ࡱ> bjbj 8cc9ppb2vL61818181818181$e47\1\1 =2?%?%?%61?%61?%?%V,@-Jzo#Z*- "1S2026-x7$.7--\7 .?%\1\1%.27p :Application of biomass sensor in the winter barley selection Ivan Abi i1, Alojzije Lali1, Vlatko Gali1, Selma Mlinari2, Lidija Begovi2 1Agricultural Institute Osijek, Ju~no predgrae 17, 31000 Osijek, Croatia (e-mail: iabicic@poljinos.hr) 2Josip Juraj Strossmayer University in Osijek, Department of Biology, Cara Hadrijana 8a, 31000 Osijek, Croatia Abstract Remote sensing by acquirement of Normalized Difference Vegetation Index (NDVI) is widely used nowadays for assessing crop biomass, yield and other traits. This study aimed to determine the variation of NDVI and its relationship with the most significant agronomic properties of 20 elite winter barley genotypes. NDVI measurements were conducted at several vegetation stages while grain yield, hectoliter and lodging were the observed traits. Results from this study showed that the influence of genotype varied significantly for grain yield and hectoliter, especially when measured biomass displayed a successive rise of genotypic variability after the onset of booting towards anthesis which was confirmed by correlation. Therefore, significant genotypic variation of NDVI values could be efficiently used for indirect identification of productive winter barley genotypes. Key words: remote sensing, barley, biomass, traits, variability Introduction Within recent years the non-invasive method of measuring canopy greenness which uses normalized difference vegetative index (NDVI) measurements from hand held devices gained popularity among plant breeders. Similar device was also used in this research, namely GreenSeeker (Trimble, Sunnyvale, CA, USA, 2017), whose sensor employs a patented technology to measure crop reflectance and calculate NDVI, NDVI = (NIR"Red)/(NIR+Red) where NIR is the fraction of emitted near infrared radiation returned from the sensed area, and Red is the fraction of emitted red radiation returned from the sensed area. Start of development and use of these techniques is firstly referenced by Raun et al. (2001) who established a non-destructive method for accurate estimate of yield potential using spectral measurements. Normalized difference vegetation index and other indices derived from canopy reflectance have been widely used for in-season estimation of grain yield in various crops, such as wheat (Walsh et al., 2013). Field phenotyping that includes different physiological traits provides valuable information about crop adaptation to unfavorable environmental conditions (Reynolds and Langridge, 2016). Additional analysis of genetic resources with relatively poor agronomic traits could identify genotypes tolerant to conditions of different kinds of abiotic stress which adds to genetic variability (Driever et al., 2014). Potential in application of these methods is also in pre-selection where breeders will be able to increase the frequency of genes associated with crop adaptation before yield testing trials (Reynolds and Langridge, 2016). This remote sensing method is fast and non-destructive, and could be used as additional selection criteria in the assessment of crop biomass during different phenology phases. Therefore, this study aimed to determine the variation of NDVI and its relationship with the most significant agronomic traits of elite winter barley genotypes from the Agricultural Institute Osijek barley breeding program. Material and methods Winter barley lines/varieties (n = 19) from Agricultural Institute Osijek breeding program and one foreign were selected for this research (Table 1). Even though all of the lines/varieties represented are high-yielding by breeding standards, there are some differences especially with lower yielding varieties, namely Osvit, Mandatar and GZ184 which are hulless by grain physiology and can be considered as outliers for the trait of grain yield and hectoliter. Field trial was set up as a randomized complete block (RCBD) with three replicates conducted through the growing season of 2017/18 at the Agricultural Institute Osijek (4532 N, 1844 E) and was grown under natural rain-fed conditions. The area itself has a continental-type climate and is 94 m above the sea level. Rainfall through growing season (October 2017 till June 2018) had 426.8 mm with 23.3 C mean maximum and -10.5 C mean minimum temperature. Soil type at the growing site is eutric cambisol with slightly acidic pH, mostly used for setting up of field trials in rotation with various crops and their generational/breeding material (maize, alfalfa, soybean, etc.). Emergence scores as well as other phenology data were recorded regularly during crop growth to determine tillering, booting, anthesis and grain filling stage (Zadoks et al., 1974). NDVI was measured once by the onset of each stage with regard to plant/plot maturity