2. Agricultural sector in Tunisia

decreasing trends of rainfall with 10–20% across North Africa [4], with average median decrease reaching 12% [5]. For Tunisia, this rainfall trend will result in a decline of water availability with up to 28% in 2030 [6, 7]. Ref. [1] also reports that water management policies can exacerbate the adverse growth impacts of CC, while good policies can go a long way toward neutralizing them. While CC is one of the major challenges facing humanity nowadays, adaptation frameworks to its, reversible and irreversible, impacts on the natural and human systems have emerged as an urgent need. It is expected to intensify risks related to natural resources availability, particularly in areas where water scarcity is already a concern [8]. In most countries, freshwater scarcity is increasing, forest fires are more frequent because of high temperature, drought is omnipresent and persistent, and desertification rates are growing [9]. Previous reports and analysis have described the Mediterranean region as a CC "hot spot" [10] including the Intergovernmental Panel on Climate Change (IPCC). Agriculture is a climate-sensitive sector subject not only to adverse impacts of CC on natural resources but also on social and economic contexts. Changes in precipitation and warming patterns are witnessed having occurred during the last century [11]. All year round widespread warming and reduction in rainfall are predicted by scientific literature for the twenty-first century [10]. Reduction in precipitation in addition to an increase in evapotranspiration would lead to water shortages particularly in regions where resources are already at a critical level and irrigated cropping areas are increasing. CC is thus contributing to narrowing the gap between water supply and demand [12] which entails more complexity on water resources management in agriculture [8]. CC is reshaping not only agriculture activ-

Agricultural Economics - Current Issues

ity patterns but also driving human existence standards, which requires a

water scarcity, engendered by CC, on the agricultural sector in the country.

32

terms of land use and irrigation in Tunisia to deal with future water scarcity. Structural change in agriculture is defined as being the adjustment of the agricultural sector to the changing conditions of demand and supply [21]. This complex and dynamic process constitutes a reallocation of land use and farm specialization,

In this chapter, we suggest to look to strategic structural adjustments needed in

restructuration of an institutional framework and a policy plan that could be able to mitigate and adapt to CC impacts. Therefore, exploring adaptive pathways [13] and climate policy is becoming a cross-scale central focus for decision and policy makers [14]. Ref. [15] demonstrated the role of regional, national, and global policies and institutions in highlighting adaptation options and tools [16] and that the development of CC adaptation as a policy field is considered as a relevant application context for the establishment of the agriculture policy [17]. In order to assess the implications of potential policy actions and to assist stakeholders in developing adequate measures to improve resilience to CC, [17] prevailed that cost-benefit analysis is a useful assessment tool; bio-economic models are more useful for an ex-ante evaluation of policy interventions by simulating agents' (farmers') behavior on the farm level. However, analyzing CC impacts on agriculture (economic, social, and environmental) requires an approach that is able to provide a detailed picture of the sector, its constituents, and the interactions within it. Agricultural models, can be built on micro-level; bio-economic models or macro-level; studies entailing the whole agricultural sector such as agricultural supply models. Agricultural supply model (ASM) provides a presentation of the agricultural sector by a sequence of behavioral equations whose objective is to maximize regional income subject to technological, environmental, and institutional constraints [17–19]. They treat a wide range of issues in agriculture; ASM has been used to predict and assess the impacts of Europe's Common Agricultural Policy (CAP) or to estimate economic value of water and land [20]. Assessing CC impacts on Tunisian agricultural sector is a propitious research field; hence, by means of an agricultural supply model, it is possible to assess the impact of

