Dryland degradation measurement techniques

Ffv V coo-1 cc THE WORLD BANK POLICY PLANNING AND RESEARCH STAFF Environment Department Dryland Degradation Measurement Techniques Stein W Bie April 1990 Environment Working Paper No. 26 ibis paper has been prepared for internal use, The views and interpretations herein are those of the author(s) and should not be attributed to the World Bank, to its affiliated organizations or to any individual acting on their behalf. This paper has been prepared by Stein W. Bie, a consultant to the Environment Department of the World Bank. He is at present Director of the Norwegian Centre for International Agricultural Development (NORAGRIC) at the Agricultural University of Norway. The author gratefully acknowledges valuable contributions from the World Bank staff and in particular from Ridley Nelson, Jon Martin Trolldalen, John English and Jeff Lewis. Similarly, unpublished reviews by Andrew Warren and Clive Agnew (University of London) and IUCN (Gland) have provided much material. The author also acknowledges valuable discussions with Stephen Sandford (ILCA, Addis Ababa), David Andere (KREMU, Nairobi), Michael Gwynne and Harvey Croze (UNEP, Nairobi), Ruben Mendez and Kebour Ghenna (UNSO, New York), Compton Tucker (Goddard Space Flight Center, Maryland) and Eva Ahlcrona (Swedish Space Corporation, Kiruna/Stockholm). Departmental Working Papers are not formal publications of the World Bank. They present preliminary and unpolished results of country analysis or research that are circulated to encourage discussion and comment; citation and the use of such a paper should take account of its provisional character. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author and should not be attributed in any manner to the World Bank, to its affiliated organizations, or to members of its Board of Executive Directors or the countries they represent. Because of the informality and to present the results of research with the least possible delay, the typescript has not been prepared in accordance with the procedures appropriate to formal printed texts, and the World Bank accepts no responsibility for errors. - ii - Dryland Degradation Measurement Techniques ABSTRACT This report is intended to provide background and practical guidelines in the evaluation of projects in dryland areas, particularly projects concerned with dryland degradation.1 The report attempts to guide the reader through a maze of recent literature and debates on the drylands to arrive at workable methodologies. These methodologies will probably not meet with universal approval, as few land use issues are more hotly contested at present than the drylands. Why is an overview of dryland degradation so important? The drylands are the marginal lands on Earth, with variable and uncertain returns on investment. Yet they harbour millions of people, tc whom both national governments and the World Community have responsibilities. They are vulnerable people - the recent drought in the Sahel demonstrated that. If the trend towards even greater marginality continues and is irreversible, the drylands offer little scope for reasonable returns on investment. Yet the costs of doing nothing inflict another burden that only relief aid can relieve. It is clearly of crucial importance to assess whether the drylands are really gone, or just suffering a temporary setback, from which they will recover to produce adequate returns. Chapter 1 of this report introduces the issues of what the drylands are. In particular it looks at the relationship between dryland degradation and desertification and traces the historical debate that reflects so much confusion and disagreement. It includes a short note on the debate on causes of degradation. Chapter 2 reviews the alternative definitions suggested for dryland degradation, and classifies them into "the systems analysis school," "the set of variables (indices) school" and "the single variable school." This chapter concludes by defining "productivity" and "resilience" as the two main tools for dryland degradation assessment. Chapter 3 considers methods of assessing productivity and resilience: by ground survey methods, aerial survey satellite remote sensing and by the use of available statistics. It concludes that all methods have their uses, that some have clear weaknesses (particularly low flying aircraft aid high-resolution satellite imagery) and that there is no substitute for ground reference data. Cost estimates for each methodology are included. Chapter 4 contains the recommendations with respect to measurements of productivity and resilience, and on important steps to safeguard historic material for future studies. 'This report does not consider irrigation issues in dryland areas, although salinization, alkalinization, water erosion, silting, crusting, waterlogging and waterborne pests and diseases are evidences of degradation associated with irrigation practices. Table of Contents DRYLAND DEGRADATION MEASUREMENT TECHNIQUES ABSTRACT CHAPTER 1: The Issue 1 1.1. Where and who 1 1.2. Living with uncertainty 2 1.3. In fear of degradation 2 1.4. Public concern 2 1.5. A history of debate 2 1.6. Are the drylands gone? 4 CHAPTER 2: Dryland degradation - In search of definitions 7 2.1. The confusing definitions 7 2.2. Utility of criteria 8 2.3. The system school 8 2.4. Sets of single variables (indices) 8 2.5. Single varia' les 9 2.5.1. Directly related variables 10 2.5.2. Indirectly related variables 10 2.6. How to choose assessment factors 10 2.7. Two broad criteria 11 2.7.1. Productivity 11 2.7.2. Resilience 12 2.7.3. Concluding productivity and resilience 12 CHAPTER 3: Assessing productivity and resilience 17 3.1. Land, air and space, statistics 17 3.2. Ground surveys 18 3.2.1. Criteria of productivity 18 3.2.2. Criteria of resilience 19 3.3. Aerial surveys 20 3.4. Air phato Interpretation 21 3.4.1. Productivity 21 3.4.2. Resilience 22 3.5. Satellite remote sensing 23 3.5.1. Productivity 23 3.5.1.1. High-resolution satellites 23 3.5.1.2. Meteorological satellites 24 3.5.2. Resillence 25 3.5.2.1. High-resolution satellites 25 3.5.2.2. Lower-resolution satellites 26 3.5.3. Statistical data 26 3.5.3.1 Productivity 26 3.5.3.2. Resilience 27 3.6. Costs of alternative methods 28 CHAPTER 4: Recommendations 29 4.1. Assessment factors 29 4.2. Promising methodology for productivity assessment 29 4.3. Proposal for transsectoral establishments 30 4.4. Air photo archives 30 4.5. Satellite imagery 30 4.6. Geographical Information systems 30 4.7. Conclusion 30 REFERENCES 31 APPENDICES -1- Chapter 1 The Issue 1.1. Where and who That part of the world that on average receives less than 800 mm annual rainfall is in this paper called *the drylands." These drylands include the deserts, semi-deserts and the driest part of the savanna zones of all continents. In Africa the Saharan, Sahel and Sudano zones fall within this area. The actual area that may be called the drylands is uncertain, for the statistics are inconsistent and not always meaningful.2 The most commonly quoted figures collected through a government questionnaire on desertification by Mabbutt (1984) have been severely criticized by many (see reviews by Dregne and Tucker, 1988; and Nelson (1988)) to the extent that they are probably best unquoted at the present time. Figure I (page 5) indicates areas in question (legend nos. 1-3). The drylands include land that is of little economic interest or even completely uninhabited. The best overview is probably World Resources 1987 (World Resources Institute, 1987; sections on Rangelands and Desertification). However, this report (and the previous World Resources 1986 (WRI, 1986) repeats figures about which there is very considerable scientific dispute. To illustrate, Dregne (1986) estimates that 770 million people live in arid lands (1980), of whom 450 million people can be expected to have felt the direct or indirect impact of desertification. Tolba (1984) puts the figure for livelihoods at risk to 850 million people. Mabbutt and Floret (1980) in the preface to the major UNESCO/UNEP/UNDP-study on desertification, estimate the people who are threatened to be between 50 and 80 million. A large proportion of the dryland peoples can be found in Africa. The US Congress's Office of Technology Assessment (OTA, 1986) estimates that 35 million people live in the Sahel. Thus, the number may be very much smaller than that suggested by the other sources quoted above may suggest. The average annual income for the Sahelians in 1983 was US$232/capita, against US$787 for lesser developed countries in general. Their Physical Quality of Life Index was 27, against 61 in other LDCs and 99 in the industrialized world (US Congress OTA, 1986). Although food production has generally increased in the Sahel, the per capita trend is declining, with large annual variability. Figures 2 and 3, produced by US Dept. of Agriculture illustrate the trends (US Congress, OTA, 1986 p. 6). 2Appendix 1 contains some statistics (from Zonn, 1986) on the drylands of the world. -2- 1.2. Living with uncertainty The drylands on all continents face the same natural phenomenon: low and unreliable rainfall. As biological production requires water as input, production itself becomes unreliable. Although water may well be the first limiting factor for vegetative production, others also appear quickly, notably phosphorous and nitrogen. Those dryland peoples whose livelihood depends on the harvesting of biological production are therefore faced with decision-making under high natural uncertainty. Different peoples react differently when faced with uncertainty. A study of the Tuareg and Foulani responses to the 1972 drought in Niger show how the Tuaregs stayed whilst the Foulanis moved when the drought hit (referred to by Mabbutt and Floret, 1980, pp. 136-137). 1.3. In fear of degradation The drylands have always been regarded as marginal for human existence and the population pressure has always been relatively low. Concern about their productivity has been voiced by many, and in particular their ability to support growing populations. There have been fears expressed that overuse of the drylands will inevitably lead to their degradation, some claim that permanent destruction is likely, with true desert-like forms developing from what is now producing enough to support populations. 1.4. Public concern The fear of degradation has been expressed by many in recent years, following the Desertification Conference in Nairobi in 1977, the major UNESCO/UNEP/UNDP study (Mabbutt and Floret, 1980) and, not the least, in the wake of the latest Sahelian disaster that culminated in 1984/85 with a large, if unknown, number of human lives lost through starvation and associated diseases. Also in the case of India, and in particular Rajasthan, recurrent famines have affected many people. In recent times there have been droughts also in 1910 - 1984, 1930, 1940 - 1944, 1967 - 1973 (US Congress OTA, 1986) (see also Mabbutt and Floret, 1980 p. 168). There has quite rightly been great public concern about the fate of peoples living on the drylands, and not the least in Africa. There are fears expr3ssed that they are victims of a naturally degrading ecosystem. Alternatively that they inflict degradation on the ecosystem through mismanagement of the natural resources. Similarly there is uncertainty as to whether degrading drylands can impact on global ecological stability. Alternatively whether external factors, particularly pollution, lead to dryland degradation. Mass media, public reports (e.g., that of the World Commission on Environment and Development, WCED, 1987) and scientific publications have alerted us all to this issue, and overwhelmingly on a concerned note. 1.5. A history of the debate Firstly, it must be stated that concern about dryland degradation is net new. Just because many now refer to such ongoing degradation, we cannot assume that it does in fact take place. There is plenty of evidence that previous claims to imminent and permanent collapse of dryland ecosystems may have been alarmist or wrong. For example, in the drier regions of Tunisia extensive land clearing for agriculture during Roman times with historic reports of land degradation exemplify that changes in land use can also be associated with an ability to support growing populations (Mabbutt and Floret, 1980, p. 14-15). Similarly, Sandford (pers. comm.) notes that approximately 100 years ago the drylands of (now) -3- Zimbabwe had around 50,000 cattle grazing there. When by 1910 the number had risen to 100,000 there were voices heard predicting imminent collapse of these drylands. To-day the total cattle population is of the order of 2.5 - 3 million (see also Sandford, 1983). Recently Dregne and Tucker (1988) have reviewed the dispute of the "encroaching Sahara" afising from papers by Borril (1921) and Stebbing (1935). It is now clear that the evidence provided both by these authors - and many of their opponents - arose from short-term observations and anecdotes (see also Nelson, 1988). In Niger