Reducing nitrate water pollution and irrigation water consumption at the river basin scale through the optimized allocation of a low-input perennial bioenergy crop within the existing cropping systems

Reducing nitrate water pollution and irrigation water consumption at the river basin scale through the optimized allocation of a low-input perennial bioenergy crop within the existing cropping systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Reducing nitrate water pollution and irrigation water consumption at the river basin scale through the optimized allocation of a low-input perennial bioenergy crop within the existing cropping systems Lamprini Kokkinaki, Maria Sismanidi, Haralampos Georgoussis, Sofia Kavalieratou, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7233578/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Dec, 2025 Read the published version in Environmental Processes → Version 1 posted 9 You are reading this latest preprint version Abstract The Pinios River Basin in Thessaly, Greece, is the country's most important agricultural region. However, intensive farming practices have led to the degradation of both surface water and groundwater quantity and quality. To address these issues within an energy vulnerable environment, the adoption of bioenergy crops into existing cropping systems offers a promising practice, combining environmental benefits at a river basin scale with the potential of producing renewable energy. The current study investigates switchgrass, a low-input, resource-efficient energy crop, as an ideal candidate for sustainable implementation in the irrigated cropland. Given the unavoidable conflicts with food, feed, and fiber production, a full examination of the environmental and economic implications is needed for its large-scale installation. The Soil and Water Assessment Tool (SWAT) was first used to develop a representative model of the Pinios River Basin and evaluate its current hydrological and nitrate (N-NO 3 ) water pollution. A multi-objective Genetic Algorithm embedded in MATLAB was linked to SWAT and an economic component and after a large number of simulations, it identified optimum spatial allocations of the bioenergy crop in the agricultural land, with respect to the net farmers' income, biomass production and water quality and quantity. The analysis of the resulting trade-off curves demonstrated highly encouraging outcomes, with the most conservative solution achieving a 5% reduction in N-NO 3 loads and a 5.6% reduction in irrigation water consumption across the entire basin. Furthermore, under this spatial allocation scheme, 0.44x10 6 tons of biomass were produced from the bioenergy crop, while maintaining the total net agricultural income at the business as usual levels. bioenergy crop genetic algorithm irrigation water multi-objective optimization nitrate pollution SWAT Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Highlights • Using efficient technological tools to assist decision making in river basin management. • A hydrological and crop growth model and a MATLAB GA assess the optimum allocation of a bioenergy crop across the basin. • Various spatial allocations of crops, including the bioenergy one, are analyzed with respect to environmental and economic criteria. • Optimum cropping patterns are demonstrated and proposed. 1 Introduction In Mediterranean rural landscapes, the intensification of agricultural activities is closely associated with the qualitative and quantitative degradation of water bodies. Balancing crop production for diverse purposes with the protection of water quantity and quality in a region is a challenge, as these objectives can be partially or wholly conflicting, and finding appropriate management practices to achieve them simultaneously is not always obvious. Proper management practices are crucial to meet these demands and the goals of the European Union’s (EU) directives. Since agricultural land is increasingly promoted worldwide as a valuable resource for sustainable energy production (Nikkhah et al., 2020 ), integrating energy crops into farming systems could be an effective strategy to mitigate these impacts. Biofuels are fuels derived from biomass, which refers to any material of biological (organic) origin. Biomass encompasses a wide range of materials that originate directly or indirectly from plants (Antar et al., 2021 ). It can be utilized to meet various energy needs, such as heating, cooling, and electricity generation, and can also be processed into liquid biofuels. Consequently, bioenergy is considered an alternative energy source with positive environmental impacts, and global policies have been adopted to promote its production and use. The need to increase bioenergy production in Greece has been recognized over the past decades (Skoulou et al., 2011). However, the national use of renewable energy sources still remains low. As reported by Tromaras et al. ( 2021 ) in 2016, renewable energy in transport accounted for less than 2%, five times lower than the EU average. The EU has implemented measures to advance biofuels through the Renewable Energy Directive (Directive 2023/2413), which is part of the European Green Deal, aiming for a climate-neutral EU by 2050. As an EU member state, Greece must comply with the European Directive, which mandates that biofuels contribute 29% to total transport energy consumption by 2030 (Directive 2023/2413). Thus, biomass and biofuels are seen as promising energy sources amid the current energy crisis in the EU, as they can be produced domestically, thereby reducing dependence on energy imports. The discharge of nitrates into surface water is one of the most serious environmental issues in extensively managed agricultural areas, since it can negatively impact the ecosystem's health. Furthermore, the high-water requirements of irrigated agriculture put additional pressure on the limited freshwater supplies, particularly in semi-arid and Mediterranean areas (Panagopoulos et al., 2014 ). The European Union has been addressing the accumulation of nutrients in aquatic systems since the early 1990s with a comprehensive legislative framework, mainly through the Nitrates Directive (91/676/EEC), which targets diffuse pollution. The Water Framework Directive (WFD) (2000/60/EC) was further established to integrate water management at the river basin level, along with changes to the Common Agricultural Policy (CAP). In Greece, River Basin Management Plans and designated Nitrate Vulnerable Zones with supplementary Action Programs have been used to incorporate these instructions into national law. However, improvements in water quality are still inadequate due to gaps in stakeholder engagement and slow environmental response. Subsequently, producing bioenergy requires extensive agricultural areas, often leading to significant land use changes that may compete with the production of food, feed, and fiber. In general, low input bioenergy crops, such as the perennial crop switchgrass can have positive environmental impacts when incorporated within existing resource-demanding cropping systems (Giannoulis et al., 2017 ). However, the reliance on land availability, the irrigation needs of energy crops in arid and semi-arid climates, and the impact of their cultivation on the water quality of nearby water bodies may pose challenges to their wider adoption. Therefore, it is essential to assess a bioenergy crop’s socio-economic and environmental footprint before selecting it for regional planning. This makes the placement of bioenergy crops a multi-objective problem requiring high-quality data on numerous parameters, including economic and environmental indicators, water quality metrics, and management practices in the study area. In Greece, no such plan has yet been proposed for any agricultural region to ensure continuous, uninterrupted production of bioenergy products. For addressing some of the previously mentioned challenges, Decision Support Tools (DST) have been used in agriculture since the 90s (Ara et al., 2021). A typical DST consists of a robust pollution estimator, often based on a hydrologic model, an economic function that can adequately represent the cost associated with different implementation scenarios along with an optimization algorithm, capable of efficiently navigating an extensive, non-linear and discontinuous solution space, such as Genetic Algorithms (GAs) (Makropoulos & Butler, 2005 ). Hydrological-based models are widely used for informed decision making for river basin diffuse pollution management. In this work, the Soil and Water Assessment Tool (SWAT) was chosen as the simulation model. SWAT is regarded as a robust, interdisciplinary tool, widely applied in Europe and globally, concluding that it is one of the most commonly used tools in addressing topics of river basin management, from water management within basins to water quality of streams, rivers and other water bodies (Gassman et al., 2014 ). Within this framework, the effects of energy crops installation on both water quality and quantity have been widely explored (Kumar et al., 2020 ; Mishra et al., 2019 ; Wang et al., 2018 ; Panagopoulos et al., 2017 ; Chen et al., 2016 ; Cibin et al., 2016 ). However, SWAT studies that focus on the optimization of bioenergy crops are less frequently discussed in international literature. Nonetheless, one can still find notable SWAT studies concerning the optimization of bioenergy scenarios (Valcu-Lisman et al.; 2016 ; Lautenbach et al., 2013 ). Thus, the study aims to adopt a modern approach by combining informative and efficient technological tools to discover regional scale cropping patterns, including a perennial energy crop, that simultaneously satisfy economic and environmental targets. One of the initial objectives of this study is the development of a user-friendly optimization tool with a combination of the SWAT model, a GA and a comprehensive economic analysis, in order to assist decision making in the study area. The main purpose was to detect the optimal spatial distribution of switchgrass under which a specified target of biomass production could be achieved with negligible environmental footprint and total net income impairment. The optimization criteria included maintaining sufficient water levels in groundwater and rivers, ensuring reductions of N-NO 3 loads, and achieving desirable biomass production for renewable energy, while minimizing the total cost of implementing the management plan. The aim was to identify compromise solutions that meet all key criteria. 2 Methods and tools 2.1 Description of the SWAT model The Soil and Water Assessment Tool (SWAT) is a semi-distributed, GIS-based and process-based model. It was developed by the US Department of Agriculture in collaboration with Texas A&M University (Williams et al., 2008 ) for use in complex agricultural landscapes. The watershed in SWAT is divided into subbasins and one reach is associated with each subbasin. The subbasins are then divided into Hydrologic Response Units (HRUs), which represent a unique combination of land use, soil type and topography. Runoff and loadings such as nutrients, sediments and pollutants transported by runoff are calculated separately in each HRU and then summed together to define the total loadings from the subbasin (Neitsch et al., 2009). Crop growth is also modeled at the HRU scale, thus increasing the accuracy of the simulation. Furthermore, each HRU is given a single shallow aquifer. In SWAT, groundwater volume in each HRU changes with percolation, water that moves from shallow aquifers to the overlying unsaturated zones, baseflow, deep aquifer recharge, and water pumping. Groundwater abstractions for irrigation or other uses are simulated at the subbasin level, meaning that water is subtracted from all HRUs in a subbasin when abstraction occurs in a single HRU of the subbasin. This approach combines small aquifers into larger ones, aligning with real-world scenarios, although with the subbasin level limitation in SWAT. The crop growth component of SWAT is a simplified version of the Erosion Productivity Impact Calculator (EPIC) model, which is capable of simulating a wide range of crop rotations, pastureland, and trees. The growth cycle is controlled by plant parameters summarized in the plant growth database, which can be modified according to the study area, the growing conditions, and by management operations. Agricultural management practices are defined in SWAT by specific management operations (Arabi et al., 2008 ) affecting every cropping and livestock system by defining planting, harvesting, tillage passes, irrigation, grazing, nutrient and pesticide applications. Thus, the aforementioned parameters can be altered when other management practices are implemented, allowing land use change scenarios to be accurately simulated. The simulation in SWAT can be run on a daily, monthly or annual basis. Depending on the print settings the output values will also be daily, monthly, or yearly. Several output files are generated in every SWAT simulation. Some output data calculated by SWAT in each HRU are sediments (t/ha), nutrients (kg/ha), irrigation water (mm) and yield (t/ha) (Neitsch et al., 2009). In the present study, the ArcSWAT 2012, Release 687, compatible with ArcGIS 10.8.2, was utilized. 2.2 Genetic Algorithms (GAs) and the MATLAB-GA Evolutionary Algorithms such as Genetic Algorithms (GAs) are meta-heuristic algorithms that perform very effectively in handling multi-objective optimization problems. GAs are based on the principle of evolution, specifically the concept of “survival of the fittest” (Sivanandam et al., 2008). Over the past years, they have gained considerable interest due to their potential as an innovative optimization method that offers simplicity in use. Multi-objective optimization is essential for resolving complicated issues, such as environmental ones, when several conflicting objectives need to be taken into account simultaneously. NSGA-II is one of the most popular evolutionary algorithms, due to its efficiency in solving multi-objective problems and its strong convergence capacity to identify a variety of solutions (Gebre et al., 2021 ). Literature review has shown that NSGA-II is very suitable for optimizing agro-environmental mathematical problems that are formulated towards crop production maximization and water usage efficiency, with changes in agricultural practices (Akdemir et al., 2025 ; Cheng et al., 2025 ; Du et al., 2024 ; Krityakierne et al., 2024 ). For the needs of this work a multi-objective controlled elitist Genetic Algorithm, that is a variant of NSGA-II (Deb, 2001 ), was selected from MATLAB’s toolbox, as the optimization engine. Controlled elitist GAs also favor individuals that can help increase the diversity of the population even if they have a lower fitness value. In MATLAB’s GA, special emphasis is placed on maintaining a well-distributed set of solutions along the Pareto front, while simultaneously preserving the best individuals (elite) in the population as the algorithm evolves over time. The use of non-dominated sorting automatically incorporates elitism, eliminating the need to explicitly define the number of top individuals to retain in the new population. The implementation of the MATLAB’s GA follows the typical optimization procedure: evaluating the population based on fitness, scaling of fitness values, selection of parents, reproduction using elitism, crossover, and mutation, and termination when a stopping criterion is met (MATLAB, 2024 ). Thus, it can be considered capable of adequately searching a large search space, examining a very large number of different crop allocation scenarios in the basin, ultimately leading to optimal solutions. 3 Methodology Assessing the allocation of agricultural management practices, including the installation of a new crop such as switchgrass, with respect to the quantity and quality of water bodies, and the farmers’ income is considered a multi-objective problem. Since there could be a significant number of possible combinations to achieve acceptable solutions, a DST is required. To address such a complex problem the developed DST requires the following elements (Panagopoulos et al., 2010): A robust pollution estimator, typically a process-based model, capable of adequately representing the effects of various crop allocation schemes within the basin area. As mentioned, SWAT was chosen in this work as the simulation model. An economic function that accurately represents the cost of implementing these different schemes at various locations. An optimization algorithm that efficiently searches through a vast, non-linear, and non-continuous solution space, such as evolutionary algorithms. A MATLAB-GA inspired by NSGA-II is selected for this work. 3.1 Study area The Thessaly region is located in Central Greece, and it is considered the most important agricultural producer in the country. Pinios River Basin (PRB) constitutes the largest river basin of the area, covering almost 11,000 km 2 . The PRB has a highly diverse geology, hydrological, and geomorphological environment, enhancing the region’s productivity. Due to these characteristics, the basin provides an ideal environment for cultivating a variety of crops, with agriculture accounting for approximately 45% of the total basin area (Pisinaras et al., 2023 ). Out of 4.000 km 2 of agricultural land, cotton and wheat are the main crops cultivated, followed by corn and much smaller areas of alfalfa. Irrigation accounts for 94% of total water usage according to the Hellenic Special Secretariat for Water (2017). Significant exploitation of groundwater resources has occurred since the 1980s as a result of the unreasonable water management methods combined with the rising irrigation and water demands. It is estimated that the source of irrigation for roughly 24% of the total irrigation abstractions is surface water, while 76% is irrigated through groundwater with legal or illegal private boreholes (Hellenic Special Secretariat for Water, 2017). However, such an intensively managed area is expected to develop water quality and quantity problems arising from agriculture. The region has inevitably been classified as a nitrate-vulnerable zone (Nitrates Directive). According to the latest regional water management plan (Hellenic Special Secretariat for Water, 2023), 2 of the 27 groundwater bodies identified in the PRB, which cover 2184 km 2 , are listed as having a "bad" quality status, while 9 of them have been designated as having "bad" quantity. These, combined with dry summers in the area, inversely affect agriculture, resulting in irrigation cutbacks and significant exploitation of surface and groundwater. Thus, it is evident that the area is a good example of existing water quality and quantity threats preventing water bodies from reaching the good ecological status required by the WFD (Directive 2000 /60/EC) and Nitrates directive (Directive 91/676/EEC). 