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Agricultural water use estimation using geospatial modeling and a geographic information system
Agricultural Water Management 67 (2004) 185–199
Agricultural water use estimation using geospatial modeling and a geographic information system夽 Vijendra K. Boken a,∗ , Gerrit Hoogenboom a , James E. Hook b , Daniel L. Thomas c , Larry C. Guerra a , Kerry A. Harrison d a
Department of Biological and Agricultural Engineering, The University of Georgia, 1109 Experiment Street, Griffin, GA 30223, USA b National Environmentally Sound Production Agriculture Laboratory, Department of Crops and Soil Science, The University of Georgia, Tifton, GA 31793, USA c Department of Biological and Agricultural Engineering, Louisiana State University, Baton Rouge, LA 70803, USA d Department of Biological and Agricultural Engineering, The University of Georgia, Tifton, GA 31793, USA Accepted 29 January 2004
Abstract Fresh water resources in the world are limited and, often, disputes occur on how to share them. In many regions, agricultural water use is significant but poorly documented. In order to contribute to solutions for water disputes involving such regions, methodologies need to be developed for regional water use estimation. In this paper we present a case study of Georgia (USA) which is locked in a water dispute with its neighboring states—Alabama and Florida. Agricultural water use in Georgia was essentially unknown because of no reporting requirement. Using a geographic information system and geospatial techniques, the depths of irrigation for cotton, peanut, and maize are estimated for the Flint, Central, and Coastal water zones of Georgia for 2000–2002. The geospatial techniques included the Inverse Distance Weighting, Global Polynomial, Local Polynomial, Radial Basis Function, Ordinary Kriging, and Universal Kriging. The volume of irrigation for these crops was estimated for 2000 and 2001. On the basis of root mean squared error, the Radial Basis Function technique was found to be the most successful one, followed by the Local Polynomial technique. The study of variograms revealed that the depth of irrigation at a site was influenced by its neighboring sites within a radius of about 40 km in the case of cotton, and within about 70 km in the case of peanut. No such influence could be detected for maize. The total volume of irrigation was highest for the Flint zone (564.2 Mm3 ), followed by the Central zone (291.9 Mm3 ) and the Coastal zone (94.1 Mm3 ) for 2000. For 2001, the irrigation volume declined by 40% for the Flint zone, 32% for the Central zone, and 16% for the 夽 The use of trade names, etc. in this paper does not imply endorsement by the University of Georgia or Louisiana State University for the product named or similar products not mentioned. ∗ Corresponding author. Tel.: +1-770-229-3436; fax: +1-770-228-7218. E-mail address: [email protected] (V.K. Boken).
0378-3774/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2004.01.003
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Coastal zone. The estimates presented in this study can be improved by including more representative sampling sites if possible, by studying the patterns of irrigated lands in Georgia, and by using satellite data for estimating irrigated area for individual crops. © 2004 Elsevier B.V. All rights reserved. Keywords: Georgia; Geostatistics; Irrigation; Spatial interpolation; Water dispute
1. Introduction Fresh water resources (e.g., lakes, rivers) of the world are limited and their demands continue to increase with rising population. People who are divided by state or national boundaries often dispute the apportioning of water they withdraw from common resources. Years and even decades pass by, but such water disputes are not resolved. One of the reasons is that the disputing parties are not convinced by each other’s estimates of the total amount of water used in the agricultural sector. Water use is often metered in all other sectors (municipal, recreational, hydropower generation, etc.) except in the agricultural sector. In this paper we examine a case study of Georgia and evaluate the use of geospatial modeling and (GIS) techniques for the estimation of agricultural water use. Alabama, Florida, and Georgia are adjoining states in the United States of America. These states currently dispute a river-water distribution. The Alabama, Coosa, and Tallapoosa (ACT), and the Apalachicola, Chattahoochee, and Flint (ACF) rivers originate in Georgia and flow through Alabama and Florida (Thomas et al., 2000). The latter two states complain, without reliable evidence, that Georgia consumes more water than its fair share, hence the dispute. The problem is that none of the states knows its yearly water usage, i.e., the volume of water used in a year. If the reliable estimates of the total water usage are available for each state, it will significantly contribute to the process of solving the water dispute satisfactorily. In Georgia, the water usage in the agricultural sector (i.e. irrigation usage) is higher than in any other sector and hence its estimation is of utmost significance in order to determine the total water usage in Georgia. Water is used for irrigating various crops in Georgia, such as cotton, peanut, maize, soybean, wheat, fruits, and vegetables. The combined irrigated area for cotton, peanut, and maize accounts for about 75% of the total irrigated area in the state (United States Department of Agriculture, 1999). In this paper, our goal was to estimate the total amount of water used by these three crops for three consecutive years (2000–2002) by employing geospatial modeling and a GIS. 1.1. Study area and objectives There are 159 counties in Georgia forming seven different water zones. But, three zones (i.e., Flint, Central, and Coastal; Fig. 1) encompass about 90% of the agricultural lands of the state. Therefore these three zones were selected to meet the following objectives: (i) to estimate the depth of irrigation (DI, cm) for cotton, peanut, and maize at a county level, for 2000–2002, (ii) to average these depths for the Flint, Central, and Coastal water zones, and finally, (iii) to determine the volume of water used by these crops in the selected zones.
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Fig. 1. Delineated water zones in the state of Georgia and sample sites for cotton.
2. Data collection The basic data required to meet the above objectives are the county level irrigated area for the selected crops and the corresponding DIs. We collected the irrigated area for 2000 from the county agents (Harrison, 2001). This information was not available for 2001 and 2002. However, we estimated the irrigated area for each crop for 2001 by assuming that the ratio of the irrigated area to harvested area for a crop within a county did not change from 2000 to 2001. We could not estimate the irrigated area for 2002 simply because the harvested area was not available for the same year. Table 1 includes the irrigated area at a zonal basis, which was derived from the county level data. A more critical component of the data collection was the DI data for the selected crops. The DI data were not available through any source and needed to be generated. In 1998, the College of Agricultural and Environmental Sciences at the University of Georgia launched a 5-year project called the Agricultural Water Pumping (AWP) project (Thomas et al., 1999, 2003; www.AgWaterPumping.net) to generate the DI data for all of the irrigated crops in Georgia. Under the auspices of the AWP project, hour meters were installed at about 400 rather randomly selected water withdrawal (i.e., pumping) sites. Factors such as a requirement of permission by farmers for collecting data at their fields and accessibility to sites influenced the site selection. Water is withdrawn from both surface as well as ground-water resources at water withdrawal sites in the study area. These AWP sites (or sample sites) constituted about 2% of the total number of permitted agricultural withdrawals in the state. The location (latitude and longitude) for each site was determined using a global positioning system (GPS). In addition, from the middle of 1999 to the end of 2002, the following data were recorded monthly by visiting each site: (i) the total operating time (in h and min) for the irrigation system and (ii) the area and names of the crops that were
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Table 1 Basic statistics of the irrigation data collected under the auspices of the Agricultural Water Pumping project in Georgia Crop
Year
Na
Observed irrigation (cm) Mean
Range
S.D.b
Irrigated area (1000 ha) Flint
Central
Coastal
Total
Cotton
2000 2001 2002
131 314 374
20.7 11.2 16.9
0–1.5 0–42.2 0–50.8
12.2 9.5 10.1
130.3 150.6
103.4 109.6
25.6 28.8
259.3 289.0
Peanut
2000 2001 2002
93 241 215
22.2 12.1 19.8
0–47.2 0–82.8 0–49.8
10.3 10.8 10.9
79.6 82.0
35.1 37.3
9.0 11.3
123.7 130.6
Maize
2000 2001 2002
53 97 135
29.4 13.9 25.0
0–60.2 0–53.1 0–95.5
16.3 12.7 15.8
44.1 38.6
20.9 21.3
10.9 9.4
75.9 69.3
a b
Number of the sample sites. Standard deviation.
irrigated. At most sites, multiple crops were irrigated using a central pivot device. The flow rate of the pumps was also measured to provide an estimate of total water use. Analysis of the data thus collected led to the determination of the DI for cotton, peanut, and maize at the sample sites for 2000–2002. Table 1 includes the basic statistics of the DI data.