level, meaning four times in total across the whole section of the trial starting from tillering (Zadoks code 21), booting (Zadoks code 41), just before anthesis (Zadoks code 61) and start of the grain filling stage (Zadoks code 71). Moving at a constant speed along the rows between plots, the GreenSeeker was held at about 60 cm above the crop canopy and its effective resolution captured approximately four inner rows out of 12 in a single plot. Also, the whole plot was harvested for grain (07th of June 2018) using a plot harvester and expressed as t ha-1. Hectoliter was measured with Infratec 1241 NIT analyzer (Foss, Denmark). Balanced ANOVA for agronomic traits and NDVI data with post-hoc LSD testing was calculated with CropStat 7.2 (IRRI, Manila, Philippines; 2008). Correlation among traits with corresponding t-test for significance was calculated with PASSaGE (Rosenberg and Anderson, 2011) software package. Table 1 List of winter barley lines/varieties mainly originated from Agricultural Institute Osijek and their contrasting traits Winter barley line/varietySpike configurationMaturity/EarlinessBingoTwo-rowedEarlyOsvitTwo-rowed (H)EarlyZlatkoTwo-rowedEarlyOS TitanSix-rowedEarlyPleterTwo-rowedEarlyBarunTwo-rowedMid-earlyMandatarTwo-rowed (H)Mid-earlyPredatorTwo-rowedMid-earlyGZ 184Two-rowed (H)Mid-earlyOsk.6.29/3-13Two-rowedMid-earlyBravoTwo-rowedMid-lateMaximTwo-rowedMid-lateOsk.4.37/14-14Two-rowedMid-lateMaestroTwo-rowedMid-latePanonacTwo-rowedMid-lateCasanova*Two-rowedLateLordSix-rowedLateOsk.5.36/25-15Two-rowedLateOsk.5.87/1-13Two-rowedLateOsk.4.36/9-14Two-rowedLate(H) hulless (no glumes attached to grain). * foreign variety. Results and discussion Mean values were given in Table 2 and for the trait of grain yield the mean was 7.207 t ha-1. Genotype with the highest result was Osk.4.37/14-14 breeding line with 8.613 t ha-1. Hectoliter showed 68.557 kg hL-1 main value and the first three varieties with the best results, quite over the mean for all genotypes, are the hulless types Mandatar, Osvit and GZ184 with 80.466, 79.766 and 78.300 kg hL-1 respectively. Most previous studies reported absence of significant association between grain weight and grain yield in wheat (Prado et al., 2017) and barley (Mirosavljevic et al., 2016) as was also corroborated in this research and even more so when considering difference in grain physiology mentioned before. Mean values were further analyzed by ANOVA where significance calculated from F-test was positive for genotype vs. grain yield, hectoliter, NDVI2, NDVI3 and NDVI4. However, LSD-test confirmed significance only in case of genotype vs. NDVI2 and NDVI3. According to the results from this paper, there was significant genotypic variation in NDVI values at booting and anthesis. These results are confirmed by the findings of the previous studies (Hazratkulova et al., 2012; Ramya et al., 2015), where NDVI also showed high levels of genotypic variability. Table 2 ANOVA for winter barley genotypes and observed traits, with post-hoc LSD test values (n = 20) Standard DeviationSources of variabilityMean (n=20)Total S. S.Residual S. S. C. V. (%)F-probability (GENOTYPE)LSD (=0.05)Grain Yield (t/ha)7.2071.1630.85611.90.003**1.861n.s.Hectoliter (kg/hL)68.5575.0411.2461.80.001**7.743n.s.Lodging (%)24.16723.52723.13092.20.380n.s.36.401n.s.NDVI1 (tillering)0.6470.0280.0253.90.062n.s.0.064n.s.NDVI2 (booting)0.8010.0300.0242.90.014*0.045*NDVI3 (anthesis)0.7470.0400.0314.10.014*0.049*NDVI4 (grain filling)0.3380.0990.04914.60.001**0.109n.s. Correlations between traits/variables observed in this study are shown in the Table 3. Statistical significance (P < 0.05) of correlation values made in bold letters highlight a positive correlation between genotype and grain yield (r = 0.24); genotype and NDVI2 (r = 0.39), NDVI3 (r = 0.57) and NDVI4 (r = 0.74). Positive relationship between NDVI values at booting, anthesis and the trait of grain yield was probably the result of high association between NDVI values and crop biomass, as previously reported in a study of Moges et al., 2005. Grain yield is negatively correlated to hectolitre (r = -0.59) and positively to NDVI2 (r = -0.42) and NDVI3 (r = -0.44). These results are in line with findings of Prado et al. (2017) who reported that high biomass was positively related with grain yield in wheat and barley. Therefore, increase of crop biomass, especially in anthesis, could represent one of the most important objectives in small grain cereals breeding programs (Zhou et al., 2014). Table 3 Correlation and statistical significance values between observed traits/variables for winter barley genotypes (n = 20) VariableGE.G.Y.HE.LO.NDVI1NDVI2NDVI3NDVI4*GE.-0.0490.0070.2160.7230.0050.0010.001P (Correlation, =0.05)G.Y.0.24-0.0140.4370.4700.0010.0050.159HE.