Agricultural Science; Vol. 2, No. 1; 2020 ISSN 2690-5396 E-ISSN 2690-4799 https://doi.org/10.30560/as.v2n1p252 252 Published by IDEAS SPREAD Environmental Conditions and Genotype Influence Upon Some Correlations Value to Few Lines of Winter Wheat Ovidiu Păniță1, Paula Iancu1 & Marin Soare1 1 Faculty of Agronomy, University of Craiova, Romania Correspondence: Paula Iancu, Faculty of Agronomy, University of Craiova, Libertatii Street, No. 19, Romania. E- mail: paula.iancu76@gmail.com Received: March 26, 2020 Accepted: April 28, 2020 Online Published: May 25, 2020 Abstract Twenty-five mutant/recombinant lines and the two parental forms of winter wheat were taken into study to assess the correlations between grain yield and some quality traits. This investigation was carried out at ARDS Caracal of University of Craiova, during 2015-2018 cropping seasons in randomized blocks design with 3 replications. It included two factors: A– influence of climatic conditions (2016-2017 favorable conditions (A1); 2017-2018 less favorable (A2) and 2018-2019 abnormal conditions (A3) and b – genotype. Observations were recorded after harvest for grain yield and some quality traits every year after harvest. All the analyzed traits such as proteins, starch, TKW, seeds number/ear, seeds weight/100, seeds weight/ear indicate the experimented material combine well high level of yield and superior quality percent in the grains. Keywords: winter wheat, PCA, correlation, yield, quality traits 1. Introduction Globally, demand for wheat by 2050 is predicted to increase by 50 percent from present’s levels, so identification of better genotypes with desirable traits is an important objective in every breeding programms. Agriculture plays a central role in the economy of the southern region of Romania. Here, modern crop technologies have come to provide record harvests on some farms, but sometimes agricultural production is highly dependent on weather conditions. In 2015, Romania was the 12th largest world grain exporter and ranked 3rd in the EU member states (28), and in 2017 it became the main cereal exporter in the European Union (https://uefiscdi.gov.ro). Innovative plant improvement techniques respond in part to current challenges. So, improving the quality of the plants has been and is still a significant objective in the activity of breeders, who must create new varieties, which will yield higher yields of useful substances per hectare, with a better nutritional or commercial value. A basic factor in improving the quality of cultivated plants is the value and diversity of biological material, which must be rich in quality genes or genotypes. The quality traits of the plants are genetic, determined monogenic or polygenic. Wheat quality is as important as production capacity. The attention paid to improving the quality is highlighted by the numerous meetings and the large volume of research undertaken. They established that both protein and gluten content are heritable traits caused by quantitative physiological differences or as a result of processes of internal elements directed by a particular gene or combination of genes. Considerable research in the past decade has been devoted to novel techniques and methodologies in wheat biotechnology. The integration of novel techniques and methods into wheat breeding programs is necessary to facilitate continued and accelerated progress in producing new wheat lines. In the past two decades, developments of in vitro culture techniques have enabled the production of large numbers of haploid plants, especially in cereals. Of these techniques, anthers culture and chromosome elimination in inter-generic crosses have been the most widely used. The production of DHs has been an important development in wheat breeding, because, after chromosome doubling to recover fertility, the recovery of homozygous lines can be achieved in a single generation. This can significantly reduce the time taken before advanced comparative trials can be made and new commercial cultivars identified. as.ideasspread.org Agricultural Science Vol. 