3. Methodology The main objective of the present study was to examine techniques for estimating DI at a county level using the DI measured at limited number of sample sites, so that the irrigation usage can be estimated at a county level. However, the estimates of irrigation usage are being reported only at zonal level in this paper due to the confidentiality guaranteed to the farmers associated with the data collection. Using the coordinate information (latitude–longitude) available for the sample sites, we created ‘point coverage’ using the ArcGIS software, Version 8.2 (Environmental Systems Research Institute, Redlands, CA) for each crop. Fig. 2 depicts the sample sites for different crops and years. The number of sites increased for each subsequent year as the installation of hour meters progressed. The DI data were available for these sample sites as determined from the AWP database. Now the question arises—How to model the DI data measured at sample sites (i.e., point locations), for a given crop and year? Once the DI is modeled, water use for non-monitored sites can be estimated. In the present study, our goal was to estimate the DI for an areal unit, i.e., a county. The easiest method to achieve this goal is by averaging the DI at sample sites within the county. But, the sample sites constituted only 2% of the total number of permitted sites and their locations were largely influenced by farmers’ voluntary participation in the AWP project. Many counties with less than 50 permitted withdrawals were devoid of any sample site (Fig. 2). Therefore, simple averaging may not yield the most representative
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Fig. 2. The location of sample sites for different crops and years in the study area in Georgia. 189
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results. In search for a more accurate method, we decided to employ geospatial techniques to model the DI data. 3.1. Geospatial techniques We examined both deterministic and geostatistical (together called geospatial) techniques for modeling the DI data. Various techniques have been described in the literature (Cressie, 1993; Baily and Gatrell, 1995; Johnston et al., 2001). These techniques were first used and even developed for geology, but lately they have been applied and found useful in climatology and hydrology for interpolation of spatial variables: such as soil parameters (Motz and Searcy, 1993), furrow irrigation characteristics (Fonteh and Podmore, 1994), climate variables at a weather station (Holdaway, 1996; Ashraf et al., 1997), and irrigation water requirements (Sousa and Pereira, 1999). In these studies, the kriging technique was found to be a useful approach for estimating a spatial variable. Inverse distance weighting was applied in selected cases. Little information is available to determine whether the geospatial techniques have been used for estimating regional water use. The deterministic techniques that we applied for estimating the regional water use included Inverse Distance Weighting (IDW), Global Polynomial (GP), Local Polynomial (LP), and Radial Basis Function (RBF) techniques. In the category of geostatistical techniques, we applied kriging techniques. A brief description of these techniques follows. The IDW technique assumes that data measured at each site has a local influence that diminishes with distance. The application of the IDW technique deserves merit because a farmer’s decision to irrigate is influenced, among other factors such as cost-benefit analysis and weather conditions, by his or her neighbor’s decision (K.A. Harrison, personal communication). The GP technique develops a multiple regression model on all of the data and creates a prediction surface. It fits a trend to the coordinates of the sample sites, which can be a polynomial surface of first order (linear), second-order (quadratic) and so forth. The LP technique is similar to the GP technique except that it uses localized windows, rather than all of the data. The LP technique captures short-range variation in input data and is sensitive to neighborhood distance within the window. The window is moved around and the surface value at the center of the window is estimated at each point by fitting a polynomial. In the present case, it would be prudent to examine both GP and LP techniques because the variation in the DI data can be attributed to both local and regional factors such as soil, weather, and ground-water levels across the study area. The RBF technique is used for calculating small surfaces from a large number of points. For each point, an RBF is defined, which depends on the Euclidean distance between the prediction site and each sample site. Geostatistical techniques create prediction surfaces by incorporating statistical properties of data measured at sample sites. The basic principle is that the data from neighboring sites tend to be more closely related than the data from sites located far apart. Common techniques included in this category are Ordinary, Simple, Universal, Probability, Indicator, and Disjunctive Kriging, along with their counterparts in co-kriging. The co-kriging technique is used when two variables are involved. The present case deals with only one variable (i.e., DI), therefore the description on co-kriging is excluded from this paper.
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The following is a general expression for a geostatistical model: Z(s) = µ(s) + ε(s)
(1)
where Z(s) is the variable of interest at site s, µ(s) can be a simple constant or a polynomial function of the coordinates of the sample sites, and ε(s) is the random error. When the coefficients used in the polynomial function of µ(s) are known, it forms the model for Simple Kriging. Otherwise, this is the model on which Ordinary Kriging (OK) or Universal Kriging (UK) is based. In present study, the coefficients were not known, therefore Simple Kriging was excluded from further discussion. Similarly, Indicator Kriging, which determines whether the predicted value is above or below a threshold, did not serve the purpose of the present study and was also excluded from further discussion. As a result, only OK and UK were selected from the kriging family of geostatistical techniques. 3.2. Developing and testing of the spatial models In the present study, we had nine different cases for developing spatial models—three (one each for 2000–2002) for each crop. For each case, we examined six spatial techniques (IDW, GP, LP, RBF, OK, and UK) to model the DI data. In order to justify the stationarity assumption for the OK technique, we transformed and detrended the input data. However, for the UK technique, this assumption was not required (Motz and Searcy, 1993). Construction of a variogram is a prerequisite for applying kriging techniques. A variogram is a graphical display of semi-variance in data (DI, in the present case) versus Euclidean distance between sites. For each case, all possible pairs of sample sites were determined and the distance as well as semi-variance between them were computed using the Variowin software (Pannatier, 1996). By choosing a lag spacing and the number of lags, the pairs were grouped into a few classes that are displayed in the form of a variogram. Fig. 3 presents the variograms for the different crops and years for studying the spatial dependence of the DI data. If the data meet the fundamental assumption for applying kriging techniques, the semi-variance should be lower for sites that are close to each other as opposed to sites that are far apart. From Fig. 3, it is evident that the variogram for maize (in 2000 and 2001) did not validate such an assumption. Hence, the kriging technique was not applied for these two maize cases. For the remaining seven cases, the specifications of the models that best fitted the semi-variograms are provided in Table 2. Exponential model and spherical model were found to be the best for five and two cases, respectively. For each of nine cases considered in the present study, the total number of sample sites was divided into two sets using the ‘create subset’ option in ‘Geostatistical Analyst’ module of the ArcGIS. The first set consisted of training sites while the second set comprised testing sites (Fig. 2). The training set with 80% of sites was used for developing a geospatial model, while the testing set with the remaining 20% of sites was used to test the performance of the model by comparing the root mean squared (RMS) error: RMS =
