Agricultural water management in a humid region: sensitivity to climate, soil and crop parameters

Agricultural Water Management 70 (2004) 51–65

Agricultural water management in a humid region: sensitivity to climate, soil and crop parameters Sudheer R. Satti a,1 , Jennifer M. Jacobs b,∗ , Suat Irmak c,2 a

c

Department of Civil and Coastal Engineering, University of Florida, P.O. Box 116580, Gainesville, FL 32611-6580, USA b Department of Civil Engineering, University of New Hampshire, 240 Gregg Hall, Durham, NH 03824, USA Department of Biological Systems Engineering, University of Nebraska - Lincoln, 234 L.W. Chase Hall, Lincoln, NE 68583-0726, USA Accepted 7 May 2004

Abstract A sensitivity analysis of irrigation water requirements at the regional scale was conducted for the humid southeastern United States. The GIS-based water resources and agricultural permitting and planning system (GWRAPPS), a regional scale, GIS-based, crop water requirement model, was used to simulate the effect of climate, soil, and crop parameters on crop irrigation requirements. The effects of reference evapotranspiration (ETo ) methods, available soil water holding capacities (ASWHC), crop coefficients (Kc ), and crop root zone depths (z) were quantified for 203 ferneries and 152 potato farms. The irrigation demand exhibited a positive relationship with Kc and z, a negative relationship with ASWHC, and seasonal variations depending on the choice of ETo methods. The average irrigation demand was most sensitive to the choice of Kc with a 10% shift in Kc values resulting in approximately 15% change in irrigation requirements. Most ETo methods performed reasonably well in estimating annual irrigation requirements as compared to the FAO-56 PM method. However, large differences in monthly irrigation estimates were observed due to the effect of the seasonal variability exhibited by the methods. Our results suggested that the selection of ETo method is more critical when modeling irrigation requirements at a shorter temporal scale (daily or monthly) as necessary for many applications, such as daily irrigation scheduling, than at a longer temporal scale (seasonal or annual). The irrigation requirements were more sensitive to z when the resultant timing of irrigation coincided with rainfall events. When compared with the overall average of the irrigation requirements differences, the site-to-site variability was low for Kc values and high for the other variables. In particular, soil properties had considerable average regional differences and variability

∗ Corresponding author. Tel.: +1 603 862 0635; fax: +1 603 862 3957. E-mail addresses: [email protected] (J.M. Jacobs), [email protected] (S. Irmak). 1 Tel.: +1 352 392 9537x1439. 2 Tel.: +1 402 472 4865.

0378-3774/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2004.05.004

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among sites. Thus, the extrapolation of site-specific sensitivity studies may not be appropriate for the determination of regional responses crop water demand. © 2004 Elsevier B.V. All rights reserved. Keywords: Soil water balance; Irrigation; Water resources planning; Crop coefficient and sensitivity analysis

1. Introduction Irrigated agriculture is the primary consumer of water in the United States. Over 81% of the total consumptive water use is by the agricultural sector (Solley et al., 1998). This study’s region, Florida, is 13th nationally for agricultural self-supplied water use and the top water user in this category in the humid southeast region (Solley et al., 1998). Agricultural self-supply is the largest user of freshwater with 45% of the total withdrawals in Florida (Marella, 1999). While quantification of regional agricultural water demand is important, irrigation demands are increasingly used to force the use of groundwater models, economic models, and runoff models to manage surface and subsurface resources, to predict crop yields and to operate water supply reservoirs (Cai et al., 2003; Vedula and Kumar, 1996). Thus, planning and management of fresh water supplies are highly sensitive to current and future irrigation demands. Regional scale water management has historically relied on relatively coarse approaches to estimate crop water requirement estimation (Hess, 1996; Plauborg et al., 1996). Recent developments in GIS and enhanced computational capabilities presented the opportunity to apply physically based soil water balance and simulation models at a regional scale to enhance water supply management (Satti and Jacobs, 2004), to optimize river basin management (Cai et al., 2003), and to study impact of management intervention. Current agro-climatic models such as CropSyst (Stockle et al., 1994), AEGIS/WIN (Engel et al., 1997), and GWRAPPS (Satti and Jacobs, 2004) are able to simulate the soil water budget by coupling crop parameters with databases of soil characteristics and long-term climate data without regard to scale. Intrinsic to the success of these models are the agronomic relationships that relate crop yield response to an indicator of crop water stress based on either soil water stress (Vedula and Kumar, 1996) or the ratio of actual evapotranspiration to potential evapotranspiration (Prajamwong et al., 1997). Soil water dynamics are also at the core of these models, quantifying groundwater recharge and reservoir demand, and are integral to the interpretation of model outcomes. While the level of sophistication depends on the application and availability of model parameters, soil water dynamics are typically modeled using a one-dimensional water balance approach that includes a reference ET value modified by a crop coefficient and soil root zone parameters including root zone depth, porosity, and soil moisture at field capacity and wilting point (e.g., Cai et al., 2003; Sethi et al., 2002; Vedula and Kumar, 1996). In contrast to site-specific irrigation scheduling applications, regional scale irrigation water models require knowledge of climatic, soils, and crop data over a range of spatial scales. As the main source of error is often observed in the measurement (or estimation) of input data (Hess, 1996), the development of effective model databases for regional models presents a significant challenge to water resources managers. Characterization of crop water

