scholarly journals Yield of Müller-Thurgau and Riesling grapevines is altered by meteorological conditions in the current and previous growing seasons

OENO One ◽  
2017 ◽  
Vol 50 (4) ◽  
Author(s):  
Markus Keller ◽  
Daniel Molitor
Keyword(s):  
Meteorological Data ◽  
Early Spring ◽  
Meteorological Conditions ◽  
Data Set ◽  
Growing Seasons ◽  
Winter Temperatures ◽  
Maximum Temperatures ◽  
Interannual Fluctuations ◽  
Yield Formation ◽  
The Impact

<p style="text-align: justify;"><strong>Background and Aims:</strong></p><p style="text-align: justify;">Grape yields show distinct interannual fluctuations caused by environmental conditions. Statistical investigations based on a 22-year data set (1993-2015) of annual yields of two grape cultivars grown in Luxembourg aimed at (i) investigating the impact of meteorological conditions during specific phases of yield formation, (ii) identifying meteorological conditions with predictive value for annual grape yield, and (iii) developing models to simulate yield based on meteorological data.</p><p class="Tabelle" style="text-align: justify;"><strong>Methods and Results:</strong></p><p class="Tabelle" style="text-align: justify;">Window pane analysis showed that pre-bloom and bloom minimum temperatures and precipitation sums in the preceding year, winter temperatures, spring temperatures, and post-veraison minimum temperatures in the current year were positively correlated with annual yield; early spring and post-harvest temperatures in the preceding year, and, for Riesling, pre-bloom precipitation sums and post-bloom maximum temperatures in the current year were negatively correlated with annual yield. Models developed from these data simulated annual yield with high accuracy (R<sup>2</sup><sub>adj</sub> = 0.88 for Riesling, and R<sup>2</sup><sub>adj</sub> = 0.92 for Müller-Thurgau).</p><p class="Tabelle" style="text-align: justify;"><strong>Conclusions:</strong></p><p style="text-align: justify;">Meteorological conditions during distinct periods of yield formation are correlated with annual yield. Yield models can be used in practical viticulture as well as in climate change impact studies.</p><p class="Tabelle" style="text-align: justify;"><strong>Significance of the study:</strong></p><p class="Tabelle" style="text-align: justify;">Enhanced understanding of the effects of meteorological conditions during specific periods of yield formation supports growers’ efforts to optimize viticultural measures aimed at achieving adequate yield levels.</p>

Soil-atmosphere CO2 and CH4 fluxes in a nutrient-poor drained peatland forest in boreal Sweden

2020 ◽  
Author(s):  
Järvi Järveoja ◽  
Matthias Peichl ◽  
Mats B. Nilsson
Keyword(s):  
Individual Component ◽  
Net Primary Production ◽  
Meteorological Data ◽  
Management Strategies ◽  
Ghg Emissions ◽  
Vegetation Removal ◽  
Growing Seasons ◽  
Soil Atmosphere ◽  
Abiotic Controls ◽  
The Impact

&lt;p&gt;In countries such as Sweden, where between 1.5 and 2.0 million hectares of natural peatlands have been drained for forestry purposes, knowledge on soil-atmosphere greenhouse gas (GHG) fluxes from these areas is required for national GHG accounting as well as for identifying suitable management strategies (e.g. forestry vs rewetting) to reduce GHG emissions. In this study, we applied the manual chamber method (incl. clear and dark chambers) to investigate the soil-atmosphere carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;) and methane (CH&lt;sub&gt;4&lt;/sub&gt;) exchanges in a nutrient-poor drained peatland forest in boreal Sweden over two growing seasons (2018-2019). Combined with an array of vegetated and vegetation-removal plots we further partitioned the soil-atmosphere CO&lt;sub&gt;2&lt;/sub&gt; exchange into its individual component fluxes of heterotrophic and autotrophic respiration as well as gross and net primary production. In addition, we collected soil environmental, vegetation and meteorological data to determine the key biotic and abiotic controls of these fluxes. All measurements were carried out along multiple transects at 5, 25 and 50 m distances from the main drainage ditch to explore their spatial variability. For comparison, we used similar GHG flux data from an automated chamber system at the nearby natural Deger&amp;#246; mire. We found divergent magnitudes and patterns in the soil-atmosphere CO&lt;sub&gt;2&lt;/sub&gt; exchange and its component fluxes between the drained peatland forest and the natural mire, altogether resulting in a close-to-zero soil-atmosphere CO&lt;sub&gt;2&lt;/sub&gt; balance at the drained site compared to a net CO&lt;sub&gt;2&lt;/sub&gt; uptake at the mire. The CH&lt;sub&gt;4&lt;/sub&gt; emissions from the drained peatland forest were significantly reduced compared to the natural mire; however, due to a relatively high mean water table level the drained site continued to act as a persistent CH&lt;sub&gt;4&lt;/sub&gt; source. Overall, these detailed data will serve as a baseline for evaluating the impact of future rewetting activities (planned for 2020 at the site) on the GHG balance and will provide the various forest stakeholders valuable decision-support for developing sustainable and climate-responsible forest management strategies.&lt;/p&gt;

