Characterisation of rainfall at a semi-arid ecotope in the Limpopo Province (South Africa) and its implications for sustainable crop production Jestinos Mzezewa1*, Titus Misi2 and Leon D van Rensburg3 1 Department of Soil Science, University of Venda, P/Bag X5050,Thohoyandou 0950 South Africa 2 Department of Statistics, University of Venda, P/Bag X5050, Thohoyandou 0950, South Africa 3 Department of Crop, Soil and Climate Sciences, University of the Free State, PO Box 339, Bloemfontein 9300, South Africa Abstract Detailed knowledge of rainfall regime is an important prerequisite for agricultural planning. Despite the importance of rain-fed agriculture to food security in the semi-arid regions of South Africa, studies to understand the spatial and temporal variability of rainfall are not widely documented. Twenty-three years (1983 to 2005) of rainfall data were analysed in order to study the basic statistical rainfall characteristics at the University of Venda ecotope. Annual and monthly rainfall was fit- ted to theoretical probability distributions. The Anderson-Darling goodness-of-fit test was used to evaluate best fit models. Probability of receiving annual and monthly rainfall was predicted using the appropriate probability distribution functions. The chance of experiencing dry spells of different durations was determined. Cumulative frequency analysis of daily rain- fall amounts and depths was characterized. It was found that the distribution of daily rainfall was highly skewed with high frequency of occurrence of low-rainfall events. The distribution of daily rainfall depths was also highly skewed, a comparatively small proportion of rainy days supplying a high proportion of the rainfall. Keywords: dry spells, ecotope, Limpopo, semi-arid, temporal rainfall analysis Introduction instances when observed distributions are neither normal nor symmetrical. It has been observed that rainfall is not necessar- Climate plays an important role in biomass production. ily normally distributed (Stephens, 1974) except in wet regions Extreme climatic conditions and high inter-annual/ seasonal (Edwards et al., 1983). Jackson (1977) has emphasized that variability of climatic parameters could adversely affect pro- annual rainfall distributions are markedly skew in semi-arid ductivity (Li et al., 2006) because rainfall governs the crop areas and the assumption of normal frequency distribution for yields and determines the choice of the crops that can be such areas is inappropriate. Research elsewhere has shown grown. The pattern and amount of rainfall are among the most that rainfall can also be described by other distributions, e.g. important factors that affect agricultural systems. The analysis Gamma distribution (Abdullah and Al-Mazroui, 1998; Aksoy, of rainfall records for long periods provides information about 2000; Garcia et al., 2007), the log-Pearson Type III distribution rainfall patterns and variability (Lazaro et al., 2001). (Chin-Yu Lee, 2005), and the Weibull and Gumbel distributions Drought mitigation can be planned by understanding daily (Tilahun, 2006). rainfall behaviour (Aghajani, 2007). Dry spell analysis assists One of the reasons for low crop production in semi- in estimating the probability of intra-season drought and arid areas is marginal and erratic rainfall, exacerbated by management practices can be adjusted accordingly (Tesfaye high runoff and evaporation losses. The in-field rainwater- and Walker, 2004; Kumar and Rao, 2005). It is of importance harvesting technique as proposed by Hensley (2000) has to know how long a wet spell is likely to persist, and what the been shown to improve the yield of maize and sunflower on probabilities are of experiencing dry spells of various dura- some benchmark ecotopes in South Africa. There has been tions at critical times during the growing season (Dennet, 1987; increasing interest recently in South Africa of making crop Sivakumar, 1992). production less risky and sustainable in semi-arid ecotopes Probability distributions are widely used in understanding through in-field rainwater harvesting (Botha et al., 2003). the rainfall pattern and computation of probabilities (Abdullah The ecotope at the University of Venda in Thohoyandou is and Al-Mazroui, 1998) and it is believed that events follow one of the areas where such studies have not been carried particular types of distributions (Tilahun, 2006). The normal out. An ecotope is defined as a homogeneous piece of land distribution is one of the most important and widely used in with a unique combination of climate, topographic and soil rainfall analysis (Kwaku and Duke, 2007). Despite the wide characteristics (Hensley et al. 2000). In order to understand applicability of the normal distribution there remain many the feasibility of establishing a water-harvesting system, rainfall analysis and the identification of prevailing rainfall patterns is required (Dennet, 1987; Rappold, 2005). The * To whom all correspondence should be addressed. main objective of this paper was