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use estimates’ sensitivity to errors in input data enables water managers to prioritize model data collection, validation, and enhancement efforts. In addition, information on the sensitivity a given model has to input parameters would help to better understand the model’s predictive capability and accuracy. Thus, this information, in turn, would help growers, their advisors, and state water regulatory agencies to make better decisions on planning, design, and management of water resources. While studies have identified the influence of one or more parameters on irrigation water requirements, there is a lack of information as to the relative importance of these parameters when water requirements are to be aggregated at a regional scale. This paper presents a sensitivity analysis to compare the relative influence of climate, soil and crop factors on estimating regional scale crop water requirements that are characteristic of the southeastern, USA. The analysis is conducted using GIS-based water resources and agricultural permitting and planning system (GWRAPPS) which readily quantifies irrigation water for both regional planning and farm scale permitting using spatially distributed soils, land-use, and long-term daily climate data. The objectives of this study were: (1) to examine the relative effects of different reference ET (ETo ) methods, available soil water holding capacity (ASWHC), crop coefficient (Kc ), and crop root zone depth (z) on the irrigation requirements, and (2) to determine the most sensitive factors in the regional irrigation requirement with respect to the regional average and the farm-to-farm variability. The sensitivity analysis is applied using farms distributed throughout Florida with a perennial and an annual crop (ferns and potato, respectively).

2. Model description 2.1. GIS-based water resources and agricultural permitting and planning system GWRAPPS is an integrated system designed as a distributed, regional scale, crop drought water requirement model that includes most regional crops and accounts for, heterogeneous soils and spatially variable climate (Satti and Jacobs, 2004). In contrast to traditional rainfall drought metrics, here, drought corresponds to those years having annual irrigation demand exceeding the median year demand. GWRAPPS operates in a Windows environment and tightly couples a geographic information system, ArcGIS (ESRI), with a crop water requirements model, the agricultural field scale irrigation requirements simulation (AFSIRS) model (Smajstrla and Zazueta, 1988). The model estimates monthly and annual irrigation requirements for statistically average and drought years using a robust frequency analysis on long-term daily irrigation estimates. GWRAPPS provides tools for estimating irrigation requirements at a farm scale and at a regional scale. It also provides a climate interpolation utility to generate distributed climate data over a given region. Satti (2002) provides a detailed description of the model and its components. 2.2. AFSIRS water budget The AFSIRS model uses a water balance approach with a two-layer soil column to simulate soil water infiltration, redistribution and extraction by evapotranspiration as steady state

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processes on a daily basis (Smajstrla, 1990). The AFSIRS model simulates the irrigation requirements for a crop based on plant physiology, soil, irrigation system, growing season, climate and basic irrigation management practice. The water balance equation for the soil column defined by the crop root zone is S = P + Inet − QGW − QSR − ETc

