Deficit irrigation with reclaimed water in a citrus orchard. Energy and greenhouse-gas emissions analysis

Agricultural Systems 159 (2018) 93–102

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Deficit irrigation with reclaimed water in a citrus orchard. Energy and greenhouse-gas emissions analysis

MARK

J.F. Maestre-Valeroa,⁎, B. Martin-Gorriza, E. Nicolasb, M.A. Martinez-Matea, V. Martinez-Alvareza a b

Escuela Técnica Superior de Ingeniería Agronómica, Universidad Politécnica de Cartagena, Paseo Alfonso XIII, 48, 30203 Cartagena, Spain CEBAS-CSIC. Campus Universitario de Espinardo, PO Box 164, 30100 Murcia, Spain

A R T I C L E I N F O

A B S T R A C T

Keywords: Water-energy productivity Non-conventional water resources Carbon footprint Water conservation

Irrigated agriculture brings important socio-economic benefits, but requires high energy consumption, which in turn generates environmental problems by emissions of greenhouse gases. To maintain agricultural activity in the Segura River Basin in the face of extant water shortages, farmers are increasingly using non-conventional water resources such as reclaimed water, and implementing water conservation techniques such as regulated deficit irrigation. The present study quantified the energy consumption and production and greenhouse gas emissions of a grapefruit orchard under the implementation of two irrigation regimes (full and regulated deficit irrigation) and the alternative use of reclaimed water instead of water transferred from the Tajo-Segura Basin for irrigation. The study additionally included the novelty of performing the analyses considering four different stages of crop development. The energy and the greenhouse gas emissions assessment was performed for each study case based on an inventory of inputs of the selected plot and their corresponding energy conversion and greenhouse gas factors. The results indicate that, under the conditions studied, the use of reclaimed water and/or the implementation of regulated deficit irrigation strategies had no significant effect on energy productivity and specific greenhouse gas emissions, irrespective of the stage of crop lifecycle analysed. Moreover, in order to increase the energy efficiency of the orchard and reduce greenhouse gas emissions, the energy consumption associated with the transportation of water to the plot, the manufacture of the irrigation system and the manufacture and transport of fertilisers should be reduced.

1. Introduction Since 1979, the Segura River Basin (SRB), in south-eastern Spain, has received an average of 196 hm3/year from the Tajo Basin in central Spain to complement its own agricultural water resources (CHS, 2015). This complementary water allocation has meant: (i) a significant increase in the net surface area devoted to irrigation, from about 170,000 ha in 1979 to 263,000 ha in 2015 (CHS, 2015), (ii) the acquisition of water rights by > 80,000 landowners in the basin (Claver, 2016), and (iii) a significant investment in the modernisation of hydraulic and irrigation infrastructures to transform rainfed and surface irrigation based agriculture to highly efficient trickle irrigation systems (Playán and Mateos, 2006), among others. In the case of the Region of Murcia, which covers 58.8% of the basin area, such complementary inter-basin water resources have allowed the surface area of irrigated woody trees to be increased from 63,947 ha in 1979 to 93,770 ha in 2015. For citrus, those values are 21,917 ha and 38,245 ha, respectively (CREM, 2015); with the latter representing 40.1% of the land surface occupied by irrigated woody trees in the ⁎

Corresponding author. E-mail address: [email protected] (J.F. Maestre-Valero).

http://dx.doi.org/10.1016/j.agsy.2017.10.017 Received 22 May 2017; Received in revised form 2 October 2017; Accepted 19 October 2017 0308-521X/ © 2017 Elsevier Ltd. All rights reserved.

region (ESYRCE, 2015). Despite this complementary resource, the SRB faces a structural water deficit of nearly 400 hm3/year (CHS, 2015), yet this irrigated agriculture must be maintained in order to provide food security to a population under continuous increase (WWAP, 2012; Faurès et al., 2013). Food security requires energy and water security (Bundschuh et al., 2014). Consequently, farmers, in order to partially confront such a water scarcity situation and to continue with sustainable agriculture, are usually forced to complement their share of conventional water resources with other non-conventional water resources such as reclaimed waters (RW) and with the implementation of regulated deficit irrigation (RDI) strategies (Maestre-Valero et al., 2016). It is of note that the volume of RW in the Region of Murcia is 105 hm3, produced in 93 wastewater treatment plants (WWTP) (ESAMUR, 2017), and which restore about 10% of the annual renewable resources (CHS, 2015). This development in irrigated land has brought associated important regional socio-economic benefits. However, modernisation in farm technology over time to achieve a high-productive agriculture has increased the amount of energy used in crop production (Rathke and

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J.F. Maestre-Valero et al.

Diepenbrock, 2006). That intensive energy consumption also generates environmental problems mainly attributed to Greenhouse Gas (GHG) emissions that contribute to global warming (Zaheli et al., 2015). In this sense, energy input-output analyses represent a valuable tool that allow different production systems to be compared by investigating and assessing energy use efficiency, environmental effects and their relationship to sustainability (Khoshnevisan et al., 2014a). Enhancing energy efficiency not only helps in increasing the productivity and profitability ratio, but also results in minimised GHG emissions and environmental impacts (Alluvione et al., 2011). The relation between energy inputs and outputs has been investigated in a wide range of crops, such as citrus (Ozkan et al., 2004; Martin-Gorriz et al., 2014), apricot (Sartori et al., 2005), olive (Guzmán and Alonso, 2008), cherry (Kizilaslan, 2009), pulse (Koocheki et al., 2011), tomato (RezvaniMoghaddam et al., 2011), plum (Tabatabaie et al., 2012), sugar beet (Asgharipour et al., 2012; Yousefi et al., 2014), cotton (Zahedi et al., 2014), and some vegetable and tree crops (Martin-Gorriz et al., 2014). Likewise, crop production GHG emissions have been calculated in some crops such as lettuce (Gunady et al., 2012), strawberry (Khoshnevisan et al., 2014b), some vegetable and tree crops (Martin-Gorriz et al., 2014), cereals (Mohammadi et al., 2014) or tomato (Ntinas et al., 2017). Overall, most of these studies analyse the energy inputs and outputs and the GHG emissions based on a general scenario, without bearing in mind the effect of other significant variables that could affect the analysis, such as (i) the implementation of water conservation irrigation techniques, (ii) the use of non-conventional water resources for irrigation, or (iii) different crop lifecycle stages. In this context, the present study has two specific aims. On the one hand, the work analyses the energy consumption and the GHG emissions of implementing several irrigation regimes: full irrigation and RDI combined or not with RW, in a ‘Star Ruby’ grapefruit orchard. This assessment has introduced the novelty of considering four different crop lifecycles stages. On the other hand, the most relevant specific inputs that affect energy demand and GHG emissions under the different productive systems are evaluated. This will provide a valuable insight into where to focus actions to improve system efficiencies.