-0.36-0.59-0.4040.5620.0020.0140.309LO.0.35-0.18-0.25-0.1940.2420.1980.207NDVI10.05-0.100.090.20-0.0730.0510.088NDVI20.390.42-0.420.270.21-0.0010.005NDVI30.570.37-0.440.430.230.82-0.002NDVI40.740.21-0.150.330.210.510.62-Correlation* statistical significance for correlations (P < 0.05). GE. genotype; G.Y. grain yield; HE. hectoliter; LO. lodging; NDVI1 tillering; NDVI2 booting; NDVI3 anthesis; NDVI4 grain filling. Conclusions Genotype had significant influence on the studied traits of winter barley varieties/lines except lodging. NDVI values at booting and anthesis were positively correlated with grain yield, while relationship with grain weight/hectoliter was negative. The presence of significant genotypic variation for NDVI values and positive correlation with grain yield indicate that NDVI measurement at booting and anthesis could be used for indirect selection of high yielding barley cultivars. Literature Driever, S.M., Lawson, T., Andralojc, P.J., Raines, C.A., & PARRY, M.A.J. (2014). Natural variation in photosynthetic capacity, growth, and yield in 64 field-grown wheat genotypes. Journal of Experimental Botany, 65, 1-15. GreenSeeker Handheld. (2017).  HYPERLINK "http://www.ntechindustries.com/handheld.html" http://www.ntechindustries.com/handheld.html International Rice Research Institute - IRRI (2008) CropStat for Windows, version 7.2. Philippines, e-mail:  HYPERLINK "mailto:GMcLaren@cgiar.org" GMcLaren@cgiar.org Hazratkulova, S., Sharma, R.C., Alikulov, S., Islomov, S., Yuldashev, T., Ziyaev, Z., Khalikulov, Z., Ziyadullaev, Z., & Turok, J. (2012). Analysis of genotypic variation for normalized difference vegetation index and its relationship with grain yield in winter wheat under terminal heat stress. Plant Breeding, 131(6), 716-721. Mirosavljevi, M., Mom ilovi, V., Pr~ulj, N., Hristov., N., Ain, V., anak, P., & Den i, S. (2016). The variation of agronomic traits associated with breeding progress in winter barley cultivars. }emdirbyst (Agriculture), 103(3), 267-272. Prado, S.A., Jgallardo, M., Kruk, B.C., Miralles, D.J. (2017). Strategies for yield determination of bread wheat and two-row barley growing under different environments: A comparative study. Field Crops Research, 203, 94-105. Ramya, K.T., Jain, N., Ramya, P., Singh, P.K., Arora, A., Singh, G.P., & Prabhu, K.V. (2015). Genotypic variation for normalized difference vegetation index and its relationship with grain yield in wheat under terminal heat stress. Indian Journal of Genetics and Plant Breeding, 75(2), 174-182. Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Lukina, E. V., Thomason, W. E., et al. (2001). In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal, 93(1), 131138. Rosenberg, M. S. and Anderson, C. D., (2011). PASSaGE: Pattern Analysis, Spatial Statistics and Geographic Exegesis. Version 2. Methods in Ecology and Evolution 2(3):229-232. Reynolds, M., and Langridge, P. (2016). Physiological breeding. 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Precision Agric (2013) 14: 343.  HYPERLINK "https://doi.org/10.1007/s11119-012-9299-9" https://doi.org/10.1007/s11119-012-9299-9 Zadoks, J.C., Chang, T.T., Konzak, C.F., (1974). A Decimal Code for the Growth Stages of Cereals. Weed Research, 14:415-421.  HYPERLINK "https://doi.org/10.1111/j.1365-3180.1974.tb01084.x" https://doi.org/10.1111/j.1365-3180.1974.tb01084.x Zhou, B., Sanz-Sez,., Elazab, A., Shen, T., Snchez-Bragado, R., Bort, J., Serret, M.D., & Araus, J.L. (2014). Physiological traits contributed to the recent increase in yield potential of winter wheat from Henan Province, China. Journal of Integrative Plant Biology, 56 (5), 492-504. Primjena senzora za biomasu u selekciji ozimog je ma Sa~etak Procjena stanja biomase, uroda zrna i ostalih svojstava skeniranjem i izra unom vegetacijskog indeksa normaliziranog spektra crvene svjetlosti (NDVI) je u danaanje vrijeme airoko rasprostranjena. Ova studija je imala za cilj odrediti varijabilnost NDVI vrijednosti i ukazati na prirodu odnosa istih prema bitnim agronomskim svojstvima kod odabranih 20 elitnih genotipova ozimog je ma. Mjerenje NDVI je provedeno kroz nekoliko klju nih vegetacijskih faza a urod zrna, hektolitar i polijeganje su promatrana svoة*0@LZnҰJLB|̶̴̶hE h1 mHsHhE hzmHsHhE h=QmHsHhE h7 mHsHUhE hT mHsHhE hcXmHsHhE hVUmHsHhE hk5mHsHhE hVU5mHsHhE h 5CJaJmHsH"jstva. Ostvareni rezultati su pokazali o ekivanu varijabilnost genotipova u odnosu na svojstvo uroda zrna i hektolitar, ali najviae prema NDVI vrijednostima mjerenima kroz fazu vlatanja i cvatnje, ato je dodatno potvreno korelacijskim faktorima. Stoga, upravo spomenuta zna ajna genotipska varijabilnost NDVI utvrena kroz klju ne faze razvoja usjeva mo~e biti efektivno koriatena kao mjerilo za indirektnu identifikaciju visokoproduktivnih genotipova ozimoga je ma. Klju ne rije i: skeniranje, je am, biomasa, svojstva, varijabilnost 6&P 1h:pj. 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