3.2 Pinios River Basin representation in SWAT For the representation of PRB in SWAT different input layers are required. A 25x25 Digital Elevation Model (DEM) was used to delineate the study area of PRB, covering almost entirely the River Basin District of Thessaly in Central Greece. The region’s elevation varies greatly, with the highest values around 2,000 m, occurring close to the basin’s edges. Data from the European Soil Database (ESDB) was used to create a soil map for PRB (Hiederer et al., 2013a; b). Additionally, the study area was divided into two slope classes: (1) ≤ 1.5% and (2) > 1.5% since slope differentiation is considered necessary for this work to further divide the agricultural area into more HRUs belonging to different slope classes. A land cover layer derived from the 2012 Corine Land Cover (CLC) dataset (Corine Land Cover 2012 -European Environment Agency) was utilized for the modeling process. Its relevance to the present was supported by a direct comparison with CLC 2018, which revealed minimal changes in land cover types within the PRB. This was further validated by examining current crop patterns on agricultural land and comparing them with data from previous decades. Analysis of both recent and historical crop allocation data from the Hellenic Statistical Authority (Hellenic Statistical Authority, Data on Crop Areas) confirmed the relative consistency of crop types and irrigated areas in the PRB over the past 20 years. For the final land use layer used in SWAT, only crops occupying more than 1% of the total agricultural area were included. The resulting classification consisted of 10 land use categories: pasture, forested areas, wetlands, urban areas, orchard trees, fallow land, and the cultivation of wheat, corn, cotton, and alfalfa. These layers were overlayed, resulting in the creation of 61 subbasins and 1850 HRUs. Figure 1 illustrates the irrigated land of the basin. As can be observed, with the blue dots in Fig. 1 a, three reservoirs operate within the basin, contributing to irrigation to a lower extent than groundwater. In addition, around 5% of cropland in the southwestern area is irrigated using water sourced externally from “Plastiras” lake, as indicated with the green color in Fig. 1 a. Groundwater is the source of irrigation for the rest 77% of the irrigated land, and satisfies irrigation needs either adequately or inadequately depending on the local availability. From the total cropland area, 55% is irrigated (Fig. 1 b), with cotton covering 81% of the irrigated cropland, receiving 420 mm per growth cycle, corn 630 mm (11% of the irrigated cropland) and alfalfa > 600 mm (8% of the irrigated cropland). The detailed schematization of the basin, along with the parameterization and the hydrological and water quality evaluation of the PRB model that is used in this study, have been thoroughly described in the recently published article of Sismanidi et al. ( 2025 ). The validated PRB model generated baseline results for hydrology, water quality, and crop production. The baseline scenario simulated an 8-year period (2016 to 2023) by providing results for the 6-year period of 2018–2023 due to a 2-year warm-up period that was neglected. Table 1 summarizes the most important water quality and quantity results of the baseline, including groundwater content at the end of the simulation period (10 6 m 3 ), total mean annual irrigation water used (10 6 m 3 ), mean annual N-NO 3 loads to surface waters (kg/ha), mean annual concentrations of N-NO 3 leached to shallow aquifers (mg/L). Table 1 SWAT estimates of water and nitrates-nitrogen in Pinios river basin at the baseline. Parameters Results in the baseline Groundwater content at the end of the simulation period (10 6 m 3 ) 5610 Total annual irrigation water consumption (10 6 m 3 )* 720 N-NO 3 loads (kg/ha) 1.48 N-NO 3 leached (mg/L) 21.2 * Including the single early irrigation dose on wheat, which was not further irrigated within its growth cycle. For the irrigated crops excluding wheat, it was 680 × 10 6 m 3 as presented in Sismanidi et al. ( 2025 ). Also, only renewable groundwater reserves were allowed for abstraction by setting the initial groundwater content at the start of the simulation (1/1/2026) at zero levels. 3.3 Bioenergy crop representation and implementation cost Switchgrass is a C4 perennial grass with a multi-year lifespan, native to North America. It is widely recognized as a resource-efficient crop since it has relatively low water and nutrient requirements (Giannoulis et al., 2016 ) and as a crop that can grow even in low-productivity areas, usually called land of marginal quality (Giannoulis et al., 2017 ). The crop is of high biomass productivity and is therefore considered for use in several bioenergy conversion processes, including cellulosic ethanol production, biogas, and direct combustion for thermal energy applications. For the representation of the bioenergy crop in SWAT several parameters were adjusted to fit the Greek conditions according to the recommendations of experts from the University of Thessaly and their past work (Giannoulis et al., 2016 ; 2017 ). For simulating switchgrass, an annual irrigation depth of 250 mm was applied during the dry period (May–September) and 150 kg/ha of N was applied annually, with an additional 30 kg N/ha applied immediately after seeding. The one-cut system was chosen as the optimal harvesting method for switchgrass in the study area. In this work, switchgrass was allowed to replace only irrigated crops, namely cotton, corn, and alfalfa. The above-mentioned N fertilization inputs of the perennial crop, particularly in comparison with those of the conventional annual crops, are significantly reduced, as can be observed in Table 2 . Table 2 Nitrogen fertilization amounts corresponding to each irrigated crop, conventional and switchgrass. Crop Annual Nitrogen (N) fertilization amounts (kg N/ha) Annual irrigation needs (mm) Alfalfa - 600 Corn 364 630 Cotton 185 420 Switchgrass 150 250 Consequently, replacing cotton with switchgrass leads to an approximate 20% reduction in N application across the corresponding HRUs. This substitution also results in a 40% decrease in irrigation water use, assuming optimal irrigation supported by available water from the source. Similarly, replacing corn with switchgrass reduces N fertilization by 59% and theoretically lowers irrigation water demand by 60%. In contrast, replacing alfalfa with switchgrass leads to an increase in N application but still results in a substantial theoretical reduction of 58% in irrigation water use. Following the modeling of the perennial crop, the next step involved collecting and analyzing economic data for all major cultivated crops in the basin, including the bioenergy crop switchgrass. This assessment aimed to evaluate the economic viability of each crop based on cultivation cost and income. The economic analysis considered a range of cost components, including seed costs (adjusted for crop life cycle in the case of perennials), fertilization (split by type and application method), and agricultural management practices such as plowing, sowing, and harvesting. Machinery-related expenses (fuel, maintenance, depreciation), labor costs, and the irrigation costs were also included. Harvesting costs for perennials (alfalfa and switchgrass) were calculated based on the yield levels and were converted to per ha values using simulated data (Giannoulis et al., 2014 ). Product sale prices and EU subsidies were incorporated to estimate income. The final assessment included calculations of the total annual expenses, income (with and without subsidies), and net income values, as presented in Table 3 . Table 3 Annual unit costs of the conventional crops and switchgrass in Pinios river basin. Unit costs Corn Cotton Alfalfa Switchgrass Product sale price (€/kg) 0.22 0.55 0.25 0.09 Product sales price (€/ha) * 2519 1705 3095 1674 Subsidies (€/ha) 550 734 83 45 Total Expenses (€/ha) 1698 1348 1875 1349 Income (€/ha)* 2519 1705 3095 1674 Income with subsidies (€/ha)* 3069 2439 3178 1719 Total Net Income (€/ha) 822 358 1220 325 Total Net Income with subsidies (€/ha) 1372 1092 1303 370 * the numbers are approximations as they are based on average 'per ha' yields. It is evident that switchgrass implementation is expected to be conflicting with the farmers’ Total Net Income due to the lower net income produced by this crop compared to conventional crops. In practice, this implies that despite the relatively low annual cultivation cost of the bioenergy perennial crop, its low biomass sale price and subsidy levels cannot result in comparable income values with those of the conventional irrigated crops. In the baseline scenario, when only conventional crops were grown in the PRB, the mean annual Total Net Income for the farmers' community was estimated as 286x10 6 €, excluding the income from the non-irrigated wheat (almost 35x10 6 €) that is a traditional crop, not allowed for replacement in the optimization problem that follows. 3.4 Multi-objective optimization scheme for Pinios River Basin A single-objective optimization, such as minimizing the total N-NO 3 loads or maximizing Total Net Income, aims to identify the best practice for each HRU and then aggregate these decisions at the catchment level. Solving this type of problem is relatively straightforward. However, when optimization involves two conflicting objectives, the scenario configuration that minimizes one objective may not lead to the optimum of the other, creating trade-offs between the two goals. Since it is essential to assess switchgrass economic and environmental footprints for informed regional planning, multi-objective optimization was chosen for this work. In Pinios basin, the optimization problem includes two environmental criteria, one productivity criterion and one economic criterion: N-NO 3 loads and total irrigation water consumption, the perennial crop’s biomass production and the cumulative Total Net Income of the scenarios, respectively. Thus, the optimization problem becomes: where N-NO 3 is nitrate-nitrogen loads (kg), irrwater is the total irrigation water consumed in the basin (m 3 ), netincome is the annual total farmers’ net income, calculated as a sum for the entire basin (€) and switchgrassyield is the perennial crop’s biomass production (kg). The i index corresponds to the HRU and the j index to the scenario applied to each one of them (conventional land use type or switchgrass). For non-irrigated HRUs, j always corresponds to the baseline landuse type (no change). This is a multi-objective optimization problem involving four conflicting objectives. It is addressed using the MATLAB's Genetic Algorithm (GA), which identifies the optimal Pareto solutions along a four-dimensional Pareto front. It is noted that this specific algorithm by default attempts to minimize all the applied objectives, therefore, for the optimization (maximization) of the Total Net Income and biomass production in this work, negative signs (-) had to be applied. Hence, while the algorithm seeks to mathematically minimize the last two objectives, their absolute values achieved are the positive desirable numbers of Income and biomass produced. The multi-objective optimization scheme is depicted in Fig. 2 . At the beginning of the optimization process SWAT was executed for 8 years, excluding the outputs of the first two (warm up period), thus simulating crop yields, N-NO 3 amounts lost from HRUs to waters, and mean annual irrigation water amounts applied to all HRUs during the six-year period 2018–2023. A Database Tool was then established as the most efficient linkage between SWAT and the optimization GA. This database replaces the dynamic connection between SWAT and the GA, while at the same time it significantly reduces the required optimization time (Panagopoulos et al., 2010). The Database Tool developed for use with the DST for the PRB stores the model’s outputs regarding mean annual N-NO 3 loads and mean annual irrigation amounts consumed in all HRUs (totally extracted from the source), mean annual biomass production from switchgrass, as well as the respective calculated Total Net Income arising from the cultivated crops to all HRUs. The developed scenarios represent the installation of switchgrass in different irrigated land uses (alfalfa, corn, cotton). The database is organized into 4 tables, where the rows of each table represent the catchment’s HRUs (1850 in total) and the two columns the aforementioned environmental parameters and associated net income. The values of the first column of each table are the results of the baseline, while those of the second column are the results from the implementation of switchgrass. Obviously, each row (HRU) values differ only for the irrigated HRUs where switchgrass was allowed for potential installation, replacing the conventional irrigated crop (cotton, corn, alfalfa). In practice, the Database Tool served as a lookup table, eliminating the need to run the SWAT model during optimization when searching for combinations of switchgrass implementation across the irrigated land. The optimization process started with the random initialization of a population through the GA. Each individual within the population was composed of a set of genes, with the number of genes corresponding to the number of decision variables, in this case, the number of HRUs in the catchment. The values of genes of an individual formed the genotype, while their real representation (phenotype) represented a combination of different allocation schemes in the HRUs of the PRB. To ensure that the algorithm generated only valid solutions (individuals), lower and upper bounds (LB and UB) were defined for each gene, and thus, the only mathematical constraints applied in the optimization problem were bound constraints. Specifically, irrigated HRUs had the bounds 1 (lower-baseline crop) and 2 (upper-switchgrass), while all others had as both bounds the '1' corresponding to the baseline output. Once properly formulated, the individuals in the population were evaluated based on fitness functions, which included the total annual N-NO 3 loads entering streams, the total annual irrigation water consumption, the annual switchgrass biomass production at the entire basin level and the associated annual Total Net Income for all HRUs, as provided by the Database Tool. The algorithm aimed to minimize the user-defined objectives through an iterative process of population evolution. After evaluating the population, the algorithm checked whether the current generation number had reached the maximum number of generations, which served as the termination criterion. If the maximum number was reached, the algorithm stopped; otherwise, the population underwent selection and genetic operations (crossover and mutation) to create a new generation. 3.5 Selected GA functions and parameters In the multi-objective GA in MATLAB the user defines all options regarding the functions and parameter values of the GA through the programming environment of the MATLAB workspace. The performed sensitivity analysis examined the most important alternative configuration options (functions and parameter values) by varying one at a time while keeping the others constant. The metric used by the algorithm to assess the spread of solutions along the Pareto front was selected to be the crowding distance, a function integrated into MATLAB’s GA toolbox (MATLAB, 2024 ). In addition, we concluded that the Pareto front is better formed using the phenotype space rather than the genotype one. The 'Rank-based' selection ranks the solutions and assigns probabilities according to their order, leading to a more uniform and stable formation of the Pareto front and preserving genetic diversity in the population. Since crossover is the primary genetic reproduction mechanism, a high crossover probability is generally considered essential for accelerating the optimization process. After several trials, the crossover probability of 0.8 was selected as the standard setting for the final optimization configuration. Consequently, the population size and the maximum number of generations until termination were examined. A larger population increases the number of individuals involved in the evolutionary process, thereby enhancing the likelihood of generating better offspring. Likewise, a higher number of generations allows producing more populations and the evaluation of a greater number of solutions, continuously improving the results. The evolution of the Pareto front after 100, 300 and 500 generations was examined. The Pareto front was enhanced further after the execution of 500 generations; however, without significant improvement in the results. Therefore, the number of 500 generations was considered sufficient. Finally, after experimentation with the population size, the number 300 was selected, demonstrating a broader and more balanced distribution and indicating improved diversity and spread of solutions. The above choices ensured reliable convergence while maintaining acceptable execution time for the optimization process. 4 Results The results of the four-criterion optimization are presented, including the minimization of N-NO 3 loads in surface waters and the minimization of the water consumed in irrigated agriculture on an annual basis, and the maximization of the Total Net Income and the biomass production from switchgrass implementation in the PRB. The produced 100 optimal Pareto solutions provide a basis for generating an equal number of maps depicting them within the PRB. For the needs of this study, five solutions are chosen to be presented. Figures 3 and 4 illustrate the position of the chosen solutions in the optimal Pareto fronts of N-NO 3 loads-Total Net Income and irrigation water consumption-Total Net Income, respectively. Solution No.1 prioritizes the N-NO 3 reduction. Solutions No.2 and No.3 align with the target of biomass production, which was set by the Thessaly’s action plan at the level of 10 6 kg (Action plan for the Region of Thessaly, 2024 ). These two solutions achieve the annual biomass production of approximately 10 6 tons (not shown on the fronts) but represent the maximum and minimum N-NO 3 reduction, respectively, achieved with this level of switchgrass biomass production at the basin level. Solution No.4 constitutes a rather more realistic and sustainable plan for PRB. It was selected based on a more rational percentage of the total irrigated cropland that can be converted to switchgrass. Specifically, changing around 10% of the total irrigated cropland to switchgrass was considered realistic to the region’s needs and characteristics. Finally, Solution No.5 represents the solution with the maximum biomass production from the bioenergy crop that, at the same time, achieves almost the maximum possible reductions in N-NO 3 loads and irrigation water consumption. Table 4 summarizes the results produced by the five solutions indicated. Table 4 Mean annual (2018–2023) results produced by the 4-criterion optimization in the 5 selected solutions and the baseline scenario. Scenarios Switchgrass as % of conv. irrig. cropland N-NO 3 (kg) Irrigation (m 3 ) Biomass production (tons) Total Net Income (€) Baseline 0 1.57x10 6 720x10 6 - 286x10 6 Solution No.1 52.0 1.38x10 6 (-12%) 550x10 6 (-23.6%) 2.01x10 6 235x10 6 (-17.8%) Solution No.2 36.0 1.41x10 6 (-10%) 595x10 6 (-17%) 1.42x10 6 261x10 6 (-8.5%) Solution No.3 25.0 1.45x10 6 (-7%) 632x10 6 (-12%) 1.03x10 6 278x10 6 (-2.5%) Solution No.4 11.6 1.49x10 6 (-5%) 680x10 6 (-5.6%) 0.44x10 6 286x10 6 (0%) Solution No.5 95.0 1.33x10 6 (-15%) 468x10 6 (-35%) 3.5x10 6 123x10 6 (57%) Figure 5 illustrates the different spatial allocations of the bioenergy crop under Solutions 1–4. Solution No.5 is not depicted with a map since in order to achieve the maximum biomass production, switchgrass is inevitably installed almost in the entire irrigated cropland. Solution No.1 involves the decrease of the mean annual N-NO 3 loads to 1.38x10 6 kg, which corresponds to a total 12% reduction from the baseline. Moreover, switchgrass allocation in the PRB according to this solution results in 550x10 6 m 3 of irrigation water consumption, 2.01x10 6 tons of biomass production with the Total Net Income at 235x10 6 €, reflecting the economic impact of such a large-scale transition (-17.8% from the baseline income). It is evident that the algorithm implemented switchgrass in extensive areas of the irrigated cropland. Specifically, 52% of the total area of conventional crops