Parameter | Value | Level |
Texture | ||
Clay (%) | 39 | |
Silt (%) | 46 | |
Sand (%) | 15 | |
Organic C (%) | 0.89 | Very low |
Total N (%) | 0.13 | Low |
C/N | 5.75 | Low |
Source: Soil laboratory of the Indonesian Cereal Testing Instrument Standard Institute. |
The objective of this study was to establish the morphological changes in the structure of Mediterranean mussel (Mytilus galloprovincialis) after frozen storage. Two hundred Mediterranean mussels (M. galloprovincialis) were collected from the Black Sea coastal waters. Forty mussels were subjected to histological analysis in fresh state. The remaining 160 mussels were divided into 4 groups and slowly frozen in a conventional freezer at −18 ℃ and subsequently stored at the same temperature for 3, 6, 9 and 12 months, respectively. The histological assessment of posterior adductor muscle and foot found a change in their morphological profile and overall structure. The fewest changes in the histostructure were recorded after a 3-month period and the most after a 12-month period of storage in frozen state. The results from that study can be used as an unambiguous marker in selecting optimum conditions for storage of mussels in frozen state.
Citation: Mariyana Strateva, Deyan Stratev, Georgi Zhelyazkov. Freezing influence on the histological structure of Mediterranean mussel (Mytilus galloprovincialis)[J]. AIMS Agriculture and Food, 2023, 8(2): 278-291. doi: 10.3934/agrfood.2023015
[1] | Huanhai Yang, Shue Liu . A prediction model of aquaculture water quality based on multiscale decomposition. Mathematical Biosciences and Engineering, 2021, 18(6): 7561-7579. doi: 10.3934/mbe.2021374 |
[2] | Xin Jing, Jungang Luo, Shangyao Zhang, Na Wei . Runoff forecasting model based on variational mode decomposition and artificial neural networks. Mathematical Biosciences and Engineering, 2022, 19(2): 1633-1648. doi: 10.3934/mbe.2022076 |
[3] | Ziyang Sun, Xugang Xi, Changmin Yuan, Yong Yang, Xian Hua . Surface electromyography signal denoising via EEMD and improved wavelet thresholds. Mathematical Biosciences and Engineering, 2020, 17(6): 6945-6962. doi: 10.3934/mbe.2020359 |
[4] | Hongli Niu, Kunliang Xu . A hybrid model combining variational mode decomposition and an attention-GRU network for stock price index forecasting. Mathematical Biosciences and Engineering, 2020, 17(6): 7151-7166. doi: 10.3934/mbe.2020367 |
[5] | Rakesh Pilkar, Erik M. Bollt, Charles Robinson . Empirical mode decomposition/Hilbert transform analysis of postural responses to small amplitude anterior-posterior sinusoidal translations of varying frequencies. Mathematical Biosciences and Engineering, 2011, 8(4): 1085-1097. doi: 10.3934/mbe.2011.8.1085 |
[6] | Yujie Kang, Wenjie Li, Jidong Lv, Ling Zou, Haifeng Shi, Wenjia Liu . Exploring brain dysfunction in IBD: A study of EEG-fMRI source imaging based on empirical mode diagram decomposition. Mathematical Biosciences and Engineering, 2025, 22(4): 962-987. doi: 10.3934/mbe.2025035 |
[7] | Tao Zhang, Hao Zhang, Ran Wang, Yunda Wu . A new JPEG image steganalysis technique combining rich model features and convolutional neural networks. Mathematical Biosciences and Engineering, 2019, 16(5): 4069-4081. doi: 10.3934/mbe.2019201 |
[8] | Enas Abdulhay, Maha Alafeef, Hikmat Hadoush, V. Venkataraman, N. Arunkumar . EMD-based analysis of complexity with dissociated EEG amplitude and frequency information: a data-driven robust tool -for Autism diagnosis- compared to multi-scale entropy approach. Mathematical Biosciences and Engineering, 2022, 19(5): 5031-5054. doi: 10.3934/mbe.2022235 |
[9] | Konki Sravan Kumar, Daehyun Lee, Ankhzaya Jamsrandoj, Necla Nisa Soylu, Dawoon Jung, Jinwook Kim, Kyung Ryoul Mun . sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms. Mathematical Biosciences and Engineering, 2024, 21(2): 2901-2921. doi: 10.3934/mbe.2024129 |
[10] | Xiaotong Ji, Dan Liu, Ping Xiong . Multi-model fusion short-term power load forecasting based on improved WOA optimization. Mathematical Biosciences and Engineering, 2022, 19(12): 13399-13420. doi: 10.3934/mbe.2022627 |
The objective of this study was to establish the morphological changes in the structure of Mediterranean mussel (Mytilus galloprovincialis) after frozen storage. Two hundred Mediterranean mussels (M. galloprovincialis) were collected from the Black Sea coastal waters. Forty mussels were subjected to histological analysis in fresh state. The remaining 160 mussels were divided into 4 groups and slowly frozen in a conventional freezer at −18 ℃ and subsequently stored at the same temperature for 3, 6, 9 and 12 months, respectively. The histological assessment of posterior adductor muscle and foot found a change in their morphological profile and overall structure. The fewest changes in the histostructure were recorded after a 3-month period and the most after a 12-month period of storage in frozen state. The results from that study can be used as an unambiguous marker in selecting optimum conditions for storage of mussels in frozen state.
Maize is a vital crop for humans. Humans rely on maize for various purposes, including food, feed, industry, and biofuel [1]. In 2022, Indonesian maize production was 16.53 million t, decreasing by around 12.50% in 2023, while maize demand increased at an increasing rate[2,3]. A challenge for increasing maize production is agricultural expansion in areas with low soil nitrogen (N) levels.
Nitrogen (N) is crucial for maize, serving as a vital nutrient for its life cycle [4]. N deficiency can reduce leaf area and photosynthesis rate because more photosynthate is allocated to roots [5]. This deficiency may also decrease plant height, increase the Anthesis-Silking Interval, accelerate senescence [6,7,8]. Additionally, nitrogen deficiency leads to decreased maize yield during harvest [9,10,11].Yields can drop by 10–50%, reaching up to 70% under severe stress conditions due to N deficiency [12,13].
The development of maize varieties with low-nitrogen (N) tolerance has addressed the challenge of cultivating crops in areas with insufficient N levels. Globally, breeding maize with low-N tolerance has been a significant focus in maize breeding. Breeding low-N-tolerant maize plants can enhance maize yield in China by 14% [14]. More than 100 inbred lines can be used as parents for breeding with low nitrogen tolerance hybrids that have high stable yield [15,16]. Various hybrid combinations with low N tolerance in maize have also been documented by [17,18,19]. In the context of Indonesia, the CY 11, G2013631, MR 14, AVLN 118-7, and AVLN 83-2 lines demonstrate good combining ability for yield in low N conditions [20,21]. It is possible to select low-nitrogen-tolerant hybrid maize lines based on secondary characteristics, stress tolerance index, and Simple Sequence Repeat (SSR) markers [22,23,24]. In Indonesia, there are 15 low-N-tolerant hybrid maize selected based on the Stress Tolerance Index and the Stress Susceptibility Index[25,26].
Low-nitrogen-tolerant hybrid maize is a potential solution for Indonesia's low soil nitrogen (N) problems. However, the current research on this crop is limited and slow. Therefore, more research is required to overcome these challenges. This research aimed to investigate the impact of nitrogen fertilization on the growth and yield of maize hybrids and assess their tolerance to N stress. The results can provide valuable insights for breeding high-yield hybrid maize under low N conditions in Indonesia, improving food security and economic growth.
This study was conducted at the Indonesian Cereal Testing Instrument Standard Institute in Maros, South Sulawesi, Indonesia, from July to November 2022. The experiment involved a total of nine promising low-nitrogen-tolerant maize hybrids (HLN 01, HLN 02, HLN 03, HLN 04, HLN 05, HLN 06, HLN 07, HLN 08, and HLN 09) and two control varieties: ADV 777 (hybrid maize that requires high nitrogen) and JH 37 (moderately tolerant to low nitrogen and drought hybrid maize). The genotype arrangement employed a three-replication nested design. The genotypes were organized within the nested structure based on the nitrogen fertilizer levels, i.e., 0 kg N ha−1, 100 kg N ha−1, and 200 kg N ha−1. The 200 kg N ha−1 level is the usual nitrogen fertilizer level farmers use for maize in Indonesia. It represents a high fertilizer level. The 100 kg N ha−1 level represents half of the usual fertilizer dose and serves as a low fertilizer level. It allows us to observe how maize responds to a reduced fertilizer level. 0 kg N ha−1 is the baseline at which no nitrogen is applied. It helps us understand the natural conditions or the minimum nitrogen requirement for maize. The experiment plot was 3 meters by 5 meters, with plants spaced at 75 cm between rows and 20 cm within rows, so there were 100 plants in one plot. This plant spacing corresponded to a population density of 66,666 plants ha−1. At 10 days after planting (DAP), the 100 kg N ha−1 treatment was applied, while the 200 kg N ha-1 treatment was split into two doses: one at 10 DAP and the other at 35 DAP, Phosphorus (P) and potassium (K) fertilizers, each at a rate of 60 kg ha−1, were applied ten days after planting (DAP). Optimal plant maintenance practices were implemented, including weeding, watering, and hoarding.