to analyse the 1983 to 2005 +2715 9628759; fax: +2715 9628598; rainfall records from the weather station in Thohoyandou e-mail: [email protected] (Limpopo Province, South Africa) as a basis for future Received 11 June 2009; accepted in revised form 7 December 2009. studies on sustainability of crop production in general and Available on website http://www.wrc.org.za 19 ISSN 0378-4738 (Print) = Water SA Vol. 36 No. 1 January 2010 ISSN 1816-7950 (On-line) = Water SA Vol. 36 No. 1 January 2010 data were not included in the calculations of averages. Where consecutive months had no recorded rain, these records were considered to indicate missing records and were not included in the calculations of averages. This was so for 1982, 2006 and 2007. A standard method of analysing of rainfall data for agricul- tural purposes that involved first summarising the daily data to give monthly and then annual totals (Abeyasekera et al., 1983) was followed. The main reason for totalling the daily data has been that the volume of data to be handled subsequently is greatly reduced and data normality would be assumed (Stern et al., 1982). Methods of data analysis Probability distributions of annual and monthly Figure 1 Location map rainfall In this study it was important to identify the probability distri- in-field rainwater harvesting in particular. The approach to butions of rainfall data. In order to determine the underlying data analysis was largely similar to those of other authors such distribution, the observed distributions were fitted to theo- as Belachew (2002) and Tilahun (2006). The latter served as retical probability distribution by comparing the frequencies observed in the data with the expected frequencies of the the main reference for our work. theoretical distribution since certain types of variables follow specific distributions (Tilahun, 2006). Materials and methods Preliminary data normality was tested using skewness and kurtosis coefficients. Probability distributions were evaluated Geographical setting by constructing probability plots and curve fitting in Minitab 14 statistical software. The study area was at the ecotope at the University of Venda Goodness-of-fit tests were based on the Anderson-Darling o o (Univen) (22 58’ S, 30 26’ E; 596 m a.m.s.l.) in Thohoyandou, test (Stephens, 1974). The Anderson-Darling statistic meas- 70 km east of Louis Trichardt in the Limpopo Province of ures how well the data follow a particular distribution. The South Africa (Fig. 1). better the distribution fits the data, the smaller this statistic The study area falls in the Lowveld of the province which is. The corresponding p-value is used to test whether the data forms part of the greater Limpopo River basin, situated in the come from a chosen distribution. If the p-value is less than o east of Southern Africa between about 20 and 26 S latitude 0.05, the null hypothesis that the data come from that distri- o and 25 and 35 E longitude (FAO, 2009). According to the bution is rejected. The p-value with the greatest magnitude Koppen Classification the basin is predominantly semi-arid was considered to be the best fit. If the p-value was the same, (Mmarete, 2003). Daily temperatures at Thohoyandou vary the smallest AD value was then used to decide the best fit. o from about 25˚C to 40 C in summer and between 22˚C and Rainfall data from October to March were considered in fit- o 26 C in winter. Rainfall is highly seasonal with 95% occurring ting distributions. The rest of the monthly rainfall data were between October and March (Mmarete, 2003), often with a not considered due to limited non-zero values. The normal mid-season dry spell during critical periods of growth (FAO, distribution, lognormal distribution, Gamma and Weibull 2009). Midsummer drought often leads to crop failure and low distributions were tested (Table 1). Fitted distributions are yields (Beukes et al., 1999). Average rainfall is about 800 mm shown in Fig. 3. but it often varies temporarily. Soils of the ecotope are predominantly deep (>1 500 mm), Aridity index (AI) dystrophic, red and yellow well-drained clays with apedal structure. Clay content is generally high (60%) and soil reac- An aridity index (AI) is a numerical indicator of the degree of tion is acidic (pH 5). The soils are formed in situ. They are dryness of the climate at a given location (Thornthwaite, 1948). classified as Hutton form (South African System of Soil clas- The agro-climatic zonation of the meteorological study area sification) (Soil Classification Working Group, 1991), Rhodic was determined using the UNESCO (1979) AI given as: Eutrustox (Soil Survey Staff, 2006) or Rhodic Ferralsol P (WRB, 2006). AI [1] ET o Data where: P is the mean annual rainfall Daily rainfall data were obtained from the South African ETO is the mean annual reference evapotranspiration Weather Service. Daily reference evapotranspiration records were provided by the Agricultural Research Council (ARC) of According to this classification, P/ETO<0.03 is hyper-arid South Africa.