Structure and validation of RICEPEST, a production situation-driven, crop growth model simulating rice yield response to multiple pest injuries for tropical Asia

Measuring and modelling crop yield losses due to invasive insect pests under climate change

Climate change and agriculture are strongly correlated, and the fast pace of climate change will have impacts on agroecosystems and crop productivity. This review summarizes potential impacts of rising temperatures and atmospheric CO₂ concentrations on insect pest-crop interactions and provides two-way approaches for integrating these impacts into crop models for sustainable pest management strategies designing. Rising temperatures and CO₂ levels affect insect physiology, accelerate their metabolism and increase their consumption, ultimately increasing population densities, which result in greater crop injury and damage, and yield loss. Whereas these direct effects are empirically demonstrated for temperature rises, they are less straightforward for CO₂ increases. Furthermore, indirect effects of rising temperatures and CO₂ levels remain largely unexploited and therefore unknown. Coupling insect pests and crops using a two-way feedback system model, whereby pest variables drive crop variables and vice versa, will improve analysis and forecasting of yield losses to better guide preparedness and intervention strategies.

A Probabilistic Bio-Economic Assessment of the Global Consequences of Wheat Leaf Rust

This study provides a bio-economic assessment of the global climate suitability and probabilistic crop-loss estimates attributable to wheat leaf rust. We draw on a purpose-built, spatially explicit, ecoclimatic suitability model for wheat leaf rust to estimate that 94.4% of global wheat production is vulnerable to the disease. To reflect the spatiotemporal variation in leaf rust losses, we used a probabilistic approach to estimate a representative rust loss distribution based on long-term, state-level annual U.S. loss estimates. Applying variants of this representative loss distribution to selected wheat production areas in 15 epidemiological zones throughout the world, we project global annual average losses of 8.6 million metric tons of grain for the period 2000 to 2050 based on a conservative, baseline scenario, and 18.3 million metric tons based on a high-loss scenario; equivalent to economic losses ranging from $1.5 to $3.3 billion per year (2016 U.S. prices). Even the more conservative baseline estimate implies that a sustained, worldwide investment of $50.5 million per year in leaf rust research is economically justified.

Modeling the Impact of Crop Diseases on Global Food Security

Plant pathology must contribute to improving food security in a safe operating space, which is shrinking as a result of declining natural resources, climate change, and the growing world population. This review analyzes the position of plant pathology in a nexus of relationships, which is mapped and where the coupled dynamics of crop growth, disease, and yield losses are modeled. We derive a hierarchy of pathogens, whereby pathogens reducing radiation interception (RI), radiation use efficiency (RUE), and harvest index increasingly impact crop yields in the approximate proportions: 1:4.5:4,700. Since the dawn of agriculture, plant breeding has targeted the harvest index as a main objective for domesticated plants. Surprisingly, the literature suggests that pathogens that reduce yields by directly damaging harvestable plant tissues have received much less attention than those that reduce RI or RUE. Ecological disease management needs to target diverse production situations and therefore must consider variation in attainable yields; this can be achieved through the reengineering of agrosystems to incorporate built-in dynamic diversity of genes, plants, and crop stands.

A Predictive Risk Model Analysis of the Potato Cyst Nematode Globodera pallida in Idaho

Globodera pallida is a major nematode pest of potato (Solanum tuberosum) and is of great economic importance for the potato industry. Assessing potato yield loss caused by the Idaho G. pallida population under field conditions was not performed due to its quarantine status in Idaho, where it is prohibited by regulatory statutes to grow potato in any infested fields. The experimental data came from three trials that were conducted under greenhouse conditions. A predictive risk model analysis was performed to: (i) determine the effect of the Idaho population of G. pallida on potato yield; (ii) estimate reproduction rate from different initial nematode densities; and (iii) simulate potato yield losses in Idaho field conditions by integrating the coefficients of potato yield into the SUBSTOR-DSSAT crop simulation model. Experiments were conducted under greenhouse conditions using five initial G. pallida soil infestation levels (0, 10, 20, 40, and 80 eggs/g soil). The coefficients of potato yield achieved under each initial nematode density were integrated into the SUBSTOR-DSSAT potato growth simulation model. The model showed that tuber weight reached a maximum yield of 96 ton/ha in noninfested soil. Based on the greenhouse trials, the model predicted a minimum yield of 12 and 58 ton/ha in trial 1 and trial 2/3 respectively, when initial nematode density was 80 eggs/g soil. In trial 1, tuber weight was significantly reduced by 44% at 40 eggs/g soil and by 87% at 80 eggs/g soil, and 20% at 40 eggs/g soil and by 39% at 80 eggs/g soil in trial 2/3. The outputs of this study should facilitate common understanding between regulators, policymakers, and potato growers on the challenges and opportunities for controlling this economically important pest in Idaho.

