Evaluation of Groundnut (Arachis hypogaea) Cultivars for Destabilized Ecosystem of North Eastern Hill Region

Groundnut (Arachis hypogaea L.), an essential food and important oilseed crop is mostly grown by small and marginal farmers in diverse agro-climatic environments of Asia and Africa (FAOSTAT, 2014). Globally, groundnut is grown in 25.0 m ha area with an average productivity of 1.7 t ha^-1, amounting to a total production of 42.0 MT (Singh et al., 2018). India is the second largest producer of groundnut, only next to China in the world. In the Eastern Himalayan Region (EHR) of India, groundnut is gaining popularity as an important food crop in the acidic upland hilly soils, which were once under the cultivation of low productive traditional cereals (rice/maize) (Singh et al., 2003).
       
The degraded uplond soils from short fallow Jhuming in the hilly ecosystems of Eastern Himalaya are vulnerable to acidity induced nutrient stress, moisture stress and severely prone to water erosion. Despite receiving 8-10 times more rainfall than the consumptive water use, high runoff loss in the Jhum degraded uplands and low utilization efficiency (<30%) often causes intermittent to terminal soil moisture stresses (Choudhury et al., 2013). Extensive use of local low yielding cultivars susceptible to abiotic stresses by the predominant marginal tribal farmers further reduced the productivity to sub-optimal level (<1.0 t/ha) (Singh et al., 2003; Datta et al., 2016; Ansari et al., 2017). To sustain the food requirement of burgeoning population in the fragile hilly rainfed uplands, improvement in agricultural productivity through adoption of improved crop cultivars suitable to targeted environments is the need of the hour (Singh et al., 2008; 2014). Diversification of cropping pattern by inclusion of oilseed crops like groundnut in the existing cropping pattern (rice/maize) in the degraded slopy uplands assures better nutritional security by providing vegetable proteins and edible oils (Singh et al., 2006; Das et al., 2016). Groundnut crop also fixes atmospheric nitrogen (N₂) in soil, reduces soil susceptibility to erosion, conserve soil moisture by smothering weed growth and thus, improves the overall soil quality (Singh et al., 2004; Konlan et al., 2013). The by-products from groundnut also can be used as feed and fodder for livestock.
       
The potential of groundnut cultivation in the Jhum degraded acid soils of hilly ecosystem of Eastern Himalaya cannot be explored fully without adequate information on suitable cultivars with adaptive traits. In the present study an attempt was made to identify cultivars with such adaptive agronomic and physiological traits associated with high production efficiency, suitable to degraded soils of rainfed hilly ecosystem of Eastern Himalaya.

Experimental site
 
The experiment was conducted for four consecutive years (2013-2016) in the uplands (Jhum degraded) at Langol farm of ICAR Research Complex for NEH Region, Manipur Centre, Imphal, India (24°49' N latitude, 93°55' E longitude and 786 m above MSL altitude) (Fig 1). Experimental site falls under humid sub-tropical climate. The mean monthly minimum and maximum temperatures during the study period (2013-2016) varied widely from 18.6°C to 32.8°C while mean annual rainfall (May to October) varied from 818.7 mm (in 2013) to 1852 mm (in 2016) (Fig 2). Monthly-average sunshine hours varied from 2.6 to 5.8 hrs while average relative humidity varied from 62 to 93%. The soils of the experimental site was sandy clay loam (sand 52.2%, silt 14.6% and clay 33.2%) in texture, acidic in reaction (pH 4.9), high in organic carbon (Walkley and Black, 1.51%), low in available nitrogen (alkaline permanganate N, 185.5 kg ha^-1), available phosphorus (Bray I P, 8.1 kg ha^-1) and available K (neutral normal ammonium acetate K, 115.5 kg ha^-1) contents.
 