Mabbutt and Floret (1980, pp. 136-137) quote comparisons between studies made in 1937, compared to 1974. Little change could be found in two areas, whereas agricultural cultivation had moved north by 150 km in another. Similarly, a US AID- report of 1972 (USAID, 1972) of both northward and southward movement of the Sahara, reaching 45 km per year, was found by Dregne and Tucker (1988) to lack any data source. The most quoted degradation statement from Africa in revent years must be from a travel report by H. Lamprey (1975), whose conclusion that the Sahara had advanced at a rate of 90-100 km in 17 years (- 5-6 km/year) in parts of the Sudan, has reappeared in numerous disguises, and appears to be widely accepted as valid for all of the Sahel. It is probably also the origin of such estimates as "annual loss of agricultural land equivalent to the size of Belgium," also - incidentally - appearing in the WCED (1987) report. Indeed, there is reason to believe that this statement is generally accepted in the informed public. Hellden (1984), L. Olsson (1985) and recently Ahlcrona (1988) have disputed the importance that can be attached to this travel report and they have been unable to find evidence in the Sudan that can substantiate Lamprey's observation. Nelson (1988), commenting on this debate, writes (p. 7): "..localized studies of this sort cannot demonstrate that, globally, land degradation or advancing deserts is not a serious problem, but they contribute small pieces to a scattered patchwork of evidence that may be starting to build up into a balanced global picture, a picture much more complex and much more mixed than is implied in the many rather poorly substantiated litanies of disaster on desertification." Dregne and Tucker (1988), using satellite imagery, found the northern limit of what they classified as green biomass in the Sahel moving as much as 250 km interannually in the 1980s, thus clearly explaining why it may be very difficult to substantiate any claims to trends of the order of 5 km/year, unless time series of 30 years or more are available. In their yet unpublished review Warren and Agnew (1988) offer much evidence as to why such disputes arise, and also make the useful point that the focus on desert encroachment has distracted from the much more important issues of dryland degradation. Although sand on the move is spectacular and dramatic (as the inhabitants of Nouakchott and other desert towns may well testify), relatively few people are affected bY drifting sands and it is, in any case, only in the most marginal of the marginal lands, or in areas where sand dunes appear more as a geological phenomenon than as a result of land degradation. It is clearly unfortunate that both multilateral organizations and non-governmental aid organizations have given such high priority to the reclamation of these low priority areas, and thereby distracted the general public from the more important issue of general dryland degradation. There is in fact a continuous history of desert distractions, from Bovill (1921), via Stebbing (1935) and Sears (1935) (the latter with a book entitled "Deserts on the March) to H. Lamprey (1975), not to mention the many press cuttings, parliamentary questions and well-meaning private organizations of the 1980s, and possibly also titles like "Rolling back the desert" (UN Desertification Control Programme Activity Centre, 1987). Warren and Agnew (1988) speculate whether "desertification" has not become an "institutional fact," one that institutions wanted to believe, one that served their purposes. See also Forse (1989). Nelson (1988) considers some of the above references, and others, and stresses the need to re-establish a position where a more meaningful dialogue between participants can be developed. Dregne (1987) makes a similar plea, and Warren and Agnew (1988) have asked for the debate to return to dryland degradation, of which desert encroachment is only a part. This must be the correct approach to take, both for scientists and for governments and donor organizations with responsibility for the drylands. -4- 1.6. Are the drylands gone? . Although it is clearly beyond the scope of this report to offer judgment as to whether a final and irreversible degradation of drylands has taken or is taking place, it is worth noting that the very definitions of the degradation processes may implicitly contain preconceived views. *Desert" has become an emotional word, portraying for the layperson a heap of totally infertile sand (although informed people will know better). "Desertification," the "diminution or destruction of the biological potential of the land, (that) can lead ultimately to desert-like conditions" (Karrar and Stiles, 1984), evokes feelings of despair if deserts are conceived as dry sand. This report is based on the notion that dryland degradation is a reversible process. I concur therefore with Nelson (1988) who in the context of desertification wrote: "..the extent of desertification as an irreversible state has probably been exaggerated." Geologists can point to the record of natural changes over millions of years, climatologists over thousands of years, historians over hundreds of years, and the life-long newspaper reader over tens of years. Drylands have come and gone. Dryland productivity has increased and decreased. Humans have migrated to and from. Investment in drylands requires knowledge about the state of the land, whether productivity is increasing or declining under present management, what the forces that affect productivity may be, and whether degradation can be stopped or prevented and productivity restored. Whether further investment in a piece of dryland is warranted relies on the outcome of the above assessment, and also what the costs of non-investment might be, including relief aid, resettlement and changing social structures. And the well-being of people. Nelson (1988) has considered this in the context of the World Bank. The aim of this report is to suggest the tools that are most easily applicable to sound assessment of dryland degradation. The causes of dryland degradation may be many, and they are hotly disputed (see e.g., Timberlake, 1985; Harrison, 1987; Ahmed, 1988). Sandford (1976) usefully described four main views on the causes of desertification: (a) structural (social and economic) (b) natural events (climate (c) human fallibility (incompetence of government, organizations, and individuals) (d) population growth (human, animal). There is in this author's view simply no sound scientific evidence favoring any one of the above factors. Most authors will agree that all are important. Nelson (1988) and Bie (1988) have both stressed climatic variations as a main contributing factor. Others emphasize anthropogenic factors. A separate scientific study will be required to evaluate the evidence - but it may be that well-documented time-series are too short for a conclusive answer. diro FIG. 1 World Distribution of Arid Regions. Source: LSESCO, 1977 一6- ，汗不烈 磚，幼卜一／I、q弋11 &！日實劉 狗勰 O寫．圖令8“各痲．h馴•n州h& 編“嫵M爛、開，矓鰍N胎“魚朧”抓 皂卹吋‘uso．州州開•。．勺此‘州·么戶闈n鳩觔馴”州““州鉤．A翰•““啊縫“”•州眾· 。坤訓”闖鱸l細）f疇Ot7“細州劇抑禹，&&“開州．戶此州鯽1馴狗· FIC。2 ludex of勿ot總1 FOod Productiou iu the Sahela,1960-85 。；戶不熙 神―、I、I\I、騙＇- &！上上劉 1綢口個歸19和唱•馮嗚，閱1閱， 物口 O細轟鹹◆馴參黝血•開義嗡閱 •）“總細駟此開．妒“臨闢糾．細坤鼠•闐細戶叩膩 以黝鹹憑U底）•州•．闔j悶向州唬．方冷闐顯物仰縴“陶h細州幼．州州滷•州闢珍細細•細頗滷· 鉀啊自目糰韋觔劇細寫jT鰓糱祕繪鯽向”劇口•開鳥妒d州開作，劇磚 FIC.3 ludex of Per Capita Food Production In thes血ela,1960一85 -7- Chapter 2: Dryland Degradation - In Search of Definitions 2.1. The confusing definitions Any rational plan of action for organizations like the World bank on the desertification issue, requires tools of communication to scientists and governments with diverse backgrounds and interests. An obvious pitfall in the current debate has been the lack of standard definitions to which participants in the debate may refer. This chapter attempts to establish some common ground. There is a great amount of disagreement on the essential terms to be discussed in this report. There is furthermore a lot of evidence that seemingly different points of view on the state of drylands may originate from different use of the same terms. More seriously, many of the commonly quoted statistics on land degradation, and desertification in particular, are highly doubtful as they are based on confusing, contradictory or ill-defined concepts. Warren and Agnew (1>88) discuss this at very considerable lengths. See also Dregne and Tucker (1988). "Land degradation" and "desertification" are two central concepts. The FAO/UNEP Provisional Methodology for Assessment and Mapping of Desertification (FAO/UNEP, 1983) regards "land degradatioe as processes leading to *desertification," whilst Ahlcrona (1988) and WArren and Agnew (1988) argue that "desertification" constitutes a particular form of "land degradation." It is exceedingly confusing to all that even the hierarchical ordering of 'land degradation" and "deserdfication" is the subject of dispute, and the issue is further confounded by the many aEd highly variable definitions of Mesertification." Glantz and Orlovsky (1983) reviewed the concepts, and Ahlcrona 1988) and Warren and Agnew (1988) have considered more recent additions to the flora. (That there are concepts like "desertization" (Le Hourou, 1975) and "deproduction" also appearing in the debate, does not ease the issue.) Probably the most commonly used definition is the one (on "desertification!) by Dregne (1983): "desertification is the impoverishment of terrestrial ecosystems under the impact of man. It is the process of deterioration in these ecosystems that can be measured by reduced productivity of desired plants, undesirable alterations in the biomass and the diversity of the micro- and macro- fauna and flora, accelerated soil deterioration, and increased hazards for human occup-mcy." In a literature review such as that contained in this report, it becomes almost impossible to ensure that views and facts presented from many authors do in fact refer to the same basic phenomena. Whilst the author will make every effort to be fair, he shares that frustrations so clearly expressed by Warren and Andrew (1988). And he also wishes to extend their short but common- sense definitions. -8- Dryland degradation is loss of resilience in drylands. With resilience is understood the ability of land under a particular land use to withstand or recover from a shock. That land use form is sustainable if land is resilient. 2.2. Utility of criteria Any organization concerned with the issue of dryland degradation will become bewildered by the large number of criteria that have been suggested to describe the large number of processes alleged to constitute or influence such degradation. Presented with reports using varying criteria, any comparison or considered opinion becomes difficult. The World Bank, evaluating alternative investment risks, is clearly in need of comparable and well-defined criteria. in the subsequent paragraphs different approaches will be considered. 2.3. The systems school There are several ways of approaching the degradation phenomenon. One school may be called the "Systems School," with roots to von Bertalanffy (1968) and Ashby (1958). This school motels empirically or statistically complete (holistic) systems. Trolldalen (1986) has discussed various applications of systems theory also using ecosystems (se his ch. 3.2). Figure 4 (page 13) from Ahlcrona (1988) illustrates the upper part of a pyramid of factors - we note that many of the factors illustrated can usefully be described by sets of simpler (single) variables. "Climate" and "Socio-economic and political factors" are themselves superfactors that can be decomposed into simpler factors, and again into variables. Figure 4 has the potential of describing a compete system. Whilst in at least in theory amenable to quantification, this has not been fully explored by the Lund group working on dryland degradation. Their subsequent approach relates more closely to the "single variables sets approach" (see 2.3. below). The systems school model is related to systems modelling of complete agricultural systems. Of particular relevance here the work of Trolldalen (1986) modelling carrying capacity in The Gambia and the Gourma region of Mali. Unlike Ahlcrona (1988), L. Olsson (1985) and K. Olsson (1985) - all working on land degradation in the Sudan - Trolldalen actually attempts to model the complete system, with carrying capacity as the indicator of degradation (here: positive or negative). Figure 5 (page 14) from Trolldalen (1986) also includes the correlation coefficients obtained for the Gambia for the period 1948- 1983. It is clear that studies like those of Trolldalen's or quantification of the Ahlcrona model require much effort and - most importantly - availability of data. In that respect The Gambia offers a much better hunting ground than The Sudan, where even basic demographic data may be hard to find (e.g., Stern, 1985). 