Report No. 9888-MAI Malawi EconomicReporton Environmental Policy (In Two Volumes) Volume Il: Technical Annexes March 20, 1992 Country Operations Division Southern Africa Department FOR OFFICIALUSEONLY Document of the World Bank Thisdocument has a restricted distribution and maybe used by recipients only In the performance of their oiticial duties.Its contents maynot otherwise be diclosed without WorldBank authorization. Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized
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Report No. 9888-MAI
MalawiEconomic Report on Environmental Policy(In Two Volumes) Volume Il: Technical Annexes
March 20, 1992
Country Operations DivisionSouthern Africa Department
FOR OFFICIAL USE ONLY
Document of the World Bank
This document has a restricted distribution and may be used by recipientsonly In the performance of their oiticial duties. Its contents may not otherwisebe diclosed without World Bank authorization.
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CURRENCY EQUIVALENTS
US$1 MK 2.79MK I US$0.36
WEIGHTS AND MEASURES
1 Kilogram (kg) - 2.2 Poundr1 Metric Ton (mt) = 2,204.6 Pounds1 Liter (1) 2.116 US Pints1 Hectare (ha) 2.471 Acres1 Cubic Meter (cm3) = 35.3 Cubic FeetI Kilometer (km) = 0.621 Miles
GLOSSARY OF ABBREVIATIONS
ADD - Agricultura! Development DivisionADMARC - Agricultural Development and Marketing CorporationAES - Agro-Economic Surveyaic - average incremental costsASA - Annual Survey of AgricultureASAC - Agricultural Sector Adjustment CreditCEM - Country Economic MemorandumCITES - Convention on Intl. Trade in Endangered Species of Wild Fauna and FloraDEVPOL - Statement of Development PoliciesDLV - Department of Lands and ValuationDNPW - Department of National Parks and WildlifeDWSF - District Water Supply FundEES - Estate Extension ServiceEPA - Environmental Planning Areaehu - erosion hazard unitEIA - Environmental Impact AssessmentEP&D - Economic Planning and Development DepartmentEU - Environment Unit, National Research CouncilFAO - Food and Agricultural OrganizationIITA - International Institute for Tropical AgricultureLHB - Land Husbandry Branchl/p/d - liters per daymai - main annual incrementMBS - Malawi Bureau of StandardsMOA - Ministry of AgricultureNCE - National Committee for the EnvironmentPFP - Policy Framework PaperRDP - Rural Development ProjectSACA - Smallholder Agriculture Credit AdministrationSan-Plat - sanitation platform latrineSLEMSA - Soil Loss Estimation Model for Southern Africat/ha/yr - tons per hectare per yearUSAID - United States Agency for International DevelopmentUSLE - Universal Soil Loss Estimation ModelVIP - ventilated improved pit latrine
GOVERNMENT OF MALAWIFISCAL YEAR
Anril 1 tn " reh t1
FOR OFFICIAL USE ONLY
Page No.VOLUME II:- TECHNICAL ANNEXES
Annex 1: Methodological Note on Estimation of Soil Erosion Rate .... ......... IAnnex 2: Autoregressive Trend Model of Relative Producer Price Stability .... .... 22Annex 3: Methodological Note on Estima.ion of Deforestation Rate and Value ... ... 26Annex 4: Methodological Note on Estimation of Water Extraction Rate .... ...... 62Annex 5: Methodological Note on Estimation of Average Incremental Costs in Water . 70Annex 6: Methodological Note on Estimation of Valuation of Protected Land ... ... 85Annex 7: Inventory of Malawi Environmental Legislation ................... 95Annex 8: Econometric Model of Private Sector Price Responsiveness in Afforestation 115
This report is based on the findings of a mission that visited Malawi in August 1990. Missionmembers were Richard Scobey (Mission Leader, AF6CO), Ben Kamugasha (AFTEN), SvenJacobi (RWSGEA), Edward Barbier, Joshua Bishop, J1janne Burgess, Michael Colby, MosheFinkel, William Hyde, Juan Seve, and Jane Walker (Consultants). Ben Kasomekera (BundaCollege, Malawi) and Edward Laisi (Water Department, Malawi) also contributed backgroundpapers to the report. William Magrath (ASTEN) provided valuable comments as peerreviewer, Brigida Tuason (AF6CO) assisted in analytical work, and Georgette Johnson(AF6CO) provided secretarial support.
This document has a restricted distribution and may be used by recipients only in the performanceof their official duties. Its contents may not otherwise be disclosed without World Bank authorization.
Annex 1Page 1 of 21
METHODOLOGICAL NOTE ON ESTIMATATION OF SOIL EROSION RATE
I. Introduction
1. Soil fertility .ecline results from the leas of organic matterand chemical nutrients, through leaching and the removal of crops andresidues, compaction and loss of soil structure, and physical erosion oftop soil by rainfall. In this study we use the latter as a proxy foroverall fertility decline. The justification for this simplificationcomes from studies showing that soil loss is a reliable predictor ofchanges in soil nutrient content, soil pH, and moisture retention (Lal1981). In the following pages, we will use estimates of soil erosion tocalculate expected annual crop yield losses, based on statisticalrelations experimentally derived in Nlgeria (Lal 1987). We furtherexpress yield losses in terms of foregone farm. income, to determinegross economic losses from land degradation, based on a simple modeldeveloped previously for Mali (Bishop, 1989).
II. Rates of Land Degradation and Soil Erosion
Existing Field Data
2. Data from field studies of fertility decline and soil loss inM4alawi are scanty. From the farmer's perspective, the most relevantmeasure of land degradation is yield decline. Results of continuousmaize trials at Chitedze Research Station, from 1955 to 1963 and for sixdifferent treatments of crop residues, reveal a mean decline of 492 overeight years for unfertilized maize, or a 9.12 average annual declineduring the period (Dept. of Agr. Annual Report for 1962/63, pub. 1965).A more recent depiction of yield decline for unfertilized local maizecompares average yields for four ADDIs in 1957-62 versus 1985-87,revealing a mean total decline of 41? over the period, or an averageannual decline of about 2? (Twyford, 1988).
3. Another measure of fertility decline is a decrease in organicmatter and-plat nutrients under cultivation. Analysis of soil sampledata from fertilizer trials carried out at Bvumbwe Agricultural ResearchStation, on land continuously cropped with tea over 25 years and withminimal application of fertilizer (45 kg N ha-' yr-1 ), revealed a 412total decline in organic matter, a 38Z decline in total Nitrogen, and a5? decline in total Phosphorus, relative to virgin land (Maida &
t ~~~Chll$ma, 1981).
4. The meisure of land degradation employed in this analysis isphysical soil loss, in tons per hectare. Se located five studies thatreported soil erosion under various crop cover and land husbandryregimes in Malawi. The reported soil losses are not strictly
-2-Annex 1
Paae 2 of 2.
comparable, due to widely varying plot sizes (from 1 - 170,000 mi). Ontha average, however, annual soil loss under traditional cultivation(i.e. maize, weeded and ridged) is about 19 t/ha. Average annualrainfall recorded at the five stations was 950 mm, and the mean slopewas 142.
Predictive Models
5. The leading predictive model for soil erosion research is theUniversal Sol.l Loss Estimation (USLE) model, developed in the U.S.A.(Wischmeier and Smith, 1978). Although widely tested and corroborated,some authors dispute the validity of the USLE model under tropicalconditions (Stocking 1987).
6. Among many proposed alternative models is the Soil LossEstimation Model for Southern Africa (SLEMSA), developed in Zimbabwe(Elwell, 1978; Elwell and Stocking 1982). SLEMSA was designed for usein countries with limited capacity to generate the physical datarequired by the USLE and other models. A preliminary evaluation ofSLEMSA under Halawian conditions compared the predictions of the modelto actual soil loss measured on experimental catchments near the BvumbweAgricultural Research Station (Mwendera 1988). The results wereinconclusive from a statistical standpoint, due to insufficient data.
7. Recently a modified version of ax MSA was developed, again inZimbabwe, for reconnaissance level evaP'- ,ion of erosion hazard(Stocking et al., 1988), The methodology is designed to make relativeassessments of the risk of erosion over large areas, expressed inErosion Hazard Units (EHU). The model uses precipitation data toestimate rainfall energy (E), which is combined with an index of soilerodibility to calculate an erosion hazard index (lb). The protectionprovided by vegetal cover is also incorporated, along with average slope(X). The authors stress that the model is not designed to predict soillosses in tons per hectare, since it fails to account for the depoaltionof eroded sediments within catchments. The technique was first appliedin an Erosion Hazard Happing of Zimbabwe (Madhiri and Manyanza, 1989).
Erosion Hazard MagRina of Malawi
S. An evalu' of erosion hazard in Malawi was carried out bytwo members of tl Husbandry Branch of the Department ofAgriculture (Khon. Ad Machira, 1987), using the methodology developedin Zimbabwe. The authors prepared a 10xlO km grid map of Malawi at11,000,000 scale, which displays the mean erosion hazard (EHU) for1,044 grid squares. The results are also presented in tabular form inan appendix to their draft report, with rainfall energy (E), erosionhazard index (ld)' vegetal cover ratio (C), mean slope (X) and ENU
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Page 3 of 21
listed for 1,048 grid squares. 1/ EHU values range from 0 to amaximum of 7,195, with a mean value of 328 (weighted by the estimatedproportion of each grid square falling inside the boundaries of Malawi).Mean slope on all areas is 6.32. In their report the authors-present asimplified EHU map (scale 1:3,000,000), for which EHU scores have beenconverted into eight categories. For each category they furtherestimate expected annual soil loss in tons per hectare.
9. While recognizing the danger of exaggeration inherent inconverting EHU into soil loss, we adopt the estimates of annual erosionmade by Khonje and Machira in the analysis that follows. The reader isasked to consider the argument presented below as an illustration of thepossible extent, distribution, and costs of land degradation, ratherthan as an exact representation.
10. The conversion rule used by Khonje and Machira is a stepfunction, and ignores intermediate values within categories. Wetransform their rule into a general equation for converting EHU intoexpected soil loss, by simple regression analysis. The best fit wasestablished with a set of three equations. A maximum soil loss rate of50 t/ha/yr was assumed for all grid squares with EHU > 1000.
11. General information on land use was derived from 1:1,000,000maps provided by the Land Husbandry Branch, showing the limits ofDistricts, Rural Development Projects (RDP), Special ̂ rop Authorities(SCA), National Parks, Forest and Game Reserves. By tracing andoverlaying all of these maps with the erosion hazard map of Khonje andMachira, and estimating the proportion of each EHU grid square lyingwithin a particular administrative unit, we compiled a data base of1,855 land use units. The mean surface area of the map units is about
I/ Our copy of ft report was noomplete and lacked pans of the appendbL Moreover, 127 gd squaresshown on the map and listed In the report do not share the sarne values. For this analysis we genwally usethe values roported In the appendix, In preference to those on the map, except where the former are msngin our copy. We were able to reornstu mean slope values for rd squares missing In the report appendix,by exapoat from EHU values shown on the map.
-4-Annex 1
PaRe 4 of 21
51 kM2. For each unit we recorded six attributes, of which the firstthree are taken directly from Khonje and Machira:
(i) grid coordinates,(ii) EHU score,(iii) mean slope (0.82, 2.62, 5.22, 9.02, or 13.52),(iv) estimated proportion of the grid square falling within the
boundaries of Malawi,(v) estimated proportion of the grid square falling within a
specific administrative area,(vi) the name of the specific administrative area.
12. The last of these attributes assigns each map unit one of 155labels, corresponding to the RDP, district, special crop authority, gameor forest reserve in which it lies. The data base thus generated shouldnot be considered a definitive analysis of land use in Malawi. It is--ough that our estimates of the surface area of major land usecategories correspond more or less to previously published figures.
Excluded Areas
13. The land use data base permits the distlnction of reservedareas, which are excluded from our analysis of the costs of soilerosion, on the assumption that most if not all of this land isuncultivated. We also assume that some unreserved swampy land is eithernot cultivated or receives significant deposits of eroded sediment (i.e.no net soil loss). Finally, the very steepest slopes are assumeduncultivated.
14. We consulted three sources which give estimatee of the totalarea of 3 uncultivable* swamps and steep slopes in Malawi: The NationalPhysical Development Plan (1986), Brunt, Mitchell and Zimmerman (FAO1984), and Stobbs and Jeffers (1985). Their figures were used to guidethe selection of rules for excluding certain grid squares from ouranalysis. In the end we define and exclude as uncultivated swampy landall those grid squares with mean slope equal to 0.81 and EHU scoresbelow 8. Uncultivated steep slopes were defined as all squares withmean slope equal to 13.5Z. The latter rule results in an excluded areasomewhat smaller than other estimates of land with slopes over 121,which are considered unarable by the Land Husbandry Branch, but are infact often cultivated.
15. With these rules of exclusion and the data base describedabove, we calculate the total surface of each administrative area,distinguishing uncultivated reserves, swampy land and steep slopes.Gross arable land is what remains and is the area assumed subject tocrop losses arising from erosion. A full tabulation of our resultsversus other estimates is given in Appendix 1.
-5-Annex 1
Paae S o_' 2.
Estimated Soil Loss
16. For each mar unit not excluded, we estimate the mean annualrate of soil erosion (t/ha) based on the equations derived from Khonjeand Machira. Summing across map units, we can calculate the mean rateof soil loss by RDP and by District on gross arable land (weightsd bythe surface area o_ each affected map unit). Detailed results arepresented in Appendix 2. For Malawi as a whole, we estimate a meancurrent rate of soil erosion of 20 t/ha/yr on gross arable land.Recalling that we assume a maximum rate of 50 t/ha/yr on any map unit,the highest estimates of erosion or. arable land occur in Nkhata BayDistrict (43 t/ha), Chirauzulu District (39 t/ha), and Dowa Hills RDP(36 tlha). The minimum estimate (10 t/ha) occurs in Balaka and KawingaRDP's.
II. Costs of Soil Erosion
Off-Site and On-Site Costs
17. Soil erosion can impose economic costs in two fundamental ways:through on-site reductions in crop productivity and farm income, andthrough off-site effects resulting from increased runoff, siltation, andwater flow irregularities. The latter may affect the quality andreliability of urban water supply, the life span of hydro-electric powerfacilities, dredging costs for irrigation schemes, and fisheriesproductivity,
18. Data to estimate the off-site costs of erosion in Malawi areunavailable, but a number of factors suggest that these costs may below. Ground water is plentiful in most areas, while filtering costs area very small fraction of water supply costs. Malawi also has littlehydro-electric and irrigation infrastructure. Fisheries may be moreseriously affected, but the data needed to determine costs imposed byeroded sediments are not available. On the other hand, the size of theagricultural sector, combined with apparent market failures which canlead farmers to deplete top soil at an inefficient rate, suggest thaton-site costs may be quite high.
19. The on-site costs of soil erosion may be captured and evaluatedin a number of ways: in terms of reduced crop yields, the replacementcost of eroded plant nutrients, or r-ost directly in terms of the reducedresale or rental values of agricultural land. The latter would be themost direct reflection of a reduction in the discounted present value ofthe income generating potential of a particular plot of land, relativeto alternative investments. Agricultural land markets in Malawi hardlyexist, however, and there is no data.
20. Evaluation of the replacement cost of eroded nutrients is anapproach that has been applied in Zimbabwe (Stocking, 1986). The methodis based on a set of statistical relations linking soil loss to nutrient
-6-Annex 1
Page 6 of 21
losses, derived from multi-year data from across Zimbabwe. Financialanalysis estimated annual losses of Nitrogen and Phosphorus worth US$150million on arable land alone (30,000 km 2 ). As pointed out in thereport, these losses understate the true cost of erosion, sa they do notaccount for losses of soil organic matter, which can affect soilstructure, water-holding capacity and nutrient availability. 2/
21. Evaluation of yield losses has the benefit of capturing all ofthe on-site effects of soil erosion on soil fertility and thus on farmproductivity. Yields reflect not only the presence of major nutrients,but many other attributes of soil fertility. The problem is to find alink between soil loss and crop yields.
