Assessing the sustainability of agricultural land in Botswana and Sierra Leone
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LAND DEGRADATION & REHABILITATION, VOL. 1,263-278 (1989)
ASSESSING THE SUSTAINABILITY OF AGRICULTURAL LAND IN BOTSWANA A N D SIERRA LEONE
Y. BIOT, M. SESSAY AND M. STOCKING School of Development Studies, University of East Anglia, Norwich, NR4 7TJ, U K
Land degradation processes, such as soil erosion, which threaten the sustainability of agricultural production have been studied for many years. While research progresses on the processes, little advance has been -ade on translating results into terms which can be used directly in the design of appropriate and economically justifiec lorms of soil conservation and land husbandry.
Research by the authors has shown how soil erosion affects the potential of land to produce crops. Simple models have been developed which provide a first approximation of the impact of soil erosion on future production, to be used as a baseline against which the benefits of soil conservation can be compared. The concepts of soil life and residual suitability have been developed as a measure of the sustainability of the production system considered. In this paper a simple experimental technique to derive the future production and the residual suitability of land is proposed.
The method is based on the principle of sequential testing and uses a simple graphical technique to translate information on production and erosion into a crop production forecast. The soil life or residual suifability of land is derived from the minimum allowable crop production which depends on socio-economic criteria. In case the minimum allowable production level cannot be reliably estimated, the concept of production half-life is introduced as a relative measure of sustainability. The method and its applications are illustrated using examples from Botswana and Sierra Leone.
KEY WORDS Sustainable agricultural production Soil life Residual suitability Production half-life Modelling Economic evaluation
We have the opportunity to reduce soil degradation much more effectively in the future than we have done in the past ...
concluded Hudson (1988: 127) in a paper to a recent conference on soil conservation. How? By changing our approach to soil and water conservation from how to stop soil moving? to which improved farming systems meet the goals of the land users and how can they assure the continued use of that land? Soil conservation may then enter centrally into the realm of rural development.
The foremost data requirement of this new approach to soil conservation is to know the degree and type of impact of soil degradation on the productive capacity of the land. If the cost of the loss of production can be quantified, soil conservationists might design appropriate and acceptable techniques, and land users and planners might better appreciate their importance (Moldenhauer, 1980). Estimates of future production are crucial in this debate.
To date, reviews of the impact of erosion on crop production and soil productivity have, despite large numbers of experiments, provided little information enabling the assessment of the decline in production over time (Stocking, 1984; 1988). In the majority of cases, researchers have established the link between production losses and cumulative soil erosion either on artificially de-surfaced or naturally eroded sites. None of the research projects reviewed have as yet attempted to simultaneously measure rates of erosion and
0898-581 2/89/O40263- 16 $08.00 0 1 9 8 9 by John Wiley & Sons, Ltd.
Received 19 November 1989 Revised 6 March 1990
264 Y. MOT. M. SESSAY AND M . STOCKING
crop production, and it is impossible to use existing production/erosion relationships directly to forecast future production.
Computer models such as EPIC (Williams, et aJ., 1984) have been developed to simulate future crop production given rates of erosion calculated using the Universal Soil LOSS Equation (USLE) (Wischmeier and Smith, 1965) and crop growth models. These models are based on the assumption that land use systems can reliably be condensed in a set of mathematical equations which, once suitably calibrated, can be used to simulate the behaviour of the system at any scale and moment in time. They demand substantial calibration and have been developed specifically for conditions of agricultural production in the USA.
Both the crop growth and the erosion component of EPIC do not provide realistic forecasts of agricultural production levels and rates of soil erosion in most tropical regions. Moreover, the sophistication and data requirement are such that it will not be possible to use it to study the impact of land degradation on crop production in these environments for many years to come.
There is thus a large gap between the demand for knowledge and usable techniques, and their provision by researchers. This paper attempts to redress this balance by presenting an alternative approach to forecasting which is based on a much simpler model of the land use system, and relationships between crop production, the rate of soil erosion and cumulative erosion which are established empirically for the area considered. The approach is based on the identification in the landscape of sites which are representative of future soil conditions. It enables rapid and relatively inexpensive assessments of future production to be made, and makes use of empirical evidence to assess both crop production and rates of erosion in the future instead of complex computer models.