Energy

FARM PROCESSES

INPUTS

INPUTS

HL, EQ

Irrigation system

Fertilisers and application

HL, F

HL, D, M, YT

Crop Plantation

Irrigation

W, E

HL, D, M

Soil preparation

Pesticides and application

HL, D, P, M

HL, D, M

Reservoir excavation

Crop Harvesting/pruning

HL, D, M

GHG Emissions

Grapefruit yield

Fig. 1. Flow diagram for grapefruit production. HL: Human labour; D: Diesel; M: Machinery; YT: Young trees; EQ: Equipment; F: Fertilisers; W: Water; E: Electricity; P: Pesticides.

units were chosen for this study; a mass-based FU defined as 1 kg of grapefruits during one annual farming period (marketable crop yield; kg/year) and a land-based FU defined as 1 ha of farmland per year. The system boundary was considered from raw material extraction to farm-gate based on grapefruit production. The processes and flows of the system boundary include inputs and outputs until the farm-gate phase. Energy consumption and GHG emissions derived from the treatment of sewage water to produce RW were considered in the study (Fig. 1). The assessment did not include: (i) nursery plantlets production, (ii) GHG emissions from the production, maintenance at the end of capital inputs life, (iii) disposal of material or waste, (iv) manufacture and construction of a shed for farm machinery storing and a plot fence.

2. Materials and methods

2.3. Data inventory

2.1. Experimental cases

Prior to performing the analysis, a data inventory was carried out from 2004 to 2014 considering four different crop lifecycle stages: (i) establishment of the plantation in late 2004, (ii) juvenile (unproductive) stage from 2005 to 2007, (iii) young productive stage from 2008 to 2010 and (iv) adult productive stage from 2011 to 2014. The inventory was performed according to the following aspects (Table 1):

The assessment of energy demand and GHG emissions was carried out from 2004 to 2014 for four cases resulting from the combinations of two different water sources and two irrigation strategies. One source (TW), with an average electrical conductivity (ECw) about 1 dS/m, was pumped from the “Tajo-Segura” water transfer canal. The other was tertiary saline RW, pumped from a WWTP. This source was automatically blended at the irrigation control-head with water from the canal to reduce its ECw value down to ≈ 3 dS/m to obtain a constant ECw during the experiment (RW). The usual blending rate was 63% of water from the WWTP and 37% of TW. Four treatments were designed, based on the water sources and the application of water deficit. On the one hand, TW and RW treatments were irrigated at 100% of the soil water lost by daily ETc during the whole season. On the other hand, the RDI treatments consisted of irrigation at 100% ETc, except during the second stage of fruit growth, 55–65 days between late-June and mid-September, when they received 50% of the water amount applied to the control. No leaching fraction was added to the irrigation doses. Irrigation with RW and the application of RDI strategies were performed from 2008 onwards. From 2005 to 2007 the whole orchard was full irrigated with TW.

2.3.1. Orchard For the study, a 0.5 ha commercial orchard located in CampotéjarMurcia, south-eastern Spain (38°07′18″ N; 1°13′15″ W) was selected. The orchard was planted in 2004 with ‘Star Ruby’ grapefruit trees (Citrus Paradisi Macf.) grafted on Macrophylla rootstock [Citrus Macrophylla Wester] with a tree spacing of 6 m × 4 m. A total of 192 trees were used in the study. The experimental design was a randomised complete design with four blocks and four experimental plots per block. The standard plot was made up of twelve trees, organised in three adjacent rows with four trees per row. The two central trees “inner trees” of the middle row were used for yield measurements and the other ten trees were guard trees so as to eliminate potential edge effects. 2.3.2. Irrigation system The irrigation system consisted of a control head equipped with pumps, a fertigation system, electrovalves, an automatic irrigation programmer and filters. The irrigation head pumped water to the plot throughout a PVC tertiary pipe 145 m in length. A total of 17 single PE irrigation laterals each measuring 100 m in length were installed on the

2.2. Functional unit and system boundary In order to perform valuable comparisons of energy demand and GHG emissions between the different cases studied, two functional 94

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Table 1 Inventory of inputs and outputs involved in the energy balance and greenhouse gas emissions analysis for 1 ha of grapefruit production. Unit/ha

A. Inputs A.1. Direct inputs A.1.1. Human labour Reservoir excavation Irrigation pipes installation Soil preparation Crop plantation Farm maintenance Preparation of fertilisers Preparation of pesticides Pruning Harvesting Total Human labour A.1.2. Diesel Reservoir excavation Soil preparation Crop plantation Application of pesticides Pruning Total Diesel A.1.3. On-farm electricity A.1.4. Off-farm electricity Total electricity A.2. Indirect inputs A.2. 1. Fertilisers N P2O5 K2O A.2. 2. Pesticides Fungicides Insecticides A.2. 3. Plant material A.2. 4. Machinery Reservoir excavation Soil preparation Crop plantation Application of pesticides Pruning Total Machinery A.2. 5. Water for irrigation A.2. 6. Irrigation system Head system PE pipelines PVC pipelines Emitters Film HDPE – Reservoir Earth work – Reservoir Earth work - Trenches B. Outputs B.1. Yield C. Others C.1. Water productivity