Existing Data on the Erosion-Yield Relation
22. There are few data linking crop yields to soil erosion inMalawi. Experiments at Nkhande Research Station on a 442 slope showyields under traditional cultivation falling 622 between 1985/86 and1986/87, from 815 to 308 kg/ha, where annual soil loss was 76 t/ha. Onan adjoining alley-cropped plot, soil loss averaged only 3.7 t/ha/yr,and yields rose over the same period from 2,050 to 2,700 kg/ha (Chome,1989). While tho example is illustrative of the effects of soil loss,it cannot provide a general rule for estimating yield losses arisingfrom erosion.
Predictive Models
23. For this analysis we use a simple model to predict crop yieldlosses from estimated rates of erosion. The model is a generalizedversion of statistizal relations between crop yields and soil loss,which were estimated using data from side-by-side, multi-year trialscarried out in Nigeria at the International Institute for TropicalAgriculture (Lal, 1987). The IITA equations predict the effects ofcumulative natural soil loss, in tons per hectare, on yields of maizeand cowpea, relative to yields on newly cleared (uneroded plots). Theyare of the form:
Y Q C-O (4)
wheres Y = ;ield in tons per hectareC - yield on uneroded (newly cleared) landp - coefficient varying by crop and slopex - cumulative soil loss (t/ha)
gy We do not use this approach for the present analysis, alhough data from the Soi Erosion Research Projectat Bvumbwe would permit an estimation of the relation between soil loss and nutini loss under Malawicondons (Amphlett, 1988).
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Page 7 of 21
24. Lal estimated eight equations, one for each crop and fourslopes (1, 5, 10, and 15Z). The estimated coefficients (p) variedbetween 0.002 and 0.036 for cowpea, and between 0.003 and 0.017 formaize, with the greatest losses recorded on gentle slopes. All but oneof the Beta coefficients are significant to 5Z.. Correlationcoefficients (r) were between 0.66 and 0.99.
25. Agricultural conditions in Malawi and southwestern Nigeria areof course not comparable, but we can assume that the general form of thecrop response to soil erosion will be similar. Note that thespecification of the equation implies a constant elasticity of yieldwith respect to cumulative erosion. In other words, the percentageyield loss in the first year is exactly the same as the percentage lossin the tenth year, assuming a constant rate of erosion. It is enough toknow the annual rate of soil loss and mean current yields to estimatecurrent crop losses. We thus drop the constant (C) and calculate apercentage yield decline for every level of soil erosion. To accountfor the uncertain sensitivity of crop yields to soil loss under Malawiconditions, we use a range of coefficients (p) and estimate a wide rangeof yield losses. The coefficients tested here are p 0.002, 0.004,0.006, 0.010, and 0.015.
26. We apply the equation described above to every map unit in thedata base, excluding reserved and unarable land. Results by RDP and byDistrict are presented in Annex 2. For Malawi as a whole, estimatedmean annual yield losses lie between 42 and 252, for p equals 0.002 and0.015 respectively. Maximum yield loss lies between 102 and 53X, fornoil loss of 50 t/ha/yr.
Crop Budgets
27. To value yield losses arising from soil erosion, we use cropbudgets provided by the PI:nning Division of the Ministry of Agriculture(MOA). We assume that farmers will reduce the use of variable inputs inthe same proportion as gross revenues decline. Applying the estimatedpercentage yield loss directly to gross crop margins, we obtain anestimate of economic losses arising from erosion. Gross muArgins aredefined as gross revenue per hectare (mean yield multililied by officialADMARC prices), less the total cost per hectare of using all recommendedinputs (seed, fertilizer, and pesticides), but not including labourinputs. In other words, intermediate inputs are excluded, leaving valueadded. Labour is assumed here to be a fixed cost of production. An41ternative financial analysis from the farmer's point of view appliesestimated percentage yield losses to net farm income, on the assumptionthat labour is not fixed.
28. Gross margins for twelve crops or crop mixtures are taken fromcurrent MOA data tables, using values for 1989/90. Where values are notavailable for specific crops, we use figures taken from the Agro-economic Survey (AES) Report No. 55 (1987). AES gross margins are
Annex 1Pame 8 of 21
inflated from 1984/85 to 1989/90, using the growth rate of gross marginsfor the same or similar crops, as reported in the MOA data tables. AESdata also includes net farm income, which re simLlarly inflate to
1989/90. Both gross margins and net income as used here are reported inAppendix 3.
Cropping Pattern
29. Estimates of the total surface area cultivated each year varywidely among different sources. The baseline figures used &re from the1987/88 3rd Crop Estimate, prepared by the Pl_nning Division (MOA).These give the total cultivated surface area, by crop and byAgricultural Development Division (ADD). According to this source, thetotal cultivated area of Malawi in the 1987/88 crop year was 18,218 km2.
30. Our calculations also account for the relative importance ofdifferent crops in each ADD. Data on cropping patterns are taken fromthe Annual Survey of Agriculture (ASA) for 1980/81 to 1985/86, asreported by the World Bank (NRDP, 1989), combined with data from the1987/88 ASA and the AES Report No. 55. Note that unfertilized 'local'(indigenous) maize accounts for about 37? of the total cultivatedsurface area of Malawi, while all maize varieties taken together accountfor 69Z of the cultivated surface. Major cash crops, including cotton,tobacco, coffee and tea only account for about 5 of total cultivatedarea. Detail for each of sixteen crops, by ADD, is presented inAppendix 3.
31. By combining information on gross margins and cropping patternswe estimate the mean contribution of each crop to average gross marginsper hectare on cultivated land. Rice is excluded from the analysis, onthe assumption that it is grown on relatively flat lowland soils, whichare not subject to serious soil erosion. We also exclude root crops,despite their importance in cropping systems, for lack of budgetarydata.
32. Summing the contributions of each crop in each ADD, wecalculate composite gross margins fer all crops taken together, inKwacha per hectare. For Malawi as a whole, composite gross margins areestimated at 249 K/ha in 1989/90 (weighted by the baseline estimate ofcultivated surface in each ADD). 3/ Again maize accounts for about702 of this figure. The highest value is in Lilongwe ADD (302 K/ha),while the lowest is in Karonga ADD (161 K/ha). Detailed results by cropand by ADD are presented in Appendix 4.
yI Multiplying composite gss margins by the baseline cultiaed sufac aea, we obtain a value of 453 millionKwacha, whih may be considered a rough estimate of dt contuton of dth crops to total 1988 agrkulturalGDP (1.224 bilion IQ.
Annex 1Page 9 of 21
Economic Losses: Baseline Results
33. Finally we apply estimated percentage yield losses, for variousvalues of p, to composite gross margins. The resul_ is an estimate ofaverage annual losses due to erosion, in Kwacha per hectare. For Malawias a whole, estimated annual losses are in the range of 10 - 64 K/ha(for P - 0.002 and 0.015, respectively), or between 4? and 262 ofcomposite gross margins, excluding rice and root crops. The greatesteconomic losses occur in Lilongwe ADD (13 - 81 K/ha, for p - 0.0C2 and0.015), due to the relatively high gross margins obtained there.
34. Multiplving mean annual losses per hectare by baselineestimates of cultivated area, we calculate total losses by ADD, forvarious values of p. Summing across ADD's, we arrive at rough estimateof the annual loss of agricu..tural income arising from soil erosion.For 0 - 0.002 and p - 0.015, we obtain roughly 18 and 116 millionKwacha, respectively. To put these numbers in perspective, theycorrespond to 0.52 and 3.12 of Malawi's gross domestic product (GDP) in1988. This is the range within which we would expect the true value ofcurrent foregone agricultural income to fall. Results by ADD arepresented in Appendix 4.
Further ManiDulations
.1- Hiaher Estimates of-Cropped Area. Some assessments of totalcultivated area by ADD are considerably higher than the baseline 3rdcrop estimates obtained from the Ministry of Agriculture. Land use datafrom Mzuzu, Kasungu, Lilongwe, Blantyre and Ngabu ADD's suggestcultivated surface areas up to twice those reported in baselineestimates. Using these higher values where available, we obtain a totalcultivated surface area of at least 25,556 km'. Aggregate income lossesare correspondingly higher, ranging between 0.7Z and 4.52 of 1988 GDP.Detailed results by ADD and for different values of p are contained inAppendix 5.
36. Financial Anal2sis. Farmers will tend to define erosion lossesmore narrowly, in terms of reduced net revenues (i.e. farm income net ofall inputs including labour). Data on net revenues for various crops isprovided by AES Report No. 55 (1987). We assume that farmers willadjust labour and other inputs in the same degree as yields decline, andthus apply percentage yield losses directly to composite nst revenues,which are calculated in the same manner as composite gross margins. Theresulting estimates of annual financial losses range from 5 to 33 K/ha,for Malawi as a whole, or between 42 and 262 of composite net farmincome. Detailed results are presented in Appendix 5.
37. Recurrent losses. Soil eroston in one year has an effect onyields in future years as well, as soil fertility declines absolutely.For simplification we may assume that the nominal loss in the baselineyear is repeated in subsequent years. The present value of current and
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Annex IPate 10 of 21
discounted future losses arising from one year of average soil loss isthus calculated as the sum of a geometric series, which simplifies as:
n+l-a 1 a ) (5)
where: L- =NPV current and future lossesL,= current one-year lossa l/l+rn time horizon (years)r D discount rate
38. The choice of a rate of time preference for discounting futureincome losses is not obvious. From a public policy perspective, a lowrate seems appropriate, since society can spread risk more effectivelythan private individuals. To suggest the social rate of timepreference, we adopt a low discount rate of 52 per year. We assume aten year planning horizon, although the scarcity of arable land inMalawi and the increasing rarity of fallowing could easily justify alonger period.
39. When we add the impact of current erosion on future yields, therange of estimated field level and aggregate losses for Malawi as awhole rise dramatically. With a 52 discount rate and a ten yearplanning horizon, estimated losses increase more than eight-foldl Usingcomposite gross margins, we thus obtain field level losses between 88and 556 KRha for every year of soil loss, or between 352 and 2242 ofcomposite (annual) gross margins (p - 0.002 to 0.015). Estimatedaggregate losses based on these figures are equivalent to 42 to 272 of1988 GDP. Detailed results of capitalizing estimated gross margin andnet revenue losses are presented in Appendix 4.
40. Private rates of time preference will generally be somewhathigher than our estimate for society as a whole. Evidence from studiesof the informal credit sector in Malawi suggest private rates ofinterest as high as 50 to 1002 per year. While interest rates are notnecessarily an accurate reflection of time preference, we can see fromequation (5) that as the discount rate (r) becomes large, L, will tendto approach L,. In other words, smallholder farmers will tend to ignoreall but current yield losses arising from soil erosion.
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Annex IPaas 11 of 21
APEDZ I LAND tt DATA
Table 1.1 KAROOA ND tUS AA CANED
TOTAL NAT PARtB COMBINE LWREBER TOTAL TOTAL SETTLE DASOS STEEP SaA,S ARDp/OI87RICT & SOWEESURFAACE & G,R. a.R. /F.R. "LPACE ARA8LE CUL-T: A INK1RS L SWAM4PS SPEB SLOPES
TOTAL NAT PARKS C01NED Lt9UV6 TOTAL TOTAL SErTLE DAos STEP SWAMPS ARDP/DSTRIC A SWES SURPACE A O.R. O.R./P.R. S04PAC* ARASLE CULT. A IN14AS L 8SWA8 SLOPES SLOW
TOTAL MAT PARKS COWItNED UWESS TOTAL TOTAL SErTLE DAM46O STEEP SWAMPS AROP/DISTRICT A SOUKES SURFACE A O.R. C.R./P.R. URFACE ARA" CULT. A INiR>A A SWAMPS SLOPES SLOPES
TOTAL MAT PARS CON910 UEW TTVAL TOTAL SETTLE DMO0 Si SWMS ARP/DISTRICT A S8WCES SIWFACE A G.R. G.R./P.R. SURMPACE ARABLE CULT.. A I4FA A SWMPS SLQPE SLOPES
NAO4INOA DISIR1CT:OUR ESTDATE I S , 15 SW0 O6 4929 8.888 1.810 280 1.540N.P.C.P./MCIN .A4 W US I Ut 4.085 1.102 101 989 s98 1.882STOOss a JBB 6.14e 5.491 1.67 el 998 47e 1,474
TOTAL NAT PARKS COISNED LOESVS TOTAL TOTAL SErrTE DAM 5T 8 P SAMI R/OSTl7Cr a SoWCs SWURACE £ O.R. C.R./P.R. SIPACE AAL CLT. A INFRAS A SWAMS SLOPES SLOPS
TOTAL NAT PARNX CSED UREWNED TOTAL TOTAL SErTLE OAs STEEP SHAWS A8DP/OISTRICT A SOWC SLAPACE A G.R. G.R./F.R. SuRFACE ARA8E CULT. A I3PAS A SWAMPS SLOPS SLOPES
Table 2.2 SOIL AND YIELD LOSS ON GROSS ARABLE LAND, BY ADD
AGRICULTURAL TOTAL GROSS CULTIVATED AREA EST. AVG. WEIGHTED AVERAGE YIELD LOSS (X)DEVELOPMENT SURFACE ARABLE BSae High SOIL LOSSDIVISION (km2) (km2) (km2) (km2) (t/ha/yr) 1!.002 .004 .-0 .010 .016
Notes: 1. ADD, regional and national *oil and yield loss weighted by gross arable area.2. Baseline astimato of cultivatod area is MOA 1987/88 3rd crop estimate. Higher
estimates are: NADD and 8LADD from LREP, LADD from Environment Discussion Paper,KADD and UZADD from NPOP.
Table 2.8 KARONGA ADD: SOIL AND YIELD LOSS ON GROSS ARABIE LAND
TOTAL GROSS EST. AVG. WEIGTED AVERAGE YIELD LOSS (3)RURAL DEVELOPMENT SURFACE ARABIA SOIL LOSSPROJECT (RDP) (km2) (km2) (t/he/yr) L%.002 .004 .006 .010 .015
Table 2.4 MZUZU ADD: SOIL AND YIELD LOSS ON GROSS ARABLE LAND
TOTAL GROSS EST. AVO. WEI2HTED AVERAGE YIELD LOSS (X)RURAL DEVELOPMENT SURFACE ARABLE SOIL LOSSPROJECT (ROP) (kh2) (km2) (t/ha/yr) 0.002 .064 .000 .610 .01O
Table 2.5 KASUNU ADD: SOIL AND YIELD LOSS ON GROSS ARABIA LAND
TOTAL GROSS EST. AVG. WEIGHTE AVERAGE YIELD LOSS (X)RURAL DEVELOPMENT SURFACE ARABIE SOIL LOSSPROJECT (RaP) (W2) (km2) (t/ha/ye) _J%-ff2 .664 .66 .010 .015
Table 2.6 LILONOWE ADD: SOIL AND YIELD LOSS ON CROSS ARABLE LAND
TOTAL GROSS EST. AVG. WEIGHTED AVERAGE YIELD LOSS (X)RURAL DEVELOPMENT SURFACE ARABLE SOIL LOSSPROJECT (ROP) (km2) (km2) (t/ho/yr) Ab.02 .2a4 .we .010 .016
Table 2.7 SALIMA ADD: SOIL AND YIELD LOSS ON GROSS ARABLE LAND
TOTAL CROSS EST. AVG. WEIGHTED AVERAGE YIELD LOSS (!)RURAL DEVELOPMENT SURFACE ARABLE SOIL LOSSPROJECT (RDP) (km2) (km2) (t/ha/yr) p.002 .064 .206 .010 .01O
Table 2.8 LIWONDE ADD: SOIL AND YIELD LOSS ON GROSS ARABLE LAND
TOTAL GROSS EST. AVG. WEIGHTED AVERAGE YIELD LOSS (X)RURAL DEVELOPMENT SURFACE ARABLE SOIL LOSSPROJECT (RDP) (km2) (ki2) (t/ha/yr) pm.002 .004 .O8 .010 .016
Table 2.9 BLANTYRE ADD: SOIL AND YIELD LOSS ON GROSS ARAJLE LAND
TOTAL CROSS EST. AVG. WEIGHTED AVERAGE YIELD LOSS (1)RURAL DEVELOPMENT SURFACE ARABLE SOIL LOSSPROJECT (RDP) (km2) (km2) (t/ha/yr) p.002 .004 .006 .610 .016
Table 2.10 NKOM ADD: SOIL AND YIELD LOSS ON GROSS ARABLE LAND
TOTAL GROSS EST. AVG. WIGHTED AVERAGE YIELD LOSS (C)RURAL DEVELOPMENT SURFACE ARABLE SOIL LOSSPROJECT (RDP) (kW2) (km2) (t/ha/yr) pa.002 .004 .006 .010 .016
Sources Agro-Uconoaic Survey (a4) Report ND. 88 (187 and MCA datatables (1989/90). oh ue standard yield figures and officialAUMAR Pliea; AE3 valuee ore inflesd from 1984/8 to 1989/90,using t.e grath rate ot grose margin. for each Crp, es=rsted tn MCtA dsa tables. Sorghum under M0A io taken frm
iD,tabl U4 (it?/il).
