Muhammed Sungur Demir

Artan nüfus, değişen tüketim alışkanlıkları ve iklim değişikliği su yönetimini küresel ölçekte kritik hale getirmiştir. Uluslararası ticaret yoluyla üretimde kullanılan su ülkeler arasında dolaylı olarak transfer edilmekte, bu durum özellikle su stresi yaşayan ülkeler açısından stratejik önem taşımaktadır. Bu çalışmada, 2015–2024 döneminde Türkiye’nin 29 stratejik tarımsal ve hayvansal ürün bazında uluslararası mavi ve yeşil sanal su akışları analiz edilmiştir. Bulgular, Türkiye’nin yıllık ortalama 20 milyar m³ sanal su ihracatı ve 35,6 milyar m³ sanal su ithalatı ile net sanal su ithalatçısı olduğunu göstermiştir. İhracatın %86’sı yeşil sudan oluşurken, net dengenin %32’sini mavi su oluşturmaktadır. Bu durum, Türkiye’nin ihracatını büyük ölçüde yağışa dayalı üretimle gerçekleştirdiğini, sulama suyu gereksinimi yüksek ürünleri ise ithalat yoluyla karşıladığını göstermektedir. Sanal su ihracatının %29’u Irak’a yönelirken, ithalatın %42’si Rusya’dan yapılmaktadır. Ürün bazında buğday unu ve ayçiçeği yağı toplam sanal su ihracatının %50’sini oluştururken, pamuk lifi mavi su ithalatının %60’ından fazlasını tek başına karşılamaktadır. Sonuçlar, Türkiye’nin yeşil su ağırlıklı ticaret yapısı sayesinde avantaj sağladığını; ancak mavi su yoğun ürünler, tedarik bağımlılığı ve yıllar arası dalgalanmalar nedeniyle su ve arz güvenliği açısından potansiyel riskler barındırdığını göstermektedir. Sanal su yönetiminin dış ticaret ve tarım politikalarıyla bütüncül ele alınması gerekmektedir.

Tarımsal su kullanımının zamansal dinamikleri, yarı kurak bölgelerde sürdürülebilir su yönetimi açısından kritik öneme sahiptir. Ancak mevcut çalışmaların büyük bölümü su ayak izini yıllık toplamlar üzerinden değerlendirdiğinden dönemsel su baskıları yeterince görünür kılınamamaktadır. Bu çalışma, Türkiye genelinde 18 ürün için mavi ve yeşil su ayak izini ilçe ölçeğinde ve aylık zaman çözünürlüğünde ilk kez kapsamlı olarak analiz etmiştir. FAO-56 metodolojisi kullanılarak 2013–2022 dönemine ait meteorolojik veriler ve 969 ilçenin tarımsal üretim istatistikleri ile hesaplanan sonuçlar, tarımsal su talebinin yıl geneline homojen dağılmadığını göstermektedir. Nisan–Mayıs–Haziran döneminde yıllık toplam mavi su kullanımının %55'inden fazlası gerçekleşmektedir. Bu dönem içinde aylık baskın ürün grupları belirgin biçimde değişmekte; Mayıs ayında tahıllar toplam mavi su ayak izinin %75'ini, Temmuz ayında sanayi bitkileri %42'sini oluşturmaktadır. Yeşil su ayak izi yağış rejimiyle doğrudan ilişkili bir mevsimsellik sergilemekte; tahıllarda yüksek yeşil su payları yağışlı dönemlerle örtüşürken, sanayi bitkileri ve sebzelerde düşük oranlar mavi su bağımlılığını belirgin biçimde artırmaktadır.Sonuç olarak, aylık zaman çözünürlüğünde gerçekleştirilen su ayak izi analizleri dönemsel su baskılarını görünür kılarak geleneksel yıllık yaklaşımların ötesinde bir çerçeve sunmaktadır. Elde edilen bulgular, sulama planlaması, ürün deseni değerlendirmesi ve kuraklık risk yönetiminde zamansal hassasiyetin artırılmasına yönelik bilimsel temel oluşturmaktadır.

Accurate estimation of reference evapotranspiration (ET ₀ ) and crop coefficients (K _c ) is critical for irrigation planning, particularly in data‐limited regions where agriculture dominates freshwater consumption. Although machine learning (ML) methods have been widely applied to ET ₀ and K _c estimation, most studies address these parameters separately or focus on a single seasonal K _c value rather than stage‐specific coefficients. This study presents an integrated machine learning framework that simultaneously estimates monthly ET ₀ and wheat stage‐specific crop coefficients (K _{c_ini} , K _{c_mid} , K _{c_end} ) using long‐term district‐level meteorological data from Türkiye. A total of 19 algorithms, including linear models, tree‐based ensembles, boosting methods, support vector machines (SVR) and deep learning models, were evaluated using 10‐fold cross‐validation and standard performance metrics. The results showed that linear models provided the most consistent performance for monthly and annual ET ₀ estimation ( R ² ≈0.85–0.87), while nonlinear ensemble models achieved superior accuracy for K _c prediction ( R ² > 0.95). Feature importance analysis identified solar radiation and temperature as dominant ET ₀ drivers, whereas elevation and relative humidity were key controls for stage‐specific K _c . The proposed framework provides a scalable approach for regional agricultural water management and supports model selection under data‐limited conditions.

