Economic Optimization of Irrigation Decisions Using Water Productivity for Crop Selection

Economic Optimization of Irrigation Decisions Using Water Productivity for Crop Selection | 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 Article Economic Optimization of Irrigation Decisions Using Water Productivity for Crop Selection Bálint Süle, Renátó Kalocsai, Lajos Kubina, Zsolt Giczi, Ottília Vámos, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9224116/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Graphical Abstract Abstract Despite decades of research, market volatility still hasn't been addressed, creating a gap between agronomic efficiency and economic viability. This study presents a decision-support framework to optimise irrigation water allocation by integrating Crop Water Productivity (CWP) with economic profitability. While traditional CWP focuses on biomass, our model incorporates fluctuating irrigation costs and market prices to calculate Net Profit per Cubic Meter of Water. The present study aims to address the critical gap between biophysical water use and financial viability by introducing a dynamic Economic-Water-Productivity (EWP) framework. In contradistinction to static models, our approach integrates stochastic market volatility with primary farm-level data, thereby enabling the quantification of the specific thresholds at which irrigation transitions from a yield-enhancer to a high-risk financial liability. Model was validated using primary data from 12 commercial farms in Western Transdanubia (2022–2024). The results show that maize exhibited the highest biological water productivity (e.g., 2.23 kg m⁻³) and the highest economic return in 2022 (€ 0.19 m⁻³). However, irrigation was not profitable in 2024 due to lower crop prices and higher costs. The analysis of the data set, which included wheat and soybeans, demonstrated that irrigation was economically non-viable in both years. Sensitivity analysis confirmed the model's robustness under price volatility scenarios of 20% and 40%. The proposed tool facilitates the prioritisation of irrigation based on direct economic efficiency, thereby providing a scalable solution for water-scarce agricultural regions. To reach the break-even point in 2024, a 22% increase in market price (to 0.209 €/kg) or a reduction in irrigation costs to 0.278 € m⁻³ would be required for maize. For soybeans, market prices would need to increase by 150% to reach the break-even threshold. Earth and environmental sciences/Environmental sciences Biological sciences/Plant sciences irrigation cost return on irrigation water productivity irrigated crops net profit 1. Introduction Since agriculture is the main consumer of global freshwater, water scarcity poses an increasingly serious threat to food security and the profitability of farms. However, the world's freshwater resources are under pressure from population growth, unsustainable practices and environmental challenges (Ködmön and Szőke, 2025 ). Irrigation is responsible for around 70% of global freshwater withdrawals and 90% of consumptive water use, which has a significant impact on various Earth system processes (McDermid and Nocco, 2023 ). Agricultural productivity growth was linked to a reduction in poverty. In Asia, every one-percent increase in productivity can reduce the number of poor people by 0.48 percent (Jirapornvaree et al., 2022 ). However, the vital role of irrigated agriculture in mitigating food shortages driven by economic and population expansion is overshadowed by its increasing water and energy demands. This poses a significant challenge to regional water security and exacerbates global climate change (Macher et al., 2025 ). A feasible and practical approach to ensure the effective use of natural resources without compromising the needs of future generations could be to assess the most appropriate irrigation treatments in terms of achieving the objectives of the Sustainable Development Goals (Heiba et al., 2023 ). There is an increasing need to optimise cropping patterns, in order to address the shortage and mismatch of land and water resources (Chen et al., 2022 ). Irrigation requirements in Hungary are comparable to those of Mediterranean countries (Tarantino et al., 2017 ). However, a mere 6% of Hungary's agricultural territory is suitable for economic irrigation, and of this, less than 50% is currently irrigated on a regular basis. This raises a key question: which crops should be prioritised for irrigation? Which plants will benefit from irrigation? Research conducted in continental regions has indicated that certain crops, including potatoes, are demonstrating increased susceptibility to risk in recent years without the benefit of irrigated support (Dwijendra et al., 2022 ). Selection of an appropriate irrigation scheduling method should be based on marketable yield (Asiimwe et al., 2022 ). In changing climates, the process of determining which plants require irrigation involves the use of specific indicators (Badawy et al., 2022 ). In light of the water shortage, it is essential to quantify irrigation water applied to accurately predict changes in yield (Ray et al., 2023 ). The study aims to identify the most profitable use of irrigation water under real market conditions. It is hypothesised that the use of the Crop Water Productivity (CWP) metric in a sophisticated manner will enable the execution of robust analyses for the purpose of selecting irrigated crops and ranking profitability. A review of the literature reveals several key gaps: Currently, Irrigation Water Productivity (IWP) and Economical Water Productivity (EWP) values are very limited in Hungary. There is a lack of decision-support tools for irrigated crop selection given the existing water constraints. The economic impact of irrigation under varying market prices is not sufficiently analysed. Research to date has been very broad, but there is no complex decision support model to clearly rank irrigated crops in terms of economic and production considerations. Our method is unique because it provides market players with a tangible, easy-to-use calculation tool for projecting their productivity and net income-generating capacity for the coming years. Agricultural companies can use it to analyze everything from the irrigation of a future plantation to the planning of next year's crop rotation. The research adds value to the current state of the literature. It provides relevant support for sustainable water use. The research is innovative in its approach, as it differentiates between irrigation costs, production costs, and purchase prices, a distinction that has not been previously made in the extant literature. This enables the aggregation of data across different units of measurement, such as kg or kg/m 3 , facilitating longitudinal comparisons. Moreover, the disaggregated treatment of irrigation costs gives a comparative analysis with dry farming, thereby enabling precise selection for farmers or researchers. 1.1 Research objectives and research questions and hypothesyses. The study's aim is to develop and validate a decision-support framework combining technical and economic indicators to optimise crop selection for irrigated farming in Western Hungary. To achieve this, we addressed the following research questions: RQ1: How do CWP and EWP vary among field crops in Western Transdanubia under different climatic conditions? RQ2: How do fluctuating irrigation costs and market prices influence the ranking of crops when prioritised by net profit per unit of water? RQ3: Is a simple decision-support equation effective at distinguishing the economic efficiency of irrigation for different crops at the farm level? The following hypotheses (H) were derived from these queries: H1: CWP and EWP are linked, but a high yield doesn't always mean profit. H2: Irrigation for maize is more sensitive to energy price fluctuations than for soybeans, due to higher water requirements. H3: Farmers can identify crop-shifting opportunities that increase net irrigation profit by at at least 15–20% with the proposed decision-support tool, compared to traditional yield-based decision-making. 1.2 Benchmarking against existing irrigation models. Although previous studies have extensively analysed Crop Water Productivity (CWP) from a physiological perspective, they often neglect the dynamic nature of economic factors such as energy-driven irrigation costs and fluctuating market prices. Our model addresses this issue by incorporating these variables into a unified decision-making equation. Unlike the CROPWAT (Iacuzzi et al., 2025 ) model, which primarily focuses on water requirements and yield response, our approach prioritises 'net profit per cubic metre', providing a direct financial key performance indicator (KPI) (Batisha, 2024 ) for farm-level management. While traditional models such as CROPWAT are still considered essential for simulating crop water requirements, they remain static in terms of economic considerations. Our approach is designed to address this limitation by integrating real-time market fluctuations. This effectively bridges the gap between agronomy and farm-level financial decision-making. 2. Materials and methods 2.1. Formulation of the research problem Currently, there is no clear tool available to irrigation stakeholders that would be suitable for forecasting the net profit of different irrigated crops and predicting yields at the same time. Research on this topic has been primarily focused on yield-based indicators to date. In contrast, the proposed methodology explicitly separates the cost of irrigation from the cost of crop production, allowing for a transparent comparison between irrigated and dryland farming. The objective was to assess whether irrigation water can be allocated in a way that maximises both profitability and productivity. To answer this question, a comparative analytical framework was developed to evaluate crop performance under irrigation. The analysis encompassed a comprehensive set of metrics, including irrigation costs, the impact on yield, water use indicators (CWP, IWP), as well as market prices and production costs. 2.2. Study region and data collection The research concentrated on Hungary's irrigable areas. The study region was selected based on three criteria: (1) low irrigation coverage (~ 6%), (2) high investment risk, and (3) significant variability in water pricing. The economic risk of irrigation investments is high. There are significant regional differences in water prices. During the research, we collected and analyzed the following data of some main crops: Irrigation costs (2018–2024), crop yields, CWP and IWP values, market prices and production costs. 2.3. Yield and water use data During the research, CWP data was collected using the FAO database CROPWAT software ( FAO, 2023 ) and international literature. 2.4. Economic analisys and production costs The average prices and production costs for 2024 were collected from the following sources. AKI Agricultural Economics Institute of Hungary. Crop market reports. In order to guarantee a reliable economic comparison, crop prices were calculated based on annual averages. For the analysis, all price data from 2022 were adjusted to 2024 present value to account for inflation and ensure comparability across the studied period. These inflation-adjusted values were used in all subsequent economic calculations and cost-benefit assessments. 2.5. Selection of crops: The crops were chosen based on the model's practical relevance, which was assessed using three strategic criteria. Economic Dominance: These crops are 70% of arable land in Western Transdanubia, making them the region's key agricultural contributors. Variability in Water Demand: The model simulates crops with different irrigation needs, from high-water-demand (Maize) to drought-tolerant (Wheat). This enables testing of decisions under varied water scarcity scenarios. Aligned with National Strategic Priorities: The selection of these crops aligns with the Hungarian National Irrigation Strategy and EU CAP objectives for climate-resilient agriculture. These crops face increased drought risk in the Carpathian Basin, so supporting these species directly impacts national food security and the irrigation sector's economic stability. 2.6. Data validation and data cleaning To validate the data, we compared the results obtained with profitability data available in the domestic literature. During data cleaning, incomplete and inconsistent records were removed. This ensures the reliability of the data for subsequent analysis and modelling. 