n (DIip − DIio ) 2 i=1
n
(2)
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V.K. Boken et al. / Agricultural Water Management 67 (2004) 185–199 Peanut 2000
300
300
300
250 200 150 100
250 200 150 100
50
50
0
0 50
100
150
200
Semi-variance
350
0
250
200 150 100
0 0
50
100 150
200
250
0
Peanut 2001 300
300
150 100
250 200 150 100
50
50
0
0 50
100
150
200
Semi-variance
300
Semi-variance
350
200
250
250 200 150 100
0 0
50
100 150
200 250
0
Cotton 2002
Peanut 2002 300
300
250 200 150 100
50
50
0
0 50
100
150
200
250
Distance (km)
Semi-variance
300
Semi-variance
350
100
150 200
250
Maize 2002
350
150
100
Distance (km)
350
0
50
Distance (km)
200
250
50
Distance (km)
250
150 200
Maize 2001
350
250
100
Distance (km)
350
0
50
Distance (km)
Cotton 2001
Semi-variance
250
50
Distance (km)
Semi-variance
Maize 2000
350
Semi-variance
Semi-variance
Cotton 2000 350
250 200 150 100 50 0
0
50
100 150
200
250
0
Distance (km)
50
100 150
200
250
Distance (km)
Fig. 3. Variograms for different crops in different years. Table 2 Specifications of models best fitting the semi-variograms while applying kriging techniques to selected cases of the present study Crop
Year
Specifications of the best model Model type
Range
Minimum error Nugget
Sil
Cotton
2000 2001 2002
Exponential Exponential Exponential
0.37 0.34 0.87
95.2 62.1 70.4
49.0 28.8 29.0
0.0416 0.0059 0.0170
Peanut
2000 2001 2002
Exponential Exponential Spherical
0.91 0.61 0.71
59.9 68.1 71.5
47.3 31.2 48.1
0.0065 0.0186 0.0060
Corn
2002
Spherical
0.61
160.0
114.6
0.0606
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where DIip is the predicted depth of irrigation for site i, DIio the observed depth of irrigation for site i, and n the total number of sites in a testing set. In each case, the performance of every model was tested and the best model (with minimum RMS error) was identified. Using the best technique, we created a prediction surface for each of the nine cases. A prediction surface was then converted into a raster surface comprising fine grids that exceeded about 90,000 (300 rows and 300 columns) depending on the spatial extent of training sites in each case. We used the ‘zonal statistics’ option available in the ‘Spatial Analyst’ module of the ArcGIS to compute the average of predicted DIs within only those counties that reported an irrigated area for the corresponding crop. Fig. 2 shows all of the counties that reported irrigated area for cotton, peanut, and maize. By multiplying the DI with the corresponding irrigated area, we estimated the total volume of water used for irrigating a crop within a county. By adding the irrigation usage for counties within a zone, the irrigation usage for the zone was determined.
4. Results and discussion In Table 3, the techniques that were considered most appropriate are presented for the different crop and years. The RBF technique provided the best predictions for six out of nine cases tested in this study. In the cases of cotton and peanut, the RBF was found to be the most appropriate technique. In the case of maize, different techniques yielded best results for different years. In fact, the LP technique had offered minimum RMS error for four (peanut 2000–2002, and maize 2002) out of nine cases, but this technique was not considered appropriate because it resulted in negative predictions for many grids that were far from the sample sites (e.g., grids within counties with no sample sites). For such cases, Table 3 The specifications of geospatial techniques yielding the lowest RMS error for each crop and year Crop
Year
Technique selected for spatial interpolation Method
RMS (cm)
Cotton
2000 2001 2002
Radial Basis Function Radial Basis Function Radial Basis Function
9.7 7.3 9.8
Peanuta
2000a 2001a 2002a
Radial Basis Function Global Polynomial, Power = 2 Radial Basis Function
9.8 9.2 8.9
Maize
2000 2001a 2002
Radial Basis Function Global Polynomial, Power = 1 Inverse Distance Weighting, Power = 1
16.7 11.6 13.2
The number of neighbors to include was 5 or at least 2 for all the techniques that required this information; completely regularized function was selected for the Radial Basis Function. a The Local Polynomial technique gave the minimum RMS error for these cases, however the LP technique resulted in negative predictions for some sites. Hence the next best technique was selected.
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Table 4 The predicted depth and volume of irrigation for the Flint, Central, and Coastal water zones of Georgia Crop
Year
Estimated depth of irrigation (cm)
Volume of water used (Mm3 )
Flint
Central
Flint
Coastal
Central
Coastal
Total
Cotton
2000 2001 2002
23.4 13.8 19.2
14.7 11.0 14.8
14.2 10.6 9.0
307.0 197.0
148.7 114.9
38.1 37.3
493.8 349.2
Peanut
2000 2001 2002
21.3 13.5 20.8
16.3 17.4 15.6
18.1 21.2 10.6
101.6 90.1
89.5 51.4
33.5 28.0
224.6 169.5
Maize
2000 2001 2002
31.0 17.2 25.7
24.6 14.4 19.7
20.9 14.3 18.0
155.6 52.6
53.7 32.8
22.5 13.7
231.8 99.1
Total
2000 2001 Change (%)
564.2 339.7 −40
291.9 199.1 −32
94.1 79.0 −16
950.2 617.8 −35
the next best technique was applied. It is therefore prudent to include sample sites within every county that requires an estimation of water use for the selected crops. Using the most appropriate techniques and following the methodology described in the previous section, the average DI and then the volume of water used for irrigating each crop was estimated for the Flint, Central, and Coastal water zone of Georgia (Table 4). In Figs. 4 and 5, the predicted DI and irrigation are also shown. In general, the DI was highest for maize followed by cotton, and peanut. The DI was higher for the Flint zone compared to other zones, and declined significantly for 2001 and 2002. The decline in the DI could be attributed to one or more of the following factors: (i) the higher availability of precipitation, (ii) the limited availability of surface water, (iii) a higher ratio of the irrigation cost to economic benefits. The average precipitation during the cropping season (March–October) was significantly lower for 2000 (Fig. 6). Hence the irrigation tended to be higher for 2000. 4.1. Impact of precipitation For 2000, the total volume of irrigation was highest for the Flint zone (564.2 Mm3 ), followed by the Central zone (291.9 Mm3 ), and the Coastal zone (94.1 Mm3 ). For 2001, the irrigation volume declined by about 40% for the Flint zone, 32% for the Central zone, and 16% for the Coastal zone (Table 4). These changes were not obviously associated with only the precipitation during the cropping season. From 2000 to 2001, the average seasonal precipitation (March–October) increased by 35% for the Flint zone, and 17% for the Central zone, but declined by 1% for the Coastal zone (Table 5). Hence, the impact of the precipitation on irrigation was significant only for the Flint zone. If additional automated rain gauges (weather stations) are installed across the study area, the relationship between precipitation and irrigation can be better examined. Currently, there are only 11 stations in the Flint zone, 13 in the Central zone, and 4 in the Coastal zone (Hoogenboom, 2001).
V.K. Boken et al. / Agricultural Water Management 67 (2004) 185–199
Irrigation Depth (cm)
40
195
2000 Flint Col 4 Coastal
30
20
10
0
Cotton
30
Flint Coastal
Peanut
Maize
2001
Irrigation Depth (cm)
Col 6
20
10
0
Cotton
Peanut
Maize
2002
30
Flint Coastal
Irrigation Depth (cm)
Col 6
20
10
0 Cotton
Peanut
Maize
Fig. 4. The predicted depth of irrigation for different crops and water zones in Georgia.
