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The research was carried out at the experimental field of the Instituto Tecnológico Superior de Tantoyuca, in the municipality of Tantoyuca, state of Veracruz, Mexico, located in the HAV region, between parallels 97°59' W and 98°24' W, and 21°06' N and 21°40' N, at an altitude of up to 300 masl. This region has a warm subhumid climate, with rainy summers, a mean annual temperature between 22 and 26 °C, an average annual rainfall between 1,100 and 1,300 mm, and a relative humidity of 44%[19]. According to FAO classification, the soil where the experiment was performed is classified as a pellic vertisol with clayey texture, a pH of 7.8, light salinity, 18.3 ppm N-NO3, 50 ppm phosphorus, 49 ppm potassium, and organic matter (> 5.1%), according to the analysis performed at the Soil Laboratory of the Veracruz University.
Evaluated germplasm and experiment design
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The vegetal material used for this study belongs to the Cenchrus purpureus (Schumach.) Morrone species, and the evaluated accessions, taken as treatments, were King grass, Maralfalfa, Elephant grass, Merkeron, Purple grass, CT-115, and Taiwan, obtained from the 'La Posta' experimental field, belonging to the National Institute of Forestry, Agricultural and Livestock Research (INIFAP), in the town of Paso del Toro, Veracruz state, Mexico. A randomized block experimental design was used, with seven treatments and four replications. The assessment period ran from July 2019 to April 2020 under rainfed conditions.
Experiment site and planting
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Twenty eight plots of 25 m2 (5 m × 5 m) were created in a 1,222 m2 area, distributed in four blocks (seven plots p/block), with a distance of 2 m between plots and blocks. Planting of the assessed accessions was done with two stems (phytomers with three internodes) per planting point, introducing two internodes in the seedbed, and placing the stems at a 45° angle. The distance between crop rows and sowing points was 80 cm, resulting in a plant density of 36 tillers per 25 m2 in every experimental unit. The seeded ground was only prepared by primary tillage (plowing and harrowing). Planting was done in September 2018, and the uniform cutting was made in July 2019, at 15 cm above the ground.
Sampling and Harvest Frequency (HF)
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A sample area of 2 m2 (1 m × 2 m) was marked, leaving a 2 m border in each experimental unit to avoid the edge effect. Harvest frequency was determined based on a mean intercepted radiation rate of ≥ 95 in the four replications of each treatment. Four consecutive cuts were performed[20].
Evaluated variables
Fresh weight (FW) and dry matter (DM)
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Cuts were done at 15 cm above the ground for FW assessment purposes, and all the biomass of the representative sample (2 m2) was weighed immediately after cutting, using a digital scale with a minimum precision of 1 g. Five whole plants (leaf and stem) were randomly selected to determine the DM and obtain a 300 g subsample, which was dried in a forced air lab oven at 65 °C to a constant weight[21]. The results were extrapolated to 10,000 m2 (ha).
Leaf: stem ratio (LSR) and accumulated dry leaf matter rate (ADLMR)
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Five plants were selected from the biomass obtained from the sample, and the leaves were separated from the stem. Once separated, the stems were all placed in a forced air lab oven at 65 °C to a constant weight. The LSR was estimated by dividing the dry weight of the leaf samples by the dry weight of the stem samples. The ADLMR was calculated by dividing the dry leaf matter yield by the number of days between cutting periods. To estimate the FW, DM, LSR, and ADLMR variables per season (dry and rainy), the values of each variable per accession and season were added up. The annual values of all variables were obtained by adding up the data from the four cuts[22].
Height (H)
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The H was determined using a measuring tape, calibrated in meters, centimeters, and millimeters, to measure five plants of each sample at every cutting period, from the soil level to the flag leaf.
Climate variables
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Some climate variables that are important for biomass production, such as average rainfall (mm), relative humidity (%), global radiation (W/m2), mean temperature (°C), and reference evapotranspiration (mm), were taken during the development of the research activities (Fig. 1). These data were obtained from the weather station located at the experimental field of the Instituto Tecnológico Superior de Tantoyuca. For the purposes of this research, 'dry season' is defined as the months of the year in which reference evapotranspiration is greater than the average rainfall because, in these conditions, crops are in a state of stress due to a lack of moisture, and this period is considered key in determining the need for irrigation[23].
