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likely behavior was predicted based on the presenceor absence of effective race-specific genes from thegreenhouse tests and behavior in the field trials. Weassumed that most lines were likely to be classifiedinto similar resistance categories in otherenvironments. Though some exceptions in eachdirection may occur, the varieties were evaluatedunder very high disease pressure in the trials inMexico. It is therefore more likely that we may haveunderestimated the level of protection from racenonspecificresistance over the area included in thisstudy.Subsequently, the midpoint of the percent leaf rustinfection relative to the susceptible check varieties(Table 2) was subtracted from 100 percent torepresent the percent yield loss avoided by eachresistance category. This was multiplied by theaverage expected farm-level loss in susceptiblevarieties by ME, as described in the followingsection.Average annual farm-level percent yield lost withsusceptible varieties. Historical farm-level data onthe average annual yields lost to rust were notavailable over the extensive spring bread wheatproducing areas of the developing world included inthis study. Nor were global data on weather,management practices, or spatial distributions ofpathogen and resistance types available to allowprediction of the annual disease pressure or theduration of resistance. In the absence of these data,we used estimates of expected losses from secondarysources. For this purpose, we initially consideredvarious sources of trial data and historical accountsfrom the literature.The grain yield losses associated with various typesof leaf rust resistance have been compared underexperimental conditions in studies conducted byCIMMYT (Singh et al. 1991; Singh and Huerta-Espino1997). However, these estimates do not necessarilyrepresent the annual yields lost in farmers’ fieldsover all the production areas included in this study.Small-plot evaluations have also been shown tooverestimate disease losses (Saari and Prescott 1985;Roelfs et al. 1992). Sayre et al. (1998) estimated theeffects of genetic resistance on yield losses from leafrust by regression analysis. Fifteen CIMMYT breadwheats released between 1966 and 1988 were grownunder farmers’ management conditions in the YaquiValley of Mexico in six trials for four seasons, withand without fungicide. The trial results indicated thedifference in percent yield loss from rust betweenbread wheats with race-specific and race-nonspecificresistance, once race-specific genes are no longereffective, and under conditions of heavy diseasepressure. These data were combined withinformation on the known or predicted longevity ofrace-specific resistance, and they were used toestimate the time path of resistance and the economicbenefits of race-nonspecific leaf rust resistance in theYaqui Valley (Smale et al. 1998). However, even inthat study, actual annual disease losses in farmers’fields were not known. Though the trial data fromSayre et al. (1998) used to estimate the yield savingsrepresented farmers’ management practices fairlyclosely, the disease pressure in the trials was heavierthan that experienced in producers’ fields in mostyears. The Yaqui Valley estimates also do notnecessarily represent the conditions in all wheat MEsincluded in this study.CIMMYT data from the International Spring WheatYield Nurseries (ISWYN) were also initiallyconsidered as a source of information. These annualtrials are conducted at locations in several MEsworldwide. They provide historical data on yield andother information—including rust infection scores—for the varieties included over different sites. Wewould have been interested in the effect of rustresistance on the yields of varieties grown at the samesite over several years. However, trial entries changeannually as new materials are developed, so the samevarieties are rarely used for more than two or threeyears. The only exception is the variety Siete Cerros, areference check that is included in all ISWYN trials inall years and at all sites. There are also someproblems in working with the ISWYN data. First, notall information has been reported, which especiallyincludes rust scores, and not all trial sites have beenused for all ISWYN years. Second, the ISWYNinformation represents data from experimentstations, whereas we were concerned with farm-leveldata. Third, when using these data, it is difficult tocontrol for the effects on farm-level yield of factorsother than rust, such as annual weather variation,changes in trial management, other biotic and abioticstresses, and degradation of the resource base of theresearch station. These factors may also affect theyield of the control variety (Collins 1995;unpublished observations by CIMMYT 1996). Wetherefore could not obtain global estimates of averageannual farm-level yield losses to leaf rust from theISWYN data.There are various historical accounts of the economicimportance of wheat rusts, and the cereal rusts havebeen described as fungal diseases with “worldwide”occurrence characterized by “frequent severe15
