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The aim of this study was to develop and validate qualitative and quantitative models to discriminate different types of maize and also estimate biochemical constituents. Spectral data were taken from the central leaf of randomly-chosen plants grown in field trials in 2011 and 2012. Leaf chlorophyll and protein content and stalk protein content were determined in the same plants. Four different Support Vector Machine (SVM) models were generated and validated in this study. In qualitative models, maize type was designated as dependent variable while Full Spectral (FS) data (400-1,000 nm) and Spectral Indices (SI) data (34 indices/bands) were independent variables. In the two quantitative models (SVMR-FS and SVMR-SI), independent variables were the same, whereas dependent variables were assigned as the quantitatively measured traits. Results showed the qualitative models to be a robust method of classification for distinguishing different maize types, such as High Oil Maize (HOM), High Protein Maize (HPM) and standard (NORMAL) maize genotypes. The SVMC-FS model was superior to SVMC-SI in terms of the genotypic classification of maize plants. Quantitative models with full spectral data gave more robust prediction than the others. The best prediction result (RMSEC= 222.4 µg g^-1, R² for Cal= 0.739, SEP= 213.3 µg g^-1; RPD= 2.04 and r= 0.877) was obtained from the SVMR-FS model developed for chlorophyll content. Indirect estimation models, based on relationships between leaf-based spectral measurements and leaf and stalk protein content, were less satisfactory.

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This study assessed the optimum water need of pepper (Capsicum annuum L. cv. California Wonder) and the critical irrigation level to be applied in order to achieve a reasonable economic yield in water shortage conditions. In a controlled field experiment involving five different treatments, seasonal evapotranspiration for pepper fluctuated from 89 mm in the severe stress treatment (I_0.00) to 1,018 mm in the excess water application (I_1.25). The highest yield was obtained in the full treatment where water in the root zone was refilled up to field capacity. In cases of water shortage, applying water of 690 mm ensures an economical yield. Maximum leaf area index was recorded in the full treatment (I_1.00), which enabled the pepper to receive more benefit from total incoming solar radiation (average, 2,387 MJ m^-2). An average of 555.45 MJ m^-2 was held by the pepper canopy throughout the whole growing season. Radiation use efficiency values on a dry yield basis were 0.69 g MJ^{-1 in 2011 and reached 1.07 g MJ-1 in 2012, since the leaf area index increased from 1.46 to 2.44. Therefore, averaged over two years, the peppers in the full treatment converted irrigation water of 888 mm and intercepted photosynthetically active radiation into the highest yield of 75.5 t ha-1}, which was more efficient than the excess and deficit water application treatments.

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Fusarium Oxysporum f. sp. Ciceris (FOC) is the causal agent of Fusarium wilt, a destructive and widespread disease of chickpea. Rapid and accurate identification and detection of plant pathogens are essential for timely Disease Management (DM) strategies with appropriate measures. This study aimed to quantitatively determine FOC by using Quantitative Real-Time Polymerase Chain Reaction (qPCR) technique with specific primer pairs [Histone (H3) and Ribosomal (J5)] in seed, root, and root collar, and to discriminate it from other pathogenic fungi [Fusarium Oxysporum formae speciales (FO f. sp.) and Ascocyhta rabiei]. Total RNAs were isolated, converted to cDNAs (limit of 5 ng/rxn.-0.05 pg/rxn.) and used as template for qPCR studies. The FOC was detected in plant samples starting from the first day after inoculation. The FOC was detected in root, root collar and seed samples and was differentiated by qPCR assay from other pathogenic fungi. Melting curves, in which no primer dimers and non-specific complementation were observed, presented a single peak. Quantification was successfully performed using specific H3 and J5 primer pairs (P< 0.05), and the FOC was distinguished from other pathogenic fungi with J5 primer (P< 0.05). The results of these studies may support the development of new biochemical and molecular methods that allow direct, faster and more accurate determination of pathogens. Thus, it will also enable us to reduce the losses caused by diseases and the costs of DM.

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This study aimed to investigate the effects of inputs such as pesticides, fertilizers, seeds, labor and machine use on wheat yield. The data used in the study were obtained from 177 wheat producers in Diyarbakir Province through a questionnaire, and Artificial Neural Networks (ANN) were used in the analysis of the data. According to the results, the average wheat yield is 5482.03 kg ha^-1, and 294.75 kg of seeds, 550.73 kg of fertilizer, 3.59 hours of machinery, 5.37 hours of labor and 2662.43 cc of pesticides were used per hectare for wheat production. According to the results of the ANN analysis, the relative importance of inputs affecting wheat yield was quantified, with the use of pesticides and fertilizer having the most significant impacts. Specifically, the sensitivity coefficients for pesticide use and fertilizer use were found to be 0.23 and 0.14, respectively. These coefficients represent the relative change in wheat yield per unit change in the input parameters.