Before the research, a soil test was done (Table 1). The total nitrogen analysis employed the Kjeldahl method [27], while soil organic carbon analysis utilized the Walkley-Black method [28]. The analysis shows that the location has a silty clay texture. The land has a very low level of organic C and low total nitrogen and C/N ratio. That level means the land is suitable for low-N-tolerant maize selection.
Parameter | Value | Level |
Texture | ||
Clay (%) | 39 | |
Silt (%) | 46 | |
Sand (%) | 15 | |
Organic C (%) | 0.89 | Very low |
Total N (%) | 0.13 | Low |
C/N | 5.75 | Low |
Source: Soil laboratory of the Indonesian Cereal Testing Instrument Standard Institute. |
The observed variables were agronomic traits and yield. The agronomic traits included plant height, ear height, stalk diameter, leaf angle, leaf length, and leaf width. The yield was corrected to t ha−1 with 15% moisture, employing the formula
Yield(tha−1)=104HAx100-GM85x EHW x SP ÷ 1.000[29] | (1) |
HA = harvested area (m2);
GM = grain moisture (%);
EHW = ear harvested weight (kg);
SP = shelling percentage (%).
An analysis of variance was performed to assess the effects of N fertilizer levels, genotype, and their interaction on the variables observed [30]. If a significant effect was found, a 5% LSD test was conducted to compare the test hybrid with control varieties.
The Stress Tolerance Index (STI) is used to measure maize hybrids' tolerance to low nitrogen (N) conditions. The STI formula is YsxYp−Y2p [31]. Ys and Yp represent the hybrid yield under low and optimum N conditions, respectively, and the average yield of all hybrids under optimum N conditions is −Y2p. The tolerance levels of the hybrids are based on their STI values: STI > 1.0 for tolerance, 0.5 < STI ≤ 1.0 for moderate tolerance, and STI ≤ 0.5 for susceptible.
The stability of the hybrid over the three N levels is another factor in determining maize hybrid tolerance. The Eberhart and Russel stability analysis [32] used bi=∑jYijIj∑jI2j, S2di=(∑jˆδ2ijj−2−s2er), where bi is the regression coefficient, S2di is the deviation from regression, i is the genotype number, j is the environment number, r is the replication number, Yij is the average yield of the ith genotype in the jth environment, Ij is the environmental index = mean index, i.e., the mean yield of the jth environment minus the mean yield of all genotypes, ∑jˆδ2ij = pooled variance, and ∑jˆδ2ij = pooled ANOVA error.
The effect of nitrogen fertilizer, genotype, and their interactions are displayed in Table 2. Table 2 demonstrates that nitrogen fertilizer and genotype significantly affected maize traits and yields. Their interaction was also significant for all variables except leaf width and angle. The variables' coefficients of variation (CVs) varied between 4.70% and 15.20%.
Variable | Mean square | CV (%) | |||||||
Nitrogen (N) | R/N | Hybrid (H) | H x N | Error | |||||
Plant height | 11777.50 | ** | 66.17 | 571.38 | * | 955.91 | ** | 218.47 | 7.40 |
Ear height | 3788.14 | ** | 28.78 | 242.23 | ** | 660.90 | ** | 64.87 | 7.60 |
Stalk diameter | 235.60 | ** | 8.28 | 14.78 | ** | 7.79 | ** | 2.84 | 7.00 |
Leaf angle | 344.93 | ** | 10.89 | 98.32 | ** | 14.82 | 14.84 | 15.20 | |
Leaf length | 1689.11 | ** | 34.30 | 152.66 | * | 45.52 | ** | 15.19 | 4.70 |
Leaf width | 9.12 | ** | 0.49 | 2.19 | ** | 0.33 | 0.44 | 6.80 | |
Yield | 289.03 | ** | 0.81 | 3.50 | ** | 3.56 | ** | 0.73 | 11.90 |
Note: * = significant at p < 0.05, ** = significant at p < 0.01, CV = coefficient of variation. |
Table 3 illustrates that the agronomic traits of maize vary with each level of fertilizer. For plant height, at 200 kg N ha−1, the range is 202.27–249.93 cm. At 100 kg N ha−1, it is 187.53–212.00 cm. At 0 kg N ha−1, it is 153.6–191.33 cm. HLN 01 and HLN 07 do not differ in plant height across the three fertilizer levels. Only HLN 01 shows no differences across the fertilizer levels for ear height. The ear height had ranges of 94.60–139.67 cm at 200 kg N ha−1, 95.47 to 115.00 cm at 100 kg N ha−1, and 61.00–111.13 cm at 0 kg N ha−1. The stalk diameter at 200 kg N ha−1 ranged from 23.80 to 29.58 mm. At 100 kg N ha−1, it ranged from 20.78 cm to 26.27 cm. At 0 kg N ha−1, it ranged from 18.44 to 24.39 mm. The leaf length was 82.13–95.91 cm at 200 kg N ha−1, 76.53–92.20 cm at 100 kg N ha−1, and 64.20–85.16 cm at 0 kg N ha−1. Only HLN 03 and JH 37 do not show any differences in stalk diameter and leaf length across all levels of fertilizers.
Hybrid | Plant height (cm) | Ear height (cm) | Stalk diameter (mm) | Leaf length (cm) | ||||||||
N2 | N1 | N0 | N2 | N1 | N0 | N2 | N1 | N0 | N2 | N1 | N0 | |
HLN 01 | 202.27 | 199.33 | 185.40 | 94.60b | 111.73 | 111.13 | 26.60 | 22.65 (x) | 20.70 (x) | 88.67 | 87.80a | 77.13 (x) |
HLN 02 | 226.60 | 202.07 (x) | 153.60 (x) | 118.53 | 115.00 | 61.00ab (x) | 26.50 | 26.27ab | 23.40a (x) | 95.91ab | 92.20ab | 85.16a (x) |
HLN 03 | 249.93 | 202.40 (x) | 177.93 (x) | 139.67 | 111.20 (x) | 99.07 (x) | 27.07 | 25.47ab | 24.39a | 89.27 | 85.33a | 81.13 (x) |
HLN 04 | 216.27 | 208.53 | 175.07 (x) | 117.00 | 95.47b (x) | 100.27 (x) | 29.55 | 24.51ab (x) | 22.39 (x) | 82.13 | 76.53 | 66.27 (x) |
HLN 05 | 210.67 | 188.47a | 182.20 (x) | 116.00 | 95.80b (x) | 105.00 | 23.80 | 22.78 | 20.86 (x) | 90.13 | 79.67 (x) | 74.53 (x) |
HLN 06 | 243.47 | 201.20 (x) | 181.60 (x) | 131.60 | 107.20 (x) | 91.80 (x) | 26.56 | 25.13ab | 21.24 (x) | 89.40 | 86.27a | 64.20 (x) |
HLN 07 | 205.93 | 187.53a | 184.73 | 113.20 | 96.33b (x) | 100.80 | 24.61 | 22.49 | 18.44 (x) | 87.33 | 86.33a | 69.07 (x) |
HLN 08 | 242.33 | 206.07 (x) | 191.33 (x) | 128.33 | 114.47 (x) | 96.87 (x) | 27.05 | 26.10ab | 21.85 (x) | 91.33a | 89.07a | 73.13 (x) |
HLN 09 | 228.33 | 212.00 | 172.93 (x) | 123.93 | 110.67 (x) | 91.27 (x) | 24.60 | 24.34ab | 20.20 (x) | 92.20ab | 88.60a | 74.93 (x) |
ADV 777 | 214.40 | 196.67 | 176.47 (x) | 122.22 | 100.00 (x) | 89.67 (x) | 29.58 | 20.78 (x) | 20.47 (x) | 84.27 | 77.20 (x) | 78.27 |
JH 37 | 215.67 | 188.33 (x) | 166.13 (x) | 114.67 | 114.40 (x) | 80.20 | 28.77 | 21.23 (x) | 22.11 (x) | 85.20 | 83.60 | 79.33 |
Mean | 223.26 | 199.33 | 177.04 | 117.96 | 96.65 | 93.37 | 26.79 | 23.8 | 21.46 | 88.71 | 84.78 | 74.83 |
LSD 5% | 24.14 | 24.14 | 24.14 | 13.15 | 13.15 | 13.25 | 2.75 | 2.75 | 2.75 | 6.36 | 6.36 | 6.36 |
Note: N0 = 0 kg N ha−1, N1 = 100 kg N ha−1, N2 = 200 kg N ha−1; in a row, (x) = significant difference from 200 kg N ha−1 by 5% LSD; in a column, a = better than ADV 777 by 5% LSD, b = better than JH 37 by 5% LSD. |
Table 4 presents the yields of the hybrids at nitrogen levels of 200 kg N ha−1, 100 kg N ha−1, and 0 kg N ha−1, along with the corresponding yield decreases and the Stress Tolerance Index (STI) levels for each fertilization level. The research study revealed that the yield of hybrid maize varied significantly with different levels of nitrogen fertilization. The yield of maize ranged from 8.42 to 12.57 t ha−1 with the application of 200 kg N ha−1. With low nitrogen fertilization of 100 kg N ha−1, the yield ranged from 4.74 to 8.09 t ha−1. However, without any nitrogen fertilizer, the yield decreased significantly. The yield of maize ranged from 4.40 to 5.33 t ha−1.
