Evaluating irrigation performance in a Mediterranean environment. I. Model and general assessment of an irrigation scheme

Irrigation Performance Evaluation in a Mediterranean Environment

Introduction

  • Assessment of irrigation performance is crucial for improving water use due to increasing water scarcity.
  • A study was conducted in the Genil–Cabra irrigation scheme (GCIS) in Andalusia, southern Spain, between 1996 and 2000.
  • The GCIS comprises about 7,000 ha of irrigated land divided into 843 parcels.
  • Crops include cereals, sunflower, cotton, garlic, and olive trees.
  • Irrigation is on-demand using a pressurized system, with hand-moved sprinklers being the most common method.
  • Six performance indicators were used to assess physical and economic performance, using parcel water-use records and a simulation model.
  • The model simulates water balance and computes an optimal irrigation schedule, checked against actual schedules.
  • The average irrigation water supply:demand ratio ranged from 0.45 to 0.64, indicating deficit irrigation.
  • Including rainfall, the supply:demand ratio increased to 0.87 in one year but was only 0.72 in the driest year.
  • Farmers efficiently used rainfall, with actual:attainable crop yields ranging from 0.72 to 0.83.
  • Water productivity (WP) ranged from 0.72 to 1.99 e/m3, averaging 1.42 e/m3.
  • Irrigation water productivity (IWP) averaged 0.63 e/m3.
  • WP is higher than IWP because WP includes production from rainfall.
  • Water availability for irrigation is expected to decrease due to demands from other sectors.
  • In Spain, freshwater demand is estimated at 35 \cdot 10^5 m^3/year, with about 70% for irrigation.
  • Irrigation demand in Southern Spain is expected to increase by about 17% in the next 10 years.
  • Modernization and rehabilitation of Spanish irrigation schemes are important for efficient water use.
  • Only 27% of irrigated area in Spain is less than 20 years old, while 37% is more than 90 years old.
  • System modernization has been emphasized, but irrigation management improvement needs more attention.
  • Assessing irrigation performance is essential for improving water management.
  • Computer simulation using hydrologic models is useful for this task.
  • Models range from empirical (Doorenbos and Pruitt 1977; Doorenbos and Kassam 1979; Allen et al. 1998) to mechanistic (Van Aelst et al. 1988).
  • Tools like remote sensing (Kite 2000; Kite and Droogers 2000) and GIS (Hartkamp et al. 1999) are combined with models.
  • Indicators are used to evaluate practices and recommend improvements (Molden and Gates 1990; Kalu et al. 1995; Malano and Burton 2001).
  • Indicators relate to water balance, economic, environmental, and social objectives, or system maintenance (Bos 1997).
  • Indicators are used for assessing trends (Sarma and Rao 1997; Droogers and Kite 1999; Droogers et al. 2000; Dechmi et al. 2003), comparing schemes (Burt and Styles 1999), resource optimization (Molden and Gates 1990), and determining compromise solutions (Kalu et al. 1995).
  • Model complexity varies from one-dimensional (Feddes 1988; Droogers and Kite 1999) to simplified FAO-based models (Dechmi et al. 2003).
  • Input data is obtained from water delivery records and consumption estimates.
  • Scheme-level assessments are needed for comparison when sub-scheme data is unavailable.
  • The study aimed to comprehensively assess irrigation performance using on-farm data and a simulation model in the GCIS.
  • The GCIS was selected due to the availability of accurate data on water use and cropping patterns.

Materials and methods

Area description

  • The study area is located within the GCIS, near Cordoba, Spain (37° 31' N, 4° 51' W).
  • The evaluated area covers 6,990 ha of irrigated land, developed around 1990 and fully supplied with water since 1995.
  • The climate is Mediterranean continental with an average annual precipitation of 606 mm and a rainless summer.
  • Average air temperature ranges from 10 °C in winter to over 27 °C in summer.
  • Predominant soils are Chromic Haploxererts (35%) and Typic Xerorthent (34.7%).
  • Cropping patterns are diverse, including winter cereals (27%), sunflower (19%), cotton (16%), and garlic (15%).
  • Other crops include olive, sugar beet, beans, maize, and horticultural crops.
  • The area is serviced by a modern pressurized irrigation-delivery system with complete flexibility.
  • Approximately 2,600 ha are watered from a gravity-fed network, and 4,400 ha are supplied by a pumped network.
  • Both networks start at the same point where water is diverted and filtered.
  • The area is divided into command areas, each with one or more parcels.
  • A parcel is an administrative unit belonging to a single owner and may be divided into fields.
  • The pressurized network supplies 44 command areas, and the gravity network supplies 39.
  • Water delivery is measured at the inlet of each parcel.
  • Farmers pay 0.02 e/m3 for energy costs and an annual fee of about 150 e/ha.
  • Land tenure: 290 parcels < 2 ha (4.3% of area), 360 parcels of 2–10 ha (22.6%), 190 parcels of 10–100 ha (65.7%), three parcels > 100 ha (8.5%).
  • Over 90% of parcels are less than 20 ha.

