Identification of efficient calcium absorbing groundnut genotypes under different intervals of gypsum fertigation

Introduction

Groundnut or peanut (Arachis hypogaea L.) is an important oil, feed, food and fodder crop in many parts of the world. It is cultivated in 111 countries in an area of 31.56 million hectares with a total production of 53.63 million tonnes (FAOSTAT, 2020) [1] . Major groundnut growing countries are China followed by India, Nigeria and United States and most of the cultivated area was confined to tropical and sub-tropical regions of the world. The nutritional profile of 100 g groundnut kernels was 25 g protein, 17.86 g carbohydrates, 10.70 g fibre, 10.71 g of saturated fatty acids and 42.86 g of unsaturated fatty acids, minerals and vitamins (USDA, Food Data Central, 2019) [2] . The monounsaturated fatty acid of kernels includes oleic acid (36-81.3%) and linoleic acid (3.9-40.2%) (Janila et al., 2016) [3] . The high oleic groundnuts (>70% oleic acid) have several health advantages viz., lower bad cholesterol, promote good cardiovascular health, confer anti-diabetic properties, and help to mitigate vascular inflammation (Wilson and Wilson, 2015) [4] . According to UN report (2017) [5] , the population is expected to reach 8.6 million in 2030 and maintaining food production for the growing world population requires increase in production and management on the current cropland. Fertilizer is one of the essential sources for accomplishing this target of high crop production. The fertilizer usage on an average increased from 82.15 kg ha -1 to 136.82 kg ha -1 in last five decades (1978-2018) in the world (FAO, 2020) [6] . In this 21 st century, managing soil nutrients to deliver a safe food supply and being environment friendly remains an immense challenge for the researchers (Zhang et al., 2012) [7] . Groundnut is an exhaustive crop and depending upon the yield, it confiscates large amount of macro-and micro-nutrients from the soil. But farmers use a very less amount of fertilizer and sometimes only one or two nutrients ensuing in severe nutrient deficiencies due to insufficient and imbalance use of nutrients. In most of the developing countries where groundnut is grown, soil infertility restricts its productivity. To improve productivity, diagnosis of nutrient deficiencies and toxicities in unfertilized soils, their rectification with minimal fertilizer treatment and development of cultivars with high nutrient efficiency in deficient soils are challenges for plant nutrition researchers. The macronutrients such as Ca, K, P and S and micronutrients such as Fe and B are playing a key role in kernel filling and oil synthesis and henceforth these minerals are required in higher extent compared to other minerals in groundnut (Singh et al., 2004) [8] . The calcium ion (Ca 2+ ) is an important secondary macronutrient essential for pod filling, development of sound mature kernels and reduces the development of "pops" (unfilled pods) and "black hearts" and improves germination percentage in kernels. It is transported upwards from roots exclusively in xylem tissue with transpiration stream but the downward movement from the leaves is almost nil through phloem (Mengel and Kirkby, 1987) [9] . When peg penetrates the soil, it cannot access the root absorbed calcium and completely depends on absorbed Ca 2+ directly from the soil which helps in the pod development. The critical levels of soil Ca are about 250 ppm in root zone and 600 ppm in pod zone (Singh et al., 2004) [8] .

Gypsum (CaSO4.2H2O) and lime (CaCO3) are most prominently used calcium sources of fertilizers in groundnut. Compared to lime, gypsum has more advantages i.e., it contains both calcium and sulphur, increases porosity of soil, no influence on soil pH, and 200 times better soluble than lime (Norton, 2008) [10] . Hence, gypsum is considered in this experiment as calcium fertilizer for evaluating groundnut genotypes. The time of application of gypsum is crucial to provide adequate calcium to groundnut during the pod development stages. As several researchers reported genotypic differences in Ca uptake from soil, efforts are needed to exploit groundnut genotypes performing well under calcium deficient soils. Hence, this study was taken up to identify best genotypes with high calcium uptake and to analyse the best time of gypsum application for better yields in groundnut.

Materials and Methods

Genetic material

For this study, 20 diverse groundnut advanced breeding lines from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India were chosen (Table 1). We applied four treatments of gypsum at different days after sowing (DAS) along with control (without gypsum application) (Table 2). The gypsum was applied on the soil surface near to the plant base at the rate of 500 kg ha -1 . The field design was laid out in spilt plot alpha-lattice and genotypes were replicated twice per each treatment and trail was conducted at ICRISAT field. The field was thoroughly characterized regarding soil properties and calcium status before the experiment.

Soil Testing

Soil samples were collected from 6 representative sites from field where experiment was taken. Samples (top 30 cm) were air-dried and sieved through a 2-mm screen, and subjected to ammonium acetate extraction (Thomas et al., 1982) [12] and saturated paste extraction (Rhoades 1982) [13] and then centrifuged for 40 min at 2000 rpm. The filtered soil solutions were analysed for calcium (Ca) concentration by Inductively

Coupled Plasma Optical Emission Spectroscopy (ICP-OES, Thermo Fisher Scientific, USA) at Charles Renard Analytical Laboratory, ICRISAT. The soil test results from the field were compared with threshold levels of calcium proposed by International Plant Nutrition Institute (IPNI), Victoria, Australia (Fig 1). The results from soil test revealed that soil is deficient in calcium content and that the conditions are suitable to conduct the experiment.

