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Approaches to Measuring the Economic Costs of Land Degradation
Implicit in the concept of land degradation (soil erosion and soil mining) is the notion that agricultural land use removes some useful nutrients from the land bringing about deterioration in its quality and reducing its productivity. Models for predicting soil land degradation’s physical impact on crop yields have been discussed in the previous section.
However, physical impacts of land degradation on crop yield entail economic costs. The economic costs of soil erosion are usually separated into two, on-site and off-site costs. Onsite refers to the direct effects of soil degradation on the quality of land resource itself, often expressed in terms of reduced agricultural productivity. Off-site costs refer to the indirect effects of soil degradation, which take the form of externalities such as siltation. These downstream damages impose costs on the other members of society not directly involved in causing the erosion.
Most economic analysis of soil erosion has been carried out in the US, where since the 1970s the issue has received much public attention (Ervin and Ervin, 1982). Earlier work on this subject mainly concentrated on conservation and adoption. Dating back to the late 1950s, literature in this area ascribes a key role to institutional factors, information and attitudes (Ciriacy-Wantrup, 1952). Researchers emphasized the need to solicit farmers’ perceptions and monitor their decisions (Ervin, 1982). However, since the 1970s, more formal modelling such as linear and dynamic programming techniques as well as optimal control models gained importance and appeal to analysing the economic costs of soil erosion [Brekke et aI., 1999; Eaton, 1996; Pagiola, 1993; McConell, 1983; Seitz and Swanson, 1980]. Other approaches included the replacement cost approach and the productivity loss approach. This section reviews the approaches that have been used to measure the economic costs of land degradation.
The approaches that have been used to measure economic costs of land degradation can be separated into two groups: those that are static in nature and those that are dynamic. A static analysis seeks an optimal number or finite set of numbers. Static optimisation models do not trace effects or changes over time. In contrast, dynamic optimisation models generate solutions for a complete optimal time path of each choice variable and not just a single optimal value (one period) (Chiang, 1984). Examples in this category include the optimal control and dynamic programming models.
The replacement cost method (RCM)
The replacement cost approach calculates the loss of major nutrients (e.g., N, P, and K) as a result of any degrading processes such as erosion or crop harvesting and assign a value to it by using the equivalent cost of replenishing the soil fertility through the application of external inputs such as commercial fertilizers. Empirical soil erosion predictive models like USLE and SLEMSA have frequently been used to estimate levels of erosion. Regression analysis is then used to establish a statistical relationship between soil erosion and losses of major soil nutrients such as N, P and K. The value of such losses is then determined through the ReM. The replacement cost method has been widely used due to its ease. Solorzano et aI., (1991) examined effects of soil erosion in Costa Rica and found that annual replacement costs were equal to 5.3-13.3 per cent of annual value-added in agriculture. Stocking (1986) working in Zimbabwe, estimated nutrient loss in terms of nitrogen, phosphorous and organic carbon, and calculated the cost of replenishing these nutrients. A set of data taken from experimental plots during the late 1950s and early 1960s was used. The data represented over 2000 individual storm soil loss events on four soil types and numerous crops, treatments and slopes.
Regression analysis was employed to establish a statistical relationship between soil erosion and losses of the three nutrients. Assuming an average rate of sheet erosion for each of the four major farming systems in the country (crop and range-land on communal and large-scale farming land), the amount of nutrients lost per year was calculated. Stockings (1986) then extrapolated the experimental data to the country as a whole for both communal and commercial farming systems engaged in grazing and arable land production. This study assumed that all nitrogen and phosphorous losses were to be replaced by fertilizer every year in order to maintain soil fertility.