19,Annez I.
Pate 19 of 21
Table 81.61 COW0SIT GROU witIAVIAUOQ CaNTtXSJION OP EACH MP TO MM NAOZI PE lTARE
AAOR OFS KARNA IUW KASIRM LILIWUWE &AMDA LINDE MAHRE NU M ALWA
2. No date for pest crop,; nt I t treated *a aorg)um.*. RICe i- excluded; *eeuaed to Incur no eroo,cn 1eea.4. Miela averv.age eichted by baelin* eativate of cultivated area.
Tale 8.4 COWSM lfEf Ri VOAR
AVCRM CTRIShION OP SUN CROP TO MET -SCE PER HEll!R
Anon. 196I. Annual Reeort of tho Dpartment ot Aqrlcuhture for the Year 1962/63 (Part II), Govemment Printer, Zomba
Anon. 1988. Natlonal Phvseia Oevelopment Plan, Vole. 1 and 0, Dept of Tovn san Couney Planning. National Physical Development Plan Project.UNDPNUNCHS (HABITAT) Project No. MLW179/012, Ulongwe.
Anon. 1087. A Production Cost Surve of Smallholder Farmner In Malawi, Agro-economic Survey Report No 55, Ulongwe. April
Anon. 1990. Annual 8ure of Agriculture, Worktables, Natonat and A.D.D. aggregatons (or 1987/88 crop year, MOA, Ulongwe.
Ampheltn, MB. 198. Soil Erosion Research Proleet Bvumbwe, Malawi, Summary Report, Hydraulics Research, WalIlngford, UK
Bishop, J. & J. Alln. 1989. The On-Sits Costs of Soil Erosion In Mall, Envlrnnment Dept, Working Paper No. 21, The Wordd Bank, Washington, D.CNovember.
Brnt MJA, A.J.B. Mithell & R.C. Zlmmnrmnn. 1984. Environmental Effect of Development, Malawi, Phase 11 Repor, Consultant Report,AG:JDP/MLW/81(001, Food and Agriculture OrganIzation of the United NatJons, Rome.
Cthwm, V.LA. 189. A ,a,ofR ExperIence In Six Years, Ref. No. NUV25/18Nol. I176. paper presented at Agrforeetry National Workshop.
Ehle, H.A. 1978. Sol loss estimation: comP led works of the Rhodesian mutdisclilnav team on soil tos estimation, Inst. Agric. Engng., Harare,Zimbabwe.
Elwell, H.A ard MA Stoolng. 1982. Developing a simole yet oractal method of soiloss estimation, In Trop. Agric. (lrlnldad) Vol. 59, No. 1, Janup. 43A4.
Khonpe, C.S. & 8.K MachIe 1S87. Erosion Hazard Maeotna of Malawl, Land Husbandry Braneh, MOA ULlongwe, December.
Lai, R. 1981. SOD erosion orobtems on Alfols In Western NIgeri. Vl. Effect of erosion on experimental slot, In QOoderma, 25, 215.
Lai, FL 1087. Eflet d erosion on cor pirducff&, In QICal Reviews In Pnt Sens, Vol. 5, Ise 4, pp. 0-367.
Madhiut, LW. & P.C. Manyanze (M. St & J. Akwman ode.). 1989. Eroslon Hazard M8oping of ti SADCC Rgion. Part 1: Zimbabwe, Report t18, Coordaton Un SADOC Sol and Water Conservation and Land Utilization Sector, Maseru, LesotIho, March.
Maida. J.HA & Z.W. CNlilma 1981. Chne In Indices o FerWiv under Continuous Crosolna, in Luso: J. Sci. Tech. (Melawr), (1981) 2(1), p..25.
M*edre, E.J. 1988. PrellmInatry Evaluation o Sof Loss Estimaton Model for utern Afrkca (SLEMSA) under Mafawv CondIions, paper presentLaud Husbandry Senior Staff Semnr, Kaaungu, 5-10 Jun 1988.
Stesbf, AR. & J.N.R. Jeffem (ed. 1. Andermon). 1985. Mand Use Survey of Mali, 19667, Land Resours Dom Centre, OveWseasDevelopme Admlnrataon, Swbr8on, Surrey, UK
Stocking, M. 198. The Os of 901 Erosion in Zimbabwe In Terms of Oe Les of Thre M_a Nutien_t, Consulants' Worki paper No. 3, SoilConsration Programne, Land a Water Devepmnt Dlv., AOLS, FAO. Romn.
St , M. 1987. MeasurIna land dearadation. in Bah , P. & Brooleld, H. (eds.), Land degradaton and socity, Mothuen, London: 4943.
Stocking. M.. Chakela, 0. & Elwel, H., 1986. An IMRond Methodoloay for Erosion Haard Madina, PaR ;: The Technkoue, in Geograftska Annale70A (3): p. 169.180.
Twylord, I.T. 1988. Development of Smallholder Ferflier Use in Malaw& , papW for FAO/FIAC mng, Rowm, 26-29 Apr11 1068.
Wischmetu, W.H. & Smith, D.D. 1978. Predictng raifl eroslon bsses a outdo to consoetion oaLngn, U.S.DA Handboolk 53, Washingt, 0
Annex 2Page 1 of 4
- 22 -
AUTOREGRESSIVE TREND MODEL OF RELATIVE PRODUCER PRICE STABILITY
1. As reviewed in Ch.'pter II of the main report, a major concernis that recent swings in relative prices in Malawi may be making itdifficult for smallholders to plan and develop viable land managementand cropping systems to counteract erosion. This is particularly thecase for the poorer smallholders who appear to rely on intercropping andrelay cropping maize, groundnuts and pulses as a means to meetingnutritional needs, maintaining soil fertility and conserving soil.However, some "progressive" farmers may abandon these mixed-croppingsystems to plant the more erosive crops, such as maize, cotton andtobacco, in pure stands if there are high relative prices for thesecrops. Thus smallholders in Malawi may be influenced by changes in therelative prices of non-erosive to erosive crops that impact on croppingsystems and land degradation through the choice of crops, croppingrotations, intercropping and relay cropping.
2. This annex examines the extent to which smallholders in Malawi* can anticipate or predict the relative price of non-erosive to erosive
crops based on past price levels and trends. As very little informationexists on actual market prices in Malawi, the price series used areofficial prices. However, as the official prices are revised every yearin light of market trends, these prices are thought to be sufficientlyrepresentative of prevailing price trends.
3. If farmers are to make significant land management investmentsin cropping patterns and systems, such as switching between arosive andnon-erosive crops or substantially improving non-erosive croppingsystems, then they would be interested in anticipating future relativeprice trends of non-erosive to erosive crops. Of particular interest isthe extent to which past relative prices and trends are an indicator offuture prices, given that the past price levels and trends are probablyall the information available t, farmers for predicting future prices.If the relative price is highly unstable, then past prices will not be agood guide to the future, and the uncertainty surrounding farmers'investments is high; on the other hand, if the relative price is stable,then past prices could be highly predictive of future trends, andfarmers would be less uncertain about the outcome of their investments.
4. Given that farmers are most likely to base their expectationsover future prices on a simple extrapolation of past price levels andtrends, fitting an autoregressive trend model to the time series ofrelative non-erosive/erosive crop prices is the most straightforwardmethod of testing the accuracy of such extrapolations. The basic modelis:
Annez 2Page 2 Of 4
- 23 -
t a + bP"1, (1)
where P. - relative price of non-erosive to erosive crops in periodt (the currert year).
Pt-, relative price of non-erosive to erosive crops in periodt-l (the previous year).
S. Additional past values of P (e.g. for years t-2, t-3, etc.) mayalso be added. Unfortunately, doing this may also add to problems ofmulticollinearity in any regression analysis, i.e. high correlationbetween two or more independent variables. However, as the major use ofthe estimated relationship is to predict future levels of P and not toprecisely estimate parameters per se, then the presence of any highmulticollinearity is less serious a problem.
6. Several versions of the basic model (1) were estimated,including adding additional past values and incorporating changes inpast values in semi-log versions of the basic model. The following werethe best results:
where LP, - natural log of P.DP - P", - P,..2. i.e. difference between last year's
price and the price prevailing the year beforeDPt2 - P,2 - Pt., i.e. difference between the price two
years ago and the price prevailing the year before
7. All estimated coefficients are significant at the 95Zconfidence level or higher. The more basic model (2) indicates thatpresent relative non-erosive/erosive crop prices can be positivelyextrapolated from ptices the year before, and given the pricefluctuations, can be negatively extrapolated from prices three yearsago. For example, on average the current relative price will be around442 of last year's price minus 56Z of the price three years ago. Thesemi-log model (3) shows how this year's prices might change in response
Annex 2Page 3 of 4
- 24 -
to changes in price differentials in the post; i.e., the coefficientsindicate how a one unit change in past price differentials might be used
to predict the percentage change in this year's prices. In thisregression, the response is very low - only 0.13Z to a marginal change
in past price differentials.
8. Unfortunately. however, the explanatory power of both models
(2) and (3) is not very good, as indicated by their respective R2 and
adjusted R2 values. This suggests that it is difficult to extrapolate
future prices from past price levels. Moreover, even if equation (3) is
accurate, it indicates that past price differentials are not a very good
guide to future price changes.
9. Models (2) and (3) can also be modified to include a long-run
price trend for non-erosive and erosive crops. The assumption would be
that smallholders take into account not only more recent prices but also
the long-run relative price trend for non-erosive and erosive crops.
The long-run trend rates of growth for non-erosive crop prices, erosive
crop prices and relative non-erosive/erosive crop prices were estimated
directly from the price series data for 1968/69 to 1989/90 using an
exponential growth function:
Non-erosive crops: 11.04Z per annumErosive crops: 11.18Z per annum
Non-erosive/erosives 0.21Z per annum.
10. However, th.e estimated regressions for deriving trend rate of
growth for the non.-erosive/erosive price ratio was a very poor fit.
This is not surprising, given the extreme fluctuation in this ratio.
Thus the trend rate of growth in the relative prices was derived
indirectly from the difference between the rate of growth of non-erosive
crop and erosive crop prices, i.e 0.14X. Using this rate of growth, a
long-run constant trend in the relative pr'ce was estimated, which was
11. The inter,retations of equations (4) and (5) are similar tothat of (2) and (3), although adding in the trend variable has changedthe coefficients in equation (4) and a 332 change in current prices inequation (5) is related to the long-run change in trend. However, theexplanatory power of the equations has improved only slightly, asindicated by the R2 and adjusted R2 values. Thus, even if smallholderswere aware of both long-run relative price changes for non-erosive anderosive crops as well as past price levels, it would be difficult toextrapolate future prices with much accuracy. A large degree ofuncertainty over future relative prices would remain.
Annez 3Page 1 of 36
° 26 -
METHODOLOGICAL NOTE ON ESTIMATION OF DEFORESTATION RATE AND VALUE
This annex provides the background data for the analysis inChapter III of the main report on the depletion rate and economicvaluation of deforestation in Malawi. Appendix 1 covers woodconsumption data, Appendix 2 covers wood supply data, Appendix 3 reviewsthe depletion rate calculation, and Appendix 4 reviews the valuationcalculation.
BASIS: Calculations based on figures given on p. 32 ofNational Energy Naster Plan -- It mass Sector PositionPapers -- Sumry Report, IPC (R. Kronen), Harch, 1988,.
Yer 1987 1M 1969 19"0 1991 12 1993 1994 1995 19% 199 1998 19 2000* a~~............................ ... . .. …t .en e ee. .eC.e ...... ....... e.. .e......
Generic factor uud in National Energ Naster Plan - BlousSeotor Position Papers, Suary Report, IPC (N. Mronen),"arch, 1981.
-'C ~ ~ ~ ~ ~ ~ ~ ,S
-31- QlAM 3Page 6 of 36
TABLE 1.4: Tobacco Estate Wood Pr9lectiLons
PAELININARY 6ROVTH ASSWIPTINS:
a) lurley grwe at 3.41 annually until 2000 and realins at thatlevel (basd on National Energy Plin (MP), p. 255)
b) All other oategorle regain at the level ot 1988/89c) All potential vood cmnsueption rate art attained by 20004) Projections vill be ade separately for poles an6 fuelvood.
Poles will be projected as per burily growth rate; tuelvoodVill remain At 1980/89 levels.
1. Recovery fsctors: .48 for Clkangawa, Zoe and Blantyre sunills;.45 for Natiaoba and .40 in Dedza and iIulan3e savmllls and other oprs.;.40 for plywood
2. NWaaMba mill not expected to produce more tan 5,000 .3/yr oflumber due to log supply, handling and drying deficincies. Couldclose down In early ninaties.
3. TZoba mill Is obsolete and badly located and assumed to stop inabout 4 years. New complex planned for late 1980s If ao pulpillIs built based on Zols resurei).
4. Slantyre mil! also obsolete aRad sold stop operations m.
5. Timber Products Limited (TPL) sill faces diminslhing ailabilityof Hl so product mix will shift to-higher share of SW.
SUCE: Forestry Sub-Sector Study, table 5, p.51, VI, Sept., 142 IPL is currently oalled ITL.
VIPLY is dsigned to praduee 7,500 s/yr of plywoo and 7,500 .3/yr ofblockboard. Lumbe production ha be etteAtd at 13,000 3/yr.
ITL has an lnstalled sawmIll apaity of 24,000 e3/yr Wille the plywocapcity is 5,000 .3/yr.
SOUE: Pardo, U.S., A Review of Foret Policy for klwi, 1S
Personal comuiatim w/ 3.6. Undo, Nuagia 1retor of ML giva uvs ill roundood inpt *i v,0 s3 vith a rot Iur reoavy of40% and a plywood roundweIut o 610, 0S vith for 2,000 m3 ofend product.
Aftef discuesios wvith International Timers td. ad VICO, it will oeassumd that total raw mterial reupire ts for the forest eroo6tsindustry wIll reaifn at the 186 level a of 199.
Assumption: from 1990 until 2000, wood reqiremts will gr1A1 at It/yrwith all growth occurring In the Northwrn region. W indstry Isstagnat; only current grwth to VIPLY and only th Nothers region hslncreental ra aterial sources.
34- ANNE 3Page 9 of 36
TABLE 1.5 Continued
Projections of Vood RequlrMts for Vood Prod4cts Industry
Brick oaklng apopar to 1 the most iwortant village industryrelying exclusively on tfwoo (NIP, p. 247)
annual brick productiont for the whole country as assustO at40000 tons ptr year, whicn require a spicific amount of2.65 e3 St of indIgenOus fuelvood per ton.
is a result, current annual fuelvod coetuptfn Dy this industry.OulO be:
106000 o3 st/ year, or, astusing:0.55 .3 solid per e3 st (generic coefficient, IPC a, p.11)
51300 i3 solid per year.
Otrer village industrie are assused by NiP to contuse:134000 o3 st/year, or:73700 a3 solid per ye.
