Assessing rangeland drought in South Africa

Agricultural Systems, Vol. 57, No. 3, pp. 367---380, 1998 PII:

~; 1998 Elsevier Science Ltd. All rights reserved Printed in Great Britain S0308-521X(98)00024-9 0308-521X/98519.00+0.00

ELSEVIER

Assessing Rangeland Drought in South Africa L. G. du Pisani, a* H. J. Fouch6 b & J. C. Venter c

aNorthern Cape Department of Agriculture, Private Bag X529, Middelburg 5900, South Africa bARC-RFI, Private Bag X01, Glen 9360, South Africa CARC-ISCW (IB501), PO Box 339, Bloemfontein 9300, South Africa (Received 20 January 1998; accepted 3 February 1998)

ABSTRACT The meteorological and agricultural assessment techniques that have been developed in South Africa are discussed, with reference to their strengths and weaknesses, application and suggested future developments. Future challenges in drought assessment as influenced by democratization are also considered. We conclude that meteorological methods have several inherent deficiencies which render them risky for the optimum allocation of drought relief. Agricultural drought assessment is suggested to be more preferable. Current agricultural drought models should nevertheless be improved to deal more successfully with the dynamic nature of rangeland. Evidence indicates that no absolute objective biophysical criteria for the quantification of the onset and the end of a drought have been identified and that all current criteria have an inherent subjectivity. Choosing appropriate interpolation techniques for mapping drought extent is a cause for concern, and there is scope for future research. Remote sensing techniques, integrated with crop modelling techniques, are options to pursue further. We finally suggest that in a democratic South Africa, more attention should be given to a multi-disciplinary approach where impacts, other than meteorological and agricultural, on previously disadvantaged black farmers are also assessed. © 1998 Elsevier Science Ltd. All rights reserved

*To whom correspondence should be addressed. 367

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INTRODUCTION Southern Africa has a highly variable climate (Schulze, 1980) (Fig. 1), with recurring droughts an endemic feature of agriculture. The early inhabitants practised transhumance to escape the ravages of drought (Danckwerts and Tainton, 1996). Colonial livestock farmers started settling permanently in the interior of South Africa from about 1850 and introduced settled pastoralism to the region. This and ensuing social and political developments, notably apartheid, shaped drought policy for many years. Unlike its neighbouring countries, who rarely supported farmers during droughts, South Africa has had a long history of drought assistance (O'Meagher et al., 1998). Apart from having an obvious racial prejudice, policy was biased towards commercial livestock farmers in arid and semiarid rangeland areas. Initially assistance was provided in a subjective and emotive manner (Skinner, 1981), not on the basis of biophysical criteria or evidence. It was gradually realized that, in the absence of objective criteria, assistance was more beneficial to incompetent managers (Skinner, 1981; Jooste, 1983; Fouch6 et al., 1985) and climatically marginal areas (Fouch6, 1992). Since 1985, policy required positive confirmation of a drought before relief was granted. This required three types of technology, i.e. criteria to quantify droughts, mechanisms to trigger provision and revocation of assistance, and mechanisms to map spatial extent. Although the 1985 and 1990 drought policies (O'Meagher et al., 1998) accelerated drought research, scientists developed objective drought assessment

Fig. 1. The relative variability (%) of annual rainfall over southern Africa (Schulze, 1980).

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techniques and criteria as far back as the 1960s. This paper reviews the various drought assessment techniques that have been developed in South Africa, discusses their strengths and weaknesses, identifies future research needs, and speculates over future challenges in a democratic society. In the context of this paper, 'drought' usually refers to 'disaster drought', which is synonymous to the term 'drought exceptional conditions' in Australia.

ASSESSING D R O U G H T

Intensity Meteorological indices Several meteorological indices have been developed or tested since the 1960s (see Table 1). They share the following basic building blocks: . Rainfall forms the basis of assessment as: (a) rainfall deficit is the most important climatic variable indicating the presence and severity of drought; (b) rainfall data are more available than other climatological data; and (c) rainfall is often correlated with other climatic variables, such as humidity, temperature, wind speed and wind direction (Zucchini and Adamson, 1984). . Intensity is calculated as the deficit between current rainfall and a measure of historical rainfall over a corresponding period of time. Central to the approach is the assumption that the water-related activities in a region should be in harmony with the amount of water which would be 'normally' available for those specific activities (Zucchini and Adamson, 1984). A significant negative deviation from the 'normal' condition is, therefore, regarded as a drought; usually the mean longterm value of the variable in question.