2.4. Sets of single variables (indices) The FAO/UNEP Provisional Methodology (FAO/UNEP, 1983) represents an approach based on a decision matrix where combinations of values of single variables are designated a value of an index. The algorithm for calculating an index is given by simple Boolean and arithmetic operators (see FAO/UNEP (1983, ch. 5). Unlike the systems approach it does not explicitly consider co- variance between variables. The required variables for this methodology are summarized in Table I (page 15). The methodology has been subject to a number of trials on all continents, with variable results (FAO/UNEP, 1982). There seems to be a growing consensus that it requires input data not frequently available or easily collected. There is also concern for the lack of formalized handling of co-variance. Much of this concerns stems from the application of the methodology to the 1:5 -9- million Desertification Map of the World, first presented by FAO/UNEP in 1984 (FAO/UNEP, 1984). Few seem to have appreciated that (a) the map presented desertification hazard and not actual desertification; and (b) that the material available for the compilers of the map was much less than assumed by the actual methodology. Thus the construction of indices and the application of the algorithms to create hazard classes was at best tentative. From a scientific point of view the premature launching of the World Map distracted from the potential value of the FAO/UNEP methodology, as the many negative reactions equated map with methodology. The FAO/UNEP Provisional Methodology is currently undergoing tests to investigate whether the methodology may be simplified to overcome the first type of objections experienced: the need for a very large set of variables. These trials are being carried out in Kenya (Marsabit - Baringo) by the Department of Resource Surveys and Remote Sensing (formerly KREMU) on contract to UNEP. Until the outcome of this research becomes clear (medio 1989), it is unlikely that the Methodology will be actively pursued by organizations involved in dryland degradation assessment. Table 2 (page 16) is a draft version of the methodology now under testing in Kenya. Of the same type as the FAO/UNEP Provisional Methodology, although relying on more easily obtainable data, is the methodology in use in the arid lands of the USSR (Babaev, 1988). As this methodology is not widely published outside the USSR< extracts are included as Appendix 2. Note that this methodology includes the use of the concept of potential productivity - a concept that has been widely claimed to be ill-defined and ephemeral. This author has yet to be convinced of a meaningful definition of this concept. This methodology will be tested in 1988-1989 in Northwestern Mali by UNEP-COM, and an evaluation of its applicability may be expected in 1990. A recent meeting in the Economic Commission for Asia and the Pacific (September 1988, in Thailand) established a consensus among 10 Asian countries (Afghanistan, China, India, Indonesia, Iran, Nepal, Pakistan, the Philippines, Thailand and the USSR) for the development of methodologies for desertification to be employed in sub-regional studies there. Methodology and mapping projects will be initiated in 1989-1990. It is proposed that the Desert Research Institutes at Ashkabad (USSR), Linzhou (China) and the Central Arid Zone Research Institute in Jodhpur (India) will play leading roles in this. The outcome of these projects will be important for dryland degradation assessment in Asia. Mabbutt and Floret (1980) include descriptions of older Asian studies (Israel, Iran, Iraq, Pakistan, India, USSR and China). 2.5. Single variables Indicators of dryland degradation may be of two kinds: (a) single variables that have direct relation to resilience (2.4.1. below) (b) single variables that may indirectly reflect resilience (proxy variables) (2.4.2. below). -10- 2.5.1. Directly related variables Examples of variables that may in themselves give indications of degree of resilience are: (a) change in vegetative cover (b) change in species composition (c) changing depth of topsoil (d) change in yield of crops or fodder 2.5.2. Indirectly related variables Some of these relate directly to the natural environment (a) precipitation (amount, distribution, intensity) (b) radiation measured from satellite or aeroplane (e.g., NDVI) (c) number of animals present (d) runoff from watershed Others may be reflecting socio-economic phenomena: (a) prices of agricultural commodities (b) human migration (c) human nutritional status (d) peoples' opinions. 2.6. How to choose assessment factors With figure 4 and table 1 as starting points it may be argued that unless an operational organization has access to the large data sets required for the systems analysis approach or single variable sets, it will get quickly lost in a mammoth data capture and data analysis task. Whilst figure 4 may well provide a sound framework for research (for which it was devised and used, re: Ahicrona, 1988; Reining, 1978; Dregne 1983), and Trolldalen's (1986) study actually providing an insight into relations in models (figure 5)) other approaches may be more attractive for rapid overviews. This is essentially the view of Warren and Agnew (1988). Warren (1984) proposed that criteria for land degradation should be: (a) clear (-unambiguous, objective, widely applicable, determinable from easily gathered information) (b) relevant (-to people living in the area, to the concepts of resilience and sustainability) (c) environmentally specific (referenced not only to socio-ecciomic changes) (d) scale specific (referenced to time and space scales). Criteria not fulfilling these specifications, he claims, should not be used. The latter aspects is clearly important, both to governments and to financing agencies. We are here dealing with marginal areas, whose productivity is likely to be low. Investment in assessment will therefore also have to be low. The problems associated with some of the criteria, i.e., the FAO/UINEP (1983) Provisional Methodology are as much associated with the need for many and costly measured -Il- variables, as with the scientific merits of the variables. Added to this is the fact that such assessments in practice have to be carried out rapidly, which may preclude the use of labour- intensive methods. With the above addition of costs, the Warren criteria for selecting criteria for dryland assessment appears to this author to be most relevant for staff in the World Bank at the present time. However, the Warren criteria of criteria appear to grow out of a frustration of cumbersome methodologies presently used. It is worth pointing out that a number of organizations, and notably UNEP through its GRID system, which is part of the Global Environment Monitoring System, are currently preparing data sets, also at regional and national level, that otherwise are difficult or impossible to obtain. Since such data sets are becoming available in integrated and digital forms (in Geographical Information Systems) they will in due course support more complex ana4yses than what is currently possible. The ease with which such data sets may be analyzed offers possibilities for use of a variety of dryland assessment techniques, including methodologies based on systems analysis, single variable sets (indices) and single variables. The crucial question now - and later - will be the establishment and maintenance of relevant data sets. 2.7. Two broad criteria Warren and Agnew (1988) following Conway (1984) therefore propose only two broad criteria for judging land degradation. (a) productivity (2.6.1.) (b) resilience (2.6.2.). 2.7.1. Productivity Productivity is defined as the rate of production. Productivity is defined separately for each production system. A decline in productivity is an indicator of land degradation, but is not sufficient to establish that degradation is taking place. Productivity is: (a) clearly defined (e.g., kg/ha), can be relatively easily measured, and universally applied (b) relevant for a defined, economically important product (c) environment specific, associated with environmental changes (disregarding here e.g., economic market changes) (d) relevant over a specified period of time - the time scale must be known (e) tied to area and must be tied to a known area of land, large or small. Thus defined, productivity fulfills the criteria of goodness for an assessment factor. Note that this definition relates to a-'tual productivity, not to potential productivity, and is therefore not subject to the type of criticism associated with this ephemeral term. -12- 2.7.2. ResiWeace Resilience is the property of a resource that makes its sustainable use possible. Land degradation occurs when resilience is damaged. Resilience is: (a) less easily defined than productivity. The ability of land to return to its origin having sustained a shock can only be measured over a period of time over a given area. Resilience is defined for each land use. Resilience is defined differently by different authors (see Holling, 1985), but in this context we employ the definition of time taken to return to the equilibrium for that particular land use,given that the shock factor no longer operates (b) environment specific and also related to the economics behind each land use. It may, for example, be part of a particular land use to include strategies that reinforces resilience, e.g., fallows or the return of eroded soil from lower to higher slopes. (c) both time and space definable. Thus defined, resilience fulfills the criteria of goodness for an assessment factor. It should be noted that although damage to resilience indicates land degradation, resilience can be restored, commonly through use of capital, technology or even rest periods. The degree of damage to resilience equals the cost of recovering resilience. If the costs exceed the benefits of or the capital available for resilience restoration, the land is "permanently" degraded (until economic factors change). This economic concept of resilience, developed by Woods (1984) for Australia, provides a useful link to economic planning. Note that it follows that no dryland can be *permanently degraded." Given enough ecvnomic and technological input all land can be restored. But as poor countries have less to input thin rich countries, damage to resilience is more serious in poor ones. 2.7.3. Concluding producivity and resilience The definitions above, based on the arguments of Warren and Agnew (1988), appear to this author to be the most useful tools in the search for assessment factors for dryland degradation. They will therefore form the basis for subsequent recommendations in this report. -13 . CLIMATE WATER SUPPLY FEavTIZE sI sas o wINZAeON .AE Pines MANURE EROSION BURNING UTRIENT STATUS OF SO NATURAL VEGETATION WIND EROSION WO 1OLLECTION CROP CULTIVATION GRAZING FOD YIELD PRESSURE PRESSUR CROP REGIONALI PRODUCTION FOOD SUPPLI LIVESTOCK FAMINE POPULATION FIG. 4 A generalized model of factors involved in the disturbance of arid and semi-arid ecosystems Source: Ahlerona, 1988. Whilst in at least in theory amenable to quantification, this has not been fully explored by the Lund group working on dryland degradation. Their subsequent approach relates more closely to the "single variables sets approach* (see 2.3. below). 的O件闐O州 00才OH,H C吋中00 吋吋，中 n 000的 化卜J弘n吋必， 日晝鎚華：「一”b羸罷了劉劉哪r，魚 0OCO&－•－一．一一馴－~&－一＿。＿--..．一。