Analysis Framework

There is an immediate need for policy decisions on how to detect, prevent and/or adapt to desertification and land degradation in general.1,4,22 However, we have no convincing basis to argue why one scale of management might be preferred over another. This is due

Table 6.3 Simulated mean and coefficient of variation of annual net primary production (ANPP) of functional types of Jornada Basin, New Mexico, in response to decadal variations in rainfall

Table 6.3 Simulated mean and coefficient of variation of annual net primary production (ANPP) of functional types of Jornada Basin, New Mexico, in response to decadal variations in rainfall

(g m-2)

CV ANPP

% Change in ANPP vs. "Normal"

"Normal" Decade (1968-77)

C3 Winter Annuals

5.19

1.20

-

(CV Rain = 0.29)

C4 Summer Annuals

14.57

1.16

-

Forbs

15.48

0.29

-

C4 Grass

22.39

1.12

-

Shrubs

34.92

0.45

-

Total

92.54

0.56

-

"Dry" Decade (1947-56)

C3 Winter Annuals

4.18

1.65

- 20 %

(CV Rain = 0.35)

C4 Summer Annuals

7.37

0.97

- 50 %

Forbs

10.79

0.59

- 30 %

C4 Grass

8.98

0.62

- 60 %

Shrubs

26.48

0.73

- 25 %

Total

57.81

0.60

- 38 %

"Wet" Decade (1984-93)

C3 Winter Annuals

11.27

1.75

+ 120 %

(CV Rain = 0.26)

C4 Summer Annuals

18.29

1.58

+ 25 %

Forbs

18.01

0.45

+ 15 %

C4 Grass

138.17

0.89

+ 500 %

Shrubs

89.57

0.33

+ 150 %

Total

275.30

0.52

(CV Rain = 0.34)

77 arid rangelands

137

0.49

Based on Reynolds and Kemp.17 See Figure 6.4 for precipation patterns during each decade.

objective of sustainable management at any given scale.23 What is certain is the need to move beyond isolated studies of various parts of the desertification problem, which has been the traditional approach.3 Analysis frameworks are needed that incorporate our best state of the art knowledge about precursors, processes, and consequences of desertification. These frameworks also need to represent the degree of uncertainty in our knowledge of the desertification puzzle and to be able to propagate these uncertainties in the analyses, thus reflecting them in the conclusions.24 The analyses presented in our case study focus attention on basic ecological feedbacks that contribute to short term ecosystem dynamics, which are precursors to longer term dynamics (Fig. 6.2).

The analysis framework we endorse is one of integrated assessment (IA). There are many approaches to the development of an assessment but, in general, assessments are often designed as a collection of knowledge on related topics prepared by a group of interested parties.25 In the case of land degradation in drylands, the interested parties should comprise expertise covering key topics in social, economic, and environmental sustainability. The scientific assessment by the Intergovernmental Panel on Climate Change (IPCC) is a good example of IA, although it represents only an initial step towards gathering of knowledge from various disciplines. We need collaborative activities where interdisciplinary research is nurtured, and the interstices of knowledge are sought out as subjects of long term interactions between economists and natural and social scientists. Hence, an IA should be more than just a model building exercise—integrated assessments are a suite of methodologies for gaining insight on an array of environmental problems that span a wide range of temporal and spatial scales, from small geographical units (such as local watersheds) to larger regions. Integrated assessment models are powerful tools that can be used to explore quantitative as well as qualitative interactions between the various elements of a problem.25

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