2, No. 1; 2020 253 Published by IDEAS SPREAD Induction of mutations is another possibility to improve quality traits and a useful method to generate new wheat lines. Mutation breeding induces mutations, usually in the seed and includes exposure of seeds to ionizing radiation, ultraviolet radiation or chemical mutagens. The yield and quality of a wheat crop is determined by the complex interaction of water availability, nutrition, environment, pest and disease and genetic make-up (Nuttall, J.G. et all., 2017). The establishment of yield performance and their correlation with some quality characteristics in some mutant/recombinant wheat lines was the purpose of this study. 2. Material and Method Field experiments were conducted in the ARDS Caracal (44"7' north latitude and 24"21' east longitude) placed in South Romania over three growing seasons (2016-2019) on a chernozem soil. 10 mutant/recombinant lines of wheat were sown in October at a seeding rate of 400 seeds m2. Every year, before planting, phosphate was added into the soil at an amount of 70 kg P/ha and nitrogen fertilizer as ammonium nitrate was incorporated also at a rate of 65 kg N/ha. Most of the experimented lines are mutant/recombinant of Izvor variety which was created at NARDI Fundulea, through sexual hybridization, followed by repeated individual selection; it is precocious and has good resistance to fall and winter frost. It is resistant to drought (it has the ormo-regulatory gene) and is recommended for production expansion in the south and east areas of the country while other lines become from breeding line F00628-24 (http://www.madr.ro/docs/cercetare/Rezultate_activitate_de_cercetare/INCDA_Fundulea.pdf). The analyzed characters were: yield, protein content, starch content, one thousand kernel weight (TKW), seeds no./ear, seeds weight/100 l, seeds weight/ear. Protein and starch concentration was determined using a Perten Infrared Analyzer. TKW and seeds weight was measured by an electronic caliper (100 randomly grains of each wheat line). Data were statistically analyzed and means were compared by least significant differences (LSD), P=0.05%. Correlation analysis and coefficients were compared after Pearson significance values and Principal Component Analysis (PCA) was performed based on the analyzed indices. Both correlation and PCA were performed by IBM SPSS Version 2011 and MS Office Excel 2016. The experiment included two factors: A factor - the environment, with 3 graduations (A1, A2 and A3) and B factor, genotype, with 10 genotypes (G1-G10). The graduations of A factor, A1 A2, A3, were considered the vegetation years 2016-2017, 2017-2018 and 2018- 2019, the environment influencing decisively the crops behavior. In the case of temperature analysis, in the 10 months of multi-annual vegetation the average is 9.850C. The closest value is recorded in 2016-2017, with an average of 9.490C, with 0.360C below the multiannual value, while in the other two years of experimentation there were registered higher values of 10.70C in 2017-2018 and a positive deviation of 0.850C and 10.690C respectively in the year 2018-2019 and a positive deviation of 0.840C. Regarding the analysis of the temperatures according to the vegetation phases of the plants, in the first part respectively from the sowing and until the exit of winter of the plants (the period of October-February), it should be noted that in comparison with the multiannual average, the first year of experimentation is noted through an extremely low period in January (average temperature -6.10C and a deviation of -70C), otherwise the monthly deviations from the multiannual monthly averages have no exaggerated values (table 1). The same situation is found in the interval in which the plants accumulate the thickness of vegetative mass (March- July), the monthly deviations from the multiannual monthly averages being rather within limits that can be considered normal. Table 1. The variation of the temperatures recorded in the years of experimentation and the calculation of deviations from the multiannual values