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Viticulture and Winemaking under Climate Change

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ISBN: 9783039219742 9783039219759 Year: Pages: 294 DOI: 10.3390/books978-3-03921-975-9 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Meteorology and Climatology
Added to DOAB on : 2020-01-07 09:08:26
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Abstract

The importance of viticulture and the winemaking socio-economic sector is acknowledged worldwide. The most renowned winemaking regions show very specific environmental characteristics, where climate usually plays a central role. Considering the strong influence of weather and climatic factors on grapevine yields and berry quality attributes, climate change may indeed significantly impact this crop. Recent trends already point to a pronounced increase in growing season mean temperatures, as well as changes in precipitation regimes, which have been influencing wine typicity across some of the most renowned winemaking regions worldwide. Moreover, several climate scenarios give evidence of enhanced stress conditions for grapevine growth until the end of the century. Although grapevines have high resilience, the clear evidence for significant climate change in the upcoming decades urges adaptation and mitigation measures to be taken by sector stakeholders. To provide hints on the abovementioned issues, we have edited a Special Issue entitled “Viticulture and Winemaking under Climate Change”. Contributions from different fields were considered, including crop and climate modeling, and potential adaptation measures against these threats. The current Special Issue allows for the expansion of scientific knowledge in these particular fields of research, as well as providing a path for future research.

Keywords

viticulture --- crop model --- phenology --- physiological processes --- climate --- micrometeorology --- microclimate --- climate change --- water limitation --- dry mass partitioning --- assimilation --- intercellular CO2 --- stomatal conductance --- leaf water potential --- Vitis vinifera L. --- production system --- S-ABA --- rate of anthocyanin accumulation --- CIRG --- bioactive compounds --- Botrytis cinerea --- low-input --- mechanical thinning --- viticultural training system --- yield formation --- leaf area --- table grapes --- photosynthesis --- berry composition --- phenolics --- natural hail --- grapevine --- phenology --- phenology modelling platform --- Touriga Franca --- Touriga Nacional --- climate change --- RCP4.5 --- EURO-CORDEX --- Douro wine region --- Portugal --- global warming --- technological and phenolic ripeness --- grape --- wine --- sensory analysis --- climate change --- elevated CO2 --- grapevine pest --- mealybug --- parasitoid --- FACE --- predawn water potential --- PRI --- remote sensing --- vineyards --- water status --- WI --- climate change --- Vitis vinifera L. --- general circulation model --- EURO-CORDEX --- phenological model --- grapevine --- Virtual Riesling --- climate change --- temperature --- plant architecture --- crop management --- modelling --- climate change --- viticulture --- adaptation --- temperature --- drought --- plant material --- rootstock --- training system --- phenology --- modeling --- Vitis vinifera --- autochthonous cultivar --- ’Uva Rey’ --- unmanned aerial vehicles --- vigour maps --- spatial variability --- normalized difference vegetation index --- crop water stress index --- crop surface model --- precision viticulture --- climate change --- multi-temporal analysis --- Vitis vinifera (L.) --- SO2 pads --- B. cinerea mold --- grape quality --- light micro-climates --- mitigation strategies --- kaolin --- irrigation --- Vitis vinifera L. --- grape berry tissues --- pulse amplitude modulated (PAM) fluorometry --- photosynthesis --- photosynthetic pigments --- viticulture --- winemaking --- climatic influence --- climate change --- adaptation measures

Sensors in Agriculture

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ISBN: 9783038974123 9783038974130 Year: Pages: 346 DOI: 10.3390/books978-3-03897-413-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 08:44:06
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Agriculture requires technical solutions for increasing production while lessening environmental impact by reducing the application of agro-chemicals and increasing the use of environmentally friendly management practices. A benefit of this is the reduction of production costs. Sensor technologies produce tools to achieve the abovementioned goals. The explosive technological advances and developments in recent years have enormously facilitated the attainment of these objectives, removing many barriers for their implementation, including the reservations expressed by farmers. Precision agriculture and ‘smart farming’ are emerging areas where sensor-based technologies play an important role. Farmers, researchers, and technical manufacturers are joining their efforts to find efficient solutions, improvements in production, and reductions in costs. This book brings together recent research and developments concerning novel sensors and their applications in agriculture. Sensors in agriculture are based on the requirements of farmers, according to the farming operations that need to be addressed.

