Gafur Gözükara

Visible near-infrared (Vis-NIR), mid-infrared (MIR), and portable X-ray fluorescence (pXRF) spectroscopy have been widely used as cost-effective, rapid, and non-destructive techniques for predicting soil properties. However, the influence of calibration sample selection on prediction performance across different soil spectral sensors and their fused datasets has not been systematically evaluated. This study aims to compare four calibration sample selection strategies, including random sampling (RS), Kennard-Stone (KS), conditioned Latin hypercube sampling (cLHS), and k-means clustering, using Vis-NIR, MIR, pXRF, and their combinations for the prediction of sand, silt, clay, electrical conductivity (EC), pH, soil organic carbon (SOC), and CaCO₃ using Cubist and PLSR models. Individual and combined Vis-NIR (350–2500 nm), MIR (4000–400 cm⁻¹), and pXRF (0–45 keV) spectra were obtained from 200 soil samples under pasture and cropland, collected from the surface layer (0–20 cm). The results demonstrated that each soil property, except CaCO₃, was significantly affected (p < 0.05) by land-use differences (pasture vs. cropland). Model performance in predicting soil properties varied with both calibration sample selection method and spectral data source. The highest prediction accuracies were obtained using RS for sand (R² = 0.62), k-means for silt (R² = 0.65) and pH (R² = 0.49), KS for clay (R² = 0.73), SOC (R² = 0.96), and CaCO₃ (R² = 0.96), and cLHS for EC (R² = 0.80). MIR generally produced the highest prediction accuracy among the individual spectral datasets for most soil properties, while fused Vis-NIR + MIR spectra improved EC prediction and fused Vis-NIR + pXRF spectra obtained higher prediction accuracy for pH. Calibration sample selection effects were higher for EC and pH, particularly in pXRF and fused spectral datasets. In contrast, SOC was consistently the least affected by calibration sample selection, whereas sand, silt, clay, and CaCO₃ generally showed low to moderate variability across the different calibration sample selection strategies. The results demonstrate that the effect of calibration sampling strategy is strongly sensor-dependent and remains significant even when spectral datasets are fused.

Demleme çay halkımızın en önemli içeceğidir. Doğu Karadeniz bölgemizin bu kıymetli bitkisinde verim ve kalitenin artırılabilmesinde, yeterli ve dengeli bir gübrelemenin etkisi yüksektir. Ancak çayın yoğun olarak yetiştirildiği Rize ili kimyasal gübre tüketiminin gübre alım gücünün düştüğü yıllarda aşırı oranlarda azalması dikkat çekici ve kabul edilebilir düzeyde değildir. Ayrıca kimyasal gübre tüketiminin azaldığı yıllarda, özellikle 25.5.10. kimyasal gübresinin aşırı tüketim azalmasına bağlı olarak; N/P2O5 ve N/K2O tüketim dalgalanmalarının çayın dengeli beslenmesini engelleyebilecek bir düzeye ulaştığı değerlendirilmelidir. Dalgalanmaların çay yaprağı verim ve kalitesi üzerine olası etkisi ve genelde çay yapraklarımızın potasyum içeriklerinin düşüklüğü dikkate alınarak; özellikle gübre fiyatlarının aşırı yükseldiği yıllarda, il düzeyinde dalgalanma boyutunu azaltıcı 25.5.10. kompoze gübresi ile ilgili özel olarak çiftçi eğitim çalışmaları ve teşvik uygulaması birlikte gerçekleştirilmelidir. 25.5.10. kompoze gübresi; bir bitkisel ürün için genel olarak uygun besin dengelerini içeren ürün bazlı kompoze gübrelerin ne denli etki üretebileceği ile ilgili iyi bir örnek olarak değerlendirilebilir. Ancak bu etkinin izleme çalışmaları ile ürün bazında etki analizleriyle değerlendirilerek gözden geçirilmesi gereklidir. Mevcut değerlendirmeler çerçevesinde; 25.5.10. kompoze gübresi, bileşiminin N/K20 dengesi yeterliliği bakımından sorgulanmalıdır.