Evaluating production forecasts Once future production levels are predicted, two types of analysis can be proposed, depending on the time- spans considered:
(1) Cost-benefit analysis (Bojo, 1986a, b; Dixon, et a/., 1986; Seckler, 1987) is used to compare the benefits of soil conservation with levels of earning achieved when letting the land degrade. This type of analysis is confined to short-term projections (typically 20 to 30 years).
(2) The concepts of soil life (Elwell and Stocking, 1984) and residual suitability (Biot, 1988a) have been introduced as a measure of the time during which production can be maintained above a given minimum allowable level. In essence these measure the sustainability of the farming system considered over the long term.
The basic information is derived from sites which are representative of different stages of the prevailing soil degradation process-a procedure referred to as sequential testing. From initial surveys of major soil types, soil characteristics such as texture and depth, their geomorphological relationships and degradation sequences are identified on a qualitative basis: e.g. if soil erosion is considered-deep, moderately deep, shallow. Alternatively, these different stages can be created artificially (Stocking, 1985). In the case of soil erosion this can be achieved by removing layers of topsoil-a process which is referred to as artificial de- surfacing. There is no guarantee, however, that the latter procedure produces soil conditions which are truly representative of the conditions which would have been achieved by the natural erosion process.
For each stage on the degradation sequence, productive potential and present rate of degradation are measured. The exact methods of doing this depend on the farming system and processes involved, scale, data and time availability. In the case of rangeland, grass production is the key variable, while on arable land, economic yields need to be assessed. Where little time and few resources are available, one may resort to soil- derived productivity indices and empirical formulae (such as the USLE) for rates of erosion. Where there is less urgency, on-farm experiments and surveys are recommended for establishing rates of soil erosion and levels of crop yield for the different degradation stages.
SUSTAINABILITY OF AGRICULTURAL LAND 265
The difference between the proposed technique and existing erosion/productivity trials on soils of various depths (Engelstad, et al., 1961 and Stone, et al., 1985) is in:
0 The selection of sampling sites on the basis of their representative value as stages of a given sequence. 0 The simultaneous measurement of both yields and rates of soil erosion (or any other process of land
Given the rates of degradation for the different stages of the process, it is possible, using mathematical integration methods, to estimate the time taken for the soil to evolve from one stage to another. Thus soil conditions at any time in the future can be forecast. These condition forecasts are then translated into a production forecast using a simple graphical technique.
degradation for that matter) on each site.
The following two examples illustrate the application of the general methodology in widely differing farming systems and the use of different experimental techniques.
Botswana The farming system considered in this example is a semi-nomadic grazing system (Ruthenberg, 1980) operating in a semiarid tree savanna. Since the beginning of the century livestock numbers have increased substantially, and the resilience of the ecosystem has been questioned (Arntzen and Veenendael, 1986). Abel, et al. (1987) have hypothesized that soil erosion is the main threat to the sustainability of this system. Biot (1988b; 1988c) has, using a modelling approach, estimated its residual suitability under present livestock densities.
The data collected for the calculation of this residual suitability are reinterpreted in order to illustrate the proposed technique.
Materials and methods. Following a reconnaissance soil and geomorphological survey of a pilot region in Central District (see Figure 1) representing the steepest sloping land in the Hardveld, sites were selected on aerial photographs which were representative of different stages of an erosion sequence. Soil depth and grass standing biomass at the end of the growing season were recorded for each site.
Grass standing biomass was converted into standing biomass at the end of the dry season using information on cattle density at the time and recommended grass consumption rate (a daily grass consumption by each animal equal to 2 per cent of the animals body weight). Annual grass production was calculated using a utilization coefficient of 0.725 and the value adjusted for a year with mean annual rainfall using a production/rainfall relationship developed for a similar environment (Dye and Spear, 1982) and local rainfall records.
