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 AND SIERRA LEONE Y. BIOT, M. SESSAY AND M. STOCKING School of Development Studies, University of East Anglia, Norwich, NR4 7TJ, UK ABSTRACT 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 INTRODUCTION ‘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. PRODUCTION FORECASTING 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 01989 by John Wiley & Sons, Ltd. Received 19 November 1989 Revised 6 March 1990

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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

ABSTRACT

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

INTRODUCTION

‘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.

PRODUCTION FORECASTING

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.

METHODOLOGY

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.

EXAMPLES

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 animal’s 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 ~

1 0

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

Discrete-time integration

60

Time (years)

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 method gives results which are very close to the first method (full line). However, extrapolation beyond the scope of the empirical evidence is now impossible: the line has to stop at a cumulative erosion of 90 cm.

Figure 4. Relationship between the angle of the line connecting two points on the circumference of a circle ( y ) and the angles of the tangents to the circumference at these points (a and /3) with the horizontal

SUSTAINABILITY OF AGRICULTURAL LAND 269

0 Average-rate integration. For a circle, the angle of the line connecting two points on the circumference with the horizontal is the mean of the angles of the tangents to the circumference at these points with the horizontal (see Figure 4).

By hypothesizing a circle between two points on the cumulative erosion over time graph, and considering again a time unit of 20 years, it is possible to derive this mean angle as follows:

) t a n a c t a n x ; arctan y

where: c1 = arctan x j = arctan y x = cm soil loss over 20 years at stage i y = cm soil loss over 20 years at stage ( i + 1) Note: Table I11 summarizes the calculations for the example considered. These average rates of loss can be used to calculate the time needed to reach any erosion stage, and to produce a cumulative erosion over time graph (see Figure 3, dashed line). The difference e , between the time taken to reach an erosion stage using the average-rate integration method, and the time taken by the continuous-time integration method is dependent upon how much the actual function relating the erosion stages differs from a circle. This difference also increases with the difference between rates of erosion at two consecutive erosion stages considered.

Production over time. The cumulative erosion forecast is converted into a production forecast using the relationship between production and cumulative erosion. This can be done graphically as in Figure 5 where the full, dotted and dashed lines refer to the continuous-time, discrete-time and average-rate integration method of deriving the cumulative erosion over time graph as explained above.

pro ductior

1000 - continuous-time integration

...... . .. discrete-time integration

average-rate integration 800

600

400

production kg ha-’ 600 4 0 0

cumulative erosion

F h a - ’

crn)

Figure 5. Derivation of grass production over time in extensive rangeland of the Hardveld of Botswana

270 Y. BIOT. M . SESSAY AND M. STOCKING

Table 111. Average rate of erosion between erosion stages assuming a circle between two consecutive stages

stage loss arctan loss average loss (cm) (cm/20 y) (degree, dec.) (cm/20 y)

0 0.16 9.09 0-3 1

30 0.48 25.64 0.69

60 0.96 43.83

90 0.06 3.4 0.44

Table IV. Time taken to reach 75 per cent and 50 per cent of the production of a deep soil by integration method (in years)

integration method 75 per cent 50 per cent

continuous-time integration 2,266 4,267 discrete-time integration 2,133 4,066 average-rate method 2,000 3,133

average 2,133 4,022 maximum deviation 266 534

The sensitivity of the results to the integration mekhod used is illustrated in Table IV. In this table, the time taken to reach 75 and 50 per cent of the production level of an un-eroded soil are

calculated using the three integration methods. In this example the maximum deviation between the three methods is about 10 to 15 per cent of the value calculated using the continuous-time integration method, down to a 50 per cent production level.

Sierra Leone The farming system considered in the Sierra Leone example (See Figure 6 ) is a fallow system in which cropping is dominated by a maize (Zea mays) and cowpea (Vigna unguiculata) intercrop.

Materials and methods. In a first phase, a soil and geomorphological study identified both the dominant soil degradation process (i.e. sheet and rill erosion) and an erosion sequence from slightly eroded soils with a thick topsoil to very eroded soils with a thin topsoil.

