CLIMATE VARIABILITY IMPLICATIONS FOR MAIZE

CLIMATE VARIABILITY IMPLICATIONS FOR MAIZE YIELD FOOD SECURITY AND RURAL POVERTY IN TANZANIA

By Prudence Y. Lugendo*, Tadesse Tsegaye, Charles Wortmann and Christopher M.U. Neale

Contents Abstracts ......................................................................................................................................... 3 1.0.

Introduction .......................................................................................................................... 4

1.1.

Background information .................................................................................................. 4

1.2.

Climatic conditions for growing maize ...........................Error! Bookmark not defined.

1.3.

Objectives of the paper ..................................................................................................... 4

2.0.

Literature review .................................................................................................................. 4

2.1.

Climate change in Tanzania ............................................................................................. 4

2.2.

Effect of climate change on maize production ................................................................. 5

2.3.

Implications of climate change on food security and rural poverty in Tanzania ............. 7

2.4.

Review of different approaches used to study climate change impact on crop yield ...... 7

3.0.

Methodology ........................................................................................................................ 8

3.1.

Data source ....................................................................................................................... 8

3.2.

Selected study areas ......................................................................................................... 9

3.3.

Trend analysis of maize yield and rainfall of the study areas ........................................ 10

3.4.

Correlation analysis for yield and rainfall for the study areas ....................................... 10

i.

Simple correlation analysis ............................................................................................ 11

ii.

Cointegration analysis .................................................................................................... 11

4.0.

Results and discussions ...................................................................................................... 12

4.1.

Correlation results between TMA data and CHIRPS..................................................... 12

4.2.

Rainfall trend for the selected stations ........................................................................... 12

4.2.1.

Monthly average rainfall ......................................................................................... 12

4.2.2.

Rainfall trend of highest rainfall months of selected station .................................. 13

4.3.

Maize yield trend for the selected study areas ............................................................... 13

4.4.

Correlation between yield and rainfall ........................................................................... 14

4.5.

Cointegration results ...................................................................................................... 15

4.5.1.

Cointegration rank between maize yield and months rainfall ................................ 15

4.5.2.

Long – run relationships between maize yield and months rainfall ....................... 16

4.5.3.

Short run relationship between maize yield and months rainfall ............................ 17

4.6.

ARIMA results for selecting forecasting model ............................................................ 18

4.6.1.

Augmented Dickey Fuller (ADF) – Test for stationarity results ............................ 18

4.6.2.

ARIMA models results ........................................................................................... 18

5.0.

Policy recommendation ..................................................................................................... 19

6.0.

Conclusion ......................................................................................................................... 20

7.0.

Reference ........................................................................................................................... 20

8.0.

Appendices ..........................................................................Error! Bookmark not defined.

Appendix 1: Correlation scatter plots for TMA data and CHIRPS Satellite rainfall data . Error! Bookmark not defined. Appendix 2: Monthly average rainfall for the selected study areas ......... Error! Bookmark not defined. Appendix 3: Plots of rainfall trends for the months with highest average rainfall ............ Error! Bookmark not defined. Appendix 4: Plots of Trend of maize yield in selected study areas ......... Error! Bookmark not defined.

Abstract Tanzania’s economy is heavily dependent on rainfed agriculture thus, climate variability has a strong impact on crop production. This study aims to assess the effects of climate variability on maize yield in Tanzania. Regression, co integration and ARIMA models were applied. The regression results showed that, April monthly rainfall for Arusha was significant (0.0027). There are significant positive long run relationships between maize yield and rainfall for Arusha, Dodoma, Songea, Tabora and Musoma districts. In the case of short run monthly rainfall for Arusha, Dodoma, Songea and Tabora were significantly positive. The fitted ARIMA (p,d,q) model for forecasting monthly rainfall at selected weather stations were computed. The study identified that there is positive relationship between yield and rainfall however; further studies need to be conducted so that we can have broader understanding of the problem for policy action.

ACKNOWLEDGEMENTS Mr. Prudence Lugendo would like to express his sincere gratitude to the USDA, University of Nebraska Lincoln (UNL) and other groups which have contributed to the success of his work. The great appreciation is extended to Prof. Tadesse Tsegaye, Prof. Charles Wortmann, Prof. Christopher M.U. Neale, Dr. Liana Calegare Ms. Sarah Hansen, Dr. Yared Ashenafi Bayissa and Dr. Getachew Demisse. The work could not have been carried out without the support provided by the people mentioned. Mr. Prudence Lugendo would, therefore, like to gratefully acknowledge the financial contribution of the USDA FAS Borlaug Fellowship Program for East and West Africa which enabled him to carry out the study, as well as to write the report.

1.0.

Introduction

1.1. Background information The agriculture sector is still the backbone of Tanzania economy (URT, 2014). It has the potential of reducing poverty. In Tanzania, agricultural production is rainfall dependent. The diverse, largely subsistence oriented, risk-prone smallholder farmers produce low crop yields due to low capacity to adopt better production practices. The recent national economic gains have not been of much benefit to the livelihood and food security of the rural poor (URT 2010 and THDR 2014). Recent studies have showed that the average annual temperatures are expected to rise by 1ºC by 2050 in Tanzania with inconsistent changes in rainfall patterns and increased climate variability such as more variable onset of the rainy season and increased severity of droughts and floods (URT 2014; Global Climate Adaptation Partnership 2011).This implies that agriculture can be negatively affected by increased climate variability with implications for lower economic growth and poverty reduction initiatives in Tanzania. Amplified pressure on water and other natural resources, together with resource degradation and increased competing uses, and reduced agricultural potential could contribute to increased strain on the livelihoods and food security, especially for rural poor.

1.2. Objectives of the paper The overarching goal of this study is to assess the impact of climate viability and trend on maize production in Tanzania. To achieve this goal, the specific objectives of the study include: i.

Analyzing the trend of maize yield and rainfall in Tanzania and then fitting the trends using Autoregressive Integrated Moving Average (ARIMA) model for time series data (rainfall)for selected districts in Tanzania.

ii.

