 2008) Report and NIMET (2009) show that climate variability may result in a shift in the present (agro) ecological zones for hundreds of kilometers horizontally, and hundreds of meters longitudinal with the hazard that some plants, especially trees and animal species cannot adjust themselves in time. This may perhaps lead to food insecurity, and ecological and environmental refugees (Beckford, & Campbell, 2013; Wu, et al., 2014). Hence, the present study examines the relationship between climate variability and tonnage of cassava yield from 1972 to 2012, intending to establish climate behaviour and its likely effects on crop yield.
Climate variability and food insecurity are critical global environmental disaster that requires prompt research action and implementation. The evidence can be seen with variation in temperature and rainfall pattern with correspondence change in tonnage of crop yield which are clarified in the present study. Recent studies indicate that an increase in daily temperature affects the period of transplanting and maturity of rice, consequently, impacting agricultural productivity and tonnage of crop yield (Challinor, 2014; FAO, 2006, 2008; Katz & Brown, 1992; Ogbuene, 2010). Climate variability over the years has caused a serious threat to food productivity and environmental disaster. This may have resulted in food insecurity and overuse of environmental resources. In combating environmental disasters and food security status; climate variability studies stand out as a fundamental measure that may be at the forefront (Zakaria & Keshav, 2014; Barnett, 2011).
Farm inputs such as fertilizer application, improved crop species, mechanization and labour appear to be very crucial in cassava production. However, the problem of irregular temperature, delayed rainfall, rainfall cessation, excessive rainfall, prolonged seasonal drought, intensive solar radiation, loss of soil moisture and increase in soil temperature may constitute serious impediments to cassava production. This is more pronounced in the study area because farmers are involved in rain-fed agriculture; hence this study is designed to predict the impact of climate variability on the tonnage of cassava yield.
The rationale for the choice of cassava: Cassava has great ease of cultivation. It can be planted in ridges, mounds or even on flat lands. The crop requires very little financial outlay and farm input. It is tolerant to other crops grown with it. The crop has the possibility of being left or “stored” in the soil for a considerable length of time provided there is no flood or fire before the farmer is ready to harvest it. Also, the extremely high and rapidly rising urban demand for garri, a major cassava food product that can be prepared into a meal in only a few minutes necessitated the choice of the crop. In addition, the increasing industrial use of cassava, especially starch and flour manufacturing, as we as high and rising market prices for cassava and its products (Igbozulike, 1986).
The study developed a predictive model for cassava yield (Manihot esculenta Crantz) in the face of climate variability. Hence, the policy implication of the study is food production in the face of climate variability which may be achieved through the application of the recommendations of this study.
2. Materials and Methods
Data used for the study are existing climatic data collected from the data base of Nigerian Meteorological Services, Oshodi Lagos, Enugu airport and ESUT weather observation as well as Federal Bureau of Statistics. The climate data and tonnage of cassava yield spanning from 1971 to 2012. The nature of seasonal climatic data collected are rainfall values, surface temperature, soil temperature at 5 cm, 10 cm, 30 cm, 50 cm and 100 cm depths, evaporation, relative humidity (R.H.) at 0900 GMT and 1500 GMT, Radiation, sunshine hours and cloud coverage (see Appendix 1, Appendixes 3-8). Questionnaire and focus group discussion was also utilized in the study. This enabled the study generate first hand information from farmers’ climate and cassava yield experience. The exercise was conducted across two seasons in 2023.
The study area is Enugu State, Nigeria. The farm practice in the area is rain-fed agriculture (Ogbuene, 2010). This study developed a predictive model equation for cassava yield (Manihot esculenta Crantz) in the face of climate variability. The study applied regression analysis to establish the relationship between seasonal climate variability and the yield of cassava in the study area. The regression analysis was utilized to develop a predictive model equation for seasonal climate variability and cassava yield over the years. Regression analysis is good for this study because it helps to compute correlation, the strength of the relationship and predictive model equation between climate variability and crop yield. Unlike Analysis of variance that can only show the significant difference between the variables of the study. However, the regression model cannot show the exerted changes in crop yield patterns over the years. Hence, Time Series Analysis was used to plot changes in cassava yield for each decade and exert climate parameters that could cause such change. To the best of our knowledge, previous work reviewed could not bring these facts into the lime light. The rainfall and temperature anomalies were also computed and plotted. A well-structured questionnaire and focus group discussion was also employed to generate first-hand data from the active farmers on rainfall pattern and implication on crop yield in the area (NIMET, 2012; Ogbuene 2010; Intergovernmental Panel on Climate Change, 2004).
3. Results and Discussions
The result of correlation, regression model, coefficient correlation and related graph of climate variability and cassava yield are shown in the study. Multiple collinearity tests were conducted to ensure that the climate variability data were significant for the analysis. The result in Table 1 showed the correlation values between cassava yield and seasonal climate variables studied. These seasonal climatic variables includes rainfall, surface temperature (minimum, average and mean), mean soil temperature at 5 cm, 10 cm, 50 cm, 100 cm, relative humidity and rate of evaporation over the years (Planting, growing and harvesting seasons). This enables the study to practically establish the different correlation levels between climate variables and cassava yield with a view to improving yield in the phase of climate variability.
Table 1. Cassava and seasonal climate variables correlation score.
S/N |
Correlation Variables |
Correlation Score |
1 |
Cassava and Rain Planting Season PS |
0.570 |
2 |
Cassava and Rain Growing Season GS |
0.756 |
3 |
Cassava and Rain Harvesting Season HS |
−0.239 |
4 |
Cassava and Temp Max |
0.344 |
5 |
Cassava and Temp Min |
0.023 |
6 |
Cassava and Temp Aver |
0.520 |
7 |
Cassava and T5cm Planting Season PS |
−0.112 |
8 |
Cassava and T5cm Growing Season GS |
−0.029 |
9 |
Cassava and T5cm Harvesting Season HS |
−0.153 |
10 |
Cassava and T10cm Planting Season PS |
−0.021 |
11 |
Cassava and T10cm Growing Season GS |
−0.254 |
12 |
Cassava and T10cm Harvesting Season HS |
−0.108 |
13 |
Cassava and T30cm Planting Season PS |
0.197 |
14 |
Cassava and T30cm Growing Season GS |
0.222 |
15 |
Cassava and T100cm Planting Season PS |
−0.336 |
16 |
Cassava and T100cm Growing Season GS |
−0.352 |
17 |
Cassava and T100cm Harvesting Season HS |
−0.352 |
18 |
Cassava and Evap Planting Season PS |
−0.309 |
19 |
Cassava and Evap Growing Season GS |
−0.282 |
20 |
Cassava and Evap Harvesting Season HS |
−0.324 |
21 |
Cassava and RH09 Planting Season PS |
0.409 |
22 |
Cassava and RH09 Growing Season GS |
0.097 |
23 |
Cassava and RH1500 Planting Season PS |
−0.241 |
24 |
Cassava and RH1500 Growing Season GS |
−0.260 |
25 |
Cassava and RH1500 Harvesting Season HS |
−0.221 |
26 |
Cassava and Sunshine Planting Season PS |
0.411 |
27 |
Cassava and Sunshine Growing Season GS |
−0.389 |
28 |
Cassava and Cloud Planting Season PS |
0.214 |
29 |
Cassava and Cloud Growing Season GS |
0.214 |
30 |
Cassava and Cloud Harvesting Season HS |
−0.075 |
31 |
Cassava and Radiation Planting Season PS |
0.534 |
32 |
Cassava and Radiation Growing Season GS |
0.288 |
33 |
Cassava and Radiation Harvesting Season HS |
0.241 |
The climate variables that have a high correlation with cassava yield are seasonal rainfall, radiation and temperature-related parameters. The implication is that rainfall, radiation and temperature are the key climatic variables that may highly influence cassava production in the study area. Hence, the process of improving the tonnage of cassava yield requires a comprehensive early warning system for the farmer to be acquainted with rainfall behavior which may probably help reduce loss.
