Response of Amaranthus Varieties (Amaranthus Spp.) to Different Seed Rates on Growth, Leaf, and Seed Yields in Jimma,southwest Ethiopia

Response of Amaranthus Varieties (Amaranthus Spp.) to Different Seed Rates on Growth, Leaf, and Seed Yields in Jimma,southwest Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Response of Amaranthus Varieties (Amaranthus Spp.) to Different Seed Rates on Growth, Leaf, and Seed Yields in Jimma,southwest Ethiopia Tilahun Deresa, Amsalu Nebiyu, Gerba Daba, Garome Shifaraw This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7606428/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The goal of the current study was to determine how varied seed rates in Jimma, Southwest Ethiopia, affect the growth, leaf, and grain production of amaranthus cultivars. The experiment was conducted with supplemental irrigation from 2020–2021. A 2x6 factorial combination with three replications was used in the experiment, which was set up in a fully randomized block design. Madiira II and AC-NL are the cultivars and the seed rate levels.The results revealed that the interaction effect of variety and seed rate was highly significant (p < 0.01) for almost all the traits. The highest number of leaves was harvested from the combined effects of Madiira-II at 2 kg ha − 1 (112.33) and AC-NL at 2.5 kg ha − 1 (111.66). The highest dry matter content was recorded for Madiira-II at a seed rate of 2.5 kg ha − 1 (29.68%), 3 kg ha − 1 (29.22%) and AC-NL at 4 kg ha − 1 (27.03%). The highest harvest index was obtained from Madiira-II at 1.5 kg ha − 1, and the lowest was obtained from AC-NL at 3 kg ha − 1 . The leaf yield was highest at a seed rate of 2.5 kg ha − 1 for Madiira-II (25.17 t ha − 1 ), with a statistically significant difference from that of AC-NL at 2.5 kg ha − 1 (26.63 t ha − 1 ), and it was highest at 3 kg ha − 1 for AC-NL ( 30.84 t ha − 1 ). The highest seed yield (3.78 g) was produced from the 1.5 kg ha − 1 treatment, and the highest seed yields (2.52, 2.64 and 2.68 t) were obtained from the 3, 3.5 and 4 kg ha − 1 treatment. Therefore, it can be concluded that the seed percentage of these two varieties can be tentatively recommended for cultivation in the study area. However, repeating the experiment over seasons and locations is suggested to provide sound and concrete recommendations. AC-NL Madiira-II competition leaf yield seed yield Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION Amaranthus is a member of the dicotyledonous genus Amaranthus L., which is grown for decorative, grain, leaf, and fodder purposes. It comprises approximately 70 species[ 1 , 2 ]. A. Amaranthus, A. Albersia, and A. Acnida are its three subgenera. The Amaranthus genus is currently widely grown and cultivated in tropical Africa, tropical South America, sections of the Pacific islands, China, India, Indonesia, Japan, and Pakistan [ 4 ]. A. dubius, A. lividus, and A. hybridus constitute the majority of vegetal species, whereas A. hypochondriacus, A. cruentus, and A. caudatus are the three primary grain species considered [ 5 , 6 , and 7 ]. In addition to the fact that most farmers and consumers dislike black grains, there is no discernible difference between the types of vegetables and grains. There are few studies on the quantitative and qualitative features that contribute to leaf and grain yields [ 8 ]. The yield of amaranthus is contingent upon the production location and is influenced by multiple factors, including the absence of better cultivars [ 9 ]. According to reports, the global average seed yield varies from 1 to 6 t ha − 1 , whereas the fresh leaf yield varies from 10 to 70 t ha-1 [ 10 ]. While the average leaf yield of amaranthus produced worldwide is approximately 14.27 t ha − 1 , it is lower in African countries such as Nigeria, where it is approximately 7.6 t ha-1. The highest yield of amaranth seeds reported in Latin America is likely related to the improvement of local varieties through the selection process, which has not been carried out to the same degree in Africa [ 11 ]. When Dmitrieva and Ivanov [ 13 ] studied various amaranthus species, they reported that there were no appreciable differences in the dry matter composition between the species. With the exception of A. spinosus, all amaranthus species presented high biological productivity in terms of their fresh green biomass output. In particular, for long stem species (A. cruentus 53.7 t ha − 1 and A. caudatus 49.0 t ha − 1 ), soil and climatic circumstances enable plants to generate well-developed stems, leaves, and inflorescences that contribute to intense photosynthesis and very high leaf yields. However, only one species of amaranth (A. cruentus, 1.32 t ha − 1 ) showed seed productivity, and in that species, seeds were able to mature to their full potential every year. In other species, however, seed maturation was not observed because of greater temperature requirements [ 13 ]. Three varieties of Amaranthus (white, red, and black) are grown in Ethiopia, according to Mekonnen et al. [ 14 ]. It is grown three times a year as a single crop or in conjunction with maize or sorghum, primarily by Menit people (Menitshasa and Menit Goldya), who live in the SNNPR [ 15 ]. The SNNPR produces 2,527 ha of amaranth, with the Bench Maji zone accounting for the largest portion, or 689 ha of the total [ 16 ]. Inadequate resources (water, light, plant nutrients, and spaces) and fewer or more plant populations are the primary causes of low yields. Resources (water, light, and plant nutrients) are made accessible for plant growth when the planting density is relatively low, and competition for these resources is generally minimal. Plant growth tends to decline when the planting density increases because of increased competition. As a result, growth is favored by an ideal planting density, meaning that higher than optimal densities inhibit growth [ 17 , 18 ]. High-yielding cultivars cannot be fully genetically harvested unless an appropriate seeding rate is ensured. Plant density impacts the leaf area index and dry matter output through competition for light, nutrients in the soil, and accessible moisture, all of which affect crop growth and production [ 23 – 24 ]. The attainment of enhanced vegetable output and resource efficiency is contingent upon the availability of suitable planting technologies and seed density. High-quality vegetable production would support household income and expand domestic, regional, and global market prospects. An alternative crop species known as amaranthus has historically been utilized for food, fiber, fodder, oil, or medicinal purposes. The potential of these products to support food security, nutrition, health, generate revenue, and environmental benefits is underutilized [ 7 ]. Owing to a number of issues, including the alarming rate of population expansion and ongoing climate change, the production of Ethiopia's key staple foods is now insufficient to meet the country's food needs [ 11 , 26 ]. Ethiopia should encourage the production and usage of amaranthus because of its many nutritional advantages [ 27 ]. Research, despite its importance, is concentrated on agronomic techniques, particularly seed rates, for the significantly lower leaf and grain yields of Amaranthus in Ethiopia. Amaranthus is a widely dispersed wild plant (i.e., weed) in the Jimma area that is utilized for a variety of functions, including personal communication, treating diarrhea, and helping expectant mothers stop bleeding after giving birth. At JUCAVM, the Eladale Research Site, Amaranthus variants have been identified since 2015 [ 25 ]. Six Amaranthus varieties (UG-AM-13, UG-AM-68, TZSMN102-Sel, AH-NL-Sel, Madiira-I and Madiira-II) were used in the experiment by Bongase et al. [ 26 ], with three rows in a row (15, 25, 35 cm); the interrow spacing was 60 cm. Madiira-II produced the highest leaf yield (18.29 t ha-1) and the highest grain yield per hectare (1.802 t ha-1 and 1.786 t ha-1) when TZSMN102-Sel and AH-NL-Sel were planted at 15 cm. The best seed rate for a particular variety of several kinds of amaranthus leaves and grains, however, has not been studied in Ethiopia. Furthermore, there is a lack of easily available information in the literature on Ethiopian agroecology concerning the impact of the seed rate for a particular variety on the productivity of Amaranthus types in vegetables or grains. Thus, it makes sense to assess numerous Amaranthus cultivars in Jimma, southwest Ethiopia, to examine how they react to various seed rate levels. The goal of the current study was to ascertain how amaranthus types in Jimma, southwest Ethiopia, respond to varying seed rates in terms of growth, leaf yield, and seed production. Specific Objectives To determine the effects of Amaranthus varieties on growth and leaf and seed yields in Jimma, southern Ethiopia The optimal seed rate that results in the highest leaf and seed yields in Jimma, southwestern Ethiopia, was determined. To investigate the interaction effects of the seed percentage and Amaranthus variety on growth and leaf and seed yields in Jimma, southwestern Ethiopia 2. MATERIALS AND METHODS 2.1. Description of the Study Area The study was carried out at the Eladale Research Site of the Jimma University College of Agriculture and Veterinary Medicine (JUCAVM) during 2020/21. The study site is located in Gudeta bula Kebele, in Jimma town, which is located 4 km from Jimma town on the way to Agaro - Bedele. It is located 1710 m above sea level and at 7° 33'N latitude and 36° 57'E longitude in the Jimma zone, Oromia National Regional State (Fig. 1 ). The mean annual maximum and minimum temperatures are 26.8 and 11.4°C, respectively, and the mean annual maximum and minimum relative humidities are 91.4 and 39.92%, respectively. The mean annual rainfall in the study area is 1250 mm. The soil at the experimental site is well-drained clay to silt clay with a pH of 5.5. The most common and dominant soil type is Nitisol [ 28 ]. 2.2. Experimental Materials Two varieties of Amaranthus (Madeira II and AC-NL) were collected from the Melkasa Agricultural Research Center (MARC) and used for the experiment. The two varieties are described below (Table 1 ). Table 1 Description of the experimental materials on the basis of their qualitative characteristics. Qualitative characteristics of the Amaranth genotypes. Genotype Madiira-II AC-NL Branching index Branches all along the stem branches all along the stem Branching pattern Highly branched Branched Spines in leaf axil Absent Absent Leaf margin Entire Entire Stem pigmentation Green Green Growth habit Erect Erect Stem pubescence Low Low Prominence of leaf veins Rogose Rogose Leaf color Green Green Petiole pigmentation Green Green Leaf shape Rhombic Ovatainate Plant Height Very tall Tall Seed color Black Brown black 2.3. Treatments and Experimental Design The treatment consisted of two varieties (Madiira II and AC-NL) and six seed rates (1.5, 2, 2.5, 3, 3.5, and 4 kg ha − 1 ) with 2×6 factor treatment combinations. The experiment was conducted in the RCBD with three replications via factorial arrangements. The reference seed rate (standard check) used in this experimental setup was 3 kg ha − 1 on the basis of Sokoto and Victor [ 24 ]. 2.4. Experimental Procedures and Crop Management. The area was cleared of undesired weeds, grasses, and plant thrashes. It was then pluckled four times with oxen and harrowed by hand until the soil was fine. Each plot included five rows spaced 60 cm apart and measured 3 m × 2.7 m (8.1 m2) [ 26 ]. Each plot and replication were separated by 0.6 and 1 m, respectively. To ensure a uniform stand and speed up the sowing process, the seeds were directly drilled and mixed with well-dried friable soil at a ratio of 1 g of seeds to 100 g of soil [ 29 ]. The seeds were planted at a depth of 1.30 cm [ 30 ], and to protect them from rain, the soil was lightly covered with well-dried mulching grasses. 2.5. Data collection Twelve randomly selected plants from the three middle rows of each plot were used to collect data on crop phenology (flowering and maturity), growth and yield, and its constituent parts. To minimize any potential border effects, the two outermost rows were not included in the data collection process. 2.6. Data Analysis Prior to data analysis, each set of data was examined to ensure that it complied with the fundamental ANOVA assumptions. The general linear model (GLM) of the Statistical Analysis System (SAS) software version 9.3 was used to conduct an analysis of variance (ANOVA) on data related to growth parameters, yield, and yield factors [ 38 ]. When an ANOVA revealed significant differences, the treatment means were compared via the least significant difference (LSD) test at the 5% probability level. The relationships between growth and yield characteristics were evaluated via Pearson's correlation coefficient. The study design model was as follows. 3. RESULTS AND DISCUSSION 3.1. Crop Phenology 3.1.1. Days to 50% flowering The results indicate that the main effects of the type and the seed rate had a substantial effect on the number of days to 50% blooming (p < 0.01). On days with 50% blooming, however, the combined impacts of the type and the seed rate were not statistically significant (Appendix Table 1). Figure 2 a shows that the Madiira II variety had the longest duration to 50% flowering, measuring 72.77 days, whereas the AC-NL variety had the shortest duration, measuring 66.83 days. Variations in the number of days from blossom initiation were attributed to variations in genotypes. This outcome is in line with the findings of Casini and La Rocca [ 39 ], Mbwambo et al. [ 35 ], Bongase et al. [ 26 ], and Dinssa et al. [ 40 ], who reported that genotypic changes were the cause of the variances shown in terms of days to 50% blooming. Furthermore, based on the 4 kg ha-1 and 3.5 kg ha-1 seed rates, the findings shown in Fig. 2 b revealed that the first days of 50% blooming were 64.5 and 66.66 days. With 74, 72.16, and 72.16 days, respectively, the 1.5 kg ha-1, 2 kg ha-1, and 2.5 kg ha-1 seed rates had the longest days on record. Three kg of seed per hectare was statistically comparable to two kg, 2.5 kg, and 3.5 kg of seed on fifty percent of the days before flowering. According to the results of this study, a relatively high seed percentage results in a relatively large plant population, which intensifies interplant competition and prompts early flowering. Variations in the time it takes for flowers to begin can be attributed to the quantity of assimilates that are accessible during the sensitive phase as well as to increased competition for resources among densities of plants, which speeds up blooming time by acting as escape mechanisms for the plant and vice versa. The current investigation aligned with Teshome and Ousman's earlier linseed study [ 41 ]. On days of phenological parameters (days of flower initiation), amaranth genotypes respond significantly to various seed rates, although there was no significant difference in interaction effects. 3.1.2. Days to 50% maturity The primary influences of the seed rate and variety were shown to have an impact on the physiological maturity from days to 50%. The varieties highly significantly differed (p < 0.01), while the seed rates substantially differed (p < 0.05). However, the interaction effects of type and seed rate did not impact physiological maturity from days to 50% physiological maturity (Appendix Table 1). The Madiira II variety had the highest recorded number of days to 50% maturity (100.94 days), whereas the AC-NL variety had the lowest number of days (90.83 days) (Fig. 2 a). The genetic diversity in maturation time across types may be the cause of the variation in days to physiological maturity. This conclusion is consistent with the findings of Dmitrieva and Ivanov [ 13 ], who reported that amaranthus prefers to grow more rapidly in developing species with a growing season of no more than 100 days and that they require more heat than moisture. According to Fig. 2 b, the days to 50% physiological maturity (91.33) recorded from a 4 kg ha-1 seed rate were statistically similar to those recorded from plants sown at 3.5 kg ha-1 (93.66 days), 3 kg ha-1 (95.16 days), and 2.5 kg ha-1 (96 days). On the other hand, the 1.5 kg ha-1 seed rate resulted in the greatest number of days to 50% maturity (100.16 days). The only seed rate that varied from 3.5 kg ha-1 to 4 kg ha-1 was the 1.5 kg ha-1 seed rate; there were no statistically significant changes in the other seed rates. One possible explanation is that greater plant densities lead to more shadowing between plants, which accelerates leaf senescence and results in leaf defoliation. The work is not supported by the current study. According to Pandey and Singh [ 43 ], there is variance in phenological days because of the unpredictable development pattern of crops and the existence of vegetative structures that persist throughout the reproductive phase [ 44 ]. Figure 2 a. Effects of Amaranthus varieties on days up to 50% to flowering and 50% to maturity. 3.1.3. Growth response 3.1.3.1. Leaf area of plant -1 The primary impacts of variety (p < 0.01) and seed rate (p < 0.01) on the leaf area per plant were highly sensitive (Appendix Table 2). Table 4 displays the largest leaf area (50.08 cm2) measured from the Madiira-II variety and the lowest leaf area (40.03 cm2) from the AC-NL variety. These findings demonstrated that genetic variations in the crop, such as variations in leaf form, influenced the leaf area. This outcome is consistent with the findings of Liu and Stützel [ 47 ], Shankar et al. [ 46 ], Srivastava [ 45 ], and others, who reported that genotypes accounted for the variation in leaf characteristics and that the leaf area per plant was affected by this variation. The findings revealed that the largest leaf areas (56.44 cm2 and 54.05 cm2) were 1.5 kg ha-1 and 2 kg ha-1, which were statistically equivalent to 50.53 cm2 from 2.5 kg ha-1. The 4 kg ha-1 (33.34 cm2) and 3.5 kg ha-1 seed rates (36.38 cm2) had the lowest leaf areas, and they were statistically similar to the 3 kg ha-1 (39.58 cm2) (Table 4 ). As the seed rate increased above 2.5 kg ha-1, the results indicated a small decrease in leaf area. This could be because there were fewer plant densities, which allowed the plant to make the most of its resources (space, nutrients, light, water, air, etc.), resulting in a larger leaf area due to the low seed rate. The results of the present study were in line with those of Rotich et al. [ 49 ], who reported that stress from high planting density decreased leaf size by reducing leaf area expansion and growth, which in turn reduced light interception and photosynthesis. Both results are therefore strongly correlated. Yarnia et al. [ 48 ] reported that a reduced leaf area with delayed sowing and increased density was due to a shorter growth period and increased competition for agricultural input, especially light, between plants. According to Poorter et al. [ 50 ], competition has a significantly greater effect on a plant's complete leaf area in terms of nutrient limitation. As a result, a plant with a smaller leaf area may have fewer leaves with higher irradiances, which would increase the plant's total leaf mass area. Higher light levels may cause leaves to become thicker than the leaf area that can benefit from more light, according to Weraduwage et al. [ 51 ]. Redirecting resources from area growth to thickening enhances the efficiency of creating a new, thicker leaf area and increases the maintenance of respiration loss in the future. 