Identifying the relationship (correlation) between maize yield and rainfall,

iii.

Provide policy recommendation based on the results.

2.0.

Literature review

2.1. Climate change in Tanzania Climate change is one of the current challenges in most of African countries. This is because most of African countries depend on weather for their agriculture activities. Climate change possesses its worst impact through interference with food security to the growing population and poverty reduction initiatives (Shemsanga et al. 2010).

Different types global climate change trends are greatly reflected in Tanzania’s climate (Shemsanga et al. 2010). Geographical location and the topographical characteristics of Tanzania offer the best opportunity to study and further understand global climate trends (Mwingira et al. 2011; Shemsanga et al. 2010). Empirical studies have suggested that, alongside other East African countries, climate variability has badly affected the country. Deteriorating water quality and quantity, loss of biodiversity and declining agricultural productivity due to climate variability, are no longer potential threats but rather threats that have already struck and caused Tanzanians repeated misery (Yanda et al. 2005; Shemsanga et al. 2010). Empirical studies show that, in Tanzania mean annual temperatures and average daily temperatures will rise from 20 C to 40C by 2075 as a direct consequence of climate change (URT 2003). Putting Tanzania into a wider African context, however, it is projected to warm up less than many countries notably north-western and southern Africa (URT 2007). Interestingly, the interior parts of the country are projected to face higher temperature increases than coastal areas whilst cold and dry seasons will warm more than warm and wet seasons (Mwandosya, et al. 1998). Current prediction studies show that precipitation in eastern Africa (including Tanzania) shows a high degree of temporal and spatial variability dominated by a variety of physical processes (Rosell and Holmer 2007; Hession and Moore 2011). Williams and Funk (2011) and Funk et al. (2008) indicate that over the last 3 decades’ rainfall has decreased across eastern Africa between March and May/June. Also, the prediction models show that,there is low confidence in projected increases of heavy precipitation over most of East Africa area (IPCC 2014). Such major changes in rainfall patterns will inevitably have severe consequences to the livelihood of the people. 2.2. Climatic conditions for growing maize Maize is grown in climates ranging from temperate to tropic during the period when mean daily temperatures are above 15°C and frost-free (FAO 2015). Maize varieties differ one climatic condition to another and much yield depend on the right choice of varieties (FAO 2015; Toshichika and Navin 2015; Porter and Semenov 2005). For tropical countries like Tanzania where mean daily temperatures during the growing season are greater than 20°C, early grain varieties take 80 to 110 days and medium varieties 110 to 140 days to mature. For some places where mean, daily temperatures are below 20°C, there is an extension in days to maturity of 10 to 20 days for each 0.5°C decrease depending on variety. The crop is very tolerant to hot and dry atmospheric conditions so long as sufficient water is available to the plant and temperatures are below 45°C (FAO 2015). The growth of maize is very responsive to radiation. Five or six leaves near and above the cob are the source of assimilation for grain filling and light must penetrate to these leaves (FAO 2015; Mansfield and Mumm 2013). The plant does well on most soils but less so on very heavy dense clay and very sandy soils (FAO 2015). The fertility demands for grain maize are relatively high and amount, for high-producing varieties, up to about 200 kg/ha N, 50 to 80 kg/ha P and 60 to 100 kg/ha K (FAO, 2015). In general, the crop can be grown continuously if soil fertility is maintained. Maize is moderately sensitive to salinity. Yield decrease under increasing soil salinity (FAO 2015).

2.3. Effect of climate change on maize production The most direct impacts of global climate change on human societies may beits potential consequences on global crop production (Berg et al. 2011; Hansen 2002). Agriculture is one of the most important human activitiesthat depends heavily on weather (Hansen 2002). Apart from average weather conditions,important factors in determining mean crop productivity levels include soil fertility and human management (Berg et al. 2011; Lobell and Field 2007).The climate change, as projected by the IPCC’s Fifth Assessment Report (2014), has continue to be the potential threats on global crops productivity. This is a challenge to the global food system, which is already facing numerous challenges like increase in population, economic development and possibly, increasing reliance on biofuels.Furthermore, it needs to do so with minimum environmental costs (Berg et al. 2011). This is particularly true for developing countries like Tanzania, where most people depend on agriculture for livelihood, the population is increasing and economic is expected to grow rapidly. For example, Collomb (1999) estimatethat by 2050 food production will need to more than five times in Africa, more than double in Asia, and nearly double in Latin America. It is doubtfully to expect this increase in production to be attained by simply expanding area under cultivation (Griffon 2006). In Sub – Saharan Africa for instance, expanding croplands to their maximum potential area while keeping crop yields constant would be insufficient to meet the projected increase in demand, while in effect resulting in complete deforestation (Berg et al. 2011; Phalan et al. 2013). Increasing yields of crops like maize (which is the staple food in Tanzania) is thus a necessary strategy, and in this context, assessing the impacts climate change may have on maize productivity is of crucial importance. There have been numerous studies on the impact of climate change on maize yields in Sub Saharan African countries, including Tanzania. According to Kindie et al. (2015) climate change will affect maize yields across SSA in 2050 and 2080, and the extent of the impact at a given period will vary considerably between input levels, regions and Maize Mega Environments (MMEs). Greater relative yield reductions may occur under medium and high-input intensification than under low intensification, in Western and Southern Africa than in Eastern and Central Africa and in lowland and dry mid-altitude than in highland and wet mid-altitude MMEs. This situation may worsen food insecurity in SSA in 2050 (Kindie et al. 2015). Review of projected climate change scenarios for Africa’s maize growing regions using the outputs of 19 global climate models study which was done by Jill et al. (2013)indicate that, by 2050, air temperatures are expected to increase throughout maize mega- environments within sub-Saharan Africa by an average of 2.1°C. Rainfall changes during the maize growing season varied with location. Given the time lag between the development of improved cultivars until the seed is in the hands of farmers and adoption of new management practices, there is an urgent need to prioritize research strategies on climate change resilient germplasm development to offset the predicted yield declines (Jill et al. 2013). Overall, empirical evidences have shown that, maize production will decrease under future climate scenarios, though the degree of impact differs among simulations (Jones and Thornton 2003; Thornton et al. 2009; Thornton et al. 2010; Nelson et al. 2009; Fischer 2009; Lobell et al. 2011; Easterling et al. 2007; Lobell and Field 2007; Muchow et al. 1990). Despite large discrepancies in projected impact on maize yield, there is a general consent that climate change

will adversely affect maize yield in East Africa. Multiple studies indicated that East Africa could lose as much as 40% of its maize production (Adhikari et al. 2015).

