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In tropical areas of Mexico, the use of bovine cattle is mainly developed through the grazing of native grasses (Axonopus and Paspalum), with low potential for forage production. Forage production takes place mainly in areas with Cynodon plectostachyus, Megathyrsus maximus, Hiparrhenia rufa, Digitaria decumbens, Pennisetum purpureum, Echinochoa polystachya and Andropogon gayanus (Enríquez et al. 2011).
García et al. (2019) reported that, in the coastal plain of Veracruz, grass is the main feeding source for cattle. However, it is scarce and has poor quality, due to the misuse of grasslands, especially during dry periods. In dual-purpose cattle farms, located in the tropical zone of the central region of Veracruz, dry matter deficit during dry season can be covered, with the application of technologies that improve the distribution of dry matter production destined for cattle during the year (Gudiño et al. 2018).
Biomass bank technology with Cuba CT-115 (Cenchrus purpureus) has been studied and extended in Cuban livestock since 1995. It consists of separating up to 30% of the area of the dairy farm sown with Cuba CT-115, from August to November, to store and graze in three rotations, between 20 and 25 t DM/ha, during dry period (Martínez and Herrera 2005).
Based on the criteria of several authors, Gudiño (2019) defines technology transfer in livestock as the application of knowledge generated from research to solving problems of animal production, considering specific conditions of production unit, with training and monitoring farmers.
The introduction of a new technology occurs depending on production, without controls in the system and with a broad relationship between the factors of the production process (Gaynor 2006).
There are methodologies, models and procedures for evaluating the impact on several factors, some general, with tendency to universality (Angelelli and Gligo 2002), others specific to particular situations or aspects (Días 2008), and others qualitative, with extensive databases and static or dynamic nature.
The impact of a technology is defined as changes achieved over time with the introduction of a technique or knowledge, determined by technological, productive, economic, social and environmental aspects, and their interrelation. Impact determination is achieved through a responsible information system, which measures the largest number of variables (Torres et al. 2019).
In the agricultural field, the Statistical Model for Impact Measurement (SMIM) has been applied in Cuba (Chacón 2009, Raez 2012 and Barreto 2012), and in countries such as Mexico (Ruiz et al. 2012) and Ecuador (Vargas et al. 2011).
According to Font and Guerrero (2005), the multiplicity of variables can be very complex, and multivariate techniques, such as principal component analysis (PCA) and hierarchical or cluster analysis, can help to interpret the complex reality of various factors, which are interrelated when evaluating the adoption of a new technology.
The objective of this study was to evaluate the impact of biomass bank technology with Cuba CT-115 in dual-purpose livestock, in a commercial dairy farm, as a representation of the livestock in tropical areas of Veracruz.
Materials and Methods
Location and management. The study was carried out from 2014 to 2019, in a commercial dairy farm, with 26 ha in a dual-purpose system, representative of the livestock of tropical areas of Veracruz, in Jamapa municipality, Mexico. This facility is located at 19°14' N and 96°14' W, at 21 m a.s.l. The climate in the area is tropical AW, according to the Köppen-Geiger classification, with a mean annual temperature of 25.9 °C and mean annual rainfall of 1,108 millimeters (Domínguez et al. 2017).
The research was developed for six years. It included three years with normal management of the area, and three with biomass bank. Data collection began in 2014, without biomass banks, when the farm had 24 cows and 11 large paddocks with grasses, and low yield and quality, in dry period. During the six years, mineral salts were always offered at will and multinutritional blocks, as well as hay and silage. It was supplied from 6 to 8 hours in the stable, only in dry period. Quantities varied according to the development of the technology, except for the block, which was offered at a rate of 0.5 kg/d. The stabulation period was from 7:00 a.m. to 4:00 p.m. The area for producing hay was composed by 1 ha of Digitaria decumbens and 1 ha of Cenchrus purpureus cv. Cuba CT-169 for silage, not included in the 26 ha of the technology. Cows were milked once a day, starting at 7:00 a.m. After milking, they went to the suckling area for two hours.
