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PRODUCTION PAPERS

Planting Date Influence on Dual-Purpose Winter Wheat Forage Yield, Grain Yield, and Test Weight

^a Dep. of Agric. Economics, Oklahoma State Univ., Stillwater, OK 74078-6026
^b Dep. of Plant and Soil Sciences, Oklahoma State Univ., Stillwater, OK 74078-6026

^* Corresponding author (epplin{at}okstate.edu).

Received for publication February 7, 2002.

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY AND CONCLUSIONS REFERENCES

 
The use of winter wheat (Triticum aestivum L.) as a dual-purpose forage and grain crop is important to the agricultural economies of the Southern Great Plains of the USA. Planting date is an important management factor in determining the economic success of a dual-purpose winter wheat enterprise. The overall objective of the research reported in this paper is to determine the economic optimal planting date for dual-purpose winter wheat production. The specific objectives are to determine wheat fall–winter forage yield, wheat grain yield, and wheat test weight response to planting date for dual-purpose winter wheat production. Field studies were conducted in north central Oklahoma from 1991–1992 through 1999–2000. The impact of alternative planting dates on dual-purpose wheat fall–winter forage yield, grain yield, and test weight was estimated. Estimated response functions illustrate that delaying the planting date from 10 to 30 September resulted in an 18% increase in expected grain yield, a 68% decrease in expected fall–winter forage yield, and only a 0.5% increase in expected test weight. Optimal planting date is sensitive to the relative value of wheat fall–winter forage and wheat grain, but not sensitive to wheat test weight discount schedules. When the value of wheat forage is high relative to the value of grain, it is more profitable to plant early to increase expected forage yield. Alternatively, when the value of grain is high relative to the value of forage, later planting generates greater net returns.

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY AND CONCLUSIONS REFERENCES

 
WHEAT IS ONE of the most important crops in the Southern Great Plains. Wheat may be grown for grain or forage only, or for both forage and grain (Redmon et al., 1995). A 1996 Oklahoma statewide survey found that two-thirds of the wheat planted in the fall of 1995 was intended for dual-purpose (True et al., 2001). Wheat pasture is a valuable source of high-quality forage; it is high in protein, energy, and minerals, and low in fiber. It is typically available in late fall, winter, and early spring, when other forage sources in the region are low in quantity and quality. In terms of crude protein and digestibility, wheat fall–winter forage is comparable to alfalfa (Medicago sativa L.). In a typical growing season in Oklahoma, winter wheat is available for grazing by livestock from late November until development of the first hollow stem, usually in early March. If livestock are removed before development of first hollow stem, the wheat will mature and produce a grain crop for harvest in June. Producers differentiate between wheat intended for dual-purpose and wheat intended for grain only (True et al., 2001). They plant dual-purpose wheat earlier than grain-only wheat to increase the likelihood of fall forage production.

Use of winter wheat as a dual-purpose crop is important to the agricultural economies of southwestern Kansas, eastern New Mexico, western Oklahoma, southeastern Colorado, and the Texas Panhandle (Pinchak et al., 1996; Redmon et al., 1995; Shroyer et al., 1993). Wheat grazing is also practiced in Argentina, Australia, Morocco, Pakistan, Syria, and Uruguay (Rodriguez et al., 1990). Krenzer (2000) identified three factors that facilitate dual-purpose winter wheat production in the southern Great Plains. First, biotic and abiotic conditions in the region reduce the risk of severe Hessian fly infestations. This enables early planting, which increases the forage production potential by extending the vegetative growth period. Second, winter grazing is enabled since extended snow cover is not common. Third, typical rains in April and May reduce concern about soil moisture limiting potential grain production.

Dual-purpose wheat production is a complicated process, mainly due to complex interactions of livestock production with wheat grain production and variable weather. Selection of wheat planting date is one of the most important management decisions for dual-purpose production. In general, fall–winter forage production is expected to be greater for earlier planted wheat. Historically, public wheat breeding and development programs conducted in the Southern plains have selected varieties based on grain yield and grain quality from planting in mid-October (Carver et al., 1991; Winter and Thompson, 1990). However, in most growing seasons, fall–winter forage production from winter wheat seeded in mid-October or later will be insufficient to support fall–winter grazing. Thus, farmers who plan to produce both forage and grain may plant in an environment different from that used in the wheat breeding programs.