Hybrid | Yield (t ha−1) | Yield reduction (t ha−1) | STI | ||||||
N2 | N1 | N0 | N2-N1 | N2-N0 | N1-N0 | N2-N1 | N2-N0 | N1-N0 | |
HLN 01 | 8.42 | 7.00a | 5.33 | 1.42 | 3.10 | 1.67 | 0.54 (MT) | 0.41 (S) | 0.86 (MT) |
HLN 02 | 12.05ab | 8.09ab | 4.40 | 3.96 | 7.66 | 3.70 | 0.89 (MT) | 0.49 (S) | 0.82 (MT) |
HLN 03 | 12.57ab | 5.61 | 4.60 | 6.96 | 7.97 | 1.01 | 0.65 (MT) | 0.53 (MT) | 0.60 (MT) |
HLN 04 | 8.96 | 6.76a | 4.53 | 2.20 | 4.44 | 2.24 | 0.56 (MT) | 0.37 (S) | 0.71 (MT) |
HLN 05 | 9.73 | 7.30a | 4.52 | 2.43 | 5.21 | 2.78 | 0.65 (MT) | 0.40 (S) | 0.76 (MT) |
HLN 06 | 11.02ab | 7.16a | 4.84 | 3.86 | 6.18 | 2.33 | 0.72 (MT) | 0.49 (S) | 0.80 (MT) |
HLN 07 | 11.45ab | 6.16a | 4.47 | 5.29 | 6.99 | 1.69 | 0.65 (MT) | 0.47 (S) | 0.64 (MT) |
HLN 08 | 12.42ab | 6.09 | 3.90 | 6.33 | 8.52 | 2.19 | 0.69 (MT) | 0.44 (S) | 0.55 (MT) |
HLN 09 | 10.83ab | 7.16a | 5.05 | 3.67 | 5.79 | 2.11 | 0.71 (MT) | 0.50 (MT) | 0.84 (MT) |
ADV 777 | 8.50 | 4.74 | 4.58 | 3.76 | 3.92 | 0.17 | 0.37 (S) | 0.36 (S) | 0.50 (MT) |
JH 37 | 8.90 | 6.18 | 4.73 | 2.72 | 4.17 | 1.46 | 0.50 (MT) | 0.39 (S) | 0.68 (MT) |
Mean | 10.44 | 6.57 | 4.63 | 3.87 | 5.81 | 1.94 | |||
SE | 0.49 | 0.49 | 0.49 | ||||||
LSD 5% | 1.40 | 1.40 | 1.40 | ||||||
Note: N0 = 0 kg N ha−1, N1 = 100 kg N ha−1, N2 = 200 kg N ha−1, a = better than ADV 777 by 5% LSD, b = better than JH 37 by 5% LSD, S = susceptible, MT = moderate tolerance. |
As per Table 4, when comparing the yield at a rate of 200 kg N ha−1 with that at 100 kg N ha−1, the yield reduction ranged from 1.42 to 6.96 t ha−1. Similarly, the yield reduction varied from 3.10 to 8.52 t ha−1 when comparing the yield at 200 kg N ha−1 to that at 0 kg N ha−1. The yield reduction from 100 kg N ha−1 to 0 kg N ha−1 ranged from 0.17 t ha−1 to 3.70 t ha−1.
The STI values ranged from 0.37 to 0.89 when fertilized with 200 kg N ha−1 and 100 kg N ha−1. Ten hybrids showed moderate tolerance, while only one hybrid was susceptible. On the other hand, when maize fertilized was with 200 kg N ha−1 and 0 kg N ha−1, the STI index was between 0.36 and 0.53. Only two hybrids demonstrated moderate tolerance, while the rest were susceptible. At rates of 100 kg N ha−1 and 0 kg N ha−1, the STI ranged from 0.50 to 0.86, and all hybrids were classified as moderate tolerance (Table 4).
The relationship pattern of tolerance levels of the hybrids based on their STIs of 200 kg N ha−1 to 100 kg N ha−1, 200 kg N ha−1 to 0 kg N ha−1, and 100 kg N ha−1 to 0 kg N ha−1 is displayed in a Venn diagram in Figure 1. Interestingly, the tolerance level at 100 kg N ha−1 to 0 kg N ha−1 was moderate, the same as for the other dose combination. Therefore, it was not included in the Venn diagram. Only STI values for the other two dose combinations (200 kg N ha−1 to 100 kg N ha−1 and 200 kg N ha−1 to 0 kg N/ha) were shown in the diagram. The diagram shows that one hybrid is susceptible at 200 kg N ha−1 to 100 kg N ha−1 and 200 kg N ha−1 to 0 kg N/ha. Additionally, eight hybrids are moderately tolerant to the first dose combination but susceptible to the second. Two maize genotypes fall into the moderately tolerant category for both dose combinations.
Table 5 shows the average yield, regression coefficient (bi), and regression deviation value (s2di) for eleven maize hybrids at three levels of N fertilizer. The average yield was 7.21 t ha−1, ranging from 5.49 t ha−1 (ADV 777) to 8.18 t ha−1 (HLN 02). Six hybrids (HLN 02, HLN 03, HLN 06, HLN 07, HLN 08, and HLN 09) had above-average yields, while five hybrids (HLN 01, HLN 04, HLN 05, ADV 777, and JH 37) had below-average yields. Most hybrids had bi values close to 1 and s2di values close to zero, except for HLN 01, HLN 03, and HLN 08.
Hybrid | Mean yield (t ha−1) | bi | s2di |
HLN 01 | 6.92 | 0.51** | 0.02 |
HLN 02 | 8.18 | 1.28 | 0.59 |
HLN 03 | 7.60 | 1.43** | 1.50** |
HLN 04 | 6.75 | 0.74 | 0.12 |
HLN 05 | 7.18 | 0.86 | 0.45 |
HLN 06 | 7.67 | 1.05 | −0.20 |
HLN 07 | 7.36 | 1.23 | 0.02 |
HLN 08 | 7.47 | 1.49** | 0.03 |
HLN 09 | 7.68 | 0.99 | −0.22 |
ADV 777 | 5.94 | 0.72 | 0.60 |
JH 37 | 6.60 | 0.72 | −0.24 |
Mean | 7.21 | ||
Note: bi: regression coefficient; s2di: deviation from regression. |
The data presented in Table 2 shows that both nitrogen fertilizer and the genotype factors significantly affect various traits and the overall yield of maize crops. Specifically, the application of nitrogen fertilizer and the use of hybrid maize varieties were found to have considerable impacts on the traits. The interaction between nitrogen fertilizer and hybrid maize was significant for most of the measured traits. This suggests that combining these two factors can result in different outcomes than expected from each individually. It implies that both factors affect growth and yield and that hybrids respond differently to nitrogen levels. [33]. However, this combined effect did not extend to all traits, as no significant interaction was observed for leaf width and angle. The coefficient of variation (CV) ranged from 4.70% to 15.20% across the variables, indicating the experiment has moderate variance and adequate precision [34].
