Modelling Management Practices in Viticulture while Considering Resource Limitations: The Dhivine Model

Abstract

Many farming-system studies have investigated the design and evaluation of crop-management practices with respect to economic performance and reduction in environmental impacts. In contrast, little research has been devoted to analysing these practices in terms of matching the recurrent context-dependent demand for resources (labour in particular) with those available on the farm. This paper presents Dhivine, a simulation model of operational management of grape production at the vineyard scale. Particular attention focuses on representing a flexible plan, which organises activities temporally, the resources available to the vineyard manager and the process of scheduling and executing the activities. The model relies on a generic production-system ontology used in several agricultural production domains. The types of investigations that the model supports are briefly illustrated. The enhanced realism of the production-management situations simulated makes it possible to examine and understand properties of resource-constrained work-organisation strategies and possibilities for improving them.

Agricultural production systems modelling and software: Current status and future prospects

“…This theme arose in many guises ranging from models that are frameworks themselves St€ ockle et al, 2014), that are applications within frameworks (Bergez et al, 2014;Donatelli et al, 2014;Herrmann et al, 2014;Martin-Clouaire et al, 2014;Whish et al, 2014) or that plead the need for frameworks with greater interoperability for various reasons (Elliott et al, 2014;McNider et al, 2014;Porter et al, 2014;Snow et al, 2014). The issue of interoperability was originally flagged by Donatelli et al (2002).…”

“…Whish et al (2014) also discuss the issues associated with additional trophic levels but here they are concerned with the pests and diseases of cropping systems. In modelling more complex systems, there is also increased demand for improved methods to model or consider management decisions as described by Martin-Clouaire et al (2014) and Moore et al (2014). It seems likely that this emergence of more complex modelling problems has partially arisen from increased capability of the simulation models and their user interfaces, but it is also likely that as we address emerging issues associated with ecosystem services (Balbi et al, 2014;Le et al, 2014) in the face of climate change.…”

On‐the‐go assessment of vineyard canopy porosity, bunch and leaf exposure by image analysis

“…Other authors have used RGB imagery acquired at night time from an autonomous moving platform, at low speed (0.3 km/h) to reconstruct a textured three‐dimensional (3D) point cloud to quantify yield components (Rose et al ). While the merit of these initiatives is valuable, the implementation of precision viticulture and variable rate machinery demands a non‐invasive system capable of assessing the main canopy features on‐the‐go, at a speed that is commercially acceptable (3–7 km/h) (Martin‐Clouaire et al ).…”

Evaluating Strategies for Adaptation to Climate Change in Grapevine Production–A Systematic Review

“…The few studies that integrated decision-making into their models are based on agent-based modeling (Delay et al, 2015 ; Tissot et al, 2017 ). Other decision models developed in viticulture could be adapted to climate change studies: VERDI (Ripoche et al, 2011 ), or DHIVINE (Martin-Clouaire et al, 2016 ). However, Corbeels et al ( 2018 ) recently challenged the ability of crop models driven by climate model projections to identify promising adaptation, given the large uncertainties of model predictions.…”