Phytopathology. 2000 Aug;90(8):788-800. doi: 10.1094/PHYTO.2000.90.8.788.
ABSTRACT A general approach was developed to predict the yield loss of crops in relation to infection by systemic diseases. The approach was based on two premises: (i) disease incidence in a population of plants over time can be described by a nonlinear disease progress model, such as the logistic or monomolecular; and (ii) yield of a plant is a function of time of infection (t) that can be represented by the (negative) exponential or similar model (zeta(t)). Yield loss of a population of plants on a proportional scale (L) can be written as the product of the proportion of the plant population newly infected during a very short time interval (X'(t)dt) and zeta(t), integrated over the time duration of the epidemic. L in the model can be expressed in relation to directly interpretable parameters: maximum per-plant yield loss (alpha, typically occurring at t = 0); the decline in per-plant loss as time of infection is delayed (gamma; units of time(-1)); and the parameters that characterize disease progress over time, namely, initial disease incidence (X(0)), rate of disease increase (r; units of time(-1)), and maximum (or asymptotic) value of disease incidence (K). Based on the model formulation, L ranges from alphaX(0) to alphaK and increases with increasing X(0), r, K, alpha, and gamma(-1). The exact effects of these parameters on L were determined with numerical solutions of the model. The model was expanded to predict L when there was spatial heterogeneity in disease incidence among sites within a field and when maximum per-plant yield loss occurred at a time other than the beginning of the epidemic (t > 0). However, the latter two situations had a major impact on L only at high values of r. The modeling approach was demonstrated by analyzing data on soybean yield loss in relation to infection by Soybean mosaic virus, a member of the genus Potyvirus. Based on model solutions, strategies to reduce or minimize yield losses from a given disease can be evaluated.
摘要 本研究提出了一种通用方法，用于预测系统性疾病感染导致作物减产的程度。该方法基于两个前提：（i）随时间推移，植物群体中的疾病发病率可以用非线性疾病进展模型（如 logistic 或单分子模型）来描述；（ii）植物的产量是感染时间（t）的函数，可以用（负）指数或类似模型（zeta(t)）来表示。在比例尺度上，植物群体的减产（L）可以表示为在极短时间间隔内新感染植物群体的比例（X'(t)dt）与 zeta(t)的乘积，该乘积在疾病流行期间进行积分。模型中的 L 可以用与直接可解释参数相关的方式表示：单株植物最大减产（alpha，通常发生在 t = 0 时）；随着感染时间的延迟，单株植物损失的减少（gamma；时间单位（-1））；以及随时间变化的疾病进展特征参数，即初始疾病发病率（X(0)）、疾病增长率（r；时间单位（-1））和疾病发病率的最大（或渐近）值（K）。基于模型的表述，L 的范围从 alphaX(0)到 alphaK，并随着 X(0)、r、K、alpha 和 gamma(-1)的增加而增加。通过模型的数值解确定了这些参数对 L 的精确影响。该模型还扩展到了预测田间各点间疾病发病率存在空间异质性以及最大单株植物减产发生在疾病流行开始后（t > 0）的情况。然而，后两种情况仅在 r 值较高时对 L 产生重大影响。通过分析大豆感染大豆花叶病毒（一种 Potyvirus 属病毒）导致的产量损失数据，对该建模方法进行了验证。基于模型的解，可以评估减少或最小化特定疾病引起的减产的策略。
