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PRODUCTION PAPER

Yield and Economic Return of Ten Peanut-Based Cropping Systems

^a Dep. of Crop Sci., North Carolina State Univ., Raleigh, NC 27695-7620
^b Dep. of Plant Pathol., Box 7619, North Carolina State Univ., Raleigh, NC 27695-7619
^c Peanut Belt Res. Stn., North Carolina Dep. of Agric. and Consumer Serv., Box 220, Lewiston-Woodville, NC 27849
^d Upper Coastal Plain Res. Stn., Rt. 2 Box 400, North Carolina Dep. of Agric. and Consumer Serv., Rocky Mount, NC 27801
^e Dep. of Agric. and Resour. Econ., Box 8109, North Carolina State Univ., Raleigh, NC 27695-8109

* Corresponding author (david_jordan{at}ncsu.edu)

Received for publication April 9, 2001.

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY REFERENCES

 
Research was conducted in North Carolina at two locations from 1997 through 2000 to determine net returns of 10 cropping systems during a 4-yr cropping cycle that included peanut (Arachis hypogaea L.), cotton (Gossypium hirsutum L.), soybean [Glycine max (L.) Merr.], and corn (Zea mays L.). Cylindrocladium black rot [caused by Cylindrocladium parasiticum] (CBR) increased when soybean was included in the rotation sequence or when peanut was grown continuously. The CBR-resistant cultivar NC 12C increased yield compared with the susceptible cultivar NC 7 when this disease was present. Cotton was a better rotation crop than corn at one of two locations with respect to peanut yield and gross economic value in the final year of the study. Net returns were substantially lower when peanut was marketed for export in the current federal program rather than at the quota price. However, the profitability ranking among cropping systems changed little regardless of marketing system. Crop yield and net return were influenced by crop selection, weather conditions, and commodity prices during the 4 yr.

Abbreviations: CBR, Cylindrocladium black rot • CR, corn • CT, cotton • PN, peanut • SB, soybean

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY REFERENCES

 
CROP ROTATION can have a dramatic impact on development of pest complexes, crop yield, and economic return. Soil characteristics, commodity prices, availability of equipment and labor, cultivar yield potential and resistance to disease and/or insects, and federal farm programs influence the producer's cropping decisions. A thorough understanding of these factors and their interactions is critical in maximizing profitability of farming enterprises.

The value of long rotations between peanut crops has been well established (Sholar et al., 1995). Producers often cite crop rotation as the most important component of their pest management strategies (Toth, 1998). Research also suggests that avoiding soybean in rotation systems with peanut often results in less disease and higher peanut yield (Ayers et al., 1989; Rodriguez-Kabana et al., 1987).

Corn, cotton, peanut, soybean, tobacco (Nicotiana tobacum L.), and wheat (Triticum aestivum L.) are important commodities in the Coastal Plain region of North Carolina. Value of these crops varies considerably, and this influences rotation decisions (Sholar et al., 1995). Producers must balance long-term benefits of rotating lower-value crops, such as disease control, with potential lower revenue. Lamb et al. (1993) reported that irrigated peanut in Georgia was often grown under shorter rotations than nonirrigated peanut, in part due to high capital investment in peanut production compared with most other agronomic crops other than tobacco. Longer rotations were more beneficial under irrigation than under dryland production (Lamb et al., 1993). Most peanut in North Carolina is not irrigated.

Cultivar selection can have a major impact on revenue due to differences in disease and insect resistance and yield potential (Lynch and Mack, 1995; Sherwood et al., 1995). Characteristics of cultivars offering disease resistance can be agronomically less favorable or may not meet demands of the market (Jordan, 2001). Virginia market type peanut cultivars offer a wide range of disease resistance, and growers often select cultivars based on these benefits (Bailey, 2001). Pesticides have helped growers remain profitable in short rotations; however, problems such as diseases and nematodes can become more severe. Peanut marketed outside of the current federal program would be less profitable due to high inputs with lower returns.

Currently, federal legislation limits importation of peanut, controls domestic production, and provides a profitable price support for peanut production in the United States (Brown, 2001a). This legislation has resulted in substantially higher prices for peanut in the United States relative to the world market price. However, farm legislation is scheduled to change in 2002, at which time price support will be eliminated or lowered approaching the world market price. Other federal legislation will allow more importation of peanut, which will compete with domestic production (R.M. Sutter, North Carolina Peanut Growers Assoc., personal communication, 2002). This would reduce the economic value of peanut at the farm level and may result in changes in cropping systems, pest management practices, or both. Addressing these issues will require a thorough understanding of the value of peanut under current and future marketing structures.

