Interannual variation in soybean yield: interaction among rainfall, soil depth and crop management

Field Crops Research 63 (1999) 237±246

Interannual variation in soybean yield: interaction among rainfall, soil depth and crop management P.A. CalvinÄoa, V.O. Sadrasb,* a CREA Tandil, Bolivar 710, Tandil 7000, Argentina Unidad Integrada INTA-UNMP, CC276, 7620, Balcarce, Argentina

b

Received 28 April 1999; accepted 22 July 1999

Abstract Using data from large, grower-managed ®elds we investigated the variation in yield of dryland soybean in an area with low and variable summer rainfall, and soils that are variable in depth and poor in phosphorus (P). First, using data from unfertilised, wide-row (0.7 m) crops grown under standard management between 1989 and 1992 (Series 1), we quanti®ed the relationship between yield and W, a rainfall-based estimate of water availability during the period of pod and grain set. Separate functions were established for deep (depth  1 m) and shallow soils (0.75 m  depth  0.5 m). Second, we partially tested these functions using two independent data sets (Series 2 and 3). Third, we evaluated the effects on yield of large (18 kg P haÿ1, Series 4) or moderate doses of P fertiliser (8±12 kg P haÿ1) in narrow-row crops (0.35 m, Series 5). To investigate water  management interaction we (i) calculated
Y, the difference between actual yield in Series 4 and 5 and yield calculated with the functions derived from Series 1, and (ii) tested the association between
Y and actual W. In a set of 24 crops (Series 1), yield varied between 2.1 and 3.1 t haÿ1 in deep soils and between 1.3 and 2.6 t haÿ1 in shallow soils; nonlinear functions described fairly well, the response of yield to W. Fertilisation with 18 kg P haÿ1 increased yield by 0.6 t haÿ1 irrespective of water availability. The combination of narrow rows and a moderate dose of fertiliser increased yield in 73% of crops in deep soil but only in 53% of crops in shallow soil. There was a positive association between
Y and W in deep soil but no relationship between these variables in shallow soil. Yield responses to management were thus differentially affected by rainfall in deep and shallow soils. # 1999 Elsevier Science B.V. All rights reserved. Keywords: Dryland agriculture; Glycine max; Multiple stresses; Phosphorus fertiliser; Soil depth; Row spacing; Water de®cit; Water-use ef®ciency

1. Introduction Variation in crop yield over years has two components. One is the more or less systematic rise in yield derived from improved cultivars, better crop manage* Corresponding author. E-mail address: [email protected] (V.O. Sadras)

ment, and the interaction between cultivars and management (Bolton, 1981). The second component is erratic and mostly related to meteorological variables, chie¯y rainfall in dryland cropping systems. Depending on the spatial scale, soil type and its interaction with weather and technology may be another important source of variation in crop yield. Evaluation of crop responses to management practices needs there-

0378-4290/99/$ ± see front matter # 1999 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 4 2 9 0 ( 9 9 ) 0 0 0 4 0 - 4

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fore to consider these sources of interannual variation in yield. Interannual variation in yield can be analysed using several tools including multivariate analysis and crop simulation models. Even powerful standard statistics may be limited in biological meaning, however, while crop simulation models have their own problems (Monteith, 1996; Passioura, 1996; Sinclair and Seligman, 1996; Sadras and TraÂpani, 1999). An alternative approach uses simple, agronomically meaningful models based upon few, key environmental variables. Examples of this approach include research on the variation in wheat yield as a function of radiation and temperature in Argentina (Magrin et al., 1993) or as a function of rainfall in Australia (French and Schultz, 1984a,b). In Argentina, soybean is well established in a belt around 338S. The southern border of the crop, about 398S in the South-eastern Buenos Aires province, is the focus of our study. In this region, maximum yield in growers' ®elds is around 3 t haÿ1 while yield potential in experimental plots is 5 t haÿ1 (Andrade, 1995). Opportunities therefore exist to adjust management practices to increase yield, provided we are able to identify the factors that generally affect the crop. Of these, water de®cit is a prime candidate as the area under study combines soils of variable depth and variable summer rainfall. Small supply of available phosphorus (P) another feature of the soils, may contribute to the development of water stress owing to the detrimental effect of P de®ciency on the growth and functionality of root systems (Radin and Eidenbock, 1984; Davis, 1994; Robinson, 1994). Using data from large, grower-managed ®elds, we investigated the in¯uence of rainfall on the yield of dryland soybean crops, and the interaction among rainfall, soil depth and selected elements of crop management. Our approach had three steps. First, we quanti®ed the relationship between yield and water availability during the period of pod and grain set. Water availability was estimated as a function of rainfall, separate functions were established for deep and shallow soils, and data series were 5 years Ð a period long enough to generate a considerable range of rainfall, and short enough to meet the criterion of unchanged technology. Second, we partially tested these functions using (i) data from a warmer location in the soybean-belt and (ii) data on yield components.

Third, using the functions developed in step 1 as a reference, we evaluated the effects on yield of (i) large (18 kg P haÿ1), and (ii) moderate doses of P fertiliser (8±12 kg P haÿ1) combined with narrow rows (0.35 m). 2. Methods 2.1. General We used data collected from commercial farms in AACREA, an organisation in which professional consultants advise groups of 8±12 growers on the basis of both on-farm trials and careful records of yield, soil, weather and economic data. Farms are grouped on the basis of agroecological and management similarities. We used data from grower-managed, large ®elds at Tandil (378S) and Pergamino (348S). Data from Tandil were used to (i) quantify the relationship between yield and water availability (Series 1), and (ii) investigate the interaction between water availability, soil depth and selected elements of crop management (Series 4 and 5). The relationship between yield and water availability derived from Series 1 was tested with data from (i) crops grown by AACREA growers in a more northern location (Pergamino, Series 2) and (ii) a study on yield components at Tandil (Series 3). Except for Series 3 (see below) yield was derived from whole, machine-harvested ®elds (20 ha) and is expressed on a basis of 13.5% grain-moisture. 2.2. Site Soils at Tandil include deep Typic Argiudols and shallow Petrocalcic Paleudolls (USDA taxonomy) with an average 6.2% organic matter and 1.6 mm available water-holding capacity cmÿ1 soil (Travasso and Suero, 1994). Available P at sowing was between 3 and 10 mg kgÿ1 (Bray and Kurtz, 1945). Shallow soils dominate areas with slopes from 2% to 5% whereas deep soils are typical of ¯at areas. In Series 1 and 5, two soil types were included: deep (depth  1 m) soils belonging to Class I or IIe (Klingebiel and Montgomery, 1961) and shallow (0.75 m  depth  0.5 m) soils of Class IIIes or IIIs. Only deep soils were included in Series 4. In Series 3,

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Fig. 1. (A) February rainfall and (B) estimated water availability during the period of pod and grain set of soybean at Tandil. Horizontal lines are (A) reference ET (long-term average) and (B) threshold for yield response derived from Figs. 2, 4 and 5A.

samples were taken along a transect of soil depth between 0.35 and >1 m. Recommended sowing date for soybean is from early November to mid December. Indeterminate cultivars of maturity groups III or IV generally set most pods and grain, i.e. stages R3±R6 (Fehr et al., 1971), in February (Andrade, 1995). Average annual rainfall is 940 mm. Although half of this generally falls during the soybean growing cycle, rainfall in February has been less than potential evapotranspiration in 22 out of 28 years (Fig. 1A). Hence water de®cit is likely to occur, particularly in shallow soils, during the period of pod and grain set. 2.3. Series 1: baseline functions between yield and water availability Each year between 1989 and 1992, yield and rainfall data were collected from 30 to 35 rainfed crops in Tandil. Management practices in all crops included: a fallow period that ensured soil water content at sowing close to maximum, 0.7 m between rows, 20 plants per metre, good control of weeds and insects, sowing dates between 5 and 25 November, and indeterminate cultivars of maturity groups III or IV inoculated by effective strains of Rhizobium japonicum. Cultivars used included Asgrow 3127, Asgrow 3205, Mitchell and Asgrow 4422. The four years of data included in Series 1 was long enough to generate a considerable range of rainfall, and short enough to meet the criterion of unchanged technology. To further reduce the in¯uence of management as a source of variation, we used yield data from only the three highest-yielding crops out of the 30±35 grown each season. An exponential ``baseline'' function with a maximum was used to describe the relationship between

yield (Y) and water availability (W): Y ˆ a ‡ b…1ÿeÿcW †;

(1)

a, b, and c are parameters estimated with Sigmaplot (SPSS, 1997); a estimates yield when available water is 0, thus a < 0 means a minimum value of W is required for grain set; a + b estimates maximum yield. On the basis of rainfall measured in each farm, three estimates of W (mm) were compared: (i) rainfall in February, (ii) rainfall in January and February, and (iii) rainfall in February plus any January rainfall exceeding reference evapotranspiration (ET) in January, up to field capacity. Of these, the later provided the best fit to the data in most cases and is the measure of W used hereafter. For simplicity, and because rainfall variability is much larger than ET variability, long-term ET average was used in the calculations (Penman, 1948). Field capacity was calculated as a function of soil depth and water-holding capacity per cm soil (Travasso and Suero, 1994). 2.4. Series 2 and 3: test of the relationship between yield and water availability Important assumptions are implicit in the relationship between yield and water availability described for Series 1 (Section 2.3) including the lack of consideration of (i) the effects of water availability during the vegetative, early reproductive, and grain-®lling stages, and (ii) the contribution of stored soil water, except for the calculation of ®eld capacity and the ®tting of separate curves for deep and shallow soil. To test the impact of these assumptions in our analysis, we used yield and rainfall data from farms at Pergamino, a warmer location where pod and grain set occurs in January (Series 2). Thus, we used Eq. (1) to test the association between yield and water availability quan-

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ti®ed, by analogy with the previous de®nition, as January rainfall plus any December rainfall exceeding reference ET in December, up to ®eld capacity. Crops were grown between 1994 and 1997 on deep, very fertile Typic Argiudolls (USDA taxonomy) belonging to Class I (Klingebiel and Montgomery, 1961). Management practices were comparable to those of Series 1 including actual sowing date within the recommended window (November 5 and 20), 0.7 m between rows, 20 plants per metre row, good control of weeds and insects, and use of indeterminate cultivars of maturity group IV (Dekalb 458, Don Mario 48, Don Mario 4700, Asgrow 4656) inoculated by effective strains of Rhizobium japonicum. An additional test of our approach involves the investigation of yield components in two farms at Tandil with similar soil and management but contrasting rainfall (Series 3). Crops were grown in 1998/99 under management practices similar to those described for Series 1 except for the use of fertiliser (8 kg P haÿ1) and narrow rows (0.35 m). At maturity, we took shoot samples at four points along a transect of soil depth, viz. 0.35, 0.5, 0.7 and >1 m. Three 1 m2 shoot samples were randomly taken at each soil depth to determine yield, grain number and individual grain weight after drying to constant weight (forced draft at 708C). 2.5. Series 4 and 5: effect of P fertiliser and narrow row In Series 4, fertilised and unfertilised treatments were compared in four trials in deep soil with initially small supply of available P (Table 1). Three replicates per treatment were established in a randomised design using plots (700 mÿ2per replicate) included in large

commercial ®elds. Except for the fertiliser treatment, crops were managed as in Series 1. In Series 5, crops were grown each year between 1993 and 1997 using practices outlined for Series 1 except for: (i) a moderate dose of fertiliser, i.e. 8± 12 kg P haÿ1and (ii) narrow rows (0.35±0.39 m). The objective of this trial was not to assess the effect of narrow row and fertiliser on yield Ð which would have required a factorial design Ð but rather to compare two management systems against the background of water availability using the analytical procedure described below. As in Series 1, soybean was grown in deep or shallow soils, and the three highestyielding crops of a series of 30±35 were considered. To assess the interaction between water availability and management factors, i.e. fertiliser in Series 4, fertiliser and narrow row in Series 5, we have: 1. Calculated
Y, the difference between actual yield in Series 4 and 5 and yield calculated using the baseline function (Eq. (1)) and actual W;
Y is therefore a measure of yield differences attributable to management factors. 2. Tested the association between
Y and W using regression analysis. A signi®cant association between these variables implies a signi®cant interaction between management and water availability. 3. Results 3.1. Baseline functions Series 1 and 2: In a set of 24 crops, yield varied between 2.1 and 3.1 t haÿ1 in deep soils, and between

Table 1 Yields of soybean obtained with 0 (ÿP) or 16±18 kg P added per ha (+P) (data Series 4, initial available soil P is also presented) Season

Farma,b

Available soil Pc (mg kgÿ1)

Yield (t haÿ1) ÿP

+P

1988/99 1989/90 1990/91 1990/91

INTA EscuelaGranja CREA Sta.Rosa INTA El Misterio CREA La Beatriz

10.4 3.8 5.8 5.6

2.8 2.9 2.7 2.5

3.6 3.6 3.2 3.1

a

Data from Darwich (1992) and CalvinÄo (1990). Space between rows was 0.7 m except at CREA Santa Rosa where 0.60 m was used. c Bray and Kurtz (1945). b

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Fig. 2. Relationship between soybean yield and estimated water availability during the period of pod and grain set in (A) deep and shallow soils at Tandil (Series 1), (B) deep soil at Pergamino (Series 2), (C) combined data for Tandil and Pergamino. Curves are: (A) deep soil, y = 0.25 + 2.79(1 ÿ eÿ0.0258x)(F2,9 = 126.6***); shallow soil, y = ÿ 1.15 + 3.81(1 ÿ eÿ0.0240x)(F2,9 = 58.8***), (B) y = ÿ 0.56 + 4.7 (1 ÿ eÿ0.0259x)(F2,23 = 24.5***), (C) y = ÿ 0.059 + 1.02 (1 ÿ eÿ0.0242x)(F2,47 = 67.5***).

1.3 and 2.6 t haÿ1 in shallow soils (Series 1, Fig. 2A). Most of the variation in yield was accounted for W (water availability, Fig. 2A) but some uncertainty remains in relation to (i) the clustering of the data, and (ii) the assumptions underlying the model, some of which have been discussed in Section 2.4. More evenly distributed data from Pergamino, however, reinforces the con®dence in the model (Fig. 2B). In all three cases in Fig. 2, the relationship between yield and water availability was non-linear with a plateau,

a + b (Eq. (1)), that re¯ects differential environmental restrictions in crops with abundant water, i.e. around 4 t haÿ1 in the soybean-belt compared with 3.1 t haÿ1 in deep and 2.7 t haÿ1 in shallow soil at the southern, Tandil, location (Fig. 2). Using a normalised scale, estimated water availability during pod and grain set accounted for 74% of the variance in the yield of the pooled data (Fig. 2C). Series 3: We compared crops grown at two farms with similar soil and management but contrasting

Fig. 3. (A) Soybean yield response to soil depth at two commercial farms at Tandil, (B) monthly rainfall and (C) relationship between yield and grain number. The regression in (C) had an intercept not different from zero (P > 0.11). Error bars are (A) one or (C) two standard errors of the mean. Data from Series 3.

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242

Fig. 4. (A) Relationship between yield of well-fertilised (*) and unfertilised (*) soybean and estimated water availability during the period of pod and grain set of soybean (W) in deep soil at Tandil. Data points are from Series 4; the baseline function derived from Series 1 is presented for comparison. (B) Relationship between
Y and W;
Y is the difference between actual yield of fertilised crops and the yield estimated using the baseline function (calculation details in Section 2.5).

Fig. 5. Relationship between the yield of narrow-row, moderately fertilised soybean crops (*) and water availability during the period of pod and grain set of soybean (W) in (A) deep and (C) shallow soil at Tandil. Data points are from Series 4; the baseline function derived from Series 1 is presented for comparison. Relationship between
Y and W is shown in (B) for deep and (D) for shallow soil.
Y is the difference between actual yield and the yield estimated using the baseline function (calculation details in Section 2.5). The line in (B) is y = ÿ 0.27 + 0.003x, r2 = 0.28; a non-linear model increased r2 to 0.65.

rainfall in 1998/99 (Fig. 3). Yield was greater and responded more to soil depth in Farm 1 than in Farm 2 (Fig. 3A). In comparison with Farm 2, crops in Farm 1 received similar rainfall during the vegetative and

early reproductive period (November±January) and less but abundant rainfall during the late reproductive period (March) (Fig. 3B). Although other factors cannot be excluded, the difference in rainfall in

Table 2 Average yield (S.E.) of soybean crops on deep and shallow soils in Series 1 and 5a Series

1 (n = 12) 5 (n = 15) a

Yield (t haÿ1)

Temperature (oC)

Deep soil

Shallow soil

December

January

February

2.6  0.11 3.0  0.09

2.0  0.16 2.5  0.07

19.3  0.42 19.0  0.34

20.7  0.06 20.5  0.26

20.1  0.26 19.0  0.14

Main differences between series were: (i) row spacing, viz. 0.7 in Series 1 vs 0.35±0.39 in Series 5, (ii) fertiliser, viz. nil in Series 1 vs 8± 12 kg P haÿ1 in Series 5. Also shown is average temperature (S.E.) during the periods of active vegetative growth (December), early reproductive growth (January) and pod and grain set (February).

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February (95 vs 35 mm), when most grain was set, probably contributed to the yield difference between farms. Consistent with this conclusion, grain number accounted for 94% of the variation in yield (Fig. 3C). 3.2. Interactions between crop management and water availability Series 4: Fertilisation with 18 kg P haÿ1 increased yield by 0.6 t haÿ1 (Table 1, Fig. 4). Crop response to fertiliser was unaffected by water availability (P > 0.79, Fig. 4B). The agreement between the yield of unfertilised crops and the base line obtained from an independent data set reinforces con®dence in the reliability of the approach (Fig. 4A). Series 5: In deep soil, the combination of narrow row and moderate fertilisation increased yield in 11 out of 15 crops (73%) (Fig. 5A). On average, yield increased by 0.4 t haÿ1 in comparison with conventional, unfertilised crops (Table 2). The responses increased with water availability (Fig. 5B). In shallow soil, narrow row and moderate fertilisation increased yield in 8 out of 15 crops (53%) (Fig. 5C). Positive responses were, however, of greater magnitude than negative responses, viz. average
Y > 0: 0.34 t haÿ1 vs average
Y < 0: ÿ0.13 t haÿ1. Thus average yield of crops in shallow soils in Series 5 was 0.5 t haÿ1 greater than in Series 1 (Table 2). Yield response was unrelated to W (P > 0.15, Fig. 5D). In both deep and shallow soils, differences in yield between Series 1 and 5 were unlikely related to temperature (Table 2). 4. Discussion Variation in yield with time results from variation in weather, technology, and the interaction between them. Depending on the spatial scale, interactions with soil factors may also be relevant. Our study dealt with variation in soybean yield in an area with low and variable summer rainfall (Fig. 1A), and soils that are variable in depth and poor in P. 4.1. Approach An important feature of our work is the use of yields from large, grower-managed ®elds. This precluded

243

conventional statistics but allowed for a realistic assessment of yield responses to rainfall, soil, crop management, and the interactions among these factors. Restriction of the data series to periods of ®ve years or less allowed us to generate a reasonable range of rainfall while meeting the assumption of unchanged technology within a series (Sadras and Villalobos, 1994). The concept of baseline functions thus developed provided a sound tool for the analysis. Also working with data from growers' paddocks, French and Schultz (1984a,b) developed a simple framework in which wheat yield in south Australia was analysed as a function of rainfall between April and October. A baseline involving the highest-yielding crops gave them a benchmark for the analysis of yield responses to the interactions among rainfall, environmental and management factors (French and Schultz, 1984a,b). Our simpli®ed estimate of water availability did not account for a number of factors including (i) water availability during the vegetative, early reproductive and grain-®lling stages, (ii) the contribution of stored soil water, (iii) run-off associated with both reduced storage capacity and the prevalent position of shallow soils in steep sections of the landscape, and run-on to foot plain, (iv) within-month rainfall distribution, and (v) variation in actual ET. Despite these limitations, the model worked reasonably well (Fig. 2) because of the dominant importance of grain number as a yield component (Fig. 3B) (Egli, 1998), and the frequent coincidence between the period when soybean sets grain and a period when shortage of rainfall is especially likely (Fig. 1). Stressful conditions during the vegetative and early reproductive stages Ð not accounted for in our approach Ð are particularly important for the yield of determinate soybean or indeterminate cultivars in late-sown crops (Tanner and Hume, 1978; Board et al., 1992; Board and Harville, 1996). In the area under study, early-season water de®cit could be important for crops in shallow soils but is unlikely to develop in crops in deep soil owing to the high rainfall and small evaporative demand in October±November. Advantages and drawbacks of simulation models as tools for research of complex interactions have been widely discussed (Passioura, 1973; Boote et al., 1996; Monteith, 1996; Passioura, 1996; Sinclair and Seligman, 1996; Sadras and TraÂpani, 1999). Following Passioura (1996), we developed a simple analytical

244

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method based on ``robust empirical relationships between the main variables''.That approach was illustrated nicely by Otegui et al. (1996) who showed that a simple model based on the dynamics of light interception, constant radiation-use ef®ciency and constant harvest index estimated maize yields better than CERES-Maize. Being developed for a speci®c cropping system, our approach lacks the ¯exibility of mathematical crop models. The number of variables tractable with our method is also limited in comparison with crop models. The baseline concept could nonetheless be readily adapted to other systems, provided variables are identi®ed that account for substantial proportions of variation in yield. 4.2. Yield responses to rainfall, soil depth and crop management Yield increased with availability of water up to W  100 mm (Figs. 2 and 5A). This threshold for yield response is consistent with evaporative demand in February (cf. threshold in Fig. 1B vs reference ET in Fig. 1A). Yield decline caused by excess water was not evident within our data range (Fig. 2). The yield response to rainfall in our study was consistent with large-scale studies based on the analysis of the El NinÄo-Southern Oscillation Index (Magrin et al., 1998) and simulation (Sinclair et al., 1992). Long-term rainfall data indicate that water availability is likely to restrict yield in 54% of the years (Fig. 1B). The pro®tability of this cropping system can therefore be substantially enhanced with practices and cultivars aimed at increasing available water and water-use ef®ciency (Cooper et al., 1987; Richards et al., 1993). Assuming soybean grain contains 0.65% P (Loomis and Connor, 1996), a crop yielding 3 t haÿ1 would remove 17 kg P haÿ1 per year. This, together with the initially low availability of P in the soil means that sustainable production would require careful management of this nutrient. Hence the importance of considering the factors that modulate crop response to fertiliser and the effect of fertiliser on water-use ef®ciency. In deep soils, a large dose of P fertiliser increased yield by 0.6 t haÿ1 (Table 2, Fig. 4). In principle, this response seemed to be independent of water availability but larger data sets are required to con®rm this result (Fig. 4). In nutrient-poor soils of northern Syria, Cooper et al. (1987) demonstrated a

positive effect of fertiliser on the yield of rainfed barley that was mediated by substantial (up to 100%) increase in water-use ef®ciency and marginal (up to 20%) increase in water availability. Narrow row spacing (<0.70 m) often increases soybean yield in relation to conventional crops grown at 0.7±1 m between rows. The actual response to row spacing, however, depends on water availability, growth habit, stem morphology, sowing date and tillage management (Taylor, 1980; Adams and Weaver, 1998; Frederick et al., 1998; Robinson and Wilcox, 1998). Crop growth analysis by Board and colleagues (Board et al., 1990a,b; Board and Harville, 1992) has helped disentangle some of these interactions. Interactions between row spacing and water availability are not straightforward. Narrow rows may improve water-use ef®ciency by earlier canopy closure, less soil evaporation and reduced risk of soil erosion and surface run-off (Frederick et al., 1998). Narrow-row crops can deplete soil water more rapidly, however, and reach critical stages with poorer water status than their wide-row counterparts, as shown by Taylor (1980). He reported that narrow-row soybean (0.25 m between rows) outyielded wide-row crops (1 m) in wet but not in dry years when crops in narrow rows showed more severe signs of water de®cit during reproductive growth (Taylor, 1980). In Series 5, the effects of row spacing cannot be separated from those of fertiliser. Nonetheless, narrow rows and moderate fertilisation enhanced yield irrespective of water availability in shallow soils (Table 2, Fig. 5C and D). In deep soils, the response to improved management was more marked in wet years (Fig. 5B). Detailed crop growth analysis is required to elucidate the physiological basis of these interactions. It is well established, however, that physical impedance and limited soil volume may restrict plant growth independently of water and nutrient availability (Ludlow et al., 1989; McConnaughay and Bazzaz, 1991; Passioura and Stirzaker, 1993). Working with soybean, Krizek et al. (1985) found that soil moisture stress reduced leaf water potential, stomatal conductance, photosynthetic rate, plastochron, shoot : root ratio, and tissue N and P concentrations. In contrast, restricted root-zone volume did not affect these traits. Impairment of growth resulting from these two stresses, they concluded, may involve different physiological processes. In our study, the difference in maximum yield between

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crops in shallow and deep soils was unlikely related to water availability (Fig. 2A). Thus physical restriction itself might be imposing an upper limit to the yield of crops in shallow soils that not necessarily would be lifted by better management of water and nutrients. Acknowledgements We thank growers of CREA Tandil, Segui, Arroyo del Medio, and J. Lieutier for access to their crops, FundacioÂn Antorchas for partial ®nancial support (Grant A-13388/1-2), and Fernando Andrade for comments on the manuscript. V.O. Sadras is member of CONICET, the Research Council of Argentina. References Adams, P.D., Weaver, D.B., 1998. Brachytic stem trait, row spacing, and plant population effects on soybean yield. Crop Sci. 38, 750±755. Andrade, F.H., 1995. Analysis of growth and yield of maize, sunflower and soybean grown at Balcarce Argentina. Field Crops Res. 41, 1±12. Board, J.E., Harville, B.G., 1992. Explanations for greater light interception in narrow-row vs wide-row soybean. Crop Sci. 32, 198±202. Board, J.E., Harville, B.G., 1996. Growth dynamics during the vegetative period affects yield of narrow-row, late-planted soybean. J. Agron. 88, 567±572. Board, J.E., Harville, B.G., Saxton, A.M., 1990a. Growth dynamics of determinate soybean in narrow and wide rows at late planting dates. Field Crops Res. 25, 203±213. Board, J.E., Harville, B.G., Saxton, A.M., 1990b. Narrow-row seed-yield enhancement in determinate soybean. J. Agron. 82, 64±68. Board, J.E., Kamal, M., Harville, B.G., 1992. Temporal importance of greater light interception to increased yield in narrow-row soybean. J. Agron. 84, 575±579. Bolton, F.E., 1981. Optimizing the use of water and nitrogen through soil and crop management. Plant Soil 58, 231±247. Boote, K.J., Jones, J.W., Pickering, N.B., 1996. Potential uses and limitations of crop models. J. Agron. 88, 704±716. Bray, R.H., Kurtz, L.T., 1945. Determination of total, organic and available form of phosphorus in soil. Soil Sci. 59, 360±361. CalvinÄo, P.A., 1990. FertilizacioÂn fosforada en soja. ReunioÂn TeÂcnica del Cultivo de Soja, Convenio AACREA-Banco de Galicia, Buenos Aires, pp. 22±30. Cooper, P.J.M., Gregory, P.J., Tully, D., Harris, H.C., 1987. Improving water use efficiency of annual crops in rainfed systems of West Asia and North Africa. Expl. Agric. 23, 113± 158.

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