2004

2005–2007

2008–2010

2011–2014

TW

TW-RDI

RW

RW-RDI

TW

TW-RDI

RW

RW-RDI

– – – – 4.4⁎ 8.7 12.0 141.0 139.0 305.1

– – – –

– – – –

– – – –

– – – – 5.6 8.7 20.0 211.5 208.5 454.3

– – – –

– – – –

– – – –

– 370.6

– – – – 4.0 8.7 6.0 71.0 104.3 194.0

6714.3 271.1 271.1 – – 7256.5 – – –

– – – 14.7 14.7 29.4 255.3 1276.4 1531.7

– – – 29.4 29.4 58.8 543.2a 2716.0a 3259.2a

– – –

– – –

– – –

– – –

– – –

– – –

58.8 472.0b 2360.1b 2832.1b

58.8 543.2a 2611.1a 3154.3a

58.8 472.0b 2268.9b 2740.9b

– – – 49.0 49.0 98.0 1006.4a 5032.0a 6038.4a

98.0 871.3b 4356.3b 5227.6b

98.0 1006.4a 4837.6a 5844.0a

98.0 871.3b 4188.1b 5059.3b

– – –

99.2 49.6 71.5

152.7 76.3 110.3

252.8 114.7 170.8

– – 417

2.0 1.0

3.0 2.0

4.0 3.0

– – – 6.0 1.5 7.5 1501.6

– – – 12.0 2.0 14.0 3195.3a

– – –

– – –

– – –

– – –

– – –

2776.6b

3195.3a

2776.6b

– – – 20.0 3.0 23.0 5920.0a

– – –

m3

234.3 34.7 34.7 – – 303.7 –

5125.1b

5920.0a

5125.1b

kg m m m m2 m3 m

150.0 1700.0 145.0 417.0 3300.0 11,600.0 145.0

– – – 834.0 – – –

–

3475.0

– – – – – – – – 28,614.7

– – – – – – – – 26,592.2

– – – – – – – – 25,544.4

– – – – – – – – 23,331.3

– – – – – – – – 76,860.0

– – – – – – – – 71,399.7

– – – – – – – – 74,825.6

– – – – – – – – 65,569.8

–

2.3

9.2

10.4

8.6

8.7

13.0

13.9

12.8

12.8

h 234.3 32.0 34.8 69.5 – – –

l

kW·h kW·h

kg

kg

units h

kg 3

Kg/m

**Letters indicate significant differences between treatments following Tukey's range test (P < 0.05). ⁎ A value shown alone in a line within a crop lifecycle stage means that said value is the same for all the cases studied.

2.3.4. Other accessories for water storing 3300 m2 of a high-density PE waterproofing membrane was considered to prevent leakage of water by infiltration.

soil surface next to the tree trunk. Three self-pressure compensating online emitters per tree discharging 4 L h− 1 each were installed at 1 m from the trunk and spaced 1 m apart. One of the emitters was installed at the establishment (2004) whereas the remaining two were installed at the end of 2006.

2.3.5. Fertilisers and pesticides All the cases received the same amounts of fertiliser applied through the drip irrigation system. In 2005, fertiliser amounts were 89–45–64 kg/ha/year (N–P2O5–K2O) and this increased by about 15% each year until the adult productive stage. Pest control practices were those commonly used by growers in the area; 3 kg/ha/year in the juvenile stage, 5 kg/ha/year in the young productive stage and 7 kg/ha/ year in the adult productive stage. No weeds were allowed to develop within the orchard.

2.3.3. Earth work for water reservoir and trenches For the construction of an agricultural water reservoir with a capacity of 10,000 m3, an earth movement of 11,600 m3 was considered. Such a capacity was defined considering storing enough water for irrigation during 21 consecutive days during the period of highest water demand by the crop (Pérez-Pérez et al., 2010; Alcón et al., 2013). In addition, 145 m of trenches to bury the tertiary pipes were taken into account. 95

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2.3.8. Machinery and diesel consumption Machinery was inventoried considering the weight (kg) of the machinery associated to each of the following farm activities during the different crop lifecycles; i.e. reservoir excavation (earth excavator plus tractor 92 kW), holes for planting (post hole digger plus tractor 60 kW), crop plantation (trailer plus tractor 60 kW), pesticides application (sprayer tank or air-blast sprayer plus tractor 60 kW) and pruning shredding (chipper plus tractor 60 kW). Diesel consumption (L) was that associated to the use of the machinery in the different farm activities.

Table 2 Energy equivalences of inputs and outputs. Type

Unit

Energy equivalent (MJ unit− 1)

Reference

A. Direct energy input Human labour Diesel Electricity

h l kW·h

2.2 38.68 12.1

Fluck (1992) Bowers (1992) Guzmán and Alonso (2008)

kg

95.57

Bowers (1992)

kg kg kg

76.5 15.9 12.7

Helsel (1992)

kg kg

199 92

Helsel (1992)

unit

9

Heichel (1980)

kg kg kg kg m

148 112 49 8.5 0.375

Batty and Keller (1980)

m3

25.4

ELCD (2017)

kg

1.5

BEDCA (2017)

B. Indirect energy input Machinery Fertilisers N P2O5 K2 O Pesticides Fungicides Insecticides Plant material Young trees Irrigation system Head system Pipeline PE Pipeline PVC Film HDPE - reservoir Earth work trenchers Earth work reservoir C. Output Grapefruit

2.3.9. Human labour Human labour (h) was that associated to the different farm activities; i.e. reservoir excavation, irrigation pipes installation, irrigation system maintenance; holes for planting, crop plantation, fertilisers application, pesticides application, pruning, pruning shredding, weeds removing and harvesting. Resources and timing for these activities have been collected from Maestre Valero et al. (2016). 2.3.10. Fruit yield Yield was registered in eight inner trees per each case studied. Yield data for each stage corresponds with the average of the period. Data shown for the period 2008–2010 have been previously published by Pedrero et al. (2015). Moreover, the irrigation water productivity (WPi) was calculated for each case as the ratio between the annual yield (kg/ ha) and the applied water (m3/ha) during the same period. 2.4. Energy analyses Energy inputs and outputs have been calculated annually from 2004 to 2014 from the farming inputs detailed in Table 1 and by applying the energy equivalences compiled in Table 2, extracted from the literature. Energy inputs in 2004 for human labour, diesel consumption, plant material, machinery and the irrigation system, while attributed to the plantation establishment, were also distributed annually, considering the lifespan of the materials installed and works carried out. The determination of the energy balance in agriculture must consider inputs and outputs, where inputs must in turn be divided into direct and indirect energy. On the one hand, direct energy demand covers human labour, electricity and diesel used in the crop production (Pimentel, 1992). Energy for human labour and diesel were calculated by multiplying the amounts the system required by their respective energy units (Table 2). Electricity, used for irrigation, has been calculated from the irrigation water consumption per crop and the specific energy for each case studied. Note that losses through generation and transport of the electricity have been estimated at 70%, and hence 1 kWh is equivalent to 12.1 MJ (Guzmán and Alonso, 2008). On the other hand, indirect use of energy includes the energy inputs from the materials consumed and the processes to produce fertilisers, pesticides, plant material, the irrigation system, the water reservoir and the agricultural machinery (Pimentel, 1992). Fertilisers, pesticides, plant material, water reservoir and irrigation systems were evaluated by multiplying their amounts used in the system by their energy units (Table 1). Fertilisers and pesticides energy units were calculated based on Helsel (1992), which included packaging and transportation of raw materials and product, and excluded the distribution cost, because in the case of fertilisers they were added into the irrigation water. Energy associated to plant material was determined according to the methodology proposed by Heichel (1980). The irrigation system was calculated following Batty and Keller (1980), albeit considering updated energy input conversion factors of raw materials (Table 2). Machinery was calculated following the methodology proposed by Bowers (1992), by adding the specific energy of raw materials (86.77 MJ/kg) and that for the manufacturing process (8.80 MJ/kg). An additional 55% was added to the value for the manufacturing

2.3.6. Water for irrigation The irrigation doses were scheduled based on the daily crop evapotranspiration (ETc) accumulated during the previous week. ETc values were estimated as reference evapotranspiration (ET0), calculated with the Penman–Monteith methodology (Allen et al., 1998), and month-specific crop coefficients (Kc). Kc were corrected during the juvenile and young productive stages (from 2005 to 2010) by reduction coefficients of 0.50 and 0.75, respectively. These coefficients accounted for an eventual decrease in evapotranspiration because of the partial soil covering by the crop canopy (Fereres et al., 1982). The total amounts of water applied were measured with inline water flow meters, placed on the four replicates of each study case. The irrigation was controlled automatically by a head-unit programmer and electro-hydraulic valves.