2.4. Implications of climate change on food security and rural poverty in Tanzania As we have already known climate change have adverse impact on crop yields. This implies that, it has negative implications on food security status and it may also compromise the effort in reducing poverty especially rural poverty where majority depend on agriculture as their source of income. Many studies have addressed these issues.For example, Paavola (2008) indicated that, in Morogoro region most agricultural households have extended cultivation, intensified agriculture, diversified livelihoods and migrated to gain access to land, markets and employment as a response to climatic and other stressors. Some of these responses have depleted and degraded natural resources such as forest, soil and water resources, which will adverse effect their livelihoods with climate variability in the future. Climate change is projected also to affect all four dimensions of food security in Tanzania: food availability, food accessibility, food utilization and food systems stability. It will have an impact on human health, livelihood assets, food production and distribution channels, as well as changing purchasing power and market flows. Its impacts will be both short term, resulting from more frequent and more intense extreme weather events, and long term, caused by changing temperatures and precipitation patterns (FAO 2008; FAO 2016; Arndt et al. 2012; Kempe 2009).It is important for the governments to put in place institutional and macro-economic conditions that support and facilitate adaptation and resilience to climate change at local, national and transnational level (Challinor et al. 2007). In realizing this truth, the government of United Republic Tanzania (URT) has done several initiatives, this include; developing of Tanzania Climate Smart Agriculture Programme for 2015 – 2025 which is coordinated by the ministry of Agriculture, Livestock and fisheries; developing of Agriculture Climate Resilience Plan for 2014 – 2019 which is implemented by the ministry of agriculture and National Climate Change Strategy of 2013 which is wide strategy for all sector including agriculture and the strategy is implemented under the vice president office. Despite all these efforts by government, implementation pace is very slow. This problem is attributed by the poor financing of those plans and programmes, lack of human resources in implementing those plans/programmes and political will to take serious consideration on climate issues. 2.5. Review of different approaches used to study climate change impact on crop yield There have been numerous studies on the impact of climate change on crop yields; these includeregional and global climate models’ projections, crop simulation models and statistical models.(Yinhong et al. 2009). The Global Climate Models (GCMs) and Regional Climate Models (RCMs) are complicated three-dimensional mathematical representations that show the processes of interactions between the atmospheres, land surface, oceans and sea ice which resulted from climate (Mearns 2009). This type of model climate projections is considered as one of efficient methods to figure out the possible futures under given emission scenarios rather than a forecasting tool (Suppiah et al. 2007). GCMs are useful tools to simulate the important aspects of current and future climates although they still have significant errors (IPCC 2007b). The

GCMs with higher spatial resolutions can perform reasonable regional climate simulations, consequently, they pro-vide climate scientists with the ability to acquire better insights into climate change impacts on a regional scale and estimate the impacts of the climate change on crop production (Mearns 2009). Blenkinsop and Fowler (2007) suggested that RCMs have some trouble when they are used to reproduce the observed duration of low sensitivity monthly rainfall amounts.GCMs have been used to predict climate scenarios and impacts in many cases using the downscaling approach (Thomas 2008). GCM data typically have a low resolution of several degrees, lack the spatial and temporal precision necessary for detailed regional analysis and in the many cases have errors to simulate even the present-day climate (Bonan et al. 2002). GCMs have uncertainties in predicting future climate data, while they can provide a reasonable accuracy about large-scale features and other variations due to climate forcing (Salinger et al. 2000). Crop simulation models have also been used to study the likely impacts of climate change on crop yield (Kazeem and Rasaq 2015).In predicting future impacts on crop yields, crop models present valuable approaches (Yinhong et al., 2009). Several crop simulation models, such as CERES-Maize (Crop Environment Resource Synthesis), CERES-Wheat, SWAP (soil–water– atmosphere–plant), and InFoCrop, have been widely used to evaluate the possible impacts of climate variability on crop production, especially to analyze crop yield-climate sensitivity under different climate scenarios (Aggarwal et al. 2006). According to Kazeem and Rasaq (2015), crop simulation models can be used to understand the effects of climate change such as elevated carbon-dioxide, changes in temperature and rainfall on crop development, growth and yield. However, crop models are not able to give accurate projections because of inadequate understanding of natural processes and computer power limitation (Kazeem and Rasaq 2015). Thus, the assessments of possible effects of climate changes are based on estimations. Moreover, most models are not able to provide reliable projections of changes in climate variability on local scale, or in frequency of exceptional events such as storms and droughts (de Wit et al. 1970). Furthermore, biological and agricultural models are reflections of systems for which the behavior of some components is not fully understood and differences between model output and real systems cannot be fully accounted for. Crop models are therefore not able to give accurate projections because of inadequate understanding of natural processes and computer power limitation. Again, methodology of model validation is still rudimentary. The main reason is that, unlike the case of disciplinary or traditional experiments, a large set of hypotheses is being tested simultaneously in a model (Murthy 2002). Econometric and statistical approaches have also been used in studying the impact of climate change on crop yield (Saravanakumar 2015; Jönsson 2011; Blanc 2012).However, the application of econometric and statistical model is limited because the important related factor, the atmospheric carbon dioxide (CO2) concentration, and in fact a driver of climate change is ignored. This means the prior econometric estimates are biased as they infer what will happen under climate change from observations in the recent past, but without consideration of CO2 effects. Furthermore, although CO2 has been varying, it has proceeded at a very linear pace and cannot be disentangled from technological progress using historical crop yield data. (Attavanich and McCarl 2011). 3.0.

