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Irrigated areas have increased throughout the globe to support the
growing global population and to cope with climate change impacts (@Siyal2023). In this context, the
{CropWaterBalance} allows users to keep track of the soil water deficit
in the root zone through the crop water balance accounting (@Andales2012). The goal of the package is to
assist users in making decisions about when and how much to irrigate.
The most important function of the package is the CWB(),
which calculates several parameters of the crop water balance, including
crop evapotranspiration (ETc), actual crop evapotranspiration, stored
water in the root zone and soil water deficit (D). The function also
suggests when irrigate, considering the management allowed depletion
(MAD) provided by the users. Although the FAO-Penman and Monteith
equation is recognized as the standard method for estimating daily
amounts of reference evapotranspiration (ETO) (@Allen1998), the high number of variables
required for calculating this model limits its operational use in
several regions in the world. Thus, the package includes the function
ETO_PM(), which estimate daily ETO amounts through the
FAO-Penman and Monteith equation, and the functions
ETO_PT() and ETO_HS()), which calculates this
agrometeorological parameter through other two alternative (and simpler)
methods: Priestley-Taylor (@Priestley1972)
and Hargreaves-Samani (@Hargreaves1985),
respectively. Additionally, the {CropWaterBalance} has other two
functions (Compare() and Descriptive()). The
first calculates measures of accuracy and agreement between two data
samples and the second calculates descriptive statistics for these
samples. Therefore, these two functions assist users in selecting
suitable ET0 estimating methods for a particular region or season.
Load the library in your R session.
library(CropWaterBalance)
#>
#> Attaching package: 'CropWaterBalance'
#> The following object is masked from 'package:methods':
#>
#> CompareReference evapotranspiration (ET0) is the combined process of
evaporation and transpiration that occurs from well-fertilized and
disease-free hypothetical grass reference crop, grown in large fields
under no soil water restriction and achieving full production (@Allen1998). The ETO_PM() function
calculates daily ETO amounts using the FAO-Penman and Monteith equation
(in millimetres).
ETO_PM() in Campinas-SP, BrazilTavg <- DataForCWB[, 2]
Tmax <- DataForCWB[, 3]
Tmin <- DataForCWB[, 4]
Rn <- DataForCWB[, 6]
WS <- DataForCWB[, 7]
RH <- DataForCWB[, 8]
G <- DataForCWB[, 9]
CpsET0PM <-
ET0_PM(
Tavg = Tavg,
Tmax = Tmax,
Tmin = Tmin,
Rn = Rn,
RH = RH,
WS = WS,
G = G,
Alt = 700
)
head(CpsET0PM)
#> ET0_PM
#> [1,] 2.440372
#> [2,] 4.171917
#> [3,] 4.290477
#> [4,] 3.665459
#> [5,] 4.848520
#> [6,] 5.669878By analyzing the ET0_PM() function, the user verify that
the FAO-Penman and Monteith equation requires several inputs, which
includes the soil heat flux (G). This latter variable is rarely measure.
This is the reason why the {CropWaterBalance} has an auxiliary function
(Soil_Heat_Flux()) that estimates G as function of daily
average air temperature values. The users may use this auxiliary
function to estimate G and then apply the ET0_PM()
function. Alternatively, the users may simply run ET0_PM()
without the G argument. In this case, this latter function will
automatically use Soil_Heat_Flux() and then estimate ETO.
See the example below.
Tavg <- DataForCWB[, 2]
Tmax <- DataForCWB[, 3]
Tmin <- DataForCWB[, 4]
Rn <- DataForCWB[, 6]
WS <- DataForCWB[, 7]
RH <- DataForCWB[, 8]
G <- Soil_Heat_Flux(Tavg)
#> Warning in Soil_Heat_Flux(Tavg): The first 3 G values were set to zero
CpsET0PM_WithG <-
ET0_PM(
Tavg = Tavg,
Tmax = Tmax,
Tmin = Tmin,
Rn = Rn,
RH = RH,
WS = WS,
G = G,
Alt = 700
)
CpsET0PM_WithoutG <-
ET0_PM(
Tavg = Tavg,
Tmax = Tmax,
Tmin = Tmin,
Rn = Rn,
RH = RH,
WS = WS,
Alt = 700
)
#> Warning in Soil_Heat_Flux(Tavg): The first 3 G values were set to zero
head(cbind(CpsET0PM_WithG, CpsET0PM_WithoutG))
#> ET0_PM ET0_PM
#> [1,] 2.293183 2.293183
#> [2,] 4.104242 4.104242
#> [3,] 4.333359 4.333359
#> [4,] 3.665304 3.665304
#> [5,] 4.848440 4.848440
#> [6,] 5.670052 5.670052ETO_PT() and ET0_HS() in
Campinas-SP, BrazilAs described in the introduction, the users may need to estimate ETO
using non-standard methods, which require less input than the FAO-Penman
and Monteith. The {CropWaterBalance} allows users to estimate this
agrometeorological parameter using the Priestley-Taylor
(ETO_PT()) and Hargreaves-Samani (ET0_HS())
methods. Note that the same considerations made for G in respect to
ETO_PM() are valid for ETO_PT().
