Comparative Evaluation of Agronomic Performance of Selected Landraces And Improved Groundnuts Cultivars In Central Uganda

Groundnut (Arachis hypogaea L.) is important for providing food, income, livestock fodder for smallholder farmers, and improving soil fertility. However, groundnut yields on farmers’ elds in Sub-Saharan Africa are still very low due to various constraints. Several groundnut cultivars are available within the farmer’s domain but the adoption of these varieties favours landraces as opposed to improved varieties. Limited information is available on performance of commonly grown cultivars for guiding selection by different user groups (farmers, breeders and other users). This study was thus designed to determine the performance of selected landraces and improved groundnut varieties in Uganda. 23 groundnut lines with varying degrees of tolerance to a range of stresses were evaluated at two sites. Results showed signicant (P ≤ 0.05 to P ≤ 0.001) differences among genotypes for yield. Signicant (P ≤ 0.05 to P ≤ 0.001) varietal differences were also observed between landraces and improved varieties for growth and physiological traits but not for pest and disease reaction. Clustering was not evident on the basis of either landraces or improved cultivars for pests and diseases incidences. However, associations of a mix of both groundnut classes of with particular diseases and/or pests were observed. Varieties such as India, Serenut 10, Kabonge, and DOK Tan associated with diseases such as rosette virus disease and its vector pest, the aphids. The results reported in this study shall be useful for driving the development of new cultivars owing to their good adaptability and acceptance thus the need to conserve and ensure sustainable use of these germplasm.


