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CropWaterBalance

Crop water balance accounting in the root zone for irrigation purposes.

Basic Description

{CropWaterBalance} is an R package designed to assist users in irrigation scheduling based on the Water Balance Approach. The package is capable of calculating reference evapotranspiration (ET0) through various methods and conducting crop water balance accounting. Additionally, {CropWaterBalance} includes auxiliary functions for comparing different ET0 estimation methods, calculating descriptive statistics for ET0 and rainfall series, and estimating soil heat flux and water stress coefficient. The functions ET0_HS(), ET0_PT(), and ET0_PM() are used to estimate daily ET0 amounts using the methods of Hargreaves-Samani, Priestley-Taylor, and FAO-56 Penman-Monteith, respectively. The Descriptive() function is specifically designed to calculate descriptive statistics for ET0 and rainfall series, including sample mean, median, standard deviation, standard error, maximum value, minimum value, and frequency of zeros. Additionally, the Compare() function may be used to calculate measures of accuracy and agreement between two ET0 or rainfall series. The Soil_Heat_Flux() function uses average air temperature data to estimate the soil heat flux, and the Water_Stress_Coef() function calculates the water stress coefficient for a crop. The package depends on R (>= 2.10) and imports functions from the R packages {PowerSDI} and {lubridate}.

Installation

devtools::install_github("gabrielblain/CropWaterBalance")

Basic Instructions

Function ET0_PM()

Calculates daily reference evapotranspiration amounts using the Penman and Monteith method.

Usage

ET0_PM(Tavg,
       Tmax,
       Tmin,
       Rn,
       RH,
       WS,
       G = NULL,
       Alt)

Arguments

Value

Daily reference evapotranspiration amounts in millimetres.

Examples

Tavg <- DataForCWB[, 2]
Tmax <- DataForCWB[, 3]
Tmin <- DataForCWB[, 4]
Rn <- DataForCWB[, 6]
WS <- DataForCWB[, 7]
RH <- DataForCWB[, 8]
G <- DataForCWB[, 9]
head(ET0_PM(Tavg, Tmax, Tmin, Rn, RH, WS, G, Alt = 700))
##        ET0_PM
## [1,] 2.440372
## [2,] 4.171917
## [3,] 4.290477
## [4,] 3.665459
## [5,] 4.848520
## [6,] 5.669878

Function ET0_PT()

Calculates daily reference evapotranspiration amounts using the Priestley-Taylor method.

Usage

ET0_PT(Tavg,
       Rn,
       G = NULL,
       Coeff = 1.26)

Arguments

Value

Daily reference evapotranspiration amounts in millimetres.

Examples

Tavg <- DataForCWB[, 2]
Rn <- DataForCWB[, 6]
G <- DataForCWB[, 9]
head(ET0_PT(Tavg, Rn, G))
##        ET0_PT
## [1,] 3.432709
## [2,] 5.849554
## [3,] 6.432616
## [4,] 5.695334
## [5,] 7.023900
## [6,] 7.817355

Function ET0_HS()

Calculates daily reference evapotranspiration amounts using the Hargreaves-Samani method.

Usage

ET0_HS(Ra,
       Tavg,
       Tmax,
       Tmin)

Arguments

Value

Daily reference evapotranspiration amounts in millimetres.

Examples

Tavg <- DataForCWB[, 2]
Tmax <- DataForCWB[, 3]
Tmin <- DataForCWB[, 4]
Ra <- DataForCWB[, 5]
head(ET0_HS(
  Ra = Ra,
  Tavg = Tavg,
  Tmax = Tmax,
  Tmin = Tmin
))
##           ET0
## [1,] 4.703700
## [2,] 5.331592
## [3,] 5.664174
## [4,] 6.163377
## [5,] 5.291303
## [6,] 6.251883

Function Soil_Heat_Flux()

Calculates the daily amounts of Soil Heat Flux.

Usage

Soil_Heat_Flux(Tavg) 

Arguments

Value

Daily amounts of soil Heat flux in MJ m-2 day-1.

Examples

Tavg <- DataForCWB[, 2]
head(Soil_Heat_Flux(Tavg))
## Warning in Soil_Heat_Flux(Tavg): The first 3 G values were set to zero

##            [,1]
## [1,]  0.0000000
## [2,]  0.0000000
## [3,]  0.0000000
## [4,]  0.3806333
## [5,] -0.7796333
## [6,] -0.2007667

Function Descriptive()

Calculates descriptive statistics for rainfall, evapotranspiration, or other variable.

Usage

Descriptive(Sample)

Arguments

Examples

Rain <- DataForCWB[, 10]
Descriptive(Sample = Rain)
##   SampleSize  Avg  Med    SD   SE MaxValue MinValue FreqZero%
## 1        129 6.53 0.25 13.06 1.15    71.37        0     48.06

Function Compare()

Calculates measures of accuracy and agreement.