2.7. Comparison and definition of the various metrics Whereas Crop Water Use Efficiency (WUE) compares an output from the system (such as yield or economic return) to crop evapotranspiration. In recent times, the focus on water productivity has broadened to encompass an analysis of the benefits and costs of water used for agriculture in terrestrial and aquatic ecosystems. Agricultural water productivity may be conceptualised as the ratio between the net benefits derived from crop, forestry, fishery, livestock and mixed agricultural systems and the quantity of water utilised to generate those benefits. The basic expression of agricultural water productivity may be defined as a measure of output produced by a given system in relation to the volume of water consumed. This may be measured for the entire system or for defined parts thereof and may be expressed as a function of time and space. It is common practice to express water productivity in terms of kg m -3 , with crop production represented in kg ha -1 and water use estimated in mm of precipitation applied or received. This is then converted to m 3 ha -1 , where 1 mm is equivalent to 10 m 3 ha -1 . As an alternative, it may be represented in terms of food (in kcal/ m 3 ) or its monetary value ( $ /m 3 ). $$\:\text{W}\text{a}\text{t}\text{e}\text{r}\:\text{p}\text{r}\text{o}\text{d}\text{u}\text{c}\text{t}\text{i}\text{v}\text{i}\text{t}\text{y}=\frac{\text{A}\text{g}\text{r}\text{i}\text{c}\text{u}\text{l}\text{t}\text{u}\text{r}\text{a}\text{l}\:\text{b}\text{e}\text{n}\text{e}\text{f}\text{i}\text{t}}{\text{W}\text{a}\text{t}\text{e}\text{r}\:\text{u}\text{s}\text{e}}$$ 1 (Drechsel et al., 2015 ) The conventional notions of irrigation efficiency, as employed by engineers, fail to incorporate economic considerations. In order to ascertain the optimal level of irrigation efficiency, the economist would require knowledge of the value of irrigation water and the cost of any increased water or management measures that would facilitate a reduction in diversion (Kijne et al., 2003 ). There are two ways of expressing water productivity: physical and economic. Specifically, economic water productivity is the economic value derived from each unit of water used, whereas physical water productivity is the ratio of agricultural output (crop yields) to the amount of water used. $$\:\text{P}\text{h}\text{y}\text{s}\text{i}\text{c}\text{a}\text{l}\:\text{w}\text{a}\text{t}\text{e}\text{r}\:\text{p}\text{r}\text{o}\text{d}\text{u}\text{c}\text{t}\text{i}\text{v}\text{i}\text{t}\text{y}\:\text{P}\text{W}\text{P}=\frac{\text{Y}\text{i}\text{e}\text{l}\text{d}\:\left(\text{k}\text{g}\right)}{\text{I}\text{r}\text{r}\text{i}\text{g}\text{a}\text{t}\text{i}\text{o}\text{n}\:\text{W}\text{a}\text{t}\text{e}\text{r}\:\text{U}\text{s}\text{e}\:\left(\text{I}\text{W}\text{U}\right)}$$ 2 $$\:\text{E}\text{c}\text{o}\text{n}\text{o}\text{m}\text{i}\text{c}\text{a}\text{l}\:\text{w}\text{a}\text{t}\text{e}\text{r}\:\text{p}\text{r}\text{o}\text{d}\text{u}\text{c}\text{t}\text{i}\text{v}\text{i}\text{t}\text{y}\:\text{E}\text{W}\text{P}=\frac{\text{G}\text{r}\text{o}\text{s}\text{s}\:\text{m}\text{a}\text{r}\text{g}\text{i}\text{n}\:\left(\text{€}\right)}{\text{I}\text{r}\text{r}\text{i}\text{g}\text{a}\text{t}\text{i}\text{o}\text{n}\:\text{W}\text{a}\text{t}\text{e}\text{r}\:\text{U}\text{s}\text{e}\:\left(\text{I}\text{W}\text{U}\right)}$$ 3 (Perelli et al., 2024 ) Normally there are three ways to improve water productivity: to transfer as much of the water as possible, to convert as much of the water into CO 2 as possible, to convert as much of the biomass as possible into a harvestable product (Ali and Talukder, 2008 ). Another approach is to look at economic water productivity. This is defined by the Food and Agriculture Organization of the United Nations as the monetary value generated from each unit of water consumed. On a dollar-for-dollar basis, the output of different crops can be compared. Economic water productivity can also be measured at whole-farm level (where farms produce multiple crops) and broader regional levels, such as counties, where regions have different crop mixes. (Frisvold and Atla, 2024 ) "Which crop and which drop". The numerator can be the total dry or fresh biomass or harvested product, expressed in physical or economic terms. Denominators may include transpiration (T), evapotranspiration (ET), irrigation volume, water inputs, etc. Often it is not explicitly stated whether fresh or dry yield is used as the numerator. (Bessembinder et al., 2005 ) The economic water productivity (EWP) of wheat (Triticum aestivum) was compared using deficit irrigation (DI). The EWP was found to be optimal at an irrigation level of 80% and was the poorest at 40% irrigation. (Shoukat Hafiza et al., 2025 ) Irrigation water productivity is for show the effect of irrigated land production. Defined as the yield produced per unit of irrigation water consumed, has become an important criterion for assessing both agricultural production and water use efficiency. It is a comprehensive indicator that shows the level of management of both the irrigation system and the crop. (Li et al., 2016 ) It is important to see, that the Crop Water Productivity and Irrigation Water Productivity correlates together as the IWP significantly higher than the CWP (Musick et al., 1994 ). The reason is under: Calculation of IWP shows that the factors are less dependent on all y climate (Luo et al., 2025 ), (Adeboye et al., 2015 ): $$\:\text{I}\text{W}\text{P}=\frac{Y}{\text{I}\text{A}}$$ 4 where Y represents the yield (kg), and IA is the total irrigation amount during the growth period (m 3 ). The agronomic practice factors, including irrigation, fertilisation, agricultural film and agricultural pesticides, contributed 20.6%, 32.8%, 42.3% and 11.1% respectively to the increase in IWP. The contribution rates of the climatic factors, i.e. daily mean temperature and solar radiation, are − 0.9% and 0.9% (Li et al., 2016 ) IWP = Y-Y0 /I (5) Where: Y= total yield; Y 0 non irrigated control; I irrigation (Yetik and Candoğan, 2022 ); (Stepanovic et al., 2021 ) In general, IWP increased with the decrease of irrigation amount and the increase of yield (Li et al., 2024 ). The WP concept can also be applied more broadly by assigning different values to the numerator [product]. This is common in water valuation approaches, where economic attributes can be given in monetary terms ( $ m -3 ); social attributes (jobs, food security, etc.), or environmental attributes (carbon sequestration, biodiversity, etc.) (Batchelor et al., 2016 ). Table 1 The following is a synopsis of the crop, irrigation, and economic water productivity indicators. Metric Analisys level Focus Application in practice CWP Plant / Field Biological effectiveness Assess crop performance and breed. IWP Field / Farm Management effectiveness Evaluate irrigation systems and schedule. EWP Farm / Regional Financial effectiveness Support policy decisions and crop selection. 2.7. Cost of irrigation calculation The cost of irrigation depends on the technology chosen and on the water consumption. The way in which the construction of these systems is financed, as well as the method of calculating the price of 1 m 3 of water, also have an influence on the cost of irrigation. (Potkonjak and Zoranović, 2013 ) The cost of providing irrigation water consists of the variable costs of treating and delivering water to end users and the fixed costs of capital depreciation, operation and maintenance. The variable costs depend on the quantity of water delivered, whereas the fixed costs do not. In most countries, fixed costs are heavily subsidised. (Lallana et al., 2001 ) It is imperative that the estimation of certain cost components be accompanied by a detailed explanatory note. A fixed cost is one that is independent of production. For an irrigation system, these are usually depreciation, interest, insurance and fixed electricity charges. They are based on the initial investment. Irrigation system operating costs should be based on planned water applications. Variable costs are those which the irrigator can control in the short term. Operating costs, such as electricity, water, labour and repairs, should be estimated as cost per m 3 . (Oosthuizen et al., 2007 ). Europe reveals a substantial variation in pricing across different countries. Findings indicate a range of prices, both within and between nations, spanning from €0.054 to €0.645 per m 3 (as observed in Greece) to €0.23 to €1.50 per m 3 (as experienced in France) (Giannakis et al., 2015 ). Twelve Hungarian irrigation farms in Western Transdanubia provided data as the basis for calculating irrigation costs. The categories of data collected: water supply fee ( € m⁻³), energy consumption (kWh/m 3 ), maintenance costs, labor costs, fixed costs (depreciation, insurance, interest costs). We anonymized the farm data and calculated average values, from which we calculated a cost price of € 0.375/m 3 for 2024. Table 2 Section presents the primary characteristics and irrigation costs of the analysed farms. Farm ID Farm Size (ha) Irrigation Technology Main Crops Analyzed Irrig. Cost (€/m³) Farm 01 145 Traveling Gun Maize, Seed maize, Wheat 0.48 Farm 02 94 Traveling Gun Soybean, Maize, Rapeseed 0.52 Farm 03 281 Center Pivot Maize, Seed maize, Wheat 0.32 Farm 04 77 Traveling Gun Sugar beet, Maize, Winter Wheat 0.33 Farm 05 157 Traveling Gun Maize, Seed maize, Wheat 0.32 Farm 06 35 Drip Irrigation Maize, Seed maize, Wheat 0.46 Farm 07 1100 Center Pivot Maize, Winter Wheat, Soybean 0.31 Farm 08 65 Traveling Gun Barley, Maize, Sunflower 0.3 Farm 09 134 Traveling Gun Soybean, Winter Wheat, Maize 0.47 Farm 10 47 Drip Irrigation Potatoes, Vegetables, Maize 0.45 Farm 11 232 Linear Move Winter Wheat, Maize, Alfalfa 0.35 Farm 12 129 Traveling Gun Maize, Rapeseed, Barley 0.21 2.8. Consistency checks The yield and water-use values have been verified against regional benchmarks. A thorough review of the spreadsheet formulae was conducted to ensure their consistency. A comparison has been made between model outputs and those of previously published case studies. 2.9. Synthesising a novel equation As part of the analysis, we calculated and compared physical and economic water use indicators. The aim is to identify the crops that yield the highest return per unit of irrigation water used. A range of measures was studied with a view to identifying the optimal representation of our aims in the context of decision support for farming companies. The most significant indicators that were analysed were as follows: Crop Water Productivity (CWP) is defined as the ratio of yield (kg/ha) to water use (m 3 /ha). Water Use Efficiency (WUE) is the proportion of irrigation water that is actually used by the crop compared to the total water input. Irrigation Water Productivity (IWP) is defined as the amount of crop yield per unit of irrigation water, expressed in kg/m 3 . Economic Water Productivity (EWP) is defined as the financial yield per unit of water (€ m⁻³). The "catchy" nature of the term "water use efficiency" leads many scientists to use it even in contexts where it doesn't apply. For this reason, WUE and CWP are two different terms and should not be used interchangeably. Although they are related, because CWP is plant-specific and controlled by other factors, a high WUE does not necessarily result in a high CWP (Perry, 2011 ). When calculating costs, we took into account the fixed and variable costs of irrigation systems, as well as the unit price of water. Following a thorough review of the relevant data, including insights from domestic irrigation farms and specialist literature, we have arrived at a cost price of € 0.375/m³ (2024) for water. In order to compare the cost-effectiveness of irrigation, we developed a simple decision support model that calculates net income per one m³ of irrigation water. The model enables direct comparison of irrigation profitability across crops. Net profit per 1m 3 = (CWP crop ​⋅ P crop ​) − C irrigation ​ − C production ​ (1) Where: CWP crop ​: Crop Water Productivity (kg/m3) P crop : crop commodity price (€/kg) C irrigation : cost of irrigation per (€ m⁻³) C production : cost of production (€/ha/year) / irrigated yield (kg/ha) In the course of processing the water productivity results in the study, the new formula was employed to examine the values for average prices in 2024. The utilisation of this methodology enables the producer to demonstrate the potential for irrigated crops by replacing the existing data. Table 3 Net profit calculation for different crops, regarding 2022 and 2024. Crop Year CWP kg/m3 Crop price €/kg Income (gross) € Irrigation cost € m⁻³ Yield - Cost of irrigation Cost of production € /ha Irrigated yield t/ha Cost per kg € Net profit € m⁻³ Maize 2022 2.23* 0.346 0.771 0.4 0.372 1226 15 0.082 0.189 Maize 2024 2.23* 0.165 0.368 0.375 -0.007 1350 15 0.090 -0.208 Wheat 2022 1.2 0.32 0.384 0.4 -0.016 1159 7,5 0.155 -0.201 Wheat 2024 1.2 0.17 0.204 0.375 -0.171 1210 7,5 0.161 -0.365 Soybean 2022 0.6 0.7 0.42 0.4 0.02 1270 4,1 0.310 -0.166 Soybean 2024 0.6 0.44 0.264 0.375 -0.111 1170 4,1 0.285 -0.282 *CWP value for maize: (Khorchani et al., 2024 ). 2.10. Sensitivity analysis of different crops Table 4 Sensitivity analisys of different crops Crop Year CWP Price Irrigation Cost Net Profit (calc) Price + 20% Price + 40% Irr. Cost − 20% Maize 2022 2.23 0.346 0.4 0.371 0.525 0.680 0.451 Wheat 2022 1.2 0.32 0.4 -0.016 0.060 0.137 0.064 Soybean 2022 0.6 0.7 0.4 0.02 0.104 0.188 0.1 Maize 2024 2.23 0.165 0.375 -0.007 0.066 0.140 0.067 Wheat 2024 1.2 0.17 0.375 -0.171 -0.130 -0.089 -0.096 Soybean 2024 0.6 0.44 0.375 -0.111 -0.058 -0.005 -0.036 The sharp decrease in profitability between 2022 and 2024 (for example, a 179% decrease for maize) highlights a critical sensitivity threshold. Our findings suggest that in scenarios involving high input costs, irrigation may no longer guarantee enhanced yields, but rather poses a significant financial risk, necessitating strict price hedging measures. 