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    Land suitability assessment for rapeseed potential cultivation in upper Tigris basin of Turkiye comparing fuzzy and boolean logic
    (Elsevier, 2024) Budak, Mesut; Kilic, Mirac; Gunal, Hikmet; Celik, Ismail; Sirri, Mesut
    Assessment of land suitability is a prerequisite for the conservation and maintenance of land productivity and the improvement of land use and management systems. This study assessed land suitability for rapeseed (Brassica napus L.) production using topography, climate, and soil data by analytical hierarchy process (AHP) and the Mamdani Fuzzy Inference System (MFIS). The study area covers 3737 km2 of land in the Diyarbakir province of southeastern Turkiye. The weights of topography, soil and climate factors in AHP were determined by expert opinions and the information in related literature. They were included in the whole process, mainly membership functions and rule base stages in the MFIS. The highest weighted factor was slope (0.264), followed by altitude (0.121), annual average temperature (0.114) and soil texture (0.112). The MFIS-based land suitability assessment indicated that the proportions of moderately (S2), marginally (S3) and currently not suitable (N1) land classes in the study area were 71.35%, 18.75% and 9.9%, respectively. The AHP results showed that 98.94% of the land was S3, and 1.06% was N1. The compatibility of AHP and MFIS methods in N1 land units was 96.05%, while the agreement for S2 and S3 land classes was not sufficiently high. The suitability of rapeseed cultivation has been more sensitively assessed by the fuzzy continuous classification obtained by the MFIS method.
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    Predicting soil hydraulic conductivity using stacked deep neural networks: Long-term tillage impacts on a Vertisol in the Eastern Mediterranean
    (Elsevier, 2026) Celik, Ismail; Kahraman, Omer Faruk; Kilic, Mirac; Gunal, Hikmet
    Accurate prediction of soil hydraulic conductivity (Ks) is crucial for understanding water movement and improving soil management, particularly under diverse tillage systems. The objective of this study was to develop and validate a Stacked Deep Neural Network (Stacked DNN) model for predicting Ks using easily measurable soil physical and hydro physical properties under long term tillage practices. Soil samples were collected from 0-15 cm and 15-30 cm depths across conventional tillage (CT), no-tillage (NT), reduced tillage (RT), and strategic tillage (ST). The Ks values at 15 cm depth were measured using a Guelph permeameter at 5 cm and 10 cm water heads, ranged from 0.024 to 1.101 cm/h for the 0-10 cm soil depth. The average values from these measurements were used for model training and validation. Predicted Ks values for 0-15 cm and 15-30 cm depths varied significantly among tillage systems and depths, reflecting the effects of soil management on hydraulic behavior. Conventional tillage with residue burning (CT2) recorded the highest predicted Ks at 0.339 cm h-1 (0-15 cm) and 0.091 cm h-1 (15-30 cm). In contrast, no-tillage (NT) exhibited the lowest values, averaging 0.081 cm h-1 and 0.128 cm h-1 at respective depths, likely due to surface compaction. Reduced tillage (RT2) demonstrated balanced performance, with predicted values of 0.484 cm h-1 (0-15 cm) and 0.186 cm h-1 (15-30 cm), suggesting enhanced water infiltration compared to other reduced tillage methods. The Stacked DNN model achieved superior predictive accuracy, with an R2 of 0.986 and an RMSE of 0.029 cm h-1, outperforming individual machine learning models such as Random Forest (R2 = 0.902) and XGBoost (R2 = 0.917). Bulk density, macroporosity, and mean weight diameter were identified as critical variables influencing Ks predictions, highlighting the model's ability to capture complex nonlinear relationships. These findings emphasize the effectiveness of ensemble machine learning approaches in modeling Ks and the significant influence of tillage practices on soil hydraulic properties. Moreover, the ability to accurately predict Ks has substantial practical implications for sustainable agriculture, as it enables the optimization of irrigation practices, informed soil management, and improved water resource conservation to support long-term crop productivity and environmental stewardship.