Agriculture is an important sector in Tunisia contributing to 8.7% of the national GDP and employing around 16.2% of the total employment in the country [24]. Major crops, in terms of cultivated area, are tree crops (especially olives and dates) followed by cereals. While tree crops are strategic for exports (Tunisia is among the top 5 world exporters of olive oil and dates), cereals remain very important for human and livestock domestic consumption. Tunisia is also characterized by low rainfall and limited renewable water resources. It is influenced by the arid and semiarid climate that covers more than three-fourths of its area [25]. The agricultural sector is also highly dependent on water resources since it consumes more than 75% of total water use in the country [26, 27]. Climate variability has major effects on agricultural production in Tunisia which results on highly variable yields along years. Other sectors might also be affected but certainly with much less extent. In fact, according to the Tunisian regulation, urban, industrial, and touristic sectors are prioritized in terms of water use during shortage periods. As an example of this fluctuation, total cereal production went from 2.9 million tons in 1996 to 0.5 million tons in 2002 and again to 2.9 million tons in 2003 [26]. This trend is observed for all cereal crops where the yield of durum wheat varies between 0.5 and 2 tons/ha, soft wheat yield ranges between 0.5 and 2.5 tons/ha, and barley yield is between 0.4 and 1.5 tons/ha. Not only yields are variable, but the cereal and fodder cropped areas are also depending stochastically on the climate conditions. For the expected "bad" years, farmers usually avoid planting cereals which engenders a decrease of both areas and yields. As strategic response to climate variability, the country has started since the early 1970s to expand its irrigated areas in order to ensure more reliable supply of agricultural commodities over the years [28]. This strategy partly succeeded in developing around 450,000 ha of irrigated areas representing around 8% of total agricultural area in the country. Although irrigated area share is low, it reflects the highest surface that can be irrigated by the available water resources, given the current levels of irrigation water use efficiency (IWUE). However, despite their low share in total agricultural land, irrigated areas in Tunisia are producing 35% of the agricultural value added, and they are contributing up to 20% of total agricultural exports and 27% of agricultural employment [26]. Around 48% of these irrigated areas are irrigated from groundwater sources, including both superficial and deep aquifers, allowing the irrigation of 48% of the total irrigated area [28]. Overall water resources in the country are estimated to be only around 4700 million m<sup>3</sup> [7] including 650 million m<sup>3</sup> of nonrenewable

resources (13.8% of the total water resources). Surface water is estimated to 2700 million m<sup>3</sup> . Another major problem of the agricultural sector in Tunisia is the small farms'size. In fact, average farm size in Tunisia in 2005 was only about 10.2 ha [27]. Total farm number is 516,000 farms, managing an area of 5.3 million ha. According to the same source, in 2005, 54% of these farms have a size lower than 5 ha, and 75% of farms have a size lower than 10 ha indicating the main structural problem facing the modernization of the agricultural sector and the irrigated areas. In this regard, the stabilization of agricultural yields and the decrease of the sector dependency to climate variations are thus necessary for enhancing food security and agricultural trade balance in Tunisia. Many solutions have been proposed including the improvement of farmers'skills, financing, mechanization, intensification, and the extension of the irrigated areas. A structural change, however, is a broader concept that permits the adjustment of agricultural sector not only upon market features but also a more sustainable management of natural resources, land and water, to reinforce resilience to climate variability and food insecurity. This paper actually aims to determine which national structural readjustments are relevant for a more efficient reallocation of resources using a country- and context-specific agricultural supply model and scenarios. The following sections explain in details the model structuring and also present and discuss the outcomes of the study.

ASc,<sup>s</sup> ¼ ∑ r

Figure 1.

strawberry

Table 1.

35

Different bioclimatic regions in Tunisia.

Durum wheat, soft wheat, barley, olive, almond, palm date, citrus, grape, peach, apple, pear, grenade, tomato, potato, pepper, onion, garlic, artichoke, melon, watermelon,

Different crops and regions considered by the ASMOT model.

RASr, <sup>c</sup>,<sup>s</sup> ¼ ∑

DOI: http://dx.doi.org/10.5772/intechopen.83568

r

Pc ∗ ð Þ Yr,c,s � ΔYr,c,s ½ �– ACr,c,s þ WP<sup>r</sup> f g ½ � ∗Xr, <sup>c</sup>,<sup>s</sup> (1)

where ASc,s is the total agricultural supply of different crops (c) and systems (s).

Systems can either be rain-fed (rai) or irrigated (irr). RASr,c,s indicates the regional agricultural supply by region (r), crop (c), and system (s). Pc is the producer price of crop c; Y is the yield expressed by region, crop, and system; and

Effects of Water Scarcity on the Performances of the Agricultural Sector and Adaptation…

Crops Governorates and aggregated

regions

Monastir, Sousse) South (SO) (Tozeur, Kebili, Tataouine, Médenine, Gabes)

North West (NW) (Bizerte, Beja, Seliana, Le Kef, Jendouba) North East (NE) (Nabeul, Ariana, Manouba, Ben Arous, Zaghouan) Center West (CW) (Sidi Bouzid, Kasserine, Kairouan, Gafsa) Center East (CE) (Sfax, Mahdia,