Technology application began after the establishment of the biomass bank with Cuba CT-115 in 7.8 ha, divided into 15 paddocks, which represents 30% of the farm. Grazing began in December 2016, with 24 crossbred Zebu and Holstein cows, weighing 450 to 500 kg and in good physical condition. The occupation period per paddock was 4, 3 and 2 d in the first, second and third rotation, respectively, for 60, 45 and 30 d with Cuba CT-115, which covered 135 d of grazing. It was fertilized with urea, at a rate of 50 kg N/ha, in specific areas, only during the first rotation. In the other 18 ha, which represent 70% of the farm, composed of Digitaria decumbens, Brachiaria decumbens, Brachiaria brizantha and Cynodon nlemfuensis, grazing was carried out after the biomass bank, in each rotation, in 10 paddocks, with occupation periods from 1 to 3 d, completing rotations of 90, 60 and 50 d, for a total of 210 d during dry period. This area was also fertilized with urea, at a rate of 50 kg N/ha after the first rotation. Twice, during dry season, irrigation with a 25 mm sheet could be applied.
Data matrix. The data matrix was organized per years and trimesters in rows. The 14 variables corresponded to columns: total cows, milking cows, dry cows, number of parturitions, cows/d/paddock, quarterly milk production, milk production/cow/d, total paddocks in Cuba CT-115, used silage (kg), silage per cow (kg), hay per cow (kg), weaned calves, produced live weight and stocking rate (LAU/ha).
Statistical analysis. To analyze and summarize the collected information, SMIM (Torres et al. 2008) was applied. This methodology combines different multivariate techniques (principal components and cluster) to carry out comprehensive analyzes and determine performance and classification of production systems (Torres 2015). Data was were processed with the statistical system IBM-SPSS (2012), version 22 for Windows.
Linear regressions were estimated between total number of cows, milk production, produced live weight, stocking rate LAU/ha (Y) and trimester (X), with the application of REG procedure of SAS (2012).
Results and Discussion
Kaiser-Meyer-Olkin test was significant (P <0.001), with a value of 0.75, which indicated that the data matrix was adequate for the application of the SMIM. In the PC analysis, the first three explained more than 83% of the studied variability. PC 1 showed the greatest variance (56.09%), and was the most important. PC 2 and 3 explained lower variance (16.47 and 11.30%, respectively) (table 1).
Table 1 Total variance explained by the PCs
| 1 | 7.85 | 56.09 | 56.09 |
| 2 | 2.31 | 16.47 | 72.56 |
| 3 | 1.58 | 11.31 | 83.87 |
Similar results to those of this research were obtained by Rodríguez et al. (2014), in a study on technical, socioeconomic and environmental evaluation of a genetic enterprise from Mayabeque, Cuba, in which they applied the SMIM with PC analysis. These authors reported that the first four components explained 63.38% of variability. PC 1 was the one with the highest relationship with productive variables, and explained 40.9% of variability. Segura et al. (2017), in a study on identification of determinant factors in milk production, in Pastaza, Ecuador, stated that impact index contributed to establish the performance and problems in the development of farms. Three components explained 78.7% of the variance, being PC1, called herd and production, which explained 38.7% of the variability.
Using the matrix of rotated components, by Varimax method, variables with preponderance values superior to 0.65 were identified (table 2). Variables total number of cows, milking cows, dry cows, number of parturitions, production per quarter, total paddocks of Cuba CT-115, number of weaned calves, produced live weight and LAU/ha were preponderant indicators in PC 1. Therefore, this new variable was called Production.
