- Email: [email protected]
Assessment of irrigation water management in the Genil-Cabra (Córdoba, Spain) irrigation district using irrigation indicators
Agricultural Water Management 120 (2013) 98–106
Contents lists available at SciVerse ScienceDirect
Agricultural Water Management journal homepage: www.elsevier.com/locate/agwat
Assessment of irrigation water management in the Genil-Cabra (Córdoba, Spain) irrigation district using irrigation indicators ˜ 1 M. Fátima Moreno-Pérez ∗ , José Roldán-Canas Edif. Leonardo Da Vinci – Campus Rabanales, Department of Agronomy, University of Córdoba, 14071 Córdoba, Spain
a r t i c l e
i n f o
Article history: Available online 12 July 2012 Keywords: Irrigation Irrigation indicators Irrigation district Irrigation management
a b s t r a c t This paper examines irrigation water management in the Genil-Cabra Irrigation District of the Province of Cordoba (southern Spain) using three irrigation indicators: relative irrigation supply (RIS), relative water supply (RWS), and relative rainfall supply (RRS). The three indicators are calculated both globally and by grouping the data according to crop type, irrigation method, soil texture, and plot size. Then, it is possible to determine the influence that each individual factor has on irrigation management and take subsequent measures to improve irrigation performance. All of the information regarding agronomic and hydraulic variables has been included in a geographical information system (GIS) to facilitate data management. The results show that irrigation is deficit given that the mean value of the RIS indicator is relatively low, around 0.60. However, the RWS indicator achieves higher mean values, normally above 0.80, indicating that evaporation demand can be met throughout the crop development cycle. The RRS indicator shows less variability with mean values around 0.40. This indicator, together with the RWS indicator permits the evapotranspiration fraction covered by rainfall to be determined. The mean values of the calculated indicators are very useful for gaining a better understanding of irrigator behavior and general irrigation trends, although the study sample is still insufficient to characterize a large irrigation area as a whole. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Water resources are under enormous pressure due to increasing demands for more and better quality water; demands which are in turn conditioned by social, political and environmental factors. The growing difficulties to ensure that water demands are met have led to greater competitiveness for scarce water resources among traditional sectors of water users, namely agriculture, industry and urban supply. This competitiveness for water use is already placing constraints on the development of many countries. Moreover, the increasing scarcity of water resources has led to higher competitiveness between regions or countries for available water resources. As a result, water has come to be considered an increasingly scarce and valuable resource requiring rigorous management and extreme care. One of the keys to overcoming these problems lies in the agricultural sector given that irrigation – particularly in arid and semiarid areas – is the chief consumer of water; accounting for 70%
∗ Corresponding author. Tel.: +34 957218512; fax: +34 957212097. E-mail addresses: [email protected] (M.F. Moreno-Pérez), ˜ [email protected] (J. Roldán-Canas). 1 Tel.: +34 957218512; fax: +34 957212097. 0378-3774/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agwat.2012.06.020
of consumption worldwide. According to data from the Federación Nacional de Comunidades de Regantes (National Federation of Irrigator Communities) (IDAE, 2008), water consumption in Spain has dropped from 80% to 67% thanks to the on-going efforts of farmers and official bodies to save water through better irrigation practices and greater investment. Nonetheless, in Spain, a much larger amount of water is allotted to agriculture than to other types of activity (Roldán, 2007). For this reason, it is of interest to determine the water management practices of irrigation communities having a large volume of available water as farmers may not implement the best water management practices or water use may be inefficient. By correcting such practices, it will be possible to use water more efficiently. Attempts to adapt farmers’ water demands to real crop demands could lead to more efficient water use as it would permit water to be delivered only when necessary without reducing crop productivity, while sustaining and/or increasing farmer income. In recent decades many authors have developed and applied irrigation performance indicators and benchmarking techniques to identify the best irrigation practices and to compare different and complex irrigation systems (Levine, 1982; Molden and Gates, 1990; Bos et al., 1994; Burt, 2001, among many others). Also, different indicators have been standardized and the definitions of the parameters that form these indicators have been adjusted so that
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
comparisons between the different water irrigation districts (WID) can be generalized (Burt et al., 1997; Malano and Burton, 2001). Two are the main objectives that have attempted to fill these jobs: on the one hand, indicators at the level of WID have been applied to measure its efficiency (Burt and Styles, 1999a; Malano et al., 2004), on the other hand, benchmarking techniques have been used in different WID so that, from their comparison, water use in such systems was improved. Thus, for example, Holden et al. (1998) and Burt and Styles (1999b) compare WID located in several countries, while other authors works at a local scale (Akkuzu et al., 2007; Pérez Urrestarazu et al., 2009; Córcoles et al., in press). In the latter group may also include Rodríguez Díaz et al. (2008) who study WID with similar characteristics using multivariate data analysis techniques. In this paper, we aim to analyze irrigation water demand and examine possible measures for modifying and rationing demand in order to achieve an efficient water management policy. To do so, it is necessary to assess water management bearing in mind existing agronomic and hydraulic processes; develop a geographical informational system in the irrigation district that relates the geospatial location of the plots with the data obtained for them; and study theoretical water requirements and their discrepancies with farmers’ actual water demands using irrigation performance indicators at plot scale. The main contribution of this paper is the study of irrigation performance indicators discriminating by type of crop, irrigation method, soil type and plot size. This allows to determine the influence of each of these factors on the irrigation and, consequently, to improve their performance. An analysis of mean RIS for greenhouse crops during crop cycle periods was also conducted in a Mediterranean greenhouse area (Fernández et al., 2007).
99
2. Materials and methods 2.1. Description of the Genil-Cabra Irrigation district To analyze discrepancies between real water demands and actual crop water requirements, we have studied the Puente Genil Irrigated Area of the Genil-Cabra Irrigation District (Fig. 1) located in the Province of Cordoba (southern Spain). The Genil-Cabra Irrigation District, and the irrigator community that manages the district, were created and began to operate in 1989 under a public initiative. The irrigation district, which belongs to the Guadalquivir River Hydrographic Basin, covers a total of 40,085 ha. The district is located on the right bank of the Genil River; the main tributary of the left bank of the Guadalquivir River. Of the total area that comprises the irrigator community, only 37,010 ha are apt for use in irrigated agriculture. The irrigator community will soon deliver water to approximately 22,000 ha and will therefore supply almost 60% of the total forecasted demand. The community is composed of 1696 users, with an average plot size of 8.9 ha. The irrigated surface area currently comprises 15,963 ha, of which 8780 ha belong to the Puente Genil Irrigated Area where this study has been conducted. The main canal supplies water to each of the network’s discharge pump stations. The canal currently runs more than 30 km in length and has a slope of 1:10,000. The canal has a capacity of 1.1 hm3 , of which 800,000 m3 are available for agriculture. The canal has a parabolic section with a maximum width of 22.86 m, a depth of 4.15 m, and is capable of conveying 40 m3 /s of water. Water is distributed by means of a pressurized system through an underground piped network. Irrigation water is delivered on demand, thus providing farmers greater flexibility in terms of the frequency, volume and duration of irrigation and permitting them to
Fig. 1. Location of the Genil-Cabra Irrigation District in Spain.
100
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
Fig. 2. Evolution of main crops in the Puente Genil Irrigated Area.
program irrigation in an optimal manner as water is available on a permanent basis. The study area has a continental Mediterranean climate characterized by cold winters and hot, dry summers with a mean annual precipitation of 606 mm chiefly in winter months. The mean temperature in the area ranges from 17 ◦ C to 18 ◦ C, with mean temperatures as low as 10 ◦ C in winter and as high as 27 ◦ C in summer. Vertisols and soils of alluvial origin are predominant in the area. In this type of soils, deep cracks form in the dry season, permitting the soil materials that are deposited in them to mix, thus favoring a ˜ homogeneous soil profile. The land relief is typical of the Campina area and is formed by gently sloping hills that permit crop mechanization in practically all of the terrain. Due to the terrain’s natural surface drainage network, rainwater is evacuated in an adequate manner. The main irrigation systems found in the study area are semipermanent or mobile sprinklers, permanent or fixed sprinklers and drip irrigation. Total coverage sprinkler irrigation systems are the least common. In recent years, drip irrigation has become more widespread than mobile sprinkler systems, particularly in crops such as olive and cotton.
2.3. RWS, RIS, and RRS irrigation performance indicators The relative water supply (RWS) irrigation performance indicator focuses on the relation between the water that enters the system (precipitation and irrigation) and the water required (evapotranspiration and leaching). The RWS is calculated, see Eq. (1), as the ratio between total water supply or water consumed and the amount of water needed for production (Levine, 1982). By analyzing the RWS index, it is possible to determine if the total amount of water (precipitation and irrigation) delivered to the crop during its
2.2. Crops, rainfall and water consumption Fig. 2 shows crop evolution in the area over ten irrigation seasons. During the 2007/2008 season, olives were the main crop, occupying 47.5% of the total surface area, followed by wheat, which accounted for 22.5%. Other crops present in the area include sunflower (5.7%), garlic (4.1%) and cotton (2.2%), while alfalfa, potatoes, onion and broad beans are less common. Crops with higher water requirements (cotton and maize) have diminished year after year. This may be due to the fact that the water supply in the area is not fully guaranteed or has decreased in a notable manner. Water consumption is influenced by the weather conditions of each irrigation season. As can be seen in Fig. 3, there is a clear relationship between water consumption in the irrigator community and rainfall in each season. In seasons with low rainfall, total water consumption in the community increased. This is the case, for example, of the 2004/2005 season which suffered a severe drought (Pérez Arellano, 2009). However, during the 2002/2003 season, consumption increased in spite of rainfall being higher than in the 2004/2005 season. This could be due to poor irrigation management practices in that particular season. Fig. 4 shows total water consumption and water consumption per hectare in the irrigator community over seven irrigation seasons.
