Special Issue "Weather and Climate Change Challenges in Agricultural and Forest Meteorology"

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Climatology and Meteorology".

Deadline for manuscript submissions: 31 July 2018

Special Issue Editors

Guest Editor
Prof. Dr. Branislava Lalic

Faculty of Agriculture, University of Novi Sad, Dositej Obradovic Sq. 8, 21000 Novi Sad, Serbia
Website | E-Mail
Interests: micrometeorology; biosphere–atmoshere interaction; agricultural meteorology; modeling dynamical systems
Guest Editor
Prof. Dr. Josef Eitzinger

Institute of Meteorology of the Department of Water, Atmosphere and Environment, University of Natural Resources and Life Sciences (BOKU), Vienna, Gregor-Mendel Str. 33, A-1180 Vienna, Austria
Website | E-Mail
Interests: agricultural meteorology; agroclimatology; microclimatology; remote sensing in agricultural meteorology; simulation models (agro-ecosystems, crops)
Guest Editor
Prof. Dr. Simone Orlandini

Department of Agrifood Production and Environmental Sciences - University of Florence Piazzale delle Cascine 18, 50144, Firenze. Italy
Website | E-Mail
Interests: agronomy; agricultural meteorology; agroclimatology; precision agriculture; modelling; sustainability

Special Issue Information

Dear Colleagues,

Agricultural and Forest Meteorology, as an application oriented science field of linking different disciplines and considering the whole biomass and food production systems, plays a key role in global food security, sustainable use of natural resources, ecosystem stability, biodiversity, and more, all affecting welfare of human kind. During the next few decades, global food and biomass demand will increase, setting further challenges for sustainable and effective use of the limited natural resources under manifold regional conditions in agriculture and forestry. Due to its significant land use share, relationships with climate system can be a key factor in GHG and extreme weather mitigation as well.

Due to the global digitalization trend, many methods developed in the past can be applied more efficiently and can better serve stakeholders in their needs. New data sources achievable from remote sensing, ground-based measurement systems, and the increasing performance of data mangement systems in combination with modelling tools, promise many useful applications in agriculture and forestry, not only for high input, but also for low input farming systems. Although there are still many gaps in agrometeorological databases, and weaknesses in applied methods or the transfer of information to farmers and its meaningful use, promising progress is also visible. In this context, we feel that this Special Issue of Atmosphere can contribute to the state-of-the-art and development of new ideas, especially in combination with challenges and new developments in meteorology applied for agriculture and forestry needs.

We invite contributions, especially in the field of atmospheric physics and meteorology relevant for agriculture and forestry (and its environmental interactions) considering global and climate change conditions, as well as application oriented research, including impact modelling, monitoring and forecasting (short and long term) supporting decision makers and stakeholders in better adapting to adverse weather conditions or changing climate.

Prof. Dr. Branislava Lalic
Prof. Dr. Josef Eitzinger
Prof. Dr. Simone Orlandini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Weather risks for agriculture and forestry
  • Agro- and Biometeorological modelling
  • (Agro)meteorological monitoring, forecasting and warning methods and tools
  • Climate change impacts and mitigation/adaptation in agriculture and forestry
  • Agriculture and forest land use–atmosphere interaction at different scales
  • Agroforestry
  • Climate smart agriculture and forestry
  • Weather and climate related impacts on agronomy and food risks and similar topics

Published Papers (1 paper)

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Open AccessArticle Comprehensive Evaluation of Machine Learning Techniques for Estimating the Responses of Carbon Fluxes to Climatic Forces in Different Terrestrial Ecosystems
Atmosphere 2018, 9(3), 83; https://doi.org/10.3390/atmos9030083
Received: 15 December 2017 / Revised: 11 February 2018 / Accepted: 22 February 2018 / Published: 25 February 2018
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Accurately estimating the carbon budgets in terrestrial ecosystems ranging from flux towers to regional or global scales is particularly crucial for diagnosing past and future climate change. This research investigated the feasibility of two comparatively advanced machine learning approaches, namely adaptive neuro-fuzzy inference
[...] Read more.
Accurately estimating the carbon budgets in terrestrial ecosystems ranging from flux towers to regional or global scales is particularly crucial for diagnosing past and future climate change. This research investigated the feasibility of two comparatively advanced machine learning approaches, namely adaptive neuro-fuzzy inference system (ANFIS) and extreme learning machine (ELM), for reproducing terrestrial carbon fluxes in five different types of ecosystems. Traditional artificial neural network (ANN) and support vector machine (SVM) models were also utilized as reliable benchmarks to measure the generalization ability of these models according to the following statistical metrics: coefficient of determination (R2), index of agreement (IA), root mean square error (RMSE), and mean absolute error (MAE). In addition, we attempted to explore the responses of all methods to their corresponding intrinsic parameters in terms of the generalization performance. It was found that both the newly proposed ELM and ANFIS models achieved highly satisfactory estimates and were comparable to the ANN and SVM models. The modeling ability of each approach depended upon their respective internal parameters. For example, the SVM model with the radial basis kernel function produced the most accurate estimates and performed substantially better than the SVM models with the polynomial and sigmoid functions. Furthermore, a remarkable difference was found in the estimated accuracy among different carbon fluxes. Specifically, in the forest ecosystem (CA-Obs site), the optimal ANN model obtained slightly higher performance for gross primary productivity, with R2 = 0.9622, IA = 0.9836, RMSE = 0.6548 g C m−2 day−1, and MAE = 0.4220 g C m−2 day−1, compared with, respectively, 0.9554, 0.9845, 0.4280 g C m−2 day−1, and 0.2944 g C m−2 day−1 for ecosystem respiration and 0.8292, 0.9306, 0.6165 g C m−2 day−1, and 0.4407 g C m−2 day−1 for net ecosystem exchange. According to the findings in this study, we concluded that the proposed ELM and ANFIS models can be effectively employed for estimating terrestrial carbon fluxes. Full article

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