3.1.3.2. Plant Height Statistical analysis revealed that the major effects of seed rate (p < 0.01) and variety (p < 0.01) significantly affected plant height. However, plant height had no effect on this interaction (Appendix Table 2). The Madiira-II variety produced more plants (151.19 cm) than did the AC-NL variety (121.76 cm), according to the mean value (Table 3 ). The genetic differences in crop growth patterns, including the length and quantity of internodes, the time it takes for flowers to initiate, and physiological maturity, may account for the variation in plant height between the two types. This outcome is consistent with earlier research by Gnan et al. [ 54 ], who proposed that an earlier transition to flowering may limit plant size and decrease seed production in annual plants, and Shahzad et al. [ 52 ], who reported that the genetic composition of a crop, along with environmental factors [ 53 ], is the primary control of crop height. Tejaswini et al. [ 55 ] reported that 27 (27) Amaranthus genotypes presented significant differences in plant height at different stages of growth, of which eight (8) genotypes were found to be Madiira-II. These findings are in strong agreement with the work of Alemu et al. [ 11 ], who reported the highest plant height of the genotype (106.77 cm), followed by AC-NL (95.62 cm). The greatest heights (165.03 cm and 152.05 cm) were measured at seed rates of 4 kg ha-1 and 3.5 kg ha-1, respectively. These values were statistically similar to the plant height when the plants were sown at 3 kg ha-1 (144.64 cm). However, the plant heights produced from 1.5 kg ha-1 and 2 kg ha-1 produced the smallest plant heights (109.65 cm and 118.02 cm), which were statistically comparable to the plant height produced from the 2.5 kg ha-1 (129.50 cm) seed rate. This could be because, on the one hand, a relatively high plant density encourages upward growth to compete for light interception; on the other hand, a relatively low plant population receives sufficient resources and more free space for lateral growth, which might limit plant height. This result is inconsistent with the findings of Henderson et al. [ 56 ] and Zubillaga et al. [ 42 ], who reported that plant population pressure can limit plant height, and with the experiments of Abbas et al. [ 57 ] and Baloch et al. [ 58 ], who reported that an increase in the seeding rate resulted in a slight decline in the heights of the plants. In contrast, the findings of the present study support the findings of Olofintoye et al. [ 59 ], who reported an increase in plant height with increasing plant density from 40,000 to 50,000 to 66,666 plants ha − 1 in A. cruentus [ 60 ]. Similar findings of Suleiman [ 61 ] and Alemayehu et al. [ 11 ] revealed that an increase in the seeding rate resulted in a slight increase in the height of the plants [ 27 ]. 3.1.3.3. Number of branch plants − 1 Both the seed rate and variety had a substantial (p < 0.01) effect on the number of branches per plant (Appendix Table 2). With 23.61 branches produced plant-1, the Madiira-II cultivar presented the highest mean value for the number of branches per plant. Nonetheless, the AC-NL cultivar yielded the fewest branches per plant—20.83—according to Table 3 . Genetic variables, such as variations in crop morphology and growth habits, may account for the branch number disparities observed in this study. The results of this study supported the findings of Mbwambo et al. [ 35 ], who noted differences in the RVI00021 and RVI000222 across seasons, and Dinssa et al. [ 40 ], who reported that there was only a slight difference in the rankings of the entries that produced a high number of branches between leaf harvest treatments for PARIS (A)-Sel (14.7 in no-leaf harvest and 14.4 in leaf harvest), DB 2006306-Sel (14.0; 13.9), IP-5-Sel (17.9; 14.5), 'Madiira-I’ (18.6; 15.2), and 'Madiira-II’ (19.6; 12.8). Horak and Loughin (2000) reported that a species's capacity to occupy space is reflected in its branching and plant volume, and they noted that A. palmeri, A. rudis, A. retroflexus, and A. albus were the species with the highest values for these traits. Additionally, according to Garba et al. [ 62 ], varietal traits have a significant effect on a plant's ability to tiller. Table 4 shows that the seed rates of 1.5 and 2 kg ha-1 produced the greatest number of branches per plant (24.16 and 24.33) and that these seed rates did not differ from those of the branches observed from 2.5 kg ha-1 (23.83). At the 4 kg ha-1 seed rate, the lowest branch number (19.33) was obtained, whereas at the 3.5 kg ha-1 seed rate, a similar branch number (19.83) was reported. Compared with the 3 kg ha-1, 3.5 kg ha-1, and 4 kg ha-1 seed rates, the 2 kg ha-1 seed rate produced 10.27%, 18.49%, and 20.54% more branches, respectively, in this study. In contrast, the use of 1.5 kg ha-1 and 2.5 kg ha-1 seed rates produced equal branch counts (0.68% and 2.05%, respectively).As a result, lower plant densities encourage vegetative growth, which includes branches and canopies, and help ensure that plants have access to sufficient nutrients. The opposite might occur from competition for assimilates when resources are depleted sooner and when a plant's ability to grow vegetatively is hindered. The findings of the present study support the findings of Caliskan et al. [ 63 ] and Chundawat et al. [ 64 ], who reported that individual plants receive more light and nutrients when there is a greater gap between plants or a lower plant density. Table 3 Main effects of varieties and seed rates on leaf area, plant height, and branch number in Jimma, southwest Ethiopia, during 2020–21. Treatments Growth Variables Seed rate (kg ha − 1 ) LAPP (cm 2 ) PH (cm) NBPP 1.5 54.05 a 109.65 c 24.16 a 2 56.44 a 118.02 c 24.33 a 2.5 50.53 ab 129.50 bc 23.83 ab 3 39.58 bc 144.64 ab 21.83 bc 3.5 36.38 c 152.05 a 19.83 cd 4 33.34 c 165.03 a 19.33 d LSD (0.05) 11.56 21.79 2.06 Varieties Madiira-II 50.08 a 151.19 a 23.61 a AC-NL 40.03 b 121.76 b 20.83 b LSD (0.05) 6.67 12.58 1.19 CV (%) 21.43 13.33 7.77 The means within columns for each variable followed by different letters are significantly different from each other at p ≤ 0.01. LAPP = leaf area per plant; PH = plant height; NBPP = number of branches per plant. 3.1.3.4. Leaf length plant -1 According to these findings, the seed percentage had a substantial effect on the LLP-1 (p < 0.01). Nevertheless, throughout the experimental treatments, neither the variety response nor the combination of variety and seed rate were noted (Appendix Table 2). With 1.5 kg ha-1, the largest leaf length (13.52 cm) was measured; this value was comparable to the leaf lengths measured with 2.5 kg ha-1 and 2 kg ha-1 seed rates (13.13 cm and 12.94 cm). At 4 kg ha-1 and 3.5 kg ha-1, the shortest leaf length (10.46 cm and 10.69 cm) was measured with statistical parity, with the leaf area recorded from 3 kg ha-1 seed rates (11.39) (Fig. 3 ). In this study, the difference in leaf length was highly dependent on seed rates rather than on the genetic makeup of the crops. The results of the present study revealed that a relatively low seed percentage yields the longest leaf length, whereas a relatively high seed percentage results in the shortest leaf length. As shown in Fig. 3 , as the seed percentage increased, the leaf length decreased. This may be due to lower seeding rates, which result in fewer plant populations per unit area, increasing open space and lessening interplant competition for resources. The results of the present study are consistent with those of Bongase et al. [ 26 ], who reported that longer and wider leaves were encouraged to grow by having more free space within the row. Majumder [ 65 ] also reported that there were significant differences in leaf length between plants with different spacings, noting that the longest leaves were found with a wider spacing (30 cm × 30 cm) than with a narrow spacing (30 cm × 10 cm) [ 26 ]. 3.1.3.5. Number of leaves per plant The results demonstrated that the main effects of the seed rates and the combined effects of the variety and seed rate had a substantial (p < 0.01) effect on the number of leaves per plant. The quantity of leaves per plant, however, was unaffected by variety (Appendix Table 2). The combination of the Madiira-II variety with 2 kg ha-1 and the AC-NL variety at 2.5 kg ha-1 produced the maximum number of leaves per plant (112.33 and 111.66), which was statistically similar to the number of leaves found from the Madiira-II variety at 2.5 kg ha-1 and the AC-NL variety with a 3 kg ha-1 seed rate (108.33 and 104). The plots treated with AC-NL at 1.5 kg ha-1 produced 88 leaves per plant, which did not differ from those of the Madiira-II variety, with 3.5 kg ha-1 (94.33); AC-NL, 2 kg ha-1 (94.66); AC-NL, 3 kg ha-1 (104); Madiira-II, 1.5 kg ha-1 (81.33); Madiira-II, 4 kg ha-1 (81); and AC-NL, 4 kg ha-1 (77.33 leaves). The combined effects of the AC-NL variety with 3.5 kg ha-1 produced the lowest leaf number (62) and did not differ statistically from those of Madiira-II at 3 kg ha-1 (66.66) and AC-NL at 4 kg ha-1 (77.33 leaves) (Table 4 ). A lower plant population that produces more branches with a larger leaf area may result in fewer leaves per plant and a shorter phonological time. Table 4 shows that the combined effect of the two amaranthus varieties with lower seed rates and higher seed rates produced similar results. Conversely, a relatively high seed percentage results in leaf defoliation because of shadowing effects that decrease assimilate production and the photosynthetically active surface area of leaves for solar radiation, particularly around the flowering phase [ 66 , 67 ]. The amaranthus variety with the ideal seed percentage had more leaves per plant than did the variety with a lower or higher seed percentage (Table 5 ). The current result was consistent with the research of Henderson et al. (2000), who noted that plant density in amaranthus could influence the number of leaves per plant. Tejaswini et al. [ 55 ] and Sokoto and Victor [ 24 ] reported that the planting technique and seed rates had an impact on amaranthus (Amaranthus spp.) leaves. 3.1.3.6. Leaf width The results of this investigation demonstrated that the primary effects of seed rates, varieties, and their interactions had a significant effect on leaf width (p < 0.01) (Appendix Table 2). Table 4 shows that the combined impact of the Madiira-II variety with a 2 kg ha-1 seed rate resulted in the broadest leaf width (3.30 cm), which differed significantly from the other seed rates. The combination of the AC-NL variety with 1.5 kg ha-1; AC-NL3 kg ha-1; AC-NL 2 kg ha-1 and Madiira-II 1.5 kg ha-1 seed rates yielded the following larger leaf widths (2.97 cm; 2.96 cm; 2.95 cm and 2.94 cm), which were statistically equivalent. Nonetheless, AC-NL (3.5 kg ha-1) and AC-NL (4 kg ha-1) had the smallest leaf widths, measuring 2.39 cm. This study's results are similar to those of Bongase et al.[ 26 ], who reported that the Madiira II variety had wider (9.749 cm) leaves with a wider intrarow spacing of 35 cm, whereas the UG-AM-13 variety planted at an intrarow spacing of 15 cm had the shortest (5.67 cm) and narrowest (4.91 cm) leaves. The variation in leaf width among treatments could be due to the combined effects of the crop's genetic makeup on growth habits, leaf shapes, and leaf structures as well as the interaction of the variety with the environment and agronomic practices. 3.1.3.7. Stem diameter The results of this investigation demonstrated that the diameter of the shoot was influenced by the seed rate (p < 0.01), main variety effect (p < 0.05), and combination effect (p < 0.01) of both the variety and the seed rate (Appendix Table 2). The Madiira-II variety treated with 1.5 kg ha-1 had the widest stem diameter (2.39 cm), whereas the Madiira-II variety treated with 2 kg ha-1 produced a statistically similar stem diameter (2.10 cm). Table 4 shows that the Madiira-II variety had the narrowest stem diameter, measuring 4 kg ha-1 (1.23 cm), followed by the Madiira-II variety, measuring 3.5 kg ha-1 (1.30 cm), the AC-NL variety, measuring 4 kg ha-1 (1.32 cm), the Madiira-II variety, measuring 3 kg ha-1 (1.38 cm), and the AC-NL variety, measuring 3.5 kg ha-1 (1.40 cm). The AC-NL variety presented the narrowest stem diameter, measuring 3 kg ha-1 (1.49 cm). he genetic makeup of crop morphology, including root morphology, root length, and the capacity to use and absorb nutrients in the soil and interact with the environment, as well as agronomic practices, was found to contribute to a trend toward differences in stem diameter. For the two amaranth species, the increase in the seed percentage led to a notable decrease in stem diameter and an increase in stem density. This is because increased seed rates naturally lead to increased stem densities, which in turn cause stem diameters to shrink as a result of clear plant competition [ 68 ]. Thin stemmed plants with a propensity to lodge are produced at relatively high plant densities [ 68 , 69 , 70 ]. These results confirm the findings of Gimplinger et al. [ 9 ], who studied two amaranth genotypes over a two-year period and reported significant decreases in plant height, stem diameter, and branch number with increasing density. In a related study, Igbokwe and Hollins [ 71 ] studied the vegetable amaranthus (Amaranthus cruentus L.) and reported that the highest stem diameter (4.6 cm) was obtained from a plant spacing of 0.9 m, and the lowest stem diameter (2.6 cm) from a plant spacing of 0.3 m. Similarly, Gondal et al. [ 68 ] and Snider et al. [ 72 ] reported similar findings, suggesting that plant stand establishment may be more favorable under narrow row spacing than under wider row spacing, i.e., 76 cm row spacing. 3.1.3.8. Fresh biomass above ground According to the results of the statistical analysis, the seed rate, variety, and interaction between the variety and the seed rate significantly affected the fresh above-ground biomass of plant-1 (p < 0.01) (Appendix Table 2). According to the data in Table 4 , the combined effects of the Madiira-II variety with a 2 kg ha-1 seed rate produced a maximum TAGFBP (195.66 g) that was much greater than those of the other varieties. The plots treated with AC-NL at 1.5 kg ha-1 and AC-NL at 3 kg ha-1 produced fresh biomass above ground per plant yields that were comparable, at 150.58 g and 145.66 g, respectively. Madiira-II (3.5 kg ha-1), Madiira-II (1.5 kg ha-1), and the amaranthus variety Madiira-II (1.5 kg ha-1) were statistically similar to the AC-NL variety at a 2 kg ha-1 seed rate. According to the results of the statistical analysis, the seed rate, variety, and interaction between the variety and the seed rate significantly affected the fresh above-ground biomass of plant-1 (p < 0.01) (Appendix Table 2). According to the data in Table 4 , the combined effects of the Madiira-II variety with a 2 kg ha-1 seed rate produced a maximum TAGFBP (195.66 g) that was much greater than those of the other varieties. The plots treated with AC-NL at 1.5 kg ha-1 and AC-NL at 3 kg ha-1 produced fresh biomass above ground per plant yields that were comparable, at 150.58 g and 145.66 g, respectively. Madiira-II (3.5 kg ha-1), Madiira-II (1.5 kg ha-1), and the amaranthus variety Madiira-II (1.5 kg ha-1) were statistically similar to the AC-NL variety at a 2 kg ha-1 seed rate. According to Patel et al. [ 73 ], total fresh weight (242.67 g plant-1) and total dry matter accumulation (67.50 g plant-1) were found to be influenced by the interaction between 45 cm row spacing and G2-GA-1 genotypes, with a 2.5 kg ha-1 seed rate. These results are consistent with the current findings. Similarly, Bongase et al. [ 26 ] reported that the fresh aboveground biomass per Madiira II plant planted at 47,619 plants ha-1, AH-NL-Sel planted at 47,619 plants ha-1 and 66,666 plants ha-1, and Madiira-I planted at 111,111 plants ha-1 plant density were 812.20 g, 719.30 g, 669.60 g, and 666 g, respectively. 3.1.3.9 Aboveground dry biomass Owing to the variety's primary effects, seed rate, and interaction effects (p < 0.01), the results for the dry biomass above ground plant-1 were highly significant (Appendix Table 2). The combination of the Madiira-II variety with 2.5 kg ha-1, the Madiira-II variety with 2 kg ha-1, and the AC-NL variety with 3 kg ha-1 yielded the greatest AGDBP (32.9 g; 32 g and 31 g). Similar results were obtained with Madiira-II at 3.5 kg ha-1, Madiira-II at 1.5 kg ha-1, Madiira-II at 3 kg ha-1, and AC-NL at 2.5 kg ha-1 in the plot treated with AC-NL at 1.5 kg ha-1. Compared with AC-NL 3.5, the combined effects of the AC-NL variety with 4 kg ha-1 produced the lowest dry biomass above ground plant-1 (18.33 g). This result is consistent with the findings of Pourfarid et al. [ 74 ], who reported that increasing the density of plants per square meter increased the dry weight of the above ground of the plants per square meter but decreased the dry biomass plant-1 as a result of the stem becoming thinner [ 26 ]. Yarnia et al. [ 48 ] reported that a reduction in organ dry matter significantly reduced plant-1 at high crop density because of the probable reduction in biomass components as a result of a reduction in the net accumulation of assimilates in plant leaves, plant height, and the number of lateral branches. According to Zubillaga et al. [ 42 ], there is a clear correlation between the number of plants at harvest and the amount of biological yield. 3.1.3.10. Dry matter content Analysis of variance revealed that dry matter in plant − 1 was significantly responsive to variety (p < 0.01) , seed rate and the interaction effects of variety and seed rate (p < 0.01) (Appendix Table 2). With respect to the combined effects of the AC-NL variety with 3.5 kg ha-1 (25.81%) and the Madiira II variety in combination with the Madiira-II variety with 2.5 kg ha-1; the Madiira-II variety with 3 kg ha-1; and the AC-NL variety with a 4 kg ha-1 seed rate, the results displayed in Table 4 show that the combination performed best in terms of dry matter yield, with percentages of 29.68%, 29.22%, and 27.037% per plant, respectively, with statistical parity. Table 4 shows that Madiira-II 2 kg ha-1, AC-NL 1.5 kg ha-1, and AC-NL 2 kg ha-1 produced the lowest DMC per plant (16.39%, 16.69%, and 17.09%, respectively). Similar statistical results were reported for AC-NL 2.5 kg ha-1 and Madiira-II 4 kg ha-1 (17.96% and 20.04%, respectively). This research further revealed that DMC is dependent on a crop's ability to utilize resources (light, nutrients, etc.) effectively at the maximum plant density; it is also associated with crop phenology and growth and yield components. According to Wilson et al. [ 75 ], leaf thickness is not as significant a correlate of resource acquisition, consumption, and availability as leaf composition variation is. This occurred because dry matter content is greater than leaf thickness. According to Baturaygil et al. [ 76 ], variation in flowering time is primarily responsible for the differences in dry matter content and plant height between the genotypes of the 10 biomass-type genotypes and the grain production variety used as a check (Bärnkrafft). This is because early flowering results in a longer seed ripening phase and improved dry matter content, whereas late flowering due to short-day genes leads to longer vegetative growth and greater plant height. However, with respect to flowering time, the results of Baturaygil et al. [ 76 ] did not align with the current findings. Consequently, Madiira II was found to be a late-flowering variety in the current study, taking 72.77 days to flower and having a higher dry matter content than AC-NL, which flowers 66.83 days after seeding. Table 4 Influences of the interaction effects of amaranth variety on the seed rate and growth parameters in Jimma, Southwest Ethiopia, from 2020–21. Treatments Growth Parameter Varieties Seed rate (kg ha − 1 ) NLPP (cm) LW (cm) SD (cm) AGFBP (g) AGDBP (g) DMC (%) Madiira 2 1.5 81.33 def 2.94 b 2.39 a 116.66 de 26.6 bc 22.94 bc 2 112.33 a 3.30 a 2.10 ab 195.66 a 32 a 16.39 e 2.5 108.33 ab 2.80 d 2.07 b 111.00 e 32.9 a 29.68 a 3 66.66 fg 2.54 f 1.37 f 84.08 f 24.43 c 29.22 a 3.5 94.33 bcd 2.86 c 1.30 f 129.33 c 27.73 b 21.58 cd 4 81.00 def 2.55 ef 1.23 f 106.00 e 21.23 de 20.04 cd e AC-NL 1.5 88.00 cde 2.97 b 1.90 bc 150.58 b 25.16 bc 16.69 e 2 94.66 bcd 2.95 b 1.75 cd 124.33 cd 21.23 de 17.09 e 2.5 111.66 a 2.59 e 1.69 cde 132.66 c 23.83 cd 17.96 de 3 104.00 abc 2.96 b 1.49 def 145.66 b 31.00 a 21.31 cd 3.5 62.00 g 2.39 e 1.40 ef 79.00 fg 20.16 ef 25.81 ab 4 77.333 efg 2.35 g 1.32 f 67.83 g 18.33 f 27.03 a LSD(0.05) 16.44 0.04 0.29 11.70 2.84 3.99 CV (%) 10.77 1.02 10.36 5.74 6.60 10.66 The means within columns for each variable followed by different letters are significantly different from each other at p ≤ 0.01. NLPP = number of leaves per plant ; LW = leaf width; SD = stem diameter; TAGFBP = total aboveground fresh biomass per plant; TAGDBP = total aboveground dry biomass per plant; DMC = dry matter content. 