＿_＿一一＿ 二嗡’&/ -15- Table 1: FAO/UNEP Provisional Methodology I: DEGRADATION OF VEGETATWE COVER For gW . consider canopy cover of perennial plants (%); biomass in kg of Dm/ha/year, production of fodder in Unit/ba/Yew, % at potential producUvity; biomass in kg of Dm/mm of rain. For , conWer degradation increment (% per year); range trend line for last 10 years (in absence of drought); woodland &rend line/yean cereal yields trend line/year. n9M999MMEW ; a. WATER, EROSION For statue. tousWier density of Ale and gullies per kni ("nsversal); surface statue; type of erosion; loss of topsoil and subsoil (%); surface affected by gullies (%); soil deposits in cm; anuence of horisons; thickness of add (A + 8) In % of original thickness; % loss of yield compared with non-eroded soils; decrease of organic matter content in % of am-eroded soils. Xo-r consider. removal or deposition above normal in Mt/ha/year or in imn/yew sediment deposits in dam in 9i of retenUou per yew. For risk, consider. slope; precipitation in mm; weight of soil led in t/ha/yr; rainfall factw. soil erodibility-, toporaphY factor. biotic factor, rainfall erosivity index per type of clium", ounimer drought (two runy seasons and winter precipitation). winter drought (two rainy seasons and summer procipitation); hwisition regunes- HL WIND EROSION A , consider. low of topooil (%). form of solian formations; total surface covered by solian formations (%); surface covered by vemer of eolian deposits; surface covered by hummocks (%) above vorma4- surface covered by moved dunse (%); nladve toncenhWAon on ground surftcs at graveis and/or stonee (%). For raW consider. removal above geologic r&W (Wlt/ba/yr); deposition above geologic rate (Mt/ha/yr); sand btasking at fine material- depth of soil removed per year (cin); growth rate of the area invaded by sand in % of aftecW area; depth of soil tranoportW by the wind (cm/yr). For risk, consider. wind erosivity Index; sand storm frequency (times per year for a 10 yew period); number of days of wind storm per year; number of hours of wind storm per year, number of days of wind atom in spring (March-Aptil) (mad dangerous in sub-ftopical atom]; --- wind speed at 2m, height (mlsec). iv. SALINIZATION For status consider. ma-i- We x 103 in n-thos/cm in upper 75 cm of soil-, mazinnim, ESP in upper 75 cm soil; plant yields (% of yields of similm non-deserUfied soils); now formations; morphological observation; salts in t/ha/I.Snr, tfim/0.75. For rate consider. EC incream in mmhoo/cm/yr in upper 76 cm of wil; ESP incresses in upper 76 cin in % per year. yields in % par you; surface affected by soluble =11s in %. FqE.d& consider climatic index for salinisation; number of dry months (in absence of Um critical depth of ground-watw table); average depth of ground-vater(cm); salt concenla%fion of irrigated water, dry residue in ipm/1 and In EC in nimbos/cm. SOIL CRUSTING AND COMPACTION Consider only for datus: ca][cic accunulation and cementation form (depth in an); gypsic accumulation and comentation form (depth in cm); ferric accumulation and camenUtion form (depth in cm). vi. REDUCTION IN SOIL ORGANIC M[ATTER. For datus consider the actual dhaUon in % of qAimmn natural level ror consider. reduction in organic nu"w in surface layer in % per year for last three yews. vii. EXCESS TOXIC SUBSTANCES IN THE SOIL (ph 6.5) Consider only for d : Lead. dnc, and copper -16- Table 2: Revised FAO/UNEP methodology on trial In Kenya 1988/89 Variable Plant cover % Soil surface texture Above ground biomass Soil permeability Primary production Salinization and alkalinization Desirable species (area %) Soil crusting Rockiness Type of erosion Organic matter Surface status Soil loss Land use Range condition Aggresivity of climate Stocking rates Topography Carrying capacity Erodibility soils Crops grown Crop yields Surface area wind erosion Change in surface Population density Windspeed Population carrying capacity Wind frequency -17- Chapter 3 Assessing Productivity and Resilience 3.1. Land, air and space, statistics There are four basic methods of collecting data that may relate to productivity and resilience, and thus to dryland degradation: (a) ground surveys (3.1.) (b) aerial surveys (3.2.) (c) satellite remote sensing (3.3.) (d) statistics (3.4.) The first three methods have been extensively described in UNEPs "Handbook of Ecological Monitoring" (Clarke, 1986). This work also discusses briefly the collection of some socio-economic variables (section 2.9.). This chapter draws heavily on this Handbook (which, with its many references is becoming a standard text), but also on primary material and newly published data. It is worth noting that the Handbook does not cover monitoring of animal habitats, and in particular the estimation of animal population. It has been claimed in the past that ecological monitoring is geared to measure all stages or deproduction (and) it can therefore monitor the process of desertification. Similarly the ILCA monograph on low-level aerial survey techniques (ILCA, 1981) contains reviews that whilst relating also to general ecological monitoring of drylands, do not specifically address productivity and resilience as defined in chapter 2. The World Bank draft report on remote sensing (Falloux, 1988) does not cover particular guidelines on the assessment of dryland productivity and resilience. However, together, these and other studies contain essential guidance on methods of assessing these two criteria, and their costs. For a further discussion of relevance to productivity and resilience, it must be noted that both these concepts are tied to individual forms of land use. Without reference to land use the concepts are not meaningful. Land use classification is an issue in itself. Unless comparable land use classes are applied, it may be difficult to extend experience gained in one area to another. It has clearly been very confusing in the dryland degradation/desertification debate that references made to one named land use type may not be applicable to other areas with the same name, as different land use classifications are employed. Standardization on one land use classification would be valuable. -18- 3.2. Ground surveys 3.2.1. Criteria of productivity Much of the arid and semi-arid lands are harvested through grazing animals. Conventionally the productivity of grazing lands is considered in terms of carrying capacity. Carrying capacity is normally defined as the number of livestock units (LU) that can graze an area in a sustainable way (over indefinite time). The concept of carrying capacity is difficult to handle empirically. Some livestock are grazers (e.g., cattle, sheep), others are browsers (goats, camels). Some animals can adjust their intake of feed with the quality of the fodder ( e.g., horses, donkeys). Others, who in the end rely on the utilization of microbial products (i.e., the ruminants) have lesser ability to adjust food intake. In general good quality fodder (high protein, easily digestible carbohydrates) will make horses and donkeys eat less, but poor quality fodder will make cattle also eat less (Clarke, 1986 pp. 91-111). The mixture of grazers and browsers, including wild game, determines the productivity of the land. Carrying capacity furthermore varies from year to year. Much of the criticism that has been levelled against the concept of potential productivity thus applies to carrying capacity. Carrying capacity is also an average term. It is meaningful when applied to a single management unit or area. Given that rainfall is notoriously patchy in dryland areas - with large differences in pasture growth over short distances - its use over larger and thus heterogeneous areas is doubtful. As land use complexity increases, carrying capacity becomes increasingly difficult to define. These aspects are usually oerlooked in the literature. Confusing as this may seem, the production and the digestibility of each type of vegetation for each type of livestock may be determined by ground surveys. Digestibility is for practical purposes tied to plant species. Digestibility data can normally be obtained from local sources, or must be determined directly (e.g., Clarke, 1986, pp. 73-81). There are three methods of sampling vegetation (Clarke, 1986, pp. 67-72): (a) Plot methods involve the establishment of a perimeter around a plot of vegetation, and then counting the plants and estimating their fodder quality within the border, (b) Intercept methods include the step-count method number and types of plants contacted by the point of the foot when walking through vegetation, line intercepts measures the interception with a tape measure, and point intercepts measures contacts on a pin passed through the vegetation; and (c) Plotless methods involve the recording of plants encountered at random intervals along transects or statistically random walks. All the methods are time and labour consuming. Whilst depending on sampling density, the general trend is for plot methods to be more expensive than intercept methods and also than plotless methods. Ground surveys, whichever method is used, require multiple sampling. Frequently recommended are samples taken at the height of the growing season, and samples taken just prior to the onset of the rains. Most dryland areas have only one rainy season per year, so that biomass is collected at maximum and minimum periods. A limiting factor in the productivity assessment is -19- usually the amount of feed available at minimum. The nomadic practices of some of the inhabitants of the drylands, utilizing pasture resources outside the actual drylands during minimum periods, may alter the calculations of productivity. Similarly animals with short gestation cycles (e.g., goats) may be able to utilize abundant fodder better as the total average annual herd may be considerably larger than the breeding herd. Again adjustment may have to be made for this. Ground sampling for productivity assessment involves obtaining a (probability) sample of the annual fodder quantity and quality. Direct ground measurement of vegetation can therefore only be done at considerable expense. Several authors have looked for proxy variables that may substitute for the costly pasture evaluations. thus in Tunisia Mabbutt and Floret (1980< p. 29) report of "rules of thumb" indicating that a vegetation cover of 20-40% by perennial species suggests that degradation is (not yet) severe. The use of proxy variables in association with ground surve, has few references. The marginal costs of assessing more and direct variables are probably small, although digestibility studies are always expensive to perform. Grounds surveys constitute a set of assessment techniques in their own, but they also provide ground reference data for aerial surveys (3.2) and satellite remote sensing (3.3.): "ground truth." When used in this fashion sampling requirements may be different from those underlying a complete ground survey. Whilst such surveys constitute an estimate of the complete population (of soil, vegetation etc.,) within an area, the needs for ground reference data relate to estimates of the classes employed in the aerial/remote sensing survey (i.e., in property space rather than geographical space). Thus, sampling intensity is normally much lower than when the ground survey is also assumed to provide limits of spatial extents of classes. 3.2.2. Criteria of resilience The ability of land under a given land use to return to its previous productivity following a negative shock, defines resilience. It therefore follows that ground surveys designed to measure resilience must cover a number of years when such shocks have occurred. Resilience is measured over a time-scale, and short-term resilience may be different from long-term. A measurement series may not pick up resilience data if no shocks occurred during the measurement period. Short-term (I to 2 years) and longer term (5 to 30 ydrs) resilience both have their value as indicators of degradation. Knowledge of short-term resilience may greatly aid in the planning of the phasing out of relief aid following a famine. Knowledge of long-term resilience may guide resettlement or longer term investments. Direct measures To measure resilience is to monitor productivity. This does not imply that land must return to its maximum productivity but to a stable state. Land returning from a lower productivity to a stable, higher productivity, has demonstrated resilience. Thus, repeated measurements as indicated in 3.2.1. (above) will perform this function. However, this is expensive. An alternative way of measuring resilience is to pair otherwise comparable areas where one may have been subject to an exogenous factor and the other not. Zonn and Orlovsky (1986) document such cases both for results of war and nuclear tests, and for degradation in the vicinity of boreholes, particularly those for the watering of livestock. Mabbutt and Floret (1980 p. 139) quoting a Niger study by Peyre de Fabregues in 1971, indicate that observation of vegetation degradation around boreholes may well serve as an indicator of resilience, in that recovery around boreholes temporarily or permanently not in use gives a measure over time. Similarly, the occurrence of patchy fire is so frequent in the drylands that resilience to grass and -20- bush fires Is easily observed. Also, since rainfall is frequently patchy in the drylands, with large local variations, careful recording of such patches may reveal the resilience of the land to (local) drought and Indicate at least short term resilience (see Mabbutt and Floret, 1980 p. 143). Indirect measures There may be indirect methods of estimating resilience. An indirect measure of resilience is the time taken for an original soil profile, once eroded, to regain its former characteristics. There are indications that in the wetter part of the drylands even severely eroded profiles can regenerate over a relatively short time (2-4 years) as observed by this author in the Wollo province of Ethiopia in 1987. Rapid pedogenesis indicates resilience, persistence of the eroded profile the lack thereof. The monitoring of soil profile development is best done by sampling (by auguring) in a stratified random design to ensure geographical coverage. As an alternative a plant ecologist may, by studying the vegetation communities, offer suggestions on how far the actual vegetation has departed from a standard e.g., the postulated climax vegetation, and whether recovery is likely. Australian experience has indicated that once the seed resource is lost and buried seeds no longer germinate, resilience to the original vegetation is lost, although a new (climax) vegetation may appear. Similarly, and following from the Wollo observation above, a trained soil scientist may estimate from looking at the existing soil profile, which layers have been removed and whether or how fast the reestablishment of the original soil profile (-resilience) may be achieved. In both cases - vegetation and soil - there is clearly great uncertainty associated with estimates based on one-time observations. This author attaches little confidence to statements of resilience based on such observations. Whilst resilience as outlined above is tied to each and every land use, the question arises whether resilience can be a disconnected variable for land, with each management (-land use) connected to it. The availability of a "universal resilience variable" needs further exploration. It is clearly related to variables like sustainability with the management factors removed. 