Fig 1:

The results of calcium content from soil test data and comparison with threshold values from IPNI

Trait Phenotyping

During the summer 2021, the following traits were phenotyped viz., single plant yield (SPY), number of pods per plant (NOP), 100-pod weight (HPW), 100-kernel weight (HKW), pod length (PL), pod width (PW), kernel length (KL), kernel width (KW) and sound mature kernel % (SMK%). Ten plants were collected from each replication of each treatment and data was recorded on SPY and NOP and mean was calculated. The PL, PW, KL and KW of twenty pods and kernels were calculated using vernier callipers and their mean was computed. The SMK% was calculated as percentage of weight of fully matured kernels (g) by total weight of kernels (g). The calcium content in kernels and shells were estimated by hydrogen peroxide digestion method proposed by Gilliham et al., (2011) [14] using closed polypropylene tubes for inductively coupled plasma optical emission spectrometry (ICP-OES, Thermo Fisher Scientific, USA) at Charles Renard Analytical Laboratory, ICRISAT. Calcium response index (CRI) was estimated separately for each treatment using the formula proposed by Furtini (2008) [15] and presented below.

PYA and PYB are corresponding pod yield under high and low calcium concentrations and (A-B) is the difference between calcium concentrations.

Statistical analysis

Two-way analysis of variance (ANOVA) was performed to assess the main and interaction effects between treatments and genotypes at 0.1, 1 and 5% level of significance. The individual variances were estimated and modelled to error distribution using residual maximum likelihood (REML) procedure using ASREML-R v4 (Butler et al., 2009) [16] . BLUPs (Best Linear Unbiased Predictors) were estimated for genotype (G), treatment levels and genotype × treatment levels and calculated pair wise comparisons using t-statistic (LSD) for significant effects using following equation.

Where, t is the cumulative student's t distribution, 0.05 is selected α level (5%), Edf is the error degrees of freedom from the ANOVA model, and SED is standard error of differences of the mean.

Where, ϭ is standard deviation and is mean Grey relation analysis (GRA) proposed by Julong (1989) [17] was used to identify interrelationships between multiple factors and multiple criteria decisions to identify the best treatment. Karl Pearson's correlation coefficient between traits in each treatment was calculated and corrplots were drawn using "SemiPar" package in R version 3.6.2. The GT-biplot was framed using "ggplot" package in R version 3.6.2. Such that PC1 scores are plotted against the PC2 scores for each genotype and treatmenttrait combination.

Results and Discussion

Response of traits to different treatments

The traits SPY, NOP and SMK% under control (T1) expressed the lowest mean, whereas at 30 DAS (T2), the same traits recorded higher mean value (Table 3, 15%, 20% and 10% over T1, T3, T4 and T5 respectively. From this study it was clear that, T2 treatment where gypsum was applied at 30 DAS was the best to increase the yield in groundnut. All gypsum applied treatments (T2, T3, T4 and T5) showed higher yield compared to control (T1), which is without gypsum application. Gypsum is a source of both calcium and sulphur, it triggers the pod yield in different ways compared to control. Calcium maintains cell membrane permeability, cell integrity, protein synthesis, activates enzymes of cell division and participates in carbohydrate transfer (Thor, 2019) [18] . Whereas sulphur is a major component of S containing amino acids, acetyl CO-A, proteins, vitamins, glutathione, chlorophyll and ferredoxin (Aarabi et al., 2020) [19] . The calcium and sulphur mechanisms probably activate the nutrient absorption, strong sink strength through development of reproductive structures, which cater to the production of assimilates under the influence of applied gypsum thus maintaining the balance between source-sink which might have resulted in increased yield attributes (Singh, 2007) [20] .

In this study application of gypsum at pegging stage (30 DAS) at the rate of 500 kg ha -1 outperforms compared to other treatments and similar type of results were also reported by several researchers. Application of gypsum at 500 kg ha -1 improved yield compared to 750 kg ha -1 and 1000 kg ha -1 (Sikhakhana, 2016) [21] , a linear response in yield was noticed when application of gypsum was increased from 200 to 500 kg ha -1 (Geethalakshmi & Lourduraj, 1998) [22] , application of 500 kg ha -1 of gypsum at pegging is beneficial compared to basal application and enhances yield (Kumaran et al.,, 2000 [23] ; Agasimani et al.,, 1992 [24] ) and similarly gypsum application improved pod yield and sound mature kernels in groundnut (Alva et al.,, 1989 [25] ; Rao et al.,, 1990 [26] ). Amending the soil with gypsum enhances the pod yield (Adams et al., 1993 [27] ; Sorenson and Butts, 2008 [28] ; Bairagi et al., 2017 [29] ), kernel yield (Murata, 2003 [30] ; Gashti et al., 2012 [31] ), number of pods per plant (Kabir et al., 2013 [32] ), sound mature kernels (Alva et al., 1989 [25] ; Rao et al., 1990 [26] ; Adams et al., 1993 [27] ; Murata, 2003 [30] ; Sorenson and Butts, 2008 [28] ), hundred pod weight (Kabir et al., 2013 [32] ), hundred kernel weight (Kamara et al., 2011) [33] .