4.2. Estimation error It would be worthwhile to examine factors that may have contributed to errors in estimation. Such factors can be related to the stratification of sampling sites, spatial variability in DI data, human error in data collection, and mapping of irrigated lands. As can be seen in
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Average Precip., March-October (cm)
Fig. 5. The predicted volume of irrigation for three primary crops and water zones in Georgia based on estimated land area in each crop.
100
90
Flint Central Coastal
80
70
60
50
2000
2001
2002
Fig. 6. The average precipitation during March–October for different water zones in Georgia.
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Table 5 The average seasonal precipitation for different water zones in Georgia during 2000–2002, based on the data from automated weather stations Year
2000 2001 2002 Change, 2000–2001 (%)
Average seasonal precipitation during March–October (cm) Flint
Central
63.2 85.1 82.6 35
64.8 75.9 79.8 17
Coastal 64.0 63.2 77.0 −1
Currently there are 11 automated weather stations in Flint zone, 13 in Central zone and 4 in Coastal zone.
Table 3, the RMS error was higher for maize than for cotton or peanut. Hence the geospatial techniques selected in the present study were more appropriate for cotton or peanut than maize. The higher RMS error for maize could be due to the smaller number of sample sites and a higher standard deviation in the DI data for maize (Table 1). The estimation error tends to be higher for data exhibiting a higher degree of spatial variability. A more accurate estimation can be expected if the number of sample sites for a particular crop within a county is proportional to the irrigated area reported for that crop and county. Such a selection of sampling sites will also reduce the spatial variability in the DI data. But, such a desired site selection was not possible in the present case due to the voluntary participation by farmers in the AWP project and due to the rotation of crops to different fields each year. Human errors in data collection contribute to errors in water use estimation. Where a single central pivot was used to irrigate multiple crops, best estimates were made by the project staff about irrigated area for individual crops during their monthly visits to sample sites. Accuracy in data collection pertaining to irrigated area may be enhanced by equipping the central pivot device with a GPS unit and thereby monitoring the daily movement of the central pivot across the crop fields by analyzing the GPS data. Besides, Advanced Very High Resolution Radiometer (AVHRR) data (Boken et al., 2004) or Moderate Resolution Imaging Spectroradiometer, i.e., MODIS data (www.gsfc.nasa.gov) may also be considered for improving estimates of irrigated area. The DI for a county was estimated by averaging the DI values predicted for every grid within the county. This was based on the assumption that the entire area within a county was irrigated, which certainly is not true. The best approach would be to map irrigated lands within a county and consider only those grids that fall within the irrigated lands. But, currently no such irrigation map is available for Georgia. The irrigation patterns are likely to change from year to year due to various factors. It would be helpful to produce a general map showing irrigated lands in Georgia, which would help enhance the accuracy of water use estimates. 4.3. Region of influence In addition to the above, there can be several other factors that can contribute to errors in estimation. But some of the additional factors are very complex to model and deserve further research. As discussed earlier, spatial variability in the DI data, particularly within
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close proximity of sample sites, tends to contribute to errors in estimation. What exactly influences a farmer’s decision on when and how much to irrigate needs further analysis. To this end, an examination of variograms for different crops is helpful in estimating a region of influence, i.e., the region within which the DI at a site is distinctly related to DI at its neighboring sites. The radius of this region can be observed from the initial portion of a variogram. By examining the variograms shown in Fig. 3, it was found that the average radius of the region of influence was 40 km (the average of approximately 30, 50, and 40 km for 2000–2002, respectively) in the case of cotton. Similarly, the average radius for peanut was about 70 km (an average of approximately 90, 60, and 70 km). In the case of maize, no such distinct relationship was found.
5. Conclusion In this study, six geospatial techniques (IDW, GP, LP, RBF, OK, and UK) were evaluated for estimating regional water use for the three main crops (cotton, peanut, and maize) that are currently irrigated in Georgia. On the basis of an RMS error, the RBF technique was found to be the most successful one, followed by the LP technique. But the LP technique was not considered appropriate and therefore not selected because it resulted in negative predictions at sites far away from sampling sites. It would therefore be prudent to include sample sites within every county with an irrigated area for the selected crops. Using the most appropriate geospatial and GIS techniques, the estimated total volume of irrigation for cotton, peanut, and maize was highest for the Flint zone (564.2 Mm3 ), followed by the Central zone (291.9 Mm3 ), and the Coastal zone (94.1 Mm3 ) for 2000. This volume declined by 40% for the Flint, 32% for the Central, and 16% for the Coastal zone, for 2001. The precipitation during the March–October period impacted the irrigation volume significantly, but only in the Flint zone. The relationship between the precipitation and irrigation can be better examined if the rain gauge network can be expanded across the study area. The water use estimates presented in this study can be improved by enhancing the representation of sampling sites as well as by limiting the estimations to irrigated areas within counties. A better understanding of other factors that influence a farmer’s decision to irrigate can also contribute to the accuracy of the estimates. Several factors affect the variation in irrigation depths, e.g., weather conditions, soil characteristics, cost-benefit analysis, and psychological factors. Examination of variograms revealed that the DI at a sample site was distinctly influenced by the neighboring sites within a radius of about 40 km in the case of cotton, and about 70 km in the case of peanut. No such influence could be detected for maize. This information could be studied along with other factors in order to analyze and model the reasons for the variability in the DI data. In summary, the geospatial and GIS approaches presented in this paper can also be applied in other parts of the world for estimating agricultural water use. However, the estimation accuracy will rely on the spatial variability in irrigation, selection of sampling sites, and accuracy of data on irrigated area. The geospatial techniques can provide useful information for improving water use planning and can significantly contribute to solutions for water disputes at regional, national, or international levels.
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Acknowledgements This work was supported in part by a special grant from USDA-CSREES and a grant from the Georgia Department of Natural Resources, Environmental Protection Division to study agricultural water use in the state of Georgia.