With the exception of the Palmer Drought Index (PDI), all models were employed at least once for practical drought assessment. Evaluation of the PDI indicated its unsuitability for rangeland conditions, as it generally did not correlate well with actual on-site conditions (Booysen, 1983). The rainfall deciles method of Erasmus (1991), and the Roux method (Roux, 1993), were the most extensively used for drought assessment. Agricultural indices o f drought intensity The advent of desktop computers in the 1980s allowed modellers to develop complex crop modelling techniques which were eventually employed for drought assessment. The most relevant techniques are listed in Table 2. Of

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Fig. 2. Geographical areas in South Africa where P U T U 11 and ZA Shrubland Model have been successfully employed for drought assessment purposes.

these, the P U T U Suite of Rangeland Models (specifically P U T U 11), the ZA Shrubland Model and the Roux Expert System made the most prominent contribution to drought assessment on an operational level. P U T U 11 has been employed for practical drought assessment in the arid and semi-arid grasslands since 1992 (Fig. 2). A map of drought-affected areas constructed with P U T U 11 is illustrated in Fig. 3. Statistical analyses of observed vs predicted values, yielded MAE and RMSE values (Wilmott, 1982) of less than 10%, and correlation coefficients of 0.8 and better. An important breakthrough of P U T U 11 was its ability to distinguish between grazing-induced droughts and climatic droughts (Fouch6 et al., 1985). The ZA Shrubland Model was employed in the arid and semi-arid shrublands (see Fig. 2). The output of the ZA Shrubland Model was tested against independent sets of yield data with a correlation coefficient of 0.75. Model performance was also tested subjectively on a number of occasions. The modus operandi included interviews with experienced farmers, as well as seasoned extension officers. Both groupings invariably concluded that the capability of the model to evaluate drought conditions is acceptably accurate.

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Fig. 3. Map showing the geographical extent of disaster drought conditions during December 1994 in the Free State Province, assessed with PUTU 11.

Both models received the same criticism from farmers, i.e. that they are optimistic in their prediction of the end of a drought. This will be discussed in more detail later in the paper. To improve the transparency and credibility of drought assessment amongst farmers, Roux (1991) developed an expert system for use by farmers serving on District Drought Committees. These results were used in conjunction with the methods described in the previous paragraphs. Although untested, this methodology enhanced farmers' understanding of drought and improved the credibility of the assessment process. Duration and spatial extent

Duration is the product of the beginning and the end of a drought. Several trigger mechanisms have been developed and are listed in Table 3. Considering that these criteria all apply to rangeland, it is noted that they differ relatively significantly. Additionally, none of the authors provided any biophysical evidence for their choice of criteria. Mapping of the geographical extend of drought is based on interpolated raster data. The most common interpolation routine is the polynomial spline method, or variations thereof (for example de Jager et al., in press).

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TABLE 3 List of Drought Duration Trigger Criteria Suggested for Rangeland Drought Assessment in South Africa Trigger

Reference

Less than 70% of the average precipitation over two consecutive growing seasons Less than 50% of average rainfall over 6 consecutive months The onset of a drought is presumed as soon as the absolute difference between the mean annual rainfall deficiency and the monthly mean totals exceed the maximum mean monthly rainfall (MMMR). As soon as this difference reverts to a value smaller than the MMMR, the end of a drought is presumed Severe rainfall deficiency is indicated when total rainfall over a 3-month period falls within decile 1 (10%). Disaster drought is indicated when total rainfall over a 3-month period falls within the lower 5% of historical rainfall events. A drought is considered to be broken when decile 7 or better rains are received in the immediate past month or when rainfall for the past 3 months exceeded decile 3 Return time of 1-in-5 years Return time of 1-in-14 years

Schulze (1980) Venter (1992) Herbst et al. (1966)

Erasmus (1991)

Fouch6 (1992) de Jager et al. (in press)

G e n e r a l l y , the r a w i n p u t d a t a are i n t e r p o l a t e d (for e x a m p l e rainfall) a n d n o t the c a l c u l a t e d d r o u g h t indices.