Analysis of variance (ANOVA)

The results from two-way ANOVA revealed that there is a significant difference between all genotypes for all the nine traits studied (Table 4). The treatment main effect was observed significant for the traits SYP and NOP. Whereas, the interaction effect of genotype x treatment was reported significant for SYP, NOP, HPW and HKW. The ANOVA revealed significant genotypic differences for all the traits under study suggesting good genetic variability for these traits in groundnut. Significant treatment difference was observed for SPY and NOP which indicates the differential response of genotypes to the gypsum treatments, and factor is an important component of the observed variation. Other traits such as SPY, NOP, HPW and HKW had significant genotype x treatment interaction indicating different genotypic response to different fertilizer treatments.

Grey relationship analysis (GRA) to identify the best treatment:

To select the most effective gypsum treatment, the traits namely SPY, NOP, HPW and HKW from different treatments were used to estimate GRA. The relative correlation value which is close to 1 is considered the most optimal and the best treatment. The data from the Table 5 indicated that the best outcome was obtained from treatment 2 followed by treatment 3, 5 and 4 and the worst comprehensively estimated treatment was the control (T1). The value of relative correlation of the five treatments was in the range of 0.33 to 0.88, which revealed that SPY, NOP, HPW and HKW varied significantly with different gypsum treatments.

ICGV 00350 showed better performance for SMK; ICGV 05155 for SPY, KW, NOP, HPW, KL and PL and ICGV 16593 for PW and HKW. The genotypes ICGV 15083, ICGV 05155, ICGV 02266 and ICGV 06040 are projected close to the ideal genotype. One novelty of GT biplot approach is the identification of superior genotypes based on the combination of traits of interest in breeding program. Combination of important traits is meaningful compared to individual traits in selecting superior cultivar. The ideal genotype is a virtual genotype represented as circle on average coordinate axis (ACA) that shows high mean for all the traits considered for analysis. A genotype is considered more desirable if it occupied position near to the ideal genotype. It was observed that genotype ICGV 02266 was projected close to ideal genotype in all the treatments including control under this study. The other genotypes such as ICGV 05155 and 06040 were close to the ideal genotype in at least two treatments. The genotypes ICGV 15090 and 15083 fell close to the ideal genotype under control and at least one treatment.

Calcium response index

The calcium response index (CRI) was estimated using pod

Calcium content in kernels and shells

The calcium content in kernels and shells was estimated from T1 and T2 treatments among all the genotypes. The calcium concentration in kernels varied from 0.73 g kg -1 to 1.62 g kg -1 in low calcium condition (T1) and 0.78 g kg -1 to 1.58 g kg -1 in T2 condition. Whereas, in shells the calcium content varied from 4.17 g kg -1 to 8.25 g kg -1 under T1 and 4.26 g kg -1 to 9.31 g kg -1 in T2 condition. In the present study, the higher values of calcium concentration in the kernels were obtained for ICGV 00351 and ICGV 06040 in T1 and ICGVs 181003, 00350 and 06040 in T2 condition. Among the cultivars, ICGV 07222 and ICGV 93468 in T1 and ICGV 181003 and ICGV 00350 in T2 showed higher calcium concentration in shells. The calcium concentration in shells in T2 was higher than T1 except for five genotypes. Whereas calcium concentration in kernels is not in a line with gypsum application, only eleven genotypes showed higher calcium compared to control. The groundnut shells accumulated more calcium than kernels in both T1 and T2. From this study, it was observed that there is a difference in calcium concentration in shells and kernels among the genotypes. III JA et al., (2017) [59] also reported significant difference in seed calcium concentration among the groundnut cultivars. In contrast, Howe et al., (2012) [60] reported that calcium content in seed was increased with gypsum application rates. From this study it was clear that there is no improvement in calcium content in kernels and shells through gypsum application and it depends on absorption capacity of genotype and micro-environment around the pod zone during gypsum application.

Conclusion

Fertilizers like gypsum which includes both calcium and sulphur are very effective for groundnut's growth and quality. The results of the present experiment showed that applying calcium fertilizer in the form of gypsum impacted the yield in groundnut. It was demonstrated that applying calcium had significant effect on single plant yield and number of pods per plant. From this study, gypsum application at 30 DAS is identified as the best treatment and the genotype ICGV 02266 was observed to be the best. This genotype can be recommended to farmers for obtaining better yield under both calcium deficient soils and soils amended with calcium.

Acknowledgement

AcknowledgmentThis research was supported under the DST-SERB by the Department of Science and Technology -Science and Engineering Research Board, New Delhi.

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