In addition, the result of the regression model for climate variability and cassava yield is shown in Table 2 and Table 3 of this work. This enables the study to quantify the strength of the relationship between climate parameters and tonnage of crop yield.
Table 2. Regression model of climate variability and cassava yield.
Model |
R |
R Square |
Adjusted R Square |
Std. Error
of the Estimate |
Change Statistics |
R Square Change |
F Change |
Df1 |
Df2 |
Sig. F Change |
1 |
0.969(a) |
0.939 |
0.16 |
11402.6 |
0.939 |
1.205 |
38 |
3 |
0.00514 |
Model 1: the result of the multiple correlation (R) value in Table 2 is 0.969. The coefficient of determination (R square) value is 0.939 with p value of 0.00514. The value of R square shows a high strength of the relationship between climate variability and cassava yield in the study area (R2 = 0.939, p = 0.00514).
To critically examine the particular climate variable that significantly influences cassava yield; the variables with the significant score were further re-run in the SPSS regression model. Hence, the climate variables selected include: Rainfall PS, Rainfall GS, Rainfall HS, Radiation GS, Relative humidity 0900 GMT GS and HS, Sunshine PS and GS, Soil Temperature @ 30cm PS and GS and Evaporation HS (see Appendix 1, Appendixes 3-9). These climatic elements selected are the key variables that affect cassava yield more in the study area. The result of re-run multiple regression is presented in Table 3 of this study.
Table 3. Regression model of key climate variables and cassava yield.
Model |
R |
R Square |
Adjusted R Square |
Std. Error
of the Estimate |
Change Statistics |
R Square Change |
F Change |
Df1 |
Df2 |
Sig. F Change |
2 |
0.748 |
0.56 |
0.378 |
9809.968 |
0.56 |
3.076 |
12 |
29 |
0.007 |
Model 2: the result of re-run multiple correlation (R) between selected climatic variables and cassava yield is 0.748. The result showed a positive correlation between the selected variables studied.
The coefficient of determination (R square) value is 0.560 with p value of 0.007. The result indicates strong explanatory power of rainfall, solar Radiation, Sunshine hours, soil temperature @ 30 cm, Evaporation and relative humidity variables on cassava yield over the years in the study area (R2 = 0.560, p = 0.007). The result showed that rainfall-related parameters are the key climate variables that may affect cassava yield more in the area. The p-value of 0.007 validates high level of significance between selected climate variables and cassava yield (p = 0.007).
The summary of the result:
Model 1: Cassava and combine climate variables: R = 0.969; R2 = 0.939; P = 0.00514 over the years.
Model 2: Cassava and key climatic variables: R = 0.748; R2 = 0.560; P = 0.007.
Model 2: the result of the regression emphasized that rainfall and temperature are the key climate parameter that influences cassava yield. Farmers practice rain fed agriculture and as a matter of fact rainfall determine farming season in the area and requires urgent management practice.
Predictive Model Equation for Cassava yield
The predictive model equation was developed from the multiple regressions of seasonal climate variables and cassava yield. This is derived from the coefficients of seasonal climate elements and cassava yield in Appendix 2. Hence, the equation is as follows:
The Multiple Regression Equation:
.
Y—output/dependent/response variable (X1, X2, ..., Xn—input/independent/explanatory variables).
a—is the Y-intercept b1, b2, …, bn—net regression coefficients of corresponding input variables.
y = −264,637 + 0.237 + (−1.073) + 077 + (−4.402) + 2.636 + 5.250 + (−8.658) + 2.054 + 1.956 + (-4.831) + 0.978 + (−0.591) + 11402.6;
y = −264,637 + 0.237 − 1.073 + 077 − 4.402 + 2.636 + 5.250 − 8.658 + 2.054 + 1.956 − 4.831 + 0.978 − 0.591 + 11,402.6.
Y = 276,032.9427 (see the coefficients detail in Appendix 2).
The predictive model at this point emphasized that improved climate conditions will result in a correspondent increase in the tonnage of cassava yield. Hence, the predictive model emphasized that seasonal rainfall; temperature, relative humidity, sunshine hours and radiation parameters are important factors in cassava production and yield as selected by the model. Consequently, the model of the study affirms that seasonal rainfall; radiation and temperature-related parameters are the likely leading climatic variables in cassava production. This is supported by rainfall and temperature anomalies (see Figure 1 and Figure 2). The pattern of rainfall and temperature anomalies may likely affect cassava yield over the years. This is highly applicable to the study area where farmers are practising rain-fed agriculture. The predictive model also validates the result of correction in Table 1 and regression mode in Table 2 and Table 3 of this study. This result simply suggests the need to develop a coordinated system approach to climate incidents and effects as well as formulate adaptive resilience in crop production in the phase of climate variability.
Furthermore, it is important to note that since the key climate variables that
Data Source: Appendix 1.
Figure 1. Trend of Rainfall Anomalies (mm).
affect cassava yield in rain-fed agricultural zones are rainfall and temperature variables, hence rainfall and temperature behaviour as well as changes in tonnage of crop yield are plotted and discussed in Figures 1-7.
Figure 1 shows rainfall anomalies in the study area. The graph is plotted with computed rainfall anomalies data in Appendix 1.
The rate of anomalies over the years is explained as fluctuations in rainfall values at point “0” in the graph. It records an increase and decrease values in the graph. It ranges from rainfall anomalies values of −478.5 to 517.8mm over the years. The result shows ten-year period of rainfall values closely related to mean rainfall of 1725.5 mm. The highest rainfall anomalies were recorded in 1974, 1981, 1991 and 1997 (with rainfall values of 517.8, 364.5, 356.6, and 442.4 mm). The lowest rainfall anomalies were observed in 1984 and 1999 (with values of −812.4 and −478.5 mm).
