PLP 6404 Epidemiology of Plant Diseases Spring 2015
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1 PLP 6404 Epidemiology of Plant Diseases Spring 2015 Ariena van Bruggen, modified from Katherine Stevenson Lecture 26: Crop loss assessment Yield levels and losses Theoretical yield The yield obtained under the best growing conditions according to calculations based on the plant and crop physiology or the maximum theoretical yield as determined by using crop growth simulation models Attainable yield The yield achieved when crops are grown using all available pest control technologies to minimize biotic stress Economic yield The level of yield where the value of the yield just exceeds the cost of production Actual yield The yield obtained under current agronomic husbandry practices Primitive yield The yield obtained without pest control (e.g. subsistence agriculture) Introduction to crop loss 1
2 The prevention of crop losses is the main reason that we study plant pathology. Zadoks & Schein (1979) considered crop losses at the end point of epidemiology; this is where the science merges with crop physiology, economics, and sociology. Definitions: Crop damage -- injuries caused by harmful biotic or abiotic agents that collectively result in a measurable loss of yield Crop loss -- a reduction in value or financial return due to damage; often measured as the difference between actual yield and attainable yield due to pests and/or pathogens The reduction in value may be the result of 1. lower quantity 2. lower quality Reliable yield loss data are always in demand (and difficult to get) and are used to: determine research priorities serve as a base of information for governmental decisions (local, regional or international levels) Potential and actual losses A loss that may occur in the absence of any management practice is a potential loss. A loss that has occurred (or is still occurring) is an actual loss. Actual losses can be direct or indirect: Losses in quality and quantity of product sustained by the grower, including costs of pest management are referred to as direct losses. Losses beyond the farm gate that arise as a consequence of plant pathogens or pests are considered indirect losses (to community, consumer, wholesaler etc.). Direct losses can be primary or secondary: Preharvest and postharvest losses of plant products due to plant disease and pests, including direct costs of pest management are primary losses. Losses caused by a reduction in the yielding capacity of future crops sustained at the grower level are considered secondary losses. The crop loss estimates can be used to: 1. establish priorities for research programs; 2. justify programs on certain crops or problems; 3. predict yield and provide crop information; 4. plan strategies for future seasons, and; 5. compare seasons, epidemics, pathogens, etc. 2
3 Measuring disease and yield in the field Four basic approaches have been employed in crop loss studies: 1. single plant or tiller method disease is assessed on a large number of individual plants ( ), covering the entire range of possible disease intensity values. yield of the individual plants is then measured and related to disease problems include high inter-plant variability and low R2 values 2. microplot experiments used primarily for soilborne pathogens allows containment of pathogens and creation of wide range of disease levels very labor intensive 3. Survey method Involves measurement of many variables concerning biological and physical properties of the crop and field, including disease intensity, usually at multiple times during the growing season 4. conventional field experiments with different levels of disease most widely used technique should repeat experiment at least 3 years A range of disease levels can be created by: Add or remove inoculum Timing or amount of fungicides applied If possible, use "typical" farming equipment and practices. Drawbacks to this approach: usually includes effects of only a single pathogen - not very realistic Note: The # of levels of disease intensity is more important than # of replications. 3
4 Models for relating disease intensity and yield loss Four categories of empirical loss models (what is an empirical model?): single-point models integral models multiple-point models generalized or nonlinear models Single-point models Single-point models relate yield loss to disease intensity at one specific time during the growing seasons or at a specific host growth stage. Most commonly used models; need relatively little data. May be either linear or non-linear Can be written in the form: L = a + bx, where L=loss and X=disease intensity Example: Linear models of yield loss in soybeans as a function of incidence of Sclerotinia stem rot (Yang, X. B., et al Plant Disease 83: ) (3 different years and 3 different locations) 4
5 Example: Linear models of yield loss of chickpea as a function of intensity of fusarium wilt (Navas-Cortes, J. A., et al., Phytopathology 90: ) Incidence (I): 0=healthy, 1=diseased Severity (S): 0=healthy, 1=1-33%, 2=34-66%, 3=67-100%, 4=dead plant Disease intensity index (DII): 5
6 Multiple-point or multiple regression models Multiple-point models relate yield loss to assessments of disease made at several times during a growing season. 6
7 More applicable in situations where yield accumulation occurs over a relatively longtime or rate of disease progress is highly variable. Example: potato late blight and tuber yield (James, 1972) L = 1.87X X X X X X X X 9 where L = % tuber yield loss and X i = weekly change in % defoliation. Difference between estimated loss and actual loss was less than 5% in 9 of 10 data sets, and has an R 2 value of.952 Integral models Yield or loss as a function of AUDPC First used by Vanderplank for stem rust of wheat, estimation of yield loss is a function of the area under the disease progress curve. Problems with this approach: AUDPC doesn t take into account when disease started that early disease may be more important that it may not be a good predictor of yield Weighting factors can be applied to disease assessments at particular times or growth stages to account for the effect of time of disease occurrence on yield. Example: Yield of soybean as a function of relative AUDPC (RAUDPC) for soybean rust epidemics (Yang, X. B., et al., Phytopathology 81: ) 7
8 Yield or loss as a function of healthy leaf area duration (HAD): Waggoner and Berger (1987): Crop yield is determined not by the area of diseased tissue, but by the area of productive, healthy tissue which is not reflected in measurements of disease severity. Yield loss is proportional to 1 - (HAD)/(LAD), where LAD is leaf area duration of a disease-free control crop. (much more difficult to measure) Example: Yield of peanut as a function of HAD and absorbed insolation. (Waggoner and Berger, Phytopathology 77: ) 8
9 Nonlinear models more powerful than strictly linear modeling account for more variation in response curve need larger data sets to fit nonlinear models Example: Relative yield of chickpeas as a function of the standardized areaunder the disease intensity indexprogress curve (Navas-Cortes, J. A., et al., Phytopathology 90: ) Negative exponential model: RY = e1 e rdx RY = relative yield x = AUDPC r d = rate of decrease (r d estimated from nonlinear regression) 9
10 Example: Seinhorst model (1965) for quantifying effects of nematodes on yield Y = m+ (1-m)N (Z-t) where Y = yield m = minimum yield t = threshold or tolerance parameter N = shape parameter Z = nematode density Effect of Meloidogyne incognita on yield of fresh spinach Assessing and modeling losses in quality of yield Losses in quality due to disease may be as important as losses in quantity. For many products, such as fresh market fruits, quality grades are established and prices received are determined by the grade. Whenever quality is an important determinant of crop value, quality losses should be considered in any investigation of the relationship between disease and loss. 10
11 Example: Effect of apple scab on value of harvested fresh market apple fruit 11
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