Statistical analysis
Analysis of variance (ANOVA)
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The Statistic software (STATISTICA V10, 2013) was used for statistical analysis; the general linear model (GLM) was as follows:
$\rm Y_{ijk}={\text μ} + {\text α}_i+{\text β}_j+{\text ε}_{ijk} $ Where: Yijk = the quantitative response variable of the i-th accession and the j-th replication. μ = Overall mean. αi = fixed effect of the i-th Cenchrus purpureus accession.
= effect of the j-th block. εijk = random error associated with each observation, where ij~NI(0,σ2), normality was analyzed with the Shapiro-Wilk test; and homoscedasticity, with Bartlett's test (p < 0.05). The means were compared with the Fisher method (p < 0.05).$\, {\text β}_{\rm j} $ Multivariate analysis
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Using the studied response variables from each cutting performed, and the monthly average climatological variables of the research site, a multiple linear analysis was executed to ascertain the weight climatological variables have on the assessed accessions. The MLR model is as follows:
$ {\text γ}_{\iota }={\text β}_{0}+{\text β}_{1}{\rm X}_{1}+{\text β}_{2}{\rm X}_{2}+{\text β}_{3}{\rm X}_{3}+{\text β}_{4}{\rm X}_{4}+{\rm e}\mathfrak{i} $ Where:
= Dependent variable of the i-th accession; β0 = Intercept; X1 = Accumulated precipitation; X2 = Relative humidity; X3 = Global radiation; X4 = Average temperature;$ {\text γ}_{{\iota }} $ = Random component.${\rm e}\mathfrak{i} $ Lastly, Principal Component Analyses (PCA) were used to observe the possible grouping of the agronomic and yield variables, as well as the accession grouping (STATISTICA V10, 2013).
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Merkeron grass had the highest value for ADLMR, statistically different (p < 0.05) from Taiwan and King grass accessions, thus demonstrating its capability to accumulate dry matter in its leaves (Table 1). Consequently, this accession had an LSR of 0.88, the greatest of all the accessions, showing a statistical difference (p < 0.05) with the CT-115 accession, which had the lowest value (0.58). CT-115 showed the highest capacity for dry matter accumulation on its stems, which is made more evident by its being one of the tallest accessions, only shorter than King grass and Maralfalfa. An important characteristic, from the perspective of productivity, is harvest frequency (HF), that is, the time (d) it takes for the plant to reach maturity and harvest time. Merkeron, Elephant, and Purple grasses were harvested at intervals ~60 d, while the rest of the accessions at ~80 d (p < 0.05) (Table 1).
Table 1. Agronomic variables of seven Cenchrus purpureus accessions in the HAV in the rainy season.
Accession Variable ADLMR (kg/ha/d) LSR H (m) HF (d) Elephant 202.44ab 0.83a 1.85b 63.00b Merkeron 220.02a 0.88a 2.06ab 62.50b Purple 200.72ab 0.85a 1.90b 62.50b Taiwan 161.95b 0.82a 2.28a 83.00a King grass 154.07b 0.78a 2.16ab 83.00a CT-115 189.82ab 0.58b 2.05ab 83.00a Maralfalfa 170.04ab 0.70ab 2.15ab 83.00a s.e. 20.35 0.06 0.13 4.13 ADLMR: Accumulated dry leaf matter rate. LSR: Leaf:stem ratio. H: Height. HF: Harvest frequency. Means within columns followed by different letters differ by Fisher test at 5% probability. Elephant grass had the highest ADLMR, showing a statistical difference (p < 0.05) with Merker grass, Taiwan A-144, King grass and CT-115. In the present study, the highest LSR was observed in Purple grass (p < 0.05). However, despite this, Purple grass had the lowest H, which was notably different from that of Elephant grass and Taiwan. There was a slight variation in HF, with Elephant grass having the lowest value, similar to King grass, and CT-115 (p > 0.05). Merkeron grass had the highest HF of all accessions (p < 0.05) (Table 2).
Table 2. Agronomic variables of seven Cenchrus purpureus accessions in the HAV on the dry season.