(1)

where S is the change in soil water storage, P the rainfall, Inet the net irrigation requirement, QGW the ground water drainage, QSR the surface runoff and ETc the crop evapotranspiration. For Florida’s flat, sandy soils, surface runoff and lateral flow are assumed to be negligible or combined with drainage. The water storage capacity (S), equivalently the total available soil water in the crop root zone, is expressed as the product of the ASWHC and the crop root zone depth (z). The ASWHC is the water stored between the field capacity (the amount of water held in the soil profile approximately 48 h after a heavy rain or irrigation under free drainage condition) and the permanent wilting point, the water content at which plants will permanently wilt and subject to irreversible damage to growth and/or yield (Smajstrla and Zazueta, 1988; Allen et al., 1998). Although water is theoretically available until wilting point, crop begins to experience stress well before the wilting point is reached. Readily available water (RAW) is the fraction of S that a crop can extract from the root zone without suffering stress and is expressed as the product of S and allowable soil water depletion (p). The AFSIRS soil database consists of the soil series database mapped by the Natural Resources Conservation Service and Institute of Food and Agricultural Sciences (IFAS). As many as three textural classifications and six soil layers are given for each soil with thickness and minimum and maximum ASWHCs defined for each soil layer. The minimum and maximum ASWHC account for the naturally occurring range of ASWHC within a soil series. For perennial crops, p values are provided on monthly basis. For annual crops, p values are given for four crop growth stages (initial or establishment, vegetative growth and development, peak growth, and maturity to harvest). The modeled soil profile depth is assumed to be equal to the crop root zone depth. The crop root zone is divided into irrigated and non-irrigated crop root zones and separate water budgets are maintained for each zone. This division of the root zone is implemented based on the common practice of irrigating only the upper portions of the crop root zone where most of the roots are located, rather than irrigating the maximum depth in which limited density of crop roots are present. This assumption has been proven to be valid in most cases for the typical sandy soils studied in AFSIRS (Smajstrla and Zazueta, 1988). The irrigated root zone is the upper 50% of the maximum expected root depth and the lower 50% is the non-irrigated root zone. It is assumed that 70 and 30% of crop ET is extracted from these zones, respectively, when water is available (SCS, 1982). This pattern of water extraction is typically assumed for well-irrigated crops on non-restrictive soil profiles (Smajstrla and Zazueta, 1988). As the non-irrigated root zone dries out during dry periods, water becomes less available in this zone, and a greater proportion of water is depleted from the irrigated zone in order to meet the crop demand. The AFSIRS crop database provides root zone information for 16 perennial and 44 annual crops. The crop root zone for perennial crops is assumed to be constant. The crop root zone development for annual crops has four growth stages. The average growth stage lengths differ by crop and are given as fractions of the

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crop-growing season. The root zone is held constant at the minimum depth throughout crop growth stage 1 (establishment of the crop). The root zone increases linearly to a maximum depth throughout growth stage 2 (vegetative growth and development). The maximum root zone is attained at the beginning of crop growth stage 3 (peak growth) and is maintained throughout growth stages 3 and 4 (maturity to harvest). The crop evapotranspiration (ETc ) is the amount of ET occurring from a specific crop. AFSIRS calculates ETc by the reference crop ET (ETo ) and the crop coefficient (Kc ) as ETc = Kc × ETo

(2)

ETo provides a standard response of the reference crop to the given atmospheric conditions. In Florida, the reference crop is typically warm-season grass under well-watered conditions. For perennial crops, the monthly Kc values are defined in AFSIRS. For annual crops, Kc values are based on the four crop growth stages. Smajstrla (1990) provides a detailed literature review conducted for the crop coefficients and effective root zone depths of each AFSIRS crop. The procedure used for estimating daily Kc values for perennial and annual crops is described in Smajstrla and Zazueta (1988). Drainage (QGW ) is the portion of rainfall in excess of rain stored in the soil profile to field capacity or depleted by crops (ETc ) as the water is redistributed in the soil. Drainage is determined based on the water content in the crop root zone by (3a) QGW = 0 if P < z 
N  if P ≥ z (3b) ETt QGW = P − (θmax − θ)z + t=1