Rates of erosion were determined using a variety of techniques. Unbounded sediment traps provided a relative measure of erosion for the different survey sites. Based on the results of a water-balance study these relative measures were converted into rates of soil erosion for an average rainfall year (see Biot, 1988b for more details).
Results. The data collected are interpreted in three sequential steps as follows: (1) grass production is plotted against cumulative soil erosion; (2) the time taken between each erosion stage is calculated; (3) the results of the first two steps (1) and (2) are combined to produce a productivity forecast.
Production versus cumulative erosion. The results of the survey conducted in the 1986/87 growing season are summarized in Table I.
It can be seen from this table that grass production declines with cumulative soil erosion, mainly because of a reduction in the overall capacity of the soil to retain moisture. As production declines, crop cover decreases and the rate of erosion increases. In the last stage, however, the rate of erosion drops to very low
266 Y. BLOT, M. SESSAY AND M . STOCKING
0 200 400km
Figure 1. Botswana survey area
Table I. Annual production and rates of erosion for mean annual rainfall in a pilot region of the Hardveld of Botswana
soil depth cumulative erosion rate of erosion grass production (cm) (cm) (cm Y Y 1 ) (kg ha- y - )
105 0 0.008 1046 75 30 0.024 771 45 60 0.048 614 15 90 0.003 460
values: this is caused by the appearance at ground-level of a gravel layer which protects the soil against further abrasion.
Cumulative erosion over time. The rates of erosion are used to estimate the time taken between each erosion stage-i.e. a cumulative erosion over time graph.
The rates of erosion determined for each erosion stage are the tangent to the erosion over time graph at each observation point. Thus the latter graph can be derived by integration of the relationship between rates of erosion and cumulative erosion. Three different integration methods are possible:
Continuous-time integration. In the method proposed by Biot (1988b; 1988c) a function is fitted through the rate over cumulative erosion curve. In the case of the Botswana Hardveld, this is done in Figure 2. This function can be integrated numerically, leading to the cumulative erosion forecast in Figure 3 (full line). Discrete-time integration. The relationship between rate of erosion and cumulative erosion can be integrated without knowledge of the exact function between rate and cumulative erosion. Starting at a cumulative loss of 0 cm, a rate of erosion equal to 0.008 cm y - is applied for a period of 20 years, giving a cumulative loss equal to 0.16 cm. The rate of erosion is then adjusted in proportion to the distance
SUSTAINABILITY OF AGRICULTURAL LAND 267
erosion (rn y -)
. O ~
cumulative erosion (cm)
Figure 2. Rate of erosion versus cumulative erosion in the Hardveld of Botswana
cumulative erosion (crn) 120 I I
__ Continuous-time integration
Figure 3. Cumulative erosion over time in the Hardveld of Botswana
between the stage reached (0.16 cm) and the next stage in the erosion sequence at which the rate of erosion is known (0.024 cm at 30 cm cumulative erosion), as follows:
0.008(30 - 0.16) + 0*024(0.16 - 0) = 0-0081
30 r =
This new rate of erosion is applied for another period of 20 years leading to the next stage and so forth. The first ten stages of this calculation are summarized in Table 11.
268 Y. BIOT, M . SESSAY AND M. STOCKING
Table 11. Derivation of cumulative erosion over time using the discrete-time integration method
erosion stage rate cumulative erosion new rate (cm 1 (cm y - I ) after 20 years (cm) (cm y - ' )
0 0.16 0.32 0.48 0.65 0.82 0.99 1.16 1.33 1.50
0.0080 0.008 1 0.0082 0.008 3 00083 00084 0.008 5 0,0086 0.0087 0.0088
0.16 0.32 0.48 0.65 0.82 0.99 1.16 1.33 1 .so 1.68
0.008 1 0.0082 0.0083 0.0083 0.0084 0.0085 0.0086 0.0087 0.008 8 0.0089
The accuracy of this forecast depends on the time-step chosen, which itself is a function of the erosion forecasting tool used. In the case of Botswana, the values for the rates of erosion were derived on the basis of climatic conditions for a period of 20 years, hence the 20 year time-step.
The resulting integration is plotted in Figure 3 (dotted line). It can be seen from this figure that the discrete-time integration metho...