13. PAPERS

Introducción a la Lectura Comprensiva de Inglés Académico para Medicina Veterinaria

FICHA #13: ARTÍCULOS DE INVESTIGACIÓN CIENTÍFICA (AIC)- RESEARCH ARTICLES (RA)-

Desde el momento en que una persona comienza sus estudios formales -inicial, primaria, secundaria y universitaria o

terciaria- está expuesta a conocimientos establecidos, aceptados por todos como verdaderos, y a los cuales accede,

mayormente, a través de la lectura de libros de circulación académica.

Cuando la persona ingresa en el mundo de los estudios de post-grado, ingresa al mundo del conocimiento

controversial, que está en progreso de formación y descubrimiento. A este tipo de conocimientos se accede,

mayormente, por la lectura de artículos de investigación científica, a los que se accede, a su vez, por sitios en

internet, contactándose con los autores, o a través de journals (publicaciones especializadas en un área determinada de

conocimientos). Es de esperar que la persona que los lee posea conocimientos básicos sobre el área en cuestión.

En la web se puede acceder a artículos en:

www.sciencedirect

www.elsevier.com

www.biblioteca.secyt.gov.ar

www.publish.csirus

El objetivo de un trabajo de investigación, y su posterior artículo –research article, or RA- es reunir diferentes puntos de

vista, evidencia y hechos nuevos acerca de un tema específico por medio de un determinado proceso y finalmente,

interpretar la información, todo lo cual se presenta siguiendo pasos específicos y un formato determinado.

Se trata de establecer una relación entre otras personas que han investigado el tema, el escritor del trabajo que

aportará nueva luz sobre el mismo, y los lectores.

Un trabajo de investigación mostrará dos cosas: lo que el escritor del mismo sabe o ha aprendido de cierto tema, o lo

que otras personas saben acerca del mismo tema. El escritor de un trabajo de investigación puede aportar su propia

opinión, o explicar ideas complejas al lector.

Cada disciplina y/o editorial puede(n) requerir un formato y estilo diferente para escribirlo. Según la investigación que se

lleve a cabo, el formato del trabajo de investigación también variará. De todos modos, podemos hablar de un formato

standard o básico que a continuación se describe.

Componentes:

TÍTULO

RESUMEN O ABSTRACT O SUMMARY

INTRODUCCIÓN

MATERIALES Y MÉTODOS (PROCEDIMIENTO)

RESULTADOS

DISCUSIÓN

CONCLUSIÓN

AGRADECIMIENTOS / RECONOCIMIENTOS

REFERENCIAS

ANEXO

A continuación veremos las partes antes mencionadas en detalle en términos de su función dentro del paper, tomando

uno como ejemplo que será analizado parte por parte. En este curso solo tenemos como objetivo que seas capaz de leer

Artículos de Investigación Científica (AIC) - Research Articles (RA) o Papers

e interpretar con eficiencia títulos y abstracts.

TÍTULO

El título informa sobre:

o el nombre del experimento,

o los nombres de los investigadores y

o la fecha del experimento.

Ejemplo:

Australian Journal of Experimental Agriculture, 2007, 47:575-582

Seasonal variation in the quality of a lucerne-based pasture and its relationship with morphological and maturity

estimates.

Machado, C.F.A, B, Morris, S.T. A, C, Hodgson, J. A, Mathew, C. A, Auza, N. B

ACollege of Sciences, Massey University, Private Bag 11222, Palmerston North 4442, New Zealand. BFacultad de Ciencias Veterinarias (UNCPBA-Tandil) Pinto 399, Tandil 7000, Argentina. CCorresponding author. Email: [email protected]

Como ya se ha expresado en otra parte de este curso, los autores de los títulos intentan informar mucho, con pocas

palabras, y ubicar al lector desde lo general a lo particular y específico, respecto del campo de estudio (general) al área

de investigación concreta dentro de ése campo (particular). Las frases nominales juegan un papel importante en este

sentido, y es primordial que las manejes con facilidad para comprender de qué trata el paper en cuestión y decidir si

resulta de utilidad o interés la lectura completa del paper. En entrevista sobre diferentes aspectos que hicieron al

proceso de escritura del trabajo de investigación, el mismo autor del paper de nuestro ejemplo comentó que, al redactar

el título, intentó que en el título quedaran plasmados los “principales componentes de la idea del paper.”

Volviendo al concepto de ubicar al lector de lo general a lo particular, y observando todos los componentes del título,

incluyendo el nombre del journal, veamos cómo sería el análisis de tema en el ejemplo citado. El paper trata de:

Agricultura

Agricultura experimental

Variación en la calidad de determinada pastura

Variación en la calidad de determinada pastura en las diferentes estaciones del año

Variación en la calidad de pastura base Lucerne en las diferentes estaciones del año calculada según morfología y

madurez


ABSTRACT O RESUMEN

El abstract es el paper en miniatura. Resume los contenidos del paper en un párrafo conciso de alrededor de cien a

ciento veinte palabras, de manera tal que el lector pueda decidir si lee el paper completo o no. De aquí su complejidad.

Un abstract contiene información sobre los siguientes aspectos de la investigación que describe:

1. qué hizo el autor.

2. cómo lo hizo.

3. qué descubrió el autor.

4. a qué conclusiones llegó el autor.

Es considerado un género en sí mismo, es decir, un texto independiente. De hecho, existen publicaciones donde solo

aparece esta parte del paper. Así mismo, existen sitios en internet donde se accede en forma gratuita a los abstracts de

los papers, no así a los papers completos.

Ejemplo:

Abstract. 1(To monitor seasonal changes in herbage quality, a Lucerne-based pasture (Medicago sativa, Bromus

wilddenowii and Dactylis glomerata) was sampled in Argentina every 2 weeks for 28 months.) 2(The pasture

was strip-grazed and samples were taken from the regrowth of a previously grazed strip, ready for regrazing, for

which herbage mass was subsequently separated into grass and Lucerne, and then into lamina plus leaflet and

stem plus pseudostem fractions. Similarly, at each sampling date, quantitatively maturity indexes -mean stage by

count and mean stage by weight- were applied to grasses and Lucerne. Samples were also analyzed for in vitro

dry matter digestibility (DMD), crude protein (CP), fibre, and non-structural carbohydrates. The dataset was

divided into morphological, maturity and nutritional variables. Analyses of variance by season for both groups of

variables were carried out using year as a block. Multiple regression analyses were performed for each season

between maturity indices and predictors of herbage quality.) 3(DMD, and consequently metabolisable energy

(ME), was significantly lower in the autumn and CP was lower in the summer compared with overall averages,

which were consistently high throughout the year (overall average of 11.5MJ ME/kg dry matter and 20.6% CP).

The sward had a higher proportion of Lucerne during summer and autumn, than winter and spring (averages

59.3 and 48.8%, respectively. The highest leaf : stem ratio (2.82) was during winter and the highest green

content (97.5%) was during spring. Grasses had a higher mean stage by count and mean stage by weight during

summer-autumn. Morphological and maturity estimates predicted satisfactorily the changes in the energy and

fibre within season, but CP content was not well predicted in summer or winter.) 4(The results provide the basis

for tactical grazing practices with further calibration.


INTRODUCCIÓN (INTRODUCTION)

La introducción refleja “la necesidad de establecer la importancia del campo de investigación elegido ante la

comunidad científica, la necesidad de situar o ubicar el trabajo de investigación en cuestión en relación con esa

importancia, y la necesidad de demostrar cómo esa ubicación o nicho será ocupado y defendido”. Tomaremos el

modelo CARS (Create a Research Space) de Swales, porque, en líneas generales, logra capturar los principales

componentes de las introducciones de papers de esta disciplina:

Movimiento 1 Establecer el territorio

Paso 1: Afirmar la centralidad del área de investigación y / o

Paso 2: Hacer generalizaciones con respecto al tema y / o

Paso 3: Revisar investigaciones previas

Movimiento 2: Establecer un nicho

Paso 1A Hacer contra-afirmaciones o

Paso 1B Indicar la brecha

Paso 1C Plantear un interrogante o

Paso 1D Continuar la tradición

Movimiento 3: Ocupar el nicho

Paso 1A Esbozar los propósitos o

Paso 1B Anunciar la investigación

Paso 2 Anunciar los principales hallazgos

Paso 3 Indicar la estructura del paper

Ejemplo:

Introduction

Mov. 1: Establecer el territorio: Generalizaciones: (Herbage nutritional quality at any time is the

weighted average of the proportions of plant components and their nutritive value. Sward quality changes

seasonally and dynamically when physiological changes take place in the plants, and grazing or harvesting

conditions interact with those changes (Nelson and Moser 1994).) Mov. 1: Establecer el territorio: Afirmar la

centralidad del area de investigación: (Matching herbage mass and quality with the nutritional requirements

of grazing animals is one of the key challenges facing graziers.) Mov. 2: Indicar la brecha: (Tactical decisions

within grazing systems are often based on scant information about herbage quality, in spite of its importance in

influencing herbage intake and animal performance (Pearson 1997). For this reason, a systematic characterisation

of herbage quality variables on sheep and beef cattle farms in four regions of New Zealand has been undertaken

recently (Litherland et al. 2002).

Mov. 1: Establecer el territorio: Afirmar la centralidad del area de investigación: (The need

for estimating herbage quality is well recognized by graziers and pastoral researchers.) Mov. 1: Revisar

investigaciones previas: (Recently, alternative and faster analytical methods than the traditional laboratory

analysis have been used, such as near infra-red reflectance spectroscopy (Corson et al. 1999). Others have

established quantitative relationships between maturity stages and herbage quality in non-defoliated grasses

(Berg and Hill 1989; Sanderson and Wedin 1989; Sanderson 1992; Elizalde et al. 1999) and legumes (Hintz and

Albretcht 1991; Owens et al. 1995; Sulc et al. 1997) and less frequently in grazed pastures (Smart et al. 2001).

Similarly the relationships between maturity and accumulated degree days (Frank and Hofmann 1989; Smart et

al. 2001) and water stress (Lynk et al. 1990) have been reported. As forage matures, the general trend is for

decreasing leaf to stem ratio (Nelson and Moser 1994) both in grasses and legumes.) Mov. 2: Establecer un

nicho: Indicar la brecha: (However, maturity is not a clear concept in multispecies pastures, as different


maturation patterns have been widely reported for different plant species under similar climatic conditions

(Sanderson 1992).) Mov. 1: Establecer el territorio: Revisar investigaciones previas (cont.): (The higher

contribution of stems and the decrease in their quality with increasing maturity is associated with an increase in

neutral detergent fibre content (NDF) and a decline in digestibility within the herbage (Sanderson and Wedin

1989). Argentinean studies of pasture quality have shown that NDF content is a good predictor of dry matter in

vitro digestibility (DMD) for lucerne, lucerne-grass pastures and winter and summer fodder crops (Pagella et al.

1996).

Herbage quality is affected by grazing management (Saul et al. 1999; Schlegel et al. 2000; Frame et al.

2002), and intensive grazing is a key tool to maintain high nutritional value of pastures (Machado et al. 2005),

keeping the sward immature with a greater leaf:stem ratio (Nelson and Moser 1994; Clark 1995; Smart et al.

2001) by preventing excessive development of structural tissue which may lead to a decline of digestible contents

(Hodgson and Brookes 1999). Herbage quality is a concept that includes multiple factors in addition to herbage

composition, such as nutrient digestion and diet selection (Bush and Burton 1994). However, in this paper the

term herbage quality is restricted to crude protein (CP,%), metabolisable energy content (ME MJ/kg DM), DMD,

neutral detergent fibre (NDF, %) and non structural carbohydrates values (NSC, %).)

Lucerne-based pastures are widely used in Argentina (Romero et al. 1995; Kloster et al. 2000), and

Australia (FitzGerald 1979; Reeve and Sharkey 1980), where they are especially noted for their high soil water

extraction capability (Dolling et al. 2005). Considering the relevance of such pastures, Mov. 3: Ocupar el nicho:

Esbozar los propósitos: (the main objective of this experiment was to study the seasonal variation of pasture

quality in a lucerne-based pasture under an intensive beef cattle grazing system in Argentina. Secondly, the

relationship between nutritional variables and potential predictors from morphological and maturity variables was

explored quantitatively.)

Así, veamos a continuación, en forma esquemática, los pasos que contiene el paper del ejemplo:

Movimiento 1 Establecer el territorio

Paso 1: Afirmar la centralidad del área de investigación

Paso 2: Hacer generalizaciones con respecto al tema

Paso 3: Revisar investigaciones previas

Movimiento 2: Establecer un nicho

Paso 1A Hacer contra-afirmaciones: X

Paso 1B Indicar la brecha

Paso 1C Plantear un interrogante: X

Paso 1D Continuar la tradición: X

Movimiento 3: Ocupar el nicho

Paso 1A Esbozar los propósitos

Paso 1B Anunciar la investigación: X

Paso 2 Anunciar los principales hallazgos: X

Paso 3 Indicar la estructura del paper: X


MATERIALES Y MÉTODOS (MATERIALS AND METHODS)

Esta sección puede consistir en una simple lista de los materiales y los métodos utilizados. Es una narrativa

completamente descriptiva, donde se detallan todos los pasos seguidos en orden. Se describe lo que realmente se hizo y

lo que realmente sucedió, no lo que se suponía que ocurriría.

Esta sección contiene información sobre:

o la ubicación del experimento,

o los materiales utilizados

o el procedimiento seguido y

o datos estadísticos

En esta sección el autor utiliza un alto porcentaje de frases nominales, así como también voz pasiva con complemento

agente ausente que se asume son los investigadores.

Ejemplo: En nuestro paper de ejemplo, hemos marcado como se indica debajo las partes de esta sección

U( ) para ubicación P( ) para procedimiento

M( ) para materiales D. E.( ) para datos estadísticos

Materials and methods

P(Herbage samples were taken regularly for analysis from a 42 ha lucerne-based pasture) which

formed part of an intensive grazing beef system U(at the Chacra Barrow Experimental Station, Argentina

(38°20”S, 60° 13”W, 120 m.a.s.).) P(The pasture was undersown) to M(wheat in 1998, with 6kg inoculated

seed of lucerne (Medicago sativa L., cv Victoria SP INTA, dormancy class 5-6) 6kg prairie grass (Bromus

willdenowii Kunth CV Porto) and 6kg cocksfoot (Dactylis glomerata L., CV Martin Fierro). The soil was an

Argiudol with an indurated calcified layer at 70-80cm restricting root penetration, and had an organic matter

content in the upper 20cm of 335g/kg DM, a pH of 6.6 and a soluble phosphorus content (Bray and Kurtz, 1945)

of 90mg/kg. P(Diammonium phosphate (18-46-0) was applied at 80kg/ha at sowing, and urea was applied) at

90 kg/ha) before P(the wheat was harvested) in December 1998. P(A crop of hay was taken) in February

2000. P(Grazing and herbage sampling started) in March 2000 and continued until July 2002, with no further

fertilizer application. P(Rainfall, temperature and evapotranspiration were recorded) daily at an observation

station within the experimental site.

P(Throughout the two years of sampling the pasture was rotationally grazed) in daily strips by a

variable number of Hereford steers. The grazing interval for individual strips was between 21 and 55 days,

reflecting seasonal differences in herbage accumulation rate. P(Representative samples of herbage were


taken,) every two weeks from March 2000 to July 2002, from previously grazed strips which were ready to be re-

grazed with a target pre-grazing herbage mass of 1700kg DM, (Litherland et al, 2002; Machado et al, 2005).

P(At each strip sampling, herbage mass (kg DM/ha) was estimated) with M(a rising plate meter

(Filip’s folding plate meter, New Zealand),) P(calibrated) against herbage mass (cut 4 cm above ground level)

for the same period and experimental site (Machado et al. 2003) according to Earle and McGowan (1979) using 8

paired samples for plate predictions. P(Fifty random herbage samples were cut) to 4 cm above ground level

from the same strip using a knife, P(bulked) in M(a plastic bag) and P-M(refrigerated)indirectamente se

informa que se utilizó una heladera immediately. P(After thorough mixing) in the laboratory, P(a sub-

sample (0.4 kg) was taken and divided) into two parts. P(The first was sorted into dead (senescent tissues) and

green. Subsequently, the green pool was separated into grasses and lucerne, and then divided into grass lamina,

lucerne leaf, grass sheath and stem and lucerne stem fractions.) The weed component was negligible and was

not considered. These fractions were M(oven dried)indirectamente se informa que se utilizó un horno

(48 h at 60ºC), P-M(weighed)indirectamente se informa que se utilizó una balanza and expressed as

dry matter percentage (DM%) as done by Astirraga et al. (2002). P(The other fraction was subdivided into two

sub-fractions: one was M(freeze-dried)indirectamente se informa que se utilizó un freezer and analyzed

for DMD (Tilley and Terry 1962); CP, determined by multiplying the N concentration (Micro Kjeldhal method:

A.O.A.C. 1960) by 6.25; NDF (Goering and Van Soest 1970); and NSC by the anthrone method (Pichard and

Alcalde 1990). The other sub fraction was sorted into components as previously described, and samples were

then freeze-dried. Five samples per season of each component were randomly selected from the whole set of

samples and analyzed for DMD and CP.

Fifty tillers per grass species and fifty lucerne shoots were collected at random at each sampling date.

Descriptions of morphological stages of development were used for lucerne (Fick and Mueller 1989) and grasses

(Simon and Park 1983). Values for each tiller and shoot were recorded and a mean arithmetic stage by count

(MSC) per species was estimated. Additionally, tillers and shoots were oven-dried (48 h at 60ºC) for each stage of

development to estimate a mean stage corrected by weight (MSW). A regression analysis was performed

between MSC and MSW for each species. )

D.E(The dataset was grouped into morphological and maturity variables (herbage mass, dry matter

content, green content, leaf: stem ratio, MSC or MSW, for each of the three species) and nutritional variables

(DMD, CP, NDF and NSC) for the whole sward. Analyses of variance by season for individual variables were


carried out using year as a block, with normally 5-6 values per season within years. Seasons were assumed as:

autumn=March-May, winter= June-August, spring=September-November and summer=December-February.

Multiple regression analyses (‘stepwise” model selection, Alpha=0.15) were performed for each season, using

alternatively MSC or MSW as the maturity predictor. CP and ME of plant components were analyzed with a

completely random design within seasons. Following a significant F-test (P<0.05), least squares means were

separated using least significant differences (Steel and Torrie 1980). All analyses used the SAS statistical analysis

system (SAS/STAT 2001). Additionally, a Bayesian smoothing analysis was applied to the complete data set of

herbage ME and CP using Flexi 3.1 (Wheeler and Upsdell 2003) to reveal possible underlying seasonal trends.

This smoothing technique employs a variance components model to fit a constant term describing the general

level of the variable and a correlated random term to describe departures of the curve from this constant. A

cycle of 365 days with fluctuating covariance (the cycle is not forced to repeat exactly) and integral equal to 0

were used to model the data as: Total fit of ME or CP = Seasonal component + long term component + error.

Graphs are presented with confidence intervals (83 % as default for Flexi) for the fitted curves (Wheeler and

Upsdell 2003). )

RESULTADOS (RESULTS)

Una vez más, en esta sección se detallan los resultados reales, no lo que debería haber ocurrido. Si bien los resultados

se presentan generalmente en forma cuantitativa, en forma de gráficos tablas, estos datos numéricos deberían

contener la misma información en forma verbal y cada bloque de datos cuantitativos debería incluir un título apropiado.

Se caracteriza por el uso del simple past en voz activa. El simple present se utiliza para expresar la interpretación de

los datos de las tablas y gráficos, porque pertenecen al “aquí y ahora” del artículo. También son comunes las

estructuras paralelas y la repetición de términos en vez del uso de sinónimos, pues se desea claridad más que

estética.

Esta sección contiene:

o evaluación de los datos numéricos,

o acuerdo con investigaciones previas

o comentarios sobre discrepancias con investigaciones previas o resultados inesperados

o reconocimiento de la necesidad de más investigaciones sobre el tema o algún aspecto en particular

del mismo.

Es importante destacar la diferencia entre un hallazgo (finding), que se expresa en forma de cantidades y porcentajes, y

una declaración o afirmación sobre esos hallazgos (claim), que es la interpretación del autor sobre esos datos numéricos

y su contribución al campo de estudio.

Ejemplo: En el paper de ejemplo, solo se expresa evaluación de datos.


Results

Climatic conditions

The annual means for rainfall, mean daily temperature and hydric balance (difference between rainfall

and potential evapotranspiration) recorded during the trial were very similar to the 18-year average (Table 1).

However, during the experimental period, February, June and December were drier and August and October were

wetter than historical data. Climate variability between the two years of the study was low, although the second

production cycle had a slightly higher rainfall (+100 mm/ha.year) than the first, mainly between March and July

(results not shown).

Table 1. Means of rainfall, daily temperature and hydric balance recorded during 2 years of trial,

and historical data (over last 18 years, within brackets)

J F M A M J J A S O N D AX

Rainfall (mm) 111

(96)

59

(78)

116

(82)

90

(80)

60

(58)

29

(43)

43

(39)

72

(47)

79

(61)

139

(76)

79

(98)

70

(87)

79

(70)

Mean daily temp. C 23

(23)

22

(21)

18

(19)

14

(14)

11

(11)

8

(8)

6

(7)

9

(9)

10

(11)

14

(14)

17

(18)

21

(21)

14

(15)

Hydric balance (mm) B -89

(-95)

-134

(-77)

-4

(-35)

9

(9)

12

(16)

-8

(11)

7

(3)

11

(-11)

1

(-18)

59

(-35)

-67

(-53)

-161

(-108)

-30

(-33)

AOverall mean of the year. BDifference between rainfall and potential evapotranspiration.

Seasonal change in nutritional quality

Between-year differences in whole-sward quality parameters were usually significant, so seasonal variations

(Table 2) were corrected for between-year contrasts. DMD (and consequently ME) was lowest (P<0.05) during

autumn (Table 2), and for the rest of the year had a fairly stable value of about 11 MJ ME/kg DM (Table 2 and

Fig. 1.a). CP was lowest (P<0.05) during summer (Table 2), although variability for the rest of the seasons was

greater than for ME content (Fig. 1.b), with two peaks occurring at the start of winter and during spring. NSC was

lowest during autumn, whereas NDF content was constant across seasons (Table 2).

Table 2. Seasonal variation of nutritional variables in a lucerne-based pasture

Mean values with different superscripts within rows indicate differences between seasons (P<0.05). Numbers of

samples (n) are indicated between brackets.


Autumn

(11)

Winter

(16)

Spring

(12)

Summer

(12)

PSE A

In vitro digestibility (% of DM) 71.7a 76.8b 78.8b 75.7ab 1.83

Metabolizable energy (MJ ME/kg DM) 10.8a 11.6b 11.9b 11.4ab 0.27

Neutral detergent fibre (% DM) 34.5 33.7 33.3 32.2 1.60

Crude protein (% DM) 21.5b 22.2b 22.0b 17.3a 1.00

Non structural carbohydrates (% of DM) 6.3a 8.8b 10.0b 8.6b 0.58

APooled standard error

A M J J A S O N D J F M

Month

ME

MJ

/kg

DM


Month

ME

MJ

/kg

DM


Month

Figure 1. Seasonal change in herbage metabolisable energy (a) and crude protein (b) in pre-grazing samples

combined data for two years.

. Plotted confidence bands represent 83% probability in the smoothing analysis

Seasonal variations in contents of ME and CP in grasses and lucerne components are presented in Table 3. The

ME of grass parts was lower (P<0.05) in summer, although ME of sheath and stem started to decrease during

spring. Lucerne leaf ME decreased during autumn and lucerne stem during summer. Dead material had the

lowest ME value during autumn. The ME content in leaf fractions (computed mean between lucerne and grasses)

had a trend to lower coefficients of variation (CV) than stem fractions (9.4 and 13.3 % CV, respectively). Lucerne

fractions tended to be more stable in ME than grass fractions, while the ME of dead components had the highest

seasonal variability. CP was lower (P<0.05) in summer in most of the sward components, but in lucerne leaflets it

was similar between seasons, and in grass stem it was also lower during autumn. CP content was highly variable

between seasons in all sward components, with an average CV of 20.6 %. However, CP in the lucerne leaf was

the least variable component (10.6 %).

a) b)


Table 3. Seasonal variation in contents of metabolisable energy and crude protein in different sward

components



APooled standard error. BCoefficient of variation.

Seasonal change of morphological and maturity variables

Seasonal variations in morphological and maturity variables in pasture, and in the alternative maturity

indices for individual species, are shown in Table 4. Pre-grazing herbage mass was similar through the seasons

(P>0.05) with an overall average of 1586 kg DM/ha (>4cm above ground level). The pasture was dominated by

lucerne throughout the year (62% of the green material). During summer and autumn, the sward had a higher

proportion of lucerne and dry matter, with lower leaf to stem ratio and green content. This last variable also

declined during autumn (P<0.05).

Table 4. Seasonal variation in morphological and maturity variables in a lucerne-based pasture



Autumn (5)

Winter (5)

Spring (5)

Summer (5)

PSE A CV B

Metabolizable energy (MJ ME/kg DM)

Grass lamina 10.2 ab 12.1 b 11.8 b 9.9 a 0.33 10.7 Grass sheath and stem 11.1 ab 12.7 b 10.0 a 9.9 a 0.53 14.7 Lucerne leaf 11.3 a 12.6 b 13.2 b 12.3 ab 0.35 8.1 Lucerne stem 10.9 b 12.8 c 10.4 ab 9.6 a 0.29 12.0 Dead 5.2 a 7.1 b 7.3 b 6.0 ab 0.54 22.2 Crude protein (% of DM) Grass lamina 22.1 20.1 21.3 18.6 1.90 21.0 Grass sheath and stem 10.8 a 15.9 b 11.9 ab 10.0 a 1.40 29.0 Lucerne leaf 32.0 b 29.4 ab 29.5 ab 26.7 a 1.20 10.6 Lucerne stem 16.0 ab 19.1 b 16.4 ab 12.6 a 1.40 23.0 Dead 8.2 ab 9.3 b 8.6 ab 7.1 a 0.7 19.1


Autumn

(11)

Winter

(16)

Spring

(12)

Summer

(12)

PSE A

Pre-grazing herbage mass

(kg DM/ha above 4 cm)

1515 1673 1519 1639 149.8

DM content (% of herbage mass) 24.6 b 19.6 a 17.6 a 31.3 c 1.29

Green (% of dry matter) 79.2 a 84.1 a 97.5 b 86.0 a 2.47

Leaf:stem ratio 1.51 ab 2.82 c 1.98 b 1.00 a 0.29

Lucerne (% of Green) 64.2 b 43.3 a 54.3 ab 85.1 c 6.45

M. sativa. (mean stage by counting) B

1.86 b 0.61 a 1.1 a 3.0 c 0.20

B. willdenowii. (mean stage by counting) C

23.6 a 25.8 a 47.1 b 40.5 b 2.82

D. glomerata. (mean stage by counting) C

22.2 a 25.8 a 36.4 b 36.6 b 2.12

M. sativa. (mean stage by weighing) B

2.1 b 0.89 a 1.6 b 3.4 c 0.26

B. willdenowii. (mean stage by weighing) C

24.5 a 26.3 a 47.3 b 53.2 b 3.74

D. glomerata. (mean stage by weighing) C

22.7 a 25.9 a 40.3 b 39.6 b 2.96

APooled standard error. BQuantitative scale (Fick and Mueller 1989). CQuantitative scale (Simon and Park 1983).

Both grasses had a higher maturity status during spring and summer than in autumn and winter, using both

MSC and MSW. Lucerne was more mature during summer-autumn using MSC. A similar pattern was obtained for

MSW but with spring also differing from winter. MSC and MSW were highly correlated in the three species (with

correlation coefficients of 0.93, 0.92 and 0.97 (n=56) for M. sativa, B. willdenowii. and D. glomerata,

respectively).

Relationships between variables

DMD was significantly correlated with NDF, NSC and CP (overall correlation coefficients of -0.62, 0.41 and

0.36, respectively), and NDF content with NSC (-0.32) (n=56). “Best fit” equations from “step-wise” regression

analysis within each season relating herbage quality variables and morphological and maturity variables of the

pasture are presented in Table 5. During summer and autumn, nutritional variables were mostly predicted from

morphological variables. CP could not be predicted either in summer or winter, and autumn presented a lower

regression coefficient (R2=0.54) than spring. The content of NSC was only predicted significantly in spring. In the

cases when MSW was selected, this could be replaced acceptably by the corresponding MSC without much loss of

prediction capability, except for the case of DMD in winter.


In order to study the overall relationship between nutritional and morphological-maturity variables across

the year, a multivariate canonical correlation analysis (Matthew et al. 1994) was also applied. Using MSW as

maturity predictor, canonical R2 was 0.81, and a similar result was obtained using MSC. From the four possible

canonical factors (the four nutritional variables constrained the number of canonical factors), one factor was

deemed significant (P<0.05), showing that the greatest variation in nutritive value (ME content and CP) was

associated with maturity in lucerne but not in the grasses (results not shown).

Table 5. Regression equations of herbage quality variables and morphological and maturity

variables in a lucerne-based pasture

Numbers of samples (n) are indicated between brackets.

Regression model Season R2 ARMSE Sig.

Autumn (n = 11)

DMD = -183 + 0.24 BGreen - 0.009 CHM + 11.23 DDACTYLISw 0.86 3.63 ***

DMD = -238 - 0.01 HM + 14.7 DDACTYLISw 0.80 3.69 ***

NDF = 30.7 + 1.23 EDM - 0.33 Green 0.65 4.34 ***

CP = 21.9 - 0.003 HM + 0.06 FLUCERNE 0.54 2.36 *

Winter (n = 16)

DMD =103.9 - 0.006 HM - 4.67 GLUCERNE w - 0.44 HBROMUSw 0.54 5.60 *

DMD = 87.3 - 0.006 HM 0.27 6.60 *

NDF = 24.1 + 5.33 LUCERNE w + 0.003 HM 0.63 3.89 *

NDF = 22.1 + 8.37 I LUCERNE c + 0.003 HM 0.52 4.41 **

Spring (n = 12)

DMD = 81.6 - 0.003 HM - 0.15 BROMUSw 0.69 2.26 **

DMD = 99.1 - 0.33 JDACTYLISc - 0.16 KBROMUSc 0.78 1.91 ***

NDF = 24.2 + 0.11 BROMUSw + 0.002 HM 0.65 1.76 **

NDF = 21.6 + 0.13 BROMUSc+ 0.003 HM 0.69 1.63 **

CP = 47.3 -0.004 HM - 0.76 DM -3.39 LUCERNE w 0.75 3.08 **

CP = 47.3 + 0.95 Green - 0.005 HM - 8.9 LUCERNE c 0.87 2.21 **

NSC= 7.9 + 0.35 DM - 0.07 BROMUSw 0.58 1.60 *

NSC= 20.4 - 0.06 LUCERNE - 0.153 BROMUSc 0.59 1.57 *

Summer (n = 12)

DMD = 41.2 + 0.4 Green 0.58 3.10 ***

NDF = 73.3 - 14.9 Green - 0.3 LLeafStem 0.87 2.40 ***


ARMSE = Root mean square error. BGreen = Green percentage. CHM = Herbage mass. DDACTYLISw = Mean

stage by weighing of D. glomerata, EDM = Dry matter percentage. FLUCERNE = Lucerne percentage. GLUCERNE

w = Mean stage by weighing of M. sativa. HBROMUSw = Mean stage by weighing of B. willdenowii, I LUCERNE c = Mean stage by counting of M. sativa . JDACTYLISc = Mean stage by counting of D. glomerata, KBROMUSc =M Mean stage by counting of B. willdenowii , LLeafStem = Leaf:stem ratio. *, P<0.05; **, P<0.01 and ***, P<0.001

DISCUSIÓN (DISCUSSION)

En esta sección se explican, analizan e interpretan los resultados, sin soslayar los problemas o errores que se pudieran

haber presentado. Esta es, quizás, la parte más importante del paper, ya que aquí el escritor del mismo demuestra que

ha comprendido y que es capaz de explicar el trabajo que ha realizado, a la vez que intenta persuadir al lector de la

importancia de su trabajo y de cómo se relaciona éste con trabajos ya realizados.

Esta sección contiene:

o información previa,

o declaraciones o afirmaciones sobre los resultados (interpretación)

o resultados inesperados

o referencia a investigaciones previas, con los dos posibles propósitos de: compararlas con, o apoyar

con ellas, el estudio presente

o explicaciones de resultados inesperados

o ejemplificación para apoyar una explicación

o deducción e hipótesis, que consiste en una declaración sobre la generalización del trabajo de

investigación en su conjunto

o recomendaciones, que generalmente consisten en afirmar la necesidad de más investigación sobre el tema en

cuestión.

Ejemplo: En nuestro paper de ejemplo aparecen las siguientes partes, y se han marcado algunos

ejemplos:

o información previa, a lo largo de toda la sección, a la que se recurre para interpretación.

o declaraciones o afirmaciones sobre los resultados (interpretación), mayormente reconocible por la

expresión is likely to

o referencia a investigaciones previas, con los dos propósitos de compararlas con, y apoyar con ellas, el estudio

presente, fácilmente identificables por los apellidos de los investigadores y los años de sus trabajos, entre paréntesis

o no.

o deducción e hipótesis, claramente indicada con la expresión In conclusion

o recomendaciones, que en este caso dos, una relacionada con el tratamiento que debería darse a los datos de las

tablas 2 y 3, y la otra en relación a la necesidad de más investigación y calibraciones antes de que se tomen

decisiones sobre estrategias de pastoreo.

Discussion

Seasonal change in nutritional quality

The herbage quality of bulk harvests was maintained to a high standard throughout the experiment and

was consistent between years, although CP content was lower in summer and ME and NSC in autumn.

Afirmaciones sobre los resultados: (This general pattern is likely to be a consequence of at least 2 main


factors. Firstly, there were no obvious adverse climatic conditions during the study (Table 1), with conditions

being consistent between years (results not shown). Secondly, the grazing management carried out during this

study ensured intensive use of the pasture area at animal production levels of 264 kg beef /ha.year, well above

values for the region for cow-calf and fattening mixed systems (Di Nezio et al. 2003).) Referencia a

investigaciones previas para comparación: (Intensive use of pastures is a key tool to keep the sward

immature by moderating the development of structural tissue (Nelson and Moser 1994; Clark 1995; Hodgson and

Brookes 1999). The results are also in agreement with the seasonal nutritional observations in a New Zealand

beef cattle finishing pasture (ryegrass and white clover) managed intensively over 3 years (Machado et al. 2005).

The observed CP levels throughout the seasons were similar to those obtained in pastures of similar

characteristics managed under intensive rotational systems over 2 years in Argentina (Kloster et al. 2000), where

the authors observed CP values by season of 23.7, 26.0, 25.8 and 23.4 % CP for autumn, winter, spring and

summer, respectively. The CP values of the present study clearly exceeded (except for the summer) the

recommended CP content of herbage required for very young growing animals, i.e. 15-18% CP (Hodgson and

Brookes 1999). )

Recomendación (1): (Patterns of variation in the nutritive value of the bulk samples (Table 2) and of

plant parts (Table 3) must be treated with caution, as sample numbers within seasons were not the same.)

However, they show that ME content of grasses fractions and lucerne stem declined during summer. Referencia

a investigaciones previas para comparación: (In the case of grass stem there was some decrease during

spring, in agreement with the changes in DMD observed by Mowat et al. (1965) in M. sativa and Berg and Hill

(1989) in D. glomerata. Leaf components had a lower coefficient of variation in ME content and less marked

variation in CP, in comparison with that observed in stems. In a study carried out over two spring periods on

lucerne (Christian et al. 1970), leaf had 4 and 19 % CV values, and stem 23.0 and 40.5 % CV values of DMD and

CP, respectively. )

Seasonal change of morphological and maturity variables

The higher proportion of lucerne during summer-autumn (75%) than winter-spring (48%) in this

experiment is in agreement with the results of Kloster et al. (2000) in pasture of a similar composition (80 and 59

% of lucerne in canopy DM for spring-summer and autumn-winter, respectively). Afirmaciones sobre los

resultados: (This seasonal pattern is likely to be associated with the different growth patterns of the species

and with the different factors for which grasses and lucerne in mixtures compete (McKenzie et al. 1999).)

Referencia a investigaciones previas para comparación: (Spring showed the highest level of green

percentage, similar to that observed by Kloster et al. (2000) in a lucerne-grass pasture under grazing. These


authors reported averages of 87.5 and 99.0% of green material for autumn-winter and spring-summer,

respectively, for two years of measurement. Dry matter content of the pasture increased significantly during

spring and summer, which is in agreement with results reported by Christian et al. (1970).

The intensive use of the pasture in this study meant that it was maintained in a relatively immature

state, and the relationship between this condition in lucerne based pastures and bloat incidence has been

established (Thompson et al. 2000). However, on the beef cattle unit where this study was developed, bloat was

controlled effectively (Majak et al. 2001),) and no cases were reported during the trial.

Leaf to stem ratio was lowest in summer. High temperature in this season promotes plant maturation

leading to proportionally more stem growth (Buxton and Fales 1994), and a marked decline in the quality of the

stems and increased senescence of green material (Christian et al. 1970; Nelson and 0Moser 1994). Clearly, the

combination of species in the mixture used in this experiment resulted in a spread of pasture maturity. Although

the three species were most mature during summer, grasses increased in maturity index during the spring, and

lucerne extended its maturity through the autumn (Table 4). Increased temperature in summer associates

positively with plant maturity, but maturity integrates multiple environmental factors and represents the

physiological and ecological status of the plant stand (Sanderson 1992).

There was a very high correlation between MSC and MSW, with an overall correlation between species

of r=0.94 (n=56), which is in agreement with that reported by others using lucerne pasture (Mueller and Fick

1989). MSW has been shown to be the best maturity predictor (Fick et al. 1994), but measurement of MSC is less

time consuming, and is simply the arithmetic average of the quantitative maturity stages of individual tillers and

stems. As MSC does not take into account the change in size of the stems and tillers, this could be an important

bias when substantial morphological plant changes are occurring. One study under grazing conditions (Smart et

al. 2001) reported that MSC became an increasingly poor predictor of leaf to stem ratio as grazing progressed in

the growing season of a big bluestem pasture. The prevention of maturity by intensive grazing in this study is

likely to have contributed to the high association between the two methods used for obtaining maturity

estimates.

Prediction of nutritive value

The overall correlation between NDF and DMD is in agreement with the observation of Pagella et al.

(1996). As expected from the seasonal changes in nutritional variables (Table 2) and morphological and maturity

variables (Table 4), nutritional predictors changed seasonally (Table 5). For this reason, no significant predictor

could be identified when the overall dataset was used without grouping by season (result not shown). Changes in

grasses became important during spring, due to earlier maturity than lucerne (Table 4), when the grass stem and

sheath components started to decrease in ME and CP content (Table 3). Stems decrease in quality faster than

leaves in most forage plants, especially when plants approach maturity (Nelson and Moser 1994), and a close


inverse association between MSW and DMD of lucerne stem has been demonstrated (Sanderson and Wedin

1989; Sanderson et al. 1989).

Poor prediction capability of plant maturity indicators has been observed when herbage is used more

intensively, therefore moderating the usual changes that affect herbage quality. The disturbance of the sward

structure caused by hard grazing resulted in poor prediction of leaf:stem ratio, CP, NDF and MSC especially when

compared with non-grazed or laxly grazed swards of Adropogon gerardii (Smart et al. 2001). However, the

combination of estimates of morphological and maturity variables applied here seemed to improve the herbage

quality predictors.

Plant maturity estimates, traditionally tested in ungrazed pastures, became weak predictors when

pasture grazing management was intensive. The inclusion of morphological descriptors is original and definitely

improves seasonal predictions (Table 5), showing a R2 mean of 0.69 (when only the best option for each variable

is included). Mixed swards are complex, but more relevant to grazing systems, and in this context the seasonal

predictions look promising. Deducción e hipótesis: (In conclusion, the significant relationships established by

different statistical tests between morphological and maturity estimates and herbage nutritional variables in a

pasture are useful to gain an understanding of the seasonal changes of herbage quality.) Recomendación (2):

(However further research and calibrations in different swards and seasons are required before their

relationships may be used for on-farm short-term tactical decisions about grazing strategies.)

CONCLUSIÓN (CONCLUSION)

En esta sección se extraen conclusiones de los resultados y la discusión de los mismos tratando de responder a la

pregunta ¿Y ahora qué? También se puede incluir una crítica del experimento y realizar recomendaciones para su

mejoramiento. Tales críticas y recomendaciones, sin embargo, deberían centrarse en el experimento como una

experiencia de aprendizaje, no ser una mera queja.

Las secciones Resultados, Discusión, y Conclusión pueden combinarse de diferentes maneras. En el caso de nuestro

ejemplo, la conclusión está incluida en la sección Discusión.

AGRADECIMIENTOS / RECONOCIMIENTOS

Acknowledgments

The senior author gratefully acknowledges The New Zealand Ministry of Foreign Affairs and Trade for providing

the scholarship for this study, and the Facultad de Ciencias Veterinarias (UNCPBA), Tandil-Argentina and PICT 0809771

(National Agency of Science and Technology of Argentina) for funding the research. Thanks to all the staff of the Chacra

Barrow Experimental Station, Argentina, particularly to Drs. J. Duhalde and L. Di Nezio and the anonymous referees for

their valuable contribution.


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Algunos papers incluyen esta sección al final, e incluye toda la bibliografía consultada a fin de realizar el experimento,

ordenada alfabéticamente. Cuando la referencia es otro paper, la información del mismo se organiza de la siguiente

manera:

1. nombre del autor o autores, con apellido completo e iniciales de los nombres, separados por comas, por ejemplo:

Berg C, Hill R

2. año de publicación entre paréntesis, por ejemplo: (1989)

3. título del paper, por ejemplo: Maturity effect on yield and quality of spring harvested orchardgrass forage.

4. nombre del journal donde fue publicado en letra cursiva, por ejemplo: Crop Science

5. número de journal dónde aparece el paper en negrita, luego una coma, por ejemplo: 29,

6. el número de páginas donde aparece el paper referenciado, por ejemplo: 944-948.

Ejemplo:

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ANEXO (APPENDIX)

Esta sección puede incluir datos sin procesar, cálculos, gráficos, y cualquier otro tipo de material cuantitativo que fue

parte del experimento y que no se incluyó en ninguna de las secciones anteriores. Nuestro paper ejemplo no cuenta con

esta sección.

Agradecemos al Dr. Claudio F. Machado por su contribución en el desarrollo de esta ficha teórica

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