An open platform to build, evaluate and simulate integrated models of farming and agro-ecosystems

Bergez, Jacques-Éric; Chabrier, Patrick; Gary, Christian; Jeuffroy, M. H.; Makowski, David; Quesnel, Gauthier; Ramat, Éric; Raynal, Hélène; Rousse, Nathalie; Wallach, Daniel; Debaeke, P.; Durand, Patrick; Duru, Michel; Dury, Jérôme; Faverdin, Philippe; Gascuel‐Odoux, Chantal; Garçia, Frédérick

doi:10.1016/j.envsoft.2012.03.011

Cited by 86 publications

(54 citation statements)

References 58 publications

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“…The model was implemented in C++ programing language on the RECORD platform (Bergez et al, 2013). To evaluate the model, two data sets were taken from the literature.…”

Section: Model Implementation and Evaluationmentioning

confidence: 99%

Modelling impacts of performance on the probability of reproducing, and thereby on productive lifespan, allow prediction of lifetime efficiency in dairy cows

Phuong

Blavy

Martin

et al. 2016

Animal

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Reproductive success is a key component of lifetime efficiency -which is the ratio of energy in milk (MJ) to energy intake (MJ) over the lifespan, of cows. At the animal level, breeding and feeding management can substantially impact milk yield, body condition and energy balance of cows, which are known as major contributors to reproductive failure in dairy cattle. This study extended an existing lifetime performance model to incorporate the impacts that performance changes due to changing breeding and feeding strategies have on the probability of reproducing and thereby on the productive lifespan, and thus allow the prediction of a cow's lifetime efficiency. The model is dynamic and stochastic, with an individual cow being the unit modelled and one day being the unit of time. To evaluate the model, data from a French study including Holstein and Normande cows fed high-concentrate diets and data from a Scottish study including Holstein cows selected for high and average genetic merit for fat plus protein that were fed high-v. low-concentrate diets were used. Generally, the model consistently simulated productive and reproductive performance of various genotypes of cows across feeding systems. In the French data, the model adequately simulated the reproductive performance of Holsteins but significantly under-predicted that of Normande cows. In the Scottish data, conception to first service was comparably simulated, whereas interval traits were slightly under-predicted. Selection for greater milk production impaired the reproductive performance and lifespan but not lifetime efficiency. The definition of lifetime efficiency used in this model did not include associated costs or herd-level effects. Further works should include such economic indicators to allow more accurate simulation of lifetime profitability in different production scenarios.Keywords: dairy cow, lifetime efficiency, productive lifespan, reproduction, nutrient partitioning Implications This paper extended an existing lifetime performance model to incorporate the impacts that varying milk yield, energy balance and body condition have on the probability of reproducing and the productive lifespan, thereby allowing prediction of a cow's lifetime efficiency. The model adequately simulated the productive and reproductive performance of various genotypes of cows across feeding systems. Thus, it can be used to study the effect of future selection and management on animal performance and efficiency.

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“…The model was implemented in C++ programing language on the RECORD platform (Bergez et al, 2013). To evaluate the model, two data sets were taken from the literature.…”

Section: Model Implementation and Evaluationmentioning

confidence: 99%

Modelling impacts of performance on the probability of reproducing, and thereby on productive lifespan, allow prediction of lifetime efficiency in dairy cows

Phuong

Blavy

Martin

et al. 2016

Animal

View full text Add to dashboard Cite

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“…By decoupling revenues from volatile rainfall patterns, irrigation can lead to substantial reductions of profit variability. Based on this background, many models focusing on agricultural water use have been extended by integrating farmers' preferences against more volatile profits by accounting for risk aversion (Bazzani et al 2005;Bergez et al, 2012;Monticino et al, 2007) 1 . However, besides reducing volatilities of yields and profits, irrigation furthermore also reduces the probability that exceptional low yields or profits occur (e.g.…”

Section: Introductionmentioning

confidence: 99%

Expanding risk consideration in integrated models – The role of downside risk aversion in irrigation decisions

Finger

2013

Environmental Modelling & Software

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“…This ensures greater transparency and ease of maintenance, also providing functionalities such as the test of input data versus their definition prior to computing any simple or integrated evaluation metric. Making it in agreement with the modern developments in software engineering, the component for model evaluation better serves as a convenient means to support collaborative model testing among the network of scientists involved in creating component-oriented models in the agroecological domain Bergez et al 2013). …”

Section: Coupling Between Simulation and Evaluationmentioning

confidence: 99%

Deliberative processes for comprehensive evaluation of agroecological models. A review

Bellocchi

Rivington

Matthews

et al. 2014

Agron. Sustain. Dev.

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The use of biophysical models in agroecology has increased in the last few decades for two main reasons: the need to formalize empirical knowledge and the need to disseminate model-based decision support for decision makers (such as farmers, advisors, and policy makers). The first has encouraged the development and use of mathematical models to enhance the efficiency of field research through extrapolation beyond the limits of site, season, and management. The second reflects the increasing need (by scientists, managers, and the public) for simulation experimentation to explore options and consequences, for example, future resource use efficiency (i.e., management in sustainable intensification), impacts of and adaptation to climate change, understanding market and policy responses to shocks initiated at a biophysical level under increasing demand, and limited supply capacity. Production concerns thus dominate most model applications, but there is a notable growing emphasis on environmental, economic, and policy dimensions. Identifying effective methods of assessing model quality and performance has become a challenging but vital imperative, considering the variety of factors influencing model outputs. Understanding the requirements of stakeholders, in respect of model use, logically implies the need for their inclusion in model evaluation methods. We reviewed the use of metrics of model evaluation, with a particular emphasis on the involvement of stakeholders to expand horizons beyond conventional structured, numeric analyses. Two major topics are discussed: (1) the importance of deliberative processes for model evaluation, and (2) the role computer-aided techniques may play to integrate deliberative processes into the evaluation of agroecological models. We point out that (i) the evaluation of agroecological models can be improved through stakeholder follow-up, which is a key for the acceptability of model realizations in practice, (ii) model credibility depends not only on the outcomes of well-structured, numerically based evaluation, but also on less tangible factors that may need to be addressed using complementary deliberative processes, (iii) comprehensive evaluation of simulation models can be achieved by integrating the expectations of stakeholders via a weighting system of preferences and perception, (iv) questionnaire-based surveys can help understand the challenges posed by the deliberative process, and (v) a benefit can be obtained if model evaluation is conceived in a decisional perspective and evaluation techniques are developed at the same pace with which the models themselves are created and improved. Scientific knowledge hubs are also recognized as critical pillars to advance good modeling practice in relation to model evaluation (including access to dedicated software tools), an activity which is frequently neglected in the context of time-limited framework programs.

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An open platform to build, evaluate and simulate integrated models of farming and agro-ecosystems

Cited by 86 publications

References 58 publications

Modelling impacts of performance on the probability of reproducing, and thereby on productive lifespan, allow prediction of lifetime efficiency in dairy cows

Modelling impacts of performance on the probability of reproducing, and thereby on productive lifespan, allow prediction of lifetime efficiency in dairy cows

Expanding risk consideration in integrated models – The role of downside risk aversion in irrigation decisions

Deliberative processes for comprehensive evaluation of agroecological models. A review

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