has been replaced by switchgrass, primarily targeting fields of corn, which is the most nutrient demanding and water consuming crop of PRB. Under the current allocation scheme, biomass production is double the target proposed by the Thessaly’s action plan. However, taking into consideration the very extensive area of switchgrass installation that endangers food/fiber security, as well as the reduced profitability mentioned, Solution No.1 is considered not applicable. Among the solutions where the production target of 10 6 tons of biomass was achieved, the ones corresponding to the maximum (Solution No.2) and minimum (Solution No.3) reduction of N-NO 3 loads are presented. Specifically, in Solution No.2, switchgrass allocation in the PRB results in 595x10 6 m 3 of irrigation water consumption, 1.42x10 6 tons of biomass production, and 1.41x10 6 kg of N-NO 3 loads with an annual Total Net Income of 261x10 6 €, reduced by 8.5% from the baseline. Switchgrass implementation was concentrated in northern, western, and southern subregions, in a similar way as was chosen in Solution No. 1, but with a more balanced extent. It is interesting to note that the large area in the eastern part that was allocated to switchgrass in Solution No.1 has now been allocated to the conventional crop (cotton). This area is irrigated by the newly constructed 'Karla' reservoir with no water deficits so to achieve a less reduced Total Net Income from the baseline under Solution No.2, the algorithm assigns this area to the most profitable crop (cotton) that maximizes its yield under full water availability. This solution continues to prioritize environmental indicators while introducing a more conservative economic impact. In this alternative configuration, 36% of the total conventional irrigated cropland in the PRB is converted to switchgrass. The reallocation includes 63% of the originally corn areas, 35% of cotton areas, and 12% of alfalfa areas being replaced. As in the previous case, corn is again the most heavily impacted crop on a percentage basis. Biomass production exceeds 10 6 tons (1.42x10 6 ), fully meeting the target set by the Thessaly’s action plan and illustrating the potential of strategic bioenergy crop deployment. In Solutions No.2 and No.3 the spatial pattern of switchgrass implementation remained largely consistent. Specifically, the spatial allocation pattern of switchgrass in Solution No.3 (Fig. 5 ) closely mirrors that of the previously described Solution No.2, despite the reduced extent of land-use change and lower environmental improvement. Under the spatial allocation of Solution No.3, 632x10 6 m 3 of irrigation water consumption, 1.03x10 6 tons of biomass production, and 1.45x10 6 kg of N-NO 3 loads are observed, with the Total Net Income being 278x10 6 €. Despite the more limited extent of land conversion, the solution meets exactly the biomass target of the Thessaly’s action plan (Action plan for the Region of Thessaly, 2024 ), with little income reduction (2.5%). The last map of Fig. 5 illustrates the spatial distribution of switchgrass as formed by the Solution No.4, which represents the most conservative approach. As shown in Table 4 , this solution involves a decrease of the mean annual N-NO 3 loads to 1.49x10 6 kg, which corresponds to a total 5% reduction from the baseline, 680 x10 6 m 3 of irrigation water consumption, 0.44x10 6 tons of biomass production, and a Total Net Income identical to the baseline one at 286x10 6 €. As observed on the map of Fig. 5 , almost all small areas in the northern and southern parts of the basin, where conventional crops were replaced in the previous solutions, are those that are solely chosen by the algorithm for conversion to switchgrass under Solution No.4. In this more realistic solution, 11.6% of the irrigated cropland is replaced by the perennial bioenergy crop. The Solution No.4 includes the reallocation of 29% of corn, 10% cotton, and 4% alfalfa areas. However, the solution fails to meet the annual biomass production target set by the Thessaly’s action plan, yielding only 0.44×10 6 tons, below the expected threshold of 10 6 tons. The two most realistic solutions, Solutions No.3 and No.4, are further analyzed in order to better assess their effectiveness and assist in decision making for the area. Figure 6 depicts the mean annual Total Net Income as calculated by the algorithm. Figure 6 a shows the Total Net Income as formed in the baseline, whereas Fig. 6 b refers to the switchgrass implementation under the Solution No.3, and Fig. 6 c refers to the switchgrass implementation under the Solution No.4. In the northern part of the basin, the baseline negative income values remain under both solutions, reflecting the constant local limitations, such as unfavorable climate (colder conditions) and soil characteristics (sloping land with shallow root depth), which constrain both conventional and bioenergy crop productivity. In Solution No.3, as shown in the circled areas of the map of Fig. 6 b, several southern HRUs display slightly increased net income, indicating favorable economic outcomes from switchgrass adoption in these zones in comparison with the conventional crops of the baseline. Actually, irrigation water deficits that did not allow adequate production of the more water demanding baseline crops in some HRUs there, has been eliminated with the use of the less demanding bioenergy crop which achieved a slightly more profitable production. Moreover, the increased income areas in the south include certain HRUs that were not directly converted to switchgrass but were indirectly benefiting from increased groundwater availability due to nearby perennial crop cultivation. In Solution No.4 (Fig. 6 c), where switchgrass was implemented at a smaller scale, the same southern HRUs still recorded improved net income. In the western and central parts of the basin indicated by circles, the changed HRUs show reduced income under both solutions, consistent with the expected economic trade-offs. Overall, the maps reflect spatial variability in economic impacts, depending on local conditions and the extent of switchgrass implementation, with the simultaneous existence of both lower and higher income subareas compared to the baseline, being responsible for maintaining the Total Net Income at levels very similar to the baseline. In Fig. 7 , the mean annual N-NO 3 loads of the baseline (Fig. 7 a and those resulted from the switchgrass installation of Solution No.3 (Fig. 7 b), and No.4 (Fig. 7 c) are presented. The introduction of switchgrass cultivation across the basin led to notable environmental benefits with respect to N-NO 3 loads, as demonstrated. In all areas where switchgrass was implemented, N-NO 3 losses were consistently lower compared to the baseline. Especially, the mean annual basin-level N-NO 3 load recorded in the baseline was 1.48 kg/ha or 1.57×10 6 kg/y, the respective loads under the Solution No.3 were 1.37 kg/ha or 1.45×10 6 kg/y, and under the Solution No.4 were 1.40 kg/ha or 1.49x10 6 kg/y. The reductions were especially evident in specific subregions in both the northern and southern parts of the basin. The Thessaly plain in the center of the PRB, which remains classified as a nitrate vulnerable zone, continued to benefit to some extent from switchgrass adoption. In Solution No.3, switchgrass implementation not only achieved high biomass yields but also substantially reduced N losses, helping to address both productivity and environmental quality objectives. Under the spatial allocation of Solution No.4, although the area of implementation was reduced compared to Solution No.3, the lower Ν input requirements still resulted in improved water quality with respect to N-NO 3 at a small area. This is particularly significant given the region’s high baseline N fertilization of crops such as corn. Another important environmental parameter is N-NO 3 leached, which refers to the amount of N, that moves through the soil and reaches the groundwater. At the subbasin level, the introduction of switchgrass resulted in substantial reductions in N-NO 3 leaching. The lower N demand of switchgrass, coupled with its deep perennial root system, effectively limited N-NO 3 movement below the root zone. Figure 8 illustrates the average N-NO 3 leaching concentration (mg/L) on a subbasin level in the baseline (Fig. 8 a) and under the implementation of Solutions No.3 (Fig. 8 b) and No.4 (Fig. 8 c). At the entire basin level, the mean annual concentration of leached N-NO 3 in the baseline (21.2 mg/L) was drastically reduced by 17.5% in Solution No.3 (17.5 mg/L) and by 17% with Solution No.4 (17.7 mg/L), demonstrating an almost identical reduction in N-NO 3 leaching. In several subbasins all across the basin where switchgrass was installed, leaching levels were reduced by more than half compared to the baseline, emphasizing the role of the bioenergy crop in improving nutrient soil retention and mitigating groundwater contamination. Particularly in the large, concentrated switchgrass area in Solution No.3 in the southern part of the basin, the concentration was decreased to levels lower than 11 mg/L. In Solution No.4, although the effect is less pronounced than in Solution No.3, it remains environmentally meaningful, especially in subbasins where switchgrass was introduced in areas that tend to promote N-NO 3 transport. In certain northern subbasins, leaching levels fell by more than 40%. Overall, the spatial correlation between switchgrass coverage and N-NO 3 leaching reductions confirms its potential as a strategic land use option for nitrate pollution control in intensively cultivated and sensitive areas. The maps in Fig. 9 depict the spatial distribution of irrigation deficits across the PRB under the baseline (Fig. 9 a), Solution No.3 (Fig. 9 b), and Solution No.4 (Fig. 9 c). The baseline showed substantial irrigation deficits across the PRB, with annual water consumption totaling approximately 720x10 6 m 3 . Under Solution No.3, the widespread implementation of switchgrass significantly reduced irrigation deficits, eliminating them entirely in 59 HRUs and lowering total water use by 12% to 632x10 6 m 3 . Solution No.4, which applied switchgrass more selectively across the 11.6% of the irrigated cropland, achieved a 5.6% reduction in total water use (680x10 6 m 3 ) and nullified deficits in 44 HRUs, with the majority of the implementation areas exhibiting deficits below 5%. Improvements were spatially distributed across the northern, southern, and western parts of the basin, with especially strong reductions where water stress was the highest, depicted with the red circles in Figs. 9 b and 9 c. In many areas with prior minor deficits, irrigation deficits were nearly eliminated. These results highlight the water-saving potential of switchgrass, even at limited scales of adoption. Figure 10 Groundwater content in the Pinios river basin at the end of the simulation period (2023), expressed in mm of water for the baseline (Fig. 10 a) and as increase from the baseline (in mm) (Figs. 10 b and 10 c) for the switchgrass according to the spatial allocation of compromise Solution No.3 and No.4 (Fig. 5 ) The spatial distribution of groundwater content across the PRB under the baseline conditions (Fig. 10 a) reveals heterogeneity, with values ranging from less than 100 mm to over 1000 mm depending on subbasin characteristics. The highest groundwater reserves are concentrated primarily in the western and northern regions, where several subbasins exceed 700 mm, and in some cases, surpass 1000 mm of groundwater content. In contrast, the central eastern regions exhibit significantly lower storage, frequently remaining below 200 mm. In Fig. 10 b, representing Solution No.3, the basin demonstrates extensive increases in groundwater content, with particularly notable improvements, often surpassing 100 mm, in the southern and southwestern subbasins. Moderate increases of 10 to 50 mm are also observed in central areas, while only minimal improvements occur in the northeastern subregions. Figure 10 c, which corresponds to Solution No.4, shows more localized yet still meaningful increases, especially in the southern subbasins, where groundwater again exceeds 100 mm. Central areas display more modest increases, generally ranging between 10 and 50 mm, indicating a positive but more conservative hydrological gain from the implementation of switchgrass. 5 Discussion The developed framework used for a multi-criteria optimization process in this study aimed to explore practical ways to improve agricultural management in the Pinios River Basin, focusing on how the implementation of a perennial bioenergy crop, switchgrass, could contribute to environmental benefits, economic stability, and adequate biomass development for subsequent use in renewable energy production. The multi-criteria optimization, performed using the MATLAB GA, proposed a wide range of different switchgrass allocation schemes for the agricultural land through the development of a four-dimensional Pareto optimal front with a very good exploration space and spread of solutions. Evaluating the solutions on these trade-off curves, as well as mapping the spatial distribution of the perennial crop across the basin, revealed a broad spectrum of alternative management strategies, corresponding to equally broad variations in N-NO 3 loads exported from land to rivers and in the overall mean annual income of the region’s agricultural community. In the previous Section, a detailed analysis of the two more realistic solutions of converting 25% or 11.6% of the conventional irrigated land to switchgrass (Solution No.3 and No.4) was presented. As explained, both had significant positive impacts on various environmental factors, and thus they are considered very effective alternatives to the existing cropping patterns of PRB. The findings emphasize the importance of spatially targeted and scale-sensitive land use strategies when integrating perennial bioenergy crops like switchgrass. The more extensive adoption of the bioenergy crop in Solution No.3 maximizes environmental returns but requires accepting some economic loss and more significant land-use changes, whereas Solution No.4 prioritizes economic stability with less evident environmental improvements and biomass production. Both solutions consistently improve N leaching loads and water use efficiency, demonstrating switchgrass contribution to sustainable agriculture and groundwater sustainability. The comparison between Solutions No.3 and No.4 highlights a clear trade-off between the extent of switchgrass adoption and its economic and environmental impacts across the PRB. Solution No.3, which converts approximately 25% of irrigated cropland to switchgrass, achieves the Thessaly Action Plan’s biomass target fully and results in substantial environmental benefits, including a 7% reduction in total N-NO 3 loads and a 12% decrease in irrigation water use basin-wide. This broader implementation also leads to significant improvements in groundwater recharge, particularly in the southern and southwestern subbasins, where groundwater content increases exceed 100 mm, which is equivalent to 1000 m 3 /ha over the 6-y simulation period. However, this more intensive approach comes with a small overall reduction in Total Net Income, although there are small southern areas in the basin that demonstrate localized economic benefits due to higher profitability with switchgrass than with the conventional crops (Fig. 6 ). On the other hand, Solution No.4 adopts a more conservative strategy, converting only 11.6% of cropland, which maintains Total Net Income at the baseline levels and still provides notable but more modest environmental benefits such as a 5% reduction in N-NO 3 loads, a 5.6% cut in irrigation water use, and localized groundwater increases generally under 50 mm. While this solution meets only half the biomass target, it preserves food and fiber production more effectively and offers a viable pathway for gradual integration of bioenergy crops without major economic disruption. Thus, it is considered as the optimum solution under the current cropping schemes and socio-economic circumstances in the area. Decision-makers must weigh these trade-offs based on subregional and local priorities, balancing renewable energy goals with conventional agricultural productivity needs and economic resilience. Overall, the results validate switchgrass incorporation into existing cropping systems as an effective practice for advancing sustainable land management and renewable energy targets in the PRB, with Solution No.3 offering a more ambitious, comprehensive approach and solution No.4 providing a cautious, economically sensitive alternative. Although the latter meets half of the annual biomass target, it can contribute towards sustainable bioenergy development in PRB, being the first crucial step for serious consideration of agriculture as a potential contributor to renewable energy production in the area. However, there still exists a significant gap between the scientific approach and the practical application, which is primarily manifested as a lack of collaboration with the farming population, an essential factor for achieving the set goals. The reasons for these barriers are obviously linked to economic factors. Farmers are often wary of adopting new land management practices, fearing, perhaps justifiably, that their income may be negatively affected. Consequently, switchgrass adoption in the existing cropping patterns could be considered controversial, because, despite the relatively low annual cultivation cost, its low biomass sale price and subsidy levels cannot result in comparable income values with those of the conventional irrigated crops. Nevertheless, with revised subsidies or a stronger biomass market, the economic attractiveness of the crop could be considerably strengthened. Some assumptions were also made in the formulation of the optimization problem that have to be discussed. First, switchgrass was allowed to be implemented across the irrigated land of the entire basin without socio-economic restrictions that could not be investigated at this stage. This means that any switchgrass introduction scheme refers to large groups of farmers, represented by the rather large HRUs of this study, implying that all of them invest simultaneously their total land for switchgrass implementation. The aggregation of several farmer holdings into the large HRUs results in the implementation of switchgrass in areas that may be affordable for some farmers but not for others. Thus, basin-scale solutions would be more realistic and practically feasible to implement if economic heterogeneities at the local level were supported by the farmers' community itself or more officially from a management body, responsible for the bioenergy crop adoption in PRB. In line with the above, the absence of restrictions led to uneven replacement of the three irrigated conventional crops, favoring the replacement of corn to a higher percentage than the other two conventional irrigated crops. Consequently, the Total Net Income referred to the aggregated income of all farmers within the PRB, and this was a key aspect of the mathematical problem which was designed to maximize the financial revenues the entire farmers' community acquires from the implementation of switchgrass and conventional crops in the basin. However, it is important to highlight that apart from the economic objectives, the environmental ones are also evaluated at the scale of the entire Pinios River Basin. As a result, not only the local income improvements or losses, but also N-NO 3 loads, and water-saving benefits are not assessed separately for different HRUs. It should be finally noted that the Pareto optimum solutions in this study are situated within a broad Total Net Income range, as it was preferable to let the GA discover all the technically feasible solutions, including those that, based on extensive replacement of conventional crops, were capable of reaching the most ambitious environmental targets at the basin scale. Hence, no income constraint was introduced to prevent the GA from discovering very unprofitable solutions. Secondly, in this study, the optimization solver used the HRU results stored in the Database Tool. This is a simplification as, due to in-stream processes, the total pollutant values exported from HRUs might not always match those at the basin outlet. The DST aimed to minimize pollutant losses from HRUs rather than reduce concentrations at the basin outlet. Besides improving computational speed, this approach may not be considered ideal for studies where the load at the outlet is severely influenced by biophysical processes occurring in the rivers. What allowed this simplification, however, is the fact that in a fast-responding Greek river, the change in pollutant load during transport is not considered significant on an annual basis (Panagopoulos et al., 2010). The geomorphology of the Greek territory often leads to a rapid response of rivers to meteorological events and, consequently, a short water residence time in the riverbed. As a result, the nutrient loads measured by authorities or simulated by the model at the basin outlet can be considered a reasonable representation of the cumulative nutrient losses from upstream areas entering the river network after leaving the land. Therefore, optimizing environmental criteria in a Greek basin using the Database Tool effectively addresses the optimization of nutrient loads or concentrations at the river basin outlet. Moreover, with the Database Tool, it is possible to address environmental pollution across the whole upstream area in a basin, where the water quality of tributary rivers could also be of concern. Finally, there is always potential for improving the current version of the DST developed in this study for agricultural management and bioenergy production in the PRB. Ideally, future improvements should stem from a close and ongoing collaboration with local stakeholders, which would allow for a more precise understanding of their needs and concerns. The general methodological framework has already proven valuable, providing opportunities for testing various scenarios and evaluating their environmental efficiency and cost-effectiveness across the entire basin. 