Next Article in Journal
Microgrid Planning Considering the Supply Adequacy of Critical Loads under the Uncertain Formation of Sub-Microgrids
Next Article in Special Issue
A Developed Framework for the Multi-District Ecological Compensation Standards Integrating Ecosystem Service Zoning in an Urban Area in China
Previous Article in Journal
Bio-Based Production Systems: Why Environmental Assessment Needs to Include Supporting Systems
Previous Article in Special Issue
Inorganic Waste Management in Greenhouse Agriculture in Almeria (SE Spain): Towards a Circular System in Intensive Horticultural Production
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 11
Issue 17
10.3390/su11174681
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Ecological and Economic Sustainability in Olive Groves with Different Irrigation Management and Levels of Erosion: A Case Study

by
Antonio Alberto Rodríguez Sousa
*,
Jesús M. Barandica
and
Alejandro Rescia
Department of Biodiversity, Ecology and Evolution (BEE), Teaching Unit of Ecology (UDECO), Faculty of Biological Sciences, Complutense University of Madrid, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(17), 4681; https://doi.org/10.3390/su11174681
Submission received: 28 July 2019 / Revised: 18 August 2019 / Accepted: 21 August 2019 / Published: 28 August 2019
(This article belongs to the Special Issue Agriculture, Landscape, Ecosystem Services and Biodiversity: New Challenges for Sustainable Development)
Browse Figures
Versions Notes

Abstract

:
In the last 50 years, both the agricultural labour force and irrigated land area have increased almost eightfold in Spain. The main objective of irrigation, in the short term, is to increase agricultural production. However, in the long term, the environmental externalities of irrigation and its direct relationship with soil erosion processes are more uncertain and still poorly studied. In this study, in an olive-growing region of Andalusia, Spain, the variation of several soil parameters related to irrigation and erosion levels was analysed. The results showed that irrigation, while increasing the productive level of the olive groves, entails a progressive alteration of the soil, modifying physical aspects (greater compaction and humidity of the soil together with lower gravel content, porosity and soil weight) and chemical aspects (reduction of the organic matter of the soil and the content of nitrates) that can aggravate the consequences of the erosive processes. In the long term, the productive benefit attributed to irrigation could be unsustainable from an ecological and, consequently, economic point of view. In addition, the lack of sustainability of olive irrigation agroecosystems could be exacerbated by the future restrictive impacts of climate change on water resources in Mediterranean environments. This situation demands spatial planning and alternative management based on soil conservation and rational and efficient forms of irrigation to ensure the sustainability of olive groves and their economic viability.

Graphical Abstract

1. Introduction

Olive grove landscapes form socioecological systems characteristic of Mediterranean environments [1,2]. They are particularly representative in Spain, covering more than 2.5 M ha [3]. In Andalusia, these olive grove landscapes, with an area of 1.5 M ha, form a multifunctional system making an essential contribution of ecosystem services to society, highlighting the production of olives and olive oil. During the last five growing seasons (2012/2013–2016/2017), the region produced an average of 1.19 M t year−1 of olive oil [4,5,6]. In addition, as with many agricultural matrix landscapes with a heterogeneity of habitats and ecotones, olive groves have been shown to serve as reservoirs of biodiversity [7,8].
The enforcement of the Common Agricultural Policy (CAP) in the European Union in 1962 led some traditional farming systems (mountain farming and agroforestry systems) to collapse due to their lack of adaptation to the new regulations (levels and forms of production). These systems were based on the optimisation of the use of natural resources and were adapted to local climatic and geomorphological conditions, but their production levels were relatively low, and they were not competitive with the environmental and sanitary standards established by the CAP or market prices [9]. Faced with this situation, farmers’ decisions showed two opposing trends: abandoning their lands in the face of declining economic benefits or intensifying (i.e., increasing or introducing) the use of synthetic agrochemical herbicides, pesticides or fertilisers; generating higher plant density and adopting technological improvements [10]. Both intensification and abandonment of farming systems can have undesirable environmental and socioeconomic consequences. On the one hand, agricultural intensification improves productivity (increasing economic benefits) but is generally accompanied by environmental damage [11], such as soil erosion [12,13,14] and spatial homogenisation (expansion of monocultures), which affects biodiversity [15]. On the other hand, the abandonment of agricultural activities leads to a total loss of profitability and the degradation of social stability [16], while at the same time causing the accumulation of biomass (scrubbing) fuel by ecological succession (passive restoration), increasing the risk of wildfires [17].
Olive grove agroecosystems have traditionally formed agricultural landscapes [18], as they are adapted to Mediterranean climates where periods of summer drought and water stress are inherent to their dynamics [19]. However, in the last 50 years, the expansion of irrigation to these agroecosystems has increased for merely productive purposes [20,21]. Irrigated olive groves in Andalusia have grown from 5% to about 35% of the total agricultural area [22,23]. Despite the resistance of olive trees to drought, irrigation is important to ensure adequate yields in years with little rainfall in order to reduce the variability of yields from year to year because of the alternate bearing and to increase olive oil production. Despite the high delivery efficiency of some irrigation systems (i.e., drip irrigation), there is high risk of water overuse. This, in turn, could impact the demand for water for human consumption in certain basins associated with large extensions of intensive olive groves and exacerbate soil erosion [24]. Specifically, the average values of soil loss were 19 t ha−1 year−1 in the 1950s but are highly variable at present, ranging from 23 to 184 t ha−1 year−1 [25,26]. To reduce erosion and prevent soil degradation or contamination, various conservation techniques, such as vegetation cover or terracing, are applied. However, a soil water balance adapted to the needs of the farming system is also important and could be achieved by increasing water infiltration, avoiding soil compaction and evaporation. In addition, to achieve this balance, it is essential to apply the appropriate irrigation system.
The impacts of the intensification of management highlight the need for policies/strategies designed ad hoc for olive grove agricultural landscapes with the objective of promoting the adequate conservation of the soil, maximising the viability of these agricultural systems and delaying the advance of erosive processes and their consequences [27,28,29]. Although several factors and management practices other than irrigation can have multiple consequences on the soil environment (i.e., agriculture mechanisation), there are few studies that quantify the consequences of erosion associated with irrigation on the soil and its impact on long-term economic profitability. Considering the restrictions that climate change will impose on the availability of water resources in the near future [30,31], and with the biophysical system serving as the foundation on which any agroecosystem is based [32], it is urgent to study the relationship between erosion, irrigation and profitability in the agricultural landscapes of olive groves. Thus, the objectives of this work, using a case study of an olive-growing region in Andalusia, were (a) to characterise the soils of the studied region considering different erosive levels and olive-growing management systems and (b) to compare, by means of medium- and long-term time projections for different erosive levels, the possible productive and economic consequences for irrigated and rainfed olive groves [33]. The use of time simulation models is a useful and valid tool to explore the uncertainty about the future consequences that may result due to the factors considered in this study (erosion, production and irrigation). Finally, the sustainability of the olive grove landscapes was evaluated based on the soil properties and profitability analysed.

2. Material and Methods

2.1. Study Area

The study area was in the olive-growing region of Estepa, in the province of Seville (Andalusia, southern Spain), corresponding to the Protected Designation of Origin (PDO) of the same name (Figure 1).
This region has 40,000 ha of olive groves along with another 20,000 ha which includes other agricultural land uses and relicts of vegetation [34,35]. The olive groves are located between 200 and 800 masl, under a temperate Mediterranean climate with an average temperature of 17.5 °C and an annual rainfall of 477 mm [36]. The soils of this region have a variable depth, between 30 and 150 cm, with a predominantly limestone substrate of alkaline pH (values between 7.2 and 8.2) [36,37]. Most of the soil texture is silty, with olive groves on Albariza soils and calcareous materials [34]. The olive groves of this region are managed, for the most part, in a certified integrated manner, with a plantation density ranging from 100 to 500 trees ha−1 and allowing the use of chemically synthesised fertilisers regulated by the Technical Control Agencies (ATC). While most olive groves are cultivated under a rainfed regime (around 90%), deficit-type irrigation (only in times of water stress and with an average endowment of 1500 cm3) has been implemented in the remaining 10% [18,36,38].

