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Agronomy Journal 95:994-999 (2003)
© 2003 American Society of Agronomy

NITROGEN MANAGEMENT

Yield Goal versus Delta Yield for Predicting Fertilizer Nitrogen Need in Corn

J. A. Lory* and P. C. Scharf

Dep. of Agron., 210 Waters Hall, Univ. of Missouri–Columbia, Columbia, MO 65211

* Corresponding author (loryj{at}missouri.edu)

Received for publication May 16, 2002.

   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Fertilizer N needs of corn (Zea mays L.) vary widely both among and within fields. Many states use yield goal to help determine differences in fertilizer N need, but some states have questioned yield goal–derived recommendations because of the poor correlation of yield with fertilizer N need. In this study, data from 298 previously reported experiments in five states (Illinois, Minnesota, Missouri, Pennsylvania, and Wisconsin) were combined to evaluate fertilizer N response of corn. Corn grain yield at the economically optimum N rate (EONR) was positively but poorly correlated with EONR (r2 = 0.02). This was consistent with others who have observed that maximum or optimum economic yield is a poor predictor of EONR. Our analysis indicated N supplied by the soil and previous management reduced N need from that predicted by yield alone at most locations. Delta yield (grain yield at optimum N rate minus grain yield of the control) was a much better predictor of EONR at these same locations (r2 = 0.47). A theoretically derived equation based on the delta yield concept was similarly capable of predicting EONR for corn. These results indicate that fertilizer recommendation systems that rely solely on yield or ignore yield entirely are limited to explaining less than 50% of the variation in EONR for corn. Farmers should be encouraged to monitor delta yield as a more effective indicator of EONR than actual yield. Greater understanding of the delta yield concept is needed before relying on it to predict fertilizer N requirements.

Abbreviations: EONR, economically optimum N rate • FNUE, fertilizer N use efficiency • Nf, estimated economically optimum N rate • Ns, soil N • Yg 0N, grain yield with no fertilizer N applied • Yg OPTN, grain yield at the economically optimum N rate • {Delta}YgFN, delta yield, the yield increase due to application of fertilizer N


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
NITROGEN FERTILIZER NEEDS of corn can vary widely, both among fields (Schmitt and Randall, 1994; Bundy and Andraski, 1995; Scharf and Lory, 2001) and within fields (Malzer et al., 1996; Blackmer and White, 1998). This variation in corn N response has been attributed to differences in soil N (Ns) supply, corn N use efficiency, and crop N need (Meisinger, 1984). For example, high inorganic N concentration in the soil profile at the start of the growing season can reduce EONR in low-rainfall regions (Olson, 1984) and humid regions (Schmitt and Randall, 1994; Blackmer et al., 1989) of the USA. Legumes, such as alfalfa (Medicago sativa L.), preceding corn in a crop rotation can also reduce EONR (Shrader et al., 1966; Lory et al., 1995).

The accuracy of corn N fertilizer recommendations has important water quality implications. Fertilizer N application in excess of crop need dramatically increases residual N in the soil profile at the end of the growing season and following spring (Olsen et al., 1970; Schuman et al., 1975; Lory et al., 1995; Andraski et al., 2000). Excess residual NO3 in the soil profile in the fall is likely to move into ground or surface waters in humid regions of the USA (Olsen et al., 1970; Schuman et al., 1975; Lory et al., 1995; Andraski et al., 2000). Accurate fertilizer N recommendations can reduce residual N in the soil profile.

Underapplication of fertilizer N is costly to the corn grower (Black, 1993). Investment in fertilizer on a field that needs N is returned many times over in the value of the increased grain yield. Fertilizer recommendations must balance the risk of overapplication to our water resources with the risk of underapplication to the profitability of the farmer.

Traditional yield-based fertilizer N recommendations are derived from the mass balance approach detailed in part by Stanford (1973) and Meisinger (1984). Economically optimum N rate is estimated as total N content of the grain less all of the other sources of grain N and adjusted for inefficiencies in the ability of the crop to recover fertilizer N from the soil:

[1]
where Nf is the estimated EONR for a selected yield goal, Ng is N content of the harvested grain, Ngs is soil N in the harvested grain, and FNUE is the fertilizer N use efficiency (the proportion of fertilizer N applied to the soil that is recovered in the grain). Fertilizer N, Ng, and Ngs all must have the same units (e.g., kg N ha-1), and FNUE is unitless. It is assumed that Ns and fertilizer N are recovered with equal efficiency by the plant.

The mass balance approach takes the following form in many fertilizer N recommendations for U.S. corn grain:

[2a]

[2b]
where Ns is the quantity of N supplied by the soil and YG is the expected grain yield. Units are kg ha-1 for Nf and Ns and Mg ha-1 for YG (Eq. [2a]) or lb acre-1 for Nf and Ns and bu acre-1 for YG (Eq. [2b]). States following this yield goal approach include Illinois (Hoeft and Peck, 2001), Minnesota (Schmitt et al., 1998), Missouri, Nebraska (Hergert et al., 1995), North Dakota (Dahnke et al., 1992), Pennsylvania (Beegle and Wolf, 2000), and South Dakota (Gerwig and Gelderman, 1996). Indiana, Michigan, and Ohio follow the same approach as Eq. [2b] but use the equation 1.36 x YG - Ns - 27, where units are lb acre-1 for Nf and Ns and bu acre-1 for YG (Vitosh et al., 1995). Nitrogen recommendation based on yield goal also have been incorporated into many regulatory and technical standards (USDA-NRCS, 1999; USEPA, 2001).