For a given planting date, if grazing is properly managed, fall–winter grazing is not expected to adversely affect grain yield of dual-purpose wheat (Christiansen et al., 1989; Winter et al., 1990; Worrell et al., 1992). Recommended management strategies include delaying livestock placement on the wheat until the plant roots are well anchored, ensuring adequate soil fertility, and removing livestock from the pasture before development of the first hollow stem stage of wheat development. Under these conditions, for a given planting date and reasonable stocking densities, fall–winter grazing is not expected to be detrimental to grain yield.

Early planting increases the total length of time that the wheat is in the field and exposed to the environment. It is associated with increased incidences of several diseases including wheat streak mosaic, High Plains mosaic, barley yellow dwarf, sharp eyespot, common root rot, and take-all root rot (Bowden, 1997). Thus, early planting increases the probability of unfavorable consequences relative to grain yield. Planting date may also influence the quality of the wheat grain. Epplin et al. (2000) estimated wheat forage and wheat grain yield response to seeding rate and planting date. However, the effect of planting date on winter wheat grain test weight has not been determined.

Wheat breeding programs, production practices, and marketing programs all recognize the importance of wheat grain quality. Test weight is used as an indicator, or proxy, for overall grain quality and soundness by domestic flour millers (Leath, 1995). Export markets also consider and use test weight as one measure of wheat grain quality. Test weight affects the productivity, efficiency, and operating costs of flour milling. Wheat grain with high test weight will usually contain kernels that reduce milling costs and increase flour yields and flour purity relative to wheat grain with low test weight (Parcell and Stiegert, 1998). As a result, lots with low test weights are discounted.

Farmers receive a lower net price for wheat grain marketed with a low test weight. A 1996 Oklahoma statewide survey found that test weight is one of the top three characteristics farmers consider (along with grain yield and forage yield) when selecting a dual-purpose variety (True et al., 2001). No prior studies have determined the impact of planting date on test weight of dual-purpose winter wheat grain.

The overall objective of the research reported in this paper is to determine the economic optimal planting date for dual-purpose winter wheat production. The specific objectives are to determine wheat fall–winter forage yield, wheat grain yield, and wheat test weight response to planting date for dual-purpose winter wheat production. Economic optimal planting dates are determined for several sets of grain and forage prices, with appropriate grain price adjustments for test weight.

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY AND CONCLUSIONS REFERENCES

 
Data
Data for this study were obtained from planting date field trials conducted over nine winter wheat production seasons, from 1991–1992 through 1999–2000, on the North Central Research Station near Lahoma, OK. The soil type was a Pond Creek silt loam (fine-silty, mixed, superactive, thermic Pachic Argiustolls). Planting date treatments ranged from late August to mid-November in a randomized complete block design. Table 1 includes the planting dates for each of the 9 yr. Plot size was 8- to 15-cm rows by 6.7 m. Each treatment was replicated four times.

To simulate grazing, the plots were mechanically clipped. The clipped forage from each plot was dried and forage yield computed and reported as kg ha^-1 oven dry forage. The first clipping was conducted in the late fall. The second clipping was conducted before first hollow stem in late winter after emergence from dormancy. Hence, the estimate of dry matter forage yield was based on the sum of the two clippings. The plants were permitted to mature and produce grain. Foliar fungicide (Tilt) was applied to all plots at the labeled rate at growth stage eight to reduce the confounding of planting date and foliar disease susceptibility. Grain yield was obtained with a small plot combine harvesting the center 5.3 m of each plot. A subsample of the combine harvested grain was cleaned and test weight was determined. All plots were fertilized to ensure that soil fertility would not be the yield-limiting factor.

Response Functions
Response functions for wheat fall–winter forage yield, wheat grain yield, and wheat test weight were estimated. Plots of observed fall–winter forage yield, grain yield, and test weight values for each planting date for each year are charted in Fig. 1, 2, and 3 , respectively. A squared term was included in the regression equations to allow for a nonlinear relationship between planting date and dependent variable.

View larger version (27K): [in this window] [in a new window]   Fig. 1. Observed winter wheat forage yield response to alternative planting dates, Lahoma, OK, from 1991–1992 through 1999–2000.

View larger version (29K): [in this window] [in a new window]   Fig. 2. Observed winter wheat grain yield response to alternative planting dates, Lahoma, OK, from 1991–1992 through 1999–2000.

View larger version (27K): [in this window] [in a new window]   Fig. 3. Observed winter wheat grain test weight response to alternative planting dates, Lahoma, OK, from 1991–1992 to 1999–2000.