Research was conducted to (i) evaluate the profitability of 10 cropping systems that included peanut, cotton, corn, and soybean over a 4-yr cycle under four marketing arrangements; (ii) monitor the development of disease under these cropping systems; and (iii) determine the value of cultivar selection during the final year of the 4-yr cycle for each of the 10 cropping systems.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY REFERENCES

 
The experiment was established in 1997 at the Peanut Belt Research Station located near Lewiston, NC, and the Upper Coastal Plain Research Station located near Rocky Mount, NC. Soil at both locations was a Norfolk sandy loam (fine-loamy, siliceous, thermic Aquic Paleudalts). Plot size was 12 rows (91-cm spacing) at Lewiston and eight rows (91-cm spacing) at Rocky Mount by 15 m. A cover crop was established in the field at Lewiston during the fall of each year by disking and seeding wheat using a drill with rows spaced 24 cm apart. A cover crop was not established at Rocky Mount. In the spring, the cover crop was terminated using glyphosate [N-(phosphonomethyl)glycine] at 0.84 kg a.e. ha^-1 applied 3 wk before planting. Tillage in the spring at this location after cover crop kill consisted of disking twice, chisel plowing, field cultivating, and ripper bedding. A similar tillage system was used at Rocky Mount but without chisel plowing in 1997. In subsequent years at Rocky Mount, tillage consisted of running the ripper bedder over plots twice without other tillage. This approach was designed to minimize soil movement into adjacent plots.

The rotation systems evaluated at both locations are listed in Table 1. Cotton cultivars at Rocky Mount and Lewiston were Stoneville 474 and Suregrow 125, respectively. Corn hybrids at these respective locations were Pioneer 3394 and Dekalb 714. Soybean cultivars were Hartz 6686 at Lewiston and Pioneer 9692 at Rocky Mount. Peanut cultivars during all years were NC 7 at Rocky Mount and NC 10C at Lewiston. In the final year of the experiment, the eight-row plots at Rocky Mount were divided, with four rows planted with NC 7 and four with NC 12C. At Lewiston, the 12-row plots were divided into three sections of four rows each and consisted of the cultivars NC 7, NC 10C, and NC 12C. The cultivar NC 7 is susceptible to most peanut diseases including CBR (Bailey, 2001). The cultivars NC 10C and NC 12C offer some resistance to CBR (Bailey, 2001). Production and pest management practices for each crop were held constant at a given location regardless of cropping system and were standard practices for the region. Fumigation was not included with peanut. Soil cores at depths of 0 to 15 and 16 to 30 cm were removed from each plot in the fall of 1997 and in the summer of 2000. Minor differences in levels of K and P were adjusted in the spring of 1998 to maximize crop yield and minimize fertility as a variable.

Net return (Paudel et al., 1998) during each year for each crop was calculated using crop prices based on North Carolina Agricultural Statistics and loan deficiency payment. Price of farmer stock peanut reflected 70% of peanut marketed as quota ($0.68 kg^-1) and 30% marketed through export contract ($0.39 kg^-1) for an average of $0.58 kg^-1 (R.M. Sutter, North Carolina Peanut Growers Assoc., personal communication, 2002). Growers often plant additional hectarage to ensure production will meet their quota should production fall due to inclement weather or unpredictable pest outbreaks. Peanut other than that marketed as quota can be sold at the export price based on the world market price. Production costs were set at $1669 ha^-1, $1037 ha^-1, $366 ha^-1, and $780 ha^-1 for peanut, cotton, soybean, and corn, respectively (Brown, 2001a, 2001b; Dunphy et al., 1998; Heiniger et al., 1999). Budgets reflected total fixed and variable costs. Market prices (including loan deficiency payment) for corn, cotton, and soybean were $0.09 kg^-1, $1.21 kg^-1, and $0.2 kg^-1, respectively. The collective economic value for each cropping system was determined over the 4-yr cycle under scenarios where (i) market prices for corn, cotton, and soybean were included and peanut was marketed under current marketing arrangements ($0.58 kg^-1 farmer stock) (referred to as federal program) and (ii) market prices for corn, cotton, and soybean were included and peanut was marketed based on average export contract ($0.39 ha^-1) (referred to as export program). Yield for the cultivars NC 10C and NC 7 were used in these calculations for Lewiston and Rocky Mount, respectively. These cultivars were included during the 4 yr of the experiment at the respective locations.