2.2. Experimental Design and Sample Processing

Stratified sampling was performed according to the erosion levels estimated from the Universal Soil Loss Equation (USLE) and the USLE-Revised USLE (RUSLE) Model (1) [39,40]:
A = R × K × L S × C × P
where A is annual soil losses, R is rain erosivity, K is soil erodibility, LS is the length and grade of the slope of the territory, C is the ground cover and P is agricultural conservation practices.
For the estimation of the erosion levels, the cadastral cartography of the Andalusia Government [41], the Spanish Land Occupancy Information System [42] and bibliographic information were used [43,44]. Specifically, factors R and LS of the USLE-RUSLE model were calibrated from specific bibliographic information for the study area [45,46]. Soil erodibility was calibrated experimentally according to criteria from Gisbert Blanquer et al. [47]. Factor C was calibrated for Estepa according to criteria from Gómez et al. (2003) [27]. Thereby, factor C varies with the type of management depending on tree density (considered low in integrated management), canopy diameter (taking an average value for the integrated olive grove of 2.5 m radius) and with the extent (width) of ground covers (applying partial coverages in the study area). Finally, factor P was considered to be 1 for all erosion situations, as it was assumed that all plots are subject to tillage practices and none are subject to specific mechanical or soil manipulation erosion control practices [45,46], regardless of ground cover as an agronomic measure considered in factor C.
In short, Table 1 shows the parameters of the USLE-RUSLE model adapted and calibrated for the Estepa region according to criteria from Rodríguez Sousa et al. [2]:
Of the total 3828 ha with moderate erosion, 1755.90 ha have deficit irrigation, while of the total 4775 ha with severe erosion, 2190.34 ha are under deficit irrigation [18]. In general, olive groves are rainfed, especially in Andalusia (almost 80%). The expansion of irrigation in recent decades had a greater incidence, in many cases, in those olive groves in more marginal environmental situations (high slopes and eroded or shallow soils). Consequently, in traditional olive groves in flat areas, such as Estepa, rainfed management tended to be maintained, with few exceptions in more restrictive areas. It is therefore uncommon to find olive groves under irrigation in areas with slight or no erosion and, in Estepa, these are not present. Thus, considering four levels of erosion in rainfed and two levels of erosion in irrigated olive groves, six treatments were obtained. Considering accessibility and the public character of the lands with olive groves, four plots were randomly sampled within each erosion level and management type (with or without irrigation), obtaining a sample size of n = 24 plots (Figure 2).
In each sampled olive grove, a transect of 1 km in length was established in which three soil samples were taken equidistantly using a core of fixed weight and volume (112.40 gr and 141.372 cm3, respectively). The soil samples used to calculate the physical–chemical parameters studied were dried at 105 °C for 24 h. Table 2 shows the most relevant parameters, their usefulness and the method of obtaining them.

2.3. Productive and Economic Data of the Olive Grove

From the surveys carried out with the farmers and owners of the sampled olive groves (n = 24), production and economic information was obtained referring to the last two years (2017–2018), considering as data the average of these years. Among other information, data about crop yields (olive oil production) linked to plantation density, selling price of the olive oil, economic incomes with or without subsidies received and production costs were collected.
With the average values from these data, long-term projections (150 years) were made on the productive level and the degree of economic profitability per hectare of an olive grove according to its erosive level and management type (rainfed or irrigated). For this purpose, an experimentally calibrated equation [2,26] was used from the samples carried out and bibliographic sources [36,37], assuming a decrease in productivity over time due to the negative effect of Erosion (2):
P r o d u c t i o n ( t ) = P i × ( c 1 + c 2 × ln ( W j −   E r j × t ) + c 3 × ( ln ( W j −   E r j × t ) ) 2 )
where Production (t) is the production of each management i at time t (t ha−1); c1, c2 and c3 are specific constants of the study area dependent on annual precipitation and soil type, being 0.7388, −0.3471 and 0.0401, respectively; Pi is the initial production (t = 0) of management i (t ha−1); Wj is the weight of soil corresponding to the erosive level j; Erj is the erosion rate proper to the erosive level j; and t is the simulation time.
Personal survey data were used to calibrate the productivity model and make temporary projections of the economic and productive profitability of each type of agricultural management. The following assumptions were made, which were considered permanent throughout the simulated period: (a) the olive grove is currently eligible for CAP subsidies under any type of management [23]; (b) the average production from the extraction of 1 L of olive oil from olives varies annually between 18% and 21% [38], with an average production of 19% being taken as general data for the study region [59]; and (c) it was assumed an average production in the integrated olive grove between 1500 and 4000 kg olives ha−1, along with the abandonment of the farming system when production is below that threshold [22]. Production and profitability data (with or without CAP subsidies) accumulated per hectare were also calculated according to their erosive level and management type.

2.4. Statistical Analysis

To verify the differences of the soil parameters at different erosive levels and the management applied (rainfed or irrigation) in the olive groves, the assumptions of normality and homoscedasticity of the samples were tested using the Shapiro–Wilk and Levene’s tests, respectively. Firstly, the possible existence of collinearity between the multiple parameters collected was tested using a principal component analysis (PCA), and the most relevant parameters to analyse the relationship between erosion levels and irrigation management were selected (see Table 2). Secondly, the possible existence of interactions between the two factors of the study (i.e., management and erosive levels) was tested to prove the existence of possible significant differences for the dependent variables (i.e., soil parameters) between equivalent erosive levels in the management types analysed (i.e., moderate and severe erosion in rainfed and irrigated olive groves) through the application of an ANOVA. For those variables that did not comply with the requirements of normality or homoscedasticity, a nonparametric Kruskal–Wallis test was employed. In addition, following the same methodology, possible differences between the considered erosion levels for each dependent variable were determined. To determine possible differences between the treatments for each dependent variable, a Tukey post hoc test for normal and homoscedastic variables or a Tamhane test for non-normal variables was carried out. The differences in production and profitability (with or without CAP subsidies) accumulated per hectare according to their erosive level and management type were tested applying a mean differences test. All statistical analyses were carried out with RStudio software [60,61], using the car library and the agricolae, dplyr and PMCMRplus packages [62,63], considering a level of significance of α = 0.05 in all analyses.

3. Results

All soil parameters analysed by PCA showed strong collinearity (determining value < 0.001) and all parameters showed a normal distribution and homoscedastic behaviour, except for the soil gravel content (G) and moisture. The interactions between olive-growing management and erosion were highly significant (p < 0.001 ***) for all dependent variables, thus leading to a separate analysis of both factors.

3.1. Soil Characterisation of the Olive Grove

3.1.1. Soil Characteristics and Water Management Regimes Considering Coincident Levels of Erosion

Table 3 shows the main results regarding the descriptive statistics and significant differences for the physical soil parameters analysed in the coincident erosive levels (i.e., moderate and severe) in rainfed and irrigated olive groves in the Estepa region.
Significant differences were found in the gravel content and moisture of the soil. Irrigated olive crops presented greater soil moisture (36.79%) than rainfed crops. In turn, decreases of up to 90.81% in the gravel content and 4.85% in the soil pores were observed in the irrigated olive groves, resulting in a more compacted soil. Irrigation did not significantly affect the finest soil particles and, therefore, their overall texture.
Table 4 shows the results for the chemical soil parameters analysed.
Irrigation significantly influenced the concentration of nitrates in soils, increasing their concentration by 21.72%. At the same time, it resulted in a 19.90% decrease in organic matter but did not significantly affect the other chemical parameters considered.

3.1.2. Soil Characteristics and Water Management Regimes Considering All Levels of Erosion

Table 5 shows the soil physical parameters for the different erosive levels in each management type of the olive groves sampled. There were highly significant differences for each dependent variable (i.e., physical parameters) in at least one of the evaluated treatments (i.e., levels of erosion) (p < 0.001 *** from the ANOVA and Kruskal–Wallis test). For each level evaluated in each variable, the results of the post hoc analysis through the realisation of a homogeneous subset matrix between the sampled treatments (i.e., null, slight, moderate and severe erosion under rainfed management, and moderate and severe erosion under irrigation) are represented. Two levels present different group classifications only when the results of the post hoc test suggested the existence of significant differences (p < 0.05 *).
For the granulometric variables, a decrease in the gravel content of the soil was detected (particles between 2 mm and 6 cm) as the level of erosion increased, with this loss of gravel being more pronounced in irrigated olive groves. In this sense, rainfed plots with severe erosion together with plots with moderate and severe erosion with irrigation formed a single statistical group differentiated from the rest of the plots due to their low gravel content.
The estimated texture, closely linked to the limestone content characteristic of the study area, corresponded to soils of medium-fine texture with good water retention. Thus, the soil of Estepa was predominantly loamy, combined mostly with sands or silts, for all erosion levels and the two types of management, with significant differences between all treatments for sand and silt content. However, the irrigated olive groves showed a higher clay content in cases of moderate erosion but not in those of severe erosion.
Regarding the porosity, in the rainfed olive groves, this parameter decreased by about 12% as the level of erosion increased. In the case of irrigated olive groves, this decrease was even more accentuated: 2.33% and 7.39% in olive agroecosystems with moderate and severe erosion, respectively. As expected, soil moisture was significantly higher in irrigated plots (up to 65.53% more in moderate erosion plots and 53.13% in severe erosion plots). Groups with significant differences in porosity and moisture were established according to different levels of erosion (increasing with the degree of erosion), maximising these differences in irrigated olive groves. Ultimately, in rainfed plots, net losses of soil weight of up to 41.04% were reached, increasing this loss with irrigation; 15.13% in olive groves with moderate erosion and 11.17% with severe erosion. The slight, moderate and severe erosion levels in the rainfed management cases together with the level of severe erosion under irrigation did not show significant differences for the weight of the soil per unit area.
Table 6 shows the chemical parameters, indirectly indicating fertility, enzymatic activity and soil contamination, and classification results obtained by means of a Tukey post hoc test. A decrease in pH was observed (almost 8%) as the degree of erosion increased, while in the irrigated olive groves, slightly lower values were observed in the coinciding erosion levels, that is, moderate and severe erosion (0.24% and 0.63%, respectively). These two erosive levels formed a differentiated group with respect to rainfed management. For the parameter organic matter, lower values were detected in olive groves with high erosion (decreases of up to 62.43%). In irrigated cases, a decrease of 18.11% was detected in olive groves with moderate erosion and 23.74% in those with severe erosion, with significant differences observed for all treatments.
The enzyme phosphatase, which plays an essential role in the mineralisation of organic P and as an indicator of soil enzymatic activity, showed an increase of more than 100% between the lowest and highest levels of erosion. However, irrigation did not influence the presence of this enzyme, which was practically the same for comparable erosive levels. The concentration of nitrates, which are indicators of fertiliser use, increased directly with erosion levels up to almost 75% in rainfed olive groves. On the other hand, irrigation increased nitrate concentration by 25% and almost 30% in moderate and severe erosive levels, respectively.