Introduction
Groundnut (Arachis hypogaea L.) is an economically valuable oilseed and cash crop grown extensively in the semi-arid tropical regions of the world. It is cultivated for direct consumption as food and for industrial use. Production of the crop is concentrated primarily in the semi-arid tropical regions of Asia and Africa, which together account for over 96% of world groundnut area and 92% of total global groundnut output. In Uganda, groundnut is the second most important legume after common bean (Okello et al. 2010;. Besides being a major food crop, it also represents a signi cant source of income, thus contributing to improved livelihoods. Groundnut seeds contain 40-50 % high quality edible oil, 20-50% easily digestible protein and 10-20% carbohydrate depending on variety. Groundnut is also a nutritional source of vitamin E, niacin, falacin, calcium, phosphorous, magnesium, zinc, iron, ribo avin, thiamine and potassium (Savage and Keenan 1994). This makes groundnut an important source of nutrition in the country. In Uganda, groundnut is grown in diverse environments; and also utilized in various ways leading to diversity in variety preferences for this crop.
Most literature on seed supply systems for crop varieties often refers to seeds as either 'modern varieties' or 'landraces'. While a 'modern variety' is understood to be a variety that is improved by a formal breeding programme (Morris et al. 2003), released under a registered name and differing from other varieties by distinctive properties for which it is uniform and breeds true, a "landrace" is de ned as dynamic population of a cultivated plant with a historical origin, distinct identity, often genetically diverse and locally adapted, and associated with a set of farmers' practices of seed selection and eld management as well as with a farmers' knowledge base (Villa et al. 2005). The main contributions of landraces to plant breeding have been traits for adaptation to stressful environments such as water stress, salinity, low-input farming system, high temperatures and several biotic stresses such as pests and diseases ( Zeven 1998 Futakuchi et al. 2003) as well as useful genes for nutrient dense foods. As such, they act as useful starting materials for variety development strategies carried out at present and also for future use.
In addition, a more e cient use of plant genetic diversity has been identi ed as a prerequisite for meeting the challenges of development, food security and poverty alleviation (FAO 1996). Therefore, it is of great importance to conserve and maintain a broad base of germplasm because they constitute valuable genetic resources for multiple desirable traits within the country's groundnut gene pool.
In Uganda, groundnut production is characterized by resource-constrained small-scale farmers who cannot afford inputs such as improved seed and fertilisers and, therefore rely on fellow farmers, farmer groups, local markets, NGOs and research organisations for these inputs (Mugisha et al. 2014). Given the lack of capital resources, the farmers are left with no option but to resort to using traditional farming methods with low levels of mechanisation, and many a time with varieties which compromise yields of the crop despite the existence of a wide range of varietal choice ranging from well adapted, but not readily available traditional varieties (due to lack of entry point into the formal seed system) to a number of released varieties which have been improved for several traits.
Several pests and diseases attack groundnuts leading to reduction of yields and lowering the quality of produce hence increasing the cost of production. The major diseases of groundnuts include: Rosette caused by a complex of three viruses namely; Groundnut Rosette Virus (GRV), Groundnut Rosette Assistor Virus (GRAV) and satellite RNA (satRNA) transmitted by a single species of aphid (Aphis craccivora Koch); Early Leaf Spot (Cercospora arachidicola); Late Leaf Spot (Phaeoisariopis personata); Root Rot; Mosaic; Rust (Puccinia arachids) and a atoxin contamination (caused by Aspergillus niger and Aspergillus avus). The Groundnut Rosette Virus Disease (GRVD) is considered the most important constraint and can cause losses of up 100% if it occurs before owering (Okello et al. 2010;Nigam et al. 2012).
The important pests of groundnut are aphids (Aphis craccivora Koch), a vector of groundnut rosette disease, leaf miner (Aproarema modicella Deventer), thrips (Thrips palmi Karny, Frankiniella schultzie Trybom, Scirtothrips dorsalis Hood) and termites (Isoptera) (Okello et al. 2010;Okello et al. 2014). Thrips and aphids are considered more important as vectors of viruses than as causing direct damage to groundnuts. In addition, the Groundnut Leaf Miner, a Lepidopteran defoliator was reported as an emerging threat to groundnut production areas in Uganda, especially those areas prone to drought (Mukankusi et al. 2000;Okello et al. 2010).
As groundnut is grown in predominantly semi-arid regions characterized by erratic rainfall and on predominantly loose sandy loam soils, drought is often a recurring production constraint. This situation also coincides with the climate change phenomenon which projects disturbances such as a decrease in the lengths of the rainy seasons especially in semi-arid tropics (SAT) where most groundnuts are grown (Pasupuleti and Nigam 2013). In addition, drought has signi cant implications on groundnut quality which undermines the value of groundnut products on local, regional and international markets.
The foregoing constraints notwithstanding, the success of a plant breeding program is anchored upon its ability to provide farmers with genotypes with guaranteed superior performance (phenotype) in terms of yield and/or quality across a range of environmental conditions. To achieve this aim, it is necessary to have an understanding of the factors leading to a good phenotype. Generally, the phenotype is the value for a trait at the end of the growing season, e. g kernel weight at maturity, and is the cumulative result of a number of continuous interactions between the genetic make-up of the plant (the genotype) and the conditions or stimuli in which that plant developed (the environment) and this varies from plant to plant.
Environments differ in the amount and quality of inputs and stimuli that they convey to plants including, e.g., the amount of water, nutrients or incoming radiation. The primary objective in plant breeding is to match genotypes and environments in such a way that improved phenotypes are obtained. As quantitatively inherited trait, seed yield performance of a genotype often varies from one environment to another, leading to a signi cant genotype by environment (GxE) interaction which can severely limit gain of selecting superior genotypes. Understanding the interaction of those factors and how they affect seed yield is crucial for maintaining high yield (David et al. 2016). It is acknowledged that there can be genotypes that do well across a wide range of conditions (widely adapted genotypes), but there are also genotypes that do relatively better than others exclusively under a restricted set of conditions (speci cally adapted genotypes). Speci c adaptation of genotypes is closely related to the phenomenon of genotypeby environment interaction (GEI). GEI exists whenever the relative phenotypic performance of genotypes depends on the environment, or in other words, when the difference in reactions of genotypes varies with the environment. Some scenarios that can occur when comparing the performances of pairs of genotypes across environments are presented in Fig. 1. The function describing the phenotypic performance of a genotype in relation to an environmental characterization is called the "norm of reaction" (Gri thsetal.1996). Figure 1A shows the case where there is no GEI, the genotype and the environment behave additively and the reaction norms are parallel. The remaining plots show different situations in which GEI occurs: divergence (Fig. 1B), convergence (Fig. 1C), and the most critical one, cross over interaction (Fig. 1D). Cross over interactions are the most important for breeders as they imply that the choice of the best genotype is determined by the environment. Therefore, given the complexity of the mechanisms and processes underlying the phenotypic response across diverse and changing environmental conditions, several analytical tools have been developed to help breeders understand GEI (Yates and Cochran 1938; Finlay and Wil-kinson 1963; Eberhart and Russell 1966;Gauch 1988;Singh et al. 1996b) among others. The use of adequate strategies to analyze GEI is a rst and important step toward more informed breeding decisions but details of how these tools are used is beyond the scope of this study. However, in nding workable solutions to limited information on performance of commonly grown groundnut landraces and improved varieties for guiding selection of groundnut varieties by different user groups (farmers, breeders and other users), the speci c objectives of this study therefore were; (i) to interpret genotype (G), Class (Landrace and improved varieties) and Site (E) main effects and GE interaction for growth, pests and disease reaction, and yield performances of 23 groundnut genotypes evaluated in two growing sites (ii) Explore the nature of GE interaction and suggest strategies for exploiting it for improved targeting of varieties to different growing sites.