Experimental design and treatment details
 
Experiment was laid out in randomized block design with twenty six improved groundnut cultivars along with one additional popular cultivar JL-24 as local check and replicated thrice. These twenty six cultivars belonged to two major groups viz. 9 early maturing cultivars, 17 medium to late maturing cultivars. Uniform plant population density (3.33 × 10⁵ plants ha^-1) was maintained and the performance of these cultivars was evaluated for four consecutive years (2013 to 2016) during Kharif season. The crop was sown to a depth of about 5 cm during first week of May and harvested during September to mid October. The experiment was well managed and kept weeds, diseases and insect pests’ free by following the recommended package of practices from planting till harvest. The recommended doses of fertilizers were applied as 20-40-40 (N, P, K) kg ha^-1 in the form of nitrogen from urea, phosphorus (P₂O₅) from single super phosphate and potassium (K₂O) from murate of potash, respectively (Singh et al., 2006).
       
Five plants from each plot were tagged at randomly from the sampling row and height of these plants was recorded from the base to the tip of the last fully opened leaf of the plant at maturity stage. Nodule plant^-1 was observed at 50 days after sowing. Five random plants from each plot were dug out by breaking the rhizospheric soils around the plants up to a depth of 50 cm with a hand hoe. Care was taken so that the root system was not disturbed during the process. The plants were then pulled out gently and put in polyethylene bags. In the laboratory, samples were kept in sieves (mesh size 0.25 mm) and washed with water to remove the soil particles. The nodules on the roots were separated and those that broke off during the course of washing were also picked up for final count.
       
Observations on number of pegs plant^-1, effective pods plant^-1, pod weight plant^-1 and kernel (seewd) weight plant^-1 were recorded at maturity stage from 5 randomly selected plants in each plot. Grain weight of each genotype was harvested separately and further shelled groundnut, 100 seeds weight (g) of each cultivar from each replication was measured. The harvests from 1 m² area for each genotype were threshed separately. The production efficiency was estimated using the following equations:

 
 ..................(1)

 
 

 
Shelling per cent was calculated by the following formula:

 
 ................(2)

 
Economics analysis
 
The gross return was calculated by multiplying the dry pod yield (economic yield) of groundnut with their minimum support price (MSP, 2016) fixed by Government of the India. The net return was worked out by subtracting the cost of cultivation from the gross return. The benefit: cost ratio (B: C ratio) was computed from the ratio of gross return and cost of cultivation.
 
Net returns (US $ ha^-1) =

                 Gross returns - Cost of cultivation          ........(3)

 
       ................(4)

 
 
Statistical analysis
 
The agronomical data were analysed of randomized block design (RBD) in SPSS v.20 software. Statistical significance was set at an alpha level of 0.05. Means were compared by the least significant difference (LSD) test if the f-value was significant. The clustering of the 27 cultivars was done based on average linkage between the groups and the proximity matrix using squared Euchlidean distance (SPSS v.20 software).

Genotypic variations in agronomical attributes
 
The plant height at maturity varied widely across the improved cultivars with the mean values ranging from 32.2 cm (GG-21) to 55.8 cm (OG-52-1) (Table 1). The height of the check cultivar (JL-24) was also comparable (41.4 cm) with the improved cultivars. The variation of groundnut cultivars had a significant (p<0.05) influence on all yield attributes viz., pegs plant^-1, effective pods plant^-1, pod and grain weight per plant exhibiting among the cultivars (Table 1). The production of number of pegs per plant and effective pods per peg were maximum in ICGS-76 (24.3) followed by ICGV-86590 (23.3) and GG-21 (20.0). Of the  27 cultivars, only few cultivars could able to produce more than >75% of total pods filled with grains while many others had only 63-65% filled pods. Highest pod weight was recorded in ICGS-76 and ICGV-86590 compared to the local check JL 24. ICGS -76 and ICGV-86590 cultivars registered a significant improvement of 61.09 % and 39.36 % over the local check JL 24 respectively. The grain weight per unit area and 100 seed weight is the major yield attributing characters also exhibited variation among the cultivars. Few improved cultivars such as ICGS-76 and ICG-86590 produced significantly (p<0.05) higher amount of grain per unit area compared to the remaining cultivars. ICGS-76 and ICG-86590 recorded an improvement of 87.67 % and 63.47%, respectively for grain weight per unit area over the local check JL-24.
       
