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Weather and Climate Change Challenges in Agricultural and Forest Meteorology

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

Deadline for manuscript submissions: closed (15 October 2018) | Viewed by 53655

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Special Issue Editors

Prof. Dr. Branislava Lalic

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Guest Editor

Faculty of Agriculture, University of Novi Sad, Dositej Obradovic Sq. 8, 21000 Novi Sad, Serbia
Interests: micrometeorology; biosphere–atmoshere interaction; agricultural meteorology; modeling dynamical systems

Prof. Dr. Josef Eitzinger

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Guest Editor

Institute of Meteorology and Climatology, University of Natural Resources and Life Sciences, Gregor-Mendel-Straße 33, 1180 Wien, Austria
Interests: agricultural meteorology; agroclimatology; microclimatology; remote sensing in agricultural meteorology; simulation models (agro-ecosystems, crops)
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Prof. Dr. Simone Orlandini

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Guest Editor

Department of Agriculture, Food, Environment and Forestry (DAGRI) - University of Florence, Piazzale delle Cascine 18, 50144 Firenze, Italy
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 submissions that pass pre-check are 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.

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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 (10 papers)

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Research

15 pages, 1357 KiB

Open AccessArticle

The Response of Spring Barley (Hordeum vulgare L.) to Climate Change in Northern Serbia

by Milena Daničić, Vladislav Zekić, Milan Mirosavljević, Branislava Lalić, Marina Putnik-Delić, Ivana Maksimović and Anna Dalla Marta

Atmosphere 2019, 10(1), 14; https://doi.org/10.3390/atmos10010014 - 05 Jan 2019

Cited by 9 | Viewed by 5256

Abstract

The present study assessed the effect of projected climate change on the sowing time, onset, and duration of flowering, the duration of the growing season, and the grain yield of spring barley in Northern Serbia. An AquaCrop simulation covered two climate model integration periods (2001–2030 and 2071–2100) using a dual-step approach (with and without irrigation). After considering the effect of climate change on barley production, the economic benefit of future supplemental irrigation was assessed. The model was calibrated and validated using observed field data (2006–2014), and the simulation’s outcomes for future scenarios were compared to those of the baseline period (1971–2000) that was used for the expected climate analysis. The results showed that the projected features of barley production for the 2001–2030 period did not differ much from current practice in this region. On the contrary, for the 2071–2100 period, barley was expected to be sown earlier, to prolong its vegetation, and to shorten flowering’s duration. Nevertheless, its yield was expected to remain stable. An economic feasibility assessment of irrigation in the future indicated a negative income, which is why spring barley will most likely remain rain-fed under future conditions. Full article

(This article belongs to the Special Issue Weather and Climate Change Challenges in Agricultural and Forest Meteorology)

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24 pages, 18986 KiB

Open AccessArticle

Filling Gaps in Hourly Air Temperature Data Using Debiased ERA5 Data

by Miloš Lompar, Branislava Lalić, Ljiljana Dekić and Mina Petrić

Atmosphere 2019, 10(1), 13; https://doi.org/10.3390/atmos10010013 - 04 Jan 2019

Cited by 15 | Viewed by 6911

Abstract

Missing data in hourly and daily temperature data series is a common problem in long-term data series and many observational networks. Agricultural and environmental models and climate-related tools can be used only if weather data series are complete. To support user communities, a technique for gap filling is developed based on the debiasing of ERA5 reanalysis data, the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses of the global climate. The debiasing procedure includes in situ measured temperature. The methodology is tested for different landscapes, latitudes, and altitudes, including tropical and midlatitudes. An evaluation of results in terms of root mean square error (RMSE) obtained using hourly and daily data is provided. The study shows very low average RMSE for all gap lengths ranging from 1.1 °C (Montecristo, Italy) to 1.9 °C (Gumpenstein, Austria). Full article

(This article belongs to the Special Issue Weather and Climate Change Challenges in Agricultural and Forest Meteorology)

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18 pages, 1081 KiB

Open AccessArticle

The Effect of Cover Crops on Soil Water Balance in Rain-Fed Conditions

by Đorđe Krstić, Svetlana Vujić, Goran Jaćimović, Paride D’Ottavio, Zoran Radanović, Pero Erić and Branko Ćupina