where θ and θ max are the current and maximum soil water contents, respectively and N is the maximum number of days for redistribution based on root zone, rainfall depth and irrigation method. The net irrigation requirement (Inet ) is calculated as the depth of water required to replenish the soil water content to field capacity in the irrigated crop root zone. Irrigation water is added to the water balance when the available soil water storage receded to RAW. The model output includes monthly and the annual irrigation requirements under median (“normal year” in terms of climatic conditions as compared to the long-term climatic data), 1-in-5-year drought and 1-in-10-year drought conditions. The median, 1-in-5-year and 1-in-10-year drought correspond to the annual irrigation requirement having a nonexceedance probability of 50, 80 and 90%, respectively. The probability of drought is calculated from a conditional probability model that uses the type I extreme value distribution for positive non-zero irrigation values (Haan, 1977; Stedinger et al., 1993). 3. Methods and materials 3.1. Study area Two counties in Florida (Fig. 1) were used as study areas for the sensitivity analysis. The ferns study was conducted on 203 ferneries comprising about 3521 ha located in Volusia County, Florida. Volusia County is located on the east (Atlantic) coast of Central Florida

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Fig. 1. Study areas in St. Johns River Water Management District (SJRWMD). The potato study is in St. Johns County and fern study is in Volusia County.

and lies between 80◦ 40 W and 81◦ 41 W longitude and 28◦ 37 and 29◦ 26 latitude. Volusia County has an average summer temperature of approximately 27 ◦ C, average winter temperature of approximately 16 ◦ C, and a mean annual rainfall of about 1220 mm. The ferneries within the county were comprised of 25 different soil types and the agricultural land is approximately 5% of the total county area. As ferns are perennial crops, the growing season of these crops are assumed to be entire year and the irrigation requirements are estimated for the entire year. The potatoes’ sensitivity analysis was conducted on 152 farms growing potatoes in St. Johns County comprising about 17,828 ha land area. St. Johns County is also located on the east coast of Florida and lies between 81◦ 10 W and 81◦ 42 W longitude and 29◦ 36 and 30◦ 16 latitude. The potato farms within the county have 16 different soil types and approximately 37% of the study area is either agricultural land or golf courses. St. Johns County, along with Flagler and Putnam County, comprise nearly 85% of the total potato production in Florida (Hochmuth and Cordasco, 2000). In St. Johns County, potatoes are typically planted in December or January and are harvested in May or June in what is considered the spring planting. In the present study, the planting and harvesting dates are January 1 and May 15, respectively.

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3.2. Reference evapotranspiration methods Based on the studies comparing ETo estimation methods by the American Society of Civil Engineers (ASCE) (Walter et al., 2000) and by Jacobs and Satti (2001) in Florida, a sensitivity study of irrigation demand to ETo method was conducted using five ETo equations. The methods considered were: the Hargreaves method (Hargreaves and Samani, 1985), the Food and Agriculture Organization of the United Nations (FAO) 24 Penman method (Doorenbos and Pruitt, 1977), the IFAS modified Penman method (Jones et al., 1984), the ASCE 1990 Penman Monteith (PM) method (Jensen et al., 1990), and the FAO-56 PM method (Allen et al., 1998). 3.3. Data The GIS data, excluding the climate layer, were obtained from the St. Johns River Water Management District GIS data repository (http://www.sjrwmd.com/). The climate layer was generated using the GWRAPPS climate interpolation utility. The climate layer provides 21 years of daily ETo and rainfall (P) values at a 20 km resolution. These data were compiled by interpolating daily climate data (ETo and P) available from nine weather stations maintained by the National Oceanic and Atmospheric Administration (NOAA). Daily ETo values for the five ETo methods were estimated from daily measured values of solar radiation, minimum and maximum air temperature, wind speed, and humidity. The AFSIRS parameter files and the GWRAPPS’ relational database provide non-spatial data including crop, soil and irrigation characteristics. 3.4. Simulation runs GWRAPPS simulations were conducted to estimate the irrigation requirements for each of the 203 ferneries and 152 potato farms. For each farm, 17 different scenarios having a range of ETo methods, soil ASWHCs, crop coefficients, and crop root zone depths were considered. The five ETo methods, identified above, were used. For each farm and crop, four deviations (−20%, −10%, +10%, +20%) of average soil ASWHC, crop coefficients and effective root zone depths from those reported in Smajstrla (1990) were considered. The baseline scenario against which all other scenarios were compared, uses the FAO-56 PM method, and the default average soil ASWHC, crop coefficients, and crop root zone depths. The baseline Kc is 1.00 for ferns throughout the growing season. The potatoes’ Kc values are 1.05 and 0.70 for growth stages 3 and 4, respectively. The irrigated and total root zone depths for ferns are 25.4 and 50.8 cm. The minimum and maximum root zone depths of potatoes are 30.5 and 45.7 cm. Standard t-tests were conducted to identify the statistical differences between the simulation scenarios at the 5% significance level. 3.5. Simulation results Table 1 summarizes the regional annual average net irrigation requirement for each scenario by crop and normal and drought conditions. For each farm, the percent difference between the baseline scenario and 16 sensitivity studies was calculated. To characterize the