3.1. Yield and yield-related response 3.1.1. Fresh leaf yield per plant The findings of the present study revealed that the fresh leaf yield per plant was significantly affected by the variety (p < 0.01), the seed rate (p < 0.01), and the interaction effect between the amaranth variety and the seed rate (p < 0.01) (Appendix Table 3). The highest fresh leaf yield per plant (42.25 g plant-1) was observed from plots allocated with the AC-NL variety at 1.5 kg ha-1, which was statistically similar to the fresh leaf yield harvested from AC-NL at 2 kg ha-1 (40.66 g), AC-NL at 2.5 kg ha-1 (40.66 g), and Madiira-II at a 2.5 kg ha-1 seed rate (38.66 g). The lowest fresh leaf yield per plant (12.66 g) was obtained from Madiira-II, with a yield of 4 kg ha-1, which was statistically significant compared with AC-NL, with a yield of 4 kg ha-1 seeds. This study revealed that when amaranth types were handled at reduced seed rates, the leaf output per plant increased. Enough room and reduced competition for nutrients and other resources (light, water, etc.) may be the cause of this. The results of the present study are comparable to those of Bongase et al. [ 26 ], who reported that planting amaranth cultivars with the widest intrarow spacing (35 cm) and the narrowest intrarow spacing (15 cm) increased the leaf output per plant. 3.1.2. Fresh leaf yield ha -1 As demonstrated in Appendix Table 3, the fresh leaf yield per plant and per hectare was found to be strongly impacted by the seed rate (p < 0.01), variety (p < 0.01), and interaction between the seed rate and amaranthus variety (p < 0.01). The highest fresh leaf yield (30.84 t ha-1) was obtained by the combined effects of the AC-NL variety with 3 kg ha-1 over all the other treatments. With respect to yields collected from plots treated with AC-NL at 2 kg ha-1 (23.54 t ha-1), the next-best yields (26.63 and 25.17 t ha-1) were from Madiira-II at 2.5 kg ha-1 and AC-NL at 2.5 kg ha-1. These yields presented statistical parity. The lowest fresh leaf yields were 14.35, 14.62, and 14.76 t/ha, respectively. According to the results of this investigation, using the AC-NL variety at 3 kg ha-1 resulted in 7.30 tons greater leaf yield than did using the AC-NL variety at 2 kg ha-1; 4.47 tons greater leaf yield than did using the AC-NL variety at 2.5 kg ha-1; and 12.00 tons greater leaf yield than did using the AC-NL variety at 3.5 kg ha-1. Similarly, the use of 2.5 kg ha-1 Madiira-II yielded 4.38 tons more fresh leaf yield than did the use of 2 kg ha-1 Madiira-II and 3.04 tons more fresh leaf yield than did the use of 3 kg ha-1 Madiira-II. A greater degree of correlation between growth and yield parameters, agronomic techniques, environmental conditions, and genetic differences could contribute to the production differences mentioned above (Table 5 ). According to Patel et al. [ 73 ], when 45 cm row spacing, the GA-1 genotype, and the 2.5 kg ha-1 seed rate are combined, the interaction effect on the green forage yield (43.53 t ha-1) and dry matter yield (3.01 t ha-1) is significantly greater. Table 5 Effects of the interaction effect of amaranth variety and seed percentage on fresh leaf yield per plant and per hectare in Jimma, southwestern Ethiopia, from 2020–21 Treatments Yield and yield related variables Varieties Seed rate (kg ha − 1 ) FLY PP FLY PH Madiira 2 1.5 32.00 d 14.35 f 2 37.33 bc 20.78 de 2.5 38.66 abc 25.17 bc 3 24.41 e 22.13 cde 3.5 20.00 ef 20.86 de 4 12.66 h 14.62 f AC-NL 1.5 42.25 a 20.66 de 2 40.66 ab 23.54 bcd 2.5 40.66 ab 26.63 b 3 34.00 cd 30.84 a 3.5 18.00 fg 18.84 e 4 13.33 gh 14.76 f LSD (0.05) 4.82 3.61 CV (%) 9.65 10.10 The means within columns for each variable followed by different letters are significantly different from each other (p ≤ 0.01). FLYPP = fresh leaf yield per plant; FLY PH = fresh leaf yield per hectare. 3.1.3. Number of inflorescences plant -1 According to the statistical results, variety (p < 0.01) and the seed percentage (p < 0.01) were the two primary influences that had the greatest impact on the number of inflorescences (Appendix Table 3). The number of inflorescences per plant was greatest for the AC-NL variety (19.14). However, the Madiira II variety had the lowest score (16.34). The capacity to effectively utilize available resources and genetic variance in flower form may be the cause of this. Gnan et al.'s research [ 54 ] revealed that larger inflorescences were better preserved by the addition of higher effective photosynthetic rates. This study corroborates the findings of Varalakshmi [ 77 ], who reported that inflorescence density varies widely, ranging from low to dense and intermediate, and Panda et al. [ 43 ]. A 1.5 kg ha-1 seed rate yielded the greatest number of inflorescence per plant (23.93), which was statistically equivalent to the number of inflorescence obtained from 2 kg ha-1 seed rates (21.15). The inflorescence recorded from the 3.5 kg ha-1 seed rate (14.56) was statistically equivalent to the inflorescence scored from the 4 kg ha-1 seed rate, which resulted in the lowest number of inflorescences per plant (12.12). According to these results, 1.5 kg ha-1 and 2 kg ha-1 produced inflorescences per plant that were comparable, with a 2.5 kg ha-1 seed rate occurring secondarily (Table 5 ). The explanation may involve increased open area, optimal light interception at a lower or lower seed rate, and, conversely, decreased resources as a result of shadowing. There was a positive correlation between vegetative size and reproductive output, indicating a trade-off between time to reproduction and reproductive output. Nevertheless, Gnan et al. [ 54 ] proposed that leaves are the main source of carbon for reproduction in plants. The study revealed that there was a significant positive correlation between the number of inflorescences per plant and the number of days of flowering, dry above ground biomass per plant, and harvest index. Conversely, there was a significant negative correlation between the number of inflorescences per plant and growth parameters such as stem diameter, branch number, fresh leaf yield per hectare, inflorescence length, seed yield per hectare, and thousand-seed weight compared with those of leaves. 3.1.4. Seed yield plant -1 and ha -1 The primary effects of variety and the seed rate were strongly linked (p < 0.01) with the seed yield per plant and per hectare (Appendix Table 3). Compared with the other varieties, the Madiira II variety produced greater seed yields (3.38 g plant-1 and 2.53 t ha-1). This difference in seed yield was substantial. The minimum seed yield per plant was 2.69 g plant-1, and the minimum seed yield per hectare was 2.04 t ha-1 for the AC-NL variety. The variation in the period of flower initiation and physiological maturity, the number and length of inflorescences, and the transport of nutrients and photosynthetic energy from seeds to other vegetative components, such as leaves, could all be contributing factors to the variation in seed output. The seed yield of the amaranth varieties was also significantly influenced by the seed rate; the plant-per-seed yield (3.78 g) was highest at 1.5 kg ha-1, which was statistically similar to that at 2 kg ha-1 (3.49 g) and 2.5 kg ha-1 (3.27 g plant-1). However, the 4 kg ha-1 (2.35) seed rate resulted in the lowest seed production per plant, matching the 3.5 kg ha-1 seed rate (2.54) (Table 6 ). The highest seed yields per hectare were obtained from 4 kg ha-1, 3.5 kg ha-1, and 3 kg ha-1, yielding 2.68 t ha-1, 2.64 t ha-1, and 2.52 t ha-1 of seed, respectively. The 1.5 kg ha-1 seed rate yielded the lowest seed yield per hectare (1.78 t ha-1) and was identical to the 2 kg ha-1 seed rate (Table 6 ). The results of the present study revealed that a low seed percentage produced the maximum seed output per plant. Higher seed rates, however, resulted in the greatest seed yield per hectare. This difference might be caused by the use of agronomic techniques (seed rate), the area that enhances light interception to increase plant photosynthetic capacity, and the correlation between grain properties. The current study confirms the findings of Khan et al. [ 78 ], who reported that plants grown with wider spacing performed better individually and had more land available to them for photosynthesis and more solar radiation to absorb. In terms of seed yield per unit area, yield is influenced by factors such as the total number of plants per unit area and yield contributing parameters in addition to the performance of each individual plant. . According to Apaza-Gutierrez et al. [ 79 ], grain yield increases linearly within the density range, stem diameter and grain yield per plant decrease quadratically with increasing plant density, and the grain yield per unit area may be directly correlated with a plant's capacity to store nutrients on the stem. Similar results were obtained by Bongase et al. [ 26 ], who reported that low plant density causes plants to generate more branches and leaves per plant, which in turn results in the creation of more inflorescences, each of which contains more seeds. Table 6 Influences of the main effects of the seed percentage and variety on the number of inflorescences per plant and the seed yield per plant and per hectare in Jimma, Southwest China, Ethiopia, from 2020–21. Treatments Yield and Yield-related variables Seed rate (kg/ha) NIPP SYPP (g plant − 1 ) SYPH (t ha − 1 ) 1.5 23.93 a 3.78 a 1.78 c 2 21.15 ab 3.49 ab 1.98 bc 2.5 19.25 b 3.27 b 2.13 b 3 15.44 c 2.79 c 2.52 a 3.5 14.56 cd 2.54 cd 2.64 a 4 12.12 d 2.35 d 2.68 a LSD (0.05) 3.16 0.29 0.29 Varieties Madiira-II 16.34 b 3.38 a 2.53 a AC-NL 19.14 a 2.69 b 2.04 b LSD (0.05) 1.82 0.16 0.17 CV (%) 14.90 7.97 10.85 The means within columns for each variable followed by different letters are significantly different from each other at p < 0.01. NIPP = number of inflorescences per plant; SYPP and SYPH = seed yield per plant and per hectare, respectively. 3.1.5. Inflorescence length The inflorescence height was analyzed, and the results revealed that the inflorescence height was significantly affected by the main effects of the variety and seed rates (p < 0.01) as well as by the combined effects of the variety and seed rate (p < 0.01) (Appendix Table 3). The greatest inflorescence height (25.66 cm) was produced by the combination of the Madiira-II variety and the 2.5 kg ha-1 seed rate, which was significantly greater than that of the other combinations. The next longest inflorescence height (24.41 cm) was recorded from the Madiira-II variety with 2 kg ha-1, which produced a similar inflorescence length (23.83 cm), followed by the longest inflorescence (19.83 cm) from AC-NL with a 4 kg ha-1 seed rate (Table 7 ). The genetic variations in growth habit, flower morphology, biological time for flower initiation, competition between vegetative and reproductive phases at critical times, and the ability of the genotype to interact with the environment and agronomic management practices could contribute to the variation in inflorescence height among the treatments. The decrease in inflorescence height observed in the Madiira II and AC-NL Amaranthus species at relatively high seed rates (over 2.5 kg ha-1) may be attributed to resource limitations caused by intense competition during critical phases of vegetative and reproductive growth. The other possible explanation is that in this study, the treatments associated with a lower seed rate (≤ 2.5 kg ha-1) for both varieties presented the highest growth parameters, including the maximum number of branches, the length of the leaf, and the thickest stem diameter. In addition, inflorescence height was strongly and positively associated with all the parameters except the dry weight of the leaf, dry matter, and height of the plant (Table 7 ).This finding aligns with the findings of Roitner-Schobesberger and Kaul [ 80 ], who demonstrated that amaranthus source strength during blooming was a greater yield-limiting factor than sink capacity. 3.1.6. Harvest index Data analysis revealed that seed rates had a substantial effect on the harvest index (p < 0.01), as did the interaction between the seed rate and amaranth variety (p < 0.01) (Appendix Table 3). With 1.5 kg ha-1, the Madiira-II variety had the highest percentage of the harvest index (16.23) of any Madiira variety, matching the AC-NL 2 kg ha-1 seed rate (14.62%). At the 3 kg ha-1 seed rate, AC-NL produced the lowest percentage (8.05) of the harvest index, suggesting that photoassimilate partition migrated into other biological yields, such as leaves, rather than reproductive portions.On the basis of this outcome, the harvest index typically ranged from 8.05 to 16.23% (Table 8 ). The genetic differences between varieties in how resources are distributed to seeds as opposed to leaves, competition between different parts of the vegetative and reproductive phases, and possible interactions between the genotype and the environment and management techniques could all contribute to the variation in the harvest index among treatments. The current result is consistent with earlier research by Guillen-Portal et al. [ 81 ], who reported that grain yields over a wide range of plant populations varying from 4 to 200 plants m − 2 at a row spacing of 0.76 m. Gimplinger et al. [ 9 ] reported that increasing density reduced the harvest index and that low plant density allowed the potential for yield to be exhausted. Comparable studies by Bongase et al. [ 26 ] revealed that, for Madiira II, which ranges from a low plant density (47,619 plants ha-1) to a high plant density (111,111 plants ha-1), a harvest index ranging from 5.71% to 7.28% was attained, whereas the harvest index ranging from 5.71% to 37.48% varied between amaranth genotypes. The plant's capacity to devote more biomass (assimilates) to leaves and biological yields than to reproductive portions is indicated by the low harvest index [ 82 ]. Thousand seed weight The results revealed that the main effects of variety, seed rate, and the interaction between amaranth variety and seed rate (p < 0.01) had a significant effect on the weight of thousands of seeds (Appendix Table 3). A greater thousand-seed weight (0.83 g and 0.81 g) was produced by the combined effects of the Madiira-II variety with 2 kg ha-1 and the Madiira-II variety with 1.5 kg ha-1 seed rates. The weight of the lowest thousand seeds (0.40 g) was from Madiira-II at 4 kg ha-1, AC-NL at 3.5 kg ha-1, and Madiira-II at 3.5 kg ha-1, and there was no significant or numerical difference between them (Table 7 ). These data suggest that amaranthus reacts to a reduced seed percentage to increase the weight of thousands of seeds. This may be the result of shade and branch overlapping effects among growth characteristics, or it may be the result of leaf defoliation at greater plant densities. Seed weight, plant-1 seed quantity, and yield components may have been impacted by this event. This study is consistent with previous research showing that diminished sources as a result of defoliation or shade decrease seed bulk and yield [ 80 , 83 ]. Table 7 Effects of the Interaction of Seed Rate and Varieties on Yield and Yield-Related Variables of Amaranth in Jimma, Southwest China, and Ethiopia during 2020–21 Treatments Yield and yield related variables Varieties Seed rate (kg/ha) IL (cm) HI (%) TSW (g) Madiira 2 1.5 23.83 bc 16.23 a 0.81 a 2 24.41 b 12.13 cd 0.83 a 2.5 25.66 a 11.13 cd 0.78 b 3 23.33 c 12.65 bc 0.55 d 3.5 21.16 d 10.22 d 0.4 f 4 21.76 d 12.08 cd 0.4 f AC-NL 1.5 23.43 c 13.06 bc 0.78 b 2 23.43 c 14.62 ab 0.76 b 2.5 23 c 12.15 cd 0.65 c 3 22 d 8.05 e 0.5 e 3.5 21.33 d 11.39 cd 0.4 f 4 19.83 e 11.75 cd 0.4 f LSD (0.05) 0.951 2.13 0.03 CV (%) 2.46 10.39 3.17 The means within columns for each variable followed by different letters are significantly different from each other (p < 0.01). IL = inflorescence length; HI = harvest index; TSW = thousand-seed weight. 3.2. Correlation analysis Fresh leaf yield ha-1 was found to be positively and significantly (p < 0.01) correlated with days to 50% maturity (r = 0.47**), leaf area (r = 0.57**), leaf length (r = 0.47**), leaf width (r = 0.53**), stem diameter (r = 0.65**), number of branches (r = 0.60**), leaf yield plant-1 (r = 0.54), above-ground fresh biomass (r = 0.59**), leaf weight (r = 0.65**), dry matter content (r = 0.47**), inflorescence length (r = 0.74**), seed yield plant-1 (r = 0.72**), and seed yield ha-1 (r = 0.55**). On the other hand, it was significantly and adversely correlated with the following parameters: plant height (r = 0.44**), inflorescence number (r = 0.65**), harvest index (r = 0.73**), and number of days to 50% blooming (r= -0.85**). LFYPH (t ha-1) did not, however, show any correlation with TSW, TAGDBPP, or NLPP. The leaf yield per hectare may be dependent on the number of plants per unit of area rather than on individual plants, which could be the cause (Table 8 ).This study is consistent with that of Bongase et al. [ 26 ], who reported positive and significant correlations between leaf yield ha-1, the plant-1 branch count, the leaf yield plant-1, the leaf area, the leaf area index, and the dry weight of the yield plant-1. The seed yield ha-1 also showed a significant (p ≤ 0.01; p ≤ 0.05) positive correlation with the following: days to 50% maturity (r = 0.79**); leaf number (r = 0.59**); leaf area (r = 0.31*); leaf length (r = 0.68**); leaf width (r = 0.58**); stem diameter (r = 0.65**); branch number (r = 0.72**); leaf fresh yield plant-1 (r = 0.68**); leaf yield ha-1 (r = 0.55**); leaf dry weight (r = 0.49**); dry matter content (r = 0.54**); inflorescence length (r = 0.48**); seed yield per plant (r = 0.76**); and thousand seed weight (r = 0.59**). Nonetheless, Table 9 shows that there was a substantial negative correlation (p ≤ 0.01; p ≤ 0.05) with days to 50% blooming (r= -0.77**), aboveground biomass (r= -0.36*), and number of inflorescences plant-1 (r= -0.35*). These findings showed that the quantity of inflorescences, dry biomass, and phenological time for flower initiation significantly impacted seed yield. Table 8. Pearson's correlation coefficient analysis for growth, leaf yield and seed yield of Amaranthus varieties ( Amaranthus var.). 