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PRODUCTION PAPERS

Planting Date, Cultivar, and Tillage System Effects on Dryland Soybean Production

^a Dep. of Agric. Econ. and Agribusiness, Fayetteville, AR 72701
^b Dep. of Crop, Soil, and Environ. Sci., Fayetteville, AR 72701
^c Agric. Stat. Lab., Univ. of Arkansas, Fayetteville, AR 72701
^d Dep. of Agric. Econ., Univ. of Kentucky, Lexington, KY 40546

* Corresponding author (mpopp{at}uark.edu)

Received for publication July 30, 1999.

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS INTERPRETIVE SUMMARY REFERENCES

 
Soybean [Glycine max (L.) Merr.] yields from nonirrigated fields in the midsouthern USA have consistently lagged behind those from irrigated fields. Nonetheless, nonirrigated fields still attract a larger share of soybean acreage in this region. This is likely due to various irrigation constraints, which include land-leasing arrangements, water shortage, lack of management time, and low levels of operating capital. The objective of this study was to identify production system components consisting of tillage, cultivar selection, and planting-date strategies for a soil series that are most suitable for enhancing economic returns to dryland soybean. Data from field experiments in three Arkansas locations in 1995 and 1996 were used for the study. Leading production systems were identified on the basis of their net returns. Results of the study show that the performance of the production systems in terms of crop yields and net returns is influenced by location and production year. While the evidence on pure planting-date effects is confounded with physical field location, cultivar yields from early soybean plantings in April and May are generally higher than those from later plantings. Furthermore, conventional and fallow production systems had higher net returns than no-till systems, largely due to higher herbicide costs associated with no-till systems. Sensitivity analysis showed that planting date and seedbed preparations are robust to changes in herbicide, fuel, and soybean prices. Further, careful attention to cultivar selection is deemed appropriate because cost differences of cultivar seeds are minor relative to net return differences that are yield driven.

Abbreviations: EPEM, early planted, early maturing • MG, maturity group

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS INTERPRETIVE SUMMARY REFERENCES

 
THE ADVERSE EFFECT OF DROUGHT STRESS on profitable soybean production is underscored by the fact that yields of dryland soybean have consistently lagged behind those of irrigated soybean in the midsouthern USA (Arkansas Agric. Stat. Serv., 1997; Bowers, 1995; Heatherly, 1996). Nonetheless, dryland soybean accounts for >65% of soybean acreage in Arkansas (Arkansas Agric. Stat. Serv., 1997). Under these conditions, two broad strategies enhance soybean yields. The first is to increase the proportion of total soybean acreage under irrigation. Research has contributed to this effort by identifying effective irrigation strategies. However, for irrigation to be successful, other limiting factors such as financial constraints, leasing agreements, and technical limitations need to be sufficiently addressed. A second strategy for improving soybean yield is to identify the components of production practices that are relatively drought resistant and that will result in superior yields under dryland conditions. It is with this second approach that research can make a more meaningful contribution in the short run. Dryland soybean producers who face potential yield losses due to drought stress can benefit from information on yield-enhancing production practices such as tillage system, planting date, and cultivar selection.

Tillage systems can impact soil moisture status because tillage influences infiltration, runoff, evaporation, and soil water storage. With conventional tillage, weeds that compete with crops for moisture and other growth resources are mechanically removed. On the other hand, conventional tillage can promote drought stress through low residue cover, increased runoff, and reduced water infiltration (Dao, 1993; Unger and Cassel, 1991; Unger and Fulton, 1989). By contrast, no-till and other conservation strategies affect soil water content through reduced runoff or erosion and improved residue cover. However, development of soil crusts that increase runoff and impede infiltration is more prevalent with conservation strategies (Dao, 1993; Pikul and Zuzel, 1994). Soil texture can further influence the choice of a suitable tillage system for optimizing yields during stress periods (Cameron and Hermawan, 1993; Heatherly and Elmore, 1983). For example, clayey soils differ from silt loam soils in their infiltration and soil moisture retention capabilities (Sopher and Baird, 1978, p. 62). For these reasons, dryland production practices need to be evaluated across locations with different soil properties.