[1]

where y is a vector of observations, ß is a vector of unknown treatment-effects parameters to be estimated, X is a known design matrix for the treatment effects that includes three columns including a column of ones, a column with planting date entered as a continuous number (for example, 1 January = 1 and 31 December = 365), and a column with planting date squared. The vector u is a vector of unobservable random effects; Z is a known design matrix for the random effects that includes 45 columns, one for each of the 9 yr and one for each of the four blocks within each of the 9 yr. The vector e is a vector of residual random errors. Both u and e are assumed normally distributed with mean 0 and variance G and R, respectively. So, y is normally distributed with mean, E(y) = Xß and variance, V(y) = V(Zu + e) = ZGZ' + R. The R matrix is equal to ²I (I denotes the identity matrix), under the assumption of homoskedasticity.

For this study, year is modeled as a random effect, because the 9 yr represent a random sample of years from the potential population of all years. In other words, the level or characteristics of a year (for example 1992, 1994) cannot be replicated exactly. This differs from a treatment variable such as planting date that can be replicated. Because the treatment variable, planting date, can be replicated it is modeled as a fixed effect.

In the randomized complete block design, within a given year, treatments (planting dates) were randomly assigned within the blocks. These blocks were randomly selected from a population of blocks on which the wheat could have been planted. So the blocks within each year are also modeled as a random effect. The G matrix has the standard diagonal variance components structure (VC option in the RANDOM statement of PROC MIXED), which assigns a distinct variance component to each random effect (SAS Inst., 1999). Littell et al. (1996) and Piepho (1999) provide a detailed discussion of the statistical methods employed by the MIXED procedure in SAS.

The regression equation to be estimated for the forage yield is:

[2]

where F is forage yield (kg ha^-1); _i are fixed effects coefficients to be estimated; PD is planting date (the day of the year, for example, 9 September = 252). The variance of forage yield is:

[3]

where ²_yr and ²_bl are variance components associated with year and blocks within year, respectively, and ²_e is variance for residual random errors.

Based on the Harvey test, the null hypothesis of homoskedasticity (equal variances) was rejected at the 5% level for the forage yield model. Initially the multiplicative or log-linear variance model, described by Harvey, was used to correct for heteroskedasticity (Greene, 1997; Littell et al., 1996). But convergence problems occurred due to demanding computations, which are common in mixed model analysis (Piepho, 1999; Sorensen and Kennedy, 1986). So, a weighted two-stage method, which has a lower computational burden, was used. Heteroskedasticity was corrected with a weighting based on reciprocals of the square root of the estimated error variances (Kennedy, 1992; Piepho, 1999). Error variances were modeled using planting date and squared planting date as the explanatory variables.

The equations for grain yield and test weight response to planting date have the same form and independent variables as the forage yield response:

[4]

[5]

where G is grain yield (kg ha^-1); T is test weight of the wheat (kg m^-3); ß and are fixed effects coefficients to be estimated associated with G and T, respectively; and other symbols are as previously defined.

The Harvey test also rejected the null hypothesis of homoskedasticity at the 5% level for both the grain yield and the test weight models. For these two equations, the multiplicative or log-linear variance model, described by Harvey, was used to correct for heteroskedasticity (Greene, 1997; Littell et al., 1996).

Optimal Planting Date
Economic optimal planting date depends on the price of wheat forage, the price of wheat grain, the test weight price adjustment, and cost differences across planting dates. It was assumed that tillage, seeding, and grain harvest costs are constant across planting dates. Some custom harvesters adjust charges based on grain yield. However, Kletke and Doye (2000) reported that the majority of observations in their custom rate survey reported a flat rate charge per acre for harvesting wheat.

Fertilizer was applied sufficiently to all plots in the field experiment so that nutrient deficiencies were not a yield-limiting factor. However, it is assumed that N requirements and N removal depend on forage and grain yield. For the purpose of economic analysis it is assumed that 1 kg of wheat forage will remove 0.03 kg of N and 1 kg of wheat grain will remove 0.0333 kg of N (Krenzer, 1994). The adjustment for N cost may be accomplished by subtracting the cost of 0.03 kg of N from the price of a kilogram of forage, and the cost of 0.0333 kg of N from the price of a kilogram of grain.

The wheat grain price was also adjusted to reflect the cost of the quantity of P removed in grain. Hard red winter wheat contains approximately 0.43% P (National Research Council, 1984). The price of wheat grain was adjusted by subtracting the cost of 0.0043 kg of P from the price of a kilogram of wheat grain. However, an adjustment was not made to the price of forage for P. A very small quantity of P is removed by grazing livestock. The grazing animal would return almost all of the P consumed to the soil in the urine and feces. The same argument could be made for N in the forage. However, N in the urine and feces is much more likely to be lost as a result of volatilization and leaching. A second reason for assessing a charge for the N used to produce the forage is that producers apply more N to wheat intended for dual-purpose use than they do for wheat intended for grain only (True et al., 2001). Hence, the price of wheat grain is adjusted to reflect the cost of N and P and the price of wheat forage is adjusted to reflect the price of N. All production costs other than that of N and P are assumed constant across planting dates.

The net returns function for the dual-purpose wheat enterprise is:

[6]

where = net returns per hectare; P_f = N cost adjusted price of wheat forage; P_g = N and P cost adjusted price of wheat grain and D is the adjustment that depends on the test weight function, T; F is the forage yield function; and G is the grain yield function. The choice variable is planting date (PD). All three functions, F, G, and T, have random error term variables. Therefore, F, G, and T are also random variables.

Assuming that the dual-purpose winter wheat producers' objective is to maximize expected net returns, the optimization problem can be stated as

[7]

where E(·) is the expectations operator. The test weight adjustment schedule determined by market forces is assumed to be independent of grain yield. By definition, the expected value of the product of two independent random variables is equal to the product of the two expected values of those variables. Eq. [7] becomes

[8]

Assuming that the random error terms of the functions are normally distributed with mean zero, the expected values of F, G, and T were approximated by the estimated F, G, and T functions, respectively. Approximation of the expected value of D requires special attention. By definition,

[9]

where Prob(·) is the probability operator, D_i is the discount associated with the relevant test weight range, T_i is the lower limit of that test weight range, and T_i+1 is the lower limit of the next range. Using the assumption that T ~ N[E(T), ²_T], the normal cumulative distribution function available in EXCEL was used to approximate the expected value of D.

	RESULTS AND DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY AND CONCLUSIONS REFERENCES

 
As shown in Fig. 1, fall–winter forage production is negligible for wheat seeded in the region after the first week of October. Therefore, only observations from plots planted before 8 October were used to estimate the forage yield response function. The estimated regression equations for the forage yield, grain yield, and test weight response functions are reported in Table 2. All estimated parameters are significantly different from zero at the 5% level. Charts of the estimated forage yield, grain yield, and test weight response to planting date functions are included in Fig. 4 . The charts show the magnitude of forage yield, grain yield, and test weight response to planting date. A 20-d change in planting date from 10 to 30 September results in an 18% increase in expected grain yield and a 68% decrease in expected forage yield, but only a 0.5% increase in expected test weight.

View larger version (21K): [in this window] [in a new window]   Fig. 4. Predicted dual-purpose winter wheat forage yield, grain yield, and test weight generated from data of field trials conducted at Lahoma, OK, from 1991–1992 through 1999–2000.

For the economic analysis, base price estimates for standing wheat forage, wheat grain, N, and P were required as well as test weight wheat grain price adjustment factors. The average wheat grain price in Oklahoma during the 1991–2000 period was $0.12 kg^-1 (USDA-NASS, 2001a). The lowest was $0.08 in 1999 and the highest was $0.17 in 1996. The economic analysis was conducted for six levels of wheat grain prices, $0.095, $0.110, $0.128, $0.147, $0.165, and $0.184 kg^-1. An estimate of the variance of test weight, T, was needed to approximate the expected value of the test weight discount, D. The procedure used to estimate the test weight regression equation parameters also provided the following estimate of the variance of T,

[10]

where e is the base of the natural logarithm (approximately 2.718). Wheat grain test weight adjustment schedules were obtained from two companies that purchase wheat grain from farmers in the region (Dunn, 1998; Peavey Company, 2000).

Prices for standing fall–winter wheat forage are not routinely reported. However, some wheat producers lease their pasture to livestock owners and, in informal surveys during the time period of the field trials, farmers reported a range on lease rates of $0.55 to $0.88 kg ^-1 of beef gain for winter wheat pasture (Doye et al., 2001). In these lease arrangements, payments from livestock owners to wheat producers are based on net live weight gain attributable to the wheat pasture. These lease arrangements are made based on cattle price expectations and are typically not changed if the price of cattle increases or decreases beyond the expected levels.

The quantity of winter wheat forage required per kilogram of beef gain has not been precisely determined. Based on the National Research Council (1984) net energy equations used to estimate livestock requirements and based on nutrient analysis of wheat forage, an average of 7 kg of forage would be required per kilogram of gain for a 200-kg steer gaining 0.9 kg d^-1 for 115 d. Seven kg would be the minimum possible allowance, assuming 100% harvest efficiency, and no allowance for nonconsumptive loss (Krenzer et al., 1996). Allowing for nonconsumptive loss, it is assumed that 1 kg of beef gain is expected to require 10 kg (dry matter) of standing wheat forage. By this measure, during the time period of the study, the value of standing fall–winter forage was approximately $0.055 to $0.088 kg^-1 dry matter. For the present study, given the lack of precision relative to forage prices, the economic analysis was conducted for five levels of forage prices, $0.055, $0.061, $0.066, $0.073, and $0.077 kg^-1 dry matter.