4. CONCLUSION Amaranthus is an indigenous and neglected vegetable crop that is widely distributed in many parts of Ethiopia. raditionally utilized as feed, edibles, and therapeutic plants during famines and food shortages, particularly in low-income homes. However, because of poor agronomic methods, such as the use of the right seed rates and a lack of better variety, its productivity and production are considerably below its yield potential. Therefore, supplemental irrigation was used to conduct this study from 2020–2021. This study employed a complete randomized block design (RCBD) with three replications, utilizing a 2x6 factorial combination to investigate the effects of six different seed rates (1.5, 2, 2.5, 3, 3.5, and 4 kg ha-1) on the growth, leaf, and seed yields of two Amaranthus varieties, Madiira-II and AC-NL, in Jimma, southwest Ethiopia. The results of this study showed that the seed rate, variety main effects, and their interaction effects all had an impact on growth, leaf yield, and seed yield. The primary effects of the amaranthus type and seed rate on the number of days to 50% flowering, number of days to 50% maturity, leaf area, plant height, number of branches, number of inflorescences, and seed production per plant and per hectare were significantly different (p<0.01; p<0.05). However, with respect to leaf number, leaf width, fresh biomass above ground, dry biomass above ground, dry matter content, leaf yield per plant, leaf yield per hectare, inflorescence length, harvest index, and thousand-seed weight, the effects of the variety and seed rate interaction were highly responsive (p<0.01). Only interaction effects were revealed to be the cause of the significant difference (p<0.05). In general, the Madiira-II variety outperformed the AC-NL variety in terms of seed yield per plant (3.38 g) and per hectare (2.53 t) according to the current findings. Nonetheless, the AC-NL variety's primary impacts generated the greatest number of inflorescences per plant (19.14). . With respect to the primary impacts of seed rates, the highest seed yield per hectare (2.52, 2.64 and 2.68 t) was obtained from the 3, 3.5 and 4 kg ha-1 seed rates, whereas the maximum seed yield per plant (3.78 g) was produced from the 1.5 kg ha-1 seed rates. In contrast, Madiira-II, with 2.5 kg ha-1 seed rates, and AC-NL, with 2.5 kg ha-1 seed rates, produced statistically identical fresh leaf yields per hectare (26.63 and 25.17 t), respectively. AC-NL revealed that the maximum fresh leaf yield per hectare was associated with AC-NL, with a 3 kg ha-1 seed rate (30.84 t). Nonetheless, Madiira-II at 1.5 kg ha-1 (14.35 t) and Madiira-II at 4 kg ha-1 seed rates (14.62 t) yielded the lowest fresh leaf yield per hectare. Thus, the farming community in the study area and other similar agroecological areas could benefit from the use of the Madiira-II variety with a 2.5 kg ha-1 seed rate, which results in the maximum leaf yield per plant (38.66 g) and per hectare (25.17 t), and the AC-NL amaranthus variety, which could result in the highest leaf yield per hectare (30.84 t).In contrast to the AC-NL variety, the Madiira-II variety yielded the maximum seed yield per plant (3.78 g) and per hectare (2.53 t). To obtain thorough advice, more trials should be carried out at different times of the year and in regions with comparable agronomic methods, as this study was performed with additional irrigation. However, as the seed rate increased, the seed yield per hectare slightly increased; hence, further research is needed to achieve maximum seed yields. More significantly, assessing the nutritional profiles and community acceptability of these two types of leaves and grains would be beneficial. Tilahun Deressa: Developed the research concept note, wrote the draft manuscript text and interpreted the data. Amsalu Nebiyu : Contributed to manuscript development and revisions Gerba Daba: Contributed to work by supervising the overall research procedures. Garome Shifaraw: Contributed to the work of managing the research data, data analysis and editing the paper for publication. Declarations Funding : This work did not receive any funding. Data Availability : All the data generated or analyzed during the study are included in this manuscript and its supplementary information files. Conflicts of interest : The authors declare that they have no conflicts of interest regarding the publication of this manuscript. Consent to publish declaration: N/A Clinical Trial: N/A References Suresh, S., Chung, J.W., Cho, G.T., Sung, J.S., Park, J.H., Gwag, J.G. and Baek, H.J., 2014. Analysis of molecular genetic diversity and population structure in Amaranthus germplasm using SSR markers. International Journal of Plant Biosystems, 148 (4): 635-644. Stetter, M.G. and Schmid, K.J., 2017. Analysis of phylogenetic relationships and genome size evolution of the Amaranthus genus using GBS indicates the ancestors of an ancient crop. Molecular phylogenetics and evolution , 109 : 80-92. Thapa, R., and M. W. Blair. 2018. Morphological assessment of cultivated and wild amaranth species diversity. Agronomy 8: 272- 275 Qiu, Y. and Liu, G., 2021. 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13:50:33","extension":"xml","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":249332,"visible":true,"origin":"","legend":"","description":"","filename":"840ee08a62fe40fcb2ecfcc5bf722bd01structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7606428/v1/5898279a1fa3f178e932d3c7.xml"},{"id":94113182,"identity":"4189d2c8-bd44-4e74-b778-6636783ccd21","added_by":"auto","created_at":"2025-10-22 13:50:33","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":262896,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7606428/v1/6fca25df6415a812dfe9e686.html"},{"id":94113152,"identity":"c73c9861-9b69-4259-a402-a82045f1f3e1","added_by":"auto","created_at":"2025-10-22 13:50:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84791,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area (Gudeta bula rural kebele), Jimma, southern Ethiopia.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7606428/v1/7b111b1842f62c4799168462.png"},{"id":94114354,"identity":"2bfae358-725a-49ad-9021-ec10c083f8cf","added_by":"auto","created_at":"2025-10-22 14:06:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":227428,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7606428/v1/024eac82aabbe8366e8a2880.png"},{"id":94114133,"identity":"02dffbee-603e-4dc5-9194-351b73d0cf96","added_by":"auto","created_at":"2025-10-22 13:58:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30752,"visible":true,"origin":"","legend":"\u003cp\u003eThe main effects of the seed percentage on leaf length per plant (cm).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe meanswithin columns for each variable followed by different letters aresignificantly different from each other at p\u003c/em\u003e\u003cu\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u003c/u\u003e\u003cem\u003e0.01.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7606428/v1/2992040bea2638c0fe29102f.png"},{"id":94122931,"identity":"ba2a819e-5354-459e-b3ae-1d582b12b911","added_by":"auto","created_at":"2025-10-22 15:31:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2225490,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7606428/v1/1f6e5b64-f1cb-439d-9d08-f0acdcd906fe.pdf"},{"id":94113153,"identity":"9c3249ea-e9bb-4ca2-9aec-df9a1f5daf44","added_by":"auto","created_at":"2025-10-22 13:50:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21549,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDICES.docx","url":"https://assets-eu.researchsquare.com/files/rs-7606428/v1/8e7884d18d5b35cda482f0ec.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eResponse of Amaranthus Varieties (Amaranthus Spp.) to Different Seed Rates on Growth, Leaf, and Seed Yields in Jimma,southwest Ethiopia\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eAmaranthus is a member of the dicotyledonous genus Amaranthus L., which is grown for decorative, grain, leaf, and fodder purposes. It comprises approximately 70 species[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. A. Amaranthus, A. Albersia, and A. Acnida are its three subgenera. The Amaranthus genus is currently widely grown and cultivated in tropical Africa, tropical South America, sections of the Pacific islands, China, India, Indonesia, Japan, and Pakistan [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. A. dubius, A. lividus, and A. hybridus constitute the majority of vegetal species, whereas A. hypochondriacus, A. cruentus, and A. caudatus are the three primary grain species considered [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, and \u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. In addition to the fact that most farmers and consumers dislike black grains, there is no discernible difference between the types of vegetables and grains.\u003c/p\u003e\n\u003cp\u003eThere are few studies on the quantitative and qualitative features that contribute to leaf and grain yields [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. The yield of amaranthus is contingent upon the production location and is influenced by multiple factors, including the absence of better cultivars [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. According to reports, the global average seed yield varies from 1 to 6 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, whereas the fresh leaf yield varies from 10 to 70 t ha-1 [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. While the average leaf yield of amaranthus produced worldwide is approximately 14.27 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, it is lower in African countries such as Nigeria, where it is approximately 7.6 t ha-1. The highest yield of amaranth seeds reported in Latin America is likely related to the improvement of local varieties through the selection process, which has not been carried out to the same degree in Africa [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. When Dmitrieva and Ivanov [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] studied various amaranthus species, they reported that there were no appreciable differences in the dry matter composition between the species. With the exception of A. spinosus, all amaranthus species presented high biological productivity in terms of their fresh green biomass output. In particular, for long stem species (A. cruentus 53.7 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and A. caudatus 49.0 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), soil and climatic circumstances enable plants to generate well-developed stems, leaves, and inflorescences that contribute to intense photosynthesis and very high leaf yields.\u003c/p\u003e\n\u003cp\u003eHowever, only one species of amaranth (A. cruentus, 1.32 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) showed seed productivity, and in that species, seeds were able to mature to their full potential every year. In other species, however, seed maturation was not observed because of greater temperature requirements [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. Three varieties of Amaranthus (white, red, and black) are grown in Ethiopia, according to Mekonnen et al. [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. It is grown three times a year as a single crop or in conjunction with maize or sorghum, primarily by Menit people (Menitshasa and Menit Goldya), who live in the SNNPR [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. The SNNPR produces 2,527 ha of amaranth, with the Bench Maji zone accounting for the largest portion, or 689 ha of the total [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. Inadequate resources (water, light, plant nutrients, and spaces) and fewer or more plant populations are the primary causes of low yields. Resources (water, light, and plant nutrients) are made accessible for plant growth when the planting density is relatively low, and competition for these resources is generally minimal. Plant growth tends to decline when the planting density increases because of increased competition. As a result, growth is favored by an ideal planting density, meaning that higher than optimal densities inhibit growth [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eHigh-yielding cultivars cannot be fully genetically harvested unless an appropriate seeding rate is ensured. Plant density impacts the leaf area index and dry matter output through competition for light, nutrients in the soil, and accessible moisture, all of which affect crop growth and production [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. The attainment of enhanced vegetable output and resource efficiency is contingent upon the availability of suitable planting technologies and seed density. High-quality vegetable production would support household income and expand domestic, regional, and global market prospects.\u003c/p\u003e\n\u003cp\u003eAn alternative crop species known as amaranthus has historically been utilized for food, fiber, fodder, oil, or medicinal purposes. The potential of these products to support food security, nutrition, health, generate revenue, and environmental benefits is underutilized [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. Owing to a number of issues, including the alarming rate of population expansion and ongoing climate change, the production of Ethiopia\u0026apos;s key staple foods is now insufficient to meet the country\u0026apos;s food needs [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Ethiopia should encourage the production and usage of amaranthus because of its many nutritional advantages [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Research, despite its importance, is concentrated on agronomic techniques, particularly seed rates, for the significantly lower leaf and grain yields of Amaranthus in Ethiopia.\u003c/p\u003e\n\u003cp\u003eAmaranthus is a widely dispersed wild plant (i.e., weed) in the Jimma area that is utilized for a variety of functions, including personal communication, treating diarrhea, and helping expectant mothers stop bleeding after giving birth. At JUCAVM, the Eladale Research Site, Amaranthus variants have been identified since 2015 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Six Amaranthus varieties (UG-AM-13, UG-AM-68, TZSMN102-Sel, AH-NL-Sel, Madiira-I and Madiira-II) were used in the experiment by Bongase et al. [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e], with three rows in a row (15, 25, 35 cm); the interrow spacing was 60 cm. Madiira-II produced the highest leaf yield (18.29 t ha-1) and the highest grain yield per hectare (1.802 t ha-1 and 1.786 t ha-1) when TZSMN102-Sel and AH-NL-Sel were planted at 15 cm. The best seed rate for a particular variety of several kinds of amaranthus leaves and grains, however, has not been studied in Ethiopia. Furthermore, there is a lack of easily available information in the literature on Ethiopian agroecology concerning the impact of the seed rate for a particular variety on the productivity of Amaranthus types in vegetables or grains. Thus, it makes sense to assess numerous Amaranthus cultivars in Jimma, southwest Ethiopia, to examine how they react to various seed rate levels. The goal of the current study was to ascertain how amaranthus types in Jimma, southwest Ethiopia, respond to varying seed rates in terms of growth, leaf yield, and seed production.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecific Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eTo determine the effects of Amaranthus varieties on growth and leaf and seed yields in Jimma, southern Ethiopia\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe optimal seed rate that results in the highest leaf and seed yields in Jimma, southwestern Ethiopia, was determined.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eTo investigate the interaction effects of the seed percentage and Amaranthus variety on growth and leaf and seed yields in Jimma, southwestern Ethiopia\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Description of the Study Area\u003c/h2\u003e\n \u003cp\u003eThe study was carried out at the Eladale Research Site of the Jimma University College of Agriculture and Veterinary Medicine (JUCAVM) during 2020/21. The study site is located in Gudeta bula Kebele, in Jimma town, which is located 4 km from Jimma town on the way to Agaro - Bedele. It is located 1710 m above sea level and at 7\u0026deg; 33\u0026apos;N latitude and 36\u0026deg; 57\u0026apos;E longitude in the Jimma zone, Oromia National Regional State (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean annual maximum and minimum temperatures are 26.8 and 11.4\u0026deg;C, respectively, and the mean annual maximum and minimum relative humidities are 91.4 and 39.92%, respectively. The mean annual rainfall in the study area is 1250 mm. The soil at the experimental site is well-drained clay to silt clay with a pH of 5.5. The most common and dominant soil type is Nitisol [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Experimental Materials\u003c/h2\u003e\n \u003cp\u003eTwo varieties of Amaranthus (Madeira II and AC-NL) were collected from the Melkasa Agricultural Research Center (MARC) and used for the experiment. The two varieties are described below (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescription of the experimental materials on the basis of their qualitative characteristics.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eQualitative characteristics of the Amaranth genotypes.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMadiira-II\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAC-NL\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBranching index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBranches all along the stem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebranches all along the stem\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBranching pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighly branched\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBranched\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpines in leaf axil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeaf margin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEntire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEntire\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStem pigmentation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrowth habit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eErect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eErect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStem pubescence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProminence of leaf veins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRogose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRogose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeaf color\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePetiole pigmentation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeaf shape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRhombic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOvatainate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlant Height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery tall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeed color\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrown black\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Treatments and Experimental Design\u003c/h2\u003e\n \u003cp\u003eThe treatment consisted of two varieties (Madiira II and AC-NL) and six seed rates (1.5, 2, 2.5, 3, 3.5, and 4 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) with 2\u0026times;6 factor treatment combinations. The experiment was conducted in the RCBD with three replications via factorial arrangements. The reference seed rate (standard check) used in this experimental setup was 3 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e on the basis of Sokoto and Victor [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Experimental Procedures and Crop Management.