Tavg <- DataForCWB[, 2]
Tmax <- DataForCWB[, 3]
Tmin <- DataForCWB[, 4]
Ra <- DataForCWB[, 5]
Rn <- DataForCWB[, 6]
G <- DataForCWB[, 9]
CpsET0PT <- ET0_PT(Tavg = Tavg, Rn = Rn, G = G)
CpsET0HS <- ET0_HS(
Ra = Ra,
Tavg = Tavg,
Tmax = Tmax,
Tmin = Tmin
)
head(cbind(CpsET0PT, CpsET0HS))
#> ET0_PT ET0
#> [1,] 3.432709 4.703700
#> [2,] 5.849554 5.331592
#> [3,] 6.432616 5.664174
#> [4,] 5.695334 6.163377
#> [5,] 7.023900 5.291303
#> [6,] 7.817355 6.251883The estimation of CpsET0PT and/or CpsET0HS raises the following
question: Can these alternative methods really replace the FAO-Penman
and Monteith model? Although the answer to this question may be regarded
as a complex function involving local weather conditions and users’
subjective choices, the (Compare()) and
(Descriptive()) functions provide statistical information,
which may assist users in such decision. In this context, the
Compare() and Descriptive() functions may be
used to verify how well an alternative ET0 estimating method approaches
ET0_PM.
Tavg <- DataForCWB[, 2]
Tmax <- DataForCWB[, 3]
Tmin <- DataForCWB[, 4]
Ra <- DataForCWB[, 5]
Rn <- DataForCWB[, 6]
WS <- DataForCWB[, 7]
RH <- DataForCWB[, 8]
G <- DataForCWB[, 9]
CpsET0PM <-
ET0_PM(
Tavg = Tavg,
Tmax = Tmax,
Tmin = Tmin,
Rn = Rn,
RH = RH,
WS = WS,
G = G,
Alt = 700
)
CpsET0PT <- ET0_PT(Tavg = Tavg, Rn = Rn, G = G)
CpsET0HS <- ET0_HS(
Ra = Ra,
Tavg = Tavg,
Tmax = Tmax,
Tmin = Tmin
)
PM_PT <- Compare(Sample1 = CpsET0PM, Sample2 = CpsET0PT)
PM_PT
#> AME RMSE dorig dmod dref RQuad
#> 1 1.69222 1.813449 0.6403158 0.376103 -0.05737454 0.8675223
Descrp_PM_PT <-
cbind(Descriptive(Sample = CpsET0PM), Descriptive(Sample = CpsET0PT))
Descrp_PM_PT
#> SampleSize Avg Med SD SE MaxValue MinValue FreqZero% SampleSize Avg
#> 1 129 3.37 3.46 0.97 0.09 5.76 1.26 0 129 5.06
#> Med SD SE MaxValue MinValue FreqZero%
#> 1 5.22 1.46 0.13 8.15 2.14 0
PM_HS <- Compare(Sample1 = CpsET0PM, Sample2 = CpsET0HS)
PM_HS
#> AME RMSE dorig dmod dref RQuad
#> 1 1.468722 1.630292 0.6033421 0.3818204 0.07924634 0.5546701
Descrp_PM_HS <-
cbind(Descriptive(Sample = CpsET0PM), Descriptive(Sample = CpsET0HS))
Descrp_PM_HS
#> SampleSize Avg Med SD SE MaxValue MinValue FreqZero% SampleSize Avg
#> 1 129 3.37 3.46 0.97 0.09 5.76 1.26 0 129 4.82
#> Med SD SE MaxValue MinValue FreqZero%
#> 1 5.11 1.09 0.1 6.73 2.25 0The results provided by the Compare() and
Descriptive() functions, indicate that none of the two
alternative methods can be used to replace the for calculating daily ET0
amounts. For instance, the values of the modified index of agreement
(dmod) (@Willmott1985) remained below 0.4
for both comparisons (PM vs PT) and (PM vs HS). Additionally, the
corresponding absolute mean errors (AME; 1.69222 and 1.468722)
represents, approximately, 50% and 43% of the average value of the
ET0_PM (3.367377).
