Received: 29/05/2024 Accepted: 16/11/2024 Published: 25/01/2025 1 of 11
https://doi.org/10.52973/rcfcv-e35460 Revista Cientíca, FCV-LUZ / Vol. XXXV
ABSTRACT
In dairy production systems, efcient pasture management is
crucial for maximizing milk output while minimizing costs. However,
many producers make decisions without considering the productive
efciency of different forage types. This research aims to address
this gap by comparing the milk production and related expenses
of Brown Swiss and Jersey cows fed with three types of grass:
Maralfalfa (Pennisetum sp.), Cameroon (Pennisetum purpureum),
and Mulato (CIAT 36087).The milk production and related expenses
for generating one liter of milk from Brown Swiss and Jersey
cows were compared when fed with Maralfalfa (Pennisetum sp.),
Cameroon (Pennisetum purpureum), and Mulato (CIAT 36087)
grasses. Productive and reproductive parameters affecting milk
production were analyzed. Milk production of the cows under
study was measured for one month when they were exclusively
pasture–fed with Mulato grass. The group of 33 cows was randomly
subdivided into three subgroups, each consisting of 11 cows,
and each subgroup was assigned to consume a specic type of
grass. This resulted in the Maralfalfa consumption group (SG1), the
Cameroun consumption group (SG2), and the control group with
Mulato pasture feeding (SG3). Daily milk production was recorded
for six weeks, with standardized management and ad libitum
feeding. Daily milk production for each cow was monitored and
recorded over the six–week period. Highly signicant differences
(P<0.01) were observed among the three studied groups from the
second week onward. The main difference was observed between
SG1 and SG3. The highest productivity, with greater milk production
volumes, was observed in cows consuming Maralfalfa. However,
variables such as the service period, live weight, and number
of calving performed better with Cameroon grass. Cost–benet
analysis favored the use of Mulato grass.
Key words: Efciency; pastures; production; cost; benet
RESUMEN
En los sistemas de producción lechera, la gestión eciente de los
pastos es crucial para maximizar la producción de leche mientras
se minimizan los costos. Sin embargo, muchos productores toman
decisiones sin considerar la eciencia productiva de los diferentes
tipos de forraje. Se comparó la producción de leche y los gastos
relacionados para generar un litro de leche de vacas Pardo Suizo y
Jersey cuando se alimentaron con pastos Maralfalfa (Pennisetum
sp.) y Cameroon (Pennisetum purpureum) y Mulato (CIAT 36087).
Se analizaron parámetros productivos y reproductivos que afectan
la producción de leche. Se midió la producción de leche de las
vacas del estudio durante un mes cuando fueron alimentadas
exclusivamente con pasto Mulato. El grupo de 33 vacas se
subdividió aleatoriamente en tres subgrupos, cada uno de ellos
formado por 11 vacas, y a cada subgrupo se le asignó un consumo
de un tipo especíco de pasto. Esto resultó en el grupo de consumo
de Maralfalfa (SG1), el grupo de consumo de Cameroon (SG2) y el
grupo de control alimentado con pasto Mulato (SG3). Se registró
la producción diaria de leche durante seis semanas, con manejo
estandarizado y alimentación ad libitum. La producción diaria
de leche de cada vaca fue monitoreada y registrada durante el
período de seis semanas. Se observaron diferencias altamente
signicativas (P<0,01) entre los tres grupos estudiados a partir
de la segunda semana. La principal diferencia se observó entre
SG1 y SG3. La mayor productividad, con mayores volúmenes de
producción de leche, se observó en las vacas que consumieron
Maralfalfa. Sin embargo, variables como el período de servicio,
el peso vivo y el número de nacimientos tuvieron un mejor
desempeño con el pasto Cameroon. El análisis costo–benecio
favoreció el uso de pasto Mulato.
Palabras clave: Eciencia; pastos; producción; costo; benecio
Comparison of dairy production based on feed of Pennisetum sp.,
Pennisetum purpureum and CIAT 36087
Comparación de la producción de leche con base en la alimentación
con Pennisetum sp., Pennisetum purpureum y CIAT 36087
Noel Ernesto Blanco–Roa1* , Hernaldo Ramón Novoa–Novoa1 , Eveling Cristina Berríos1 , Omar Alfredo Soto–Gutiérrez1 ,
Carlos Alberto Zúniga–González1 , Silvio Javier Sáenz–Rojas1 , José de Jesús Nuñez–Rodriguez2
1National Autonomous University of Nicaragua,
Leon.
Knowledge Area of Agrarian and Veterinary Sciences. Leon, Nicaragua.
2University of Santander, Faculty of Economic, Administrative and Accounting Sciences. Cúcuta, Colombia.
*Corresponding author: nblanco@ev.unanleon.edu.ni
Comparison of feed-based dairy production / Blanco-Roa et al.________________________________________________________________________
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INTRODUCTION
Nicaragua consistently faces challenges concerning the feeding
of its bovine cattle, especially in the dry corridor regions, as part of
its ongoing efforts to maintain or increase milk production levels,
all in the backdrop of climate change [1, 2, 3].
