Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
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We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\n
Throughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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\r\n\tThe goal of this book on Topics in Regional Anesthesia is to review selected subjects of importance in daily practice. Since the first years of the introduction of cocaine by Carl Koller in 1884, the evolution of regional anesthesia has been continuous, gradual and safe. Its development has been based on anatomy, the pharmacology of local anesthetics and adjuvant drugs, as well as advances in the various blocking techniques, with ultrasound guidance being the most recent advent. The use of ultrasound in regional anesthesia has shown the reduction of complications, which makes it mandatory to knowledge and acquire skills in all ultrasound-guided techniques.
\r\n
\r\n\tUltrasound-guided regional blocks will be reviewed extensively, as well as intravenous regional anesthesia, thoracic spinal anesthesia. The role of regional anesthesia and analgesia in critically ill patients is of paramount importance. In addition, we will review the current role of regional techniques during the Covid-19 pandemic. Complications and malpractice is another topic that should be reviewed. Regional anesthesia procedures in some specialties such as pediatrics, orthopedics, cancer surgery, neurosurgery, acute and chronic pain will be discussed.
",isbn:"978-1-83969-570-4",printIsbn:"978-1-83969-569-8",pdfIsbn:"978-1-83969-571-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"264f7f37033b4867cace7912287fccaa",bookSignature:"Prof. Víctor M. Whizar-Lugo and Dr. José Ramón Saucillo-Osuna",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10708.jpg",keywords:"Regional Anesthesia, Ultrasound-Guided Regional Anesthesia, Local Anesthetics, Preventive Analgesia, Peripheral Blocks, Pediatric Regional Anesthesia, Intravenous Regional Anesthesia, Techniques, Complications, Adjuvants in Regional Anesthesia, Opioids, Alfa2 Agonists",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 23rd 2021",dateEndSecondStepPublish:"March 23rd 2021",dateEndThirdStepPublish:"May 22nd 2021",dateEndFourthStepPublish:"August 10th 2021",dateEndFifthStepPublish:"October 9th 2021",remainingDaysToSecondStep:"18 days",secondStepPassed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Dr. Whizar-Lugo has published more than 100 publications on Anesthesia, Pain, Critical Care, and Internal Medicine. He works as an anesthesiologist at Lotus Med Group and belongs to the Institutos Nacionales de Salud as an associated researcher.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"169249",title:"Prof.",name:"Víctor M.",middleName:null,surname:"Whizar-Lugo",slug:"victor-m.-whizar-lugo",fullName:"Víctor M. Whizar-Lugo",profilePictureURL:"https://mts.intechopen.com/storage/users/169249/images/system/169249.jpg",biography:"Víctor M. Whizar-Lugo graduated from Universidad Nacional Autónoma de México and completed residencies in Internal Medicine at Hospital General de México and Anaesthesiology and Critical Care Medicine at Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán in México City. He also completed a fellowship at the Anesthesia Department, Pain Clinic at University of California, Los Angeles, USA. Currently, Dr. Whizar-Lugo works as anesthesiologist at Lotus Med Group, and belongs to the Institutos Nacionales de Salud as associated researcher. He has published many works on anesthesia, pain, internal medicine, and critical care, edited four books, and given countless conferences in congresses and meetings around the world. He has been a member of various editorial committees for anesthesiology journals, is past chief editor of the journal Anestesia en México, and is currently editor-in-chief of the Journal of Anesthesia and Critical Care. 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Dr. Saucillo-Osuna has lectured at multiple national and international congresses and is an adjunct professor at the Federación Mexicana de Colegios de Anestesiología, AC, former president of the Asociación Mexicana de Anestesia Regional, and active member of the Asociación Latinoamericana de Anestesia Regional.",institutionString:"Centro Médico Nacional de Occidente",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"347258",firstName:"Marica",lastName:"Novakovic",middleName:null,title:"Dr.",imageUrl:"//cdnintech.com/web/frontend/www/assets/author.svg",email:"marica@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"6550",title:"Cohort Studies in Health Sciences",subtitle:null,isOpenForSubmission:!1,hash:"01df5aba4fff1a84b37a2fdafa809660",slug:"cohort-studies-in-health-sciences",bookSignature:"R. 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1. Introduction
The influence of climatic variables during the vineyard growing seasons on wine quality is well known because they influence the grapevine growth and then the berry composition. For wine grape crops, plant phenology, wine quality, and yield are very dependent on climate at regional, local and microclimatic scales [1, 2, 3, 4]. For any of these spatial scales, considerations of grape site selection, cultural practices, and water management are important, which are very important issues for potential adaptations to different climate scenarios; however, large-scale climate has been the focus for assessing the climate change impacts [5, 6, 7, 8].
The optimal vineyard response to air temperature (Ta) ranges between 20 and 35°C [9]. According to Fraga et al. [3], a 10°C basal temperature (Tb) is needed for the growing season onset. However, high thermal conditions contribute to increase sugar content in grapes, resulting in large alcohol concentration and low acidity in wines, rising pH [2, 10, 11, 12]. There are also important secondary thermal effects, such as increase of pest and disease risks, like downy and powdery mildew, especially under rainy conditions [4, 13, 14].
Rising thermal conditions (Ta > 30°C) should increase suspended solid concentrations, but high Brix levels may be attributed to large evapotranspiration (ET) rates [15]. According to Webb et al. [16], high Ta during the harvest period may reduce berry quality due to increasing ET. These issues make the rational water management an important issue for controlling water deficiencies and excesses, and the need of vineyard water requirements (WR) quantification [12, 17, 18].
Taking into account the water balance, on the one hand, warming conditions can directly affect the vineyard WR, which, together with low precipitation (P) amounts, promote high levels of aridity and water demand. On the other hand, high soil moisture throughout the growing season may cause excessive vigor, increased risks of pest and diseases, and other problems related to wine quality and to the balance of its chemical components [4, 9, 19, 20, 21]. All these thermohydrological effects during the vineyard growing seasons on wine quality and production show the importance of water accounting to delimitate areas and seasons with suitable climatic aptitude for winemaking processes.
A large number of climate models have been used worldwide to classify winemaking regions by using different methodologies. For aptitude delimitation, aiming at grape and wine production, one can apply bioclimatic indices based on the thermohydrological requirements. The multicriteria climatic classification (MCC) system proposed by Tonietto and Carbonneau [22] has been used under temperate climate conditions in Europe [23] and in South America [24, 25]. However, the method has worked well considering a single, 6-month growing season per year under temperate climate conditions.
Over the last years, the Brazilian semiarid region has appeared among the main tropical wine-producing areas in the country, typically growing under irrigation conditions and trained mainly in vertical shoot-positioning systems. With proper irrigation and cultural management practices, the farmers can produce grapes and carry out winemaking at any time of the year, allowing a potential average of between two and three vineyard-growing cycles per year, in accordance with and depending on each variety. The Ta rising with a consequent increase in ET rates and aridity in this region will affect both the wine grape quality and vineyard water requirements.
The coupled effect of increasing water consumption and decreasing precipitation, together with land use change, makes it important to elaborate and upscale indices for subsidizing winemaking adaptations and water productivity improvements. Vineyard water variables have been quantified in this region by point measurements [26, 27] but to upscale these punctual results, tools such as remote sensing and geographic information system (GIS) can be used. For the vineyard climatic suitability determination, one can apply water balance indices by using GIS and long-term weather data [4, 18, 21, 28]. For the vineyard water balance indices used in this chapter, distinctions are important between reference (ET0) and actual (ET) evapotranspiration. ET0 is the water flux from a reference surface, not a shortage of water, which may be considered as a hypothetical grass surface with specific characteristics, while ET is the real water flux occurring from the surfaces in a specific situation involving all environmental conditions [29].