2.3.7. Electricity In the case of the TW study case, electricity was evaluated considering both (i) the off-farm conveyance energy cost from the sources to the supplying point of the irrigation districts and then the water allocation to each farm hydrant; established at 0.85 kW·h/m3, and (ii) also the on-farm energy cost for irrigation; established at 0.17 kW·h/m3 (MAGRAMA, 2014; Martin-Gorriz et al., 2014). Accordingly, 1.02 kW·h to provide each m3 of TW to the farm was used. For the RW case studied, electricity was mainly associated to the energy required to produce reclaimed water in the WWTP (0.72 kW·h/ m3), the transport of the water to the plot (0.07 kW·h/m3), and the onfarm energy for irrigation (0.17 kW·h/m3) (Martin-Gorriz et al., 2014). Electricity was then calculated as the product of the proportion of TW used in the mix (37%) per its weighted average energy (1.02 kW·h) and the product of the proportion of water coming from the WWTP (63%) per the energy required to produce, transport and apply each m3 of water (0.96 kW·h) (Martin-Gorriz et al., 2014). Accordingly, a weighted average energy of 0.98 kW·h to provide each m3 of RW to the farm was used.

96

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the electricity supply company was on average 0.30 kg CO2eq/kW·h between 2005 and 2007, 0.26 kg CO2eq/kW·h between 2008 and 2010 and 0.23 kg CO2eq/kW·h between 2011 and 2014 (Iberdrola, 2017). Additionally, the following CO2eq indices were used: (i) specific GHG emissions (kgCO2eq/kg), computed as the ratio between the amount of CO2eq emissions (kgCO2eq/ha) and yield (kg/ha), and (ii) areal GHG emissions (kgCO2eq/ha) calculated as the ratio between CO2eq emissions per hectare.

process to consider the energy required for machinery maintenance during its life cycle (Fluck, 1985). Regarding energy outputs, the energy content in the crop production was taken from the Spanish national nutrient database for standard reference (BEDCA, 2017). Finally, for the sake of comparisons between study cases, the following energy indices for analyzing the efficiency of crop production were used: (i) energy use efficiency (dimensionless), also called energy ratio (ER), as the ratio between energy output (MJ/ha) to energy input (MJ/ha), and (ii) energy productivity (EP; kg/MJ) as the yield (kg/ha) divided by the total energy used (MJ/ha).

2.6. Statistical analyses The results obtained from the different treatments were analysed separately by life-cycle periods, that is, the young productive and the adult productive stages. A one-way variance analysis (ANOVA) was used to test the hypothesis of equal means. Additionally, when differences were significant Tukey's range test, at 95% confidence level, was carried out.

2.5. GHG emission analyses GHG from agricultural production was assessed based on four major groups of emission sources: (i) GHG emissions due to the use of fossil fuels and electricity (Lal, 2004), (ii) GHG emissions from the machinery and the irrigation system (Berge, 2009; Iberdrola, 2017), (iii) GHG emissions due to the production, transportation, storage and transfer of agricultural chemicals (Lal, 2004), and (iii) GHG emissions of N2O from soils due to N-fertiliser application (IPCC, 2006). GHG emissions were managed as CO2 equivalent produced (CO2eq). CO2eq emissions have been calculated annually from 2004 to 2014 from the farming inputs detailed in Table 1 and by applying the emission factors presented in Table 3. CO2eq emissions at the establishment stage in 2004, while attributed to plantation establishment, were also distributed annually considering the lifespan of the materials installed and work carried out. Table 3 compiles the CO2eq emission factors associated to the farming inputs. Accordingly, CO2eq emissions were calculated as the product between the input application rate (electricity, diesel, machinery, irrigation system, fertilisers and pesticides) and its corresponding CO2eq emission factor. In the specific case of electricity, the emission factor includes indirect emissions attributable to the extraction, production and transport of electricity as well as to the electricity lost in delivery in the network. For these calculations, it was assumed that the electricity in Spain is generated by several sources. Accordingly, the electricity mix factor by

3. Results and discussion 3.1. Analysis of input-output energy Table 4 presents the energy conversion of the farming inputs for the four cases studied and the four crop lifecycle stages. At the establishment of the plantation, total direct and indirect energy inputs were 202,260.9 MJ/ha (Fig. 2) with the maximum values attributed to the irrigation system materials (70.6% of total energy inputs) (Fig. 3a). It is important to bear in mind that energy inputs at this stage have also been distributed among all the years of the plantation's lifespan. Regarding direct energy inputs, the highest values were attributed to diesel consumption devoted to the reservoir excavation, the soil preparation and the crop plantation (46,939.5 MJ/ha; 99.3% of total direct inputs and 23.2% of total inputs), whilst in the case of indirect inputs, the highest energy values were associated to the irrigation system materials (142,762.6 MJ/ha; 92.1% of total indirect inputs); especially to the PE film for the reservoir waterproofing (53,295.0 MJ/ha). As a general rule for the juvenile (2005–2007), young (2008–2010)

Table 3 Greenhouse gas (GHG) emission factor of inputs. Activity A. GHG emissions due to the use of fuel and electricity Electricity Diesel

Gas

Unit

CO2 CH4 NO2

kg kg kg kg

B. GHG emissions from machinery and irrigation system Machinery CO2 Irrigation system PVC sheet CO2 Pipeline PE CO2 Pipeline PVC CO2 Head system CO2 Film HDPE - reservoir CO2 C. GHG emissions due to production, transportation, storage and transfer of agricultural Fertilisers N CO2 P2O5 CO2 K2O CO2 Pesticides Fungicides CO2 Insecticides CO2 D. GHG emissions of NO2 from soils due to N-fertiliser application Direct N2O from N inputs (fertilisers) NO2 Direct N2O from N leaching or runoff NO2 Indirect N2O from atmospheric decomposition of N volatilised as NH3 and NOx Synthetic fertiliser NO2

