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
aaFacultad de AgronomõÂa, Universidad de Buenos Aires, Av. San Martin 4453, 1417 Buenos Aires, Argentina bUniversidad 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 biologically-founded 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, whereR
andTare average solar radiation and air temperature respectively, during the 50-day period bracketing anthesis; andTbis 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 whileQaccounted for 23% of the varia-tion 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
0308-521X/01/$ - see front matter#2001 Elsevier Science Ltd. All rights reserved. P I I : S 0 3 0 8 - 5 2 1 X ( 0 0 ) 0 0 0 4 8 - 2
www.elsevier.com/locate/agsy
* Corresponding author. Tel.: +54-11-4524-8053; fax: 54-11-4524-8053.
emphasised the value of combining experimental studies Ð which provide biological back-ground 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 haÿ1for 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 haÿ1owing to the com-bination 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 stabil-ity, 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 Con-sortia (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).
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 combi-nation 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 least-square 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,
QR T ÿTbÿ1 1
whereRis average solar radiation, andTis average air temperature during the period from 30 days before to 20 days after anthesis, and Tb is a base tem-perature of 4C (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 frac-tion of seasonal water use that occurs after anthesis (Passioura, 1977; Sadras and Connor, 1991).
2.2. Site
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.
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.
Table 1
Soil types in the agroecological zones under study
Zone Major soil type Other soils
1 Entic Haplustoll Typic Argiustoll
2 Entic Hapludoll Typic Hapludoll
3 Typic Hapludoll Entic Hapludoll
Thaptoargic Hapludolla Thaptonatric Hapludolla
4 Entic Hapludoll Thaptonatric Hapludolla
5 Entic Haplustoll Lithic Argiudolla
Petrocalcic Natracuolla Petrocalcic Paleustolla
6 Typic Hapludoll Petrocalcic Paleustolla
Typic Argiudoll
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 per-centile and 33 perper-centile 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; YYmax 2a
if GC4threshold; YaÿbGC 2b
where GC is canopy ground cover (%),Yis yield,Ymax,aandbare 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 at-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
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 num-ber ranged from 2400 to 5400 mÿ2and individual grain mass between 40 and 69 mg. Grain number accounted for about 40% of the variation in yield (P<0.01) (Fig. 2B); no relationship was found between yield and grain mass (P>0.1). 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 Variable
Crop
Phenological development Date of sowing Date of ¯owering
Date of physiological maturity Structural and functional aspects 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 and its components Yield (kg haÿ1)e
Individual grain mass (mg)f
Daily maximum and minimum temperature (C)j
Daily solar radiation (MJ mÿ2)j
Soil 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
Biological Weed cover at ¯owering (%)p
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 inQwith time between October and March; (2) the decline inQwith increasing latitude; and (3) the seasonal variation in Qwithin a zone. The photo-thermal 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 MJÿ1C] was 50522640 and 8183,
respec-tively (Fig. 4C).
3.3. Water availability
Rainfall had substantial spatial and seasonal variation (Fig. 5A). Seasonal varia-tion was smallest in Zone 6 and largest in Zone 1 where total rainfall varied over a 2-fold range (Fig. 5A). Variation among farms within a zone was also large. Stored
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.
b 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,
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<0.03); rough estimates of net miner-alisation indicate a contribution of up to 80 kg N haÿ1seasonÿ1.
3.5. Crop management and biotic stresses
Plant population density ranged from 3.2 to 5.1 plants mÿ2with a considerable variation among seasons and zones, and within zones (not shown). Weed management ensured weed cover below 20% in most zones and seasons. Intensity of Verticillium
wilt (Verticillium dahliae) was most important in 1996 and 1997 when Zones 2 and 5 were particularly aected (Fig. 7). Scores of around 2 in our scale are associated with a yield loss of about 1 t haÿ1. Incidence of Verticillium wilt did not increase much between ¯owering and maturity (not shown). Intensity of Sclerotinia head rot (Sclerotinia sclerotiorum) at ¯owering was low in general. During grain ®lling in 1997, however, head rot was widespread and intense (data not shown).
3.6. Resource capture and yield
We used the concept of resource capture to further analyse environmental and technological restrictions to sun¯ower yield [assumptions (3) and (4), Section 2.1]. An envelope curve was ®tted to describe the relationship between farm yield and canopy ground cover Ð an estimate of the ability of the crop to capture resources (Eq. (2a) and (2a)) Fig. 8). Although poor ground cover contributed to low yield in 25% of the crops, 70% of crops with covers higher than 75.8% (envelope threshold) yielded less than 2400 kg while the envelope reached a plateau of 3397 kg. Number of grains per unit area was strongly related to the variation among farms (Table 3). With this exception, no other variable showed a common pattern of response among zones; yield constraints particular to each zone were therefore investigated. Departure
from the curve of maximum yield in each zone (Fig. 9) was related to variables identi®ed in Table 3, including:
1. In Zone 6, variables associated with shallow soil. Consistently, lowest yielding crops were those in shallower soil, with shorter fallow, low availability of water in the soil at sowing, and less rainfall during grain®ll. Other factors causing signi®cant yield reduction were low photothermal quotient, low plant density, and high incidence of Verticillium wilt.
2. In Zone 5, low availability of P, late sowing, linked to late ¯owering and lowQ
around anthesis.
3. In Zone 4, Crop spatial heterogeneity, excess rainfall and lowQ. LowQand rainfall probably favoured Verticillium wilt, whose incidence was signi®cantly greater in the crops further away from the envelope curve.
4. In Zone 3, shallow soil, and excess rainfall that favoured Sclerotinia head rot and lodging.
5. In Zone 2, slightly acidic soils and less available P.
6. In Zone 1, little soil nitrogen, large weed cover and lodging.
Some counterintuitive results were also found that were not listed above (Table 3). For example, in Zone 2, low yield was also associated with greater Q, more soil nitrogen, and more rainfall during grain ®ll that was not related to disease incidence
Fig. 7. Intensity (on a 0±3 scale) of Verticillium wilt in commercial sun¯ower crops. Bar colouring and errors bars as in Fig. 2.
Table 3
Yield, grain number, and key crop, environmental and management variables for crops grown in commercial ®elds in six agroecological zones during three seasonsa
Variable Agroecological zone
1 2 3 4 5 6
U L U L U L U L U L U L
Grain yield (kg haÿ1) 2887 1434* 2574 1560*** 2433 1559*** 2675 1629*** 2542 1717*** 2849 1650***
Grain number (mÿ2) 4198 2771 6434 3399*** 4629 3615** 4448 2396*** 5111 3658** 4828 3115***
Sowing date (day of year) 307 302 299 304 293 293 298 294 306 319** 297 297
Flowering date (day of year) 377 373 377 380 370 368 373 374 383 394*** 374 376
Plant density (pl mÿ2) 3.9 4.1 4.8 4.6 4.6 4.5 4.0 3.8 4.3 4.4 4.2 3.6***
Temporal uniformity (0±3) 1.0 1.0 1.3 1.2 1.5 1.3 2. 0 1.3** 1.8 0.7** 1.5 1.0
Spatial uniformity (CV%) 47 47 60 57 56 58 33 51** 63 69 80 52**
Lodging (%) 0.0 18.2** 0.0 4.7 1.8 11.9* 0.0 1.3 14.8 8.9 3.3 0.0
Preanthesis rainfall (mm) 397 292 270 347 261 375* 145 347*** 235 283 264 238
Postanthesis rainfall (mm) 109 134 96 133* 151 196 93 107 178 183 141 60***
Q(MJ mÿ2dayÿ1Cÿ1) 1.24 1.25 1.46 1.55*** 1.44 1.47 1.40 1.37* 1.50 1.43* 1.46 1.39*
Verticillium intensity (0±3) 0.0 0.2 1.1 0.9 0.0 0.1 0.1 0.7* 0.8 0.6 0.0 0.5**
Sclerotinia intensity (0±3) 0.0 0.0 0.0 0.0 0.1 0.6* 0.2 0.0 0.1 0.3 0.2 0.7
Table 3 (continued)
Variable Agroecological zone
1 2 3 4 5 6
U L U L U L U L U L U L
Previous crop (%)
Grazed winter cereal 0 18 50 50 27 25 35 36 64 33 0 40
Wheat 0 0 0 0 0 0 0 0 36 44 92 40
Summer crop 50 71 42 33 36 38 55 55 0 11 8 20
Pasture 50 12 8 17 36 38 10 9 0 11 0 0
Fallow duration (days) 64 55 53 46 61 69 44 57 63 60 223 79***
a U are crops above the 66% model (Figs. 8 and 9); L are crops below the 33% model (Figs. 8 and 9). Asterisks indicate signi®cance oft-test ort-test
modi®ed by Satterthwhite (Steel and Torrie, 1985) when variances were dierent (Levenne test). *P<0.05.
**P<0.01. ***P< 0.001.
Mercau
et
al.
/
Agricultura
l
Systems
67
(2001)
83±103
as in Zone 4. In Zones 4 and 5, high-yielding crops had greater temporal hetero-geneity in emergence. These apparent ``anomalies'' could have been derived from errors in the measurement of variables, or could be re¯ecting complex interactions that yield counterintuitive results. Crop heterogeneity, for instance, increases yield in some cases (Sadras, 1996; Hide et al., 1997).
To complement this analysis, which identi®ed speci®c sets of variables underlying variation in yieldwithineach zone, we analysed the variation in yieldamong zones (regional scale) on the grounds of assumption (4) (Section 2.1). Harvest index in determinate species is a function of the ratio between water used from anthesis to maturity, and seasonal water use (Passioura, 1977; Richards and Townley-Smith, 1987; Sadras and Connor, 1991). Taking seasonal distribution of rainfall as a sur-rogate for seasonal partitioning of water use, we found a signi®cant association between yield and partitioning of water use (Fig. 10).
4. Discussion
In the last three decades, oilseed crops bloomed in the Pampas. Soybean acreage increased from an average 0.7 million ha in the 1970s to 6.0 million ha in the 1990s whilst sun¯ower acreage increased from 1.6 to 3.0 million ha (SAGYP, 1998). In the same period, actual yield of sun¯ower in Argentina increased at a steady rate of about 50 kg haÿ1yearÿ1owing to the combination of hybrid seed technology and improved management (LoÂpez Pereira et al., 1999). In this study, we investigated environmental and management factors underlying the gap between actual and attainable yield (Loomis and Connor, 1996).
Our analysis was centred on two main concepts: grain number as the main yield component, and resource capture by the crop. Most of the variation in yield was accounted for grain number and a signi®cant proportion of the variation in grain number among zones was accounted for temperature and radiation as summarised in the photothermal coecient developed by Fischer (1985). Furthermore, Qalso accounted for a signi®cant part of the variation in yield within Zones 4±6 (Table 3). Working at a regional scale comparable to ours, Magrin et al. (1993) also found a strong association between wheat kernel number and Q. In all six agroecological zones, Qfalls with time during the most likely period of ¯owering, from 1.6 to 1.2 MJ mÿ2 Cÿ1 (Fig. 4B). Hence it is important to manage sowing date and hybrid phenology to achieve early ¯owering as to ensure highQfavourable for grain set.
It is worth noting that the relationships between yield and grain number and between grain number and Qin the rainfed commercial crops under study agreed qualitatively with the relationships derived from experimental plots, despite the fact that experimental plots were heavily fertilised and irrigated (Figs. 1B and 4C). Quantitatively, however, models derived from experimental studies by Cantagallo et al. (1997) and LoÂpez Pereira et al. (1999) overestimated grain number and yield, respectively. This is not surprising owing to the close-to-potential conditions
mon to their studies. Qualitative agreement and quantitative discrepancies with experimental results in Figs. 1C and 4C highlight the value of combining experi-mental studies Ð which provide biological background Ð and on-farm research that realistically quanti®es yield response to key factors.
Yield depends on the ability of the crop to capture resources (Monteith, 1994). Major resources Ð radiation, water and nutrients Ð also modulate leaf expansion and root growth, feeding back to the ability of the crop to capture resources. Ground cover at anthesis was taken as a measure of the capacity of the canopy to capture light, and a measure of crop size that also re¯ects its ability to capture other resources. Yield seemed restricted by ground cover in a small proportion of crops (Figs. 8 and 9). Most of the cases of restricted ground cover were in Zones 1 and 5 (Fig. 9). This was probably due to shortage of available water during the period of canopy expansion (Connor and Hall, 1997; Sadras and TraÂpani, 1999). In Zone 5, shallow soils compounded the problem associated with low rainfall. In other zones, where variation in rainfall was less and/or where stored soil water may have buered shortage of rainfall, ground cover was over the threshold required to maximise yield. Although leaf expansion is also very sensitive to nitrogen de®cit (Sadras and TraÂ-pani, 1999), no relationship was found between available N at sowing and ground cover (P>0.1).
(P<0.0001). Despite the coarse assessment of disease incidence, and the likely noise owing to person-to-person variation in visual assessment, we were able to detect signi®cant associations between yield and disease incidence.
5. Conclusion
We quanti®ed crop, environmental and management variables in commercial farms. These variables were analysed within a framework derived from com-plementary models of yield determination based upon numeric components or resource capture by crops. This approach led us to the identi®cation of general constraints for production across distinct agroecological areas, including the com-bined eect of radiation and temperature that modulates the number of grain set, stored soil water at sowing, and seasonal distribution of rainfall. We also identi®ed particular sets of variables that are relevant sources of variation within each zone, and highlighted the two-fold role of rainfall, as a positive factor that favours canopy expansion, resource capture, growth and yield, and the negative in¯uence of abun-dant moisture that favours key fungal diseases. These results set the bases to estab-lish a wide net of on-farm and simulation experiments oriented to develop adequate crop management technologies to sort out the limiting factors found.
Acknowledgements
This work is part of a joint project involving AACREA and the University of Buenos Aires, ®nancially supported by Zeneca and Molinos Rio de la Plata. We thank AACREA growers and consultants for access to their crops, H. Alippe, M. Bosch, G. Duarte, E. Friedericks, G. Gallo, P. Mussat, J. GonzaÂlez Montaner for their technical advice, and S. Perelman for help with statistics. AJH, EHS, and VOS are members of CONICET, the Research Council of Argentina.
References
Abawi, G.S., Grogan, R.G., 1979. Epidemiology of diseases caused bySclerotiniaspecies. Phytopatology 69, 899±904.
Anon., 1996. Bolsa de Cereales. NuÂmero estadõÂstico 1994/95-1995/96. No 57. Buenos Aires.
Byerlee, D., Triomphe, B., Sebillotte, M., 1991. Integrating agronomic and economic perspectives into the diagnostic stage of on-farm research. Exp. Agricult. 27, 95±114.
CalvinÄo, P.A., Sadras, V.O., 1999. Interannual variation in soybean yield: interaction among rainfall, soil depth and crop management. Field Crops Res. 63, 237±246.
Cantagallo, J., Chimenti, C., Hall, A., 1997. Number of seeds per unit area in sun¯ower correlates well with a photothermal quotient. Crop Sci. 37, 1780±1786.
Connor, D.J., Hall, A.J., 1997. Sun¯ower Physiology. In: Schneiter, A.A. (Ed.), Sun¯ower Technology and Production, Monograph No. 35. ASA, CSSA, SSSA, Madison, pp. 113±182.
Duarte, G., 1994. Labranzas y fertilizacioÂn en el movimiento CREA. Proc. 2nd Simposio TecnoloÂgico AACREA, Buenos Aires.
Duarte, G., Satorre, O., Repetto, I., Trasmonte, D., Alippe, H., SaÂnchez, M., 1993. AnaÂlisis de las lab-ranzas en el cultivo de girasol. Proc. 6th Congreso CREA-Zona Oeste Arenoso, CoÂrdoba.
EcheverrõÂa, H.E., GarcõÂa, F.O., 1998. GuõÂa para la fertilizacioÂn fosfatada de trigo, maõÂz, girasol y soja (BoletõÂn TeÂcnico No. 149), INTA, Balcarce.
Fischer, R.A., 1985. Number of kernels in wheat crops and the in¯uence of solar radiation and tempera-ture. J. Agric. Sci. 105, 447±461.
Hall, A.J., Rebella, C.M., Ghersa, C.M., Culot, J.P., 1992. Field-crop systems of the Pampas. In: Pearson, C.J. (Ed.), Field Crop Ecosystems. Elsevier, Amsterdam, pp. 413±450.
Hide, G.A., Welham, S.J., Read, P.J., Ainsley, A.E., 1997. Eects of planting mixtures of dierent sizes of potato seed tubers on the yield and size of tubers. J. Agric. Sci. 128, 173±180.
Kenney, D.R., Nelson, D.W., 1982. Nitrogen inorganic form. In: Page, A., Miller, R.H., Feeney, D.H. (Eds.), Methods of soil analysis (Part 2. 2nd ed. Agron. Monogr. 9). ASA and SSSA, Madison, WI, pp. 643±689.
Loomis, R.S., Connor, D.J., 1996. Crop Ecology. Productivity and Management in Agricultural Systems, Cambridge University Press, Cambridge.
LoÂpez Pereira, M., Sadras, V.O., TraÂpani, N., 1999. Genetic improvement of sun¯ower in Argentina between 1930 and 1995. I. Yield and its components. Field Crops Res. 65, 157±166.
Magrin, G.O., Grondona, M.O., Travasso, M.I., BoulloÂn, D.R., RodrõÂguez, G.R., Messina, C.D., 1998. Impacto del fenoÂmeno ``El NinÄo'' sobre la produccioÂn de cultivos en la RegioÂn Pampeana, INTA, Castelar, Argentina.
Magrin, G.O., Hall, A.J., Baldy, C., Grondona, M.O., 1993. Spatial and interannual variations in the photothermal quotient: implications for the potential kernel number of wheat crops in Argentina. Agric. Forest Meteorol. 67, 29±41.
Masirevic, S., Gulya, T.J., 1992. Sclerotinia and Phomopsis Ð two devastating sun¯ower pathogens. Field Crops Res. 30, 271±300.
Monteith, J., 1977. Climate and eciency of crop production in Britain. Phil Trans Royal Soc London, Series B 281, 277±294.
Monteith, J.L., 1994. Principles of resource capture by crop stands. In: Monteith, J.L., Scott, R.K., Unsworth, M.H. (Eds.), Resource capture by crops. Proc 52nd Easter School, Univ of Nottingham, School of Agriculture. Nottingham Univ. Press, Nottingham, pp. 1±15.
Olsen, S.R., Sommers, L.E., 1982. Phosphorus. In: Page, A., Miller, R.H., Feeney, D.R. (Eds.), Methods of soil analysis (Part 2. 2nd ed. Agron. Monogr. 9). ASA and SSSA, Madison, WI, pp. 403±430. Passioura, J., 1977. Grain yield, harvest index and water use of wheat. J. Aust. Inst. Agric. Sci. 43, 117±120. Pereyra, V.R., Escande, A.R., 1994. Enfermedades del girasol, INTA, Balcarce.
Podesta, G.P., Messina, C.D., Grondona, M.O., Magrin, G.O., 1999. Association between grain crop yields in Central-Eastern Argentina and El NinÄo-Southern Oscilation. J. Appl. Meteor. 38, 1488±1498. Richards, R.A., Townley-Smith, T.F., 1987. Variation in leaf area development and its eect on water
use, yield and harvest index of droughted wheat. Aust. J. Agric. Res. 38, 922±983.
Ritcher, M., von Wistinghausen, E., 1981. Undercheidbarkeit von humusfrackionen in boden bei under-schiedlicher bewirtschaftung. Z. P¯anzenernaehr. Bodenk. 144, 395±406.
Sadras, V.O., 1996. Population-level compensation after loss of vegetative buds: Interactions among damaged and undamaged cotton neighbours. Oecologia 106, 432±439.
Sadras, V.O., Connor, D.J., 1991. Physiological basis of the response of harvest index to the fraction of water transpired after anthesis. A simple model to estimate harvest index for determinate species. Field Crops Res. 26, 227±239.
Sadras, V.O., TraÂpani, N., 1999. Leaf expansion and phenologic development: key determinants of sun-¯ower plasticity, growth and yield. In: Smith, D.L., Hamel, C. (Eds.), Physiological control of growth and yield in ®eld crops. Springer-Verlag, Berlin, pp. 205±232.
Sadras, V.O., Whit®eld, D.M., Connor, D.J., 1991. Regulation of evapotranspiration and its partitioning between transpiration and soil evaporation by sun¯ower crops. A comparison of hybrids of dierent stature. Field Crops Res. 28, 17±37.
SAGYP, 1998. SecretarõÂa de Agricultura, GanaderõÂa, Pesca y AlimentacioÂn. Programa de Servicios AgrõÂ-colas Provinciales (PROSAP). Sistema Integrado de InformacioÂn Agropecuaria (SIIA), Buenos Aires Ð Argentina. Available: http://siiap.sagyp.mecon.ar/home/aseries.htm.
Scharf, F.S., Juanes, F., Sutherland, M., 1998. Inferring ecological relationship from the edges of scatter diagrams: comparison of regression techniques. Ecology 79, 448±460.
SÏkoric, D., 1992. Achievements and future directions of sun¯ower breeding. Field Crops Res. 30, 231±270.
Steel, R.G.D., Torrie, J.H., 1985. Principles and Procedures of Statistics Ð A Biometrical Approach, McGraw-Hill Book Co, Mexico.
Villalobos, F., Ritchie, J., 1992. The eect of temperature on leaf emergence rates of sun¯ower genotypes. Field Crops Res. 29, 37±46.
Vranceanu, A., Stonescu, F., Pirvu, N., 1988. Genetic progress in sun¯ower breeding in Romania. Proc 12th Int. Sun¯ower Conf., 25±29 July, 1988, Novi Sad, Yugoslavia. Yugoslav Assoc. Producers of Plant Oil and Fat, Novi Sad, pp. 404±410.
