Directory UMM :Data Elmu:jurnal:A:Agricultural Systems:Vol67.Issue2.2001:
Agricultural Systems 67 (2001) 83±103
www.elsevier.com/locate/agsy
On-farm assessment of regional and seasonal
variation in sun¯ower yield in Argentina
J.L. Mercau a,*, V.O. Sadras b, E.H. Satorre a, C. Messina a,
C. Balbi a, M. Uribelarrea a, A.J. Hall a
a
Facultad de AgronomõÂa, Universidad de Buenos Aires, Av. San Martin 4453, 1417 Buenos Aires, Argentina
b
Universidad de Mar del Plata-INTA Balcarce, Argentina
Received 21 March 2000; received in revised form 14 August 2000; accepted 12 September 2000
Abstract
Using an on-farm approach, we investigated constraints to actual yield of sun¯ower in six
agroecological zones within the Argentine Pampas during three growing seasons. In 249 large,
grower-managed paddocks, we quanti®ed a series of variables related to: (1) crop phenology,
growth, and yield; (2) the physical and biological environment; and (3) management practices.
Variation in yield among zones and seasons was analysed on the basis of four biologicallyfounded assumptions: (1) grain number accounts for a large proportion of the variation in
yield; (2) grain number is associated with a photothermal coecient, Q=R (T-Tb)ÿ1, where R
and T are average solar radiation and air temperature respectively, during the 50-day period
bracketing anthesis; and Tb is a base temperature; (3) crop growth and yield are proportional
to light interception, and therefore proportional to canopy ground cover; and (4) yield is
proportional to the fraction of seasonal rainfall that occurs after anthesis. Average yield
ranged from 1.1 to 2.7 t haÿ1, grain number from 2400 to 5400 mÿ2, individual grain mass
between 40 and 69 mg and grain oil concentration between 42 and 52%. Grain number
accounted for 43% of the variation in average yield while Q accounted for 23% of the variation in grain number. Low yield was associated with de®cient ground cover in 25% of the
crops; part of the remaining variation in yield was accounted for by sets of measured variables
particular to each zone, including soil shallowness, low available P, low initial water content,
weeds and diseases Ð chie¯y Verticillium wilt (Verticillium dahliae) and Sclerotinia head rot
(Sclerotinia sclerotiorum). Across zones and seasons, the proportion of seasonal rainfall
occurring after anthesis accounted for 28% of the variation in crop yield. A trade-o is
highlighted whereby bene®cial eects of rainfall that favours growth and yield may be oset
by the detrimental eect of abundant moisture that favours major fungal diseases. We
* Corresponding author. Tel.: +54-11-4524-8053; fax: 54-11-4524-8053.
E-mail address: [email protected] (J.L. Mercau).
0308-521X/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved.
PII: S0308-521X(00)00048-2
84
J.L. Mercau et al. / Agricultural Systems 67 (2001) 83±103
emphasised the value of combining experimental studies Ð which provide biological background in the form of working hypotheses Ð with on-farm research that realistically quanti®es yield response to key factors. # 2001 Elsevier Science Ltd. All rights reserved.
Keywords: Helianthus annuus; Technology; Yield potential; Attainable yield; Agroecology; Rainfall;
Radiation; Temperature; Verticillium dahliae; Sclerotinia sclerotiorum
1. Introduction
Rainfed production of sun¯ower in Argentina commenced in the early 1930s with
open-pollinated varieties brought by European immigrants. After some ups and
downs in acreage, the crop is currently well established and the national harvest in
the 1990±1995 period accounted for 16 to 22% of world production (Anon., 1996).
Actual yield remained stable around 0.7 t haÿ1 for a long period from 1930 to the
early 1970s, when the ®rst hybrids were released (LoÂpez Pereira et al., 1999). In
the last three decades, actual yield increased to about 1.4 t haÿ1 owing to the combination of hybrid seed technology and improved management (LoÂpez Pereira et al.,
1999). A gap of at least 2 t haÿ1, however, remains between actual and potential
yield estimated in experimental plots (LoÂpez Pereira et al., 1999). Opportunities
therefore exist to lift actual sun¯ower yield, provided the main restrictions to crop
growth and yield are identi®ed.
The purpose of a diagnostic stage, when aiming to increase crop yield and stability, is to describe and understand the farming system and to identify production
constraints (Byerlee et al., 1991). On farm experimentation and farm surveys have
usually been the approaches used in this stage. In Argentina, private associations of
farmers, including the Argentine Association of Agricultural Experimentation Consortia (AACREA), are the main sources of information on major cropping systems.
