Crop yield response to water - Cra

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After LAI of the planting is estimated, the corresponding CC can be read off Figure 1 or similarrelations, and used as CC x for the simulation. As is obvious in Figure 1, for cases where thecanopy is full or nearly full and the plant density is not widely different from that of thereference, the CC x estimate made with the above procedure should be accurate within a fewpercentage points. The estimates become less and less reliable as the difference in densitybecomes greater, or if CC x or reference CC x is substantially less than full cover. On the otherhand, for cases where there is little interplant competition for PAR because of a sparse canopy(e.g. CC < 0.5), Equation 1 can be used along with Figure 1 to obtain a reasonable estimate ofCC x by setting F adj close to 1.0.Canopy growth coefficient (CGC)CGC is a measure of the intrinsic ability of the canopy to expand. A CGC of 0.11, for example,means that each day the CC is 11 percent greater than the CC of the day before during thefirst half of canopy development. CGC is virtually a constant when temperature effects areaccounted for by using GDD as the driver and there is no stress. Because CGC is based on firstorder kinetics (Bradford and Hsiao, 1982), a good way to derive CGC is to plot the log of CCvs. time and take the slope of the linearly fitted curve to be CGC, provided that only CC datameasured from shortly after seedling emergence to approximately 60 percent cover, that donot include periods of heavy fruit load on the crop, are used. If CC data are too limited forthe period specified above, but additional data have been collected up to canopy closure ornear full cover, CGC can be parameterized using the canopy growth components of AquaCrop.Instead of running the model, which is more time consuming, a simple Excel programmelimited only to canopy growth is available on the FAO AquaCrop website for this purpose.Commonly, both cc o and CGC would be unknown, requiring trial-and-error iterations to findthe best values for the two parameters. As general guiding principles for parameterizing CGC,the main considerations appear to be whether the crop is C 3 or C 4 , and whether the crop ismore efficient in the capture of PAR. For maize and sorghum, two important C 4 crops alreadyparameterized for AquaCrop, the CGC is 0.17 per day on a calendar time basis and 0.013 on aGDD basis. For a number of C 3 species, the CGC is around 0.09 to 0.12 per day on a calendartime basis. There are exceptions. One is the C 3 crop sunflower, its CGC is in the order of 0.22per day (calendar time), presumably because of its solar tracking ability to capture more PARper unit of canopy. In the trial-and-error runs to parameterize cc o and CGC, several scenariosof outcome are possible when the simulated CC over time are compared with the measureddata. These are listed in the first column of Table 3. In the second column are given possiblecauses for the lack of agreement and adjustments to make.If the comparison of simulated vs. measured data does not follow any of the scenarios in thetable, it is possible that either the experimental data are questionable, or the weather datamay be deficient. The weather data are particularly suspect if 10-day or monthly minimum andmaximum temperature are used instead of daily valuesAquaCrop has built in an alternative to estimate CGC, based on the time required for CC toreach CC x . This feature is provided for users who want to simulate roughly the production andwater use of a crop with some or many of the crop parameters not known. It should not berelied on to parameterize CGC, because in such cases cc o and plant density or initial canopycover (CC o ), which are equally important in determining the time to reach maximum cover, aremost certainly not known.AquaCrop parameterization, calibration, and validation Guide 81
Table 3Comparison of simulated with measured canopy cover and possible adjustments in the modelparameters to improve the match.Agreement between simulated CC(CC sim ) and measured CC (CC meas )CC sim is either lower or higher than CC meas fromtime of emergence to CC x . Same CC x reached butat different times. Slopes of the two curves forthe period of rapid canopy growth are similarPossible cause(s) of discrepancyand suggested remedial actionEither cc o is too low or plant density is too low,or, respectively, cc o is too high or plant densityis too high. Check plant density data and trylarger (or smaller) cc o . CGC and CC x probably OKCC sim coincides with CC meas early in season butgradually becomes either lower or higher. SameCC x reached but at different timesCGC is either too low or too high, respectively.Make the appropriate adjustment in CGC. CC xand cc o probably OKCC sim is either lower or higher than CC meas earlyin season but the trend reverses gradually laterand the same CC x is reached but at differenttimesEither cc o is too low or plant density is too low,or, respectively, cc o is too high or plant densityis too high. CGC is either too high or too lowrespectively. Check plant density data and trylarger (or smaller) cc o and lower (or higher)CGC. CC x probably OKCC sim coincide well with CC meas over the seasonValues of cc o , CGC, and CC x are good for this setof experimental dataCanopy decline coefficient (CDC)After the canopy begins to senesce, CC is reduced progressively by applying an empirical canopydecline coefficient (CDC) (Raes et al., 2011). If there are LAI data spanning the senescencephase, they should be converted to CC using Equation 1 and a value for CDC selected to matchthe simulated CC decline with the measured values. Regrettably, in many studies detailed LAIdata are lacking for this phase. In this case, CDC may be set initially according to observationsof the canopy's speed of yellowing, and then refined by trial-and-error simulations to findthe CDC that gives the best fit of the measured biomass data during the senescence phase.In terms of predicting biomass and yield, AquaCrop is not very sensitive to the extent of CCdecline near maturity, because the model assumes a continuous decrease in the efficiency ofconverting normalized Tr to biomass for that period.Normalized water productivity (WP*)The water productivity (WP) of concern here is the ratio of biomass produced to the amount ofwater transpired (WP B/Tr ), and the normalized water productivity (WP*) is the ratio of biomassproduced to water transpired, normalized for the evaporative demand and CO 2 concentrationof the atmosphere.WP normalized for evaporative demandTranspiration, the denominator of WP, is extremely difficult to measure and separate from soilevaporation in the field. Fortunately, there are numerous sets of data on biomass productionvs. consumptive water use, which can be used to derive WP B/Tr , and hence WP* if the requiredweather data are available. Plots of biomass vs. normalized ET, based on sequential samplingover the season, should exhibit a portion of rising slope at the beginning followed by a straight-82crop yield response to water
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Crop yieldresponse to waterFAOIRRIG
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1. IntroductionFood production and