58 Table 1 Summary of the irrigation requirements estimated for two crops, ferns and potatoes, using GIS-based water resources and agricultural permitting and planning system (GWRAPPS) Sensitivity

ASWHC variation (%)

Kc variation (%)

Root zone variation (%)

Ferns

Potatoes

Normal irrigation (cm)

1-in-5 irrigation (cm)

1-in-10 irrigation (cm)

Normal irrigation (cm)

1-in-5 irrigation (cm)

1-in-10 irrigation (cm)

Baseline

FAO-56 PM

0

0

0

58.4

65.9

69.0

14.5

18.8

21.1

Climate

Hargreaves IFAS Penman FAO-24 Penman ASCE90 PM

0 0 0 0

0 0 0 0

0 0 0 0

64.0 57.4 60.6 60.3

70.9 66.5 67.9 67.7

73.7 70.5 70.9 70.8

14.6 13.0 15.2 15.2

18.3 17.0 19.1 19.2

20.1 19.1 21.0 21.2

Soil

FAO-56 PM FAO-56 PM FAO-56 PM FAO-56 PM

−20 −10 +10 +20

0 0 0 0

0 0 0 0

59.9 59.1 57.7 57.0

67.0 66.5 65.3 64.7

70.0 69.6 68.5 67.9

15.4 14.9 14.0 13.6

19.7 19.2 18.3 18.0

21.8 21.4 20.6 20.3

Crop coefficient

FAO-56 PM FAO-56 PM FAO-56 PM FAO-56 PM

0 0 0 0

−20 −10 +10 +20

0 0 0 0

42.1 50.1 67.2 76.2

48.1 56.7 75.2 84.9

50.9 59.8 78.8 88.8

10.3 12.4 16.8 19.0

13.7 16.2 21.5 24.1

15.6 18.3 23.9 26.7

Root zone

FAO-56 PM FAO-56 PM FAO-56 PM FAO-56 PM

0 0 0 0

0 0 0 0

−20 −10 +10 +20

59.9 59.0 57.6 52.1

70.9 69.4 68.3 63.5

67.1 66.3 65.1 60.0

15.5 15.0 14.0 13.5

19.7 19.3 18.2 17.7

21.8 21.5 20.5 20.0

The reference evapotranspiration (ETo ) methods considered are: Hargreaves, Institute of Food and Agricultural Sciences (IFAS) Penman, Food and Agricultural Organization (FAO) 24 Penman method, American Society of Civil Engineers (ASCE) 90 Penman Monteith (PM), and FAO-56 PM methods.

