The purpose of this study was to evaluate the potential of using fuzzy C-means clustering, AMMI and GGE biplot analysis methods to predict the yield of chickpea (Cicer arietinum L.) genotypes grown in various environments. The trials were conducted in the Central, Silvan and Hazro districts of Diyarbakir province and Kiziltepe district of Mardin province in the Southeastern Anatolia Region of Türkiye. During the 2016 growing season, 19 chickpea genotypes were tested across four distinct environments. Multiple location experiments were used to assess the genotypes’ performance and stability. The study employed a two-factor experimental design in randomized blocks with four replications in each environment. As a result, the genotype FLIP98-206C showed the highest performance for yield (1727.3 kg ha⁻¹) at the Diyarbakır location among all locations. On the other hand, the Diyar-95 variety showed the lowest yield (723.5 kg ha⁻¹) at the Hazro location among all locations. The Diyarbakir location was determined as an ideal test environment for genotype selection in fuzzy C-means clustering, AMMI and GGE biplot analysis. The Silvan region was determined as the weakest environment for this purpose. It is considered that the determination of genotypes with high yield and stability in this research, in which different analysis methods were used in combination, will contribute to agricultural production.

An experiment was carried out at the Dicle University, Agriculture Faculty, southeast Anatolia of Turkiyeduring the years 2020 and 2021. The study was conducted to determine the effect of chicken manure levels (2.0 and 3.0 t ha-1), nitrogen and phosphorus levels (40:60, 40:80 and 40:100 kg ha-1) and their combinations (chicken manure levels + NP levels) on the growth, yield and yield components of common beans (Phaseolus vulgaris L.) The experiment was laid out in a randomized complete block design with three replications. Fertilizers on the soil surface were applied at sowing time. Dry biomass, dry stem weight, dry leaf weight and leaf area were investigated at 15 days intervals starting from 20 days after the emerging (DAE) until80 DAE. Seed yield and yield components were evaluated at the maturity time. The effect of fertilizer treatments on growth, yield and yield components was significant, and fertilizer treatments increased the growth, yield and yield components compared to the control. The highest dry biomass, dry stem weight, dry leaf weight and leaf area values were observed when chicken manure 3 tha-1 +40N+100P kg ha-1at65 DAE was applied. The highest seed yield, number of primary and secondary branches and pod weight plant-1 were observed at the application of chicken manure2 t ha-1 + 40N + 60P kg ha-1combination. Seed yield ranged from 1521.5 kg ha-1 in control plots to 2742.1 kg ha-1 in chicken 2 t ha-1 + 40N + 100P kg ha-1. Therefore, adding chicken manure (2 t ha-1) and chemical fertilizers (40N+100P kg ha-1) combinations to nutrient management systems was deduced more efficient than chemical fertilizer or chicken manure alone treatments on growth and yield of common bean. Keywords: Chicken manure, Common bean, Chemical fertilizer, Turkiye, Yield, Yield components

This study aimed to investigate the temporal effects of different fertilization strategies on the physiological, morphological, and biomass-related traits of soybean under controlled greenhouse conditions. Individual and combined applications of urea, zinc (Zn), and microbial inoculants were evaluated using a multi-sensor approach. Optical parameters (SPAD, NDVI), plant height, and thermal imaging were monitored across days after onset (DAO ) , and post-harvest biomass traits were measured to establish integrative relationships. The findings demonstrated that SPAD values increased by 18–27% and NDVI by up to 22%, with combined treatments (Urea + Microbial, Zn + Microbial) showing the most pronounced improvements. Urea + Microbial increased plant height by 15% and fresh biomass by 28% compared to the control. Thermal imaging revealed a 1.8–2.5 °C reduction in canopy temperature under combined treatments, indicating enhanced stomatal regulation and water-use efficiency. Strong positive correlations ( r = 0.71–0.84) between SPAD/NDVI and post-harvest biomass confirmed the reliability of early-stage sensor measurements for predicting yield-related traits. Importantly, the integration of microbial inoculants with mineral fertilizers enhanced both physiological resilience and water-use efficiency, while the identification of tentative threshold values for SPAD (~ 35) and NDVI (~ 0.60) provides practical benchmarks for fertilization decisions and automation in precision agriculture. Overall, this study highlights the utility of combining optical and thermal sensing with morphological and biomass assessments to optimize fertilization strategies in soybeans. The results provide novel insights into the role of micronutrient (Zn) and microbial management in crop monitoring and underline the potential of sensor-based approaches to improve nutrient efficiency and support sustainable agricultural production.

Bu araştırma baklada gübre uygulamalarının verim ve verim unsurlarına etkisini araştırmak amacıyla, 2018-2019 yılları arasında iki yıl süreyle Dicle Üniversitesi Ziraat Fakültesi Araştırma alanında yürütülmüştür. Araştırmada Filiz-90, Eresen-87, Salkım bakla çeşitleri ve azot, fosfor, Organik-1, Organik-2 gübreleri kullanılmış, deneme tesadüf bloklarında bölünmüş parseller deneme desenine göre düzenlenmiş, ekimler Şubat ayında, hasatlar Haziran ayında yapılmıştır. İncelenen özellikler arasında uygulamaların bitki boyu, bitki ve bakla ağırlığı, bitkide bakla ve tane sayısı, bitkide tane verimine etkisi önemli bulunurken, tane verimi, 100 tane ağırlığı ve hasat indeksi üzerine etkisi önemsiz bulunmuştur. Bu çalışmada bitki boyu, bitkide tane sayısı, bitki ağırlığı ve bitkide tane verimi üzerine fosfor uygulamalarının diğer uygulamalara kıyasla önemli etkide bulunduğu tespit edilmiştir.

Identifying superior wheat genotypes under rainfed semi-arid conditions and understanding the traits that drive yield remain challenging, and multi-trait selection indices and explainable machine learning are seldom used together. This study combined the Multi-Trait Genotype–Ideotype Distance Index (MGIDI) with explainable machine learning to evaluate 20 bread wheat (Triticum aestivum L.) genotypes for 14 phenological, physiological and yield-component traits in a randomised complete block design in a single rainfed season in Diyarbakır, Türkiye. Broad-sense heritability was high for all traits (H² = 0.84–0.99), and the close agreement between genotypic and phenotypic coefficients of variation indicated a predominant genetic contribution to phenotypic variation. Grain yield, the number of grains per spike, and grain weight per spike showed the highest expected genetic advance (GAM = 17–22%). MGIDI-based selection of the top 25% of genotypes increased grain yield by 9.50%, with concurrent gains of 6–13% in spike length, spikelets per spike and grain weight per spike, and the relationship between MGIDI and grain yield was negative and significant (R² = 0.58, p < 0.001). Among five learners evaluated under block-stratified GroupKFold cross-validation, Gradient Boosting performed best (CV R² = 0.740; training R² = 0.998; RMSE = 30.55 kg da⁻¹; MAE = 24.43 kg da⁻¹). SHAP analysis identified grain weight per spike, plant height and days to flowering as the main yield drivers, and these rankings were corroborated by model-independent permutation importance. Combining MGIDI with explainable machine learning allowed genotype selection and yield-determining traits to be evaluated within a single framework, and the selection decisions were biologically consistent.

Seed size is a fundamental characteristic determining germination success and early seedling development in legumes. However, its influence on water uptake kinetics and physiological responses during early development in common bean (Phaseolus vulgaris L.) has not been sufficiently investigated. This study aimed to evaluate the effects of four seed size categories (small, medium, large, and extra-large) on seed hydration dynamics, moisture content, germination behavior, and root development under controlled conditions. Morphometric analyses revealed significant differences (P