Based on the soil and geomorphological maps, four sites were selected which are representative of four stages of the erosion process considered. On each site experiments were conducted to determine production levels and rates of erosion under the traditional maize and cowpea intercrop and an improved cropping system. The improved system differed from the traditional system only in its use of NPK fertilizer in a 150:60:40 ratio, which is the recommended optimum fertilizer level based on local fertilizer trials and represents the upper limit of human intervention in fertility.

Results Production over cumulatioe erosion. The results of this work are summarized in Table V. Here again it can be seen that production decreases with erosion. The rate of erosion, however, keeps on

increasing up to stage four, which means that, unlike in the Botswana example, indurated layers have not reached the surface yet.

SUSTAINABILITY OF AGRICULTURAL LAND 27 1

Figure 6. Sierra Leone survey area

Cumulative erosion over time. The cumulative erosion over time curves for both the traditional and the improved systems are represented in Figures 7 and 8 respectively, using the same conventions as used in the Botswana example. It can be seen from these figures that, over the range of erosion stages sampled, the three integration methods give very similar results.

The figures also illustrate the importance of having accurate mathematical models for the numerical integration method: according to Figures 7 and 8, the rate of erosion of the improved system would be faster than under the traditional conditions beyond a cumulative soil erosion of 100 cm. This is highly unlikely as