Usage

Compare(Sample1, Sample2)

Arguments

Value

Examples

Tavg <- DataForCWB[, 2]
Tmax <- DataForCWB[, 3]
Tmin <- DataForCWB[, 4]
Rn <- DataForCWB[, 6]
WS <- DataForCWB[, 7]
RH <- DataForCWB[, 8]
G <- DataForCWB[, 9]
Sample1 <-
  ET0_PM(
    Tavg = Tavg,
    Tmax = Tmax,
    Tmin = Tmin,
    Rn = Rn,
    RH = RH,
    WS = WS,
    G = G,
    Alt = 700
  )
Sample2 <- ET0_PT(Tavg = Tavg, Rn = Rn, G = G)
Compare(Sample1 = Sample1, Sample2 = Sample2)
##       AME     RMSE     dorig     dmod        dref     RQuad
## 1 1.69222 1.813449 0.6403158 0.376103 -0.05737454 0.8675223

Function CWB()

Calculates several parameters of the crop water balance. It also suggests when and how much to irrigate.

Usage

CWB(
  Rain,
  ET0,
  AWC,
  Drz,
  Kc = NULL,
  Irrig = NULL,
  MAD = NULL,
  InitialD = 0,
  start.date
)

Arguments

Value

Examples

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)
Rain <- DataForCWB[, 10]
Drz <- DataForCWB[, 11]
AWC <- DataForCWB[, 12]
MAD <- DataForCWB[, 13]
Kc <- DataForCWB[, 14]
Irrig <- DataForCWB[, 15]
head(CWB(
  Rain = Rain,
  ET0 = ET0,
  AWC = AWC,
  Drz = Drz,
  Kc = Kc,
  Irrig = Irrig,
  MAD = MAD,
  start.date = "2023-11-23"
))
##            DaysSeason Rain Irrig ET0 Kc WaterStressCoef_Ks ETc (P+Irrig)-ETc
## 2023-11-23          1 45.5     0 2.4  1                  1 2.4          43.0
## 2023-11-24          2  0.3     0 4.2  1                  1 4.2          -3.9
## 2023-11-25          3  0.0     0 4.3  1                  1 4.3          -4.3
## 2023-11-26          4 11.4     0 3.7  1                  1 3.7           7.8
## 2023-11-27          5  0.3     0 4.8  1                  1 4.8          -4.6
## 2023-11-28          6  0.0     0 5.7  1                  1 5.7          -5.7
##            NonStandardCropEvap ET_Defict  TAW SoilWaterDeficit d_MAD D>=dmad
## 2023-11-23                 2.4         0 45.7              0.0  13.7      No
## 2023-11-24                 4.2         0 45.7              3.9  13.7      No
## 2023-11-25                 4.3         0 45.7              8.2  13.7      No
## 2023-11-26                 3.7         0 45.7              0.4  13.7      No
## 2023-11-27                 4.8         0 45.7              5.0  13.7      No
## 2023-11-28                 5.7         0 45.7             10.7  13.7      No

Function CWB_FixedSchedule()

Calculates several parameters of the crop water balance. It also suggests how much irrigate.

Usage

CWB_FixedSchedule(
  Rain,
  ET0,
  AWC,
  Drz,
  Kc = NULL,
  Irrig = NULL,
  MAD = NULL,
  InitialD = 0,
  Scheduling,
  start.date
)

Arguments

Value

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)
Rain <- DataForCWB[, 10]
Drz <- DataForCWB[, 11]
AWC <- DataForCWB[, 12]
MAD <- DataForCWB[, 13]
Kc <- DataForCWB[, 14]
Irrig <- DataForCWB[, 15]
Scheduling <- 5
head(CWB_FixedSchedule(
  Rain = Rain,
  ET0 = ET0,
  AWC = AWC,
  Drz = Drz,
  Kc = Kc,
  Irrig = Irrig,
  MAD = MAD,
  Scheduling = Scheduling,
  start.date = "2023-11-23"
))
##            DaysSeason   Rain Irrig   ET0 Kc WaterStressCoef_Ks   ETc
## 2023-11-23          1 45.470     0 2.440  1                  1 2.440
## 2023-11-24          2  0.254     0 4.172  1                  1 4.172
## 2023-11-25          3  0.000     0 4.290  1                  1 4.290
## 2023-11-26          4 11.430     0 3.665  1                  1 3.665
## 2023-11-27          5  0.254     0 4.849  1                  1 4.849
## 2023-11-28          6  0.000     0 5.670  1                  1 5.670
##            (P+Irrig)-ETc NonStandardCropEvap ET_Defict   TAW SoilWaterDeficit
## 2023-11-23        43.030               2.440         0 45.72            0.000
## 2023-11-24        -3.918               4.172         0 45.72            3.918
## 2023-11-25        -4.290               4.290         0 45.72            8.208
## 2023-11-26         7.765               3.665         0 45.72            0.444
## 2023-11-27        -4.595               4.849         0 45.72            5.038
## 2023-11-28        -5.670               5.670         0 45.72           10.708
##             d_MAD            Scheduling
## 2023-11-23 13.716                    No
## 2023-11-24 13.716                    No
## 2023-11-25 13.716                    No
## 2023-11-26 13.716                    No
## 2023-11-27 13.716 Time to Irrigate 5 mm
## 2023-11-28 13.716                    No

DataForAWC: Soil texture and plant available water capacity (AWC).