3.2. Time Projection of Profitability in Rainfed and Irrigated Management

Table 7 shows the production and economic data obtained from surveys of farmers. These data correspond to each of the integrated management types considered: rainfed (n = 16) and deficit irrigation (n = 8). The responses of the farmers indicated that the decision to apply irrigation was related to the objective of increasing crop production, above any other consideration (for example, cost of implantation or related erosion problems).
On average, the production of olive groves is considerably higher under irrigation, with an annual production of 4249.50 kg of olives ha−1 (807.89 L of oil ha−1), compared with 2749.50 kg of olives ha−1 (522.72 L of oil ha−1) produced under rainfed management. In the olive groves with higher production per hectare, the estimated average production per tree is lower than in plots with lower production. This is due to the higher density of plants present in the olive groves of high production per unit area, where the trees are smaller and, therefore, their productive level is lower. In economic terms, the annual benefit (difference between sales revenue and costs) of 1 ha with an irrigated olive grove was 682.65 € ha−1, which increased to 1182.65 € ha−1 when considering CAP subsidies. In the rainfed plots, an annual profit of 922.60 € ha−1 was observed, increasing to 1322.60 € ha−1 when including the CAP subsidies.
Figure 3 shows the time projection of production and benefits per hectare of rainfed and irrigated olive groves, considering their levels of erosion.
In the olive groves with null, slight and moderate erosion, under rainfed management, the production remained relatively constant (with a small decrease with slight and moderate erosion), between 3000 and 2500 kg of olives ha−1 year−1 for the period considered (150 years). In the olive groves with moderate erosion, the production of irrigated olive groves was higher than that of unirrigated olive groves (4250 and 2565 kg of olives ha−1 year−1, respectively), maintaining a slight decline during the simulation time. The olive groves with severe erosion showed better production in irrigated management compared with unirrigated groves (3700 and 2200 kg of olives ha−1 year−1, respectively, in the first year of the period considered). However, in an interval of 100 years for rainfed olive groves and 145 years for irrigated olive groves, those with severe erosion would be abandoned due to a decrease in productivity below 1500 kg of olives ha−1 (following the criterion of Gómez-Calero [22]).
In economic terms, with moderate erosion, the profitability of an olive grove, either rainfed or irrigated, was close to 750 € ha−1. In rainfed olive groves, the value was practically constant around this value during the study period, while in the case of irrigation, it fell from a value of 700 to around 600 € ha−1 in the last year of the study period. With severe erosion, in the olive groves without irrigation, the estimated profitability was 500 € ha−1, while the irrigated olive groves presented a value of profitability of 250 € ha−1. Profitability would disappear at 100 years under rainfed management and 44 years for irrigated olive groves because the operating costs of the latter would exceed the minimum income from sales considered profitable (3263.28 kg of olive ha−1). With slight or no erosion, rainfed olive groves were profitable throughout the study period, with profits of 760 and 900 € ha−1, respectively. The CAP subsidies received by farmers obviously improve their profits but do not prevent the long-term loss of profitability of severely eroded olive groves, which must be abandoned after 90 and 40 years of simulation for rainfed and irrigated olive groves, respectively.
Table 8 shows the production and economic data per hectare accumulated throughout the simulation time. It was observed that soil erosion levels negatively affect production under both types of management in a highly significant way (p < 0.001 ***). In the rainfed olive groves, severe erosion presented production values markedly lower than other levels of erosion. Logically, the decrease in production due to erosion significantly impacted the profitability of olive groves, with this significant relationship increasing in irrigated olive groves despite their increased production due to increased farm costs.

4. Discussion

4.1. Influence of Irrigation on the Soil Characteristics

The irrigation in the studied olive grove showed some soil effects, which were more accentuated in high erosive levels. For example, as in other studies [64,65], a decrease from 21.07% to 9.29% was detected in the soil weight caused by the significant interaction of erosion and irrigation. This soil weight loss associated with the loss of the soil depth (essential fertility aspect) could be mitigated using vegetation cover [66,67,68,69], a very uncommon agricultural practice in Estepa. According to farmers’ responses to the surveys conducted, vegetation cover was present in 54% of the olive groves under a rainfed regime and 33% of the irrigated lands. Another logical and expected effect of irrigation was the increase in soil moisture content. This fact, together with the loss in soil weight, causes greater soil compaction that may hinder root development of plants and increase the speed of circulation of runoff water [70,71,72]. An increase in fine particles in the soil could be a side effect of textural modification by irrigation in our study. A greater presence of silts and clays associated with crop irrigation was detected by Dong et al. [73] and this increase favoured the retention of water, carbon and organic matter. However, in Estepa, the irregular distribution of clays in the different erosive levels could be associated with the type of localised deficit irrigation that would avoid this effect of modifying the texture of the soil [19,36,74,75,76].
Regarding the chemical parameters of soils, erosive levels with irrigation consolidated differentiated groups with respect to rainfed management, except for pH and phosphatase. Soil pH influences the rate of synthesis, release and stability of phosphatase. Despite significant differences in pH values, the soils remained basic, with values below 7, and irrigation only slightly lowered the pH of the soils. In irrigation management, the organic matter content was lower than in rainfed management and the severe erosive level presented a lower percentage of organic matter for both management types. There could be a reciprocal effect, considering that organic matter can act as an erosion mitigating agent [77,78]; that is, irrigation increases organic matter loss, enhancing the erosion effect, and the low concentration of organic matter enhances the erosive effect of irrigation. The enzymatic activity of the soil was, in general terms, similar in irrigated olive groves compared to rainfed ones, although the increase in moisture could have favoured phosphatase activity and survival of the microbiota and microbial activity [53,79]. This was probably not appreciated in our study because the soils were basic.
Zhou et al. (2019) [80] highlighted that fertilisation is an important factor, especially when using fertilisers with N, which influences crop yield and affects the efficient use of water and nutrients. In Estepa, nitrate content, an indicator of N fertilisation, increased directly with erosion levels and its concentration was higher in irrigated olive groves. Numerous studies have shown that irrigation and fertilisation influence nitrate leaching in agricultural ecosystems [81]. According to the results of our study, in agreement with [81], the use of fertilisers should be managed not only considering the amount of fertiliser that is applied but also the irrigation management measures (amount of water, irrigation time and irrigation method). Therefore, irrigation could be as important as fertilisation in leaching water quality, and optimal irrigation combined with optimal fertilisation could reduce the potential environmental risk caused by excessive fertilisation in intensive systems of olive groves. In fact, diffuse pollution is considered one of the main negative environmental externalities of olive groves [1,14]. Regarding erosion, it should be considered that nitrates accumulate mainly in the soil layer of 0–60 cm and, therefore, erosive processes can transport this nutrient along with soil particles over long distances, contaminating other areas farther away [82].

4.2. Influence of Irrigation on the Ecological and Economic Sustainability of the Olive Groves

In Spain, irrigation practices in agriculture make up 19% of the cultivated area and are responsible for 60% of agricultural production and 80% of water consumption [83]. In the socioecological systems of olive groves, rainfed management predominates, with the use of irrigation in these systems being relatively recent [84,85]. Irrigation has been used to achieve greater production and respond to agricultural demand, increasing the benefits for farmers [86]. In addition, irrigation management allows the population to settle in rural areas. In Spain, irrigated agriculture employs almost 8 times the labour per unit area compared with rainfed agriculture [87]. In Estepa and, in general, Andalusian olive groves, a type of localised and deficit irrigation predominates to alleviate the limiting consequences of the water deficit on agricultural production without causing serious environmental damage [88,89]. However, despite these advantages of irrigation (in Estepa, irrigated olive groves showed an increase in production of up to 55%), the environmental externalities on the soil detected in our study should also be considered. This could condition the ecological sustainability of irrigated olive groves in the long term.
Of concern is the degree of misinformation on the part of farmers regarding regulations that have an impact on the long-term ecological sustainability of olive grove plantations. For example, all the farmers surveyed stated that they were not obliged to use plant covers; however, this agricultural practice is compulsory in integrated management (the overwhelming majority of groves in Estepa) according to Royal Decree 1201/2002 [90]. This agricultural practice (soil cover) is even a highly recommended measure in the Andalusian Olive Grove Master Plan [23]. In this sense, it would be relevant to implement and consolidate different scales of active channels of information from the administration to farmers and other involved stakeholders on environmental management practices appropriate to mitigating the erosive processes that condition the sustainability of olive groves over time [28,91].
Clearly, the greater production derived from irrigation is associated with greater economic benefit. However, the results of our study suggest that the medium- and long-term economic sustainability of irrigated olive groves should also be evaluated. According to the surveys carried out, farmers make the decision to irrigate their crops to obtain greater production, assuming, in the short term, a positive linear relationship between the production level and the economic benefits. A more detailed and rigorous evaluation should consider the increase in the costs of olive grove management derived from the maintenance of irrigation (irrigation water pricing and control of water use efficiency) [10] and the lower sale price at source of the olives and olive oil produced under this management [58,59]. The results of the study showed that, despite the higher production of irrigated olive groves, their benefits are similar to those of rainfed olive groves, both in the present and the time projections carried out. In the short term, the decrease in the economic sustainability of irrigated olive groves with severe erosion could be attributed to the need for economic investment in a type of irrigation that increases the efficiency of water resources and avoids aggravating surface erosion by irrigation [21]. In the medium to long term, economic decline could occur due to greater environmental impacts (i.e., diffuse pollution [92] and erosion) that could result from inadequate irrigation (excessive volume of water).