Table V. Yields of maize and cowpea and rates of erosion for four erosion stages on an erosion sequence in Sierra Leone

~~~~~

traditional improved

erosion stage erosion production erosion production (kg ha-') (kg ha-')

(cumulative erosion, cm) (cm y - ' ) maize cowpea (cm y - ' ) maize cowpea

0 0.060 4,300 1,286 0.037 6,316 2,852 12 0.080 2,266 829 0.045 4,658 1,355 21 0.089 1,568 449 0.064 3,794 733 31 0-097 628 166 0.084 2,224 526

272 Y. BIOT, M . SESSAY AND M. STOCKING

Time (years)

Figure 7. Cumulative erosion over time in the traditional agricultural system in Sierra Leone

cumulative erosion (cm) 120

I

60

0 0.00 250 500 750 1 ) O

Time (years)

Figure 8. Cumulative erosion over time in the improved agricultural system in Sierra Leone

we can expect higher crop production, hence higher crop cover, under the improved conditions. This discrepancy is caused by an inadequate mathematical model for the rate versus cumulative erosion relationship, which allows fair interpolation between points but produces unrealistic extrapolation beyond the limits of the empirical evidence.

Production ouer time. Future maize and cowpea production are forecast in Figure 9 and 10 respectively, based on the auerage-rate integration method of soil erosion. In the case of maize, production decline tends to accelerate and cowpea losses decrease at lower production levels.

SUSTAINABILITY OF AGRICULTURAL LAND

yiek - improved system __-- traditional system

6000

213

:g ha-’)

2000

yield ,T (hg ha-’) 4000

cunulative arosio~ m)

Figure 9. Derivation of maize yield over time in the Sierra Leone example

APPLICATIONS

Short-term economic analyses The purpose of production forecasting is to provide information about potential future food production and earnings from agricultural activities. In land use planning, economic and financial analyses of projected and/ or existing types of land utilization are carried out in which costs are compared to benefits over a given planning horizon (usually in the order of 20 to 30 years) (Gittinger, 1984). The declining production forecasts made using the proposed methodology can be included as declining benefits and thus the ‘price’ to be paid for not combating land degradation can be assessed. Alternatively, the analysis can be carried out on the basis of estimated costs to restore crop production at (near) maximum-level. They can also be used as the without- project baseline against which the benefits earned from soil and water conservation projects can be compared.

Production declines caused by erosion are usually very low over a period of 20 years. In the examples above, 0.31 cm soil lost over a period of 20 years in Botswana will be responsible for a grass production decline of 1,046 to 1,043 kg ha-’ y- l , a drop of 0.3 per cent. In the traditonal agricultural system in Sierra Leone 1.20 cm soil lost in 20 years will cause a 5 and 4 per cent drop and, in the improved system a 0-74 cm loss in 20 years will cause a 2 and 3 per cent drop in maize and cowpea yields respectively. It is unlikely that losses of this magnitude will be responsible for perceivable differences in cost-benefit analyses over a planning horizon of 20 years.

Long-term environmental impact The above short-term economic analyses illustrate how we can demonstrate that the cost of land degradation may not warrant intervention on pure financial or economic grounds, unless off-site damages compound the on-site losses dramatically.

274 Y. BIOT, M. SESSAY AND M. STOCKING

Figure 10. Derivation of cowpea yield over time in the Sierra Leone example

When using much longer time-spans, losses can become appreciable: according to the forecast made, the traditional maize-cowpea intercropping system could lose up to 25 per cent of its average annual production over 100 years-a catastrophic decline for a subsistence type agricultural society.

The soil 1i;fe and residual suitability of extensive rangeland in Botswana. As noted earlier, the concepts of soil !i$e and residual suitability of agricultural land estimate the time for an agricultural system to reach a productive level which only just meets the demands put upon it by land users-the minimum allowable production level.

Minimum allowable levels of grass production under different stocking rates expressed as livestock units per hectare (LU ha-') can be derived from data on the daily grass consumption and the utilization coefficient introduced earlier. These calculations are summarized in Table VI.

The time taken to reach these minimum allowable production levels can be derived from Figure 5. The results are summarized in Table VII.

At the present average stocking rate of 1 :6.6 LU ha-', and an average soil depth of 45 cm, a minimum annual production of 534 kg ha-' of grass is needed and the residual suitability equals 650 years. A small increase in stocking rate, however, could bring this figure down very rapidly.

The impact of different stocking rates on the residual suitability of two soils of 45 and 75cm depth respectively is illustrated in Figure 11. Considering a minimum acceptable soil life of 100 years, the deeper soils permit a denser stocking rate (1:44 LU ha-') than the shallower soils (1:5.8 LU ha-'). The relationships between stocking rate and soil life in the above figure can be used as a guide for management of given areas of rangeland if the area can be classified according to soil depth.

SUSTAINABILITY OF AGRICULTURAL LAND 215

Table VI. Calculation of the grass production required to support different stocking rates in the communal range- lands of the Hardveld of Botswana

stocking rate grass consumption grass production (LU ha- ' ) (kg ha- ' ) needed (kg ha- ' )

1 : l O 256 1:7.5 340 1:5 511

353 469 706

Soil life (years)

2000

1000

minimm accept Soil life

Figure 11. Relationship between soil life and stocking rate for two different soils