Generally, the observation variable tends to decline as fertilizer diminishes. Lower nitrogen levels reduced maize growth indicators such as plant height, leaf area, chlorophyll, stalk diameter, ear length, and kernel number [35,36]. Nitrogen (N) is essential for plant growth and development. Maize needs N throughout its life cycle, from the vegetative to the reproductive stage [37]. Maize requires nitrogen to synthesize proteins and chlorophyll and for other metabolic pathways [38]. Chlorophyll, the green pigment for photosynthesis, contains much nitrogen. Without sufficient nitrogen, plant leaves lose their green colour and become pale and yellow due to less chlorophyll [39]. Leaf area index (LAI) and leaf chlorophyll content are crucial in evaluating a plant's photosynthetic capacity, nutrient status, and overall health. LAI is a valuable indicator of the plant's light interception capability for photosynthesis, while leaf chlorophyll content reflects the plant's nutrient status and photosynthetic efficiency [40,41]. The reduced photosynthesis rate affects the plant's ability to generate energy and biomass, inhibiting plant growth and development. The addition of N fertilizer can enhance the vascular tissue in the stem and the synthesis of enzymes and nucleic acids that regulate protein accumulation and post-translational protein modification [42,43].
Root traits are critical for resource uptake and crop performance under low nitrogen conditions. Maize responds to nitrogen deficiency by enhancing root depth and steepening root growth angles [44,45]. Fine roots exhibit greater nitrogen uptake compared to thicker roots [46]. Root architecture plays a significant role in determining nutrient acquisition efficiency, particularly through root length and density [47,48]. A deeper root system with increased lateral root length increases nitrogen acquisition efficiency [49].
The interaction between genotype and environment is beneficial for breeders in plant-stress fields. The interaction causes each genotype to show different responses to different fertilization levels. The response is due to differences in genetic backgrounds. Tolerant genotypes will perform more stable than susceptible ones. Therefore, plant breeders can use these differences to select the desired genotypes according to their purposes [50,51].
The maize yield at each N level is varied. At a 200 kg N ha−1 rate, HLN 03 had the maximum yield (12.57 t ha−1), while HLN 01 had the minimum (8.42 t ha−1). All maize hybrids, except HLN 01, HLN 04, and HLN 05, differed from the control at this level. However, at 100 kg N ha−1, HLN 02 was the best, and ADV 777 was the worst. HLN 02 had a significant difference from the controls, achieving a yield of 8.09 t ha−1, whereas ADV 777 had the lowest yield at 4.74 t ha−1. At a 0 kg ha−1 nitrogen rate, HLN 01 exhibited the highest yield at 5.33 t ha−1, while HLN 08 had the lowest at 3.90 t ha−1 (Table 4). The interaction of genotype and N fertilizer dose led to differences in yield for each genotype at each N fertilization level [52].
The yield reduction between each level of nitrogen fertilization differs depending on the hybrid maize variety. Table 4 shows that the yield at the rate of 200 kg N ha−1 instead of 100 kg N ha−1 is reduced by 1.42–6.96 t ha−1. HLN 03 has the highest yield reduction, and HLN 01 has the lowest. The yield reduction ranges from 3.10 to 8.52 t ha−1 when the yield at rate 0 kg N ha−1 is compared to that at 200 kg N ha−1, with HLN 08 having the most considerable reduction and HLN 01 having the smallest. The yield reduction from 100 kg N ha−1 to 0 kg N ha−1 ranged from 0.17 t ha−1 (ADV 777) to 3.70 t ha−1 (HLN 03). The absence of nitrogen in the soil led to a restricted presence of starch metabolizing enzymes and hormone levels in maize, consequently causing a reduction in yield [19].
Table 4 shows the hybrid maize tolerance index values based on STI for different fertilization levels. For 200 kg N ha−1 and 100 kg N ha−1, the STI values varied from 0.37 (ADV 777) to 0.89 (HLN 02). According to the STI criteria, all hybrid maize corresponds to a moderate-tolerance group, except for ADV 777 (susceptible). For 200 kg N ha−1 and 0 kg N ha−1, the STI values ranged from 0.36 to 0.53. HLN 03 had the highest STI value, and ADV 777 had the lowest. Only HLN 03 and HLN 09 were encompassed in the moderate-tolerance criteria at this fertilization level, while the rest were susceptible. For 100 kg N ha−1 and 0 kg N ha−1, the STI values spanned from 0.50 to 0.86. ADV 777 showed the lowest STI value, and HLN 01 showed the highest based on the STI criteria. All hybrid maize belonged to a moderate-tolerance group at this fertilization level.
The STI index can identify maize genotypes with high yields under normal and stressful conditions. The STI index can screen genotypes with high yield potential and tolerance under both normal and stressful conditions [53,54]. Table 4 shows that maize hybrids with above-average yields at three fertilization levels were classified as tolerant or moderately tolerant. A similar pattern in wheat was also found, where genotypes with high yields under heat stress and normal conditions had high STI values, while genotypes with low yields had low STI values [55]. This finding was in line with previous studies by [56,57,58].
Figure 1 is a Venn diagram that shows the different groups of maize genotypes that can handle 200 kg N ha−1 and 100 kg N ha−1 of nitrogen fertilization based on their STI values. A Venn diagram is a graphical representation of the relationships among different data sets based on intersections or combinations of several sets [59,60]. Venn diagrams can categorize data by intersections or combinations of sets and are more informative than heat maps and tables for up to five variables in some cases [61,62]. In a Venn diagram, each set is shown as a transparent circle. The overlapping regions indicate the elements that belong to more than one set [63,64,65]. In Figure 1, the hybrid ADV 777 was classified as susceptible to both fertilizer conditions, meaning it had low yields under both N levels. Eight hybrids (HLN 01, HLN 02, HLN 04, HLN 05, HLN 06, HLN 07, HLN 08, and JH 37) were rated as moderately tolerant at STI 100 kg N ha−1 at susceptible to STI 0 kg N ha−1. When N levels were normal, their yields were high, but when N levels were stressed, their yields were low. Two maize hybrids (HLN 03 and HLN 09) were classified as moderately tolerant to both fertilizer conditions, meaning they had moderate yields under both N levels.
The bi and s2di values determine the maize hybrid stability. Based on these values, maize hybrids can be classified into four categories [66,67]. The first category consists of hybrids with bi values not significantly different from 1 and s2di values not significantly different from 0. These hybrids are considered stable across environments. The second category comprises hybrids with bi values significantly different from 1 and s2di values not significantly different from 0. These hybrids are adapted to specific environments. The third category includes hybrids with bi values not significantly different from 1 and s2di values significantly different from 0. The fourth category contains hybrids with bi values significantly different from 1 and s2di values significantly different from 0. Hybrids in the third and fourth categories are unstable across environments.
The HLN 03 maize hybrid is unstable due to its bi value of 1.43 (significantly different from 1) and its s2di value of 1.30 (significantly different from 0). These values indicate that HLN 03 has a high level of interaction with the environment. HLN 01 is a genotype-specific hybrid for low-N soil locations. The bi value of 0.51 (significantly lower than 1) and the s2di value of 0.02 (not significantly different from 0) of the HLN 01 maize hybrid indicate that it is suitable for cultivation in marginal environments. The HLN 08 maize hybrid has a bi value of 1.49 (significantly higher than 1) and s2di value of 0.02 (not significantly different from 0), which implies that HLN 08 is a genotype-specific hybrid for optimal environments (high N soil locations). Maize hybrids with bi values close to 1 and s2di values close to 0 have low environmental interaction and are categorized as stable hybrids. Genotypes HLN 02, HLN 04, HLN 05, HLN 06, HLN 07, HLN 09, ADV 777, and JH 37 belong to this category of stable hybrid (Table 5).