This chapter aims to elaborate on and apply water balance indices to be scaled up by using a GIS in the wine grape growing regions of Petrolina and Juazeiro counties, located respectively in the semiarid regions of Pernambuco (PE) and Bahia (BA) states, simulating different pruning dates along the year. These indices were delimitated and analyzed, generating criteria for a rational expansion of irrigated and rain-fed vineyards with higher probability of success for tropical wine elaboration. The results generate criteria for a rational expansion of irrigated and rain-fed vineyards with higher probability of success for the Brazilian tropical wine elaboration, under the actual scenario of rising water competition by irrigated agriculture and nonagricultural sectors.
2. Materials and methods
2.1. Study region and data set
A net of agrometeorological stations was used throughout interpolation processes in a GIS environment. The stations are spread in the Petrolina (PE) and Juazeiro (BA) counties, with seven of them inside irrigated farms and the other seven in the natural vegetation, called “Caatinga.” The gridded weather data well characterize the horizontal thermohydrological contrast between these mixed agroecosystems (Figure 1).
Figure 1.
Petrolina and Juazeiro counties, respectively in Pernambuco (PE) and Bahia (BA) states, in the Brazilian Northeast, together with the agrometeorological stations used for the interpolation processes.
According to Teixeira [19], in the Brazilian Northeast semiarid region, disturbed currents from the South, North, East, and West influence the climatology. Excluding the places of high altitude, all areas present long-term annual Ta higher than 24°C, with the average maximum of 33°C and the average minimum of 19°C. The warmest months are October and November when the Sun is close to the zenith position with low cloud cover, and the coldest ones are June and July at winter solstice in the Southern hemisphere. The thermal homogeneity strongly contrasts with the spatial and temporal heterogeneity of the rainfall regime, with the rainy period from November to April (90% of the annual total), the period January to April representing 68% of the annual rainfall. The weather variables in the current study involved a 10-year period (2003–2012). Monthly data were used to calculate the reference evapotranspiration (ET0) by the Penman-Monteith method [29].
2.2. Modeling vineyard water balance indices
Previous Bowen ratio energy balance data from Teixeira et al. [26] for the cv. Syrah in the Brazilian semiarid region were used. Figure 2 shows the details of the field experiment carried out in Petrolina (PE), Northeast of Brazil.
Figure 2.
Measurements of gradients of air temperature, vapor pressure and wind speed; incident and reflected shortwave; net radiation; acquisition data system; and soil moisture in Bowen ratio system of wine grape.
The energy balance equation for the wine grape can be expressed by means of bulk energy and heat fluxes:
Rn−λE−H−G=0E1
where Rn is the net radiation, λE is the latent heat flux, H is the sensible heat flux, and G is the soil heat flux.
The vineyard λE was obtained by a partitioning parameter:
λE=Rn−G1+βE2
where β is the Bowen ratio:
β=γΔTΔeE3
and γ (kPa °C−1) is the psychrometric constant, ΔT(°C) the temperature gradient measured by dry thermocouples, and ∆e (kPa) is the water vapor pressure gradient measured by the difference between dry and wet thermocouples over the height interval above the vineyard canopy surface.
Actual evapotranspiration (ET) was derived from the latent heat of vaporization (λ), density of water, and λE. The field experiment was close (3 km) to Bebedouro station (Figure 1), which ET0 data allowed the acquirement of the crop coefficient (Kc) along the crop stages [29]:
Kc=ETET0E4
Considering a base temperature (Tb) of 10°C, Kc was related with the accumulated degree days DDac [30]:
Kc=aDDac2+bDDac+cE5
where a = −2 × 10−7, b = 4 × 10−4 and c = 0.54 are the regression coefficients (R2 > 0.70).
Further, Kc was used to obtain the ET under potential conditions, which in turn considered the vineyard water requirements (WR), using the cv. Syrah as a reference wine grape in the study region. WR for a growing season (GS) was acquired by simulating different pruning date and considering a 4-month mean GS duration under the Brazilian semiarid conditions:
WRGS=KcEGST0GSE6
Five Kc values were taken into account, being DDac zero at the start of a GS, while the other DDac values were calculated, along the GS, with the average Ta for the subsequent months. The five-Kc averaged values and the total ET0 for a GS (ET0GS) were considered for acquiring WRGS.
Another indicator, the water balance difference (WBd), was applied to quantify the magnitude of excess or deficiencies of water in the vineyards on large scales for a GS, where PGS is the total growing season precipitation:
WBdGS=PGS−WRGSE7
Neglecting the water storage in the root zones in the vineyard water balance, difficult to consider in large-scale analyses, positive WBd values are a quantification of vineyard water excess, while the negative ones are related to the vineyard water deficiencies.
The WRGS together with PGS values allowed the development and application of the water balance ratio (WBr) indicator:
WBrGS=PGSWRGSE8
WBr takes into account the thermohydrological conditions, and it is a measure of the water availability in the vineyard root zone. When it is around 1.00, imply the feasibility for rain-fed wine grape, while those much higher should indicate moisture excess problems, independently of the absence or not of irrigation. Low WBr values mean possibility of natural water deficiencies and the degree of irrigation needs according to the pruning dates.
3. Results and discussion
Figure 3 shows the TGS maps, for different wine grape pruning dates along the year, considering a mean 4-month GS, and the 10-year period from 2003 to 2012.
Figure 3.
Spatial averages of mean air temperature values for a 4-month wine grape-growing season (TGS), and a 10-year period (2003–2012), simulating different pruning dates, in the Petrolina (PE) and Juazeiro (BA) counties, Northeast Brazil. The mean pixel values and standard deviations are also indicated.
The coldest and the hottest growing seasons (GS) are those for pruning dates between April and July and from September to December, respectively. Considering the standard deviation (SD) values, there are low thermal spatial variations, due to the proximity of the counties to the equator. The lowest air temperatures for a growing season (TGS) occur at the winter solstice time in the Southern hemisphere, while the highest ones are when the Sun is around the zenith position over the Brazilian tropical wine grape growing region. For pruning done during the coldest periods, several pixels present TGS values lower than 24°C, while one can see all the areas with TGS higher than 26°C, for pruning in the hottest months.
There are no thermal limitations for wine grape crop in the Brazilian semiarid region, with pruning dates in the middle of the year. On the one hand, for all pruning periods, TGS pixels are below 30°C, which conditions around or above this value should increase suspended solid concentrations [15]. On the other hand, TGS values are not below the threshold of 10°C, which could introduce a dormancy stage in temperate climates [3]. The TGS values in the study area are between 23°C and 28°C, inside the optimum thermal range pointed by Gouveia et al. [9].
However, when the pruning is done from September to December, many areas with TGS above 27°C often occur and could affect negatively the wine quality. These latter conditions will contribute to high sugar content in grapes but wines with increasing levels of alcohol, low acidity, and large pH values. These effects together will promote a wine unbalance with instability for the phenolic and aromatic composition [2, 3, 10, 11, 12].
Figure 4 shows the PGS maps, for different wine grape pruning dates along the year, considering a mean 4-month GS, and the 10-year period from 2003 to 2012.
Figure 4.
