Reverse modelling to estimate yield losses caused by crop diseases

Abstract

Yield loss analysis is critical to inform tactical and strategic decisions in crop health management, and requires quantification of three elements: the levels of injury caused by disease or pest, the actual (injured) yield, and the attainable (uninjured) yield. Reverse modelling allows reconstruction of an object or a process from limited information combined with a mathematical model. This approach is applied to estimate yield losses caused by diseases in winter wheat using a process-based simulation model (WHEATPEST), in combination with field data generated by a network of experiments across France, where multiple disease injuries and actual yields, but not attainable yields, were measured. The analysis covers 70 [year 9 region 9 variety 9 crop management] combinations encompassing five years (2004)(2005)(2006)(2007)(2008), four French regions, two winter wheat varieties (one high-yielding and one hardy variety), and two levels of crop management corresponding to two levels of chemical intensification. The analysis involved three main successive modelling steps where actual yield, attainable yield and yield losses associated with individual diseases were simulated. Overall, simulated yield losses to combined diseases ranged from 0 to 4.2 t ha À1 , and averaged 0.80 t ha À1 . Septoria tritici blotch caused the highest mean yield loss of 0.66 t ha À1 . The results highlight the contribution of varietal improvement to agricultural sustainability and performances. Reverse modelling can be applied to other crops and diseases or pests, in order to estimate individual and combined disease yield losses.

An outlook on wheat health in Europe from a network of field experiments

“…The variation in levels of multiple-disease intensity according to cultivar and fungicide use (Figure 7) reveals that the effect of cultivar resistance can be masked by fungicide use: differences between cultivars are reduced as the level of fungicide protection increases. This was already documented in other studies (e.g., Willocquet et al, 2018). This echoes a common situation in farmers' practices (Jørgensen et al, 2014), whereby the decision to use fungicides does not take into account the level of host plant resistance of the cultivar used.…”

“…On the other hand, yields are affected by the combined effects of weather, crop management, disease management, and disease levels. Such relationships were quantitatively estimated for wheat in France using a process-based modelling approach (Willocquet et al, 2018), and in Sweden using logistic regression models (Djurle et al, 2018).…”

“…Because wheat health problems vary over time (from season to season) and space, such an assessment needs to be conducted every year, in a range of geographical locations. Such assessments are not available currently for wheat in EU: only fragmented information of wheat health status is available, at the scale of Europe (e.g., Jørgensen et al, 2014;Figueroa et al, 2018;Singh et al, 2016;Savary et al, 2017;2019), or at the country scale (e.g., Jahn et al, 2012;Savary et al, 2016a;Djurle et al, 2018;Willocquet et al, 2018). In the same way, information on fungicide use is also incomplete, and the information pertaining, e.g., to the number of fungicide applications on wheat crops in Europe, is seldom available.…”

A Plant Stress-Responsive Bioreporter Coupled With Transcriptomic Analysis Allows Rapid Screening for Biocontrols of Necrotrophic Fungal Pathogens

“…Pathogens and pests cause severe damage to crops and are estimated to cost the global economy billions of dollars each year (Bradshaw et al, 2016;Savary et al, 2019). Yield reductions, on average, range from 20 to 40%, or higher (Ficke et al, 2017;Willocquet et al, 2018). Some disease management strategies using chemical fungicides are losing effectiveness as pathogens evolve resistance and overcome their modes of action (Hahn 2014).…”

Investigation into the emerging soil borne disease of peanut – Neocosmospora root rot

“…Firester et al (2018) developed a weather-based simulation model to map the spread of Phytophthora infestans at a regional scale from season-to-season while determining seasonal risk of disease outbreak. A similar mathematical simulation model has been developed by Willocquet et al (2018) to evaluate yield loss due to specific pathogens of winter wheat on a seasonal basis. While the two simulation models differ, it would be beneficial to look at a combination of risk evaluation and potential yield loss as a result of Neocosmospora root rot outbreaks.…”