AWC is the amount of water between field capacity and permanent wilting point. Given in millimetre of water per centimetre of soil. Extracted from: Irrigation Scheduling: The Water Balance Approach Fact Sheet No. 4.707 by A. A. Andales, J. L. Chávez, T. A. Bauder..

Usage

DataForAWC

Format

Source

https://extension.colostate.edu/topic-areas/agriculture/.

Examples

DataForAWC
##        Soil.Texture AWC.Low AWC.High AWC.Average
## 1      Coarse sands      50       70          60
## 2        Fine sands      70       80          80
## 3       Loamy sands      70      100          80
## 4       Sandy loams     100      130         120
## 5  Fine sandy loams     130      170         150
## 6  Sandy clay loams     130      180         160
## 7             Loams     180      210         200
## 8        Silt loams     170      210         190
## 9  Silty clay loams     130      170         150
## 10        Clay loam     130      170         150
## 11       Silty clay     130      140         130
## 12             Clay     110      130         120

DataForCWB: Data for Water Balance Accounting.

Daily meteorological data from a weather station in Campinas, Brazil and other parameters required for calculating the crop water balance. The meteorological data belongs to the Agronomic Institute of the state of Sao Paulo.

Usage

DataForCWB

Format

Source

http://www.ciiagro.org.br/.

Examples

head(DataForCWB)
##         Date   tmed  tmax  tmin       Ra       Rn    W    RH     G   Rain
## 1 11/23/2010 23.000 27.26 18.74 42.07246  6.04422 2.16 76.58 -0.64 45.470
## 2 11/24/2010 23.730 29.00 18.46 42.12238 11.08968 2.57 64.50 -0.30  0.254
## 3 11/25/2010 24.650 30.33 18.97 42.17043 12.71410 2.80 70.37  0.19  0.000
## 4 11/26/2010 24.795 31.46 18.13 42.21660 11.46852 1.86 73.03  0.38 11.430
## 5 11/27/2010 22.340 27.86 16.82 42.26093 12.89778 2.61 57.80 -0.78  0.254
## 6 11/28/2010 23.400 30.70 16.10 42.30341 15.02158 2.42 48.06 -0.20  0.000
##      Drz AWC MAD Kc Irrig
## 1 0.3048 150 0.3  1     0
## 2 0.3048 150 0.3  1     0
## 3 0.3048 150 0.3  1     0
## 4 0.3048 150 0.3  1     0
## 5 0.3048 150 0.3  1     0
## 6 0.3048 150 0.3  1     0

BugReports:

<https://github.com/gabrielblain/CropWaterBalance/issues >

License:

MIT

Authors:

Gabriel Constantino Blain, Graciela da Rocha Sobierajski, Regina Célia Matos Pires, Adam H. Sparks, Letícia L. Martins. Maintainer: Gabriel Constantino Blain, gabriel.blain@sp.gov.br

Acknowledgments:

The package uses data from the Fact Sheet number 4707 Irrigation Scheduling: The Water Balance Approach, by A. A. Andales, J. L. Chávez, and T. A. Bauder. The authors greatly appreciate this initiative.

References

Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop evapotranspiration. In Guidelines for Computing Crop Water Requirements. Irrigation and Drainage Paper 56; FAO: Rome, Italy, 1998; p. 300.

Andales, A.A.; Chávez, J.L.;Bauder, T.A. 2012. Irrigation Scheduling: The Water Balance Approach. Fact Sheet number 4707, crop series | irrigation. https://extension.colostate.edu/docs/pubs/crops/04707.pdf

Hargreaves, G.H.; Samani, Z.A. 1985.Reference crop evapotranspiration from temperature. Appl. Eng. Agric,1, 96–99.

Package ‘lubridate’, Version 1.9.3, Author Vitalie Spinu et al., https://CRAN.R-project.org/package=lubridate

Package ‘PowerSDI’, Version 1.0. 0, Author Gabriel C. Blain et al., https://CRAN.R-project.org/package=PowerSDI

Priestley, C.H.B., Taylor, R.J., 1972. On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters. Monthly Weather Review, 100 (2), 81–92.

These 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.
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