3.3. Aerial surveys Originally designed for the counting of animals, domestic or wild, the use of observations from low-flying aircraft has become an essential tool in the monitoring of livestock and game. The UNEP Handbook on Ecological Monitoring (Clarke, 1986) is largely based on discussions of this technique. Earlier overviews of this technique may be found in e.g., ILCA (1981). There seems to be a consensus that the application of low-flying aircraft observation techniques has lesser application for essential elements of dryland degradation than originally thought. Although indispensable for animal censuses the variables likely to be most closely associated with productivity and resilience are not easily assessed in a rapid fly-by. Three features, however, that are often not well recorded on maps or easily visible from aerial photographs (see 3.2.2.) may be relatively easily observed from low-flying aircraft finer erosion patterns, presence of components of vegetation (rather than botanical composition) and the presence and type of smallholder cultivation (Clarke, 1986, p. 173). Recently R. Lamprey and de Leeuw (1988) - albeit in a different context (see 3.4. below) - have demonstrated the close correspondence between aircraft-flown radiometers and those in satellites for measuring biomass. There seems to be considerable scope for extending systematic low-level flights, employing observers and modified 35 mm camera, to radiometers. Flying at much lower altitude the atmospheric correction problems associated with e.g., NOAA AVHRR-imagery in the tropics and semi-tropics are greatly lessened, although difficult geometric corrections remain. However, as this is the first study of its kind it would seem premature to recommend this otherwise -21- promising technique both for the assessment ot productivity of the land and the resilience. It is plausible, however, that further extension of this UNEP/ILCA-project may be found to offer a powerful addition to both absolute and relative measurements of biomass and thereby give value input to the assessment of dryland degradation. In its own right the counting of animals from low- flying aircraft may offer indirect measurements of both productivity and resilience. However, the numbers of livestock are often controlled as much by outside socio-economic forces (including import/export from better-endowed areas) as by the carrying capacity of the land. Great surges in game population, e.g., the 8-foid increase in wildebeest on the Serengeti Plains in the 1960s and '70s, may have to be explained in terms of population dynamics rather than assessed by factors commonly used in degradation studies (Clarke, 1986, p. 142). Livestock and game numbers relate to the concept of overgrazing, an often-claimed cause of dryland degradation. Overgrazing again relates to the concept of carrying capacity. Overgrazing must be defined in relation to the grazing or browsing animal, as they exploit the vegetation differently. Changes in livestock numbers, or the abundance of game, are indicators of degradation risks, but in the view of this author they do not constitute a direct measurement technique of degradation. Pastoral peoples, utilizing the drylands, have many socio-economic mechanisms for adding or removing stock from a grazing area. Livestock and game numbers are useful indicators, however, as to whether direct measurement techniques should be employed. To conclude, low-flying aircraft do not have a proven capability of producing data that can be directly employed in dryland degradat.on assessment. However, the use of radiometers offers considerable hope of expanding a proven animal census technique also to this field. In this context the work of R. Lamprey and de Leeuw (1988) may extend the pioneering efforts of Gwynne, Croze, Norton-Griffith, Andere and others (see e.g., Clarke, 1986). It is of course paradoxical that ELCA, so long devoted to such surveys, have now largely focussed on other applications. The ILCA work in Kenya referred to above, and its efforts in Mali by P. Hiernoux, may yet offer hope of a return of interest to rangeland monitoring, an essential part of dryland assessment. 3.4. Air photo Interpretation This is a classic and well-proven methodology for the mapping of land. A.p.i. has over the last 50 years found its use in topographic mapping, in a wide variety of thematic mapping tasks, and in the monitoring of changes. It is clearly beyond the scope of this paper to offer a comprehensive guideline to a.p.i. techniques - there are very large reference works to this effect (e.g., Manual of remote sensing, 1986). instead this report will focus on the direct use of aerial photographs for pertinent dryland degradation variables. 3.4.1. Productivity Air photographs, whether black and white, color, infrared or a combination of spectral bands (e.g., Clarke, 1986, pp. 230-234) offer insight into a large number of spatial phenomena related to dryland productivity. Whilst features i.lating to productivity may be observed and quantified directly by an interpreter with local field knowledge, accurate and consistent assessment normally relies on calibration with ground observations (see 3.1. above). Thus, ground observations and a.p.i. together form an assessment technique. Features relating to acreage of different crops grown, including various types of rangeland, woodland, solitary trees, bare ground (including sand dunes), areas affected by fire, floods or salinity and alkalinity are relatively easily quantified by a.p.i. Similarly a trained interpreter will be able to estimate the extent of major types of soil and of erosion areas and types. Backed by knowledge of the local vegetation and interpreter may well be able to offer estimates on woody biomass, e.g., by the estimation of canopy or canopy shadows (e.g., K. Olsson, 1986). -22- There is such a vast and established literature on a.p.i. in its various forms that there can remain little doubt that air photographs in combination with ground surveys offer a proven and often cost-effective assessment method of dryland productivity. Even to the most ardent satellite supporters it must be obvious that air photographs offer a resolution of small scale phenomena related to productivity that tatellite sensors cannot obtain with present technology. As the same supporters no doubt will point out, air photographs also have obvious drawbacks. Of prime importance here is the timing of the photos. Estimates of productivity are often best related to productivity at maximum and minimum. Given that dryland productivity is closely linked to precipitation, it often happens that photographic conditions are far from ideal at the time of maximum productivity. Similarly any traveller of the drylands can vouch to haze and sandstorms being associated with the driest times of the year. Optimal timing may be more than difficult to achieve. Granted that standard air photos are often taken to serve many purposes, including topographic mapping, geological surveys, land use 3urvays, hydrographic investigations etc., each of which may have their own optimal timing, productivity assessment may receive little consideration from the planners of a.p.i. campaigns. Repeated series within one year are rare - what may happen is that parts of larger areas may have been flown at different periods, making productivity assessment difficult or impossible. In practice, therefore, a.p.i. on standard air photo series obtained for alternative uses, may have serious drawbacks for productivity assessment. 3.4.2. Resilience Whilst a.p.i. for productivity assessment has obvious pitfalls, there are at least some aspects of resilience that can be assessed and quantified even if air photographs are not optimally tied to the productivity cycle. In particular this relates to estimates of phenomena like trees- clumped or solitary - and bare ground, including sand dunes. Changes in numbers and areas of such features over years offer perhaps the best historical record of changes in the landscape and thus of resilience. Similarly areas of cropped lands and of actively used grazing lands (including patterns developing round watering points), offer clues to the states of the land. In the view of this author the single most important clue to the resilience of drylands lies in the comparisons of air photos taken at various times over a long period. The colonial past of many of the World's dry countries has ensured that many have extensive series of air photographs taken over the last 50-60 years, often for mapping or military purposes. It is a paradox that at the time of high technology virtually nothing is being done to safeguard this most valuable source of resilience data. Many countries, donor agencies and consultants have spent much more money in obtaining one-time space imagery than what it would cost to secure negatives and positives that now offer the best indicators of resilience. So much valuable material is currently being lost through poor archiving (lack of air conditioning, poor administrative routines) and the inherent fragility of photographic material. Strict adherence to security rules makes it sometimes difficult for outside agencies to mount rescuing services. It is also a fact that ex-colonial powers may have the only copies of air photos in their own archives, without encouraging the transfer of this material to the independent countries (with possible loss of the material). International consultants may also wish to safeguard their commercial interests by not releasing photos to others. Recently proposals by IGN (France) in 1987 and ILCA in 1988 on the use of historic air photos to monitor changes in the Sahel and in rangelands more generally, illustrate the value now appreciated of such archives. It is much to be hoped that these studies can add significantly to our knowledge of resilience assessment. -23- 3.5. Satellite remote sensing as with air photo interpretation there is a very large literature on satellite remote sensing, ranging from the comprehensive Manual of Remote Sensing (1985), through more introductory texts (e.g., Harris, 1987) to more specialized treatises in e.g.a Clarke (1986) and Falloux (1988). the task of this report is to evaluate the contribution that data from a variety of space-borne sensors can make for the assessment of productivity and resilience in drylands. It is assumed that the reader has basic knowledge of the type of sensors used and their resolution. 