3. Results and discussion Irrigation systems are increasingly vulnerable in volatile markets. While technology can boost water productivity, economic sustainability is driven by price and cost. Irrigation strategies should assess economic risk alongside agronomic optimisation. Irrigation profitability saw a decline from 2022 to 2024, with all crops becoming unprofitable due to lower crop prices (e.g., maize net profit decreased from + 0.189 to − 0.208 € m⁻³). A sensitivity analysis was conducted, which demonstrated a strong correlation with market conditions. Maize demonstrated threshold behaviour, becoming profitable under moderate price increases (e.g., + 40%), while wheat remained unprofitable even under improved scenarios (e.g., − 0.365 € m⁻³ at baseline). Soybean prices exhibited moderate improvement (for example, from − 0.282 to near break-even with higher prices) but remained negative overall. The impact of fluctuations in irrigation costs proved to be less significant in comparison to that of changes in crop prices. The results provide clear insights into irrigation profitability under changing market conditions. The results indicate that the low grain prices in 2024, along with the production costs of arable crops and industrial crops, significantly affect the economic viability of irrigated arable farming. Recent data indicates a general increase in energy and energy unit reservation fees. If these trends persist, the number of economically viable irrigated crops is expected to decline. The formula and research materials presented in this article demonstrate a methodology for determining the most profitable crop composition for irrigated areas. The financial implications of irrigation, including both operational costs and purchase prices, are subject to periodic fluctuations. However, the efficacy of this approach remains constant. Research has demonstrated that irrigated crops are particularly susceptible to annual market price fluctuations. Consequently, utilising decision support tools based on historical average CWP or IWP values, coupled with next year's commodity markets forecasts, can accurately determine the number of positive years a farm is likely to experience in the long term. This information is crucial for strategic planning and ensuring financial stability. 3.1. Dataset representativeness and conclusions scope In order to ensure high internal validity, a multi-case study approach was employed. This facilitated the incorporation of high-fidelity primary data, encompassing actual pumping logs and energy invoices, thereby offering a more granular economic insight than regional statistical averages. While the study is based on a primary dataset of 12 commercial farms, these were strategically selected to provide a high level of representation of high-intensity irrigated agriculture in Western Transdanubia. The following points justify the representativeness of this sample: This research contrasts with national-scale studies relying on secondary statistics by using farm-level primary data e.g. energy bills and pumping logs. The 12 farms investigated collectively manage 2600 hectares of irrigated land, using Hungary's most common irrigation technologies. A National Model for the Whole Country. Western Transdanubia is a good example of the country's modernising irrigation sector. The decision-making here can be used as a model for other Hungarian regions with similar conditions. Rather than a broad statistical survey, this study uses a case study approach, a scientifically accepted method for validating new decision-support models. We provide concrete decision scenarios based on the BEP (Break Even Point) for the 2024 growing season. These show the economic viability of irrigation in volatile market conditions. Break-even Crop Price (P BE ) P BE = (C prod + C irrig ) / CWP P BE : Break-even price (€/kg) C prod : The total production cost, excluding irrigation costs (€ per ha) C irrig : The unit cost of irrigation is expressed in Euros per cubic meter CWP: is the Crop Water Productivity of the examined crops (kg m -3 ) Table 5 Break Even Point for major crops in Hungary in 2024. Crop 2024 CWP (kg m -3 ) Price (€ -kg ) Irrigation Cost (€ m⁻³) Production Cost (€/ha) Yield (kg/ha) Cost per kg (€ -kg ) Revenue per m 3 Profit per m 3 Break-even Price (€/kg) Break-even Irrigation Cost € m⁻³ Maize 2.230 0.165 0.375 1350 15000 0.090 0.368 -0.097 0.209 0.278 Wheat 1.200 0.170 0.375 1210 7500 0.161 0.204 -0.332 0.447 0.043 Soybean 0.600 0.440 0.375 1170 4100 0.285 0.264 -0.396 1.101 -0.021 These figures provide a baseline for producers to evaluate whether current market prices cover the rising costs of water use and production for maize, wheat, and soybean. The break-even analysis (Table 5 .) quantifies the economic vulnerability of the sector; the fact that maize requires a 22% price premium just to cover irrigation costs suggests that technological efficiency alone cannot compensate for adverse market shifts. 4. Conclusions This study demonstrates that integrating economic and water productivity indicators significantly improves irrigation decision-making. The novelty of the results lies in the fact that they directly link water use efficiency to production costs and return on investment, thus providing a more complex picture of the economics of irrigation than previous analyses, which were mostly based on yield or water requirements alone. The quantification of trends indicates that, in comparison with the 2022 year under consideration, the economic advantage of grain maize irrigation has diminished by 179% in 2024. The net profit of wheat production under irrigated conditions decreased by 81.6% between 2022 and 2024. It was also not financially viable to irrigate wheat in 2022. This assertion pertains to the irrigation of soybeans. The year 2022 yielded a modest profit of 0.189 € m - ³, however, in 2024, a substantial loss was incurred due to escalating Operational Expenditures (OPEX) and unfavourable commodity prices. The results highlight the increasing financial risk of irrigation under volatile market conditions. The practical benefit of the study is that the model presented helps to tailor irrigation strategies and support producers' risk management decisions. The study supports what was previously discovered by Li et al., when he was studying Maximum Economic Water Productivity (EWPmax) and Maximum Irrigation Productivity (IWP max) (Li et al., 2025 ). The findings published in the article are extremely important from the perspective of domestic irrigation of Hungary, as production is currently shifting towards low value-added crops, while maize and wheat (wheat productivity is lower, as are its stock market prices) do not cover the cost of irrigation. Therefore, there is a need to move towards other crops, which generate the highest net profit under irrigation. The research group will work on an extensive database to help producers make decisions. This model is thus proposed as a scalable diagnostic tool for semi-arid and transition regions, with the suggestion that sustainable irrigation requires a strategic shift from low-value cereal monocultures towards high-margin, water-efficient crop rotations. Declarations Financial interest The authors have no relevant financial or non-financial interests to disclose. Consent to participate: Informed consent was obtained from all individual participants, Bálint Süle, Renátó Kalocsai, Lajos Kubina, Zsolt Giczi, Viktor Nagy, Ottília Vámos, Nóra Gombkötő included in the study. Ethics declaration not applicable. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution All authors, Bálint Süle, Renátó Kalocsai, Lajos Kubina, Zsolt Giczi, Viktor Nagy, Ottília Vámos, Nóra Gombkötő reviewed the manuscript. Data Availability The datasets generated during and analyzed duringthe current study are not publicly available but are available from thecorresponding author on reasonable request. References Adeboye, O. B., Schultz, B., Adekalu, K. O. & Prasad, K. Crop water productivity and economic evaluation of drip-irrigated soybeans (Glyxine max L. Merr). Springer Nat. - Agric. Food Secur. 4 , 10. https://doi.org/10.1186/s40066-015-0030-8 (2015). Ali, M. H. & Talukder, M. S. U. Increasing water productivity in crop production—A synthesis. Agric. Water Manage. 95 , 1201–1213. https://doi.org/10.1016/j.agwat.2008.06.008 (2008). Asiimwe, G., Jaafar, H., Haidar, M. & Mourad, R. Soil Moisture or ET-Based Smart Irrigation Scheduling: A Comparison for Sweet Corn with Sap Flow Measurements. J. Irrig. Drain. Eng. 148 , 04022017. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001668 (2022). Badawy, A., Elmahdi, A., Abd El-Hafez, S. & Ibrahim, A. Water Profitability Analysis to Improve Food Security and Climate Resilience: A Case Study in the Egyptian Nile Delta. Climate 10 , 17. https://doi.org/10.3390/cli10020017 (2022). Batchelor, C., Hoogeveen, Faurès, J. M. & Peiser, L. Water Accounting and Auditing - A Sourcebook (FAO, 2016). Batisha, A. Multi-disciplinary strategy to optimize irrigation efficiency in irrigated agriculture. Sci. Rep. 14 , 11433. https://doi.org/10.1038/s41598-024-61372-0 (2024). Bessembinder, J. J. E., Leffelaar, P. A., Dhindwal, A. S. & Ponsioen, T. C. Which crop and which drop, and the scope for improvement of water productivity. Agric. Water Manage. 73 , 113–130. https://doi.org/10.1016/j.agwat.2004.10.004 (2005). Chen, Y. et al. Crop pattern optimization for the coordination between economy and environment considering hydrological uncertainty. Sci. Total Environ. 809 , 151152. https://doi.org/10.1016/j.scitotenv.2021.151152 (2022). Drechsel, P., Heffer, P., Magen, H., Mikkelsen, R. & Wichelns, D. Managing water and fertilizer for sustainable agricultural intensification (International Water Management Institute (IWMI), Montpellier, 2015). Dwijendra, N. K. A. et al. The effect of various irrigation technologies and strategies on water resources management. J. Water Land. Dev. 143–147. https://doi.org/10.24425/jwld.2022.140790 (2022). FAO. Release Note AquaCrop version 7.1. FAO, Rome, Italy. (2023). Frisvold, G. B. & Atla, J. Agricultural Economic Water Productivity Differences across Counties in the Colorado River Basin. Hydrology 11 , 125. https://doi.org/10.3390/hydrology11080125 (2024). Giannakis, E., Bruggeman, A., Djuma, H., Kozyra, J. & Hammer, J. Water pricing and irrigation across Europe: opportunities and constraints for adopting irrigation scheduling decision support systems. Water Supply . 16 , 245–252. https://doi.org/10.2166/ws.2015.136 (2015). Heiba, Y., Ibrahim, M. G., Mohamed, A. E., Fujii, M. & Nasr, M. Developing smart sustainable irrigation matrix (SIM)-based model for selection of best irrigation techniques: A framework to achieve SDGs. J. Clean. Prod. 420 , 138404. https://doi.org/10.1016/j.jclepro.2023.138404 (2023). Iacuzzi, N. et al. Crop Water Requirement Estimated with Data-Driven Models Improves the Reliability of CROPWAT 8.0 and the Water Footprint of Processing Tomato Grown in a Hot-Arid Environment. Agronomy 15. (2025). https://doi.org/10.3390/agronomy15071533 Jirapornvaree, I., Suppadit, T. & Kumar, V. Assessing the environmental impacts of agrifood production. Clean Technol. Environ. Policy . 24 , 1099–1112. https://doi.org/10.1007/s10098-021-02153-5 (2022). Khorchani, M. et al. Long-term croplands water productivity in response to management and climate in the Western US Corn Belt. Agric. Water Manage. 291 , 108640. https://doi.org/10.1016/j.agwat.2023.108640 (2024). Kijne, J. W., Barker, R., Molden, D. J. & Colombo Water Productivity in Agriculture: Limits and Opportunities for Improvement, International Water Management Institute.CABI, Sri Lanka. (2003). Ködmön, Z. & Szőke, J. Sustainability of water security in Burkina Faso—a review. Clean Technol. Environ. Policy . 27 , 8967–8979. https://doi.org/10.1007/s10098-025-03198-6 (2025). Lallana, C., Krinner, W., Nixon, S., Leonard, J. & Berland, J. M. Sustainable water use in Europe (No. 19), Environmental issue report (European Environment Agency, 2001). Li, G., Zhang, C., Huo, Z. & Liu, Y. Achieving comprehensive water productivity improvement: A multi-objective simulation-optimization model for water productivity-oriented irrigation water management. Agric. Water Manage. 309 , 109316. https://doi.org/10.1016/j.agwat.2025.109316 (2025). Li, X. et al. Irrigation water productivity is more influenced by agronomic practice factors than by climatic factors in Hexi Corridor, Northwest China. Nature 6 , 37971. https://doi.org/10.1038/srep37971 (2016). Li, Y. et al. Effects of irrigation scheduling on the yield and irrigation water productivity of cucumber in coconut coir culture. Nature 14 , 2944. https://doi.org/10.1038/s41598-024-52972-x (2024). Luo, A., Li, J., Xiao, Y., He, Z. & Liang, J. Engineering Soil Quality and Water Productivity Through Optimal Phosphogypsum Application Rates. Agronomy 15 , 35. https://doi.org/10.3390/agronomy15010035 (2025). Macher, G. Z., Beke, D. & Torma, A. Water quality of harvested rainwater from asbestos cement roofs and its suitability for irrigation. Clean Technol. Environ. Policy . 27 , 8955–8965. https://doi.org/10.1007/s10098-025-03197-7 (2025). McDermid, S. & Nocco, M. Irrigation in the Earth system. Nat. reviews earth Environ. 4 , 435–453. https://doi.org/10.1038/s43017-023-00438-5 (2023). Musick, J. T., Jones, O. R., Stewart, B. A. & Dusek, D. A. Water-Yield Relationships for Irrigated and Dryland Wheat in the U.S. Southern Plains. Agron. J. 86 , 980–986. https://doi.org/10.2134/agronj1994.00021962008600060010x (1994). Oosthuizen, L., Botha, P., Grové, B. & Meiring, J. Cost-estimating procedures for drip-, micro- and furrow-irrigation systems: technical note. Water SA . 405. https://doi.org/10.4314/wsa.v31i3.5214 (2007). Perelli, C., Branca, G., Corbari, C. & Mancini, M. Physical and Economic Water Productivity in Agriculture between Traditional and Water-Saving Irrigation Systems: A Case Study in Southern Italy. Sustainability 16 , 4971. https://doi.org/10.3390/su16124971 (2024). Perry, C. Accounting for water use: Terminology and implications for saving water and increasing production. Agric. Water Manage. 