We studied horizonation of 150 Alfisols on a research farm in Wisconsin, USA. Most of these Alfisols had developed in loess covering dense glacial till; common horizons were an Ap over a glossic horizon (E/Bt or Bt/E) over a Bt covering the glacial till (2Bt). In addition to field identification, we distinguished the horizons by combining soil properties and spectra (Vis–NIR, MIR, and XRF) using a random forest (RF) model. A total of 503 soil samples from 150 pedons were analyzed. Soil samples were scanned using visible–near infrared (Vis–NIR) (350–2500 nm), mid‐infrared (MIR) (4000–600 cm ⁻¹ ), and X‐ray fluorescence (XRF) (0–50 keV) spectrometers. Higher silt, pH, soil organic carbon (SOC), Ca, K, P, Si, Ti, Zn, and Zr contents were measured in the Ap horizon, whereas the glossic horizons were characterized by higher CIE L * (lightness) and b * (yellow‐blue) values. The underlying Bt horizon had higher clay, Al, and Fe contents, while the 2B(t) horizon was distinguished by higher sand, coarse fragments, and CIE a * values. The combination of soil properties and spectra (Vis–NIR, MIR, XRF) achieved a higher overall accuracy for predicting soil horizons compared to their individual use. The Ap horizons were best predicted using MIR or XRF (R ² = 0.98), the glossic horizons using combined Vis–NIR and XRF spectra (R ² = 0.98), the Bt horizons using MIR (R ² = 0.90), whereas the 2B(t) horizons were best predicted using combined spectra (Vis–NIR + MIR + XRF) (R ² = 0.50). In the absence of soil data, these spectra can be used with high accuracy to distinguish A and various B horizons of Alfisols.

Land use and soil drainage influence soil organic carbon (SOC) dynamics by impacting organic matter accumulation, decomposition and redox conditions. A total of 158 A horizon samples were collected from Alfisols under forest, pasture, and crop‐pasture rotation. The soil samples were scanned using a MIR spectrometer (4000–400 cm ⁻¹ ). Aerial imagery was used to determine cultivation periods and the age and extent of forest cover. Poorly drained soils had higher SOC stocks and concentrations, bulk density (BD), soil inorganic carbon (SIC) and A horizon thickness (AHT). Moderately well drained soils had lower SOC stock, SOC concentration, BD and AHT. Soils under short‐term (12‐year) pasture and crop‐pasture rotation areas had higher SOC concentration than other land uses. SIC exhibited large variation across land use and drainage classes. After > 75 years, converting forest to cropland increased SOC stock from 34.6 to 62.6 Mg ha ⁻¹ due to a thicker A horizon, but it decreased SOC concentration from 21.1 to 19.5 g kg ⁻¹ compared to soils under forest. Over time, SOC stock and SOC concentration decreased in pasture areas. Soils under forest differed from other soils in some regions of MIR spectra because of specific clay mineral peaks (3695, 3620 and 916 cm ⁻¹ ), alkyl groups (2930 and 2850 cm ⁻¹ ), carbonates (2517 cm ⁻¹ ), amides (1640 cm ⁻¹ ), aromatics (1510 cm ⁻¹ ), carbohydrates (1050 cm ⁻¹ ), and quartz (811, 790 and 693 cm ⁻¹ ). The spectral points at 3695, 3620 and 1050 cm ⁻¹ were associated with differences in soil drainage. It was concluded that SOC stock and SOC concentration are influenced by long‐term land use and drainage conditions, and the MIR spectrometer can be a valuable tool for identifying their impacts on soil.