Therefore, the total annual ftuelvod conumptil for villagiIndustrl wouvld be;
1320 solid pe year.
actordifg to UEP (P. 248), the introdctiom of IW*ved pred6timette could rtduce brick bWning fuebmo demn b W.Thtrefore, for projctions ve vill atoi eoestlof rminsconstant.
ProJeetlons of Wood Rplre ts for V11lae Ilndtries
thenaM 031Year 1M6 1o 1M 19"0 1991 19I2 1993 194 1 9 I? 1" 199 2010o S f e l e a no....... ......... . ,......e.. ..
Other priv. plantat*lonsLocal authority 0.7 1.0 1.0 2.7BcFP* *4.6 4.8
mTL 2.5 2.5
Subtotal 0.7 1.0 6.2 10.0
TOTAL PLANTATIONS 56.0 27.1 36.6 123.7
TOTAL FOREST LAND 193.0 1332.1 667.3 4162.2
maCpP * Slantyre Clty Puslwood Projeot=*ITL a Imperial T1ib Limited
~.
.40.- AN 3Page 15 of 36
TABLE 2.1 Continued
SOURCES:
1. Forest area on oustomary land obtained from ZPCz. National EnergyMaster Plan-Biomass Seotor Position Papers--Summary Report(Kronen), Maroh, 1966, p. 7.
2. Regarding Forest Reserves:Total figure obtained from various souro-e Inoluding WO SAR,p. 4; R. Pardo's Common Property Resouroe Management, 190 p.5;Forestry Department, pers. comm. per r. Bakanda.Regional breakdown obtained from Armitage, J., An EvolvingStrategy for Managing Wood Energy: A Case Study for Malawi,1966. p. 2.
3. Regarding Government Plantations:Total figure obtained from ZPC, National Eiergy Master Plan--Biomass Seotor Position Papers--Summary Report (Kronen), March,1908, p. S. These are 1967 data. Regional breakdown assumeS3,000 ha In the Northern Region (oorresponding to the Vlphyaplantations (per Forestry Dept). Remainder Is asumed to beevenly split between the Southern and Central Regions.
4. Regardlng plantations on estates:Total are of plantations on tobacoo estates obtanied from IPC,1966 (Kronen), Annex Ui.S. Plantations on tea etates (all inSouthern Region) obtalned from IPC (Kronen), p. 6. Regionalbreakdown of total plantations on estates "sum" to be 50 inSouthern, 30% in Central and 20% In Northern Reglons basd onregonal distribution of tea and tobaooo estates.
5. On other private plantations:Looal Authority Plantations baed on WH Projeot CompletlonReport, Annex 1 Table 3. BCFP figures based on BCfP plantingschedule per FORINDECO Mwrket Prios ftudy for Fuswood and Polep. 23. ITL plantation area obtained from Mr. D.G. Lloyd,Managing Diretor of MTL.
- 41 -ANNU 3Page 16 of 36
TABLE 2.2: Growing Stock Coefficients(m3la)
NORATERN CENTRAL SOUTHERNREGION REGION REGION COPOSITE
A. Indig. For. Res. 100 80 s0
B. Indig. oust. Id. 120 40 30
C. Government plant. 202 84
D. Private plantations 71 115
SOURCES:
1. For Indigenous Forest Reserves and Indigenous oustomary land,ZPC (Kronen), p. V.
2. For plantations, both Government and private, figures wereoaloulated from Romahn, S., Deforestatlon In Halawl-DisoussionPaper for the National Eoonoseo Counoil, May, B9, Table 2,p.2.
-42 -
Pa.. 17 of 36
TAIL 2.3: Stock In 1990(tbousand u3)
OEtESTO POTHERN CENTRAL SOUTHGENCATEGORY REGION REGION REGSON TOTAL
TOTAL GROWING 6TOT 23135.4 7006.1 46439.2 347857?.
USCFP a Slantyre City Fuelvood ProjeotN*sTL a perlal Timesr Limited
OSSRVATIONS:
I e For Government Plantations In the Central Reglon, the growingstock seffioelent of the Swithern Region (64 mI/ha) has beeappliLed.
2. For planttats en tobaooo estates in the Northern Reglon, thegrowing eOok oeffiioent tor preovate plantatins In the CentralRegion (7TL /ha) has bem applied. Otherwise, plantations enestate us the growing stock oeffiolents for privateplantatiens In the respective reglnas.
3. Local authority plantation In the Northern R"ion use thegrowing stook coeficient foe' private plantations In the Central
tegion C71 mI/ha). Otherwis, leal authority plantatiens usethe growing stook cosfficients for private plantateons In therespeotve regions.
4. SCP and KTL use the growing stook o"fficients for privateM*l iv * 34 of I t , n -0*3 Dn '
- 43 -3
Page 18 of 36
TABLE 2.4: Mean Annual Increment Coefficients
_ _ 1sl.(/ha/yr)
NORTHERN CENTRAL SOUTHERNREGION REGION REGION COMPOSITE
A. ndi9g. For. Rie. 1.2 1 1
B. Indlg. oust. Ld. 1 0.8 0.5
C. Government plant. 20 12 12
D. Private plantatlons 10 10 20
SOURCES
* o1. For indigenous Forest Reserves MAX ooeffiolonts wer eetimatedbase on an average impliolt sustalned yield of 1.11 mS1/ha/yrcaloulated from IPC (Kronen), p. 69 and spread acrose reglonswith the Nothern region having the hlghest produotlivty, as Inthe oase of Indigenous forests 6n customary land.
2. For Indigenous forest on oustomay land, MAX ooeffioIeft@i wereobtained froe IPC (Kronen), p. V.
3. For Government plantations MAX ooeffioients wer also obtainedfroe IPC (Kronen), p. V. For the Northrn Reglon the Vlphyasoftwood plantation ooeffioient (20 3/ha/yr) was used. For theCentral and Sothern Regions, the 12 .3/ha/yr ooefficientoorresponds to softwood plantations other than Viphya orEucalyptus plantations for wood energy.
4. MAI oosffioients for private plantations were also obtained tromIPC (Kronen), p. V, with the Nothern and Central regions usingthe Euoalyptus figure for tobaooo estates (10 m3/ha/yr), and theSouthern region uslng the flgure for tsea state (20 &3/M/yr).
.44- ANNEX 3Page 19 of 36
TABLE 2.5: Mean Annual Increment In 1990(tbousand .3)
FOREST NORTHERN CENTRAL SOTHERNCATEGORY REGION REGION REGION TOTAL
1. Blantyre City Fuelwood Project plants 1,750 ha/yr from 1989/90to 1992/93. This is assumed to continue.
2. The Forestry Extension Services Division has provided over130 million seedlings since 1976e This implies 9.3 millionseedlings per year. Assuming 2,500 trees planted per hectareand a 30% survival rate, this gives approximately 1,100hectares per year.
3. Between 1981/82 and 1985/86, Local Authority plantationsincreased at a rate of 2,710.5/4 - 677.5 per year, roundedoff to 700 ha/yr. Distribution was 27% in Northern, 36% inCentral and 36% in Southern regions.
4. Between 19.81/82 and 1985/86 Government plantations increasedat a rate of 10,899.9/4 - 2,725 ha/yr, rounded off to 3,000ha/yr.
5. Based on the above, the following annual planation scenariois assumed for years 1991 to 2,000.
b) Snw pric for 1990 wilt emAW atK 5.61 jS pe wdste of pita rum
- of Jtiy 19M, owwat to m tiLd.
C) Rwatt ww%s of Om farq. fta ftow of,m mitt b bin refartirg Wa m Sw Omtilf ftro fbi tc be sAatiro
*D A=air ptmuafcm mof 10 diAv/w wdWd tkire Vi rAtes of f fto WMR.wl forckaddknin wr 2300 m foWtt:
Sm'. it. I3
CAM. A. X3W..................
=tAL dw
Vim so tot ov 0mpd to cw viis dmdeimAd be n'fattl:
tVd
...........................
SpAh. R. 31 SMD
C.w. a. m 4,..... ........... ---------
AL. 695 IOD.C
reultIr In a NOl d rmtfrdy 70,00 ML
558- AM= 3
Page 33 of 36
TABLE 4.1 Continued
*) Am 1 ta arms Is p t Ii in lt Witpwftiar. Fro 1WO to 2D irct1, this ispxlmswy 61 timrd hwtar to bep&ef avml,y. This wilt be bW m d. bet%nal Saothum ̂ d curast egI am i, torfialt rupinsu sas foallo:
tCumwd hcotaris r
Sahu. A. 36 56S3D
CAw. R. 28 43.674...... ,..........
TOTAL 64 MM
f) it wiLi be in.ad t 50.OM of Ute tifamt0cun to bewd for pint erioual.
IN=e io iuu " an uwuts frum filewiM d., fe 1u' d for fasnre .auLd be asfollo:
!amrdfrgtyflue ftaAdr tdAe dna mAsti1eieforesd hostaiwm awv td to f*Mr.
j d~~~~~Moi hwte
Yr_ 19 1991 199 1993 19 195 19 199 19 199 9 m.................................................................................................................SmAwu R. 49 100 152 2as u 295 316 DO 39 381 4u
cwutuR. 25 51 71 107 36 17 ¶9W 2 ag 3D 342............................................................... ................................................................
TTAL 76 151 z30 .312 0 461 s1s 570 62 6w. WA
g) mf asteai t miAW at1 1000 Au.bond aw hbif of co ~eu in UMIEW a tprimc Jn dr-1Wd frum fipm e. a a Z.This alLm for a t cast .iWh is applsa tslvacm hof f Vs in 1w T pimatians wdstatt in 80 to t* aR of loer plan0rg
cost of inUstI 'dr.
59 UAN 3Page 34 of 36
TABLE 4.1 Continued
h) RodI rat wits of wriaAtl uW. a gsinyielo IO kgft witl b _md (info.
4fd by Jth idcp). Priam fw miza in1"RA deuid htm ministry of Agriutuw, perih l i *p It o.a CM/k. An oum iWofto of IS.OI witll h mwifad ircnsi.
1) hpgdidr scteslitte, wmian iill axr antfmted lac Utat is rat fsnic Akm sbjwtto oria la isafols:
1wr I_O 11 1992 16 19 1992 ¶99 17 1990 199" aom
StuAhaM R. 49 51 52 54 68 22 22 22 22 22 22
raeel A. 7S 2 Z 3D 31 32 3. 5 37 3................... ............ .... ....... ..... ...... ....
TOTAL 74 77 go U 9 52 56 55 57 5 60
J) md mi mo1ptf, In Wm 7.5 of WS , it isu md On ach hscUre aLbjet to .rion mittaffect 0.5 hwcta of WiAutul WM cwirge a4.at dltirw in uvAl miz# pr*tivity. Trhfore
kith an wnAt pain yield f 1000 kaft,utl fer tlvlty lm mXt km t
*V W hsv abject to ermimi At a pie ofd, / 8w l _tst wllt K 5.2 w
METHODOLOGICAL NOTE ON ESTIMATION OF WATER EXTRACTION RATE
1. This annex provides the background analysis for the discussionin Chapter IV of the main report on the extraction rate of waterresources.
I. Total Water SuDply
Rainfall
2. Malawi's climate is influenced by its large lake, high plateausand rugged relief. Rainfall is measured at more than 500 stations. Therainfall is seasonal from November to March in the south extending toApril and May in the center and north. Tropical cyclones occur but notfrequently. Malawi is in the high rainfall zone of Africa, with a meanannual rainfall of 1,037 mm. The rainfall/elevation distribution is asfollowst 63 percent of the country receives 650-1,000 mm, 17 percentreceives 1,000-1,200 mm, and 20 percent receives more than 1,200 mm.
3. The windward slopes of Mount Mulanje, the Shire highlands,Zomba Plateau, and the eastern slopes of the Viphya and Nyika Plateausexperience very high rainfall. The lakeshore near Nkhotakota, NkhataBay and Chintheche, where the lakeshore is set against the direction ofthe wind, are also subject to heavy annual rainfall. The remaining highplateaus are climatically defined as semi-arid. The southern lakeshoreand the Shire Valley are classified as arid.
Surface Water
4. Malawi has a broad network of river systems with substantialsurface water resources. Most of the rivers, with the exception of theShire River, have a seasonal pattern of flow. The flow begins to risein November, following the rains, with high flows occurring mainly fromJanuary to March. In April through May, the flow begins to recede.June to October are the driest months with practically no rainfall.Many of the small tributaries of the main rivers in the plateau areasand in the upper reaches of the main rivers are seasonal. Most of theShire tributaries and the streams flowing towards the Chilwa basin, too,are seasonal. All lakeshore rivers are perennial.
5. The Shire River is the only outlet from Lake Malawi. Theextent of flow in the Shire at the outlet at Mangochi is governed by thelake levels. The contribution of water to the Shire from catchmentsdownstream is small in relation to amounts of outflow from the lake.
6. The hydrology section of the Water Resources Branch of theDepartment of Water maintains a network of river gauging stations
Annez 4Page 2 of 8
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covering all the river systems in Malawi. Water levels are observedtwice a day and discharge records cover low and high flow seasons.Processing of these records, analysis and evaluation of the basic datawere part of the National Water Resources Master Plan Project completedin 1986.
7. Table 1 gives details of rainfall, runoff and percentage ofrunoff for the 17 major watersheds of Malawi. The average annual runofffor the whole country is 19Z of the average annual rainfall. Along thelakeshore the runoff is higher. The North Rumphi river, too, has a highaverage runoff factor (33.1Z) as does the Ruo watershed. The totalrunoff can be expressed as 196 mm. or as 586 cu.m. per second.
TABLE 1: RIVER BASINS OF MALAWI, MEAN ANNUAL RAINFALL, AND RUNOFF…-- - - -- - - - -- - - - - -- - - - -- - - - - -- - - - -- - - - _
WRA/River Basin Catchment Rainfall Runoff 2Area mm mm m3/s Runoff
1 Shire 18,945 902 137 82 15.22 Lake Chilwa 4.981 1,053 213 34 20.23 South West
TOTAL 94,276 1,037 196 588 18.9… …__------------------------------------------------------
SOURCE: National Water Resources Master Plan, 1986
Annex 4Page 3 of 8
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Groundwater
8. The greater part of the country is underlain by crystallinemetamorphic and igneous rocks referred to as the Basement Complex.Younger consolidated rocks are limited to minor occurrences of Karoosedimentary and volcanic rocks at the northern and southern tips of thecountry. The most imposing structural feature is the Rift Valleyoccupied by Lake Malawi and the Shire River. Variable thicknessquaternary sediments occur along the lakeshore, around Lake Chilwa andin the Lower Shire Valley. The Rift Valley dominates the topography,and the major physiographic divisions (uplands, plateau, escarpment,alluvial plains) define the occurrenc- of groundwater.
9. There are two main aquifers: (i) the basement aquifer which isextensive but low yielding; and (ii) the alluvial aquifers that arehigher yielding but limited to the lakeshore plains and the ShireValley. The weathered zone in the basement complex is generally 15 to30 meters thick and less towards the escarpment. It is found usually 5-20 meters below the ground in most areas. The yield is usually only 1-2liters per second in the plains and on the escarpment the yields areeven lower.
10. The depth of water in the alluvial aquifers varies but they aregenerally deeper with higher yields. Yields of up to 15 liters persecond were found in the alluvial plains along the Salima Lakeshore,Bwanje Valley and the Lower Shire Valley.
11. Groundwater levels have been monitored with autographicrecorders since 1980. These give the seasonal fluctuations of the waterlevels, which could contribute to an evaluation of changes in the volumeof groundwater. The available data has indicated that the normalseasonal fluctuations are in the range of im - 5m in the weatheredbasement aquifers and lm - 3m in the alluvial aquifers. The recordsfurther indicate that there is no evidence of declining levels over thepast 10 years in any of the aquifers. This indicates that the extractionis still lower than the recharge.
12. Estimates of the recharge of the aquifers have been made. Onthe basis of hydrological considerations the annual recharge wasestimated at 15 mm - 80 mm to basement aquifers and 3 mm - 80 mm to thealluvial aquifers. In the alluvial aquifers recharge also occursthrough seepage from the perennial river beds.