Atmosphere 2018, 9(12), 492; https://doi.org/10.3390/atmos9120492 - 11 Dec 2018

Cited by 21 | Viewed by 7075

Abstract

Soil and water conservation benefits of cover crops have been hypothesized as a way to mitigate and adapt to changing climatic conditions, but they can also have detrimental effects if rainfall is limited. Our objective was to quantify effects of winter cover crops on soil water storage and yield of silage maize under the agro-ecological conditions within Vojvodina Province in Serbia. The experiment was conducted under rain-fed conditions at three locations and included a control (bare fallow) plus three cover crop and two N rate treatments. The cover crop treatments were common vetch (Vicia sativa L.), triticale (x Triticosecale Wittm. ex A. Camus) and a mixture of the two species. All were managed as green manure and subsequently fertilized with either 120 or 160 kg N ha⁻¹ before planting silage maize (Zea mays L.). Cover crop effects on soil water storage were calculated for two periods, March–May and May–September/October. A Standardized Precipitation Index (SPI) used to characterize drought severity for 2011/2012 and 2012/2013, showed values of 3 and 9, respectively, for the two periods. Soil water storage was reduced by all cover crop treatments, with the greatest deficiency occurring during the extremely dry year of 2012. Previous studies have shown cover crop growth reduced by soil water depletion during their growing season and negative effects on early-season growth and development of subsequent cash crops such as silage maize, but if rainfall is extremely low it can also reduce cash crop yield. This detrimental effect of cover crops on soil water balance was confirmed by correlations between soil water storage and maize silage yield. Full article

(This article belongs to the Special Issue Weather and Climate Change Challenges in Agricultural and Forest Meteorology)

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17 pages, 3876 KiB

Open AccessArticle

Toward a Weather-Based Forecasting System for Fire Blight and Downy Mildew

by Ana Firanj Sremac, Branislava Lalić, Milena Marčić and Ljiljana Dekić

Atmosphere 2018, 9(12), 484; https://doi.org/10.3390/atmos9120484 - 07 Dec 2018

Cited by 2 | Viewed by 4410

Abstract

The aim of this research is to present a weather-based forecasting system for apple fire blight (Erwinia amylovora) and downy mildew of grapevine (Plasmopara viticola) under Serbian agroecological conditions and test its efficacy. The weather-based forecasting system contains Numerical Weather Prediction (NWP) model outputs and a disease occurrence model. The weather forecast used is a product of the high-resolution forecast (HRES) atmospheric model by the European Centre for Medium-Range Weather Forecasts (ECMWF). For disease modelling, we selected a biometeorological system for messages on the occurrence of diseases in fruits and vines (BAHUS) because it contains both diseases with well-known and tested algorithms. Several comparisons were made: (1) forecasted variables for the fifth day are compared against measurements from the agrometeorological network at seven locations for three months (March, April, and May) in the period 2012–2018 to determine forecast efficacy; (2) BAHUS runs driven with observed and forecast meteorology were compared to test the impact of forecasted meteorological data; and (3) BAHUS runs were compared with field disease observations to estimate system efficacy in plant disease forecasts. The BAHUS runs with forecasted and observed meteorology were in good agreement. The results obtained encourage further development, with the goal of fully utilizing this weather-based forecasting system. Full article

(This article belongs to the Special Issue Weather and Climate Change Challenges in Agricultural and Forest Meteorology)

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18 pages, 4449 KiB

Open AccessArticle

Expected Changes of Montenegrin Climate, Impact on the Establishment and Spread of the Asian Tiger Mosquito (Aedes albopictus), and Validation of the Model and Model-Based Field Sampling

by Mina Petrić, Branislava Lalić, Igor Pajović, Slavica Micev, Vladimir Đurđević and Dušan Petrić

Atmosphere 2018, 9(11), 453; https://doi.org/10.3390/atmos9110453 - 17 Nov 2018

Cited by 9 | Viewed by 5501

Abstract

Aedes albopictus has become established in many parts of Europe since its introduction at the end of the 20th century. It can vector a range of arboviruses, of which Chikungunya and Dengue are most significant for Europe. An analysis of the expected climate change and the related shift in Köppen zones for Montenegro and impact on the establishment of Ae. albopictus was conducted. Outputs of a mechanistic Aedes albopictus model were validated by 2245 presence/absence records collected from 237 different sites between 2001 and 2014. Finally, model-based sampling was designed and performed at 48 sites in 2015, in a previously unexplored northern part of Montenegro, and results were validated. The Eta Belgrade University (EBU)-Princeton Ocean Model (POM) regional climate model was used with the A2 emissions scenario for the 2001–2030 and 2071–2100 integration periods. The results point to a significant increase in suitability for the mosquito and a vertical shift to higher altitudes by the end of the century. The model showed excellent results with the area under the receiver operating characteristic curve (AUC) of 0.94. This study provides a tool for prioritizing surveillance efforts (model-based surveillance), especially when resources are limited. This is the first published analysis of Climate Change that incorporates observations from the national synoptic grid and the subsequent impact on Ae. albopictus in Montenegro. Full article