Figure 2 shows temperature anomalies in the study area. The graph is plotted with computed temperature anomalies data in Appendix 1.
Data Source: Appendix 1.
Figure 2. Temperature Anomalies (˚C).
The mean annual temperature for the years is 27.6˚C while the temperature anomalies range from −1.2˚C to 2.3˚C over the years. The year 1971 records the lowest temperature anomalies and 1999 records the highest temperature anomalies. The period of 12 years out of the 42 years analysed records temperature anomalies above the mean annual temperature and 17 years records temperature below it. The analysis also indicates that 14 years records temperature anomalies closely related to the mean temperature at point “0” in the graph.
Changes in the tonnage of cassava yield from 1970 to 2012 were clearly shown in Figure 3. The time series graph is plotted with data from the tonnage of cassava yield over the years in Appendix 1 of the study.
The time series graph in Figure 3 shows variation in the tonnage of cassava over the years. The fluctuation in tonnage of cassava yield exhibits different patterns of decrease and increase over the years. The peak tonnage of cassava yield was recorded in the year 1999 with a value of 34,094 tonnes. While lowest tonnage of cassava yield was recorded in the year 1993 with 161 tonnes. The fluctuation in the tonnage of cassava yield may be attributed to the pattern of rainfall trend over the years in the study area (see Figures 4-7).
Data Source: Appendix 1.
Figure 3. Time series graph of change in tonnage of crops.
Data Source raw data: Appendix 1.
Figure 4. Time series graph of rainfall and cassava yield trend in decade I (1971 to 1980).
The decadal analysis of the fluctuation in rainfall and cassava yield was plotted in Figures 4-7 and is used to clarify the link between rainfall and cassava yield over the years.
The trend in decade I; showed a significant increase and decrease in rainfall pattern with a corresponding change in cassava yield. This implies that the change in rainfall may determine the crop yield behaviour from the period of 1971 to 1980.
Data Source: Appendix 1.
Figure 5. Time series graph of rainfall and cassava yield trend in decade II (1981-1990).
Data Source: Appendix 1.
Figure 6. Time series graph of rainfall and cassava yield trend in decade III (1991 to 2000).
Data Source: Appendix 1.
Figure 7. Time series graph of rainfall and cassava yield trend in decade IV (2001-2012).
In decade II (1981-1990), there was excessive rainfall in 1981, 1982 and 1983 with corresponding low cassava yield. There is also increasing rainfall with significant increase and decrease in related crop yield values. However, there was a rapid increase in cassava yield in 1989 and 1990. This could be attributed to adequate rainfall distribution within the farming season and improved cassava species.
Decade III; which ranges from 1991 to 2000, records significant correlations between rainfall and cassava yield especially from 1992 to 1996. However, from 1997 to 2000 exhibited a serious increase in cassava yield without a corresponding increase in rainfall values. Other factors outside adequate rainfall distribution within the planting and growing seasons, such as improved cassava species may be responsible for the improvement in cassava yield in the period.
There is a significant correlation between rainfall values and cassava yield over the years in decade IV (2001-2012). The years 2001, 2006, 2010 and 2012 recorded outstanding increases in cassava yield over the period. This may be attributed to good rainfall distribution and improved cassava species within the period. Rainfall and temperature behaviour can also be predicted and plotted for sustainable farm planning and crop yield improvement.
The findings of the study showed that climate variability may have a strong positive relationship on cassava yield in the study area (R2 = 0.939; P = 0.00514). The result indicates that climate variability especially rainfall-related parameters may have a significant effect on cassava yield. The findings are significantly related to the study of Wilcox (2006), Sundström, et al. (2014) and Salisu, (2013). Their study maintained that crop yields potentially increased with more rainfall and decreased with higher temperatures. The study also maintained that in some cases, crop yield decreased with excessive rainfall. The result of the study is related to Adejoro (2001) and Barnett, (2011). Their study emphasized that the most significant climate variability expected during the 21st century was rainfall and temperature behaviour. Hence, accurate prediction of climate variability may help in effective farm planning which may perhaps boost crop yield. This was validated by the response of the farmer during our fieldwork and the response generated through questionnaire administration and focus group discussion in the area. The farmers emphasized that rainfall amount, intensity, duration and pattern has a significant impact on their farming seasons, activities and tonnage of yield over the years (see Figure 8). Hence, there is an urgent need for a comprehensive early warning system to farmer in their local dialect as this will help reduce loss and maximise profit which is directly caused by change in rainfall pattern. This is in alignment with FAO (2008) estimates which emphasized that for each 1˚C rise in temperature, farm profits in Africa will drop by nearly 10%.
Figure 8 shows that the active farmers in the study area experienced more late onset and early onset of rainfall as well as rainfall cessation over the years. They hardly experienced normal rainfall patterns. The farmers practice rain-fed agriculture and may always wait for the rain to derive their farming seasons giving way to the need for urgent development of an irrigation scheme. The study strongly re-emphasized that late onset and early onset of rain as well as rainfall cessation are important factors for predictive model in efficient cassava yield and hence, prompted the recommendations of the study.
Figure 8. Perception of active farmers on change in rainfall pattern in enugu state.
4. Recommendations
The findings from the study have prompted the following recommendations
1) A comprehensive early warning system on climate variability incidence which can be translated to local farmers in their indigenous language by agro-meteorological extension officers should be developed and practiced.
2) Development of sustainable coordinated system approach on climate variability incidence, effect, adaptation and management strategies.
3) Construction of intensive and functional weather stations in accordance with WMO standards in the study area.
4) Research should focus on improved crop species that can adapt to harsh weather, and mature within the shortest time frame. The research should develop improved storage facilities.
5) Development of functional and improved irrigation scheme. Improving irrigation scheduling is an important element in effective water management and food production.
6) There is an urgent need for water conservation and improved tree-planting culture. This will help improve the hydrological cycle and atmospheric water content. This could be achieved through effective training, awareness campaigns and continuous capacity building.
7) There is an urgent need for a policy implication strategic document on climate variability-induced problems.
5. Conclusion
The predictive model in this study selected seasonal rainfall, radiation, relative humidity, sunshine, soil temperature and evaporation as important climatic variables that may influence cassava yield more. The predictive model equation for cassava yield and climatic elements is:
y = a + b1 + b2 + b3 + b4 + b5 + b6 + b7 + b8 + b9 + b10 + b11 + b12 + E;
y = −264,637 + 0.237 − 1.073 + 077 − 4.402 + 2.636 + 5.250 − 8.658 + 2.054 + 1.956 − 4.831 + 0.978 − 0.591 + 11.402.6.