\u003c/h2\u003e\n \u003cp\u003eThe area was cleared of undesired weeds, grasses, and plant thrashes. It was then pluckled four times with oxen and harrowed by hand until the soil was fine. Each plot included five rows spaced 60 cm apart and measured 3 m \u0026times; 2.7 m (8.1 m2) [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Each plot and replication were separated by 0.6 and 1 m, respectively. To ensure a uniform stand and speed up the sowing process, the seeds were directly drilled and mixed with well-dried friable soil at a ratio of 1 g of seeds to 100 g of soil [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. The seeds were planted at a depth of 1.30 cm [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e], and to protect them from rain, the soil was lightly covered with well-dried mulching grasses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Data collection\u003c/h2\u003e\n \u003cp\u003eTwelve randomly selected plants from the three middle rows of each plot were used to collect data on crop phenology (flowering and maturity), growth and yield, and its constituent parts. To minimize any potential border effects, the two outermost rows were not included in the data collection process.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6. Data Analysis\u003c/h2\u003e\n \u003cp\u003ePrior to data analysis, each set of data was examined to ensure that it complied with the fundamental ANOVA assumptions. The general linear model (GLM) of the Statistical Analysis System (SAS) software version 9.3 was used to conduct an analysis of variance (ANOVA) on data related to growth parameters, yield, and yield factors [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. When an ANOVA revealed significant differences, the treatment means were compared via the least significant difference (LSD) test at the 5% probability level. The relationships between growth and yield characteristics were evaluated via Pearson\u0026apos;s correlation coefficient.\u003c/p\u003e\n \u003cp\u003eThe study design model was as follows.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Crop Phenology\u003c/h2\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1. Days to 50% flowering\u003c/h2\u003e\n \u003cp\u003eThe results indicate that the main effects of the type and the seed rate had a substantial effect on the number of days to 50% blooming (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). On days with 50% blooming, however, the combined impacts of the type and the seed rate were not statistically significant (Appendix Table 1). Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea shows that the Madiira II variety had the longest duration to 50% flowering, measuring 72.77 days, whereas the AC-NL variety had the shortest duration, measuring 66.83 days. Variations in the number of days from blossom initiation were attributed to variations in genotypes. This outcome is in line with the findings of Casini and La Rocca [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e], Mbwambo et al. [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e], Bongase et al. [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e], and Dinssa et al. [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e], who reported that genotypic changes were the cause of the variances shown in terms of days to 50% blooming.\u003c/p\u003e\n \u003cp\u003eFurthermore, based on the 4 kg ha-1 and 3.5 kg ha-1 seed rates, the findings shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb revealed that the first days of 50% blooming were 64.5 and 66.66 days. With 74, 72.16, and 72.16 days, respectively, the 1.5 kg ha-1, 2 kg ha-1, and 2.5 kg ha-1 seed rates had the longest days on record. Three kg of seed per hectare was statistically comparable to two kg, 2.5 kg, and 3.5 kg of seed on fifty percent of the days before flowering. According to the results of this study, a relatively high seed percentage results in a relatively large plant population, which intensifies interplant competition and prompts early flowering. Variations in the time it takes for flowers to begin can be attributed to the quantity of assimilates that are accessible during the sensitive phase as well as to increased competition for resources among densities of plants, which speeds up blooming time by acting as escape mechanisms for the plant and vice versa. The current investigation aligned with Teshome and Ousman\u0026apos;s earlier linseed study [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. On days of phenological parameters (days of flower initiation), amaranth genotypes respond significantly to various seed rates, although there was no significant difference in interaction effects.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2. Days to 50% maturity\u003c/h2\u003e\n \u003cp\u003eThe primary influences of the seed rate and variety were shown to have an impact on the physiological maturity from days to 50%. The varieties highly significantly differed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while the seed rates substantially differed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the interaction effects of type and seed rate did not impact physiological maturity from days to 50% physiological maturity (Appendix Table 1). The Madiira II variety had the highest recorded number of days to 50% maturity (100.94 days), whereas the AC-NL variety had the lowest number of days (90.83 days) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). The genetic diversity in maturation time across types may be the cause of the variation in days to physiological maturity. This conclusion is consistent with the findings of Dmitrieva and Ivanov [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e], who reported that amaranthus prefers to grow more rapidly in developing species with a growing season of no more than 100 days and that they require more heat than moisture.\u003c/p\u003e\n \u003cp\u003eAccording to Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb, the days to 50% physiological maturity (91.33) recorded from a 4 kg ha-1 seed rate were statistically similar to those recorded from plants sown at 3.5 kg ha-1 (93.66 days), 3 kg ha-1 (95.16 days), and 2.5 kg ha-1 (96 days). On the other hand, the 1.5 kg ha-1 seed rate resulted in the greatest number of days to 50% maturity (100.16 days). The only seed rate that varied from 3.5 kg ha-1 to 4 kg ha-1 was the 1.5 kg ha-1 seed rate; there were no statistically significant changes in the other seed rates. One possible explanation is that greater plant densities lead to more shadowing between plants, which accelerates leaf senescence and results in leaf defoliation. The work is not supported by the current study. According to Pandey and Singh [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e], there is variance in phenological days because of the unpredictable development pattern of crops and the existence of vegetative structures that persist throughout the reproductive phase [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea. Effects of Amaranthus varieties on days up to 50% to flowering and 50% to maturity.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.3. Growth response\u003c/h2\u003e\n \u003cdiv id=\"Sec14\" class=\"Section4\"\u003e\n \u003ch2\u003e3.1.3.1. Leaf area of plant\u003csup\u003e-1\u003c/sup\u003e\u003c/h2\u003e\n \u003cp\u003eThe primary impacts of variety (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and seed rate (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) on the leaf area per plant were highly sensitive (Appendix Table 2). Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e displays the largest leaf area (50.08 cm2) measured from the Madiira-II variety and the lowest leaf area (40.03 cm2) from the AC-NL variety. These findings demonstrated that genetic variations in the crop, such as variations in leaf form, influenced the leaf area. This outcome is consistent with the findings of Liu and St\u0026uuml;tzel [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e], Shankar et al. [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e], Srivastava [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e], and others, who reported that genotypes accounted for the variation in leaf characteristics and that the leaf area per plant was affected by this variation.\u003c/p\u003e\n \u003cp\u003eThe findings revealed that the largest leaf areas (56.44 cm2 and 54.05 cm2) were 1.5 kg ha-1 and 2 kg ha-1, which were statistically equivalent to 50.53 cm2 from 2.5 kg ha-1. The 4 kg ha-1 (33.34 cm2) and 3.5 kg ha-1 seed rates (36.38 cm2) had the lowest leaf areas, and they were statistically similar to the 3 kg ha-1 (39.58 cm2) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). As the seed rate increased above 2.5 kg ha-1, the results indicated a small decrease in leaf area. This could be because there were fewer plant densities, which allowed the plant to make the most of its resources (space, nutrients, light, water, air, etc.), resulting in a larger leaf area due to the low seed rate. The results of the present study were in line with those of Rotich et al. [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e], who reported that stress from high planting density decreased leaf size by reducing leaf area expansion and growth, which in turn reduced light interception and photosynthesis. Both results are therefore strongly correlated. Yarnia et al. [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e] reported that a reduced leaf area with delayed sowing and increased density was due to a shorter growth period and increased competition for agricultural input, especially light, between plants. According to Poorter et al. [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e], competition has a significantly greater effect on a plant\u0026apos;s complete leaf area in terms of nutrient limitation. As a result, a plant with a smaller leaf area may have fewer leaves with higher irradiances, which would increase the plant\u0026apos;s total leaf mass area. Higher light levels may cause leaves to become thicker than the leaf area that can benefit from more light, according to Weraduwage et al. [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. Redirecting resources from area growth to thickening enhances the efficiency of creating a new, thicker leaf area and increases the maintenance of respiration loss in the future.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\" class=\"Section4\"\u003e\n \u003ch2\u003e3.1.3.2. Plant Height\u003c/h2\u003e\n \u003cp\u003eStatistical analysis revealed that the major effects of seed rate (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and variety (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) significantly affected plant height. However, plant height had no effect on this interaction (Appendix Table 2). The Madiira-II variety produced more plants (151.19 cm) than did the AC-NL variety (121.76 cm), according to the mean value (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The genetic differences in crop growth patterns, including the length and quantity of internodes, the time it takes for flowers to initiate, and physiological maturity, may account for the variation in plant height between the two types. This outcome is consistent with earlier research by Gnan et al. [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e], who proposed that an earlier transition to flowering may limit plant size and decrease seed production in annual plants, and Shahzad et al. [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e], who reported that the genetic composition of a crop, along with environmental factors [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e], is the primary control of crop height. Tejaswini et al. [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e] reported that 27 (27) Amaranthus genotypes presented significant differences in plant height at different stages of growth, of which eight (8) genotypes were found to be Madiira-II. These findings are in strong agreement with the work of Alemu et al. [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e], who reported the highest plant height of the genotype (106.77 cm), followed by AC-NL (95.62 cm).\u003c/p\u003e\n \u003cp\u003eThe greatest heights (165.03 cm and 152.05 cm) were measured at seed rates of 4 kg ha-1 and 3.5 kg ha-1, respectively. These values were statistically similar to the plant height when the plants were sown at 3 kg ha-1 (144.64 cm). However, the plant heights produced from 1.5 kg ha-1 and 2 kg ha-1 produced the smallest plant heights (109.65 cm and 118.02 cm), which were statistically comparable to the plant height produced from the 2.5 kg ha-1 (129.50 cm) seed rate. This could be because, on the one hand, a relatively high plant density encourages upward growth to compete for light interception; on the other hand, a relatively low plant population receives sufficient resources and more free space for lateral growth, which might limit plant height.\u003c/p\u003e\n \u003cp\u003eThis result is inconsistent with the findings of Henderson et al. [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e] \u003cem\u003eand\u003c/em\u003e Zubillaga et al. [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e], who reported that plant population pressure can limit plant height, and with the experiments of Abbas et al. [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e] and Baloch et al. [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e], who reported that an increase in the seeding rate resulted in a slight decline in the heights of the plants. In contrast, the findings of the present study support the findings of Olofintoye et al. [\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e], who reported an increase in plant height with increasing plant density from 40,000 to 50,000 to 66,666 plants ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in \u003cem\u003eA. cruentus\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e]. Similar findings of Suleiman [\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e] and Alemayehu et al. [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e] revealed that an increase in the seeding rate resulted in a slight increase in the height of the plants [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section4\"\u003e\n \u003ch2\u003e3.1.3.3. Number of branch plants \u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/h2\u003e\n \u003cp\u003eBoth the seed rate and variety had a substantial (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) effect on the number of branches per plant (Appendix Table 2). With 23.61 branches produced plant-1, the Madiira-II cultivar presented the highest mean value for the number of branches per plant. Nonetheless, the AC-NL cultivar yielded the fewest branches per plant\u0026mdash;20.83\u0026mdash;according to Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Genetic variables, such as variations in crop morphology and growth habits, may account for the branch number disparities observed in this study.\u003c/p\u003e\n \u003cp\u003eThe results of this study supported the findings of Mbwambo et al. [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e], who noted differences in the RVI00021 and RVI000222 across seasons, and Dinssa et al. [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e], who reported that there was only a slight difference in the rankings of the entries that produced a high number of branches between leaf harvest treatments for PARIS (A)-Sel (14.7 in no-leaf harvest and 14.4 in leaf harvest), DB 2006306-Sel (14.0; 13.9), IP-5-Sel (17.9; 14.5), \u0026apos;Madiira-I\u0026rsquo; (18.6; 15.2), and \u0026apos;Madiira-II\u0026rsquo; (19.6; 12.8). Horak and Loughin (2000) reported that a species\u0026apos;s capacity to occupy space is reflected in its branching and plant volume, and they noted that A. palmeri, A. rudis, A. retroflexus, and A. albus were the species with the highest values for these traits. Additionally, according to Garba et al. [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e], varietal traits have a significant effect on a plant\u0026apos;s ability to tiller.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the seed rates of 1.5 and 2 kg ha-1 produced the greatest number of branches per plant (24.16 and 24.33) and that these seed rates did not differ from those of the branches observed from 2.5 kg ha-1 (23.83). At the 4 kg ha-1 seed rate, the lowest branch number (19.33) was obtained, whereas at the 3.5 kg ha-1 seed rate, a similar branch number (19.83) was reported. Compared with the 3 kg ha-1, 3.5 kg ha-1, and 4 kg ha-1 seed rates, the 2 kg ha-1 seed rate produced 10.27%, 18.49%, and 20.54% more branches, respectively, in this study. In contrast, the use of 1.5 kg ha-1 and 2.5 kg ha-1 seed rates produced equal branch counts (0.68% and 2.05%, respectively).As a result, lower plant densities encourage vegetative growth, which includes branches and canopies, and help ensure that plants have access to sufficient nutrients. The opposite might occur from competition for assimilates when resources are depleted sooner and when a plant\u0026apos;s ability to grow vegetatively is hindered. The findings of the present study support the findings of Caliskan et al. [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e] and Chundawat et al. [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e], who reported that individual plants receive more light and nutrients when there is a greater gap between plants or a lower plant density.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain\u003c/strong\u003e effects of varieties and seed rates on leaf area, plant height, and branch number in Jimma, southwest Ethiopia, during 2020\u0026ndash;21.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eGrowth Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeed rate (kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAPP (cm\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePH (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNBPP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.05 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.65 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.16 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.44\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.02 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.33\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.53 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.50 \u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.83\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.58 \u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144.64 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.83\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.38\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152.05 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.83\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.34 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165.03 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.33 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSD (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21.79\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVarieties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMadiira-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.08 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151.19\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.61 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAC-NL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.03 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121.76 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.83\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSD (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21.43\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eThe means within columns for each variable followed by different letters are significantly different from each other at p\u003c/em\u003e\u0026thinsp;\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;\u003cem\u003e0.01. LAPP\u0026thinsp;=\u0026thinsp;leaf area per plant; PH\u0026thinsp;=\u0026thinsp;plant height; NBPP\u0026thinsp;=\u0026thinsp;number of branches per plant.