Site description
The study was carried out in the Central Wooded Savanna ecological zone of Uganda, in Nakaseke (0°43'29" N, 32° 54'04" E) and Nakasongola (1°18'32" N, 32° 27'23"E) districts. In Nakasongola, the annual daily temperatures range from 18 o C to 35 o C, with mean daily maximum of 30°C. Rainfall ranges between 500 to 1000 mm per annum and there are two rain seasons. The vegetation in the study site mainly comprises three vegetation cover types depending on the extent of anthropogenic activities/disturbance on speci c ranch sites. The three vegetation cover types include dense vegetation cover (>50% basal cover), sparse vegetation cover (25 to 50% basal cover) and bare ground. The area is characterized by prolonged droughts and oods due to shifting rainfall pattern (Nimusiima et al. 2013). Hitherto dominated by livestock grazing, the area is increasingly changing in land use, with crop farming especially for maize production becoming common. The altitudinal range is 600-1160msl.
Nakaseke site has traditionally been described as the coffee-banana farming system. This area falls within an altitudinal range of 1086-1280 masl, with mean annual rainfall of up to 1100 mm. The annual daily temperatures range from 16 •C to 30 •C.
Bi-modal rainfall distribution characterizes the two districts with the rst rainy season extending from March to June, while the second rainy season starts in late August or early September to November-December (Ogwang et al.2016). The main rain season occurs from March-April to June July while the second rain season follows from August to October November. A long dry season occurs from December to February while a short spell comes around July-August.
Trial gardens were established in two districts of Nakasongola and Nakaseke, each district taken as a site. In each site replication was done in four villages. In Nakaseke trial gardens were established in the villages of Kiziba, Namirali, Kalagala and Kyamutakasa while in Nakasongola they were established in Naitondo, Kasambya, Kalobokwe and Kiralamba (

Data collection
Data was taken on common groundnut diseases and pests. The diseases included; Groundnut Rosette, Leaf Spot, Root-rot, and Groundnut Mosaic while the pests included aphids and leaf minor. Disease and pest incidences were assessed (at 1, 2 and 3 months after planting by counting the number of plants affected per variety (NaSARRI, unpublished). In addition, plant growth performance was assessed by measuring plant height while germination uniformity was scored 2 weeks from germination (Wood and Roper, 2000). Using a scale of 1 to 4, where 1= poor and 4 = very good performance. Stay green as a measure of drought tolerance was also evaluated using a scale of 1 to 4 which were subsequently

Leaf miner and aphids
Observations on leaf miner incidence and defoliator damage were recorded regularly at 15-days interval.
Observations were made on top ve leaves of ve randomly selected plants in each replication for number of lea ets damaged by leaf miner and extent of defoliation by defoliators. From these observations, per cent incidence of leaf miner and per cent defoliation were calculated.
Agronomic data Data on selected agronomic traits were collected on plot basis. Yield data were recorded from ve middle rows, excluding plants at the end of rows for each of the variety, based on number of pods per plot. All plants were clipped at the soil surface, the pods were dug up and pods were then detached, bulked together and counted.