II. IMPROVED WATER SUPPLY
Urban Water SuPPly
13. The Blantyre Water Board is responsible for supplying water tothe City of Blantyre and its surrounding areas, which currently cover
Annex 4Page 4 o, 8
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about 38,700 ha. It serves an estimated population of about 340,000people. The main source of water supply is the Shire River about 40 km
from the city. The water is pumped after treatment to storagereservoirs for distribution. Other sources of water are minor. Theseare the Hynde and Mudi dams and reservoirs located in the Limbe area.The Blantyre Water Board presently has the capacity to supply 60,000 cu.m. per day, which is likely to meet the demand for water uxitil 1992-93.
14. The Lilongwe Water Board draws water from the Kamuzu Dam on the
Lilongwe River. The present capacity is 29,000 cu. m. per day to apopulation ,f 160,000 people.
Smaller Urban Schemes
15. Urban water supply to the siX main district centers (Karonga,Hzuzu, Kasungu. Ntcheu, Liwonde and Zomba) is the responsibility of theDepartment of Water at the Ministry of Works. The total productioncapacity of these schemes is 10,600 cu. m. per day supplying a
population of 115,000 people.
16. The water supply to the remaining semi-urban centers is alsothe responsibility of the Department of Water. These schemes have beenin operation with varying levels of service. At present these schemesproduce about 16,000 cu.m. per day supplying a population of about185,000 people.
17. Table 2 summarizes service coverage for the urban population.
TABLE 2: ESTIMATE OF URBAN POPULATION SERVED (1989)
Area Total Population Percent WaterPopulation Served Served Produced
TOTAL 1,267,000 800.000 63 115,600SOURCE:--Water--Departme-t-___ _ _
SOURCE: Water Department
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Rural Water Su121l
18. In rural areas two basic water resources prevail: (i) surfacewater supplied by gravity through a piped system to communal waterpoints; and (ii) groundwater supplied from shallow dug-wells and throughboreholes equipped with handpumps. The rural water supply sub-sector isthe responsibility of the Department of Water. A considerable amount ofappropriate technology has been incorporated in system design andconsiderable effort has been devoted to encouraging communityinvolvement in planning, implementing and maintaining the systems. Therural piped schemes are under the responsibility of the Rural PipedWater Supply Section in the Water Supply Branch, whereas theborehole/well schemes come under the Groundwater Section in the WaterResources Branch.
19. A total of 55 piped gravity schemes have been completedproviding water to approximately 1.24 million people. With only a fewexceptions, the water is untreated. No charge is levied for the water,but the beneficiary communities are expected to organize themselves intocovmuttees to provide self-help labor inputs, local constructionmaterials, and long-term maintenance services. On a country-wide basisit can be found that the average service level for the piped ruralschemes is 150 people per tap, equivalent to 20-25 families.
20. Development of groundwater supplies for the rural populationdates back to the 1930s, when boreholes and open dug wells were providedin rural areas. It is estimated that there are about 9,000 boreholesand 4,000 dug wells, most of which are equipped with handpumps.However, despite substantial investment of manpower and financialresources in maintenance, it is estimated that 302 to 402 of these unitsare out of order at any given time. It is assumed that each boreholewith handpump serves 250 people and each shallow well handpump serves125.
21. The estimated rural population served through gro-mndwater isgiven in Table 3.
TABLE 3: ESTIMATED RURAL POPULATION SERVED BY GROUNDWATERe___________0_0___0______________0_________0_n_____________________
Boreholes with handpumps 1,290,000Boreholes with motor pumps 170,000Protected shallow wells 400,000
Total 1,860,000
SOURCE: Water Department
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III. WATER DEMAND
Unit Demande
22. The unit consumption figures used for planning and designpurposes in the domestic sector are shown in Table 4. The correspondingfigures for the commercial, industrial and institutional sectors arepresented in Table 5.
TABLE 4: UNIT CONSUMPTION - DOMESTIC SECTOR…------------------------------------------------------------__---
Housing Category Unit Consumption (l/cld)
Traditional housing 25Permanent housing:
- High density 75
- Medium density 125
- Low density 200
SOURCE: Water Department
TABLE 5: UNIT CONSUMPTION - NON-DOMESTIC SECTOR
User Category Unit Consumption (l/p/d)
Rest House 200Bar/restaurant as housing categoriesMarket 2.5 cu.mShope as housing categoriesIndustry differentiated demandHealth institutions:
Administrations- offices 30- staff housing as housing categories
SOURCE ------------ Wae Department------------------____SOURCE: Water Department
Annex 4Page 7 of 8
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Total Domestic Demand
23. The future water demand depends on many factors. Assumptionsmust be made as to the future population growth- rates, rates ofdevelopment of the urban and semi-urban centers, development of commerceand industry, shifts in per capita water consumption with changes in thestandard of living and distribution of water points, installation ofwater-borne sewage systems, water supply efficiency and other factors.For example, in studies conducted by the Center for Social Research ofthe University of Malawi it was found that per capita water consumptionin rural areas doubles from 20 l/p/d to 40 l/p/d when individual homesare connected. This may double again when homes are connected to septictanks and a sewage system is installed.
24. The National Water Resources Master Plan has attempted toestimate the future domestic water demand for Malawi. Tables 6 and 7are based on their estimates and were amended to extend the time horizonto the year 2010. It is seen that the total demand for year 2000 isestimated to be 737,000 cu.m. per day or 269 MCM/yr, and the totaldemand for the year 2010 is estimated to be 1,567,200 cu.m. per day or572 MCM/yr.
TABLE 6: TOTAL DOMESTIC WATER DEMAND - 2000…--------------------------------------------------------------------
Population Demand Demandper capita per capital/pld cu.m/day
TOTAL WATER DEMAND 16,190,000 97 1,567,000…---------------------------------------------------------------__--
SOURCE: National Watet Resources Master Plan, 1986; Staff Estimates
25. In addition to the consumptive use of water for domesticdemand, there is also demand for water for irrigation, estimated at 300MOK/year In 2010, and for animal use, estimated at 15 MCM/year in 2010.
Annex 5Paae 1 of 15
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METHODOLOGICAL NOTE ON ESTIMATION OF AVERAGE INCREMENTAL COSTS IN WATER
1. This annex provides the background analysis for the discussionin Chapter IV of She main report on the average incremental costs (AIC)of urban and rural water supply systems. All data is presented inconstant 1990 Malawi Kwacha.
Rural Water Supply
2. The AIC of several piped rural water supply schemes have beencalculated based on available feasibility studies and design reports.The results of this analysis indicate a wide range of real resourcecosts for rural piped water. The feasibility studies used as base dataemployed a rarge of design criteria, on average representing between 27and 36 liters per capita per day. The detailed calculations areincluded in Appendix 1.
3. As might be expected the augmentation and rehabilitationschemes, ie. Mulanje West and Zomba East, show a lower cost per m3. TheSekwa Rural Piped Water Scheme includes treatment by slow sandfiltration. This is a new development in rural gravity fed rural watersystems as previously tapped catchments were more remote and did notpresent pollution problems. Estimates indicate that including treatmentin rural gravity schemes may increase costs as much as 50Z.
4. The AIC for Mpira-Balaka is high. Mpira-Balaka can beconsidered a unique project in terms of rural water supply as itincludes a number of components such as a dam, treatment plant (threetreatment works), considerable engineering design and constructionequipment not usually associated with rural water projects. It must benoted that 202 of the water demand is intended to be used by Balaka townand therefore the project can be categorized as a joint rural-urbanproject. Further, the initial appraisal reports of the project (Danida,1986) commented that the unit costs for Mpira-Balaka are "quite high...even if components normally not included in rural water schemes areexcluded dams, treatment works etc, the unit costs are stillapproximately twice the costs reported on for instance other ongoingprojects.'
5. AIC analyses were also undertaken for two borehole pumpheadtypes, the Climax and the Afridev. The detailed calculations areincluded in Appendix 2.
6. The AICs for both pumphead choices are similar. The AICreflects the initial high front loaded costs of drilling and installingthe casings, etc. The Climax has been shown to be a robust pump, givenits relatively high maintenance costs. The pumphead is expected to be
Annex 5Page 2 of 15
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able to last up to 20 years with correct maintenance. The Afridev hasbenefitted from a low cost maintenance requirement as it has beendesigred to be community 'self-maintained". It-is a "light" pump withplastic components. This abrogates the need for heavy lifting equipmentfor pump maintenance and replacement of parts. For the purpose of theanalysis it is assumed the pumphead has a life of 10 years. Further, anallowance for the cost of community training for the Afridev pump, whichis part of the initial component of the project and is suggested willresult in much lower maintenance, has been estimated at 152 of theinitial capital cost.
Smaller Urban Water SUPglY
7. AIC analyses for several of the new projects to be undertakenin the small urban sector are set cut in Appendix 3.
Urban Water SuDPlY
8. An indicative AIC analysis is presented for the BWB and LWBtaking into account increases in water consumption as a result of thenew program of capital works for each authority. These are presented inAppendix 4.
AMUIX 1:t AT= IICRMI L COSTS RURAL PIPD mATER
h.* .: s0i RUL PUIUD VAIU SCa
ProJlct year 1 2 3 4 i t 1 3 9 1 11 t2 13 14 Is 1I 11 1t 1t 20
notes:Feasibility Study for Rebahilitatios and mpeotatioe of U Piped EM.'t %pply fcleoeJanuary aa9oAssuoption are for population in 1990 to be about 120,000 rising -. in 2005
llotes:Preparation of Detailed esigns of the Pro,e lProvmts to Notke Bay Vatt Supply1 Deig ip wrt (Draft), Rinistry of Sorts, later Deportamet Deceeber I9,January 9O T0loies 6.3 and 6.6
Notes:F-eparatiao of Detailed Design of the propsed lspraomeots to Duasi later Supply, Dhsig Report If0raft), flinistry af Works, Niter Departeot December 398
llotes:Preparation of bthilud Nuip of ti propud lopovemts to tobo Plata Mar SMat l1, kslp Spar (Draft)Nlinistry of wts, ltr kWrtmt kcusbr 19 TaIln 6.3 aid 6.
Naintenace costs are estimated at 11 r year.
O J~ gu'iugn 5 w3.id aum - F P. lmq a PA" _ PM* Ps q PM 11 Yp _maq
p At or ap apw n. qa sumimm ow awuimf Pa
mapgIna 461161 E tvj ipm& 885 S15 pu mq analt.,
nfnSXC*4ltZUZla2WtCZ*f @I3Sfl
*~~~~~~~~~~~~~~~~~W n
'l DIN* U.* U
* mu qug~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .uw i....... 5ns
p uria n qIq pamw isas
IO fm IsU
Ila lif a Ila lilt n.u tIu m Ila dua u an nu mu Ima uan au aU m a a mana nim g maa mm "anal U 6 UK ui n. PPail ius ail liltfl ilsStS LtZu SitS a au amu am au an an au anim ac m an Dalu amg an am au an m. "WIN an au uno UVi 411 n wuW Iu.1I own%
* * 0 0 0 S G 0 6 0 I I I I 0* 0 S 0 S * 0 * 0 * I S I 0 S I S I 0* V W" Snoa_@ egoss e 9 *gI ,I a , g *I 9 I.a I,a ,*, *m.u wN u mu smib
a It a it it u It u a n 8 69 u a a a a a a a aa a n a a & 6 I I s niva I * t uVI"
z'uuns nvm Sven Kg ULW lvUWasm :rj lgismu
TABLt 4.2: LIAIE VAT SUPPLY
-s sew*errsssslleseseat||estsxnstse_ ;__Us _________________ r s
rgjetIts * 1 2 3 4 S l6l I i Se 1l a 83 147 1? 1S 12 219 321 2 21 N n 29 3031 3n 2 34 35..... _._ ........ _.... .. . . ._.
tCaital l s,ut M 1S111 U II66 I 3 * 0*0 0 e e * e e 0 6 Q 0 00000 0 0 0 0 e 0 0 a 0Otlw lsgtmt 2&3anl33112l121f U 6 I 6 0*0 * 0 0 I a 0 9 0 0 a 0 6 I 0 i 0 0 60 0 0 0 0 09 rtlwS lalmto 231 } 4 03 2 M 93e 1 im 12 1711 111U 111 l lil m Il 1713 l 111 11 Il Ills Ills Iia iam 1 11s6 111 1 s 111 1711e Ills 1713 71 1116 1113 1713 1113 17111813
_ tal 1e0,1 21119 14o 1M 3111 393 4113 U391 1t11 31711 1711117 Is llt 111 Il17 1713 ItU 11am 3l13 Il 113 1 1U Ir7 1713a11if IG Is iIJ0 1?0 171113 I712 I113 l lIl 7113
* I{MS) 1433 2412 Sill M31 S41392 612 IS N71 O 1113 35 OM m9 3 11a111151m5l of esgm n e m91 ane 5nml159am n 11 19 mlgm3ml1got m51 859138591
& ap Imutel ists IOt U A L r pd
31 .91lt e.n6in3 1.51m2 1.23
*'e'ep Iecreuel Costs WIll.32 .11
101 0.3III 3.01
U 0.20Ut .29
121 0.26
$Pi" 1190
If miugue water min, (C.-,p.t.o is 1910-9 adimi 3.a Appail 19eo Mb pouf og!XgJitsl costs to PerIl gotta cusueti, a s am Is Capital mitlion, Prejit Istit.mii of vine,,
qioteaase costs nsleai te at 3K .NPr .3
Annex 6Page 1 of 10
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METHODOLOGICAL NOTE ON ESTIMATION OF VALUATION OF PROTECTED LAND
1. This annex provides the background analysis for the discussion inChapter V of the main report on the costs and benefits of protecting land in
national parks and reserves.
I. Review of Costs and Benefits of Protected Land
2. There are three main types of costs of lands gazetted for wildlifeprotection: (i) Cost of protected area management, including planning &
training, infrastructure development, and poaching control (this cost is so
low relative to other types of land management in Malawi that it canpractically be ignored); (ii) Cost of crop damage by marauding wildlife; and
(iii) Opportunity costs of foregone production from agriculture, wood, and
minerals. Of course, each of these alternative uses (benefits) also entail
capital and recurrent costs, such as management and training, infrastructure,and/or extension.
3. There are several types of benefits: (i) Existence value of wildanimals, biodiversity and ecosystem resilience in general, and areas of
special aesthetic beauty; (ii) Economic value of tourism; (iii) Sustainablehusbandry of wildlife for safari hunting and live exports, particularly sable,
roan, and cichlid fishes- (iv) Sustainable harvesting of wood for energy andconstruction; (v) Regional watershed management; (vi) Non-tourism, localizedtypes of wildlife utilization, such as nyala and crocodiles for meat & hides,bees for honey & beeswax, and local fisheries for consumption; and (vii)Plant biodiversity for indigenous use, such as plants for medicinal, fiber, or
food uses.
II. Review of Protected Areas in Malawi
4. Mwabvi is the on?y remaining area in Malawi which hosts blackrhino (perhaps as many as 6-:0), a seriously endangered species throughoutAfrica. In actuality, about 60 percent of the area that appears on maps as
Mwabvi Reserve was degazetted back in 1977 and is already available for
agriculture. Portions have been settled. Tourism (and infrastructure for it)
is currently minimal, though it would be useful for some safari-trophy hunting(of species other than rhino).
5. Lengwe National Park is the only remaining habitat in Malawi forthe Nyala, a large and attractive antelope. It currently serves about 6,000
visitors per year, mostly residents from Blantyre. Parts of Lengwe may alsobe useful for sugar, but there appears to be an excess of sugar-suitable landoutside protected areas, and the sugar estate beside it has recently taken
some of its own land out of sugar production in order to ranch Nyala, forwhich the currently unutilized "western extension" of the Park is also being
considered. This area of the park could be considered for multiple use zoning
(the game ranch, wood, perhaps some agroforestry).