#### 3. Methodology and analysis

The ASMOT model is an agricultural supply model that is built based on primary and secondary data of farming inputs and outputs for different crops, regions, and systems (rain-fed and irrigated). ASMOT is the first regionally disaggregated ASM developed for Tunisia. The model includes 21 of the most strategic crops of the country (including the most important cereals, trees/fruits, and vegetables). It also includes a representation of 67% of the total agricultural areas of Tunisia (around 3.34 million ha) and 78% of the total irrigated areas (around 352,000 ha). The ASMOT model is built based on regional disaggregated data, including 24 governorates of Tunisia. These governorates have been aggregated into five regions (North West (NW), North East (NE), Center West (CW), Center East (CE), and South (SO)) based on bioclimatic homogeneity (Figure 1).

The model was calibrated through Positive Mathematical Programming (PMP) [29] and using official 2011 data about observed crop areas by region and system (irrigated/rain-fed) as recorded by the Ministry of Agriculture, Hydraulic Resources and Fisheries of Tunisia [30]. Regional irrigation water availability was also included into the model based on official secondary data about existing water reservoirs in the different regions of the country.

Regional agricultural value added are optimized by ASMOT and aggregated into a national domestic agricultural value added. Various types of biophysical and economic constraints are considered in parallel to this optimization process. These can be found in the next section presenting the main mathematical structure of the model. The model also considers crop evapotranspiration and their respective effect on yield gaps. The different crops and regions included in the ASMOT model are shown in Table 1.

#### 3.1 Structure of the ASMOT model

The aggregated agricultural supply (Eq. (1)) of the model calculates the aggregated gross value of agricultural supply (AS) in Tunisia as the sum of regional agricultural gross production values (RAS). Eq. (1) can be read as follows:

Effects of Water Scarcity on the Performances of the Agricultural Sector and Adaptation… DOI: http://dx.doi.org/10.5772/intechopen.83568

$$\mathbf{AS}\_{\mathbf{c},s} = \sum\_{r} \mathbf{RAS}\_{\mathbf{t},\mathbf{c},s} = \sum\_{r} \left\{ \left[ \mathbf{P}\_{\mathbf{c}} \ast \left( \mathbf{Y}\_{r,\mathbf{c},s} - \Delta \mathbf{Y}\_{r,\mathbf{c},s} \right) \right] \cdot \left[ \mathbf{AC}\_{\mathbf{r},\mathbf{c},s} + \mathbf{WP}\_{r} \right] \right\} \ast \mathbf{X}\_{\mathbf{t},\mathbf{c},\mathbf{s}} \tag{1}$$

where ASc,s is the total agricultural supply of different crops (c) and systems (s). Systems can either be rain-fed (rai) or irrigated (irr). RASr,c,s indicates the regional agricultural supply by region (r), crop (c), and system (s). Pc is the producer price of crop c; Y is the yield expressed by region, crop, and system; and

#### Figure 1.

resources (13.8% of the total water resources). Surface water is estimated to 2700

farms'size. In fact, average farm size in Tunisia in 2005 was only about 10.2 ha [27]. Total farm number is 516,000 farms, managing an area of 5.3 million ha. According to the same source, in 2005, 54% of these farms have a size lower than 5 ha, and 75% of farms have a size lower than 10 ha indicating the main structural problem facing the modernization of the agricultural sector and the irrigated areas. In this regard, the stabilization of agricultural yields and the decrease of the sector dependency to climate variations are thus necessary for enhancing food security and agricultural trade balance in Tunisia. Many solutions have been proposed including the improvement of farmers'skills, financing, mechanization, intensification, and the extension of the irrigated areas. A structural change, however, is a broader concept that permits the adjustment of agricultural sector not only upon market features but also a more sustainable management of natural resources, land and water, to reinforce resilience to climate variability and food insecurity. This paper actually aims to determine which national structural readjustments are relevant for a more efficient reallocation of resources using a country- and context-specific agricultural supply model and scenarios. The following sections explain in details the model

structuring and also present and discuss the outcomes of the study.

South (SO)) based on bioclimatic homogeneity (Figure 1).

reservoirs in the different regions of the country.

shown in Table 1.