98 , 1840–1846. https://doi.org/10.1016/j.agwat.2010.10.002 (2011). Potkonjak, S. & Zoranović, T. Investments and costs of irrigation in function of agricultural sustainable development. (No. 46006), Sustainable Agriculture and Rural Development in the Function of Accomplishing Strategic Objectives of the Republic of Serbia in the Danube Region. CABI Digital Library, University of Novi Sad. (2013). Ray, L. I. P., Swetha, K., Singh, A. K. & Singh, N. J. Water productivity of major pulses – A review. Agric. Water Manage. 281 , 108249. https://doi.org/10.1016/j.agwat.2023.108249 (2023). Shoukat Hafiza, B., Ishaque, W., Ahmad, S., Ali, S. & El-Sheikh, M. A. Optimizing wheat productivity and water productivity through deficit irrigation strategies in semi-arid environments. Nature 15 , 20630. https://doi.org/10.1038/s41598-025-04618-9 (2025). Stepanovic, S., Rudnick, D. & Kruger, G. Impact of maize hybrid selection on water productivity under deficit irrigation in semiarid western Nebraska. Agric. Water Manage. 244 , 106610. https://doi.org/10.1016/j.agwat.2020.106610 (2021). Tarantino, E. et al. Agro-industrial Treated Wastewater Reuse for Crop Irrigation: Implication in Soil Fertility. Chem. Eng. Trans. 58 , 679–684. https://doi.org/10.3303/CET1758114 (2017). Yetik, A. K. & Candoğan, B. N. Optimisation of irrigation strategy in sugar beet farming based on yield, quality and water productivity. Plant. Soil. Environ. 68 , 358–365. https://doi.org/10.17221/234/2022-PSE (2022). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 27 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviews received at journal 16 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor invited by journal 07 Apr, 2026 Editor assigned by journal 26 Mar, 2026 Submission checks completed at journal 26 Mar, 2026 First submitted to journal 25 Mar, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9224116","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":622738147,"identity":"60a5f8c4-68cd-4c43-8d0a-062ab60f2447","order_by":0,"name":"Bálint Süle","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYFAC5gZmBgYLHiDjAJAnIUOEFkaQFgkeBja2BAYwg1gtDAxsPAYgLmEt8jMSGx8X1EjImMv3fH51owboQvbDRzfg02JwI7HZeMYxCR7LNt5t1jlABgNPWtoNvFokEtukedgkeAyO8W4zzgEygN4xw6sF6LD23zz/QFp4nhnn/CNCC8ONxDZm3jawFubHuW1EaDE487BZemYfSEuaGXMukMFGyC/y7ckHPxd8s7E3OHz48eecb3Vy/OyHj+F3GBJgkwCTxCoHAeYPpKgeBaNgFIyCkQMA6FFA4fPiJIMAAAAASUVORK5CYII=","orcid":"","institution":"Széchenyi István University","correspondingAuthor":true,"prefix":"","firstName":"Bálint","middleName":"","lastName":"Süle","suffix":""},{"id":622738148,"identity":"89ac9469-397d-46da-9c42-e4fce80d3007","order_by":1,"name":"Renátó Kalocsai","email":"","orcid":"","institution":"Széchenyi István University","correspondingAuthor":false,"prefix":"","firstName":"Renátó","middleName":"","lastName":"Kalocsai","suffix":""},{"id":622738149,"identity":"7f28056b-941a-4baa-8ff7-fc5ad7ade39b","order_by":2,"name":"Lajos Kubina","email":"","orcid":"","institution":"Széchenyi István University","correspondingAuthor":false,"prefix":"","firstName":"Lajos","middleName":"","lastName":"Kubina","suffix":""},{"id":622738151,"identity":"9cf2e601-d4d6-4c33-b613-d305baf88d39","order_by":3,"name":"Zsolt Giczi","email":"","orcid":"","institution":"Széchenyi István University","correspondingAuthor":false,"prefix":"","firstName":"Zsolt","middleName":"","lastName":"Giczi","suffix":""},{"id":622738153,"identity":"a4398210-b276-4b00-8af0-8b611a77be89","order_by":4,"name":"Ottília Vámos","email":"","orcid":"","institution":"Széchenyi István University","correspondingAuthor":false,"prefix":"","firstName":"Ottília","middleName":"","lastName":"Vámos","suffix":""},{"id":622738160,"identity":"f9ef7447-07c5-47d2-8103-3e9c3095b6df","order_by":5,"name":"Nóra Gombkötő","email":"","orcid":"","institution":"Széchenyi István University","correspondingAuthor":false,"prefix":"","firstName":"Nóra","middleName":"","lastName":"Gombkötő","suffix":""}],"badges":[],"createdAt":"2026-03-25 14:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9224116/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9224116/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107017548,"identity":"14952253-6a82-420a-ac47-0f8aeed188db","added_by":"auto","created_at":"2026-04-15 20:09:51","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"graphical-abstract","size":28859,"visible":true,"origin":"","legend":"Despite decades of research, market volatility still hasn't been addressed, creating a gap between agronomic efficiency and economic viability. This study presents a decision-support framework to optimise irrigation water allocation by integrating Crop Water Productivity (CWP) with economic profitability. While traditional CWP focuses on biomass, our model incorporates fluctuating irrigation costs and market prices to calculate Net Profit per Cubic Meter of Water. The present study aims to address the critical gap between biophysical water use and financial viability by introducing a dynamic Economic-Water-Productivity (EWP) framework. In contradistinction to static models, our approach integrates stochastic market volatility with primary farm-level data, thereby enabling the quantification of the specific thresholds at which irrigation transitions from a yield-enhancer to a high-risk financial liability. Model was validated using primary data from 12 commercial farms in Western Transdanubia (2022\u0026ndash;2024). The results show that maize exhibited the highest biological water productivity (e.g., 2.23 kg m⁻\u0026sup3;) and the highest economic return in 2022 (\u0026euro; 0.19 m⁻\u0026sup3;). However, irrigation was not profitable in 2024 due to lower crop prices and higher costs. The analysis of the data set, which included wheat and soybeans, demonstrated that irrigation was economically non-viable in both years. Sensitivity analysis confirmed the model's robustness under price volatility scenarios of 20% and 40%. The proposed tool facilitates the prioritisation of irrigation based on direct economic efficiency, thereby providing a scalable solution for water-scarce agricultural regions. To reach the break-even point in 2024, a 22% increase in market price (to 0.209 \u0026euro;/kg) or a reduction in irrigation costs to 0.278 \u0026euro; m⁻\u0026sup3; would be required for maize. For soybeans, market prices would need to increase by 150% to reach the break-even threshold.","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9224116/v1/40e409fe5842dfaf5823138f.png"},{"id":107480665,"identity":"a112835c-2075-492a-8e14-e3190037c0ed","added_by":"auto","created_at":"2026-04-22 02:12:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":716582,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9224116/v1/5c81263e-02fc-479e-94ee-6395c222a923.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Economic Optimization of Irrigation Decisions Using Water Productivity for Crop Selection","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSince agriculture is the main consumer of global freshwater, water scarcity poses an increasingly serious threat to food security and the profitability of farms. However, the world's freshwater resources are under pressure from population growth, unsustainable practices and environmental challenges (K\u0026ouml;dm\u0026ouml;n and Szőke, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Irrigation is responsible for around 70% of global freshwater withdrawals and 90% of consumptive water use, which has a significant impact on various Earth system processes (McDermid and Nocco, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Agricultural productivity growth was linked to a reduction in poverty. In Asia, every one-percent increase in productivity can reduce the number of poor people by 0.48 percent (Jirapornvaree et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the vital role of irrigated agriculture in mitigating food shortages driven by economic and population expansion is overshadowed by its increasing water and energy demands. This poses a significant challenge to regional water security and exacerbates global climate change (Macher et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A feasible and practical approach to ensure the effective use of natural resources without compromising the needs of future generations could be to assess the most appropriate irrigation treatments in terms of achieving the objectives of the Sustainable Development Goals (Heiba et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). There is an increasing need to optimise cropping patterns, in order to address the shortage and mismatch of land and water resources (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Irrigation requirements in Hungary are comparable to those of Mediterranean countries (Tarantino et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, a mere 6% of Hungary's agricultural territory is suitable for economic irrigation, and of this, less than 50% is currently irrigated on a regular basis. This raises a key question: which crops should be prioritised for irrigation? Which plants will benefit from irrigation? Research conducted in continental regions has indicated that certain crops, including potatoes, are demonstrating increased susceptibility to risk in recent years without the benefit of irrigated support (Dwijendra et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Selection of an appropriate irrigation scheduling method should be based on marketable yield (Asiimwe et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In changing climates, the process of determining which plants require irrigation involves the use of specific indicators (Badawy et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In light of the water shortage, it is essential to quantify irrigation water applied to accurately predict changes in yield (Ray et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The study aims to identify the most profitable use of irrigation water under real market conditions. It is hypothesised that the use of the Crop Water Productivity (CWP) metric in a sophisticated manner will enable the execution of robust analyses for the purpose of selecting irrigated crops and ranking profitability. A review of the literature reveals several key gaps: Currently, Irrigation Water Productivity (IWP) and Economical Water Productivity (EWP) values are very limited in Hungary. There is a lack of decision-support tools for irrigated crop selection given the existing water constraints. The economic impact of irrigation under varying market prices is not sufficiently analysed. Research to date has been very broad, but there is no complex decision support model to clearly rank irrigated crops in terms of economic and production considerations. Our method is unique because it provides market players with a tangible, easy-to-use calculation tool for projecting their productivity and net income-generating capacity for the coming years. Agricultural companies can use it to analyze everything from the irrigation of a future plantation to the planning of next year's crop rotation. The research adds value to the current state of the literature. It provides relevant support for sustainable water use. The research is innovative in its approach, as it differentiates between irrigation costs, production costs, and purchase prices, a distinction that has not been previously made in the extant literature. This enables the aggregation of data across different units of measurement, such as kg or kg/m\u003csup\u003e3\u003c/sup\u003e, facilitating longitudinal comparisons. Moreover, the disaggregated treatment of irrigation costs gives a comparative analysis with dry farming, thereby enabling precise selection for farmers or researchers.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Research objectives and research questions and hypothesyses.\u003c/h2\u003e \u003cp\u003eThe study's aim is to develop and validate a decision-support framework combining technical and economic indicators to optimise crop selection for irrigated farming in Western Hungary.\u003c/p\u003e \u003cp\u003eTo achieve this, we addressed the following research questions:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eRQ1: How do CWP and EWP vary among field crops in Western Transdanubia under different climatic conditions?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRQ2: How do fluctuating irrigation costs and market prices influence the ranking of crops when prioritised by net profit per unit of water?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRQ3: Is a simple decision-support equation effective at distinguishing the economic efficiency of irrigation for different crops at the farm level?\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe following hypotheses (H) were derived from these queries:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eH1: CWP and EWP are linked, but a high yield doesn't always mean profit.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eH2: Irrigation for maize is more sensitive to energy price fluctuations than for soybeans, due to higher water requirements.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eH3: Farmers can identify crop-shifting opportunities that increase net irrigation profit by at at least 15\u0026ndash;20% with the proposed decision-support tool, compared to traditional yield-based decision-making.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Benchmarking against existing irrigation models.