References Ashraf, M., Loftis, J.C., Hubbard, K.G., 1997. Agric. For. Meteor. 84, 255–271. Baily, T.C., Gatrell, A.C., 1995. Interactive Spatial Data Analysis. Addison-Wesley/Longman, Essex. Boken, V.K., Hoogenboom, G., Kogan, F.N., Hook, J.E., Thomas, D.L., Harrison, K.A., 2004. Potential of using NOAA-AVHRR data for estimating irrigated area to help solve an inter-state water dispute. Int. J. Remote Sens. 25 (12), (in press). Cressie, N.C., 1993. Statistics for Spatial Data. Wiley, New York. Fonteh, M.F., Podmore, T., 1994. Application of geostatistics to characterize spatial variability of infiltration in furrow irrigation. Agric. Water Manage. 25, 153–165. Harrison, K.A., 2001. Agricultural irrigation trends in Georgia. In: Hatcher, K.J. (Ed.), Proceedings of the Georgia Water Resources Conference, Institute of Ecology, The University of Georgia, Athens, Georgia, 2001, pp. 114–117. Holdaway, M., 1996. Spatial modeling and interpolation of monthly temperature using kriging. Clim. Res. 6, 215–225. Hoogenboom, G., 2001. Weather monitoring for management of water resources. In: Hatcher, K.J. (Ed.), Proceedings of the Georgia Water Resources Conference, Institute of Ecology, The University of Georgia, Athens, Georgia, 2001, pp. 778–781. Johnston, K., ver Hoef, J.M., Krivoruchko, K., Lucas, N., 2001. Using ArcGIS Geostatistical Analyst. Environmental Systems Research Institute, Redlands, CA. Motz, D.S., Searcy, S.W., 1993. Interpolation methods for spatially variable data. ASAE Paper 93–3561, 1–12. Pannatier, Y., 1996. VARIOWIN: Software for Spatial Data Analysis in 2D. Springer-Verlag, New York, NY. Sousa, V., Pereira, L.S., 1999. Regional analysis of irrigation water requirements using kriging—application to potato crop (Solanum tuberosum L.) at Tras-os-Montes. Agric. Water Manage. 40, 221–233. Thomas, D.L., Myers-Roche, C., Harrison, K.A., Hook, J.E., Tyson, A.W., Hoogenboom, G., Segars, W.I., 1999. Agricultural Water Pumping: a new program to evaluate agricultural water use in Georgia. In: Hatcher, K.J. (Ed.), Proceedings of the 1999 Georgia Water Resources Conference, Institute of Ecology, The University of Georgia, Athens, Georgia, pp. 560–562. Thomas, D.L., Harrison, K., Hook, J.E., Hoogenboom, G., Stooksbury, D., 2000. Current water resources issues in Georgia: their potential impact on irrigation. In: Flug, M., Frevert, D. (Eds.), Proceedings of the Watershed Management and Operations Management Symposium, Track E, Section 6. American Society of Civil Engineers, 2000. Thomas, D.L., Harrison, K.A., Hook, J.E., Hoogenboom, G., McClendon, R.W., Wheeler, L., 2003. Agricultural water use in Georgia: results from the Ag. Water pumping program. In: Hatcher, K.J. (Ed.), Proceedings of the Georgia Water Resources Conference, Institute of Ecology, The University of Georgia, Athens, Georgia, 1999, pp. 566–570. United States Department of Agriculture, 1999. 1997 Census of Agriculture, CD-ROM Set. National Agricultural Statistics Service, Washington, DC.
Agricultural water use estimation using geospatial modeling and a geographic information system夽 Vijendra K. Boken a,∗ , Gerrit Hoogenboom a , James E. Hook b , Daniel L. Thomas c , Larry C. Guerra a , Kerry A. Harrison d a
Department of Biological and Agricultural Engineering, The University of Georgia, 1109 Experiment Street, Griffin, GA 30223, USA b National Environmentally Sound Production Agriculture Laboratory, Department of Crops and Soil Science, The University of Georgia, Tifton, GA 31793, USA c Department of Biological and Agricultural Engineering, Louisiana State University, Baton Rouge, LA 70803, USA d Department of Biological and Agricultural Engineering, The University of Georgia, Tifton, GA 31793, USA Accepted 29 January 2004
Abstract Fresh water resources in the world are limited and, often, disputes occur on how to share them. In many regions, agricultural water use is significant but poorly documented. In order to contribute to solutions for water disputes involving such regions, methodologies need to be developed for regional water use estimation. In this paper we present a case study of Georgia (USA) which is locked in a water dispute with its neighboring states—Alabama and Florida. Agricultural water use in Georgia was essentially unknown because of no reporting requirement. Using a geographic information system and geospatial techniques, the depths of irrigation for cotton, peanut, and maize are estimated for the Flint, Central, and Coastal water zones of Georgia for 2000–2002. The geospatial techniques included the Inverse Distance Weighting, Global Polynomial, Local Polynomial, Radial Basis Function, Ordinary Kriging, and Universal Kriging. The volume of irrigation for these crops was estimated for 2000 and 2001. On the basis of root mean squared error, the Radial Basis Function technique was found to be the most successful one, followed by the Local Polynomial technique. The study of variograms revealed that the depth of irrigation at a site was influenced by its neighboring sites within a radius of about 40 km in the case of cotton, and within about 70 km in the case of peanut. No such influence could be detected for maize. The total volume of irrigation was highest for the Flint zone (564.2 Mm3 ), followed by the Central zone (291.9 Mm3 ) and the Coastal zone (94.1 Mm3 ) for 2000. For 2001, the irrigation volume declined by 40% for the Flint zone, 32% for the Central zone, and 16% for the 夽 The use of trade names, etc. in this paper does not imply endorsement by the University of Georgia or Louisiana State University for the product named or similar products not mentioned. ∗ Corresponding author. Tel.: +1-770-229-3436; fax: +1-770-228-7218. E-mail address: [email protected] (V.K. Boken).
0378-3774/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2004.01.003
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Coastal zone. The estimates presented in this study can be improved by including more representative sampling sites if possible, by studying the patterns of irrigated lands in Georgia, and by using satellite data for estimating irrigated area for individual crops. © 2004 Elsevier B.V. All rights reserved. Keywords: Georgia; Geostatistics; Irrigation; Spatial interpolation; Water dispute
1. Introduction Fresh water resources (e.g., lakes, rivers) of the world are limited and their demands continue to increase with rising population. People who are divided by state or national boundaries often dispute the apportioning of water they withdraw from common resources. Years and even decades pass by, but such water disputes are not resolved. One of the reasons is that the disputing parties are not convinced by each other’s estimates of the total amount of water used in the agricultural sector. Water use is often metered in all other sectors (municipal, recreational, hydropower generation, etc.) except in the agricultural sector. In this paper we examine a case study of Georgia and evaluate the use of geospatial modeling and (GIS) techniques for the estimation of agricultural water use. Alabama, Florida, and Georgia are adjoining states in the United States of America. These states currently dispute a river-water distribution. The Alabama, Coosa, and Tallapoosa (ACT), and the Apalachicola, Chattahoochee, and Flint (ACF) rivers originate in Georgia and flow through Alabama and Florida (Thomas et al., 2000). The latter two states complain, without reliable evidence, that Georgia consumes more water than its fair share, hence the dispute. The problem is that none of the states knows its yearly water usage, i.e., the volume of water used in a year. If the reliable estimates of the total water usage are available for each state, it will significantly contribute to the process of solving the water dispute satisfactorily. In Georgia, the water usage in the agricultural sector (i.e. irrigation usage) is higher than in any other sector and hence its estimation is of utmost significance in order to determine the total water usage in Georgia. Water is used for irrigating various crops in Georgia, such as cotton, peanut, maize, soybean, wheat, fruits, and vegetables. The combined irrigated area for cotton, peanut, and maize accounts for about 75% of the total irrigated area in the state (United States Department of Agriculture, 1999). In this paper, our goal was to estimate the total amount of water used by these three crops for three consecutive years (2000–2002) by employing geospatial modeling and a GIS. 1.1. Study area and objectives There are 159 counties in Georgia forming seven different water zones. But, three zones (i.e., Flint, Central, and Coastal; Fig. 1) encompass about 90% of the agricultural lands of the state. Therefore these three zones were selected to meet the following objectives: (i) to estimate the depth of irrigation (DI, cm) for cotton, peanut, and maize at a county level, for 2000–2002, (ii) to average these depths for the Flint, Central, and Coastal water zones, and finally, (iii) to determine the volume of water used by these crops in the selected zones.
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Fig. 1. Delineated water zones in the state of Georgia and sample sites for cotton.