\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section4\"\u003e\n \u003ch2\u003e3.1.3.4. Leaf length plant\u003csup\u003e-1\u003c/sup\u003e\u003c/h2\u003e\n \u003cp\u003eAccording to these findings, the seed percentage had a substantial effect on the LLP-1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Nevertheless, throughout the experimental treatments, neither the variety response nor the combination of variety and seed rate were noted (Appendix Table 2). With 1.5 kg ha-1, the largest leaf length (13.52 cm) was measured; this value was comparable to the leaf lengths measured with 2.5 kg ha-1 and 2 kg ha-1 seed rates (13.13 cm and 12.94 cm). At 4 kg ha-1 and 3.5 kg ha-1, the shortest leaf length (10.46 cm and 10.69 cm) was measured with statistical parity, with the leaf area recorded from 3 kg ha-1 seed rates (11.39) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn this study, the difference in leaf length was highly dependent on seed rates rather than on the genetic makeup of the crops. The results of the present study revealed that a relatively low seed percentage yields the longest leaf length, whereas a relatively high seed percentage results in the shortest leaf length. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, as the seed percentage increased, the leaf length decreased. This may be due to lower seeding rates, which result in fewer plant populations per unit area, increasing open space and lessening interplant competition for resources. The results of the present study are consistent with those of Bongase et al. [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e], who reported that longer and wider leaves were encouraged to grow by having more free space within the row. Majumder [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e] also reported that there were significant differences in leaf length between plants with different spacings, noting that the longest leaves were found with a wider spacing (30 cm \u0026times; 30 cm) than with a narrow spacing (30 cm \u0026times; 10 cm) [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section4\"\u003e\n \u003ch2\u003e3.1.3.5. Number of leaves per plant\u003c/h2\u003e\n \u003cp\u003eThe results demonstrated that the main effects of the seed rates and the combined effects of the variety and seed rate had a substantial (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) effect on the number of leaves per plant. The quantity of leaves per plant, however, was unaffected by variety (Appendix Table 2). The combination of the Madiira-II variety with 2 kg ha-1 and the AC-NL variety at 2.5 kg ha-1 produced the maximum number of leaves per plant (112.33 and 111.66), which was statistically similar to the number of leaves found from the Madiira-II variety at 2.5 kg ha-1 and the AC-NL variety with a 3 kg ha-1 seed rate (108.33 and 104). The plots treated with AC-NL at 1.5 kg ha-1 produced 88 leaves per plant, which did not differ from those of the Madiira-II variety, with 3.5 kg ha-1 (94.33); AC-NL, 2 kg ha-1 (94.66); AC-NL, 3 kg ha-1 (104); Madiira-II, 1.5 kg ha-1 (81.33); Madiira-II, 4 kg ha-1 (81); and AC-NL, 4 kg ha-1 (77.33 leaves). The combined effects of the AC-NL variety with 3.5 kg ha-1 produced the lowest leaf number (62) and did not differ statistically from those of Madiira-II at 3 kg ha-1 (66.66) and AC-NL at 4 kg ha-1 (77.33 leaves) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eA lower plant population that produces more branches with a larger leaf area may result in fewer leaves per plant and a shorter phonological time. Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the combined effect of the two amaranthus varieties with lower seed rates and higher seed rates produced similar results. Conversely, a relatively high seed percentage results in leaf defoliation because of shadowing effects that decrease assimilate production and the photosynthetically active surface area of leaves for solar radiation, particularly around the flowering phase [\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe amaranthus variety with the ideal seed percentage had more leaves per plant than did the variety with a lower or higher seed percentage (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The current result was consistent with the research of Henderson et al. (2000), who noted that plant density in amaranthus could influence the number of leaves per plant. Tejaswini et al. [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e] and Sokoto and Victor [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] reported that the planting technique and seed rates had an impact on amaranthus (Amaranthus spp.) leaves.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\" class=\"Section4\"\u003e\n \u003ch2\u003e3.1.3.6. Leaf width\u003c/h2\u003e\n \u003cp\u003eThe results of this investigation demonstrated that the primary effects of seed rates, varieties, and their interactions had a significant effect on leaf width (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Appendix Table 2). Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the combined impact of the Madiira-II variety with a 2 kg ha-1 seed rate resulted in the broadest leaf width (3.30 cm), which differed significantly from the other seed rates. The combination of the AC-NL variety with 1.5 kg ha-1; AC-NL3 kg ha-1; AC-NL 2 kg ha-1 and Madiira-II 1.5 kg ha-1 seed rates yielded the following larger leaf widths (2.97 cm; 2.96 cm; 2.95 cm and 2.94 cm), which were statistically equivalent. Nonetheless, AC-NL (3.5 kg ha-1) and AC-NL (4 kg ha-1) had the smallest leaf widths, measuring 2.39 cm.\u003c/p\u003e\n \u003cp\u003eThis study\u0026apos;s results are similar to those of Bongase et al.[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e], who reported that the Madiira II variety had wider (9.749 cm) leaves with a wider intrarow spacing of 35 cm, whereas the UG-AM-13 variety planted at an intrarow spacing of 15 cm had the shortest (5.67 cm) and narrowest (4.91 cm) leaves. The variation in leaf width among treatments could be due to the combined effects of the crop\u0026apos;s genetic makeup on growth habits, leaf shapes, and leaf structures as well as the interaction of the variety with the environment and agronomic practices.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec20\" class=\"Section4\"\u003e\n \u003ch2\u003e3.1.3.7. Stem diameter\u003c/h2\u003e\n \u003cp\u003eThe results of this investigation demonstrated that the diameter of the shoot was influenced by the seed rate (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), main variety effect (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and combination effect (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) of both the variety and the seed rate (Appendix Table 2). The Madiira-II variety treated with 1.5 kg ha-1 had the widest stem diameter (2.39 cm), whereas the Madiira-II variety treated with 2 kg ha-1 produced a statistically similar stem diameter (2.10 cm). Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the Madiira-II variety had the narrowest stem diameter, measuring 4 kg ha-1 (1.23 cm), followed by the Madiira-II variety, measuring 3.5 kg ha-1 (1.30 cm), the AC-NL variety, measuring 4 kg ha-1 (1.32 cm), the Madiira-II variety, measuring 3 kg ha-1 (1.38 cm), and the AC-NL variety, measuring 3.5 kg ha-1 (1.40 cm). The AC-NL variety presented the narrowest stem diameter, measuring 3 kg ha-1 (1.49 cm).\u003c/p\u003e\n \u003cp\u003ehe genetic makeup of crop morphology, including root morphology, root length, and the capacity to use and absorb nutrients in the soil and interact with the environment, as well as agronomic practices, was found to contribute to a trend toward differences in stem diameter. For the two amaranth species, the increase in the seed percentage led to a notable decrease in stem diameter and an increase in stem density. This is because increased seed rates naturally lead to increased stem densities, which in turn cause stem diameters to shrink as a result of clear plant competition [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]. Thin stemmed plants with a propensity to lodge are produced at relatively high plant densities [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e]. These results confirm the findings of Gimplinger et al. [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e], who studied two amaranth genotypes over a two-year period and reported significant decreases in plant height, stem diameter, and branch number with increasing density. In a related study, Igbokwe and Hollins [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e] studied the vegetable amaranthus (Amaranthus cruentus L.) and reported that the highest stem diameter (4.6 cm) was obtained from a plant spacing of 0.9 m, and the lowest stem diameter (2.6 cm) from a plant spacing of 0.3 m. Similarly, Gondal et al. [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e] and Snider et al. [\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e] reported similar findings, suggesting that plant stand establishment may be more favorable under narrow row spacing than under wider row spacing, i.e., 76 cm row spacing.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec21\" class=\"Section4\"\u003e\n \u003ch2\u003e3.1.3.8. Fresh biomass above ground\u003c/h2\u003e\n \u003cp\u003eAccording to the results of the statistical analysis, the seed rate, variety, and interaction between the variety and the seed rate significantly affected the fresh above-ground biomass of plant-1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Appendix Table 2). According to the data in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the combined effects of the Madiira-II variety with a 2 kg ha-1 seed rate produced a maximum TAGFBP (195.66 g) that was much greater than those of the other varieties. The plots treated with AC-NL at 1.5 kg ha-1 and AC-NL at 3 kg ha-1 produced fresh biomass above ground per plant yields that were comparable, at 150.58 g and 145.66 g, respectively. Madiira-II (3.5 kg ha-1), Madiira-II (1.5 kg ha-1), and the amaranthus variety Madiira-II (1.5 kg ha-1) were statistically similar to the AC-NL variety at a 2 kg ha-1 seed rate. According to the results of the statistical analysis, the seed rate, variety, and interaction between the variety and the seed rate significantly affected the fresh above-ground biomass of plant-1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Appendix Table 2). According to the data in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the combined effects of the Madiira-II variety with a 2 kg ha-1 seed rate produced a maximum TAGFBP (195.66 g) that was much greater than those of the other varieties. The plots treated with AC-NL at 1.5 kg ha-1 and AC-NL at 3 kg ha-1 produced fresh biomass above ground per plant yields that were comparable, at 150.58 g and 145.66 g, respectively. Madiira-II (3.5 kg ha-1), Madiira-II (1.5 kg ha-1), and the amaranthus variety Madiira-II (1.5 kg ha-1) were statistically similar to the AC-NL variety at a 2 kg ha-1 seed rate.\u003c/p\u003e\n \u003cp\u003eAccording to Patel et al. [\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e], total fresh weight (242.67 g plant-1) and total dry matter accumulation (67.50 g plant-1) were found to be influenced by the interaction between 45 cm row spacing and G2-GA-1 genotypes, with a 2.5 kg ha-1 seed rate. These results are consistent with the current findings. Similarly, Bongase et al. [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] reported that the fresh aboveground biomass per Madiira II plant planted at 47,619 plants ha-1, AH-NL-Sel planted at 47,619 plants ha-1 and 66,666 plants ha-1, and Madiira-I planted at 111,111 plants ha-1 plant density were 812.20 g, 719.30 g, 669.60 g, and 666 g, respectively.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec22\" class=\"Section4\"\u003e\n \u003ch2\u003e3.1.3.9 Aboveground dry biomass\u003c/h2\u003e\n \u003cp\u003eOwing to the variety\u0026apos;s primary effects, seed rate, and interaction effects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), the results for the dry biomass above ground plant-1 were highly significant (Appendix Table\u0026nbsp;2). The combination of the Madiira-II variety with 2.5 kg ha-1, the Madiira-II variety with 2 kg ha-1, and the AC-NL variety with 3 kg ha-1 yielded the greatest AGDBP (32.9 g; 32 g and 31 g). Similar results were obtained with Madiira-II at 3.5 kg ha-1, Madiira-II at 1.5 kg ha-1, Madiira-II at 3 kg ha-1, and AC-NL at 2.5 kg ha-1 in the plot treated with AC-NL at 1.5 kg ha-1. Compared with AC-NL 3.5, the combined effects of the AC-NL variety with 4 kg ha-1 produced the lowest dry biomass above ground plant-1 (18.33 g).\u003c/p\u003e\n \u003cp\u003eThis result is consistent with the findings of Pourfarid et al. [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e], who reported that increasing the density of plants per square meter increased the dry weight of the above ground of the plants per square meter but decreased the dry biomass plant-1 as a result of the stem becoming thinner [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Yarnia et al. [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e] reported that a reduction in organ dry matter significantly reduced plant-1 at high crop density because of the probable reduction in biomass components as a result of a reduction in the net accumulation of assimilates in plant leaves, plant height, and the number of lateral branches. According to Zubillaga et al. [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e], there is a clear correlation between the number of plants at harvest and the amount of biological yield.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec23\" class=\"Section4\"\u003e\n \u003ch2\u003e3.1.3.10. Dry matter content\u003c/h2\u003e\n \u003cp\u003eAnalysis of variance revealed that dry matter in plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e was significantly responsive to variety \u003cem\u003e(p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e, seed rate and the interaction effects of variety and seed rate \u003cem\u003e(p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e (Appendix Table 2). With respect to the combined effects of the AC-NL variety with 3.5 kg ha-1 (25.81%) and the Madiira II variety in combination with the Madiira-II variety with 2.5 kg ha-1; the Madiira-II variety with 3 kg ha-1; and the AC-NL variety with a 4 kg ha-1 seed rate, the results displayed in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e show that the combination performed best in terms of dry matter yield, with percentages of 29.68%, 29.22%, and 27.037% per plant, respectively, with statistical parity. Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows that Madiira-II 2 kg ha-1, AC-NL 1.5 kg ha-1, and AC-NL 2 kg ha-1 produced the lowest DMC per plant (16.39%, 16.69%, and 17.09%, respectively). Similar statistical results were reported for AC-NL 2.5 kg ha-1 and Madiira-II 4 kg ha-1 (17.96% and 20.04%, respectively). This research further revealed that DMC is dependent on a crop\u0026apos;s ability to utilize resources (light, nutrients, etc.) effectively at the maximum plant density; it is also associated with crop phenology and growth and yield components.\u003c/p\u003e\n \u003cp\u003eAccording to Wilson et al. [\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e], leaf thickness is not as significant a correlate of resource acquisition, consumption, and availability as leaf composition variation is. This occurred because dry matter content is greater than leaf thickness. According to Baturaygil et al. [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e], variation in flowering time is primarily responsible for the differences in dry matter content and plant height between the genotypes of the 10 biomass-type genotypes and the grain production variety used as a check (B\u0026auml;rnkrafft). This is because early flowering results in a longer seed ripening phase and improved dry matter content, whereas late flowering due to short-day genes leads to longer vegetative growth and greater plant height. However, with respect to flowering time, the results of Baturaygil et al. [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e] did not align with the current findings. Consequently, Madiira II was found to be a late-flowering variety in the current study, taking 72.77 days to flower and having a higher dry matter content than AC-NL, which flowers 66.83 days after seeding.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInfluences of the interaction effects of amaranth variety on the seed rate and growth parameters in Jimma, Southwest Ethiopia, from 2020\u0026ndash;21.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTreatments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eGrowth Parameter\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVarieties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeed rate\u003c/p\u003e\n \u003cp\u003e(kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLPP\u003c/p\u003e\n \u003cp\u003e(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLW\u003c/p\u003e\n \u003cp\u003e(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003cp\u003e(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGFBP (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGDBP (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDMC\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eMadiira 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.33 \u003csup\u003e\u003cstrong\u003edef\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.94 \u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.39 \u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.66\u003csup\u003e\u003cstrong\u003ede\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.6 \u003csup\u003e\u003cstrong\u003ebc\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n 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\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00 \u003csup\u003e\u003cstrong\u003eg\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.39 \u003csup\u003e\u003cstrong\u003ee\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u003csup\u003e\u003cstrong\u003eef\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.00 \u003csup\u003e\u003cstrong\u003efg\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.16\u003csup\u003e\u003cstrong\u003eef\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.81\u003csup\u003e\u003cstrong\u003eab\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.333\u003csup\u003e\u003cstrong\u003eefg\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.35 \u003csup\u003e\u003cstrong\u003eg\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32 \u003csup\u003e\u003cstrong\u003ef\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.83 \u003csup\u003e\u003cstrong\u003eg\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.33\u003csup\u003e\u003cstrong\u003ef\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.03\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLSD(0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.44\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.70\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCV (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.66\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eThe means within columns for each variable followed by different letters are significantly different from each other at p\u003c/em\u003e\u0026thinsp;\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;\u003cem\u003e0.01. NLPP\u0026thinsp;=\u0026thinsp;number of leaves per plant\u003c/em\u003e; \u003cem\u003eLW\u0026thinsp;=\u0026thinsp;leaf width; SD\u0026thinsp;=\u0026thinsp;stem diameter; TAGFBP\u0026thinsp;=\u0026thinsp;total aboveground fresh biomass per plant; TAGDBP\u0026thinsp;=\u0026thinsp;total aboveground dry biomass per plant; DMC\u0026thinsp;=\u0026thinsp;dry matter content.