5. Conclusions

The results showed that irrigation in the olive agroecosystems in Estepa considerably increases the level of production immediately and in the short term but negatively affects their ecological and economic sustainability due to the degradation of the different soil parameters studied. The generalised irrigation type in the study area is localised and deficient in nature, with less environmental impact than other types of irrigation, such as sprinkler or blanket irrigation (also called flood or surface irrigation). Despite this, the results showed that, when comparing rainfed and irrigated olive groves with equivalent erosive levels, there were significant differences in some soil characteristics. The irrigated olive groves presented lower gravel content and higher soil moisture, decreasing their soil weight. Irrigation also gave rise to a loss of organic matter and a higher content of nitrates. Although irrigation increases production per hectare, this productive bonus may not be directly correlated with the economic benefits for farmers due to a lower selling price on the market for oils from irrigated olive groves and the cost of water. In this context, the promotion of deficit localised irrigation programmes would contribute to increasing water efficiency and saving, especially in situations of water scarcity, which are very common in Mediterranean olive grove areas.
We believe that the results of this study should be considered with some caution since the data analysed correspond to a single period and the absence of irrigation management in olive groves with null and slight erosion. Despite these limitations, the results show that management decisions must be taken in an integrated manner, considering not only economic–productive factors but also ecological aspects. This means that the notable expansion of irrigation in Andalusia should follow more rigorous guidelines considering the trade-offs that can occur with environmental aspects such as soil erosion. In any case, future lines of research should be oriented towards the optimisation of water yield [78,90], with the aim of improving the efficiency of this resource and increasing the general sustainability of olive groves. Exhaustive research should be carried out on the erosion–productivity relationship [2,26]. In addition, considering the restrictions on water resources anticipated in the coming years due to climate change [93,94,95], measures should be considered to increase rainfed agricultural yields, especially in Mediterranean areas characterised by water stress.

Author Contributions

Conceptualisation, A.A.R.S.; Data curation, A.A.R.S. and A.R.; Formal analysis, A.A.R.S. and J.M.B.; Investigation, A.A.R.S., J.M.B. and A.R.; Methodology, A.A.R.S. and J.M.B.; Project administration, A.R.; Resources, J.M.B. and A.R.; Software, A.A.R.S. and J.M.B.; Supervision, J.M.B. and A.R.; Validation, J.M.B. and A.R.; Visualisation, A.A.R.S. and A.R.; Writing—original draft, A.A.R.S. and A.R.; Writing—review and editing, A.A.R.S., J.M.B. and A.R.

Funding

This research was not funded by any public or private institution.

Acknowledgments

The corresponding author is grateful for the opportunity to do a PhD thanks to a predoctoral contract of researcher in training (UCM-Santander scholarship) granted by the University Complutense of Madrid. Also, the authors would like to thank Moisés Caballero, Secretary of the Estepa Denomination of Origin, and M. Aurora Rodríguez Sousa for her comments and suggestions on the first drafts of the manuscript. Finally, the authors thank a native English speaker for correcting the grammar, punctuation, spelling and overall style of the English.

Conflicts of Interest

The authors declare that they have no conflict of interest.

