Contents lists available at ScienceDirect Ecological Informatics journal homepage: www.elsevier.com/locate/ecolinf Use of ROOT to build a software optimized for parameter estimation and simulations with Distributed Delay Model Luca Rossini a, ⁎ , Maurizio Severini b , Mario Contarini a , Stefano Speranza a a Università degli Studi della Tuscia, Viterbo, Dipartimento di Scienze Agrarie e Forestali (DAFNE), Via San Camillo de Lellis snc, 01100 Viterbo, Italy b Università degli Studi della Tuscia, Viterbo, Dipartimento di Scienze Ecologiche e Biologiche (DEB), Loc. Riello snc, 01100 Viterbo, Italy ARTICLE INFO Keywords: Crop protection Development rate function ROOT Insects' life stage forecast ABSTRACT ROOT is a software package developed by a CERN project started in 1994 by René Brun for statistical analysis in high-energy physics. This software package can also be used in the field of protecting plants against pest insects. Indeed, for a long time there has been a significant use of the Distributed-Delay Model, but there is no specific software available to date that is useful for following the research from the first step to simulations and field validations. This work, through ROOT's libraries, builds a series of macros that consent to do non-linear fits with functions such as Erlang PDF, linear-rate, Logan, Briére, Sharpe and De Michele, thereby giving support to the parameters- estimate step in laboratory sessions and then numerically solving the Distributed-Delay Model equations. This study supplies results both graphically and numerically. 1. Introduction In Agricultural, Forest and Environmental Sciences, and especially in the context of risk management, modelling has become an important tool. In particular, entomologists started using several models to fore- cast pest insects' life cycles for two primary reasons: safeguarding human and environmental health, and because of the restrictive laws regarding pesticide use. The difference between a monitoring system and a forecasting system results from the possibility of having an idea of the risk situation of an agricultural field or an urban and peri-urban area (Speranza et al. 2007). There is a need to develop a control strategy different from the conventional one. Despite monitoring and forecast systems seeming similar, in reality there is a conspicuous dif- ference between them. In the first case, data collected with traps placed in fields provides empirical information on insect presence. Capinera defines the term “monitoring” as “careful observation of pest abundance and damage” (Capinera 2001). The inconvenience is that monitoring provides no information about the future trends of the monitored po- pulation. On the other hand, a simulation could not report the exact field situation, but it is helpful to know the future development of the population. In other words, forecasting is “the process of making pre- dictions based on past and present data”. These predictions can support a series of research projects that involve using natural enemies such as predators, parasitoids, entomopathogenic fungi and bacteria for the control of insect pests. Indeed, natural enemy complex plays a pre- eminent role in controlling invasive and autochthonous species in dif- ferent environmental contexts. For example, the entomopathogenic fungus Entomophaga maimaiga in areas where it has been introduced is effective against the gipsy moth Lymantria dispar (Lepidoptera: Ere- bidae) populations (Pilarska et al., 2006), encouraging further experi- mentation aimed at diffusing this pathogen in new areas across the world (Contarini et al. 2016). In the agronomic context, the biological control applications of the entomopathogenic bacteria Bacillus thur- ingiensis (Alsaedi et al. 2017; González-Cabrera et al. 2011) or the predator Zelus obscuridorsis (Hemiptera: Heteroptera: Reduviidae) (Speranza et al. 2014) in the tomato leaf miner, Tuta absoluta (Lepi- doptera: Gelechiidae) might be mentioned. The inconvenience of this type of control is that it requires highly qualified technicians, quick response and accurate knowledge of the most susceptible life stage of the insect pest if one is to maximize the efficacy of the use of biological control. On the other hand, if it is impossible to apply a biological control strategy, to know the pests' population trends helps one perform chemical control to reduce the quantity of the pesticide in question. One of the widely used models is the Distributed Delay Model (DDM). Beginning with its introduction by Manetsch (1976), the DDM has garnished interest from entomologists and environmental scientists to describe the life cycle of poikilothermic organisms. One of the strengths concerns the possibility of linking environmental and species' https://doi.org/10.1016/j.ecoinf.2019.02.002 Received 31 May 2018; Received in revised form 16 January 2019; Accepted 3 February 2019 ⁎ Corresponding author. E-mail address: luca.rossini@unitus.it (L. Rossini). Ecological Informatics 50 (2019) 184–190 Available online 04 February 2019 1574-9541/ © 2019 Elsevier B.V. All rights reserved. T