Spatial averages of total precipitation for a 4-month wine grape-growing season (PGS), and a 10-year period (2003–2012), simulating different pruning dates, in the Petrolina (PE) and Juazeiro (BA) counties, Northeast Brazil. The mean pixel values and standard deviations are also indicated.
The pruning dates with the highest PGS are those from December to February, with several pixel values larger than 300 mm GS−1 in the Petrolina County. During this period, the largest moisture spatial variation is also verified according to the SD values. The lowest PGS are for pruning between May and July. High pixel values occur in the northwestern side of Petrolina (PE), while the lowest ones occur in the southwestern area of Juazeiro (BA).
Taking into account all pruning dates, the rainfall amounts in Petrolina (PE) are 61% larger than in Juazeiro (BA). Thus, in the first county, there are more possibilities of matching the vineyard water requirements with rainfall together with supplementary irrigations, whenever irrigation water is available. However, as a first guess, risks of pest and diseases and other problems related to wine quality and to the balance between its chemical components are higher for pruning done from December to February [4, 9, 19, 20, 21].
Considering the cv. Syrah as reference for wine grapes in the growing regions of Petrolina (PE) and Juazeiro (BA), and the long-term weather conditions (2003–2012), the WRGS spatial values for a 4-month mean GS are presented in Figure 5.
Figure 5.
Spatial averages of the water requirements for a 4-month wine grape-growing season (WRGS), and 10-year period (2003–2012), simulating different pruning dates, in the Petrolina (PE) and Juazeiro (BA) counties, Northeast Brazil. The mean pixel values and standard deviations are also indicated.
The pruning dates with the highest WRGS are from August to October, with average pixel values larger than 420 mm GS−1, when, according to the SD values, there are also the highest spatial variations. Pruning done from March to May will promote the lowest water consumptions, with mean WRGS below 350 mm GS−1, and the smallest SD values, below 25 mm in March and April. Large WRGS occur in the northwestern side of Petrolina (PE), what might correspond to good grape yield and wine quality if water is available together with techniques to avoid natural water excesses in the root zones [4, 12]. However, special attention should be given under water scarcity conditions, when there is ample room for water productivity improvements in situations of lower atmospheric demands [21].
Taking into account all pruning dates along a year, the water demands in the Petrolina (PE) county are 10% larger, when comparing with those for Juazeiro (BA) one. Daily average WR values in the study region were between 2.7 and 3.6 mm day−1, being similar to the ET rates found throughout field experiments in different wine grapes growing regions of the world [27, 30, 31], bringing confidence to the upscaling techniques applied in the current case study.
Table 1 resumes the averages and SD values of the vineyard water balance indices for each 4-month pruning date per county, for the wine grape, cv. Syrah considering the period of weather data from 2003 to 2012 in the growing region of Petrolina – Pet (PE) e Juazeiro – Jua (BA), in the semiarid region of Northeast Brazil.
Pruning date
TGS (°C)
PGS (mm GS−1)
WRGS (mm GS−1)
Pet
Jua
Pet
Jua
Pet
Jua
January
26.7 ± 0.1
26.8 ± 0.3
396 ± 65
253 ± 52
398 ± 23
362 ± 20
February
26.2 ± 0.1
26.3 ± 0.3
329 ± 57
209 ± 47
372 ± 22
337 ± 18
March
25.6 ± 0.1
25.6 ± 0.3
224 ± 47
139 ± 35
342 ± 19
313 ± 18
April
25.0 ± 0.1
24.9 ± 0.3
130 ± 28
78 ± 22
340 ± 19
310 ± 19
May
24.6 ± 0.2
24.4 ± 0.3
58 ± 14
32 ± 11
361 ± 22
327 ± 20
June
24.8 ± 0.2
24.5 ± 0.3
40 ± 10
17 ± 7
399 ± 25
360 ± 22
July
25.5 ± 0.2
25.3 ± 0.3
44 ± 8
18 ± 7
439 ± 28
396 ± 24
August
26.5 ± 0.2
26.3 ± 0.3
65 ± 11
37 ± 7
461 ± 29
416 ± 24
September
27.3 ± 0.2
27.2 ± 0.3
105 ± 17
62 ± 12
461 ± 29
417 ± 23
October
27.7 ± 0.1
27.7 ± 0.3
191 ± 28
121 ± 21
448 ± 27
404 ± 22
November
27.6 ± 0.1
27.6 ± 0.3
292 ± 40
189 ± 33
441 ± 26
396 ± 22
December
27.2 ± 0.1
27.3 ± 0.3
365 ± 57
233 ± 45
420 ± 24
368 ± 21
Mean
26.1 ± 0.1
26.2 ± 0.3
187 ± 32
116 ± 25
407 ± 24
367 ± 21
Table 1.
Mean values and standard deviations (SD) of the vineyard water balance indices for the wine grape, cv. Syrah, considering a 4-month average growing season (GS) and a 10-year period (2003–2012), in the Petrolina—Pet (PE) and Juazeiro—Jua (BA) counties, Northeast Brazil.
Air temperature (TGS); Precipitation (PGS); and Water requirements (WRGS).
No significant differences arise among the TGS mean values from Petrolina (PE) and those from Juazeiro (BA) with average for all pruning periods of 26°C GS−1 for both counties; however, the second one presents larger spatial thermal variation, according to the SD values.
In case of PGS, the values for Juazeiro (BA) are lower those for Petrolina (PE), indicating higher possibility of rainfall water use by the vineyards in the second county. As the vineyard thermal conditions between them did not differ so much, the WRGS values for Juazeiro (BA) were, in average, 90% of those for Petrolina (PE). These differences could be attributed to the effect of relative humidity (RH) in the ET0 calculations, as lower PGS reduce RH in Juazeiro (BA) when comparing with Petrolina (PE).
Keeping in mind that the wine quality depends on both thermal and water conditions, these conditions were analyzed throughout the mean pixel values and standard deviations (SD) of WBd and WBr for a 4-month average growing season and according to the pruning dates considering the 10-year period of weather data (2003–2012) (Figure 6).
Figure 6.
Mean pixel values and standard deviations (SD) for the water balance indices, considering a 10-year period (2003–2012) and an average 4-month wine grape-growing season, cv. Syrah according to the simulated pruning dates, in the Petrolina—Pet (PE) and Juazeiro—Jua (BA) counties, Northeast Brazil: (a) water balance difference (WBd) and (b) water balance ratio (WBr).
According to Figure 6a, there are no positive WBd mean values, meaning that considering the whole area and the average conditions; in general, there is absence of vineyard water excesses for any pruning dates. Disregarding the water storage in the root zones in the vineyard water balance, the pruning periods with the highest water deficiencies (the most negative WBd) are from June to September, when both counties presented average WBd pixel values lower than −340 mm GS−1 and also the lowest SD, around 23 mm GS−1. The less negative WBd values obtained for pruning dates are from December to February, when the average was above −150 mm GS−1. These last thermohydrological conditions indicated the feasibility of rain-fed wine grape with supplementary irrigation. Natural water deficiency in Petrolina is 87% of that for Juazeiro, with better chances of success for rain-fed wine grapes, once rainfall-water storage techniques are applied.
As, in average, PGS in Petrolina is 61% higher than that for Juazeiro, but WRGS is only 11% larger (Table 1), the differences regarding rainfall amounts will affect more the water balance than the different evapotranspiration rates between the counties. Similarly to the WBd index, pruning dates from December to February present the highest WBr, with averages ranging from 0.60 to 1.00, meaning that rainfall amounts met from 60 to 100% of the vineyard water demands during these pruning periods; however, SD values for both of them are around 0.10 (Figure 6b).