Here, we review the prediction accuracy for soil properties using portable X‐ray fluorescence (pXRF), mid‐infrared (MIR), and visible near‐infrared (Vis‐NIR) and the factors impacting predictions and its accuracy. In total, 305 published papers were reviewed, and most of them were from Australia, Brazil, China, and the United States. About 44% of papers focused on the prediction of soil organic carbon (SOC) using Vis‐NIR spectra. Partial least squares regression was most frequently used. Most studies sampled Alfisols, Inceptisols, and Entisols, and up to 40‐cm depth. Researcher‐based factors (type or brand of spectrometers, which differ in hardware, spectral range, resolution, and calibration protocols; preprocessing methods; prediction models; and soil analysis methods for calibration) and soil‐based factors (horizon and depth) were explored. MIR spectra had better prediction accuracy with a mean R ² over 0.8 for sand, clay, total N, total C (TC), SOC and soil inorganic carbon (SIC), and cation exchange capacity compared to Vis‐NIR and pXRF. In the past 20 years, prediction accuracy tended to increase for sand, silt, clay, SIC, soil organic matter, and EC when using MIR and Vis‐NIR spectra, and for TC and CaCO ₃ when using pXRF spectra. Preprocessing methods, spectral range, calibration, type of the prediction models (i.e., machine and deep learning), and source of soil spectra (Vis‐NIR, MIR, and pXRF), which are used to reduce noise and multicollinearity, calibrate data, and smooth spectra, all affected the prediction. In general, MIR spectra obtained the highest prediction accuracy for most soil properties. Future studies should focus on the effects of soil‐based factors (parent material, soil mineralogy, pedogenesis, soil type, and horizon/depth) on the prediction accuracy of soil physical and chemical properties.

In soil science, the allocation of soil samples to their respective origins holds paramount significance, as it serves as a crucial investigative tool. In recent times, with the increasing use of proximal sensing and advancements in machine-learning techniques, new approaches have accompanied these developments, enhancing the effectiveness of soil utilization in soil science. This study investigates soil classification based on four parent materials. For this purpose, a total of 59 soil samples were collected from 12 profiles and the vicinity of each profile at a depth of 0–30 cm. Surface soil samples were analyzed for elemental concentrations using X-Ray fluorescence (XRF) and inductively coupled plasma–optical emission spectrometry (ICP-OES) and soil spectra using a visible near-infrared (Vis-NIR) spectrometer. Soil samples collected from soil profiles (12 soil samples) and surface (47 soil samples) were used to classify parent materials using machine learning-based algorithms such as Support Vector Machine (SVM), Ensemble Subspace k-Near Neighbor (ESKNN), and Ensemble Bagged Trees (EBTs). Additionally, as a validation of the classification techniques, the dataset was subjected to five-fold cross-validation and independent sample set splitting (80% calibration and 20% validation). Evaluation metrics such as accuracy, F score, and G mean were used to evaluate prediction performance. Depending on the dataset and algorithm used, the classification success rates varied between 70% and 100%. Overall, the ESKNN (99%) produced better results than other classification methods. Additionally, Relief algorithms were employed to identify key variables for each dataset (ICP-OES: CaO, Fe₂O₃, Al₂O₃, MgO, and MnO; XRF: SiO₂, CaO, Fe₂O₃, Al₂O, and MnO; Vis-NIR: 567, 571, 572, 573, and 574 nm). Subsequent soil reclassification using these reduced variables revealed reduced accuracies using Vis-NIR data, with ESKNN still yielding the best results.

Global attention to climate change issues, especially air temperature changes, has drastically increased over the last half-century. Along with population growth, greater surface temperature, and higher greenhouse gas (GHG) emissions, there are growing concerns for ecosystem sustainability and other human existence on earth. The contribution of agriculture to GHG emissions indicates a level of 18% of total GHGs, mainly from carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O). Thus, minimizing the effects of climate change by reducing GHG emissions is crucial and can be accomplished by truly understanding the carbon footprint (CF) phenomenon. Therefore, the purposes of this study were to improve understanding of CF alteration due to agricultural management and fertility practices. CF is a popular concept in agro-environmental sciences due to its role in the environmental impact assessments related to alternative solutions and global climate change. Soil moisture content, soil temperature, porosity, and water-filled pore space are some of the soil properties directly related to GHG emissions. These properties raise the role of soil structure and soil health in the CF approach. These properties and GHG emissions are also affected by different land-use changes, soil types, and agricultural management practices. Soil management practices globally have the potential to alter atmospheric GHG emissions. Therefore, the relations between photosynthesis and GHG emissions as impacted by agricultural management practices, especially focusing on soil and related systems, must be considered. We conclude that environmental factors, land use, and agricultural practices should be considered in the management of CF when maximizing crop productivity.