Stay green trait
Starting from pod initiation to physiological maturity, visual scoring for stay-green were carried out at two-weeks intervals. The stay-green characteristic of the genotypes was scored on a scale of 1 to 5 based on the proportion of the total leaf area that had senesced with 1 being no leaf senescence and 5 completely senesced plant (Xu et. 2000). The stay green scores were used to compute the leaf area under greenness (LAUG) values (Joshi et al. 2017).

Data analysis
Data for each variety was summarised using descriptive statistics with means presented with respective standard error of the means. All variables were tested for normality using Shapiro-Wilk test and the strongly skewed variables were transformed prior to analyses of variance where necessary, to meet the assumption of normality and homogeneity of variances. Variables expressed as percentages (%) were arcsine-square-root (+0.5) transformed, while counts of individuals were log (log (x +1)) transformed. Where transformation was not su cient to improve data shape, an appropriate non-parametric test was applied. The differences among varieties in yield performance was compared using analysis of variance (ANOVA), with post-hoc means separation tested using Tukey (HSD) at 5% probability level. Differences in medians of germination rate (%), growth (%), pest and disease incidence, and drought tolerance among varieties were compared using Kruskal-Wallis test, with Mann-Whitney post-hoc medians separation at 5% probability level. Site and variety interactions in growth performance, pest, and disease and drought tolerance were tested with General Linear Model (GLM) two-way analysis of variance (ANOVA). Where the GLM test indicated significant differences, post-hoc Tukey (HSD) test was used. To assess similarity among varieties, hierarchical cluster analysis using Bray-Curtis distance measure was used to depict variety performance similarity with dendrogram. Correspondence analysis ordination with symmetric scaling was used to assess associations between pest and diseases, and the various varieties. All the tests were done using PAST software (Oyvind 2002).

Evaluation of the groundnut cultivars for yield performance
The results of the evaluation of the genotypic variation among the groundnut genotypes are presented in Table 1. The one-way ANOVA showed large and signi cant (P ≤ 0.05 to P ≤ 0.001) mean squares (MS) for differences between groundnut lines for yield performance. The yield of these varieties per se varied from 1,575,284 (Muddugavu) to 4545201(Erudu Red) pods per hectare, with an average yield of 2,551,235 pods/hectare, regardless of the class of groundnut variety evaluated.
The combined analysis for varieties tested over two growing sites for yield showed non-signi cant (P > 0.05) MS for variation between classes of groundnuts (i.e., between the landraces and improved varieties ( Table 2 and Table 4). Moreover, the yield of these varieties varied from 1,940,111 (DOK Red) to 3,143,422 (Serenut 12 R) and from 1,575,284 (Muddugavu) to 4,545,201(Erudu Red) pods per hectare, with an average yield of 2,494,851 and 2,612,317 pods/hectare for improved and landraces, respectively.  The results of U-test for difference in reaction to pests and diseases by improved and landrace groundnuts lines are presented in Fig. 1. Although improved varieties showed better resistance to pests and diseases (Fig. 1), the difference between landrace and improved varieties was not signi cant (P > 0.05).
Results of association of diseases and pests with test varieties, using correspondence ordination analysis with symmetric scaling explained by principal components accounting for 65 % of total variation among the accessions, showed that the most resistant genotypes to virus diseases by graphical positioning were Gabon, Serenut 5, Serenut 14, Serenut 5, Serenut 12, Ogwara, Otira and Egoromoit ( Fig. 2  and Fig. 3). However, all the varieties were tolerant to leafspot diseases owing to the position and separation between varieties and leaf spot diseases. Moreover, varieties such as India, Serenut 8, Serenut 6, Serenut 10, Kabonge, Mudugavu, DOR red, DOK clustered with diseases (Yellow Mosaic and Green Rosette) and their vector pests such as aphids (Fig. 3). However, their positioning closer to the origin and being far away from the particular diseases or pests shows they were tolerant to the diseases or pests.
These correspondence ordination with symmetrical scaling for association of pests, diseases and varieties results were also supported by those from hierarchical clustering (Fig. 3). Generally, the genetic distances between varieties were small which can be owed to low genetic diversity in the set of varieties used in this study. Moreover, there were no speci c clusters for pests and diseases of reaction for landraces or improved varieties but rather a mixture of both landraces and improved varieties ( Fig. 2 and  Fig. 3). In relation to reaction of groundnut accessions to aphids and Rosette virus disease, three clusters (I, II & III) could be identi ed as resistant, tolerant and susceptible, respectively (Fig. 3).