References

  1. López-Pintor, A.; Salas, E.; Rescia, A. Assessment of Agri-Environmental Externalities in Spanish Socio-Ecological Landscapes of Olive Groves. Sustainability 2018, 10, 2640. [Google Scholar] [CrossRef]
  2. Rodríguez Sousa, A.A.; Barandica, J.M.; Sanz-Cañada, J.; Rescia, A.J. Application of a dynamic model using agronomic and economic data to evaluate the sustainability of the olive grove landscape of Estepa (Andalusia, Spain). Landsc. Ecol. 2019, 34, 1547–1563. [Google Scholar] [CrossRef]
  3. INE. Agriculture, Forestry and Fishing; INE (Instituto Nacional de Estadística/Statistical Spanish Office): Madrid, Spain, 2013; Available online: http://www.ine.es (accessed on 16 January 2019).
  4. INE. Agriculture and Environment; INE (Instituto Nacional de Estadística/Statistical Spanish Office): Madrid, Spain, 2014; Available online: http://www.ine.es (accessed on 17 January 2019).
  5. Sastre, B.; Barbero-Sierra, C.; Bienes, R.; Marques, M.J.; García-Díaz, A. Soil loss in an olive grove in Central Spain under cover crops and tillage treatments, and farmer perceptions. J. Soil Sediment 2017, 17, 873–888. [Google Scholar] [CrossRef]
  6. IOC. Cifras Aceite De Oliva; IOC (International Olive Council): Madrid, Spain, 2019; Available online: http://www.internationaloliveoil.org/ (accessed on 1 April 2019). (In Spanish)
  7. Granado-Díaz, R.; Villanueva, A.J.; Gómez-Limón, J.A.; Rodríguez-Entrena, M. Analysis of heterogeneity in the demand for public goods provided by mountain olive groves in Andalusia. ITEA 2018, 114, 158–182. [Google Scholar]
  8. Pérez, L.P.; Egea, P.; de-Magistris, T. When agrarian multifunctionality matters: Identifying heterogeneity in societal preferences for externalities of marginal olive groves in Aragon, Spain. Land Use Policy 2019, 82, 85–92. [Google Scholar] [CrossRef]
  9. Taguas, E.V.; Gómez, J.A. Vulnerability of olive orchards under the current CAP (Common Agricultural Policy) regulations on soil erosion: A study case in Southern Spain. Land Use Policy 2015, 42, 683–694. [Google Scholar] [CrossRef]
  10. Gómez-Limón, J.A.; Riesgo, L. Irrigation water pricing: Differential impacts on irrigated farms. Agric. Econ. 2004, 31, 47–66. [Google Scholar] [CrossRef]
  11. Parrot, L.; Meyer, W.S. Future landscapes: Managing within complexity. The Ecological Society of America. Front. Ecol. Environ. 2012, 10, 382–389. [Google Scholar] [CrossRef]
  12. Caraveli, H. A comparative analysis on intensification and extensification in Mediterranean agriculture: Dilemmas for LFAs policy. J. Rural Stud. 2000, 16, 231–242. [Google Scholar] [CrossRef]
  13. Allen, H.D.; Randall, R.E.; Amable, G.S.; Devereux, B.J. The impact of changing olive cultivation practices on the ground flora of olive groves in the Messara and Psiloritis regions, Crete, Greece. Land Degrad. Dev. 2006, 17, 249–273. [Google Scholar] [CrossRef]
  14. Martínez, J.R.F.; Zuazo, V.H.D.; Raya, A.M. Environmental impact from mountainous olive orchards under different soil-management systems (SE Spain). Sci. Total Environ. 2006, 358, 46–60. [Google Scholar] [CrossRef] [PubMed]
  15. Nekhay, O.; Arriaza, M.; Guzmán-Álvarez, J.R. Spatial analysis of the suitability of olive plantations for wildlife habitat restoration. Comput. Electron. Agric. 2009, 65, 49–64. [Google Scholar] [CrossRef]
  16. Mann, S.; Wüstemann, H. Multifunctionality and a new focus on externalities. J. Socio-Econ. 2008, 37, 293–307. [Google Scholar] [CrossRef]
  17. Lasanta, T.; González-Hidalgo, J.C.; Vicente-Serrano, S.M.; Sferi, E. Using landscape ecology to evaluate an alternative management scenario in abandoned Mediterranean mountain areas. Landsc. Urban Plan. 2006, 78, 101–114. [Google Scholar] [CrossRef]
  18. Martínez, J.D.S.; Simón, V.J.G.; Jiménez, E.A. El olivar andaluz y sus transformaciones recientes. Estud. Geogr. 2011, 72, 203–229. (In Spanish) [Google Scholar] [CrossRef]
  19. Connor, D.J. Adaptation of olive (Olea europaea L.) to water-limited environments. Aust. J. Agric. Res. 2005, 56, 1181–1189. [Google Scholar] [CrossRef]
  20. Loumou, A.; Giourga, C. Olive groves: “The life and identity of the Mediterranean”. Agric. Hum. Values 2003, 20, 87–95. [Google Scholar] [CrossRef]
  21. Ali, M.H.; Talukder, M.S.U. Increasing water productivity in crop production—A synthesis. Agric. Water Manag. 2008, 95, 1201–1213. [Google Scholar] [CrossRef]
  22. Gómez-Calero, J.A. Olivar Sostenible: Prácticas para una Producción Sostenible de Olivar en Andalucía; Instituto de Agricultura Sostenible, Centro Superior de Investigaciones Científicas: Córdoba, Spain, 2010; Available online: https://www.ias.csic.es/sostenibilidad_olivar/BPA_VF_Jan2010.pdf (accessed on 8 January 2019). (In Spanish)
  23. BOJA. Plan Director del Olivar Andaluz Decreto 103/2015; BOJA (Boletín Oficial de la Junta de Andalucía/Official Regional Government of Andalusia Bulletin): Sevilla, Spain, 2015; Available online: http://www.webcitation.org/77MO1YwQe (accessed on 3 April 2019). (In Spanish)
  24. Gómez-Limón, J.A.; Riesgo, L. Sustainability assessment of olive grove in Andalusia: A methodological proposal. New Medit 2012, 11, 39–49. [Google Scholar]
  25. BOJA. El Pronóstico de la Erosión de Suelos Como Parte del Proceso de Evaluación; BOJA (Boletín Oficial de la Junta de Andalucía/Official Regional Government of Andalusia): Sevilla, Spain, 2002; Available online: http://www.webcitation.org/77MNrCQZD (accessed on 3 April 2019). (In Spanish)
  26. Gómez, J.A.; Infante-Amate, J.; De Molina, M.G.; Vanwalleghem, T.; Taguas, E.V.; Lorite, I. Olive cultivation, its impact on soil erosion and its progression into yield impacts in Southern Spain in the past as a key to a future of increasing climate uncertainty. Agriculture 2014, 4, 170–198. [Google Scholar] [CrossRef]
  27. Gómez, J.A.; Battany, M.; Renschler, C.S.; Fereres, E. Evaluating the impact of soil management on soil loss in olive orchards. Soil Use Manag. 2003, 19, 127–134. [Google Scholar] [CrossRef]
  28. Milgroom, J.; Gómez, J.A.; Soriano, M.A.; Fereres, E. From experimental research to an on-farm tool for participatory monitoring and evaluation: An assessment of soil erosion risk in organic olive orchards. Land Degrad. Dev. 2007, 18, 397–411. [Google Scholar] [CrossRef]
  29. Gómez, J.A.; Sobrinho, T.A.; Giráldez, J.V.; Fereres, E. Soil management effects on runoff, erosion and soil properties in an olive grove of Southern Spain. Soil Tillage Res. 2009, 102, 5–13. [Google Scholar] [CrossRef]
  30. Gómez, J.A.; Rodríguez-Carretero, M.T.; Lorite, I.J.; Fereres, E. Modeling to evaluate and manage climate change effects on water use in Mediterranean olive orchards with respect to cover crops and tillage management. In Practical Applications of Agricultural System Models to Optimize the Use of Limited Water; American Society of Agronomy, Inc.: Wisconsin Madison, WI, USA, 2014; pp. 237–266. Available online: https://doi.org/10.2134/advagricsystmodel5.c10 (accessed on 2 January 2019).
  31. Tanasijevic, L.; Todorovic, M.; Pereira, L.S.; Pizzigalli, C.; Lionello, P. Impacts of climate change on olive crop evapotranspiration and irrigation requirements in the Mediterranean region. Agric. Water Manag. 2014, 144, 54–68. [Google Scholar] [CrossRef]
  32. Figueiredo, T.D.; Almeida, A.; Araújo, J. Edaphic characteristics of olive-tree areas in the Tras-os-Montes Region (Portugal): A map-based approach. Acta Hortic. 2002, 151–154. Available online: http://hdl.handle.net/10198/6483 (accessed on 15 February 2019).
  33. Peterson, G.D.; Cumming, G.S.; Carpenter, S.R. Scenario planning: A tool for conservation in an uncertain world. Conserv. Biol. 2003, 17, 358–366. [Google Scholar] [CrossRef]
  34. Guzmán Álvarez, J.R. Geografía de los Paisajes del Olivar Andaluz; JA (Junta de Andalucía/Official Regional Government of Andalusia); Ministry of Agriculture and Fisheries, Official Regional Government of Andalusia: Sevilla, Spain, 2004; Available online: http://www.webcitation.org/77MQ7Gu6V (accessed on 3 April 2019). (In Spanish)
  35. Rescia, A.J.; Sanz-Cañada, J.; Del Bosque-González, I. A new mechanism based on landscape diversity for funding farmer subsidies. Agron. Sustain. Dev. 2017, 37, 9. [Google Scholar] [CrossRef]
  36. BOJA. Pliego de Condiciones de la Denominación de Origen Protegida Estepa; BOJA (Boletín Oficial de la Junta de Andalucía/Official Regional Government of Andalusia Bulletin); Consejería de Agricultura, Pesca y Desarrollo Rural: Sevilla, Spain, 2016; Available online: http://www.webcitation.org/77MOBd5Gh (accessed on 3 April 2019). (In Spanish)
  37. SEISnet. Datos Andalucía; SEISnet (Sistema Español de Información de Suelos/Spanish Soil Information System): Treviso, Italy, 2019; Available online: http://evenor-tech.com/banco/seisnet/seisnet.htm (accessed on 28 January 2019). (In Spanish)