34

3.1 Structure of the ASMOT model

The ASMOT model is an agricultural supply model that is built based on primary

The model was calibrated through Positive Mathematical Programming (PMP) [29] and using official 2011 data about observed crop areas by region and system (irrigated/rain-fed) as recorded by the Ministry of Agriculture, Hydraulic

Regional agricultural value added are optimized by ASMOT and aggregated into

The aggregated agricultural supply (Eq. (1)) of the model calculates the aggregated gross value of agricultural supply (AS) in Tunisia as the sum of regional agricultural gross production values (RAS). Eq. (1) can be read as follows:

Resources and Fisheries of Tunisia [30]. Regional irrigation water availability was also included into the model based on official secondary data about existing water

a national domestic agricultural value added. Various types of biophysical and economic constraints are considered in parallel to this optimization process. These can be found in the next section presenting the main mathematical structure of the model. The model also considers crop evapotranspiration and their respective effect on yield gaps. The different crops and regions included in the ASMOT model are

and secondary data of farming inputs and outputs for different crops, regions, and systems (rain-fed and irrigated). ASMOT is the first regionally disaggregated ASM developed for Tunisia. The model includes 21 of the most strategic crops of the country (including the most important cereals, trees/fruits, and vegetables). It also includes a representation of 67% of the total agricultural areas of Tunisia (around 3.34 million ha) and 78% of the total irrigated areas (around 352,000 ha). The ASMOT model is built based on regional disaggregated data, including 24 governorates of Tunisia. These governorates have been aggregated into five regions (North West (NW), North East (NE), Center West (CW), Center East (CE), and

3. Methodology and analysis

Agricultural Economics - Current Issues

. Another major problem of the agricultural sector in Tunisia is the small

million m<sup>3</sup>

Different bioclimatic regions in Tunisia.


#### Table 1. Different crops and regions considered by the ASMOT model.

ΔY is the variation of yields which can be due to water stress (higher temperatures and evaporations). AC is the average cost of crop production excluding water costs. AC is expressed by region and system. WP is the irrigation water price in different regions. Finally, Xc,r,s is the positive variable of the total area for crop (c) under system (s) and in region (r). Observed Xc,r,s of the year 2011 was used for the calibration of Eq. (1). Once calibrated, X becomes variable and can be optimized under different scenarios. Yield variation ΔY is calculated as follows:

$$
\Delta Y\_{\text{r,c,s}} = \text{Y} \ast \text{ky} \ast \left(1 - \frac{\text{Eta}}{\text{ETM}}\right) \tag{2}
$$

where ky is the yield variation coefficient, which has a constant value for each crop, and Eta and ETM are, respectively, the real and maximal evapotranspiration:

$$\sum\_{c\_2, s} X\_{r, c, s} \le A\_r \tag{3}$$

AC r,c,s ¼ α r,c,s þ βr,c,s Xr, <sup>c</sup>,<sup>s</sup> (8)

Eq. (8) was replaced by Eq. (1) which will generate a calibrated nonlinear objective function. To validate the calibrated model, we optimize Eq. (1) under all constraints while excluding the initial calibration in Eq. (7). If the resulting model will generate the same land allocation observed during the base year, then we can assume that our model is well validated and can be used for scenario simulations. ASMOT validation and calibration performances are presented in the result section.

Effects of Water Scarcity on the Performances of the Agricultural Sector and Adaptation…

The data used for the ASMOT model was of different types and thus collected from various sources. Specific crop input and output levels for different regions and systems were collected through farmer questionnaires which were conducted for the season 2012–2013, in all regions of Tunisia in the framework of the Eau Virtuelle et Sécurité Alimentaire en Tunisie(EVSAT, funded by the IDRC) research project. Many focus groups with regional experts in crop production were conducted afterward in order to revise the average input and output values in respective regions and systems for all considered crops. Some coefficients of the model, such as the annual growth rates of tree crops, were calculated using FAO data [40]. Other secondary data regarding water availability, initial crop area distribution, irrigated areas, etc. were collected from official national datasets, especially available at the level of [30]. Water requirements in addition to evapotranspiration coefficients of different crops in different regions and systems were measured by the EVSAT