Table 2 Matrix of rotated components by Varimax method
| Total number of cows | 0.97 | -0.14 | 0.12 |
| Milking cows | 0.84 | 0.00 | 0.44 |
| Dry cows | 0.91 | -0.22 | -0.09 |
| Parturitions | 0.79 | 0.07 | -0.21 |
| Cows/d/paddock | -0.02 | 0.74 | 0.50 |
| Milk per quarter | 0.74 | -0.08 | 0.62 |
| Milk per cow per day | -0.02 | -0.22 | 0.82 |
| Area with CT-115 | 0.86 | -0.43 | -0.14 |
| Used silage, kg | 0.04 | 0.92 | -0.26 |
| Silage per cow, kg | -0.37 | 0.87 | -0.17 |
| Hay per cow, kg | -0.51 | 0.70 | -0.19 |
| Weaned calves | 0.68 | -0.38 | 0.19 |
| Produced liveweight | 0.69 | -0.40 | 0.23 |
| Stocking rate, LAU ha | 0.97 | -0.14 | 0.12 |
| Variance percentage | 56.09 | 16.47 | 11.31 |
| Accumulated variance percentage | 56.09 | 72.56 | 83.87 |
Variables cows/d/paddock, used silage, silage per cow and hay per cow were the indicators with the highest preponderance of PC 2, and it was called Supplementation. Variable milk production per cow per day was the only preponderant indicator in PC 3, and it was called Productivity.
Lok et al. (2009) also obtained high and positive ponderance indexes, when evaluating indicators and animal performance of the entire herd in the Genetic 4 productive unit of a farm in the Institute of Animal Science, located in Mayabeque, Cuba, with the introduction of Cuba CT-115 biomass bank technology, which turned out to be a viable alternative to fulfill food deficit during dry season.
Martínez et al. (2013), in a study to measure impact of factors that influence on milk production in farms from Ciego de Ávila, Cuba, demonstrated that the mathematical approach described the impact index of the variables with the highest preponderance in the principal component analysis, by explaining variability in factors affecting production efficiency.
According to Torres et al. (2013), coefficients of factorial measures express impact indexes for each scenario, based on the principal components. Results of this analysis for PC Production are presented in figure 1. Bars show the impacts in the scenarios, from 2014 to 2019. In the first 12 trimesters, biomass bank technology was not applied, with stocking rate of 0.96 LAU/ha. The changes from negative to positive values in the scenarios indicate the impact of the biomass bank on results, which refer to the last 12 studied quarters, corresponding to the period from 2017 to 2019. This demonstrates that PC Production changed since the beginning of biomass bank technology application.
A similar situation was presented in a study carried out by Martínez et al. (2012) on the analysis of the impact of biomass bank technology with Cuba CT-115. The cited authors used rotated matrices to determine impact indexes in each scenario. These changed their values, from negative to positive, over the years, which shows the impact of this technique.
In figure 2, bars represent the impacts of PC Supplementation in each scenario. Negative values correspond to the quarters of rainy season, which did not receive hay or silage, while positive values correspond to periods in which it was supplemented. Lower positive values are appreciated from 2017, indicating a decrease in the amount of hay and silage used during the stable hours during dry periods.
The previous showed that PC Supplementation is not related to the PC Production, and explains less the variability. In addition, it presented lower impact values during the years that the biomass bank was used. In practice, this indicates that before biomass bank, supplementation with preserved foods was insufficient to grow in the main productive indicators.
In figure 3, bars represent impacts of PC Productivity. The variable milk production per cow per day was the preponderant indicator. This, by itself, has low variability, and although the following tables show discrete increases in productivity, it is not the fundamental cause of productive increase. This result was also influenced by the productive management of the farm, since due to the low price and demand in the milk market, part of the herd is managed with a cow-breeding system, and not as a dairy farm, during the year.
Similar results were obtained by Martínez et al. (2012), who, in the study of implementation and use of biomass banks in nine dairy farms for 10 years, reported increases of 0.2 L/milking cow per year and little variability. A more detailed form of showing the results with the productive indicators was the use of cluster procedure for grouping the scenarios with the greatest similarity.
Table 3 shows means and standard deviations of studied variables for each of the three groups.