6 Conclusions This study developed and applied a robust, GIS-based decision support tool integrating the SWAT hydrological model with a Genetic Algorithm, embedded in MATLAB, to optimize the spatial allocation of switchgrass in the PRB, balancing environmental sustainability with economic viability. Through multi-criteria optimization, the tool generated 100 Pareto-optimal solutions that equally considered the mean annual N-NO 3 loads, irrigation water consumption, biomass production, and Total Net Income of the region’s agricultural community. Among the chosen solutions on the Pareto fronts, the solution that demonstrated the highest environmental benefit achieved significant N-NO 3 and irrigation reductions while exceeding biomass targets, but was considered unrealistic due to high income losses and extensive land conversion. Another solution presented a more balanced trade-off but still involved considerable economic impacts. In contrast, two other solutions offered more practical compromises. One of them met the biomass target with a small 2.5% annual income reduction, alongside a 7% reduction in N-NO 3 and 12% in water use. The second was selected as the most viable, maintaining baseline income while still achieving moderate reductions in N-NO 3 (5%) and irrigation water use (5.6%), though producing only half of the biomass target. Overall, the integration of SWAT with an optimization algorithm proved efficient for evaluating land use strategies, enabling accurate exploration of the areas with N-NO 3 pollution and high water exploitation. The results highlight the potential of the present optimization tool to guide river basin scale decisions that support both environmental and agricultural production sustainability. As a broader conclusion, it can be said that switchgrass, being a drought-tolerant, low-input, and high-yielding crop, could play a central role in improving water quality and quantity, contributing to the overall sustainability of agricultural practices in the region and the production of significant biomass amounts for bioenergy production. An ideal combination of the existing crops and the perennial bioenergy one would form a comprehensive and sustainable action plan to enhance and balance the environmental issues of the Pinios river basin and the Thessaly water District in central Greece. Declarations Acknowledgments:We would like to thank the reviewers for their constructive and insightful comments that helped us improve our article. Funding: This research study was carried out within the framework of the National Recovery and Resilience Plan Greece 2.0, funded by the European Union—NextGenerationEU (Implementation body: HFRI). More specifically, this research was supported under the Basic Research Financing Action “Horizontal support of all sciences”, Sub-action 1 (Project Number: 16425; project title: BIOGRASS). Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Author Contributions: Conceptualization L.K, M.S and Y.P.; methodology, L.K., M.S., K.G., and Y.P.; software, L.K., M.S., S.K. and H.G.; validation, L.K., M.S., S.K., H.G. and K.G.; formal analysis, L.K., M.S., and Y.P.; investigation, L.K., M.S., S.K. and H.G.; resources, L.K., M.S. and Y.P.; data curation, L.K., M.S., K.G. and Y.P.; writing, original draft preparation, L.K. and M.S.; writing-review and editing, L.K., M.S., S.K., H.G., K.G. and Y.P.; visualization, L.K., M.S., S.K., H.G. and Y.P.; supervision, Y.P.; project administration, Y.P.; funding acquisition Y.P. All authors have read and agreed to the published version of the manuscript. References Action plan for the Region of Thessaly (2024) Region of Thessaly. https://www.thessaly.gov.gr/images/right/sxedio.pdf . Accessed 01 May 2025 Akdemir EA, Kern J, Smith JP, Limb BJ, Quinn JC, Field JL, Pack T (2025) Multi-objective optimization of sustainable aviation fuel production pathways in the US Corn Belt. 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Accessed 15 June 2025 Mishra SK, Negri MC, Kozak J, Cacho JF, Quinn J, Secchi S, Ssegane H (2019) Valuation of ecosystem services in alternative bioenergy landscape scenarios. GCB Bioenergy 116:748–762. https://doi.org/10.1111/gcbb.12602 Neitsch SL, Arnold JG, Kiniry JR, Srinivasan R, Williams JR (2009b) Soil and Water assessment tool - input/Output file documentation-version 2005. Blackland Research Center, Texas. Agricultural Experiment Station, Temple, Texas (BRC Report 02–05). swat.tamu.edu/media/69296/swat-io-documentation-2012.pdf . Accessed 26 June 2025 Neitsch SL, Arnold JG, Kiniry JR, Williams JR (2009a) Soil and Water Assessment Tool (SWAT) Theoretical Documentation. Blackland Research Center, Texas. Agricultural Experiment Station, Temple, Texas (BRC Report 02–05). Available online at: https://swat.tamu.edu/media/69296/swat-io-documentation-2012.pdf . Accessed 20 June 2025 Nikkhah A, Assad MEH, Rosentrater KA, Ghnimi S, Van Haute S (2020) Comparative review of three approaches to biofuel production from energy crops as feedstock in a developing country. Bioresource Technol Rep 10:100412. https://doi.org/10.1016/j.biteb.2020.100412 Panagopoulos Y (2010) Decision making for planning sustainable measures for water quality protection against non-point source pollution. PhD Dissertation. School of Civil Engineering, National Technical University of Athens Panagopoulos Y, Gassman PW, Kling CL, Cibin R, Chaubey I (2017) Water quality assessment of large-scale bioenergy cropping scenarios for the upper Mississippi and Ohio‐Tennessee river basins. JAWRA J Am Water Resour Association 53:6: 1355–1367. https://doi.org/10.1111/1752-1688.12594 Panagopoulos Y, Makropoulos C, Gkiokas A, Kossida M, Evangelou L, Lourmas G, Mimikou M (2014) Assessing the cost-effectiveness of irrigation water management practices in water stressed agricultural catchments: The case of Pinios. Agric Water Manage 139:31–42. https://doi.org/10.1016/j.agwat.2014.03.010 Pisinaras V, Herrmann F, Panagopoulos A, Tziritis E, McNamara I, Wendland F (2023) Fully Distributed Water Balance Modelling in Large Agricultural Areas—The Pinios Basin (Greece). Sustainability 15.5: 4343. https://doi.org/10.3390/su15054343 Sismanidi M, Kokkinaki L, Kavalieratou S, Georgoussis H, Giannoulis KD, Dimitriou E, Panagopoulos Y (2025) Assessing the Effects of Bioenergy Cropping Scenarios on the Surface Water and Groundwater of an Intensively Agricultural Basin in Central Greece. Hydrology 124:66. https://doi.org/10.3390/hydrology12040066 Sivanandam SN, Deepa SN (2008) Genetic algorithms. In: Springer Berlin Heidelberg (eds) Genetic algorithms, pp 15–37. Available online at: https://link.springer.com/content/pdf/10.1007/978-3-540-73190-0_2.pdf Tromaras A, Margaritis D, Moschovou T (2021) Energy Consumption and Perspectives on Alternative Fuels for the Transport Sector: A National Energy Policy for Greece. In Advances in Mobility-as-a-Service Systems: Proceedings of 5th Conference on Sustainable Urban Mobility, Virtual CSUM2020. June 17–19, 2020, Greece (pp. 347–356). Springer https://doi.org/10.1007/978-3-030-61075-3_34 Valcu-Lisman AM, Kling CL, Gassman PW (2016) The optimality of using marginal land for bioenergy crops: tradeoffs between food, fuel, and environmental services. Agric Resour Econ Rev 45(2):217–245. https://doi.org/10.1017/age.2016.20 Vasiliades L, Loukas A, Liberis N (2011) A water balance derived drought index for Pinios River Basin, Greece. Water Resour Manage 25:4: 1087–1101. https://doi.org/10.1007/s11269-010-9665-1 Wang G, Jager HI, Baskaran LM, Brandt CC (2018) Hydrologic and water quality responses to biomass production in the Tennessee river basin. Gcb Bioenergy 1011:877–893. https://doi.org/10.1111/gcbb.12537 Williams JR, Arnold JG, Kiniry JR, Gassman PW, Green CH (2008) History of model development at Temple, Texas. Hydrol Sci J 535:948–960. https://doi.org/10.1623/hysj.53.5.948 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Dec, 2025 Read the published version in Environmental Processes → Version 1 posted Editorial decision: Revision requested 13 Oct, 2025 Reviews received at journal 22 Aug, 2025 Reviews received at journal 20 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers invited by journal 16 Aug, 2025 Editor assigned by journal 07 Aug, 2025 Submission checks completed at journal 30 Jul, 2025 First submitted to journal 28 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7233578","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504322144,"identity":"12960ef8-b57f-45b8-9f7a-cf3e2a7d9990","order_by":0,"name":"Lamprini Kokkinaki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACPgY2BsaGAxIM/HAhCQJa2GBaJBugIjxEamFgMDhAtBb2Y4kPZ5yxyDM+fvzahw8Mdon7pRuYX1fg08KTdthwww2JYrMzOcUzZzAkJ/bIHGCzPIPXYeltkg8+SCRuO5CTzMzDwJzYI5HAZtiATwv/c4iWzf1vQFrqidAikXZMEuiwxA0S6YeBWg6DtDA/xK/lWbLhjDMSiTNuvGFmnGFw3LjnRmIbIz4t/Pxphg97jtUl9venP2b4UFEt2z4j+fBHfFqQAI8BMHZADMY2QlEDA+wPYCzmD0RqGQWjYBSMgpEBALtSTtogd4LhAAAAAElFTkSuQmCC","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":true,"prefix":"","firstName":"Lamprini","middleName":"","lastName":"Kokkinaki","suffix":""},{"id":504322146,"identity":"638798b9-dc8b-4f84-b5c5-f19479585b5c","order_by":1,"name":"Maria Sismanidi","email":"","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Sismanidi","suffix":""},{"id":504322148,"identity":"609c5b01-a4f1-4d2e-aabd-781bed018d66","order_by":2,"name":"Haralampos Georgoussis","email":"","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Haralampos","middleName":"","lastName":"Georgoussis","suffix":""},{"id":504322149,"identity":"42b386aa-d7bd-4fb6-bd30-9ae0040439b7","order_by":3,"name":"Sofia Kavalieratou","email":"","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Sofia","middleName":"","lastName":"Kavalieratou","suffix":""},{"id":504322150,"identity":"c653139d-1c6c-4c35-943c-3a793dfb77c3","order_by":4,"name":"Kyriakos D. Giannoulis","email":"","orcid":"","institution":"University of Thessaly","correspondingAuthor":false,"prefix":"","firstName":"Kyriakos","middleName":"D.","lastName":"Giannoulis","suffix":""},{"id":504322151,"identity":"89aea841-4fdd-4dda-bce9-8ef4619c1e05","order_by":5,"name":"Yiannis Panagopoulos","email":"","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Yiannis","middleName":"","lastName":"Panagopoulos","suffix":""}],"badges":[],"createdAt":"2025-07-28 11:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7233578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7233578/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40710-025-00809-8","type":"published","date":"2025-12-04T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89843130,"identity":"aa260184-25b5-4391-b2fd-e61f116ecd4c","added_by":"auto","created_at":"2025-08-25 15:39:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":331528,"visible":true,"origin":"","legend":"\u003cp\u003eIrrigated areas (colored) with irrigation sources (Fig. 1a) and irrigated cropland (Fig. 1b)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7233578/v1/fdfe81f92fe46d267b559c07.png"},{"id":89843133,"identity":"5853c081-62fd-4783-9373-169ecbb4052b","added_by":"auto","created_at":"2025-08-25 15:39:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103366,"visible":true,"origin":"","legend":"\u003cp\u003eThe multi-objective optimization scheme\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7233578/v1/018eee18f4f53760ac64bb35.png"},{"id":89843142,"identity":"84a868b5-1e64-40a1-b833-7dfe4a527c31","added_by":"auto","created_at":"2025-08-25 15:39:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53154,"visible":true,"origin":"","legend":"\u003cp\u003eCompromise solutions presented in the optimal trade-off curve between annual N-NO\u003csub\u003e3\u003c/sub\u003e loads from land to waters and Total Net Income from the 4-criterion optimization. Income appears with negative sign for optimization purposes (the GA seeks to minimize the objectives), while its absolute (positive) numbers are the desirable ones\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7233578/v1/3ba9c7e4660e5d15dedfb3d7.png"},{"id":89844784,"identity":"4f0d8cbc-9ab2-4026-af40-318a76e775aa","added_by":"auto","created_at":"2025-08-25 15:55:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50025,"visible":true,"origin":"","legend":"\u003cp\u003eCompromise solutions presented in the optimal trade-off curve between annual irrigation water consumption and Total Net Income from the 4-criterion optimization. Income appears with negative sign for optimization purposes (the GA seeks to minimize the objectives), while its absolute (positive) numbers are the desirable ones\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7233578/v1/b75b65075ae89d24053685c8.png"},{"id":89843872,"identity":"16cfef45-32a5-47ec-a6b3-218324ef5e93","added_by":"auto","created_at":"2025-08-25 15:47:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":611591,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial allocation of switchgrass under Solutions No.1, No.2, No.3 and No.4 of\u003cstrong\u003e \u003c/strong\u003eFigs. 3 and 4\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7233578/v1/c3196c652fba040a7dea17d4.png"},{"id":89843156,"identity":"760a7d60-f302-4e85-8af5-4149641105e2","added_by":"auto","created_at":"2025-08-25 15:39:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":413113,"visible":true,"origin":"","legend":"\u003cp\u003eMean annual (2018-2023) Total Net Income produced in €/ha. Fig. a: baseline scenario, Fig. b: switchgrass according to the spatial allocation of compromise Solution No.3 (Fig. 5), Fig. c: switchgrass according to the spatial allocation of compromise Solution No.4 (Fig. 5)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7233578/v1/4ffbdf580a5c836a0887c545.png"},{"id":89843152,"identity":"07f36b06-48f5-4758-9dcb-cb1afe61d5a2","added_by":"auto","created_at":"2025-08-25 15:39:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":418205,"visible":true,"origin":"","legend":"\u003cp\u003eMean annual (2018-2023) N-NO3 loads in kg/ha. Fig. a: baseline scenario, Fig. b: switchgrass according to the spatial allocation of compromise Solution No.3 (Fig. 5), Fig. c: switchgrass according to the spatial allocation of compromise Solution No.4 (Fig. 5)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7233578/v1/d9b27491b44f9ceebe2bb506.png"},{"id":89843148,"identity":"beb7e2c3-2c60-4d63-a92e-3dd01bec27e7","added_by":"auto","created_at":"2025-08-25 15:39:14","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":352818,"visible":true,"origin":"","legend":"\u003cp\u003eMean annual (2018-2023) concentrations of leached N-NO\u003csub\u003e3\u003c/sub\u003e to the shallow aquifers of each subbasin in mg/L. Fig. a: baseline scenario, Fig. b: switchgrass according to the spatial allocation of compromise Solution No.3 (Fig. 5), Fig. c: switchgrass according to the spatial allocation of compromise Solution No.4 (Fig. 5)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7233578/v1/b9b1d278ab5bea7cc1efc530.png"},{"id":89843143,"identity":"0263b868-d613-49dd-9f94-e367e850dd47","added_by":"auto","created_at":"2025-08-25 15:39:14","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":414130,"visible":true,"origin":"","legend":"\u003cp\u003eMean annual (2018-2023) irrigation deficit of the Pinios river basin as % difference from the theoretical crop water needs under: Fig. a: baseline scenario, Fig. b: switchgrass according to the spatial allocation of compromise Solution No.3(Fig. 5), Fig. c: switchgrass according to the spatial allocation of compromise Solution No.4 (Fig. 5)\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7233578/v1/c54c838d419537eb385b3cd2.png"},{"id":89843873,"identity":"e283a4bf-acce-4897-ab04-de091c739398","added_by":"auto","created_at":"2025-08-25 15:47:14","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":390464,"visible":true,"origin":"","legend":"\u003cp\u003eGroundwater content in the Pinios river basin at the end of the simulation period (2023), expressed in mm of water for the baseline (Fig. 10a) and as increase from the baseline (in mm) (Figs. 10b and 10c) for the switchgrass according to the spatial allocation of compromise Solution No.3 and No.4 (Fig. 5)\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7233578/v1/e072d0e12658a3515545d5d8.png"},{"id":97724092,"identity":"8053c76d-2145-494f-9817-3ee70fe1eadb","added_by":"auto","created_at":"2025-12-08 16:11:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3738444,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7233578/v1/d311de40-b524-4b75-b436-0e8753f784f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reducing nitrate water pollution and irrigation water consumption at the river basin scale through the optimized allocation of a low-input perennial bioenergy crop within the existing cropping systems","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; Using efficient technological tools to assist decision making in river basin management.\u003c/p\u003e\u003cp\u003e\u0026bull; A hydrological and crop growth model and a MATLAB GA assess the optimum allocation of a bioenergy crop across the basin.\u003c/p\u003e\u003cp\u003e\u0026bull; Various spatial allocations of crops, including the bioenergy one, are analyzed with respect to environmental and economic criteria.