Although rainy conditions having the beneficial aspect of natural water availability, increasing soil moisture may reduce the ripening capacity of grapes, and the difficulty of water stress management is unfavorable for the organoleptic wine quality. In this sense, care should be also taken for improving drainage for both irrigated and rain-fed vineyards during the periods of high WBr.
The natural climate dryness conditions occur when the pruning is done from May to August. Under these circumstances, the WBr values are around 0.10 with almost no spatial variation, favoring more the irrigated vineyards. These conditions avoid plant diseases, root respiration problems, and direct damage to the berries promoted by excess of precipitation, favoring the quality of must and wine [3, 4, 9, 12, 20, 21].
4. Conclusions
Water balance indices are successfully developed and applied, allowing the large-scale analyses of the thermohydrological conditions for wine grape production under the semiarid conditions of the Brazilian Northeast, considering different pruning dates along the year.
On the one hand, under irrigation conditions, the best wine grape pruning dates in the Brazilian Northeast are from May to August, with the thermohydrological conditions favoring a better tropical wine quality. On the other hand, the most problematic pruning periods for irrigated crops are from December to February because the joint effects of higher air temperatures and precipitations. Considering the possibility of rainfed crops, this last period should be considered in situations with the possibility of supplementary irrigation applying rainfall water storage techniques.
The spatial delimitations carried out in the current research, joined with other environmental characteristics, are important for the success of the commercial tropical wine production expansion, considering also the sustainability of the activity in the Brazilian semiarid region, where the land use and climate changes are happening together with water competition during the last decades.
Acknowledgments
The authors acknowledge the National Council for Scientific and Technological Development – CNPq (Processe 404229/2013-1), for the financial support to a project. The research deals with agricultural water productivity in hydrological basins with land use changes in Brazil.
\n',keywords:"evapotranspiration, crop coefficient, vineyard adaptation, water resources, Vitis vinifera",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/58458.pdf",chapterXML:"https://mts.intechopen.com/source/xml/58458.xml",downloadPdfUrl:"/chapter/pdf-download/58458",previewPdfUrl:"/chapter/pdf-preview/58458",totalDownloads:747,totalViews:166,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"June 1st 2017",dateReviewed:"November 27th 2017",datePrePublished:"December 20th 2017",datePublished:"February 28th 2018",dateFinished:null,readingETA:"0",abstract:"Over the last few decades, the Brazilian semiarid region has appeared as one of the main tropical wine production areas in the country. The aim of this research was the elaboration and application of water balance indices to upscale them in the wine grape growing regions of the Petrolina and Juazeiro counties in the states of Pernambuco (PE) and Bahia (BA), respectively, simulating different pruning dates along the year. Previous energy balance measurements were used for relating the crop coefficient (Kc) with the accumulated degree-days (DDac). The model was applied to upscale the water balance indices during the growing seasons (GS). It was concluded that if irrigation water is available, the best pruning periods are for GS from May to July because of better natural thermal and moisture conditions. Much care should be taken for pruning done in other periods of the year, with regard to the effect of increasing thermal conditions on wine quality. The classifications and delimitations done, joined with other environmental characteristics, are important for a rational planning of the commercial tropical wine production expansion, mainly in the actual situations of climate and land use changes together with rising water competition along the years in the Brazilian semiarid region.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/58458",risUrl:"/chapter/ris/58458",book:{slug:"grapes-and-wines-advances-in-production-processing-analysis-and-valorization"},signatures:"Antônio Heriberto de Castro Teixeira, Jorge Tonietto and Janice F.\nLeivas",authors:[{id:"212840",title:"Dr.",name:"Antônio",middleName:null,surname:"Teixeira",fullName:"Antônio Teixeira",slug:"antonio-teixeira",email:"heriberto.teixeira@embrapa.br",position:null,institution:{name:"Brazilian Agricultural Research Corporation",institutionURL:null,country:{name:"Brazil"}}},{id:"212843",title:"Dr.",name:"Jorge",middleName:null,surname:"Tonietto",fullName:"Jorge Tonietto",slug:"jorge-tonietto",email:"jorge.tonietto@embrapa.br",position:null,institution:null},{id:"213180",title:"Dr.",name:"Janice",middleName:null,surname:"Leivas",fullName:"Janice Leivas",slug:"janice-leivas",email:"janice.leivas@embrapa.br",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Materials and methods",level:"1"},{id:"sec_2_2",title:"2.1. Study region and data set",level:"2"},{id:"sec_3_2",title:"2.2. Modeling vineyard water balance indices",level:"2"},{id:"sec_5",title:"3. Results and discussion",level:"1"},{id:"sec_6",title:"4. Conclusions",level:"1"},{id:"sec_7",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Jones GV, Webb LB. Climate change, viticulture, and wine: Challenges and opportunities. Journal of Wine Research. 2010;21:03-106. DOI: 10.1080/09571264.2010.530091'},{id:"B2",body:'Fraga H, Malheiro AC, Moutinho-Pereira J, Santos JA. An overview of climate change impacts on European viticulture. Food and Energy Security. 2012;1:94-110. DOI: 10.1002/fes3.14'},{id:"B3",body:'Fraga H, Malheiro AC, Moutinho-Pereira J, Santos JÁ. Climate factors driving wine production in Portuguese Minho region. Agricultural and Forest Meteorology. 2014;185:26-36. 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ISSN: 0042-7500'},{id:"B24",body:'Ferrer M, Pedocchi R, Michelazzo M, Gonzalez-Neves G, Carbonneau A. Delimitación y descripción de regiones vitícolas del Uruguay en base al método de clasificación climática multicriterio utilizando índices bioclimáticos adaptados a las condiciones del cultivo. Agrociencia. 2007;11:47-56. ISSN: 1405-3195'},{id:"B25",body:'Montes C, Perez-Quezada JF, Peña-Neira A, Tonietto J. Climatic potential for viticulture in Central Chile. Australian Journal of Grape and Wine Research. 2012;18:20-28. DOI: 10.1111/j.1755-0238.2011.00165.x'},{id:"B26",body:'Teixeira AH de C, Bastiaanssen WGM, Bassoi LH. Crop water parameters of irrigated wine and table grapes to support water productivity analysis in Sao Francisco River basin, Brazil. Agricultural Water Management. 2007;94:31-42. DOI: 10.1016/j.agwat.2007.08.001'},{id:"B27",body:'Azevedo PV, Soares JM, Silva V de P, Silva BB, Nascimento T. Evapotranspiration of “superior” grapevines under intermitente irrigation. Agricultural Water Management. 2008;95:301-308. DOI: 10.1016/j.agwat.2007.10.011'},{id:"B28",body:'Teixeira AH de C, Scherer-Warren M, Hernandez FBT, Andrade RG, Leivas JF. Large-scale water productivity assessments with MODIS images in a changing semi-arid environment: A Brazilian case study. Remote Sensing. 2013;5:5783-5804. DOI: 10.3390/rs5115783'},{id:"B29",body:'Allen RG, Pereira LS, Raes D, Smith M. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. Rome: Food and Agriculture Organization of the United Nations; 1998. 300 p'},{id:"B30",body:'Williams LE, Ayars JE. Grapevine water use and the crop coefficient are linear functions of the shaded area measured beneath the canopy. Agricultural and Forest Meteorology. 2005;132:201-211. DOI: 10.1016/j.agrformet.2005.07.010'},{id:"B31",body:'Ortega-Farias S, Carrasco M, Olioso A, Acevedo C, Poblete C. Latent heat flux over Cabernet Sauvignon vineyard using the Shuttleworth and Wallace model. Irrigation Science. 2007;25:161-170. DOI: 10.1007/s00271-006-0047-7'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Antônio Heriberto de Castro Teixeira",address:"heriberto.teixeira@embrapa.br",affiliation:'
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1. Introduction
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Population genetic studies deal with allele frequencies and processes that shape their variation within and among populations. Multiple studies have addressed genetic variation and their structure based on the screening of molecular markers such as allozymes (began with Lewontin and Hubby [1]), random amplified polymorphic DNA (RAPD) [2], amplified fragment length polymorphism (AFLP) [3], microsatellites or simple sequence repeats (SSR) [4], intersimple sequence repeats (ISSR) [5] and single nucleotide polymorphisms (SNP). The use of allozyme markers started up a series of population genetic studies, allowing relatively precise estimation of heterozygosity levels due to their codominance nature. Those markers were largely employed until the end of the 1990s. The development of techniques for screening directly at the level of DNA has accelerated the discovery of numberless markers in humans, animals, plants, fungi, and other organisms. RAPD, ISSR, and AFLP, in general, are more limited in describing genetic variation due to their dominance. In contrast, several SSR markers have been developed for studying a diverse set of species, enabling precise estimates of genetic diversity, gene flow, spatial genetic structure, paternity, linkage, and association mapping.