(This article belongs to the Special Issue Weather and Climate Change Challenges in Agricultural and Forest Meteorology)

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26 pages, 4523 KiB

Open AccessArticle

The Impact of Adverse Weather and Climate on the Width of European Beech (Fagus sylvatica L.) Tree Rings in Southeastern Europe

by Stefan Stjepanović, Bratislav Matović, Dejan Stojanović, Branislava Lalić, Tom Levanič, Saša Orlović and Marko Gutalj

Atmosphere 2018, 9(11), 451; https://doi.org/10.3390/atmos9110451 - 15 Nov 2018

Cited by 9 | Viewed by 4469

Abstract

European beech (Fagus sylvatica L.) is the most important deciduous tree species in Europe. According to different climate scenarios, there is a relatively high probability of a massive decline in and loss of beech forests in southern Europe and in the southern part of central Europe. Thus, the authors of this study explored the dynamics of tree diameter increments and the influence of extremely dry years on the width of tree rings. This study used dendroecological methods to analyze the growth and diameter increments of European beech trees at locations in Serbia and the Republic of Srpska. The sampling was conducted along the vertical distribution of beech forests, at five sites at the lower limit of the distribution, at five optimal sites of the distribution, and at five sites at the upper limit of the distribution. Long-term analyses indicate that dry conditions during a growing season can reduce tree-ring width, but a reduction in tree growth can be expected as a result of more than one season of unfavorable conditions. Low temperatures in autumn and winter and prolonged winters can strongly affect upcoming vegetation and reduce tree development even under normal thermal conditions during a growing season. Full article

(This article belongs to the Special Issue Weather and Climate Change Challenges in Agricultural and Forest Meteorology)

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16 pages, 1909 KiB

Open AccessArticle

Environmentally-Related Cherry Root Cambial Plasticity

by Mirjana Ljubojević, Ivana Maksimović, Branislava Lalić, Ljiljana Dekić, Tijana Narandžić, Nenad Magazin, Jovana Dulić, Maja Miodragović, Goran Barać and Vladislav Ognjanov

Atmosphere 2018, 9(9), 358; https://doi.org/10.3390/atmos9090358 - 17 Sep 2018

Cited by 6 | Viewed by 3687

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 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

(This article belongs to the Special Issue Weather and Climate Change Challenges in Agricultural and Forest Meteorology)

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14 pages, 4340 KiB

Open AccessArticle

A Statistical Parameter Correction Technique for WRF Medium-Range Prediction of Near-Surface Temperature and Wind Speed Using Generalized Linear Model

by Jinmyeong Jeong and Seung-Jae Lee

Atmosphere 2018, 9(8), 291; https://doi.org/10.3390/atmos9080291 - 27 Jul 2018

Cited by 17 | Viewed by 5447

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 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

(This article belongs to the Special Issue Weather and Climate Change Challenges in Agricultural and Forest Meteorology)

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25 pages, 3263 KiB

Open AccessArticle

Effects of Different Spatial Precipitation Input Data on Crop Model Outputs under a Central European Climate

by Sabina Thaler, Luca Brocca, Luca Ciabatta, Josef Eitzinger, Sebastian Hahn and Wolfgang Wagner

Atmosphere 2018, 9(8), 290; https://doi.org/10.3390/atmos9080290 - 26 Jul 2018

Cited by 15 | Viewed by 4936

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 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 (SM2R_ASC) and a regression analysis (RA_ASC) 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

(This article belongs to the Special Issue Weather and Climate Change Challenges in Agricultural and Forest Meteorology)

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30 pages, 3227 KiB

Open AccessArticle

Comprehensive Evaluation of Machine Learning Techniques for Estimating the Responses of Carbon Fluxes to Climatic Forces in Different Terrestrial Ecosystems

by Xianming Dou and Yongguo Yang

Atmosphere 2018, 9(3), 83; https://doi.org/10.3390/atmos9030083 - 25 Feb 2018

Cited by 18 | Viewed by 4197

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 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

(This article belongs to the Special Issue Weather and Climate Change Challenges in Agricultural and Forest Meteorology)

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