Fig. 3. Annual evolution of rainfall and total water consumption.
Fig. 4. Total water consumption and consumption per hectare by irrigation season.
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
growth cycle has been excessive, sufficient or deficient (Rodríguez Nadales, 2002). RWS values in the range of 0.9–1.2 are considered adequate. RWS is useful in that it serves as a basis for the comparative study and analysis of irrigated areas located in different regions with diverse characteristics: RWS =
R + Pe Water supply = ETc Actual crop evapotranspiration
(1)
where R is the irrigation; Pe the effective precipitation and ETc is the actual crop evapotranspiration. The relative irrigation supply (RIS) indicator relates, see Eq. (2), the volume of irrigation water supplied to users during the irrigation season to the volume of irrigation water required for the crop throughout its life cycle (Perry, 1996). This indicator provides information about the quality of the irrigation water delivered to the crop as it relates water supply demand to net water requirements. The RIS indicates if the farmer has taken into account crop evapotranspiration and lixiviation requirements when using a given volume of water. The optimal value of this indicator is around 1, meaning that water requirements not covered by rainfall are met. A value below 1 indicates an irrigation deficit: RIS =
Irrigation water supply R = ETc − Pe Crop water requirements
(2)
Both indicators provide information about the scarcity or excess of water and how water supply is adjusted to fit water demand (Molden et al., 1998). The relative rainfall supply (RRS) indicator has also been studied. It relates, see Eq. (3), the effective precipitation to the total amount of water needed for production. This indicator, which only takes rainfall into account, allows determining to what extent crop water requirements have been met in a natural manner (Pérez Urrestarazu et al., 2005). When the RRS value is equal to the RWS value in the same period, it means that rainfall is the only water supplied to the crop and irrigation does not take place: RRS =
Pe Effective precipitation = ETc Actual crop evapotranspiration
(3)
In this paper, we have calculated the three indicators mentioned above (RWS, RIS and RRS) for 31 plots belonging to the Genil-Cabra Irrigation District from the 2000/2001 to the 2007/2008 irrigation seasons. Rodríguez Nadales (2002) and Romero (2008) calculated the RIS and RWS indicators for only 16 plots. To assess the agronomic management of the Genil-Cabra Irrigation District, it is necessary to calculate the water requirements of the crops cultivated on the plots during the seasons studied. To do so, we used the CROPWAT 4.2 program (Clarke, 1998). Climatic data was taken from the weather station located within the irrigation district in the town of Santaella. 2.4. Geographic information system A Geographic Information System (ESRI, 2002) that was initially developed in previous works (Romero, 2008) was expanded and improved upon in order to analyze irrigation water demand and examine the possibilities for modifying and rationing demand to achieve an efficient water management policy in the Genil-Cabra Irrigation District. The variables introduced in the GIS used in this study with their corresponding geo-referenced data were: geographical location, climatic data (temperature and precipitation); edaphological data corresponding to several soil samples (texture, bulk density, cation exchange capacity, exchange ions, calcium, sodium, potassium, total and active lime, available phosphorus, oxidable organic matter, organic nitrogen and pH) (Romero, 2008); plot information; irrigation sectors and groups; primary and secondary distribution
101
Table 1 Irrigation management by crop according to the RWS value. Crop
RWS
Irrigation management
Olive Cotton Sunflower Maize Garlic Wheat Asparagus Sugar beet
0.90 0.91 0.96 0.88 1.91 1.53 0.58 0.74
Adequate Adequate Adequate Adequate Excess Excess Deficient Deficient
networks; discharge pumping stations; agronomic variables; plots with irrigation indicators and manual flow discharge readings (Díaz, 2009). Chemical soil analyses were excluded from the study as they did not reveal problems regarding the quality of the water used for irrigation (Romero, 2008). Data regarding crop, irrigation method and volume supplied to each plot correspond to the 2000/01–2007/08 irrigation seasons. The indicators were calculated during this period for the 31 plots selected. 3. Results and discussion 3.1. Analysis of indicators by crop type The irrigation performance indicators were calculated for the largest crops in the area (Díaz, 2009). Using the mean values obtained from the RWS indicator, irrigation management can be classified by crop type into three categories (see Table 1). Of the eight crops studied, six showed irrigation performance values below 1. Given that four of the crops (olive, cotton, sunflower and maize) showed RWS values close to 1 and the water deficit is negligible, irrigation management in these crops can be considered adequate. Irrigation is sufficient to meet the evaporation demand throughout the development cycle of the olive, cotton and sunflower crops as the mean RWS value is equal to or greater than 0.90. Similar results were found by Lorite et al. (2004b) in the same area of irrigation for cotton and corn. However, their values for sunflower and olive were somewhat lower. In the case of sugar beet and asparagus, the deficit increases, with RWS values below 0.8 indicating a permanent scarcity of water for crops that could lead to a reduction in crop yield. The results show that irrigation is clearly deficient. In this case it is necessary to increase the irrigation depth. Results obtained in the northeastern Spain (Dechmi et al., 2003) also show that, in general, crops were severely water stressed. Taking into account the annual rainfall in the area, a good irrigation strategy would be to match irrigation water demand to crop water requirements, that is, RIS = 1. Irrigation management was found to be inefficient in the wheat and garlic crops due to an excess supply of water. This excess of water is the result of the fact that these winter crops are usually irrigated in spite of natural rainfall supply. In this case, the best strategy would be to reduce the irrigation water depth as in the previous cases. By the contrary, Lorite et al. (2004b) found RWS values less than one for these winter crops over several irrigation seasons in the late nineties. However, our data confirm the application of excess irrigation water. To study irrigation water quality in greater depth, the results focus on olive crops. Olive groves are the most extensive crop and have continued to expand in recent irrigation seasons. The results discussed here correspond to the 17 plots shown in Tables 2a and 2b. The results for the first nine plots were taken from the studies by Rodríguez Nadales (2002) and Romero (2008). The evolution of the indicators RIS and RWS in the eight irrigation seasons (2000/2001–2007/2008) and their mean values are also
102
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
Table 2a RIS values for olive groves. PLOT
00/01
01/02
02/03
03/04
04/05
05/06
06/07
07/08
Mean RIS
213-A 213-B-01 220 225 237 238 253 293 I-104 218 219 221 292 291 402-I I-105 403-01 Mean RIS
0.60 0.30 0.35 0.30 0.31 0.88 0.36 0.61 0.44 0.72 – 0.34 0.18 0.24 – 1.04 0.26 0.46
0.78 0.32 1.20 0.23 0.85 1.22 0.37 0.99 0.40 0.57 – 0.45 0.26 0.46 – 1.31 0.16 0.64
0.53 0.24 0.42 – 0.35 0.81 0.66 0.56 2.47 0.58 – 0.25 0.25 0.31 – 0.47 0.41 0.59
0.35 0.43 0.45 – 0.37 0.66 0.43 0.36 – 0.94 0.82 0.27 0.46 0.59 – 0.08 0.54 0.48
0.41 0.55 0.36 – 0.75 0.97 0.51 0.50 2.48 0.31 1.05 0.26 0.13 0.30 0.17 1.01 0.76 0.58
0.31 0.11 0.24 0.31 0.29 0.57 0.16 0.20 0.91 0.74 2.35 0.42 0.69 0.42 0.82 2.03 0.78 0.67
0.56 1.20 0.47 0.27 0.74 0.58 0.60 0.37 2.15 0.71 1.02 0.15 0.74 0.73 0.15 1.53 0.70 0.69
0.45 0.42 0.44 0.49 0.33 1.22 0.54 0.49 0.53 0.75 1.22 0.04 0.65 0.29 0.14 0.56 0.59 0.53
0.50 0.45 0.49 0.32 0.50 0.86 0.45 0.51 1.34 0.67 1.29 0.27 0.41 0.42 0.32 0.75 0.50 0.59
Table 2b RWS values for olive groves. PLOT
00/01
01/02
02/03
03/04
04/05
05/06
06/07
07/08
Mean RWS
213-A 213-B-01 220 225 237 238 253 293 I-104 218 219 221 292 291 402-I I-105 403-01 Mean RWS
1.15 0.93 0.85 0.81 0.82 1.22 0.85 1.03 0.91 1.18 – 0.94 0.83 0.87 – 1.39 0.88 0.98
1.05 0.78 1.29 0.73 1.08 1.30 0.81 1.17 0.83 0.92 – 0.85 0.74 0.86 – 1.37 0.67 0.96
0.82 0.60 0.74 – 0.69 1.03 0.91 0.84 2.27 1.02 – 0.80 0.79 0.84 – 0.94 0.90 0.94
0.58 0.64 0.65 – 0.59 0.81 0.64 0.59 – 1.26 1.19 0.86 0.97 1.05 – 0.75 1.03 0.83
0.57 0.69 0.53 – 0.85 1.03 0.65 0.65 2.25 0.50 1.08 0.46 0.36 0.49 0.39 0.27 0.69 0.72
0.56 0.46 0.51 0.56 0.55 0.75 0.45 0.48 0.99 0.96 1.97 0.76 0.93 0.77 1.01 1.76 0.99 0.85
0.90 1.32 0.84 0.71 1.02 0.91 0.92 0.77 1.94 0.95 1.12 0.65 0.97 0.97 0.65 0.85 0.95 0.97
0.92 0.90 0.91 0.95 0.84 0.92 0.98 0.95 0.97 1.02 1.31 0.57 0.89 0.72 0.63 0.89 0.92 0.90
0.82 0.79 0.79 0.75 0.80 1.00 0.78 0.81 1.45 0.98 1.33 0.74 0.81 0.82 0.67 1.03 0.88 0.90
Table 2c RRS values for olive groves. PLOT
00/01
01/02
02/03
03/04
04/05
05/06
06/07
07/08
Mean RRS
RRS
0.71
0.58
0.63
0.70
0.26
0.50
0.56
0.54
0.56
shown. Table 2c shows the value of RRS indicator in all irrigation seasons. The values of the RIS indicator remain relatively constant, around 0.60 indicating that water use is deficit, across the irrigation seasons, reaching minimum values during the 2003/2004 season (the wettest season in the study). The 2003/2004 season showed the smallest range in values (Díaz, 2009), while the 2004/2005 season had the widest range of values (the driest year). The RIS mean value, around 0.40, obtained by Lorite et al. (2004b) is even lower than ours. The RWS value is close to 1 in the majority of the irrigation seasons. The highest value was recorded during the 2000/2001 season with a value of 0.98; a year that can be considered wet. The lowest RWS value (0.72) was recorded during the 2004/2005 season. When evaluating these results, it is important to highlight that the RWS indicator includes total water required, evapotranspiration and leaching. In our case, however, we have limited our study to evapotranspiration as leaching is not a common practice. The mean RRS value for all of the seasons is 0.56. When comparing this value to the RWS value, we can deduce that 40% of crop
water requirements are supplied by rainfall, while the remaining requirements are supplied by irrigation. Fig. 5 shows the evolution of the RWS and RRS values throughout the irrigation seasons. Given that the proportion of water supplied by natural means is indicated by the RRS curve, the interval between both curves denotes the water supplied by irrigation in the different seasons.