Data for collective economic value were subjected to combined analysis of variance for the two location by 10 rotation systems. Data from 2000 (peanut was the only crop planted), consisting of percentage CBR, peanut pod yield, and gross economic value, were subjected to analyses of variance by location for a 2 or 3 (cultivar) x 10 (rotation system) factorial treatment arrangement. Means of significant main effects and interactions were separated using Fisher's Protected LSD Test at P < 0.05. Although not analyzed statistically, data for crop yield are presented to help explain differences in net returns. Break-even crop yield was calculated based on commodity prices and fixed and variable costs (Table 2).

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY REFERENCES

Differences in net return were affected by crops in the rotation, the year the crop was planted within a rotation system, and weather conditions. For example, peanut yield at Lewiston in 1997 ranged from 4270 to 4870 kg ha^-1 (Table 2). Yield of cotton at this location was 640 kg ha^-1 while corn yield ranged from 3610 to 4250 kg ha^-1. Yield for these crops was lower at Rocky Mount in 1997 (Table 2). Cotton yields in 1998 were higher at both locations compared with yields in either 1997 or 1999. In 1998, average yield of cotton was 930 and 840 kg ha^-1 at Lewiston and Rocky Mount, respectively (Table 2). Peanut yield at Lewiston also increased in 1998 as evidenced by average yield of 5810 kg ha^-1 (excluding continuous peanut). Average yield in 1998 at Rocky Mount was 3900 kg ha^-1 when continuous peanut was excluded. Corn yield at Lewiston and Rocky Mount in 1998 was 3090 and 3230 kg ha^-1, respectively. Although these corn yields are relatively low, corn yields within this range are often observed on sandy loam soils without irrigation in the Coastal Plain region of North Carolina.

Excessive rainfall from three hurricanes during September and early October negatively affected cotton and most likely peanut yield in 1999 (Pearce et al., 2000). Although peanut yield was lower in 1999 than in 1998 in the continuous peanut cropping system, a general decline in yield may have resulted from lack of rotation out of peanut. Even though early-season rainfall contributed to greater yield potential of corn at Lewiston in 1999, a hailstorm in mid-June damaged corn and most likely reduced yield (average of 3330 kg ha^-1). Cotton was also damaged from hail at this location. At Rocky Mount, average corn yield was 5660 kg ha^-1 in 1999. At both locations, corn was harvested before hurricanes. Rainfall in 2000 was sufficient for optimum peanut yields at both locations. For example, peanut yield for several cropping systems exceeded 5000 kg ha^-1.

Break-even yield for peanut marketed as quota or for export was 2890 and 4280 kg ha^-1, respectively, based on fixed and variable costs used in these studies. Break-even yield for corn was 8670 kg ha^-1. Cotton break-even yield was 860 kg ha^-1. Break-even yield for soybean was 1830 kg ha^-1. Corn did not break even at either location regardless of cropping system (Table 2). Cotton did not break even in 1997 or 1999 at either location. However, soybean was profitable at both locations regardless of cropping system. Peanut was profitable in both federal and export programs during 1997 at Lewiston in all cropping systems except PN–CR–CR–PN. In contrast, peanut during this year at Rocky Mount was profitable only when marketed in the current federal program for all cropping systems except PN–CR–CR–PN. Cotton and soybean in all cropping systems and peanut in the federal program were profitable at Lewiston in 1998. Cotton was profitable at Rocky Mount in only one of three cropping systems in 1998. None of the crops were profitable in 1999 at either location. With the exception of PN–SB–CR–PN and CR–SB–CT–PN at Lewiston, PN–CT–CR–CN at Rocky Mount, and continuous peanut cropping systems at both locations, peanut was profitable in 2000 under both marketing systems.

Cultivar Comparison
Main effects of cropping system and cultivar were significant for percentage CBR at Lewiston and Rocky Mount in 2000. The interaction of these treatment factors was significant at Rocky Mount but not at Lewiston. When pooled over cultivars, percentage CBR was higher in the PN–SB–CR–PN cropping system compared with CT–PN–CT–PN, PN–CT–CT–PN, and CR–CT–CR–PN cropping systems (Table 4). No differences in percentage CBR were noted among the other cropping systems. At Rocky Mount, percentage CBR was highest in the continuous peanut cropping system when NC 7 was planted compared with percentage CBR in the other cropping systems. Previous research (Black and Beute, 1984; Sidebottom and Beute, 1989) suggests that peanut grown after soybean had less CBR than peanut grown after peanut. The percentage CBR did not differ among cropping systems when NC 12C was planted (Table 4). Benefits of cultivar resistance were also obvious when examining main effects of cultivar on percentage CBR (Table 5). When pooled over cropping systems at Lewiston, percentage CBR was 16, 4, and 2% for the cultivars NC 7, NC 10C, and NC 12C, respectively. Likewise, when pooled over cropping systems, NC 7 had 6% CBR compared with 1% on NC 12C at Rocky Mount (Table 5). At Rocky Mount, NC 12C had 8% CBR when planted in continuous peanut compared with 45% for NC 7 in this same cropping system (Table 4). Previous results support these data demonstrating greater resistance to CBR by NC 10C and NC 12C compared with NC 7 (Bailey, 2001).