scholarly journals VINESYM: an integrated vine and grapevine mathematical model for vegetative development and production quality forecast

2019 ◽  
Vol 13 ◽  
pp. 02005
Author(s):  
Vittorio Faluomi ◽  
Iacopo Borsi
Keyword(s):  
Mathematical Model ◽  
Acid Content ◽  
Vegetative Growth ◽  
Meteorological Data ◽  
Mechanistic Model ◽  
Sugar Accumulation ◽  
Quality Model ◽  
Data Set ◽  
Carbon Demand ◽  
The Impact

The present work deal with the development of a mathematical model able to predict, using time dependent meteorological data, soil and vine characteristics, the growing of a vine and grapevine in terms of leaf area, shoot length, fruit and vegetative mass and finally sugar and acid content of the berry. The model is based upon a source-sink relationship approach, integrated with a soil-atmosphere model, where water accumulation in soil, sap flow across vine are coupled with potential carbon demand functions to directly consider possible water and temperature stresses. The model includes also a N2 sink-source approach, limiting growth rate following N2 availability. Finally, a mechanistic model to evaluate sugar accumulation and a correlation-based model for acid concentration evaluation in the berry is coupled with vegetative growth, to provide the information required to manage vineyard operations and evaluate the impact to the potential wine quality. The primary distinctive trait of this model is then the integration and feedback among prediction of grapevine quality model (sugar an acid content) and vegetative growth model, using a common initial ad boundary conditions data set.

scholarly journals Application of the Data Mining Methods to Assess the Impact of Meteorological Conditions on the Episodes of High Concentrations of PM10 along the Polish – Czech Border

2018 ◽  
Vol 28 ◽  
pp. 01027
Author(s):  
Leszek Ośródka ◽  
Ewa Krajny ◽  
Marek Wojtylak
Keyword(s):  
Data Mining ◽  
Air Pollution ◽  
Particulate Matter ◽  
Concentration Level ◽  
Meteorological Data ◽  
Meteorological Conditions ◽  
Mining Methods ◽  
High Concentrations ◽  
The Impact ◽  
Derivatives Of

The paper presents an attempt to use selected data mining methods to determine the influence of a complex of meteorological conditions on the concentrations of PM10 (PM2.5) proffering the example of the regions of Silesia and Northern Moravia. The collection of standard meteorological data has been supplemented by increments and derivatives of measurable weather elements such as vertical pseudo-gradient of air temperature. The main objective was to develop a universal methodology for the assessment of these impacts, i.e. one that would be independent of the analysed pollution. The probability of occurrence (at a given location) of the assumed concentration level as exceeding the value of the specified distributional quintile was adopted as the discriminant of the incidence. As a result of the analyses conducted, incidences of elevated concentrations of air pollution particulate matter PM10 have been identified and the types of weather responsible for the emergence of such situations have also been determined.

scholarly journals Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations

2016 ◽  
Vol 20 (7) ◽  
pp. 3059-3076 ◽  
Author(s):  
Patricia López López ◽  
Niko Wanders ◽  
Jaap Schellekens ◽  
Luigi J. Renzullo ◽  
Edwin H. Sutanudjaja ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Observation System ◽  
Data Set ◽  
Coarse Resolution ◽  
The Impact