Emission factor

Reference

0.30–0.23 74.1 × 10− 3 21 × 10− 5 19 × 10− 5

Iberdrola (2017) IPCC (2006)

74.1 × 10− 3

Berge (2009)

5.7 2.2 3.0 1.8 2.2

Berge (2009)

kg CO2eq kg− 1 N kg CO2eq kg− 1 P2O5 kg CO2eq kg− 1 K2O

1.3 0.2 0.15

Lal (2004)

kg CO2eq kg− 1 kg CO2eq kg− 1

3.9 5.1

Lal (2004)

kg CO2eq kg− 1 N input kg CO2eq kg− 1 N input

4.87 1.096

IPCC (2006)

kg CO2eq kg− 1 N input

0.487

CO2eq CO2eq CO2eq CO2eq

kW·h− 1 MJ− 1 MJ− 1 MJ− 1

kg CO2eq MJ− 1 kg CO2eq kg CO2eq kg CO2eq kg CO2eq kg CO2eq chemicals

97

m− 2 kg− 1 kg− 1 kg− 1 kg− 1

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Table 4 Energy balance considering the farming inputs and yield for the four cases studied (TW; TW-RDI; RW; RW-RDI) and the four crop lifecycle stages (crop establishment; juvenile unproductive; young; adult). Units in MJ/ha. 2004

2005–2007

2008–2010 TW

A. Inputs A.1. Direct inputs A.1.1. Human labour Reservoir excavation Irrigation pipes installation Soil preparation Crop plantation Farm maintenance Preparation of fertilisers Preparation of pesticides Pruning Harvesting Total human labour A.1.2. Diesel Reservoir excavation Soil preparation Crop plantation Application of pesticides Pruning Total diesel A.1.3. On-farm electricity A.1.4. Off-farm electricity Total electricity Total direct energy inputs A.2. Indirect inputs A.2. 1. Fertilisers N P2O5 K2O Total fertilisers A.2. 2. Pesticides Fungicides Insecticides Total pesticides A.2. 3. Plant material A.2. 4. Machinery Reservoir excavation Soil preparation Crop plantation Application of pesticides Pruning Total machinery A.2. 5. Irrigation system Head system PE pipelines PVC pipelines Emitters Film HDPE – Reservoir Earth work – Reservoir Earth work - Trenches Total irrigation system Total indirect energy inputs Total energy inputs B. Outputs B.1. Yield C. Others C.1. Energy ratio (output/input) C.2. Energy productivity (kg/MJ)

2011–2014 TW-RDI

RW

RW-RDI

TW

TW-RDI

RW

RW-RDI

10,542.3b 52,711.4b 70,988.4b 70,988.4b

12,177.4a 58,535.5a 78,447.7a 78,447.7a

10,542.3b 50,675.5b 68,952.5b 68,952.5b

3.8 7.6 8.8 19.1 13.2 156.2 229.4 444.2

2.6⁎ 3.5 3.8 7.6 9.7 19.1 26.4 310.2 305.8 688.7

2.6 3.5 3.8 7.6 12.3 19.1 44.0 465.3 458.7 1017.0

25.971.0 10.484.2 10.484.2 – – 46,939.5 – – – 47,290.8

1298.6 524.2 524.2 1137.2 290.1 3774.3 3088.8 15.444.1 18.532.9 22,751.4

1298.6 524.2 524.2 2274.4 386.8 5008.2 6572.7a 32,863.3 45,132.8a 45,132.8a

1298.6 524.2 524.2 3790.6 580.2 6717.8 12,177.4a 60,887.2a 80,799.4a 80,799.4a

– – – –

7587.7 788.5 908.6 9284.9

11,679.0 1213.7 1401.2 14.293.9

19,335.7 1823.3 2169.8 23.328.7

– – – 3753.0

398.0 92.0 490.0 187.7

597.0 184.0 781.0 187.7

796.0 276.0 1072.0 187.7

2736.5 1660.0 4057.9 – – 8454.5

136.8 83.0 202.9 741.1 164.2 1328.1

136.8 83.0 202.9 1482.2 219.0 2124.0

136.8 83.0 202.9 2470.4 328.5 3221.6

22,200.0 11,424.0 25,175.4 116.8 53,295.0 29,464.0 1087.5 142,762.6 154,970.1 202,260.9

1110.0 571.2 1258.8 13.6 2664.8 1473.2 54.4 7138.1 18,436.5 41,187.9

1110.0 571.2 1258.8 17.5 2664.8 1473.2 54.4 7149.8 24,536.4 69,669.1a

64,501.5b

68,399.8a

63,398.5b

1110.0 571.2 1258.8 17.5 2664.8 1473.2 54.4 7149.8 34,959.8 115,759.2a

105,948.3b

113,407.5a

103,912.3b

0

5212.5

42.922.0

39.888.4

38.316.6

34.997.0

115.289.9

107.099.5

112.238.4

98.354.7

– –

0.13 0.08

0.62 0.411

0.62 0.412

0.56 0.37

0.55 0.37

1.00 0.66

1.01 0.67

0.99 0.66

0.95 0.63

51.6 70.4 76.5 152.9 – – – – – 351.3

2.6

5711.4b 28,556.9 39,965.1b 39,965.1b

6572.7a 31,593.9 43,863.5a 43,863.5a

5711.4b 27,453.9 38,862.2b 38,862.2b

**Letters indicate significant differences between treatments following Tukey's range test (P < 0.05). ⁎ A value shown alone in a line within a crop lifecycle stage means that said value is the same for all the study cases.

ha (Fig. 2). At this stage, the orchard presented a low average water productivity of 2.03 kg/m3, as a consequence of the average water used for irrigation (1501.6 m3/ha) and the low yield obtained (3475 kg/ha) (Table 1). These results were also reflected in the low values of ER (0.13) and EP (0.08 kg/MJ) observed (Table 4). At the young and adult stages, energy inputs were on average 66,492.2 ± 3014.7 MJ/ha and 109,756.8 ± 7409.7 MJ/ha, respectively. At the adult stage, these results were quite similar to those

and adult (2011–2014) crop stages, the maximum energy inputs were attributed to off-farm electricity for irrigation (mean value of 46.8% of total energy inputs; Fig. 3a, and 72.5% of total direct inputs). Secondly, energy inputs were also associated to the production, transportation, storage and transfer of nitrogenous fertilisers (mean value of 17.7% of total energy inputs and 50.3% of total indirect inputs) (Fig. 3a). In the case of the juvenile unproductive stage, direct (22,751.4 MJ/ ha) and indirect (18,436.5 MJ/ha) energy inputs totalled 41,187.9 MJ/ 98

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Reduction of energy outputs and GHG emissions (%)

J.F. Maestre-Valero et al.