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Ultimately, SNP markers have arisen as powerful markers for fine-scale genetic diversity, structure, and association mapping studies. The direct comparison among sequences of specific fragments generated by Sanger sequencing allowed the discovery of the first set of SNP. However, the revolution in sequencing technology of the last decade has provided numberless sequences for comparing individuals and deciphering population genetic mechanisms with high accuracy. The next-generation sequencing platforms generate millions of sequences that often result in thousands of SNP markers.
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Nonetheless, the sole use of molecular data provides no definitive responses on evolutionary mechanisms operating in populations. An examination of the ecological factors, that drive the fate of individuals over generations or how current mechanisms impact in their adaptation or acclimation, is a much-needed task to better understand all species. Adequate statistical methods combining genetic and environmental variables are then necessary. Landscape genetics emerged as a field for the improvement of our understanding of the influence of geographical and environmental variables on the genetic structure of populations [6]. It diverges from the traditional basis of population genetics in the sense of more profound tests of the influence of landscape and environmental factors such as altitude, topography, and ground cover on population processes such as gene flow and population structure [7]. The rapid boost in genome-scale analyses also generated the terminology landscape genomics, as proposed by Joost et al. [8]. Landscape genomics differs from landscape genetics in the sense that it has become a powerful approach for scanning genes involved in complex adaptation mechanisms of species at populations and individual levels [9, 10].
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This chapter is intended to provide brief concepts that cover the subject of landscape genetics and genomics. Furthermore, we outline potential applications of landscape genetic studies in the comprehension of adaptive traits of plants and animals and how such results may assist in the design of conservation strategies for endangered species. It is not our intent to provide an exhaustive panorama of landscape genetics studies so far, but rather contextualize concepts and applications with chosen case studies. Moreover, we briefly contextualize how landscape genetics is contributing in the comprehension of historical human migrations and the dispersion of human diseases.
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2. Molecular markers and population structure studies
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The most popular molecular markers employed in population genetic studies are SSR [4] and SNP. Simple sequence repeats are tandem repeated motifs with 1–6 bp [11] or up to 10 bp [12] with high frequency in genomes of all organisms. Plants commonly have AT-type repeats, whereas animals have the AC motif as the most common repeat unit [13]. High mutation rates are characteristics of microsatellite markers [12] providing markers with several alleles. SSR are codominant, hypervariable, and Mendelian inherited [14], which is implicated in high heterozygosity levels, increasing the discriminatory power among individuals and populations. Originally, SSR were developed from DNA libraries that required extensive laboratory work. Currently, however, the easiest way of discovering novel microsatellites if though direct sequencing of genomes and transcriptomes generated from NGS platforms [12]. With that available, SNP markers have actually been the most studied markers in recent years. SNP markers are the most abundant polymorphisms along plant and animal genomes. SNP consist on single base-pair changes present in the genome sequence that can occur as transitions or transversions, as nucleotide substitutions [15]. They can reach much higher density than all other types of markers in genomes. Next-generation sequencing can generate large amounts of sequence data, enabling the detection of thousands of SNP [16].
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Microsatellites and SNP markers are powerful tools for population genetic analyses. They have been extensively employed in studies with humans as well as animal and plant models and non-model species. The codominance and multiallelic nature of microsatellites make them suitable for estimating variables such as heterozygosity, inbreeding, gene flow, outcrossing rates, differentiation among populations and population structure [17]. SNP markers are generally employed for determining population structure as well, but with much higher density of markers and therefore genomic coverage to explain such subdivision. A series of studies have used SNP to dissect complex traits with QTL mapping and genome-wide association studies (GWAS) [15].
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3. The concepts of landscape genetics and landscape genomics
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Landscape genetics is concerned with testing the effects of landscape features on gene flow and genetic population structure. In general, the first studies of landscape genetics involved an exploratory phase, by geographically widespread sampling of populations and analysis of the effects of various landscape variables [18]. Landscape features or variables consist of any biotic, climatic, soil, or other conditions that comprise the habitat of organisms [6]. The population structure means the organization of genetic variation as influenced by a combination of evolutionary forces such as recombination, mutation, drift, natural selection, and historic demographic processes [19]. This leads to the idea that a group of subpopulations that exchange migrants in an occasional fashion are part of metapopulations [6].
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The current status of genomic technologies allows the discovery of thousands of SNP markers, which has increased the resolution power for studying the association of environmental variables with specific genomics regions, also with a much deeper understanding of evolutionary processes. Genotyping-by-sequencing has enabled the discovery of SNP markers even in non-model species, which may lack a reference genome so far [20, 21]. This is where the concept of landscape genomics comes forward. Landscape genomics focuses on detecting candidate genes under selection as putative signals of local adaptation. The design of a landscape genomics experiment involves replicated sampling of environmental factors that might be driving selection, augmenting the resolution for detection of candidate loci under selection [10].
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4. A briefing on statistical approaches in landscape genetics
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In a landscape genetics study, two steps of analyses are normally required. The first involves the analysis of patterns of genetic variation. Next, such patterns are correlated with landscape variables based on statistical methods [22]. To test for association of environmental variables with genetic data, one of the simplest and commonly used methods is the Mantel’s test, originally developed for identifying time-space clustering of diseases [23]. The test uses permutations to address the significance of the linear correlation coefficient between two pair-wise similarity or dissimilarity matrices [22]. One of the simplest examples of its application in landscape genetics is to correlate the genetic distances between individuals with their geographic location [24].
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The methods for determining association of genetic data with environmental variables can be broadly categorized into approaches that deal with (i) pair-wise landscape data and (ii) location-specific landscape data, as reviewed by Balkenhol et al. [22]. The development of methods in landscape genomics, however, expanded the range of tests for detecting loci under selection using genome scans, approaches for candidate gene discovery, QTL mapping and GWAS. Genome scans use two methods for detecting loci under selection, the differentiation outlier methods and the genetic-environmental association test, as reviewed by Storfer et al. [10]. Novel methods are continuously being developed, as more genomes are becoming sequenced or resequenced in populations.