Significant positive correlation (r= 0.90-0.98*, p<0.005) among these three yield attributes (effective pods per plant, pod and grain weight) with dry pod yield also affirmed it.  Phakamas et al., (2008) also reported the significant effect of major yield attributing traits of groundnut on yield. Crop yield is an integrated result of various processes, including canopy photosynthesis, conversion of assimilates to biomass and partitioning of assimilates to grain and higher yield attributing traits (Datta et al., 2016; Frimpong et al., 2017; Singh et al., 2018). However, the efficiency of conversion of assimilates into pod or grain vary with genetic make-up of the cultivars coupled with its suitability of the cultivars to the growing environment (Nautiyal et al., 2012, Singh et al., 2014; Singh et al., 2018).
       
Active nodule (pink or red in colour) count in roots varied widely among the cultivars and it ranged from 10.9 in SG-99 to 33.3 per plant in ICGS-76 at 50 DAS (Table 1). The highest nodulating improved cultivers ICGS-76 recorded 2.2-fold increase in nodulation over local check (JL-24: 15.2 plant^-1). Variation is observed in nodule formation among groundnut cultivars. This variation is primarily due to differences in emergence of auxiliary root hairs on lateral roots (Tajima et al., 2008) and similar observation has been made in this present study also.
 
Genotypic variation in pod yield and economics
 
The wide variability among the 27 cultivars was studied across seasons from 2013 to 2016 (Table 2). Wide variation was observed among the cultivars for dry pod yield per hectare. ICGS-76 and ICGV-86590 produced more than 39% higher pod yield and ICGS-5, TKG-19 A, TG-37-A, GG-11 and GG-21 gave 19-38%  significantly (p<0.05) higher pod yield over the check JL 24 across years. Similarly, mean shelling percentage of the pods among the cultivars ranged from 45.1 (BG-3) to 72.6% (ICG-86590). Only two cultivars viz., ICGS-76 and ICGV-86590 could register a mean improvement of more than 15% over the check JL-24 for shelling percentage across years. Mean production efficiency (PE), an estimated parameter from the productivity  (grain) per unit area and crop duration also varied widely among the cultivars. The medium duration cultivars ICGS-76 with maximum pod yield obviously recorded the highest PE followed by early duration ICGV-86590 with second highest pod yield. The cultivars TKG-19-A also produced significantly higher yield with better shelling percentage and PE than other cultivars as compared to the local check JL-24. Differential maturity groups (early and medium to late) among the cultivars marginally influenced the pod yield as evident from group average values in early (2.38 t ha^-1) groups as against the medium to late maturing group (2.23 t ha^-1). Even with shorter crop growth period in majority of the early duration cultivars, production efficiency (PE) did not increase since low pod yield produced by these cultivars offset the gain in early crop duration. Differences in pod yield among the cultivars are the major factor causing variation in net return (437- 1569 $ ha^-1) and benefit: cost ratio (2.0 - 4.5) among the cultivars (Table 2). 12 cultivars ICGS-76, ICGV-86590, ICGS-5, TG 37 A, GG21, GG 11, TKG 19 A, GG 8, SG 99, OG 52-1, GG-16 and GG-20 showed significantly higher yield than JL-24 as the check cultivar. The higher pod yield in ICGS-76, ICGV-86590 and TKG-19-A and net return as estimated from the gross return after deducting cost of cultivation and benefit : cost ratio were significantly (p<0.05) higher than the other improved cultivars. Among the 27 test cultivars, ICGS-76 and ICG-86590 were superior for yield attributing traits recording significant improvement over the check JL-24.
       