Table 3 Means and standard deviations of studied variables, grouped in three cluster analyzes
|
|
|
|
||||
|---|---|---|---|---|---|---|
| Total number of cows | 24.33 | 1.41 | 40.56 | 10.96 | 54.67 | 0.52 |
| Milking cows | 13.08 | 2.02 | 15.85 | 4.23 | 25.06 | 4.03 |
| Dry cows | 11.26 | 1.95 | 24.41 | 7.30 | 29.56 | 3.88 |
| Number of parturitions | 4.56 | 2.07 | 7.22 | 1.99 | 11.33 | 1.86 |
| Cows/d/paddock | 237.02 | 9.83 | 150.37 | 29.77 | 235.16 | 33.14 |
| Production per quarter | 4860.50 | 1016.81 | 5839.58 | 1800.24 | 9801.79 | 2362.50 |
| Production/cow/day | 4.12 | 0.56 | 4.03 | 0.39 | 4.25 | 0.31 |
| Area with CT-115 | 0.55 | 0.30 | 6.80 | 1.22 | 7.65 | 0.00 |
| Used silage, kg | 19221.33 | 9302.64 | 9403.33 | 11238.21 | 17732.50 | 11012.73 |
| Silage per cow, kg | 786.67 | 382.13 | 252.78 | 330.56 | 325.15 | 202.27 |
| Hay per cow, kg | 457.11 | 238.71 | 142.25 | 219.54 | 60.33 | 93.47 |
| Weight at weaning, kg | 132.78 | 6.04 | 135.31 | 8.04 | 138.73 | 15.88 |
| Weaned calves | 3.44 | 1.01 | 8.22 | 2.91 | 8.33 | 2.34 |
| Produced LW, kg | 451.79 | 124.34 | 1111.44 | 387.04 | 1171.83 | 353.71 |
| Stocking rate, LAU/ha | 0.93 | 0.06 | 1.56 | 0.42 | 2.11 | 0.02 |
The fact that group I gathered the initial quarters with much lower indicators, demonstrates that, over time, there was a well-defined productive impact, capable of separating the most productive cases in group III. The total number of cows, milking cows, dry cows, weaned calves, milk production (kg), produced live weight (kg) and stocking rate LAU/ha, increased by 124.7, 91.6, 162.5, 142.2, 101.7, 159.4 and 126.9%, respectively, between group I and III. As a consequence, the implementation of this technology had better capacity to feed animals and increase size of herd under production. Hay and silage gradually decreased in each group, due to the increase of forage produced in the biomass bank.
The above is corroborated in the study by Fortes et al. (2014) on growth of Cuba CT-115 in biomass bank technology. These authors refer, as main result, the increase of stocking rate capacity, and the consequent increase of herd production. It is also confirmed in results of Alarcón et al. (2015), who developed an investigation about the transfer of livestock technology in Santiago de Cuba, and concluded that the increase of food base with Cuba CT-115 allows to increase herd yields.
Statistical regression analyzes among total number of cows, milk production (kg), produced live weight (kg), stocking rate LAU/ha and quarters, were highly significant (P<0.001) (figure 4) and quantify the impact of biomass bank technology in the estimators of herd productive parameters. Total number of cows increased by 1.84 ± 0.14 animals per quarter and milk production by 298.37 ± 45.87 kg per quarter. Produced live weight increased by 44.81 ± 9.59 kg per quarter and the stocking rate was increased by 0.057 ± 0.07 LAU/ ha per each quarter.
Figure 4 Linear regressions of the total number of cows, milk production, produced live weight and stocking rate LAU/ha and quarters
Díaz et al. (2014) reported important increases in milk production with biomass banks of Cuba CT-115, in a study carried out in farms of leading farmer, in Campeche, Mexico. Martínez and Medina (2018), when analyzing the influence of the use of biomass banks with Cuba CT-115 on seasonal performance of milk production in Siboney de Cuba cows, concluded that this technology is an alternative to face climate effects in tropical regions, which have from four to six dry months, because milk production per cow and per hectare increases, with greater stability in the annual productive process.
Conclusions
The application of biomass bank technology with Cuba CT-115 in a dual-purpose dairy farms, representative of the livestock in tropical areas of Veracruz, showed a favorable impact, according to the results of the impact measurement model, particularly in the principal component more related to the response to changes in production.
With the establishment of this technology, forage production was higher, which allowed increasing stocking rate, milk production, total number of cows in herd, live weight of weaned animals and the reduction of supplementation. This way, food deficit, caused by the dry period in the ecosystem of the tropical zone of central Veracruz, was possible to be overcome.