\u003c/p\u003e\u003cp\u003e\u0026bull; Optimum cropping patterns are demonstrated and proposed.\u003c/p\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eIn Mediterranean rural landscapes, the intensification of agricultural activities is closely associated with the qualitative and quantitative degradation of water bodies. Balancing crop production for diverse purposes with the protection of water quantity and quality in a region is a challenge, as these objectives can be partially or wholly conflicting, and finding appropriate management practices to achieve them simultaneously is not always obvious. Proper management practices are crucial to meet these demands and the goals of the European Union\u0026rsquo;s (EU) directives. Since agricultural land is increasingly promoted worldwide as a valuable resource for sustainable energy production (Nikkhah et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), integrating energy crops into farming systems could be an effective strategy to mitigate these impacts.\u003c/p\u003e\u003cp\u003eBiofuels are fuels derived from biomass, which refers to any material of biological (organic) origin. Biomass encompasses a wide range of materials that originate directly or indirectly from plants (Antar et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It can be utilized to meet various energy needs, such as heating, cooling, and electricity generation, and can also be processed into liquid biofuels. Consequently, bioenergy is considered an alternative energy source with positive environmental impacts, and global policies have been adopted to promote its production and use. The need to increase bioenergy production in Greece has been recognized over the past decades (Skoulou et al., 2011). However, the national use of renewable energy sources still remains low. As reported by Tromaras et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in 2016, renewable energy in transport accounted for less than 2%, five times lower than the EU average. The EU has implemented measures to advance biofuels through the Renewable Energy Directive (Directive 2023/2413), which is part of the European Green Deal, aiming for a climate-neutral EU by 2050. As an EU member state, Greece must comply with the European Directive, which mandates that biofuels contribute 29% to total transport energy consumption by 2030 (Directive 2023/2413). Thus, biomass and biofuels are seen as promising energy sources amid the current energy crisis in the EU, as they can be produced domestically, thereby reducing dependence on energy imports.\u003c/p\u003e\u003cp\u003eThe discharge of nitrates into surface water is one of the most serious environmental issues in extensively managed agricultural areas, since it can negatively impact the ecosystem's health. Furthermore, the high-water requirements of irrigated agriculture put additional pressure on the limited freshwater supplies, particularly in semi-arid and Mediterranean areas (Panagopoulos et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The European Union has been addressing the accumulation of nutrients in aquatic systems since the early 1990s with a comprehensive legislative framework, mainly through the Nitrates Directive (91/676/EEC), which targets diffuse pollution. The Water Framework Directive (WFD) (2000/60/EC) was further established to integrate water management at the river basin level, along with changes to the Common Agricultural Policy (CAP). In Greece, River Basin Management Plans and designated Nitrate Vulnerable Zones with supplementary Action Programs have been used to incorporate these instructions into national law. However, improvements in water quality are still inadequate due to gaps in stakeholder engagement and slow environmental response.\u003c/p\u003e\u003cp\u003eSubsequently, producing bioenergy requires extensive agricultural areas, often leading to significant land use changes that may compete with the production of food, feed, and fiber. In general, low input bioenergy crops, such as the perennial crop switchgrass can have positive environmental impacts when incorporated within existing resource-demanding cropping systems (Giannoulis et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, the reliance on land availability, the irrigation needs of energy crops in arid and semi-arid climates, and the impact of their cultivation on the water quality of nearby water bodies may pose challenges to their wider adoption. Therefore, it is essential to assess a bioenergy crop\u0026rsquo;s socio-economic and environmental footprint before selecting it for regional planning. This makes the placement of bioenergy crops a multi-objective problem requiring high-quality data on numerous parameters, including economic and environmental indicators, water quality metrics, and management practices in the study area. In Greece, no such plan has yet been proposed for any agricultural region to ensure continuous, uninterrupted production of bioenergy products.\u003c/p\u003e\u003cp\u003eFor addressing some of the previously mentioned challenges, Decision Support Tools (DST) have been used in agriculture since the 90s (Ara et al., 2021). A typical DST consists of a robust pollution estimator, often based on a hydrologic model, an economic function that can adequately represent the cost associated with different implementation scenarios along with an optimization algorithm, capable of efficiently navigating an extensive, non-linear and discontinuous solution space, such as Genetic Algorithms (GAs) (Makropoulos \u0026amp; Butler, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Hydrological-based models are widely used for informed decision making for river basin diffuse pollution management. In this work, the Soil and Water Assessment Tool (SWAT) was chosen as the simulation model. SWAT is regarded as a robust, interdisciplinary tool, widely applied in Europe and globally, concluding that it is one of the most commonly used tools in addressing topics of river basin management, from water management within basins to water quality of streams, rivers and other water bodies (Gassman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Within this framework, the effects of energy crops installation on both water quality and quantity have been widely explored (Kumar et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mishra et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Panagopoulos et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cibin et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, SWAT studies that focus on the optimization of bioenergy crops are less frequently discussed in international literature. Nonetheless, one can still find notable SWAT studies concerning the optimization of bioenergy scenarios (Valcu-Lisman et al.; \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lautenbach et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThus, the study aims to adopt a modern approach by combining informative and efficient technological tools to discover regional scale cropping patterns, including a perennial energy crop, that simultaneously satisfy economic and environmental targets. One of the initial objectives of this study is the development of a user-friendly optimization tool with a combination of the SWAT model, a GA and a comprehensive economic analysis, in order to assist decision making in the study area. The main purpose was to detect the optimal spatial distribution of switchgrass under which a specified target of biomass production could be achieved with negligible environmental footprint and total net income impairment. The optimization criteria included maintaining sufficient water levels in groundwater and rivers, ensuring reductions of N-NO\u003csub\u003e3\u003c/sub\u003e loads, and achieving desirable biomass production for renewable energy, while minimizing the total cost of implementing the management plan. The aim was to identify compromise solutions that meet all key criteria.\u003c/p\u003e"},{"header":"2 Methods and tools","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Description of the SWAT model\u003c/h2\u003e\u003cp\u003eThe Soil and Water Assessment Tool (SWAT) is a semi-distributed, GIS-based and process-based model. It was developed by the US Department of Agriculture in collaboration with Texas A\u0026amp;M University (Williams et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) for use in complex agricultural landscapes. The watershed in SWAT is divided into subbasins and one reach is associated with each subbasin. The subbasins are then divided into Hydrologic Response Units (HRUs), which represent a unique combination of land use, soil type and topography. Runoff and loadings such as nutrients, sediments and pollutants transported by runoff are calculated separately in each HRU and then summed together to define the total loadings from the subbasin (Neitsch et al., 2009). Crop growth is also modeled at the HRU scale, thus increasing the accuracy of the simulation. Furthermore, each HRU is given a single shallow aquifer. In SWAT, groundwater volume in each HRU changes with percolation, water that moves from shallow aquifers to the overlying unsaturated zones, baseflow, deep aquifer recharge, and water pumping. Groundwater abstractions for irrigation or other uses are simulated at the subbasin level, meaning that water is subtracted from all HRUs in a subbasin when abstraction occurs in a single HRU of the subbasin. This approach combines small aquifers into larger ones, aligning with real-world scenarios, although with the subbasin level limitation in SWAT.\u003c/p\u003e\u003cp\u003eThe crop growth component of SWAT is a simplified version of the Erosion Productivity Impact Calculator (EPIC) model, which is capable of simulating a wide range of crop rotations, pastureland, and trees. The growth cycle is controlled by plant parameters summarized in the plant growth database, which can be modified according to the study area, the growing conditions, and by management operations. Agricultural management practices are defined in SWAT by specific management operations (Arabi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) affecting every cropping and livestock system by defining planting, harvesting, tillage passes, irrigation, grazing, nutrient and pesticide applications. Thus, the aforementioned parameters can be altered when other management practices are implemented, allowing land use change scenarios to be accurately simulated.\u003c/p\u003e\u003cp\u003eThe simulation in SWAT can be run on a daily, monthly or annual basis. Depending on the print settings the output values will also be daily, monthly, or yearly. Several output files are generated in every SWAT simulation. Some output data calculated by SWAT in each HRU are sediments (t/ha), nutrients (kg/ha), irrigation water (mm) and yield (t/ha) (Neitsch et al., 2009). In the present study, the ArcSWAT 2012, Release 687, compatible with ArcGIS 10.8.2, was utilized.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Genetic Algorithms (GAs) and the MATLAB-GA\u003c/h2\u003e\u003cp\u003eEvolutionary Algorithms such as Genetic Algorithms (GAs) are meta-heuristic algorithms that perform very effectively in handling multi-objective optimization problems. GAs are based on the principle of evolution, specifically the concept of \u0026ldquo;survival of the fittest\u0026rdquo; (Sivanandam et al., 2008). Over the past years, they have gained considerable interest due to their potential as an innovative optimization method that offers simplicity in use. Multi-objective optimization is essential for resolving complicated issues, such as environmental ones, when several conflicting objectives need to be taken into account simultaneously. NSGA-II is one of the most popular evolutionary algorithms, due to its efficiency in solving multi-objective problems and its strong convergence capacity to identify a variety of solutions (Gebre et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Literature review has shown that NSGA-II is very suitable for optimizing agro-environmental mathematical problems that are formulated towards crop production maximization and water usage efficiency, with changes in agricultural practices (Akdemir et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Du et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Krityakierne et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor the needs of this work a multi-objective controlled elitist Genetic Algorithm, that is a variant of NSGA-II (Deb, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), was selected from MATLAB\u0026rsquo;s toolbox, as the optimization engine. Controlled elitist GAs also favor individuals that can help increase the diversity of the population even if they have a lower fitness value. In MATLAB\u0026rsquo;s GA, special emphasis is placed on maintaining a well-distributed set of solutions along the Pareto front, while simultaneously preserving the best individuals (elite) in the population as the algorithm evolves over time. The use of non-dominated sorting automatically incorporates elitism, eliminating the need to explicitly define the number of top individuals to retain in the new population. The implementation of the MATLAB\u0026rsquo;s GA follows the typical optimization procedure: evaluating the population based on fitness, scaling of fitness values, selection of parents, reproduction using elitism, crossover, and mutation, and termination when a stopping criterion is met (MATLAB, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, it can be considered capable of adequately searching a large search space, examining a very large number of different crop allocation scenarios in the basin, ultimately leading to optimal solutions.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Methodology","content":"\u003cp\u003eAssessing the allocation of agricultural management practices, including the installation of a new crop such as switchgrass, with respect to the quantity and quality of water bodies, and the farmers\u0026rsquo; income is considered a multi-objective problem. Since there could be a significant number of possible combinations to achieve acceptable solutions, a DST is required. To address such a complex problem the developed DST requires the following elements (Panagopoulos et al., 2010):\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eA robust pollution estimator, typically a process-based model, capable of adequately representing the effects of various crop allocation schemes within the basin area. As mentioned, SWAT was chosen in this work as the simulation model.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eAn economic function that accurately represents the cost of implementing these different schemes at various locations.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eAn optimization algorithm that efficiently searches through a vast, non-linear, and non-continuous solution space, such as evolutionary algorithms. A MATLAB-GA inspired by NSGA-II is selected for this work.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Study area\u003c/h2\u003e\n \u003cp\u003eThe Thessaly region is located in Central Greece, and it is considered the most important agricultural producer in the country. Pinios River Basin (PRB) constitutes the largest river basin of the area, covering almost 11,000 km\u003csup\u003e2\u003c/sup\u003e. The PRB has a highly diverse geology, hydrological, and geomorphological environment, enhancing the region\u0026rsquo;s productivity. Due to these characteristics, the basin provides an ideal environment for cultivating a variety of crops, with agriculture accounting for approximately 45% of the total basin area (Pisinaras et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Out of 4.000 km\u003csup\u003e2\u003c/sup\u003e of agricultural land, cotton and wheat are the main crops cultivated, followed by corn and much smaller areas of alfalfa. Irrigation accounts for 94% of total water usage according to the Hellenic Special Secretariat for Water (2017). Significant exploitation of groundwater resources has occurred since the 1980s as a result of the unreasonable water management methods combined with the rising irrigation and water demands. It is estimated that the source of irrigation for roughly 24% of the total irrigation abstractions is surface water, while 76% is irrigated through groundwater with legal or illegal private boreholes (Hellenic Special Secretariat for Water, 2017). However, such an intensively managed area is expected to develop water quality and quantity problems arising from agriculture.\u003c/p\u003e\n \u003cp\u003eThe region has inevitably been classified as a nitrate-vulnerable zone (Nitrates Directive). According to the latest regional water management plan (Hellenic Special Secretariat for Water, 2023), 2 of the 27 groundwater bodies identified in the PRB, which cover 2184 km\u003csup\u003e2\u003c/sup\u003e, are listed as having a \u0026quot;bad\u0026quot; quality status, while 9 of them have been designated as having \u0026quot;bad\u0026quot; quantity. These, combined with dry summers in the area, inversely affect agriculture, resulting in irrigation cutbacks and significant exploitation of surface and groundwater. Thus, it is evident that the area is a good example of existing water quality and quantity threats preventing water bodies from reaching the good ecological status required by the WFD (Directive \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e/60/EC) and Nitrates directive (Directive 91/676/EEC).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Pinios River Basin representation in SWAT\u003c/h2\u003e\n \u003cp\u003eFor the representation of PRB in SWAT different input layers are required. A 25x25 Digital Elevation Model (DEM) was used to delineate the study area of PRB, covering almost entirely the River Basin District of Thessaly in Central Greece. The region\u0026rsquo;s elevation varies greatly, with the highest values around 2,000 m, occurring close to the basin\u0026rsquo;s edges. Data from the European Soil Database (ESDB) was used to create a soil map for PRB (Hiederer et al., 2013a; b). Additionally, the study area was divided into two slope classes: (1)\u0026thinsp;\u0026le;\u0026thinsp;1.5% and (2)\u0026thinsp;\u0026gt;\u0026thinsp;1.5% since slope differentiation is considered necessary for this work to further divide the agricultural area into more HRUs belonging to different slope classes. A land cover layer derived from the 2012 Corine Land Cover (CLC) dataset (Corine Land Cover \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e-European Environment Agency) was utilized for the modeling process. Its relevance to the present was supported by a direct comparison with CLC 2018, which revealed minimal changes in land cover types within the PRB. This was further validated by examining current crop patterns on agricultural land and comparing them with data from previous decades. Analysis of both recent and historical crop allocation data from the Hellenic Statistical Authority (Hellenic Statistical Authority, Data on Crop Areas) confirmed the relative consistency of crop types and irrigated areas in the PRB over the past 20 years. For the final land use layer used in SWAT, only crops occupying more than 1% of the total agricultural area were included. The resulting classification consisted of 10 land use categories: pasture, forested areas, wetlands, urban areas, orchard trees, fallow land, and the cultivation of wheat, corn, cotton, and alfalfa. These layers were overlayed, resulting in the creation of 61 subbasins and 1850 HRUs. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the irrigated land of the basin.