Fig. 5. Evolution of RWS and RRS in olive groves.
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
103
Fig. 6. Evolution of indicators in drip irrigation.
Fig. 7. Evolution of indicators in sprinkler irrigation.
As can be seen, a lower volume of irrigation water was delivered during the 2003/2004 season (the rainiest year in the study) than in the other seasons, while in the following season (a drier year) more irrigation water was supplied. Hence, irrigators modify the water supply depending on climatological conditions. The volume of water supplied in the other seasons remained relatively constant.
by sprinkler irrigation systems tends to be deficit, the RWS and RIS values are observed to be higher in sprinkler systems than in drip irrigation systems. This is to be expected given that sprinkler systems deliver a larger volume of water to crops than localized irrigation systems. The highest RIS value (1.07) was found for the 2001/2002 season, while the RWS values remained constant and close to 1, meaning that crop evaporation demand was adequately met in the crops that were irrigated using this system. Given that the RRS value reached its minimum during the 2004/2005 season, it is necessary to supply a greater volume of water to achieve an adequate RWS value. The mean values of the three indicators for each irrigation method are compared in Fig. 8. As seen for the values of each season, the mean values of the RIS and RWS indicators were also observed to be lower for drip irrigation. The value of the RIS indicator is 0.64 for drip irrigation, indicating that water management is deficit since crop water requirements are not met with the volume of water supplied. Although the value of the RIS indicator for sprinkler irrigation (0.8) is also insufficient, this is an acceptable value given that crop evaporation demand is met to a large extent. Moreover, farmers who have sprinkler irrigations systems were observed to use water more efficiently since the RIS and RWS values are close to 1. The mean value of the RWS for sprinkler irrigation (0.98) shows that the volume of water required and the volume of water supplied were practically the same. In contrast, the mean value of RRS is higher in drip irrigation since the majority of crops irrigated using this system are arboreous crops (almost exclusively olive groves) with a development cycle that spans the entire season. Crop evaporation demand is therefore met more efficiently with irrigation water as the high evapotranspiration values of the summer months are compensated for by higher rainfall in the fall and winter. Fig. 8 also includes the standard deviation values to show the dispersion of the mean values for the three indicators. In this case,
3.2. Analysis of indicators by irrigation method Fig. 6 shows the evolution of the RIS, RWS and RRS values calculated for plots with drip irrigation systems. Farmers who use drip irrigation systems tend to supply less water to the crops than the amount they actually need. The mean value of the RIS indicator is influenced by the fact that the majority of plots with olive groves are drip irrigated. Moreover, given that the RIS values are lower (0.59) for the olive groves than the rest of the crops (with the exception of sugar beet and asparagus, both of which occupy a small surface area), the final RIS value is significantly lower. During the eight seasons studied, the RIS values that indicate irrigation quality are deficits. The highest RIS value, 0.70, was reached in the 2007/2008 season. However, the RWS indicator shows a value close to 1 in four of the irrigation seasons, meaning that the irrigation drip systems met crop evaporation demand during these seasons although irrigation was deficit. The RRS indicator is influenced by the type of irrigation given that the result takes into account both rainfall and crop evapotranspiration. The mean RRS value is 0.52. As can be observed in Fig. 6, the value of this indicator was very low (0.23) during the 2004/2005 season, although this was not the season with the highest irrigation supply (calculated as the difference between RWS and RRS). In contrast, irrigation water supply was found to be lower during the 2003/2004 season, the rainiest season of the study. The results of the three indicators for sprinkler irrigation systems are shown in Fig. 7. Although the volume of water supplied
Fig. 8. Comparison of the mean values and standard deviation of the indicators by irrigation method.
104
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
Fig. 9. Evolution of the indicators for silt clay loam soils.
As mentioned above (see Table 1), irrigation water management in this crop is highly inefficient. The mean values of the indicators differ only slightly with regard to soil texture (see Fig. 11), suggesting that the farmers do not take into account the characteristics of the soil when irrigating. Unlike silt clay loam soils, which have a greater water retention capacity, more water must generally be supplied to crops planted in sandy loam soils since these soils retain less water and a greater volume of water is lost due to percolation and runoff. The higher mean RWS values in silt clay loam soils (see Fig. 11) indicate that the water requirements of the crops planted in these soils is met in a more efficient manner. The RRS value regarding soil texture shows the greatest variability, with higher values in the case of silty clay loam soils given the greater water retention capacity of these soils; a factor that permits a greater amount of rainfall water to be supplied to the crops. The values of the standard deviation that appear next to the mean values in Fig. 11 are relatively high in the sandy loam soils for the RWS indicator and for the RIS indicator in particular. This means that not all farmers interpret irrigation water requirements in the same way. Indeed, many farmers deliver an excess of water as they fear that given the lower retention capacity of these soils, an insufficient amount of water will be stored in the soil. 3.4. Analysis of indicators by plot size
Fig. 10. Evolution of the indicators for sandy loam soils.
the standard deviation is relatively small, indicating that the mean values are quite representatives. 3.3. Analysis of indicators by soil type Figs. 9 and 10 show the evolution of the values of the three indicators in two dominant types of soils with a silty clay loam and sandy loam texture, respectively. The soil analyses performed on the study plots have been taken from previous works (Pérez Urrestarazu, 2007; Romero, 2008). The majority of the plots have soils with a silt clay loam texture. The RIS and RWS values obtained in the plots with silt clay loam soils (see Fig. 9) are relatively high, indicating that water management was adequate in at least five of the eight irrigation seasons studied. In the case of plots having sandy loam soils (see Fig. 10), the three indicators were observed to be higher during the 2001/2002 irrigation season, with the RIS indicator reaching a value of 1.85 due to the large amount of garlic planted in these soils during this season.
The plots were classified into four categories according to their size: smaller than 2 ha; from 2 to 5 ha; from 5 to 10 ha; and larger than 10 ha (Pérez Arellano, 2009). The mean values and standard deviations of the RIS and RWS indicators are shown in Fig. 12. As can be observed, the best results are obtained for the smallest and the largest plots. As regards the smallest plots, this finding can be explained by the fact that it is easier for farmers to control and monitor crop irrigation. In plots over 10 ha in size, this may be due to the fact that farmers attach greater importance and pay more attention to their irrigation practices. Moreover, many of these plots are sprinkler irrigated; a method for which higher indicator values have been observed (see Fig. 8). The standard deviation values are acceptable with the exception of the RIS of the largest plots. This may be due to the fact that these plots have a wider range of sizes (10–30 ha) than the plots in the other segments studied, thus resulting in more disperse results. 3.5. Global analysis of indicators for the irrigated area The global calculations of the irrigation indicators permit the overall performance of the entire irrigated area to be determined.
Fig. 11. Comparison of mean values and standard deviations of the indicators by soil texture.