2. Data collection The basic data required to meet the above objectives are the county level irrigated area for the selected crops and the corresponding DIs. We collected the irrigated area for 2000 from the county agents (Harrison, 2001). This information was not available for 2001 and 2002. However, we estimated the irrigated area for each crop for 2001 by assuming that the ratio of the irrigated area to harvested area for a crop within a county did not change from 2000 to 2001. We could not estimate the irrigated area for 2002 simply because the harvested area was not available for the same year. Table 1 includes the irrigated area at a zonal basis, which was derived from the county level data. A more critical component of the data collection was the DI data for the selected crops. The DI data were not available through any source and needed to be generated. In 1998, the College of Agricultural and Environmental Sciences at the University of Georgia launched a 5-year project called the Agricultural Water Pumping (AWP) project (Thomas et al., 1999, 2003; www.AgWaterPumping.net) to generate the DI data for all of the irrigated crops in Georgia. Under the auspices of the AWP project, hour meters were installed at about 400 rather randomly selected water withdrawal (i.e., pumping) sites. Factors such as a requirement of permission by farmers for collecting data at their fields and accessibility to sites influenced the site selection. Water is withdrawn from both surface as well as ground-water resources at water withdrawal sites in the study area. These AWP sites (or sample sites) constituted about 2% of the total number of permitted agricultural withdrawals in the state. The location (latitude and longitude) for each site was determined using a global positioning system (GPS). In addition, from the middle of 1999 to the end of 2002, the following data were recorded monthly by visiting each site: (i) the total operating time (in h and min) for the irrigation system and (ii) the area and names of the crops that were
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Table 1 Basic statistics of the irrigation data collected under the auspices of the Agricultural Water Pumping project in Georgia Crop
Year
Na
Observed irrigation (cm) Mean
Range
S.D.b
Irrigated area (1000 ha) Flint
Central
Coastal
Total
Cotton
2000 2001 2002
131 314 374
20.7 11.2 16.9
0–1.5 0–42.2 0–50.8
12.2 9.5 10.1
130.3 150.6
103.4 109.6
25.6 28.8
259.3 289.0
Peanut
2000 2001 2002
93 241 215
22.2 12.1 19.8
0–47.2 0–82.8 0–49.8
10.3 10.8 10.9
79.6 82.0
35.1 37.3
9.0 11.3
123.7 130.6
Maize
2000 2001 2002
53 97 135
29.4 13.9 25.0
0–60.2 0–53.1 0–95.5
16.3 12.7 15.8
44.1 38.6
20.9 21.3
10.9 9.4
75.9 69.3
a b
Number of the sample sites. Standard deviation.
irrigated. At most sites, multiple crops were irrigated using a central pivot device. The flow rate of the pumps was also measured to provide an estimate of total water use. Analysis of the data thus collected led to the determination of the DI for cotton, peanut, and maize at the sample sites for 2000–2002. Table 1 includes the basic statistics of the DI data.
3. Methodology The main objective of the present study was to examine techniques for estimating DI at a county level using the DI measured at limited number of sample sites, so that the irrigation usage can be estimated at a county level. However, the estimates of irrigation usage are being reported only at zonal level in this paper due to the confidentiality guaranteed to the farmers associated with the data collection. Using the coordinate information (latitude–longitude) available for the sample sites, we created ‘point coverage’ using the ArcGIS software, Version 8.2 (Environmental Systems Research Institute, Redlands, CA) for each crop. Fig. 2 depicts the sample sites for different crops and years. The number of sites increased for each subsequent year as the installation of hour meters progressed. The DI data were available for these sample sites as determined from the AWP database. Now the question arises—How to model the DI data measured at sample sites (i.e., point locations), for a given crop and year? Once the DI is modeled, water use for non-monitored sites can be estimated. In the present study, our goal was to estimate the DI for an areal unit, i.e., a county. The easiest method to achieve this goal is by averaging the DI at sample sites within the county. But, the sample sites constituted only 2% of the total number of permitted sites and their locations were largely influenced by farmers’ voluntary participation in the AWP project. Many counties with less than 50 permitted withdrawals were devoid of any sample site (Fig. 2). Therefore, simple averaging may not yield the most representative
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Fig. 2. The location of sample sites for different crops and years in the study area in Georgia. 189
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results. In search for a more accurate method, we decided to employ geospatial techniques to model the DI data. 3.1. Geospatial techniques We examined both deterministic and geostatistical (together called geospatial) techniques for modeling the DI data. Various techniques have been described in the literature (Cressie, 1993; Baily and Gatrell, 1995; Johnston et al., 2001). These techniques were first used and even developed for geology, but lately they have been applied and found useful in climatology and hydrology for interpolation of spatial variables: such as soil parameters (Motz and Searcy, 1993), furrow irrigation characteristics (Fonteh and Podmore, 1994), climate variables at a weather station (Holdaway, 1996; Ashraf et al., 1997), and irrigation water requirements (Sousa and Pereira, 1999). In these studies, the kriging technique was found to be a useful approach for estimating a spatial variable. Inverse distance weighting was applied in selected cases. Little information is available to determine whether the geospatial techniques have been used for estimating regional water use. The deterministic techniques that we applied for estimating the regional water use included Inverse Distance Weighting (IDW), Global Polynomial (GP), Local Polynomial (LP), and Radial Basis Function (RBF) techniques. In the category of geostatistical techniques, we applied kriging techniques. A brief description of these techniques follows. The IDW technique assumes that data measured at each site has a local influence that diminishes with distance. The application of the IDW technique deserves merit because a farmer’s decision to irrigate is influenced, among other factors such as cost-benefit analysis and weather conditions, by his or her neighbor’s decision (K.A. Harrison, personal communication). The GP technique develops a multiple regression model on all of the data and creates a prediction surface. It fits a trend to the coordinates of the sample sites, which can be a polynomial surface of first order (linear), second-order (quadratic) and so forth. The LP technique is similar to the GP technique except that it uses localized windows, rather than all of the data. The LP technique captures short-range variation in input data and is sensitive to neighborhood distance within the window. The window is moved around and the surface value at the center of the window is estimated at each point by fitting a polynomial. In the present case, it would be prudent to examine both GP and LP techniques because the variation in the DI data can be attributed to both local and regional factors such as soil, weather, and ground-water levels across the study area. The RBF technique is used for calculating small surfaces from a large number of points. For each point, an RBF is defined, which depends on the Euclidean distance between the prediction site and each sample site. Geostatistical techniques create prediction surfaces by incorporating statistical properties of data measured at sample sites. The basic principle is that the data from neighboring sites tend to be more closely related than the data from sites located far apart. Common techniques included in this category are Ordinary, Simple, Universal, Probability, Indicator, and Disjunctive Kriging, along with their counterparts in co-kriging. The co-kriging technique is used when two variables are involved. The present case deals with only one variable (i.e., DI), therefore the description on co-kriging is excluded from this paper.