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5. Applications of landscape genetics
\n
Several applications of landscape genetics or genomics can be described. We briefly account for case studies in plant and animal systems within this section. Moreover, a few examples of studies applied to humans are also given. In general, landscape genetics or genomics studies have provided association among geographic, abiotic, and biotic factors and genetic data provided by the screening of molecular markers in populations of diverse organisms. It has increased our power to detail inferences of movement and gene flow and potential adaptation to the landscape populations occur. However, studies for several organisms are still scarce or inexistent.
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Cultivated crops such as maize, soybean, rice, and common bean were domesticated from wild progenitors which reflect their current adaptation to distinct environments. Landscape genomics studies have enabled a deeper understanding of processes shaping their distribution across multiple environments. Common bean (Phaseolus vulgaris L.) is an exceptional example of a widespread species original from America. Molecular data of wild germplasm identified two major gene pools, the Andean from Argentina to Colombia, and the Mesoamerican from Colombia to Mexico [25, 26]. A third smaller pool of wilds is also distinctive in a narrow area between Peru-Equador [27]. Microsatellites markers were broadly used to screen the genetic structure of wild and domesticated accessions of common bean (Phaseolus vulgaris L.), distinguishing from the broadest Andean and Mesoamerican gene pools to further subdivision within each one of them [25]. SNP markers from single fragments sequenced by Sanger also allowed an accurate distinction between Andean and Mesoamerican accessions, as well as their subdivisions [28]. The recognition of a parallel domestication event in each of the two major pools was also possible based on the detection of SNP markers in specific genomic regions of Andean and Mesoamerican genotypes [29]. Recent landscape genomics approaches enabled a more detailed description of the major events that determined the range expansion of P. vulgaris in America and how they were accompanied by environmental changes [26]. The climatic variability was also associated with differential drought adaptation and specific SNP markers were statistically related to root and shoot traits varying in a Mesoamerican panel of genotypes originated from regions with distinct precipitation regimes throughout the year [30].
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Another application of landscape genomics concerns with the understanding of range expansion and ecological dominance of insect pests. The first step toward that is to know the population structure, gene flow and how natural selection is affecting adaptation. Zucchi et al. [31] described and addressed such problem by examining the population structure of Piezodous guildiniis, a soybean pest, in the United States and Brazil. A GBS-based set of SNP markers revealed genetic structure according to their geographic environment of origin. About 10% of loci were under positive selection, and their annotation revealed genes involved in genome reorganization, neuropeptides, and energy mobilization [31]. Addressing such problem is to assist future endeavors at managing pest spreading in cultivated crops.
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Another equally important questions addressed by landscape genomics are the consequences of climate change and human intervention to natural populations of wilds plants and animals. Euterpe edulis Martius is a palm species native to the Atlantic Rain Forest in Brazil, known as heart-of-palm [32]. The species is the list of endangered species to extinction [33]. Several studies have addressed the genetic diversity and structure of natural populations of this palm (for a compilation see [34]). Soares et al. [35] studied the genetic diversity and structure of remnant fragments of E. edulis in Bahia state and related the data to landscape metrics such as composition and configuration and local variables including the logging activity as human disturbance variable. No evidence of spatial genetic structure was detected, but distinct genetic clusters could be identified, suggesting a reduction in gene flow between the fragments of this study [35]. Natural populations located in other regions of Brazil, such as in Sao Paulo state, revealed to have high genetic diversity, as shown from microsatellite markers. Adjacent populations that have been generated though germplasm collection for management and cultivation showed similar genetic diversity. Those genetic materials could be used for recovering overexploited populations [36].
\n
Landscape genetics studies with wild animals have been focused in recognizing their patterns of moving across their habitats. On terrestrial lands, landscape genetics of animals has particular features in comparison to aquatic environments or even to terrestrial plants. Landscape patterns interfere with organism behavior, thereby affecting mating and dispersal and reflecting on population processes [37].
\n
\n
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6. Landscape genetics and human populations and diseases
\n
Genomic technologies have also enabled studies to uncover historical human migrations and the genetic structure and diversity of human populations. For example, a genome-wide study of Malaysian ethnic groups using a SNP array revealed that humans from the peninsular area of Malaysia had higher genetic diversity, which the authors associated with a contact zone for recent human migrations in the Asian continent [38]. Such an example suggests the association between the genetic structure of human populations with geographic variables. In fact, Peter et al. [39] show that genetic differentiation generally tends to increase over higher geographic distances; however, distortions in those patterns also frequently occur. The human population structure, then, seems to be quite dynamic.
\n
Landscape genetics also has been employed in epidemiological studies of human diseases. Statistical methods can be used in the identification of hotspot areas of disease movement [40]. This will have important implications in designing strategies for spread containment. One challenge, however, has been the application of landscape genetics methods in vector-borne diseases, which was reviewed by Hemming-Schroeder [40]. A few studies have been dedicated to such goal with human diseases. One interesting example is the correlation found between the genetic structure of Aedes mcintochi, a major vector for Rift Valley fever in Kenya, and mean precipitation values [41].
\n
In 2020, one of the major global health issues concerns the new COVID-19. Sequencing technologies coupled with landscape genomics approaches have the potential to identify dispersal patterns of the virus in order to contain its spreading. Landscape genetic approaches have the power of assisting the decision-making process.
\n
\n
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7. Concluding remarks
\n
Climate change and human interference are no longer to be neglected on natural ecosystems. Among several fields of study devoted to deciphering the impact of these processes, landscape genetics will provide a better comprehension of the interaction between organisms and their environment of origin. The boost in sequencing technologies is enabling the study of the most diverse range of organisms. In fact, the Earth BioGenome Project is intended to sequence, catalog, and characterize all eukaryotic diversity in the forthcoming decade [42]. With that information available, resequencing to the level of population and their association with landscape variables will provide information for designing appropriate strategies for the conservation of endangered forms of life as well as any other species. The resequencing of several human genomes will also enable a better comprehension of the human population structure throughout the world and how the landscape shapes its organization. This has been and will be continuing valuable information to comprehending the dispersion of human diseases as well.