Month Year Oct. Nov. Dec. Jan. Feb. March. Apr. May June July Average 2016-2017 Value 10.7 5.5 0.7 -6.1 0.00 8.8 10.6 16.9 23.5 24.3 9.49 Deviation -0.5 0.2 0.5 -7 -1.4 2.8 -0.9 -0.6 2.2 1.1 -0.36 as.ideasspread.org Agricultural Science Vol. 2, No. 1; 2020 254 Published by IDEAS SPREAD 2017-2018 Value 12.1 6.5 3.1 0.8 1.00 3.8 16.1 19.6 22.1 21.9 10.70 Deviation 0.9 1.2 2.9 -0.1 -0.4 -2.2 4.6 2.1 0.8 -1.3 0.85 2018-2019 Value 13.8 5.1 0.2 0.5 3.20 9.1 12 17.1 22.8 23.1 10.69 Deviation 2.6 -0.2 0 -0.4 1.8 3.1 0.5 -0.4 1.5 -0.1 0.84 Multianual 11.2 5.3 0.2 0.9 1.40 6 11.5 17.5 21.3 23.2 9.85 In the case of precipitation analysis, the multiannual average amount was 429 mm, the highest value being recorded in 2016-2017 with an amount of 604.4 mm and a positive deviation of 175.1 mm. In the second year of experimentation, a precipitation amount of 448 mm was recorded, with a positive deviation from the average multiannual amount of 18 mm (Table 2). The lowest value is recorded in the case of 2018-2019 with a value of 387.5 mm/year and a negative deviation compared to the multiannual of 41.8 mm. In the analysis of the monthly values, in the first part of vegetation period, the first year of experimentation is noted by higher levels of precipitation compared to the multiannual average, both at arise and tillering, the winter period bringing in quantitative significant precipitations. In the period of intense growth in the spring (March-April) were also recorded higher values well above the multiannual average, which favored the intense development of the plants. The month was poorer in precipitation, but the negative deviation was reduced in value (-8.1 mm), being supplemented by the precipitations that occurred in March and April. Regarding the experimentation year 2017-2018, although at dawn there was sufficient rainfall to allow germination and planting, in winter the accumulated rainfall was modest in value, with negative deviations from the average multiannual values, as in small quantities of water were accumulated, which did not allow a good development of the plants in the first part of the spring. Only towards the end of March, when the plants had entered well into the vegetation and on a drought background in the soil, there were consistent precipitations. April came much closer to the normal values recorded, while the month of May came with consistent rainfall, but in this same period the plants had finished their period of growth and accumulation of vegetative mass. Regarding the year 2018-2019, October was a very poor one in precipitation, only towards the end of November, significant quantities of precipitation were recorded, so that the plants had major difficulties in the germination and sprouting process. Subsequently, during the winter, in the background of the drought in the autumn, the water supplies from the soil were not restored, which was accentuated in the first part of the spring, so that the plants suffered from water stress. Only in May it was recorded precipitations close to normal, but their development cycle had been seriously affected. Table 2. The variation of precipitation recorded during the years of experimentation and the calculation of deviations from the multiannual values Month Year Oct. Nov. Dec. Jan. Feb. March Apr. May June July Sum 2016-2017 Value 46 63.8 103 38.8 56.4 86.4 104.6 55.6 10.2 39.6 604.40 Deviation 5.6 11.4 56.3 0.7 18.5 45.6 52.7 -8.1 7.3 -14.9 175.10 2017-2018 Value 56 48 14 6.8 12.4 53 54 84.8 17.6 101.4 448.00 Deviation 15.6 -4.4 -32.7 -31.3 -25.5 12.2 2.1 21.1 14.7 46.9 18.70 2018-2019 Value 7.4 46.8 53.4 38.6 14.2 25.2 44.4 69 28.5 60 387.50 Deviation -33 -5.6 6.7 0.5 -23.7 -15.6 -7.5 5.3 25.6 5.5 -41.80 Multianual 40.4 52.4 46.7 38.1 37.9 40.8 51.9 63.7 2.9 54.5 429.30 as.ideasspread.org Agricultural Science Vol. 2, No. 1; 2020 255 Published by IDEAS SPREAD 3. Results and Discussions Quality characteristics (content of proteins and starch,) are strongly influenced by environment conditions and fertilization. Proteins are stable constituents in dry grains. Proteins content from wheat grains may vary from less than 6 % to more than 20% and are distributed over the whole grain. The applying of fertilization during growing is essential for optimal plant development. Nitrogen (N) fertilization is, in particular, important for common wheat, because a high N supply provides high protein content. It seems that for the experimented areal, the lines presented superior genetic potential, both for yield and some productivity elements and for quality. 