  38. AEMO. Aproximación a los Costes del Cultivo del Olivo. Cuaderno de Conclusiones del Seminario AEMO; AEMO (Asociación Española de Municipios del Olivo/Spanish Association of Municipalities of Olive groves): Córdoba, Spain, 2012; Available online: http://www.webcitation.org/77MCvuNPx (accessed on 3 April 2019). (In Spanish)
  39. Wischmeier, W.H.; Smith, D.D. A Universal Soil-Loss Equation to Guide Conservation Farm Planning, 1st ed.; International Society of Soil Science: Madison, WI, USA, 1961; pp. 418–425. [Google Scholar]
  40. Diodato, N. Predicting RUSLE (Revised Universal Soil Loss Equation) monthly erosivity index from readily available rainfall data in Mediterranean area. Environmentalist 2006, 26, 63–70. [Google Scholar] [CrossRef]
  41. IECA. Datos Espaciales de Referencia de Andalucía (DERA): G17 Divisiones Administrativas; IECA (Instituto de Estadística y Cartografía de Andalucía/ Institute of Statistics and Cartography of Andalusia): Sevilla, Spain, 2018; Available online: http://www.webcitation.org/77MQd2rHN (accessed on 3 April 2019). (In Spanish)
  42. SIOSE. Plan Nacional para la Observación del Territorio: Sistema de Información Sobre Ocupación del Suelo de España; SIOSE (Sistema de Información sobre Ocupación del Suelo de España/Information System on Land Use in Spain): Madrid, Spain, 2011; Available online: www.siose.es (accessed on 11 January 2019). (In Spanish)
  43. Moreira-Madueño, J.M. Capacidad de Uso y Erosión de Suelos. Una Aproximación a la Evaluación de Tierras en Andalucía; Junta de Andalucía, Agencia del Medio Ambiente: Sevilla, Spain, 1991; Available online: http://www.juntadeandalucia.es/medioambiente/web/Red_informacion_ambiental/productos/Publicaciones/articulos/articulos_pdf/Paralelo.PDF (accessed on 18 January 2019). (In Spanish)
  44. MAPAMA. Mapa de Estados Erosivos (1987–2001); MAPAMA (Ministerio de Agricultura, Alimentación y Medio Ambiente/Ministry of agriculture, Food and Environment): Madrid, Spain, 2017; Available online: http://www.mapama.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/mapas_estados_erosivos.aspx (accessed on 11 January 2019). (In Spanish)
  45. Gómez, J.A.; Giráldez, J.V. Sostenibilidad de la Producción de Olivar en Andalucía, 1st ed.; Consejería de Agricultura y Pesca, Junta de Andalucía: Sevilla, Spain, 2010; p. 472. Available online: http://www.ias.csic.es/sostenibilidad_olivar/Sost_2009/Sostenibilidad_de_la_Producci%F3n_de_Olivar_en_Andaluc%EDa3.pdf (accessed on 12 July 2019). (In Spanish)
  46. Sánchez Escobar, F. Sistemas Complejos: Una Aplicación para el Análisis de los Balances Energéticos y Económicos en el Agrosistema de Olivar de Estepa. Ph.D. Thesis, Universidad de Sevilla, Sevilla, Spain, 2015. [Google Scholar]
  47. Gisbert Blanquer, J.M.; Ibañez Asensio, S.; Moreno Ramón, H. El Factor K de la Ecuación Universal de Pérdidas de Suelo (USLE); Universitat Politècnica de València: València, Spain, 2012; Available online: http://hdl.handle.net/10251/16850(In Spanish). (accessed on 12 July 2019). (In Spanish)
  48. Helson, O.; Beaucour, A.L.; Eslami, J.; Noumowe, A.; Gotteland, P. Physical and mechanical properties of soilcrete mixtures: Soil clay content and formulation parameters. Constr. Build. Mater. 2017, 131, 775–783. [Google Scholar] [CrossRef]
  49. Narayanan, N.; Ramamurthy, K. Structure and properties of aerated concrete: A review. Cem. Concr. Compos. 2000, 22, 321–329. [Google Scholar] [CrossRef]
  50. Bouyoucos, G.J. A recalibration of the hydrometer method for making mechanical analysis of soils. Agron. J. 1951, 43, 434–438. [Google Scholar] [CrossRef]
  51. Stadler, A.; Rudolph, S.; Kupisch, M.; Langensiepen, M.; van der Kruk, J.; Ewert, F. Quantifying the effects of soil variability on crop growth using apparent soil electrical conductivity measurements. Eur. J. Agron. 2015, 64, 8–20. [Google Scholar] [CrossRef]
  52. Laudicina, V.A.; Novara, A.; Barbera, V.; Egli, M.; Badalucco, L. Long-term tillage and cropping system effects on chemical and biochemical characteristics of soil organic matter in a Mediterranean semiarid environment. Land Degrad. Dev. 2015, 26, 45–53. [Google Scholar] [CrossRef]
  53. Acosta-Martínez, V.; Zobeck, T.M.; Gill, T.E.; Kennedy, A.C. Enzyme activities and microbial community structure in semiarid agricultural soils. Boil. Fertil. Soils 2003, 38, 216–227. [Google Scholar] [CrossRef]
  54. Adetunji, A.T.; Lewu, F.B.; Mulidzi, R.; Ncube, B. The biological activities of β-glucosidase, phosphatase and urease as soil quality indicators: A review. J. Soil Sci. Plant Nutr. 2017, 17, 794–807. [Google Scholar] [CrossRef]
  55. Cuoco, E.; Darrah, T.H.; Buono, G.; Verrengia, G.; De Francesco, S.; Eymold, W.K.; Tedesco, D. Inorganic contaminants from diffuse pollution in shallow groundwater of the Campanian Plain (Southern Italy). Implications for geochemical survey. Environ. Monit. Assess. 2015, 187, 46. [Google Scholar] [CrossRef] [PubMed]
  56. Varekar, V.; Karmakar, S.; Jha, R.; Ghosh, N.C. Design of sampling locations for river water quality monitoring considering seasonal variation of point and diffuse pollution loads. Environ. Monit. Assess. 2015, 187, 376. [Google Scholar] [CrossRef] [PubMed]
  57. Duan, X.W.; Yun, X.; Feng, Y.J.; Yin, S.Q. Study on the method of soil productivity assessment in black soil region of Northeast China. Agric. Sci. China 2009, 8, 472–481. [Google Scholar] [CrossRef]
  58. Vaezi, A.R.; Hasanzadeh, H.; Cerdà, A. Developing an erodibility triangle for soil textures in semi-arid regions, NW Iran. Catena 2016, 142, 221–232. [Google Scholar] [CrossRef]
  59. Caballero, M.; (Denominación de Origen Protegida Estepa, Sevilla, Andalucía, Spain). Personal Communication, 2018.
  60. González, C.G.; Lise, A.V.; Felpeto, A.B. Tratamiento de Datos con R, Statistica y SPSS, 1st ed.; Ediciones Díaz de Santos: Madrid, Spain, 2013; pp. 217–415. (In Spanish) [Google Scholar]
  61. RStudio. Open Source and Enterprise-Ready Professional Software for R; RStudio Version 0.98.1102; RStudio Inc.: Boston, MA, USA, 2009; Available online: https://www.rstudio.com/products/RStudio/ (accessed on 25 February 2019).
  62. Lawson, J. Design and Analysis of Experiments with R, 1st ed.; Chapman and Hall/CRC: New York, NY, USA, 2014; pp. 65–150. [Google Scholar]
  63. Gandrud, C. Reproducible Research with R and R Studio, 2nd ed.; Chapman and Hall/CRC: New York, NY, USA, 2016; pp. 29–78. [Google Scholar]
  64. Metzidakis, I.; Martinez-Vilela, A.; Nieto, G.C.; Basso, B. Intensive olive orchards on sloping land: Good water and pest management are essential. J. Environ. Manag. 2008, 89, 120–128. [Google Scholar] [CrossRef] [PubMed]
  65. Nunes, J.P.; Bernard-Jannin, L.; Rodríguez Blanco, M.L.; Santos, J.M.; Coelho, C.D.O.A.; Keizer, J.J. Hydrological and erosion processes in terraced fields: Observations from a humid Mediterranean region in Northern Portugal. Land Degrad. Dev. 2018, 29, 596–606. [Google Scholar] [CrossRef]
  66. Pleguezuelo, C.R.; Zuazo, V.D.; Fernandez, J.M.; Peinado, F.M.; Tarifa, D.F. Litter decomposition and nitrogen release in a sloping Mediterranean subtropical agroecosystem on the coast of Granada (SE, Spain): Effects of floristic and topographic alteration on the slope. Agric. Ecosyst. Environ. 2009, 134, 79–88. [Google Scholar] [CrossRef]
  67. Kosmas, C.; Danalatos, N.; Cammeraat, L.H.; Chabart, M.; Diamantopoulos, J.; Farand, R.; Gutiérrez, L.; Jacob, A.; Marques, H.; Martínez-Fernández, J.; et al. The effect of land use on runoff and soil erosion rates under Mediterranean conditions. Catena 1997, 29, 45–59. [Google Scholar] [CrossRef]
  68. Kairis, O.; Karavitis, C.; Kounalaki, A.; Salvati, L.; Kosmas, C. The effect of land management practices on soil erosion and land desertification in an olive grove. Soil Use Manag. 2013, 29, 597–606. [Google Scholar] [CrossRef]
  69. Panagos, P.; Borrelli, P.; Meusburger, K.; Alewell, C.; Lugato, E.; Montanarella, L. Estimating the soil erosion cover-management factor at the European scale. Land Use Policy 2015, 48, 38–50. [Google Scholar] [CrossRef]
  70. Raper, R.L. Agricultural traffic impacts on soil. J. Terramech. 2005, 42, 259–280. [Google Scholar] [CrossRef]
  71. García-Zamorano, F.; Ruíz-Coleto, F.; Cano-Rodríguez, J.; Pérez-García, J.; Molina de la Rosa, J. Suelo, Riego, Nutrición y Medio Ambiente del Olivar; Junta de Andalucía, Consejería de Agricultura y Pesca: Sevilla, Spain, 2010; p. 192. [Google Scholar]
  72. Kumar, A. Evaluation of Soil Compaction as Affected by Different Tillage Practices. Ph.D. Thesis, Punjab Agricultural University, Ludhiana, India, 2017. [Google Scholar]
  73. Dong, L.; Zhang, H.; Wang, L.; Yu, D.; Yang, F.; Shi, X.; Saleem, H.; Akhtar, M.S. Irrigation with sediment-laden river water affects the soil texture and composition of organic matter fractions in arid and semi-arid areas of Northwest China. Geoderma 2018, 328, 10–19. [Google Scholar] [CrossRef]