However, Norse and Saigal (1992) summarized the pioneering work of Stocking (1986) and concluded that Stocking’s study overestimated the costs of soil erosion in Zimbabwe by almost 20 per cent due to its neglect of nutrient input sources. The replacement approach used by Stocking may over-state on-site costs since it is based on replacing the entire mineral stock, whilst the rate at which nutrients become available for crop growth and the low actual uptake of minerals means that fertility may be maintained without complete replenishment (Bishop, 1992). The replenishment cost approach does not take into account the thresh-hold beyond which the effects of erosion are irreversible and cannot be rectified. Soil erosion affects several yield determining parameters, such as soil depth and nutrient availability [Hailu and Runge-Metzger, 1992]. Thus, when soil erosion has destroyed the soil physical structures like rooting depth, nutrient replenishment approach may under-state effects of soil erosion. Another major weakness of this approach is that it is a cost-based rather than benefit based valuation. This approach is remedial in focus unlike the benefit-based valuation e.g., computing the marginal value of soil quality. The latter approach instils in the user a sense that soil is an asset and has a value. The speed of the asset depreciation will thus depend on the way the asset is used and cared for. Comparably, where one is concerned with sustainable use of soil resource, the benefit-based valuation, which indicates a marginal value of soil quality, is more proactive in approach. For example, if producers are made aware of the marginal value of their land’s quality they would protect and put it to the best use possible.
CHAPTER I INTRODUCTION
1.1 Background and Statement ofthe Problem
1.2 Objectives of the Study
1.3 Approaches and Methods of the Study
1.4 Organization ofthe Thesis
CHAPTER II AGRICULTURE AND SOIL RESOURCES OF MALAWI
2.1 Agricultural Sector in Malawi
2.2 Food Security Situation in Malawi
2.3 Existing Policy Framework
2.4 Malawi Soil Resource
2.5 The Major Soils of Malawi
2.6 Soil Nutrient Balances
2.7 Concluding summary
CHAPTER III MEASURING THE ECONOMIC IMPACTS OF SOIL DEGRADATION: Survey of the Literature
3.1 Introduction
3.2 Soil Fertility and Soil Degradation
3.3 The Relationship Between Soil Properties and Productivity
3.4 Predicting Soil Erosion Impact on Productivity
3.5 Approaches to Measuring the Economic Costs of Land Degradation
3.6 Concluding Summary
CHAPTER IV STUDY APPROACH TO MODELING THE DYNAMICS OF OPTIMAL SOIL FERTILITY MANAGEMENT IN MALAWI
4.1 The Analytical Framework and The Optimal Control Approach
4.2 Modelling Agricultural Output and Soil Mining
4.3 The Optimal Control of Soil Quality Depletion
4.4 Interpreting FOCs
4.5 Input Substitution
4.6 Socially Optimal Use of Soil Nutrient Stock
4.7 Comparing Dynamic with Static Optimisation Solutions of Farmers
CHAPTER V SPECIFICATION OF THE OPTIMAL CONTROL MODEL, EMPIRICAL RESULTS, DISCUSSION AND CONCLUSION
5.1 Specification of the Empirical Soil Mining Model for Malawi
5.2 Solutions of the Optimal Soil Mining Model
5.3 Estimation of the Specified Model Parameters
5.4 Using estimated model to determine dynamic optima for soil resources use
5.5 Empirical Results ofthe Optimal Control Model, Discussion and Conclusion
5.6 Sensitivity Analysis
CHAPTER VI FACTORS INFLUENCING INCIDENCE AND·EXTENT OF ADOPTION OF SOIL CONSERVATION TECHNOLOGIES AMONG SMALLHOLDER FARMERS IN MALAWI: A Selective Tobit Model Analysis
6.1 Introduction
6.2 Soil Conservation in Malawi
6.3 Investing in Soil Conservation
6.4. Approach and methods of the study
6.5 Specification ofthe Empirical Model l
6.6 Choice of Variables
6.7 Data and Data Limitations
6.8 Household Characteristics in the Study Areas
6.9 Concluding Summary
CHAPTER VII EMPIRICAL RESULTS OF THE SELECTIVE TOBIT ANALySIS
7.1 Introduction
7.2 Empirical Results and Discussion
7.3 Concluding Summary
CHAPTER VIII SUMMARy, CONCLUSIONS AND IMPLICATIONS FOR POLICY AND RESEARCH
REFERENCES
APPENDICES