epidemics” and “huge annual losses” (Agrios 1997).However, the number and significance of recordedrust epidemics vary widely. Estimated productionlosses have typically been reported anecdotally forthe developing world (Saari and Prescott 1985; Smaleet al. 1998). Even when occurrence of the disease maybe recorded, it is seldom accompanied by data onyield losses or the relationship to wheat prices,output levels, or imports. There are also problemswhen measuring rust losses in practice (Saari andPrescott 1985; Roelfs et al. 1992). Losses of less than10% are difficult to measure statistically under mostcircumstances. Consequently, disease developmentmust be severe to measure losses more accurately. Itis also difficult to disaggregate rust-occasioned lossesfrom those due to other biotic and abiotic stresses.These may often occur simultaneously andcontribute to observed losses.Accounts in the literature of leaf rust losses for theAsian subcontinent include Barclay (1892), Howardand Howard (1909), Nagarajan and Joshi (1975,1985), Joshi (1980), Joshi et al. (1980), Nagy (1984),Joshi et al. (1985), Bajwa et al. (1986), and Khan(1987). Accounts for Mexico include Borlaug (1954,1968), Dubin and Torres (1981), and Smale et al.(1998), and for the Southern Cone, Kohli (1985). ForAfrica and other developing countries, as well asdeveloped countries in Asia, Europe, North America,and Oceania, see Chester et al. (1951), Stakman andHarrar (1957), Saari and Prescott (1985), Roelfs andBushnell (1985), and Oerke et al. (1994). In theaccounts mentioning them, the estimated yield lossesfrom leaf rust range between environments andyears, and by the scale of the area covered.Table 3 shows examples of the yield loss estimatesreported in the literature, and these examples areraised to demonstrate the importance of the area andtime period represented. The disease lossencountered for any variety in any year is generallyhigher in zones of high disease pressure, such as inlocalized “hot spots.” Estimated losses are also muchhigher in epidemic years, especially in areas wherelosses cannot be averted by chemical control. Farmlevelyield losses averaged over several years, largeareas, and various production environments areclearly smaller. Such annual losses vary from a traceto usually less than 10% (Roelfs et al. 1992), and theyrarely exceed 15% (Singh et al. 1991). Oerke et al.(1994:272) estimate that the global average, includingdeveloped and developing countries, of actual lossescaused by all wheat diseases (excluding pests andweeds) over the three-year period from 1988 to 1990was 12.4%. This means that on a global basis, annuallosses averaged over a longer time period for leaf rustalone should be less.Comprehensive annual yield loss data at the statelevel in the USA were obtained from the CerealDisease Laboratory (http://www.cdl.umn.edu) for aperiod of 25 years from 1976 to 2000. The averageannual losses to leaf rust for the USA in total rangedbetween traces in some years, up to 2.7% (Table 3),but they differed between locations and years. Thesedata demonstrate the point that annual lossesaveraged over large areas are smaller. However, theseestimates do not represent the production conditionsand disease pressure prevailing in all spring breadwheat environments included in this study. They alsodo not represent the situation in most developingcountries, where few farmers use fungicides tocontrol leaf rust. Previous estimates by CIMMYT(1985) suggest an area-weighted average annual yieldloss of 3.7% to leaf rust, when calculated over a tenyearperiod for 22 developing countries producingmore than 100,000 hectares of wheat. Thisinformation was, however, not attached to MEs.In view of all these considerations, we based ourupper-bound estimates of the average annual farmlevelpercent yield loss in susceptible varieties onthose provided by the CIMMYT Wheat Program bywheat-producing environment (Table 1). Estimates inall MEs are less than 10% and thus in line with thegeneral global guideline of less than 10% (Roelfs et al.1992:2). The estimates are moreover based on yieldlosses in susceptible varieties in environments wherea mosaic of resistant and susceptible cultivars is used.This reduces the build-up and spread of rust overlarge areas. Losses exceeding 25%, as reported innorthwestern Mexico by Dubin and Torres (1981),might occur in most regions classified as ME 1 andME 5 if only susceptible cultivars were used. This isbecause water and nitrogen, which favor diseasedevelopment, are usually not limiting in theseproduction regions. Wheat could not be grownwithout using fungicides under this scenario. Higheraverage annual losses than those assumed in Table 1would therefore have been likely if all cultivars sownin the developing world were in fact fully susceptible.In addition to using these estimates to solveequations (1) to (3), we performed a sensitivityanalysis by arithmetically calculating the minimumaverage annual yield that would have had to havebeen lost by susceptible varieties in ME 1 to recoverCIMMYT’s wheat breeding investment since 1967.16
Page 1 and 2: E c o n o m i c s P r o g r a m P a
Page 3 and 4: CIMMYT® (www.cimmyt.org) is an int
Page 5 and 6: TablesTable 1. Summary of parameter
Page 8 and 9: The Economic Impact in DevelopingCo
Page 10 and 11: origins or pedigrees are selected f
Page 12 and 13: Most assessments of the returns on
Page 14 and 15: MethodologyIn the capital investmen
Page 18 and 19: Table 3. Estimated yield losses fro
Page 20 and 21: esults showed that the annual progr
Page 22 and 23: decreasing percentage of the bread
Page 24 and 25: arising from this assumption may be
Page 26 and 27: Table 6. Discounted gross benefits
Page 28 and 29: our base scenario was minimal (Tabl
Page 30 and 31: esistance. It is conceptually and p
Page 32 and 33: ReferencesAdusei, E.O. 1988. Evalua
Page 34 and 35: Nagarajan, S., and L.M. Joshi. 1985
Page 36 and 37: Appendix ADefinition of CIMMYT mega
Page 38: ISSN: 1405-7735International Maize

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