Modelling the impacts of pests and diseases on agricultural systems

The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.

Balancing competition for resources with multiple pest regulation in diversified agroecosystems: a process-based approach to reconcile diversification and productivity

Agroecosystem plant diversification can enhance pest biological regulation and is a promising alternative to pesticide application. However, the costs of competition for resources between plants may exceed the benefits gained by pest regulation. To disentangle the interactions between pest regulation and competition, we developed a generic process‐based approach that accounts for the effects of an associated plant and leaf and root pests on biomass production. We considered three crop–plant associations that differ in competition profiles, and we simulated biomass production under wide ranges of both pest regulation rates and resources’ availability. We analyzed outputs to quantify the pest regulation service level that would be required to attain monoculture yield and other production goals. Results showed that pest regulation requirements were highly dependent on the profile of resource interception of the associated plant and on resources’ availability. Pest regulation and the magnitude of competition between plants interacted in determining the balance between nitrogen and radiation uptake by the crop. Our findings suggest that productivity of diversified agroecosystems relative to monoculture should be optimized by assembling plants whose characteristics balance crops’ resource acquisition. The theoretical insights from our study draw generic rules for vegetation assemblage to optimize trade‐offs between pest regulation and production. Our findings and approach may have implications in understanding, theorizing and implementing agroecosystem diversification. By its generic and adaptable structure, our approach should be useful for studying the effects of diversification in many agroecosystems.

Synergisms between microbial pathogens in plant disease complexes: a growing trend

Plant diseases are often thought to be caused by one species or even by a specific strain. Microbes in nature, however, mostly occur as part of complex communities and this has been noted since the time of van Leeuwenhoek. Interestingly, most laboratory studies focus on single microbial strains grown in pure culture; we were therefore unaware of possible interspecies and/or inter-kingdom interactions of pathogenic microbes in the wild. In human and animal infections, it is now being recognized that many diseases are the result of multispecies synergistic interactions. This increases the complexity of the disease and has to be taken into consideration in the development of more effective control measures. On the other hand, there are only a few reports of synergistic pathogen-pathogen interactions in plant diseases and the mechanisms of interactions are currently unknown. Here we review some of these reports of synergism between different plant pathogens and their possible implications in crop health. Finally, we briefly highlight the recent technological advances in diagnostics as these are beginning to provide important insights into the microbial communities associated with complex plant diseases. These examples of synergistic interactions of plant pathogens that lead to disease complexes might prove to be more common than expected and understanding the underlying mechanisms might have important implications in plant disease epidemiology and management.

Injury Profile SIMulator, a qualitative aggregative modelling framework to predict crop injury profile as a function of cropping practices, and the abiotic and biotic environment. I. Conceptual bases

The limitation of damage caused by pests (plant pathogens, weeds, and animal pests) in any agricultural crop requires integrated management strategies. Although significant efforts have been made to i) develop, and to a lesser extent ii) combine genetic, biological, cultural, physical and chemical control methods in Integrated Pest Management (IPM) strategies (vertical integration), there is a need for tools to help manage Injury Profiles (horizontal integration). Farmers design cropping systems according to their goals, knowledge, cognition and perception of socio-economic and technological drivers as well as their physical, biological, and chemical environment. In return, a given cropping system, in a given production situation will exhibit a unique injury profile, defined as a dynamic vector of the main injuries affecting the crop. This simple description of agroecosystems has been used to develop IPSIM (Injury Profile SIMulator), a modelling framework to predict injury profiles as a function of cropping practices, abiotic and biotic environment. Due to the tremendous complexity of agroecosystems, a simple holistic aggregative approach was chosen instead of attempting to couple detailed models. This paper describes the conceptual bases of IPSIM, an aggregative hierarchical framework and a method to help specify IPSIM for a given crop. A companion paper presents a proof of concept of the proposed approach for a single disease of a major crop (eyespot on wheat). In the future, IPSIM could be used as a tool to help design ex-ante IPM strategies at the field scale if coupled with a damage sub-model, and a multicriteria sub-model that assesses the social, environmental, and economic performances of simulated agroecosystems. In addition, IPSIM could also be used to help make diagnoses on commercial fields. It is important to point out that the presented concepts are not crop- or pest-specific and that IPSIM can be used on any crop.

Quantification and modeling of crop losses: a review of purposes

This review considers the cascade of events that link injuries caused by plant pathogens on crop stands to possible (quantitative and qualitative) crop losses (damage), and to the resulting economic losses. To date, much research has focused on injury control to prevent this cascade of events from occurring. However, this cascade involves a complex succession of components and processes whereby knowledge on crop loss generates entry points for management. Proposed here is a framework linking different types of knowledge on crop loss to a range of decision categories, from tactical to strategic short- or long-term. Important advances in this field are now under way, including a probabilistic treatment of the injury-damage relationship, or analyses of the sources of uncertainty attached to some components of the decision process. Management of injury profiles, rather than individual injuries, and shifts in dimensionality of crop losses are anticipated to contribute to the design of sustainable agricultural systems, and address global issues concerning food security and food safety.