The result of rainfall and temperature anomalies, decadal change in the trend of cassava yield and the opinion of farmers on changes in rainfall season validates the predictive model developed in this study. The application of the recommendations of this study could help in food security policy making which could encourage efficient cassava production despite of climate variability incidence.
Acknowledgements
We acknowledge the Director of the Centre for Environmental Management and Control, University of Nigeria, Prof. Anene Moneke, for providing meaningful guidance and effective coordination, which aided in the collection of samples for this study and the developing of the predictive model. We also thank Prof. Obinna Onwujekwe, the Director of Research, the University of Nigeria, for ensuring that this work met the criteria for the approved University standards and rules regarding integrity tests and sound scientific practices in undertaking the study. We also, acknowledge the Management of the Enugu State University of Science and Technology (ESUST), particularly, the staff of the Department of Geography and Meteorology, for contributing their technical expertise in generating meteorological observatory parameters that aided in creating the predictive model. Also, all authors agreed on the sequence of authorship as listed in this paper.
Funding
We, the authors declare that no funds, grants, or other support were received before, during, and after the preparation of this manuscript. Every activity was undertaken voluntarily with self-funding.
Authors’ Contributions
All the authors contributed to the conception and design of this work. Material preparation, data collection and analysis were performed by Emeka Bright Ogbuene, Obianuju Gertrude Aloh, Tonia Nkiru Nwodo, and Josiah Chukwuemeka Ogbuka. The first draft of the manuscript was written by Obianuju Gertrude Aloh, Vivian Amarachi Ozorme, Fred Emeka Achoru, and Obiageli Jacinta Okolo. All authors read and commented on the previous versions of the manuscript, as well as approved the manuscript before submission.
Data Availability
The datasets generated during, and/or analysed during this study are not publicly available due to the risk of violating the privacy of respondents/participants, who provided most of the information that makes up the study data. However, the datasets are available with the corresponding author on reasonable request.
Compliance with Ethical Standards
All relevant authorities granted their informed consent to participate in the research before the commencement of the study. Exemption from ethical precautions was granted by the Centre for Environmental Management and Control, University of Nigeria, due to the use of plants for the study. Essentially, the study was conducted according to the Nigerian University-wide ethical guidelines and review processes, as well as the internal guidelines of the Research and Ethics Directorate of the University of Nigeria and Enugu State University of Science and Technology.
Appendix 1: Cassava Yield, Rainfall and Temperature Anomalies in the Study Area (1971-2012) (Sourced from NIMET Lagos and Enugu, 2016, 2013 and 2009; FBS, 2013)
Years |
Total Annual
Rainfall |
Rainfall
Anomalies |
Mean
Temperature |
Temperature
Anomalies |
Tonnage of
Cassava Yield |
1971 |
1507.1 |
−218.4 |
26.5 |
−1.2 |
870 |
1972 |
1986.2 |
260.7 |
27.7 |
0.1 |
950 |
1973 |
1450.4 |
−275.1 |
27.6 |
0 |
650 |
1974 |
2243.3 |
517.8 |
27.1 |
−0.5 |
690 |
1975 |
1979.7 |
254.2 |
27.7 |
0.1 |
860 |
1976 |
1417.7 |
−307.8 |
27.2 |
−0.4 |
1340 |
1977 |
1827.6 |
102.1 |
26.7 |
−0.9 |
1570 |
1978 |
1545.8 |
−179.7 |
27.6 |
0 |
1630 |
1979 |
1988.1 |
262.6 |
27.1 |
−0.5 |
1680 |
1980 |
1696 |
−29.5 |
27.2 |
−0.4 |
1578 |
1981 |
2090 |
364.5 |
27.3 |
−0.3 |
1506 |
1982 |
1639 |
−86.5 |
27.3 |
−0.3 |
873 |
1983 |
1566.6 |
−158.9 |
27.2 |
−0.2 |
581 |
1984 |
913.1 |
−812.4 |
28.4 |
0.8 |
909 |
1985 |
1779.4 |
53.9 |
28.2 |
0.6 |
1174 |
1986 |
1930.6 |
205.1 |
28.1 |
0.5 |
1192 |
1987 |
1450.6 |
−274.9 |
27.6 |
0 |
1930 |
1988 |
1415.6 |
−309.9 |
28.1 |
0.5 |
3151 |
1989 |
1461 |
−264.5 |
27.5 |
−0.1 |
9066 |
1990 |
1643 |
−82.5 |
27.6 |
0 |
10768 |
1991 |
2082.1 |
356.6 |
28 |
0.4 |
20680 |
1992 |
1960.8 |
235.3 |
27.3 |
−0.3 |
567 |
1993 |
1704.7 |
−20.8 |
27.3 |
−0.3 |
161 |
1994 |
1576.9 |
−148.6 |
27.5 |
−0.1 |
1029 |
1995 |
1454.9 |
−270.6 |
27.5 |
−0.1 |
1107 |
1996 |
2167.9 |
442.4 |
27 |
−0.6 |
2087 |
1997 |
1963.1 |
237.6 |
28.5 |
0.9 |
32928 |
1998 |
1824 |
98.5 |
27 |
−0.6 |
33495 |
1999 |
1247 |
−478.5 |
29.9 |
2.3 |
34094 |
2000 |
1647.4 |
−78.1 |
27.5 |
−0.1 |
33506 |
2001 |
1947.3 |
221.8 |
27.6 |
0 |
33698 |
2002 |
1676.3 |
−49.2 |
26.7 |
−0.9 |
890 |
2003 |
1722.2 |
−3.3 |
27.5 |
−0.1 |
1120 |
2004 |
1890 |
164.5 |
28.2 |
0.6 |
2030 |
2005 |
1770.1 |
44.6 |
28.4 |
0.8 |
1080 |
2006 |
1819.6 |
94.1 |
27.2 |
−0.2 |
22240 |
2007 |
1911.2 |
185.7 |
27.6 |
0 |
1029 |
2008 |
1738.4 |
12.9 |
28.1 |
0.5 |
1107 |
2009 |
1769.7 |
44.2 |
27.3 |
−0.3 |
2087 |
2010 |
1669.5 |
−56 |
27.5 |
−0.1 |
32928 |
2011 |
1724.1 |
−1.4 |
28.2 |
0.6 |
1120 |
2012 |
1676.3 |
−49.2 |
28.4 |
0.8 |
33698 |
Note: Rainfall Total = 72474.3 mm; Mean Rainfall = 1725.5 mm; Mean Annual Temperature = 27.6˚C.