The selection that considers the tolerance and stability index in the stress conditions can identify both tolerant and widely adapted genotypes. This method has been employed in various crops, such as rice in saline conditions [68], bread wheat in drought conditions [69], and maize under waterlogging conditions [70]. In the current research, HLN 02, HLN 06, and HLN 07 are stable hybrids with yields higher than average. However, these hybrids exhibit only moderate tolerance at STI 100 kg N ha−1 and are susceptible at STI 0 kg N ha−1. In contrast, HLN 09 was identified as the most suitable maize hybrid for low-N environments. HLN 09 exhibited a relatively high yield of 7.68 t ha−1, surpassing the mean yield of 7.21 t ha−1 for all hybrids. The HLN 09 yields at 0 kg N ha−1, 100 kg N ha−1, and 200 kg N ha−1 were 5.05 t ha−1, 7.16 t ha−1, and 10.83 t ha−1, respectively, greater than mean yields at each fertilizer level (4.63 t ha−1, 6.57 t ha−1, and 10.44 t ha−1). The Stress Tolerance Index (STI) values for HLN 09 were 0.71 for N2-N1, 0.50 for N2-N0, and 0.84 for N1-N0. These STI values demonstrate that HLN 09 consistently maintained higher stress tolerance across varying nitrogen levels. Additionally, HLN 09 was characterized as a stable hybrid (bi = 0.99, s2di = −0.22). These facts indicate that HLN 09 has superior performance to other hybrids. As such, HLN 09 represents a stable and promising hybrid for low-N environments.
A decrease in nitrogen fertilizer dosage for maize significantly affected agronomic traits, followed by a yield decrease among the tested maize hybrid genotypes. This fact indicates that optimal nitrogen levels are essential to optimizing maize yield. Among the tested genotypes, the HLN 09 maize hybrid showed remarkable tolerance to nitrogen-deficient conditions, sustaining both stability and high yield. The hybrid's tolerance to low nitrogen suggests its potential for cultivation in environments with limited nitrogen availability.
The authors declare they have not used artificial intelligence (AI) tools in the creation of this article.
The author appreciates the Indonesian Cereal Testing Instrument Standard Institute chief for permission and the staff for carrying out the research well.
The authors declare no conflict of interest.
Conceptualization: M.A.; data curation: A.M.; formal analysis: R.I. and S.B.P.; investigation: R.I. and N.N.A.; methodology: A.M; project administration: R.E.; resources: S and R.E.; software: S.B.P. and N.N.A.; supervision: M.A.; validation: A.M; visualization: S; writing—original draft: R.E.; writing—review and editing: S.B.P. All authors have read and agreed to the published version of the manuscript.
[1] |
Subramaniam T, Lee HJ, Jeung HD, et al. (2021) Report on the annual gametogenesis and tissue biochemical composition in the Gray mussel, Crenomytilus grayanus (Dunker 1853) in the subtidal rocky bottom on the east coast of Korea. Ocean Sci J 56: 424–433. https://doi.org/10.1007/s12601-021-00042-y doi: 10.1007/s12601-021-00042-y
![]() |
[2] |
Fitori A, Ishag IA, Masoud AN, et al. (2021) Assessment of some heavy metals using sediments and bivalvia (Mytilus galloprovincialis) samples collected from Tobruk coast. Sci J Fac Sci-Sirte Univ 1: 25–31. https://doi.org/10.37375/sjfssu.v1i2.79 doi: 10.37375/sjfssu.v1i2.79
![]() |
[3] |
Czech A, Grela ER, Ognik K (2015) Effect of frying on nutrients content and fatty acid composition of muscles of selected freezing seafoods. J Food Nutr Res 3: 9–14. https://doi.org/10.12691/jfnr-3-1-2 doi: 10.12691/jfnr-3-1-2
![]() |
[4] |
Tan K, Zhang HK, Li SK, et al. (2022) Lipid nutritional quality of marine and freshwater bivalves and their aquaculture potential. Crit Rev Food Sci Nutr 62: 6990–7014. https://doi.org/10.1080/10408398.2021.1909531 doi: 10.1080/10408398.2021.1909531
![]() |
[5] | Gurdal AA, Caglak E (2021) Investigation of nutritional, some quality changes of mussels covered with edible films prepared using extracts of persimmon, Cherry Laurel and Likapa. Fresenius Environ Bull 30: 1823–1836. |
[6] |
Afsa S, De Marco G, Giannetto A, et al. (2022) Histological endpoints and oxidative stress transcriptional responses in the Mediterranean mussel Mytilus galloprovincialis exposed to realistic doses of salicylic acid. Environ Toxicol Pharmacol 92: 103855. https://doi.org/10.1016/j.etap.2022.103855 doi: 10.1016/j.etap.2022.103855
![]() |
[7] |
Peycheva K, Panayotova V, Stancheva R, et al. (2022) Effect of steaming on chemical composition of Mediterranean mussel (Mytilus galloprovincialis): Evaluation of potential risk associated with human consumption. Food Sci Nutr 10: 3052–3061. https://doi.org/10.1002/fsn3.2903 doi: 10.1002/fsn3.2903
![]() |
[8] |
Tan MT, Ye JX, Chu YM, et al. (2021) The effects of ice crystal on water properties and protein stability of large yellow croaker (Pseudosciaena crocea). Int J Refrig 130: 242–252. https://doi.org/10.1016/j.ijrefrig.2021.05.040 doi: 10.1016/j.ijrefrig.2021.05.040
![]() |
[9] |
Zhu SC, Yu HJ, Chen X, et al. (2021) Dual cryoprotective strategies for ice-binding and stabilizing of frozen seafood: A review. Trends Food Sci Technol 111: 223–232. https://doi.org/10.1016/j.tifs.2021.02.069 doi: 10.1016/j.tifs.2021.02.069
![]() |
[10] |
Lee S, Jo K, Jeong HG, et al. (2022) Freezing-induced denaturation of myofibrillar proteins in frozen meat. Crit Rev Food Sci Nutr, 1–18. https://doi.org/10.1080/10408398.2022.2116557 doi: 10.1080/10408398.2022.2116557
![]() |
[11] |
Al-Jeddawi W, Dawson P (2022) The effect of frozen storage on the quality of Atlantic Salmon. J Food Sci Nutr Res 5: 552–569. https://doi.org/10.26502/jfsnr.2642-11000098 doi: 10.26502/jfsnr.2642-11000098
![]() |
[12] |
Gao YP, Jiang HL, Lv DD, et al. (2021) Shelf-life of half-shell mussel (Mytilus edulis) as affected by pullulan, acidic electrolyzed water, and stable chlorine dioxide combined ice-glazing during frozen storage. Foods 10: 1896. https://doi.org/10.3390/foods10081896 doi: 10.3390/foods10081896
![]() |
[13] |