\u003c/h2\u003e \u003cp\u003eAlthough previous studies have extensively analysed Crop Water Productivity (CWP) from a physiological perspective, they often neglect the dynamic nature of economic factors such as energy-driven irrigation costs and fluctuating market prices. Our model addresses this issue by incorporating these variables into a unified decision-making equation. Unlike the CROPWAT (Iacuzzi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) model, which primarily focuses on water requirements and yield response, our approach prioritises 'net profit per cubic metre', providing a direct financial key performance indicator (KPI) (Batisha, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for farm-level management. While traditional models such as CROPWAT are still considered essential for simulating crop water requirements, they remain static in terms of economic considerations. Our approach is designed to address this limitation by integrating real-time market fluctuations. This effectively bridges the gap between agronomy and farm-level financial decision-making.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Formulation of the research problem\u003c/h2\u003e \u003cp\u003eCurrently, there is no clear tool available to irrigation stakeholders that would be suitable for forecasting the net profit of different irrigated crops and predicting yields at the same time. Research on this topic has been primarily focused on yield-based indicators to date. In contrast, the proposed methodology explicitly separates the cost of irrigation from the cost of crop production, allowing for a transparent comparison between irrigated and dryland farming. The objective was to assess whether irrigation water can be allocated in a way that maximises both profitability and productivity. To answer this question, a comparative analytical framework was developed to evaluate crop performance under irrigation. The analysis encompassed a comprehensive set of metrics, including irrigation costs, the impact on yield, water use indicators (CWP, IWP), as well as market prices and production costs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study region and data collection\u003c/h2\u003e \u003cp\u003eThe research concentrated on Hungary's irrigable areas. The study region was selected based on three criteria: (1) low irrigation coverage (~\u0026thinsp;6%), (2) high investment risk, and (3) significant variability in water pricing. The economic risk of irrigation investments is high. There are significant regional differences in water prices. During the research, we collected and analyzed the following data of some main crops: Irrigation costs (2018\u0026ndash;2024), crop yields, CWP and IWP values, market prices and production costs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Yield and water use data\u003c/h2\u003e \u003cp\u003eDuring the research, CWP data was collected using the FAO database CROPWAT software \u003cb\u003e(\u003c/b\u003eFAO, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e and international literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Economic analisys and production costs\u003c/h2\u003e \u003cp\u003eThe average prices and production costs for 2024 were collected from the following sources. AKI Agricultural Economics Institute of Hungary. Crop market reports. In order to guarantee a reliable economic comparison, crop prices were calculated based on annual averages. For the analysis, all price data from 2022 were adjusted to 2024 present value to account for inflation and ensure comparability across the studied period. These inflation-adjusted values were used in all subsequent economic calculations and cost-benefit assessments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Selection of crops:\u003c/h2\u003e \u003cp\u003eThe crops were chosen based on the model's practical relevance, which was assessed using three strategic criteria.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEconomic Dominance: These crops are 70% of arable land in Western Transdanubia, making them the region's key agricultural contributors.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eVariability in Water Demand: The model simulates crops with different irrigation needs, from high-water-demand (Maize) to drought-tolerant (Wheat). This enables testing of decisions under varied water scarcity scenarios.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAligned with National Strategic Priorities: The selection of these crops aligns with the Hungarian National Irrigation Strategy and EU CAP objectives for climate-resilient agriculture. These crops face increased drought risk in the Carpathian Basin, so supporting these species directly impacts national food security and the irrigation sector's economic stability.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Data validation and data cleaning\u003c/h2\u003e \u003cp\u003eTo validate the data, we compared the results obtained with profitability data available in the domestic literature. During data cleaning, incomplete and inconsistent records were removed. This ensures the reliability of the data for subsequent analysis and modelling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Comparison and definition of the various metrics\u003c/h2\u003e \u003cp\u003eWhereas Crop Water Use Efficiency (WUE) compares an output from the system (such as yield or economic return) to crop evapotranspiration. In recent times, the focus on water productivity has broadened to encompass an analysis of the benefits and costs of water used for agriculture in terrestrial and aquatic ecosystems. Agricultural water productivity may be conceptualised as the ratio between the net benefits derived from crop, forestry, fishery, livestock and mixed agricultural systems and the quantity of water utilised to generate those benefits. The basic expression of agricultural water productivity may be defined as a measure of output produced by a given system in relation to the volume of water consumed. This may be measured for the entire system or for defined parts thereof and may be expressed as a function of time and space. It is common practice to express water productivity in terms of kg m\u003csup\u003e-3\u003c/sup\u003e, with crop production represented in kg ha\u003csup\u003e-1\u003c/sup\u003e and water use estimated in mm of precipitation applied or received. This is then converted to m\u003csup\u003e3\u003c/sup\u003e ha\u003csup\u003e-1\u003c/sup\u003e, where 1 mm is equivalent to 10 m\u003csup\u003e3\u003c/sup\u003e ha\u003csup\u003e-1\u003c/sup\u003e. As an alternative, it may be represented in terms of food (in kcal/ m\u003csup\u003e3\u003c/sup\u003e) or its monetary value (\u003cspan\u003e$\u003c/span\u003e/m\u003csup\u003e3\u003c/sup\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{W}\\text{a}\\text{t}\\text{e}\\text{r}\\:\\text{p}\\text{r}\\text{o}\\text{d}\\text{u}\\text{c}\\text{t}\\text{i}\\text{v}\\text{i}\\text{t}\\text{y}=\\frac{\\text{A}\\text{g}\\text{r}\\text{i}\\text{c}\\text{u}\\text{l}\\text{t}\\text{u}\\text{r}\\text{a}\\text{l}\\:\\text{b}\\text{e}\\text{n}\\text{e}\\text{f}\\text{i}\\text{t}}{\\text{W}\\text{a}\\text{t}\\text{e}\\text{r}\\:\\text{u}\\text{s}\\text{e}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e(Drechsel et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe conventional notions of irrigation efficiency, as employed by engineers, fail to incorporate economic considerations. In order to ascertain the optimal level of irrigation efficiency, the economist would require knowledge of the value of irrigation water and the cost of any increased water or management measures that would facilitate a reduction in diversion (Kijne et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). There are two ways of expressing water productivity: physical and economic. Specifically, economic water productivity is the economic value derived from each unit of water used, whereas physical water productivity is the ratio of agricultural output (crop yields) to the amount of water used.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\text{P}\\text{h}\\text{y}\\text{s}\\text{i}\\text{c}\\text{a}\\text{l}\\:\\text{w}\\text{a}\\text{t}\\text{e}\\text{r}\\:\\text{p}\\text{r}\\text{o}\\text{d}\\text{u}\\text{c}\\text{t}\\text{i}\\text{v}\\text{i}\\text{t}\\text{y}\\:\\text{P}\\text{W}\\text{P}=\\frac{\\text{Y}\\text{i}\\text{e}\\text{l}\\text{d}\\:\\left(\\text{k}\\text{g}\\right)}{\\text{I}\\text{r}\\text{r}\\text{i}\\text{g}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\text{W}\\text{a}\\text{t}\\text{e}\\text{r}\\:\\text{U}\\text{s}\\text{e}\\:\\left(\\text{I}\\text{W}\\text{U}\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\text{E}\\text{c}\\text{o}\\text{n}\\text{o}\\text{m}\\text{i}\\text{c}\\text{a}\\text{l}\\:\\text{w}\\text{a}\\text{t}\\text{e}\\text{r}\\:\\text{p}\\text{r}\\text{o}\\text{d}\\text{u}\\text{c}\\text{t}\\text{i}\\text{v}\\text{i}\\text{t}\\text{y}\\:\\text{E}\\text{W}\\text{P}=\\frac{\\text{G}\\text{r}\\text{o}\\text{s}\\text{s}\\:\\text{m}\\text{a}\\text{r}\\text{g}\\text{i}\\text{n}\\:\\left(\\text{\u0026euro;}\\right)}{\\text{I}\\text{r}\\text{r}\\text{i}\\text{g}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\text{W}\\text{a}\\text{t}\\text{e}\\text{r}\\:\\text{U}\\text{s}\\text{e}\\:\\left(\\text{I}\\text{W}\\text{U}\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e(Perelli et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eNormally there are three ways to improve water productivity: to transfer as much of the water as possible, to convert as much of the water into CO\u003csub\u003e2\u003c/sub\u003e as possible, to convert as much of the biomass as possible into a harvestable product (Ali and Talukder, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Another approach is to look at economic water productivity. This is defined by the Food and Agriculture Organization of the United Nations as the monetary value generated from each unit of water consumed. On a dollar-for-dollar basis, the output of different crops can be compared. Economic water productivity can also be measured at whole-farm level (where farms produce multiple crops) and broader regional levels, such as counties, where regions have different crop mixes. (Frisvold and Atla, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\"Which crop and which drop\". The numerator can be the total dry or fresh biomass or harvested product, expressed in physical or economic terms. Denominators may include transpiration (T), evapotranspiration (ET), irrigation volume, water inputs, etc. Often it is not explicitly stated whether fresh or dry yield is used as the numerator. (Bessembinder et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) The economic water productivity (EWP) of wheat (Triticum aestivum) was compared using deficit irrigation (DI). The EWP was found to be optimal at an irrigation level of 80% and was the poorest at 40% irrigation. (Shoukat Hafiza et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) Irrigation water productivity is for show the effect of irrigated land production. Defined as the yield produced per unit of irrigation water consumed, has become an important criterion for assessing both agricultural production and water use efficiency. It is a comprehensive indicator that shows the level of management of both the irrigation system and the crop. (Li et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) It is important to see, that the Crop Water Productivity and Irrigation Water Productivity correlates together as the IWP significantly higher than the CWP (Musick et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). The reason is under: Calculation of IWP shows that the factors are less dependent on all y climate (Luo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), (Adeboye et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e):\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:\\text{I}\\text{W}\\text{P}=\\frac{Y}{\\text{I}\\text{A}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere Y represents the yield (kg), and IA is the total irrigation amount during the growth period (m\u003csup\u003e3\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eThe agronomic practice factors, including irrigation, fertilisation, agricultural film and agricultural pesticides, contributed 20.6%, 32.8%, 42.3% and 11.1% respectively to the increase in IWP. The contribution rates of the climatic factors, i.e. daily mean temperature and solar radiation, are \u0026minus;\u0026thinsp;0.9% and 0.9% (Li et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIWP\u0026thinsp;=\u0026thinsp;Y-Y0 /I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(5)\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\u003eWhere: Y= total yield; Y\u003csub\u003e0\u003c/sub\u003e non irrigated control; I irrigation (Yetik and Candoğan, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); (Stepanovic et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) In general, IWP increased with the decrease of irrigation amount and the increase of yield (Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The WP concept can also be applied more broadly by assigning different values to the numerator [product]. This is common in water valuation approaches, where economic attributes can be given in monetary terms (\u003cspan\u003e$\u003c/span\u003e m\u003csup\u003e-3\u003c/sup\u003e); social attributes (jobs, food security, etc.), or environmental attributes (carbon sequestration, biodiversity, etc.) (Batchelor et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe following is a synopsis of the crop, irrigation, and economic water productivity indicators.