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Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"molecular studies, environmental variables, population structure, genetic diversity, single nucleotide polymorphisms",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/71705.pdf",chapterXML:"https://mts.intechopen.com/source/xml/71705.xml",downloadPdfUrl:"/chapter/pdf-download/71705",previewPdfUrl:"/chapter/pdf-preview/71705",totalDownloads:231,totalViews:0,totalCrossrefCites:0,dateSubmitted:"April 23rd 2019",dateReviewed:"March 6th 2020",datePrePublished:"April 8th 2020",datePublished:"January 20th 2021",dateFinished:"April 8th 2020",readingETA:"0",abstract:"Landscape genetics combines population genetics and landscape ecology to understand processes that shape the distribution and organization of human, animal, or plant populations. This field of genetics emerged from the availability of several studies with classical molecular markers, such as isozymes, RAPD, AFLP, and microsatellites. Population genetic studies enabled the detection of population structure with those markers, but a more comprehensive analysis of natural populations was only possible with the development of statistical methods that combined both molecular data and environmental variables. Ultimately, the rapid development of sequencing technologies allowed studies at the genomic level, augmenting the resolution of association with environment factors. This chapter outlines basic concepts in landscape genetics, the main statistical methods used so far, and the perspectives of this field of knowledge into strategies for conservation of natural populations of plant and animal species. Moreover, we briefly describe the application of the field to understand historical human migration processes as well as how some diseases are spread throughout the world.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/71705",risUrl:"/chapter/ris/71705",signatures:"Enéas Ricardo Konzen and Maria Imaculada Zucchi",book:{id:"9569",title:"Methods in Molecular Medicine",subtitle:null,fullTitle:"Methods in Molecular Medicine",slug:"methods-in-molecular-medicine",publishedDate:"January 20th 2021",bookSignature:"Yusuf Tutar",coverURL:"https://cdn.intechopen.com/books/images_new/9569.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"196963",title:"Dr.",name:"Enéas Ricardo",middleName:null,surname:"Konzen",fullName:"Enéas Ricardo Konzen",slug:"eneas-ricardo-konzen",email:"erkonzen@gmail.com",position:null,institution:{name:"Federal University of Rio Grande do Sul",institutionURL:null,country:{name:"Brazil"}}},{id:"305359",title:"Prof.",name:"Maria Imaculada",middleName:null,surname:"Zucchi",fullName:"Maria Imaculada Zucchi",slug:"maria-imaculada-zucchi",email:"mizucchi@apta.sp.gov.br",position:null,institution:{name:"Agência Paulista de Tecnologia dos Agronegócios",institutionURL:null,country:{name:"Brazil"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Molecular markers and population structure studies",level:"1"},{id:"sec_3",title:"3. The concepts of landscape genetics and landscape genomics",level:"1"},{id:"sec_4",title:"4. A briefing on statistical approaches in landscape genetics",level:"1"},{id:"sec_5",title:"5. Applications of landscape genetics",level:"1"},{id:"sec_6",title:"6. Landscape genetics and human populations and diseases",level:"1"},{id:"sec_7",title:"7. Concluding remarks",level:"1"},{id:"sec_11",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'\nLewontin RC, Hubby JL. A molecular approach to the study of genic heterozygosity in natural populations. II. Amount of variation and degree of heterozygosity in natural populations of Drosophila pseudoobscura. Genetics. 1966;54(2):595\n'},{id:"B2",body:'\nWilliams JG, Kubelik AR, Livak KJ, Rafalski JA, Tingey SV. DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Research. 1990;18(22):6531-6535\n'},{id:"B3",body:'\nVos P, Hogers R, Bleeker M, Reijans M, Lee TV, Hornes M, et al. AFLP: A new technique for DNA fingerprinting. Nucleic Acids Research. 1995;23:4407-4414\n'},{id:"B4",body:'\nLitt M, Luty JA. A hypervariable microsatellite revealed by in vitro amplification of a dinucleotide repeat within the cardiac muscle actin gene. American Journal of Human Genetics. 1989;44(3):397\n'},{id:"B5",body:'\nLagercrantz U, Ellegren H, Kakanuga T. The abundance of various polymorphic microsatellite motifs differs between plants and vertebrates. Nucleic Acids Research. 1993;21:1111-1115\n'},{id:"B6",body:'\nManel S, Schwartz MK, Luikart G, Taberlet P. Landscape genetics: Combining landscape ecology and population genetics. Trends in Ecology & Evolution. 2003;18(4):189-197\n'},{id:"B7",body:'\nStorfer A, Murphy MA, Evans JS, Goldberg CS, Robinson S, Spear SF, et al. Putting the ‘landscape’ in landscape genetics. Heredity. 2007;98(3):128\n'},{id:"B8",body:'\nJoost S, Bonin A, Bruford MW, Després L, Conord C, Erhardt G, et al. A spatial analysis method (SAM) to detect candidate loci for selection: Towards a landscape genomics approach to adaptation. Molecular Ecology. 2007;16(18):3955-3969\n'},{id:"B9",body:'\nLi Y, Zhang XX, Mao RL, Yang J, Miao CY, Li Z, et al. Ten years of landscape genomics: Challenges and opportunities. Frontiers in Plant Science. 2017;8:2136\n'},{id:"B10",body:'\nStorfer A, Patton A, Fraik AK. Navigating the interface between landscape genetics and landscape genomics. Frontiers in Genetics. 2018;9:68\n'},{id:"B11",body:'\nKalia RK, Rai MK, Kalia S, Singh R, Dhawan AK. Microsatellite markers: An overview of the recent progress in plants. Euphytica. 2011;177(3):309-334\n'},{id:"B12",body:'\nVieira MLC, Santini L, Diniz AL, Munhoz CDF. Microsatellite markers: What they mean and why they are so useful. Genetics and Molecular Biology. 2016;39(3):312-328\n'},{id:"B13",body:'\nPowell W, Morgante M, Andre C, Hanafey M, Vogel J, Tingey S, et al. The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Molecular Breeding. 1996;2(3):225-238\n'},{id:"B14",body:'\nAbdul-Muneer PM. Application of microsatellite markers in conservation genetics and fisheries management: Recent advances in population structure analysis and conservation strategies. Genetics Research International. 2014;2014:1-11\n'},{id:"B15",body:'\nNadeem MA, Nawaz MA, Shahid MQ , Doğan Y, Comertpay G, Yıldız M, et al. DNA molecular markers in plant breeding: Current status and recent advancements in genomic selection and genome editing. Biotechnology & Biotechnological Equipment. 2018;32(2):261-285\n'},{id:"B16",body:'\nXia W, Luo T, Zhang W, Mason AS, Huang D, Huang X, et al. Development of high-density SNP markers and their application in evaluating genetic diversity and population structure in Elaeis guineensis. Frontiers in Plant Science. 2019;10:130\n'},{id:"B17",body:'\nKonzen ER. Towards conservation strategies for forest tree endangered species: The meaning of population genetic statistics. Advances in Forestry Science. 2014;1(1):45-51\n'},{id:"B18",body:'\nStorfer A, Murphy MA, Spear SF, Holderegger R, Waits LP. Landscape genetics: Where are we now? Molecular Ecology. 2010;19(17):3496-3514\n'},{id:"B19",body:'\nAndam CP, Challagundla L, Azarian T, Hanage WP, Robinson DA. Population structure of pathogenic bacteria. In: Genetics and Evolution of Infectious Diseases. Amsterdam: Elsevier; 2017\n'},{id:"B20",body:'\nElshire RJ, Glaubitz JC, Sun Q , Poland JA, Kawamoto K, Buckler ES, et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. 2011;6(5):e19379\n'},{id:"B21",body:'\nPoland JA, Brown PJ, Sorrells ME, Jannink JL. Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS One. 2012;7(2):e32253\n'},{id:"B22",body:'\nBalkenhol N, Gugerli F, Cushman SA, Waits LP, Coulon A, Arntzen JW, et al. Identifying future research needs in landscape genetics: Where to from here? Landscape Ecology. 2009;24(4):455\n'},{id:"B23",body:'\nMantel N. The detection of disease clustering and a generalized regression approach. Cancer Research. 1967;27(2 part 1):209-220\n'},{id:"B24",body:'\nDiniz-Filho JAF, Soares TN, Lima JS, Dobrovolski R, Landeiro VL, Telles MPDC, et al. Mantel test in population genetics. Genetics and Molecular Biology. 2013;36(4):475-485\n'},{id:"B25",body:'\nKwak M, Gepts P. Structure of genetic diversity in the two major gene pools of common bean (Phaseolus vulgaris L., Fabaceae). Theoretical and Applied Genetics. 