Yield goal alone typically does not correlate well with EONR (Vanotti and Bundy, 1994; Fox and Piekielek, 1995; Kachanoski et al., 1996; Bundy, 2000). Vanotti and Bundy (1994) found no correlation between yield and EONR (r2 = 0.02) in a 24-yr N rate study. Fox and Piekielek (1995) found no correlation with yield goal and EONR in a summary of 57 site-years in Pennsylvania (r2 = 0.08). Bundy (2000) found no correlation of yield goal and EONR in 101 site-years of data summarizing Wisconsin experiments from 1989 to 1999. A summary of 300 N fertilizer experiments in Ontario concluded that maximum yield explained less than 15% of the variability in the EONR (Kachanoski et al., 1996).

The limited ability of yield goal alone to predict EONR is anticipated in N recommendation systems based on yield goal. Economically optimum N rate is a function of yield goal and Ns (Eq. [2]). Yield goal will be poorly correlated with EONR if Ns is variable among years and locations and is a significant source of grain N in at least some years and locations.

Soil N supply is expected to vary among years and location. Components of Ns include soil organic matter, residual organic and inorganic N from previous N applications, atmospheric N fixed by legumes and free-living N-fixing bacteria, and atmospheric deposition (Legg and Meisinger, 1982). Corn N recommendations typically include a system of N credits to account for conditions that increase the quantity Ns available to the crop. Nitrogen credits are usually given for elevated inorganic N in the profile, recent cropping history that includes legumes, recent applications of manure, and in a few cases, high soil organic matter (Vitosh et al., 1995; Gerwig and Gelderman, 1996; Hergert et al., 1995; Schmitt et al., 1998). In practice, many of these credits are ignored because of cost (e.g., soil inorganic N test) or difficulty in accurately predicting their contribution.

Yield goal has been eliminated from N recommendations in at least two states (Vanotti and Bundy, 1994; Bundy, 2000; Blackmer et al., 1997). In Wisconsin, recommended fertilizer N rate is based on regional soil characteristics (Vanotti and Bundy, 1994). The state was divided into four regions based on soil characteristics anticipated to affect productivity and FNUE. A standard fertilizer N rate for corn was determined for each soil region based on fertilizer N response experiments. These recommendations are further adjusted for annual and locational variations in soil organic matter, residual N levels in the soil, and other management factors that affect Ns. Iowa also developed statewide recommendations independent of yield goal. The recommendation is based only on cropping system. Soil N tests can be used to hone the recommendation to field-specific Ns conditions. Information supporting the Iowa and Wisconsin recommendation systems both cite the lack of correlation between yield goal and EONR as one of the reasons for adopting the current recommendation system (Vanotti and Bundy, 1994; Blackmer et al., 1997).

The elimination of a yield component from fertilizer N recommendations is a fundamental shift in fertilizer N management. In the Iowa and Wisconsin systems, a site that has potential for a large increase in yield from fertilizer N can have the same recommendation as a site that has only a small potential for increasing yield. Fertilizer recommendations in most other states are predicated on the assumption that more fertilizer N is needed at locations where there is a greater potential yield increase from applied N.

Delta yield ({Delta}YgFN) is the yield increase due to application of fertilizer N:

[3]
where Yg OPTN is the grain yield at the EONR and Yg 0N is the grain yield with no fertilizer N applied. Kachanoski et al. (1996) suggested that delta yield might improve variable fertilizer N rate recommendations because it was better correlated with EONR in Canadian studies than yield goal. Delta yield has shown promise to predict within-field variation in EONR of corn (Kachanoski et al., 1996; Braum et al., 1999).

Our objective in this paper is to evaluate the utility of grain and delta yield in predicting EONR in corn. In this effort, we derive both empirical and theoretical equations predicting EONR from delta yield.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
We created a data set from previously published data from 298 fertilizer N response experiments in Illinois (n = 54), Minnesota (n = 54), Missouri (n = 32), Pennsylvania (n = 57), and Wisconsin (n = 101). These sites represented a wide range of management systems, soil types, and climate conditions within the humid region of the USA. Tillage systems included no-till, moldboard plow, and chisel plow. Previous crops included corn (n = 179), soybean [Glycine max (L.) Merr.; n = 67], small grain {wheat (Triticum aestivum L.) and sorghum [Sorghum bicolor (L.) Moench]; n = 18}, and alfalfa (n = 33); 74 locations had animal manure applications in the past 3 yr. Further details of the experiment locations and the management of the fertilizer N response experiments can be found in Brown (1996), Schmitt and Randall (1994), Scharf and Lory (2001), Scharf and Lory (2002), Fox and Piekielek (1995), and Bundy (2000).