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnalisys level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFocus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApplication in practice\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlant / Field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiological effectiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssess crop performance and breed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eField / Farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eManagement effectiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvaluate irrigation systems and schedule.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarm / Regional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinancial effectiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSupport policy decisions and crop selection.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Cost of irrigation calculation\u003c/h2\u003e \u003cp\u003eThe cost of irrigation depends on the technology chosen and on the water consumption. The way in which the construction of these systems is financed, as well as the method of calculating the price of 1 m\u003csup\u003e3\u003c/sup\u003e of water, also have an influence on the cost of irrigation. (Potkonjak and Zoranović, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) The cost of providing irrigation water consists of the variable costs of treating and delivering water to end users and the fixed costs of capital depreciation, operation and maintenance. The variable costs depend on the quantity of water delivered, whereas the fixed costs do not. In most countries, fixed costs are heavily subsidised. (Lallana et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) It is imperative that the estimation of certain cost components be accompanied by a detailed explanatory note. A fixed cost is one that is independent of production. For an irrigation system, these are usually depreciation, interest, insurance and fixed electricity charges. They are based on the initial investment. Irrigation system operating costs should be based on planned water applications. Variable costs are those which the irrigator can control in the short term. Operating costs, such as electricity, water, labour and repairs, should be estimated as cost per m\u003csup\u003e3\u003c/sup\u003e. (Oosthuizen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Europe reveals a substantial variation in pricing across different countries. Findings indicate a range of prices, both within and between nations, spanning from \u0026euro;0.054 to \u0026euro;0.645 per m\u003csup\u003e3\u003c/sup\u003e (as observed in Greece) to \u0026euro;0.23 to \u0026euro;1.50 per m\u003csup\u003e3\u003c/sup\u003e (as experienced in France) (Giannakis et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Twelve Hungarian irrigation farms in Western Transdanubia provided data as the basis for calculating irrigation costs. The categories of data collected: water supply fee (\u003cb\u003e\u0026euro;\u003c/b\u003e m⁻\u0026sup3;), energy consumption (kWh/m\u003csup\u003e3\u003c/sup\u003e), maintenance costs, labor costs, fixed costs (depreciation, insurance, interest costs). We anonymized the farm data and calculated average values, from which we calculated a cost price of \u0026euro; 0.375/m\u003csup\u003e3\u003c/sup\u003e for 2024.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSection presents the primary characteristics and irrigation costs of the analysed farms.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFarm ID\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFarm Size (ha)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIrrigation Technology\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMain Crops Analyzed\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eIrrig. Cost (\u0026euro;/m\u0026sup3;)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm 01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraveling Gun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaize, Seed maize, Wheat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm 02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraveling Gun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoybean, Maize, Rapeseed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm 03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCenter Pivot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaize, Seed maize, Wheat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm 04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraveling Gun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSugar beet, Maize, Winter Wheat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm 05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraveling Gun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaize, Seed maize, Wheat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm 06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrip Irrigation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaize, Seed maize, Wheat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm 07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCenter Pivot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaize, Winter Wheat, Soybean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm 08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraveling Gun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBarley, Maize, Sunflower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm 09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraveling Gun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoybean, Winter Wheat, Maize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrip Irrigation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotatoes, Vegetables, Maize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLinear Move\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter Wheat, Maize, Alfalfa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraveling Gun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaize, Rapeseed, Barley\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Consistency checks\u003c/h2\u003e \u003cp\u003eThe yield and water-use values have been verified against regional benchmarks. A thorough review of the spreadsheet formulae was conducted to ensure their consistency. A comparison has been made between model outputs and those of previously published case studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Synthesising a novel equation\u003c/h2\u003e \u003cp\u003eAs part of the analysis, we calculated and compared physical and economic water use indicators. The aim is to identify the crops that yield the highest return per unit of irrigation water used. A range of measures was studied with a view to identifying the optimal representation of our aims in the context of decision support for farming companies. The most significant indicators that were analysed were as follows:\u003c/p\u003e \u003cp\u003eCrop Water Productivity (CWP) is defined as the ratio of yield (kg/ha) to water use (m\u003csup\u003e3\u003c/sup\u003e/ha). Water Use Efficiency (WUE) is the proportion of irrigation water that is actually used by the crop compared to the total water input. Irrigation Water Productivity (IWP) is defined as the amount of crop yield per unit of irrigation water, expressed in kg/m\u003csup\u003e3\u003c/sup\u003e. Economic Water Productivity (EWP) is defined as the financial yield per unit of water (\u0026euro; m⁻\u0026sup3;). The \"catchy\" nature of the term \"water use efficiency\" leads many scientists to use it even in contexts where it doesn't apply. For this reason, WUE and CWP are two different terms and should not be used interchangeably. Although they are related, because CWP is plant-specific and controlled by other factors, a high WUE does not necessarily result in a high CWP (Perry, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen calculating costs, we took into account the fixed and variable costs of irrigation systems, as well as the unit price of water. Following a thorough review of the relevant data, including insights from domestic irrigation farms and specialist literature, we have arrived at a cost price of \u0026euro; 0.375/m\u0026sup3; (2024) for water. In order to compare the cost-effectiveness of irrigation, we developed a simple decision support model that calculates net income per one m\u0026sup3; of irrigation water. The model enables direct comparison of irrigation profitability across crops.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet\u0026nbsp;profit\u0026nbsp;per\u0026nbsp;1m\u003csup\u003e3\u003c/sup\u003e = (CWP\u003csub\u003ecrop\u003c/sub\u003e ​\u0026sdot; P\u003csub\u003ecrop\u003c/sub\u003e​) \u0026minus; C\u003csub\u003eirrigation\u003c/sub\u003e​ \u0026minus; C\u003csub\u003eproduction\u003c/sub\u003e ​\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eWhere:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eCWP\u003csub\u003ecrop\u003c/sub\u003e​: Crop Water Productivity (kg/m3)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eP\u003csub\u003ecrop\u003c/sub\u003e: crop commodity price (\u0026euro;/kg)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eC\u003csub\u003eirrigation\u003c/sub\u003e: cost of irrigation per (\u0026euro; m⁻\u0026sup3;)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eC\u003csub\u003eproduction\u003c/sub\u003e: cost of production (\u0026euro;/ha/year) / irrigated yield (kg/ha)\u003c/p\u003e \u003cp\u003eIn the course of processing the water productivity results in the study, the new formula was employed to examine the values for average prices in 2024. The utilisation of this methodology enables the producer to demonstrate the potential for irrigated crops by replacing the existing data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNet profit calculation for different crops, regarding 2022 and 2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCrop\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCWP kg/m3\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCrop price \u0026euro;/kg\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eIncome (gross) \u0026euro;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eIrrigation cost \u0026euro; m⁻\u0026sup3;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eYield - Cost of irrigation\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eCost of production \u0026euro; /ha\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eIrrigated yield t/ha\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eCost per kg \u0026euro;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eNet profit \u0026euro; m⁻\u0026sup3;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.23*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.23*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoybean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoybean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.282\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\u003e*CWP value for maize: (Khorchani et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Sensitivity analysis of different crops\u003c/h2\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\u003eSensitivity analisys of different crops\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCrop\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCWP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePrice\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eIrrigation Cost\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eNet Profit (calc)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePrice\u0026thinsp;+\u0026thinsp;20%\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ePrice\u0026thinsp;+\u0026thinsp;40%\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eIrr. Cost\u0026thinsp;\u0026minus;\u0026thinsp;20%\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoybean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoybean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.036\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\u003eThe sharp decrease in profitability between 2022 and 2024 (for example, a 179% decrease for maize) highlights a critical sensitivity threshold. Our findings suggest that in scenarios involving high input costs, irrigation may no longer guarantee enhanced yields, but rather poses a significant financial risk, necessitating strict price hedging measures.