In this Nicaraguan context, there has been a notable increase
in the adoption of specialized cut–and–carry feeding systems for
bovine cattle. This is done to ensure high–quality year–round feeding
for the animals, consequently maximizing land utilization, increasing
stocking rates per hectare, and ultimately achieving higher volumes
of milk and meat production per unit of land area [4, 5].
However, the utilization of improved forages is not a simple
practice for small and medium–sized producers, as it tends to
elevate their operational costs. According to Martin et al. [6],
intensive systems that involve irrigation and fertilization in improved
pastures can be a viable option for high–genetic–potential dairy
cows, provided that the investment is justied. In general, small and
medium–sized producers in Nicaragua often do not possess high–
genetic–potential dairy cattle (Bos taurus) [7, 8]. The establishment
and maintenance of improved pastures come with a high cost, and
when combined with poor management due to a lack of technical
knowledge among producers, it often leads to the waste of forage
resources [9] The pastures most commonly employed in these
feeding systems include Cameroon (Pennisetum purpureum),
Maralfalfa (Pennisetum purpureum), King Grass (Pennisetum
purpureum), Napier (Pennisetum purpureum), Pasto Cuba 22
(Pennisetum purpureum), CT-115 (Pennisetum purpureum), Maize
(Zea mays), among others [10] . Few studies have compared the
milk production capacity of these cut–and–carry forages and the
breeds considered in this study.
The primary aim of this research was to determine the efciency
in production and analyze the relationship between costs and
benets in milk production through the utilization of pastures such
as Maralfalfa (Pennisetum sp.), Cameroon (Pennisetum purpureum),
and Mulato II (CIAT 36087). By doing so, it provides farmers with
a tool to make informed decisions regarding the type of pasture
to use in their dairy production.
The signicance of this study lies in providing farmers and
livestock breeders with guidance to choose the most cost–effective
pasture options available in the region, based on productive
efciency and cost–benet outcomes [11, 12] the ndings of
this research can have a substantial economic impact by offering
insights into which pasture is the most protable in terms of
production costs and milk yield.
This research goes beyond merely comparing milk production
among cattle breeds; it also takes into account three different
types of pastures. This broadens the scope of the study, allowing
for the evaluation of multiple variables and their interactions.
In summary, this research lls a crucial gap by addressing the
interaction between multiple variables (cattle breed and pasture
types), considering cost and profitability perspectives, and
recognizing livestock diversity and the signicance of management in
milk production. These elements make the study unique and highly
relevant in the eld of dairy production and livestock feeding [12, 13].
MATERIALS AND METHODS
Randomly selected from a population of 240 milking cows, 33
cows of Brown Swiss and Jersey breeds were chosen for this study.
These cows were part of the Santa Teresa farm located in Villanueva
Chinandega, Nicaragua (12°45'21.4" N | 87°01'07.1" W). In the study
area, the environmental conditions are tropical savanna climate,
ranging from the Pacic area and the western foothills of the central
mountain range. It has average temperatures between 21°C and
30°C and maximum temperatures up to 41°C. It is characterized by
a dry season from November to April, the maximum annual rainfall
is 2,000 mm and the minimum between 700 and 800 mm annually.
Parameters considered for selecting the cows in this study
included lactation status (not more than 60 days (d) open), healthy
udders, and the absence of physiopathological issues [14] and the
data is disposal in the Mendeley repository [15].
The milk production per cow per day was assessed over 30
consecutive d for the selected cows, with them being exclusively
pasture–fed with Mulato II (CIAT 36087) to obtain their initial
productions for subsequent comparisons. Milk production was
measured using volumetric methods, utilizing BouMatic Xcalibur
equipment, manufactured in the United States, which is commonly
employed for precise measurement in dairy farming. After the
initial monitoring period for individual daily cow production, the
group of cows was randomly subdivided into three subgroups, each
containing 11 cows, and each subgroup was assigned a specic
type of grass to consume.
Consumption Subgroup 1 (SG1), grazing is the primary method.
Regrowth days are managed carefully to optimize yield and
quality, with a rotation plan ensuring adequate recovery periods
between grazing sessions. The stocking rate is adjusted based on
forage availability and growth rates. In Consumption Subgroup 2
(SG2), which includes Cameroon (Pennisetum purpureum), the
forage is cut and fed in stalls every 60 d. This method allows
for controlled regrowth, ensuring the forage reaches optimal
quality before harvesting. Specic plot numbers are utilized for
rotation to minimize overgrazing and promote healthy regrowth.
For Consumption Subgroup 3 (SG3), which features Mulato II,
continuous grazing is employed, maintaining the forage at an
optimal size. This practice allows for consistent availability while
supporting regrowth. Stocking rates are monitored closely to
prevent overgrazing, ensuring the pasture remains healthy and
productive. From a genetic (breed) standpoint, the three groups
were heterogeneous, consisting of Brown Swiss and Jersey cows
in very similar proportions, and thus, no a prior advantage was
assumed for any group. Environmental factors (temperature,
housing, humidity, etc.) were the same for all three groups, except
for the feed, which was our independent variable, and from which
we expected to generate productivity differences.
The individual milk production per cow per day was measured
for a period of 42 d using calibrated liters. The measurements
were conducted using BouMatic Xcalibur equipment (USA),
ensuring accurate and consistent data collection. Environmental
and housing conditions for the cows were standardized across
all three subgroups. Feeding was ad libitum and represented
the independent variable since the type of grass consumed was
different for each subgroup.
Comparison of feed-based dairy production / Blanco-Roa et al.________________________________________________________________________ _________________________________________________________________________________________________Revista Cientica, FCV-LUZ / Vol.XXXV
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TABLE I
Basic statistical characteristics of biological variables for group1
(G1): OD, weight, production dierence, age, and calvings
O.D. Weight Production
dierence Age Calvings
NValid 11 11 11 11 11
Lost 0 0 0 0 0
Mean 47.091 409.182 43.1909 6.182 2.818
Medium 47.000 419.000 41.8000 6.000 3.000
Tip. Dev. 8.3241 30.6360 7.84251 1.7215 1.1677
Range 24.0 95.0 29.50 6.0 4.0
Minimum 37.0 355.0 29.60 3.0 1.0
Maximum 61.0 450.0 59.10 9.0 5.0
Percentiles
25 39.000 387.000 37.6000 5.000 2.000
50 47.000 419.000 41.8000 6.000 3.000
75 56.000 435.000 49.7000 8.000 4.000
Source: Livestock farm records, processed by ANOVA system.
The study analyzed and compared the biological conversion
efciency, as well as the economic aspects of the production system
to quantify the impact of the independent variable within the system.
For data analysis, the basic package of descriptive statistics
in Excel was used to calculate mean, standard deviations,
and coefcients of variation. To estimate differences between
subgroups, ANOVA [16] and the Tukey test were used to identify
signicant differences [17].
The productive and reproductive parameters analyzed in this
study included daily milk production per cow, live weight, age at
rst calving, open days, and the number of calvings, with their
values derived from individual records [18, 19] .
P = Tm × Mc × Pd (1)
Where:
P = Production cow.milk-1.day-1
Tm = total production of milk
Mc = # milking cow
Pd = # production days
The productive variable, Milk production·ha
-1
·year
-1
, was
calculated using the formula (2):
PR = PLT × VO × ha (2)
Where:
PR= Production (L.cow-1.ha-1.year-1).
PLT: Total Production of milk (L).
VO: # Cows in milking.
ha: # hectares dedicated to livestock per year
The methodology employed for comparing the productive capacity
of the studied types of grass was based on the differences in
percentages and average calculations within the three analyzed
subgroups to determine which of the grasses yielded the best results.
The net benet was calculated using the formula (3) proposed
by Wadsworth [20] and applied to livestock:
BN = IT - CT (3)
Where:
BN: Benet–Cost Net
IT: Total Income (USD$)
CT: Total Costs (USD$)
The cost–benet relationship was calculated using formula (4)
proposed by Vargas and Cuevas [14]:
RBC = IT . CT-1 (4)
Where:
RBC= B/C: Benet–Cost Ratio
IT: Total Income (USD$).
CT: Total Costs (US$?).
The cost of one liter of milk was determined using the method
proposed by [21] Holman (1993), in which the total costs incurred
for the sale of milk are calculated, then divided by the total liters
of milk produced (formula 5) :
CL = CT . PTL-1 (5)
Where:
CL: Cost per liter of milk (USD$).
CT: Total Cost (USD$).
PTL: Total milk production (L).
RESULTS AND DISCUSSION
The means and standard deviations of the productive and
reproductive parameters of group SG1 are presented in TABLE
I. The means for open days (OD), live weight, differences in
weekly milk production, and number of calving were 47.09 days,
409.18kg, 43.19 liters, and 2.8 births, respectively.
The fundamental statistical parameters of the biological
variables of group SG2 are presented in TABLE II. The means for
open days, live weight, number of births, and the differences in
weekly milk production were 41.27 d, 339.9 kg, 2.63 births, and
36.32 liters, respectively. These results are similar to the study
by Cruz–Hernández et al. [22], which also found that different
management practices influence milk production. The higher
levels of milk production observed in groups fed with Maralfalfa
(Pennisetum sp) and Cameroon (Pennisetum purpureum) under
stall–feeding conditions suggest that dietary choices signicantly
impact productivity [23, 24 and 25].
In TABLE III, the statistics of the biological variables of SG3 are
shown. The means for open days, live weight, number of births, and
the differences in weekly milk production were 46.81 d, 386.90kg,
Comparison of feed-based dairy production / Blanco-Roa et al.________________________________________________________________________
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TABLE II
Basic statistical characteristics of biological variables for group2
(G2): OD, weight, production dierence, age, and calvings
O.D. Weight Production
dierence Age Calvings
NValid 11 11 11 11 11
lost 0 0 0 0 0
Mean 41.273 339.909 36.3282 5.455 2.636
Medium 41.000 324.000 35.4000 6.000 3.000
Tip. Dev . 4.6063 43.0127 7.33130 1.5076 1.0269
Range 13.0 113.0 24.79 5.0 3.0
Minimum 36.0 300.0 25.21 3.0 1.0
Maximum 49.0 413.0 50.00 8.0 4.0
Percentiles
25 37.000 310.000 30.2000 5.000 2.000
50 41.000 324.000 35.4000 6.000 3.000
75 45.000 400.000 41.3000 6.000 3.000
Source: Livestock farm records, processed by ANOVA system
TABLE III
Basic statistical characteristics of biological variables for group
3 (G3): OD, weight, production dierence, age, and calvings
O.D. Weight Production
dierence Age Calvings
NValid 11 11 11 11 11
lost 0 0 0 0 0
Mean 46.818 386.909 24.9873 5.727 3.000
Medium 47.000 398.000 24.1000 6.000 3.000
Tip. Dev . 5.0758 33.9719 6.90207 1.5551 1.2649
Range 18.0 103.0 21.80 5.0 4.0
Minimum 37.0 327.0 15.50 3.0 1.0
Maximum 55.0 430.0 37.30 8.0 5.0
Percentiles
25 43.000 359.000 19.8800 5.000 2.000
50 47.000 398.000 24.1000 6.000 3.000
75 51.000 417.000 32.7000 7.000 4.000
Source: Livestock farm records, processed by ANOVA system
3 births, and 24.98 liters, respectively. These parameters are similar
to the study by Cordero [26], and Esquivel–Mimenza et al. [27],
which highlight that various feeding strategies and management
conditions can influence these biological variables. Our study also
reveals that feeding with Maralfalfa (Pennisetum sp.) and Cameroon
(Pennisetum purpureum), particularly under stall–feeding conditions,
tends to support higher milk production levels. This suggests that
while SG3’s parameters are comparable to those in other studies,
different feeding strategies might still impact production outcomes.
has been shown to enhance milk production, which aligns with
the results of this study. These results are consistent with ndings
from Villanueva et al. [28], WingChing [29], and St–Pierre [30].
All the studys reproductive females were at the peak of their
lactation curve, as this was a prerequisite for their selection in the
study, and, as a result, it did not affect the average productivity.
The mean number of births per group indicates that SG3 had the
most lactations, being in its third lactation cycle, which could
translate to an advantage in terms of production compared to the
other groups (25, 31) (TABLE IV).
TABLE IV displays the mean, standard deviation, and a 95%
condence interval for the three groups. Upon analyzing the upper
and lower limits, it is evident that there is no overlap between the
values of the groups in any of the weeks, suggesting signicant
differences among the groups under study.
TABLE V presents the results of comparing the means of milk
production in each of the weeks of the experiment for the three
subgroups under study. The following results were obtained: In
weeks 0 and 1, there was no signicant difference (P=0.700) and
(P=0.078), respectively, conrming the null hypothesis.
Starting from the 2
nd
week and onwards (weeks 3, 4, 5, and 6), the
differences in productive means among the subgroups of breeders
became signicant (P<0.001). This leads us to afrm that it is from the
second week onwards that the nutritional differences of each of the
pastures in the milk production of the breeders are fully established,
especially regarding Maralfalfa grass, this would be consistent with
the findings of Calzada–Marín et al. [10]. When comparing the
means between subgroups throughout the experimental period,
highly signicant differences were found (P<0.001), consistent with
Martin et al. [6] regarding the productive response of dairy cattle fed
improved pastures with proper agroecological management. Similarly,
it is expressed and presented by Milera et al. [32].
TABLE VI, shows the results of applying the Tukey post hoc tests for
the subgroups analyzed. These results indicate that the difference in
milk production is observed between subgroup SG3 and subgroups
SG1 and SG2. This is consistent with Calzada–Marín et al. [10].
FIG. 1 illustrates the evolution of milk production for the
experimental subgroups. It is evident that all three subgroups
started with similar levels of milk production from week 0 to week1.
However, the change in the feeding regimen for subgroups SG1 and
SG2 became apparent in their milk production from week 2 onward.
SG1 experienced the most signicant increase in production,
followed by SG2, and nally SG3. Arias and Camargo [33] reported
that females fed with Maralfalfa grass (Pennisetumsp.) recorded
higher levels of milk production, which aligns with our ndings.
Additionally, considering cost–benet is fundamental for making
nancial decisions. This study also found that Mulato II grass
was the most cost–effective option, generating higher prots
compared to Cameroon (Pennisetum purpureum) and Maralfalfa
(Pennisetumsp.) [34, 35].
Similarly, Milera et al. [32] demonstrated that cultivars in intensive
systems with irrigation and fertilization of improved grasses are a
viable option for dairy cows with high genetic potential, which justies
the investment, while also considering the environmental impacts.
The most important biological factors influencing the productive
differences in female cattle (as evident in the average productivity of
the three groups in this study) are genetics (breed), the environment
(facilities, temperature, humidity, altitude, and primarily nutrition
in our case), and the number of births. These ndings support that
nutrition, specically the type of forage, plays a crucial role in milk
production. For instance, feeding with Maralfalfa and Cameroun
Comparison of feed-based dairy production / Blanco-Roa et al.________________________________________________________________________ _________________________________________________________________________________________________Revista Cientica, FCV-LUZ / Vol.XXXV
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TABLE IV
Basic statistical characteristics of milk production variables by groups
Descriptive NMean Typical
Deviation
Typical
error
Condence interval
for the mean at 95% Minimum Maximum
Lower limit Upper limit
MP
SG1 11 63.8000 3.57099 1.07670 61.4010 66.1990 55.00 69.30
SG2 11 64.8900 3.76897 1.13639 62.3580 67.4220 57.40 70.00
SG3 11 63.1218 6.77389 2.04240 58.5711 67.6726 55.30 74.20
Total 33 63.9373 4.82809 0.84046 62.2253 65.6492 55.00 74.20
Week 1
SG1 11 67.2000 4.34948 1.31142 64.2780 70.1220 57.20 73.70
SG2 11 66.8318 3.71081 1.11885 64.3389 69.3248 58.60 71.20
SG3 11 63.1636 5.12919 1.54651 59.7178 66.6095 56.20 72.30
Total 33 65.7318 4.67553 0.81391 64.0739 67.3897 56.20 73.70
Week 2
SG1 11 76.100 4.6844 1.4124 72.953 79.247 64.4 80.6
SG2 11 73.245 4.2276 1.2747 70.405 76.086 67.1 80.5
SG3 11 64.000 4.6951 1.4156 60.846 67.154 56.7 71.0
Total 33 71.115 6.8437 1.1913 68.688 73.542 56.7 80.6
Week 3
SG1 11 87.809 4.3418 1.3091 84.892 90.726 78.8 94.2
SG2 11 81.436 4.8876 1.4737 78.153 84.720 72.3 89.1
SG3 11 72.155 4.3115 1.3000 69.258 75.051 65.9 79.8
Total 33 80.467 7.8594 1.3681 77.680 83.253 65.9 94.2
Week 4
SG1 11 95.527 7.0166 2.1156 90.813 100.241 84.0 106.0
SG2 11 89.918 5.4393 1.6400 86.264 93.572 80.8 100.1
SG3 11 81.964 5.0208 1.5138 78.591 85.337 73.7 90.3
Total 33 89.136 8.0277 1.3974 86.290 91.983 73.7 106.0
Week 5
SG1 11 101.555 8.4248 2.5402 95.895 107.214 86.8 112.4
SG2 11 96.109 5.8391 1.7606 92.186 100.032 88.4 106.3
SG3 11 86.418 5.5427 1.6712 82.695 90.142 76.0 92.7
Total 33 94.694 9.1020 1.5845 91.467 97.921 76.0 112.4
Week 6
SG1 11 106,991 8.6123 2.5967 101,205 112,777 92.2 122.1
SG2 11 101,218 5.9476 1.7933 97,223 105,214 91.0 111.0
SG3 11 88,109 5.7861 1.7446 84,222 91,996 77.0 94.5
Total 33 98,773 10.4430 1.8179 95,070 102,476 77.0 122.1
Dierence
SG1 11 43.1909 7.84251 2.36460 37.9222 48.4596 29.60 59.10
SG2 11 36.3282 7.33130 2.21047 31.4029 41.2534 25.21 50.00
SG3 11 24.9873 6.90207 2.08105 20.3504 29.6241 15.50 37.30
Total 33 34.8355 10.44065 1.81748 31.1334 38.5375 15.50 59.10
SG1: Cows fed with Maralfalfa grass. SG2: Cows fed with Cameroun grass. SG3: Cows fed with Mulato II grass. MP: stands for milk
production, measured in liters per week
In week 0, the average production of all three study groups
ranged from 60 to 70 liters. However, as the study concluded in the
last week (week 6), the production averages varied. SG1 showed
approximately 100 to 110 liters, SG2 ranged between 90 and 110
liters, and SG3’s production was in the range of 80 to 90 liters.
These results indicate that SG1 had the most substantial growth in
milk production over the course of the 6-week study. These results
differ from those presented by Campos Granados [36], who states
that the nutritional differences between these varieties in terms of
composition do exist, but are not as pronounced as once thought.
The results of the random comparison of the distribution
of Jersey cows in the subgroups are shown in TABLE VII. The
distribution was 4, 7, and 4 for SG1, SG2, and SG3, respectively.
The average milk production for these groups was 98 L for SG2,
101.4 L for SG1, and 92.4 L for SG3. The highest standard deviation
was 9.4 for SG1, and the lowest was 2.87 for SG3.
TABLE VIII displays the results for the random distribution of
Brown Swiss cows, with the highest number of Brown Swiss cows
placed in SG3 and SG1, each with 7, and 4 in SG2. The average
milk production for these groups was 106.8, 110.1, and 85.6 L
for SG2, SG1, and SG3, respectively. The standard deviation was
3.17 for SG2, the lowest degree of dispersion, and 6.77 for SG1,
the highest.