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
105
Fig. 12. Comparison of mean values and standard deviations of the RIS and RWS indicators by plot size.
Table 3 Mean values of the indicators at entire irrigated area and plot scale 2006/2007 season.
Entire irrigated area Selected plots
RIS
RWS
RRS
0.36 0.64
0.72 0.80
0.49 0.40
Moreover, by comparing these calculations to those obtained for the 31 plots must give us an idea of the representativity of the plots. Table 3 shows the results obtained when calculating the coefficients using data from the 2006/2007 irrigation season for all the crops planted. According to the meter readings in all the plots, near 12,300,000 m3 of water was consumed; an amount equivalent to 1400 m3 /ha (see Fig. 4). This is a relatively low figure due to the fact that the hydrographic basin agency imposed water restrictions during the 2006/2007 season, permitting a maximum volume of 2.500 m3 /ha. The global value of the indicators is solely orientational as it does not take into account water that is lost during pumping, conveyance or distribution. In addition, fallow land, buildings and greenhouses were excluded from the calculation. The differences observed between the indicators are due to the water losses mentioned above, and the fact that the number of plots studied (31) is small in relation to the 1700 plots that exist in the area. Moreover, given that the irrigators were allowed a relatively small water supply, the percentage of water losses increases. However, Lorite et al. (2004a) found similar values to those obtained by us in the 31 plots for the whole irrigation area using data from ten years ago. 4. Conclusions Three irrigation indicators have been used to study water management within a large irrigated area: RIS (relative irrigation supply), RWS (relative water supply) and RRS (relative rainfall supply). Irrigation water management can be improved using the obtained results. RIS is the most important indicator as it permits farmers’ actual irrigation management practices to be determined and interpreted. A mean RIS value of around 0.60 indicates that irrigation is deficit. The values of the RWS indicator are higher (normally above 0.80), meaning that irrigation management is adequate since the water supplied is sufficient to meet evaporation demand during the crop development cycle. However, values close to 0.80 indicate that the crop can suffer from water stress at some point in its life cycle. The RRS indicator values show less variability, with values around 0.40. Nonetheless, it is also important to take crop type into account to determine whether or not the rainfall fraction adequately meets the net crop water requirements. A
comparison of the RWS and RRS indicators shows the proportion of evapotranspiration covered by rainfall. By calculating the irrigation indicators according to crop type, irrigation method, soil texture or plot size, it is possible to determine the influence that each individual factor has on irrigation management and take subsequent measures to improve irrigation performance. The simple extrapolation of the results obtained in the study plots to analyze irrigator behavior and irrigation performance in the entire irrigated zone was not satisfactory due to the small number of plots and the need to evaluate water losses in the system. The use of geographical information systems to characterize irrigator communities and the inclusion of data on agronomic and hydraulic variables can significantly improve water management efficiency or that of any other agricultural variable. Acknowledgments The authors would like to thank the project AGL2006-13795C02-01/AGR, funded by the Ministry of Science and Innovation, and titled “Use of artificial intelligence for precipitation estimation and for efficient management in irrigation districts”, for partially funding this study. References Akkuzu, E., Unal, H.B., Karatas, B.S., Avci, M., Asik, S., 2007. General irrigation planning performance of water user associations in the Gediz basin in Turkey. Journal of Irrigation and Drainage Engineering 133 (1), 17–26. Bos, M.G., Murray-Rust, D.H., Merrey, D.J., Johnson, H.G., Snellen, W.B., 1994. Methodologies for assessing performance of irrigation and drainage management. Irrigation and Drainage 4, 345–365. Burt, C.M., Clemmens, A.J., Strelkoff, T.S., Solomon, K.H., Bliesner, R.D., Hardy, L.A., Howell, T.A., Eisenhauer, D.E., 1997. Irrigation performance measures: efficiency and uniformity. Journal of Irrigation and Drainage Engineering 123 (6), 423–442. Burt, C.M. and Styles, S.W., 1999a. Modern water control and management practices: impact on performance. Water Report 19, FAO, Rome, Italy. Burt, C.M., Styles, S.W., 1999b. Water balance-related performance indicators for international projects. In: Conference on Benchmarking Irrigation System Performance using Water Measurement and Water Balances, pp. 337–353. Burt, C.M., 2001. Proceso de evaluación rápida (RAP) y comparación con el patrón de referencia (benchmarking). Explicaciones y herramientas. Irrigation Training and Research Center Report (ITRC), No R 01-009, FAO, Bangkok, Thailand. Clarke, D., 1998. Cropwat for windows user guide. Food and Agricultural Organization of the United Nations. Souhamton University: National Water Research, Egypt. Córcoles, J.I., de Juan, J.A., Ortega, J.F., Tarjuelo, J.M., Moreno, M.A., 2011. Evaluation of irrigation systems by using benchmarking techniques. Journal of Irrigation and Drainage Engineering, in press. Dechmi, F., Playán, E., Faci, J.M., Tejero, M., 2003. Analysis of an irrigation district in northeastern Spain. I. Characterisation and water use assessement. Agricultural Water Management 61, 75–92. Díaz, M.P., 2009. Integración de variables agronómicas e hidráulicas en un entorno SIG para la gestión eficiente del agua en la Zona Regable del Genil-Cabra (Córdoba). Final professional work. Universidad de Córdoba, Córdoba, Spain. ESRI, 2002. Getting started with ArcGis. Redlands, New York (USDA).
106
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
˜ J., Pérez, C., Bonachela, S., 2007. Analysis Fernández, M.D., González, A.M., Carreno, of on-farm irrigation performance in Mediterranean greenhouses. Agricultural Water Management 89, 251–260. IDAE, Instituto para la Diversificación y Ahorro de la Energía., 2008. Ahorro y eficiencia energética en las Comunidades de Regantes. Madrid, Spain. Levine, G. 1982. Relative Water Supply: an explanatory variable for irrigation systems. Technical Report. Lorite, I., Mateos, L., Fereres, E., 2004a. Evaluating irrigation performance in a Mediterranean environment. I. Model and general assessment of an irrigation scheme. Irrigation Science 23, 77–84. Lorite, I., Mateos, L., Fereres, E., 2004b. Evaluating irrigation performance in a Mediterranean environment. II. Variability among crops and farmers. Irrigation Science 23, 85–92. Malano, H., Burton, M., 2001. Guidelines for benchmarking performance in the irrigation and drainage sector. In: International Programme for Technology and Research in Irrigation and Drainage (IPTRID), FAO, Rome, Italy. Malano, H., Burton, M., Makin, I., 2004. Benchmarking performance in the irrigation and drainage sector: a tool for change. Irrigation and Drainage 53, 119–133. Molden, D., Gates, J.K., 1990. Performance measures for evaluation of irrigation water delivery systems. Journal of Irrigation and Drainage Engineering 116, 804–823, no. 6. Cornell University, Ithaca, New York, USA. Molden, D.; Sakthivadivel, R.; Perry, C.J.; de Fraiture, C. Kloezen, W. 1998. Indicators for comparing performance of irrigated agricultural systems. Research report 20. International Water Management Institute. Colombo, Sri Lanka. Pérez Arellano, R., 2009. Estudio de la demanda de agua de riego de la Zona Regable del Genil-Cabra mediante sistemas de telecontrol para una gestión eficiente. Final professional work. Universidad de Córdoba, Córdoba, Spain.
˜ Pérez Urrestarazu, L., Rodríguez Díaz, J.A., Camacho Poyato, E., Ibánez Verdú, M., Deltell Padilla, G., 2005. Análisis estacional y espacial del uso del agua en una zona regable. In: XXIV Congreso Nacional de Riegos, Elche, Spain. Pérez Urrestarazu, L. 2007. Aplicación de los indicadores para el análisis de las acciones de mejora en zonas regables y para el desarrollo de un modelo de gestión integral del agua de riego. PhD. Dissertation. Universidad de Córdoba, Córdoba, Spain. Pérez Urrestarazu, L., Rodríguez Díaz, J.A., Camacho Poyato, E., López Luque, R., 2009. Quality of service in irrigation distributios networks: case of Palos de la Frontera irrigation district, Spain. Journal of Irrigation and Drainage Engineering 135 (6), 755–762. Perry, C.J., 1996. Quantification and measurement of a minimum set of indicators of the performance of irrigation systems. International Irrigation Management Institute, Colombo, Sri Lanka. Rodríguez Díaz, J.A., Camacho Poyato, E., López Luque, R., Pérez Urrestarazu, L., 2008. Benchmarking and multivariate data analysis techniques for improving the efficiency of irrigation districts: an application in Spain. Agricultural Systems 96, 250–259. Rodríguez Nadales, J.R., 2002. Caracterización de la demanda de agua de riego en la zona regable del Genil-Cabra (sectores II-III y IV-VII). Final professional work. Universidad de Córdoba, Córdoba, Spain. Roldán, J. 2007. Mejora del uso del agua en el regadío. In: E. Cabrera and L. Babiano ˜ Ministerio de Medio Ambiente, Madrid, Spain, (coords.), La sequía en Espana. pp. 228-247. Romero, J.L., 2008. Estudio de la distribución de agua y cultivos a escala de parcela en la zona regable de Genil-Cabra. Desarrollo y utilización de un SIG para tratamiento de la información. Final professional work. Universidad de Córdoba, Córdoba, Spain.