Qualification of a Plant Disease Simulation Model: Performance of the LATEBLIGHT Model Across a Broad Range of Environments

ABSTRACT The concept of model qualification, i.e., discovering the domain over which a validated model may be properly used, was illustrated with LATEBLIGHT, a mathematical model that simulates the effect of weather, host growth and resistance, and fungicide use on asexual development and growth of Phytophthora infestans on potato foliage. Late blight epidemics from Ecuador, Mexico, Israel, and the United States involving 13 potato cultivars (32 epidemics in total) were compared with model predictions using graphical and statistical tests. Fungicides were not applied in any of the epidemics. For the simulations, a host resistance level was assigned to each cultivar based on general categories reported by local investigators. For eight cultivars, the model predictions fit the observed data. For four cultivars, the model predictions overestimated disease, likely due to inaccurate estimates of host resistance. Model predictions were inconsistent for one cultivar and for one location. It was concluded that the domain of applicability of LATEBLIGHT can be extended from the range of conditions in Peru for which it has been previously validated to those observed in this study. A sensitivity analysis showed that, within the range of values observed empirically, LATEBLIGHT is more sensitive to changes in variables related to initial inoculum and to weather than to changes in variables relating to host resistance.

Simulation of Potato Late Blight in the Andes. II: Validation of the LATEBLIGHT Model

ABSTRACT LATEBLIGHT, a mathematical model that simulates the effect of weather, host growth and resistance, and fungicide use on asexual development and growth of Phytophthora infestans on potato foliage, was validated for the Andes of Peru. Validation was needed due to recent modifications made to the model, and because the model had not been formally tested outside of New York State. Prior to validation, procedures to estimate the starting time of the epidemic, the amount of initial inoculum, and leaf wetness duration were developed. Observed data for validation were from field trials with three potato cultivars in the Peruvian locations of Comas and Huancayo in the department of Junín, and Oxapampa in the department of Pasco in 1999 and 2000 for a total of 12 epidemics. These data had not been used previously for estimating model parameters. Observed and simulated epidemics were compared graphically using disease progress curves and numerically using the area under the disease progress curve in a confidence interval test, an equivalence test, and an envelope of acceptance test. The level of agreement between observed and simulated epidemics was high, and the model was found to be valid according to subjective and objective performance criteria. The approach of measuring fitness components of potato cultivars infected with isolates of a certain clonal lineage of P. infestans under controlled conditions and then using the experimental results as parameters of LATEBLIGHT proved to be effective. Fungicide treatments were not considered in this study.

Research priorities for rice pest management in tropical Asia: a simulation analysis of yield losses and management efficiencies

ABSTRACT A simulation study was conducted to assess the current and prospective efficiency of rice pest management and develop research priorities for lowland production situations in tropical Asia. Simulation modeling with the RICEPEST model provided the flexibility required to address varying production situations and diverse pest profiles (bacterial leaf blight, sheath blight, brown spot, leaf blast, neck blast, sheath rot, white heads, dead hearts, brown plant-hoppers, insect defoliators, and weeds). Operational definitions for management efficacy (injury reduction) and management efficiency (yield gain) were developed. This approach enabled the modeling of scenarios pertaining to different pest management strategies within the agroecological contexts of rice production and their associated pest injuries. Rice pests could be classified into two broad research priority-setting categories with respect to simulated yield losses and management efficiencies. One group, including weeds, sheath blight, and brown spot, consists of pests for which effective pest management tools need to be developed. The second group consists of leaf blast, neck blast, bacterial leaf blight, and brown plant-hoppers, for which the efficiency of current management methods is to be maintained. Simulated yield losses in future production situations indicated that a new type of rice plant with high-harvest index and high-biomass production ("New Plant Type") was more vulnerable to pests than hybrid rice. Simulations also indicated that the impact of deployment of host resistance (e.g., through genetic engineering) was much larger when targeted against sheath blight than when targeted against stem borers. Simulated yield losses for combinations of production situations and injury profiles that dominate current lowland rice production in tropical Asia ranged from 140 to 230 g m(-2). For these combinations, the simulated efficiency of current pest management methods, expressed in terms of relative yield gains, ranged from 0.38 to 0.74. Overall, the analyses indicated that 120 to 200 x 10(6) tons of grain yield are lost yearly to pests over the 87 x 10(6) ha of lowland rice in tropical Asia. This also amounts to the potential gain that future pest management strategies could achieve, if deployed.