Comparison of feed-based dairy production / Blanco-Roa et al.________________________________________________________________________
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TABLE V
Comparison of the productive means of the subgroups
Sum of
squares DF Quadratic
meaning F Sig. or P
MP
Inter–groups 17.506 2 8.753 0.360 0.700
Intra – groups 728.427 30 24.281
Total 745.933 32
Week 1
Inter–groups 109.572 2 54.786 2.786 0.078
Intra – groups 589.967 30 19.666
Total 699.539 32
Week 2
Inter–groups 880.135 2 440.068 21.342 0.000
Intra – groups 618.607 30 20.620
Total 1.498.742 32
Week 3
Inter–groups 1.363.372 2 681.686 33.346 0.000
Intra – groups 613.282 30 20.443
Total 1.976.653 32
Week 4
Inter–groups 1.021.933 2 510.966 14.736 0.000
Intra – groups 1.040.264 30 34.675
Total 2.062.196 32
Week 5
Inter–groups 1.293.146 2 646.573 14.284 0.000
Intra – groups 1.357.933 30 45.264
Total 2.651.079 32
Week 6
Inter–groups 2.059.551 2 1029.775 21.600 0.000
Intra – groups 1.430.235 30 47.674
Total 3.489.785 32
Dierences
Inter–groups 1.859.314 2 929.657 17.122 0.000
Intra – groups 1.628.913 30 54.297
Total 3.488.227 32
Note: MP=Milk production
TABLE VI
Tukey’s post hoc test
Group 1 NSubset for alpha = 0.05
1 2
SG3 11 24.9873
SG2 11 36.3282
SG1 11 43.1909
Tukey B a to. Use the sample size of the harmonic mean = 11,00
FIGURE 1: Evolution of milk production of the experimental subgroups:
Cameroon (SG2), Marafalfa (SG1) and Grazing or Mulato (SG3)
TABLE VII
Descriptive one–way ANOVA for the random
distribution of the Jersey breed
NMean Typical
Deviation
Typical
Error
Condence interval
for the mean at 95% Min. Max.
Lower
limit
Upper
limit
SG2 7 98.000 4.5494 1.7195 93.793 102.207 91.0 105.3
SG1 4 101.400 9.4865 4.7432 86.305 116.495 92.2 112.0
SG3 4 92.400 2.8787 1.4393 87.819 96.981 88.4 94.5
Total 15 97.413 6.4672 1.6698 93.832 100.995 88.4 112.0
TABLE VIII
Descriptive one–way ANOVA for the random
distribution of the Brown Swiss breed
NMean Typical
Deviation
Typical
Error
Condence interval
for the mean at 95% Min. Max.
Lower
limit
Upper
limit
SG2 4 106.850 3.1723 1.5861 101.802 111.898 103.6 111.0
SG1 7 110.186 6.7731 2.5600 103.922 116.450 102.0 122.1
SG3 7 85.657 5.6891 2.1503 80.396 90.919 77.0 92.0
Total 18 99.906 12.9556 3.0537 93.463 106.348 77.0 122.1
TABLE IX
Post hoc multiple comparison of milk production in Jersey cows
(j)
group
(p)
group
Mean
dierence
(jp)
Typical
Error Next.
Condence interval
for the mean at 95%
Lower limit Upper limit
SG2 SG1 -6.720 3.75156 0.214 -16.7287 3.2887
SG3 0.205 3.75156 0.998 -9.8037 10.2137
SG1 SG2 6.720 3.75156 0.214 -3.2887 16.7287
SG3 6.925 4.23233 0.269 -4.3663 18.2163
SG3 SG2 -0.205 3.75156 0.998 -10.2137 9.8037
SG1 -6.925 4.23233 0.269 -18.2163 4.3663
j = Jersey p = Brown Swiss
When conducting multiple comparisons of milk production among
Jersey cows fed different types of forage, no signicant differences
were found, indicating that there is no advantage of having 7 Jersey
cows in SG1 and SG2 over the 4 Jersey cows in SG2. See TABLE IX.
When conducting multiple comparisons of milk production among
Brown Swiss cows in the different groups, a signicant difference
(P<0.05) was found between SG3 and the SG1 and SG2 groups,
respectively. This indicates a positive productive response capacity
of the Brown Swiss breed to changes in forage with different
nutritional levels, in our case, in favor of Maralfalfa (TABLE X).
Comparison of feed-based dairy production / Blanco-Roa et al.________________________________________________________________________ _________________________________________________________________________________________________Revista Cientica, FCV-LUZ / Vol.XXXV
7 of 11
FIGURE 2. Milk production of breeds by groups
TABLE X
Post hoc multiple comparison of the production of Swiss Brown cows
(j)
group
(p)
group
Mean
dierence
(jp)
Typical
Error Next.
Condence interval
for the mean at 95%
Lower limit Upper limit
SG2 SG1 -2.58571 3.60162 0.757 -11.9408 6.7694
SG3 22.06286* 3.60162 0.000 12.7078 31.4180
SG1 SG2 2.58571 3.60162 0.757 -6.7694 11.9408
SG3 24.64857* 3.07147 0.000 16.6705 32.6266
SG3 SG2 -22.06286* 3.60162 0.000 -31.4180 -12.7078
SG1 -24.64857* 3.07147 0.000 -32.6266 -16.6705
j = Jersey, p = Brown Swiss
TABLE XI
Descriptive inuence of calving on milk production
p N mean Typical
Deviation
Typical
Error
Condence interval
for the mean at 95% Min. Max.
Lower
limit
Upper
limit
1 4 33.2000 13.38158 6.69079 11.9069 54.4931 17.80 50.00
2 9 33.9322 11.51742 3.83914 25.0792 42.7853 15.50 49.70
3 12 35.6483 10.38692 2.99844 29.0488 42.2479 20.18 59.10
4 5 38.2200 9.85175 4.40584 25.9874 50.4526 22.70 50.00
5 3 30.8333 9.57932 5.53062 7.0370 54.6297 24.10 41.80
Total 33 34.8355 10.44065 1.81748 31.1334 38.5375 15.50 59.10
TABLE XII
One–factor ANOVA of parity number
Dierence Sum of
squares DF Quadratic
meaning F Sig. or P
Inter–groups 131.297 4 32.824 0.274 0.892
Intra – groups 3356.930 28 119.890
Total 3488.227 32
Furthermore, it can be observed that the thermoregulation
capacity and milk production of the Brown Swiss breed are affected
under high–temperature conditions during grazing. The Brown
Swiss breed exhibited higher levels of milk production in the groups
fed with Maralfalfa and Cameroun under stall–feeding conditions.
This nding is signicant and supports the importance of feeding
and management conditions in the milk production of the Brown
Swiss breed [23, 24]. The fact that higher levels of milk production
were recorded in the groups fed with Maralfalfa (Pennisetum sp.)
and Cameroon (Pennisetum purpureum), especially under stall–
feeding conditions, suggests that these grasses and environments
provide favorable support for the lactation of this breed.
This may have practical implications for the livestock industry
as it indicates that the choice of diet and care environment can
have a positive impact on the productivity of Brown Swiss breeders
[37]. Furthermore, it could be considered as an important factor
in decision–making in dairy livestock management to optimize
production and protability. This result may also be relevant to
other researchers and breeders involved in breeding cattle of
this breed, as it suggests the importance of selecting appropriate
feed and management practices to maximize milk yield in Brown
Swiss [38, 39].
In FIG. 2, milk productions of the breeds within the groups are
illustrated, and signicant differences (P<0.01) are found between
SG3 and SG1, as well as between SG3 and SG2 regarding the Brown
Swiss breed. The productive response of both breeds was superior
when consuming Maralfalfa grass, resulting in higher milk production
compared to their lower production when consuming Mulato II.
TABLE XI, presents descriptive data regarding the number of
calvings per cow in the 3 study groups, consisting of a total of 33
cows. It is observed that the most frequent value is 3 calvings, with
a total of 12 cows achieving this number. The highest productive
average is found in 5 cows that have had 4 calvings, with an average
production of 38.2 liters. These results reflect a physiological
pattern established by the lactation curve, theoretically indicating
that the peak of milk production is reached in the third or fourth
calving of the cows.
In TABLE XII see the results of the one–way ANOVA applied
to the number of calvings of cows in the different groups and
their milk production are shown, and no signicant influence was
found (P=0.892).
TABLE XIII illustrates the distribution and quantity of cows based
on the number of calvings (1, 2, 3, 4, and 5) in the different groups.
It is observed that cows with 3 calvings are the most numerous,
totaling 12 cows, with 5 in SG2, 3 in SG1, and 4 in SG3. This random
distribution could potentially represent a productive advantage
for SG2 in relation to SG1. Cows with 4 calvings numbered 5 in
total, with 2 in SG2, 2 in SG1, and 1 in SG3. The distribution could
represent a productive advantage for SG3 in relation to SG2.
The costs of establishing 1 ha of improved forage in the study
are presented in TABLE XIV. The establishment cost for 1 ha of
Comparison of feed-based dairy production / Blanco-Roa et al.________________________________________________________________________
8 of 11 9 of 11
TABLE XIII
Contingency table of calving in the study groups
Count
Group 1
Total
G2 G1 G3
Calving cow 1
12114
22439
3 5 3 4 12
42215
50123
Total 11 11 11 33
Table XIV
The cost of production of one L of milk by groups of breeders in our study
Grass Cameroon Mulato II Maralfalfa
Investment per cycle ($) 1,635.06 2,930.27 1708.96
Fresh Biomass (kg) 38,020 35,808 33,100
Income ($) 2,630.09 2,330.47 2,635.99
Intake (kg) 44.86 38.72 54.00
Cost per kilogram ($) 0.43 0.82 0.52
Ration Cost ($) 1.38 0.49 2.18
Total production (L·group-1) 5,596.35 4,957.20 5,602.60
Production Milk / Dairy Liter 12.11 10.72 12.12
Sale price ($) 0.47 0.47 0.47
Cost.per litre ($) 0.11 0.05 0.18
Source: Taken from farm records
FIGURE 3. Production cost of 1 liter of milk in the dierent groups under study
Maralfalfa grass is $1,707.96, for Mulato II grass is $2,930.27,
and for Cameroun grass is $1,635.06.