InitialD() and CWB() in Campinas-SP,
BrazilConsidering the previous results, we applied the CWB()
using daily values of rainfall and ET0_PM obtained/estimated from daily
data of the weather station of Campinas. We included this meteorological
data in the package (DataForCWB), along with parameters required for
calculating the crop water balance: depth of the root zone (Drz),
available water capacity (AWC; amount of water between field capacity
and permanent wilting point), management allowed depletion (MAD), and
crop coefficient (Kc). The example below loaded DataForCWB to apply
CWB() in Campinas-SP. Only for the sake of simplicity, Kc
values were set to 1 for the entire period. The initial D value
(Dinitial) required for initiating the crop water balance account was
obtained using function DInitial() with teta_FC and AWC
values obtained from two data sets included in the package
(DataForSWC.rda and DataForAWC). These data sets provide soil water
content values (m3/m3) for the effective root zone at the field capacity
and at permanent wilting point, and AWC values (mm/m), respectively
(teta_obs was measured in the field). We applied the CWB()
function considering two scenarios. Scenario 1 in which no irrigation
was applied and scenario 2 in which the package’s recommendation about
when and how much to irrigate was met.
Tavg <- DataForCWB[, 2]
Tmax <- DataForCWB[, 3]
Tmin <- DataForCWB[, 4]
Rn <- DataForCWB[, 6]
WS <- DataForCWB[, 7]
RH <- DataForCWB[, 8]
G <- DataForCWB[, 9]
ET0 <- ET0_PM(Tavg, Tmax, Tmin, Rn, RH, WS, G, Alt = 700)
Dinitial <-
Dinitial(teta_FC = 0.30,
teta_Obs = 0.17,
Drz = DataForCWB[1, 11])
Rain <- DataForCWB[, 10]
Drz <- DataForCWB[, 11]
AWC <- DataForCWB[, 12]
MAD <- DataForCWB[, 13]
Kc <- DataForCWB[, 14]
Irrig <- DataForCWB[, 15]
Scenario1 <- CWB(
Rain = Rain,
ET0 = ET0,
AWC = AWC,
Drz = Drz,
Kc = Kc,
Irrig = Irrig,
MAD = MAD,
InitialD = Dinitial,
start.date = "2011-11-23"
)
Scenario1[1:8, ]
#> DaysSeason Rain Irrig ET0 Kc WaterStressCoef_Ks ETc (P+Irrig)-ETc
#> 2011-11-23 1 45.5 0 2.4 1 1.0 2.4 43.0
#> 2011-11-24 2 0.3 0 4.2 1 1.0 4.2 -3.9
#> 2011-11-25 3 0.0 0 4.3 1 1.0 4.3 -4.3
#> 2011-11-26 4 11.4 0 3.7 1 1.0 3.7 7.8
#> 2011-11-27 5 0.3 0 4.8 1 1.0 4.8 -4.6
#> 2011-11-28 6 0.0 0 5.7 1 1.0 5.7 -5.7
#> 2011-11-29 7 0.0 0 5.8 1 0.9 5.8 -5.8
#> 2011-11-30 8 0.5 0 3.0 1 0.8 3.0 -2.5
#> NonStandardCropEvap ET_Defict TAW SoilWaterDeficit d_MAD
#> 2011-11-23 2.4 0.0 45.7 0.0 13.7
#> 2011-11-24 4.2 0.0 45.7 3.9 13.7
#> 2011-11-25 4.3 0.0 45.7 8.2 13.7
#> 2011-11-26 3.7 0.0 45.7 0.4 13.7
#> 2011-11-27 4.8 0.0 45.7 5.0 13.7
#> 2011-11-28 5.7 0.0 45.7 10.7 13.7
#> 2011-11-29 5.3 0.5 45.7 16.5 13.7
#> 2011-11-30 2.5 0.5 45.7 19.0 13.7
#> D>=dmad
#> 2011-11-23 No
#> 2011-11-24 No
#> 2011-11-25 No
#> 2011-11-26 No
#> 2011-11-27 No
#> 2011-11-28 No