Methodology

3.1. Data source Rainfall data from 41 weather stations werecollected from Tanzania Meteorological Agency (TMA). The TMA data collected range from 1996 to 2013. The dataset was having many missing values and the 1996 – 2013 time was not sufficient for designing good predictive model. Therefore, we use satellite-derived data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). The CHIRPS data has 5kmspatial resolution (Funk et al. 2015). We used the location (Latitude and Longitude) of twenty-two stations. Those points for stations were imported in the ArcGIS software. The monthly rainfall data for those stations from 1981 – 2015 were extracted per the pixel of the stations in the CHIRPS data layers. The extraction of data uses the sampling techniques for spatial analysis in ArcGIS. Then, theextracted satellite-derived monthly rainfall data were compared with TMA data by running the regression analysis between the TMA and CHIRPS data for making comparison. The maize yield data were collected from the Ministry of Agriculture, Livestock and Fisheries. The ministry collects those data though the Monitoring, Evaluation and statistics (MES) unit under the Department of the policy and planning. The data are collected up to the district level. The Monitoring and Evaluation (M&E) officers in the Local Government Authorities (LGA) compile the yield data and then send them directly to the ministry. Ministry of Agriculture, Livestock and Fisheries is the one with the authority and mandate of publishing those data. 3.2. Selected study areas The study areas were selected based on agro-ecological zones per definition of the MALF and data availability. In Each agro-ecological zone at least one station was selected. Selection also considers the availability of time series data of rainfall and maize yield. The selected study areas were Arusha, Dodoma, Morogoro, Mtwara, Tabora, Songea, Mbeya and Musoma. Table 1: Summary of climatic conditions of selected study areas Location

Arusha Dodoma Morogoro Mtwara Songea

Agro ecological Zone

Elevation from the sea level (m) 1361- 1400 500 - 2000 400 – 2400 0 – 84 800 – 1300

Latitude

Northern zone -3.365803 Central zone -6.352208 Coastal zone -6.450001 Southern zone -10.666664 Southern -10.676803 Highlands Mbeya Southern 1500 - 2400 -8.8996843 Highlands Tabora Western zone 900 - 1300 -5.042495 Musoma Lake zone 800 - 1148 -1.500001 Source: TMA data, MALF and Regional Social Economic Profiles

Longitude

36.674402 35.700071 38.154007 39.000011 35.655785

Highest recorded Rainfall month April February April January December

Average annual rainfall (mm) 1143.291 601.336 742.574 1055.682 1108.680

33.450556

January

1091.582

32.819733 33.800003

March April

934.627 804.912

Figure 1: Map of Tanzania showing the selected study area (districts)

3.3. Trend analysis of maize yield and rainfall of the study areas Trend graphsof the historical rainfall and maize yield data for selected zones in Tanzania were produced.The trend analyses for maize yield show that there were positive trends in all the zones. This positive trend could be due to improvement in technology and farming practices. Therefore, to avoid the impact of human intervention, the historical data for maize yield were detrended before running correlation with rainfall. ARIMAmethod was used to fit the model for predicting rainfall. It is expressed as ARIMA (p,d,q) which means that; p = the number of autoregressive terms; d = the number of no seasonal differences and q = the number of moving-average terms.It is a statistical approach, that was first presented by Box and Jenkins (1976), for model building and forecasting time series data.This modeling approach has been used by various researchers to forecast weather parameters like temperature and rainfall. Thus, the ARIOMA method is a powerful approach to the solution of many forecasting problems (Johnson and Montgomery, 1976) that can provide accurate forecasts of time series and offers a formal structured approach to model building and analysis (Chowdhury and Biswas 2016). In fitting ARIMA (p,d,q) model, the first step is to determine whether the time series is stationary or non-stationary.Augmented Dickey–Fuller (ADF) test and Phillips Perron Unit test are used to test for stationarity of data. If it is non-stationary, it is transformed into a stationary time series by applying suitable degree of differencing by selecting proper value of d. The

appropriate values of p and q are chosen by examining the autocorrelation function (ACF) and partial autocorrelation function (PACF) of the time series.

3.4.

Correlation analysis for yield and rainfall for the study areas

i.

Multiple Regression Analysis Multiple Linear Regression analysis was used to measure the strength and direction of the linear relationship between monthly rainfall (mm) and yield in Ton/Ha. Linear regression is an approach for modeling the relationship between a scalar dependent variable and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. This kind of analysis has been used in various studies to assess the relationship between rainfall and crops yields (Peprah 2012; Poudel and Shaw 2015).

ii.

Cointegration analysis Econometric approach of cointegration analysis was employed to check how rainfall influence yield in short- and long-term. This approach has been used in various studies on assessing the impact of climate change on crop yields (Blanc 2012; Amikuzuno and Donkoh 2012; Abu 2015 and Mushtaq and Dawson 2003). Most of time series are non-stationary and in general OLS regressions between nonstationary data arespurious (Mushtaq and Dawson 2003). The presence of unit roots in the autoregressive representation of a time series leads to no stationarity and such series must be first-differenced to render them stationary or integrated. Where integrated series move together and their linear combination is stationary, the series are cointegrated and the problem of spurious regression does not arise (Mushtaq and Dawson 2003). Cointegration implies the existence of a meaningful long-run equilibrium between time series variables (Granger 1988). Since a cointegrating relationship cannot exist between two variables which are integrated of a different order, in cointegration analysis we first test for the order of integration of the variables. In doing so Augmented Dickey Fuller (ADF) test and Phillips Perron Unit test are used to test for non-stationarity of the data. The lags in the ADF-equation is selected to safeguard that serial correlation is absent using the Breusch-Godfrey statistic (Greene 2000). After running the ADF test or Phillips Perron Test and Breusch – Godfrey statistic then cointegration ranking is performed using Cointegration rank analysis of Vector Error Correction Model (VECM). This analysis was done so that, we can be sure if there co movement between rainfall of place and maize yield of the same place. Lastly, the VECM regression analysis is performed to see if that movement is for short run or long run.