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ETo method

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range of variability among the farms, summary statistics for the farm-based percent differences were determined (Table 2). The statistics include the minimum and maximum percent differences, the average percent difference, the standard deviation of the percent difference, and the coefficient of variation of the percent differences. Standard t-tests showed that the irrigation requirements were significantly different at a 95% confidence level for 14 scenarios. Two scenarios, the irrigation requirements using the IFAS Penman method for ferns and the Hargreaves method for potatoes, did not show statistically significant differences between those requirements calculated from the baseline scenario, at 95% confidence level. The irrigation requirements exhibited varying sensitivities to the ETo estimation method used. On an annual basis, most methods performed reasonably well with the average net irrigation requirements ranging from 57.4 to 64.0 cm for ferns and 13.0 to 15.2 cm for potatoes (Table 1). However, the magnitude of variation in the monthly irrigation requirements among different ETo methods was high and showed considerable variations throughout the growing season. The low magnitude of variation in annual estimates is due to the counteracting effect of the seasonal variation exhibited by the ETo methods. For instance, the Hargreaves and the IFAS Penman methods underestimated during colder months and overestimated during warmer months, whereas FAO-24 Penman method overestimated during colder months and underestimated during hotter months. Results using the FAO-24 Penman and the ASCE90 PM methods were relatively consistent when compared with the results from the FAO-56 PM method, but had a small positive bias. The IFAS Penman method gave the best irrigation estimates for ferns and the largest differences for potatoes. The Hargreaves method provided the best irrigation estimates for potatoes with annual average differences of 0.2% but also had the largest annual average differences of 9.8% for ferns (Table 2). Inconsistencies and variability in estimated irrigation requirements were attributed to the combinations of both large seasonal variation observed in the monthly irrigation estimates and to the inconsistent performances of some of the ETo methods in growing seasons that have different climatic characteristics. For example, Hargreaves is an empirical method that heavily depends on air temperature. The inconsistent and poor estimates of the temperature methods are mostly because they do not account for solar radiation, vapor pressure deficit, or sunshine percentage which play important role when estimating ETo especially in humid regions where the variations in ETo are more often due to variations in these factors than to variations in air temperature (Irmak et al., 2003a,b). Stomatal resistance describes the resistance of vapor flow through the transpiring crop and evaporating soil surface and is a critical component in calculating ETc (McCabe and Wolock, 1992; Singh et al., 1993). Neither the IFAS Penman, nor the FAO-24 PM methods consider stomatal resistance in their ETo estimates. The ±20% variation of average soil ASWHC considered for the sensitivity analysis was within the minimum and maximum ASWHC range provided in Smajstrla (1990). The potatoes’ annual average net irrigation requirements were more sensitive to soil ASWHC than the ferns’ requirement. A soil with high ASWHC can supply plant water for longer period than soils with lower ASWHC. Thus, the observed negative correlation between ASWHC and irrigation was anticipated. A fairly linear relationship between variation in soil ASWHC and irrigation requirements was observed for both ferns and potatoes. A 20% decrease in soil ASWHC resulted in an increase of 2.7 and 6.5% in average net irrigation requirements for ferns and potatoes, respectively. A 20% increase in soil ASWHC

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Sensitivity

ETo method

ASWHC (%)

Kc (%)

Root zone (%)

Ferns

Potatoes

Average

S.D.

CV

Minimum

Maximum

Average

S.D.