Annex 6Page 2 of 10
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6. The Hajete Reserve, which has very stony and steep terrain, is animportant watershed in the Lower Shire Valley. There is some encroachment for
firewood. It could be quite useful for trophy hunting, with a littledevelopment, perhaps through joint government-private sector ventures (no
economic benefits are currently derived from Majete). There may be some
agricultural potentiul on the eastern side; the western part might be useful
for some grazing or forestry, but not agriculture.
7. Liwonde Park is flooded throughout the rainy season, and the
boundaries were established after an extensive survey of agriculturalsuitability, explicitly to avoid conflicts with agriculture. In the late
1980s. it was discovered that up to 1,000 people had moved into a 10 square km
area inside the eastern boundary. This area was degazetted for them, although
there are reports that they may already be abandoning this land asunproductive. Poaching became a severe problem in 1988, but seems to be
declining substantially since enforcement efforts were strengthened.Currently, poaching seems to be mostly for subsistence fishing and animal
trapping -- activities that might even be considered for legalization if
efforts to increase the benefits to local populations were to include
recognition of traditional utilization.
8. Basically, the only productive use of Liwonde's land is forwildlife. The Malawi Government is beginning a three year, US$ 1 million
project to upgrade Liwonde's roads and facilities for this purpose. Itcontains perhaps the world's highest concentration of the rare, spectacularSable antelope, which could serve as a lucrative species to export live to
other countries (negotiations with South Africa are already in progress).
Crocodile farming appears to have high potential on the Shire River, whichflows along the western side of the park. An attempt has been made to
introduce bee-keeping, but it appears to be unviable in Liwonde, both socially
and ecologically.
9. In addition, some of the wildlife (Uspecially elephante) mayeventually need to be cropped (or more lucratively, trophy-hunted),particularly as the park is now being fenced to reduce the problem of crop
damage. Some preliminary research, to be expanded upon in the next growing
season, indicates that the total value of damage to the maize crop around
Liwonde may be around MK3S,000 per year). Such fencing is likely to reduce
the effective wildlife carrying capacity of the Park, as there are indications
that some animals, particularly elephants, may migrate between the park and
the forest reserves to its northeast.
10. Lake Malawi Park, which has been designated a World Heritage Sitebecause of its unique biodiversity value, already allows pre-existingsettlements to remain in the park. Much of the remaining land is extremelysteep and rocky. It is the most popular camping site in the country and hasconsiderable potential for increased tourism revenues, if its facilities areimproved. Certainly much would be lost, and nothing gained, from degazettingeither Lake Malawi or Liwonde. There is plenty of scope for increasing theirbenefits to the people who live around them, while maintaining their protectedstatus.
Annex 6Page 3 of 10
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11. There are only two wildlife areas in the Central Region, though they areboth considerably larger than those in the south. Nkk'otakota Reserve on theeastern side is very important for watershed and soil protection for centralLake Malawi and the Bua River, the one remaining river that the Lake Salmonspawn in. There is a crocodile farm nearby which has depended on this riverfor its stock. The land, at least iA the southern portion, is very poor foragriculture, and presently is visited by few tourists, but it has considerablepotential for safari hunting.
12. On the western side, opposite Nkhotakota, Kasungu Park has someagricultural potential, mainly for tobacco. However, tobacco is aparticularly erosive crop, and Kasungu is the headwater for the Dwangwa River,which provides irrigation water for a major sugar estate. Kasungu is also thearea with the most investment so far in tourism infrastructure and wildlifemanagement. The western side of the Park is in the process of being fenced soas to reduce crop damage by wildlife. It has the largest elephant and buffalopopulations in Malawi (800-1,000 for the former), and together withNkhotakota, provides the only remaining Brachyetegia woodland ecosystem (whichonce covered most of the central region).
13. There are also two large protected areas in the Northern Region. Nyikalark is experiencing the fastest growth in tourism and serves as a vitalwatershed for the Lake. Bee-keeping by local reeidents has been allowedinside the Park and is proving quite profitable. Vwasa Marsh Reserve hassuffered from some encroachment. Multiple use zoning, and fencing the easternside to reduce crop damage, might reduce most of this pressure. Safarihunting again has potential to provide revenues and development for the localcommunities.
1I1. Asseslina the Agricultural Value of Protected Areas
14. Using the maps of the Land Resources Evaluation Project (LREP), one canestimate the relative suitability and value of agriculture production inprotected areas. These maps indicate that some portions of two areas in thefar south, Mwabvi Reserve and Lengw6 Park, are moderately suitable for somecrops such as sorghum and millet, and also cotton (see Table 1). I/ Theprojected value of agriculture production in Mwabvi and Lengwe National Park(with the latter divided into two portions, the small core "Old '-engwen andthe larger more
recent "Extension,") was estimated based on the following equation:
GMx - (SUM(xi1n4): rA*Siz*Yiz*Pl) - A*I
AMx - GMx I A
where:
x = type of crop:i - the suitability level for that crop (High, Moderate, Marginal,
Zero);GMx - gross margin of area for crop x;The underlined term is the total production of crop x for the givenarea (figures obtained from "Improved Traditional Technology" maps aredivided by 5 to obtain yields expected under "traditional" farmingmethods);A - total area in hectares;S - fraction of the area at suitability level i for crop x;Y - average yield (kg/ha) for suitability level i, crop x;P - producer price for crop x (MK/kg);I - input costs (MK/ha);AMx - average margin (profit) in MK/ha for area for crop x.
The resulting estimates of agricultural production value are summarized inTables 2 and 3.
IV. Assessing the Tourism and Safari Hunting Values of Protected Areas
15. Current government revenues from tourism are provided in Table 4. Itshould be noted that this is a considerable under-estimation of the currentvalue of tourism, as it includes only park entrance fees and in-park lodging,while it excludes all other expenditures by tourists. Unfortunately, thestatistics needed to make an accurate assessment of the true economic value oftourism in Malawi, by the "travel cost methodology", for instance, are notavailable.
16. Table 5 provides the Department of National Parks and Wildlife's (DNPW)most recent estimates of the potential proceeds from safari hunting in thefour game reserves. They estimate that including parts of the national parksfor hunting would increase revenues from this activity by a factor of 4. Ofcourse, this would have to be very carefully managed, as Malawi has had badexperience in the past with safari hunting programs.
17. These estimates of the value of hunting may be low, however, for fourreasons. First, the value of meat and hides, which are usually left behind byhunters, was not included. Second, the proposed unit trophy fees are low byinternational standards (Zimbabwe rates are assumed in our analysis). Third,the proposed hunting charges of USS 450/day for the big four game andUS$200/day for everything else are also low by international standards (ouranalysis assumes $750/day for cats and/or elephant hunts, $500/day forbuffalo, and $250/day for all
Annez 6Page S of 10
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ot.hers, but including some of the rarer/larger animals only in a longer huntthan the basic 7-day). Fourth, the proposed hunting system, comprising huntso.' 7 days for the basic animals (antelope, zebra, and hippo) and 21 days fora1l of them plus the rarer game (buffalo, leopard, lion, elephant), results ina sub-optimal return from the harvest. Since the rarer game safaris generatehigher daily hunting charges (US$500 for buffalo and US$750 for cats andelephant), it would be more efficient to create Ointermediate" hunts of 10-15days. This would allow bunters to take just one or two of the rarer game inaddition to the antelopes, which results in a greater number of total huntingdays at the higher rates, and thus, much higher revenues.
18. Table 6 lists the unit value of the different kinds of hunts. Table 7provides the grand total from several combinations of hunts reaching the totalquota of each species proposed. The DNPW proposal is provided as the firstscenario. All of them include the revenue from skins and meat, plus theincreased trophy fees as described in Table 5. 2/
19. As noted above, DNPW estimates that total revenues from safari huntingwould increase by a factor of 4 if national parks were used in this way too.Since Mwabvi is the smallest reserve, if one assumes that only one-eighth ofthe total game reserve hunting was done in Mwabvi, under the more flexiblehunting scheme outlined in tables 6-7, this would yield an average annualreturn of US$47,000. Thus, this direct use of the reserve alone is of thesame order of magnitude in value as the food crops investigated in Table 4($55,000-126,000), before including the protection of rhino, watershed, andother harder-to-quantify values (see below). If Parks are included and Lengwenets a 1/8 share, for $190,000 plus increasing tourism, the return is also inthe same range as it might be from agriculture ($143,00-379,000).
y Se Agnes Kss {Ed.). 1990, LMna with Wildlife: Widlife Resource Management with Local ParticiPation infrba, Would Bank; Chapter by F. Mur ndagomo on 'CAMPFIRE Program - Zimbabwe' and B. Child, "Annex 4:
Notes en te Safar Hunting Indusby."
90 Annex 6Page 6 of 10
TABLE 1: AGRICULTURAJ. SUITABILITY OF LENGWE AN) b7ABVI
INVNTOQR OF NALM! NIRONKE AL LECIS!ATIONUs. LEOZILA?1g UmY o UWisIs pSUiIs COMernucMMA,wst
I SM ACT Prelde for mbbe. rStoos.e tlew TheW UI1et9 1 Ieepeed to faeted 1 1le, th et CAP: WStO in blesl, ahebe eebewp, p6gb roeistetm mad preulet... tor reveod es_ 1Gl tbl_As. The IN&
Ot Pivatbe eed. Te ot ftebbw pseelti., I.e. sa time of N0 a"d rVIm Wee In 1361. All _ooesbeprovtd.e" C4o W0I& ef lea NW Ioep ett ehe. bmve bow oIncorporatd Is Act.- 1*prelblbAee st Wbadpe, ef _eer.mt ste a.y orders zsd. whie1h eoeb't thew eP .tel eeepetloe of lad. moe of certain apecilIc *ers, ea
thee have alee bos lecorpored.
2 hSOISlU Preeldee tee re Ss1belee of titlg of Ap e.abwatbe Of pwovlelo. of, the act mm e_octe is too A sads 1iWe ACT 1 ead sadell Is ead so Act easetsa time of MM &leprloesb 1970,11,61. All asdsonto have to doCAP, 811e lregbebed. sr 0 yre. Esfereset .f cewe.ot Is e with th rogistration et land. Lad
prh'm doe be loch of rolotretlon Ison oupoalvo procedureoesapwodoquet trsaepsrt a NWd mppere tO he h sale hedilcep.oerdilotlea by *1I relevant Dept. inditfterat ectors.
S OSTOUM? Pregds tee whea fl _eerwstl_ . Pr ampeea6tswe' tleefmo peaSty of. Thle Act mm seatod la 1361 sa hasLM r1obe I lebes la custom" lead a flee of up te 610 sad Ieprbe.n..t up nct bn revwled ela1 th_a. "oad_(DI ems et ohe ebjestwe Is t bprobe to 0 a sete Ie provided. revIse.ACt bber we1colurl do _e pato.
We 5001 WM ebeay lea. LeW Cmittes eedhweee be l ead elSetlb_
p_l__iplee. ____ I_8 e) Ll1S This ede deerlhe the boeladee'et
' I it|l li }]{1'S i .l,,.X- ,s,X,~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~4
| jtis-s jie3tat! 1l ii 2i1 Es~~~aL IL |3 3 il |$
INVUTORY OF NAIMI ENVIRONNENTML LECISLATION
lb. LESIATM _A7 U POVISIWUS pSATIES COAlTS/RECU0ADIDTIoo~~~~~~~~~~~~~~~ -w_ - A -9*__-_ --- *--------------------'----------------*-------______ _______-____
et U% inienimp
* Um Prvdge for conte A we of ster A pine she coatrsvo the The Act m essed to Ig Nd kwRSICS ACT mrcof Usbluli. Part V provide proleioe of te Act say be senteneed bew relomd orly three til.
CAPt M2t0S for ,.Il..* voerleblee tm lnstlon to a fine of up to K1,000 and Prop"lIs to revew the Act sar Ina 1 dmleltteo ets. r Isteo l, iprieeent for e year. advrnced stage.pollatleo of P*bi1e wset. Thor lafurther pr"WOOI ter as d.cter.loeof controlle14e1 s t foW PupOSe of Act
... f
* o8) vatsr Plovde afw as met of est Poselti wilt *o be viewed as the he for levtes.Gelorao pet ntl.e. The dloohe. me of sar prepel to revew the Act la wtor
(Water ow ofe leastIle, publie Mse set cosedo let.ptll ttioe assent to preibltd. Wr are elseControl) prowlolen sIth respec to ameelyol ofRegettloeo0 st" w offleant.weds, under 1oetie4. -
* WAEW'JHI P1rvides fow thestebllslowms of Any eotevoftlen of the provisieo of This Act we enecd Is IMS end hasAC wate Seards a wter Ares a fo me te Act oxmct. e slate.. flne o f X4 sinc been reiew several ties to
CAP2 11:0 msetabi I.smt Am sote0e1 8e of end a asalmun fie of X20. take Into accoet p"oe lndspendenesear,orhs to euc won. Also ebsoge.. Reqlres furthr rview.Pr1010 fOlOtIN % IDJV67,p r o l o t n o r l e t l s t o l a ) rPoolutlose of Wr eNW the serart.
10 MM fAS P idest fo rP seay ooa eoe f th Enacted tn t1wo0, rviewe tce aSNPM ACT A,ur.mwleel eIo of the Act, the monism lwoked en 211617 a 6/1/70. Repoe
CAP. :lifot f elt vesslse poelty la a fine of f600 and propse . Oheft bIll esbmltted bythe saft*y of p _osngs a corpe tuprisEoneet for two years. conga Ilte to Umlaul Govoti vt forhe conpetecy of ew . ft V proes eonsideration. Ad&tI. propesoIfor dsclsretiee f nleld voters a provides fort ew, dmilinetratles AeBremoto s/other centries,per to Intl egreete effecting _ritles i1v.evempee poWvision. incoemletonts/trestles.
_____________________________________--- ---- …
IVM3lJKV OF KAWL KUVIENTAL LUMSIATLON
lb. UUEAIN MwO W PUISI(INS PENALTIES CS11INRShC1AnTION
, 51 inUX,
10 ) tal P d 9. f pw bl, atr .etle ef e pastle d l. lelele*1atere earring of simle, meet carryln reptatlae Omte a tin, at S. so atted aboe. atIt e100 etfect Urn.Shbipplag meled 1- 'l*l corrA *f slung ubsIdIry lIegItle. it to(brbe*or) pobo. - be saw ubath r of Mtbptatl 56" Govessmmt wIll acomptthe prpil i
d er bitt.
I0 b) aIsead owlsI mertals tea 1t* beSbers hasb he OA rpoet te Lob.Shtppieg
of Areas _Harbour.)
owtiom~~~~~~~~~~~~~~~~~~~
11 ULMItRE WTe Lo proldee for eod.iebratie Sectie 411 nb.. it as offee to Tb Act e mac lad 1 A2. It _.d.UATUS *11d _oberbtea of he Slmbyre Water peolls ewat. revUe en otr.ngtIwemg of theACT oe s ftr bard end for the entorcemn,t machinery.