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Lead AuthorsMartin Smith(formerly F
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3. Yield response to waterof herbac
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The WP parameter introduced in Aqua
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figure 7 The root zone depicted as
Page 47 and 48: threshold and 1.0 at the lower thre
Page 50 and 51: also calculated by multiplying with
Page 53: FIGURE 14 Schematic representation
Page 57 and 58: figure 17ClimateInput data defining
Page 59 and 60: Table 1 Conservative crop parameter
Page 62: figure 18 The Main AquaCrop menu.di
Page 67: Applications to Irrigation Manageme
Page 72: Box 1 Simulating deficit irrigation
Page 75 and 76: for each planting date. If there ar
Page 77: ox 2 (CONTINUED)FIGURE 1 Difference
Page 83 and 84: Heng, L.K., Hsiao,T.C., Evett, S.,
Page 88 and 89: Table 2Additional information and d
Page 90 and 91: capacity (FC) and permanent wilting
Page 95: densities. This range is referred t
Page 99 and 100: In Equation 3 C a is the mean air C
Page 102: REFERENCESAllen, R., Pereira, L., R
Page 107 and 108: Lead AuthorSenthold Asseng(formerly
Page 109 and 110: Figure 1 World wheat harvested area
Page 113 and 114: When nutrition is limiting, yield p
Page 116: wheat 101
Page 120 and 121: Figure 1 World rice harvested area
Page 123: Response to StressesBecause rice ev
Page 129 and 130: Lead AuthorTheodore C. Hsiao(Univer
Page 132 and 133: emergence to flowering is about 65
Page 135 and 136: ReferencesAyers, R.S. & Westcot, D.
Page 140: Figure 1 World soybean harvested ar
Page 144 and 145: A number of studies indicate that s
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ReferencesBhatia, V.S., Piara Singh
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Figure 1 World barley harvested are
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Barley development may be thought o
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The seasonal water requirements for
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Lead AuthorSuhas P. Wani(ICRISAT, A
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sowing usually starts in late Septe
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loss. If water stress is severe eno
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ReferencesFAO. 2011. FAOSTAT online
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Figure 1 World cotton harvested are
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Response to StressesCotton stands o
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FAO. 2011. FAOSTAT online database,
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spring. In double cropping, sowing
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size as affected by soil water defi
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sunflower 171
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to produce energy (electricity from
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Response to stressLow temperatureSu
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Sugarcane 181
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Figure 1 World potato harvested are
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initiation, vigorous canopy, profus
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ReferencesBradshaw, J.E. 2009. A ge
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Tomato requires soils with proper w
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and there is frequent wetting of ex
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is so excessive that fruit setting
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Lead AuthorsMichele Rinaldi(CRA, Ba
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North Africa and near East ranges f
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Water use & ProductivitySugar beets
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ReferencesAllen, R.G., Pereira, L.S
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Figure 1 Alfalfa harvested area (SA
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Water use & ProductivityAs a perenn
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SalinityAlfalfa is tolerant to rela
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Lead AuthorAsha Karunaratne(formerl
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Generally, the growing season begin
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FAO. 2011. FAOSTAT online database,
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*Flower and grain colour presented
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Figure 1 World quinoa harvested are
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with typical values around 10.5 g/m
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ReferencesAlvarez-Jubete, L., Arend
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Growth and developmentThe common me
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soils (EARO, 2002). Tef has some to
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tef 243
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Lead AuthorsElias Fereres(Universit
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equirements per unit land area for
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figure 6 The water balance of an or
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ox 2 Understanding the transpiratio
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Orchard transpirationTree Tr is det
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ox 4 Sample calculation of E dz , E
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ox 5 Computing olive tree transpira
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FIGURE 10 Crop coefficient (K c ) c
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For training systems on a vertical
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ox 7Consumptive and non-consumptive
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are seeking more precision in their
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ox 9 Examples of soil water monitor
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ox 10 (CONTINUED)The major limitati
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ox 12Definition of CWSI and an exam
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The water budget methodWith this me
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ox 15 Evolution of soil water under
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opening and photosynthesis relative
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that occur during the periods of fr
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The crop, where price can vary more
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Modify horticultural practicesPruni
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FIGURE 13Comparison of yield per un
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season. Thus the risks of salinity
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4.1 Fruit trees and vinesEditor:Eli
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Figure 1 Production trends for oliv
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Figure 2Occurrence and duration of