Accession Variables ADLMR(kg/ha/d) LSR H (m) HF (d) Elephant grass 228.51a 0.93b 1.94a 79.00e Merkeron grass 108.84bc 0.91b 1.79ab 104.50a Purple grass 198.68a 1.22a 1.70b 98.00ab Taiwan 79.52c 0.92b 1.92a 94.00cb King grass 110.97bc 0.89b 1.84ab 85.50cde CT-115 91.02c 0.86b 1.72b 84.00de Maralfalfa 187.45ab 0.93b 1.86ab 92.50bcd s.e. 30.56 0.06 0.06 3.34 ADLMR: Accumulated dry leaf matter rate. LSR: Leaf:stem ratio. H: Height. HF: Harvest frequency. Means within columns followed by different letters differ by Fisher test at 5% probability. The ADLMR variable in Elephant grass, which had a value of 228.51 kg/ha/d, was the highest in all treatments, showing statistical differences (p < 0.05) with Merkeron grass, Taiwan, King grass, and CT-115, and a greater capability to deposit dry matter on the leaves (Table 3). Even though Elephant grass showed an adequate LRS, it was surpassed by Purple grass, which was statistically different (p < 0.05) from King grass, CT-115, and Maralfalfa. Measure H was not statistically different (p > 0.05) among the assessed accessions. The shortest HF was 71 d, and it corresponded to Elephant grass, while the longest corresponded to the Taiwan accession, with statistical differences between the two (p < 0.05).
Table 3. Annual analysis of agronomic variables in seven Cenchrus purpureus accessions in the HAV region.
Accession Variables ADLMR (kg/ha/d) LSR H (m) HF (d) Elephant grass 215.47a 0.88ab 1.89a 71.0b Merkeron grass 164.42ab 0.89ab 1.92a 83.5ab Purple grass 199.70ab 1.03a 1.80a 80.2ab Taiwan 120.73b 0.87ab 2.09a 88.5a King grass 132.51b 0.83b 2.00a 84.2ab CT-115 140.42ab 0.72b 1.88a 83.5ab Maralfalfa 178.74ab 0.81b 2.00a 83.5ab Standard error 19.08 0.04 0.07 3.2 Season Rainy 185.58a 0.78b 2.06a 74.2b Dry 143.56b 0.95a 1.82b 91.0a s.e. 10.20 0.02 0.37 1.7 ADLMR: Accumulated dry leaf matter rate. LSR: Leaf:stem ratio. H: Height. HF: Harvest frequency. Means within columns followed by different letters differ by Fisher test at 5% probability. The differences in climatic factors between the dry and rainy seasons (Fig. 2) influenced the agronomic behavior of the assessed accessions. Noteworthy values of ADLMR, H, and HF were reported during the rainy season. On the other hand, LSR was greatest in the dry season (p < 0.05).
Figure 2.
Grouping of Cenchrus purpureus accessions according to two main components: Factor 1. Agronomic characteristics and Factor 2. Yield parameters. (a), (c) dry season, (b), (d) rainy season.
The FW of the assessed accessions was not statistically different during the rainy season; nevertheless, the DM results indicate that CT-115 was quantitatively superior to all the other accessions, and statistically different (p < 0.05) from Elephant and Merkeron grass. Conversely, the FW of Maralfalfa was greatest in the dry season, and it was statistically different from CT-115 and Taiwan (p < 0.05). In turn, Elephant grass had the highest accumulation of dry matter (t/ha), followed by Purple grass and Maralfalfa (p < 0.05) in the same period. The annual cumulative fresh weight was greatest in Maralfalfa, and statistically different (p < 0.05) from Taiwan, while Elephant grass had the greatest amount of accumulated dry matter of all the accessions after a year of assessment (Table 4).
Table 4. Cumulative yields of fresh weight and dry matter per season and cumulative annual yield of seven accessions of Cenchrus purpureus in the HAV region.