  74. Tovar, M.J.; Romero, M.P.; Alegre, S.; Girona, J.; Motilva, M.J. Composition and organoleptic characteristics of oil from Arbequina olive (Olea Europaea L) trees under deficit irrigation. J. Sci. Food Agric. 2002, 82, 1755–1763. [Google Scholar] [CrossRef]
  75. Melgar, J.C.; Mohamed, Y.; Navarro, C.; Parra, M.A.; Benlloch, M.; Fernandez-Escobar, R. Long-term growth and yield responses of olive trees to different irrigation regimes. Agric. Water Manag. 2008, 95, 968–972. [Google Scholar] [CrossRef]
  76. Ribeiro, H.; Abreu, I.; Cunha, M. Olive crop-yield forecasting based on airborne pollen in a region where the olive groves acreage and crop system changed drastically. Aerobiologia 2017, 33, 473–480. [Google Scholar] [CrossRef]
  77. Lal, R. Soil erosion and carbon dynamics. Soil Tillage Res. 2005, 81, 137–142. [Google Scholar] [CrossRef]
  78. Zuazo, V.H.D.; Pleguezuelo, C.R.R. Soil-Erosion and Runoff Prevention by Plant Covers: A Review. Agron. Sustain. Dev. 2009, 28, 65–86. [Google Scholar] [CrossRef]
  79. López-Piñeiro, A.; Albarrán, A.; Nunes, J.R.; Peña, D.; Cabrera, D. Long-term impacts of de-oiled two-phase olive mill waste on soil chemical properties, enzyme activities and productivity in an olive grove. Soil Tillage Res. 2011, 114, 175–182. [Google Scholar] [CrossRef]
  80. Zhou, H.; Niu, X.; Yan, H.; Zhao, N.; Zhang, F.; Wu, L.; Yin, D.; Kjelgren, R. Interactive Effects of Water and Fertilizer on Yield, Soil Water and Nitrate Dynamics of Young Apple Tree in Semiarid Region of Northwest China. Agronomy 2019, 9, 360. [Google Scholar] [CrossRef]
  81. Li, Y.; Li, J.; Gao, L.; Tian, Y. Irrigation has more influence than fertilization on leaching water quality and the potential environmental risk in excessively fertilized vegetable soils. PLoS ONE 2018, 13, e0204570. [Google Scholar] [CrossRef] [PubMed]
  82. Yang, R.; Wang, X. Effects of nitrogen fertilizer and irrigation rate on nitrate present in the profile of a sandy farmland in Northwest China. Procedia Environ. Sci. 2011, 11, 726–732. [Google Scholar] [CrossRef] [Green Version]
  83. Gómez-Limón, J.A.; Berbel, J. Multicriteria analysis of derived water demand functions: a Spanish case study. Agric. Syst. 2000, 63, 49–72. [Google Scholar] [CrossRef]
  84. Moreno, B.; Garcia-Rodriguez, S.; Cañizares, R.; Castro, J.; Benitez, E. Rainfed olive farming in south-eastern Spain: Long-term effect of soil management on biological indicators of soil quality. Agric. Ecosyst. Environ. 2009, 131, 333–339. [Google Scholar] [CrossRef]
  85. Soriano, M.-A.; Alvarez, S.; Landa, B.B.; Gomez, J.A. Soil properties in organic olive orchards following different weed management in a rolling landscape of Andalusia, Spain. Renew. Agric. Food Syst. 2012, 29, 83–91. [Google Scholar] [CrossRef]
  86. Tilman, D.; Cassman, K.G.; Matson, P.A.; Naylor, R.; Polasky, S. Agricultural sustainability and intensive production practices. Nature 2002, 418, 671. [Google Scholar] [CrossRef]
  87. Berbel, J.; Gómez-Limón, J.A. The impact of water-pricing policy in Spain: an analysis of three irrigated areas. Agric. Water Manag. 2000, 43, 219–238. [Google Scholar] [CrossRef]
  88. Hernandez-Santana, V.; Fernández, J.; Cuevas, M.; Perez-Martin, A.; Diaz-Espejo, A. Photosynthetic limitations by water deficit: Effect on fruit and olive oil yield, leaf area and trunk diameter and its potential use to control vegetative growth of super-high density olive orchards. Agric. Water Manag. 2017, 184, 9–18. [Google Scholar] [CrossRef] [Green Version]
  89. Parra-López, C.; Calatrava-Requena, J.; De-Haro-Giménez, T. A multi-criteria evaluation of the environmental performances of conventional, organic and integrated olive-growing systems in the south of Spain based on experts’ knowledge. Renew. Agric. Food Syst. 2007, 22, 189–203. [Google Scholar] [CrossRef]
  90. BOE. Real Decreto 1201/2002, de 20 de Noviembre, por el que se Regula la Producción Integrada de Productos Agrícolas; BOE (Boletín Oficial del Estado/State Official Bulletin): Madrid, Spain, 2002. (In Spanish) [Google Scholar]
  91. Guzmán, G.I.; López, D.; Román, L.; Alonso, A.M. Participatory action research in agroecology: Building local organic food networks in Spain. Agroecol. Sustain. Food Syst. 2013, 37, 127–146. [Google Scholar] [CrossRef]
  92. Ramankutty, N.; Mehrabi, Z.; Waha, K.; Jarvis, L.; Kremen, C.; Herrero, M.; Rieseberg, L.H. Trends in Global Agricultural Land Use: Implications for Environmental Health and Food Security. Annu. Rev. Plant Boil. 2018, 69, 789–815. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  93. White, K.D.; Vaddey, S.V.; Hamlet, A.F.; Cohen, S.; Neilsen, D.; Taylor, W. Integrating climate impacts in water resource planning and management. In Proceedings of the 13th International Conference on Cold Regions Engineering, Orono, ME, USA, 23–26 July 2006. [Google Scholar] [CrossRef]
  94. Elliott, J.; Deryng, D.; Müller, C.; Frieler, K.; Konzmann, M.; Gerten, D.; Glotter, M.; Flörke, M.; Wada, Y.; Best, N.; et al. Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proc. Natl. Acad. Sci. USA 2014, 111, 3239–3244. [Google Scholar] [CrossRef] [PubMed]
  95. Schewe, J.; Heinke, J.; Gerten, D.; Haddeland, I.; Arnell, N.W.; Clark, D.B.; Dankers, R.; Eisner, S.; Fekete, B.M.; Colón-González, F.J.; et al. Multimodel assessment of water scarcity under climate change. Proc. Natl. Acad. Sci. USA 2014, 111, 3245–3250. [Google Scholar] [CrossRef] [PubMed]
Sustainability 11 04681 g001 550
Figure 1. Geographical location of the region of Estepa in Andalusia, province of Seville, Spain, Europe. The images correspond to the different integrated olive grove management approaches in the study area. The maps were georeferenced according to the Universal Transverse Mercator (UTM) system, under the geodesic datum ETRS89 (European Terrestrial Reference System 1989), specifically projecting the 30S grid, in which the study area is framed.
Figure 1. Geographical location of the region of Estepa in Andalusia, province of Seville, Spain, Europe. The images correspond to the different integrated olive grove management approaches in the study area. The maps were georeferenced according to the Universal Transverse Mercator (UTM) system, under the geodesic datum ETRS89 (European Terrestrial Reference System 1989), specifically projecting the 30S grid, in which the study area is framed.
Sustainability 11 04681 g001
Sustainability 11 04681 g002 550
Figure 2. Map of the region of Estepa, which shows the olive-growing territory classified according to its erosion and rainfed or deficit irrigation regimes. Sampling points are also shown on olive groves belonging to the different erosive levels (null, slight, moderate and severe).
Figure 2. Map of the region of Estepa, which shows the olive-growing territory classified according to its erosion and rainfed or deficit irrigation regimes. Sampling points are also shown on olive groves belonging to the different erosive levels (null, slight, moderate and severe).
Sustainability 11 04681 g002
Sustainability 11 04681 g003 550
Figure 3. Production per hectare for each erosive level (a), in rainfed (a1) and irrigated (a2) management, and benefits without and with CAP subsidies (b and c, respectively) for each hectare of each erosive level in rainfed (b1,c1) and irrigated (b2,c2) management.
Figure 3. Production per hectare for each erosive level (a), in rainfed (a1) and irrigated (a2) management, and benefits without and with CAP subsidies (b and c, respectively) for each hectare of each erosive level in rainfed (b1,c1) and irrigated (b2,c2) management.
Sustainability 11 04681 g003
Table
Table 1. Classification of the olive groves of Estepa in erosive levels according to the Universal Soil Loss Equation-Revised Universal Soil Loss Equation (USLE-RUSLE) model. The factor LS is also expressed as a percentage; C and P are dimensionless.
Table 1. Classification of the olive groves of Estepa in erosive levels according to the Universal Soil Loss Equation-Revised Universal Soil Loss Equation (USLE-RUSLE) model. The factor LS is also expressed as a percentage; C and P are dimensionless.
Erosive LevelOlive Grove Area in ha (%)FactorsA (t ha−1 y−1)
RKLSCP
(J ha−1)(Mg J−1)
Null22,494 (57.00)109.70.820.00 (0%)0.161
Slight8366 (21.20)109.70.890.18 (3%)0.1612.81
Moderate3828 (9.70)109.70.560.70 (7%)0.1616.88
Severe4775 (12.10)109.70.952.20 (15%)0.16136.68
Table
Table 2. Soil physical–chemical parameters analysed, indicating corresponding units, abbreviations, their usefulness as indicators and the procedure followed to calculate them (equations and methodology).
Table 2. Soil physical–chemical parameters analysed, indicating corresponding units, abbreviations, their usefulness as indicators and the procedure followed to calculate them (equations and methodology).
Parameters (units)AbbreviationsUsefulnessEquations and Methodology
Gravel (%)GSoil structural indicators Sample sieving [48,49]
Sands (%)SandsSoil textural indicators. USDA criteria (sands: 2 mm to 50 μm; silts: 50–2 μm; clays: <2 μm)Bouyoucos densitometer [48,50]
