Rice (Oryza sativa L.) which was originated from the tropics and subtropics is widely cultivated in diverse environments. The tremendous growth of human population worldwide has increased the demand for rice production , requiring an improvement of 50% by the year 2025 . Due to its origin in tropical and subtropical regions, rice is more sensitive to cold stress than other cereals crop such as Wheat and Barley. Therefore, the production of rice is severely limited by cold stress in temperate areas . Cold stress is the major factor affecting rice growth, productivity, its distribution worldwide . Production of rice is affected primary due to its vulnerability to cold stress at seedling stage, as well as reproductive stage leading to spikelet sterility.
In Africa, rice also constantly increasing as staple food and there has been increasing demand in Africa in the past three decades from 1999-2018 ; however, these demands have not been commensurate with the total production and most of African countries are net importer of milled rice, which costs 6.4 billion USD annually . Although rice has recently introduced to Ethiopia, recognizing its importance as a food security crop and a source of income and employment opportunity . The production, productivity and consumption of rice in Ethiopia is constantly increasing in the country . Ethiopia’s geography is noticeable by immense depressions and mountains. Consequently vast arable lands are located at high altitudes more than 2000 meter above sea level. Rice can grow in wide agro climatic zones; however, low temperature stresses are serious challenges for rice farmers at high elevations in the tropics. Lack of cold tolerant rice varieties in the high lands of Ethiopia is the main constraints for the promotion of rice. Therefore, the main objectives of these studies were to evaluate the performance and stability of introduced lowland greed super rice genotypes for their wider or specific recommendation in the North-West Ethiopia and similar agro ecologies.
Materials and Methods
Two sets of low land Green Super Rice (GSR) experiment were conducted. For the first experiment, a total of 16 genotypes (Table 1) were introduced from China/CAAS in 2014 with major objectives of cold tolerance and high grain yield whereas 40 GSR genotypes (Table 2) were introduced from IRRI in 2014 for the second experiment with major objective of high grain yield. Laboratory and field quarantine/observation for both sets of experiment were conducted in Holeta agricultural research center and Andasa, respectively in 2014 main cropping season. Both sets of preliminary variety trials were conducted in 2015 in woreta on station. For the second experiment, 36 genotypes including one check were planted again as preliminary variety trial in a place where temperature and humidity are high (Pawe). The plot size and type of design for observation and preliminary variety trial are not standard and vary based on the amount of seed and experimental area.
The experiment was conducted in Pawe and Assosa from 2016-2018. The locations are where the trials conducted differ in soil type, annual rain fall, altitude and annual temperature based on the experiments nature (Table 1). A total of 20 genotypes including one check were used (Table 2). The trial was laid out in randomized complete block design with three replications for both sets of experiments in all locations. Each plot had a size of 7.5 m2 (Six rows with 5 m long with 0.25 m row spacing). Seed rate of 60 kg/ha was used and direct seeding methods in a row was applied. Fertilizer (UREA and DAP) were applied based on each location recommendation. All DAP was applied at the time of sowing. For UREA, split application was applied; 1/3 at sowing, 1/3 at active tillering and the remaining 1/3 during panicle initiations. Other agronomic practices were applied according to each location recommendations. The data were subjected to the GLM procedure for analysis of variance using SAS software V.9.0. And Genotype x environment and stability analysis were done by using Genstat 18th edition software.
Table 1: Description of study environment for Lowland GSR experiment
Table 2: List of genotypes used for national variety trial
Result and Discussion
Combined analysis of variance
The combined analysis of variance for grain yield, days to maturity, days to heading, plant height and thousand grains weight showed significant difference (P≤0.01); panicle length revealed significant different (P≤0.05). There was no significant difference observed among tested genotypes on filled grain per panicle. The analysis of environment effect also revealed significant difference (P≤0.01) for grain yield and other agronomic characters. Except panicle length and filled grain per panicle, the three way interaction of genotypes x environment x years were showed significant variation (P≤0.01) for yield and other agronomic characters (table 8).
The significant interaction difference of the three way interaction of genotype x environment x years revealed that the possibility of getting genotypes which can be adapted widely/or specifically. As indicated (table 8), the mean grain yield of the 20 lowland green super rice genotypes ranged from 2730.30 kgha-1 (G7) to 3683.40 kg ha-1 (G13) with the mean grain yield of 3310.04 kgha-1. Compared to the standard check (G20), seven genotypes (G13, G14, G1, G12, G15, G4, G8,) were statistically high yielder than the check. However there is no genotype showed significant difference than the standard check on grain yield. This revealed that the tested genotypes failed to give high grain yield than the standard check (KOMBOKA) and there is no genotype for wider adaptability. Even though all tested genotypes failed on combined analysis for both locations, the separate location combined analysis revealed that there is a significant difference on grain yield and other agronomic characteristics than the standard check.