Keywords

wireless sensor network (WSN) --- Wi-SUN --- vine --- mandarin orange --- thermal image --- fluorescent measurement --- X-ray fluorescence spectroscopy --- visible and near-infrared reflectance spectroscopy --- heavy metal contamination --- spectral pre-processing --- feature selection --- machine-learning --- LiDAR --- light-beam --- plant localization --- Kinect --- leaf area index --- radiative transfer model --- neural networks --- GF-1 satellite --- wide field view --- big data --- geo-information --- plant phenotyping --- grapevine breeding --- Vitis vinifera --- ambient intelligence --- wireless sensor --- fuzzy logic --- smart irrigation --- virtual organizations of agents --- CIE-Lab --- precision plant protection --- optical sensor --- weed control --- classification --- NIR hyperspectral imaging --- chemometrics analysis --- weeds --- UAS --- RPAS --- one-class --- machine learning --- remote sensing --- geoinformatics --- plant disease --- pest --- deep convolutional neural networks --- real-time processing --- detection --- hyperspectral imaging --- soil type classification --- total nitrogen --- texture features --- data fusion --- Fusarium --- near-infrared --- spectroscopy --- hulled barely --- partial least squares-discriminant analysis --- remote sensing --- precision agriculture --- crop monitoring --- data fusion --- speckle --- diffusion --- scattering --- biological sensing --- apparent soil electrical conductivity --- ECa-directed soil sampling --- electromagnetic induction --- proximal sensor --- response surface sampling --- salt tolerance --- boron tolerance --- soil mapping --- soil salinity --- spatial variability --- irrigation --- energy balance --- water management --- semi-arid regions --- on-line vis-NIR measurement --- total nitrogen --- total carbon --- spiking --- gradient boosted machines --- artificial neural networks --- random forests --- rice --- striped stem-borer --- hyperspectral imaging --- texture feature --- data fusion --- greenhouse --- wireless sensor network --- data fusion --- dynamic weight --- dataset --- agriculture --- obstacle detection --- computer vision --- cameras --- stereo imaging --- thermal imaging --- LiDAR --- radar --- object tracking --- crop area --- remote sensing image classification --- area frame sampling --- stratification --- regression estimator --- agriculture --- meat spoilage --- vegetable oil --- quality assessment --- electronic nose --- electrochemical sensors --- spectral analysis --- feature selection --- genetic algorithms --- classification --- vegetation indices --- vineyard --- diseases --- spatial data --- sensor --- data fusion --- change of support --- geostatistics --- precision agriculture --- management zones --- event detection --- back propagation model --- multivariate water quality parameters --- time-series data --- spatial-temporal model --- connected dominating set --- water supply network --- SS-OCT --- Capsicum annuum --- germination --- salt concentration --- deep learning --- clover-grass --- precision agriculture --- dry matter composition --- proximity sensing --- 3D reconstruction --- RGB-D sensor --- crop inspection platform --- water depth sensors --- soil moisture sensors --- temperature sensors --- rice field monitoring --- irrigation --- silage --- packing density --- moisture content --- compound sensor --- simultaneous measurement --- birth sensor --- bovine embedded hardware --- ambient intelligence --- virtual organizations of agents --- Fusarium --- near infrared --- discrimination --- hulled barely --- naked barley --- wheat --- dielectric probe --- apple shelf-life --- dielectric dispersion --- electronic nose --- pest scouting --- pest management --- gas sensor --- noninvasive detection --- nitrogen --- near infrared sensors --- drying temperature --- SPA-MLR --- PLS --- CARS --- hyperspectral camera --- handheld --- sensor evaluation --- case studies --- soil --- moisture --- sensor --- landslide --- rice leaves --- chromium content --- laser-induced breakdown spectroscopy --- laser wavelength --- preprocessing methods --- agricultural land --- field crops --- land cover --- photograph-grid method --- remote sensing --- data validation and calibration --- mobile app --- wireless sensor networks (WSN) --- energy efficiency --- distributed systems --- processing of sensed data --- WSN distribution algorithms --- recognition patterns --- agriculture

Sensors in Agriculture

Author:
ISBN: 9783038977445 9783038977452 Year: Pages: 354 DOI: 10.3390/books978-3-03897-745-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 08:44:06
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Abstract

Agriculture requires technical solutions for increasing production while lessening environmental impact by reducing the application of agro-chemicals and increasing the use of environmentally friendly management practices. A benefit of this is the reduction of production costs. Sensor technologies produce tools to achieve the abovementioned goals. The explosive technological advances and developments in recent years have enormously facilitated the attainment of these objectives, removing many barriers for their implementation, including the reservations expressed by farmers. Precision agriculture and ‘smart farming’ are emerging areas where sensor-based technologies play an important role. Farmers, researchers, and technical manufacturers are joining their efforts to find efficient solutions, improvements in production, and reductions in costs. This book brings together recent research and developments concerning novel sensors and their applications in agriculture. Sensors in agriculture are based on the requirements of farmers, according to the farming operations that need to be addressed.

