Socio economic aspects of land degradation

At some level, national statistics will always be less informative of individual agricultural performance than disaggregated surveys of agriculture.

Both require a combination of census and sample survey data. Another problem occurs as the scope of the regression is decreased. Grepperud, S.

causes and effects of land degradation

However, these results indicate that investigations into the relationship between global economic phenomena and the local management of natural resources are quite important, and suggest that improvements in some measures of economic welfare may be positively associated with increased environmental degradation, a position that is frequently cited in the debate over the implications of globalization for resource management patterns.

The non-use value is divided into three categories namely; bequest, altruistic and existence values.

Socio economic factors of environmental degradation

References Anselin, L. However, a study of the interactions between soil degradation and socio-economic factors is not impossible using the current GLASOD dataset. Surprisingly, degradation decreases with slope. Downloadable AEZWin software. Roberts, M. The estimation task is twofold: to measure the effects of soil degradation on the grower's socio-economic situation and to measure the effect of the grower's socio-economic situation on soil degradation. The determinants inhibiting the adoption of SLM practices are also possible to promote land degradation.

Detailed empirical studies in developing countries include that of Pagiola in Kenya, Nakhumwa and Hassan in Malawi, Shiferaw and HoldenGebremedhin and Swinton and Bekele and Drake in Ethiopia. Perhaps most importantly, the authors of the studies have usually spent significant time in the research area, gaining essential insight and experience concerning the region.

Environmental impacts of land degradation

Robust checks tare carried out to check these misspecifications. The spatial weighting matrix is included in the statistical model. PRM is preferred because it takes considers the non-negative and binary nature of the data Winkelmann and Zimmermann Analogous to the maps generated for the Africa-wide regression, Figure 16 presents the share of soil degradation of poverty inducing factors in Ghana. However, there is potential for the use of simulation models to deal with endogeneity in regressions. It is desirable and possible to perform a more sophisticated integration that incorporates information on the variance and correlation of the geophysical datasets, but this is a nontrivial task that is beyond the scale of the current work. Use of such techniques may provide more significant results than those obtained with the OLS method; however there needs to be more rigorous work conducted on the appropriate explanatory variables and the underlying theoretical model before the results of such an estimation could be considered valid. Land degradation may also result in greater yield variability, and thus greater costs to risk-averse farmers.

These maps provide a graphic presentation of statistical analyses in a form that facilitates communication with policy-makers as well as other field-based practitioners and researchers.

Although the causal assumption is a practical and valid course of action, it must be kept in mind when interpreting the results of the study.

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Economics of Land Degradation and Improvement in Tanzania and Malawi