Contents lists available at SciVerse ScienceDirect
Agricultural Water Management journal homepage: www.elsevier.com/locate/agwat
Assessment of irrigation water management in the Genil-Cabra (Córdoba, Spain) irrigation district using irrigation indicators ˜ 1 M. Fátima Moreno-Pérez ∗ , José Roldán-Canas Edif. Leonardo Da Vinci – Campus Rabanales, Department of Agronomy, University of Córdoba, 14071 Córdoba, Spain
a r t i c l e
i n f o
Article history: Available online 12 July 2012 Keywords: Irrigation Irrigation indicators Irrigation district Irrigation management
a b s t r a c t This paper examines irrigation water management in the Genil-Cabra Irrigation District of the Province of Cordoba (southern Spain) using three irrigation indicators: relative irrigation supply (RIS), relative water supply (RWS), and relative rainfall supply (RRS). The three indicators are calculated both globally and by grouping the data according to crop type, irrigation method, soil texture, and plot size. Then, it is possible to determine the influence that each individual factor has on irrigation management and take subsequent measures to improve irrigation performance. All of the information regarding agronomic and hydraulic variables has been included in a geographical information system (GIS) to facilitate data management. The results show that irrigation is deficit given that the mean value of the RIS indicator is relatively low, around 0.60. However, the RWS indicator achieves higher mean values, normally above 0.80, indicating that evaporation demand can be met throughout the crop development cycle. The RRS indicator shows less variability with mean values around 0.40. This indicator, together with the RWS indicator permits the evapotranspiration fraction covered by rainfall to be determined. The mean values of the calculated indicators are very useful for gaining a better understanding of irrigator behavior and general irrigation trends, although the study sample is still insufficient to characterize a large irrigation area as a whole. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Water resources are under enormous pressure due to increasing demands for more and better quality water; demands which are in turn conditioned by social, political and environmental factors. The growing difficulties to ensure that water demands are met have led to greater competitiveness for scarce water resources among traditional sectors of water users, namely agriculture, industry and urban supply. This competitiveness for water use is already placing constraints on the development of many countries. Moreover, the increasing scarcity of water resources has led to higher competitiveness between regions or countries for available water resources. As a result, water has come to be considered an increasingly scarce and valuable resource requiring rigorous management and extreme care. One of the keys to overcoming these problems lies in the agricultural sector given that irrigation – particularly in arid and semiarid areas – is the chief consumer of water; accounting for 70%
∗ Corresponding author. Tel.: +34 957218512; fax: +34 957212097. E-mail addresses: [email protected] (M.F. Moreno-Pérez), ˜ [email protected] (J. Roldán-Canas). 1 Tel.: +34 957218512; fax: +34 957212097. 0378-3774/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agwat.2012.06.020
of consumption worldwide. According to data from the Federación Nacional de Comunidades de Regantes (National Federation of Irrigator Communities) (IDAE, 2008), water consumption in Spain has dropped from 80% to 67% thanks to the on-going efforts of farmers and official bodies to save water through better irrigation practices and greater investment. Nonetheless, in Spain, a much larger amount of water is allotted to agriculture than to other types of activity (Roldán, 2007). For this reason, it is of interest to determine the water management practices of irrigation communities having a large volume of available water as farmers may not implement the best water management practices or water use may be inefficient. By correcting such practices, it will be possible to use water more efficiently. Attempts to adapt farmers’ water demands to real crop demands could lead to more efficient water use as it would permit water to be delivered only when necessary without reducing crop productivity, while sustaining and/or increasing farmer income. In recent decades many authors have developed and applied irrigation performance indicators and benchmarking techniques to identify the best irrigation practices and to compare different and complex irrigation systems (Levine, 1982; Molden and Gates, 1990; Bos et al., 1994; Burt, 2001, among many others). Also, different indicators have been standardized and the definitions of the parameters that form these indicators have been adjusted so that
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
comparisons between the different water irrigation districts (WID) can be generalized (Burt et al., 1997; Malano and Burton, 2001). Two are the main objectives that have attempted to fill these jobs: on the one hand, indicators at the level of WID have been applied to measure its efficiency (Burt and Styles, 1999a; Malano et al., 2004), on the other hand, benchmarking techniques have been used in different WID so that, from their comparison, water use in such systems was improved. Thus, for example, Holden et al. (1998) and Burt and Styles (1999b) compare WID located in several countries, while other authors works at a local scale (Akkuzu et al., 2007; Pérez Urrestarazu et al., 2009; Córcoles et al., in press). In the latter group may also include Rodríguez Díaz et al. (2008) who study WID with similar characteristics using multivariate data analysis techniques. In this paper, we aim to analyze irrigation water demand and examine possible measures for modifying and rationing demand in order to achieve an efficient water management policy. To do so, it is necessary to assess water management bearing in mind existing agronomic and hydraulic processes; develop a geographical informational system in the irrigation district that relates the geospatial location of the plots with the data obtained for them; and study theoretical water requirements and their discrepancies with farmers’ actual water demands using irrigation performance indicators at plot scale. The main contribution of this paper is the study of irrigation performance indicators discriminating by type of crop, irrigation method, soil type and plot size. This allows to determine the influence of each of these factors on the irrigation and, consequently, to improve their performance. An analysis of mean RIS for greenhouse crops during crop cycle periods was also conducted in a Mediterranean greenhouse area (Fernández et al., 2007).
99
2. Materials and methods 2.1. Description of the Genil-Cabra Irrigation district To analyze discrepancies between real water demands and actual crop water requirements, we have studied the Puente Genil Irrigated Area of the Genil-Cabra Irrigation District (Fig. 1) located in the Province of Cordoba (southern Spain). The Genil-Cabra Irrigation District, and the irrigator community that manages the district, were created and began to operate in 1989 under a public initiative. The irrigation district, which belongs to the Guadalquivir River Hydrographic Basin, covers a total of 40,085 ha. The district is located on the right bank of the Genil River; the main tributary of the left bank of the Guadalquivir River. Of the total area that comprises the irrigator community, only 37,010 ha are apt for use in irrigated agriculture. The irrigator community will soon deliver water to approximately 22,000 ha and will therefore supply almost 60% of the total forecasted demand. The community is composed of 1696 users, with an average plot size of 8.9 ha. The irrigated surface area currently comprises 15,963 ha, of which 8780 ha belong to the Puente Genil Irrigated Area where this study has been conducted. The main canal supplies water to each of the network’s discharge pump stations. The canal currently runs more than 30 km in length and has a slope of 1:10,000. The canal has a capacity of 1.1 hm3 , of which 800,000 m3 are available for agriculture. The canal has a parabolic section with a maximum width of 22.86 m, a depth of 4.15 m, and is capable of conveying 40 m3 /s of water. Water is distributed by means of a pressurized system through an underground piped network. Irrigation water is delivered on demand, thus providing farmers greater flexibility in terms of the frequency, volume and duration of irrigation and permitting them to
Fig. 1. Location of the Genil-Cabra Irrigation District in Spain.
100
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
Fig. 2. Evolution of main crops in the Puente Genil Irrigated Area.
program irrigation in an optimal manner as water is available on a permanent basis. The study area has a continental Mediterranean climate characterized by cold winters and hot, dry summers with a mean annual precipitation of 606 mm chiefly in winter months. The mean temperature in the area ranges from 17 ◦ C to 18 ◦ C, with mean temperatures as low as 10 ◦ C in winter and as high as 27 ◦ C in summer. Vertisols and soils of alluvial origin are predominant in the area. In this type of soils, deep cracks form in the dry season, permitting the soil materials that are deposited in them to mix, thus favoring a ˜ homogeneous soil profile. The land relief is typical of the Campina area and is formed by gently sloping hills that permit crop mechanization in practically all of the terrain. Due to the terrain’s natural surface drainage network, rainwater is evacuated in an adequate manner. The main irrigation systems found in the study area are semipermanent or mobile sprinklers, permanent or fixed sprinklers and drip irrigation. Total coverage sprinkler irrigation systems are the least common. In recent years, drip irrigation has become more widespread than mobile sprinkler systems, particularly in crops such as olive and cotton.
2.3. RWS, RIS, and RRS irrigation performance indicators The relative water supply (RWS) irrigation performance indicator focuses on the relation between the water that enters the system (precipitation and irrigation) and the water required (evapotranspiration and leaching). The RWS is calculated, see Eq. (1), as the ratio between total water supply or water consumed and the amount of water needed for production (Levine, 1982). By analyzing the RWS index, it is possible to determine if the total amount of water (precipitation and irrigation) delivered to the crop during its
2.2. Crops, rainfall and water consumption Fig. 2 shows crop evolution in the area over ten irrigation seasons. During the 2007/2008 season, olives were the main crop, occupying 47.5% of the total surface area, followed by wheat, which accounted for 22.5%. Other crops present in the area include sunflower (5.7%), garlic (4.1%) and cotton (2.2%), while alfalfa, potatoes, onion and broad beans are less common. Crops with higher water requirements (cotton and maize) have diminished year after year. This may be due to the fact that the water supply in the area is not fully guaranteed or has decreased in a notable manner. Water consumption is influenced by the weather conditions of each irrigation season. As can be seen in Fig. 3, there is a clear relationship between water consumption in the irrigator community and rainfall in each season. In seasons with low rainfall, total water consumption in the community increased. This is the case, for example, of the 2004/2005 season which suffered a severe drought (Pérez Arellano, 2009). However, during the 2002/2003 season, consumption increased in spite of rainfall being higher than in the 2004/2005 season. This could be due to poor irrigation management practices in that particular season. Fig. 4 shows total water consumption and water consumption per hectare in the irrigator community over seven irrigation seasons.