Abstract. The coarse spatial resolution of global hydrological models (typically  >  0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally tuned river models. A possible solution to the problem may be to drive the coarse-resolution models with locally available high-spatial-resolution meteorological data as well as to assimilate ground-based and remotely sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study, we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee River basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with downscaled global meteorological data (downscaled from 0.5° to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high-resolution, gauging-station-based gridded data set (0.05°). Downscaled satellite-derived soil moisture (downscaled from  ∼  0.5° to 0.08° resolution) from the remote observation system AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE). Furthermore, results show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. We conclude that it is possible to improve PCR-GLOBWB simulations forced by coarse-resolution meteorological data with assimilation of downscaled spaceborne soil moisture and streamflow observations. These improved model results are close to the ones from a local model forced with local meteorological data. These findings are important in light of the efforts that are currently made to move to global hyper-resolution modelling and can help to advance this research.

scholarly journals Influence of temperature and moisture conditions of locality on the yield formation of sunflower (Helianthus annuus L.)

2011 ◽  
Vol 59 (6) ◽  
pp. 99-104 ◽  
Author(s):  
Ivan Černý ◽  
Alexandra Veverková ◽  
Marek Kovár ◽  
Vladimír Pačuta ◽  
Juliana Molnárová
Keyword(s):  
Helianthus Annuus ◽  
Soil Analysis ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Technological System ◽  
Characteristic Analysis ◽  
Mild Winter ◽  
Moisture Conditions ◽  
Yield Formation ◽  
The Impact

Field polyfactorial experiments were realized on fields of the Agricultural Co-operative in Nitrianska Blatnica in years 2007−2009. Experimental field is located in the maize production area (climatic region: warm; climatic sub region dry; climatic zone: warm, dry with mild winter and long sunshine) in altitude 250 m above sea level, with brown soil. We observed the influence of both temperature and moisture conditions of experimental area on sunflower yield of achenes (conventional, medium-late hybrids: NK Brio, NK Armoni). Preceding crop of sunflower (Helianthus annuus L.) every experimental year was wheat (Triticum aestivum L.). Technological system of sunflower cultivation was realized in accordance with conventional technology of cultivation. The basic fertilization was made by balance method on the base of agrochemical soil analysis for expected yield 3 t.ha−1. The meteorological data were got out from agrometeorological station of the Central Controlling and Testing Institute in Agriculture in Veľké Ripňany. During every experimental year the change of inner energy (ΔU) was evaluated for thermodynamic characteristic analysis (security of the temperature and moisture) and the impact of changes on yield forming with maximal yield (Ymax in 2008) and minimal yield (Ymin in 2009). Achieved value of yield from thermal and precipitation energy introduces concrete energy amount, which is available in given period for concrete height of yield. From the results follow, the sunflower has got critical thermodynamic phase in the period of months from July to August. For the yield formation is requirement, that input power of precipitation prevailed over the thermal during moths July to August. Achieved results confirmed statistically high significant dependence of the yield on weather conditions and for high annual variations in climatic characteristics the consideration is needed about potential changes some agrotechnological measures of technological system of sunflower cultivation.

scholarly journals A field study to estimate the impact of noise barriers on mitigation of near road air pollution

2021 ◽  
Author(s):  
Ranga Rajan Thiruvenkatachari ◽  
Yifan Ding ◽  
David Pankratz ◽  
Akula Venkatram
Keyword(s):  
Air Pollution ◽  
Field Study ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Dispersion Models ◽  
Noise Barriers ◽  
Data Set ◽  
Wind Speeds ◽  
Sonic Anemometers ◽  
The Impact

AbstractAir pollution associated with vehicle emissions from roadways has been linked to a variety of adverse health effects. Wind tunnel and tracer studies show that noise barriers mitigate the impact of this pollution up to distances of 30 times the barrier height. Data from these studies have been used to formulate dispersion models that account for this mitigating effect. Before these models can be incorporated into Federal and State regulations, it is necessary to demonstrate their applicability under real-world conditions. This paper describes a comprehensive field study conducted in Riverside, CA, in 2019 to collect the data required to evaluate the performance of these models. Eight vehicles fitted with SF6 tracer release systems were driven in a loop on a 2-km stretch of Interstate 215 that had a 5-m tall noise barrier on the downwind side. The tracer, SF6, was sampled at over 40 locations at distances ranging from 5 to 200 m from the barrier. Meteorological data were measured with several 3-D sonic anemometers located upwind and downwind of the highway. The data set, corresponding to 10 h collected over 4 days, consists of information on emissions, tracer concentrations, and micrometeorological variables that can be used to evaluate barrier effects in dispersion models. An analysis of the data using a dispersion model indicates that current models are likely to overestimate concentrations, or underestimate the mitigation from barriers, at low wind speeds. We suggest an approach to correct this problem.