In AACREA professional consultants advise groups of 8±12 growers on the basis of
both on-farm trials and careful records of crop, soil, weather and economic data. In
the last 14 years, the association, which include 1500 farmers and 150 professional
consultants, has developed a comprehensive database on local cropping systems that
has been instrumental in the analysis of current and novel production techniques
(Duarte et al., 1993; Duarte, 1994).
The database was also aimed to determine yield limiting factors and sources of
yield variability. Nonetheless, recognition of the limits to analysis imposed by the
nature of the data (Alippe and Bosch, personal communication), led to a project
aiming at: (1) improving the database by inclusion of key crop ecological and physiological variables; and (2) identifying major constraints for sun¯ower production
in the Pampas. The project involved industry members, consultants and researchers,
and the approach combined comprehensive ®eld evaluation of cropping systems and
simulation techniques. This paper reports the results of the ®eldwork, which was
based on four components. First, we developed a biological framework for the
analysis of yield variation, including two complementary models of yield determination based on: (1) numeric components and photothermal coecient (Fischer,
1985; Cantagallo et al., 1997); and (2) resource capture by the crop and partitioning
J.L. Mercau et al. / Agricultural Systems 67 (2001) 83±103
85
of growth before and after anthesis (Sadras and Connor, 1991; Monteith, 1994).
Second, working in large, grower-managed ®elds, we quanti®ed a series of variables
related to: (1) crop phenology, growth and yield; (2) the physical and biological
environment; and (3) management practices. Third, we considered variation among
six agroecological zones during three growing seasons. The time series was short
enough to met the criterion of constant technology within each zone while combination of zones and seasons ensured a large variation in growing conditions (Sadras
and Villalobos, 1994; CalvinÄo and Sadras, 1999). Fourth, we explored the main
restrictions to yield using regression analysis, and an edge approach based on leastsquare regression (Scharf et al., 1998). Emphasis was placed on the physical and
biological meaning of the statistical relationships between yield and key variables.
2. Methods
2.1. Biological framework
Two complementary approaches were used to investigate seasonal and regional
variation in grain yield. First, based on the work of Fischer (1985) and Cantagallo et
al. (1997) we postulated that:
(1) Grain number accounts for a large proportion of the variation in yield.
(2) Grain number is associated with a photothermal coecient,
Q R T ÿ Tb ÿ1
1
where R is average solar radiation, and T is average air temperature during the
period from 30 days before to 20 days after anthesis, and Tb is a base temperature of 4 C (Villalobos and Ritchie, 1992).
Second, based on Monteith (1977) and Sadras and Connor (1991), respectively, we
proposed that
(3) yield is proportional to light interception during the growing season, and
therefore proportional to canopy groundcover.
(4) yield is proportional to the fraction of seasonal rainfall that occurs after
anthesis. This is based on the relationship between harvest index and the fraction of seasonal water use that occurs after anthesis (Passioura, 1977; Sadras
and Connor, 1991).
2.2. Site
Soil, climate and technology of dryland cropping systems in the Pampas have been
described by Hall et al. (1992). Brie¯y, the Pampas extend over 52 million ha originally covered by grasslands. The area sown to sun¯ower in the 1990s averaged 3
million ha and yield averaged 1.7 t haÿ1. Crop-to-pasture ratio in the most common
86
J.L. Mercau et al. / Agricultural Systems 67 (2001) 83±103
cropping rotations range from 0.6 to 0.8 in the best to 0.3 in the poorest soils.
Dominant agricultural soils are mollisols formed over loessic sediments. Annual
rainfall decreases and the proportion of summer-to-annual rainfall increases from
east to west. Details of the agroecological zones under study are summarised in
Table 1 and Fig. 1.
Table 1
Soil types in the agroecological zones under study
Zone
Major soil type
Other soils
1
2
3
Entic Haplustoll
Entic Hapludoll
Typic Hapludoll
Thaptoargic Hapludolla
Entic Hapludoll
Entic Haplustoll
Petrocalcic Natracuolla
Typic Hapludoll
Typic Argiudoll
Typic Argiustoll
Typic Hapludoll
Entic Hapludoll
Thaptonatric Hapludolla
Thaptonatric Hapludolla
Lithic Argiudolla
Petrocalcic Paleustolla
Petrocalcic Paleustolla
4
5
6
a
Shallow soil.
Fig. 1. Main sun¯ower growing areas in the Pampas (intensity of grey indicates percentage presence of
the crop in the cultivated area of each department). October±March total rainfall isohyets and January
mean temperature isotherms are also presented. Location of agroecological zones (1±6) de®ned in this
analysis are also indicated.
J.L. Mercau et al. / Agricultural Systems 67 (2001) 83±103
87
2.3. Database and statistical analysis
We collected data from commercial farms belonging to AACREA (Section 1).
Farms were grouped on the basis of agroecological similarities into six zones (Fig. 1,
Table 1). Restriction of the data series to three seasons allowed for a reasonable
range of weather conditions while meeting the assumption of unchanged technology
(Sadras and Villalobos, 1994; CalvinÄo and Sadras, 1999). We assessed a series of
variables summarised in Table 2. Assessment of some variables was improved as
research progressed, e.g. ground cover was evaluated only at ¯owering in the ®rst
two seasons while it was also evaluated 20 days after ¯owering in the third. From
the initial 285 paddocks we discarded 36 owing to incomplete data.
Variables in Table 2 were analysed using descriptive statistics and Student t-test
for comparison of means. Association between variables, averaged for each zone
and year, was investigated using correlation analysis. To investigate the relationship
between canopy ground cover and yield we applied an edge approach based on
least-square regression (Scharf et al., 1998). Yield data Ð pooled across seasons and
zones Ð were divided into size classes corresponding to 10% increments of ground
cover. Within each size class, data points corresponding to the maximum, 66 percentile and 33 percentile were paired with average cover for the size class. Thereafter
a bi-linear model was ®tted to data for each of these three categories using a broken
stick procedure, i.e.
if
GC > threshold;
if
GC4threshold;
Y Ymax
Y a ÿ b GC
2a
2b
where GC is canopy ground cover (%), Y is yield, Ymax, a and b are parameters. A
broken stick procedure was used since it produced a better distribution of residuals
than a simple linear regression approach (Steel and Torrie, 1985) and because it is
consistent with the expected relationship between yield and ground cover at high
ground cover values. We then investigated the factors underlying departure from the
envelope (maximum) model (Eq. (2a) and (2b)) by making comparisons between
deviations lying above the model ®tted to the 66 percentiles values with these falling
below the model ®tted to the 33 percentiles. Factors means for the two categories
were then compared using a t-test or a t-test modi®ed by Satterthwhite (Steel and
Torrie, 1985) when variances were dierent (Levenne test).
3. Results
3.1. Seasonal and spatial variation in yield and its components
There was a 2.5-fold range in average yield, some zones showed large seasonal
variation (e.g. Zones 1 and 6) whilst others showed greater yield stability (e.g. Zone
3; Fig. 2A). Variation in yield among ®elds within a zone was similar across zones
88
J.L. Mercau et al. / Agricultural Systems 67 (2001) 83±103
and was fairly stable in all three seasons (see error bars in Fig. 2A). Two yield
components were considered, grain number and individual grain mass. Grain number ranged from 2400 to 5400 mÿ2 and individual grain mass between 40 and 69 mg.
Grain number accounted for about 40% of the variation in yield (P0.1). Individual
grain mass was negatively associated with grain number (Fig. 2C).
Grain oil concentration varied between 42 and 52% (Fig. 3). There was a large
variation among zones; with few exceptions (e.g. Zone 4) seasonal variation within
zones was low. Grain oil concentration was negatively associated with availability of
soil nitrogen (r=ÿ0.49, P=0.07). Inverse relationships between supply of soil
nitrogen and oil content seem to be mediated by a trade-o between protein and oil
content in the grain typical of oil seed crops including sun¯ower (Connor and
Sadras, 1992) and soybean (Wilcox, 1998).
Table 2
Variables assessed in commercial farms to characterise key aspects of the crop, its environment and
management
Features of the croping system
Crop
Phenological development
Structural and functional aspects
Yield and its components
Environment
Weather
Soil
Biological
Variable
Date of sowing
Date of ¯owering
Date of physiological maturity
Hybrid
Plant population density (plants mÿ2)a
Temporal uniformity of emergenceb
Spatial uniformity of emergencec
Ground cover at ®rst ¯ower, and 20 days after end of ¯oweringd
Lodging (%)
Yield (kg haÿ1)e
Individual grain mass (mg)f
Grain number (mÿ2)g
Grain oil content (%)h
Daily rainfall (mm)i
Hail stormsi
Daily maximum and minimum temperature (C)j
Daily solar radiation (MJ mÿ2)j
Soil type (USDA classi®cation)
Eective depth (m)k
pH (1 soil:2.5 water)
Available phosphorus (mg kgÿ1)l
Carbon content (%)m
Inorganic nitrogen at sowing (kg haÿ1)n
Water content at sowing (mm)o
Weed cover at ¯owering (%)p
Intensity of diseases at ¯oweringq
J.L. Mercau et al. / Agricultural Systems 67 (2001) 83±103
89
Table 2 (continued)
Features of the croping system
Variable
Management
Previous crop
Fallow duration (days from ®rst tillage or herbicide application)
Date of harvest
a
Assessed in 10 randomly distributed linear plots of 14.3 m each.
Standard deviation of number of leaves (>4 cm) in 20 randomly chosen plants.
c
Coecient of variation of the distance between successive plants in 3 rows of 14.3 m.
d
Proportion of shade measured at noon with a 1-m ruler placed diagonally on the ground beneath the
canopy.
e
Measured in three randomly chosen rows (7 m) before mechanical harvest.
f
Measured in ®ve sub-samples of 200 grains each.
g
Derived from e and f.
h
Nuclear magnetic resonance with standard sun¯ower oil.
i
Measured on farm.
j
Measured on nearby meteorological stations.
k
Depth of duripan.
l
Measured in 0±0.2 m, method: Ritcher and vonWistinghausen (1981).
m
Measured in 0±0.2 m, method: Olsen and Sommers (1982).
n
Measured in 0±0.4 m, method: Kenney and Nelson (1982).
o
Measured gravimetrically to a soil depth of 0.6 m.
p
Visually estimated.
q
Intensity estimated using a scale with four classes, i.e. 3=very intense, 2=intense, 1=moderate,
0=nil. Main diseases included Sclerotinia head rot (Sclerotinia sclerotiorum (Lib.) de Bary), Verticillium
wilt (Verticillium dahliae Klebahn), and Black stem (Phoma macdonaldii Boerema).
b
3.2. Radiation and temperature
In the commercial crops under study, grain number was the component more
closely associated with yield (Fig. 1B) in agreement with studies in experimental
plots including those by Cantagallo et al. (1997). These authors also showed that
grain number was positively related to radiation and negatively to temperature, and
integrated these variables in a photothermal coecient (Eq. (1)). Fig. 4 illustrates:
(1) the decline in Q with time between October and March; (2) the decline in Q with
increasing latitude; and (3) the seasonal variation in Q within a zone. The photothermal coecient accounted for 23% of the variation in average grain number
among zones and seasons (Fig. 4c). Intercept [grains mÿ2] of the relationship
between grain number and Q in our sampled commercial crops was ÿ28833612
(P>0.44) compared to 328 in the irrigated single-cultivar experiments of Cantagallo
et al. (1997) whereas the slope [grains MJÿ1 C] was 50522640 and 8183, respectively (Fig. 4C).
3.3. Water availability
Rainfall had substantial spatial and seasonal variation (Fig. 5A). Seasonal variation was smallest in Zone 6 and largest in Zone 1 where total rainfall varied over a 2fold range (Fig. 5A). Variation among farms within a zone was also large. Stored
90
J.L. Mercau et al. / Agricultural Systems 67 (2001) 83±103
Fig. 2. Spatial and seasonal variation in sun¯ower yield and its components for crops grown in large,
grower-managed ®elds. (A) Grain yield; (B) relationship between yield and grain number, and (C) relationship between grain number and individual grain mass. Agroecological zones are characterised in Fig.
1 and Table 1. In (A) data for 1995/96, 1996/97 and 1997/98 seasons are represented by white, grey and
black bars respectively, error bars are standard deviations. In (B) and (C) each point is the average of all
®elds surveyed in each zone and season.
J.L. Mercau et al. / Agricultural Systems 67 (2001) 83±103
91
Fig. 3. Spatial and seasonal variation in grain oil content of sun¯ower crops grown in large, growermanaged ®elds. Bar colouring and errors bars as in Fig. 2.
soil water at sowing contributed to the spatial and seasonal variation in total water
availability (Fig. 5B). In some cases, limited stored soil water compounded the eect
of low rainfall during the period from sowing to maturity (e.g. Zone 1, Fig. 5B); in
others, stored water attenuated the eects of shortage of rainfall (e.g. Zone 3, Fig.
5B) being almost 20% of the total water (rainfall+soil water) available to the crop
in the driest years. Yield was unrelated to seasonal rainfall whereas initial soil water
accounted for 30% of the variation in yield (Fig. 5C and D), highlighting the
importance of growing conditions during the early stage of crop establishment.
3.4. Soil nutrients
Inorganic N at sowing was highly variable among seasons and zones, and within
zones (Fig. 6A). Available P was also highly variable among zones and among ®elds
in each zone; with few exceptions (Zone 2), it was fairly stable among seasons (Fig.
6B). Local studies indicate P fertiliser enhances sun¯ower yield in soils with P below
12 mg kgÿ1 (EcheverrõÂa and GarcõÂa, 1998); clearly many ®elds in Zones 3±6 were
short in P (Fig. 6B). However, relationships between yield and soil nutrients was
weak for P (r2=0.14, P=0.09) and weaker steel for inorganic N (r2=0.09, P>0.26).
A much stronger association of yield with%C in soil indicates an important role of
mineralisation during the season (r2=0.25, P
www.elsevier.com/locate/agsy
On-farm assessment of regional and seasonal
variation in sun¯ower yield in Argentina
J.L. Mercau a,*, V.O. Sadras b, E.H. Satorre a, C. Messina a,
C. Balbi a, M. Uribelarrea a, A.J. Hall a
a
Facultad de AgronomõÂa, Universidad de Buenos Aires, Av. San Martin 4453, 1417 Buenos Aires, Argentina
b
Universidad de Mar del Plata-INTA Balcarce, Argentina
Received 21 March 2000; received in revised form 14 August 2000; accepted 12 September 2000
Abstract
Using an on-farm approach, we investigated constraints to actual yield of sun¯ower in six
agroecological zones within the Argentine Pampas during three growing seasons. In 249 large,
grower-managed paddocks, we quanti®ed a series of variables related to: (1) crop phenology,
growth, and yield; (2) the physical and biological environment; and (3) management practices.
Variation in yield among zones and seasons was analysed on the basis of four biologicallyfounded assumptions: (1) grain number accounts for a large proportion of the variation in
yield; (2) grain number is associated with a photothermal coecient, Q=R (T-Tb)ÿ1, where R
and T are average solar radiation and air temperature respectively, during the 50-day period
bracketing anthesis; and Tb is a base temperature; (3) crop growth and yield are proportional
to light interception, and therefore proportional to canopy ground cover; and (4) yield is
proportional to the fraction of seasonal rainfall that occurs after anthesis. Average yield
ranged from 1.1 to 2.7 t haÿ1, grain number from 2400 to 5400 mÿ2, individual grain mass
between 40 and 69 mg and grain oil concentration between 42 and 52%. Grain number
accounted for 43% of the variation in average yield while Q accounted for 23% of the variation in grain number. Low yield was associated with de®cient ground cover in 25% of the
crops; part of the remaining variation in yield was accounted for by sets of measured variables
particular to each zone, including soil shallowness, low available P, low initial water content,
weeds and diseases Ð chie¯y Verticillium wilt (Verticillium dahliae) and Sclerotinia head rot
(Sclerotinia sclerotiorum). Across zones and seasons, the proportion of seasonal rainfall
occurring after anthesis accounted for 28% of the variation in crop yield. A trade-o is
highlighted whereby bene®cial eects of rainfall that favours growth and yield may be oset
by the detrimental eect of abundant moisture that favours major fungal diseases. We
* Corresponding author. Tel.: +54-11-4524-8053; fax: 54-11-4524-8053.
E-mail address: [email protected] (J.L. Mercau).
0308-521X/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved.
PII: S0308-521X(00)00048-2
84
J.L. Mercau et al. / Agricultural Systems 67 (2001) 83±103
emphasised the value of combining experimental studies Ð which provide biological background in the form of working hypotheses Ð with on-farm research that realistically quanti®es yield response to key factors. # 2001 Elsevier Science Ltd. All rights reserved.
Keywords: Helianthus annuus; Technology; Yield potential; Attainable yield; Agroecology; Rainfall;
Radiation; Temperature; Verticillium dahliae; Sclerotinia sclerotiorum
1. Introduction
Rainfed production of sun¯ower in Argentina commenced in the early 1930s with
open-pollinated varieties brought by European immigrants. After some ups and
downs in acreage, the crop is currently well established and the national harvest in
the 1990±1995 period accounted for 16 to 22% of world production (Anon., 1996).
Actual yield remained stable around 0.7 t haÿ1 for a long period from 1930 to the
early 1970s, when the ®rst hybrids were released (LoÂpez Pereira et al., 1999). In
the last three decades, actual yield increased to about 1.4 t haÿ1 owing to the combination of hybrid seed technology and improved management (LoÂpez Pereira et al.,
1999). A gap of at least 2 t haÿ1, however, remains between actual and potential
yield estimated in experimental plots (LoÂpez Pereira et al., 1999). Opportunities
therefore exist to lift actual sun¯ower yield, provided the main restrictions to crop
growth and yield are identi®ed.
The purpose of a diagnostic stage, when aiming to increase crop yield and stability, is to describe and understand the farming system and to identify production
constraints (Byerlee et al., 1991). On farm experimentation and farm surveys have
usually been the approaches used in this stage. In Argentina, private associations of
farmers, including the Argentine Association of Agricultural Experimentation Consortia (AACREA), are the main sources of information on major cropping systems.
In AACREA professional consultants advise groups of 8±12 growers on the basis of
both on-farm trials and careful records of crop, soil, weather and economic data. In
the last 14 years, the association, which include 1500 farmers and 150 professional
consultants, has developed a comprehensive database on local cropping systems that
has been instrumental in the analysis of current and novel production techniques
(Duarte et al., 1993; Duarte, 1994).
The database was also aimed to determine yield limiting factors and sources of
yield variability. Nonetheless, recognition of the limits to analysis imposed by the
nature of the data (Alippe and Bosch, personal communication), led to a project
aiming at: (1) improving the database by inclusion of key crop ecological and physiological variables; and (2) identifying major constraints for sun¯ower production
in the Pampas. The project involved industry members, consultants and researchers,
and the approach combined comprehensive ®eld evaluation of cropping systems and
simulation techniques. This paper reports the results of the ®eldwork, which was
based on four components. First, we developed a biological framework for the
analysis of yield variation, including two complementary models of yield determination based on: (1) numeric components and photothermal coecient (Fischer,
1985; Cantagallo et al., 1997); and (2) resource capture by the crop and partitioning
J.L. Mercau et al. / Agricultural Systems 67 (2001) 83±103
85
of growth before and after anthesis (Sadras and Connor, 1991; Monteith, 1994).
Second, working in large, grower-managed ®elds, we quanti®ed a series of variables
related to: (1) crop phenology, growth and yield; (2) the physical and biological
environment; and (3) management practices. Third, we considered variation among
six agroecological zones during three growing seasons. The time series was short
enough to met the criterion of constant technology within each zone while combination of zones and seasons ensured a large variation in growing conditions (Sadras
and Villalobos, 1994; CalvinÄo and Sadras, 1999). Fourth, we explored the main
restrictions to yield using regression analysis, and an edge approach based on leastsquare regression (Scharf et al., 1998). Emphasis was placed on the physical and
biological meaning of the statistical relationships between yield and key variables.
2. Methods
2.1. Biological framework
Two complementary approaches were used to investigate seasonal and regional
variation in grain yield. First, based on the work of Fischer (1985) and Cantagallo et
al. (1997) we postulated that:
(1) Grain number accounts for a large proportion of the variation in yield.
(2) Grain number is associated with a photothermal coecient,
Q R T ÿ Tb ÿ1
1
where R is average solar radiation, and T is average air temperature during the
period from 30 days before to 20 days after anthesis, and Tb is a base temperature of 4 C (Villalobos and Ritchie, 1992).
Second, based on Monteith (1977) and Sadras and Connor (1991), respectively, we
proposed that
(3) yield is proportional to light interception during the growing season, and
therefore proportional to canopy groundcover.
(4) yield is proportional to the fraction of seasonal rainfall that occurs after
anthesis. This is based on the relationship between harvest index and the fraction of seasonal water use that occurs after anthesis (Passioura, 1977; Sadras
and Connor, 1991).
2.2. Site
Soil, climate and technology of dryland cropping systems in the Pampas have been
described by Hall et al. (1992). Brie¯y, the Pampas extend over 52 million ha originally covered by grasslands. The area sown to sun¯ower in the 1990s averaged 3
million ha and yield averaged 1.7 t haÿ1. Crop-to-pasture ratio in the most common
86
J.L. Mercau et al. / Agricultural Systems 67 (2001) 83±103
cropping rotations range from 0.6 to 0.8 in the best to 0.3 in the poorest soils.
Dominant agricultural soils are mollisols formed over loessic sediments. Annual
rainfall decreases and the proportion of summer-to-annual rainfall increases from
east to west. Details of the agroecological zones under study are summarised in
Table 1 and Fig. 1.
Table 1
Soil types in the agroecological zones under study
Zone
Major soil type
Other soils
1
2
3
Entic Haplustoll
Entic Hapludoll
Typic Hapludoll
Thaptoargic Hapludolla
Entic Hapludoll
Entic Haplustoll
Petrocalcic Natracuolla
Typic Hapludoll
Typic Argiudoll
Typic Argiustoll
Typic Hapludoll
Entic Hapludoll
Thaptonatric Hapludolla
Thaptonatric Hapludolla
Lithic Argiudolla
Petrocalcic Paleustolla
Petrocalcic Paleustolla
4
5
6
a
Shallow soil.
Fig. 1. Main sun¯ower growing areas in the Pampas (intensity of grey indicates percentage presence of
the crop in the cultivated area of each department). October±March total rainfall isohyets and January
mean temperature isotherms are also presented. Location of agroecological zones (1±6) de®ned in this
analysis are also indicated.
J.L. Mercau et al. / Agricultural Systems 67 (2001) 83±103
87
2.3. Database and statistical analysis
We collected data from commercial farms belonging to AACREA (Section 1).
Farms were grouped on the basis of agroecological similarities into six zones (Fig. 1,
Table 1). Restriction of the data series to three seasons allowed for a reasonable
range of weather conditions while meeting the assumption of unchanged technology
(Sadras and Villalobos, 1994; CalvinÄo and Sadras, 1999). We assessed a series of
variables summarised in Table 2. Assessment of some variables was improved as
research progressed, e.g. ground cover was evaluated only at ¯owering in the ®rst
two seasons while it was also evaluated 20 days after ¯owering in the third. From
the initial 285 paddocks we discarded 36 owing to incomplete data.
Variables in Table 2 were analysed using descriptive statistics and Student t-test
for comparison of means. Association between variables, averaged for each zone
and year, was investigated using correlation analysis. To investigate the relationship
between canopy ground cover and yield we applied an edge approach based on
least-square regression (Scharf et al., 1998). Yield data Ð pooled across seasons and
zones Ð were divided into size classes corresponding to 10% increments of ground
cover. Within each size class, data points corresponding to the maximum, 66 percentile and 33 percentile were paired with average cover for the size class. Thereafter
a bi-linear model was ®tted to data for each of these three categories using a broken
stick procedure, i.e.
if
GC > threshold;
if
GC4threshold;
Y Ymax
Y a ÿ b GC
2a
2b
where GC is canopy ground cover (%), Y is yield, Ymax, a and b are parameters. A
broken stick procedure was used since it produced a better distribution of residuals
than a simple linear regression approach (Steel and Torrie, 1985) and because it is
consistent with the expected relationship between yield and ground cover at high
ground cover values. We then investigated the factors underlying departure from the
envelope (maximum) model (Eq. (2a) and (2b)) by making comparisons between
deviations lying above the model ®tted to the 66 percentiles values with these falling
below the model ®tted to the 33 percentiles. Factors means for the two categories
were then compared using a t-test or a t-test modi®ed by Satterthwhite (Steel and
Torrie, 1985) when variances were dierent (Levenne test).
3. Results
3.1. Seasonal and spatial variation in yield and its components
There was a 2.5-fold range in average yield, some zones showed large seasonal
variation (e.g. Zones 1 and 6) whilst others showed greater yield stability (e.g. Zone
3; Fig. 2A). Variation in yield among ®elds within a zone was similar across zones
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and was fairly stable in all three seasons (see error bars in Fig. 2A). Two yield
components were considered, grain number and individual grain mass. Grain number ranged from 2400 to 5400 mÿ2 and individual grain mass between 40 and 69 mg.
Grain number accounted for about 40% of the variation in yield (P0.1). Individual
grain mass was negatively associated with grain number (Fig. 2C).
Grain oil concentration varied between 42 and 52% (Fig. 3). There was a large
variation among zones; with few exceptions (e.g. Zone 4) seasonal variation within
zones was low. Grain oil concentration was negatively associated with availability of
soil nitrogen (r=ÿ0.49, P=0.07). Inverse relationships between supply of soil
nitrogen and oil content seem to be mediated by a trade-o between protein and oil
content in the grain typical of oil seed crops including sun¯ower (Connor and
Sadras, 1992) and soybean (Wilcox, 1998).
Table 2
Variables assessed in commercial farms to characterise key aspects of the crop, its environment and
management
Features of the croping system
Crop
Phenological development
Structural and functional aspects
Yield and its components
Environment
Weather
Soil
Biological
Variable
Date of sowing
Date of ¯owering
Date of physiological maturity
Hybrid
Plant population density (plants mÿ2)a
Temporal uniformity of emergenceb
Spatial uniformity of emergencec
Ground cover at ®rst ¯ower, and 20 days after end of ¯oweringd
Lodging (%)
Yield (kg haÿ1)e
Individual grain mass (mg)f
Grain number (mÿ2)g
Grain oil content (%)h
Daily rainfall (mm)i
Hail stormsi
Daily maximum and minimum temperature (C)j
Daily solar radiation (MJ mÿ2)j
Soil type (USDA classi®cation)
Eective depth (m)k
pH (1 soil:2.5 water)
Available phosphorus (mg kgÿ1)l
Carbon content (%)m
Inorganic nitrogen at sowing (kg haÿ1)n
Water content at sowing (mm)o
Weed cover at ¯owering (%)p
Intensity of diseases at ¯oweringq
J.L. Mercau et al. / Agricultural Systems 67 (2001) 83±103
89
Table 2 (continued)
Features of the croping system
Variable
Management
Previous crop
Fallow duration (days from ®rst tillage or herbicide application)
Date of harvest
a
Assessed in 10 randomly distributed linear plots of 14.3 m each.
Standard deviation of number of leaves (>4 cm) in 20 randomly chosen plants.
c
Coecient of variation of the distance between successive plants in 3 rows of 14.3 m.
d
Proportion of shade measured at noon with a 1-m ruler placed diagonally on the ground beneath the
canopy.
e
Measured in three randomly chosen rows (7 m) before mechanical harvest.
f
Measured in ®ve sub-samples of 200 grains each.
g
Derived from e and f.
h
Nuclear magnetic resonance with standard sun¯ower oil.
i
Measured on farm.
j
Measured on nearby meteorological stations.
k
Depth of duripan.
l
Measured in 0±0.2 m, method: Ritcher and vonWistinghausen (1981).
m
Measured in 0±0.2 m, method: Olsen and Sommers (1982).
n
Measured in 0±0.4 m, method: Kenney and Nelson (1982).
o
Measured gravimetrically to a soil depth of 0.6 m.
p
Visually estimated.
q
Intensity estimated using a scale with four classes, i.e. 3=very intense, 2=intense, 1=moderate,
0=nil. Main diseases included Sclerotinia head rot (Sclerotinia sclerotiorum (Lib.) de Bary), Verticillium
wilt (Verticillium dahliae Klebahn), and Black stem (Phoma macdonaldii Boerema).
b
3.2. Radiation and temperature
In the commercial crops under study, grain number was the component more
closely associated with yield (Fig. 1B) in agreement with studies in experimental
plots including those by Cantagallo et al. (1997). These authors also showed that
grain number was positively related to radiation and negatively to temperature, and
integrated these variables in a photothermal coecient (Eq. (1)). Fig. 4 illustrates:
(1) the decline in Q with time between October and March; (2) the decline in Q with
increasing latitude; and (3) the seasonal variation in Q within a zone. The photothermal coecient accounted for 23% of the variation in average grain number
among zones and seasons (Fig. 4c). Intercept [grains mÿ2] of the relationship
between grain number and Q in our sampled commercial crops was ÿ28833612
(P>0.44) compared to 328 in the irrigated single-cultivar experiments of Cantagallo
et al. (1997) whereas the slope [grains MJÿ1 C] was 50522640 and 8183, respectively (Fig. 4C).
3.3. Water availability
Rainfall had substantial spatial and seasonal variation (Fig. 5A). Seasonal variation was smallest in Zone 6 and largest in Zone 1 where total rainfall varied over a 2fold range (Fig. 5A). Variation among farms within a zone was also large. Stored
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J.L. Mercau et al. / Agricultural Systems 67 (2001) 83±103
Fig. 2. Spatial and seasonal variation in sun¯ower yield and its components for crops grown in large,
grower-managed ®elds. (A) Grain yield; (B) relationship between yield and grain number, and (C) relationship between grain number and individual grain mass. Agroecological zones are characterised in Fig.
1 and Table 1. In (A) data for 1995/96, 1996/97 and 1997/98 seasons are represented by white, grey and
black bars respectively, error bars are standard deviations. In (B) and (C) each point is the average of all
®elds surveyed in each zone and season.
J.L. Mercau et al. / Agricultural Systems 67 (2001) 83±103
91
Fig. 3. Spatial and seasonal variation in grain oil content of sun¯ower crops grown in large, growermanaged ®elds. Bar colouring and errors bars as in Fig. 2.
soil water at sowing contributed to the spatial and seasonal variation in total water
availability (Fig. 5B). In some cases, limited stored soil water compounded the eect
of low rainfall during the period from sowing to maturity (e.g. Zone 1, Fig. 5B); in
others, stored water attenuated the eects of shortage of rainfall (e.g. Zone 3, Fig.
5B) being almost 20% of the total water (rainfall+soil water) available to the crop
in the driest years. Yield was unrelated to seasonal rainfall whereas initial soil water
accounted for 30% of the variation in yield (Fig. 5C and D), highlighting the
importance of growing conditions during the early stage of crop establishment.
3.4. Soil nutrients
Inorganic N at sowing was highly variable among seasons and zones, and within
zones (Fig. 6A). Available P was also highly variable among zones and among ®elds
in each zone; with few exceptions (Zone 2), it was fairly stable among seasons (Fig.
6B). Local studies indicate P fertiliser enhances sun¯ower yield in soils with P below
12 mg kgÿ1 (EcheverrõÂa and GarcõÂa, 1998); clearly many ®elds in Zones 3±6 were
short in P (Fig. 6B). However, relationships between yield and soil nutrients was
weak for P (r2=0.14, P=0.09) and weaker steel for inorganic N (r2=0.09, P>0.26).
A much stronger association of yield with%C in soil indicates an important role of
mineralisation during the season (r2=0.25, P