Appendix 2: Coefficients of Climate Elements and Cassava Yield
Coefficients |
ModeI |
unstandardized
Coefficients |
Standardized Coefficients Beta |
t |
Sig. |
95% Confidence
lnterval for B |
Correlations |
Collinearity
Statistics |
B |
Std. Error |
Lower Bound |
Upper Bound |
Zero-order |
Partial |
Part |
Tolerance |
VIF |
1.000 |
{Constant) · |
−264637.000 |
1466422 |
|
−0.180 |
0.868 |
−4931448.009 |
4402173.352 |
|
|
|
|
|
|
RainPS |
19.234 |
43.889 |
0.237 |
0.438 |
0.691 |
−120.441 |
158.909 |
0.170 |
0.245 |
0.063 |
0.070 |
14.319 |
|
RainGS |
−55.827 |
33.402 |
−1.073 |
−1.671 |
0.193 |
−162.128 |
50.474 |
0.055 |
−0.694 |
−0.239 |
0.050 |
20.111 |
|
RainHS |
18.953 |
103.368 |
0.077 |
0.183 |
0.866 |
−310.011 |
347.916 |
−0.239 |
0.105 |
0.026 |
0.117 |
8.538 |
|
TempMax |
−17775.700 |
25266.745 |
−1.507 |
−0.704 |
0.532 |
−98185.801 |
62634.317 |
0.344 |
−0.376 |
−0.101 |
0.004 |
223.770 |
|
TempMin |
10852.696 |
35775.155 |
0.348 |
0.303 |
0.781 |
−102999.813 |
124705.205 |
0.023 |
0.173 |
0.043 |
0.016 |
64.023 |
|
TempAver |
34939.873 |
48782.426 |
1.711 |
0.716 |
0.526 |
−120307.576 |
190187.323 |
0.320 |
0.382 |
0.103 |
0.004 |
278.539 |
|
T5cmPS |
−9199.600 |
20655.311 |
−0.729 |
−0.445 |
0.686 |
−74934.019 |
56534.819 |
−0.112 |
−0.249 |
−0.064 |
0.008 |
130.542 |
|
T5cmGS |
−14572.700 |
32200.820 |
−0.917 |
−0.453 |
0.682 |
−117050.059 |
87904.660 |
0.029 |
−0.253 |
−0.065 |
0.005 |
200. 170 |
|
T5cmHS |
24431.013 |
27877.686 |
1.735 |
0.876 |
0.445 |
−64288.226 |
113150.252 |
−0.153 |
0.451 |
0.125 |
0.005 |
191.252 |
|
T10cmPS |
4237.5&7 |
5608.602 |
−0.442 |
−0.756 |
0.505 |
−22086.672 |
13611.478 |
−0.021 |
−0.400 |
−0.108 |
0.060 |
16.718 |
|
T10cmGS |
−6295.312 |
9049.988 |
−0.693 |
−0.696 |
0.537 |
−35096.413 |
22505.788 |
−0.254 |
−0.373 |
−0.100 |
0.021 |
48.482 |
|
T10cmHS |
4480.265 |
3751.640 |
0.377 |
1.194 |
0.318 |
−7459.129 |
16419.658 |
−0.108 |
0.568 |
0.171 |
0.206 |
4.852 |
|
T30cmPS |
−45894.600 |
29123.533 |
−4.402 |
−1.576 |
0.213 |
−138578.638 |
46789.524 |
0.197 |
−0.673 |
−0.226 |
0.003 |
380.737 |
|
T30cmGS |
27917.007 |
12330.183 |
2.638 |
2.264 |
0.109 |
−11323.137 |
67157.152 |
0.222 |
0.794 |
0.324 |
0.015 |
66.132 |
|
T30cmHS |
11765.883 |
17160.094 |
1.189 |
0.686 |
0.542 |
−42845.196 |
66376.961 |
0.300 |
0.368 |
0.098 |
0.007 |
146.657 |
|
T50cmPS |
−5193.213 |
8140.863 |
−0.321 |
−0.638 |
0.569 |
−31101.072 |
20714.645 |
−0.192 |
−0.346 |
−0.091 |
0.081 |
12.380 |
|
T50cmGS · |
−1516.414 |
6813.953 |
−0.075 |
−0.223 |
0.838 |
−23253.400 |
20168.824 |
0.025 |
−0.127 |
−0.032 |
0.178 |
5.612 |
|
T50cmHS |
−6531.004 |
5642.373 |
−0.438 |
−1.157 |
0.331 |
−24487.584 |
11425.516 |
0.194 |
−0.556 |
−0.166 |
0.144 |
6.926 |
|
T100cmPS |
79101.444 |
97469.326 |
3.780 |
0.812 |
0.476 |
−231089.453 |
389292.341 |
−0.336 |
0.424 |
0.116 |
0.001 |
1058.114 |
|
T100cmGS |
2959.047 |
83989.398 |
0.133 |
0.035 |
0.974 |
−264332.703 |
270250.797 |
−0.352 |
0.020 |
0.005 |
0.001 |
697.463 |
|
T100cmHS |
−0.971 |
84634.438 |
−4.283 |
−1.148 |
0.334 |
−366467.987 |
172221.122 |
−0.352 |
−0.552 |
−0.164 |
0.001 |
679.465 |
|
EvapPS |
−0.945 |
78072.150 |
−5.432 |
−1.210 |
0.313 |
−342912.737 |
154008.116 |
−0.309 |
−0.573 |
−0.173 |
0.001 |
983.457 |
|
EvapGS |
2523.428 |
26237.456 |
0.139 |
0.096 |
0.929 |
−80975.868 |
86022.723 |
−0.324 |
0.055 |
0.014 |
0.010 |
101.850 |
|
EvapHS |
92797.556 |
69506.377 |
5.250 |
1.335 |
0.274 |
−128402.757 |
313997.870 |
−0.284 |
0.610 |
0.191 |
0.001 |
754.439 |
|
RH09PS |
19119.983 |
16238.470 |
7.299 |
1.177 |
0.324 |
−32558.075 |
70798.041 |
0.428 |
0.562 |
−0.169 |
0.001 |
1874.513 |
|
RH09GS |
−22535.000 |
17594.928 |
−8.658 |
−1.252 |
0.299 |
−798023 |
34732.880 |
0.409 |
−0.586 |
−0.179 |
0.000 |
2331.851 |
|
RH09HS |
5110.307 |
3353.692 |
2.054 |
1.524 |
0.225 |
−5562.637 |
15783.252 |
0.097 |
0.661 |
0.218 |
0.011 |
88.616 |
|
RH 1500PS |
·12090.2 |
11343.677 |
−4.056 |
−1.066 |
0.365 |
−48190.893 |
24010.395 |
−0.241 |
−0.524 |
−0.153 |
0.001 |
706.475 |
|
RH 1500GS |