\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Yield and yield-related response\u003c/h2\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1. Fresh leaf yield per plant\u003c/h2\u003e\n \u003cp\u003eThe findings of the present study revealed that the fresh leaf yield per plant was significantly affected by the variety (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), the seed rate (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the interaction effect between the amaranth variety and the seed rate (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Appendix Table\u0026nbsp;3). The highest fresh leaf yield per plant (42.25 g plant-1) was observed from plots allocated with the AC-NL variety at 1.5 kg ha-1, which was statistically similar to the fresh leaf yield harvested from AC-NL at 2 kg ha-1 (40.66 g), AC-NL at 2.5 kg ha-1 (40.66 g), and Madiira-II at a 2.5 kg ha-1 seed rate (38.66 g). The lowest fresh leaf yield per plant (12.66 g) was obtained from Madiira-II, with a yield of 4 kg ha-1, which was statistically significant compared with AC-NL, with a yield of 4 kg ha-1 seeds. This study revealed that when amaranth types were handled at reduced seed rates, the leaf output per plant increased. Enough room and reduced competition for nutrients and other resources (light, water, etc.) may be the cause of this. The results of the present study are comparable to those of Bongase et al. [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e], who reported that planting amaranth cultivars with the widest intrarow spacing (35 cm) and the narrowest intrarow spacing (15 cm) increased the leaf output per plant.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2. Fresh leaf yield ha\u003csup\u003e-1\u003c/sup\u003e\u003c/h2\u003e\n \u003cp\u003eAs demonstrated in Appendix Table\u0026nbsp;3, the fresh leaf yield per plant and per hectare was found to be strongly impacted by the seed rate (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), variety (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and interaction between the seed rate and amaranthus variety (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The highest fresh leaf yield (30.84 t ha-1) was obtained by the combined effects of the AC-NL variety with 3 kg ha-1 over all the other treatments. With respect to yields collected from plots treated with AC-NL at 2 kg ha-1 (23.54 t ha-1), the next-best yields (26.63 and 25.17 t ha-1) were from Madiira-II at 2.5 kg ha-1 and AC-NL at 2.5 kg ha-1. These yields presented statistical parity. The lowest fresh leaf yields were 14.35, 14.62, and 14.76 t/ha, respectively.\u003c/p\u003e\n \u003cp\u003eAccording to the results of this investigation, using the AC-NL variety at 3 kg ha-1 resulted in 7.30 tons greater leaf yield than did using the AC-NL variety at 2 kg ha-1; 4.47 tons greater leaf yield than did using the AC-NL variety at 2.5 kg ha-1; and 12.00 tons greater leaf yield than did using the AC-NL variety at 3.5 kg ha-1. Similarly, the use of 2.5 kg ha-1 Madiira-II yielded 4.38 tons more fresh leaf yield than did the use of 2 kg ha-1 Madiira-II and 3.04 tons more fresh leaf yield than did the use of 3 kg ha-1 Madiira-II. A greater degree of correlation between growth and yield parameters, agronomic techniques, environmental conditions, and genetic differences could contribute to the production differences mentioned above (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAccording to Patel et al. [\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e], when 45 cm row spacing, the GA-1 genotype, and the 2.5 kg ha-1 seed rate are combined, the interaction effect on the green forage yield (43.53 t ha-1) and dry matter yield (3.01 t ha-1) is significantly greater.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffects of the interaction effect of amaranth variety and seed percentage on fresh leaf yield per plant and per hectare in Jimma, southwestern Ethiopia, from 2020\u0026ndash;21\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTreatments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYield and yield related variables\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVarieties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeed rate (kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLY PP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLY PH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eMadiira 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.00 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.35 \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.33 \u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.78 \u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.66 \u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.17 \u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.41 \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.13 \u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.00 \u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.86 \u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.66 \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.62 \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eAC-NL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.25 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.66 \u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.66 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.54 \u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.66 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.63 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.00 \u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.84 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.00 \u003csup\u003efg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.84 \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.33 \u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.76 \u003csup\u003e\u003cstrong\u003ef\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLSD (0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.82\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.61\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCV (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eThe means within columns for each variable followed by different letters are significantly different from each other (p\u003c/em\u003e\u0026thinsp;\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;\u003cem\u003e0.01). FLYPP\u0026thinsp;=\u0026thinsp;fresh leaf yield per plant; FLY PH\u0026thinsp;=\u0026thinsp;fresh leaf yield per hectare.\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.3. Number of inflorescences plant\u003csup\u003e-1\u003c/sup\u003e\u003c/h2\u003e\n \u003cp\u003eAccording to the statistical results, variety (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and the seed percentage (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were the two primary influences that had the greatest impact on the number of inflorescences (Appendix Table\u0026nbsp;3). The number of inflorescences per plant was greatest for the AC-NL variety (19.14). However, the Madiira II variety had the lowest score (16.34). The capacity to effectively utilize available resources and genetic variance in flower form may be the cause of this. Gnan et al.\u0026apos;s research [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e] revealed that larger inflorescences were better preserved by the addition of higher effective photosynthetic rates. This study corroborates the findings of Varalakshmi [\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e], who reported that inflorescence density varies widely, ranging from low to dense and intermediate, and Panda et al. [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eA 1.5 kg ha-1 seed rate yielded the greatest number of inflorescence per plant (23.93), which was statistically equivalent to the number of inflorescence obtained from 2 kg ha-1 seed rates (21.15). The inflorescence recorded from the 3.5 kg ha-1 seed rate (14.56) was statistically equivalent to the inflorescence scored from the 4 kg ha-1 seed rate, which resulted in the lowest number of inflorescences per plant (12.12). According to these results, 1.5 kg ha-1 and 2 kg ha-1 produced inflorescences per plant that were comparable, with a 2.5 kg ha-1 seed rate occurring secondarily (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The explanation may involve increased open area, optimal light interception at a lower or lower seed rate, and, conversely, decreased resources as a result of shadowing.\u003c/p\u003e\n \u003cp\u003eThere was a positive correlation between vegetative size and reproductive output, indicating a trade-off between time to reproduction and reproductive output. Nevertheless, Gnan et al. [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e] proposed that leaves are the main source of carbon for reproduction in plants. The study revealed that there was a significant positive correlation between the number of inflorescences per plant and the number of days of flowering, dry above ground biomass per plant, and harvest index. Conversely, there was a significant negative correlation between the number of inflorescences per plant and growth parameters such as stem diameter, branch number, fresh leaf yield per hectare, inflorescence length, seed yield per hectare, and thousand-seed weight compared with those of leaves.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.4. Seed yield plant\u003csup\u003e-1\u003c/sup\u003e and ha\u003csup\u003e-1\u003c/sup\u003e\u003c/h2\u003e\n \u003cp\u003eThe primary effects of variety and the seed rate were strongly linked (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with the seed yield per plant and per hectare (Appendix Table\u0026nbsp;3). Compared with the other varieties, the Madiira II variety produced greater seed yields (3.38 g plant-1 and 2.53 t ha-1). This difference in seed yield was substantial. The minimum seed yield per plant was 2.69 g plant-1, and the minimum seed yield per hectare was 2.04 t ha-1 for the AC-NL variety. The variation in the period of flower initiation and physiological maturity, the number and length of inflorescences, and the transport of nutrients and photosynthetic energy from seeds to other vegetative components, such as leaves, could all be contributing factors to the variation in seed output.\u003c/p\u003e\n \u003cp\u003eThe seed yield of the amaranth varieties was also significantly influenced by the seed rate; the plant-per-seed yield (3.78 g) was highest at 1.5 kg ha-1, which was statistically similar to that at 2 kg ha-1 (3.49 g) and 2.5 kg ha-1 (3.27 g plant-1). However, the 4 kg ha-1 (2.35) seed rate resulted in the lowest seed production per plant, matching the 3.5 kg ha-1 seed rate (2.54) (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe highest seed yields per hectare were obtained from 4 kg ha-1, 3.5 kg ha-1, and 3 kg ha-1, yielding 2.68 t ha-1, 2.64 t ha-1, and 2.52 t ha-1 of seed, respectively. The 1.5 kg ha-1 seed rate yielded the lowest seed yield per hectare (1.78 t ha-1) and was identical to the 2 kg ha-1 seed rate (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The results of the present study revealed that a low seed percentage produced the maximum seed output per plant. Higher seed rates, however, resulted in the greatest seed yield per hectare. This difference might be caused by the use of agronomic techniques (seed rate), the area that enhances light interception to increase plant photosynthetic capacity, and the correlation between grain properties.\u003c/p\u003e\n \u003cp\u003eThe current study confirms the findings of Khan et al. [\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e], who reported that plants grown with wider spacing performed better individually and had more land available to them for photosynthesis and more solar radiation to absorb. In terms of seed yield per unit area, yield is influenced by factors such as the total number of plants per unit area and yield contributing parameters in addition to the performance of each individual plant.\u003c/p\u003e\n \u003cp\u003e. According to Apaza-Gutierrez et al. [\u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e], grain yield increases linearly within the density range, stem diameter and grain yield per plant decrease quadratically with increasing plant density, and the grain yield per unit area may be directly correlated with a plant\u0026apos;s capacity to store nutrients on the stem. Similar results were obtained by Bongase et al. [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e], who reported that low plant density causes plants to generate more branches and leaves per plant, which in turn results in the creation of more inflorescences, each of which contains more seeds.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInfluences of the main effects of the seed percentage and variety on the number of inflorescences per plant and the seed yield per plant and per hectare in Jimma, Southwest China, Ethiopia, from 2020\u0026ndash;21.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eYield and Yield-related variables\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeed rate (kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNIPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSYPP (g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSYPH (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.93 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.78\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.15 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.49 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.98 \u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.25 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.27\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.44 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.79 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.52 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.56 \u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.54\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.64 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.12\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.35\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.68 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSD (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVarieties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMadiira-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.34 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.38 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.53\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAC-NL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.14 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.69\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSD (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eThe means within columns for each variable followed by different letters are significantly different from each other at p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. NIPP\u0026thinsp;=\u0026thinsp;number of inflorescences per plant; SYPP and SYPH\u0026thinsp;=\u0026thinsp;seed yield per plant and per hectare, respectively.\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.5. Inflorescence length\u003c/h2\u003e\n \u003cp\u003eThe inflorescence height was analyzed, and the results revealed that the inflorescence height was significantly affected by the main effects of the variety and seed rates (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) as well as by the combined effects of the variety and seed rate (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Appendix Table 3). The greatest inflorescence height (25.66 cm) was produced by the combination of the Madiira-II variety and the 2.5 kg ha-1 seed rate, which was significantly greater than that of the other combinations. The next longest inflorescence height (24.41 cm) was recorded from the Madiira-II variety with 2 kg ha-1, which produced a similar inflorescence length (23.83 cm), followed by the longest inflorescence (19.83 cm) from AC-NL with a 4 kg ha-1 seed rate (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe genetic variations in growth habit, flower morphology, biological time for flower initiation, competition between vegetative and reproductive phases at critical times, and the ability of the genotype to interact with the environment and agronomic management practices could contribute to the variation in inflorescence height among the treatments.\u003c/p\u003e\n \u003cp\u003eThe decrease in inflorescence height observed in the Madiira II and AC-NL Amaranthus species at relatively high seed rates (over 2.5 kg ha-1) may be attributed to resource limitations caused by intense competition during critical phases of vegetative and reproductive growth. The other possible explanation is that in this study, the treatments associated with a lower seed rate (\u0026le;\u0026thinsp;2.5 kg ha-1) for both varieties presented the highest growth parameters, including the maximum number of branches, the length of the leaf, and the thickest stem diameter. In addition, inflorescence height was strongly and positively associated with all the parameters except the dry weight of the leaf, dry matter, and height of the plant (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).This finding aligns with the findings of Roitner-Schobesberger and Kaul [\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e], who demonstrated that amaranthus source strength during blooming was a greater yield-limiting factor than sink capacity.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.6. Harvest index\u003c/h2\u003e\n \u003cp\u003eData analysis revealed that seed rates had a substantial effect on the harvest index (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), as did the interaction between the seed rate and amaranth variety (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Appendix Table 3). With 1.5 kg ha-1, the Madiira-II variety had the highest percentage of the harvest index (16.23) of any Madiira variety, matching the AC-NL 2 kg ha-1 seed rate (14.62%). At the 3 kg ha-1 seed rate, AC-NL produced the lowest percentage (8.05) of the harvest index, suggesting that photoassimilate partition migrated into other biological yields, such as leaves, rather than reproductive portions.On the basis of this outcome, the harvest index typically ranged from 8.05 to 16.23% (Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). The genetic differences between varieties in how resources are distributed to seeds as opposed to leaves, competition between different parts of the vegetative and reproductive phases, and possible interactions between the genotype and the environment and management techniques could all contribute to the variation in the harvest index among treatments.\u003c/p\u003e\n \u003cp\u003eThe current result is consistent with earlier research by Guillen-Portal et al. [\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e], who reported that grain yields over a wide range of plant populations varying from 4 to 200 plants m\u0026thinsp;\u0026minus;\u0026thinsp;2 at a row spacing of 0.76 m. Gimplinger et al. [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e] reported that increasing density reduced the harvest index and that low plant density allowed the potential for yield to be exhausted.