Rotten Hazelnuts Prediction via Simulation Modeling—A Case Study on the Turkish Hazelnut Sector

Abstract

The quality defects of hazelnut fruits comprise changes in morphology and taste, and their intensity mainly depends on seasonal environmental conditions. The strongest off-flavor of hazelnuts is known as rotten defect, whose candidate causal agents are a complex of fungal pathogens, with Diaporthe as the dominant genus. Timely indications on the expected incidence of rotten defect would be essential for buyers to identify areas where hazelnut quality will be superior, other than being useful for farmers to have the timely indications of the risk of pathogens infection. Here, we propose a rotten defect forecasting model, and we apply it in the seven main hazelnut producing municipalities in Turkey. We modulate plant susceptibility to fungal infection according to simulated hazelnut phenology, and we reproduce the key components of the Diaporthe spp. epidemiological cycle via a process-based simulation model. A model sensitivity analysis has been performed under contrasting weather conditions to select most relevant parameters for calibration, which relied on weekly phenological observations and the post-harvest analyses of rotten incidence in the period 2016–2019, conducted in 22 orchards. The rotten simulation model reproduced rotten incidence data in calibration and validation datasets with a mean absolute error below 1.8%. The dataset used for model validation (321 additional sampling locations) has been characterized by large variability of rotten incidence, in turn contributing to decrease the correlation between reference and simulated data (R2 = 0.4 and 0.21 in West and East Black Sea region, respectively). This denotes the key effect of other environmental and agronomic factors on rotten incidence, which are not yet taken into account by the predictive workflow and will be considered in further improvements. When applied in spatially distributed simulations, the model differentiated the rotten incidence across municipalities, and reproduced the interannual variability of rotten incidence. Our results confirmed that the rotten defect is strictly dependent on precipitation amount and timing, and that plant susceptibility is crucial to trigger fungal infections. Future steps will envisage the application of the rotten simulation model to other hazelnut producing regions, before being operationally used for in-season forecasting activities.

DEFHAZ: A Mechanistic Weather-Driven Predictive Model for Diaporthe eres Infection and Defective Hazelnut Outbreaks

“…In terms of the crop susceptibility windows, we established the DEFHAZ model to have possible infection until the end of May, which we based on the regression analysis using a stepwise approach, and which is in partial agreement with Valeriano et al [22], who considered hazelnut to be most susceptible to infections during female flowering, and therefore over a narrower period. To confirm the reasonable period for primary infection, we require studies with the artificial inoculation of D. eres α conidia in planta, and possibly with the subsequent incubation at different temperatures.…”

“…In a more recent paper, the authors present a model to predict rotten hazelnut kernels; in this case, they did not focus on a specific causal agent but included the Diaporthe spp. [22]. The authors confirmed the strict dependence of rotten hazelnuts on the precipitation amount and timing, and on the role of the plant susceptibility, and they suggest the effects of other environmental and agronomic factors on the rot incidence that they did not include in the model.…”

“…We based this model on weather, and we did not include any other factors. Based on the results reported by Pscheidt and Heckert [21] and Valeriano et al [22], other factors are relevant to enhancing defective hazelnuts, such as the variety susceptibility to fungal infection. Therefore, the prediction capacity of the DEFHAZ model is adequate; however, it could be improved by including other variables.…”

“…Data were available, but only under a narrow range of RH conditions (94%, 97%, and 100% RHs; [18]), We need more detailed experiments to better describe the conidia germination potential with different RH values, as well as time regimes. In a recently developed model already mentioned [22], the authors neglected this aspect and assumed that the dispersed conidia were suitable for the infection under the proper combination of leaf wetness and temperature, which could have had a strong negative impact on the model performances.…”

Diversity of hazelnut varieties and changes in plant development during introduction in the semi-arid zone

“…However, in this case, as our studies have shown, it is desirable to leverage different characteristics of varieties for the development and generation of yields, which allows for a more flexible strategy (Kizilkaya et al, 2022;Valeriano et al, 2022). The generation of yield depends on many parameters of both the plant itself and its fruit, which has been repeatedly noted above.…”