Fig. 3. Annual evolution of rainfall and total water consumption.
Fig. 4. Total water consumption and consumption per hectare by irrigation season.
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
growth cycle has been excessive, sufficient or deficient (Rodríguez Nadales, 2002). RWS values in the range of 0.9–1.2 are considered adequate. RWS is useful in that it serves as a basis for the comparative study and analysis of irrigated areas located in different regions with diverse characteristics: RWS =
R + Pe Water supply = ETc Actual crop evapotranspiration
(1)
where R is the irrigation; Pe the effective precipitation and ETc is the actual crop evapotranspiration. The relative irrigation supply (RIS) indicator relates, see Eq. (2), the volume of irrigation water supplied to users during the irrigation season to the volume of irrigation water required for the crop throughout its life cycle (Perry, 1996). This indicator provides information about the quality of the irrigation water delivered to the crop as it relates water supply demand to net water requirements. The RIS indicates if the farmer has taken into account crop evapotranspiration and lixiviation requirements when using a given volume of water. The optimal value of this indicator is around 1, meaning that water requirements not covered by rainfall are met. A value below 1 indicates an irrigation deficit: RIS =
Irrigation water supply R = ETc − Pe Crop water requirements
(2)
Both indicators provide information about the scarcity or excess of water and how water supply is adjusted to fit water demand (Molden et al., 1998). The relative rainfall supply (RRS) indicator has also been studied. It relates, see Eq. (3), the effective precipitation to the total amount of water needed for production. This indicator, which only takes rainfall into account, allows determining to what extent crop water requirements have been met in a natural manner (Pérez Urrestarazu et al., 2005). When the RRS value is equal to the RWS value in the same period, it means that rainfall is the only water supplied to the crop and irrigation does not take place: RRS =
Pe Effective precipitation = ETc Actual crop evapotranspiration
(3)
In this paper, we have calculated the three indicators mentioned above (RWS, RIS and RRS) for 31 plots belonging to the Genil-Cabra Irrigation District from the 2000/2001 to the 2007/2008 irrigation seasons. Rodríguez Nadales (2002) and Romero (2008) calculated the RIS and RWS indicators for only 16 plots. To assess the agronomic management of the Genil-Cabra Irrigation District, it is necessary to calculate the water requirements of the crops cultivated on the plots during the seasons studied. To do so, we used the CROPWAT 4.2 program (Clarke, 1998). Climatic data was taken from the weather station located within the irrigation district in the town of Santaella. 2.4. Geographic information system A Geographic Information System (ESRI, 2002) that was initially developed in previous works (Romero, 2008) was expanded and improved upon in order to analyze irrigation water demand and examine the possibilities for modifying and rationing demand to achieve an efficient water management policy in the Genil-Cabra Irrigation District. The variables introduced in the GIS used in this study with their corresponding geo-referenced data were: geographical location, climatic data (temperature and precipitation); edaphological data corresponding to several soil samples (texture, bulk density, cation exchange capacity, exchange ions, calcium, sodium, potassium, total and active lime, available phosphorus, oxidable organic matter, organic nitrogen and pH) (Romero, 2008); plot information; irrigation sectors and groups; primary and secondary distribution
101
Table 1 Irrigation management by crop according to the RWS value. Crop
RWS
Irrigation management
Olive Cotton Sunflower Maize Garlic Wheat Asparagus Sugar beet
0.90 0.91 0.96 0.88 1.91 1.53 0.58 0.74
Adequate Adequate Adequate Adequate Excess Excess Deficient Deficient
networks; discharge pumping stations; agronomic variables; plots with irrigation indicators and manual flow discharge readings (Díaz, 2009). Chemical soil analyses were excluded from the study as they did not reveal problems regarding the quality of the water used for irrigation (Romero, 2008). Data regarding crop, irrigation method and volume supplied to each plot correspond to the 2000/01–2007/08 irrigation seasons. The indicators were calculated during this period for the 31 plots selected. 3. Results and discussion 3.1. Analysis of indicators by crop type The irrigation performance indicators were calculated for the largest crops in the area (Díaz, 2009). Using the mean values obtained from the RWS indicator, irrigation management can be classified by crop type into three categories (see Table 1). Of the eight crops studied, six showed irrigation performance values below 1. Given that four of the crops (olive, cotton, sunflower and maize) showed RWS values close to 1 and the water deficit is negligible, irrigation management in these crops can be considered adequate. Irrigation is sufficient to meet the evaporation demand throughout the development cycle of the olive, cotton and sunflower crops as the mean RWS value is equal to or greater than 0.90. Similar results were found by Lorite et al. (2004b) in the same area of irrigation for cotton and corn. However, their values for sunflower and olive were somewhat lower. In the case of sugar beet and asparagus, the deficit increases, with RWS values below 0.8 indicating a permanent scarcity of water for crops that could lead to a reduction in crop yield. The results show that irrigation is clearly deficient. In this case it is necessary to increase the irrigation depth. Results obtained in the northeastern Spain (Dechmi et al., 2003) also show that, in general, crops were severely water stressed. Taking into account the annual rainfall in the area, a good irrigation strategy would be to match irrigation water demand to crop water requirements, that is, RIS = 1. Irrigation management was found to be inefficient in the wheat and garlic crops due to an excess supply of water. This excess of water is the result of the fact that these winter crops are usually irrigated in spite of natural rainfall supply. In this case, the best strategy would be to reduce the irrigation water depth as in the previous cases. By the contrary, Lorite et al. (2004b) found RWS values less than one for these winter crops over several irrigation seasons in the late nineties. However, our data confirm the application of excess irrigation water. To study irrigation water quality in greater depth, the results focus on olive crops. Olive groves are the most extensive crop and have continued to expand in recent irrigation seasons. The results discussed here correspond to the 17 plots shown in Tables 2a and 2b. The results for the first nine plots were taken from the studies by Rodríguez Nadales (2002) and Romero (2008). The evolution of the indicators RIS and RWS in the eight irrigation seasons (2000/2001–2007/2008) and their mean values are also
102
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
Table 2a RIS values for olive groves. PLOT
00/01
01/02
02/03
03/04
04/05
05/06
06/07
07/08
Mean RIS
213-A 213-B-01 220 225 237 238 253 293 I-104 218 219 221 292 291 402-I I-105 403-01 Mean RIS
0.60 0.30 0.35 0.30 0.31 0.88 0.36 0.61 0.44 0.72 – 0.34 0.18 0.24 – 1.04 0.26 0.46
0.78 0.32 1.20 0.23 0.85 1.22 0.37 0.99 0.40 0.57 – 0.45 0.26 0.46 – 1.31 0.16 0.64
0.53 0.24 0.42 – 0.35 0.81 0.66 0.56 2.47 0.58 – 0.25 0.25 0.31 – 0.47 0.41 0.59
0.35 0.43 0.45 – 0.37 0.66 0.43 0.36 – 0.94 0.82 0.27 0.46 0.59 – 0.08 0.54 0.48
0.41 0.55 0.36 – 0.75 0.97 0.51 0.50 2.48 0.31 1.05 0.26 0.13 0.30 0.17 1.01 0.76 0.58
0.31 0.11 0.24 0.31 0.29 0.57 0.16 0.20 0.91 0.74 2.35 0.42 0.69 0.42 0.82 2.03 0.78 0.67
0.56 1.20 0.47 0.27 0.74 0.58 0.60 0.37 2.15 0.71 1.02 0.15 0.74 0.73 0.15 1.53 0.70 0.69
0.45 0.42 0.44 0.49 0.33 1.22 0.54 0.49 0.53 0.75 1.22 0.04 0.65 0.29 0.14 0.56 0.59 0.53
0.50 0.45 0.49 0.32 0.50 0.86 0.45 0.51 1.34 0.67 1.29 0.27 0.41 0.42 0.32 0.75 0.50 0.59
Table 2b RWS values for olive groves. PLOT
00/01
01/02
02/03
03/04
04/05
05/06
06/07
07/08
Mean RWS
213-A 213-B-01 220 225 237 238 253 293 I-104 218 219 221 292 291 402-I I-105 403-01 Mean RWS
1.15 0.93 0.85 0.81 0.82 1.22 0.85 1.03 0.91 1.18 – 0.94 0.83 0.87 – 1.39 0.88 0.98
1.05 0.78 1.29 0.73 1.08 1.30 0.81 1.17 0.83 0.92 – 0.85 0.74 0.86 – 1.37 0.67 0.96
0.82 0.60 0.74 – 0.69 1.03 0.91 0.84 2.27 1.02 – 0.80 0.79 0.84 – 0.94 0.90 0.94
0.58 0.64 0.65 – 0.59 0.81 0.64 0.59 – 1.26 1.19 0.86 0.97 1.05 – 0.75 1.03 0.83
0.57 0.69 0.53 – 0.85 1.03 0.65 0.65 2.25 0.50 1.08 0.46 0.36 0.49 0.39 0.27 0.69 0.72
0.56 0.46 0.51 0.56 0.55 0.75 0.45 0.48 0.99 0.96 1.97 0.76 0.93 0.77 1.01 1.76 0.99 0.85
0.90 1.32 0.84 0.71 1.02 0.91 0.92 0.77 1.94 0.95 1.12 0.65 0.97 0.97 0.65 0.85 0.95 0.97
0.92 0.90 0.91 0.95 0.84 0.92 0.98 0.95 0.97 1.02 1.31 0.57 0.89 0.72 0.63 0.89 0.92 0.90
0.82 0.79 0.79 0.75 0.80 1.00 0.78 0.81 1.45 0.98 1.33 0.74 0.81 0.82 0.67 1.03 0.88 0.90
Table 2c RRS values for olive groves. PLOT
00/01
01/02
02/03
03/04
04/05
05/06
06/07
07/08
Mean RRS
RRS
0.71
0.58
0.63
0.70
0.26
0.50
0.56
0.54
0.56
shown. Table 2c shows the value of RRS indicator in all irrigation seasons. The values of the RIS indicator remain relatively constant, around 0.60 indicating that water use is deficit, across the irrigation seasons, reaching minimum values during the 2003/2004 season (the wettest season in the study). The 2003/2004 season showed the smallest range in values (Díaz, 2009), while the 2004/2005 season had the widest range of values (the driest year). The RIS mean value, around 0.40, obtained by Lorite et al. (2004b) is even lower than ours. The RWS value is close to 1 in the majority of the irrigation seasons. The highest value was recorded during the 2000/2001 season with a value of 0.98; a year that can be considered wet. The lowest RWS value (0.72) was recorded during the 2004/2005 season. When evaluating these results, it is important to highlight that the RWS indicator includes total water required, evapotranspiration and leaching. In our case, however, we have limited our study to evapotranspiration as leaching is not a common practice. The mean RRS value for all of the seasons is 0.56. When comparing this value to the RWS value, we can deduce that 40% of crop
water requirements are supplied by rainfall, while the remaining requirements are supplied by irrigation. Fig. 5 shows the evolution of the RWS and RRS values throughout the irrigation seasons. Given that the proportion of water supplied by natural means is indicated by the RRS curve, the interval between both curves denotes the water supplied by irrigation in the different seasons.