Long-term addition of manure increases soil organic carbon (SOC), provides nutrient supply, enhances soil quality and crop yield (CY), but may also increase global warming potential (GWP). In this study, a long-term experiment was conducted to investigate impacts of organic dairy manure and inorganic fertilizer on the spatial distribution of soil quality indicators in field scale. The experiment was initiated in 2008 (seven years), and includes three manure and two inorganic fertilizer treatments along with a control (no manure or no inorganic fertilizer addition). The study was set into a randomized complete block design with six treatments and four replications in a total of 24 plots with an equal size each of 6 × 18 m (108 m²). Soil physical, chemical and biological properties (total 26 properties) were considered as the total data set and principal component analysis (PCA) was used to determine long-term organic and inorganic fertilizer-induced changes in soil quality. Ordinary kriging interpolation methods were used to predict the spatial distributions of soil quality index (SQI) and mean soil quality values were compared with fertilization treatments by using Duncan’s test. Results showed that most measured soil quality index parameters showed significant differences (p < 0.05). The long-term dairy manure applications had positive impacts on soil quality index parameters where overall SQI scores were higher under high manure (HM) compared to medium manure (MM), low manure (LM), medium fertilizer (MF), high fertilizer (HF), control (CK) by 25%, 27%, 47%, 55% and 92%. A similar trend was observed for CY and GWP. This indicates that long-term dairy manure can be an option to increase SQI values and provide higher CY, however, this may lead to greater GWP.

Bu araştırmada, Eskişehir İli Seyitgazi ilçesi sınırlarında buğday yetiştirilen Kahverengi Büyük Toprak Grubu ve Kahverengi Orman Büyük Toprak Grupları topraklarının fiziksel ve kimyasal özellikleri belirlenerek, verimlilik durumlarının ortaya konulması ve toprak kaynaklı bitki besleme sorunlarına çözüm getirilmesi amaçlanmıştır. Araştırmada, 0-20 cm derinlikten toplam 45 adet toprak örneği (9 farklı bölgeden x 5 toprak örneği) alınmıştır. Toprak örneklerinde; tekstür, pH, EC, kireç (CaCO3), organik madde, azot (N), fosfor (P), potasyum (K), kalsiyum (Ca), magnezyum (Mg), demir (Fe), mangan (Mn), çinko (Zn), bakır (Cu) ve bor (B) analizleri yapılmıştır. Araştırma sonuçlarına göre; Kahverengi Büyük Toprak Grubunda topraklar genellikle kumlu-killi-tın, hafif alkalin, orta kireçli, organik madde miktarı orta düzeyde ve tuz miktarı düşük düzeyde, toplam N ve alınabilir P yeterli, değişebilir K, Ca ve Mg ise fazla olarak belirlenmiştir. Kahverengi Orman Büyük Toprak Grubu ait tarım arazileri genellikle killi, nötr ve hafif alkalin, tuzluluk riski olmayan, az kireçli, organik madde miktarı iyi, yeterli düzeyde toplam N ve alınabilir P kapsamına sahiptir. Bu toprak grubundaki P miktarı diğer toprak grubundan daha yüksek olmakla birlikte, değişebilir K, Ca ve Mg kapsamlarının fazla olduğu belirlenmiştir. Alınabilir Fe içeriği Kahverengi Büyük Toprak Grubunda daha az olmasına rağmen, her iki büyük toprak grubunda da alınabilir Mn ve Zn içeriği az ve Cu içeriği yeterli seviyededir. Kahverengi Büyük Toprak Grubu topraklarının alınabilir B içeriği yeterli, Kahverengi Orman Büyük Toprak Grubunda ise az olarak belirlenmiştir. Genel olarak, buğday yetiştirmek için Kahverengi Orman Büyük Toprak Grubu topraklarının verimlilik parametreleri Kahverengi Büyük Toprak Grubu’ndan daha iyi özelliklere sahip olduğu sonucuna varılmıştır.