Data collection

  • The study was carried out during four irrigation seasons (1996/1997 to 1999/2000).
  • No irrigation restrictions were imposed during these seasons.
  • The 1998/1999 season had very limited rainfall (150 mm compared to the 4-year average of 559 mm and long-term average of 606 mm).
  • Crops on each parcel were recorded, and water-meter readings were taken four or five times each season.
  • Soil maps and characteristics were obtained from previous studies.
  • Parcel information and irrigation system characteristics were provided by the district manager.
  • Each parcel was visited annually to confirm crop information and describe the method of irrigation.
  • Portable sprinkler systems were most common for herbaceous crops, while drip irrigation was used in olive groves.
  • Frequency and duration of irrigation and sowing dates were obtained from interviews and questionnaires (10% response rate).
  • Sowing dates were assigned randomly each year based on a normal distribution for cotton, sunflower, wheat, and garlic.
  • Daily meteorological data were obtained from an automated weather station.
  • Attainable crop yields were estimated from interviews and expert opinions, and marketable prices were compiled from local bulletins.

Simulation model

  • A mass-balance model was developed to simulate water use in the GCIS.
  • It included sub-models for calculating all water-balance components and quantifying the effects of water stress on crop yield.
  • The model compares calculated optimum schedules with actual schedules based on water-meter readings.
  • The model calculates the soil water-balance components for each computation unit on a daily basis.
  • GIS tools were used for overlaying the parcel with soil maps to characterize each parcel's soil.
  • The simulation model was then applied to each of these computation units and the results aggregated to obtain average values.
Soil water balance
  • A daily soil water-balance model with multiple soil layers was developed.
  • Rainfall and irrigation were inflows, while capillary rise and lateral flow were not considered.
  • Outflows were crop transpiration, soil evaporation, surface runoff, and deep percolation.
  • The soil profile was divided into 10-cm layers up to the maximum root system depth.
  • Surface runoff was calculated with the SCS curve number method.
  • Water extraction by roots was calculated for each soil layer as a function of its water content and root-length density.
  • The water in excess of the maximum storage for each soil layer flowed in a cascade mode, and deep percolation was computed.
  • The soil water balance took into account the extraction of water by crops after the last irrigation and before crop maturity.
  • Deep percolation due to irrigation was also caused by application non-uniformity.
Crop water requirements and yield
  • Crop water requirements (ETc) were calculated using FAO equations (Allen et al. 1998): ETc = ETo \cdot (Ks \cdot Kcb + Ke) Where:
    • ETo is the daily reference evapotranspiration.
    • Kcb is the basal crop coefficient.
    • Ke is the soil evaporation coefficient.
  • Ke is obtained by calculating the amount of energy available at the soil surface (Allen et al. 1998): Ke = Kr \cdot (Kc_{max} - Kcb) Where:
    • Kr is a dimensionless evaporation reduction coefficient.
    • Kc_{max} is the maximum value of Kc following rainfall or irrigation.
  • Water stress is addressed using Ks, a water-stress coefficient: Ks = \frac{WHC - Dr}{(1 - p)WHC} Where:
    • WHC is the water-holding capacity of the root zone.
    • Dr is the root zone depletion.
    • p is the fraction of the WHC below which the root zone water content limits crop transpiration.
  • Seasonal maximum evapotranspiration (ETc{max}), seasonal actual evapotranspiration (ETc) and the crop yield response factor (Ky) are used to estimate crop yield reduction: 1 - \frac{Ya}{Ym} = Ky \cdot (1 - \frac{ETc}{ETc{max}}) Where:
    • Ya is the actual crop yield.
    • Ym is the maximum expected crop yield.
    • Ky is an empirical crop response factor.
  • Severe ET deficits affect harvest index more than biomass production (Fereres 1984).
  • The production function was valid until a seasonal ETc deficit of 40% of ETc_{max} was reached.
  • At that threshold point, a linear reduction in crop yield was assumed, reaching a zero yield at an ETc deficit of 80% of ETc_{max}.
  • Deficit coefficient (Cd) is defined as the ratio between the mean deficit and the required depth:
    ETc = ETc_{max} - (Cd \cdot HR)
  • HR is defined as: HR = ETc_{max} - Rain - DS Where:
    • Rain is the effective rainfall along the irrigation season.
    • DS is the increase or decrease in root zone water storage.
Irrigation scheduling
  • Two scheduling strategies were analyzed: optimum and actual.
  • Under the optimum strategy, the model simulates the irrigation schedule using allowable depletion equal to p adjusted from the data of Allen et al. (1998).
  • The non-uniformity of the irrigation application implies the need for an additional water depth to compensate for the lack of uniformity.
  • An economic optimum was defined for each crop based on yield price and water cost (Wu 1988).
  • The optimum strategy includes additional water up to the economic optimum depth.
  • The actual irrigation schedule was derived from the total amount of water used in each parcel.
  • We estimated the number of irrigation events carried out by each farmer.
  • The simulation model was run under the assumption that the farmer would distribute the water by attempting to reach the same level of allowable depletion in the root zone.