Planting date is another production component that can be manipulated to counter the adverse effects of drought stress. This is accomplished through early plantings so that a soil moisture deficit is avoided during the critical stages of plant growth (Miller, 1994; Bowers, 1995; Heatherly, 1996). Although early maturing cultivars usually produce lower yields when compared with full-season cultivars at normal planting dates, early planted, early maturing (EPEM) cultivars may produce superior yields by avoiding late-summer drought conditions (Miller, 1994; Bowers, 1995; Heatherly, 1996). These cultivars would have passed critical reproductive stages before stored soil water is exhausted, which ameliorates the effects of drought on crop growth (Miller, 1994). Growth characteristics of soybean cultivars offer an additional means of achieving a satisfactory level of drought tolerance. For instance, indeterminate soybean cultivars (common in the midwestern USA) have reduced growth rates under drought stress and resume normal growth rates when such stress is removed (Beuerlein, 1988). This may be an important growth attribute to consider if producers expect considerable soil moisture deficits due to several short, intermittent droughts during the growing season.

The objective of this study was to identify the components of soybean production and cultural practices encompassing tillage, planting date, and cultivars that increase returns and/or reduce the variability of net economic returns with differing soil texture and soil moisture conditions. This information may aid producers in adopting practices that will maintain the competitiveness of dryland soybean when resources and other production constraints make it difficult to irrigate soybean fields.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS INTERPRETIVE SUMMARY REFERENCES

 
Agronomics
Field experiments were conducted in 1995 and 1996 at three Arkansas locations with different soil series (Table 1). At each experimental location, four adjacent plots of land were selected and randomly assigned a planting date of mid-April, mid-May, mid-June, or mid-July. The randomized complete block experimental design at each location and planting date was used with four replications at Keiser and Pine Tree and three replications at Little Rock. The treatment design at each location and planting date was a split plot. The 9-m-wide by 7-m-long main plots were tillage levels, i.e., no-till, fallow, and conventional. Each main plot was separated from adjacent plots by a 3-m-wide border. Each replication of main plots was separated from the next by a 9-m-wide alley. All nonplot areas within the experiment as well as a 6-m-wide border around the experiment were planted to the earliest-maturing cultivar used at that planting date. Main plots were split into three 3-m-wide by 7-m-long subplots. Each subplot was planted to a soybean cultivar selected to be representative of the group of recommended cultivars provided by SOYVA (Arkansas soybean variety selection computer program; Ashlock et al., 1998) (Table 1). Treatment combinations were applied to the same plots in subsequent years. The plots were not irrigated. Weather data were collected at each location, and all production inputs were recorded for each planting date and production practice. Full details on the attributes of these experiments are presented in Table 1.

Weed management systems were designed for effective weed control (Table 1). Two weeks before planting, the no-till system received a preplant foliar application of glyphosate (see Table 1 for herbicide chemical formulations) to desiccate winter weeds and emerged summer annuals. No-till and fallow systems then received a tank mix of metolachlor plus a premix of metribuzin and chlorimuron applied preemergence. A tank mix of trifluralin plus metribuzin and a premix of chlorimuron was preplant-incorporated in the conventional system. All tillage systems received fomesafen followed by clethodim as a postemergence, over-the-top application as needed for weed control during the growing season. Dates of postemergence herbicide applications varied.

As mentioned previously, cultivars were selected using SOYVA (Ashlock et al., 1998) and varied with planting date. Cultivars in Maturity Groups (MGs) III and IV were used for the mid-April planting, and cultivars in MG IV, V, and VI were used for the mid-May, mid-June, and mid-July plantings. Both indeterminate and determinate cultivars were used. Because information concerning the recommendation of cultivars from different MGs at different planting dates was common knowledge (Univ. of Arkansas Staff, 2000), we only had to select representative cultivars for a particular planting date (i.e., there was no need to plant MG V in April because that MG is not recommended for that planting date; further, ‘Williams 82’ would be representative). This reduced the total size of the experiment by one-half.

It was decided to keep all of the plots for one planting date physically together. Because planting date affects the scheduling of the herbicide program, herbicide drift problems that can easily occur in small plots (especially before the general use of hooded sprayers) with burn-down herbicides would be avoided using this design. As a result, a trade-off exists from the potential bias caused by herbicide drift vs. the deliberate confounding of physical field location with planting date. Because the experimental areas were quite uniform in their surface drainage and soil type, we felt that the effect of physical field location would be small compared with the planting-date effect. However, effects from drifting glyphosate (applied in some plots several times during the season at different times with different planting dates) could easily be severe enough to invalidate the experimental results if not avoided. Drift problems with glyphosate that are severe enough to cause substantial yield losses are sometimes very difficult to detect as well.

Soybean seeds were planted on a flat soil surface in 18- to 20-cm-wide rows with John Deere 750 no-till drills. Seeding rate was 9 to 12 seeds m^-1 row (which was within the range of recommended seeding rates and deemed to be close enough, as it is extremely difficult to set the same seeding rate with different cultivars with a drill). A 1.5-m-wide strip in the center of each plot was harvested with a plot combine at maturity. Yields were adjusted to 130 g moisture kg^-1 seed.

Soil moisture in the tillage production systems was measured gravimetrically beginning at planting and every week thereafter during the growing season, except after rainfall when soils were saturated. Soil samples were taken at random from the 0- to 8-cm depth at Keiser and from the 0- to 60-cm depth at the other locations from each tillage method plot at planting and after planting in 1995. In 1996, soil samples were taken from the 0- to 60-cm depth in 15-cm depth increments at all locations. These were later composited mathematically into one 0- to 60-cm-deep sample. Soil sampling was discontinued when the earliest-maturing cultivar in the planting date reached the R6 growth stage (Fehr and Caviness, 1977).

An analysis of variance was performed using the SAS MIXED procedure where the fixed effects are the main effects and all interactions of planting date, tillage, cultivar, and year were evaluated (Table 2). The random effects, each nested in planting date, are block, block x tillage, block x tillage x cultivar, block x year, and block x tillage x year. The degrees-of-freedom option was satterth. The method of variance estimation was REML. Mean separation was done using the LSMEANS option.

View this table: [in this window] [in a new window]   Table 2. Probability of greater values of F resulting from Type III tests of fixed effects for yield and net returns above total specified costs (NRAT) obtained from SAS PROC MIXED analysis.

The 10-yr average price of soybean ($0.219 kg^-1) in Arkansas from 1990 to 1999 (http://www.nass.usda.gov/ar/histcrop.htm; verified 18 Sept. 2001) was applied to the respective soybean yields in each year to obtain gross returns per hectare. A 10-yr average price is used to remove any market effects due to years with abnormally high or low prices. Production cost estimates issued annually by Arkansas Agricultural Cooperative Extension Service (Windham and Sills, 2000) were used to obtain the relevant input requirements and prices. In order to take account of changes in input prices over time, sensitivity analysis (Petersen and Lewis, 1994) was performed on inputs that vary most across treatments in this study—i.e., fuel, herbicides, and output price. For example, fuel prices used in this study were $0.159 L^-1, reflective of low-cost conditions that producers faced for some time before 2000. By 2001, producers have seen a price increase to $0.370 L^-1. Further, herbicide expenses have nearly halved over the last 5 yr, led by reductions in glyphosate and competing herbicides. Finally, a last scenario shows returns that can be expected by changing prices to reflect concerns over the current low-price environment.

Operating expenses were estimated from input requirements for seed, fertilizer, pesticides, custom hire, repairs, maintenance, and fuel. Guidelines of the American Society of Agricultural Engineers (ASAE) were followed to determine the ownership costs for machinery, operator's labor, fuel, and repair and maintenance (ASAE, 1993, p. 328–334.; Flynn et al., 1996). Costs of field operations that were equally performed across all strategies were the same, but the costing process duly recognized the differences in input requirements of various systems.

Ownership costs include depreciation, insurance, property taxes, and interest on capital invested in farm machinery. Some of these ownership costs are a function of the machinery complement required. Total specified costs were the sum of both the ownership and operating costs. These total specified costs did not include charges for land, risk, overhead, crop insurance, and management. Net returns, gross return less total specified costs, may be interpreted as long-term returns to land, risk, and management resources devoted to soybean production and are used in this study to differentiate across the different production methods.

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS INTERPRETIVE SUMMARY REFERENCES

 
Agronomics
On the basis of the PROC MIXED results (Table 2), the yield results for all locations are presented as a three-way interaction among years, cultivars, and planting dates (Table 3). The other three-way interaction among years, tillage, and planting dates at Pine Tree (Table 2) is considered an anomaly due to extreme weather in 1996 (i.e., heavy rains in June). At a location, no statistically significant differences in water stored in the top of the soil profile occurred between different tillage systems. However, soil water storage was related to planting date and rainfall distribution (Fig. 1) . (The 1995 Keiser data were discarded because the soil was sampled at too shallow a depth to reflect the crop-extractable soil water reservoir.) In general, delaying planting, while maintaining a vegetation-free surface condition, conserved soil water. However, the soil water stored as a result of later planting was not necessarily conducive to higher yields (Table 3). For example, the mid-July planting had more stored soil moisture, but this resulted in the highest yields only at Little Rock in 1995. The EPEM soybean production system used for escaping drought was either the superior system or was one of the superior systems for both years on the clay soil at Keiser and one year on the alluvial silt loam at Little Rock. On the shallow silt loam soils at Pine Tree, yield from the mid-June planting was not measurably different from the highest yields obtained in any year. On clay and alluvial silt loam, the EPEM system yielded no better than the May planting.

View larger version (38K): [in this window] [in a new window]   Fig. 1. Soil water is the 0 to 60 cm soil depth as a function of planting date and rainfall distribution at Keiser, Pine Tree, and Little Rock, AR. An average LSD for comparing any two data points for a given location within a year is estimated by averaging the standard errors of all data points reported for a year and location. The average t-value used was 2.00 in every situation.

Economics
The magnitude of net returns for various alternative practices is the first evidence of the viability and superiority of one strategy over another. As shown in Table 2, net returns exhibited a three-way interaction among planting date, cultivar, and year. The means of this interaction are shown in Table 4. These results mirror those found in Table 3 because averaging across seedbed preparation methods eliminates major cost differences; therefore, net return results are similar to yield results. In addition, the results show that cultivar selection, once the selection of planting date is made, can affect returns by as little as $11 ha^-1 but also by as much as $146 ha^-1. While there are no clear trends in the data, a producer would be well advised to pay careful attention to cultivar selection because cultivar differences in net returns are much larger on average than differences across tillage methods (Table 5). Furthermore, seed cost differences across cultivars tend to be minimal; therefore, paying attention to cultivar differences may net as much as $146 ha^-1.

Analyzing average net returns over the 2-yr study period, clear production system recommendations emerge in Table 6. At the Keiser and Pine Tree locations, the preferred planting date is May, whereas Little Rock allows for June planting. Little Rock and Pine Tree utilize conventional seedbed preparation while at Keiser, the somewhat more costly fallow seedbed preparation process has attendant yield sufficient to offset the additional costs. Noteworthy in Table 6 is the sensitivity analysis in the bottom half of the table, which suggests that planting date and seedbed preparation choices are very robust to price changes at Little Rock and Pine Tree and to a lesser extent at Keiser. At Keiser, either a 33% increase in herbicide price or a 19% decline in soybean price to $0.177 kg^-1 leads to a change in the production system from the overall preferred strategy of fallow seedbed preparation in May to conventional seedbed preparation with an April planting date. Another concern is the effect of fuel prices. The sensitivity analysis indicates that fuel prices would have to change tremendously to elicit a change in production practices. A large price change (approximately a 7- to 14.5-fold increase in fuel costs) would be required to affect the optimal production strategy because fuel costs account for only a small proportion (typically <5%) of total specified costs. The fuel cost analysis was done with the assumption that other indirect effects of fuel price changes—i.e., the impact of changes in fuel prices on other inputs, such as fertilizer and chemicals—are not included.

	INTERPRETIVE SUMMARY

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS INTERPRETIVE SUMMARY REFERENCES

	ACKNOWLEDGMENTS

 
The authors are grateful to Dr. Lanny Ashlock of Arkansas Cooperative Extension Service for his help in cultivar selection. Also, Patrick Manning and David Annis, Jr. are thanked for their efforts. This research was made possible by funding from the Arkansas Soybean Promotion Board.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS INTERPRETIVE SUMMARY REFERENCES

 

• [ASAE] American Society of Agricultural Engineers. 1993. ASAE standards 1993: Standards, engineering practices and data adopted by the American Society of Agricultural Engineers. 40th ed. ASAE, St. Joseph, MI.
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• Ashlock, L.O., D. Beaty, R. Klerk, B. Bridges, and W. Haynes. 1998. Soybean variety selection program. SOYVA. User's guide 1998. Univ. of Arkansas Coop. Ext. Serv., Little Rock.
• Beuerlein, J.E. 1988. Yield of indeterminate and determinate semidwarf soybean for several planting dates, row spacings, and seeding rates. J. Prod. Agric. 1:300–303.
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