Trade-off between disease resistance and crop yield: a landscape-scale mathematical modelling perspective

Abstract

The deployment of crop varieties that are partially resistant to plant pathogens is an important method of disease control. However, a trade-off may occur between the benefits of planting the resistant variety and a yield penalty, whereby the standard susceptible variety outyields the resistant one in the absence of disease. This presents a dilemma: deploying the resistant variety is advisable only if the disease occurs and is sufficient for the resistant variety to outyield the infected standard variety. Additionally, planting the resistant variety carries with it a further advantage in that the resistant variety reduces the probability of disease invading. Therefore, viewed from the perspective of a grower community, there is likely to be an optimal trade-off and thus an optimal cropping density for the resistant variety. We introduce a simple stochastic, epidemiological model to investigate the trade-off and the consequences for crop yield. Focusing on susceptible–infected–removed epidemic dynamics, we use the final size equation to calculate the surviving host population in order to analyse the yield, an approach suitable for rapid epidemics in agricultural crops. We identify a single compound parameter, which we call the efficacy of resistance and which incorporates the changes in susceptibility, infectivity and durability of the resistant variety. We use the compound parameter to inform policy plots that can be used to identify the optimal strategy for given parameter values when an outbreak is certain. When the outbreak is uncertain, we show that for some parameter values planting the resistant variety is optimal even when it would not be during the outbreak. This is because the resistant variety reduces the probability of an outbreak occurring.

Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens

“…crop rotation [ 24 , 25 ]; mixtures [ 26 – 28 ]; pyramiding [ 29 , 30 ]; mosaics [ 31 – 42 ]) or a combination of strategies (e.g. mixture and pyramiding: [ 43 ]; mosaic and pyramiding: [ 44 , 45 ]). Only a few studies explicitly compare two types of strategies [ 46 – 50 ] and only two studies evaluated more than two strategies [ 51 , 52 ].…”

“…As a result, a comprehensive evaluation of different deployment schemes is complicated, and currently only feasible via pairwise comparisons [ 53 , 54 ]. The situation for quantitative resistance is similar, since often only one [ 28 , 34 , 41 , 42 , 55 ], two [ 36 , 37 , 56 ], or a combination [ 26 , 44 , 49 ] of pathogen aggressiveness components are targeted, although quantitative resistance can affect several life-history traits of the pathogen. As articulated above, this current gap in our ability to predict which strategy will maximise our ability to control disease epidemics as well as pathogen evolutionary potential (or indeed whether these goals are compatible) emphasises the need for models that can compare different deployment schemes within the same framework, using standardised assumptions.…”

Identification of Stem Rust Resistance Genes in the Winter Wheat Collection from Southern Russia

“…It is necessary to obtain stable and high grain yields in order to provide the population with food. The varieties developed by breeders have a high productive potential, which cannot fully be realized because of crop diseases [2].…”

Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens

“…crop rotation [24,25]; mixtures [26][27][28]; pyramiding [29,30]; mosaics [31][32][33][34][35][36][37][38][39][40][41][42]) or a combination of strategies (e.g. mixture and pyramiding: [43]; mosaic and pyramiding: [44,45]). Only a few studies explicitly compare two types of strategies [46][47][48][49][50] and only two studies evaluated more than two strategies [51,52].…”

“…As a result, a comprehensive evaluation of different deployment schemes is complicated, and currently only feasible via pairwise comparisons [53,54]. The situation for quantitative resistance is similar, since often only one [28,34,41,42,55], two [36,37,56], or a combination [26,44,49] of pathogen aggressiveness components are targeted, although quantitative resistance can affect several life-history traits of the pathogen. As articulated above, this current gap in our ability to predict which strategy will maximise our ability to control disease epidemics as well as pathogen evolutionary potential (or indeed whether these goals are compatible) emphasises the need for models that can compare different deployment schemes within the same framework, using standardised assumptions.…”