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The following is a general expression for a geostatistical model: Z(s) = µ(s) + ε(s)
(1)
where Z(s) is the variable of interest at site s, µ(s) can be a simple constant or a polynomial function of the coordinates of the sample sites, and ε(s) is the random error. When the coefficients used in the polynomial function of µ(s) are known, it forms the model for Simple Kriging. Otherwise, this is the model on which Ordinary Kriging (OK) or Universal Kriging (UK) is based. In present study, the coefficients were not known, therefore Simple Kriging was excluded from further discussion. Similarly, Indicator Kriging, which determines whether the predicted value is above or below a threshold, did not serve the purpose of the present study and was also excluded from further discussion. As a result, only OK and UK were selected from the kriging family of geostatistical techniques. 3.2. Developing and testing of the spatial models In the present study, we had nine different cases for developing spatial models—three (one each for 2000–2002) for each crop. For each case, we examined six spatial techniques (IDW, GP, LP, RBF, OK, and UK) to model the DI data. In order to justify the stationarity assumption for the OK technique, we transformed and detrended the input data. However, for the UK technique, this assumption was not required (Motz and Searcy, 1993). Construction of a variogram is a prerequisite for applying kriging techniques. A variogram is a graphical display of semi-variance in data (DI, in the present case) versus Euclidean distance between sites. For each case, all possible pairs of sample sites were determined and the distance as well as semi-variance between them were computed using the Variowin software (Pannatier, 1996). By choosing a lag spacing and the number of lags, the pairs were grouped into a few classes that are displayed in the form of a variogram. Fig. 3 presents the variograms for the different crops and years for studying the spatial dependence of the DI data. If the data meet the fundamental assumption for applying kriging techniques, the semi-variance should be lower for sites that are close to each other as opposed to sites that are far apart. From Fig. 3, it is evident that the variogram for maize (in 2000 and 2001) did not validate such an assumption. Hence, the kriging technique was not applied for these two maize cases. For the remaining seven cases, the specifications of the models that best fitted the semi-variograms are provided in Table 2. Exponential model and spherical model were found to be the best for five and two cases, respectively. For each of nine cases considered in the present study, the total number of sample sites was divided into two sets using the ‘create subset’ option in ‘Geostatistical Analyst’ module of the ArcGIS. The first set consisted of training sites while the second set comprised testing sites (Fig. 2). The training set with 80% of sites was used for developing a geospatial model, while the testing set with the remaining 20% of sites was used to test the performance of the model by comparing the root mean squared (RMS) error: RMS =
n (DIip − DIio ) 2 i=1
n
(2)
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V.K. Boken et al. / Agricultural Water Management 67 (2004) 185–199 Peanut 2000
300
300
300
250 200 150 100
250 200 150 100
50
50
0
0 50
100
150
200
Semi-variance
350
0
250
200 150 100
0 0
50
100 150
200
250
0
Peanut 2001 300
300
150 100
250 200 150 100
50
50
0
0 50
100
150
200
Semi-variance
300
Semi-variance
350
200
250
250 200 150 100
0 0
50
100 150
200 250
0
Cotton 2002
Peanut 2002 300
300
250 200 150 100
50
50
0
0 50
100
150
200
250
Distance (km)
Semi-variance
300
Semi-variance
350
100
150 200
250
Maize 2002
350
150
100
Distance (km)
350
0
50
Distance (km)
200
250
50
Distance (km)
250
150 200
Maize 2001
350
250
100
Distance (km)
350
0
50
Distance (km)
Cotton 2001
Semi-variance
250
50
Distance (km)
Semi-variance
Maize 2000
350
Semi-variance
Semi-variance
Cotton 2000 350
250 200 150 100 50 0
0
50
100 150
200
250
0
Distance (km)
50
100 150
200
250
Distance (km)
Fig. 3. Variograms for different crops in different years. Table 2 Specifications of models best fitting the semi-variograms while applying kriging techniques to selected cases of the present study Crop
Year
Specifications of the best model Model type
Range
Minimum error Nugget
Sil
Cotton
2000 2001 2002
Exponential Exponential Exponential
0.37 0.34 0.87
95.2 62.1 70.4
49.0 28.8 29.0
0.0416 0.0059 0.0170
Peanut
2000 2001 2002
Exponential Exponential Spherical
0.91 0.61 0.71
59.9 68.1 71.5
47.3 31.2 48.1
0.0065 0.0186 0.0060
Corn
2002
Spherical
0.61
160.0
114.6
0.0606
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where DIip is the predicted depth of irrigation for site i, DIio the observed depth of irrigation for site i, and n the total number of sites in a testing set. In each case, the performance of every model was tested and the best model (with minimum RMS error) was identified. Using the best technique, we created a prediction surface for each of the nine cases. A prediction surface was then converted into a raster surface comprising fine grids that exceeded about 90,000 (300 rows and 300 columns) depending on the spatial extent of training sites in each case. We used the ‘zonal statistics’ option available in the ‘Spatial Analyst’ module of the ArcGIS to compute the average of predicted DIs within only those counties that reported an irrigated area for the corresponding crop. Fig. 2 shows all of the counties that reported irrigated area for cotton, peanut, and maize. By multiplying the DI with the corresponding irrigated area, we estimated the total volume of water used for irrigating a crop within a county. By adding the irrigation usage for counties within a zone, the irrigation usage for the zone was determined.
4. Results and discussion In Table 3, the techniques that were considered most appropriate are presented for the different crop and years. The RBF technique provided the best predictions for six out of nine cases tested in this study. In the cases of cotton and peanut, the RBF was found to be the most appropriate technique. In the case of maize, different techniques yielded best results for different years. In fact, the LP technique had offered minimum RMS error for four (peanut 2000–2002, and maize 2002) out of nine cases, but this technique was not considered appropriate because it resulted in negative predictions for many grids that were far from the sample sites (e.g., grids within counties with no sample sites). For such cases, Table 3 The specifications of geospatial techniques yielding the lowest RMS error for each crop and year Crop
Year
Technique selected for spatial interpolation Method
RMS (cm)
Cotton
2000 2001 2002
Radial Basis Function Radial Basis Function Radial Basis Function
9.7 7.3 9.8
Peanuta
2000a 2001a 2002a
Radial Basis Function Global Polynomial, Power = 2 Radial Basis Function
9.8 9.2 8.9
Maize
2000 2001a 2002
Radial Basis Function Global Polynomial, Power = 1 Inverse Distance Weighting, Power = 1
16.7 11.6 13.2
The number of neighbors to include was 5 or at least 2 for all the techniques that required this information; completely regularized function was selected for the Radial Basis Function. a The Local Polynomial technique gave the minimum RMS error for these cases, however the LP technique resulted in negative predictions for some sites. Hence the next best technique was selected.
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Table 4 The predicted depth and volume of irrigation for the Flint, Central, and Coastal water zones of Georgia Crop
Year
Estimated depth of irrigation (cm)
Volume of water used (Mm3 )
Flint
Central
Flint
Coastal
Central
Coastal
Total
Cotton
2000 2001 2002
23.4 13.8 19.2
14.7 11.0 14.8
14.2 10.6 9.0
307.0 197.0
148.7 114.9
38.1 37.3
493.8 349.2
Peanut
2000 2001 2002
21.3 13.5 20.8
16.3 17.4 15.6
18.1 21.2 10.6
101.6 90.1
89.5 51.4
33.5 28.0
224.6 169.5
Maize
2000 2001 2002
31.0 17.2 25.7
24.6 14.4 19.7
20.9 14.3 18.0
155.6 52.6
53.7 32.8
22.5 13.7
231.8 99.1
Total
2000 2001 Change (%)
564.2 339.7 −40
291.9 199.1 −32
94.1 79.0 −16
950.2 617.8 −35
the next best technique was applied. It is therefore prudent to include sample sites within every county that requires an estimation of water use for the selected crops. Using the most appropriate techniques and following the methodology described in the previous section, the average DI and then the volume of water used for irrigating each crop was estimated for the Flint, Central, and Coastal water zone of Georgia (Table 4). In Figs. 4 and 5, the predicted DI and irrigation are also shown. In general, the DI was highest for maize followed by cotton, and peanut. The DI was higher for the Flint zone compared to other zones, and declined significantly for 2001 and 2002. The decline in the DI could be attributed to one or more of the following factors: (i) the higher availability of precipitation, (ii) the limited availability of surface water, (iii) a higher ratio of the irrigation cost to economic benefits. The average precipitation during the cropping season (March–October) was significantly lower for 2000 (Fig. 6). Hence the irrigation tended to be higher for 2000. 4.1. Impact of precipitation For 2000, the total volume of irrigation was highest for the Flint zone (564.2 Mm3 ), followed by the Central zone (291.9 Mm3 ), and the Coastal zone (94.1 Mm3 ). For 2001, the irrigation volume declined by about 40% for the Flint zone, 32% for the Central zone, and 16% for the Coastal zone (Table 4). These changes were not obviously associated with only the precipitation during the cropping season. From 2000 to 2001, the average seasonal precipitation (March–October) increased by 35% for the Flint zone, and 17% for the Central zone, but declined by 1% for the Coastal zone (Table 5). Hence, the impact of the precipitation on irrigation was significant only for the Flint zone. If additional automated rain gauges (weather stations) are installed across the study area, the relationship between precipitation and irrigation can be better examined. Currently, there are only 11 stations in the Flint zone, 13 in the Central zone, and 4 in the Coastal zone (Hoogenboom, 2001).