3.1 Influence of A Factor As concern yield analysis, TKW, no. of seeds/ear and seeds weight/ear, the highest value is registered at the A1 graduation, which recorded significant differences compared to the values recorded by the other two graduations and between these there is a significant difference. In the case of protein content, the highest value was registered at the A3 graduation, which records significant differences compared to the values recorded by the other two graduations and between these, there is also a significant difference. Regarding the starch content, the highest value is registered at the A2 graduation, with significant differences compared to the values recorded by the other two and between these, there is a significant difference. In the case of seeds weight/100 l, the highest value was registered at the A1 graduation with significant differences compared to the values recorded by the other two and between the latter not registering a significant difference (table 3). Aslani, F. et all., 2013 stated that water stress and high temperature are the principle environmental parameters affecting wheat grain quality under Mediterranean conditions. Also, drought stress environments are generally in relation to a rise in protein content (Pompa, M. et al., 2009). It can be seen from these results that abiotic stress influences yield and quality and it is difficult to calculate exactly how it will affect crops. In the future, the productivity of major crops is expected to decline in many countries of the world due to global warming, lack of water and other environmental impacts (Ali Raza et all., 2019). Analysis of variance revealed significant differences among a category of amphidiploids and mutant/recombinant genotypes for many morphological characters of wheat, due mostly to the influence of the climatic conditions of the region (Iancu Paula et all., 2019 a). Table 3. Influence of A factor upon studied characters’ variability Character A graduations Yield (kg/ha) Protein (%) Starch (%) TKW (g) Seeds no./ear Seeds weight/100 l Seeds weight/ear A1 6998.2a 12.36b 74a 49.20a 65.81a 78.18a 2.97a A2 5467.6b 11.69c 72.32b 45.39b 49.86c 77.73ab 2.88b A3 3846.9c 12.92a 71.33c 43.48c 58.23b 76.81b 2.74c Average 5437.5 12.32 72.55 46.02 57.97 77.57 2.86 LSD 109.38 0.321 0.379 1.056 1.248 1.014 0.107 3.2 Influence of B Factor In the case of yield analysis the first five genotypes differ significantly from all the others and between them there are no statistical differences. For protein content, the highest value was recorded by genotype B8, this differing significantly compared to the last 5 classified genotypes. In the case of starch content, the most productive genotype B1 records the highest content, differing significantly from all other genotypes, except with the exception of the second classified genotype. As concern TKW analysis, as.ideasspread.org Agricultural Science Vol. 2, No. 1; 2020 256 Published by IDEAS SPREAD the most valuable result was recorded by genotype B2, which differs significantly from all other analyzed genotypes (table 4). Regarding the seeds no./ear, the first classified genotype differs significantly compared to the last 7 classified, this being the most productive. For seeds weight/100 l, the best results are recorded by genotypes B5 and B6, which differ significantly compared to the last 4 classified genotypes. Concerning seeds weight/ear, the first 3 classified genotypes, which are also the most productive, register significant differences compared to all the other genotypes analyzed. Table 4. Influence of B factor upon