3.5.1. Productivity 3.5.1.1. High-resolution satellites Early Landsat sensors were specifically designed for the measurement of crop yields, particularly of cereals. Later sensors, with improved spatial resolutions, have been applied in similar fashion. Thus both MSS and Thematic Mapper on Landsat-satellites have a proven record of acreage and yield prediction granted that a reasonable amount of ground reference data ("ground truth") is available. The technology has proven particularly valuable for dryland conditions, where vegetation cover is limited. Similar evidence is beginning to accumulate on the more recently launched SPOT- I satellite, and there is little doubt that data from the Japanese MOS- 1 and from Indian and Chinese satellites may be used in a similar fashion. There is considerable published evidence of the use of Soviet space imagery for land use and yield predictions. Unlike air photography most high-resolution satellites will give repeat coverage of an area, normally 15-30 times per year. Although many of the images of an aiea may be of little value due to cloud or haze (variable with region and time of year) the drylands of the world have a fair chance of multiple images being available. However, there is less certainty as to whether these will be timed to coincide with maximum and minimum so as to assess overall productivity. In practice the commercial vendors of space imagery, be it in paper form or as digital tapes, have shown limited ability to provide multi-temporal sets of data that can routinely serve productivity assessment of drylands. Whilst the remote sensing literature has numerous papers on experiments showing the utility (see e.g., AhIcrona (1988) for her work in the Sudan, and references to earlier studies (Ch. 8- 9)), there is virtually no proven track-record on operationality. Whilst this author remains convinced of the technical feasibility of estimating important aspects of productivity from Landsat MSS and TM, SPOT, MOS and similar space imagery with pixel side sizes of 10-80 m on the ground (using a variety of vegetation indices), I have serious doubt about the present operational capabilities of the commercial companies in providing such data routinely - at any price. A World Bank study by Falloux (1988) addresses specifically this problem. There appears to be a consensus by many African remote sensing specialists that this operational bottleneck can only be overcome by the construction of several receiving stations on the African continent. The advantages of this is to shorten the route between satellite and end consumer, allow for better scheduling of recorded scenes (e.g., SPOT), and relieve satellite on-board tape recorders. Smaller data by conventional or digital means. Ultimately there is a hope expressed that local capability for radiometric and geometric correction of images, image enhancement and further processing may reduce the costs associated with such imagery. Although costs per unit area may be low, the price of space data constitutes at the moment a limiting factor in application of high resolution imagery. Multi-temporal data sets are required. Image analysis systems, the infrastructure and expertise associated with their operation also constitute sizeable investments, that require significant production volumes to be justified. The technical potential in high-resolution imagery for dryland productivity assessment was demonstrated as early as 1978. The logistics involved have, however, proved difficult. Large-scale use is a must to cover heavy investments in people and equipment - alternatively the cost of -24- employing outside contractors. There is considerable uncertainty about operational use of such imagery for dryland productivity assessment in the nearest future. Financial commitment and indigenous user requests for productivity assessed from high-resolution satellites have not been forthcoming from dryland countries. In the longer term applications of this technology should be user-driven (Falloux, 1988). If the user has access to the required technology and funds project-oriented use of high- resolution imagery may well be cost-effective, both as an insurance policy against unwise investment in low-productivity areas, and as a guideline for planning. However, the logistic constraints mentioned above still exist. 3.5.1.2. Meteorological satellites Weather satellites in polar orbits have been in routine operation for the last 8 years. Their data flow, whilst originally intended for meteorological applications, has been widely used in the assessment of dryland productivity. With pixel sizes of Ikm x l km (at nadir), sometimes aggregated to 4km x 4km (LAC and GAC modes), these satellites (including the NOAA series) offer frequent coverages of all dryland zones. Geostationary satellites (e.g., Meteosat) have much poorer spatial and radiometric resolution and are less suitable for productivity assessment. The polar orbiting NOAA satellites equipped with the Advanced Very High Resolution Radiometer (AVHRR) offer each at least 2 daily day-time passes over any point on the globe. Thus the frequency of passes, and the chance of obtaining cloud-free images at relevant periods for maximum and minimum productivity are greatly increased compared to Landsat, SPOT and similar. The tracking and reception of NOAA AVHRR-data is technically little different from the high-resolution satellites. Thus major investments in satellite reception must be expected. But as meteorological services may be the main customers of the data, costs may be shared with this major user. Even so there are very few NOAA receiving stations in poorer countries, and as the satellites do not carry tape recorders there are large gaps in the geographical coverage in archives kept at major receiving stations. Currently, however, global coverage is possible at special request, and NOAA and NASA are both developing complete global datasets. The costs of NOAA AVHRR data are not borne by the end user, as with Landsat and SPOT (and others), but by the meteorological community, and that in the US in particular. Consequently even the purchase of archived data is relatively inexpensive. Delivery has, however, been erratic, both from NOAA and processed data from NASA, although improvements in delivery time has improved in the last year. Pioneering work employing vegetation indices to assess green biomass has been done at NASA Goddard Space Center, primarily by Tucker and co-workers (in a long series of papers Tucker and Justice (1986) may serve as an introduction and overview). As with high-resolution imagery the calculation of green biomass makes use of the absorption of sunlight at frequencies where the chlorophyll in plant cells is active and frequencies where there is high reflectance (Channel 1: 0.55-0.68 micrometers and Channel 2: 0.73-1.1 micrometers respectively). Of the many ways of -25- using these data most work has been done with the Normalized Difference Vegetation Index (NDVI) defined as: (CH. 2-Ch. 1)/(Ch. 2+Ch.1) This index compensates for changing illumination, surface slopes and viewing aspect. There has, however, been considerable uncertainty as to whether NDVI corresponds sufficiently with productivity to be of predictive value for this variable. The arguments of concern have been: (a) NDVI does not give plant species composition, thus no data on palatability or digestibility for various types of grazers/browsers (b) NDVI only relates to photosynthetically active plants, thus gives no information on the dry states of grasses so important in productivity assessment. It has been argued that whilst limits of green biomass may well be visible from NOAA AVHRR (e.g., Dregne and Tucker, 1988) the correspondence to productivity remains unproven. In a recent study R. Lamprey and de Leeuw (1988) have found good correlations between AVHRR NDVIs and NDVIs measured by an airborne radiometer In various parts of arid and semi-arid Kenya, and further interpreted by aerial photography. Whilst not providing the final answer as to the correlation between NDVI and aerial and ground reference data. Incidentally, their paper contains a very extensive bibliography on the subject, including biomass calibration studies by ILCA and others in Mali, Niger and in Botswana. See also Hellden (1987) and Hellden and Eklundh (1988). This author has come to accept that AVHRR NDVI and similar indices offer very considerable scope for productivity assessment, particularly of the herbaceous cover. The low cost of imagery allows for calibration with ground and aerial reference data. The recent wave of calibration studies is likely to confirm that coarse productivity assessment of dryland productivity may be achieved by using NOAA AVHRR imagery, preferably in LAC mode. It is more uncertain whether other types of land use, e.g., wood fuel production, can be estimated equally well. There are here conflicting data (e.g., Langaas, 1987; Hellden, 1987; R. Lamprey and de Leeuw, 1988). 3.5.2. Resilience 3.5.2.1. High-resolution satellites Multi-temporal studies covering many years are still in their infancy with Landsat and SPOT imagery. It has proven difficult to obtain relevant and timely cloud-free images over many years. Although progress has been made with geometrical corrections to allow (digital) superimposition, this is still heavy computing work required. With costs of high-resolution imagery remaining relatively high, it is clearly uncertain whether such studies can be undertaken for anything but smaller project areas. At the present time high-resolution satellites do not appear to offer an operational method for resilience assessment in dryland degradation studies. This may, however, change in the future, whereby the early scenes recorded by Landsat (dating back to 1972) may provide vital data in longer time-series. Analogous to the argument about preservation of historic aerial photographs (3.4.1. above), it seems important to safeguard the digital recordings of early Landsat passages. This author has been informed about the possibilities of deteriorating early Landsat-tapes. Considering their potential historic importance in future resilience studies, it is hard to comprehend that funds are not available for securing these datasets. -26- 3.5.2.2. Lower-resolution satellites From the arguments of 3.5.1.2. (above) it follows that within the limits of calibration the meteorological satellites of NOAA type offer an interesting method for estimating green biomass response to external shocks. Attention is drawn to the two review articles by Tucker and Justice (1986) and Dregne and Tucker (1988) on this issue, particularly relating to the African drylands. Time-series (up to 8 years) are now becoming available for the measurements of resilience in grassland ecosystems in the Sahel. As longer time-series become available in the future, lower-resolution imagery will become increasingly interesting for resilience studies. To illustrate this Dregne and Tucker (1988) point to the need for a 20-40 year monitoring study to investigate whether the desert boundary in the Sahel is in fact moving, or just being subject to annual fluctuations. There would appear presently to be no other low-cost alternative to AVHRR-data for long-term resilience assessment over large areas. Particular prhise should be given to NOAA and NASA for securing the historic data of NOAA AVHRR recordings in the form of decadal datasets going back to 1981. This effort may be an inspiration for e.g., analogous actions on early Landsat data (see 3.5.2.1.). 3.5.3. Statistical data 3.5.3.1. Productivity Production estimates are frequently carried out at times of drought, by governmental and non-governmental organizations, or as part of early warning systems. Such estimates are prone to serious errors and to translate these into productivity estimates is not always meaningful. They should - in the opinion of this author - be treated with extreme caution. This applies particularly to livestock estimates, where the deathrate, imports and exports of animals into an area are notoriously difficult to evaluate. YIeld data Data on yield are frequently proposed as proxy data for productivity. Decline in yields have frequently been used as indicators of dryland degradation. Detailed studies in Northern Sudan by L. Olsson (1985) and Ahlcrona (1988) indicate cereal yield correlations of r = 0.7-0.8 with number of days with significant rainfall. At least in this area yield data are thus of limited value in indicate that degradation is taking place. Yield data (e.g., t/ha) furthermore do not reflect a normal farming response to declining productivity, namely the increased use of more marginal areas. Tilling of marginal lands was a widespread response to recent droughts in the Sahel, particularly during the first few years. There do not seem to be studies relating changes in arable acreages to degradation. Rainfall data Biological production in the drylands is closely linked to availability of soil water. Soil water is linked to precipitation, but indirectly as infiltration and permeability of the soil determine some of the soil water availability to plant roots. (There are also other factors, e.g., nutrients, pans.). As argued above (3.4.3.1.1.) there is considerable statistical material available to suggest that the rate of production (=productivity) is closely related to available soil water. It is not easy to derive soil moisture data from rainfall figures. Studies by Hellden, L. Olsson and Ahlcrona (see e.g., Ahlcrona, 1988) suggest that in the Sudan the best correlations are achieved by considering rainy days with precipitation in excess of between 3 and 10 mm/day, while Langaas (1987) working in the Gambia -27- considered rainfall over 10-day periods as guidelines to soil moisture. it seems probable that similar relations may be established for other dryland areas, but research will have to ascertain these. Note that although originally point data (rain gauges) rainfall data may be interpolated to areas in two ways: by surface modelling (e.g., contouring) or by the use of satellite imagery of periods following rainfall. The patchiness of rainfall in dryland areas make surface modelling uncertain. The application of satellite imagery has, however, shown considerable promise (Hielkema et al., 1986; R. Lamprey and de Leeuw, 1988). Interviews In the social sciences interviews constitute an important source of information. In the context of productivity assessment Clarke (1986, section 2.9) has offered guidelines on the selection of interviewees and interview techniques. 