11533.200 |
10173.420 |
4.067 |
1.134 |
0.339 |
−20843.182 |
43909.563 |
−0.260 |
0.548 |
0.182 |
0.002 |
627.722 |
|
RH 1500HS · |
−2813.106 |
3186.781 |
−1.037 |
−0.883 |
0.442 |
−12954.867 |
7328.655 |
−0.221 |
−0.454 |
−0.126 |
0.015 |
67.361 |
|
Sunshine.PS |
39542.461 |
29423.294 |
1.956 |
1.344 |
0.272 |
−54095.591 |
133180.513 |
−0.411 |
0.613 |
0.192 |
0.010 |
103.333 |
|
SunshineGS |
−97987.800 |
76323.638 |
−4.831 |
·1.284 |
0.289 |
−340883.648 |
144908.113 |
−0.389 |
−0.595 |
−0.184 |
0.001 |
690.825 |
|
Sunshine.HS |
45965.031 |
67790.078 |
2.230 |
0.678 |
0.546 |
−169773.252 |
261703.315 |
−0.344 |
0.365 |
0.097 |
0.002 |
527.814 |
|
CloudPS |
−4549.814 |
28352.764 |
−0.074 |
−0.160 |
0.883 |
−94780.962 |
85681.335 |
0.214 |
−0.092 |
−0.023 |
0.095 |
10.487 |
|
CloudHS |
29932.059 |
27560.685 |
1.616 |
1.086 |
0.357 |
−57778.340 |
117642.4−58 |
−0.075 |
0.531 |
0.155 |
0.009 |
108.057 |
|
RadiationPS |
45932.299 |
36161.141 |
0.978 |
1.270 |
0.294 |
−69148.589 |
161013.187 |
0.534 |
0.591 |
0.182 |
0.035 |
28.926 |
|
RadiationGS |
·18591.8 |
13670.922 |
−0.591 |
−1.360 |
0.267 |
−82098.793 |
24915.158 |
0.288 |
−0.618 |
−0.195 |
0.109 |
9.201 |
|
RadiationHS |
35311.037 |
38236.741 |
0.701 |
0.923 |
0.424 |
−86375.338 |
156997.413 |
0.241 |
0.470 |
0.132 |
0.036 |
28.112 |
SPSS Regression Analysis.
Appendix 3: Mean Soil Temperature Values at 5 cm Depth from 1971 to 2012
Years |
T5PS |
T5GS |
T5HS |
1971 |
29.3 |
29.1 |
29.2 |
1972 |
29.6 |
29.2 |
29.4 |
1973 |
29.5 |
29.4 |
29.3 |
1974 |
29.1 |
28.9 |
29 |
1975 |
29.7 |
29.5 |
29.6 |
1976 |
31.2 |
30.8 |
30.9 |
1977 |
29.6 |
29.2 |
29.4 |
1978 |
29.7 |
29.3 |
29.5 |
1979 |
30.1 |
29.8 |
29.9 |
1980 |
30.3 |
30.1 |
30.2 |
1981 |
29.9 |
29.5 |
29.7 |
1982 |
29.7 |
29.3 |
29.5 |
1983 |
29.9 |
29.7 |
29.4 |
1984 |
29.8 |
29.6 |
29.3 |
1985 |
30.1 |
29.9 |
29.7 |
1986 |
30.6 |
30.4 |
30.2 |
1987 |
29.4 |
29.3 |
29.1 |
1988 |
29.7 |
29.5 |
29.3 |
1989 |
29.9 |
29.6 |
29.4 |
1990 |
30.1 |
29.8 |
29.6 |
1991 |
29.7 |
29.5 |
29.2 |
1992 |
28.6 |
28.4 |
28.1 |
1993 |
29.1 |
28.9 |
28.6 |
1994 |
29.6 |
29.4 |
29.7 |
1995 |
29.4 |
29.3 |
29.1 |
1996 |
29.5 |
29.3 |
29.2 |
1997 |
29.1 |
28.9 |
28.7 |
1998 |
27.2 |
27.9 |
27.8 |
1999 |
31 |
30.9 |
30.8 |
2000 |
29.7 |
29.5 |
29.3 |
2001 |
29.4 |
29.2 |
29.1 |
2002 |
28.7 |
28.5 |
28.3 |
2003 |
27.4 |
27.3 |
27.1 |
2004 |
28.3 |
28.1 |
27.9 |
2005 |
29.1 |
28.9 |
28.7 |
2006 |
27.3 |
27.1 |
26.9 |
2007 |
29.1 |
29.4 |
28.7 |
2008 |
27.2 |
29.3 |
27.8 |
2009 |
31 |
29.3 |
30.8 |
2010 |
29.7 |
30.9 |
29.3 |
2011 |
27.4 |
29.5 |
29.1 |
2012 |
28.3 |
29.2 |
27.9 |
Source: NIMET Lagos and Enugu, 2013 and 2009; Author’s Field work, 2013.
Appendix 4: Mean Soil Temperature Values at 10cm Depth from 1971 to 2012
Years |
T10PS |
T10GS |
T10HS |
1971 |
30.8 |
31.9 |
32.9 |
1972 |
31.1 |
32.4 |
33.4 |
1973 |
30.9 |
30.1 |
32.1 |
1974 |
31.2 |
30.8 |
31.2 |
1975 |
33.1 |
31.9 |
33.9 |
1976 |
32.6 |
31.7 |
33 |
1977 |
31.8 |
32.4 |
32.3 |
1978 |
30.4 |
32.6 |
32.8 |
1979 |
33.7 |
30.3 |
32.8 |
1980 |
31.1 |
30.4 |
33.6 |
1981 |
29.9 |
30.6 |
30.9 |
1982 |
32.7 |
30.2 |
30.8 |
1983 |
32.6 |
29.5 |
34.6 |
1984 |
30.6 |
30.3 |
31 |
1985 |
33.9 |
28.8 |
31.2 |
1986 |
32 |
28.9 |
32.5 |
1987 |
33.7 |
32.6 |
32.6 |
1988 |
30.1 |
32.4 |
33.3 |
1989 |
29.9 |
32.6 |
32.6 |
1990 |
30.2 |
30.1 |
32.4 |
1991 |
31.6 |
30.2 |
32 |
1992 |
30.8 |
32.2 |
33 |
1993 |
30.4 |
32.1 |
32.9 |
1994 |
31 |
32.1 |
33.1 |
1995 |
29.7 |
32.2 |
31.8 |
1996 |
30.4 |
28.5 |
32.5 |
1997 |
29.3 |
28.6 |
32.1 |
1998 |
29.5 |
30.3 |
31.6 |
1999 |
33.7 |
30.6 |
30.3 |
2000 |
30.2 |
28.4 |
`34.4 |
2001 |
31.1 |
28.6 |
33.2 |
2002 |
30.9 |
28.9 |
33.6 |
2003 |
30.2 |
28.6 |
31.2 |
2004 |
30.8 |
30.1 |
33 |
2005 |
30.1 |
30.2 |
33.6 |
2006 |
30.8 |
32.4 |
33.2 |
2007 |
30.2 |
30.1 |
32.4 |
2008 |
31.6 |
30.2 |
32 |
2009 |
29.5 |
30.3 |
31.6 |
2010 |
33.7 |
30.6 |
30.3 |
2011 |
30.2 |
28.6 |
31.2 |
2012 |
30.8 |
30.1 |
33 |
Source: NIMET Lagos and Enugu, 2013 and 2009; Author’s Field work, 2013.