4.0.

Results and discussions

4.1. Correlation results between TMA data and CHIRPS The correlation between the gauged TMA monthly rainfall data and CHIRPS monthly rainfall data was done so that to verify use of CHIRPS dataset instead of TMA dataset which was having many missing values and few number of years which was not enough for running model like ARIMA. Simple regression analysis was used to verify that. The values of R and R square indicates whether the CHIRPS data is good or bad representative of the TMA data. If the correlation is high, it means CHIRPS dataset is a good representation for TMA data set and therefore we can use CHIRPS dataset in the study analysis. As shown in table 2 below there is strong correlation between the TMA data and CHIRPS data. The value of R for all stations range from 0.83 to 0.95 while for R square range from 0.7 to 0.9 which is very strong. The same results were obtained in the study which was done by Moctar and Sander (2016) which show that, the correlation coefficient between the Gauged station and CHIRPS data was 0.96. Not only that, but also other study which show the same results include Hessels (2015) on his thesis which tried to compare the Gauged data with other Open Access Remotely Sensed Rainfall Products for the Nile Basin. In his thesis, the value of correlation coefficient was =0.92. Therefore, results of comparison between TMA dataset and CHIRPS indicate that CHIRPS dataset can be used for the selected study area for further analysis of relationship between yield and rainfall. Table 2: Correlationbetween TMA gauged data and CHIRPS monthly rainfall data Location Arusha Dodoma Morogoro R 0.858 0.876 0.867 R - Square 0.737 0.768 0.752 Intercept 7.414 3.657 -3.049 Slope 0.581* 0.897* 1.135* Note: * Significant at 95% level of confidence

4.2.

Mtwara 0.856 0.734 0.952 0.949*

Songea 0.951 0.905 2.542 0.924*

Musoma 0.875 0.767 0.784 1.136*

Mbeya 0.898 0.804 -2.233 0.847*

Tabora 0.911 0.831 0.509 0.925*

Rainfall trend for the selected stations

4.2.1. Monthly average rainfall Table 3 below show some key results on monthly average rainfall for selected stations. The months which is having maximum monthly rainfall reflect the month in farming season of the specific area in which the rainfall is highest. This monthly rainfall intensity might have significant impact on maize yield. Therefore,these results will be used in determining which monthly rainfall might have stronger correlation with maize yield and in determining which monthly rainfall data will be used in fitting ARIMA for forecasting rainfall and predicting crop yield in future.

The variation of average maximum and minimum rainfall of all selected stations is very high as standard deviations range from 47 – 109. Musoma, Mtwara Morogoro and Arusha are stations which receive much annual rainfall compared to other stations as their coefficient of variations (CVs) are lower compared to others. Dodoma is the station which receive less average annual rainfall compared to other stations as her CV is very high.

Table 3. Summary of monthly average rainfall of study areas Location Arusha Dodoma Morogoro Mtwara Songea Mbeya Tabora Musoma

Minimum (mm) 9.91(Sept) 0.01(July) 10.28(Aug) 9.46(Aug) 0.71(June) 1.14(Aug) 0.38(July) 3.54(July)

Maximum (mm) 330.93(April) 151.17(Dec) 162.41(April) 208.32(March) 288.06(Jan) 231.51(Dec) 203.38(Dec) 146.51(April)

CV 0.998 1.243 0.769 0.897 1.188 1.037 0.956 0.767

Std. Deviation 95.155 62.276 47.616 78.961 109.807 94.343 80.616 47.694

4.2.2. Rainfall trend of highest rainfall months of selected station Table 4 show the summary of the trend analysis for months which recorded highest average rainfall. The slopes for Arusha, Morogoro, Songea and Musoma districts are negative which implies that, rainfall for the month which receive highest rainfall in the year for those districts have been decreasing over years. The slopes for Dodoma, Mtwara, Mbeya and Tabora districts have positive slopes which implies that, the rainfall for the month which received highest rainfall in those areas have been increasing over years. The variation of rainfall from mean rainfall for the months which receive highest rainfall in selected station is very high as the standard deviation range from 44 – 117. Table 4. Summary of the Rainfall trend in selected study areas Location Arusha Dodoma Morogoro Mtwara Songea Mbeya Tabora Musoma

Mean 330.932 151.174 162.414 208.320 288.061 231.508 203.387 146.507

Standard Deviation 116.872 86.037 46.048 73.371 71.054 81.096 78.560 44.191

Sign of trend slope -0.6709 0.3440 -1.1080 1.4683 -0.1918 1.5122 0.7329 -0.2451

4.3. Maize yield trend for the selected study areas Generally, slope of most of the selected districts for Maize yield is positive implying that maize yields from selected districts have been increasing over years. However, the trend for Mtwara is negative indicating that the maize yield in Mtwara district have been decreasing over years. The increase in maize yield is attributed by other factors apart from rainfall. This include government initiatives in agriculture sectorsuch as Agriculture Sector Development Programme on (ASDP I) which was implemented by the Agriculture Sector Lead Ministries (ASLMs), kilimo Kwanza initiatives which is implemented through Southern Agricultural Growth Corridor (SAGCOT)

Initiatives and Comprehensive African Agriculture Development Programme (CAADP) which is implemented through Tanzania Agriculture and Nutrition Investment Plan (TAFSIP). These programmes and initiatives enabled smallholder’s farmers to get loans for hiring/purchasing farming machines and implements which results into increase in productivity. Therefore, yield data for the selected districts were detrended to remove the effect of trend over time before running correlation and econometric model for yield against rainfall. The variation of mean yield of maizeover time was very high as the standard deviation range from 0.4 to 2.9. Highest average yield over time was observed in Songea, Mbeya, Musoma and Morogoro. which is 2.2 Tons/ha and the last one was Dodoma and Mtwara record show they have lower maize yield. These resultsare well supported by previous studies on maize crops in Tanzania. The studies indicate that, the highest producing zone for maize in Tanzania is in Southern Highlands which Songea and Mbeya are found (Croon et al. 1984; Lyimo et al. 2014 and USAID 2010). The trend slopes of all selected districts are positive expect for Mtwara which is negative. This implies that, in all selected districts the trend of maize yield is increasing over years expect for Mtwara which is decreasing. Table 5. Summary of Trend analysis of maize yield in the study areas Location Arusha Dodoma Morogoro Mtwara Songea Mbeya Tabora Musoma