studied characters’ variability Literature ensures greater variation in protein content. It has been reported wider germplasm screens with the comparison of 212.600 lines in the World Wheat Collection showing a range from about 7 to 22% protein on a dry weight basis (Vogel, K.P. et all., 1976). Lines with higher grain protein contents presented lower starch percent. Iancu Paula et all., 2018 a, also reported some amphidiploids lines which presented high quality characteristics in combination with other agronomic desirable characters and represent valuable genes sources which can be incorporated into future wheat breeding programs and other genetic studies. As concern protein content of the whole grains flour of some mutant/recombinant lines, highly significant differences were detected and some of them could be grown as a new and suitable release for the experimented region (Iancu Paula et all., 2018 b). Starch is the major storage carbohydrate of cereals and an important part of human nutrition and lately became also an important feedstock for bioethanol or biogas production. The result for starch percent for the mutant/recombinant lines ranged from 70.87% (B10) to 74.07% (B1). Shewry, P.R., 2013 sustained that the increases in starch content are largely responsible for the increases in grain size achieved by breeding programs in order to produce high-yielding wheat varieties (table 5). Table 5. Influence of A and B factors interaction upon studied characters’ variability Character B graduations Yield (kg/ha) Protein (%) Starch (%) TKW (g) Seeds no./ear Seeds weight/100 l Seeds weight/ear B1 5945.00a 11.67e 74.07a 45.47b-e 62.07a 78.30ab 3.30a B2 5958.33a 11.97de 73.40ab 52.28a 61.67ab 78.12ab 3.26a B3 5858.67a 12.07c-e 73.13b 46.64bc 60.73ab 77.73ab 3.23a B4 5773.33a 12.27b-d 73.03b 45.53b-d 59.53bc 78.17ab 2.92b B5 5785.00a 12.23b-e 72.87bc 44.96c-e 58.20cd 79.03a 2.89b B6 5156.33b 12.47a-d 72.27cd 47.02b 56.87d 78.98a 2.88b B7 5253.33b 12.37a-d 72.20cd 44.94c-e 56.53d 77.12b 2.65c B8 4934.00c 12.90a 71.97d 43.67de 56.27de 76.93b 2.59c B9 4780.67c 12.70ab 71.70d 46.18bc 54.07ef 76.60bc 2.56c B10 4930.33c 12.60a-c 70.87e 43.55de 53.73f 74.73c 2.34d Average 5437.50 12.32 72.55 46.02 57.97 77.57 2.86 LSD 5% 199.70 0.586 0.692 1.929 2.279 1.851 0.196 Character aibi variant Yield (kg/ha) Protein (%) Starch (%) TKW (g) Seeds no./ear Seeds weight/ 100 l Seeds weight/ear a1b1 7900.00a 12.00d-i 75.30a 48.55b-f 70.20a 79.10a-d 3.47a a1b2 7840.00a 12.10d-h 74.90ab 57.20a 69.60a 79.00a-d 3.36ab a1b3 7827.00a 12.40b-g 74.30a-c 49.50b-e 67.60ab 78.60a-d 3.32a-c as.ideasspread.org Agricultural Science Vol. 2, No. 1; 2020 257 Published by IDEAS SPREAD PCA analysis indicates that the first two components account for 78.78% of the total version, of which the first component registers 60.8% and the second component 17.93% (table 6). Table 6. Total Variance Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1. 4.256 60.807 60.807 4.256 60.807 60.807 2. 1.255 17.932 78.739 1.255 17.932 78.739 3. 0.671 9.581 88.319 4. 0.405 5.791 94.110 5. 0.274 3.908 98.018 6. 0.082 1.164 99.183 7. 0.057 0.817 100.000 a1b4 7760.00a 12.10d-h 74.20a-d 48.28b-f 66.40a-c 77.00a-e 3.02c-h a1b5 7735.00a 12.00d-i 73.90b-e 47.65c-g 65.10b-d 79.80a 3.01c-h a1b6 6320.00b 12.60b-f 73.80b-e 50.08bc 64.80b-d 79.75ab 2.98d-h a1b7 6320.00b 12.10d-h 73.70b-f 50.00b-d 64.60b-d 77.85a-e 2.77g-k a1b8 6170.00bc 12.60b-f 73.50c-g 46.67d-h 64.00b-d 77.65a-e 2.70h-l a1b9 6092.00bc 12.80b-e 73.30c-h 49.46b-e 63.20cd 77.00a-e 2.69h-m a1b10 6016.00b-d 12.90bd 73.10d-h 44.63g-k 62.60c-e 76.05d-f 2.37m-o a2b1 5750.00de 11.00i 74.40ab 46.44e-i 53.60hi 78.40a-d 3.32a-c a2b2 5864.00c-e 11.30hi 72.90e-i 51.09b 53.40hi 78.25a-e 3.31a-d a2b3 5642.00d-f 11.40gi 72.80e-i 42.98j-n 53.00h-j 77.50a-e 3.30a-d a2b4 5635.00ef 11.60f-i 72.60f-i 47.50c-g 52.00i-k 79.30a-c 2.90e-i a2b5 5520.00ef 11.70f-i 72.40g-j 44.16h-l 50.40i-k 79.20a-d 2.87f-i a2b6 5312.00fg 11.80e-i 72.30h-j 45.61f-j 49.20jk 79.20a-d 2.87f-i a2b7 5628.00ef 12.00d-i 72.30h-j 44.87g-j 48.80kl 77.00a-e 2.68h-m a2b8 5075.00g 12.00d-i 