250,000 Energy outputs Energy indirect inputs

200,000 Energy (MJ/ha)

Energy direct inputs 150,000 TW

RW TW-RDI

RW-RDI

100,000 TW

TW-RDI RW RW-RDI

Juvenile

0

Establishment

50,000

Young

Adult

Adult Young

14%

Adult state TW-RDI RW-RDI TW-RDI (GHG) RW-RDI (GHG)

12% 10% 8% 6% 4% 2% 7.0%

7.5%

8.0%

8.5%

9.0%

(Table 4). Concerning RDI strategies, water savings in the grapefruit orchard (about 13%) led to direct energy consumption reductions of 11.4% at the young stage and 12.1% at the adult stage, irrespective of the type of water used for irrigation: TW or RW (Table 4). In addition, such water savings also reduced the energy outputs by 7.1% in the case of using TW for irrigation, 8.7% in the case of using RW for irrigation at the young stage, and 12.4% in the case of using RW for irrigation at the adult stage (Fig. 4). At the young stage, regardless of the type of water used, the percentages of reduction for outputs, inputs and yield were very similar and hence ER and EP values were also very similar; 0.62 and 0.41 kg/MJ for the TW and TW-RDI study cases and 0.56 and 0.37 kg/MJ for the RW and RW-RDI study cases (Table 4). At the adult stage, saving TW led to similar reductions for outputs (7.1%), inputs (8.5%) and yield (7.1%), hence obtaining also similar ER and EP ratios (1.01 and 0.67 kg/MJ). However, reducing RW led to higher reductions of yield and outputs (about 12.1%) in comparison with inputs (8.4%) (Fig. 4), hence reducing the ER and EP from 1.01 and 0.67 kg/MJ in the RW case to 0.95 and 0.63 kg/MJ in the RW-RDI case (Table 4). Irrigation with RW did not have any effect on the energy inputs as slight variations were only observed in the calculation of the electricity for irrigation (reductions of 3.2% for direct energy inputs irrespective of the crop lifecycle stage). The orchard yield was also very similar between the TW and RW cases (Table 1) and hence, orchard energy outputs were also very similar (reductions of 10.7% and 2.6% for energy outputs at the young and adult stages, respectively). At the young stage, as the percentage of output energy reduction (10.7%) was higher than that of energy inputs (1.8%), ER for the RW case studied was reduced from 0.62 in the TW case to 0.56 in the RW case. In addition, as the yield was reduced by 10.7% in the RW case, EP was also reduced from 0.41 kg/MJ in the TW case to 0.37 kg/MJ in the RW case (Table 4). At the adult stage, such percentages of reduction for outputs (2.65%), inputs (2.00%) and yield (2.65%) were quite similar, hence giving similar results of ER (1.00) and EP (0.66 kg/MJ) (Table 4).

a) Human labour

RW-RDI RW

Diesel

TW-RDI TW

Electricity

RW-RDI

Fertilisers

RW

Pesticides

TW-RDI TW

Plant material

Juvenile

Machinery

Establishment

Irrigation system

b) Diesel

RW-RDI

Adult

16%

Young state

TW-RDI RW-RDI TW-RDI (GHG) RW-RDI (GHG)

Fig. 4. Relationship between the percentage of reduction of energy inputs and the percentage of reduction of outputs and GHG emissions for the young and adult stages when regulated deficit irrigation with transferred water (TW) and reclaimed water (RW) was performed.

Fig. 2. Indirect and direct energy inputs and outputs for the four lifecycle stages and the four irrigation cases studied.

RW TW-RDI

Electricity

TW

Fertilisers

Young

18%

Reduction of energy inputs (%) Crop lifecycle stages

RW-RDI

Pesticides

RW TW-RDI

N2O Emissions

TW

Juvenile

Machinery

Establishment 0%

20%

Irrigation system 20%

40%

60%

80%

100%

Fig. 3. Distribution of the main energy inputs (a) and GHG emissions (b) associated with the production of grapefruits per hectare for the four lifecycle stages and the four irrigation cases considered in the study.

calculated by Martin-Gorriz et al. (2014) for lemon, mandarin and orange citrus trees in south-eastern Spain (98,481.3 ± 6871.7 MJ/ha), and notably higher than those presented by Ozkan et al. (2004) in Turkey (57,589 ± 7646 MJ/ha). In this latter case, it was due to the lower amounts of water and electricity used for irrigation; 330 m3/ha and 624.9 kW·h, respectively. At these two stages, differences between the cases studied were only observed in in total indirect inputs, specifically those associated to the amount and type of water resource used for irrigation. Similar results were observed when the cumulative energy values from 2008 to 2014 were analysed (Table 6). As expected, ER and EP increased as the orchard became older and more efficient

3.2. Analysis of GHG emissions Table 5 shows the GHG emissions for the four cases studied and the four crop lifecycle stages. At the establishment stage, the highest GHG emissions were attributed to the irrigation system (21,984.3 kgCO2/ha; 84.2% of total 99

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Table 5 GHG emission sources for the four cases studied and the four crop lifecycle stages. Units in kg CO2eq/ha. 2004

2005–2007

2008–2010 TW

A. GHGs emissions due to the use of fossil fuel and electricity A.1. Electricity Off-farm electricity for irrigation – 382.9 814.8a On-farm electricity for irrigation – 76.6 163.0a Total electricity – 459.5 977.8a A.2. Diesel Reservoir excavation 1934.8 96.7 96.7⁎ Soil preparation 781.1 39.1 39.1 Crop plantation 781.1 39.1 39.1 Application of pesticides – 84.7 169.4 Pruning – 21.6 28.8 Total Diesel 3497.0 281.2 373.1 Total A group 3497.0 740.7 1220.5a B. GHGs emissions from machinery and irrigation system B.1. Machinery Reservoir excavation 202.8 10.1 10.1 Soil preparation 123.0 6.2 6.2 Crop plantation 300.7 15.0 15.0 Application of pesticides 54.9 109.8 Pruning 12.2 16.2 Total Machinery 626.5 98.4 157.4 B.2. Irrigation system 21,984.3 1100.7 1099.2 TOTAL B group 22,610.8 1199.2 1256.6 C. GHG emissions due to production, transportation, storage and transfer of agricultural C.1. Fertilisers N – 128.9 198.5 P2O5 – 9.9 15.3 K2O – 10.7 16.6 Total Fertilisers – 149.6 230.3 C.2. Pesticides Fungicides – 7.8 11.7 Insecticides – 5.1 10.2 Total Pesticides – 12.9 21.9 Total C group – 162.5 252.2 D. GHG emissions of NO2 from soils due to N-fertiliser application D.1. Direct N2O from N inputs – 483.0 743.5 D.2. Direct N2O from N leaching or runoff – 108.7 167.3 D3. Indirect N2O from atmospheric decomposition of N volatirised as NH3 and NOx Synthetic fertiliser – 48.3 74.3 Total D group – 640.0 985.2 Areal GHG emissions (kg CO2eq/ha) 26,107.8 2742.4 3844.8a Specific GHG emissions (kg CO2eq/kg) – 0.789 0.134

