Chief EditorT. Mohapatra
Print ISSN 0367-8245
Online ISSN 0976-058X
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Phenotyping and Association Analysis of Grain Zinc and Iron Content with Seed Yield in Diverse Local Germplasm of Rice
Methods: Zinc and iron content of a set of 92 rice genotypes was estimated in the aliquot of seed extract by using an Inductive Coupled Plasma-Optical Emission Spectrophotometer (ICP-OES) to explore donors and to study the degree of association of these micronutrients with agro-economic traits including grain quality traits and seed yield.
Result: The top Fe (≥50 ppm) and Zn dense (≥40 ppm) genotypes identified were P44 mutant selection-1, ORCZ 75-3-1, Basudha, Malliphulajhuli, Tikimahsuri and Nikipankhia. P44 mutant Sel.-1 and ORCZ 75-3-1 had good yield potential (44 q ha-1). Grains/panicles and the number of effective bearing tillers/hill maintained an appreciably strong positive association with seed yield, while plant height had an inverse relationship. Grain Fe content positively correlated with panicle length. On the other hand, Zn is positively associated with tillering ability and grains/panicle. Grain Fe and Zn revealed strong a inter se positive association. Interestingly, grain Fe revealed no association, but grain Zn revealed significant positive relationship with seed yield. This envisaged better scope for genetic enhancement of grain Zn content along with substantial increase in grain Fe without any yield penalty.
MATERIALS AND METHODS
Observations were recorded on seven agro-morphological traits including seed yield and nine quality traits including zinc and iron content of brown rice. Basing on grain length and grain length (L)/Breadth(B) ratio, rice genotypes were classified into seven grain types as per Govindaswamy (1985). Fine ground samples of brown rice of each of the genotypes in three replicates were digested by di-acid mixture of nitric acid (HNO3): and perchloric acid (HClO4) as per Jahan et al., (2013). Fe and Zn content were estimated using Inductive Coupled Plasma-Optical Emission Spectrophotometer (ICP-OES) at 238.2nm and 206.2nm wavelength respectively (Fig 1) at Central Instrumentation Facility (CIF), OUAT, Bhubaneswar.
Analysis of variance and covariance were done as per Singh and Choudhury (1985) using sample means of various traits under study. Estimates of the correlation coefficient for each pair of characters were computed following as per Al-Jibouri et al., (1958) and the significance of correlation coefficients was tested by the t- test at n -2 degrees of freedom.
RESULTS AND DISCUSSION
Modern high yielding rice varieties are deficient in Fe and Zn. The breeding target is approximately fixed at 13-15 ppm for iron (Bouis et al., 2011) and 28-30 ppm for zinc (Shahzad et al., 2014) biofortification for polished rice. About 57% of iron and 40% of Zn are lossed during milling and polishing of rice grains (Hansen et al., 2009). In the present study, grain Fe and Zn content ranged from 8.3-52.15 ppm and .3.0-52.7 ppm respectively in brown rice. Most of the local landraces showed rich source of above minerals as Anandan et al., (2011) also reported. The top Fe dense (≥40 ppm) genotypes identified were Tikimahsuri (52.15 ppm), Jabaphulla (52.15 ppm), Kala Kusuna (52.1 ppm), OR CZ 75-3-1(51.95 ppm), P 44 mutant selection-1(51.9 ppm), CR 2327-23(51.4 ppm), Budhidhan (51.15ppm) and Kalamakhi (50.15 ppm) (Fig 2). Interestingly, Basudha, Malliphulajhuli, Tikimahsuri and Nikipankhia also revealed higher grain Zn content (>40.0 ppm) in addition to iron. These lowland land races recorded very low seed yield (20.2-27.9 q ha-1) except Nikipankhia (34.0 qtl ha-1). Some landraces (Roy and Sharma, 2014), basmati types (Brar et al., 2011) and wild rice (Banerjee et al., 2010) are also reported to retain high grain Fe and Zn content but japonica rice harbour the least (Anuradha et al., 2012a). The iron and zinc dense genotypes identified in this study may serve as potential donors for biofortification in rice.
Inter-relationship of morpho-agronomic traits with seed yield
Grains/panicle and effective bearing tillers(EBT)/hill followed by panicle length and days to maturity exhibited positive significance relationship with seed yield but plant height maintained inverse relationship with tillers per plant (0.555**), grains per panicle (0.440**) and seed yield (-0.529**) and indicating the fact that semi-dwarf plant types are associated to high tillering and higher number of grains per panicle leading to perform better for seed yield than intermediate to tall plant types (Table 1). This corroborates the findings of Nagesh et al., (2013) and Bekele et al., (2013). Besides, panicle length associated favourably with ear bearing tillers, grains per panicle and grain weight as also reported by Sri Devi et al., (2019). Thus, such desirable panicle feature is likely to result high productivity.
Inter se relationship with physical quality traits
Yield has shown positive correlation with grain length, grain length/breadth, grain type, kernel length, kernel length/breadth and zinc content. But productivity was shown to be negatively associated with kernel breadth (Table 1). This indicates that increase in yield in rice may be achieved by selecting long grain with slender kernel types. Besides, kernel length was shown to have a favourable association with panicle length, effective bearing tillers/hill and grains/panicle indicating the possibility of increasing productivity through correlated selection response based on the above component traits. Chouhan (1996) reported significant positive association of kernel length and kernel L/B ratio but a negative association with kernel breadth.
Correlation with grain Fe and Zn content
Grain iron and Zinc content determine the success of biofortification in rice. The present study revealed no relationship between grain iron content and seed yield but grain zinc content maintained a significant positive relationship with seed yield (Table 1) indicating the possibility of the role of Zn than Fe for the increase in sink size in rice. Linear regression analysis shown in Fig 3 further confirms such relationship. This supports the findings of Ajmera et al., (2017) and Sri Devi et al., (2019) though other workers opined inverse relationship (Shivani et al., 2019 and Inabangan-Asilo et al., 2019) or even no association (Pandey et al., 2018) of both Fe and Zn with seed yield. However, Moreno-Moyano et al., (2016) made it clear that over-accumulation beyond 23 µg Fe/g and 40µg Zn/g of rice grain (in os-NAS/IR 64 transgenic progenies) had significant negative impact in a number of agro-morphological traits affecting grain yield.
Our study revealed significant positive association of grain Fe- content with zinc (r=0.609) possibly due to co-location of QTLs for Fe and Zn in chromosome 7 and chromosome 12 in brown rice (Anuradha et al., 2012b). This corroborates the findings of Maganti et al., (2019) and Inabangan-Asilo et al., (2019). The linear regression shown in Fig 2 further confirms such strong relationship between the above micronutrients. This means that the varieties which have efficient transport of zinc would have also higher translocation of iron and loading of the same in the grain. However, no such strong relationship can be realized between these trace elements in polished rice (Sala et al., 2013) possibly due to loss of the major fraction of Fe during milling compared to Zinc, as rice bran contains 55% and 34% Fe and Zn respectively.
Grain zinc content positively correlated with effective bearing tillers and grains/panicle which might be a cause for its erstwhile mentioned positive significant association with seed yield. On the other hand, iron content was shown to have significant positive correlation with panicle length indicating possibility of increasing iron content by selecting long panicle types. Grain and kernel dimension (length, breadth and length/breadth) revealed no correlation with either iron or zinc content indicating their no interference with genetic enhancement for Fe and Zn. In contrast, Anuradha et al., (2012a) revealed strong negative association of Zn concentration with grain elongation in a set of 126 germplasm lines and Bekele et al., (2013) showed positive correlation of grain Zn concentration with grain weight and grain length.
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