71.80i-k 43.21i-n 48.60k-m 76.80a-e 2.60i-o a2b9 5125.00g 12.00d-i 71.30j-l 44.50g-k 45.00lm 76.50b-e 2.58i-o a2b10 5125.00g 12.10c-h 70.40mn 43.57h-m 44.60m 75.10ef 2.34no a3b1 4185.00h 12.00d-i 72.50g-i 41.42k-n 62.40c-e 77.40a-e 3.11b-f a3b2 4171.00h 12.50b-f 72.40g-j 44.55g-k 62.00de 77.10a-e 3.10b-g a3b3 4107.00hg 12.40b-g 72.30h-j 43.44h-m 61.60de 77.10a-e 3.07b-g a3b4 3925.00h-i 13.10a-c 72.30h-j 40.80mn 60.20d-f 78.20a-e 2.83f-j a3b5 4100.00hg 13.00b-d 72.30h-j 43.08j-n 59.10e-g 78.10a-e 2.78f-k a3b6 3837.00h-j 13.00b-d 70.70k-m 45.38f-j 56.60f-h 78.00a-e 2.78f-k a3b7 3812.00g-j 13.00b-d 70.60l-n 39.96n 56.20gh 76.50b-e 2.50j-o a3b8 3557.00j 14.10a 70.60l-n 41.12l-n 56.20gh 76.35c-e 2.47k-o a3b9 3125.00k 13.30ab 70.50mn 44.57g-k 54.00hi 76.30c-e 2.42l-o a3b10 3650.00ij 12.80b-e 69.10n 42.44j-n 54.00hi 73.05f 2.31o LSD 345.88 1.015 1.199 3.341 3.948 3.206 0.340 as.ideasspread.org Agricultural Science Vol. 2, No. 1; 2020 258 Published by IDEAS SPREAD Extraction Method: Principal Component Analysis. In a similar experience, but in groundnuts, PCA analysis accounted 95.57% with the first PC of 74.38 % whereas the second PC of 21.19% (Iancu Paula et all., 2019 b). Analysis of the first component indicate positive values for all analyzed characters except the protein content, so this component can be considered as an identifier for the variants with high production results, with good starch content and high value yield elements. For the second component, positive values for protein and seeds no./ear are recorded, so it is an identifier for high-protein variants with high values for TKW and seeds no./ear (table 7). Table 7. Character Component score Method: Principal Component Analysis 2 components extracted The first group consists of 9 variants that have both positive components, these variants being: a1b1, a1b2, a1b3, a1b4, a1b5, a1b6, a1b7, a1b8 and a1b9. This group is characterized by the fact that almost all genotypes are present at A1 graduation. In other words, A1 graduation determines for all genotypes the highest production values, high protein and starch content and very high values of the production elements (chart 1). The second group consists of 6 variants respectively a2b1, a2b2, a2b3, a2b4, a2b5, and a2b6, variants having the first positive and the second negative. Characteristic for this group is the fact that it was identify the first 6 genotypes as yield potential under the conditions offered by the A2 graduation, this resulting in lower yields compared to the first graduation, the lowest protein content, high starch content and yield elements with highest values for seeds weight/100 l and respectively for seeds weight/ear. The third group consists of 5 variants respectively a2b7, a2b8, a2b9, a2b10 and respectively, a3b1, variants that have both negative components, being genotypes less productive under the conditions offered by A2 graduation or B1 genotype under A3 graduation conditions. A2 graduation provides for these genotypes poor production results and low protein and starch content, with low production elements. The fourth group consists of 10 respective variants a1b10, a3b2 a3b3, a3b4, a3b5, a3b6, a3b7, a3b8, a3b9 and a3b10, variants having the first negative component and the second positive. Characteristic for these groups is the fact that it is represented by all genotypes in the conditions offered by the A3 graduation, which determines the poorest production results, the highest content of protein, the lowest content of starch and production elements with modest values. Character Component Yield (kg/ha) Protein (%) Starch (%) TKW (g) Seeds no./ear Seeds weight/100 l Seeds weight/ear 1 0.870 -0.542 0.954 0.803 0.607 0.765 0.836 2 0.028 0.798 0.100 0.112 0.744 -0.112 -0.170 as.ideasspread.org Agricultural Science Vol. 2, No. 1; 2020 259 Published by IDEAS SPREAD Chart 1. Distribution of the analyzed variants by groups according to PCA analysis Analysis of cereal productivity data for the last few decades reveals considerable increase in yield, but it appears that negative impact of climate change