CV

Minimum

Maximum

0 0 0 0

0 0 0 0

0 0 0 0

9.8 −1.7 4.0 3.4

1.6 1.1 1.8 0.6

0.2 −0.7 0.5 0.2

7.3 −7.0 −0.9 2.2

20.4 2.9 9.7 6.3

0.2 −10.8 4.4 5.0

2.6 2.5 2.0 1.2

15.2 −0.2 0.5 0.2

−5.1 −15.7 −0.8 2.0

5.1 −6.8 10.2 7.3

FAO-56 PM FAO-56 PM FAO-56 PM FAO-56 PM

−20 −10 +10 +20

0 0 0 0

0 0 0 0

2.7 1.3 −1.2 −2.5

2.0 1.0 1.0 1.9

0.7 0.8 −0.8 −0.8

0.0 −0.6 −5.1 −9.9

8.9 4.9 1.5 1.5

6.5 2.9 −3.6 −6.6

2.3 1.8 1.9 2.6

0.4 0.6 −0.5 −0.4

2.5 −1.0 −8.8 −13.9

10.5 7.7 −0.5 −3.5

Crop coefficient

FAO-56 PM FAO-56 PM FAO-56 PM FAO-56 PM

0 0 0 0

−20 −10 +10 +20

0 0 0 0

−28.2 −14.3 15.2 31.0

3.2 1.6 2.0 4.3

−0.1 −0.1 0.1 0.1

−43.6 −22.9 13.1 26.4

−25.1 −12.6 24.0 51.5

−29.2 −15.0 16.1 31.3

3.1 2.5 2.7 5.0

−0.1 −0.2 0.2 0.2

−33.4 −20.3 11.6 23.4

−23.0 −11.0 22.6 41.3

Root zone

FAO-56 PM FAO-56 PM FAO-56 PM FAO-56 PM

0 0 0 0

0 0 0 0

−20 −10 +10 +20

2.7 1.2 −1.4 −10.7

2.5 2.3 2.3 2.4

0.9 2.0 −1.6 −0.2

−13.5 −14.2 −17.4 −23.7

20.2 17.1 18.1 7.1

6.9 3.3 −3.9 −6.7

3.5 2.4 2.3 2.8

0.5 0.7 −0.6 −0.4

−0.9 −2.7 −9.4 −12.2

12.8 9.0 −0.9 0.5

Climate

Hargreaves IFAS Penman FAO-24 Penman ASCE90 PM

Soil

The ETo methods considered were: Hargreaves, Institute of Food and Agricultural Sciences (IFAS) Penman, Food and Agricultural Organization (FAO) 24 Penman method, American Society of Civil Engineers (ASCE) 90 Penman Monteith (PM), and FAO-56 PM methods.

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Table 2 Statistics of the percent differences on an individual farm basis between the baseline irrigation requirements and the estimated requirements for ETo methods, five water holding capacities (ASWHCs), five crop coefficients (Kc ) and five crop root zone depths by crop

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Fig. 2. Relationship between magnitude of irrigation variation and variation in crop and soil parameters.

resulted in a decrease of 2.5 and 6.6% in average net irrigation requirements for ferns and potatoes, respectively (Fig. 2). The annual 1-in-10 drought net irrigation requirements exhibited similar variations with a difference of 2.1 cm for ferns and 1.5 cm for potatoes. The magnitude of the variation in irrigation requirements was less during drier months when most of the rainfall was considered as effective rainfall. During these months, under conditions of minimal drainage and no overland flow, Eq. (1) dictates that the irrigation requirement is mainly a function of the difference between ET and P. Application of the crop coefficient approach to estimate ETc uses Kc as a scaling factor for ETo throughout the growing season. Therefore, the Kc variations are expected to be strongly and, positively correlated with irrigation requirement. Large differences in the irrigation requirements resulted from modest changes in Kc . The annual average irrigation requirements ranged from 42.1 to 76.2 cm for ferns and 10.3 to 19.0 cm for potatoes (Table 1). A 20% increase in Kc resulted in over a 31% increase in irrigation requirements for ferns and potatoes. In addition, individual sites had considerable site-to-site variability with

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differences of up to 43.6 and 41.3% for ferns and potatoes, respectively (Table 2). However, the consistently low coefficients of variation indicate that this variability is relatively low considering the average magnitude of differences among fields. Both crops exhibited varying sensitivity to changes in crop root zone depth. Potatoes were more sensitive to the crop root zone depths during the later growth stages than ferns. Deeper crop root zones have more soil volume from which to deplete soil water and thereby reduce irrigation demand. Accordingly, the potatoes’ irrigation requirements increased by 6.9% when the maximum crop root zone is reduced by 20% and decreased by 6.7% when the maximum crop root zone is increased by 20%. Ferns also exhibited similar variation with the irrigation requirements decreasing with increasing crop root zone. However, a large change in the magnitude of variation, from 1.4 to 10.7%, was observed when the crop root zone depth increased from 10 to 20%. This large difference was mainly due to the influence of the crop root zone depth on the soil water content at which irrigation is required and thus on the timing of irrigation. For this case study, it was observed that the total number of irrigation events reduced considerably when the root zone was increased from 10 to 20%, mainly due to more rainfall events coinciding with days when irrigation is required.

4. Discussion The current study shows that irrigation demand is highly sensitive to crop coefficient values and supports the findings of McCabe and Wolock (1992) and Singh et al. (1993) who conducted similar sensitivity analyses using water balance based models and found that the models were most sensitive to the stomatal resistance of the crop. In the current study, each 10% difference in Kc values results in approximately a 15% change in the regional irrigation requirements. Such Kc values will change crop ET and may cause significant impacts including on agricultural profits and return flow through field drainage (Cai et al., 2003). While Kc values for a crop are considered to be independent of climatic conditions, variations exist due to differences in crop variety, growth conditions, and soil and management conditions. Stegman (1988) reported that Kc curves were different even within a region, depending on production and management practices. Jagtap and Jones (1989) demonstrated that significant errors in estimation of irrigation amount could occur when crop coefficients developed under one set of conditions are used in other conditions. They reported that the largest errors could occur when humidity, wind speed or solar radiation conditions were very different among sites. Tasumi et al. (in press) used remote sensing techniques to identify variations in the regional population of Kc values for eight crops in Idaho and showed that the Kc distributions evolve over a growing season with differences among crops. Allen et al. (1998) concur with these assessments of Kc values and suggest that the values be modified based on regional climate characteristics. Presently, few studies use crop coefficients developed in the region of application or for crop varieties specific interest. Instead, regional studies (e.g., Cai et al., 2003; Vedula and Kumar, 1996) apply crop coefficients cited in the literature (Doorenbos and Pruitt, 1977; Allen et al., 1998) that were developed based on studies conducted at a specific location and under a specific management practice and do not account for any of the noted variations. Based on previous study results, errors in Kc values