Accession Rainy season Dry season Annual FW (t/ha) DM (t/ha) FW (t/ha) DM (t/ha) FW (t/ha) DM (t/ha) DM (%) Elephant grass 261.31a 54.39a 155.18abc 80.09b 416.50ab 134.49a 34.1a Merkeron grass 269.78a 55.60a 179.11a 46.71a 448.89ab 102.31ab 24.2b Purple grass 264.66a 61.65ab 153.46ab 69.30b 418.13ab 130.95a 32.7a Taiwan 252.66a 60.05ab 104.96c 29.21a 357.62a 89.27b 27.5ab King grass 259.79a 61.32ab 156.66ab 41.11a 416.46ab 102.44ab 26.3ab CT-115 321.14a 79.81b 120.01bc 32.20a 441.16ab 112.01ab 27.2ab Maralfalfa 295.95a 67.03ab 190.57a 67.37b 486.52b 134.41a 30.0ab s.e. 25.08 7.22 13.77 6.05 32.62 11.23 2.84 FW: Fresh weight. DM: Dry matter. Means within columns followed by different letters differ by Fisher test at 5% probability. Table 5 shows the average FW and DM per cutting period. The superior capability of Elephant grass to produce dry matter (DM) under the particular climatic conditions of the HAV is evident. Statistical differences exist in FW and DM variables between seasons (p < 0.05). The rainy season yielded the highest values for these variables.
Table 5. Average annual yields per cut of fresh weight and dry matter of seven Cenchrus purpureus accessions in the HAV region.
Accession Variable FW (t/ha) DM (t/ha) Elephant grass 208.25ab 67.24a Merkeron grass 224.44a 51.15ab Purple grass 209.06ab 65.47a Taiwan 178.81b 44.63b King grass 208.23ab 51.22ab CT-115 220.58ab 56.00ab Maralfalfa 243.26a 67.20a s.e. 15.01 6.19 Season Rainy 275.04a 62.84a Dry 151.42b 52.28b s.e. 8.02 3.31 FW: Fresh weight. DM: Dry matter. Means within columns followed by different letters differ by Fisher test at 5% probability. The correlation analysis used to determine the degree of association between the DM and other agronomic variables indicated positive correlations with H, ADLMR, and a negative correlation with LSR. Nevertheless, it was found that there is little association with the FW variable (r = 0.147), showing a value of p= 0.083. For the case of the H and LSR variables, the associations were low (r = 0.192 and r = 0.262, respectively). There was a high degree of association with the ADLMR variable (r = 0.889), with a value of p = 0.001 (Table 6).
Table 6. Correlation analysis between the DM and the agronomic variables of seven Cenchrus purpureus accessions grown in the HAV.
Variable r r2 α s.e. (a) p (a) β s.e. (b) p (b) DM H 0.192 0.037 12.826 6.743 0.059 8.218 3.580 0.023 LSR 0.262 0.069 44.658 5.333 0.001 -19.545 6.116 0.002 DLMAR 0.884 0.782 3.967 1.227 0.002 0.153 0.007 0.001 FW 0.147 0.022 26.466 1.525 0.001 0.000 0.001 0.083 ADLMR: Accumulated dry leaf matter rate. LSR: Leaf:stem ratio. H: Height. FW: Fresh weight. r2: Determination coefficient. a: intercept. b: slope. p: p-value. MLR was implemented for the creation of statistical models to describe the magnitude of the association between climatological and agronomic variables of Cenchrus purpureus in the HAV is presented in Table 7. From all the performed analysis, the one that obtained a medium to high value in the goodness-of-fit indicators (r = 0.73 and r2adjusted = 0.52) was the FW variable, where the Forward method included all the climatic variables. The goodness-of-fit indicators for the DM mathematical model (r = 0.22 and r2adjusted = 0.03) do not indicate a good fit of the data, and likewise with the LSR and ADLMR. The MLR model (Forward method) for the H variable showed a medium-high degree of association (r = 0.70 and r2adjusted = 0.48), only including temperature and global radiation.
Table 7. Multiple linear regression models of the agronomic variables of seven Cenchrus purpureus accessions, associated with climatological variables of the HAV.