\u003c/p\u003e\n \u003cp\u003eAs can be observed, with the blue dots in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea, three reservoirs operate within the basin, contributing to irrigation to a lower extent than groundwater. In addition, around 5% of cropland in the southwestern area is irrigated using water sourced externally from \u0026ldquo;Plastiras\u0026rdquo; lake, as indicated with the green color in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea. Groundwater is the source of irrigation for the rest 77% of the irrigated land, and satisfies irrigation needs either adequately or inadequately depending on the local availability. From the total cropland area, 55% is irrigated (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb), with cotton covering 81% of the irrigated cropland, receiving 420 mm per growth cycle, corn 630 mm (11% of the irrigated cropland) and alfalfa\u0026thinsp;\u0026gt;\u0026thinsp;600 mm (8% of the irrigated cropland).\u003c/p\u003e\n \u003cp\u003eThe detailed schematization of the basin, along with the parameterization and the hydrological and water quality evaluation of the PRB model that is used in this study, have been thoroughly described in the recently published article of Sismanidi et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). The validated PRB model generated baseline results for hydrology, water quality, and crop production. The baseline scenario simulated an 8-year period (2016 to 2023) by providing results for the 6-year period of 2018\u0026ndash;2023 due to a 2-year warm-up period that was neglected. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the most important water quality and quantity results of the baseline, including groundwater content at the end of the simulation period (10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e), total mean annual irrigation water used (10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e), mean annual N-NO\u003csub\u003e3\u003c/sub\u003e loads to surface waters (kg/ha), mean annual concentrations of N-NO\u003csub\u003e3\u003c/sub\u003e leached to shallow aquifers (mg/L).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSWAT estimates of water and nitrates-nitrogen in Pinios river basin at the baseline.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResults in the baseline\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroundwater content at the end of the simulation period (10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal annual irrigation water consumption (10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN-NO\u003csub\u003e3\u003c/sub\u003e loads (kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN-NO\u003csub\u003e3\u003c/sub\u003e leached (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e* Including the single early irrigation dose on wheat, which was not further irrigated within its growth cycle. For the irrigated crops excluding wheat, it was 680 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e as presented in Sismanidi et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Also, only renewable groundwater reserves were allowed for abstraction by setting the initial groundwater content at the start of the simulation (1/1/2026) at zero levels.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Bioenergy crop representation and implementation cost\u003c/h2\u003e\n \u003cp\u003eSwitchgrass is a C4 perennial grass with a multi-year lifespan, native to North America. It is widely recognized as a resource-efficient crop since it has relatively low water and nutrient requirements (Giannoulis et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) and as a crop that can grow even in low-productivity areas, usually called land of marginal quality (Giannoulis et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). The crop is of high biomass productivity and is therefore considered for use in several bioenergy conversion processes, including cellulosic ethanol production, biogas, and direct combustion for thermal energy applications.\u003c/p\u003e\n \u003cp\u003eFor the representation of the bioenergy crop in SWAT several parameters were adjusted to fit the Greek conditions according to the recommendations of experts from the University of Thessaly and their past work (Giannoulis et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). For simulating switchgrass, an annual irrigation depth of 250 mm was applied during the dry period (May\u0026ndash;September) and 150 kg/ha of N was applied annually, with an additional 30 kg N/ha applied immediately after seeding. The one-cut system was chosen as the optimal harvesting method for switchgrass in the study area. In this work, switchgrass was allowed to replace only irrigated crops, namely cotton, corn, and alfalfa. The above-mentioned N fertilization inputs of the perennial crop, particularly in comparison with those of the conventional annual crops, are significantly reduced, as can be observed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNitrogen fertilization amounts corresponding to each irrigated crop, conventional and switchgrass.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrop\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnnual Nitrogen (N) fertilization amounts (kg N/ha)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnnual irrigation needs\u003c/p\u003e\n \u003cp\u003e(mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlfalfa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e630\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCotton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSwitchgrass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eConsequently, replacing cotton with switchgrass leads to an approximate 20% reduction in N application across the corresponding HRUs. This substitution also results in a 40% decrease in irrigation water use, assuming optimal irrigation supported by available water from the source. Similarly, replacing corn with switchgrass reduces N fertilization by 59% and theoretically lowers irrigation water demand by 60%. In contrast, replacing alfalfa with switchgrass leads to an increase in N application but still results in a substantial theoretical reduction of 58% in irrigation water use.\u003c/p\u003e\n \u003cp\u003eFollowing the modeling of the perennial crop, the next step involved collecting and analyzing economic data for all major cultivated crops in the basin, including the bioenergy crop switchgrass. This assessment aimed to evaluate the economic viability of each crop based on cultivation cost and income. The economic analysis considered a range of cost components, including seed costs (adjusted for crop life cycle in the case of perennials), fertilization (split by type and application method), and agricultural management practices such as plowing, sowing, and harvesting. Machinery-related expenses (fuel, maintenance, depreciation), labor costs, and the irrigation costs were also included. Harvesting costs for perennials (alfalfa and switchgrass) were calculated based on the yield levels and were converted to per ha values using simulated data (Giannoulis et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Product sale prices and EU subsidies were incorporated to estimate income. The final assessment included calculations of the total annual expenses, income (with and without subsidies), and net income values, as presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnnual unit costs of the conventional crops and switchgrass in Pinios river basin.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit costs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCorn\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCotton\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlfalfa\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSwitchgrass\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProduct sale price (\u0026euro;/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProduct sales price (\u0026euro;/ha) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubsidies (\u0026euro;/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Expenses (\u0026euro;/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1349\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome (\u0026euro;/ha)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome with subsidies (\u0026euro;/ha)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Net Income (\u0026euro;/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Net Income with subsidies (\u0026euro;/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e* the numbers are approximations as they are based on average \u0026apos;per ha\u0026apos; yields.\u003c/p\u003e\n \u003cp\u003eIt is evident that switchgrass implementation is expected to be conflicting with the farmers\u0026rsquo; Total Net Income due to the lower net income produced by this crop compared to conventional crops. In practice, this implies that despite the relatively low annual cultivation cost of the bioenergy perennial crop, its low biomass sale price and subsidy levels cannot result in comparable income values with those of the conventional irrigated crops. In the baseline scenario, when only conventional crops were grown in the PRB, the mean annual Total Net Income for the farmers\u0026apos; community was estimated as 286x10\u003csup\u003e6\u003c/sup\u003e \u0026euro;, excluding the income from the non-irrigated wheat (almost 35x10\u003csup\u003e6\u003c/sup\u003e \u0026euro;) that is a traditional crop, not allowed for replacement in the optimization problem that follows.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Multi-objective optimization scheme for Pinios River Basin\u003c/h2\u003e\n \u003cp\u003eA single-objective optimization, such as minimizing the total N-NO\u003csub\u003e3\u003c/sub\u003e loads or maximizing Total Net Income, aims to identify the best practice for each HRU and then aggregate these decisions at the catchment level. Solving this type of problem is relatively straightforward. However, when optimization involves two conflicting objectives, the scenario configuration that minimizes one objective may not lead to the optimum of the other, creating trade-offs between the two goals. Since it is essential to assess switchgrass economic and environmental footprints for informed regional planning, multi-objective optimization was chosen for this work. In Pinios basin, the optimization problem includes two environmental criteria, one productivity criterion and one economic criterion: N-NO\u003csub\u003e3\u003c/sub\u003e loads and total irrigation water consumption, the perennial crop\u0026rsquo;s biomass production and the cumulative Total Net Income of the scenarios, respectively. Thus, the optimization problem becomes:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003ewhere \u003cem\u003eN-NO\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e is nitrate-nitrogen loads (kg), \u003cem\u003eirrwater\u003c/em\u003e is the total irrigation water consumed in the basin (m\u003csup\u003e3\u003c/sup\u003e), \u003cem\u003enetincome\u003c/em\u003e is the annual total farmers\u0026rsquo; net income, calculated as a sum for the entire basin (\u0026euro;) and \u003cem\u003eswitchgrassyield\u003c/em\u003e is the perennial crop\u0026rsquo;s biomass production (kg). The \u003cem\u003ei\u003c/em\u003e index corresponds to the HRU and the \u003cem\u003ej\u003c/em\u003e index to the scenario applied to each one of them (conventional land use type or switchgrass). For non-irrigated HRUs, \u003cem\u003ej\u003c/em\u003e always corresponds to the baseline landuse type (no change).\u003c/p\u003e\n \u003cp\u003eThis is a multi-objective optimization problem involving four conflicting objectives. It is addressed using the MATLAB\u0026apos;s Genetic Algorithm (GA), which identifies the optimal Pareto solutions along a four-dimensional Pareto front. It is noted that this specific algorithm by default attempts to minimize all the applied objectives, therefore, for the optimization (maximization) of the Total Net Income and biomass production in this work, negative signs (-) had to be applied. Hence, while the algorithm seeks to mathematically minimize the last two objectives, their absolute values achieved are the positive desirable numbers of Income and biomass produced. The multi-objective optimization scheme is depicted in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eAt the beginning of the optimization process SWAT was executed for 8 years, excluding the outputs of the first two (warm up period), thus simulating crop yields, N-NO\u003csub\u003e3\u003c/sub\u003e amounts lost from HRUs to waters, and mean annual irrigation water amounts applied to all HRUs during the six-year period 2018\u0026ndash;2023. A Database Tool was then established as the most efficient linkage between SWAT and the optimization GA. This database replaces the dynamic connection between SWAT and the GA, while at the same time it significantly reduces the required optimization time (Panagopoulos et al., 2010). The Database Tool developed for use with the DST for the PRB stores the model\u0026rsquo;s outputs regarding mean annual N-NO\u003csub\u003e3\u003c/sub\u003e loads and mean annual irrigation amounts consumed in all HRUs (totally extracted from the source), mean annual biomass production from switchgrass, as well as the respective calculated Total Net Income arising from the cultivated crops to all HRUs. The developed scenarios represent the installation of switchgrass in different irrigated land uses (alfalfa, corn, cotton). The database is organized into 4 tables, where the rows of each table represent the catchment\u0026rsquo;s HRUs (1850 in total) and the two columns the aforementioned environmental parameters and associated net income. The values of the first column of each table are the results of the baseline, while those of the second column are the results from the implementation of switchgrass. Obviously, each row (HRU) values differ only for the irrigated HRUs where switchgrass was allowed for potential installation, replacing the conventional irrigated crop (cotton, corn, alfalfa). In practice, the Database Tool served as a lookup table, eliminating the need to run the SWAT model during optimization when searching for combinations of switchgrass implementation across the irrigated land.\u003c/p\u003e\n \u003cp\u003eThe optimization process started with the random initialization of a population through the GA. Each individual within the population was composed of a set of genes, with the number of genes corresponding to the number of decision variables, in this case, the number of HRUs in the catchment. The values of genes of an individual formed the genotype, while their real representation (phenotype) represented a combination of different allocation schemes in the HRUs of the PRB. To ensure that the algorithm generated only valid solutions (individuals), lower and upper bounds (LB and UB) were defined for each gene, and thus, the only mathematical constraints applied in the optimization problem were bound constraints. Specifically, irrigated HRUs had the bounds 1 (lower-baseline crop) and 2 (upper-switchgrass), while all others had as both bounds the \u0026apos;1\u0026apos; corresponding to the baseline output. Once properly formulated, the individuals in the population were evaluated based on fitness functions, which included the total annual N-NO\u003csub\u003e3\u003c/sub\u003e loads entering streams, the total annual irrigation water consumption, the annual switchgrass biomass production at the entire basin level and the associated annual Total Net Income for all HRUs, as provided by the Database Tool. The algorithm aimed to minimize the user-defined objectives through an iterative process of population evolution. After evaluating the population, the algorithm checked whether the current generation number had reached the maximum number of generations, which served as the termination criterion. If the maximum number was reached, the algorithm stopped; otherwise, the population underwent selection and genetic operations (crossover and mutation) to create a new generation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Selected GA functions and parameters\u003c/h2\u003e\n \u003cp\u003eIn the multi-objective GA in MATLAB the user defines all options regarding the functions and parameter values of the GA through the programming environment of the MATLAB workspace. The performed sensitivity analysis examined the most important alternative configuration options (functions and parameter values) by varying one at a time while keeping the others constant.\u003c/p\u003e\n \u003cp\u003eThe metric used by the algorithm to assess the spread of solutions along the Pareto front was selected to be the crowding distance, a function integrated into MATLAB\u0026rsquo;s GA toolbox (MATLAB, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, we concluded that the Pareto front is better formed using the phenotype space rather than the genotype one.\u003c/p\u003e\n \u003cp\u003eThe \u0026apos;Rank-based\u0026apos; selection ranks the solutions and assigns probabilities according to their order, leading to a more uniform and stable formation of the Pareto front and preserving genetic diversity in the population. Since crossover is the primary genetic reproduction mechanism, a high crossover probability is generally considered essential for accelerating the optimization process. After several trials, the crossover probability of 0.8 was selected as the standard setting for the final optimization configuration.\u003c/p\u003e\n \u003cp\u003eConsequently, the population size and the maximum number of generations until termination were examined. A larger population increases the number of individuals involved in the evolutionary process, thereby enhancing the likelihood of generating better offspring. Likewise, a higher number of generations allows producing more populations and the evaluation of a greater number of solutions, continuously improving the results. The evolution of the Pareto front after 100, 300 and 500 generations was examined. The Pareto front was enhanced further after the execution of 500 generations; however, without significant improvement in the results. Therefore, the number of 500 generations was considered sufficient. Finally, after experimentation with the population size, the number 300 was selected, demonstrating a broader and more balanced distribution and indicating improved diversity and spread of solutions. The above choices ensured reliable convergence while maintaining acceptable execution time for the optimization process.