Silts (%)Silts
Clays (%)Clays
Porosity (%)PorosityTotal amount of soil pores Volume   sample − Volume   dry   soil = Volume   pores ; %   Porosity = ( Volume   pores × V o l u m e   s a m p l e − 1 ) × 100 [49]
Moisture (%)MoistureAmount of soil pores with water (Porosity − Moisture = Aeration) Weight   sample + Water − Weight   dry   soil = Weight   water ;   %   Moisture = ( Weight   water × W e i g h t   s a m p l e + W a t e r − 1 ) × 100 [49]
Soil weight (t ha−1)WAmount of soil per unit area W e i g h t = 100 × h e i g h t × A p p a r e n t   d e n s i t y [43]
pH (---)pHIndicator of acidity or basicity of soilDirect estimation [51]
Organic matter (%)OMSoil fertility indicator O M = 1.724   [ C a r b o n ] ; indirect calculation through the estimation, by colourimetry, of edaphic carbon (%) [52]
Phosphatase (µmol p-nitrophenol gr−1 h−1)PhosphataseTransformation from organic phosphorus to phosphates (primary plant nutrient)Colourimetry [53,54]
Nitrates (ppm)NitratesDiffuse soil contamination indicatorColourimetry [55,56,57]
Texture (---)TextureSoil textural classificationTextures triangle [58]
Table
Table 3. Mean values (x) and standard deviation (SD) of the physical soil parameters measured in the olive groves sampled for the different water management regimes, also including the estimation of soil texture. p-values (p < 0.05 *: significant value; p < 0.01 **: very significant value; p < 0.001 ***: highly significant value). G: gravel; W: soil weight.
Table 3. Mean values (x) and standard deviation (SD) of the physical soil parameters measured in the olive groves sampled for the different water management regimes, also including the estimation of soil texture. p-values (p < 0.05 *: significant value; p < 0.01 **: very significant value; p < 0.001 ***: highly significant value). G: gravel; W: soil weight.
ManagementRainfedIrrigationp-Value
x ± SDx ± SD
G (%)1.96 ± 1.870.18 ± 0.180.030 *
Sands (%)55.85 ± 13.4261.67 ± 4.120.261
Silts (%)25.74 ± 6.1722.57 ± 5.710.304
Clays (%)18.40 ± 7.2515.76 ± 1.600.332
Porosity (%)59.93 ± 3.6857.02 ± 1.710.085
Moisture (%)23.83 ± 6.5437.70 ± 4.78<0.001 ***
W (t ha−1)11,207.14 ± 2133.969695.78 ± 1580.050.130
Texturesandy loamsandy loam---
Table
Table 4. Mean values (x) and standard deviation (SD) of the soil chemical parameters measured in the olive groves sampled for the different water management regimes. p-values (p < 0.05 *: significant value; p < 0.01 **: very significant value; p < 0.001 ***: highly significant value). OM: organic matter.
Table 4. Mean values (x) and standard deviation (SD) of the soil chemical parameters measured in the olive groves sampled for the different water management regimes. p-values (p < 0.05 *: significant value; p < 0.01 **: very significant value; p < 0.001 ***: highly significant value). OM: organic matter.
ManagementRainfedIrrigationp-Value
x ± SDx ± SD
pH8.04 ± 0.158.00 ± 0.170.671
OM (%)1.96 ± 0.611.57 ± 0.550.196
Phosphatase (µmol p-nitrophenol gr−1 h−1)0.47 ± 0.070.46 ± 0.070.890
Nitrates (ppm)4.72 ± 0.306.03 ± 0.51<0.001 ***
Table
Table 5. Mean values (x) and standard deviation (SD) of the physical soil parameters measured in the olive groves sampled for the different levels of erosion and water management regimes, also including the estimation of soil texture. The superindexes, a–f, indicate the classification groups generated in the post hoc tests to establish similar categories. G: gravel; W: soil weight.
Table 5. Mean values (x) and standard deviation (SD) of the physical soil parameters measured in the olive groves sampled for the different levels of erosion and water management regimes, also including the estimation of soil texture. The superindexes, a–f, indicate the classification groups generated in the post hoc tests to establish similar categories. G: gravel; W: soil weight.
ManagementRainfedIrrigation
Erosive LevelNull (x ± SD)Slight (x ± SD)Moderate (x ± SD)Severe (x ± SD)Moderate (x ± SD)Severe (x ± SD)
G (%)11.28 ± 0.42 a7.25 ± 0.04 b3.71 ± 0.06 c0.22 ± 0.01 d0.36 ± 0.02 d0.00 ± 0.00 d
Sands (%)36.18 ± 0.19 a61.51 ± 0.09 b43.29 ± 0.08 c68.41 ± 0.05 d65.52 ± 0.11 e57.82 ± 0.06 f
Silts (%)52.41 ± 0.02 a24.05 ± 0.10 b31.52 ± 0.03 c19.97 ± 0.02 d17.23 ± 0.01 e27.91 ± 0.04 f
Clays (%)11.41 ± 0.17 a14.44 ± 0.01 b25.18 ± 0.09 c11.62 ± 0.07 a17.25 ± 0.12 d14.27 ± 0.02 b
Porosity (%)68.33 ± 0.77 a62.58 ± 0.03 b60.04 ± 0.81 c59.83 ± 0.06 d58.64 ± 0.01 e55.41 ± 0.06 f
Moisture (%)35.10 ± 0.35 a31.03 ± 0.02 b25.30 ± 0.06 c22.38 ± 0.01 d41.12 ± 0.26 e34.27 ± 0.04 f
W (t ha−1)15,728.70 ± 541.60 a13,921.60 ± 112.75 b13,140.00 ± 315.71 b9272.80 ± 266.18 c11,151.78 ± 150.46 d8236.80 ± 136.81 c
Texturesilty loamsandy loamloamsandy loamsandy loamsandy loam
Table
Table 6. Mean values (x) and standard deviation (SD) of the soil chemical parameters measured in the olive groves sampled for the different levels of erosion and water management regimes. The superindexes, a–f, indicate the classification groups generated in the post hoc tests to establish similar categories. OM: organic matter.
Table 6. Mean values (x) and standard deviation (SD) of the soil chemical parameters measured in the olive groves sampled for the different levels of erosion and water management regimes. The superindexes, a–f, indicate the classification groups generated in the post hoc tests to establish similar categories. OM: organic matter.
ManagementRainfedIrrigation
Erosive LevelNull (x ± SD)Slight (x ± SD)Moderate (x ± SD)Severe (x ± SD)Moderate (x ± SD)Severe (x ± SD)
pH8.55 ± 0.03 a8.27 ± 0.01 b8.18 ± 0.01 c7.90 ± 0.02 d8.16 ± 0.02 c7.85 ± 0.01 d
OM (%)3.70 ± 0.08 a2.90 ± 0.02 b2.54 ± 0.02 c1.39 ± 0.01 d2.08 ± 0.02 e1.06 ± 0.03 f
Phosphatase (µmol p-nitrophenol gr−1 h−1)0.26 ± 0.01 a0.46 ± 0.02 b0.41 ± 0.01 b0.53 ± 0.02 c0.40 ± 0.01 b0.53 ± 0.01 c
Nitrates (ppm)2.85 ± 0.14 a3.88 ± 0.15 b4.48 ± 0.12 c4.97 ± 0.02 c5.61 ± 0.15 d6.46 ± 0.11 e
Table
Table 7. Economic and production data for rainfed and irrigated olive groves. PlantD (plantation density, trees ha−1); production (kg olive ha−1); tree production (kg olive tree−1); selling price (€ kg olive); CAP (environmental subsidy received from the European Union through the Common Agricultural Policy, € ha−1 year−1) and costs (operating costs, including machinery, personnel and application of phytosanitary products, € ha−1 year−1).
Table 7. Economic and production data for rainfed and irrigated olive groves. PlantD (plantation density, trees ha−1); production (kg olive ha−1); tree production (kg olive tree−1); selling price (€ kg olive); CAP (environmental subsidy received from the European Union through the Common Agricultural Policy, € ha−1 year−1) and costs (operating costs, including machinery, personnel and application of phytosanitary products, € ha−1 year−1).
ManagementRainfedIrrigation
MaximumMinimumMeanMaximumMinimumMean
PlantD199100150.00499200350.00
Production349920002749.50449940004249.50
Tree production17.582018.339.012012.14
Selling price0.890.700.790.790.600.69
CAP449350400.00549450500.00
Costs149910001249.50249920002249.50
Table
Table 8. Production (kg olive ha−1) and economic profitability (Bnf, € ha−1) of the different types of management and levels of erosion for the simulation period considered (150 years). Without or with CAP (Common Agricultural Policy) refers to receiving or not receiving the environmental subsidies covered by this European policy.
Table 8. Production (kg olive ha−1) and economic profitability (Bnf, € ha−1) of the different types of management and levels of erosion for the simulation period considered (150 years). Without or with CAP (Common Agricultural Policy) refers to receiving or not receiving the environmental subsidies covered by this European policy.
ManagementRainfedIrrigation
Erosive levelsNullSlightModerateSevereModerateSevere
Production412,000392,000381,000192,000631,000406,000
Bnf (without CAP)138,000122,000113,00025,90097,7006780
Bnf (with CAP)198,000182,000173,00063,100173,00028,800

Share and Cite

MDPI and ACS Style

Rodríguez Sousa, A.A.; Barandica, J.M.; Rescia, A. Ecological and Economic Sustainability in Olive Groves with Different Irrigation Management and Levels of Erosion: A Case Study. Sustainability 2019, 11, 4681. https://doi.org/10.3390/su11174681

AMA Style

Rodríguez Sousa AA, Barandica JM, Rescia A. Ecological and Economic Sustainability in Olive Groves with Different Irrigation Management and Levels of Erosion: A Case Study. Sustainability. 2019; 11(17):4681. https://doi.org/10.3390/su11174681

Chicago/Turabian Style

Rodríguez Sousa, Antonio Alberto, Jesús M. Barandica, and Alejandro Rescia. 2019. "Ecological and Economic Sustainability in Olive Groves with Different Irrigation Management and Levels of Erosion: A Case Study" Sustainability 11, no. 17: 4681. https://doi.org/10.3390/su11174681

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop