The effects of season and enterprise on cow milk production indicators between 2008 and 2012 in the province of Ciego de Ávila,  Cuba, are presented. Official information of the productive, economic, and financial indicators from the Economic and Production Departments of the enterprises evaluated was used. The mean productivity and effi-ciency indicators were calculated from the basic information collected. Descriptive statistical  analysis and variable comparisons between seasons and enterprises were made.  The bio-economic indicators in the rainy season  produced the least unfavorable results. More than one milk kg/cow was produced in the rainy season, and yields per area had a mean of 8.2 and 12.9 kg of milk/ha/month in the dry and rainy seasons, respectively.  Different results were observed in annual  efficiency and production  indicators, both primary and secondary.  Ruta  Invasora  was  the enterprise with the best productive results; whereas Orlando González Enterprise had the highest overall yields

Justiz López, Yosvany

Pedraza Olivera, Redimio M.

English

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Rev. prod. anim., 27 (2): 2015 I S S N  2 2 2 4 - 7 9 2 0  Season and Enterprise Effects on Cow Milk Production Indicators in Ciego de Ávila Redimio M. Pedraza Olivera* and Yosvany Justiz López** * Center for Development of Animal Production Studies (CEDEPA), Faculty of Agricultural Sciences, Ignacio Agramonte Loynaz University of Camagüey, Cuba ** Youth Working Army, Ministry of the Armed Forces (MINFAR), Ciego de Ávila, Cuba redimio.pedraza@reduc.edu.cu ABSTRACT The effects of season and enterprise on cow milk production indicators between 2008 and 2012 in the province of Ciego de Ávila, Cuba, are presented. Official information of the productive, economic, and financial indicators from the Economic and Production Departments of the enterprises evaluated was used. The mean productivity and effi-ciency indicators were calculated from the basic information collected. Descriptive statistical analysis and variable comparisons between seasons and enterprises were made. The bio-economic indicators in the rainy season produced the least unfavorable results. More than one milk kg/cow was produced in the rainy season, and yields per area had a mean of 8.2 and 12.9 kg of milk/ha/month in the dry and rainy seasons, respectively. Different results were observed in annual efficiency and production indicators, both primary and secondary. Ruta Invasora was the enterprise with the best productive results; whereas Orlando González Enterprise had the highest overall yields. Key words: productivity, efficiency, cow milk, climate, enterprise  INTRODUCTION Productivity of dairy systems based on the use of pastures and forages must be determined with different technological variants, in order to design efficient alternatives, improved strategies, or re-structuring of systems with productive limitations (Curbelo et al., 2010). However, productivity and efficiency of herds are mainly determined by the use it has been given, including the control over several different factors, such as the level of sup-plies applied to the soil-plant system, its natural potentialities, animal potential, and the volume of supplements in terms quality and quantity (Gue-vara et al., 2003); all influenced by the climate and executive decision making factors. Certain barriers that can affect sustainability may be un-veiled (Marchand et al, 2014), regardless of the tools used to assess a farm. This paper deals with season effect on cattle milk production indicators in the province of Cie-go de Avila, Cuba, in the 2008-2012 period. MATERIALS AND METHODS This study included information collected over a five-year period, between January 2008 and De-cember 2012, from every Basic Unit of Coope-rated Production (UBPC); and from four state-owned cattle enterprises, Ministry of Agriculture: Ruta Invasora, Orlando González, Chambas and Bolivia, located in four municipalities of the province of Ciego de Ávila, Cuba.  The climate in the province is humid tropical savannah, with 6-7 month seasonal humidity, and annual mean precipitation values of 1 319 mm; around 80 % of total precipitations occur between May and October; the other 20 % takes place from November to April. The mean temperature values in the period were 27.3° C, ranging be-tween 22.5° C and 24.3° C, during the dry season, and 26.4° C-30.0° C in the rainy season, according to data provided by the Weather Forecast Service in Ciego de Ávila. Official information of productive, economic, and financial indicators was collected from the Economics and Production Departments at the en-terprises assessed. The primary information com-piled every month was, area, number of animals per category, number of workers, salaries, milk delivered to industry, milk production per cow per day, fuel purchases (Diesel and gasoline), power consumption, and total solid contents in the milk (monthly reports by the dairy industry in Ciego de Ávila).  The primary information used to calculate mean productivity indicators (Barrios 2008) and effi-ciency was,   Milk kg/ha: the mean monthly milk kg is divided by the total hectares of the enter-prise.  Season and Enterprise Effects on Cow Milk Production Indicators in Ciego de Ávila Rev. prod. anim., 27 (2): 2015  Milk kg/total females: the mean monthly milk kg is divided by the mean monthly number of females in reproduction.   Milk kg/Diesel l: the mean monthly milk kg is divided by the mean monthly vo-lume of Diesel acquired.   Milk kg/gasoline l: the mean monthly milk kg is divided by the mean monthly volume of gasoline acquired.   Milk kg/kW: the mean monthly milk pro-duction kg is divided by the mean monthly amount of kW consumed.   Solid kg/ha: the mean monthly total of solid concentration is divided by mean monthly amount of hectares.   Milk kg/total salary of workers: the mean monthly milk kg is divided by the mean monthly salary paid to all workers.   The season´s features were studied (dry be-tween November and April, and rainy between May and October), and the enterprises, all the indicators, both from official primary informa-tion and secondary productivity information, es-timated from the former values.  The Kolmogorov-Smirnov test was made to de-termine normality, and the variables with normal distribution were analyzed by simple variance analysis, using the linear general model. The mean differences were set with the Tukey test (P ≤ 0.05). Descriptive statistical analysis and comparisons of abnormal variables by the Mann Whitney test, for season; and Kruskal Wallis H, for enterprise, were made. SPSS 15 was used for statistical analysis. RESULTS AND DISCUSSION It is well known that dry land pastures are more widespread in the rainy season, sharply shrinking in the dry season, when temperature and solar radiations are lower, and the days are shorter, in comparison to the months in the rainy season. Significant differences were observed in the milk production per season (P ≤ 0.001), a mean of 2.85 kg/cow/day in the dry season; whereas in the rainy season it was 3.95 kg/cow/day. Several stu-dies (De Loyola, 2010; Guevara et al., 2010 and Soto, 2010), showed that the season effect on milk production is more associated to pasture availability increase, than to lower temperature values during the season, when biomass produc-tion decreases.  There are differences in milk production per cow. Orlando González enterprise shows the lowest means ranges; the other companies have similar values per cow (Table 1) Studies conducted in the first decade of the 2000s, in the province of Camagüey, (Andújar, 2006; del Risco et al., 2007; De Loyola, 2010; Soto, 2010) reported the effects of season and climatic factors on pasture growth, and their rela-tion with better consumption by animals, leading to increases in production and other bio-economic indicators.  Table 2 shows no significant differences be-tween the seasons, concerning indicators like lac-tation days and total solids percent in the milk; however, significant differences were observed in milking cows; and highly significant differences were seen in the monthly production and mean salary indicators.  The calving number increased during the rainy season, and in the late dry season, in the province of Havana (Évora et al., 2002), and Camagüey (De Loyola, 2010 and Soto, 2010), which also contributed to higher efficiency in production during the rainy season (Guevara et al, 2012). Senra (2007) has stressed on the need to imple-ment alternatives to improve quality and yields of grasslands and soil fertility, as vital elements for milk production at any time of the year. In that sense, season variation would be less striking, as for the case of Leucaena-graminaneae associated systems improved for dry lands to stimulate milk production in Mambí de Cuba cows (Sánchez, 2007). Table 3 shows differences milk kg/gasoline li-ter, and highly significant differences in mid ranges of milk kg/area, milk kg total females, milk kg/Diesel l consumed, milk kg/kW con-sumed, total solid/hectares, and mean salary/milk produced. Usually, Cuban cattle systems do not include secondary indicators that measure the ef-ficiency of energy sources needed to produce milk.   García and Perón (2000) have claimed that one of the most commonly widespread forms used to measure productivity of dairy cattle is milk kg/cow (kg/c). Additionally, various research pa-pers only measure milking cow yields, regardless of the number of females in reproduction.  Table 4 shows the mean ranges of official va-riables per enterprise; highly significant differences R. M. Pedraza and Y. Justiz Rev. prod. anim., 27 (2): 2015 were observed in indicators like milking cows, lactating days, monthly production, total solids and mean monthly salary. Milking cows, lactation days, and monthly production, are indicators for the system´s productivity, depending on reproduc-tion levels, like natality and annual calving distri-bution, with a marked influence on annual milk production, income and system efficiency (Évora et al, 2002). Key factors to achieve greater productivity with the cow´s genetic potential are better pasture use (more production and harvest of dry matter/ha), and implementation of more persistent and stable complementing and supplementing strategies to minimize risks, both associated to weather condi-tions, and the ups and downs of the market (Gallar-do, 2012).  Table 5 shows that all indicators for the compa-nies had significant differences. The differences between enterprises were dependent on the kind of production management, supplies for soil-plant, natural potentialities, animal potential and pasture quality, availability and quality, and quan-tity of supplements (Guevara et al., 2003). Another element to consider is the stocking rate, which has been defined by many researchers (Mott, 1960, cited by Guevara et al, 2010; Mc Meekan, 1963, cited by Soto 2010) as the main aspect of pasture management, and one of the efficiency indicators in cattle systems. The stocking rate may be different in each of the enterprises studied, but it was not determined in this paper, which solely included the number of animals and the total area, regard-less of land use. Further, more specific studies are needed to determine the effect of that indicator on every enterprise. Today, widespread infestation of grasslands with sickle bush (D. cinerea) is one of the main causes of increased stocking rates in Cu-ba.  Enterprises have contrasts in other social and economic aspects that bring about significant dif-ferences in the areas used, like total females in re-production, and breeds, ultimately in productivity indicators, which are critical to achieve proper ef-ficiency (Gallardo, 2012). Holmes (2001) notes that besides measuring dairy efficiency in kg/cow and kg/ha, it can also be measured from milk solids produced per sup-plement, per area, per salary, or even solids pro-duces per fuel type consumed.  A more rational use of raw materials and energy sources is currently one of the downsides of milk production systems, because the greater the vo-lume, the lesser efficient and sustainable they turn (Flores and Gómez, 2006). One vital element for worker motivation and in-creased performance is income through salary, which contribute to meet personal needs. In that sense, there were inequalities among enterprises, associated with differences in productivity, and diversification, since not in all enterprises income depends on milk sales to the same extent; other agricultural productions and services have a part, too. Furthermore, salaries were considered, whether direct or indirect milk production labor takes place (the main item in dairy systems). In general, the productive results achieved at Ruta Invasora Enterprise were better; whereas Orlando González had the best performances. CONCLUSIONS The bio economic indicators got the least unfa-vorable results for the rainy season, which is mainly associated with more milk production, due to more pasture and forage availability.  The results of primary and secondary indicators for efficiency and productivity varied from enter-prise to enterprise. The best productive results were observed at Ruta Invasora; whereas Orlando González had, in general terms, the lowest per-formances. REFERENCES ANDÚJAR, O. (2006). Influencia de la productividad primaria del pastizal, los suplementos y la estrate-gia estacional de partos anuales en la producción de leche. Master´s thesis on sustainable Animal Pro-duction, University of Camagüey, Cuba. BARRIOS, G. (2008). Análisis de la eficiencia técnica en UBPC cañeras en la provincia Villa Clara. PhD thesis on Economic Sciences, Martha Abreu  Cen-tral University of Las Villas, Villa Clara, Cuba.  CURBELO, L. M.; GUEVARA, R. V.; SOTO, S. A.; GUEVARA, G. E.; SENRA, A. F. y GARCÍA, R. (2010). Eficiencia alimentaria en sistemas de pro-ducción de leche con pariciones concentradas al inicio del período de máximo crecimiento de la hierba. Journal of Animal Production, 22 (2). DE LOYOLA (2010). Efectos de una mayor intensidad de partos al inicio de la época lluviosa, sobre la eficiencia bioeoconómica de vaquerías comercia-les. PhD Thesis in Sciences (pre-dissertation), Ig-nacio Agramante y Loynaz University of Cama-guey, Cuba. Season and Enterprise Effects on Cow Milk Production Indicators in Ciego de Ávila Rev. prod. anim., 27 (2): 2015 DEL RISCO, S. (2007). Evaluación del comportamiento productivo de vaquerías comerciales en razón del patrón de pariciones anuales. Master´s Thesis on Sustainable Animal Production, University of Ca-maguey, Cuba. ÉVORA, J. C.; GUERRA, D. y GONZÁLEZ, D. (2002). Programación de los partos y la eficiencia en la producción de leche. Rev. ACPA, (4), 44. FLORES, J. y GÓMEZ, J. A. (2006). Planificación multi-criterio de explotaciones agrarias en áreas tropica-les. El caso de la zona protectora de recursos natu-rales Guanare-Masporro Venezuela. Económica agraria y recursos naturales, 6, 81. GALLARDO, M. (2012). Factores nutricionales que afectan la producción y composición de la leche. Dpto. de Producción Animal, Facultad de Ciencias Agronómicas, Universidad de Chile. Retrieved on July 2011, from,  desde http://www.engormix.com/producir19o26sarticulos538GDL.htm. GARCÍA, R. y PERÓN, E. (2000). Indicadores de la pro-ducción de leche. Rendimiento Lácteo. Rev. ACPA, 1, 24-26. GUEVARA R; SOTO, S.; CURBELO, L.; DE LOYOLA, C.; GUEVARA, G.; BERTOT, J. A.; SENRA, A.; GARCÍA, R. y DEL RISCO, SONIA (2010). Factores que pue-den afectar la eficiencia bioeconómica y ambiental en sistemas estacionales cubanos de producción de leche (artículo reseña). Rev. prod. anim., 22 (2), 87-95. GUEVARA, R.; SPENCER, M.; SOTO, S.; GUEVARA, G.; CURBELO, L.; LOYOLA, C.; BERTOT, J. (2012). In-fluencia de la estrategia de pariciones anuales en la eficiencia bioeconómica de microvaquerías en una empresa pecuaria. I. Concentración de partos en lluvia y seca. Rev. prod. anim., 24 (1). GUEVARA, G.; GUEVARA, R.; CURBELO, L. y SPENCER, M. (2003). Evolución y eficiencia de los sistemas de producción de leche en un municipio de Cama-güey, Cuba, período 1959 a 2002. Retrieved in March, from http://www.reduc.edu.cu/147/05//14705107.pdf. HOLMES, C. W. (2001). Features of Dairy Production Systems in Competition Countries. Dairy Farming annual, Massey University.  MARCHAND, F.; DEBRUYNE, L.; TRISTE, L.; GERRARD, C.; PADEL, S. y LAUWERS, L. (2014). Key Charac-teristics for Tool Choice in Indicator-Based Sustai-nability Assessment at Farm Level. Ecology and Society, 19 (3), 46. Retrieved in April, 2015, from http://dx.doi.org/10.5751/ES-06876-190346. SÁNCHEZ, T. (2007). Evaluación productiva de una asociación de gramíneas mejoradas y Leucaena leucocephala cv. Cunningham con vacas Mambí de Cuba en condiciones comerciales. PhD Thesis, University of Camaguey, Cuba. SENRA, A. (2007). Posibilidades de la producción esta-cional de leche en Cuba en forma sostenible. Rev. Prod. Anim., 19 (Número Especial). SOTO, S. (2010). Impacto bioeconómico de la produc-ción lechera estacional en ecosistemas ganaderos de Camagüey. Retrieved in December 2012, from http://www.monografias.com/trabajos81/impacto-bioeconomico-produccion-lechera-estacional/impacto-bioeconomico-produccion-lechera-estacional.html. . Received: 23-6-2015 Accepted: 25-7-2015  Table 1. Individual milk production per cow, per company (kg/vaca/día) Enterprise Mean Ruta Invasora Orlando González Chambas Bolivia 3.43ª 3.00b 3.46a 3.41a ES  0.041 Sig. *  *P ≤ 0.05; different letters in the means indicate significant differences            Table 2. Mean official primary variable ranges by season, according to the Mann Whitney test (means within parenthesis) Indicators Mean ranges Sig. Dry Rainy Milking cows, n 344 (99) 376 (111) * Lactation days, days  360 (171) 360 (171) NS Monthly production, kg/mes 299 (7875) 421 (13208) *** Total solid in milk, % 350.00 (12.94) 370.9 (13.04) NS Mean salary, CUP/month 256.7 (319.28) 464.2 (412.35) *** *P ≤ 0.05;  *** P ≤ 0.001; NS no significant differences CUP (Cuban Peso)   Table 3. Mean ranges of monthly secondary variables by season, according to the Mann Whitney test(means within parenthesis) Indicators Mean ranges Sig. Dry Rainy Milk per area, kg/ha/month  308.9 (8.16) 912.1 (12.89) *** Milk per total females, kg/females/month 278.6 (0.90) 442.4 (1.4) *** Milk per Diesel,  kg/L Diesel/month 312.3 (21.65) 408.7 (11.27) *** Milk per gasoline,  kg/L gasolina 343.7 (21.65) 377.4 (36.85) * Milk per kW, kg/kW/month 307.9 (3.56) 413.0 (5.60) *** Solid/ha,  kg/ha/month 308.6 (3.55) 412.9 (5.58) *** Total salary/month, CUP/kg 331.3 (0.34) 389.7 (0.45) *** *P ≤ 0.05;  *** P ≤ 0.001; NS no significant differences. CUP (Cuban Peso)  Table 4. Mean ranges of official variables by companies, according to the Kruskal-Wallis H test (means within parenthesis) Indicators Mean ranges R. Invasora O. González Chambas Bolivia Sig. Milking cows, n/month 502       (161) 381       (113.5) 283       (90,0) 257         (77.0) *** Lactation days, Days 323       (169.5) 217      (167.0) 431        (172.0) 328     (170.0) *** Monthly production, kg/month 468.8    (16017) 340.0   (9775) 307.7    (8411) 287.2     (7160) *** Total solid, % 488.4   (13.62) 426.3    (13.26) 258.2    (12.70) 327.7    (13.02) *** Mean salary, CUP/month 301.3     (335.69) 466.2  (488.86) 389.1     (377.62) 354.6  (353.83) ***            Table 5. Mean ranges of secondary variables by company, according to the Kruskal-Wallis H test (means within parenthesis) Indicators  R. Invasora O. González Chambas Bolivia Sig. Milk per area, kg/ha/month 547  (24.49) 300    (9.2) 277        (8.29) 224        (6.9) *** Milk per total females, kg/hembras/month 345     (1.0) 386       (1.1) 383       (1.2) 324       (1.0) * Milk per Diesel, kg/L Diesel/month 408    (12.73) 258       (5.52) 397        (9.59) 226    (3.90) *** Milk per gasoline, kg/L gaso-line 554    (353.20) 445        (52.0) 208      (0.000) 253        (3.60) *** Milk per kW, kg/kW/month 404        (5.2) 231        (3.0) 273        (3.10) 558        (8.6) *** Solid per area, kg/ha/month 404       (5.16) 231        (2.96) 273        (3.13) 558    (8.62) *** Total salary per milk kg, CUP/kg 471       (0.58) 109        (0.18) 378      (0.46) 221       (0.23) *** *P≤0.05;  *** P≤0.001. CUP (Cuban Peso)  

Season and Enterprise Effects on Cow Milk Production Indicators in  Ciego de Ávila.

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Season and Enterprise Effects on Cow Milk Production Indicators in Ciego de Ávila.

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