Table VII. Residual suitability of extensive rangeland soils of varying depths in the Hardveld of Botswana

stocking rate cumulative erosion (cm)

(LU ha- ' ) 0 30 60 90

1 : l O >5,150 >3,150 >2,350 >1,000 1 : 7.5 4,050 2,150 1,250 ~

1:5 2,250 333

Note: > : soil life beyond empirical evidence

- -

- : soil life exceeded

276 Y. BIOT, M. SESSAY AND M. STOCKING

Table VIII. Production ‘half-life’ of a tradi- tional and improved maize-cowpea intercrop in Sierra Leone

cumulative erosion (cm)

system 0 12 21 31

traditional maize 180 170 90 30* cowpea 230 130 80 40*

improved maize 490 290 150* 90* cowpea 270 220 230* 170*

Note: * based on extrapolation

The production ‘half-life’ of an agricultural system. A similar reasoning can be applied in the case of the Sierra Leone data. In the absence of estimates of the minimum allowable production levels, which are the subject of present investigations, the production ‘half-life’ can be used as a relative measure of the sustainability of this agricultural system.

The concept of production ‘half-life’ is defined here as the time it will take for an agricultural system to reach half of today’s production level. In the case of Sierra Leone, the production ‘half-life’ can be derived from Figures 9 and 10. The results are summarized in Table VIII.

can be seen from these calculations that:

The traditional agricultural system is characterized by production ‘half-lives’ equal to about 50 per cent of those of the improved system; The simple introduction of fertilizer can prolong the productive life of a soil considerably. This is achieved by increasing production levels on the one hand and by reducing the rate of erosion through increased soil protection on the other.

CONCLUSION

We have demonstrated how a simple and relatively rapid technique involving sequential testing can provide information directly relevant to decision-makers and policy-makers with regard to soil conservation.

In the Botswana example we estimate a residual suitability of the soils of slightly over 500 years. This rangeland system shows itself to be resilient, but it is for the planners, economists and decision makers to choose the minimum allowable production level. The modelling methodology outlined here gives them access to the necessary data, so that they can make value-judgements as to the sustainability of the system and the need for conservation. Figure 11 is especially valuable in that respect.

In the Sierra Leone example, we have shown that, in the medium-term, the agricultural system is quite safe, but that the next generation of subsistence farmers may face substantial production declines. It may be unpalatable to the conservationist, but sometimes (and perhaps often), conservation is not needed over a medium-term planning horizon, and should certainly not be a burden imposed upon a peasant population. In this case, the safeguarding of the productive capacity of land for tomorrow’s farmers is clearly the responsibility of a wider group of people.

The techniques proposed in this paper have been conceived with a view to interpreting the results obtained from ongoing research projects based on the design recommended by Stocking (1985); see also Stocking (1988). In these experiments, rates of erosion and production levels are monitored on erosion plots which have undergone different levels of de-surfacing. They are conducted over a wide ranging set of soil, climatic and land use conditions in South America, Africa and South East Asia. It is hoped that interpreted in the way

SUSTAINABILITY OF AGRICULTURAL LAND 277

Table IX. Guidelines for the interpretation of the residual suitability of agricultural land

residual suitability interpretation

0- 10 years 10-20 years 20-50 years 50-100 years > 100 years

very poor poor

moderate good

very good

after Biot (1988a:263)

we have discussed, the results obtained from these experiments will be able to provide guidelines as to the sustainability of a range of land utilization strategies under tropical conditions.

The production forecasts and values of the residual soil suitability have to be assessed in the light of a number of uncertainties. Even in the case of the continuous-time integration method, the model used might be unrepresentative of the physical reality; production both in Botswana and Sierra Leone was estimated from a one-off sample adjusted for average climatic conditions; the measurement of rates of erosion is prone to a very wide range of errors (Stocking, 1987; Biot, 1990); each sampling exercise is accompanied by sampling errors. It is obvious, therefore, that absolute values of the soil life cannot be directly used, and that they have to be interpreted carefully. Guidelines for such interpretation are given in Table IX.

REFERENCES

Abel, N. 0. J., Flint, M., Hunter, M. D., Chandler, D. and Maka, G 1987. Cattle Keeping, Ecological Change and Communal Management in Ngwaketse. International Livestock Centre for Africa (ILCA), the Integrated Farming Pilot Project (IFPP) and the Overseas Development Group (ODG). Addis Ababa, Gaborone and Norwich.

Arntzen, J. M. and Veenendaal, E. M. 1986. A Profile ofEnvironment and Development in Botswana. Institute for Environmental Studies (IES) and National Institute of Development Research and Documentation (NIR). Amsterdam and Gaborone.

Biot, Y. 1988a. Calculating the residual suitability of agricultural land based on routine land resource surveys, pp. 261-265 in: J. Bouma and A. K. Bregt (eds.) Land Qualities in Time and Space. Proceedings of a symposium organised by the ISSS, Wageningen. PUDOC, Wageningen.

Biot, Y. 1988b. Forecasting Productioity Losses Caused by Sheet and Rill Erosion in Semi-arid Rangeland a case study from the communal areas of Botswana. PhD thesis, School of Development Studies, University of East Anglia, Norwich 225 pp.

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