CAP, lIgU2 dmo.ItIegmatad mseat m atoneooastaser Is the enter are.e
13 LILUI Pe'e,top eav .deto blee of The ton" rag Ie1 RIO ned Kfl_. This ts a fierty a Lot _amle hoeUT|1 _S loupet1 later Area. eotehitm of so"_ of the offenc_n ho penales encted In 1so.n
ACT Li leaso later Saued A dew $PMt A which cao accru by mS per dayCa PSIm *elotanmee. of weterwoebl In throgu the dertlae of tm#relatng to pr_v_6tlee of pot ltion.
as FISEIES ACT Prods tfe reg.aloti _d control For say ceeravelem Ohw. In a Act enaced la 1073. Ide OfCAP: Wa8S of fshing9 & ?fr penalty of a elee of up to MMO a perittleg cortal flsherles Off
purcb I-,eolo.WIZatlng,Plio,alqhg. lap leprlmeemet. for I ypr Proawlae for to he progatere in1 ffenes mudeorb A ouport and ceesrvatlen of fish. fortolt.uro of feAct, 1 god o. Such OffleGns aN to
Prehibloo me of .*le,ewelee on sed We .emlttl.o effeec. reseli. be pro"pei bsed of the ame bt
flae,l.11MC4Us pleh.wors. am tom to be l*edequto A I.t of diechere thig fuecti. ofecttl,olg.
presommie eeele oftomne c..rdinettee In ofee_t eectors ewipeuelse of Ch P.A. probem.
n Ad--_ ----------------------- =
Is ) FPle i PrlIbIts th _e o e1s New to(P.blblte Let biee1.
rooieMag)
suds emlefteso
a3 b) Plehsele ProhIbitbte Wmeam of %MI st in the(ProbiblIleos stemp of Lake Bolee.
ofTeePleMme)Order. me"
_ .
is *) pletwe.'e obgslste fishleg em a eemeweleI(Cesescelel heel. Is ase satoe of obleel.Plues")
mo and
egotIsm as
14 UnSma Preldiem fer Wme emeel a proteetlee Peeeltp for sap osetravemblo.t of the The Aet see amete Is 136.
ACT of evc doleo, the .ooIetiee of the pr"owlele.s of the Act Is.e fine of upCVS 6011se hetleg s.d veering of ercdI too sem to kIO NWd Impr leemment for eOm per.,
redias to ereaeile, prdeste.*MAtIe ithest.a fiee"" le
Ib%Ie S rohIbIte.
IMEY MTOR OF NAIAVI UVIROU WAL LEGISlATIONlb. ILEiAn SIM, V SvImt PENALTIES CMMftS/ECO ^DATIOM
Cs fLMW MSMM s
as PSU ACT Pelde for am oaled A mletis Camprehnive provistes fare TWIN Act wee eaced no 14 t siamCAPs S5a1 of foret preodcte. E*pee inIser nonecsplince slit Act. Mamlm fine the th"'a have bee. gertgl pest
to declre say public er customary 100 1 yr Ve- rsp l_ensnt. Provide for IndePendence _ at. to It. Filein" to be a forest reseve. Provide. enfoeremet by fest or polce sod enforcemet mechanim requireiWAtent .ulinhla@ powe by Vlinoter officers but et effewtivoly review.to aOW tt.n foret I'plemene.prded_U I teplanting
16 a) Thero are Mainly deeI .5th the dsscriptles of Ther Is nowi to declar re rmseveral the _arn ¢ feabt reserve in In order to lacose pereeteg ofo t0 e_ l ide for forest r_esurces.
reservesdeclared byWme Minete
*of teLt
specified isthe echedeletot
-- -- -- ---- --as b) Forest Preulda for pretest, of fercete by Any centroeveatles of Uteae Rules or There have bees reseonable efftert to
tepees meade, prohbiting felling of trese*lightlng coditimon ce"tOised therein is review thee. rlse regularly, the amos% under seties firsee1 & for regulating greaing punishable with a fine of up to ffI36 reeet revise wa In 193.
a end la esmalms. Sch activities can be doe. and Imprieftemmnt for teolve o set.underV the auhrity et the proper
ceataine list of special prolectodseceies
--- - ---------------------------- ----
10 PLMGT Provide for th, oerdication of pests The penalty provided for any The Act mee enacted In 1360 VWd sinceMIIECTION OAad dles,a destructive, to Plante. contravention of the Act Is a flonw of then it Nee, not bwee reviewed. It
ACT Conseoin provisions to prewent up to N~OW end lmprieonsent for 4 fee. ehould be reviewed so eoon asoCAP: 64:01 introduction A spread of pests a The Act provides for the confieCetion possible.
diesese destructive to plants. such of the growing medium Plant. or other
IUVItR!t UOP ULAWII LRVIUIUlA IA LA iblAtiU -
b. UEWSU? UinY P N!siIeS PUMUSSTB COMMIS/KCONUEIATIIS
Co ftm WUcos
det Is Ismes opsen ead sowes". thiap e on default.
6 a) Plean Prvid for tWe empe of plsat only Thaw reile1m were mede la 1&61.
PretwMeft *e ada I lo ,1_ lta In so.rd_ma They hould be riewed as tbs appea
(Rupert) with the repllehm. Thmee to be Inedeate to meet the cheged
ietlsa% egletime de *IF te he epti s es.
made ug,ds of hun oII, Mea sete SAU lat. oeefe
getlee 12 cu flemer, fro* fruit ad,egetablee.
as b) Pleat Preldb fo tWe prutibitsa1 of The Sspect*er appoldl Under the Act T_es reuiIsleae were mad to 111
Prote" slee INPert1ets Of O*etei pleat mi4heet he"s pesoee to eseare that Its. eldnce the they have not busn
(Impor) thewit. Cortala pleate se eww*pte prevsonse ad retatlo are reviewd. Rteis ia r1 qured.
Reguiehiee, free such requrmt A prewielea Cp o_Id with.stb e6 lletlw 1qw *"_ al
* ed undter 11 estritin i_rt tr I eb led * .. b...-.-. Tbese ee .sw 1 .. -
Prtetiem me pleat cemedity, building for pres*t at fumlistilon a nsr eialpot
(Pum"iei) th peepse of dnetsp ljureu se"d Is isI r rd atndrd. Row. ce"sla
eletlioe e- lit of fmigation qwlpat. Rep. adeb
made undP ert i d pa%sold be revIewd. V1ei K
sectice Isits 1 nesto nw _mre te desl wfrecent O J
meslyb u *tater lajuriee ttce P
1?t UIIIWS beerelip previge Pet the ordilclatle eliletO to cmply With pro641-Isns 1 The ACt we enacte In I61 but bee
ACT of Rseieeo Weeds. Act Is punlehable by a tfine of t69 elnee bee relewe to take leto
CA ee2 lmliseomt for a mnths. Inpector a t
app_Inted uner the Act esre that The lest reiew Wa In 196. Thie Act
the provisleos of the Act are compiled should be reviewed.with.
IIVQRT OF IAIAI LEZISIATIOlb. &MIIUT S? Uv PsVIS PTlES pTScoU
aeeeeeeeee.O Cee------------------- _
51 a bs o flowe RIdlinmISw, Estop fla b mmwos et reuesd aw in .eml"U bwo o is m a ,..io Shmuld row be du for esethe rewlow.of NMI)..
eabeeee ab- -O O ____ _ _ _ _ - - -ISb PECIAL tWS Pldms fee us developmentb sod Pal ears to alyp with pr,I.ee. of This 1to0 v er l * rt s Act. It
ACTrhl.g of sp.la erop aid for the Act carries a l *o .f up to *2e00 A replethe th g Ing of m CAPS SllsOl dsSoobIas @ eopesli are". Isi_OM for S pre. It swct Impeortt crops In the cooatrp, which
el_ s "orItlwe for prdef seg offence Conti"" a time of K20 a day fore tth ecoelc backb_e ef *Mlail.OA tfechelag e ob f A Impr igmest for a.m wook for teahspeslol on". day dories ibech th Ofloes cOV-Nome
to provided for.
- - -- -- ,,- - - - - - ,*
a) $pselaS The eo %bot he" so top 0Crepe u*.re special Oer. ar . s
Dec _leslo cOffD _etes G ste, in_acsdasMads "WdM aruo, ts, sad flee cared tobacco.
betsleee oRe-
I9 b) Kamm gm1m atrlty for Flue-IFlueCurd ued lob.. sa t eleari dot isag
Aetsl toe or"
Is c) a seletlee pod far a tbe poeities for .e-_osiIss_ with'Fleo-Cured .Orecmr' glceez. the provisions o. Wh regletlcee areTobacco a floe of RICO se lopelcinmet forAutherltp three sete.LiclealaRegalatios.
Annex 7-3103 - Page 9 of 20
§ 1 I ~~~~~~i sEE i l'
'jI I! 'It l *i,{ 1.Il li
14~~~~~~~ 'VSI11S]a { }Siv11
IR Mm O! KAUMI VJRtoDIIAL LECISLATIONlb. LEZUtSAIA u v or ,sw PE TIES ClA _ Cn I
-_- -------------- -- ----
Ordwe, atol eil thOoeI ess o or"wsgrwd eashs 1ewe asI hel wadra "le
4 *ri heega _,g Cog. Ltd.
St TlOM ACf A1 " t As d A" h I. AMy e..tr.,O.s of th Mt e be law VW 4eee il 317 "A weuap SU robstiag nt i4%asd"I ., flee of 51000 WAd iuptie.mmt 1fr sinm boe rwlemed ores I .
msste.seo sa mvt.tbS of . ebes.. as pee.The Ash esle grsime* hEs Woseproeblog ef Imewn plaos oebId be
.)9 Tebes Proestbo D_e w_of eoriewlg taebeee Pameties for say .eatrev.m.al. of r. ft. lot),. Vleft Os s 10,I tf7(ebed of _ es" em cwetemor lend. fretIde th rfeg tbesere a fine oi up to hove been rfFol **C., IsiM.Cielng) emangl=3 fr so& r1oplr . RIsteO 5100 eand IupuIet for thmeeSussletieme, et.modsl undeseelh l,, .
tS IN CTol Preol. fo ts smeIldotlef gh Amp ceetseetl.e of the pirolole. of this At - eted 1t1o sat WAMACT I_ o roetleg to the Pedt ISO, Wm Act Is eeble wt e a lie of be" relm seveal WIm to take
CMP, ISa1W _r1"eeel sa serhte of sette. up t. MM00 Na l.pr I*e.Ai 1.ster a ota sceesat _e ps iI Ace'O Illlteletee to _psred under th Pet*d of hreel seeth. _, esthe or dewelpeat. Do At sbeid heAA t orde Wh dsetlee of s pipast w fa be dmotreqs_". rmisse.*01es tor epw len hu,m _f.
It CeCIL P5 Pm A prides for Vs domepeemt l ffle"ITe low. Etsesrchfollow WAd msemftv % t oaf etrtss eNd pwie slones shod he strs.gtheesd byIENSAIU Me betaftesiogds a.s so mtVlsI her lt.i eatmaodla mit. .49 theo cae) J.S1ANlC et Sleol. It ablbebee e W Cou,loOARMD O fOr Wat NIo 11Herhevis s1 d W *et..
1M A"C Gos olfa st .bl lCAP# ?,tf1
INVEATO or 0 UWA1! LI=vuwmAI LUSL&TIUUNo. LIIMA?tU ElSE OF PMIWISIU POSALvtIs COon?SiWIcOMSr
u miww.L pr.,te .I..e fohe bflo A of a p1Seali of * tles of vp to M1.000 The 1c mm .o9eto 1WO. Revl.?..pAW ACT ma.ilea. pSAt, Woo .. cw,et1 s of ga" tnocleeoms for IV* l yeors Ie at real ,M.wwy.CAP: 6s:0? smlea. vas, tabla 1Ad .bjet of proJwidd for Day coetroveetion of thU
specIa l _ 1terest Ist moer path. Act.
"~~~~~~ ~~ ~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ *) -l -- -o - - -l" - -St a) Utmiles ksnle aesu'Wa. a.ma3aIs hmi to be"a blo i PM e.g. P1 _1
22 b) iotlol Peloo fe *b We psew _e of Us Amp untrarnilen of the RoplatiseP par PM.* by lopealesetale esliles same" a flto of 3100 end laprle.ment.gIctI^eao, ta h is be beoevd. for .1. meat).._edt _ de
13 UK ACT Pmlds ter the pro1ervetlae Sa Pesaitle tfartgo ceetraveethe of rh This Aet mm meted in Im 1a haCAPmI esu ct&i f pm.. Vh.th, Is te Act are a fine St VP to K1.S "a s bees reiw" d levar t1ime. TM lest
* ek "mmse ad_ _p ewe rlo for feThe rerhe w. ln 1161. SVield be.Wmest * ome to probibited. are also previes relating to hU stt etr m tloed t brie It to flew lith
torfeitere of agape" or ean ve ie proposed teravis .? the Nttlulan"d In t celeslee of ms effsoc. Patr Act.
oflelee atlmm it ' t mddla of hethee h_1ee.path, mad.o
1s b) one T_e loals mks pewIslme fw th6bles, Made pr.htbit.e . hest op oteld _nde m se selmle. cstteg ad beveg op
4. ~~wegoetieth.
e a) Provides fer as pro lbltioe otNIpp..tms bestale hlgpMepein.
Rols, maobeader osete40 (b)
to rewde foa the peblstie at ophtsPPrebblhl s esmle mtetapmbof 1WAIIF
-3 CUNIN be Co sin ideoe NW me l . ascmp WV _at.grveall a flue of up to The Act mseectd to 136. is *
*ISEA OIF V elati,3 tome seatui sW dlma IUOO l.prlsomon_t for slx mei shold be r.o,ld.MIMS ACV of s.l.ui.. Them or pm,leiea peelded.
CAP$ 086 r1241tS to the Go-vow of mebleiWAd dmotler of lafeeed am. TM
AC% sloe promdu for the maw toeblhi ewcm_n cm be d)mpsuW of.
34 a) Sims._ Pomdo for Wo 41dimes t. Wm _ulm _oe nI.c s.. la am of Aeluue teotmtof eSt a sie from VW$.. and NO" bam rewIo.ad wol _tlma
leeo adds dlarees St dipping tombe. to take oate accout the palo
une sadieI I dm oelIsPo I ot th. lb e
* now to rwel tme. tfrther.
16UIE -mm leegulol thme amemeb a Olson of Vileatleseof time to. la memo can" This Aft mem uIde Ft la3 m NWU11M9S ACT mlsemie. Part VII Ants wth cerri e a tlme et of op to 0,000 end rouesed once In 1s36. Thin la 0
CAP: 61201 prOtlettle of mWteame It metersl laelomnt for too years. oemplo of a very e.mpr.ientv medroeresm modern Act.protetlo Ceeits"sroibtlltttlon of ores d_magd by
INV OF PALAWI EMIV NThL LECISLATIOUlb. LMUU TI U mI5I6PA PEALIIE S COMuNS/12CcO.NA,I.S
16 e) Pd.rolam Proid for OM to -WStt Ioee and(bo.ereI stus, befwoe poetole dtsicavered
Pploletoe) cem1ellp t dpi led, d fop theuplil.a, we my of m sle.
mode medor
1? KYlE Provide for ahe"Plotless, aetrl Cetravettee cartie a emrateae et Lea_ SI 166. Amedd Io 1368.ACT acql.tUee. m_nfacte, met, md se Iseleoommt for hp to to.w mre. mle reqire review as "et*1 tsy-
CP 14a6 o eofoslote. u pe he. ope" is tc e lastOe.
8a) etlowo. Prvde for ats bhelog, bleotlte, The s,ei peaet 7 for ceetreaett.sReulettlee, itleri mmd _eeet me of oupleelwe. of 18 Act so o fle of KIM2Q0
sods gudsa lap~~~~1wel sem.t for three. Maths.eofttes 18.
U ,* Povideewl for On rgletaeo Peaetp for ceetrw" eatee of Thts Act Wms need Is 1t10 has metFARM FEEIDS & fetlila, ferm fe,, etriltaleg oleeo i fts of et20D Na ums rwiemd siee tHea. noh111ES ACT posel ad oset- eedtoes. The Act leplwe et for 0 mse. A pare redletien he. bee _ds with
CAPO rebat A roeter ts6 " eslo of ce*vlcted _er the Act he iS respect to tofu fees. Review of thi*fotila, fom fe b crtals cmedti ff ted.t r e is s rgtoly rewired.
Isde A eshetecee of ealmi orllr preveles for mepolatat of ImeonpctarIltaded for mofecter of a ljpatoe np.red to eferiller. prelslca of 1 cal led *1It_
U0 8) Perttttie Provide for .tert112tIONle Of .. 10.1end Farm predeete.Feed_steetitsetto
S rUTES IMe Prowidse for the publicattio of Enacted In 11S4. Prit to msCO%VWIIDO owlt I tmtOlOSl trettle of thlis Act . tsrot"esce.tt..PIUmCATIU c.wstlaas ad amto to ehich oe being powleshd in the ibisesACT 0.,erm_a bee seesdId or bae Teaty S.ws.