In relation to the overall objective of the chapter, our scenario development considers the current water scarcity situation faced by Tunisia, where water availability is expected to decrease by 28% at the end of the next decade [6]. Based on this, our first scenario suggests a cut of water availability by 25%, while second and third scenarios will consider improvements of IWUE and producers' prices as possible options to deal with this shortage and offer market incentives to enhance farmers' adaptation capacities. Only 69% of the total irrigated areas in Tunisia are fitted with water-saving technologies, thus leading to an average water use efficiency of about 55% at the national level [41]. This shows a wide scope to improve IWUE through appropriate investments in the farmer's skills and modernization of the irrigation networks. On the other side, it is well known that better integration of farmers along commodity value chains may offer enhanced producer prices [42], which can be considered as market incentives allowing farmers to enhance their technical investments and adaptation capacities [43, 44]. Based on these arguments, scenarios which were simulated using the ASMOT model can explicitly be read as

• Scenario 1. Cutting total fresh water availability by 25%. This reduction is

• Scenario 2. Cutting total fresh water availability by 25% and improving IWUE by 10%. The improvement of IWUE is interpreted in our modeling as a

supposed to be the same across all regions of the country.

decrease of water volumes applied for different crops by 10%.

3.2 Source of data

DOI: http://dx.doi.org/10.5772/intechopen.83568

3.3 Water scenarios

follows:

37

research team through field experimentations.

Constraint 3 is a land constraint, indicating that the total cultivated areas in each region should not, in the short term, exceed the currently observed agricultural areas (Ar):

$$\sum\_{\mathbf{c}\_{\bullet} \ (s=irr)} X\_{r,c,s} \le I A\_r \tag{4}$$

Constraint 4 indicates that the sum of crop irrigated areas in each region should not exceed the total irrigable areas (IAr) available in that region:

$$\sum\_{r(c=tres)\_rs} X\_{r,c,s} \le TA\_r + (1 + \chi\_{c=tres}) \tag{5}$$

Constraint 5 bounds the annual tree area expansion to the observed annual growth rates of these areas in Tunisia during the last two decades which is about 5%. This constraint is also set at the regional level, where TAr is the current tree area in region r and γ is the annual growth rate of tree areas which is set to be equal to 5%:

$$\sum\_{c\_2, s} w\_{r, c, s} \* X\_{r, c, s} \le W A\_r \tag{6}$$

Constraint 6 indicates that the sum of water requirement of all crops cultivated under different systems in a given region (Wr,c,s) should not exceed the water availability in that region (WAr):

$$X\_{r,c,s} \le X\_{r,c,s}^{\vartheta} \ast (1+\varepsilon) \tag{7}$$

Finally, constraint 7 is a calibration constraint which was used in the first PMP step in order to estimate the cost function calibration coefficients (αr,c,s and βr,c,s). The average cost AC function is a nonlinear expression (Eq. (8)) estimated using two main calibration coefficients (ααr,c,s and βr,c,s) which were calculated by solving Eq. (1) under the set of all considered constraints (3–7), including the calibration constraint [31, 29]. Coefficients ααr,c,s and βr,c,s were calculated using the dual values of constraint 7, and following the approach of [31, 32], where exogenous information about land rents was used for estimating the values of α and β. These PMP approaches have been widely validated and used for different sectors and other farm-type modeling and calibrations [33–39]:

Effects of Water Scarcity on the Performances of the Agricultural Sector and Adaptation… DOI: http://dx.doi.org/10.5772/intechopen.83568

$$AC\_{r,c,s} = \alpha\_{r,c,s} + \beta\_{r,c,s} \text{ X}\_{\text{t,c,s}} \tag{8}$$

Eq. (8) was replaced by Eq. (1) which will generate a calibrated nonlinear objective function. To validate the calibrated model, we optimize Eq. (1) under all constraints while excluding the initial calibration in Eq. (7). If the resulting model will generate the same land allocation observed during the base year, then we can assume that our model is well validated and can be used for scenario simulations. ASMOT validation and calibration performances are presented in the result section.