Mean 1.354 0.871 1.608 0.875 1.855 1.952 1.146 2.271

Standard Deviation 0.721 0.646 0.684 0.743 0.575 0.788 0.415 1.260

Sign of slope + + + + + + +

4.4. Correlation between yield and rainfall Table 6 below shown the results of regression analysis between maize yield and monthly rainfall in selected districts. In the analysis, we considered only those months which are the farming season because we assume the intensity of rainfall during that period has significant effects on crop yield. Most of coefficients are not statistical significant expect for coefficient of April rainfall in Arusha.The coefficients are not very strong because of the following reasons: first rainfall is not the only constraint to maize yield, there are many other factors which need to be considered in the future studies. These factors are like temperature, excessive rainfall, farming management practices seed varieties and soil nutrients.Therefore, to get the real strength of correlation between rainfall and maize yield simple regression analysis might not be informative enough. Secondly, the comparison was made between the station point data for rainfall and average maize yield for the whole district which the station is found. This affect the strength of correlation coefficient because within the same district the intensity of rainfall may vary from one point to another point. Table 6. Multiple linear regression analysis for maize yield against monthly rainfall Location Arusha

Months March April May

Coefficient -0.0014 0.0027* -0.0015

Std. Error 0.0027 0.0013 0.0028

t - statistics -0.52 2.11 -0.55

P>|t| 0.610 0.050 0.588

Y-Intercept December January February Y-Intercept Morogoro March April May Y-Intercept Mtwara January February March April Y-Intercept Songea January February March Y-Intercept Mbeya December January February March Y-Intercept Tabora January February March April Y-Intercept Musoma March April May Intercept Note: *significant at 95% Dodoma

4.5.

-0.4117 0.00234 0.00339 -0.00350 -0.41088 -0.0069 0.0057 -0.0066 0.3168 -0.0028 0.0014 -0.0009 -0.0042 1.0118 -0.0019 0.0139 0.0098 -4.4661 0.0009 0.0004 -0.0023 0.0009 -0.0867 -0.00132 0.00116 -0.00099 -0.00181 0.48223 -0.00162 -0.00413 -0.00055 0.84183

0.7596 0.0018 0.0035 0.0038 0.6703 0.0053 0.0038 0.0066 0.7701 0.0019 0.0024 0.0033 0.0030 1.2161 0.010 0.012 0.013 4.663 0.0031 0.0051 0.0061 0.0043 1.5242 0.00206 0.00279 0.00221 0.00192 0.76978 0.00635 0.00602 0.01008 1.54382

-0.54 1.31 0.97 -0.93 -0.61 -1.31 1.50 -1.00 0.41 -1.490 0.590 -0.280 -1.420 0.830 -0.180 1.200 0.730 -0.960 0.300 0.070 -0.370 0.200 -0.060 -0.64 0.42 -0.45 -0.94 0.63 -0.250 -0.690 -0.050 0.550

0.595 0.207 0.348 0.365 0.548 0.210 0.153 0.333 0.686 0.157 0.566 0.781 0.177 0.418 0.858 0.248 0.474 0.352 0.767 0.945 0.714 0.843 0.955 0.532 0.683 0.661 0.362 0.540 0.802 0.503 0.957 0.593

Cointegration results

4.5.1. Cointegration rank between maize yield and months rainfall Cointegration ranking analysis was the first step in the analysis of Vector Error Correction Model (VECM). The purpose of the ranking is to identify if there is relationship/co – movement between maize yield and farming season monthly rainfall of selected stations. The number of test hypothesis depend on the number of variables of interest which are moving together. The rule of thumb is if the Trace statistics is greater than critical value we reject the null hypothesis. Thus, based on that, the results on table 7 indicate that, in all districts there is at least one co – movement between maize yield and monthly rainfall because all the trace static for 0 which means there is no co – movement are greater that critical values at 95% and 99% level of significance. In Arusha, there is 2 co – movement between maize yield and rainfall months. In Dodoma, there is one co- movement, in Morogoro there is 2 co – movement, in Mtwara there is one co – movement, in Songea there is 3 co – movement, in Mbeya there is 2 co – movement, in Tabora there is 2 co- movement and in Musoma there is one co – movement. This ranking indicates that there are relationships between maize yield and rainfall months which are either long run relationships or short run relationships. To identify that, then VECM model was analyzed to check if the relationships between rainfall and yield if for long run or short run.

Table 7. Cointegration rank of Vector Error Correction Model (VECM) Location Arusha

Cointegration rank Trace Statistic 0 169.0474 2 13.2926** Dodoma 0 142.6739 1 25.6489** Morogoro 0 155.0086 2 9.3375** Mtwara 0 125.2490 1 44.1248** Songea 0 111.1368 3 4.2839* Mbeya 0 120.8187 2 25.3777** Tabora 0 110.4375 1 53.9416** 2 22.8492*** Musoma 0 72.9796 1 17.8405* Note: ** significant at both 5% and 1%; *Significant at either 5% or 1%

1%Critical value 54.46 20.04 54.46 35.65 54.46 20.04 76.07 54.46 35.65 6.65 76.07 35.65 76.07 54.46 35.65 54.46 20.04

5%Critical value 47.21 15.41 47.21 29.68 47.21 15.41 68.52 47.21 29.68 3.76 68.52 29.68 68.52 47.21 29.68 47.21 15.41

4.5.2. Long – run relationships between maize yield and months rainfall After identifying the existence of a co integrating relationship between the maize yield and rainfall months as shown in table 7 in the Johansen co integration test, a VECM was estimated. The results in table 8 below shows the long-term relationship between maize yield and rainfall months. Maize yield in Arusha has long-term relationship with rainfall. This is because the Coefficient of Error (CE) correction term which shows the speed of adjustment towards equilibrium is negative and significant at 90% level of confidence. The long-term relationship means that as rainfall continue to change either by increasing or decreasing over years even farmers also increase or decrease their investment effort towards farming maize. For example, if in this year there was a rainfall shortage and farmers get loss for what they were farming in the coming year some of them will not put much effort in investing in farming and if it repeats again farmers will continue to reduce their efforts in farming because there is no incentive in farming and thus decrease in yield over years. This trend stops if there is the introduction of new technology like irrigation which solve the problem rainfall shortage. For Dodoma, the CE is negative and significant at both 90% and 95% level of confidence. This implies that there is long-term relationship between rainfall and maize yield in Dodoma district. In Songea, Musoma and Tabora the values of CE are negative and significant which means there are also long run relationship between rainfall and maize yields in the respectively districts. The value for CE in Mbeya, Mtwara and Morogoro were not significant. Similar results were found in various previous studies. Ayindea et al. (2011) use the same approach in analyzing the effect of climate change on agriculture productivity in Nigeria and found that climate parametershave significant effect on agricultural productivity in the long-term. Abu (2015) use the same approach to analyses the relationship of rainfall, sorghum yield and producer prices of sorghum in Nigeria and found that, there is long run relationship between producer’s prices and rainfall. The study

which used the same approach in Tunisia found that, the short-run climate effects on crop yield are smaller than the long-run effects (Zaied and Cheikh 2014).

Table 8. Long run relationship between Yield and rainfall Location CE Arusha -0.6855905* Dodoma -0.5451023** Morogoro 0.0570269 Mtwara 0.0224723 Songea -1.267571** Mbeya -0.0006321 Tabora -0.0479612** Musoma -1.360284** Note: * Significant at 90% and ** significant at 95%

Z -1.71 -2.50 1.26 0.28 -3.49 -0.19 -2.42 -2.26

P>|z| 0.087 0.012 0.247 0.779 0.000 0.846 0.016 0.024

4.5.3. Short run relationship between maize yield and months rainfall Short run effect or relationship is the immediately effect of shortage of rainfall on yield. For example, if there is shortage in rainfall in this year we are expecting to have shortage in maize yield. Table 9 shows the results of short run relationships between maize yield and rainfall. In Arusha district, almost all rainfall months in the farming season were having significant impact on yield in the short run. In Dodoma, rural district January and February rainfall were having significant short run effects. The same results were found in Songea. In Tabora rainfall in February, March and April were having significant effect in short run on maize yield. The short run effect affect yield in the same year. This means that, if there is increase or decrease of rainfall in the specific year, it affects directly the maize yield for that year. Table 9. Short run relationship between Yield and Rainfall Location Arusha

Dodoma

Morogoro

Mtwara

Songea

Mbeya

Tabora

Rainfall Month March April May December January February March April May January February March April January February March December January February March January

Chi-Square 5.42 8.57 15.30 2.73 9.14 11.38 2.84 3.33 5.88 0.97 3.91 2.28 4.48 4.64 4.68 4.96 0.18 1.16 2.72 3.23 0.56

Prob. Chi square 0.0666* 0.0728* 0.0180** 0.2560 0.0501** 0.0773* 0.2412 0.5048 0.4368 0.3244 0.1418 0.8920 0.8115 0.0312** 0.0963* 0.1749 0.6727 0.5593 0.4367 0.5207 0.4561

February 9.44 March 12.18 April 12.49 Musoma March 0.86 April 1.18 May 2.94 Note: * Significant at 90%; ** Significant at 90% and 95% and ***Significant at 90%, 95% and 99%.

4.6.

0.0089*** 0.0068*** 0.0140** 0.3532 0.5547 0.4009

ARIMA results for selecting forecasting model

4.6.1. Augmented Dickey Fuller (ADF) – Test for stationarity results Table 10 present results on Augmented Dickey Fuller (ADF) test which take into consideration the problem of autocorrelation. The aim of running the test was to check if the time series rainfall data sets are stationary or non-stationary before fitting the ARIMA model. This is because ARIMA model fit forecasting model for time series data which are stationary. The results show that the rainfall data are stationary as the test statistics for rainfall data for all selected stations are significant. Table 10. ADF test for Unit Root for rainfall time series data Location Arusha

Test statistic 1% Critical value -13.300*** -3.446 -13.288*** -3.983 -9.102*** -2.580 Dodoma -10.344*** -3.446 -10.329*** -3.983 -7.979*** -2.58 Morogoro -11.981*** -3.446 -12.000*** -3.983 -7.276*** -2.58 Mtwara -11.321*** -3.446 -11.307*** -3.983 -7.668*** -2.58 Songea -9.002*** -3.446 -8.992*** -3.983 -6.784*** -2.58 Mbeya -8.812*** -3.446 -8.798*** -3.983 -6.137*** -2.58 Tabora -10.191*** -3.446 -10.176*** -3.983 -6.948*** -2.58 Musoma -12.241*** -3.446 -12.24*** -3.983 -7.381*** -2.58 Note: *** Significant at 90%, 95% and 99%.