#> 2011-11-29 Yes. Irrigate 16 mm
#> 2011-11-30 Yes. Irrigate 19 mm
Irrig[7] <- 16
Scenario2 <- CWB(
Rain = Rain,
ET0 = ET0,
AWC = AWC,
Drz = Drz,
Kc = Kc,
Irrig = Irrig,
MAD = MAD,
InitialD = Dinitial,
start.date = "2011-11-23"
)
Scenario2[1:8, ]
#> DaysSeason Rain Irrig ET0 Kc WaterStressCoef_Ks ETc (P+Irrig)-ETc
#> 2011-11-23 1 45.5 0 2.4 1 1 2.4 43.0
#> 2011-11-24 2 0.3 0 4.2 1 1 4.2 -3.9
#> 2011-11-25 3 0.0 0 4.3 1 1 4.3 -4.3
#> 2011-11-26 4 11.4 0 3.7 1 1 3.7 7.8
#> 2011-11-27 5 0.3 0 4.8 1 1 4.8 -4.6
#> 2011-11-28 6 0.0 0 5.7 1 1 5.7 -5.7
#> 2011-11-29 7 0.0 16 5.8 1 1 5.8 10.2
#> 2011-11-30 8 0.5 0 3.0 1 1 3.0 -2.5
#> NonStandardCropEvap ET_Defict TAW SoilWaterDeficit d_MAD D>=dmad
#> 2011-11-23 2.4 0 45.7 0.0 13.7 No
#> 2011-11-24 4.2 0 45.7 3.9 13.7 No
#> 2011-11-25 4.3 0 45.7 8.2 13.7 No
#> 2011-11-26 3.7 0 45.7 0.4 13.7 No
#> 2011-11-27 4.8 0 45.7 5.0 13.7 No
#> 2011-11-28 5.7 0 45.7 10.7 13.7 No
#> 2011-11-29 5.8 0 45.7 0.5 13.7 No
#> 2011-11-30 3.0 0 45.7 3.0 13.7 NoIn both scenarios, the package suggested irrigating on 29/11/2011 because the soil water deficit had become larger than dmad. Since in scenario 1 we applied no irrigation, the Ks coefficient started to assume values smaller than 1 and the NonStandardCropEvap becomes smaller than ETc, which may prevent the crop from achieving its potential yield. In scenario 2, we followed the package’s recommendation and applied irrigation on day 29 (Irrig = 16 mm). In this case, no crop evapotranspiration deficit was observed, indicating no water shortage in the root zone.
InitialD() and CWB_fixedSchedule in
Campinas-SP, BrazilUntil now, we have assumed that the decision of when and how much to
irrigate was solely dependent on soil-plant-atmosphere factors. However,
it is well-known that this decision may also be significantly influenced
by the design and operation of the irrigation system, as well as the
availability of labor and water. This is why we included in the package
the CWB_fixedSchedule fuction that, as the
CWB(), also calculates the crop water balance in the root
zone. However, it allows users to specify the number of days between
consecutive irrigations.This is accompliched by setting the parameter
Scheduling to the number of days defining this fixed interval between
two consecutive irrigations. The estimation of how much to irrigate is
performed on this specific days. This is why we included in the package
the CWB_fixedSchedule() function that, as the
CWB, also calculates the crop water balance in the root
zone. However, it allows users to specify the number of days between
consecutive irrigations.This is accomplished by setting the parameter
Scheduling to the number of days defining this fixed interval between
two consecutive irrigations. The estimation of how much to irrigate is
performed on this specific days.