Fig. 5. Evolution of RWS and RRS in olive groves.
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
103
Fig. 6. Evolution of indicators in drip irrigation.
Fig. 7. Evolution of indicators in sprinkler irrigation.
As can be seen, a lower volume of irrigation water was delivered during the 2003/2004 season (the rainiest year in the study) than in the other seasons, while in the following season (a drier year) more irrigation water was supplied. Hence, irrigators modify the water supply depending on climatological conditions. The volume of water supplied in the other seasons remained relatively constant.
by sprinkler irrigation systems tends to be deficit, the RWS and RIS values are observed to be higher in sprinkler systems than in drip irrigation systems. This is to be expected given that sprinkler systems deliver a larger volume of water to crops than localized irrigation systems. The highest RIS value (1.07) was found for the 2001/2002 season, while the RWS values remained constant and close to 1, meaning that crop evaporation demand was adequately met in the crops that were irrigated using this system. Given that the RRS value reached its minimum during the 2004/2005 season, it is necessary to supply a greater volume of water to achieve an adequate RWS value. The mean values of the three indicators for each irrigation method are compared in Fig. 8. As seen for the values of each season, the mean values of the RIS and RWS indicators were also observed to be lower for drip irrigation. The value of the RIS indicator is 0.64 for drip irrigation, indicating that water management is deficit since crop water requirements are not met with the volume of water supplied. Although the value of the RIS indicator for sprinkler irrigation (0.8) is also insufficient, this is an acceptable value given that crop evaporation demand is met to a large extent. Moreover, farmers who have sprinkler irrigations systems were observed to use water more efficiently since the RIS and RWS values are close to 1. The mean value of the RWS for sprinkler irrigation (0.98) shows that the volume of water required and the volume of water supplied were practically the same. In contrast, the mean value of RRS is higher in drip irrigation since the majority of crops irrigated using this system are arboreous crops (almost exclusively olive groves) with a development cycle that spans the entire season. Crop evaporation demand is therefore met more efficiently with irrigation water as the high evapotranspiration values of the summer months are compensated for by higher rainfall in the fall and winter. Fig. 8 also includes the standard deviation values to show the dispersion of the mean values for the three indicators. In this case,
3.2. Analysis of indicators by irrigation method Fig. 6 shows the evolution of the RIS, RWS and RRS values calculated for plots with drip irrigation systems. Farmers who use drip irrigation systems tend to supply less water to the crops than the amount they actually need. The mean value of the RIS indicator is influenced by the fact that the majority of plots with olive groves are drip irrigated. Moreover, given that the RIS values are lower (0.59) for the olive groves than the rest of the crops (with the exception of sugar beet and asparagus, both of which occupy a small surface area), the final RIS value is significantly lower. During the eight seasons studied, the RIS values that indicate irrigation quality are deficits. The highest RIS value, 0.70, was reached in the 2007/2008 season. However, the RWS indicator shows a value close to 1 in four of the irrigation seasons, meaning that the irrigation drip systems met crop evaporation demand during these seasons although irrigation was deficit. The RRS indicator is influenced by the type of irrigation given that the result takes into account both rainfall and crop evapotranspiration. The mean RRS value is 0.52. As can be observed in Fig. 6, the value of this indicator was very low (0.23) during the 2004/2005 season, although this was not the season with the highest irrigation supply (calculated as the difference between RWS and RRS). In contrast, irrigation water supply was found to be lower during the 2003/2004 season, the rainiest season of the study. The results of the three indicators for sprinkler irrigation systems are shown in Fig. 7. Although the volume of water supplied
Fig. 8. Comparison of the mean values and standard deviation of the indicators by irrigation method.
104
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
Fig. 9. Evolution of the indicators for silt clay loam soils.
As mentioned above (see Table 1), irrigation water management in this crop is highly inefficient. The mean values of the indicators differ only slightly with regard to soil texture (see Fig. 11), suggesting that the farmers do not take into account the characteristics of the soil when irrigating. Unlike silt clay loam soils, which have a greater water retention capacity, more water must generally be supplied to crops planted in sandy loam soils since these soils retain less water and a greater volume of water is lost due to percolation and runoff. The higher mean RWS values in silt clay loam soils (see Fig. 11) indicate that the water requirements of the crops planted in these soils is met in a more efficient manner. The RRS value regarding soil texture shows the greatest variability, with higher values in the case of silty clay loam soils given the greater water retention capacity of these soils; a factor that permits a greater amount of rainfall water to be supplied to the crops. The values of the standard deviation that appear next to the mean values in Fig. 11 are relatively high in the sandy loam soils for the RWS indicator and for the RIS indicator in particular. This means that not all farmers interpret irrigation water requirements in the same way. Indeed, many farmers deliver an excess of water as they fear that given the lower retention capacity of these soils, an insufficient amount of water will be stored in the soil. 3.4. Analysis of indicators by plot size
Fig. 10. Evolution of the indicators for sandy loam soils.
the standard deviation is relatively small, indicating that the mean values are quite representatives. 3.3. Analysis of indicators by soil type Figs. 9 and 10 show the evolution of the values of the three indicators in two dominant types of soils with a silty clay loam and sandy loam texture, respectively. The soil analyses performed on the study plots have been taken from previous works (Pérez Urrestarazu, 2007; Romero, 2008). The majority of the plots have soils with a silt clay loam texture. The RIS and RWS values obtained in the plots with silt clay loam soils (see Fig. 9) are relatively high, indicating that water management was adequate in at least five of the eight irrigation seasons studied. In the case of plots having sandy loam soils (see Fig. 10), the three indicators were observed to be higher during the 2001/2002 irrigation season, with the RIS indicator reaching a value of 1.85 due to the large amount of garlic planted in these soils during this season.
The plots were classified into four categories according to their size: smaller than 2 ha; from 2 to 5 ha; from 5 to 10 ha; and larger than 10 ha (Pérez Arellano, 2009). The mean values and standard deviations of the RIS and RWS indicators are shown in Fig. 12. As can be observed, the best results are obtained for the smallest and the largest plots. As regards the smallest plots, this finding can be explained by the fact that it is easier for farmers to control and monitor crop irrigation. In plots over 10 ha in size, this may be due to the fact that farmers attach greater importance and pay more attention to their irrigation practices. Moreover, many of these plots are sprinkler irrigated; a method for which higher indicator values have been observed (see Fig. 8). The standard deviation values are acceptable with the exception of the RIS of the largest plots. This may be due to the fact that these plots have a wider range of sizes (10–30 ha) than the plots in the other segments studied, thus resulting in more disperse results. 3.5. Global analysis of indicators for the irrigated area The global calculations of the irrigation indicators permit the overall performance of the entire irrigated area to be determined.
Fig. 11. Comparison of mean values and standard deviations of the indicators by soil texture.
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
105
Fig. 12. Comparison of mean values and standard deviations of the RIS and RWS indicators by plot size.
Table 3 Mean values of the indicators at entire irrigated area and plot scale 2006/2007 season.