5% Critical value -2.873 -3.423 -1.950 -2.873 -3.423 -1.95 -2.873 -3.423 -1.95 -2.873 -3.423 -1.95 -2.873 -3.423 -1.95 -2.873 -3.423 -1.95 -2.873 -3.423 -1.95 -2.873 -3.423 -1.95

10% Critical value -2.570 -3.130 -1.620 -2.57 -3.13 -1.62 -2.57 -3.13 -1.62 -2.57 -3.13 -1.62 -2.57 -3.13 -1.62 -2.57 -3.13 -1.62 -2.57 -3.13 -1.62 -2.57 -3.13 -1.62

4.6.2. ARIMA models results ARIMA analysis was done for determining the good forecast model for future projection of rainfall for the selected study areas. In fitting ARIMA model the following criteria were used: First is the lower Akaike's information criterion (AIC) and Bayesian information criterion (BIC),

the best ARIMA model is that which have lower AIC and BIC; second is the significance of coefficient and lastly is the coefficient which have lower correlation with Autoregressive term (Ar), this means that, Ar is not the only factor which influence the time series variable. There are other variables that have not been included in the model which affect the time series variable. Based on those criteria the ARIMA (2,0,2) was selected for forecasting April rainfall in Arusha district. ARIMA (2,0,1) was selected for forecasting December rainfall in Dodoma district. ARIMA (1,1,1) was selected for forecasting April rainfall in Morogoro. ARIMA (2,1,0) was selected for forecasting February rainfall in Mtwara. ARIMA (6,1,0) was selected for forecasting February rainfall in Songea while for Mbeya ARIMA (2,1,0) was selected for forecasting February rainfall. In Tabora ARIMA (1,1,0) was selected for forecasting February rainfall. ARIMA (2,1,0) was selected for forecasting April rainfall. These findings will help building the prediction model for yield based on rainfall months that have strong positive correlation with maize yield and higher average rainfall. Table 11. ARIMA model for the study areas Location

ARIMA

Arusha

Dodoma

Morogoro

Mtwara Songea

Mbeya

Tabora

Musoma

Ar Coefficient

z

P>|z|

AIC

BIC

(1,0,1) (2,0,2) (2,0,1) (3,1,1) (3,1,2) (3,0,1) (3,0,2) (2,0,1) (4,1,1) (2,1,1) (1,1,1) (3,1,0) (1,1,0) (2,1,0)

-0.285 -0.629* 0.172 0.105 -0.023 -0.046 0.371 -0.483* -0.495 -0.035 -0.448* -0.431 -0.493 -0.343

-0.45 -1.94 0.68 0.40 -0.07 -0.18 1.69 -2.42 -2.51 -0.16 -2.12 -2.49 -2.91 -2.06

0.654 0.053 0.496 0.687 0.941 0.856 0.091 0.016 0.012 0.871 0.034 0.013 0.004 0.039

436.91 439.40 435.05 408.41 405.32 411.57 410.01 409.65 364.63 358.53 358.57 362.21 413.29 411.90

443.13 448.73 441.28 417.56 414.48 420.91 419.33 417.43 375.32 364.64 364.67 369.84 417.87 418.01

(6,1,0) (1,1,0) (2,1,0) (1,1,1) (1,1,0) (2,1,0)

-0.465* -0.526 -0.560 -0.099 -0.510 -0.336*

-2.62 -3.03 -3.32 -0.57 -2.91 -1.95

0.009 0.002 0.001 0.566 0.004 0.051

399.55 411.49 400.49 366.62 379.44 377.57

411.76 416.07 406.60 371.19 384.02 383.68

(1,1,0) (2,1,0) (1,0,1) (3,0,3) (2,0,2) (2,1,0) (1,1,0) (1,0,1)

-0.620* -0.317 0.651 -0.703 -0.747 -0.501* -0.819 -0.665

-5.30 -1.72 2.41 -2.92 -3.25 -2.35 -6.52 -1.41

0.000 0.085 0.016 0.004 0.001 0.019 0.000 0.158

391.23 389.98 385.91 391.47 389.86 358.92 364.49 365.28

395.81 396.08 392.13 400.80 397.64 365.03 369.07 371.51

Note: * is the Autoregressive coefficient of selected ARIMA model and ARIMA (p,d,q) means; p is the number of lags, d is the degree of differencing order and q is the moving average order

5.0.

Policy recommendation

Based on the literature which have been reviewed and the results of this study, the following policy and actions are recommended: 1. Encouraging more research on climate viability/change and their possible impact on the agriculture sector in Tanzania. This study has attemptedto enlighten the possible impact of climate variability on maize yield, but the study is not comprehensive enough to inform the agriculture sector of Tanzania because of three challenges; the first one is data limitation, second is time limitation and third is the broader range of agricultural commodities which are being affected differently by climate change. 2. Increasing the pace of implementation of Climate Smart Agriculture Programme in Tanzania for 2015 – 2025 and Agriculture Climate Resilience Plan for 2014 – 2019.The results of this study showed that there is a correlation between rainfall (which is the climate parameter) and maize yield (which is the food security crop) in Tanzania. Also, most of the people in Tanzania still depend on agriculture as means for their livelihood. In addition, the current initiatives of moving the country (Tanzania) into the industrial economy depend on climate and agriculture as the source of energy and source of raw materials for most of manufacturing industries. This means that, the country need to consider climate change as serious issue by increase pace on implementing those plans and programmes. 6.0. Conclusion The results show that, there is positive correlation between maize yield and monthly rainfall. Cointegration test show that, there is also short run and long run relationship between maize yield and monthly rainfall. This means if there is monthly rainfall shortage in future the country might face the food shortage problem. Empirical evidence shows that, climate change is happening. What is not discussed or is little researched is the potential devastating impact of climate change on socioeconomic development in developing countries like Tanzania and the policy measures available for adaptation. Therefore, as a country we need to consider climate change as a serious issue for the future development of Tanzania. The study also has managed to fit and select the ARIMA models for the monthly rainfall of selected stations. The monthly rainfall data of 35 years (1981-2015) were used in this study. Models were selected based on minimum AIC, and BIC values, the significance of AR coefficient and lower value of AR coefficient. ARIMA (2,0,2);(2,0,1);(1,1,1);(2,1,0);(6,1,0);(2,1,0); (1,1,0); (2,1,0) were selected for Arusha, Dodoma, Morogoro, Mtwara, Songea, Mbeya, Tabora, and Musoma districts respectively. Therefore, the selected model could be fairly used to forecast the upcoming rainfall inselected districts.

7.0.

Reference

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