Table 3: Mean grain yield and other yield related parameters of 20 lowland green super rice genotypes at Assosa and Pawe over three years (2016-2018)Note: *, **, and *** refers to significant at 5%, 1% and 0.1% level, NS=non -significant, CV= coefficient of variation, LSD= list significance different, G*E= genotype by environment, G*E*Y= genotype by environment by year, DH= days to 50% heading, DM= days to 85% maturity, FGP= filled grains/panicle, PH= plant height (cm), PL= panicle length (cm) and Gykgha= grain yield (kg/ha)
In Assosa, the three years combined grain yield ranged from1872.5 kgha-1 to 3809.7kgha-1 and mean grain yield of 2836.23kgha-1. Seven genotypes (G13, G12, G6, G4, G3, G15 and G8) showed statistically higher grain yield than the standard check (table 9). Only one genotype (G13) showed significant different than the check and gave grain yield advantage of 26.7% than the check.
Table 4: Mean grain yield and other yield related parameters of 20 lowland green super rice genotypes at Assosa over three years (2016-2018)Note: *, **, and *** refers to significant at 5%, 1% and 0.1% level, NS=non -significant, CV= coefficient of variation, LSD= list significance different, G*E= genotype by environment, DH= days to 50% heading, DM= days to 85% maturity, FGP= filled grains/panicle, PH= plant height (cm), PL= panicle length (cm), TGW=thousand grain weight and Gykgha= grain yield (kg/ha)
In Pawe, The combined analysis of variance (table 10) for grain yield, days to maturity, days to heading and plant height showed significant difference (P≤0.01); thousand grain weights revealed significant different (P≤0.05). There was no significant difference observed among tested genotypes on fertile tillers per plant and fertile grain per panicle. Except plant height (P≤0.01) and grain yield (P≤0.05), there is no genotype by year interaction significant difference. The mean grain yield ranges from 3416.7 kgha-1to 4545.8 kgha-1. Two genotypes (G14 and G1) showed significant different than the check and gave yield advantage of 21.7% and 20.6%, respectively (table 10).
Table 5: Mean grain yield and other yield related parameters of 20 lowland green super rice genotypes at Pawe over three years (2016-2018)Note: *, **, and *** refers to significant at 5%, 1% and 0.1% level, NS=non -significant, CV= coefficient of variation, LSD= list significance different, G*E= genotype by environment, DH= days to 50% heading, DM= days to 85% maturity, FGP= filled grains/panicle, PH= plant height (cm), PL= panicle length (cm), TGW=thousand grain weight and Gykgha= grain yield (kg/ha)
Polygon view of GGE biplot analysis
The polygon view of GGE biplot (Fig1) is the best way for the identification which-won-where by visualizing the interaction patterns between genotypes and environments  . In this GGE biplot, the vertex genotypes were G13, G14, G19, G16, G7 and G6 having the largest distance from the origin. In the present study, seven rays in Fig.1, the biplot was divided into seven sectors and the environments only into three of them. The rest four sectors in the polygon had no test environment. The vertex genotypes (G13 and G14) were the highest yielding genotypes in E5 and E3, respectively. G6, G10, G9 and G18 were not adapted to any of the tested environments. Both discriminating ability and representativeness view of the GGE biplot are the most important measures of testing environment, which profide not only valuable but also unbiased information about the testing genotypes .
Figure 1: The polygon view of GGE biplot to the identification of winning genotypes and their related mega-environments (here, G= Genotypes, E=Environment)
Conclusion and Recommendations
The present study revealed that there are no genotypes higher than the standard check for grain yield and related agronomic traits. However in Pawe two genotypes, G14 (GSR IR1-12-Y4-D1-Y2 and G1 (GSR IR1-17-Y16-Y3-Y2) gave higher grain yield with yield advantage of 21.7% and 20.6%, respectively than the check. And in Assosa G13 (GSR IR1-12-D10-S1-D1) gave grain yield advantage of 26.7% than the check. Therefore further investigation of regional variety trial is necessary for Pawe and Assosa areas to recommend for their specific locations.
Authors are indebted acknowledged Ethiopian Institute of Agricultural Research for financial support and International Rice Research Institute (IRRI) for the germplasm resources. The authors highly appreciated the efforts made by rice research team Fogera National Rice Research and Training Center, Pawe Agricultural Research and Assosa Agricultural Research Center for field management and data collection during the execution of filed trial.