3.5.3.2. Resilience Yield data Time-series of yield data offer interesting possibilities for the monitoring of resilience. It is important, however, to consider them in view of the land use practices employed. In section 2.6.2. resilience was related to costs. The degree of damage to resilience equals the costs of restoring resilience (Woods, 1984). The costs of maintaining constant yields may be seen as the costs of maintaining resilience. However, physical deterioration of the land may nevertheless take place, and yield data, or the economic data associated with the inputs required to maintain yields, are but indicative of resilience. Monitoring of physical variables is essential. The sole uses yield data as a resilience measure corresponds to single variable assessment, and is risky. Rainfall data The understanding of the dryland environment is helped greatly through records of precipitation. Long-term rainfall data as presented by Lamb (see Kerr, 1985 or Bie, 1988), L. Olsson (1985), Ahlcrona (1988) or Langaas 91987) offer guidelines to when other physical factors of the landscape should be measured. As a direct variable indicating resilience rainfall data are not useful. As an indicator of when to look at other assessment factors (times of drought, times of plenty of soil moisture) they are most valuable. Pollen analysis Sampling of pollen from sediments is an additional tool for the study of medium to long term resilience and perhaps the most quantitative insight into past patterns. It offers interesting possibilities for estimates of species composition and frequency. Although pollen analysis suffers from uncertainties as to whether the sediments sampled really represent a probability sample of the vegetation, they are indicative. The long-term (1.1 million years) study of the drylands of Central China by An and Liu (quoted by Yeh and Fu, 1985) give unique insight into long-term resilience. Shorter-term analysis of Lake Chad sediments similarly gives clues on recent Sahel vegetation patterns over the last few hundred years (e.g., Nicholson, 1982). -28- Interviews Old people remember. Their own experiences and the tales of their forefathers play an important role in the lives of many dryland peoples. Years may be given names that reflect droughts or rains. A recent project proposal by ELCA (Sandford, pers. comm. 1988) outlines procedures for exploring such data. Substantiated data of this type may constitute a most valuable source - and a largely untapped one - for resilience assessment over medium to long periods. In Chapter 1 the anecdotal nature of much historic material on drylands was pointed out. Although much caution must be exercised in the use of historic records relating to resilience, there is little doubt that early work may contain information of value, particularly relating to periods of drought and periods of recovery. 3.6. Costs of alternative methods The costs of carrying out ground surveys, aerial surveys and surveys based on satellite remote sensing vary greatly. They clearly depend on the type of organization involved and its procedures for calculating project costs. Thalen (in ELCA, 1981, p. 85-97) has given a graphical overview of a number of methods (Fig. 6). Falloux (1988) has prepared cost estimates for different types of maps. As the presentation of degradation and resilience often take map form, these figures may be used as a starting point for estimates. Falloux' figures have been modified to reflect productivity/resilience estimates and supplemented by various personal communications from surveyors: Ground surveys Productivity US$ 1 - 5 per hectare Resilience US$ 0.5 - 2/ha/observation set Aerial surveys Low-flying aircraft USS 0.05 - 0.12/ha (5% sample) Air photo + interpretation USS 0.5 - 1/ha Satellite surveys High-resolution US$ 0.2 - 0.5/ha Lower-resolution USS 0.05 - 0.1/ha -29- Chapter 4 Recommendations 4.1. Assessment factors Dryland degradation is at present usefully assessed through considerations of productivity and resilience. Productivity is defined as rate of production for each production system (e.g., kg/ha). Productivity is tied to an area and a time period. Declining productivity is an indicator of degradation, but not sufficient to establish that degradation is taking place. Productivity relating to drylands may be useful measured by weight, palatability and digestibility of vegetation for domestic animals and game through ground surveys. Such surveys are expensive, and should in most cases be limited to providing ground reference data for aerial and satellite surveys. Productivity of croplands is measured by kg/ha of each produce. Sampling by ground survey is normally preferential to yield records. Ground survey also produces reference data for aerial survey (a.p.i.) estimates. Where good correlations are established between productivity and proxy variables (e.g., precipitation functions), meteorological statistics may provide estimates of productivity. Resilience is the ability of land to return to its previous state having been subjected to a shock. Land degradation occurs when resilience is damaged. Estimates of resilience is normally obtained through time series of productivity (see above). Alternative approaches to measurements of resilience include comparisons of degraded and unaffected test areas within larger areas (by ground survey), both for vegetation and soil profile development. Air photo interpretation provides a cost-effective way of extrapolating ground reference data to larger areas. The safeguarding of historic air photos should get highest priority. The measurement of resilience by the monitoring of green biomass from low-resolution weather satellites appears the most cost-effective method yet devised, provided ground referencing is done, there is much promise in this technique. Indications of resilience may also be obtained from yield records, meteorological long-term statistics, pollen analysis, and from interviews of elders and historical records. Measurements are more uncertain. 4.2. Promising methodology for productivity assessment With the growing opposition to productivity assessment through the FAO/UNEP Provisional Methodology, hope must now be pinned on the revised methodology now under UNEP testing by KREMU in Kenya. Also, studies in West Africa by IGN (Paris) and in Mali by UNEP-COM deserve attention over the next 1-2 years. -30- Following the completion of these studies, approx. 1990, a wajor international effort should be undertaken to secure one methodology for use by international agencies. It will then become most important that major environmental data bases, and UNEPs GRID system in particular, give priority to the collection of data that constitute input in this methodology (e.g., Bie and Lamp, 1981). Lists of current GRID data sets, both at global and national level, may be found in Appendix 3. 4.3. Proposal for transectoral establishments The basic thoughts behind the KREMU, IGN and UNEP-COM transects should be followed up, through the establishment of series of transects in dryland areas. The use of local universities and research institutions towards this end is recommended. Since dryland degradation normally takes place over medium to long periods, frequent measurements along transects are desirable but not essential. 4.4. Air photo archives A major effort should be initiated to assemble and safeguard historic air photos now in imminent danger of destruction in many countries. Scattered collections, in old colonial archives, in the files of international consultants and in national survey departments should be consolidated, possibly by the use of modern scanning techniques and storage on optical discs. The consolidated material should be put at the disposal of the individual countries. 4.5. Satellite imagery The providers of high-resolution satellite imagery must be made to understand that the current track record of deliveries of images prevents operational use of their data. Older high- resolution imagery (e.g., the early Landsat MSS) will probably provide valuable information for resilience studies in years to come. It is of great importance to ensure that these images are not lost through lack of adequate digital maintenance of tape archives. Operational use of satellite imagery, also low-resolution data (e.g., NOAA AVHRR), will require the creation of competence centers in the dryland areas, e.g., through regional centers. 4.6. Geographical Information systems The future availability of larger data sets both on productivity and resilience, and the presence of large data sets in the form of digitized air photos and digital satellite imagery, will provide compatible input for geographical informatqon systems (GIS). This author believes that GIS- technology will play a major role in dryland degradation assessment in the future. The general availability of lower-cost decentralized GISs will extend analytical capability to research institute level, also in lesser developed countries, and to decision-making desk officers. 4.7. Conclusion We are as yet uncertain as to the resilience of drylands. Periods of apparent degradation have in the past been followed by recovery. Climatic factors have largely dominated. Increased population pressure, and the impact of a possibly changed global climate through human interference, may have altered this historical picture. Investment in drylands may depend on our appreciation of the forces at work. Dryland productivity and dryland resilience deserve the attention of our best survey and monitoring methods. -31- REFERENCES AhIcrona, E. (1988) The impact of climate and man on land transformation in Central Sudan. Meddel. Luns Univ. Geogr. Inst. Avhandl. 103. Ashby, W.R. (1958) An introduction to cybernetics. John Wiley, New York. Babaev, A.G. (1988) Explanatory note to the map of man-made desertification of the USSR arid lands scale 1: 2,500,000. Turkmen SSR Academy of Sciences. Ashkabad. von Bertalanffy, L. (1968) General systems theory. George Buaziller, New York. Bie, S.W. and Lamp, J. (1981) Criteria, hardware and software for a global land and soil monitoring system. GRID information series 1. UNEP, Nairobi. Bie, S.W. (1988) Desertification Ecological or economic imbalance? In One earth - one world. NAVF, Oslo, 179-184. Bovill, E.W. (1921) The encroachment of the Sahara on the Sudan. Jorun. African Soc., 20, 174- 185; 259-269. Clarke, R. ed. (1986) The handbook of ecological monitoring. Oxford Science Publications, Oxford. Conway, G. (1984) Rural Resource Conflicts in the U.K. and the Third World. SPRU, Univ. of Sussex. Dregne, H.E. (1983) Desertification of Arid Lands. Adv. arid land techn. and devt. 3. Harwood academic Publ., Chur. Dregne, H.E. (1986) Magnitude and spread of the desertification process. In Glantz, M.H. (ed.) Arid Land Development and the Combat against Desertification an Integrated Approach. USSR Commission for UNEP, Moscow. 10-16. Dregne, H.E. (1987) Reflections on the PACD. 10th anniv., UNCOD issue, Nov. 15. Dregne, H.E. and Tucker, C.J. (1988) Desert encroachment. Desertification Control Bulletin 16, 16- 19. Falloux, F. (1988) Draft The role of remote sensing in natural resource management in subsaharan Africa. World Bank Techn. Paper, Washington DC. FAO/UNEP (1983) Provisional methodology for assessment and mapping of desertification. Rome. FAO/UNEP (1984) 1: 5 million Map of desertification hazards. Rome. Forse, B. (1989) The Myth of the Marching Desert. New Scientist (4 Feb. 31-32. Manual of Remote Sensing (1986) American Society of Photogrammetry. Nelson, R. (1988) Dryland Management The "Desertification" Problem. World Bank Environmental Dept. Working Paper No. 8. -32- Nicholson, S.E. (1982) The Sahel - a climatic perspective. CILSS/OECD Club du Sahel Rep. D (1982). Olsson, K. (1985) Remote sensing for fuelwood resources and land degradation studies in Kordofan, the Sudan. Meddel. Lunds Univ. geogr. inst. Avhandl. 100. Olsson, L. (1985) An integrated study of desertification: applications of remote sensing, GIS and spatial models in semi-arid Sudan. Meddel. Lunds Univ. geogr. inst. Avhandl. 98. Reining, P. (1978) Handbook on desertification indicators. American Ass. for the Advancement of Science. Washington DC. Sandford, S. (1976) Pastoralism under pressure. ODI rev. 2, 45-68. Sandford, S. (1983) Management of Pastoral Development in the Third World. John Wiley and Sons, Chichester. Sears, P.B. (1935) Deserts on the March. Univ. Oklahoma Press, Norman. Stebbing, E.P. (1935) The encroaching Sahara. Geogr. Journ. 85, 506-524. Stem, M. (1985) Cencus from Heaven? Population estimates with remote sensing techniques. Meddel. Lunds Univ. geogr. 99. Timberlake, L. (1985) Africa in Crisis. Earthscan, London. Tolba, M. (1984) Harvest of Dust. Desertification Bull., 10, 2-4. Trolldalen, J.M. (1986) On the Fringe - A systems approach to the evolution of the environment and the agricultural production in the Gambia, West Africa. Dr. scient. thesis, Dept. of Geography, Univ. of Oslo. Tucker, C.J. and Justice, C.O. (1986) Satellite remote sensing of desert spatial extent. Desertification Control Bulletin 13, 2-5. UN Desertification Control Programme Activity Centre (1987) Rolling back the Desert. UNEP, Nairobi. USAID (1972) Desert encroachment on arable lands. Washington DC. US Congress Office of Technology Assessment (1986) Continuing the Commitment Agricultural Development in the Sahel. Special Report OTA-F-308. Washington DC. US Govt. Printing Office. Warren, A. (1984) Productivity, variability and sustainability as criteria of desertification. In: Fantechi, R. and Margaria, N.S. Proc. Inf. Symp. EEC Progr. Climatology. D. Reidel, Dordrecht. 