For the analysis, two N prices were used. A price of $0.31 kg^-1 N was used to represent a low price situation and a price of $0.61 kg^-1 N was used to represent a high price situation. For all situations analyzed, the price of P was held constant at $0.56 kg^-1 P₂O₅ (USDA-NASS, 2001b). The SOLVER option in EXCEL was used to solve the optimization problem to determine the optimal planting date.

Table 3 includes the estimated planting dates that result in maximum net returns for 30 different combinations of wheat forage and wheat grain prices with a N price of $0.31 kg^-1. When the price of wheat forage is high ($0.077 kg^-1) and the price of wheat grain is low ($0.095 kg^-1) the optimal planting date is late August. Alternatively, when the price of wheat forage is low ($0.055 kg^-1) relative to the price of wheat grain ($0.184 kg^-1), the optimal planting date is 27 September. Based on the estimated functions, fertilizer prices, and test weight discount schedules, when the price of forage is $0.066 kg^-1 and the price of wheat grain $0.128 kg^-1, the optimal planting date is 6 September.

Table 5 includes the optimal planting dates for the same combinations of wheat grain, wheat forage, N, and P prices as used to determine the dates reported in Table 3, but under the assumption that none of the wheat grain prices were adjusted for differences in test weight. For a wheat grain price of $0.095 kg^-1, and a wheat forage price of $0.055 kg^-1, the optimal planting date is 24 August if the test weight adjustment is included, but 28 August when the test weight adjustment is ignored. Based on the estimated response function, the early planted wheat has a lower expected test weight. Inclusion of the test weight adjustment decreases the price of wheat grain relative to the price of wheat forage. Forage becomes relatively more valuable and planting 4 d earlier is expected to increase production of the relatively more valuable forage. However, as the price of wheat grain increases, for example to $0.184 kg^-1, the optimal planting date occurs in late September, and inclusion of the test weight adjustment in the optimization model does not change the optimal date. As shown in Tables 3 and 5, the optimal planting dates are the same across all forage prices when the wheat grain price is $0.184 kg^-1. It can be concluded that the optimal planting date is relatively insensitive to the test weight discount schedules when grain prices are relatively high.

	SUMMARY AND CONCLUSIONS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY AND CONCLUSIONS REFERENCES

Based on the estimated response functions, a 20-d delay in planting date from 10 to 30 September results in an 18% increase in expected grain yield and a 68% decrease in expected forage yield, but only a 0.5% increase in expected test weight. Producers whose sole objective is to maximize forage production would be expected to plant early. The expected fall–winter forage yield from the earliest planting date used in the field trials, 24 August, is 3277 kg ha^-1. However, the expected grain yield from a 24 August planting date is only 1879 kg ha^-1. Based on the estimated wheat grain yield response function, the maximum wheat grain yield of 3196 kg ha^-1 is expected to result from planting on 8 October. However, if planting is delayed until 8 October, the expected forage yield declines to 246 kg ha^-1. As the planting date changes from 24 August to 8 October, the expected fall–winter forage yield declines by 3031 kg ha^-1, but the expected wheat grain yield increases by 1317 kg ha^-1.

The estimated economic optimal planting date for dual-purpose winter wheat ranged from 24 August to 29 September, depending on the relative prices of wheat forage and wheat grain. When the price of fall–winter wheat forage is high relative to the price of wheat grain, it is optimal to plant early. Alternatively, when the price of wheat grain is high relative to the value of standing wheat forage, it is economically optimal to plant later. However, planting 1 wk earlier or 1 wk later than the optimal date is expected to decrease expected net returns by <$2.00 ha^-1. Finally, it was also determined that the optimal planting date is relatively insensitive to wheat price test weight adjustments when wheat grain prices are relatively high.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY AND CONCLUSIONS REFERENCES

 
This is journal paper AEJ-253 of the Oklahoma Agric. Exp. Stn., Project H-2403. This material is based on work supported in part by the Coop. State Research, Education, and Extension Service, USDA, under Agreement no. 93-34198-8410, 97-34198-3970, and 99-34198-7481.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY AND CONCLUSIONS REFERENCES

 

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