Entire irrigated area Selected plots
RIS
RWS
RRS
0.36 0.64
0.72 0.80
0.49 0.40
Moreover, by comparing these calculations to those obtained for the 31 plots must give us an idea of the representativity of the plots. Table 3 shows the results obtained when calculating the coefficients using data from the 2006/2007 irrigation season for all the crops planted. According to the meter readings in all the plots, near 12,300,000 m3 of water was consumed; an amount equivalent to 1400 m3 /ha (see Fig. 4). This is a relatively low figure due to the fact that the hydrographic basin agency imposed water restrictions during the 2006/2007 season, permitting a maximum volume of 2.500 m3 /ha. The global value of the indicators is solely orientational as it does not take into account water that is lost during pumping, conveyance or distribution. In addition, fallow land, buildings and greenhouses were excluded from the calculation. The differences observed between the indicators are due to the water losses mentioned above, and the fact that the number of plots studied (31) is small in relation to the 1700 plots that exist in the area. Moreover, given that the irrigators were allowed a relatively small water supply, the percentage of water losses increases. However, Lorite et al. (2004a) found similar values to those obtained by us in the 31 plots for the whole irrigation area using data from ten years ago. 4. Conclusions Three irrigation indicators have been used to study water management within a large irrigated area: RIS (relative irrigation supply), RWS (relative water supply) and RRS (relative rainfall supply). Irrigation water management can be improved using the obtained results. RIS is the most important indicator as it permits farmers’ actual irrigation management practices to be determined and interpreted. A mean RIS value of around 0.60 indicates that irrigation is deficit. The values of the RWS indicator are higher (normally above 0.80), meaning that irrigation management is adequate since the water supplied is sufficient to meet evaporation demand during the crop development cycle. However, values close to 0.80 indicate that the crop can suffer from water stress at some point in its life cycle. The RRS indicator values show less variability, with values around 0.40. Nonetheless, it is also important to take crop type into account to determine whether or not the rainfall fraction adequately meets the net crop water requirements. A
comparison of the RWS and RRS indicators shows the proportion of evapotranspiration covered by rainfall. By calculating the irrigation indicators according to crop type, irrigation method, soil texture or plot size, it is possible to determine the influence that each individual factor has on irrigation management and take subsequent measures to improve irrigation performance. The simple extrapolation of the results obtained in the study plots to analyze irrigator behavior and irrigation performance in the entire irrigated zone was not satisfactory due to the small number of plots and the need to evaluate water losses in the system. The use of geographical information systems to characterize irrigator communities and the inclusion of data on agronomic and hydraulic variables can significantly improve water management efficiency or that of any other agricultural variable. Acknowledgments The authors would like to thank the project AGL2006-13795C02-01/AGR, funded by the Ministry of Science and Innovation, and titled “Use of artificial intelligence for precipitation estimation and for efficient management in irrigation districts”, for partially funding this study. References Akkuzu, E., Unal, H.B., Karatas, B.S., Avci, M., Asik, S., 2007. General irrigation planning performance of water user associations in the Gediz basin in Turkey. Journal of Irrigation and Drainage Engineering 133 (1), 17–26. Bos, M.G., Murray-Rust, D.H., Merrey, D.J., Johnson, H.G., Snellen, W.B., 1994. Methodologies for assessing performance of irrigation and drainage management. Irrigation and Drainage 4, 345–365. Burt, C.M., Clemmens, A.J., Strelkoff, T.S., Solomon, K.H., Bliesner, R.D., Hardy, L.A., Howell, T.A., Eisenhauer, D.E., 1997. Irrigation performance measures: efficiency and uniformity. Journal of Irrigation and Drainage Engineering 123 (6), 423–442. Burt, C.M. and Styles, S.W., 1999a. Modern water control and management practices: impact on performance. Water Report 19, FAO, Rome, Italy. Burt, C.M., Styles, S.W., 1999b. Water balance-related performance indicators for international projects. In: Conference on Benchmarking Irrigation System Performance using Water Measurement and Water Balances, pp. 337–353. Burt, C.M., 2001. Proceso de evaluación rápida (RAP) y comparación con el patrón de referencia (benchmarking). Explicaciones y herramientas. Irrigation Training and Research Center Report (ITRC), No R 01-009, FAO, Bangkok, Thailand. Clarke, D., 1998. Cropwat for windows user guide. Food and Agricultural Organization of the United Nations. Souhamton University: National Water Research, Egypt. Córcoles, J.I., de Juan, J.A., Ortega, J.F., Tarjuelo, J.M., Moreno, M.A., 2011. Evaluation of irrigation systems by using benchmarking techniques. Journal of Irrigation and Drainage Engineering, in press. Dechmi, F., Playán, E., Faci, J.M., Tejero, M., 2003. Analysis of an irrigation district in northeastern Spain. I. Characterisation and water use assessement. Agricultural Water Management 61, 75–92. Díaz, M.P., 2009. Integración de variables agronómicas e hidráulicas en un entorno SIG para la gestión eficiente del agua en la Zona Regable del Genil-Cabra (Córdoba). Final professional work. Universidad de Córdoba, Córdoba, Spain. ESRI, 2002. Getting started with ArcGis. Redlands, New York (USDA).
106
M.F. Moreno-Pérez, J. Roldán-Ca˜ nas / Agricultural Water Management 120 (2013) 98–106
˜ J., Pérez, C., Bonachela, S., 2007. Analysis Fernández, M.D., González, A.M., Carreno, of on-farm irrigation performance in Mediterranean greenhouses. Agricultural Water Management 89, 251–260. IDAE, Instituto para la Diversificación y Ahorro de la Energía., 2008. Ahorro y eficiencia energética en las Comunidades de Regantes. Madrid, Spain. Levine, G. 1982. Relative Water Supply: an explanatory variable for irrigation systems. Technical Report. Lorite, I., Mateos, L., Fereres, E., 2004a. Evaluating irrigation performance in a Mediterranean environment. I. Model and general assessment of an irrigation scheme. Irrigation Science 23, 77–84. Lorite, I., Mateos, L., Fereres, E., 2004b. Evaluating irrigation performance in a Mediterranean environment. II. Variability among crops and farmers. Irrigation Science 23, 85–92. Malano, H., Burton, M., 2001. Guidelines for benchmarking performance in the irrigation and drainage sector. In: International Programme for Technology and Research in Irrigation and Drainage (IPTRID), FAO, Rome, Italy. Malano, H., Burton, M., Makin, I., 2004. Benchmarking performance in the irrigation and drainage sector: a tool for change. Irrigation and Drainage 53, 119–133. Molden, D., Gates, J.K., 1990. Performance measures for evaluation of irrigation water delivery systems. Journal of Irrigation and Drainage Engineering 116, 804–823, no. 6. Cornell University, Ithaca, New York, USA. Molden, D.; Sakthivadivel, R.; Perry, C.J.; de Fraiture, C. Kloezen, W. 1998. Indicators for comparing performance of irrigated agricultural systems. Research report 20. International Water Management Institute. Colombo, Sri Lanka. Pérez Arellano, R., 2009. Estudio de la demanda de agua de riego de la Zona Regable del Genil-Cabra mediante sistemas de telecontrol para una gestión eficiente. Final professional work. Universidad de Córdoba, Córdoba, Spain.
˜ Pérez Urrestarazu, L., Rodríguez Díaz, J.A., Camacho Poyato, E., Ibánez Verdú, M., Deltell Padilla, G., 2005. Análisis estacional y espacial del uso del agua en una zona regable. In: XXIV Congreso Nacional de Riegos, Elche, Spain. Pérez Urrestarazu, L. 2007. Aplicación de los indicadores para el análisis de las acciones de mejora en zonas regables y para el desarrollo de un modelo de gestión integral del agua de riego. PhD. Dissertation. Universidad de Córdoba, Córdoba, Spain. Pérez Urrestarazu, L., Rodríguez Díaz, J.A., Camacho Poyato, E., López Luque, R., 2009. Quality of service in irrigation distributios networks: case of Palos de la Frontera irrigation district, Spain. Journal of Irrigation and Drainage Engineering 135 (6), 755–762. Perry, C.J., 1996. Quantification and measurement of a minimum set of indicators of the performance of irrigation systems. International Irrigation Management Institute, Colombo, Sri Lanka. Rodríguez Díaz, J.A., Camacho Poyato, E., López Luque, R., Pérez Urrestarazu, L., 2008. Benchmarking and multivariate data analysis techniques for improving the efficiency of irrigation districts: an application in Spain. Agricultural Systems 96, 250–259. Rodríguez Nadales, J.R., 2002. Caracterización de la demanda de agua de riego en la zona regable del Genil-Cabra (sectores II-III y IV-VII). Final professional work. Universidad de Córdoba, Córdoba, Spain. Roldán, J. 2007. Mejora del uso del agua en el regadío. In: E. Cabrera and L. Babiano ˜ Ministerio de Medio Ambiente, Madrid, Spain, (coords.), La sequía en Espana. pp. 228-247. Romero, J.L., 2008. Estudio de la distribución de agua y cultivos a escala de parcela en la zona regable de Genil-Cabra. Desarrollo y utilización de un SIG para tratamiento de la información. Final professional work. Universidad de Córdoba, Córdoba, Spain.