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Results","content":"\u003cp\u003eThe results of the four-criterion optimization are presented, including the minimization of N-NO\u003csub\u003e3\u003c/sub\u003e loads in surface waters and the minimization of the water consumed in irrigated agriculture on an annual basis, and the maximization of the Total Net Income and the biomass production from switchgrass implementation in the PRB. The produced 100 optimal Pareto solutions provide a basis for generating an equal number of maps depicting them within the PRB. For the needs of this study, five solutions are chosen to be presented. Figures\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrate the position of the chosen solutions in the optimal Pareto fronts of N-NO\u003csub\u003e3\u003c/sub\u003e loads-Total Net Income and irrigation water consumption-Total Net Income, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSolution No.1 prioritizes the N-NO\u003csub\u003e3\u003c/sub\u003e reduction. Solutions No.2 and No.3 align with the target of biomass production, which was set by the Thessaly\u0026rsquo;s action plan at the level of 10\u003csup\u003e6\u003c/sup\u003e kg (Action plan for the Region of Thessaly, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These two solutions achieve the annual biomass production of approximately 10\u003csup\u003e6\u003c/sup\u003e tons (not shown on the fronts) but represent the maximum and minimum N-NO\u003csub\u003e3\u003c/sub\u003e reduction, respectively, achieved with this level of switchgrass biomass production at the basin level. Solution No.4 constitutes a rather more realistic and sustainable plan for PRB. It was selected based on a more rational percentage of the total irrigated cropland that can be converted to switchgrass. Specifically, changing around 10% of the total irrigated cropland to switchgrass was considered realistic to the region\u0026rsquo;s needs and characteristics. Finally, Solution No.5 represents the solution with the maximum biomass production from the bioenergy crop that, at the same time, achieves almost the maximum possible reductions in N-NO\u003csub\u003e3\u003c/sub\u003e loads and irrigation water consumption. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the results produced by the five solutions indicated.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean annual (2018\u0026ndash;2023) results produced by the 4-criterion optimization in the 5 selected solutions and the baseline scenario.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScenarios\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSwitchgrass as % of conv. irrig. cropland\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN-NO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e(kg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIrrigation\u003c/p\u003e\u003cp\u003e(m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBiomass\u003c/p\u003e\u003cp\u003eproduction (tons)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal Net\u003c/p\u003e\u003cp\u003eIncome (\u0026euro;)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.57x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e720x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e286x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolution No.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.38x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e550x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-23.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.01x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e235x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-17.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolution No.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.41x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e595x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.42x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e261x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-8.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolution No.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.45x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e632x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.03x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e278x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolution No.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.49x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e680x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-5.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.44x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e286x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolution No.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.33x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e468x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.5x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e123x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(57%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the different spatial allocations of the bioenergy crop under Solutions 1\u0026ndash;4. Solution No.5 is not depicted with a map since in order to achieve the maximum biomass production, switchgrass is inevitably installed almost in the entire irrigated cropland.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSolution No.1 involves the decrease of the mean annual N-NO\u003csub\u003e3\u003c/sub\u003e loads to 1.38x10\u003csup\u003e6\u003c/sup\u003e kg, which corresponds to a total 12% reduction from the baseline. Moreover, switchgrass allocation in the PRB according to this solution results in 550x10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e of irrigation water consumption, 2.01x10\u003csup\u003e6\u003c/sup\u003e tons of biomass production with the Total Net Income at 235x10\u003csup\u003e6\u003c/sup\u003e \u0026euro;, reflecting the economic impact of such a large-scale transition (-17.8% from the baseline income). It is evident that the algorithm implemented switchgrass in extensive areas of the irrigated cropland. Specifically, 52% of the total area of conventional crops has been replaced by switchgrass, primarily targeting fields of corn, which is the most nutrient demanding and water consuming crop of PRB. Under the current allocation scheme, biomass production is double the target proposed by the Thessaly\u0026rsquo;s action plan. However, taking into consideration the very extensive area of switchgrass installation that endangers food/fiber security, as well as the reduced profitability mentioned, Solution No.1 is considered not applicable.\u003c/p\u003e\u003cp\u003eAmong the solutions where the production target of 10\u003csup\u003e6\u003c/sup\u003e tons of biomass was achieved, the ones corresponding to the maximum (Solution No.2) and minimum (Solution No.3) reduction of N-NO\u003csub\u003e3\u003c/sub\u003e loads are presented. Specifically, in Solution No.2, switchgrass allocation in the PRB results in 595x10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e of irrigation water consumption, 1.42x10\u003csup\u003e6\u003c/sup\u003e tons of biomass production, and 1.41x10\u003csup\u003e6\u003c/sup\u003e kg of N-NO\u003csub\u003e3\u003c/sub\u003e loads with an annual Total Net Income of 261x10\u003csup\u003e6\u003c/sup\u003e \u0026euro;, reduced by 8.5% from the baseline. Switchgrass implementation was concentrated in northern, western, and southern subregions, in a similar way as was chosen in Solution No. 1, but with a more balanced extent. It is interesting to note that the large area in the eastern part that was allocated to switchgrass in Solution No.1 has now been allocated to the conventional crop (cotton). This area is irrigated by the newly constructed 'Karla' reservoir with no water deficits so to achieve a less reduced Total Net Income from the baseline under Solution No.2, the algorithm assigns this area to the most profitable crop (cotton) that maximizes its yield under full water availability. This solution continues to prioritize environmental indicators while introducing a more conservative economic impact. In this alternative configuration, 36% of the total conventional irrigated cropland in the PRB is converted to switchgrass. The reallocation includes 63% of the originally corn areas, 35% of cotton areas, and 12% of alfalfa areas being replaced. As in the previous case, corn is again the most heavily impacted crop on a percentage basis. Biomass production exceeds 10\u003csup\u003e6\u003c/sup\u003e tons (1.42x10\u003csup\u003e6\u003c/sup\u003e), fully meeting the target set by the Thessaly\u0026rsquo;s action plan and illustrating the potential of strategic bioenergy crop deployment.\u003c/p\u003e\u003cp\u003eIn Solutions No.2 and No.3 the spatial pattern of switchgrass implementation remained largely consistent. Specifically, the spatial allocation pattern of switchgrass in Solution No.3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) closely mirrors that of the previously described Solution No.2, despite the reduced extent of land-use change and lower environmental improvement. Under the spatial allocation of Solution No.3, 632x10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e of irrigation water consumption, 1.03x10\u003csup\u003e6\u003c/sup\u003e tons of biomass production, and 1.45x10\u003csup\u003e6\u003c/sup\u003e kg of N-NO\u003csub\u003e3\u003c/sub\u003e loads are observed, with the Total Net Income being 278x10\u003csup\u003e6\u003c/sup\u003e \u0026euro;. Despite the more limited extent of land conversion, the solution meets exactly the biomass target of the Thessaly\u0026rsquo;s action plan (Action plan for the Region of Thessaly, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with little income reduction (2.5%).\u003c/p\u003e\u003cp\u003eThe last map of Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the spatial distribution of switchgrass as formed by the Solution No.4, which represents the most conservative approach. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, this solution involves a decrease of the mean annual N-NO\u003csub\u003e3\u003c/sub\u003e loads to 1.49x10\u003csup\u003e6\u003c/sup\u003e kg, which corresponds to a total 5% reduction from the baseline, 680 x10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e of irrigation water consumption, 0.44x10\u003csup\u003e6\u003c/sup\u003e tons of biomass production, and a Total Net Income identical to the baseline one at 286x10\u003csup\u003e6\u003c/sup\u003e \u0026euro;. As observed on the map of Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, almost all small areas in the northern and southern parts of the basin, where conventional crops were replaced in the previous solutions, are those that are solely chosen by the algorithm for conversion to switchgrass under Solution No.4. In this more realistic solution, 11.6% of the irrigated cropland is replaced by the perennial bioenergy crop. The Solution No.4 includes the reallocation of 29% of corn, 10% cotton, and 4% alfalfa areas. However, the solution fails to meet the annual biomass production target set by the Thessaly\u0026rsquo;s action plan, yielding only 0.44\u0026times;10\u003csup\u003e6\u003c/sup\u003e tons, below the expected threshold of 10\u003csup\u003e6\u003c/sup\u003e tons.\u003c/p\u003e\u003cp\u003eThe two most realistic solutions, Solutions No.3 and No.4, are further analyzed in order to better assess their effectiveness and assist in decision making for the area. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e depicts the mean annual Total Net Income as calculated by the algorithm. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea shows the Total Net Income as formed in the baseline, whereas Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb refers to the switchgrass implementation under the Solution No.3, and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec refers to the switchgrass implementation under the Solution No.4.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the northern part of the basin, the baseline negative income values remain under both solutions, reflecting the constant local limitations, such as unfavorable climate (colder conditions) and soil characteristics (sloping land with shallow root depth), which constrain both conventional and bioenergy crop productivity. In Solution No.3, as shown in the circled areas of the map of Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, several southern HRUs display slightly increased net income, indicating favorable economic outcomes from switchgrass adoption in these zones in comparison with the conventional crops of the baseline. Actually, irrigation water deficits that did not allow adequate production of the more water demanding baseline crops in some HRUs there, has been eliminated with the use of the less demanding bioenergy crop which achieved a slightly more profitable production. Moreover, the increased income areas in the south include certain HRUs that were not directly converted to switchgrass but were indirectly benefiting from increased groundwater availability due to nearby perennial crop cultivation. In Solution No.4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), where switchgrass was implemented at a smaller scale, the same southern HRUs still recorded improved net income. In the western and central parts of the basin indicated by circles, the changed HRUs show reduced income under both solutions, consistent with the expected economic trade-offs. Overall, the maps reflect spatial variability in economic impacts, depending on local conditions and the extent of switchgrass implementation, with the simultaneous existence of both lower and higher income subareas compared to the baseline, being responsible for maintaining the Total Net Income at levels very similar to the baseline.\u003c/p\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the mean annual N-NO\u003csub\u003e3\u003c/sub\u003e loads of the baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea and those resulted from the switchgrass installation of Solution No.3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb), and No.4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec) are presented.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe introduction of switchgrass cultivation across the basin led to notable environmental benefits with respect to N-NO\u003csub\u003e3\u003c/sub\u003e loads, as demonstrated. In all areas where switchgrass was implemented, N-NO\u003csub\u003e3\u003c/sub\u003e losses were consistently lower compared to the baseline. Especially, the mean annual basin-level N-NO\u003csub\u003e3\u003c/sub\u003e load recorded in the baseline was 1.48 kg/ha or 1.57\u0026times;10\u003csup\u003e6\u003c/sup\u003e kg/y, the respective loads under the Solution No.3 were 1.37 kg/ha or 1.45\u0026times;10\u003csup\u003e6\u003c/sup\u003e kg/y, and under the Solution No.4 were 1.40 kg/ha or 1.49x10\u003csup\u003e6\u003c/sup\u003e kg/y. The reductions were especially evident in specific subregions in both the northern and southern parts of the basin. The Thessaly plain in the center of the PRB, which remains classified as a nitrate vulnerable zone, continued to benefit to some extent from switchgrass adoption. In Solution No.3, switchgrass implementation not only achieved high biomass yields but also substantially reduced N losses, helping to address both productivity and environmental quality objectives. Under the spatial allocation of Solution No.4, although the area of implementation was reduced compared to Solution No.3, the lower Ν input requirements still resulted in improved water quality with respect to N-NO\u003csub\u003e3\u003c/sub\u003e at a small area. This is particularly significant given the region\u0026rsquo;s high baseline N fertilization of crops such as corn.\u003c/p\u003e\u003cp\u003eAnother important environmental parameter is N-NO\u003csub\u003e3\u003c/sub\u003e leached, which refers to the amount of N, that moves through the soil and reaches the groundwater. At the subbasin level, the introduction of switchgrass resulted in substantial reductions in N-NO\u003csub\u003e3\u003c/sub\u003e leaching. The lower N demand of switchgrass, coupled with its deep perennial root system, effectively limited N-NO\u003csub\u003e3\u003c/sub\u003e movement below the root zone. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates the average N-NO\u003csub\u003e3\u003c/sub\u003e leaching concentration (mg/L) on a subbasin level in the baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea) and under the implementation of Solutions No.3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb) and No.4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt the entire basin level, the mean annual concentration of leached N-NO\u003csub\u003e3\u003c/sub\u003e in the baseline (21.2 mg/L) was drastically reduced by 17.5% in Solution No.3 (17.5 mg/L) and by 17% with Solution No.4 (17.7 mg/L), demonstrating an almost identical reduction in N-NO\u003csub\u003e3\u003c/sub\u003e leaching. In several subbasins all across the basin where switchgrass was installed, leaching levels were reduced by more than half compared to the baseline, emphasizing the role of the bioenergy crop in improving nutrient soil retention and mitigating groundwater contamination. Particularly in the large, concentrated switchgrass area in Solution No.3 in the southern part of the basin, the concentration was decreased to levels lower than 11 mg/L. In Solution No.4, although the effect is less pronounced than in Solution No.3, it remains environmentally meaningful, especially in subbasins where switchgrass was introduced in areas that tend to promote N-NO\u003csub\u003e3\u003c/sub\u003e transport. In certain northern subbasins, leaching levels fell by more than 40%. Overall, the spatial correlation between switchgrass coverage and N-NO\u003csub\u003e3\u003c/sub\u003e leaching reductions confirms its potential as a strategic land use option for nitrate pollution control in intensively cultivated and sensitive areas.\u003c/p\u003e\u003cp\u003eThe maps in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e depict the spatial distribution of irrigation deficits across the PRB under the baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea), Solution No.3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb), and Solution No.4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe baseline showed substantial irrigation deficits across the PRB, with annual water consumption totaling approximately 720x10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e. Under Solution No.3, the widespread implementation of switchgrass significantly reduced irrigation deficits, eliminating them entirely in 59 HRUs and lowering total water use by 12% to 632x10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e. Solution No.4, which applied switchgrass more selectively across the 11.6% of the irrigated cropland, achieved a 5.6% reduction in total water use (680x10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e) and nullified deficits in 44 HRUs, with the majority of the implementation areas exhibiting deficits below 5%. Improvements were spatially distributed across the northern, southern, and western parts of the basin, with especially strong reductions where water stress was the highest, depicted with the red circles in Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec. In many areas with prior minor deficits, irrigation deficits were nearly eliminated. These results highlight the water-saving potential of switchgrass, even at limited scales of adoption.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e Groundwater content in the Pinios river basin at the end of the simulation period (2023), expressed in mm of water for the baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea) and as increase from the baseline (in mm) (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ec) for the switchgrass according to the spatial allocation of compromise Solution No.3 and No.4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe spatial distribution of groundwater content across the PRB under the baseline conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea) reveals heterogeneity, with values ranging from less than 100 mm to over 1000 mm depending on subbasin characteristics. The highest groundwater reserves are concentrated primarily in the western and northern regions, where several subbasins exceed 700 mm, and in some cases, surpass 1000 mm of groundwater content. In contrast, the central eastern regions exhibit significantly lower storage, frequently remaining below 200 mm. In Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb, representing Solution No.3, the basin demonstrates extensive increases in groundwater content, with particularly notable improvements, often surpassing 100 mm, in the southern and southwestern subbasins. Moderate increases of 10 to 50 mm are also observed in central areas, while only minimal improvements occur in the northeastern subregions. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ec, which corresponds to Solution No.4, shows more localized yet still meaningful increases, especially in the southern subbasins, where groundwater again exceeds 100 mm. Central areas display more modest increases, generally ranging between 10 and 50 mm, indicating a positive but more conservative hydrological gain from the implementation of switchgrass.\u003c/p\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eThe developed framework used for a multi-criteria optimization process in this study aimed to explore practical ways to improve agricultural management in the Pinios River Basin, focusing on how the implementation of a perennial bioenergy crop, switchgrass, could contribute to environmental benefits, economic stability, and adequate biomass development for subsequent use in renewable energy production. The multi-criteria optimization, performed using the MATLAB GA, proposed a wide range of different switchgrass allocation schemes for the agricultural land through the development of a four-dimensional Pareto optimal front with a very good exploration space and spread of solutions. Evaluating the solutions on these trade-off curves, as well as mapping the spatial distribution of the perennial crop across the basin, revealed a broad spectrum of alternative management strategies, corresponding to equally broad variations in N-NO\u003csub\u003e3\u003c/sub\u003e loads exported from land to rivers and in the overall mean annual income of the region\u0026rsquo;s agricultural community.\u003c/p\u003e\u003cp\u003eIn the previous Section, a detailed analysis of the two more realistic solutions of converting 25% or 11.6% of the conventional irrigated land to switchgrass (Solution No.3 and No.4) was presented. As explained, both had significant positive impacts on various environmental factors, and thus they are considered very effective alternatives to the existing cropping patterns of PRB. The findings emphasize the importance of spatially targeted and scale-sensitive land use strategies when integrating perennial bioenergy crops like switchgrass. The more extensive adoption of the bioenergy crop in Solution No.3 maximizes environmental returns but requires accepting some economic loss and more significant land-use changes, whereas Solution No.4 prioritizes economic stability with less evident environmental improvements and biomass production. Both solutions consistently improve N leaching loads and water use efficiency, demonstrating switchgrass contribution to sustainable agriculture and groundwater sustainability.\u003c/p\u003e\u003cp\u003eThe comparison between Solutions No.3 and No.4 highlights a clear trade-off between the extent of switchgrass adoption and its economic and environmental impacts across the PRB. Solution No.3, which converts approximately 25% of irrigated cropland to switchgrass, achieves the Thessaly Action Plan\u0026rsquo;s biomass target fully and results in substantial environmental benefits, including a 7% reduction in total N-NO\u003csub\u003e3\u003c/sub\u003e loads and a 12% decrease in irrigation water use basin-wide. This broader implementation also leads to significant improvements in groundwater recharge, particularly in the southern and southwestern subbasins, where groundwater content increases exceed 100 mm, which is equivalent to 1000 m\u003csup\u003e3\u003c/sup\u003e/ha over the 6-y simulation period. However, this more intensive approach comes with a small overall reduction in Total Net Income, although there are small southern areas in the basin that demonstrate localized economic benefits due to higher profitability with switchgrass than with the conventional crops (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, Solution No.4 adopts a more conservative strategy, converting only 11.6% of cropland, which maintains Total Net Income at the baseline levels and still provides notable but more modest environmental benefits such as a 5% reduction in N-NO\u003csub\u003e3\u003c/sub\u003e loads, a 5.6% cut in irrigation water use, and localized groundwater increases generally under 50 mm. While this solution meets only half the biomass target, it preserves food and fiber production more effectively and offers a viable pathway for gradual integration of bioenergy crops without major economic disruption. Thus, it is considered as the optimum solution under the current cropping schemes and socio-economic circumstances in the area.\u003c/p\u003e\u003cp\u003eDecision-makers must weigh these trade-offs based on subregional and local priorities, balancing renewable energy goals with conventional agricultural productivity needs and economic resilience. Overall, the results validate switchgrass incorporation into existing cropping systems as an effective practice for advancing sustainable land management and renewable energy targets in the PRB, with Solution No.3 offering a more ambitious, comprehensive approach and solution No.4 providing a cautious, economically sensitive alternative. Although the latter meets half of the annual biomass target, it can contribute towards sustainable bioenergy development in PRB, being the first crucial step for serious consideration of agriculture as a potential contributor to renewable energy production in the area.\u003c/p\u003e\u003cp\u003eHowever, there still exists a significant gap between the scientific approach and the practical application, which is primarily manifested as a lack of collaboration with the farming population, an essential factor for achieving the set goals. The reasons for these barriers are obviously linked to economic factors. Farmers are often wary of adopting new land management practices, fearing, perhaps justifiably, that their income may be negatively affected. Consequently, switchgrass adoption in the existing cropping patterns could be considered controversial, because, despite the relatively low annual cultivation cost, its low biomass sale price and subsidy levels cannot result in comparable income values with those of the conventional irrigated crops. Nevertheless, with revised subsidies or a stronger biomass market, the economic attractiveness of the crop could be considerably strengthened.\u003c/p\u003e\u003cp\u003eSome assumptions were also made in the formulation of the optimization problem that have to be discussed. First, switchgrass was allowed to be implemented across the irrigated land of the entire basin without socio-economic restrictions that could not be investigated at this stage. This means that any switchgrass introduction scheme refers to large groups of farmers, represented by the rather large HRUs of this study, implying that all of them invest simultaneously their total land for switchgrass implementation. The aggregation of several farmer holdings into the large HRUs results in the implementation of switchgrass in areas that may be affordable for some farmers but not for others. Thus, basin-scale solutions would be more realistic and practically feasible to implement if economic heterogeneities at the local level were supported by the farmers' community itself or more officially from a management body, responsible for the bioenergy crop adoption in PRB. In line with the above, the absence of restrictions led to uneven replacement of the three irrigated conventional crops, favoring the replacement of corn to a higher percentage than the other two conventional irrigated crops. Consequently, the Total Net Income referred to the aggregated income of all farmers within the PRB, and this was a key aspect of the mathematical problem which was designed to maximize the financial revenues the entire farmers' community acquires from the implementation of switchgrass and conventional crops in the basin. However, it is important to highlight that apart from the economic objectives, the environmental ones are also evaluated at the scale of the entire Pinios River Basin. As a result, not only the local income improvements or losses, but also N-NO\u003csub\u003e3\u003c/sub\u003e loads, and water-saving benefits are not assessed separately for different HRUs. It should be finally noted that the Pareto optimum solutions in this study are situated within a broad Total Net Income range, as it was preferable to let the GA discover all the technically feasible solutions, including those that, based on extensive replacement of conventional crops, were capable of reaching the most ambitious environmental targets at the basin scale. Hence, no income constraint was introduced to prevent the GA from discovering very unprofitable solutions.\u003c/p\u003e\u003cp\u003eSecondly, in this study, the optimization solver used the HRU results stored in the Database Tool. This is a simplification as, due to in-stream processes, the total pollutant values exported from HRUs might not always match those at the basin outlet. The DST aimed to minimize pollutant losses from HRUs rather than reduce concentrations at the basin outlet. Besides improving computational speed, this approach may not be considered ideal for studies where the load at the outlet is severely influenced by biophysical processes occurring in the rivers. What allowed this simplification, however, is the fact that in a fast-responding Greek river, the change in pollutant load during transport is not considered significant on an annual basis (Panagopoulos et al., 2010). The geomorphology of the Greek territory often leads to a rapid response of rivers to meteorological events and, consequently, a short water residence time in the riverbed. As a result, the nutrient loads measured by authorities or simulated by the model at the basin outlet can be considered a reasonable representation of the cumulative nutrient losses from upstream areas entering the river network after leaving the land. Therefore, optimizing environmental criteria in a Greek basin using the Database Tool effectively addresses the optimization of nutrient loads or concentrations at the river basin outlet. Moreover, with the Database Tool, it is possible to address environmental pollution across the whole upstream area in a basin, where the water quality of tributary rivers could also be of concern.\u003c/p\u003e\u003cp\u003eFinally, there is always potential for improving the current version of the DST developed in this study for agricultural management and bioenergy production in the PRB. Ideally, future improvements should stem from a close and ongoing collaboration with local stakeholders, which would allow for a more precise understanding of their needs and concerns. The general methodological framework has already proven valuable, providing opportunities for testing various scenarios and evaluating their environmental efficiency and cost-effectiveness across the entire basin.\u003c/p\u003e"},{"header":"6 Conclusions","content":"\u003cp\u003eThis study developed and applied a robust, GIS-based decision support tool integrating the SWAT hydrological model with a Genetic Algorithm, embedded in MATLAB, to optimize the spatial allocation of switchgrass in the PRB, balancing environmental sustainability with economic viability. Through multi-criteria optimization, the tool generated 100 Pareto-optimal solutions that equally considered the mean annual N-NO\u003csub\u003e3\u003c/sub\u003e loads, irrigation water consumption, biomass production, and Total Net Income of the region\u0026rsquo;s agricultural community. Among the chosen solutions on the Pareto fronts, the solution that demonstrated the highest environmental benefit achieved significant N-NO\u003csub\u003e3\u003c/sub\u003e and irrigation reductions while exceeding biomass targets, but was considered unrealistic due to high income losses and extensive land conversion. Another solution presented a more balanced trade-off but still involved considerable economic impacts. In contrast, two other solutions offered more practical compromises. One of them met the biomass target with a small 2.5% annual income reduction, alongside a 7% reduction in N-NO\u003csub\u003e3\u003c/sub\u003e and 12% in water use. The second was selected as the most viable, maintaining baseline income while still achieving moderate reductions in N-NO\u003csub\u003e3\u003c/sub\u003e (5%) and irrigation water use (5.6%), though producing only half of the biomass target. Overall, the integration of SWAT with an optimization algorithm proved efficient for evaluating land use strategies, enabling accurate exploration of the areas with N-NO\u003csub\u003e3\u003c/sub\u003e pollution and high water exploitation. The results highlight the potential of the present optimization tool to guide river basin scale decisions that support both environmental and agricultural production sustainability.\u003c/p\u003e\u003cp\u003eAs a broader conclusion, it can be said that switchgrass, being a drought-tolerant, low-input, and high-yielding crop, could play a central role in improving water quality and quantity, contributing to the overall sustainability of agricultural practices in the region and the production of significant biomass amounts for bioenergy production. An ideal combination of the existing crops and the perennial bioenergy one would form a comprehensive and sustainable action plan to enhance and balance the environmental issues of the Pinios river basin and the Thessaly water District in central Greece.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments:We would like to thank the reviewers for their constructive and insightful comments that helped us improve our article.\u003c/p\u003e\n\u003cp\u003eFunding: This research study was carried out within the framework of the National Recovery and Resilience Plan Greece 2.0, funded by the European Union—NextGenerationEU (Implementation body: HFRI). More specifically, this research was supported under the Basic Research Financing Action “Horizontal support of all sciences”, Sub-action 1 (Project Number: 16425; project title: BIOGRASS).\u003c/p\u003e\n\u003cp\u003eCompeting Interests: The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions: Conceptualization L.K, M.S and Y.P.; methodology, L.K., M.S., K.G., and Y.P.; software, L.K., M.S., S.K. and H.G.; validation, L.K., M.S., S.K., H.G. and K.G.; formal analysis, L.K., M.S., and Y.P.; investigation, L.K., M.S., S.K. and H.G.; resources, L.K., M.S. and Y.P.; data curation, L.K., M.S., K.G. and Y.P.; writing, original draft preparation, L.K. and M.S.; writing-review and editing, L.K., M.S., S.K., H.G., K.G. and Y.P.; visualization, L.K., M.S., S.K., H.G. and Y.P.; supervision, Y.P.; project administration, Y.P.; funding acquisition Y.P. 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Hydrol Sci J 535:948\u0026ndash;960. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1623/hysj.53.5.948\u003c/span\u003e\u003cspan address=\"10.1623/hysj.53.5.948\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-processes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enpr","sideBox":"Learn more about [Environmental Processes](https://www.springer.com/journal/40710)","snPcode":"40710","submissionUrl":"https://submission.nature.com/new-submission/40710/3","title":"Environmental Processes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"bioenergy crop, genetic algorithm, irrigation water, multi-objective optimization, nitrate pollution, SWAT","lastPublishedDoi":"10.21203/rs.3.rs-7233578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7233578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Pinios River Basin in Thessaly, Greece, is the country's most important agricultural region. However, intensive farming practices have led to the degradation of both surface water and groundwater quantity and quality. To address these issues within an energy vulnerable environment, the adoption of bioenergy crops into existing cropping systems offers a promising practice, combining environmental benefits at a river basin scale with the potential of producing renewable energy. The current study investigates switchgrass, a low-input, resource-efficient energy crop, as an ideal candidate for sustainable implementation in the irrigated cropland. Given the unavoidable conflicts with food, feed, and fiber production, a full examination of the environmental and economic implications is needed for its large-scale installation. The Soil and Water Assessment Tool (SWAT) was first used to develop a representative model of the Pinios River Basin and evaluate its current hydrological and nitrate (N-NO\u003csub\u003e3\u003c/sub\u003e) water pollution. A multi-objective Genetic Algorithm embedded in MATLAB was linked to SWAT and an economic component and after a large number of simulations, it identified optimum spatial allocations of the bioenergy crop in the agricultural land, with respect to the net farmers' income, biomass production and water quality and quantity. The analysis of the resulting trade-off curves demonstrated highly encouraging outcomes, with the most conservative solution achieving a 5% reduction in N-NO\u003csub\u003e3\u003c/sub\u003e loads and a 5.6% reduction in irrigation water consumption across the entire basin. Furthermore, under this spatial allocation scheme, 0.44x10\u003csup\u003e6\u003c/sup\u003e tons of biomass were produced from the bioenergy crop, while maintaining the total net agricultural income at the business as usual levels.\u003c/p\u003e","manuscriptTitle":"Reducing nitrate water pollution and irrigation water consumption at the river basin scale through the optimized allocation of a low-input perennial bioenergy crop within the existing cropping systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-25 15:39:09","doi":"10.21203/rs.3.rs-7233578/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-13T12:31:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-22T16:34:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-20T14:52:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162912270086562727939897105392006698736","date":"2025-08-17T14:51:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274793440452610126230181479853192604620","date":"2025-08-16T18:06:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-16T16:49:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-07T20:16:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-30T07:45:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Processes","date":"2025-07-28T11:47:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-processes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enpr","sideBox":"Learn more about [Environmental Processes](https://www.springer.com/journal/40710)","snPcode":"40710","submissionUrl":"https://submission.nature.com/new-submission/40710/3","title":"Environmental Processes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e4f0f0e4-2507-4b4b-b5a6-3eeb851b5755","owner":[],"postedDate":"August 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:06:26+00:00","versionOfRecord":{"articleIdentity":"rs-7233578","link":"https://doi.org/10.1007/s40710-025-00809-8","journal":{"identity":"environmental-processes","isVorOnly":false,"title":"Environmental Processes"},"publishedOn":"2025-12-04 15:57:58","publishedOnDateReadable":"December 4th, 2025"},"versionCreatedAt":"2025-08-25 15:39:09","video":"","vorDoi":"10.1007/s40710-025-00809-8","vorDoiUrl":"https://doi.org/10.1007/s40710-025-00809-8","workflowStages":[]},"version":"v1","identity":"rs-7233578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7233578","identity":"rs-7233578","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}