Shelling percentage is mostly influenced by the pod size, volume, kernel weight of cultivars as well as availability of calcium (Ca) in the soil (Misra et al., 2000; Misra, 2004; Singh et al., 2018). In (strong acidic) soils, exchangeable Ca²⁺⁺ availability was 1.5 meq 100^-1 g soil, which was just above the critical threshold limits (Ca++1.0 meq 100^-1 g soil) for the acid soils of the region (Patiram, 2007). Therefore, the wide variability in shelling percentage among the cultivars was mostly contributed by the differences among the cultivars for pod characters including grain yield per pod. The two high yielding cultivars ICGS-76 and ICGV-86590 recorded significantly (higher number of effective pods over others including local check. A strong positive correlation of these pod attributes with shelling percentage (r= 0.738 - 0.921*, p<0.0005) and higher shelling percentage of more than 72% in these two cultivars further affirmed our assumption was in confirmation with the earlier reports (Jnr et al., 2017). Variation in production efficiency was highly influenced by the dry pod yield (per ha) produced by the respective cultivars and was evident from a strong positive correlation (r=0.94, p<0.0005) of production efficiency (PE) with dry pod yield. Since all the cultivars were grown under similar management practices including variable cost (cost of human labour, seed and sowing, crop management, seed material and fertilizers), the variation in the estimated net return as well as benefit cost ratio was due to the pod yield/ha of the respective cultivars. It was also evident from the strong positive correlation of dry pod yield with net returns (r= 0.96*, p<0.0005) and benefit cost ratio (r= 0.97*, p<0.0005). As a result, higher dry pod yield producing cultivars namely ICGS-76 and ICGV-86590 is expected to improve the net income to a tune of 59-93% over local check (JL-24) while increasing the benefit: cost ratio by 39-60%. Similar observations of higher net return on adoption of improved cultivars of groundnut were reported by other workers in other porots of the country. (Singh et al., 2006; Datta et al., 2016).
 
Cluster analysis
 
The clustering based on average linkage between the groups and dissimilarity matrix using squared euchlidean distance for dry pod yield ha^-1, grouped the 27 cultivars in two main clusters (MC-1 and MC-2). The MC-2 cluster comprised of only one genotype, that is, ICGS-76 which out-yielded (3.35 t ha^-1) other cultivars. The remaining 26 cultivars in MC-1 cluster were grouped under two distinct sub-clusters (SC-A with 20 cultivars and SC-B with 6 cultivars). The SC-A was again represented by two sub-sub-clusters (SSC-1A with 18 cultivars and SSC-2A with 2 cultivars). The SSC-2A comprised of two high yielding cultivars viz., TKG-19-A (2.72 t ha^-1) and ICGV-86590 (2.90 t ha^-1); while 18 cultivars in SSC-1A represented the medium yielding group with dry pod yield ranging from 2.03 to 2.56 t ha^-1. The SC-B comprised six low yielding cultivars represented in two sub-sub-clusters viz. SSC-1B (B-95, G-3 and Tirupati-4) and SSC-2B (ICGV-88448, HNG-69 and GG-14). Among the cultivars, maximum dissimilarity matrix of 3.572 was recorded between B-95 and ICGS-76, followed by BG-3 and ICGS-76 (3.349). These cultivars from divergent clusters would serve as appropriate parental lines for attaining highest genetic advance in respect of dry pod yield in groundnut.

High yielding cultivars such as ICGS-76, ICGV-86590, TKG-19-A, ICGS-5, TG 37 A, GG-21 and GG-11 have greater adaptability in the degraded strong acid soils of hilly ecosystem of Eastern Himalaya. Better yield attributing traits of these cultivars helped them to withstand multi-ferrous abiotic stresses (soil degradation from Jhuming, acidity) and thus, they could produce up to 60.3% higher mean dry pod yield over local check under similar agro-ecosystem management. Adoption of these cultivars will better productivity and profitability with higher net return without any extra cost to the existing production system. This may also offer a viable alternative to the existing low profit cereal based cropping systems (rice/maize-fallow) in the region, there by improving sub-optimal cropping intensity (<150%) and area diversification in the Jhum degraded acidic soils of rainfed hilly ecosystem.

The authors are thankful to the Director, ICAR-RC for NEH Region, Meghalaya, India for funding assistance. The authors express their sincere gratitude to Director, Directorate of Groundnut Research, Junagadh, Gujarat for providing the groundnut germplasm for current study under AICRP.