Tian J, Walayat N, Ding YT, et al. (2022) The role of trifunctional cryoprotectants in the frozen storage of aquatic foods: Recent developments and future recommendations. Compr Rev Food Sci Food Saf 21: 321–339. https://doi.org/10.1111/1541-4337.12865 doi: 10.1111/1541-4337.12865
![]() |
[14] |
Bardales JR, Cascallana JL, Villamarín A (2011) Differential distribution of cAMP-dependent protein kinase isoforms in various tissues of the bivalve mollusc Mytilus galloprovincialis. Acta Histochem 113: 743–748. https://doi.org/10.1016/j.acthis.2010.11.002 doi: 10.1016/j.acthis.2010.11.002
![]() |
[15] |
Cappello T, Maisano M, Mauceri A, et al. (2017) 1 H NMR-based metabolomics investigation on the effects of petrochemical contamination in posterior adductor muscles of caged mussel Mytilus galloprovincialis. Ecotoxicol Environ Saf 142: 417–422. https://doi.org/10.1016/j.ecoenv.2017.04.040 doi: 10.1016/j.ecoenv.2017.04.040
![]() |
[16] |
Castro-Claros JD, Checa A, Lucena C, et al. (2021) Shell-adductor muscle attachment and Ca2+ transport in the bivalves Ostrea stentina and Anomia ephippium. Acta Biomater 120: 249–262. https://doi.org/10.1016/j.actbio.2020.09.053 doi: 10.1016/j.actbio.2020.09.053
![]() |
[17] |
Mediodia DP, Santander-de Leon SMS, Añasco N, et al. (2017) Shell Morphology and Anatomy of the Philippine Charru Mussel Mytella charruana (d'Orbigny 1842). Asian Fish Sci 30: 185–194. https://doi.org/10.33997/j.afs.2017.30.3.004 doi: 10.33997/j.afs.2017.30.3.004
![]() |
[18] |
Yamada A, Yoshio M, Nakamura A, et al. (2004) Protein phosphatase 2B dephosphorylates twitchin, initiating the catch state of invertebrate smooth muscle. J Biol Chem 279: 40762–40768. https://doi.org/10.1074/jbc.M405191200 doi: 10.1074/jbc.M405191200
![]() |
[19] |
McElwain A, Bullard SA (2014) Histological atlas of freshwater mussels (Bivalvia, Unionidae): Villosa nebulosa (Ambleminae: Lampsilini), Fusconaia cerina (Ambleminae: Pleurobemini) and Strophitus connasaugaensis (Unioninae: Anodontini). Malacologia 57: 99–239. https://doi.org/10.4002/040.057.0104 doi: 10.4002/040.057.0104
![]() |
[20] | Simone LRL (2019) Modifications in adductor muscles in bivalves. Malacopedia 2: 1–12. |
[21] |
Vitellaro-Zuccarello L, De Biasi S, Amadeo A (1990) Immunocytochemical demonstration of neurotransmitters in the nerve plexuses of the foot and the anterior byssus retractor muscle of the mussel, Mytilus galloprovincialis. Cell Tissue Res 261: 467–476. https://doi.org/10.1007/BF00313525 doi: 10.1007/BF00313525
![]() |
[22] |
Balamurugan S, Subramanian P (2021) Histopathology of the foot, gill and digestive gland tissues of freshwater mussel, Lamellidens marginalis exposed to oil effluent. Austin J Environ Toxicol 7: 1033. https://doi.org/10.26420/austinjenvirontoxicol.2021.1033 doi: 10.26420/austinjenvirontoxicol.2021.1033
![]() |
[23] |
Lee JS, Lee YG, Park JJ, et al. (2012) Microanatomy and ultrastructure of the foot of the infaunal bivalve Tegillarca granosa (Bivalvia: Arcidae). Tissue Cell 44: 316–324. https://doi.org/10.1016/j.tice.2012.04.010 doi: 10.1016/j.tice.2012.04.010
![]() |
[24] |
Radwan EH, Saad GA, Hamed SSh (2016) Ultrastructural study on the foot and the shell of the oyster Pinctada radiata (leach, 1814), (Bivalvia Petridae). J Biosci Appl Res 2: 274–282. doi: 10.21608/jbaar.2016.107548
![]() |
[25] |
Angane M, Gupta S, Fletcher GC, et al. (2020) Effect of air blast freezing and frozen storage on Escherichia coli survival, n-3 polyunsaturated fatty acid concentration and microstructure of GreenshellTM mussels. Food Control 115: 107284. https://doi.org/10.1016/j.foodcont.2020.107284 doi: 10.1016/j.foodcont.2020.107284
![]() |
[26] |
Park JJ, Lee JS, Lee YG, et al. (2012) Micromorphology and Ultrastructure of the Foot of the Equilateral Venus Gomphina veneriformis (Bivalvia: Veneridae). CellBio 1: 11–16. https://doi.org/10.4236/cellbio.2012.11002 doi: 10.4236/cellbio.2012.11002
![]() |
[27] |
Mohamed AS, Bin Dajem S, Al-Kahtani M, et al. (2021) Silver/chitosan nanocomposites induce physiological and histological changes in freshwater bivalve. J Trace Elem Med Biol 65: 126719. https://doi.org/10.1016/j.jtemb.2021.126719 doi: 10.1016/j.jtemb.2021.126719
![]() |
[28] |
Parisi MG, Baranzini N, Dara M, et al. (2022) AIF-1 and RNASET2 are involved in the inflammatory response in the Mediterranean mussel Mytilus galloprovincialis following Vibrio infection. Fish Shellfish Immun 127: 109–118. https://doi.org/10.1016/j.fsi.2022.06.010 doi: 10.1016/j.fsi.2022.06.010
![]() |
[29] |
Parisi MG, Maisano M, Cappello T, et al. (2019) Responses of marine mussel Mytilus galloprovincialis (Bivalvia: Mytilidae) after infection with the pathogen Vibrio splendidus. Comp Biochem Phys C 221: 1–9. https://doi.org/10.1016/j.cbpc.2019.03.005 doi: 10.1016/j.cbpc.2019.03.005
![]() |
[30] |
Sheir SK, Handy RD, Galloway TS (2010) Tissue injury and cellular immune responses to mercuric chloride exposure in the common mussel Mytilus edulis: Modulation by lipopolysaccharide. Ecotoxicol Environ Saf 73: 1338–1344. https://doi.org/10.1016/j.ecoenv.2010.01.014 doi: 10.1016/j.ecoenv.2010.01.014
![]() |
[31] |
Al-Subiai SN, Moody AJ, Mustafa SA, et al. (2011) A multiple biomarker approach to investigate the effects of copper on the marine bivalve mollusc, Mytilus edulis. Ecotoxicol Environ Saf 74: 1913–1920. https://doi.org/10.1016/j.ecoenv.2011.07.012 doi: 10.1016/j.ecoenv.2011.07.012
![]() |
[32] |
Nieto-Ortega S, Melado-Herreros Á, Foti G, et al. (2021) Rapid differentiation of unfrozen and frozen-thawed tuna with non-destructive methods and classification models: Bioelectrical impedance analysis (BIA), Near-infrared spectroscopy (NIR) and Time domain reflectometry (TDR). Foods 11: 55. https://doi.org/10.3390/foods11010055 doi: 10.3390/foods11010055
![]() |
[33] |
Yang SB, Hu YQ, Takaki K, et al. (2021) Effect of water ice-glazing on the quality of frozen swimming crab (Portunus trituberculatus) by liquid nitrogen spray freezing during frozen storage. Int J Refrig 131: 1010–1015. https://doi.org/10.1016/j.ijrefrig.2021.06.035 doi: 10.1016/j.ijrefrig.2021.06.035
![]() |