2011–2014 TW-RDI

RW

RW-RDI

TW

TW-RDI

RW

RW-RDI

708.0b 141.6b 849.6b

783.3a 163.0a 946.3a

680.7b 141.6b 822.3b

1509.6a 301.9a 1811.5a

1306.9b 261.4b 1568.3b

1451.3a 301.9a 1753.2a

1256.4b 261.4b 1517.8b

1085.8b

96.7 39.1 39.1 282.4 43.2 500.5 1768.5a

1598.3b

1727.7a

1562.9b

5445.7b 0.076

5630.7a 0.075

5395.3b 0.082

1109.5b

1193.2a

10.1 6.2 15.0 109.8 16.2 238.7 1099.2 1337.9 chemicals 328.6 22.9 25.6 377.1 15.6 15.3 30.9 408.0 1230.9 277.0

3716.7b 0.140

3813.3a 0.149

3689.3b 0.158

123.1 1631.0 5689.0a 0.074

**Letters indicate significant differences between treatments following Tukey's range test (P < 0.05). ⁎ A value shown alone in a line within a crop lifecycle stage means that said value is the same for all the cases studied. Table 6 Cumulative energy and GHG emissions for electricity during the period 2008–2014 for the four cases studied. Energy inputs (MJ/ha)

On-farm electricity Off-farm electricity Total electricity

GHG emissions (kg CO2eq/ha)

TW

TW-RDI

RW

RW-RDI

TW

TW-RDI

RW

RW-RDI

68,427.7a 342,138.6 410,566.3a

59,303.3b 296,516.3 355,819.5b

68,427.7a 328,923.7 397,351.4a

59,303.3b 285,063.5 344,366.8b

1696.56a 8482.78a 10,179.33a

1470.33b 7351.64b 8821.97b

1696.56a 8155.13a 9851.69a

1470.33b 7067.69b 8538.02b

**Letters indicate significant differences between treatments following Tukey's range test (P < 0.05).

GHG emissions) (Table 5; Fig. 3b), whereas the diesel used by machinery for the reservoir excavation, the soil preparation and the crop plantation and its GHG emissions played a secondary role (13.4% of total GHG emissions). It must be recalled that GHG emissions at this stage have also been distributed among all the years of the plantation's lifespan. In general, for the juvenile (2005–2007), young (2008–2010) and adult (2011–2014) crop stages, maximum GHG emissions were associated to nitrogenous fertilisation (33.1%, of which 5.8% corresponds to GHG emissions due to the production, transportation, storage and transfer of nitrogen and the remainder, 27.3%, to emissions of NO2

from soils as a consequence of nitrogen fertiliser application), the irrigation system (mean value of 26.3%), and the electricity for irrigation (mean value of 25.8%) (Fig. 3b). At the juvenile stage, up to two thirds of the total GHG emissions (2741.1 kgCO2/ha) were mainly associated to the irrigation system (40.1%) and the nitrogenous fertilisers (28.0%). At this stage, electricity for irrigation ranked third as trees canopies only partially covered the soil and hence the water supply for irrigation (ETc) was reduced by half (Fereres et al., 1982). At the young and adult stages, total GHG emissions increased up to 3766 ± 75 kgCO2/ha and 5540 ± 141 kgCO2/ha, respectively. At 100

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organic fertilisers and (iii) the determination of the exact crop nutrients needs through periodic soil and water characterisation. The implementation of RDI or the irrigation with RW have had minor effects on the energy ratio, the energy productivity and the specific GHG emissions. Therefore, under the conditions of this study and the expected limitation of conventional water resources availability, the use of RDI and RW are positioned as alternative environmentally-feasible strategies to maintain sustainable agriculture. Special mention should be made of the fact that the regular water source used for irrigation in this study comes from the Tajo Basin, in central Spain, with an average specific energy of 1.02 kW·h/m3. Therefore, computed energy and GHG emissions in other regions with access to surface water, with much lower specific energy, could show RW and RW-RDI case studies as being more unfavourable scenarios from the environmental point of view.

both stages, differences were observed specifically associated to the amount and the type of water resource used for irrigation. Such differences were also observed when the cumulative analysis for GHG emissions between 2008 and 2014 was performed (Table 6). As already mentioned for energy consumption, these results were quite similar to those calculated by Martin-Gorriz et al. (2014) for some lemon, mandarin and orange citrus trees (3737.0 ± 456.3 kgCO2/ha) in southeastern Spain. It is of note that the net ecosystem exchange for an adult citrus orchard can range between 5500 kgCO2/ha and 6500 kgCO2/ha (Liguori et al., 2009; Consoli et al., 2013; Maestre-Valero et al., 2017); a value that is higher than the CO2 emissions calculated in any case studied and hence evidencing the potential sink capacity of citrus orchards. RDI strategies, irrespective of the type of water used, led to GHG emission reductions of 3.3% and 4.3% at the young and adult stages, respectively (Table 5; Fig. 4). As the percentages of reduction in yield for the RDI cases studied (7.1% and 8.7% in the TW-RDI and RW-RDI cases at the young stage, respectively, and 7.1% and 12.4% in the TWRDI and RW-RDI cases at the adult stage, respectively) were higher than those for GHG emissions; specific GHG emissions were slightly higher in the RDI cases (0.140 kgCO2/kg and 0.158 kgCO2/kg for the TW-RDI and RW-RDI cases, respectively at the young stage and 0.076 kgCO2/kg and 0.082 kgCO2/kg for the TW-RDI and RW-RDI cases, respectively, at the adult stage (Table 5). As observed for energy consumption, irrigation with RW had hardly any effect on the GHG emissions as slight variations were only observed in the calculation of the electricity for irrigation (reductions of 3.2% regardless of the crop lifecycle stage). At the young stage, irrigation with RW led to an orchard yield 10.7% lower, whereas areal GHG emissions were only reduced by 0.8%. Accordingly, specific GHG emissions increased up to 0.149 kgCO2/kg; i.e. 11.1% higher than the orchard irrigated with TW (Table 5). At the adult stage, irrigation with RW led to similar percentages of reduction for yield (2.6%) and emissions (0.9%), and hence no effect was detected for specific GHG emissions (about 0.077 kgCO2/kg for all cases studied). It is noteworthy that the older the orchard is, the higher the areal GHG emissions and the lower the specific GHG emissions are.