Keywords

wireless sensor network (WSN) --- Wi-SUN --- vine --- mandarin orange --- thermal image --- fluorescent measurement --- X-ray fluorescence spectroscopy --- visible and near-infrared reflectance spectroscopy --- heavy metal contamination --- spectral pre-processing --- feature selection --- machine-learning --- LiDAR --- light-beam --- plant localization --- Kinect --- leaf area index --- radiative transfer model --- neural networks --- GF-1 satellite --- wide field view --- big data --- geo-information --- plant phenotyping --- grapevine breeding --- Vitis vinifera --- ambient intelligence --- wireless sensor --- fuzzy logic --- smart irrigation --- virtual organizations of agents --- CIE-Lab --- precision plant protection --- optical sensor --- weed control --- classification --- NIR hyperspectral imaging --- chemometrics analysis --- weeds --- UAS --- RPAS --- one-class --- machine learning --- remote sensing --- geoinformatics --- plant disease --- pest --- deep convolutional neural networks --- real-time processing --- detection --- hyperspectral imaging --- soil type classification --- total nitrogen --- texture features --- data fusion --- Fusarium --- near-infrared --- spectroscopy --- hulled barely --- partial least squares-discriminant analysis --- remote sensing --- precision agriculture --- crop monitoring --- data fusion --- speckle --- diffusion --- scattering --- biological sensing --- apparent soil electrical conductivity --- ECa-directed soil sampling --- electromagnetic induction --- proximal sensor --- response surface sampling --- salt tolerance --- boron tolerance --- soil mapping --- soil salinity --- spatial variability --- irrigation --- energy balance --- water management --- semi-arid regions --- on-line vis-NIR measurement --- total nitrogen --- total carbon --- spiking --- gradient boosted machines --- artificial neural networks --- random forests --- rice --- striped stem-borer --- hyperspectral imaging --- texture feature --- data fusion --- greenhouse --- wireless sensor network --- data fusion --- dynamic weight --- dataset --- agriculture --- obstacle detection --- computer vision --- cameras --- stereo imaging --- thermal imaging --- LiDAR --- radar --- object tracking --- crop area --- remote sensing image classification --- area frame sampling --- stratification --- regression estimator --- agriculture --- meat spoilage --- vegetable oil --- quality assessment --- electronic nose --- electrochemical sensors --- spectral analysis --- feature selection --- genetic algorithms --- classification --- vegetation indices --- vineyard --- diseases --- spatial data --- sensor --- data fusion --- change of support --- geostatistics --- precision agriculture --- management zones --- event detection --- back propagation model --- multivariate water quality parameters --- time-series data --- spatial-temporal model --- connected dominating set --- water supply network --- SS-OCT --- Capsicum annuum --- germination --- salt concentration --- deep learning --- clover-grass --- precision agriculture --- dry matter composition --- proximity sensing --- 3D reconstruction --- RGB-D sensor --- crop inspection platform --- water depth sensors --- soil moisture sensors --- temperature sensors --- rice field monitoring --- irrigation --- silage --- packing density --- moisture content --- compound sensor --- simultaneous measurement --- birth sensor --- bovine embedded hardware --- ambient intelligence --- virtual organizations of agents --- Fusarium --- near infrared --- discrimination --- hulled barely --- naked barley --- wheat --- dielectric probe --- apple shelf-life --- dielectric dispersion --- electronic nose --- pest scouting --- pest management --- gas sensor --- noninvasive detection --- nitrogen --- near infrared sensors --- drying temperature --- SPA-MLR --- PLS --- CARS --- hyperspectral camera --- handheld --- sensor evaluation --- case studies --- soil --- moisture --- sensor --- landslide --- rice leaves --- chromium content --- laser-induced breakdown spectroscopy --- laser wavelength --- preprocessing methods --- agricultural land --- field crops --- land cover --- photograph-grid method --- remote sensing --- data validation and calibration --- mobile app --- wireless sensor networks (WSN) --- energy efficiency --- distributed systems --- processing of sensed data --- WSN distribution algorithms --- recognition patterns --- agriculture

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english (3)


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2019 (3)