[34] | Noomhorm A, Vongsawasdi P (2004) Freezing shellfish, In: Hui YH, Legarretta IG, Lim MH, et al. (Eds.), Handbook of frozen foods, New York: Marcel Dekker, 309–324. |
[35] |
Stella R, Mastrorilli E, Pretto T, et al. (2022) New strategies for the differentiation of fresh and frozen/thawed fish: Non-targeted metabolomics by LC-HRMS (part B). Food Control 132: 108461. https://doi.org/10.1016/j.foodcont.2021.108461 doi: 10.1016/j.foodcont.2021.108461
![]() |
[36] |
Tinacci L, Armani A, Guidi A, et al. (2018) Histological discrimination of fresh and frozen/thawed fish meat: European hake (Merluccius merluccius) as a possible model for white meat fish species. Food Control 92: 154–161. https://doi.org/10.1016/j.foodcont.2018.04.056 doi: 10.1016/j.foodcont.2018.04.056
![]() |
[37] |
Tinacci L, Armani A, Scardino G, et al. (2020) Selection of histological parameters for the development of an analytical method for discriminating fresh and frozen/thawed common octopus (Octopus vulgaris) and preventing frauds along the seafood chain. Food Anal Methods 13: 2111–2127. https://doi.org/10.1007/s12161-020-01825-0 doi: 10.1007/s12161-020-01825-0
![]() |
[38] |
Furnesvik L, Erkinharju T, Hansen M, et al. (2022) Evaluation of histological post-mortem changes in farmed Atlantic salmon (Salmo salar L.) at different time intervals and storage temperatures. J Fish Dis 45: 1571–1580. https://doi.org/10.1111/jfd.13681 doi: 10.1111/jfd.13681
![]() |
[39] |
Bouchendhomme T, Soret M, Devin A, et al. (2022) Differentiating between fresh and frozen-thawed fish fillets by mitochondrial permeability measurement. Food Control 141: 109197. https://doi.org/10.1016/j.foodcont.2022.109197 doi: 10.1016/j.foodcont.2022.109197
![]() |
[40] |
Shafieipour A, Sami M (2015) The effect of different thawing methods on chemical properties of frozen pink shrimp (Penaeus duorarum). Iran J Vet Med 9: 1–6. https://doi.org/10.22059/ijvm.2015.53226 doi: 10.22059/ijvm.2015.53226
![]() |
[41] |
Anderssen KE, Kranz M, Syed S, et al. (2022) Diffusion tensor imaging for spatially-resolved characterization of muscle fiber structure in seafood. Food Chem 380: 132099. https://doi.org/10.1016/j.foodchem.2022.132099 doi: 10.1016/j.foodchem.2022.132099
![]() |
[42] |
Zhang LH, Zhang M, Mujumdar AS (2021) Technological innovations or advancement in detecting frozen and thawed meat quality: A review. Crit Rev Food Sci Nutr, 1–17. https://doi.org/10.1080/10408398.2021.1964434 doi: 10.1080/10408398.2021.1964434
![]() |
Parameter | Value | Level |
Texture | ||
Clay (%) | 39 | |
Silt (%) | 46 | |
Sand (%) | 15 | |
Organic C (%) | 0.89 | Very low |
Total N (%) | 0.13 | Low |
C/N | 5.75 | Low |
Source: Soil laboratory of the Indonesian Cereal Testing Instrument Standard Institute. |
Variable | Mean square | CV (%) | |||||||
Nitrogen (N) | R/N | Hybrid (H) | H x N | Error | |||||
Plant height | 11777.50 | ** | 66.17 | 571.38 | * | 955.91 | ** | 218.47 | 7.40 |
Ear height | 3788.14 | ** | 28.78 | 242.23 | ** | 660.90 | ** | 64.87 | 7.60 |
Stalk diameter | 235.60 | ** | 8.28 | 14.78 | ** | 7.79 | ** | 2.84 | 7.00 |
Leaf angle | 344.93 | ** | 10.89 | 98.32 | ** | 14.82 | 14.84 | 15.20 | |
Leaf length | 1689.11 | ** | 34.30 | 152.66 | * | 45.52 | ** | 15.19 | 4.70 |
Leaf width | 9.12 | ** | 0.49 | 2.19 | ** | 0.33 | 0.44 | 6.80 | |
Yield | 289.03 | ** | 0.81 | 3.50 | ** | 3.56 | ** | 0.73 | 11.90 |
Note: * = significant at p < 0.05, ** = significant at p < 0.01, CV = coefficient of variation. |
Hybrid | Plant height (cm) | Ear height (cm) | Stalk diameter (mm) | Leaf length (cm) | ||||||||
N2 | N1 | N0 | N2 | N1 | N0 | N2 | N1 | N0 | N2 | N1 | N0 | |
HLN 01 | 202.27 | 199.33 | 185.40 | 94.60b | 111.73 | 111.13 | 26.60 | 22.65 (x) | 20.70 (x) | 88.67 | 87.80a | 77.13 (x) |
HLN 02 | 226.60 | 202.07 (x) | 153.60 (x) | 118.53 | 115.00 | 61.00ab (x) | 26.50 | 26.27ab | 23.40a (x) | 95.91ab | 92.20ab | 85.16a (x) |
HLN 03 | 249.93 | 202.40 (x) | 177.93 (x) | 139.67 | 111.20 (x) | 99.07 (x) | 27.07 | 25.47ab | 24.39a | 89.27 | 85.33a | 81.13 (x) |
HLN 04 | 216.27 | 208.53 | 175.07 (x) | 117.00 | 95.47b (x) | 100.27 (x) | 29.55 | 24.51ab (x) | 22.39 (x) | 82.13 | 76.53 | 66.27 (x) |
HLN 05 | 210.67 | 188.47a | 182.20 (x) | 116.00 | 95.80b (x) | 105.00 | 23.80 | 22.78 | 20.86 (x) | 90.13 | 79.67 (x) | 74.53 (x) |
HLN 06 | 243.47 | 201.20 (x) | 181.60 (x) | 131.60 | 107.20 (x) | 91.80 (x) | 26.56 | 25.13ab | 21.24 (x) | 89.40 | 86.27a | 64.20 (x) |
HLN 07 | 205.93 | 187.53a | 184.73 | 113.20 | 96.33b (x) | 100.80 | 24.61 | 22.49 | 18.44 (x) | 87.33 | 86.33a | 69.07 (x) |
HLN 08 | 242.33 | 206.07 (x) | 191.33 (x) | 128.33 | 114.47 (x) | 96.87 (x) | 27.05 | 26.10ab | 21.85 (x) | 91.33a | 89.07a | 73.13 (x) |
HLN 09 | 228.33 | 212.00 | 172.93 (x) | 123.93 | 110.67 (x) | 91.27 (x) | 24.60 | 24.34ab | 20.20 (x) | 92.20ab | 88.60a | 74.93 (x) |
ADV 777 | 214.40 | 196.67 | 176.47 (x) | 122.22 | 100.00 (x) | 89.67 (x) | 29.58 | 20.78 (x) | 20.47 (x) | 84.27 | 77.20 (x) | 78.27 |
JH 37 | 215.67 | 188.33 (x) | 166.13 (x) | 114.67 | 114.40 (x) | 80.20 | 28.77 | 21.23 (x) | 22.11 (x) | 85.20 | 83.60 | 79.33 |
Mean | 223.26 | 199.33 | 177.04 | 117.96 | 96.65 | 93.37 | 26.79 | 23.8 | 21.46 | 88.71 | 84.78 | 74.83 |
LSD 5% | 24.14 | 24.14 | 24.14 | 13.15 | 13.15 | 13.25 | 2.75 | 2.75 | 2.75 | 6.36 | 6.36 | 6.36 |
Note: N0 = 0 kg N ha−1, N1 = 100 kg N ha−1, N2 = 200 kg N ha−1; in a row, (x) = significant difference from 200 kg N ha−1 by 5% LSD; in a column, a = better than ADV 777 by 5% LSD, b = better than JH 37 by 5% LSD. |
Hybrid | Yield (t ha−1) | Yield reduction (t ha−1) | STI | ||||||
N2 | N1 | N0 | N2-N1 | N2-N0 | N1-N0 | N2-N1 | N2-N0 | N1-N0 | |
HLN 01 | 8.42 | 7.00a | 5.33 | 1.42 | 3.10 | 1.67 | 0.54 (MT) | 0.41 (S) | 0.86 (MT) |
HLN 02 | 12.05ab | 8.09ab | 4.40 | 3.96 | 7.66 | 3.70 | 0.89 (MT) | 0.49 (S) | 0.82 (MT) |
HLN 03 | 12.57ab | 5.61 | 4.60 | 6.96 | 7.97 | 1.01 | 0.65 (MT) | 0.53 (MT) | 0.60 (MT) |
HLN 04 | 8.96 | 6.76a | 4.53 | 2.20 | 4.44 | 2.24 | 0.56 (MT) | 0.37 (S) | 0.71 (MT) |
HLN 05 | 9.73 | 7.30a | 4.52 | 2.43 | 5.21 | 2.78 | 0.65 (MT) | 0.40 (S) | 0.76 (MT) |
HLN 06 | 11.02ab | 7.16a | 4.84 | 3.86 | 6.18 | 2.33 | 0.72 (MT) | 0.49 (S) | 0.80 (MT) |