CAPs ase *1ipts", or of Ns article, toe,coueaeator pevmli.. costs led
madee a" too e
ato) Trebetio Lles. h biII erelt vise oboltti
and_lex -blullal -_ -----
CeoveatiACT MaImwiplbs. thsi s Act remileafwt.e Tepate awtt c r w heAtotc nRSm
1Pubieatiee
I As)
a@ -
8 _
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114 - Anex 7Page 20 of 20
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Annex 8Page 1 of 13
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ECONOMETRIC MODEL OF PRIVATE SECTOR PRICE RESPONSIVENESS INAFFORESTATION
1. As reviewed in Chapter III of the main report, the privatesector iA Malawi is extremely responsive to price changes for woodssmall relative price increases appear to induce large in^reases inplanting, even under the most conservative assumptions about seedlingsurvival and forest yield. This finding reinforces the critical role ofcontinued price reform in addressing Malawi's deforestation problem.The underlying analysis behind the conclusion of strong priceresponsiveness is summarized below.
Conceptual Model
2. Figure 1 summarizes the conceptual model. Aggregate demandD(t) is the sum of sectoral demands for wood at time t. It shifts withtime as the sum of demands from individual wood consuming economicsectors shift.
3. There are two important components to supply: the supply ofindigenous trees MC;(t), which is drawn down over time, and thesustainable supply of exotic plantings MC.. Together, these sum tocurrent supply MC,(t) at time t. The private cost if indigenousharvests is simply the wood collection cost (or the collection cost plussome small royalty paid to the Forestry Department). Indigenous growthcannot keep up with harvests. As the indigenous growing stock is drawndown, the residual stock becomes more remote and collection costs rise.Therefore, the supply for indigenous trees MC, must shift left overtime. Furthermore, indigenous tree removal without replacement impliesoff-site environmental (watershed) costs of unknown magnitude.Therefore, the social marginal cost of indigenous harvests is somewhereto the left of MC, at all times.
4. The sustainable supply schedule MC. does not change over time(in the absence of cost-reducing technical change in tree growth).,Sustainable supply implies both growing and collecting costs.Therefore, it occurs at a higher cost for any output level than theinitial indigenous harvest (collecting) costs MC;(t). Sustainablesupply creates an unidentified level of off-site environmental benefits.Therefore, the social marginal cost curve for plantation wood issomewhere to the right of MC,.
5. As time passes, aggregate demand shifts outward, and theindigenous stock will be drawn down. Wood collection costs rise and M.Cshifts to the left. Prices probably rise from the initial marketequilibrium at pt and the market will reach a sustainable equilibriumwhere eventual demand D(t+n) equilibrates with MC.. The mostaccessible, lower cost, indigenous forest cover will be gone.
Annex 8PaRe 2 of 13
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Some less-accessible, higher-c.:lection-cost, indigenous forest coverwill remain, but no one will hdrvest it in preference to the lower cost,more accessible and sustainable, introduced forest. The indigenousstock, while it may be part of the physical forest inventory, is not animportant part of effective aggregate supply at the eventual marketclearing price Ptn-
6. The two components of supply also imply something aboutproperty rights. Sustained supply occurs on managed land with secureproperty rights--otherwise there would be no incentive to manage it on along-term sustainable basis. Indigenous supply may occur on land whichis open access or on which access restrictions are incompletelyenforced. As the indigenous supply is drawn down (as collection costsrise and MC, shifts left), open access harvest opportunities diminish.The open access market failure for wood resources must eventuallydisappear as prices rise (to Ptn in Figure 1) and sustainable supplyeventually comes to dominate all harvests. Indeed, while the openaccess availability of some current supply hastens the drawdown of theindigenous forest, it also precipitates the price rise and induces morerapid conversion to sustainable supply.
Empirical Specification
7. Tables 1-3 record the input data, which are based on supply anddemand data presented in Annex 3. The important demand sectors arerural and urban households (Table 1) and tobacco estates (Table 2'. Theform of our basic household demand functions is
Qd = (bO + bl*price) (b2*gl),_ (g2),_
where bO is a constant, bl is the price elasticity, b2 is the incomeelasticity, gl is the income growth factor, and g2 is a populationgrowth factor. The subscripts in t refer to the time period. Specificregional data create four regional household demand functions, two eachfor urban and rural households.
8. ' The household price elasticities are speculative. They reflectthe greater opportunity for rural household substitution cf 1)agricultural residues for fuelwood and 2) household labor co collectfuelwood for fuelwood purchased with discretionary income. The incomeelasticities derive from survey data from the Centre for SocialResearch. Fuel income elasticities for low income households in otherparts of the world are commonly in the neighborhood of 1.1. Thisgeneral observation is consistent with the upper limit of our urbanincome elasticities. Lower income urban households and for ruralhouseholds may have more discretionary time than discretionary income.Our lower income elasticities, particularly in the rural areas, mayreflect the greater opportunities of lower income households tosubstitute household labor for fuelwood collection. Household
Annex 8Pare 3 of 13
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technology in Table 2 refers to opportunities to use more efficientstoves. Positive rates of technological change reflect data from theEnergy Unit of EP&D about improved stoves in urban areas, as well as thepresumption that increasing scarcity must induce some technicalresponse. The analytical results will be checked for sensitivity toeach of these uncertain elasticity and technology estimates.
9. The tobacco estate data originate with a sample of twenty loanapplicants to a local commercial bank. Econometric estimates from thesesample data show no significant level of price responsiveness for eitherfuelwood or pole demand by the estates. The absence of a price responseconverts the tobacco demand functions into simple growth paths. Thisconclusion contrasts with the Tobacco Research Authority's evidence offuelwood technology adoption and with more casual observations ofeucalyptus plantings on tobacco estates. The remaining demand sectorsare either self-sufficient (tea plantations) or smaller consumers ofwood.
10. Supply is composed of indigenous supply and sustainable,plantation supply. Indigenous supply is the residual stock of standinghardwood timber at any year in time. The indigenous supply is adjustedeach year for prior year harvests and prior year growth. Sustainablesupply is always harvested first in our analysis because it is moreaccessible. Sustainable supply is
Q. = b4 + bS*price + b5*price 2
where b4 is a constant and b5 is the supply price elasticity. Specificregional data explain two regional sustainable supply functions.
11. The noteworthy supply assumption (Table 3) is plantation priceresponsiveness (or the plantation supply price elasticity). The lowestimate of twelve depends on thirty percent survival of all seedlingsdistributed by the Forestry Department to smallholders, 2500 seedlingsplanted per hectare, and 6 m3 /ha annual growth. The higher priceresponse estimate of twenty depends on thirty percent survival of 2500seedlings/ha, but 10 me/ha annual smallholder growth.' This range of12-20 reflects private plantation price responsiveness only--but in thepresence of the current public and donor plantation structure.
12. The high elasticity estimate of 144 adds all ForestryDepartment and international donor plantations and assumes 12 mr/ha
The Forestry Department distributed 130 million seedlings between 1976 and 1990. At 2500 seedlings/ha andthirty percent survival, approximately 110 smailholder hectares were planted in an average year.
Annex 8Page 4 of 13
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annual growth. 2 Surely the market would induce some plantings now madeby donors and the Forestry Department. On the other hand, some ForestryDepartment plantations are intended to protect watersheds. These willprobably be unavailable for future legal harvests and, consequently, areexcluded from the analysis.
13. In sum, even in the most cautious case, plantation growth andlong-term sunply are remarkably price responsive. This observationbears particular notice, as it will largely explain the optimism of themodeling results. It also supports our anticipations about Malawi'srapid reforestation response.
14. Finally, one important modeling simplification must beintroduced. There is inadequate data about 1) the substitution betweena) the collection time to extract harvests from indigenous naturalstands and b) the timber growth and management costs for new plantationsas well as 2) the rate indigenous forests are drawn down in response torelative fuelwood price increases. Without one of these pieces ofinformation, the economic solution path for demand and supply equilibriaover time cannot be assessed (that is, annual price and quantityestimates).
15. As an alternative to projecting the estimated "real" path, itis possible to review what would hiappen to prices, harvests, growth, andresidual indigenous stock if it 'is assumed that aggregate annualharvests never fall below some "acceptable" level; say, their currentlevel. Since harvests have fallen approximately thirty percent infourteen years, this would be a conservative policy. That is, thesocial fabric of Malawi, and the market alone, have permitted even morerapid response in the recent past than this conservative analysisanticipates for the near future.
16. This policy rule says that we will never allow the economy tobecome physically worse off for wood than it is now. This ruleanticipates further harvests of indigenous forests and further priceincreases -- just as we observe occurring now. It should alsoanticipate some additional planting in response to the price increase.The important questions become: Is there enough indigenous forest tosupport the market until plantations can replace the indigenous supply?How long will that take? How high must relative fuelwood prices risebefore that can happen?
2 Annual growth estimates between 6 m3/ha and 12 m3/ha are conservative, at least for public plantations. Thecurrent average yields for all public and private plantations are greater than 14 m3/ha for the southem region and 11m31ha for the central region. Smallholder yields may be less and they may pull the regional averages down.
Annex 8Pase 5 of 13
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17. For technical purposes, this means that the analytical modelfirst searches for the demand price that yields a demand quantity equalto tr.e current (1990) level of consumption. It then determinessustainable supply at this price and takes as much of this consumptionas possible from any year's available sustainable supply. Residualconsumption, if there is any, is taken from the residual stand ofindigenous forest. Higher prices induce more sustainable production andreduced harvest levels from the residual indigenous stand. Eventually,prices rise such that all harvests become sustainable, even at thecurrent level of annual consumption.
Analytical Results
18. Table 4 summarizes the results for each region -- and forsensitivity tests of the various demand and supply assumptions.
19. Table 5 shows the analytical results for one sample case, theSouthern Region with a base case supply elasticity of 12. This tablemay help show the adjustments occurring in various of our criticalparameters. Year 1 refers to 1990 with an indigenous forest stock of39,640,000 in, an urban household market clearing price of approximately120 KIm8, and regional demand of 4,031,000 ne originating mostly fromindigenous forest (3,556 n3 ) but including some sustainable plantationsupply (475 m). Demand remains conitant at the current level set byour policy/equilibrating rule. The indigenous forest is drawn down, ascut exceeds growth, for nine years. This drawdown occurs at an everdeclining rate, however, as the sustainable supply of plantation woodgrows and replaces more indigenous supply each year. By the tenth year,year 2000, sustainable production satisfies the full market demand.Further indigenous harvests become unnecessary. Price has risen toMX196 per in.
20. Botlh regional result sets are relatively insensitive toindependent tests for various sectoral demand elasticities and rates oftechnical change. The results for all sensitivity tests cluster around60-75 percent price increases in just over one decade, or about fourpercent annually. In just over one decade, sustainable tree plantingssatisfy the full market demand for wood. The notable regionaldifference in these demand sensitivity results shows up in the southernindigenous forest loss of approximately one-quarter of its currentstanding volume (from 42.6 to 30-32 million in) in the adjustmentprocess. Central Region indigenous forests dQcline by only eightpercent from their 1990 level (from 67.9 to 63-55 million te).
21. The regional result sets are more sensitive to variations insupply. The regional demand sensitivity cases use a supply elasticityof 12. This is the supply price responsiveness for private plantingsonly -- assuming 30 percent seedling survival, 2500 seedlings/ha, and 6me/ha annual yields. More realistic supply responses might include
Annex 8Page 6 of 13
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better survival and greater annual yields. Therefore, the solution pathfor a moderately more responsive supply with an elasticity of 20 is alsoexamined. Table 4 shows that the more elastic supply causes theadjustment period to drop below one decade in both regions. Prices donot rise as much before they induce a sustainable yield from plantationsand the final indigenous stock is slightly larger in both regions.3
22. Changing the policy rule to allow demand and supplyequilibration at some decreasing level of annual physical yield would bemore in keeping with recent history and with economic expectations.This would decrease the adjustment period, and decrease the temporarydrawdown of indigenous forest. Changing the policy rule to allowequilibrium at an increasing level of annual physical yield would onlyextend the adjustment period. It may also increase the temporarydrawdown of indigenous stock during the adjustment period. In thislatter case, the regional forest economies would still go through aprocess of price increases inducing planting, but inducing somewhatlater sustainable yields.
23. In all realistic cases, for all demand and supply scenarios andall policy solution rules, Malawi's rate of deforestation tapers off,plantation harvests begin to replace harvests of indigenous trees, anddeforestation eventually disappears as an issue. Relative wood pricesdo increase, however, and the new plantings of introduced speciesprobably occur on higher quality and more accessible land than the oldindigenous forest.
24. Price increases are an indicator of greater potential socialburden. If per capita income grows at a rate greater than the rate ofwood price increase, then the wood pricn increase is no real burden. Ifincome growth is less than the relative wood price increase, thenincreasing hardship may be anticipated, particularly for the lowestincome households for whom wood energy for heating and cooking comprisea large budget share. Assuming an annual GDP growth rate of 4-5 percentand an annual population growth rate 3.5 percent, per capita incomegrowth of 0.5-1.5 is expected. Together with the projected wood priceincreases of 4 percent per annum for the next decade, these imply an
3 More realistic supply responses yet would include some Forestry Department and donor plantations yields, andan improved annual yield level, perhaps as great as 12 m3/ha. The results from our first two supply sensitivity analysesare impressive enough. Sensitivity analyses reflecting these final uncertain adjustments are unnecessary.
Annex 8Page 7 of 13
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increasing wood price burden on Malawian society.4 Undoubtedly thiswill induce some conservation, some substitution to wood alternativesand, perhaps, some unanticipated technical change.
25. In sunmary, it appears that rising prices induce tree plantingand a largely sustainable wood supply, regardless of variations in ourdemand or supply aseumptions. In both the Southern and Central Regions,the drawdown of indigenous forests continues past the year 2000, but therate of drawdown slackeis. Indeed, our analysis projects only a smallincrement of additional deforestation in the central region. At least3/4 of the current indigenous forest will remain, even in the South,after the economy becomes more dependent on steady plantation yieldssometime before 2010.
4 The burden would be less for less conservative projection scenarios where seedling survival or annual plantationyields per hectare are greater; therefore, where supply price responsiveness Is greater. The burden would also be lessfor a less conservative policy adjustment scenario that allows diminished consumption in response to Increasing prices.Therefore, we have probably overestimated the annual rate of price increase and the anticipated equity problem.
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Paae 8 of 13
FIGUR 1:. Forestry sector Supply AdDemand Kodel
Price/ms
McX (t+n) ( (+n)
Mc (t) _ +MCKi (t)
tt / \ ) D(t)
0 volume (m3)
Table 1: Household Demand Assumptions
ii _______ South CentLal
Urban householdstotal quantity: soli mO (firewood and 609,000 m3 425,000 .3
land area (1990) 848.700 ha 1,305,000 hastanding volume (1990) 42.6 mIllion mo 67.9 millilon m3current growth (1990) 596,000 m3/yr. 19122,00 m3/yr.growth rate 1.4 Z 1.65 X
hAricultural land conversion: assume all wood from land being converted to agriculture io usedin either household or comercLal consumption.
co
Table 4: Analytical Results
Demand Sensitivity
adjustmnt nltial final indiflous StoSW& satanabile 2unle private
Southern 9-12 yrs 120 t/)3 193-7 f/1.3 42.600 *2 30-32.000 a 425 wa 4.1-4,300* 9. 3 KIa 700 haCentral 12-15 yrs 166 ff1.3 256-312 f/.3 67.900 .' 63-65.000 s3 311 s3 3,4-3,500 S 3 7.3 be 575 ha
Sunnlv Sensitivity e. - 12. 20 !Southern 7-10 yrs 120 It/.s 174-96 ff/.3 42,600 m3 31-35,000 m3 425 a' 4,200 w' '9.3 ha 700 haCentral 8-14 yrs 168 K/a3 256-312 KJW3 67,900 .' 64-65.800 a 311 .' 3,500 a' 7.3 ha 575 ha
* in thousands
- 127. ARM 8Page 13 of 13
Table 5: Sample reaults: Southern region vith base case supply Tespone of 12.