Evaluation of growth and yield performance of groundnuts at the two study sites
Results of combined analysis for growth and yield over two growing locations are presented in Table 4 and Fig. 4. Signi cant (P < 0.05 P < 0.001) MS for drought tolerance and germination but non-signi cant (P > 0.05) MS for yield and growth were observed for variation due to class of genotype. Signi cant (P < 0.05 to P < 0.001) MS due to variation in the locations (sites) were observed for all traits. The interactions MS between class and site were also signi cant (P < 0.05 to P < 0.001) for all the traits. Graphical representation of mean yield performance of improved and landraces at different growing sites is presented in Fig. 4. The interaction between site and mean genotypic performance for traits can be described as qualitative (crossover) for the case of yield and quantitative (non-crossover) for other traits (growth, drought tolerance and germination (Fig. 4).

Discussion
Comparison of yield performance of selected improved groundnut varieties and landraces

Comparison of germination performance of selected improved and landraces
A higher germination of seeds depends on availability of favourable environmental factors like adequate temperature, light, salinity, moisture, water (Cokkizgin 2012) and presence of seed borne pathogens (Ahmed et al. 2017). In this study, landraces performed better than improved varieties when judged in terms of germination. The difference in germination between these classes of groundnut could be related to adaptability of these lines to growing environments. This observation is supported by that of Salasya Moreover, Salasya et al. (2007) reported that improved groundnut varieties took longer days to emerge and ower due to poor adaptation to the environment, but the landrace grew better because they were well-adapted to the environment.

Comparison of drought tolerance level of selected improved groundnut varieties and landraces
Stay green is an important trait that allows plants to retain their leaves in an active photosynthetic state when exposed to stress conditions (Thomas and Howarth 2000). Stay-greenness has been used as a criterion for selecting stay-green genotypes in various crops (Christopher et  Signi cance of GE has serious implications for deployment of varieties (Kang 1993). While non-crossover GE can be exploited by improving the growing environment, the crossover GE calls for selection and deployment of speci cally adapted varieties. Cross-over GE was only observed for yield implying that high performers from both improved and landraces could be speci cally deployed in the respective sites.
On the other hand, non-cross over GE could be overcome by modifying the production sites. Related to this recommendation, biophysical description of the two sites indicates that although Nakaseke site has better conditions for crop growth, Nakasongola has the largest share of locations suitable for groundnut production; which also con rms the inherent drought tolerant nature of this crop (Erickson and Ketring 1985;Reddy et al. 2003). Therefore, strategies such as irrigation, use of conservation agriculture, and adoption of drought tolerant varieties could boost performance of both improved varieties and landraces in Nakasongola.

Conclusion
The current study aimed to explore the presence of genetic and geographic structures in a collection of groundnuts representing the existing variability for this species in the Ugandan gene pool. There were no clear structures for either landraces or improved varieties with respect to the traits investigated. The differences or similarities observed could largely be explained by the genetic pools managed by the breeding programmes operating in the country and those in the hands of farmers coupled with differences in the target growing environments. The results reported in the current study may be used to facilitate development of new cultivars building on inherent strengths of both well adapted landraces and improved varieties with desirable traits thus the need to conserve these varieties for sustainable use.

Declarations Con icts of Interest/competing interests
The authors declare no con icts of interest. The sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Data Availability of data and materials
Data used to support these ndings can be sourced from the corresponding author. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Code Availability
The analysis code can be accessed from corresponding author  Hierarchical clustering using distance matrices showing similarities between varieties in relation to aphids and Rosette virus disease reaction