have large influence in vegetation period. Drought conditions are a frequent impediment to maximized production. Plants are sensitive to short-term changes in weather and to seasonal, annual and longer-term variations in climate. For every degree increase in mean temperature, grain yield decreased by 428 Kg/ha (Khan, S.A., et all., 2009). Climate change proved to have negative effect on yield and productivity of agricultural crops. Genetic variability and character association is a pre-request for improvement of any crop and for selection of superior genotypes and improvement of any trait (Krishnaveni, B. et al., 2006). In this study, the correlation coefficients between the studied characters were calculated according to each group resulting from the PCA analysis (table 6). The correlation between yield and protein is very strong in productive genotypes both under favorable conditions (A1) and less favorable (A2) and weak at low productive genotypes or under abnormal conditions (A3), irrespective of the biological production potential of genotypes. The correlation between yield and starch is strong for all genotypes under favorable conditions (A1) or abnormal conditions (A3) and becomes insignificant for genotypes with lower biological potential under A2 conditions. Also, the correlation between yield and TKW is very strong in genotypes with lower biological potential under A2 conditions. Regardless of conditions or biological potential between yield and seeds no./ear there is a very strong correlation. Seeds weight/100 l influence production only on genotypes with high biological potential and under A2 conditions. Under favorable conditions (A1) or less favorable (A2), regardless of the biological potential seeds weight/ear strongly influences yield. The relationship between protein and starch is strong and indirect under favorable conditions (A1) and normal conditions (A2) regardless of biological potential. No relationship between protein and TKW was identified regardless of biological potential or experimental conditions. The relationship between protein and seeds no./ear is strong and inverse under normal conditions (A2) to genotypes with high biological potential. The correlation between protein and seeds weight/100 l is strong and inverse under normal conditions for genotypes with low biological potential. The relationship between protein and seeds weight/ear is strong and a1b1 a1b2 a1b3 a1b4 a1b5 a1b6 a1b7 a1b8 a1b9 a1b10 a2b1 a2b2 a2b3 a2b4 a2b5 a2b6 a2b7 a2b8 a2b9a2b10 a3b1 a3b2 a3b3 a3b4 a3b5 a3b6 a3b7 a3b8 a3b9 a3b10 yield protein starch TKW seeds no./ear seeds weight/100l seeds weight/ear -2.00 -1.50 -1.00 -0.50 0.00 0.50 1.00 1.50 -2.25 -1.75 -1.25 -0.75 -0.25 0.25 0.75 1.25 1.75 2.25 co m po ne nt 2 component 1 as.ideasspread.org Agricultural Science Vol. 2, No. 1; 2020 260 Published by IDEAS SPREAD inverse, regardless of biological potential or experimental conditions. Only under favorable conditions (A1) can a relationship between starch and TKW be identified. The relationship correlation between starch and seeds no./ear is strong and inverse regardless of the biological potential or conditions of experimentation. There is a strong direct relationship between starch and seeds weight/100 l in abnormal conditions (A3) regardless of biological potential. The correlation between starch and seeds weight/ear is strong and direct regardless of the biological potential. The relationship between TKW and seeds no./ear is strong and inverse under normal conditions (A2) genotypes with low biological potential. The relationship between TKW and seeds weight/100 l was not identified, regardless of the biological potential and experimental conditions. A strong inverse relationship between TKW and Seeds weight/ear normal conditions (A2) can be identified in genotypes with low biological potential. The correlation between seeds no./ear and seeds weight/100 l is very strong under normal conditions regardless of the biological potential. The correlation between seeds no./ear and seeds weight/ear is very strong and direct