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of 10–20% are not improbable. This study shows that modeled required irrigation demand is highly sensitive to these errors and indicates that efforts should be made to improve crop coefficients prior to their application at a regional scale. The choice of reference ET method is important both with respect to variations among methods and in combination with crop coefficients to predict ETo . Kc values are sensitive to the ETo method used to develop the coefficients. Interpretation of results and intercomparison among studies is difficult as studies apply the Kc values using a variety of reference ET methods including pan evaporation (Vedula and Kumar, 1996) and the Hargreaves method (Sethi et al., 2002) or altogether neglect to define which ETo method was used. Additionally, the new standard reference ET method (Allen et al., 1998) applied in combination with literature Kc values, developed using other reference ET methods, may bias potential ET values. Additional studies are advisable to ascertain the transferability of Kc developed using earlier ET methods. The present study, as well as studies by Irmak et al. (2003b) and Itenfisu et al. (2003), identifies variations among reference ET methods. Itenfisu et al.’s (2000) findings that the choice of ETo method could result in water loss differences of up to 7% is comparable to this study’s maximum differences of 9.8 and 10.8% for ferns and potatoes, respectively. Additionally, this study’s results indicate that the choice of ETo method is more critical when modeling irrigation requirements at a shorter temporal scale (daily or monthly) as necessary for precision agriculture and daily irrigation scheduling than at a longer temporal scale (seasonal or yearly) as applied in planning and management purposes. The present study found that ASWHC had a relatively minor impact on net irrigation. These results differ from McCown’s (1973) findings in a study of the ASWHC influence on the growing season length and yield of tropical pastures. His study found that differences among soils in the ASWHC have a substantial influence on the growing season length and thus on irrigation demand. The present study constrains plant response and independently examines parameters. However, interactions among responses may enhance or mitigate water demand. Thus, additional impacts might be identified using irrigation models that include plant growth models and allow for a variable growing season length.

5. Conclusion GWRAPPS was used to study the average regional sensitivity of the irrigation water requirements to critical variables and the variability among sites within a region. The irrigation requirement was most sensitive to crop coefficients, followed by ETo method, crop root zone depth, and ASWHC. Thus, the accurate determination of crop coefficients should be the highest priority to improve estimates of irrigation demand and to enhance management of water resources. On an annual basis, most ETo methods performed relatively well while larger differences were observed when methods were compared on a monthly basis. Due to seasonal variability effects, choice of ETo method is most critical when irrigation requirements are estimated on a daily or monthly basis. Irrigation requirements are sensitive to ASWHC with the sensitivity decreasing in drier soil conditions. Changes in crop root zone depth leads to significant changes in irrigation requirements when the resultant timing of irrigation events coincides with rainfall events.

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Crop coefficients exhibited limited site-to-site variability of irrigation requirements as compared to the overall average magnitude of the differences. For the remaining variables examined, there is considerable variability among the results for individual sites. In particular, soil properties had considerable average regional differences and variability among sites. Thus, the extrapolation of site-specific sensitivity studies may not be appropriate for the determination of regional responses crop water demand.

Acknowledgements This work was funded by the St. Johns River Water Management District (SJRWMD) under contract number 1073190. The authors wish to acknowledge Ms. Katherine Pordeli and Ms. Beth Wilder, SJRWMD for their support for the project. The authors are grateful to two anonymous reviewers and the Editor whose comments and advice improved the quality of the manuscript. This manuscript is dedicated to the memory of Sudheer Reddy Satti (1979–2004).

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