HLN 07 | 11.45ab | 6.16a | 4.47 | 5.29 | 6.99 | 1.69 | 0.65 (MT) | 0.47 (S) | 0.64 (MT) |
HLN 08 | 12.42ab | 6.09 | 3.90 | 6.33 | 8.52 | 2.19 | 0.69 (MT) | 0.44 (S) | 0.55 (MT) |
HLN 09 | 10.83ab | 7.16a | 5.05 | 3.67 | 5.79 | 2.11 | 0.71 (MT) | 0.50 (MT) | 0.84 (MT) |
ADV 777 | 8.50 | 4.74 | 4.58 | 3.76 | 3.92 | 0.17 | 0.37 (S) | 0.36 (S) | 0.50 (MT) |
JH 37 | 8.90 | 6.18 | 4.73 | 2.72 | 4.17 | 1.46 | 0.50 (MT) | 0.39 (S) | 0.68 (MT) |
Mean | 10.44 | 6.57 | 4.63 | 3.87 | 5.81 | 1.94 | |||
SE | 0.49 | 0.49 | 0.49 | ||||||
LSD 5% | 1.40 | 1.40 | 1.40 | ||||||
Note: N0 = 0 kg N ha−1, N1 = 100 kg N ha−1, N2 = 200 kg N ha−1, a = better than ADV 777 by 5% LSD, b = better than JH 37 by 5% LSD, S = susceptible, MT = moderate tolerance. |
Hybrid | Mean yield (t ha−1) | bi | s2di |
HLN 01 | 6.92 | 0.51** | 0.02 |
HLN 02 | 8.18 | 1.28 | 0.59 |
HLN 03 | 7.60 | 1.43** | 1.50** |
HLN 04 | 6.75 | 0.74 | 0.12 |
HLN 05 | 7.18 | 0.86 | 0.45 |
HLN 06 | 7.67 | 1.05 | −0.20 |
HLN 07 | 7.36 | 1.23 | 0.02 |
HLN 08 | 7.47 | 1.49** | 0.03 |
HLN 09 | 7.68 | 0.99 | −0.22 |
ADV 777 | 5.94 | 0.72 | 0.60 |
JH 37 | 6.60 | 0.72 | −0.24 |
Mean | 7.21 | ||
Note: bi: regression coefficient; s2di: deviation from regression. |
Parameter | Value | Level |
Texture | ||
Clay (%) | 39 | |
Silt (%) | 46 | |
Sand (%) | 15 | |
Organic C (%) | 0.89 | Very low |
Total N (%) | 0.13 | Low |
C/N | 5.75 | Low |
Source: Soil laboratory of the Indonesian Cereal Testing Instrument Standard Institute. |
Variable | Mean square | CV (%) | |||||||
Nitrogen (N) | R/N | Hybrid (H) | H x N | Error | |||||
Plant height | 11777.50 | ** | 66.17 | 571.38 | * | 955.91 | ** | 218.47 | 7.40 |
Ear height | 3788.14 | ** | 28.78 | 242.23 | ** | 660.90 | ** | 64.87 | 7.60 |
Stalk diameter | 235.60 | ** | 8.28 | 14.78 | ** | 7.79 | ** | 2.84 | 7.00 |
Leaf angle | 344.93 | ** | 10.89 | 98.32 | ** | 14.82 | 14.84 | 15.20 | |
Leaf length | 1689.11 | ** | 34.30 | 152.66 | * | 45.52 | ** | 15.19 | 4.70 |
Leaf width | 9.12 | ** | 0.49 | 2.19 | ** | 0.33 | 0.44 | 6.80 | |
Yield | 289.03 | ** | 0.81 | 3.50 | ** | 3.56 | ** | 0.73 | 11.90 |
Note: * = significant at p < 0.05, ** = significant at p < 0.01, CV = coefficient of variation. |
Hybrid | Plant height (cm) | Ear height (cm) | Stalk diameter (mm) | Leaf length (cm) | ||||||||
N2 | N1 | N0 | N2 | N1 | N0 | N2 | N1 | N0 | N2 | N1 | N0 | |
HLN 01 | 202.27 | 199.33 | 185.40 | 94.60b | 111.73 | 111.13 | 26.60 | 22.65 (x) | 20.70 (x) | 88.67 | 87.80a | 77.13 (x) |
HLN 02 | 226.60 | 202.07 (x) | 153.60 (x) | 118.53 | 115.00 | 61.00ab (x) | 26.50 | 26.27ab | 23.40a (x) | 95.91ab | 92.20ab | 85.16a (x) |
HLN 03 | 249.93 | 202.40 (x) | 177.93 (x) | 139.67 | 111.20 (x) | 99.07 (x) | 27.07 | 25.47ab | 24.39a | 89.27 | 85.33a | 81.13 (x) |
HLN 04 | 216.27 | 208.53 | 175.07 (x) | 117.00 | 95.47b (x) | 100.27 (x) | 29.55 | 24.51ab (x) | 22.39 (x) | 82.13 | 76.53 | 66.27 (x) |
HLN 05 | 210.67 | 188.47a | 182.20 (x) | 116.00 | 95.80b (x) | 105.00 | 23.80 | 22.78 | 20.86 (x) | 90.13 | 79.67 (x) | 74.53 (x) |
HLN 06 | 243.47 | 201.20 (x) | 181.60 (x) | 131.60 | 107.20 (x) | 91.80 (x) | 26.56 | 25.13ab | 21.24 (x) | 89.40 | 86.27a | 64.20 (x) |
HLN 07 | 205.93 | 187.53a | 184.73 | 113.20 | 96.33b (x) | 100.80 | 24.61 | 22.49 | 18.44 (x) | 87.33 | 86.33a | 69.07 (x) |
HLN 08 | 242.33 | 206.07 (x) | 191.33 (x) | 128.33 | 114.47 (x) | 96.87 (x) | 27.05 | 26.10ab | 21.85 (x) | 91.33a | 89.07a | 73.13 (x) |
HLN 09 | 228.33 | 212.00 | 172.93 (x) | 123.93 | 110.67 (x) | 91.27 (x) | 24.60 | 24.34ab | 20.20 (x) | 92.20ab | 88.60a | 74.93 (x) |
ADV 777 | 214.40 | 196.67 | 176.47 (x) | 122.22 | 100.00 (x) | 89.67 (x) | 29.58 | 20.78 (x) | 20.47 (x) | 84.27 | 77.20 (x) | 78.27 |
JH 37 | 215.67 | 188.33 (x) | 166.13 (x) | 114.67 | 114.40 (x) | 80.20 | 28.77 | 21.23 (x) | 22.11 (x) | 85.20 | 83.60 | 79.33 |
Mean | 223.26 | 199.33 | 177.04 | 117.96 | 96.65 | 93.37 | 26.79 | 23.8 | 21.46 | 88.71 | 84.78 | 74.83 |
LSD 5% | 24.14 | 24.14 | 24.14 | 13.15 | 13.15 | 13.25 | 2.75 | 2.75 | 2.75 | 6.36 | 6.36 | 6.36 |
Note: N0 = 0 kg N ha−1, N1 = 100 kg N ha−1, N2 = 200 kg N ha−1; in a row, (x) = significant difference from 200 kg N ha−1 by 5% LSD; in a column, a = better than ADV 777 by 5% LSD, b = better than JH 37 by 5% LSD. |
Hybrid | Yield (t ha−1) | Yield reduction (t ha−1) | STI | ||||||
N2 | N1 | N0 | N2-N1 | N2-N0 | N1-N0 | N2-N1 | N2-N0 | N1-N0 | |
HLN 01 | 8.42 | 7.00a | 5.33 | 1.42 | 3.10 | 1.67 | 0.54 (MT) | 0.41 (S) | 0.86 (MT) |
HLN 02 | 12.05ab | 8.09ab | 4.40 | 3.96 | 7.66 | 3.70 | 0.89 (MT) | 0.49 (S) | 0.82 (MT) |
HLN 03 | 12.57ab | 5.61 | 4.60 | 6.96 | 7.97 | 1.01 | 0.65 (MT) | 0.53 (MT) | 0.60 (MT) |
HLN 04 | 8.96 | 6.76a | 4.53 | 2.20 | 4.44 | 2.24 | 0.56 (MT) | 0.37 (S) | 0.71 (MT) |
HLN 05 | 9.73 | 7.30a | 4.52 | 2.43 | 5.21 | 2.78 | 0.65 (MT) | 0.40 (S) | 0.76 (MT) |
HLN 06 | 11.02ab | 7.16a | 4.84 | 3.86 | 6.18 | 2.33 | 0.72 (MT) | 0.49 (S) | 0.80 (MT) |
HLN 07 | 11.45ab | 6.16a | 4.47 | 5.29 | 6.99 | 1.69 | 0.65 (MT) | 0.47 (S) | 0.64 (MT) |
HLN 08 | 12.42ab | 6.09 | 3.90 | 6.33 | 8.52 | 2.19 | 0.69 (MT) | 0.44 (S) | 0.55 (MT) |
HLN 09 | 10.83ab | 7.16a | 5.05 | 3.67 | 5.79 | 2.11 | 0.71 (MT) | 0.50 (MT) | 0.84 (MT) |
ADV 777 | 8.50 | 4.74 | 4.58 | 3.76 | 3.92 | 0.17 | 0.37 (S) | 0.36 (S) | 0.50 (MT) |
JH 37 | 8.90 | 6.18 | 4.73 | 2.72 | 4.17 | 1.46 | 0.50 (MT) | 0.39 (S) | 0.68 (MT) |
Mean | 10.44 | 6.57 | 4.63 | 3.87 | 5.81 | 1.94 | |||
SE | 0.49 | 0.49 | 0.49 | ||||||
LSD 5% | 1.40 | 1.40 | 1.40 | ||||||
Note: N0 = 0 kg N ha−1, N1 = 100 kg N ha−1, N2 = 200 kg N ha−1, a = better than ADV 777 by 5% LSD, b = better than JH 37 by 5% LSD, S = susceptible, MT = moderate tolerance. |
Hybrid | Mean yield (t ha−1) | bi | s2di |
HLN 01 | 6.92 | 0.51** | 0.02 |
HLN 02 | 8.18 | 1.28 | 0.59 |
HLN 03 | 7.60 | 1.43** | 1.50** |
HLN 04 | 6.75 | 0.74 | 0.12 |
HLN 05 | 7.18 | 0.86 | 0.45 |
HLN 06 | 7.67 | 1.05 | −0.20 |
HLN 07 | 7.36 | 1.23 | 0.02 |
HLN 08 | 7.47 | 1.49** | 0.03 |
HLN 09 | 7.68 | 0.99 | −0.22 |
ADV 777 | 5.94 | 0.72 | 0.60 |
JH 37 | 6.60 | 0.72 | −0.24 |
Mean | 7.21 | ||
Note: bi: regression coefficient; s2di: deviation from regression. |