The green matter production per ha for Cameroun grass was
38,020 kg·ha
-1
with an average cost per ton of $43.00, for MulatoII
grass (CIAT 36087) it was 35,808 kg·ha
-1
with an average cost
per tonne of $81.85, and for Maralfalfa grass (Pennisetum sp.),
it was 33,100 kg·ha
-1
of green matter with an average cost per
tonne of $199.43.
The costs of investment per cycle and type of grass are presented
in TABLE XIV, with Maralfalfa being clearly the most expensive at
$1,708.96, compared to $1,635.06 for Cameroun.
The cost of different pastures for the production of one litre of
milk is shown in FIG. 3. It can be produced more affordably at $0.49
per litre with Mulato II grass (CIAT 36087) and more expensively
at $0.18 per litre with Maralfalfa grass (Pennisetumsp.), followed
by Cameroon grass (Pennisetum purpureum) with a cost of $0.05
per litre. Productivity and efciency of specialized dairy farms in
the Valle del Cauca (Colombia) by Morales – Vallecilla and Ortiz–
Grisales [40] present lower costs, despite being in larger and more
technologically advanced operations.
However, we assume that despite the production costs of one litre
of milk with Maralfalfa, in intensive production systems and with
dairy cows with high genetic potential for milk production, costs
can be signicantly reduced due to higher production volumes. It
is worth noting that Mulato II grass proved to be the most cost–
effective option, generating higher prots compared to Cameroon
(Pennisetum purpureum) and Maralfalfa (Pennisetum sp.) [23, 24].
This nding indicates that the production cost per litre of milk is
lower in breeders fed with Mulato II (CIAT 36087) [41]. However,
it is important to consider that, despite this lower cost, Maralfalfa
(Pennisetum sp.) demonstrated a superior productive response.
This observation suggests that, if milk production volumes with
Maralfalfa (Pennisetum sp.) can be increased, overall costs could
be lowered, thereby increasing protability.
In practical terms, this discussion highlights the importance of
balancing cost efciency and production to maximize protability
in dairy farming [42]. It is crucial to consider both production
performance and the costs associated with feeding to make
informed decisions in livestock management and pasture selection.
Additionally, these results can have signicant implications for
producers seeking to optimize their milk production and protability
in the livestock industry [43].
CONCLUSION
According to our results, the group of breeders that consumed
Maralfalfa grass (Pennisetum sp.) showed the best productive
response with higher milk production volumes compared to the
groups fed with Mulato II (CIAT 36087) and Cameroon (Pennisetum
purpureum) from the second week of the experiment.
The values of the variables (Service period, live weight, and
number of calving) showed a more uniform and compact pattern in
the Cameroon group, with greater variability in the Maralfalfa and
Mulato groups. The Brown Swiss breeders exhibited higher levels of
milk production in the groups fed with Maralfalfa (Pennisetumsp.)
and Cameroon (Pennisetum purpureum) under stall–feeding
conditions. However, a lower productive response was observed
in the group fed with Mulato while grazing, suggesting better
adaptability of this breed to comfortable feeding and management
conditions, in contrast to its lower adaptability to stress from
high temperatures.
Comparison of feed-based dairy production / Blanco-Roa et al.________________________________________________________________________ _________________________________________________________________________________________________Revista Cientica, FCV-LUZ / Vol.XXXV
9 of 11
In terms of cost–benet, the implementation of Cameroon
(Pennisetum sp.), Maralfalfa (Pennisetum sp.), and Mulato II (CIAT
36087) grasses in milk production proved to be more protable
for Mulato II grass (CIAT 36087), as it generated higher prots.
The cost of producing one litre of milk was lower in the breeders
fed with Mulato II grass, followed by Cameroon (Pennisetum
purpureum) and Maralfalfa (CIAT 36087). However, given the
superior productive response of Maralfalfa grass, it is possible to
reduce costs with higher milk volumes.
Ethics statement
No experiments were performed on animals or people. The
protocol to carry out this research was reviewed and conrmed to
proceed by the National Autonomous University of Nicaragua Leon.
No formal ethical approval was required for this study according
to the Personal Data Protection Act. LAW NO. 787, regarding the
ethical approval requirements for this type of study. Verbal consent
was used instead of written consent because the aforementioned
law does not require written consent to be bound by it.
Conflict of interest
The authors declare that they have no competing interests.
Grant information
No funding for this research was disclosed.
Data availability
Underlying data Mendeley: Data for Comparison of milk
production of Maralfalfa (Pennisetum sp.), Cameroun (Pennisetum
purpureum ) and Mulato (CIAT 36087) in dairy cattle. https://data.
mendeley.com/datasets/n7mfwys6sy/1 [15].
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