#### 3.2 Source of data

ΔY is the variation of yields which can be due to water stress (higher temperatures and evaporations). AC is the average cost of crop production excluding water costs. AC is expressed by region and system. WP is the irrigation water price in different regions. Finally, Xc,r,s is the positive variable of the total area for crop (c) under system (s) and in region (r). Observed Xc,r,s of the year 2011 was used for the calibration of Eq. (1). Once calibrated, X becomes variable and can be optimized under different scenarios. Yield variation ΔY is calculated as follows:

<sup>Δ</sup>Yr, <sup>c</sup>,<sup>s</sup> <sup>¼</sup> <sup>Y</sup><sup>∗</sup> ky <sup>∗</sup> <sup>1</sup> � Eta

∑ c, s

∑ c,ð Þ <sup>s</sup>¼irr

not exceed the total irrigable areas (IAr) available in that region:

∑ c, s

other farm-type modeling and calibrations [33–39]:

availability in that region (WAr):

Agricultural Economics - Current Issues

∑ ð Þ <sup>c</sup>¼trees , <sup>s</sup>

areas (Ar):

36

where ky is the yield variation coefficient, which has a constant value for each crop, and Eta and ETM are, respectively, the real and maximal evapotranspiration:

Constraint 3 is a land constraint, indicating that the total cultivated areas in each region should not, in the short term, exceed the currently observed agricultural

Constraint 4 indicates that the sum of crop irrigated areas in each region should

Constraint 5 bounds the annual tree area expansion to the observed annual growth rates of these areas in Tunisia during the last two decades which is about 5%. This constraint is also set at the regional level, where TAr is the current tree area in region r and γ is the annual growth rate of tree areas which is set to be equal to 5%:

Constraint 6 indicates that the sum of water requirement of all crops cultivated

Finally, constraint 7 is a calibration constraint which was used in the first PMP step in order to estimate the cost function calibration coefficients (αr,c,s and βr,c,s). The average cost AC function is a nonlinear expression (Eq. (8)) estimated using two main calibration coefficients (ααr,c,s and βr,c,s) which were calculated by solving Eq. (1) under the set of all considered constraints (3–7), including the calibration constraint [31, 29]. Coefficients ααr,c,s and βr,c,s were calculated using the dual values of constraint 7, and following the approach of [31, 32], where exogenous information about land rents was used for estimating the values of α and β. These PMP approaches have been widely validated and used for different sectors and

under different systems in a given region (Wr,c,s) should not exceed the water

Xr,c,s ≤ X<sup>o</sup>

ETM 

Xr,c,s ≤ Ar (3)

Xr,c,s ≤ IAr (4)

Xr,c,s ≤ TAr þ 1 þ γ<sup>c</sup>¼trees ð Þ (5)

wr,c,s ∗Xr,c,s ≤ WAr (6)

r,c,s ∗ ð Þ 1 þ ε (7)

(2)

The data used for the ASMOT model was of different types and thus collected from various sources. Specific crop input and output levels for different regions and systems were collected through farmer questionnaires which were conducted for the season 2012–2013, in all regions of Tunisia in the framework of the Eau Virtuelle et Sécurité Alimentaire en Tunisie(EVSAT, funded by the IDRC) research project. Many focus groups with regional experts in crop production were conducted afterward in order to revise the average input and output values in respective regions and systems for all considered crops. Some coefficients of the model, such as the annual growth rates of tree crops, were calculated using FAO data [40]. Other secondary data regarding water availability, initial crop area distribution, irrigated areas, etc. were collected from official national datasets, especially available at the level of [30]. Water requirements in addition to evapotranspiration coefficients of different crops in different regions and systems were measured by the EVSAT research team through field experimentations.

#### 3.3 Water scenarios

In relation to the overall objective of the chapter, our scenario development considers the current water scarcity situation faced by Tunisia, where water availability is expected to decrease by 28% at the end of the next decade [6]. Based on this, our first scenario suggests a cut of water availability by 25%, while second and third scenarios will consider improvements of IWUE and producers' prices as possible options to deal with this shortage and offer market incentives to enhance farmers' adaptation capacities. Only 69% of the total irrigated areas in Tunisia are fitted with water-saving technologies, thus leading to an average water use efficiency of about 55% at the national level [41]. This shows a wide scope to improve IWUE through appropriate investments in the farmer's skills and modernization of the irrigation networks. On the other side, it is well known that better integration of farmers along commodity value chains may offer enhanced producer prices [42], which can be considered as market incentives allowing farmers to enhance their technical investments and adaptation capacities [43, 44]. Based on these arguments, scenarios which were simulated using the ASMOT model can explicitly be read as follows:


• Scenario 3. Cutting total fresh water availability by 25%, in addition to an increase of IWUE with 10% and higher producer prices offered to farmers. The suggested increase of producer prices are as follows: +5% for cereal prices and +10% for fruits and vegetable prices.