scholarly journals Impact of Early Spring Weather Factors on the Risk of Tomato Spotted Wilt in Peanut

Plant Disease ◽  
2009 ◽  
Vol 93 (8) ◽  
pp. 783-788 ◽  
Author(s):  
R. O. Olatinwo ◽  
J. O. Paz ◽  
S. L. Brown ◽  
R. C. Kemerait ◽  
A. K. Culbreath ◽  
...  
Keyword(s):  
Frankliniella Occidentalis ◽  
Meteorological Data ◽  
Monitoring Network ◽  
Early Spring ◽  
Daily Minimum Temperature ◽  
Economic Losses ◽  
Weather Factors ◽  
Management Options ◽  
Tomato Spotted Wilt ◽  
The Impact

Peanut growers in the southeastern United States have suffered significant economic losses due to spotted wilt caused by Tomato spotted wilt virus (TSWV). The virus is transmitted by western flower thrips, Frankliniella occidentalis, and tobacco thrips, F. fusca, and was first reported in the southeast in 1986. The severity of this disease is extremely variable in individual peanut fields, perhaps due to the sensitivity of the vector population to changing weather patterns. The objective of this study was to investigate the impact of early spring weather on spotted wilt risk in peanut. On-farm surveys of spotted wilt severity were conducted in Georgia peanut fields in 1998, 1999, 2002, 2004, and 2005. The percent spotted wilt intensity (%) for cv. Georgia Green was recorded and categorized into three intensity levels: low, moderate, and high. Meteorological data were obtained from the Georgia Automated Environmental Monitoring Network for the period between March 1 and April 30. Statistical analysis was conducted to identify weather variables that had significant impact on spotted wilt intensity. The results indicated a high probability of spotted wilt if the number of rain days during March was greater than or equal to 10 days and planting was before 11 May or after 5 June. The total evapotranspiration in April (>127 mm) and the average daily minimum temperature in March (>6.8°C) similarly increased the risk of spotted wilt. Knowing in advance the level of spotted wilt risk expected in a peanut field could assist growers with evaluating management options and significantly improve the impact of their decisions against spotted wilt risk in peanut.

Examining water storage variations as a function of meteorology using GRACE and GLDAS

2021 ◽  
Author(s):  
Caleb Kelly ◽  
Nicholas Hamm ◽  
Craig Hancock ◽  
Stephen Grebby ◽  
Stuart Marsh
Keyword(s):  
Surface Water ◽  
Water Availability ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Water Storage ◽  
Meteorological Conditions ◽  
P Value ◽  
Increasing Trend ◽  
Terrestrial Water ◽  
The Impact