Acknowledgements This study was supported by IRRIQUAL (EU-FP6-FOOD-CT-2006023120) and SIRRIMED (FP7-KBBE-2009-3-245159) projects. We are also grateful to two SENECA projects (05665/PI/07 and 11872/PI/09) and SENECA-Excelencia Científica (19903/GERM/15), CONSOLIDER INGENIO 2010 (MECCSD2006-0067) and two CICYT projects (AGL2010-17553 and AGL2013-49047-C2-2-R) for providing funds to finance this research. References Alcón, F., Martin-Ortega, J., Pedrero, F., Alarcon, J.J., de Miguel, M., 2013. Incorporating non-market benefits of reclaimed water into cost-benefitanalysis: a case study of irrigated mandarin crops in southern Spain. Water Resour. Manag. 27, 1809–1820. Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration guidelines for computing crop water requirements. In: FAO Irrigation and Drainage Paper 56. Rome, Italy, pp. 15–27. Alluvione, F., Moretti, B., Sacco, D., Grignani, C., 2011. EUE (energy use efficiency) of cropping systems for a sustainable agriculture. Energy 36, 4468–4481. Asgharipour, M.R., Mondani, F., Riahinia, S., 2012. Energy use efficiency and economic analysis of sugar beet production system in Iran: a case study in Khorasan Razavi province. Energy 44, 1078–1084. Batty, J.C., Keller, J., 1980. Energy Requirements for Irrigation. In: Pimentel, D. (Ed.), Handbook of Energy Utilization in Agriculture. CRC Press, Boca Raton, FL, pp. 35–44. BEDCA, 2017. Spanish Food Composition Database. Ministry of Science and Innovation Available at. http://www.bedca.net (Accessed February 2017). Berge, B., 2009. The Ecology of Building Materials. Elsevier, Oxford, UK. Bowers, W., 1992. Agricultural field equipment. In: Fluck, R.C. (Ed.), Energy in World Agriculture. Vol. 6. Elsevier, Amsterdam, pp. 117–129. Bundschuch, J., Chen, G., Mushtaq, S., 2014. Towards a Sustainable Energy Technologies Based Agriculture. In: Sustainable Energy Solutions in Agriculture. CRC Press, The Netherlands, pp. 3–15. CHS, 2015. Estudio General sobre la Demarcación Hidrográfica del Segura [General study on the River Basin District of the Segura]. Confederación Hidrográfica del Segura, Murcia. Claver, J.M., 2016. The Tagus-Segura water transfer: a vital and sustainable Infrastructure for the southeast of Spain. In: Informe técnico Sindicato Central de Regantes Acueducto Tajo Segur. SCRATS (13pp.). Consoli, S., Facini, O., Motisi, A., Nardino, M., Papa, R., Rossi, F., Barbagallo, S., 2013. Carbon balance and energy fluxes of a Mediterranean crop. J. Agric. Eng. XLIV (s2), e6. CREM, 2015. Centro regional de estadística de Murcia. (Accessed February 2017). ELCD, 2017. European Life Cycle Database. European Commission Available from. http://eplca.jrc.ec.europa.eu/ELCD3/index.xhtml?stock=default (Accessed March 17). Encuesta de Superficies y Rendimientos de Cultivos, 2015 (ESYRCE). (Accessed February 2017). ESAMUR, 2017. Entidad de saneamiento y depuración de aguas residuales de la región de Murcia. http://www.esamur.com (Accessed May 2017). Faurès, J.M., Bartley, D., Bazza, M., Burke, J., Hoogeveen, J., Soto, D., Steduto, P., 2013. Climate Smart Agriculture Sourcebook. FAO, Rome, pp. 557. Fereres, E., Martinich, D.A., Aldrich, T.M., Castel, J.R., Schulbach, E.H., 1982. Drip irrigation saves money in young almond orchards. Calif. Agric. 36, 12–13. Fluck, R.C., 1985. Energy sequestered in repairs and maintenance of agricultural machinery. Trans. ASAE 28, 738–744. http://dx.doi.org/10.1016/j.envint.2004.03.005. Fluck, R.C., 1992. Energy in human labor. In: Fluck, R.C. (Ed.), Energy in World Agriculture. Vol. 6. Elsevier, Amsterdam, pp. 31–37. Gunady, M.G.A., Biswas, W., Solah, V.A., James, A.P., 2012. Evaluating the global warming potential of the fresh produce supply chain for strawberries, romaine/cos lettuces (Lactuca sativa), and button mushrooms (Agaricus bisporus) in Western

4. Conclusions and mitigation strategies This study has evaluated, for a grapefruit orchard, the energy consumption and the GHG emissions of implementing regulated deficit irrigation (RDI) strategies and/or irrigating with reclaimed water (RW). The analysis has considered four different lifecycles of the crop; i.e. establishment of the plantation in late 2004, juvenile (unproductive) stage from 2005 to 2007, young productive stage from 2008 to 2010 and adult productive stage from 2011 to 2014. At the orchard establishment stage, the irrigation system was the main energy consumer with the highest greenhouse gas (GHG) emissions. Consequently, recycled materials or those which have a longer lifespan may require less energy consumption and emit less GHG, and thus could be used to reduce the environmental impact. For the rest of the crop lifecycle stages evaluated (juvenile, young and adult stages), electricity for water transport and distribution and nitrogenous fertilisers represent the highest share of energy consumption and GHG emissions. Therefore, in the case of electricity for water transport, efforts must be directed at substituting, as far as possible, non-renewable energy sources with renewable ones; i.e. solar or wind power generated electricity, in order to notably reduce direct energy consumption. It should be noted that the electric energy distributor in this study currently uses 55% of renewable energy (Iberdrola, 2017). In addition, more efficient irrigation management, such as the herein evaluated RDI strategies, needs to be implemented. Then, in the case of fertilisers, an environmental friendly solution must deal with: (i) the adoption of more efficient techniques in fertiliser production, especially that of nitrogenous fertilisers, (ii) the substitution of chemical with 101

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