Efficient agricultural water management is essential to address global water scarcity. Soil moisture serves as a key indicator for irrigation scheduling, crop yield forecasting, and hydrological modeling. However, traditional in-situ measurements are costly, labor-intensive, and spatially limited. This study develops a machine learning framework for predicting soil moisture at multiple depths (20, 40, and 80 cm) using readily available environmental variables. Türkiye was selected as the study area due to its pronounced climatic and pedological heterogeneity, providing an ideal testbed for training robust models. Seven machine learning algorithms were systematically evaluated using data from 201 meteorological stations spanning 2016–2024. Extreme Gradient Boosting (XGBoost) outperformed all alternatives following hyperparameter optimization, achieving strong accuracy across all depths (R² = 0.74, 0.69, and 0.66 at 20, 40, and 80 cm, respectively). Feature importance analysis indicated a depth-dependent shift in the controls on soil moisture. Near the surface (20 cm), short-term meteorological variability and seasonal dynamics were the primary drivers, whereas deeper layers were increasingly regulated by stable soil hydraulic properties, with clay dominating at intermediate depths, and large-scale spatial gradients represented by latitude and elevation. This pattern reflects a transition from weather-driven processes at shallow depths to long-term regulation by soil structure and regional climate patterns in the subsurface. The model maintained robust performance across diverse environmental conditions, with seasonal accuracy varying by nearly 10% between winter and autumn. Spatial analysis revealed regional variations in controlling factors: soil properties (particularly organic carbon) dominated in the northern regions, while their relative importance decreased southward where climatic and temporal variables contributed more substantially, reflecting heightened sensitivity of moisture dynamics to meteorological fluctuations. The framework provides a scalable, cost-effective solution for soil moisture monitoring in data-scarce regions, supporting irrigation optimization, drought early warning, and sustainable water governance.

Agricultural water use in semi-arid regions faces three critical challenges: stabilizing agricultural production, maintaining district-level revenues, and conserving increasingly scarce water resources. This study develops the first district-level water footprint (WF) optimization framework for Türkiye, covering 969 districts and 18 crops, while enforcing production quotas and revenue-neutrality constraints. Türkiye serves as a representative case study due to its semi-arid climate, extensive irrigated agriculture, and pronounced spatial heterogeneity in hydroclimatic conditions. District-level WF maps were constructed using meteorological data from 756 stations (2013–2022) and the FAO-56 methodology; they reveal that Türkiye’s agriculture consumes 106.2 Gm³ annually (67.3 Gm³ blue, 38.9 Gm³ green). Wheat and barley account for 60% of national use, with critical stress concentrated in Central Anatolia and the southeast, where efficiency gaps are most pronounced. Building on these spatial patterns, a strictly linear programming model (17,442 variables, 2,956 constraints, solved via MATLAB linprog) was developed to minimize blue water consumption while maintaining national production quotas and district-level revenues. Under idealized assumptions of perfect spatial flexibility, the optimization indicates potential efficiency gains: blue water use decreases by ≈ 32%, total WF by 25%, and cultivated area by 22%. Sensitivity analyses across interpolation methods, quota adjustments (± 10%), and climate perturbations indicate that these savings estimates and spatial efficiency rankings remain robust to methodological variations. The framework demonstrates how binding production and revenue constraints can be integrated into spatial WF optimization for semi-arid regions facing similar water-agriculture trade-offs.

Agricultural activities account for the majority of global freshwater consumption, prompting extensive research into strategies for reducing water demand in the sector. Crop pattern optimization has emerged as a key strategy for achieving significant water savings. However, most existing studies prioritize yield maximization over minimizing water consumption. Additionally, water-based optimization efforts often rely on low temporal resolution data and rarely offer practical recommendations for crop pattern design. This study aims to evaluate water-saving potential in agriculture through crop pattern optimization based on water footprint (WF) assessments, focusing on the Ceyhan River Basin—an important hotspot for diverse crop production in Türkiye. The method effectively balances blue, green, and grey water components by integrating high-resolution spatial WF data from the Soil and Water Assessment Tool (SWAT) hydrological model into a multi-objective optimization framework. We developed six scenarios: Scenario S1 prioritizes total WF reduction, while Scenario S6 focuses solely on blue WF, with intermediate scenarios representing linear blends of these priorities. The results show that Scenario S1 achieves a 23.4% reduction in total WF (614 million m³)—comparable to the annual wheat consumption of 1.8 million people—whereas Scenario S6 attains a 47.9% decrease in blue WF (464 million m³), potentially meeting the daily water needs of 5.6 million individuals. Notably, an intermediate scenario (S5) provides a balanced reduction across WF components with a more uniform crop distribution, enhancing its practical appeal. Recognizing that farmer practices and economic concerns may hinder implementation, we recommend establishing targeted incentive mechanisms—such as financial support for farmers who adopt optimized cropping patterns—to facilitate the transition toward water-efficient practices. Overall, this research offers a robust decision-support tool for policymakers and water resource managers, providing clear guidance for aligning agricultural practices with sustainable water management and enhancing food and water security in water-scarce regions.

Water footprint (WF) assessment has emerged as a vital tool for understanding and managing agricultural water consumption amid increasing water scarcity. This review synthesizes current knowledge and methodological developments in agricultural WF research, examining both volumetric and Life Cycle Assessment approaches. We analyze key methodological challenges, including water component differentiation, effective precipitation estimation, and calculation standardization across spatial scales. Recent trends show a shift from global assessments toward regional analyses, enabling precise water management strategies. The review evaluates WF reduction methods, demonstrating that optimized crop patterns and improved agricultural practices can significantly reduce water consumption. Integration of hydrological models with WF assessments shows promise for improving accuracy. Climate change, with varying regional impacts, emerges as a critical factor influencing future WF calculations. While WF methodology offers valuable insights for sustainable water management, its practical application requires careful consideration of regional contexts and limitations.