&lt;p&gt;The Upper East Region (UER) of Ghana, located between 10.2&amp;#8211;11.2&amp;#176;N, 1.6&amp;#176;W&amp;#8211;0.03&amp;#176;E, is characterised by a long dry season and annual floods that are exacerbated by the opening of the Bagre Dam in neighbouring Burkina Faso. The UER lies within the Volta Basin, which has been the subject of numerous hydrological studies. The basin spans several jurisdictions with varying meteorological conditions; thus, basin-wide studies may not truly reflect localised dynamics of water storage over the UER. Data from the Gravity Recovery and Climate Experiment (GRACE) mission and hydrological models, e.g., the Global Land Data Assimilation System (GLDAS), have been used for hydrological studies. Nonetheless, GRACE&amp;#8217;s resolution may restrict its application to large areas (&amp;#8805;150,000 km&lt;sup&gt;2&lt;/sup&gt;) or smaller areas with storage variations of &amp;#8805;8 km&lt;sup&gt;3&lt;/sup&gt;, while GLDAS does not model surface water. With this in mind, this research evaluates GRACE and GLDAS for water storage analysis over the UER (~9000 km&lt;sup&gt;2&lt;/sup&gt;).&lt;/p&gt;&lt;p&gt;We used the latest mass concentration solution from the Centre for Space Research, GLDAS-NOAH, and the Global Precipitation Measurement (GPM) from April 2002 to June 2017. The long-term mean (2004&amp;#8211;2009) was removed from GPM and NOAH. The GRACE time series was characterised by an increasing trend in terrestrial water storage anomalies (TWSA) (6.2 mm/yr), annual and semi-annual amplitudes of 99.4 mm and 10.5 mm, and annual and semi-annual phases of 39.1&amp;#176; and 13.6&amp;#176;, respectively. The minimum variation (-150.8 mm, -47.4 km&lt;sup&gt;3&lt;/sup&gt;) in TWSA occurred in May 2003, while the maximum (222.3 mm, 69.9 km&lt;sup&gt;3&lt;/sup&gt;) occurred in September 2012, both of which are during the rainy season. Rainfall anomalies showed a declining trend at a rate of 0.25 mm/yr. A Pearson correlation coefficient (r) between rainfall and TWSA revealed a low r = 0.30 (p-value &lt;&lt; 0.01 ). Conversely, time-lagged r = 0.60, one and two months after rainfall. The largest (r = 0.66) occurred two months &amp;#160;after rainfall. NOAH-based evapotranspiration anomalies (ETA) indicated a slow, but increasing, trend (0.4 mm/yr). Furthermore NOAH-derived TWSA underestimated storage, yielding a rate of decline of 2.1 mm/yr, which could be due to unmodelled surface water. However, NOAH-derived TWSA were comparatively strongly correlated with rainfall (r = 0.69 and 0.87 at lags 0 and 1). As rainfall is the only source of input to the water balance equation and as rates of ETA suggest conditions in the UER support water loss, these results may indicate a strong contribution to TWSA from the yet unmodelled water from the Bagre Dam.&lt;/p&gt;&lt;p&gt;This study was the first to investigate the impact of meteorological conditions on water availability in the UER using GRACE and GLDAS. The results show that GLDAS-NOAH underestimated storage, and that TWSA increased, although this increase is not entirely explained by rainfall. Subsequent experiments will incorporate the contribution of water from the Bagre Dam as well as other meteorological data (e.g., wind speed, humidity) to better explain the differences in those parameters and fully characterise the impact of meteorological conditions on water availability in the UER.&amp;#160;&lt;/p&gt;

Association between air pollution and chronic rhinosinusitis: a nested case-control study using meteorological data and national health screening cohort data

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
J.H. Wee ◽  
C. Min ◽  
H.J. Jung ◽  
M.W. Park ◽  
H.G. Choi
Keyword(s):  
Air Pollution ◽  
Chronic Rhinosinusitis ◽  
National Health ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Health Screening ◽  
Meteorological Conditions ◽  
Control Group ◽  
Temperature Range ◽  
The Impact

Background: Inconsistent results about the effect of air pollution on chronic rhinosinusitis (CRS) have been reported. This study aimed to evaluate the impact of meteorological conditions/air pollution on the prevalence of CRS in adult Koreans. Methodology: The data from the Korean National Health Insurance Service-Health Screening Cohort from 2002 through 2015 were used. A CRS group (defined as ICD-10 codes J32, n=6159) was matched with a control group (n=24,636) in 1:4 ratios by age, sex, income, and region of residence. The meteorological conditions and air pollution data included the daily mean, highest, and lowest temperature (°C), daily temperature range (°C), relative humidity (%), ambient atmospheric pressure (hPa), sunshine duration (hr), and the rainfall (mm), SO2 (ppm), NO2 (ppm), O3 (ppm), CO (ppm), and PM10 (μg/m3) levels before the CRS diagnosis. Crude and adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for CRS were analyzed using logistic regression analyses. Results: When the NO2 level increased by 0.1 ppm, the odds for CRS increased 5.40 times, and when the CO level increased by 1 ppm and PM10 increased by 10 μg/m3, the odds for CRS decreased 0.75 times and 0.93 times, respectively. Other meteorological conditions, such as the mean/highest/lowest temperature, temperature range, rainfall and other air pollution, such as SO2 and O3, were not statistically significant. NO2 for 90 days before the index date increased the risk of CRS in all subgroups, except for the nasal polyp and older age subgroups. Conclusion: CRS is related to high concentrations of NO2.

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