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: closed (15 October 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

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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.

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Keywords

  • 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 (4 papers)

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Research

Open AccessArticle Environmentally-Related Cherry Root Cambial Plasticity
Atmosphere 2018, 9(9), 358; https://doi.org/10.3390/atmos9090358
Received: 10 August 2018 / Revised: 12 September 2018 / Accepted: 14 September 2018 / Published: 17 September 2018
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Abstract
The general aim of this research was to determine whether the cherry root cambium possesses similar water-stress adaptation abilities as the scion. Specifically, this study aimed to determine whether there is a shift in root xylem structure due to precipitation fluctuations and temperature
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The general aim of this research was to determine whether the cherry root cambium possesses similar water-stress adaptation abilities as the scion. Specifically, this study aimed to determine whether there is a shift in root xylem structure due to precipitation fluctuations and temperature increase during the growing season in two cherry species. Oblačinska sour cherry and European ground cherry roots with secondary structure were anatomically surveyed in detail, and correlated with meteorological conditions occurring during the vegetation when the roots were formed. Under environmental signals, both investigated species altered their radial root growth imprinting stops and starts in a cambial activity that resulted in the occurrence of intra-annual false growth rings. Changing environmental conditions triggered the shifts of large and small vessels throughout the false growth rings, but their size seemed to be mainly genetically controlled. Taking into consideration all the above, genotypes with moderate vessel lumen area—lesser or around 1200 μm2 in the inner zone, as well as no greater than 1500 μm2 in the outer zone—are presumed to be both size-controlling and stable upon the drought events. Thus, further field trials will be focused on the SV2 European ground cherry genotype, and OV13, OV32, and OV34 Oblačinska sour cherry genotypes. Full article
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Open AccessArticle A Statistical Parameter Correction Technique for WRF Medium-Range Prediction of Near-Surface Temperature and Wind Speed Using Generalized Linear Model
Atmosphere 2018, 9(8), 291; https://doi.org/10.3390/atmos9080291
Received: 12 June 2018 / Revised: 18 July 2018 / Accepted: 23 July 2018 / Published: 27 July 2018
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Abstract
A statistical post-processing method was developed to increase the accuracy of numerical weather prediction (NWP) and simulation by matching the daily distribution of predicted temperatures and wind speeds using the generalized linear model (GLM) and parameter correction, considering an increase in model bias
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A statistical post-processing method was developed to increase the accuracy of numerical weather prediction (NWP) and simulation by matching the daily distribution of predicted temperatures and wind speeds using the generalized linear model (GLM) and parameter correction, considering an increase in model bias when the range of the prediction time lengthens. The Land Atmosphere Modeling Package Weather Research and Forecasting model, which provides 12-day agrometeorological predictions for East Asia, was employed from May 2017 to April 2018. Training periods occurred one month prior to and after the test period (12 days). A probabilistic consideration accounts for the relatively short training period. Based on the total and monthly root mean square error values for each test site, the results show an improvement in the NWP accuracy after bias correction. The spatial distributions in July and January were compared in detail. It was also shown that the physical consistency between temperature and wind speed was retained in the correction procedure, and that the GLM exhibited better performance than the quantile matching method based on monthly Pearson correlation comparison. The characteristics of coastal and mountainous sites are different from inland automatic weather stations, indicating that supplements to cover these distinctive topographic locations are necessary. Full article
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Open AccessArticle Effects of Different Spatial Precipitation Input Data on Crop Model Outputs under a Central European Climate
Atmosphere 2018, 9(8), 290; https://doi.org/10.3390/atmos9080290
Received: 30 May 2018 / Revised: 16 July 2018 / Accepted: 23 July 2018 / Published: 26 July 2018
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Abstract
Crop simulation models, which are mainly being utilized as tools to assess the consequences of a changing climate and different management strategies on crop production at the field scale, are increasingly being used in a distributed model at the regional scale. Spatial data
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Crop simulation models, which are mainly being utilized as tools to assess the consequences of a changing climate and different management strategies on crop production at the field scale, are increasingly being used in a distributed model at the regional scale. Spatial data analysis and modelling in combination with geographic information systems (GIS) integrates information from soil, climate, and topography data into a larger area, providing a basis for spatial and temporal analysis. In the current study, the crop growth model Decision Support System for Agrotechnology Transfer (DSSAT) was used to evaluate five gridded precipitation input data at three locations in Austria. The precipitation data sets consist of the INtegrated Calibration and Application Tool (INCA) from the Meteorological Service Austria, two satellite precipitation data sources—Multisatellite Precipitation Analysis (TMPA) and Climate Prediction Center MORPHing (CMORPH)—and two rainfall estimates based on satellite soil moisture data. The latter were obtained through the application of the SM2RAIN algorithm (SM2RASC) and a regression analysis (RAASC) applied to the Metop-A/B Advanced SCATtermonter (ASCAT) soil moisture product during a 9-year period from 2007–2015. For the evaluation, the effect on winter wheat and spring barley yield, caused by different precipitation inputs, at a spatial resolution of around 25 km was used. The highest variance was obtained for the driest area with light-textured soils; TMPA and two soil moisture-based products show very good results in the more humid areas. The poorest performances at all three locations and for both crops were found with the CMORPH input data. Full article
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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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Abstract
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
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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 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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