regardless of the biological potential and conditions of experimentation. The correlation between seeds weight/100 l and seeds weight/ear is very strong and direct, regardless of the biological potential under favorable conditions (A1) or abnormal (A3) and inverse less favorable conditions (A2) (table 8). Table 8. Variation of correlation coefficients between studied characters depending on variant groups resulted after PCA analysis Crt. no. r values Correlation relationship PCA groups according to components Co m p. 1 Co m p. 2 Co m p. 1 Co m p. 2 Co m p. 1 Co m p. 2 Co m p. 1 Co m p. 2 + + + - - - - + 1. yield/protein -0.733*** -0.808*** 0.104ns -0.324ns 2. yield/starch 0.807*** 0.576* -0.269 0.687*** 3. yield/TKW 0.235ns 0.574* 0.945*** 0.281ns 4. yield/seeds no./ear 0.815*** 0.937*** -0.807*** 0.733*** 5. yield/seeds weight/100 l 0.355ns -0.541* -0.316ns 0.043ns 6. yield/seeds weight/ear 0.853*** 0.782*** -0.697** -0.018ns 7. protein/starch -0.660*** -0.927*** -0.834*** -0.342ns 8. protein/TKW -0.170ns -0.289ns 0.023ns -0.370ns 9. protein/seeds no./ear -0.615ns -0.897*** -0.406ns -0.495** 10. protein/seeds weight/100 l -0.325ns 0.624** -0.928*** 0.001ns 11. protein/seeds weight/ear -0.551** -0.885*** -0.641** -0.522** 12. starch/TKW 0.418* 0.152ns -0.298ns 0.235ns 13. starch/seeds no./ear 0.989*** 0.697** 0.736** 0.941*** 14. starch/seeds weight/100 l 0.409* -0.384 0.971*** 0.622*** 15. starch/seeds weight/ear 0.944*** 0.682** 0.859*** 0.555** 16. TKW/seeds no./ear 0.483* 0.353ns -0.826*** 0.219ns 17. TKW/seeds weight/100 l 0.218ns 0.107ns -0.290ns 0.093ns 18. TKW/seeds weight/ear 0.421* 0.234ns -0.656** 0.203ns 19. seeds no./ear/seeds weight/100 l 0.378ns -0.709*** 0.694*** 0.461* 20. seeds no./ear/seeds weight/ear 0.958*** 0.885*** 0.952*** 0.619*** 21. seeds weight/100 l/seeds weight/ear 0.533** -0.891*** 0.855*** 0.668*** 22. r significance P 5% 0.380 0.468 0.514 0.361 as.ideasspread.org Agricultural Science Vol. 2, No. 1; 2020 261 Published by IDEAS SPREAD 23. levels P1% 0.486 0.589 0.641 0.462 24. P 0.1% 0.597 0.708 0.760 0.570 Two tailed significance levels of the Pearson correlation coefficient 4. Conclusions The first graduation of factor A is decisive regarding the expression of the yield potential for all genotypes, while the A3 graduation is decisive regarding the protein content. Concerning the B factor influence, the genotype, on yield potential, it is decisive in the A1 graduation conditions, where even the least productive genotypes obtain superior results compared with the results obtained by the best genotypes under A2 and A3 graduations conditions. Under the conditions offered by A3 graduation, almost all genotypes obtain the highest protein content and record the lowest production results. The first two graduations of factor A are decisive regarding the first two groups of variants. Thus, the variants in the first group are almost all genotypes on A1 graduation, while the variants in the second group are the first 6 genotypes as biological potential under the conditions offered by the A2 graduation. In relation to the analysis of the correlations between the studied characters, certain correlations are universally valid regardless of the group of variables analyzed, among them we mention: yield and seeds no./ear, starch and seeds weight/ear, seeds no./ear and seeds weight/ear, seeds weight/100 l if seeds weight/ear. Only one relationship was found to be insignificant regardless of the analyzed group, the one between TKW and seeds weight/100 l, otherwise said regardless of genotype and environment for the analyzed genotypes, there is no relationship between the two characters. The relationship between yield and protein is strongly indirect only in favorable conditions that allow the expression of a high biological potential, in unfavorable conditions, the drastic decrease of the yield no longer allows the identification of a relationship between the two analyzed characteristics. References Aslani, F., Mehrvar, M. R., Nazeri, A., & Juraimi, A. S. (2013). 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