DISCUSSION

AND

CONCLUSIONS

Meteorological drought indices F o r m a n y years, m e t e o r o l o g i c a l d r o u g h t indices singularly c o n t r i b u t e d to o u r k n o w l e d g e a n d u n d e r s t a n d i n g o f the d r o u g h t p h e n o m e n o n . T h e s e nevertheless h a d several i n h e r e n t deficiencies w h i c h raised criticism f r o m b o t h f a r m e r s a n d agriculturalists. T h e r e a s o n is t h a t m e t e o r o l o g i c a l d r o u g h t s d o n o t necessarily coincide with p e r i o d s o f agricultural d r o u g h t (Wilhite a n d G l a n t z , 1987). Several r e a s o n s c a n be offered for this a n o m a l y : 1. C e n t r a l to the m e t e o r o l o g i c a l m e t h o d o l o g y is the a s s u m p t i o n t h a t the w a t e r - r e l a t e d activities in a region s h o u l d be in h a r m o n y with the a m o u n t o f w a t e r w h i c h w o u l d n o r m a l l y be available ( Z u c c h i n i a n d A d a m s o n , 1984). In reality, s o m e activities require less o r m o r e w a t e r t h a n the m e a n available.

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2. Meteorological models assume that the vegetation that occurs naturally within a particular region is adapted to the average climatic variability. This is at least in part true, but the soil-plant-climate ecosystem is infinitely more complex. The effects of other factors, like soil, rangeland population dynamics, rainfall intensity and distribution, cardinal and critical temperatures, etc., are, therefore, negated. Owing to these inherent deficiencies, we are of the opinion that decisions relating to drought assistance should preferably not be based on meteorological indices. What we need for optimum allocation of drought assistance is not so much percentile five rainfall, but percentile five pasture production (Brook and Carter, 1994).

Agricultural drought indices The variables that induce agricultural droughts derive from the same ones associated with meteorological droughts (Zucchini and Adamson, 1984). But, the primary argument for using agricultural drought indices is that meteorological condition alone does not easily capture the important issues of rainfall in relation to the state of the agricultural system (Keating et al., 1996). The interactions between both the amount and timing of rainfall (and other meteorological variables), as well as the plant available water in the soil profile in relation to crop growth and development, are reflected in an agricultural drought index (Lourens, 1995). We, therefore, suggest that agricultural indices of drought should be used in preference to meteorological methods. The crop models that are operational are quite successful given isolated problems relating to poor availability of climatic and other input data, other than rainfall. Some points, however, need further investigation. Rangelands are highly dynamic ecosystems with rainfall variability and seasonality the primary driving forces in plant productivity and population dynamics (Roux and Vorster, 1983; O'Connor and Roux, 1995). The crop models used in South Africa deal fairly successfully with the production variability associated with rainfall variability, but not with changes associated with plant composition, mortalities, seedling emergence, density and cover during and after droughts. This is often evident after a protracted drought, when the models overoptimistically predict the end of the drought, while the ecosystem is still in a recovery phase. This can be directly ascribed to the models' inability to deal with the dynamic nature of vegetation during and after protracted droughts. It is important that this deficiency receives attention to improve the equitable allocation of drought assistance, and to allow for the protection of the rangeland resource through policy formulation.

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et al.

Duration

Quantifying the onset and the end of a drought is one of the components of drought assessment that has been severely criticized by livestock producers. Although farmers would naturally prefer to receive financial assistance as soon as possible and for as long as possible, we recognize that they may have a valid basis for argument (at least in part). This assumption is based on the following: 1. Drought is not instantaneous, but associated with a progressive deterioration of conditions (Drought Policy Review Task Group, 1990). The same is true after a drought has been broken, when conditions improve gradually. One could thus argue that drought is a non-event and it is, therefore, difficult, if not impossible, to define and measure the onset and the end of a drought in absolute biophysical terms. This is corroborated by the fact that none of the authors listed in Table 3 provided scientific evidence for their choice of triggers. It is thus suggested that the current criteria are fairly arbitrary, inherently subjective and need further investigation. 2. The character of drought is distinctly regional, reflecting unique meteorological, hydrological, agricultural, and socio-economic characteristics (Wilhite, 1993). Drought is, furthermore, crop specific, as energy flow differs between ecosystems (Fouch6, 1992). Drought should, therefore, be understood regionally and locally in terms of supply and demand, as well as the long-term balances in nature (Schulze, 1987), and should reflect equity and consistency. Current crop models do not take sufficient cognisance of rangeland dynamics and are consequently optimistic in predicting the end of a drought. The solution to the conflicts created by the inability of science to adequately assess drought duration, is to base the start and the end of a disaster drought on criteria previously agreed upon between producers and policy makers. We suggest, however, that objective drought assessment methodologies should still serve as a guide when negotiating the criteria. The criteria should never fail to support sustainable resource utilization. If this occurs, the success of drought relief would be negated, how ever well intended. We also suggest that phased drought assistance would be appropriate. Assistance could then be gradually phased in or out as conditions deteriorate or improve. Scientists have employed mainly the return time of drought as a means of identifying drought. However, Schulze (1987) warned against the careless use of the return periods of droughts, as the statistical confidence levels are