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The use of displacement sensors to
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Figure 4 Relationship between relat
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Table 3 Sample calculation of month
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clayey soils. If supply is very lim
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Lead AuthorDavid A. Goldhamer(forme
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Fruit growth during this stage is t
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Season-long stressSeveral studies h
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Table 1Published monthly crop coeff
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Four crop-water-production function
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size distribution toward more favou
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Lead AuthorSAmos Naor(GRI, Universi
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Apples tend to have a biennial bear
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water stress and thus highly respon
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indicate that deficit irrigation ad
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Figure 7Effect of midday light inte
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Figure 10Response of marketable fru
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Failla, O., Zocchi, Z., Treccani, C
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Figure 1 Production trends for plum
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soil water. In young orchards, post
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Figure 3 Relationships between rela
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ReferencesAllen, R.G., Pereira, L.S
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Figure 1 Production trends for almo
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FIGURE 2The three stages of almond
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Figure 3Differences in the cultivar
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Indicators of tree water statusTo p
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nuts are rapidly expanding and late
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ReferencesAyars, J.E., Johnson, R.
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Table 2 (Continued)Year TreatmentWa
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Table 3 (continued)Potential900 mmA
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Figure 1 Production trends for pear
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(Elkins et al., 2007). The appearan
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out in Spain under more common grow
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Figure 4Relationships between the p
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Data in Figure 5 suggest that there
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e saved, but this causes a reductio
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pear 389
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Figure 1 Production trends for peac
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Figure 2bEvolution of vegetative (s
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The postharvest period is important
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the midday stem-water potential in
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PHOTOPeach leaf appearance under th
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FIGURE 5Relation between the crop c
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In applying RDI strategies an impor
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peach 407
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Figure 1 Production trends of walnu
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1 100 mm, a team in California appl
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Figure 1 Production trends for pist
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There are two types of shoot growth
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FIGURE 3Time course development of
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Stage III was the most stress sensi
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(see Chapter 4), as in other specie
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Table 2 Suggested RDI strategies fo
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Lead AuthorSCristos Xiloyannis(Univ
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is completed within 20 days; therea
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or peach. However, because fruit is
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to a midday value varying between -
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Lead AuthorRaúl Ferreyraand Gabrie
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The most critical developmental per
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Figure 4Effects of the level of app
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Lead AuthorJordi Marsal(IRTA, Lleid
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water stress should not be imposed
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Under certain growing conditions, s
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ReferencesAntunez A., Stockle, C. &
Page 463 and 464:
Lead AuthorSVictor O. Sadras(SARDI
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Current season inflorescences becom
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the actual timing of each critical
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environments, rainfall pulses and l
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Figure 6Yield reduction with increa
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Table 3Yield and yield components o
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Figure 9Wine quality score as a fun
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Figure 11Wine attributes of Tempran
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(McCarthy and Coombe, 1999), but ev
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Figure 15Relative yield as a functi
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Shiraz, Grenache and Mourvèdre in
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Girona, J., Mata, M., del Campo, J.
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Lead AuthorSCristos Xiloyannis(Univ
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Figure 2Seasonal pattern of the lea
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Figure 5Fraction of the exposed and
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FIGURE 9Soil volume explored by roo
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Water Requirements and IrrigationMa
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5. EpilogueThis publication, Irriga
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operating systems (e.g., Linux, Uni
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Crop yield response to waterAbstrac
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