Performance indicators

  • Six performance indicators were chosen:
    • Annual relative irrigation supply (ARIS).
    • Annual relative water supply (ARWS).
    • Drainage ratio (DR).
    • Crop yield ratio (CYR).
    • Water productivity (WP).
    • Irrigation water productivity (IWP).
  • Other indicators such as those used for evaluating equity or dependability (Molden and Gates 1990) were not considered as important.
Performance indicators related to water use
  • \text{ARIS} = \frac{\text{Annual volume of irrigation water inflow}}{\text{Annual volume of crop irrigation demand}}
  • \text{ARWS} = \frac{\text{Annual volume of total water supply}}{\text{Annual volume of crop water demand}}
  • \text{DR} = \frac{\text{Drained volume of water from irrigation}}{\text{Annual volume of irrigation water inflow}}
  • \text{CYR} = \frac{\text{Actual crop yield}}{\text{Intended crop yield}}
Performance indicators related to economics
  • \text{WP (Euros/m}^3\text{)} = \frac{\text{Annual value of agricultural production}}{\text{Annual volume of irrigation water inflow}}
  • \text{IWP (Euros/m}^3\text{)} = \frac{\text{Increase in annual value of agricultural production due to irrigation}}{\text{Annual volume of irrigation water inflow}}

Results and discussion

  • The GCIS is characterized by a low ARIS and a relatively high WP.
  • The average parcel ARIS for the whole area was always less than 1.0 (from 0.45 to 0.64).
  • ARIS values between years differed significantly depending on rainfall.
  • Standard deviations of the parcel ARIS values were quite large.
  • ARIS average values published for different irrigation areas around the world are generally higher than those presented.
  • The ARWS and the CYR showed similar behavior because both indexes were correlated.
  • Average values for the four irrigation seasons showed less variation than did ARIS.
  • ARWS varied from 0.72 to 0.87.
  • CYR varied from 0.72 to 0.83.
  • The DR index varied very little in the four irrigation seasons.
  • WP was influenced by weather conditions and by irrigation management.
  • Average WP values varied from 1.99 e/m3 to 0.72 e/m3.
  • The lowest WP value corresponded to the year of lowest rainfall.
  • IWP varied less and was highest in the dry year.
  • The high WP of the GCIS is probably due to the presence of high WP crops and the low water consumption in the GCIS.
  • IWP did not show important variations between the 4 years.
  • For all performance indicators studied, the variability among fields was substantial.

Conclusions

  • The traditional cliche´ of a wasteful use of water for irrigation clearly does not apply to the GCIS.
  • The scheme is in a deficit-irrigation situation, as shown by the average ARIS value.
  • The conjunctive use of rainfall and irrigation makes efficient use of the total water available.
  • Yields are not limited by the deficit irrigation.
  • The estimated average CYR varies from 0.72 to 0.83 and is lowest in the driest season.
  • The relatively high water productivity found in the GCIS is due to a combination of deficit irrigation.