Tavg <- DataForCWB[, 2]
Tmax <- DataForCWB[, 3]
Tmin <- DataForCWB[, 4]
Rn <- DataForCWB[, 6]
WS <- DataForCWB[, 7]
RH <- DataForCWB[, 8]
G <- DataForCWB[, 9]
ET0 <- ET0_PM(Tavg, Tmax, Tmin, Rn, RH, WS, G, Alt = 700)
Dinitial <-
Dinitial(teta_FC = 0.30,
teta_Obs = 0.17,
Drz = DataForCWB[1, 11])
Rain <- DataForCWB[, 10]
Drz <- DataForCWB[, 11]
AWC <- DataForCWB[, 12]
MAD <- DataForCWB[, 13]
Kc <- DataForCWB[, 14]
Scheduling <- 5
Irrig <- DataForCWB[, 15]
Scenario_FixedSchedule <-
CWB_FixedSchedule(
Rain = Rain,
ET0 = ET0,
AWC = AWC,
Drz = Drz,
Kc = Kc,
Irrig = Irrig,
MAD = MAD,
InitialD = Dinitial,
Scheduling = Scheduling,
start.date = "2011-11-23"
)
Scenario_FixedSchedule[1:15, ]
#> DaysSeason Rain Irrig ET0 Kc WaterStressCoef_Ks ETc
#> 2011-11-23 1 45.470 0 2.440 1 1.000 2.440
#> 2011-11-24 2 0.254 0 4.172 1 1.000 4.172
#> 2011-11-25 3 0.000 0 4.290 1 1.000 4.290
#> 2011-11-26 4 11.430 0 3.665 1 1.000 3.665
#> 2011-11-27 5 0.254 0 4.849 1 1.000 4.849
#> 2011-11-28 6 0.000 0 5.670 1 1.000 5.670
#> 2011-11-29 7 0.000 0 5.757 1 0.914 5.757
#> 2011-11-30 8 0.508 0 3.009 1 0.836 3.009
#> 2011-12-01 9 35.810 0 3.588 1 1.000 3.588
#> 2011-12-02 10 0.000 0 3.929 1 1.000 3.929
#> 2011-12-03 11 11.940 0 3.179 1 1.000 3.179
#> 2011-12-04 12 21.340 0 3.180 1 1.000 3.180
#> 2011-12-05 13 0.000 0 4.239 1 1.000 4.239
#> 2011-12-06 14 10.920 0 3.925 1 1.000 3.925
#> 2011-12-07 15 3.302 0 3.737 1 1.000 3.737
#> (P+Irrig)-ETc NonStandardCropEvap ET_Defict TAW SoilWaterDeficit
#> 2011-11-23 43.030 2.440 0.000 45.72 0.000
#> 2011-11-24 -3.918 4.172 0.000 45.72 3.918
#> 2011-11-25 -4.290 4.290 0.000 45.72 8.208
#> 2011-11-26 7.765 3.665 0.000 45.72 0.444
#> 2011-11-27 -4.595 4.849 0.000 45.72 5.038
#> 2011-11-28 -5.670 5.670 0.000 45.72 10.708
#> 2011-11-29 -5.757 5.263 0.495 45.72 16.465
#> 2011-11-30 -2.501 2.516 0.494 45.72 18.967
#> 2011-12-01 32.222 3.588 0.000 76.20 0.000
#> 2011-12-02 -3.929 3.929 0.000 76.20 3.929
#> 2011-12-03 8.761 3.179 0.000 76.20 0.000
#> 2011-12-04 18.160 3.180 0.000 76.20 0.000
#> 2011-12-05 -4.239 4.239 0.000 76.20 4.239
#> 2011-12-06 6.995 3.925 0.000 76.20 0.000
#> 2011-12-07 -0.435 3.737 0.000 76.20 0.435
#> d_MAD Scheduling
#> 2011-11-23 13.716 No
#> 2011-11-24 13.716 No
#> 2011-11-25 13.716 No
#> 2011-11-26 13.716 No
#> 2011-11-27 13.716 Time to Irrigate 5 mm
#> 2011-11-28 13.716 No
#> 2011-11-29 13.716 No
#> 2011-11-30 13.716 No
#> 2011-12-01 22.860 No
#> 2011-12-02 22.860 Time to Irrigate 4 mm
#> 2011-12-03 22.860 No
#> 2011-12-04 22.860 No
#> 2011-12-05 22.860 No
#> 2011-12-06 22.860 No
#> 2011-12-07 22.860 Time to Irrigate 0 mmThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.