2009;118(5):979-992\n'},{id:"B26",body:'\nAriani A, Mier B, Teran JC, Gepts P. Spatial and temporal scales of range expansion in wild Phaseolus vulgaris. Molecular Biology and Evolution. 2017;35(1):119-131\n'},{id:"B27",body:'\nGepts P, Osborn TC, Rashka K, Bliss FA. Phaseolin-protein variability in wild forms and landraces of the common bean (Phaseolus vulgaris): Evidence for multiple centers of domestication. Economic Botany. 1986;40(4):451-468\n'},{id:"B28",body:'\nBitocchi E, Nanni L, Bellucci E, Rossi M, Giardini A, Zeuli PS, et al. Mesoamerican origin of the common bean (Phaseolus vulgaris L.) is revealed by sequence data. Proceedings of the National Academy of Sciences. 2012;109(14):E788-E796\n'},{id:"B29",body:'\nBitocchi E, Bellucci E, Giardini A, Rau D, Rodriguez M, Biagetti E, et al. Molecular analysis of the parallel domestication of the common bean (Phaseolus vulgaris) in Mesoamerica and the Andes. New Phytologist. 2013;197(1):300-313\n'},{id:"B30",body:'\nTeran JCBM, Konzen ER, Medina V, Palkovic A, Ariani A, Tsai SM, et al. Root and shoot variation in relation to potential intermittent drought adaptation of Mesoamerican wild common bean (Phaseolus vulgaris L.). Annals of Botany. 2019;124(6):917\n'},{id:"B31",body:'\nZucchi MI, Cordeiro EM, Allen C, Novello M, Viana JPG, Brown PJ, et al. Patterns of genome-wide variation, population differentiation and SNP discovery of the red banded stink bug (Piezodorus guildinii). Scientific Reports. 2019;9(1):1-11\n'},{id:"B32",body:'\nReis MS, Guerra MP, Nodari RO, Ribeiro RJ, Reis A. Distribuição geográfica e situação atual das populações na área de ocorrência de Euterpe edulis Martius. Sellowia. 2000a;49-52:324-335\n'},{id:"B33",body:'\nMartinelli M, Moraes A. Livro Vermelho da Flora Do Brasil. Rio de Janeiro: Instituto de Pesquisas Jardim Botânico do Rio de Janeiro; 2013. p. 1100\n'},{id:"B34",body:'\nKonzen ER, Martins MP. Contrasting levels of genetic diversity among populations of the endangered tropical palm Euterpe edulis Martius. Cerne. 2017;23(1):31-42\n'},{id:"B35",body:'\nSoares LASS, Cazetta E, Santos LR, França DDS, Gaiotto FA. Anthropogenic disturbances eroding the genetic diversity of a threatened palm tree: A multi-scale approach. Frontiers in Genetics. 2019;10:1090\n'},{id:"B36",body:'\nNovello M, Viana JPG, Alves-Pereira A, de Aguiar Silvestre E, Nunes HF, Pinheiro JB, et al. Genetic conservation of a threatened Neotropical palm through community-management of fruits in agroforests and second-growth forests. Forest Ecology and Management. 2018;407:200-209\n'},{id:"B37",body:'\nWaits LP, Cushman SA, Spear SF. Applications of landscape genetics to connectivity research in terrestrial animals [chapter 12]. In: Balkenhol N, Cushman SA, Storfer AT, Waits LP, editors. Landscape Genetics: Concepts, Methods, Applications. 1st ed. West Sussex: John Wiley and Sons Ltd; 2016. pp. 199-219\n'},{id:"B38",body:'\nDeng L, Hoh BP, Lu D, Fu R, Phipps ME, Li S, et al. The population genomic landscape of human genetic structure, admixture history and local adaptation in peninsular Malaysia. Human Genetics. 2014;133(9):1169-1185\n'},{id:"B39",body:'\nPeter BM, Petkova D, Novembre J. Genetic landscapes reveal how human genetic diversity aligns with geography. Molecular Biology and Evolution. 2019;37(4):943-951\n'},{id:"B40",body:'\nHemming-Schroeder E, Lo E, Salazar C, Puente S, Yan G. Landscape genetics: A toolbox for studying vector-borne diseases. Frontiers in Ecology and Evolution. 2018;6:21\n'},{id:"B41",body:'\nCampbell LP, Alexander AM. Landscape genetics of Aedes mcintoshi (Diptera: Culicidae), an important vector of rift valley fever virus in northeastern Kenya. Journal of Medical Entomology. 2017;54(5):1258-1265\n'},{id:"B42",body:'\nLewin HA, Robinson GE, Kress WJ, Baker WJ, Coddington J, Crandall KA, et al. Earth BioGenome project: Sequencing life for the future of life. Proceedings of the National Academy of Sciences. 2018;115(17):4325-4333\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Enéas Ricardo Konzen",address:"erkonzen@gmail.com",affiliation:'
Interdisciplinary Department, Center of Limnological, Coastal and Marine Studies, Federal University of Rio Grande do Sul, Brazil
Secretary of Agriculture and Supply of São Paulo State, Brazil
Institute of Biology, University of Campinas, Brazil
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The combined printing and delivery times for orders vary from 12-20 business days, depending on the printed quantity and destination. This period does not include any customs clearance difficulties that may arise and that are beyond our control. Once your order has been printed and shipped, you will receive a confirmation email that includes your DHL tracking number. You can then track your order at www.dhl.com.
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My order has not arrived, what do I do?
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If you do not receive your order within 30 days, please contact us to inquire about the shipping status at orders@intechopen.com.
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Return policy
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POD products are non-returnable and non-refundable, except in the event of poor print quality or an error in quantity. If we delivered the item to you in error or the item is faulty, please contact us. Inspect your order carefully when it arrives. Any problems should be immediately reported to orders@intechopen.com.
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Taxes and customs
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Taxes: Residents of European Union countries need to add a Book Value-Added Tax of 5%. Institutions and companies, registered as VAT taxable entities in their own EU member state, will not pay VAT by providing us their VAT registration number. This is made possible by the EU reverse charge method.
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Customs: Shipping costs do not include any duties, taxes or clearing charges levied by the destination country. These charges are the responsibility of the customer and will vary from country to country.
Orders have to be prepaid in advance and before printing. We accept payment in GBP, EUR and USD. Payments can be made by bank transfer or cheque, by credit card (Visa, MasterCard, American Express, Discover Card) and PayPal worldwide online payments system. In accordance with the best security practice, we do not accept card orders via email.
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General shipping and delivery information
\n\n
The combined printing and delivery times for orders vary from 12-20 business days, depending on the printed quantity and destination. This period does not include any customs clearance difficulties that may arise and that are beyond our control. Once your order has been printed and shipped, you will receive a confirmation email that includes your DHL tracking number. You can then track your order at www.dhl.com.
\n\n
My order has not arrived, what do I do?
\n\n
If you do not receive your order within 30 days, please contact us to inquire about the shipping status at orders@intechopen.com.
\n\n
Return policy
\n\n
POD products are non-returnable and non-refundable, except in the event of poor print quality or an error in quantity. If we delivered the item to you in error or the item is faulty, please contact us. Inspect your order carefully when it arrives. Any problems should be immediately reported to orders@intechopen.com.
\n\n
Taxes and customs
\n\n
Taxes: Residents of European Union countries need to add a Book Value-Added Tax of 5%. Institutions and companies, registered as VAT taxable entities in their own EU member state, will not pay VAT by providing us their VAT registration number. This is made possible by the EU reverse charge method.
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Customs: Shipping costs do not include any duties, taxes or clearing charges levied by the destination country. These charges are the responsibility of the customer and will vary from country to country.
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