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eIrrigation systems are increasingly vulnerable in volatile markets. While technology can boost water productivity, economic sustainability is driven by price and cost. Irrigation strategies should assess economic risk alongside agronomic optimisation. Irrigation profitability saw a decline from 2022 to 2024, with all crops becoming unprofitable due to lower crop prices (e.g., maize net profit decreased from +\u0026thinsp;0.189 to \u0026minus;\u0026thinsp;0.208 \u0026euro; m⁻\u0026sup3;). A sensitivity analysis was conducted, which demonstrated a strong correlation with market conditions. Maize demonstrated threshold behaviour, becoming profitable under moderate price increases (e.g., +\u0026thinsp;40%), while wheat remained unprofitable even under improved scenarios (e.g., \u0026minus;\u0026thinsp;0.365 \u0026euro; m⁻\u0026sup3; at baseline). Soybean prices exhibited moderate improvement (for example, from \u0026minus;\u0026thinsp;0.282 to near break-even with higher prices) but remained negative overall. The impact of fluctuations in irrigation costs proved to be less significant in comparison to that of changes in crop prices. The results provide clear insights into irrigation profitability under changing market conditions. The results indicate that the low grain prices in 2024, along with the production costs of arable crops and industrial crops, significantly affect the economic viability of irrigated arable farming. Recent data indicates a general increase in energy and energy unit reservation fees. If these trends persist, the number of economically viable irrigated crops is expected to decline. The formula and research materials presented in this article demonstrate a methodology for determining the most profitable crop composition for irrigated areas. The financial implications of irrigation, including both operational costs and purchase prices, are subject to periodic fluctuations. However, the efficacy of this approach remains constant. Research has demonstrated that irrigated crops are particularly susceptible to annual market price fluctuations. Consequently, utilising decision support tools based on historical average CWP or IWP values, coupled with next year's commodity markets forecasts, can accurately determine the number of positive years a farm is likely to experience in the long term. This information is crucial for strategic planning and ensuring financial stability.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Dataset representativeness and conclusions scope\u003c/h2\u003e \u003cp\u003eIn order to ensure high internal validity, a multi-case study approach was employed. This facilitated the incorporation of high-fidelity primary data, encompassing actual pumping logs and energy invoices, thereby offering a more granular economic insight than regional statistical averages. While the study is based on a primary dataset of 12 commercial farms, these were strategically selected to provide a high level of representation of high-intensity irrigated agriculture in Western Transdanubia. The following points justify the representativeness of this sample:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThis research contrasts with national-scale studies relying on secondary statistics by using farm-level primary data e.g. energy bills and pumping logs.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe 12 farms investigated collectively manage 2600 hectares of irrigated land, using Hungary's most common irrigation technologies.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA National Model for the Whole Country. Western Transdanubia is a good example of the country's modernising irrigation sector. The decision-making here can be used as a model for other Hungarian regions with similar conditions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRather than a broad statistical survey, this study uses a case study approach, a scientifically accepted method for validating new decision-support models.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eWe provide concrete decision scenarios based on the BEP (Break Even Point) for the 2024 growing season. These show the economic viability of irrigation in volatile market conditions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBreak-even Crop Price (P\u003c/b\u003e \u003csub\u003e \u003cb\u003eBE\u003c/b\u003e \u003c/sub\u003e \u003cb\u003e)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eP\u003c/b\u003e \u003csub\u003e \u003cb\u003eBE\u003c/b\u003e \u003c/sub\u003e \u003cb\u003e= (C\u003c/b\u003e\u003csub\u003e\u003cb\u003eprod\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e+ C\u003c/b\u003e\u003csub\u003e\u003cb\u003eirrig\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e) / CWP\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eP\u003c/b\u003e \u003csub\u003e \u003cb\u003eBE\u003c/b\u003e \u003c/sub\u003e: \u003cb\u003eBreak-even price (\u0026euro;/kg)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eC\u003c/b\u003e \u003csub\u003e \u003cb\u003eprod\u003c/b\u003e \u003c/sub\u003e: \u003cb\u003eThe total production cost, excluding irrigation costs (\u0026euro; per ha)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eC\u003c/b\u003e \u003csub\u003e \u003cb\u003eirrig\u003c/b\u003e \u003c/sub\u003e: \u003cb\u003eThe unit cost of irrigation is expressed in Euros per cubic meter\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eCWP: is the Crop Water Productivity of the examined crops (kg m\u003c/b\u003e \u003csup\u003e \u003cb\u003e-3\u003c/b\u003e \u003c/sup\u003e \u003cb\u003e)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBreak Even Point for major crops in Hungary in 2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop 2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCWP (kg m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrice (\u0026euro; \u003csup\u003e-kg\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIrrigation Cost (\u0026euro; m⁻\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProduction Cost (\u0026euro;/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYield (kg/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCost per kg (\u0026euro; \u003csup\u003e-kg\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRevenue per m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eProfit per m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBreak-even Price (\u0026euro;/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eBreak-even Irrigation Cost \u0026euro; m⁻\u0026sup3;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoybean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.021\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\u003eThese figures provide a baseline for producers to evaluate whether current market prices cover the rising costs of water use and production for maize, wheat, and soybean. The break-even analysis (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.) quantifies the economic vulnerability of the sector; the fact that maize requires a 22% price premium just to cover irrigation costs suggests that technological efficiency alone cannot compensate for adverse market shifts.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis study demonstrates that integrating economic and water productivity indicators significantly improves irrigation decision-making. The novelty of the results lies in the fact that they directly link water use efficiency to production costs and return on investment, thus providing a more complex picture of the economics of irrigation than previous analyses, which were mostly based on yield or water requirements alone. The quantification of trends indicates that, in comparison with the 2022 year under consideration, the economic advantage of grain maize irrigation has diminished by 179% in 2024. The net profit of wheat production under irrigated conditions decreased by 81.6% between 2022 and 2024. It was also not financially viable to irrigate wheat in 2022. This assertion pertains to the irrigation of soybeans. The year 2022 yielded a modest profit of 0.189 \u0026euro; m\u003csup\u003e-\u003c/sup\u003e\u0026sup3;, however, in 2024, a substantial loss was incurred due to escalating Operational Expenditures (OPEX) and unfavourable commodity prices. The results highlight the increasing financial risk of irrigation under volatile market conditions. The practical benefit of the study is that the model presented helps to tailor irrigation strategies and support producers' risk management decisions. The study supports what was previously discovered by Li et al., when he was studying Maximum Economic Water Productivity (EWPmax) and Maximum Irrigation Productivity (IWP max) (Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The findings published in the article are extremely important from the perspective of domestic irrigation of Hungary, as production is currently shifting towards low value-added crops, while maize and wheat (wheat productivity is lower, as are its stock market prices) do not cover the cost of irrigation. Therefore, there is a need to move towards other crops, which generate the highest net profit under irrigation. The research group will work on an extensive database to help producers make decisions. This model is thus proposed as a scalable diagnostic tool for semi-arid and transition regions, with the suggestion that sustainable irrigation requires a strategic shift from low-value cereal monocultures towards high-margin, water-efficient crop rotations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eFinancial interest\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent to participate:\u003c/h2\u003e \u003cp\u003eInformed consent was obtained from all individual participants, B\u0026aacute;lint S\u0026uuml;le, Ren\u0026aacute;t\u0026oacute; Kalocsai, Lajos Kubina, Zsolt Giczi, Viktor Nagy, Ott\u0026iacute;lia V\u0026aacute;mos, N\u0026oacute;ra Gombk\u0026ouml;tő included in the study.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics declaration\u003c/h2\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors, B\u0026aacute;lint S\u0026uuml;le, Ren\u0026aacute;t\u0026oacute; Kalocsai, Lajos Kubina, Zsolt Giczi, Viktor Nagy, Ott\u0026iacute;lia V\u0026aacute;mos, N\u0026oacute;ra Gombk\u0026ouml;tő reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and analyzed duringthe current study are not publicly available but are available from thecorresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdeboye, O. B., Schultz, B., Adekalu, K. O. \u0026amp; Prasad, K. Crop water productivity and economic evaluation of drip-irrigated soybeans (Glyxine max L. Merr). \u003cem\u003eSpringer Nat. - Agric. Food Secur.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40066-015-0030-8\u003c/span\u003e\u003cspan address=\"10.1186/s40066-015-0030-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli, M. H. \u0026amp; Talukder, M. S. U. Increasing water productivity in crop production\u0026mdash;A synthesis. \u003cem\u003eAgric. Water Manage.\u003c/em\u003e \u003cb\u003e95\u003c/b\u003e, 1201\u0026ndash;1213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agwat.2008.06.008\u003c/span\u003e\u003cspan address=\"10.1016/j.agwat.2008.06.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsiimwe, G., Jaafar, H., Haidar, M. \u0026amp; Mourad, R. Soil Moisture or ET-Based Smart Irrigation Scheduling: A Comparison for Sweet Corn with Sap Flow Measurements. \u003cem\u003eJ. Irrig. Drain. Eng.\u003c/em\u003e \u003cb\u003e148\u003c/b\u003e, 04022017. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1061/(ASCE)IR.1943-4774.0001668\u003c/span\u003e\u003cspan address=\"10.1061/(ASCE)IR.1943-4774.0001668\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBadawy, A., Elmahdi, A., Abd El-Hafez, S. \u0026amp; Ibrahim, A. Water Profitability Analysis to Improve Food Security and Climate Resilience: A Case Study in the Egyptian Nile Delta. \u003cem\u003eClimate\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cli10020017\u003c/span\u003e\u003cspan address=\"10.3390/cli10020017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBatchelor, C., Hoogeveen, Faur\u0026egrave;s, J. M. \u0026amp; Peiser, L. \u003cem\u003eWater Accounting and Auditing - A Sourcebook\u003c/em\u003e (FAO, 2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBatisha, A. Multi-disciplinary strategy to optimize irrigation efficiency in irrigated agriculture. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 11433. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-024-61372-0\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-61372-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBessembinder, J. J. E., Leffelaar, P. A., Dhindwal, A. S. \u0026amp; Ponsioen, T. C. Which crop and which drop, and the scope for improvement of water productivity. \u003cem\u003eAgric. Water Manage.\u003c/em\u003e \u003cb\u003e73\u003c/b\u003e, 113\u0026ndash;130. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agwat.2004.10.004\u003c/span\u003e\u003cspan address=\"10.1016/j.agwat.2004.10.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Y. et al. Crop pattern optimization for the coordination between economy and environment considering hydrological uncertainty. \u003cem\u003eSci. Total Environ.\u003c/em\u003e \u003cb\u003e809\u003c/b\u003e, 151152. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2021.151152\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2021.151152\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrechsel, P., Heffer, P., Magen, H., Mikkelsen, R. \u0026amp; Wichelns, D. \u003cem\u003eManaging water and fertilizer for sustainable agricultural intensification\u003c/em\u003e (International Water Management Institute (IWMI), Montpellier, 2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDwijendra, N. K. A. et al. The effect of various irrigation technologies and strategies on water resources management. \u003cem\u003eJ. Water Land. Dev.\u003c/em\u003e 143\u0026ndash;147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24425/jwld.2022.140790\u003c/span\u003e\u003cspan address=\"10.24425/jwld.2022.140790\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO. Release Note AquaCrop version 7.1. FAO, Rome, Italy. (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrisvold, G. B. \u0026amp; Atla, J. Agricultural Economic Water Productivity Differences across Counties in the Colorado River Basin. \u003cem\u003eHydrology\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 125. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/hydrology11080125\u003c/span\u003e\u003cspan address=\"10.3390/hydrology11080125\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiannakis, E., Bruggeman, A., Djuma, H., Kozyra, J. \u0026amp; Hammer, J. Water pricing and irrigation across Europe: opportunities and constraints for adopting irrigation scheduling decision support systems. \u003cem\u003eWater Supply\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e, 245\u0026ndash;252. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2166/ws.2015.136\u003c/span\u003e\u003cspan address=\"10.2166/ws.2015.136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeiba, Y., Ibrahim, M. G., Mohamed, A. E., Fujii, M. \u0026amp; Nasr, M. Developing smart sustainable irrigation matrix (SIM)-based model for selection of best irrigation techniques: A framework to achieve SDGs. \u003cem\u003eJ. Clean. Prod.\u003c/em\u003e \u003cb\u003e420\u003c/b\u003e, 138404. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2023.138404\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2023.138404\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIacuzzi, N. et al. Crop Water Requirement Estimated with Data-Driven Models Improves the Reliability of CROPWAT 8.0 and the Water Footprint of Processing Tomato Grown in a Hot-Arid Environment. Agronomy 15. (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy15071533\u003c/span\u003e\u003cspan address=\"10.3390/agronomy15071533\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJirapornvaree, I., Suppadit, T. \u0026amp; Kumar, V. Assessing the environmental impacts of agrifood production. \u003cem\u003eClean Technol. Environ. Policy\u003c/em\u003e. \u003cb\u003e24\u003c/b\u003e, 1099\u0026ndash;1112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10098-021-02153-5\u003c/span\u003e\u003cspan address=\"10.1007/s10098-021-02153-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhorchani, M. et al. Long-term croplands water productivity in response to management and climate in the Western US Corn Belt. \u003cem\u003eAgric. Water Manage.\u003c/em\u003e \u003cb\u003e291\u003c/b\u003e, 108640. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agwat.2023.108640\u003c/span\u003e\u003cspan address=\"10.1016/j.agwat.2023.108640\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKijne, J. W., Barker, R., Molden, D. J. \u0026amp; Colombo Water Productivity in Agriculture: Limits and Opportunities for Improvement, International Water Management Institute.CABI, Sri Lanka. (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK\u0026ouml;dm\u0026ouml;n, Z. \u0026amp; Szőke, J. Sustainability of water security in Burkina Faso\u0026mdash;a review. \u003cem\u003eClean Technol. Environ. Policy\u003c/em\u003e. \u003cb\u003e27\u003c/b\u003e, 8967\u0026ndash;8979. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10098-025-03198-6\u003c/span\u003e\u003cspan address=\"10.1007/s10098-025-03198-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLallana, C., Krinner, W., Nixon, S., Leonard, J. \u0026amp; Berland, J. M. \u003cem\u003eSustainable water use in Europe (No. 19), Environmental issue report\u003c/em\u003e (European Environment Agency, 2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, G., Zhang, C., Huo, Z. \u0026amp; Liu, Y. Achieving comprehensive water productivity improvement: A multi-objective simulation-optimization model for water productivity-oriented irrigation water management. \u003cem\u003eAgric. Water Manage.\u003c/em\u003e \u003cb\u003e309\u003c/b\u003e, 109316. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agwat.2025.109316\u003c/span\u003e\u003cspan address=\"10.1016/j.agwat.2025.109316\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, X. et al. Irrigation water productivity is more influenced by agronomic practice factors than by climatic factors in Hexi Corridor, Northwest China. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 37971. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep37971\u003c/span\u003e\u003cspan address=\"10.1038/srep37971\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Y. et al. Effects of irrigation scheduling on the yield and irrigation water productivity of cucumber in coconut coir culture. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 2944. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-024-52972-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-52972-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, A., Li, J., Xiao, Y., He, Z. \u0026amp; Liang, J. Engineering Soil Quality and Water Productivity Through Optimal Phosphogypsum Application Rates. \u003cem\u003eAgronomy\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy15010035\u003c/span\u003e\u003cspan address=\"10.3390/agronomy15010035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacher, G. Z., Beke, D. \u0026amp; Torma, A. Water quality of harvested rainwater from asbestos cement roofs and its suitability for irrigation. \u003cem\u003eClean Technol. Environ. Policy\u003c/em\u003e. \u003cb\u003e27\u003c/b\u003e, 8955\u0026ndash;8965. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10098-025-03197-7\u003c/span\u003e\u003cspan address=\"10.1007/s10098-025-03197-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDermid, S. \u0026amp; Nocco, M. Irrigation in the Earth system. \u003cem\u003eNat. reviews earth Environ.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 435\u0026ndash;453. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43017-023-00438-5\u003c/span\u003e\u003cspan address=\"10.1038/s43017-023-00438-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMusick, J. T., Jones, O. R., Stewart, B. A. \u0026amp; Dusek, D. A. Water-Yield Relationships for Irrigated and Dryland Wheat in the U.S. Southern Plains. \u003cem\u003eAgron. J.\u003c/em\u003e \u003cb\u003e86\u003c/b\u003e, 980\u0026ndash;986. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2134/agronj1994.00021962008600060010x\u003c/span\u003e\u003cspan address=\"10.2134/agronj1994.00021962008600060010x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOosthuizen, L., Botha, P., Grov\u0026eacute;, B. \u0026amp; Meiring, J. Cost-estimating procedures for drip-, micro- and furrow-irrigation systems: technical note. \u003cem\u003eWater SA\u003c/em\u003e. 405. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4314/wsa.v31i3.5214\u003c/span\u003e\u003cspan address=\"10.4314/wsa.v31i3.5214\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerelli, C., Branca, G., Corbari, C. \u0026amp; Mancini, M. Physical and Economic Water Productivity in Agriculture between Traditional and Water-Saving Irrigation Systems: A Case Study in Southern Italy. \u003cem\u003eSustainability\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 4971. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su16124971\u003c/span\u003e\u003cspan address=\"10.3390/su16124971\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerry, C. Accounting for water use: Terminology and implications for saving water and increasing production. \u003cem\u003eAgric. Water Manage.\u003c/em\u003e \u003cb\u003e98\u003c/b\u003e, 1840\u0026ndash;1846. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agwat.2010.10.002\u003c/span\u003e\u003cspan address=\"10.1016/j.agwat.2010.10.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePotkonjak, S. \u0026amp; Zoranović, T. Investments and costs of irrigation in function of agricultural sustainable development. (No. 46006), Sustainable Agriculture and Rural Development in the Function of Accomplishing Strategic Objectives of the Republic of Serbia in the Danube Region. CABI Digital Library, University of Novi Sad. (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRay, L. I. P., Swetha, K., Singh, A. K. \u0026amp; Singh, N. J. Water productivity of major pulses \u0026ndash; A review. \u003cem\u003eAgric. Water Manage.\u003c/em\u003e \u003cb\u003e281\u003c/b\u003e, 108249. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agwat.2023.108249\u003c/span\u003e\u003cspan address=\"10.1016/j.agwat.2023.108249\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShoukat Hafiza, B., Ishaque, W., Ahmad, S., Ali, S. \u0026amp; El-Sheikh, M. A. Optimizing wheat productivity and water productivity through deficit irrigation strategies in semi-arid environments. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 20630. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-04618-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-04618-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStepanovic, S., Rudnick, D. \u0026amp; Kruger, G. Impact of maize hybrid selection on water productivity under deficit irrigation in semiarid western Nebraska. \u003cem\u003eAgric. Water Manage.\u003c/em\u003e \u003cb\u003e244\u003c/b\u003e, 106610. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agwat.2020.106610\u003c/span\u003e\u003cspan address=\"10.1016/j.agwat.2020.106610\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTarantino, E. et al. Agro-industrial Treated Wastewater Reuse for Crop Irrigation: Implication in Soil Fertility. \u003cem\u003eChem. Eng. Trans.\u003c/em\u003e \u003cb\u003e58\u003c/b\u003e, 679\u0026ndash;684. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3303/CET1758114\u003c/span\u003e\u003cspan address=\"10.3303/CET1758114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYetik, A. K. \u0026amp; Candoğan, B. N. Optimisation of irrigation strategy in sugar beet farming based on yield, quality and water productivity. \u003cem\u003ePlant. Soil. Environ.\u003c/em\u003e \u003cb\u003e68\u003c/b\u003e, 358\u0026ndash;365. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17221/234/2022-PSE\u003c/span\u003e\u003cspan address=\"10.17221/234/2022-PSE\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"irrigation cost, return on irrigation, water productivity, irrigated crops, net profit","lastPublishedDoi":"10.21203/rs.3.rs-9224116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9224116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Despite decades of research, market volatility still hasn't been addressed, creating a gap between agronomic efficiency and economic viability. This study presents a decision-support framework to optimise irrigation water allocation by integrating Crop Water Productivity (CWP) with economic profitability. While traditional CWP focuses on biomass, our model incorporates fluctuating irrigation costs and market prices to calculate Net Profit per Cubic Meter of Water. The present study aims to address the critical gap between biophysical water use and financial viability by introducing a dynamic Economic-Water-Productivity (EWP) framework. In contradistinction to static models, our approach integrates stochastic market volatility with primary farm-level data, thereby enabling the quantification of the specific thresholds at which irrigation transitions from a yield-enhancer to a high-risk financial liability. Model was validated using primary data from 12 commercial farms in Western Transdanubia (2022\u0026ndash;2024). The results show that maize exhibited the highest biological water productivity (e.g., 2.23 kg m⁻\u0026sup3;) and the highest economic return in 2022 (\u0026euro; 0.19 m⁻\u0026sup3;). However, irrigation was not profitable in 2024 due to lower crop prices and higher costs. The analysis of the data set, which included wheat and soybeans, demonstrated that irrigation was economically non-viable in both years. Sensitivity analysis confirmed the model's robustness under price volatility scenarios of 20% and 40%. The proposed tool facilitates the prioritisation of irrigation based on direct economic efficiency, thereby providing a scalable solution for water-scarce agricultural regions. To reach the break-even point in 2024, a 22% increase in market price (to 0.209 \u0026euro;/kg) or a reduction in irrigation costs to 0.278 \u0026euro; m⁻\u0026sup3; would be required for maize. For soybeans, market prices would need to increase by 150% to reach the break-even threshold.","manuscriptTitle":"Economic Optimization of Irrigation Decisions Using Water Productivity for Crop Selection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-15 20:09:43","doi":"10.21203/rs.3.rs-9224116/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-27T13:27:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T01:04:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T10:46:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183464749253348923748068064795747159743","date":"2026-04-14T06:21:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127170011481909921933412819061085437389","date":"2026-04-09T23:45:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T21:58:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-07T14:34:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-26T10:27:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-26T10:27:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-25T13:50:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fc4e1b5f-e50c-4dca-8187-e91e300695f6","owner":[],"postedDate":"April 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":66266213,"name":"Earth and environmental sciences/Environmental sciences"},{"id":66266214,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-04-27T13:38:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-15 20:09:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9224116","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9224116","identity":"rs-9224116","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}