Appendix 5: Mean Soil Temperature Values at 30 cm Depth from 1970 to 2012
Years |
T30PS |
T30GS |
T30HS |
1971 |
27 |
26.9 |
26.7 |
1972 |
27.1 |
27 |
26.8 |
1973 |
27.5 |
27.3 |
27.1 |
1974 |
27.6 |
27.4 |
27.2 |
1975 |
27.3 |
27.2 |
27.1 |
1976 |
27.9 |
27.6 |
27.3 |
1977 |
28.1 |
27.8 |
27.6 |
1978 |
30.2 |
29.9 |
29.7 |
1979 |
29.2 |
29.1 |
28.9 |
1980 |
27.4 |
27.2 |
27.1 |
1981 |
30.3 |
30.1 |
29.9 |
1982 |
30.2 |
29 |
29.8 |
1983 |
30.2 |
30 |
29.7 |
1984 |
27.8 |
27.6 |
27.4 |
1985 |
29.6 |
29.4 |
29.2 |
1986 |
30.1 |
29.9 |
29.7 |
1987 |
29.8 |
29.6 |
29.4 |
1988 |
30.2 |
30 |
29.8 |
1989 |
30.3 |
30.1 |
29.9 |
1990 |
30.7 |
30.6 |
30.5 |
1991 |
29.8 |
29.6 |
29.5 |
1992 |
29.9 |
29.7 |
29.6 |
1993 |
27.7 |
27.5 |
27.3 |
1994 |
30.2 |
30.1 |
29.9 |
1995 |
27.9 |
27.6 |
27.5 |
1996 |
27.9 |
27.7 |
27.5 |
1997 |
27.7 |
27.6 |
27.4 |
1998 |
29.6 |
29.4 |
29.3 |
1999 |
30.3 |
30.1 |
30.2 |
2000 |
29.8 |
29.4 |
29.9 |
2001 |
30.2 |
30.1 |
30.2 |
2002 |
29.9 |
29.6 |
30.1 |
2003 |
29.8 |
29.6 |
28.1 |
2004 |
30.1 |
30 |
29.9 |
2005 |
29.9 |
29.7 |
27.5 |
2006 |
27.8 |
27.6 |
27.5 |
2007 |
27.7 |
27.7 |
27.4 |
2008 |
29.6 |
27.6 |
29.3 |
2009 |
30.3 |
29.4 |
30.2 |
2010 |
29.8 |
29.4 |
29.9 |
2011 |
30.2 |
30.1 |
27.4 |
2012 |
29.9 |
29.6 |
29.2 |
Source: NIMET Lagos and Enugu, 2013 and 2009; Author’s Field work, 2013.
Appendix 6: Mean Soil Temperature Values at 50 cm Depth from 1970 to 2012
Years |
T50PS |
T50GS |
T50HS |
1971 |
31.6 |
28.6 |
31.6 |
1972 |
31.1 |
28.2 |
32.7 |
1973 |
30.4 |
28.3 |
31.9 |
1974 |
31.3 |
28.5 |
32 |
1975 |
32.6 |
28.9 |
31.3 |
1976 |
30.4 |
28.2 |
34.4 |
1977 |
30.1 |
28.4 |
32.6 |
1978 |
30.2 |
30 |
31.7 |
1979 |
30.4 |
29.1 |
34.3 |
1980 |
30.6 |
28.2 |
31 |
1981 |
30.7 |
28.7 |
31.3 |
1982 |
30.6 |
28.6 |
32.4 |
1983 |
31.9 |
28.7 |
32.6 |
1984 |
31 |
28.3 |
31.8 |
1985 |
30.3 |
28.4 |
32 |
1986 |
30.4 |
28.1 |
32.9 |
1987 |
30.6 |
28.3 |
31.4 |
1988 |
30.1 |
28.4 |
32.3 |
1989 |
30.4 |
28.2 |
32.4 |
1990 |
30 |
28.4 |
31.7 |
1991 |
29.3 |
28.2 |
31.6 |
1992 |
29.3 |
30.6 |
31.8 |
1993 |
31.3 |
28.2 |
31.3 |
1994 |
31.7 |
25.9 |
31.9 |
1995 |
32.6 |
28.2 |
32.6 |
1996 |
31.5 |
28.4 |
32.7 |
1997 |
29.2 |
28.2 |
34.4 |
1998 |
30.9 |
28.5 |
32.5 |
1999 |
30.4 |
28.6 |
32.3 |
2000 |
30.7 |
28.5 |
32.3 |
2001 |
31.2 |
28.4 |
32.1 |
2002 |
30 |
28.6 |
32.7 |
2003 |
30.4 |
28.2 |
32 |
2004 |
30.2 |
28.9 |
31.6 |
2005 |
30.3 |
28.3 |
32.6 |
2006 |
30.6 |
28.5 |
34.3 |
2007 |
30 |
28.4 |
31.7 |
2008 |
29.3 |
28.2 |
31.6 |
2009 |
30.9 |
28.5 |
32.5 |
2010 |
30.4 |
28.6 |
32.3 |
2011 |
30.4 |
28.2 |
32 |
2012 |
30.2 |
28.9 |
31.6 |
Source: NIMET Lagos and Enugu, 2013 and 2009; Author’s Field work, 2013.