83-94. . / r . -33- APPENDIК 1 Агааа о1 Arid Teггitorles bY СоnlИми . М km� (аltег Р. Meigs, 1958) ргоропlое at аНб lапд апа Соаиаwп ��� Агiд �!� te1a1 • !о �as1 о1 ruлUnaat (9й1 Аиыгаliа - з.ав 2.sz в.за вз Atгics 4.88 Т.30 8.10 17.90 S9 Asia 1.OS Т.9! �.S1 16.4Т 38 Noгth аад Cent- 0.03 1.28 2.68 3.9Т 10 гаi America South АтеНса 0.1Т 122 1.63 3.02 8 Витодв - 0.17 0.84 1.01 1 . Wor1d Tota1 5.81 21.1� 21.26 48.81 34 о 34 Extent of Hyperarid, Arid and Semiarid Regions in Countries with Marc Than a 50% Moisture Deficit I Rogers, 19811 Country's Region Country antAl And smand (X IOM kari IX 1000 M04 % 1iX1OOOkWJ % 11X1OOOkMJ Latin America Argentina 2776.9 - - 638.7 23 1166.6 42 Mexico 1972.5 19.7 1 493.1 25 394.5 20 Asia Bahrein 0.6 - - 0.6 100 - - Iran 1648.0 33.0 2 $57.0 52 527.4 32 Iraq 434.9 - - 322.0 74 65.2 15 Israel 20,8 0.4 2 1.4 7 17.4 84 Jordan 92.2 16.6 IS 69.1 75 5.5 6 Kuwait 16.1 - - 16.0 100 - - Oman 212.4 95.0 40 80.7 38 46.7 22 Saudi Arabia 21149.7 94.6 44 1161.0 54 43.0 2 Syria 185.2 - - q2.6 50 72.2 39 United Arab 83.6 7,5 9 76.1 91 - - Emirates (UAE) YAR 195.0 29.2 16 89.7 46 50.7 26 PDRY 297.7 5.7 2 267.5 93 14.3 5 Afghanistan 647.5 - - 233.1 36 246.0 38 Mongolia 1564.9 - - 344.3 22 860.7 55 Pakistan 895.5 - - 438.8 49 264.8 29 AFRICA Algeria 2381.7 14*76.7 62 595.4 25 190.5 8 Botswana 600.4 - - 60.0 10 504.3 84 Chad 1284.1 115.5 9 642.0 50 296.3 23 Djibouti 23.0 - - 23.0 100 - - Egypt 1002.0 921.9 92 80.2 8 - - Ethiopia 1222.0 12.3 1 415.5 34 342A 28 Kenya 582.6 - - 285.5 49 215.6 37 Libya 1759.5 1566.0 89 167.1 95 156.6 1 Mail 1239.7 223.1 IS 384.3 31 520.6 42 Mauritania 1119.3 481.3 43 570.8 51 67.1 6 Morocco 623.8 62.4 10 355.6 57 156.0 25 Namibia 823.2 90.5 11 370.4 45 324.0 39 Niger 1267.0 532.1 42 544.8' 43 190.0 .15 Senegal 196.7 - - 21.6 11 104.2 53 Somalia 637.6 38.3 6 484.6 75 89.3 14 South 1222.2 12.2 1 366.6 30 269.0 22 African Republic Sudan 2505.8 526.2 21 626.4 25 701.6 28 Tunisia 164.1 23.0 14 75.5 46 23.0 14 AUSTRALIA Australia 7686.8 - 1-13766.5 149 11537.3 120 - 35 - Land Use In Arid Countries Total area. Specided: Count y thectates Arabic land Permanent Forest and Irragated Other IX 10001 aind land under e X ) permanent cropa passures Woodland land* 7 htares) AFRICA Algeria 238174 7497 36323 4384 336 189970 Botswana 60037 1360 44000 962 2 12215 Djibouti 2200 I 244 6 - 1947 Egypt 100145 2848 - 2 2848 96695 Ethiopia 122190 13730 4545 26930 - 23990 Kenya 58265 2270 3770 2560 46 48325 Libya 175954 2564 6700 534 135 166156 Mali 124000 2050 30000 8840 100 81110 Mauritania 103070 195 39250 15134 8 48461 Morocco 44655 7719 12500 5195 46 19216 Namibia 82429 656 52906 10427 8 18340 Niger 126700 3290 9668 2960 36 10752 Senegal 19619 5200 5700 5318 180 2982 Somalia 63766 1066 28850 8910 165 23908 SAR 122104 ;4620 81100 4600 1020 21784 Sudan 250581 12400 56000 49250 1700 119950 TuntNia 16361 4970 2550 500 140 7516 Bourkina Faso 27420 2563 10000 7260 5 7557 (Upper Volta) LATIN AMERICA Argentina 176689 35120 141300 60100 1560 35149 Mexico 197255 I 23220 I 74499 49030 I 5100 j 45555 ASIA Afghanistan 64750 8050 50000 1400 252000* 4800 Bahrein 62 2 4 - - 56 Iran 164800 15950 44000 18000 5900 85650 Iraq 43492 5450 4000 1500 1730 32447 Israel 2077 413 818 116 189 686 Jordan 9774 1370 100 125 85 8123 Kuwait 1782 1 134 2 1 1645 Mongolia 366500 160 123553 15178 32 16609 Pakistan 80394 20175 5000 2810 14450 49887 Qatar 1100 2 50 - - 1048 Saudi Arabia 214969 1105 85000 1610 395 127263 Syria 18518 5686 8274 459 539 3990 UAE 8360 12 200 2 5 8146 YAR 19500 2790 7000 1600 243 810 PDRY 33297 205 9095 2460 67 21567 AUSTRALIA Australia I 768685 I 43900 448393 107000 I 1490 162500 FAO Production Yearbook, vol. 34. 1980. 1979 Data. Excluding irrigated lands of non*arid countries having no arid territories. 1* 3976 Data. - 36 - APPfiRX, 2 CRITZRIA MR ASSESSG DEMADATIM OF 'GEATION COVER Aspects Assessment foctors clasp limits I Slight i Moderate s Severe t Very severe 2 2 Status I.ChaoteVistica of vege- lia comau- Long existing Ephomeral so- Deatruction of tion cover nities, seconda- secondary vegetation cover slightly ry communi- communities changed ties 2.Present productivity, per cent of potential >90 90-60 60-30 (30 productivity Rate I.Decline in biomass pro- duction, per cent per 410 10-25 25-50 >50 year 2.Pasture conditions 4o(- radation, per cent per (2*5 2,5-5,0 5,0-75 >7,5 year 3.Cutting' of foreats (porcontaca of non- 42,5 2,5-5*0 5,0-7,5 -restored forest area per year) 4.Dooroase in forae production* Pur cent <I1-4 -/> per year. Inherent I.Stability of 00- -Stable co- Comparatively Non-stablo Non-stable * rick systems slotems of stable eco- ecocyntems ecusys,tem Of .Joy, loam systems of of river sandy deserts, and -ravel locas desert, vallies, ecosystems of steep deserts ecosystoms ecosystems mountain slopes Ad developed on of gentle badlands sandy loam mountain soils and mo- al0oe and untain slopes clay piedmot: plains 2.Potential for Pee- IaMations W - zear according to (3 - 3.5 540 >10 projeot -37- CRITERIA FOR ASSESSOG WIND ERCSION Table 2 Aspeot Assessment factors 14.limits -----Sliht M9oderate $-Severe Ve severo Status 1. ind erosion features 25 per cent 25-50 per cent Slip slopes 6ovement of are covered are covered formation sand on the total with precipi- with blowouts, on shifting area ced blowouts sand ripples sand an bare surface * 2.Sod cover,per cent 50-30 30-10 * 10 none 3*Ratio of shrub density (per cent) and sod 20-3O T..0- cover (per cent) 40-80 40-10 10-5 5 4.Depth of blowing out of root layer (for non (10 10-25 25-50 >50 sandy soil) in per cent Rate I.Increase in eroded area, per cent per year <I 1-2 2-5 > 5 2.Amount of sand tr*nsported * over I running weLro per <0,5 0,5-1,0 1$0-5,0 >5,0 year, a 3.Decrcase in biomass pro- - i** - -- Inherent I.'ind erodibility Croups Silt,clay Loam, sandy Lony band Sand ric (texturo of uoil) clay loUM oM 2.Numa impact en sandy Streamline Shrub cut- Doestruction Earth works disturbanca tinLr, over- of uod cover without Zad surface relief foa- trasinG stabilization. tures - 38 - Table 3 CRITRIA FOR ASSESSING WATER EROSION Cleas limlits Aspects Assessment factor$ Slight Modorate Severe Very severe 2 3 4 6 Status I.Surface features Gravel and sto- Gravel and sto- Boulders Boulders no cover 10 per ne cover 10- and rocks and rocks cent or loss 25 per cent -cover 25- exposures 50 per cent cover 50 per cant or more 2.Type of water erosion Slight surfa- Uoderate sur- Heavy sar- Vory heavy 0e run off face run Off and face run off surface run off and rt11 ero- :111 erosion and Gully and gully sion (from (from modora- erosion orosion sliGht to mo- te to hoevy) derate) 3.umber of raving, Cullies and rats per Irunning km <b 5-10 >10 4.Subsoillexposed,per cant <10 10-25 25-50 >50 5.Soil thickness, cM >90 90-50 50-10 <10 6.Loss of soil depth over root layer, per cent a.0riainal soil dopth I m <25 25-50 50-75 >75 7 11-- - - - - --- ----- - - - - b.Oriuil soil dnpth I m <70 30-60 60-90 >90 Rare I.Incrosse in oroded area, per <1 1-2 2-5 >5 cent per your 2.Soils loss, UT ha/year - 40,5 0,5-1,0 2-5 >5 3.Sediment depootion in re- a.vatrue ms /o. < 60 60-200 200-500 >500 b.watershod 500 km <40 40-100 100-250 >250 4e.Annu loss of 4torage, (0,2 0,2-0,4 0,4-0,1 >1,0 Inherent I.TopoGraphy SliGhtly Hilly Uountsinswith Mountains undulated guntlv~ and with steep risk steep slopes slopes and talus 2.Sopes 4 5 5-15 15-30 >30 3.Density of shrub and tree vegetation (or semi-shrubs), > 0 0 15-5 45 per cent >0 3-51- 4.Percentage of area covered with sod (in the areas with dominant herbacious 0IO veget ation) )750 50-30 30-10(1 -39- _2 3------------ Lj - 5*Coofficieut of h7droGraghic not density (ratio of the total length of river-bed kmo to watershod size, km<0 >0,5 Table 4 0IlT;3A 90:. A=!C UG EGIL MALINIZATION1 Cluzu Jimits Aspects Asasocant fact ors ASpoeto Ass n fcSlimht toderuto Sovero Very covers Status I.Salinization ratoz S a.Dfctse residue, per 0,21-0,40 0,40-0,00(0,80) 0,60(0?80)-1,00 1,00 cent includinG Cl 0,001-0,03 0;0(0,0()- 0,100-0,230 0,230 (0,06) *0,100 Na 0,23-0,046 0;046-0,092 0,092-0,184 0,184 b.Toxic salts ,total .amount 0,28 0,28*0,40 0,49-0,69 0,69 2.Salt layer location in Deep solonchak Solonchak sali- Solonchak-liko The whole Soil soil profile salinization, nization (calts salinization profile is (salts are be- are below 30- (Salts ure in Uaturated wizh 1dw 80-100 cam) 60 (80) cam) 30 cam layer) * salts 3.orpholoeical features Salts are not Rare small spe- Frequent small Salts in the of salinisation, now visible cks of salts in spots of mould* form of nests, formation the upper dry salt coating mould spots part of profile in 0-60(100)cm specks, crys- or in clay ho- horizon, salt talls through. rizons accumulation in out the pro- clay horizons file or a sall crust 4.Salinisation chemistry Chlorido-cul- Chloride-sul- Sulphate-chlo- Chloride phato, sulphate phate, sul- ride and.chlo- phato-chlo- ride ride Table C1TM;lA FOR ASEI'G TC-:0AE:;IC l t to calculated fron the indices tiven in Table 7. Let'a'.consider an qxaple of calculation for the mapping unit in which the formula is given: Aspects s Assessment factores: Class lixito Slightt 2oderate tSevere The content of this formula can be explained in the Statuo I.Destruction of the following ways vegetation cover o a.Cutting tree and Symbol Aspect Index shrub vegetation, 2 DS(moderate) 6 ift per cent (25 25-50 50-70 2 DS(severe) 12 b-Dentruction of I:D(Moevere) 12 sod cover, in a per cent <25 25-50 50-70 iv 13r(very severe) 26 2.Percentage of ero- I AP(severe) 2 ded area caused by rKnevere) 8 irregular move-T ments of vehicles end machines (10 10-50 50-70 According to the sum of indices the mapping unit under c*Percentage 9t area conzileration should be illuminated brown. That means under nogenic sands (10 10-25 25-50 tai3 condition DI is severe. According to Table 7 slight DII Rate I-Inicrease of area is illu=inated b7 yellow, moderate by orange and very severe with technoge-r by red colour. ';e reco.mend to illuminate green the areps 0 nic features, in riz;out slight chunkes occuring during last 20 years. In the year (for 5 year araj desertification is practically non-existant. period) (2 2-5 5-10 AP is'calculated in the following way. We estimate the Inherent IW'ind erodibility Silt,clay,LoAm, Loany cta prezont livestock density in per cent of potential livestock risk groups (texture clay loomlandj carr7inG capacity. The scale is of Doil) loam rreseat livestock AP 2.Percentag eof area density in per cent involvedtby projects in road construc- of potential livestock tion and other carrjin; capacity technozenio trans- 200 Very heavy formation Cexept 20 Vey eavy the construction 1o0eat works in the p- IM-2 . pulated areas3o (10 10-30 30-50 66-I0 Loderate PP is esti-mated by population density. The following scrle is proposel: 31iht - I person per I M2 Zoderate - i-10 persons per I km Reavy - 10-25 persona per I km2 Very heavy - * >25 persons per I k02 -41- Table 4 5.DOOreas* of ageitl- tural Crops produe- tion (cotton),percent <15 15-40 40-80 >80 6.PeroenteG of area under Badliaisation (5 5-20 20-50 >50 ?.Density of cotton plants, thou per ha 80-50 50-30 30-10 410 8.Cotton height in the stage of ripeninG, c 120-80 80-40 40-20 c20 Rate I.Iacrease in salinizod aea, per cent per year <1 * -2 2-5 >5 2.Decrease in agricultural crops production, per oent per year - <2 2-4 4-8 >8 3.Seasonal accumulation of saltalper cent 00,20-0'0 0 "--, 0,6! (1.2) UT per ha 16-30 .31-4.5 45-90 90 Inherent I.Uon annual depth of under- risk ground water, cm 500-3000 300-100 100-50 450 2.Ground water ineraliza- tion g/1 3-6 6-10 10-30 >30 3.Drainlace quality of the aeration zone Moderate Weak Low Vry low Tuble 4 1'2 4 5 4.T..pogruphy of the ter- Plainz and Flat plains Deprosions and Depression rain hifh plains romuntu in ri- romsui.tc in wUV vallits river Vaillos, depression bev- woon cones 5.Procet drainate not- work, in per cunt of the projected lonj;th >80 80-50 50-lo <IO 6.Violation of acrotoch- nical recommendations (luaching,waturing coil treatcent,applicaVion of mineral fertilizers), in per cent of the project (10 10-40 40-90 >90 7.Lithology of soil in the Sandy loom, Roavy and light Loamy soil domi- Clay or parent aeravion -zone sandy soils soils in equal nates in the rock outorcps underlayed proportion aeration zone on the surface by clay at the depth of 3- 5 l +) Excluding heavy and very heavy saline soils and soloachaks of the piedmont flexure, of chloride-sulphate and sulphate composition, related to the soil formation conditions. • � Ф О 1Ф-• Ф лG Ф � � r и 4 . т � � �. о у .. е р. � о t0 . � .r '° Ф и�л � у ,,Я� СЯ�, ►+ г�+ � Ф М/М+ Ф Я � �' 4р1 М Яер► Я Ф �Ф О Ф N •'•' й М '�' ` С1 N Q� W н Ф � в'! 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