The development of a methodology using fuzzy logic to assess the performance of cropping systems based on a case study of maize in the Po Valley

Carozzi, Marco; Bregaglio, Simone; Scaglia, Barbara; Bernardoni, E.; Acutis, Marco; Confalonieri, Roberto

doi:10.1111/sum.12066

Cited by 9 publications

(3 citation statements)

References 33 publications

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“…According to Zhang [13], the fuzzy model obtained the most accurate results in the determination of phosphorus uptake by plants. In the same way, Carozzi et al [71] determined the performance of cropping systems, using corn as case study, and found lower error using fuzzy modeling in comparison to regression analysis. Applications with fuzzy modeling in agriculture have shown results with greater precision, as in the energy savings in a rotary dryer calculated with a fuzzy multivariable control application, which reduced biomass consumption by 9% [72].…”

Section: Discussionmentioning

confidence: 95%

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Fuzzy Modeling Development for Lettuce Plants Irrigated with Magnetically Treated Water

Ferrari Putti,

Cremasco,

Neto

et al. 2023

Plants

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Due to the worldwide water supply crisis, sustainable strategies are required for a better use of this resource. The use of magnetic water has been shown to have potential for improving irrigation efficacy. However, a lack of modelling methods that correspond to the experimental results and minimize error is observed. This study aimed to estimate the replacement rates of magnetic water provided by irrigation for lettuce production using a mathematical model based on fuzzy logic and to compare multiple polynomial regression analysis and the fuzzy model. A greenhouse study was conducted with lettuce using two types of water, magnetic water (MW) and conventional water (CW), and five irrigation levels (25, 50, 75, 100 and 125%) of crop evapotranspiration. Plant samples for biometric lettuce were taken at 14, 21, 28 and 35 days after transplanting. The data were analyzed via multiple polynomial regression and fuzzy mathematical modeling, followed by an inference of the models and a comparison between the methods. The highest biometric values for lettuce were observed when irrigated with MW during the different phenological stage evaluated. The fuzzy model provided a more exact adjustment when compared to the multiple polynomial regressions.

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Section: Discussionmentioning

confidence: 95%

“…Carozzi et al [71] used the fuzzy model for the determination of corn response producing and found that the least error occurred when compared with regression analysis.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy Modeling Development for Lettuce Plants Irrigated with Magnetically Treated Water

Ferrari Putti,

Cremasco,

Neto

et al. 2023

Plants

View full text Add to dashboard Cite

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“…A new evaluation-interpretation cycle can be run any time that new versions (solutions) of the modeling system are developed. Again, a well-designed, component-based evaluation system can be easily extended toward including further evaluation approaches to keep up with evolving methodologies, i.e., statistical or fuzzy-based (e.g., Carozzi et al 2013;Fila et al 2014).…”

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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Modelling fuzzy combination of remote sensing vegetation index for durum wheat crop analysis

Semeraro

Mastroleo

Pomes³

et al. 2019

Computers and Electronics in Agriculture

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The development of a methodology using fuzzy logic to assess the performance of cropping systems based on a case study of maize in the Po Valley

Cited by 9 publications

References 33 publications

Fuzzy Modeling Development for Lettuce Plants Irrigated with Magnetically Treated Water

Fuzzy Modeling Development for Lettuce Plants Irrigated with Magnetically Treated Water

Deliberative processes for comprehensive evaluation of agroecological models. A review

Modelling fuzzy combination of remote sensing vegetation index for durum wheat crop analysis

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