When pooled over cultivars at Lewiston, pod yield and gross economic value were higher in CT–PN–CT–PN, PN–CT–CT–PN, and CR–CT–CR–PN cropping systems compared with CR–PN–CR–PN, PN–CT–CR–PN, PN–SB–CR–PN, PN–SB–CT–PN, PN–CR–CR–PN, CR–SB–CT–PN, and continuous peanut cropping systems (Table 6). Additionally, pod yield and gross economic value of the cropping systems CR–PN–CR–PN, PN–CT–CR–PN, and PN–SB–CT–PN exceeded that of the CR–SB–CT–PN and PN–PN–PN–PN cropping systems. Continuous peanut yielded the lowest and provided the least revenue at this location. At Rocky Mount, cropping systems other than continuous peanut yielded and provided gross economic value equal to one another (Table 6). As was noted at Lewiston, continuous peanut was the lowest-yielding cropping system and provided the lowest gross value.

	SUMMARY

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY REFERENCES

 
In comparing net returns among cropping systems, economic benefits of peanut were demonstrated when marketing included the current federal program. Continuous peanut was a liability when marketed for export, due to the high production costs associated with peanut compared with the gross value obtainable in these studies.

Including peanut in the cropping system and growing peanut continuously reduced yield potential and gross value of peanut in the final year of the 4-yr cycle at one location compared with cropping systems including at least 2 yr of cotton combined with 2 yr of peanut or when 2 yr of corn and 1 yr of cotton were included with 1 yr of peanut.

These studies reveal the unpredictability of crop production as the result of environmental conditions. Excessive rainfall in the form of hurricanes and a hail storm in 1999 reduced yield and revenue for crops in one of the 4 yr. The economic shortfall was exacerbated by low commodity prices. Cotton was present in 4 of the 10 cropping systems during 1999, and excessive rainfall greatly reduced yield and economic return. Although corn (present in 4 of 10 cropping systems in 1999) did not break even, this crop was harvested before hurricanes in 1999, which prevented greater losses. Substantial investment in production of cotton had been made throughout the season before hurricane damage, and weather conditions greatly reduced profitability of cotton production.

Selection of profitable crop rotations requires a thorough understanding of yield potential and market value of each commodity. Short-term decisions must account for the long-term productivity of fields when diseases such as CBR are present. Although beyond the scope of this study, evaluation of peanut-based cropping systems using optimization modeling may suggest a preferred rotation system under specific resource constraints.

	ACKNOWLEDGMENTS

 
The North Carolina Peanut Growers Association provided partial financial support for these studies. Appreciation is expressed to staff at the Upper Coastal Plain Research Station and the Peanut Belt Research Station for assistance with these studies. Almond Stallings, Joel Alston, Lewis Pitt, and Carl Murphy provided technical support.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY REFERENCES

 

• Ayers, A.R., H.E. Duncan, K.R. Barker, and K.M. Beute. 1989. Effects of crop rotation and nonfumigant nematicides on peanut and corn yields in fields infested with Criconemella species. J. Nematol. 21:268–275.
• Bailey, J.E. 2001. Peanut disease management. p. 85–104. In 2001 peanut information. Publ. AG-331. North Carolina Coop. Ext. Serv., Raleigh.
• Bailey, J.E., and C.A. Matyac. 1989. A decision model for use of fumigation and resistance to control Cylindrocladium black rot of peanuts. Plant Dis. 73:323–326.
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• Lynch, R.E., and T.P. Mack. 1995. Biological and biotechnical advances for insect management in peanut. p. 95–159. In H.E. Pattee and H.T. Stalker (ed.) Advances in peanut science. Am. Peanut Res. and Educ. Soc., Stillwater, OK.
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This Article Abstract Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (3) Citing Articles via Google Scholar Google Scholar Articles by Jordan, D. L. Articles by Johnson, P. D. Search for Related Content PubMed Articles by Jordan, D. L. Articles by Johnson, P. D. Agricola Articles by Jordan, D. L. Articles by Johnson, P. D. Related Collections Other Legumes Economics Crop Rotation Systems Other Crop Management Other Oil Crops Plant Disease Production Agriculture