V.K. Boken et al. / Agricultural Water Management 67 (2004) 185–199
Irrigation Depth (cm)
40
195
2000 Flint Col 4 Coastal
30
20
10
0
Cotton
30
Flint Coastal
Peanut
Maize
2001
Irrigation Depth (cm)
Col 6
20
10
0
Cotton
Peanut
Maize
2002
30
Flint Coastal
Irrigation Depth (cm)
Col 6
20
10
0 Cotton
Peanut
Maize
Fig. 4. The predicted depth of irrigation for different crops and water zones in Georgia.
4.2. Estimation error It would be worthwhile to examine factors that may have contributed to errors in estimation. Such factors can be related to the stratification of sampling sites, spatial variability in DI data, human error in data collection, and mapping of irrigated lands. As can be seen in
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Average Precip., March-October (cm)
Fig. 5. The predicted volume of irrigation for three primary crops and water zones in Georgia based on estimated land area in each crop.
100
90
Flint Central Coastal
80
70
60
50
2000
2001
2002
Fig. 6. The average precipitation during March–October for different water zones in Georgia.
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Table 5 The average seasonal precipitation for different water zones in Georgia during 2000–2002, based on the data from automated weather stations Year
2000 2001 2002 Change, 2000–2001 (%)
Average seasonal precipitation during March–October (cm) Flint
Central
63.2 85.1 82.6 35
64.8 75.9 79.8 17
Coastal 64.0 63.2 77.0 −1
Currently there are 11 automated weather stations in Flint zone, 13 in Central zone and 4 in Coastal zone.
Table 3, the RMS error was higher for maize than for cotton or peanut. Hence the geospatial techniques selected in the present study were more appropriate for cotton or peanut than maize. The higher RMS error for maize could be due to the smaller number of sample sites and a higher standard deviation in the DI data for maize (Table 1). The estimation error tends to be higher for data exhibiting a higher degree of spatial variability. A more accurate estimation can be expected if the number of sample sites for a particular crop within a county is proportional to the irrigated area reported for that crop and county. Such a selection of sampling sites will also reduce the spatial variability in the DI data. But, such a desired site selection was not possible in the present case due to the voluntary participation by farmers in the AWP project and due to the rotation of crops to different fields each year. Human errors in data collection contribute to errors in water use estimation. Where a single central pivot was used to irrigate multiple crops, best estimates were made by the project staff about irrigated area for individual crops during their monthly visits to sample sites. Accuracy in data collection pertaining to irrigated area may be enhanced by equipping the central pivot device with a GPS unit and thereby monitoring the daily movement of the central pivot across the crop fields by analyzing the GPS data. Besides, Advanced Very High Resolution Radiometer (AVHRR) data (Boken et al., 2004) or Moderate Resolution Imaging Spectroradiometer, i.e., MODIS data (www.gsfc.nasa.gov) may also be considered for improving estimates of irrigated area. The DI for a county was estimated by averaging the DI values predicted for every grid within the county. This was based on the assumption that the entire area within a county was irrigated, which certainly is not true. The best approach would be to map irrigated lands within a county and consider only those grids that fall within the irrigated lands. But, currently no such irrigation map is available for Georgia. The irrigation patterns are likely to change from year to year due to various factors. It would be helpful to produce a general map showing irrigated lands in Georgia, which would help enhance the accuracy of water use estimates. 4.3. Region of influence In addition to the above, there can be several other factors that can contribute to errors in estimation. But some of the additional factors are very complex to model and deserve further research. As discussed earlier, spatial variability in the DI data, particularly within
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close proximity of sample sites, tends to contribute to errors in estimation. What exactly influences a farmer’s decision on when and how much to irrigate needs further analysis. To this end, an examination of variograms for different crops is helpful in estimating a region of influence, i.e., the region within which the DI at a site is distinctly related to DI at its neighboring sites. The radius of this region can be observed from the initial portion of a variogram. By examining the variograms shown in Fig. 3, it was found that the average radius of the region of influence was 40 km (the average of approximately 30, 50, and 40 km for 2000–2002, respectively) in the case of cotton. Similarly, the average radius for peanut was about 70 km (an average of approximately 90, 60, and 70 km). In the case of maize, no such distinct relationship was found.
5. Conclusion In this study, six geospatial techniques (IDW, GP, LP, RBF, OK, and UK) were evaluated for estimating regional water use for the three main crops (cotton, peanut, and maize) that are currently irrigated in Georgia. On the basis of an RMS error, the RBF technique was found to be the most successful one, followed by the LP technique. But the LP technique was not considered appropriate and therefore not selected because it resulted in negative predictions at sites far away from sampling sites. It would therefore be prudent to include sample sites within every county with an irrigated area for the selected crops. Using the most appropriate geospatial and GIS techniques, the estimated total volume of irrigation for cotton, peanut, and maize was highest for the Flint zone (564.2 Mm3 ), followed by the Central zone (291.9 Mm3 ), and the Coastal zone (94.1 Mm3 ) for 2000. This volume declined by 40% for the Flint, 32% for the Central, and 16% for the Coastal zone, for 2001. The precipitation during the March–October period impacted the irrigation volume significantly, but only in the Flint zone. The relationship between the precipitation and irrigation can be better examined if the rain gauge network can be expanded across the study area. The water use estimates presented in this study can be improved by enhancing the representation of sampling sites as well as by limiting the estimations to irrigated areas within counties. A better understanding of other factors that influence a farmer’s decision to irrigate can also contribute to the accuracy of the estimates. Several factors affect the variation in irrigation depths, e.g., weather conditions, soil characteristics, cost-benefit analysis, and psychological factors. Examination of variograms revealed that the DI at a sample site was distinctly influenced by the neighboring sites within a radius of about 40 km in the case of cotton, and about 70 km in the case of peanut. No such influence could be detected for maize. This information could be studied along with other factors in order to analyze and model the reasons for the variability in the DI data. In summary, the geospatial and GIS approaches presented in this paper can also be applied in other parts of the world for estimating agricultural water use. However, the estimation accuracy will rely on the spatial variability in irrigation, selection of sampling sites, and accuracy of data on irrigated area. The geospatial techniques can provide useful information for improving water use planning and can significantly contribute to solutions for water disputes at regional, national, or international levels.
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Acknowledgements This work was supported in part by a special grant from USDA-CSREES and a grant from the Georgia Department of Natural Resources, Environmental Protection Division to study agricultural water use in the state of Georgia.
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