\u003c/p\u003e\n \u003cp\u003eComparable studies by Bongase et al. [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] revealed that, for Madiira II, which ranges from a low plant density (47,619 plants ha-1) to a high plant density (111,111 plants ha-1), a harvest index ranging from 5.71% to 7.28% was attained, whereas the harvest index ranging from 5.71% to 37.48% varied between amaranth genotypes. The plant\u0026apos;s capacity to devote more biomass (assimilates) to leaves and biological yields than to reproductive portions is indicated by the low harvest index [\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eThousand seed weight\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe results revealed that the main effects of variety, seed rate, and the interaction between amaranth variety and seed rate (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) had a significant effect on the weight of thousands of seeds (Appendix Table 3). A greater thousand-seed weight (0.83 g and 0.81 g) was produced by the combined effects of the Madiira-II variety with 2 kg ha-1 and the Madiira-II variety with 1.5 kg ha-1 seed rates. The weight of the lowest thousand seeds (0.40 g) was from Madiira-II at 4 kg ha-1, AC-NL at 3.5 kg ha-1, and Madiira-II at 3.5 kg ha-1, and there was no significant or numerical difference between them (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThese data suggest that amaranthus reacts to a reduced seed percentage to increase the weight of thousands of seeds. This may be the result of shade and branch overlapping effects among growth characteristics, or it may be the result of leaf defoliation at greater plant densities. Seed weight, plant-1 seed quantity, and yield components may have been impacted by this event. This study is consistent with previous research showing that diminished sources as a result of defoliation or shade decrease seed bulk and yield [\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffects of the Interaction of Seed Rate and Varieties on Yield and Yield-Related Variables of Amaranth in Jimma, Southwest China, and Ethiopia during 2020\u0026ndash;21\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTreatments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eYield and yield related variables\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVarieties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeed rate (kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSW (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eMadiira 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.83 \u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.23 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.41 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.13 \u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.66 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.13 \u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.33 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.65 \u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.16 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.22 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4 \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.76 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.08 \u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4 \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eAC-NL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.43 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.06 \u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.43 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.62 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.15 \u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.05 \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5 \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.33 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.39 \u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4 \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.83 \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.75 \u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4 \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSD (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.951\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.46\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eThe means within columns for each variable followed by different letters are significantly different from each other (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). IL\u0026thinsp;=\u0026thinsp;inflorescence length; HI\u0026thinsp;=\u0026thinsp;harvest index; TSW\u0026thinsp;=\u0026thinsp;thousand-seed weight.\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Correlation analysis\u003c/h2\u003e\n \u003cp\u003eFresh leaf yield ha-1 was found to be positively and significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) correlated with days to 50% maturity (r\u0026thinsp;=\u0026thinsp;0.47**), leaf area (r\u0026thinsp;=\u0026thinsp;0.57**), leaf length (r\u0026thinsp;=\u0026thinsp;0.47**), leaf width (r\u0026thinsp;=\u0026thinsp;0.53**), stem diameter (r\u0026thinsp;=\u0026thinsp;0.65**), number of branches (r\u0026thinsp;=\u0026thinsp;0.60**), leaf yield plant-1 (r\u0026thinsp;=\u0026thinsp;0.54), above-ground fresh biomass (r\u0026thinsp;=\u0026thinsp;0.59**), leaf weight (r\u0026thinsp;=\u0026thinsp;0.65**), dry matter content (r\u0026thinsp;=\u0026thinsp;0.47**), inflorescence length (r\u0026thinsp;=\u0026thinsp;0.74**), seed yield plant-1 (r\u0026thinsp;=\u0026thinsp;0.72**), and seed yield ha-1 (r\u0026thinsp;=\u0026thinsp;0.55**).\u003c/p\u003e\n \u003cp\u003eOn the other hand, it was significantly and adversely correlated with the following parameters: plant height (r\u0026thinsp;=\u0026thinsp;0.44**), inflorescence number (r\u0026thinsp;=\u0026thinsp;0.65**), harvest index (r\u0026thinsp;=\u0026thinsp;0.73**), and number of days to 50% blooming (r= -0.85**). LFYPH (t ha-1) did not, however, show any correlation with TSW, TAGDBPP, or NLPP. The leaf yield per hectare may be dependent on the number of plants per unit of area rather than on individual plants, which could be the cause (Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).This study is consistent with that of Bongase et al. [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e], who reported positive and significant correlations between leaf yield ha-1, the plant-1 branch count, the leaf yield plant-1, the leaf area, the leaf area index, and the dry weight of the yield plant-1.\u003c/p\u003e\n \u003cp\u003eThe seed yield ha-1 also showed a significant (p\u0026thinsp;\u0026le;\u0026thinsp;0.01; p\u0026thinsp;\u0026le;\u0026thinsp;0.05) positive correlation with the following: days to 50% maturity (r\u0026thinsp;=\u0026thinsp;0.79**); leaf number (r\u0026thinsp;=\u0026thinsp;0.59**); leaf area (r\u0026thinsp;=\u0026thinsp;0.31*); leaf length (r\u0026thinsp;=\u0026thinsp;0.68**); leaf width (r\u0026thinsp;=\u0026thinsp;0.58**); stem diameter (r\u0026thinsp;=\u0026thinsp;0.65**); branch number (r\u0026thinsp;=\u0026thinsp;0.72**); leaf fresh yield plant-1 (r\u0026thinsp;=\u0026thinsp;0.68**); leaf yield ha-1 (r\u0026thinsp;=\u0026thinsp;0.55**); leaf dry weight (r\u0026thinsp;=\u0026thinsp;0.49**); dry matter content (r\u0026thinsp;=\u0026thinsp;0.54**); inflorescence length (r\u0026thinsp;=\u0026thinsp;0.48**); seed yield per plant (r\u0026thinsp;=\u0026thinsp;0.76**); and thousand seed weight (r\u0026thinsp;=\u0026thinsp;0.59**). Nonetheless, Table 9 shows that there was a substantial negative correlation (p\u0026thinsp;\u0026le;\u0026thinsp;0.01; p\u0026thinsp;\u0026le;\u0026thinsp;0.05) with days to 50% blooming (r= -0.77**), aboveground biomass (r= -0.36*), and number of inflorescences plant-1 (r= -0.35*). These findings showed that the quantity of inflorescences, dry biomass, and phenological time for flower initiation significantly impacted seed yield.\u003c/p\u003e\n \u003cp\u003eTable 8.\u0026nbsp;Pearson\u0026apos;s correlation coefficient analysis for growth, leaf yield and seed yield of Amaranthus varieties (\u003cem\u003eAmaranthus var.).\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003cimg 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\"\u003e\u003c/em\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. CONCLUSION","content":"\u003cp\u003eAmaranthus is an indigenous and neglected vegetable crop that is widely distributed in many parts of Ethiopia. raditionally utilized as feed, edibles, and therapeutic plants during famines and food shortages, particularly in low-income homes. However, because of poor agronomic methods, such as the use of the right seed rates and a lack of better variety, its productivity and production are considerably below its yield potential. Therefore, supplemental irrigation was used to conduct this study from 2020\u0026ndash;2021. This study employed a complete randomized block design (RCBD) with three replications, utilizing a 2x6 factorial combination to investigate the effects of six different seed rates (1.5, 2, 2.5, 3, 3.5, and 4 kg ha-1) on the growth, leaf, and seed yields of two Amaranthus varieties, Madiira-II and AC-NL, in Jimma, southwest Ethiopia.\u003c/p\u003e\n\u003cp\u003eThe results of this study showed that the seed rate, variety main effects, and their interaction effects all had an impact on growth, leaf yield, and seed yield. The primary effects of the amaranthus type and seed rate on the number of days to 50% flowering, number of days to 50% maturity, leaf area, plant height, number of branches, number of inflorescences, and seed production per plant and per hectare were significantly different (p\u0026lt;0.01; p\u0026lt;0.05). However, with respect to leaf number, leaf width, fresh biomass above ground, dry biomass above ground, dry matter content, leaf yield per plant, leaf yield per hectare, inflorescence length, harvest index, and thousand-seed weight, the effects of the variety and seed rate interaction were highly responsive (p\u0026lt;0.01). Only interaction effects were revealed to be the cause of the significant difference (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003eIn general, the Madiira-II variety outperformed the AC-NL variety in terms of seed yield per plant (3.38 g) and per hectare (2.53 t) according to the current findings. Nonetheless, the AC-NL variety\u0026apos;s primary impacts generated the greatest number of inflorescences per plant (19.14).\u003c/p\u003e\n\u003cp\u003e. With respect to the primary impacts of seed rates, the highest seed yield per hectare (2.52, 2.64 and 2.68 t) was obtained from the 3, 3.5 and 4 kg ha-1 seed rates, whereas the maximum seed yield per plant (3.78 g) was produced from the 1.5 kg ha-1 seed rates. In contrast, Madiira-II, with 2.5 kg ha-1 seed rates, and AC-NL, with 2.5 kg ha-1 seed rates, produced statistically identical fresh leaf yields per hectare (26.63 and 25.17 t), respectively. AC-NL revealed that the maximum fresh leaf yield per hectare was associated with AC-NL, with a 3 kg ha-1 seed rate (30.84 t). Nonetheless, Madiira-II at 1.5 kg ha-1 (14.35 t) and Madiira-II at 4 kg ha-1 seed rates (14.62 t) yielded the lowest fresh leaf yield per hectare.\u003c/p\u003e\n\u003cp\u003eThus, the farming community in the study area and other similar agroecological areas could benefit from the use of the Madiira-II variety with a 2.5 kg ha-1 seed rate, which results in the maximum leaf yield per plant (38.66 g) and per hectare (25.17 t), and the AC-NL amaranthus variety, which could result in the highest leaf yield per hectare (30.84 t).In contrast to the AC-NL variety, the Madiira-II variety yielded the maximum seed yield per plant (3.78 g) and per hectare (2.53 t). To obtain thorough advice, more trials should be carried out at different times of the year and in regions with comparable agronomic methods, as this study was performed with additional irrigation. However, as the seed rate increased, the seed yield per hectare slightly increased; hence, further research is needed to achieve maximum seed yields. More significantly, assessing the nutritional profiles and community acceptability of these two types of leaves and grains would be beneficial.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTilahun Deressa:\u003c/strong\u003e Developed the research concept note, wrote the draft manuscript text and interpreted the data.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAmsalu Nebiyu\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eContributed to manuscript development and revisions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGerba Daba:\u003c/strong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eContributed to work by supervising the overall research procedures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGarome Shifaraw:\u003c/strong\u003e Contributed to the work of managing the research data, data analysis and editing the paper for publication.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This work did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAvailability\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e All\u0026nbsp;the\u0026nbsp;data generated or analyzed during the study are included in this manuscript and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003einterest\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The authors declare that they have no conflicts of interest regarding the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish declaration: N/A\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial: N/A\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSuresh, S., Chung, J.W., Cho, G.T., Sung, J.S., Park, J.H., Gwag, J.G. and Baek, H.J., 2014. 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Effect of row spacing and seed rate on growth, fodder productivity and economics of amaranth genotypes. \u003cem\u003eKarnataka Journal of Agricultural Sciences\u003c/em\u003e, \u003cstrong\u003e\u003cem\u003e24\u0026nbsp;\u003c/em\u003e(5):\u0026nbsp;\u003c/strong\u003e643-650\u003cstrong\u003e.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e[74]\u003c/strong\u003e Pourfarid, A., Kamkar, B. and Akbari, G.A., 2014. The effect of density on yield and some agronomical and physiological traits of Amaranth (\u003cem\u003eAmaranthus spp\u003c/em\u003e). \u003cem\u003eInternational Journal of Farming and Allied Sciences\u003c/em\u003e, \u003cstrong\u003e\u003cem\u003e3\u0026nbsp;\u003c/em\u003e(12):\u003c/strong\u003e 1256-1259.\u003c/li\u003e\n \u003cli\u003eWilson, P.J., Thompson, K.E.N. and Hodgson, J.G., 1999. 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Effect of row spacing and seeding rates on growth yield and yield components of chickpea. \u003cem\u003eSarhad Journal of Agriculture\u003c/em\u003e, \u003cstrong\u003e\u003cem\u003e26\u0026nbsp;\u003c/em\u003e(2):\u0026nbsp;\u003c/strong\u003e201-211.\u003c/li\u003e\n \u003cli\u003eApaza-Gutierrez, V., Romero-Saravia, A., Guillen-Portal, F.R. and Baltensperger, D.D., 2002. Response of grain amaranth production to density and fertilization in Tarija, Bolivia. Trends in new crops and new uses\u003cem\u003e. ASHS Press, Alexandria\u003c/em\u003e, 107-109.\u003c/li\u003e\n \u003cli\u003eRoitner-Schobesberger, B. and Kaul, H.P., 2013. Source capacity during flowering affects grain yield of amaranth (\u003cem\u003eAmaranthus sp\u003c/em\u003e.). \u003cem\u003ePlant, Soil and Environment\u003c/em\u003e, \u003cstrong\u003e\u003cem\u003e59\u0026nbsp;\u003c/em\u003e(10):\u0026nbsp;\u003c/strong\u003e472-477.\u003c/li\u003e\n \u003cli\u003eGuillen-Portal, F.R., Baltensperger, D.D. and Nelson, L.A., 1999. Plant population influence on yield and agronomic traits in Plainsman grain amaranth. Perspectives on New Crops and New Uses.\u003cem\u003e\u0026nbsp;ASHS Press, Alexandria\u003c/em\u003e, 190-193.\u003c/li\u003e\n \u003cli\u003eWnuk, A., G\u0026oacute;rny, A.G., Bocianowski, J. and Kozak, M., 2013. Visualizing harvest index in crops. \u003cem\u003eCommunications in Biometry \u0026amp; Crop Science\u003c/em\u003e, \u003cstrong\u003e\u003cem\u003e8\u0026nbsp;\u003c/em\u003e(2):\u0026nbsp;\u003c/strong\u003e48\u0026ndash;59\u003c/li\u003e\n \u003cli\u003eBorr\u0026aacute;s, L., Slafer, G.A. and Otegui, M.E., 2004. Seed dry weight response to source\u0026ndash;sink manipulations in wheat, maize and soybean: a quantitative reappraisal. \u003cem\u003eField Crops Research\u003c/em\u003e, \u003cstrong\u003e\u003cem\u003e86\u0026nbsp;\u003c/em\u003e(2-3):\u0026nbsp;\u003c/strong\u003e131-146.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AC-NL, Madiira-II, competition, leaf yield, seed yield","lastPublishedDoi":"10.21203/rs.3.rs-7606428/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7606428/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe goal of the current study was to determine how varied seed rates in Jimma, Southwest Ethiopia, affect the growth, leaf, and grain production of amaranthus cultivars. The experiment was conducted with supplemental irrigation from 2020\u0026ndash;2021. A 2x6 factorial combination with three replications was used in the experiment, which was set up in a fully randomized block design. Madiira II and AC-NL are the cultivars and the seed rate levels.The results revealed that the interaction effect of variety and seed rate was highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for almost all the traits. The highest number of leaves was harvested from the combined effects of Madiira-II at 2 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (112.33) and AC-NL at 2.5 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (111.66). The highest dry matter content was recorded for Madiira-II at a seed rate of 2.5 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (29.68%), 3 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (29.22%) and AC-NL at 4 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (27.03%). The highest harvest index was obtained from Madiira-II at 1.5 kg ha\u003csup\u003e\u0026minus;\u003c/sup\u003e1, and the lowest was obtained from AC-NL at 3 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The leaf yield was highest at a seed rate of 2.5 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for Madiira-II (25.17 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), with a statistically significant difference from that of AC-NL at 2.5 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (26.63 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and it was highest at 3 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for AC-NL \u003cb\u003e(\u003c/b\u003e30.84 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The highest seed yield (3.78 g) was produced from the 1.5 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1 treatment,\u003c/sup\u003e and the highest seed yields (2.52, 2.64 and 2.68 t) were obtained from the 3, 3.5 and 4 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e treatment. Therefore, it can be concluded that the seed percentage of these two varieties can be tentatively recommended for cultivation in the study area. However, repeating the experiment over seasons and locations is suggested to provide sound and concrete recommendations.\u003c/p\u003e","manuscriptTitle":"Response of Amaranthus Varieties (Amaranthus Spp.) to Different Seed Rates on Growth, Leaf, and Seed Yields in Jimma,southwest Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 13:50:28","doi":"10.21203/rs.3.rs-7606428/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92bb950e-bc5c-41a8-b58c-bb9e030027a4","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T15:23:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 13:50:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7606428","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7606428","identity":"rs-7606428","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}