Variable Y = α + β1 + β2 + β3 + β4 + c r r2adjusted p MSE Method FW −27.2 0.10 −33.01 2.12 3.15 34.68 0.73 0.52 0.01 1202.92 F p value 0.84 0.10 0.01 0.01 0.01 DM 11.08 − −1.50 0.15 − 14.33 0.22 0.03 0.02 205.45 F p value 0.50 − 0.29 0.06 − H 3.64 0.00 −0.28 0.01 − 0.24 0.70 0.48 0.01 0.06 F p value 0.01 0.17 0.01 0.01 − LSR 2.90 − − −0.01 −0.01 0.16 0.53 0.28 0.01 0.02 B p value 0.01 − − 0.01 0.01 ADLMR 80.19 − −16.99 1.45 − 80.36 0.32 0.08 0.01 6458.51 F p value 0.39 − 0.036 0.01 − Y model = α + β1X1 + β2X2 + β3X3 + β4X4 + c; α: Intercept; β1−4: Slope factor; X1: Accumulated precipitation (mm). X2: Average temperature (°C). X3: Global radiation (W/m2). X4: Relative humidity (%). c: Estimate error. FW: Fresh weight. DM: Dry matter. r2adjusted: Adjusted correlation coefficient. p: Probability value. MSE: Mean Square Error. F: forward method. B: backward method. (−): Variable not included in the model. The principal component analysis (PCA) was conducted using all estimated variables (rainy and dry seasons) of the seven Cenchrus purpureus accessions, resulting in the creation of two groups of orthogonally independent variables. The first group (Factor 1) corresponds to those known as agronomic characteristics, and the second group (Factor 2) is made up of estimated yield variables (Fig. 2).
The PCA implemented in the dry and rainy seasons (Fig. 2) formed three groups of Cenchrus purpureus accessions: a) outstanding agronomic characteristics and fresh weight and dry matter, b) outstanding yield characteristics, and c) unremarkable agronomic and yield characteristics. The Cenchrus purpureus accessions integrating each group were different in both seasons. Their genetic characteristics and interactions with the environment define their performance.
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The assessed accessions showed variations in their agronomic behavior and yield indicators, attributed to changes in the climatic variables between the rainy and dry seasons, and to genetic variability. Elephant grass and Maralfalfa achieved the highest DM and FW in both seasons, even though there were minimal differences between them, proving their genetic capability to maintain a stable production in different environmental conditions. It is necessary to simultaneously evaluate the agronomic characteristics, yield, and quality indicators of the assessed accessions to identify those who show the best levels in these attributes.
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About this article
Cite this article
Arrieta-González A, Silva-Martínez KL, Vite-Cristóbal C, Rodríguez-Andrade A, Hernández-Beltrán A, et al. 2024. Agronomic traits of seven accessions of Cenchrus purpureus under rainfed conditions in the tropical region of Veracruz, México. Grass Research 4: e023 doi: 10.48130/grares-0024-0022
Agronomic traits of seven accessions of Cenchrus purpureus under rainfed conditions in the tropical region of Veracruz, México
- Received: 02 July 2024
- Revised: 30 October 2024
- Accepted: 01 November 2024
- Published online: 28 November 2024
Abstract: The purpose of this study was to determine the agronomic characteristics and yield of seven Cenchrus purpureus accessions (Taiwan grass, King grass, Elephant grass, Merkeron, CT-115, Purple grass, and Maralfalfa) to identify the best options to use as fodder resources in the Huasteca Alta Veracruzana (HAV) ruminant production units. The research was conducted at the Instituto Tecnologico Superior de Tantoyuca in 2019 and 2020, under rainfed conditions. Sowing density was established at 36 tillers per 25 m2, with a spacing between rows of 0.8 m and 0.8 m between plants. Fresh weight, dry matter, daily rates of dry matter accumulation in leaves, leaf/stem ratio, height, and harvest frequency were assessed. Rainfall amount, temperature, relative humidity, and global radiation levels were recorded to assess their effect on the evaluated variables. The one-way analysis of variance and multivariate analysis techniques were used, such as multiple linear regression and clustering analysis with Statistica V10 software. There were no statistical differences between the fresh weight of different accessions in the rainy season. On the other hand, CT-115 presented a higher dry matter content than Merkeron or Elephant grass (p < 0.05). The correlation analysis showed a high association between dry matter and daily rates of dry matter accumulation in leaves. The results varied due to weather phenomena and C. purpureus genetic variability. Quality parameters must be evaluated to identify the accessions with the best nutritional values and their effect on animal performance.
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Key words:
- Mathematical models /
- Fodder /
- Yield /
- Drought /
- Tropic