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usually large. For example, the computed answer from a 68 year rainfall record at Cedara (Natal), indicated that by 1983 the area would be in the grips of a 1-in-143 year drought. Within 95% confidence limits it could have been anything from a 1-in-30 to a 1-in-250year drought. Another problem with the return time of drought, is its inability to adequately assess the impact of low intensity, extended droughts. Their impact can be more detrimental than high intensity, short duration droughts; so much so that either a quadratic or logarithmic impact rather than a linear impact can be expected (Fouch6, 1992). Further investigation into these matters is required.

Spatial extent Farmers have levelled a fair amount of criticism to the so-called 'lines on maps' phenomenon. Two arguments normally prevail, i.e. areas adjacent to the drought demarcated zones experience similar conditions, while some areas are not demarcated at all. As with drought duration, we submit that these arguments may be valid (in part at least) and the following are put forward as possible explanations: 1. As already discussed, drought is not instantaneous; nor are there necessarily abrupt transitions from one condition to another. Current deficiencies in mapping technology and drought duration definitions do not recognize this; thus, the incidence of 'lines on maps'. 2. It is obvious that a dense network of data points is a prerequisite for successful geographical interpolation (Rudolf et al., 1994). This is emphasized by the fact that in arid and semi-arid regions, small regional pockets can continue to experience devastating drought conditions within years and regions of abundant rainfall (Laing, 1990). Smith et al. (1992) suggested that spatial interpolation is more reliable when the drought indices rather than the spatially variable raw climatic inputs are interpolated. It can, however, be assumed that the accuracy does not strongly depend on the method of interpolation if the data availability is sufficient, but more on the given density, location and quality of the data (Rudolf et al., 1994). Over data sparse regions, however, an improvement in the accuracy of the estimates could be expected by the use of a procedure which includes aerographic-climatological relationships (Rudolf et al., 1994). It would seem appropriate to use historical records to generate sitespecific interpolation functions (e.g. Johnson, 1982). Choosing appropriate interpolation techniques to determine the spatial extent of drought from point data is a cause for concern and there is scope for further work in this field in South Africa. Using remote sensing techniques

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may be part of the solution, since this would be a natural way with which to interpolate the data, whether measured or generated by models (Barrett and Martin, 1981). Possibly the best solution would be to integrate satellite data and rangeland growth models, as proposed by Brook and Carter (1994) for Australia.

F U T U R E C H A L L E N G E S IN A D E M O C R A T I C SOUTH A F R I C A South Africa, with its juxtaposition of first and third world conditions, a well-developed commercial farming sector alongside an emerging and subsistence farming sector, would need more comprehensive drought assessment technologies. The emerging and subsistence farming segments, formerly excluded from drought assistance, are the most at risk to the ravages of drought. Their requirements are such that, where previous policy attempted to secure national food security, current and future policy would concentrate more on household food, water, health and welfare issues. Historically, drought assessment criteria held little regard for water and social issues. Although meteorological and agricultural drought assessment technology would remain important for optimum allocation of assistance, they do not identify or quantify victims of potential hazards. Other indicators and types of information that would need to be developed are those relevant to nutrition, health, welfare, rural employment and water issues, This would necessitate a multi-disciplinary approach where agriculturists, meteorologists, hydrologists, information technologists, sociologists and health and welfare employees work side by side.

ACKNOWLEDGEMENTS We acknowledge the constructive and valued criticism of two anonymous referees. REFERENCES Barrett, E. C. and Martin, D. W. (1981) The Use of Satellite Data in Rainfall Monitoring. Academic Press, London. Booysen, J. (1983) Two methods for the quantitative simulation of growth stages of climax grass (Afrikaans). MSc thesis, University of the Orange Free State, Bloemfontein, South Africa. Brook, K. D. and Carter, J. O. (1994) Integrating satellite data and pasture growth models to produce feed deficit and land condition alerts. Agricultural System and Information Technology--Climate and Risk 6(2), 54-56.

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