Appendix 7: Mean Soil Temperature Values at 100 cm Depth from 1970 to 2012
Years |
T100PS |
T100GS |
T100HS |
1971 |
30.1 |
30 |
30.2 |
1972 |
30.2 |
29.9 |
30.1 |
1973 |
30.4 |
30.2 |
30.6 |
1974 |
30.7 |
30.5 |
30.8 |
1975 |
30.8 |
30.4 |
30.6 |
1976 |
30.2 |
29.9 |
30.3 |
1977 |
30.3 |
30.1 |
30.2 |
1978 |
30.5 |
30.2 |
30.4 |
1979 |
30.9 |
30.6 |
30.8 |
1980 |
30.3 |
30.1 |
30.2 |
1981 |
30.6 |
30.4 |
30.5 |
1982 |
29.9 |
29.6 |
29.8 |
1983 |
30.3 |
30.1 |
30.2 |
1984 |
30.7 |
30.5 |
30.6 |
1985 |
30.4 |
30.1 |
30.3 |
1986 |
30.3 |
30.2 |
30.4 |
1987 |
30 |
29.8 |
30.1 |
1988 |
29.9 |
29.7 |
29.8 |
1989 |
30.6 |
30.2 |
30.5 |
1990 |
30.3 |
30.1 |
30.2 |
1991 |
30.3 |
30.2 |
30.4 |
1992 |
30.5 |
30.1 |
30.3 |
1993 |
30.4 |
30.2 |
30.5 |
1994 |
30.7 |
30.3 |
30.6 |
1995 |
30.6 |
30.4 |
30.7 |
1996 |
28.1 |
28 |
28.4 |
1997 |
30.1 |
29.8 |
30.2 |
1998 |
29.9 |
29.6 |
29.8 |
1999 |
30.6 |
30.2 |
30.5 |
2000 |
30.3 |
30.1 |
30.4 |
2001 |
30.4 |
30.2 |
30.3 |
2002 |
30.3 |
30 |
30.2 |
2003 |
30.5 |
30.3 |
30.6 |
2004 |
30.7 |
30.4 |
30.8 |
2005 |
30.6 |
30.3 |
30.5 |
2006 |
28.9 |
28.7 |
28.8 |
2007 |
30.4 |
30.2 |
30.5 |
2008 |
30.7 |
30.3 |
30.6 |
2009 |
30.6 |
30.4 |
30.7 |
2010 |
28.1 |
28 |
28.4 |
2011 |
30.1 |
29.8 |
30.2 |
2012 |
29.9 |
29.6 |
29.8 |
Source: NIMET Lagos and Enugu, 2013 and 2009; Author’s Field work, 2013.
Appendix 8: Seasonal Mean Evaporation Values from 1970 to 2012 in the Study Area
Years |
Evaporation
Planting Season |
Evaporation
Growing Season |
Evaporation
Growing Season |
1971 |
6 |
5.8 |
6.1 |
1972 |
6.2 |
6 |
6.3 |
1973 |
6.1 |
5.9 |
6.2 |
1974 |
5.9 |
5.6 |
5.9 |
1975 |
5.7 |
5.4 |
5.8 |
1976 |
5.1 |
4.8 |
5.2 |
1977 |
5.4 |
5.1 |
5.5 |
1978 |
5.8 |
5.2 |
5.9 |
1979 |
5.6 |
5 |
5.8 |
1980 |
5.7 |
5.1 |
5.9 |
1981 |
5.6 |
5 |
5.7 |
1982 |
5.6 |
5.1 |
5.8 |
1983 |
5.2 |
4.8 |
5.4 |
1984 |
6 |
5.4 |
6.2 |
1985 |
5.1 |
4.6 |
5.3 |
1986 |
5.4 |
4.5 |
5.6 |
1987 |
6 |
5.3 |
6.2 |
1988 |
6.1 |
5.2 |
6.4 |
1989 |
5.4 |
5 |
5.6 |
1990 |
4.9 |
4.2 |
4.9 |
1991 |
4.8 |
4.1 |
4.9 |
1992 |
4.7 |
4.2 |
4.8 |
1993 |
6.1 |
5.8 |
6.2 |
1994 |
5.6 |
5 |
5.7 |
1995 |
5 |
4.6 |
5 |
1996 |
4.7 |
4.2 |
4.9 |
1997 |
3.9 |
3.3 |
4 |
1998 |
3.2 |
3 |
3.4 |
1999 |
5 |
4.6 |
5.2 |
2000 |
5.6 |
5.1 |
5.8 |
2001 |
4.5 |
4.1 |
4.9 |
2002 |
4.3 |
4 |
4.5 |
2003 |
4 |
3.8 |
4.2 |
2004 |
5.4 |
5.1 |
5.6 |
2005 |
4.6 |
4.4 |
4.8 |
2006 |
4.9 |
4.7 |
5.1 |
2007 |
3.9 |
3.7 |
4.1 |
2008 |
4.1 |
3.9 |
4.3 |
2009 |
5.1 |
4.9 |
5.3 |
2010 |
5.6 |
5.2 |
5.8 |
2011 |
4.5 |
4.3 |
4.7 |
2012 |
5.4 |
5.1 |
5.6 |
Source: NIMET Lagos and Enugu, 2013 and 2009; Author’s Field work, 2013.
Appendix 9: Relative Humidity (R.H) @ 0900 GMT from 1970 to 2012
Years |
Relative Humidity
(@0900GMT)
Planting Season |
Relative Humidity
(@0900GMT)
Growing Season |
Relative Humidity
(@0900GMT)
Harvesting Season |
1971 |
63 |
75 |
53 |
1972 |
64 |
76 |
54 |
1973 |
66 |
78 |
56 |
1974 |
60 |
72 |
50 |
1975 |
66 |
78 |
56 |
1976 |
71 |
83 |
61 |
1977 |
69 |
81 |
59 |
1978 |
60 |
72 |
50 |
1979 |
64 |
76 |
54 |
1980 |
65 |
79 |
55 |
1981 |
60 |
72 |
50 |
1982 |
59 |
71 |
49 |
1983 |
58 |
70 |
48 |
1984 |
70 |
82 |
49 |
1985 |
61 |
73 |
50 |
1986 |
59 |
71 |
51 |
1987 |
58 |
70 |
54 |
1988 |
59 |
71 |
53 |
1989 |
60 |
72 |
56 |
1990 |
61 |
73 |
52 |
1991 |
64 |
74 |
50 |
1992 |
63 |
75 |
55 |
1993 |
66 |
78 |
58 |
1994 |
62 |
74 |
61 |
1995 |
60 |
72 |
63 |
1996 |
65 |
77 |
52 |
1997 |
68 |
80 |
58 |
1998 |
71 |
83 |
48 |
1999 |
73 |
85 |
60 |
2000 |
62 |
74 |
49 |
2001 |
68 |
80 |
55 |
2002 |
58 |
70 |
45 |
2003 |
70 |
82 |
57 |
2004 |
71 |
83 |
58 |
2005 |
60 |
72 |
47 |
2006 |
66 |
78 |
53 |
2007 |
56 |
68 |
43 |
2008 |
68 |
80 |
55 |
2009 |
56 |
68 |
43 |
2010 |
68 |
80 |
55 |
2011 |
55 |
67 |
42 |
2012 |
67 |
79 |
54 |
Source: NIMET Lagos and Enugu, 2013 and 2009; Author’s Field work, 2013.