In each of these experiments, multiple fertilizer N rates had been applied to corn, and grain yield for each treatment was measured at physiological maturity. Experimental locations were typically in farmer fields, and all sites had uniform N management before the initiation of treatments. In some cases, starter fertilizer containing N had been uniformly applied to the entire experimental area before application of the N treatments. This methodology determines the response to fertilizer N for conditions at the time fertilizer N treatments were applied. Typically, the quadratic plateau model was used to model yield response to fertilizer N at each site responsive to applied N. We obtained estimates of control grain yield (no fertilizer N applied), the EONR, and the yield at the EONR from each previously published experiment. For the Missouri and Pennsylvania data, we had access to the regression coefficients and calculated EONR and the associated optimum yield based on a fertilizer/crop value ratio of 0.1. In the remaining three states, we relied on reported EONR and associated yields. From this process, each location was reduced to a single data point for our analysis. To assess the ability of delta yield to predict grain N concentration, we evaluated a subset of 38 Minnesota locations where grain N concentration had been measured and grain yield was responsive to fertilizer N.

Linear and nonlinear regression methods were used to determine best fit models (SAS Inst., 1987) and to test the hypothesis that regression lines from different states had the same slope and intercept (Weisberg, 1985, p. 179–183).


   RESULTS AND DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
CONCLUSIONS
 REFERENCES
 
Economic optimum yield for corn grain at the 298 locations in Pennsylvania, Missouri, Minnesota, Illinois, and Wisconsin had a mean of 9.7 Mg ha-1 (155 bu acre-1), a range from 4.3 to 14.7 Mg ha-1, and a normal distribution (P < W = 0.28). Grain yield of control plots had a mean of 7.8 Mg ha-1 (124 bu acre-1) and a range from 1.9 to 14.7 Mg ha-1. Yield was not affected by fertilizer N at 105 of 298 locations (EONR = 0). Omitting the nonresponsive locations, mean EONR was 125 kg N ha-1 (111 lb acre-1) and ranged from 29 to 222 kg N ha-1 for 193 responsive locations.

Correlation of Yield Components with Optimum Nitrogen Rate
Grain yield at EONR at responsive sites was positively but poorly correlated with EONR (Fig. 1) . These results are consistent with others that have observed that maximum or optimum yield is a poor predictor of EONR (Vanotti and Bundy, 1994; Fox and Piekielek, 1995; Kachanoski et al., 1996; Bundy, 2000).



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  		Fig. 1. The effectiveness of yield at the economically optimum N rate (EONR) rate as a predictor of EONR for corn. Data summarize 298 experiments of corn response to fertilizer N in five states.

 
Delta grain yield was highly correlated with EONR in each state (Fig. 2) . Kachanoski et al. (1996) suggested a nonlinear model that approached a plateau N rate of approximately 175 kg N ha-1 for a subset of N response trials in southwestern Ontario. Their model indicated that N need is independent of delta yield at high delta yield sites; that is, above a critical value, increasing yield was obtained only from increased FNUE with no additional need for fertilizer N. We rejected this explanation for the five states we evaluated because there was no evidence of curvilinearity in the relationship between delta yield and EONR in any individual state (Illinois, P = 0.39; Minnesota, P = 0.42; Missouri, P = 0.41; Pennsylvania, P = 0.67; Wisconsin, P = 0.69). The positive linear increase in EONR with delta yield emphasizes that yield is an important component of accurate fertilizer N recommendations (Fig. 2). Locations that have a greater ability to increase yield with additions of fertilizer N required higher fertilizer N rates to attain optimum yield. Consequently, fertilizer N recommendations at these more responsive sites should be greater than recommendations at less responsive sites. This result supports yield-based fertilizer N recommendation systems based on Eq. [2] if a comprehensive N credit system is in place to account for variability of Ns supply.



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  		Fig. 2. The effectiveness of delta yield [grain yield at the economically optimum N rate (EONR) minus grain yield of the control] as a predictor of EONR. Data summarize 193 experiments of corn response to fertilizer N in five states where fertilizer N increased corn yield.

 
Separate regression lines for each state did not significantly improve model fit compared with a single line for all states (Fig. 2; P = 0.23), indicating that a single relationship could be used to relate EONR to delta yield among the five states. Slope of the five-state model (Fig. 2a) was 19.4 kg N Mg-1 (1.1 lb N bu-1), a value similar to 21.4 kg N Mg-1 (1.2 lb N bu-1) used to estimate total N need for corn in Illinois, Minnesota, Missouri, Pennsylvania, and many other states (Hoeft and Peck, 2001; Schmitt et al., 1998; Beegle and Wolf, 2000). The essential role of N credits in yield-based fertilizer N recommendations based on Eq. [2] is emphasized by Fig. 3 . Almost all data points in Fig. 3 fall above the 1:1 line, indicating that yield goal strategies with no Ns adjustment typically recommended more than the optimum N rate.



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  		Fig. 3. Correlation between economically optimum N rate (EONR) and N rate calculated as 24.1 kg N Mg-1 (1.2 lb N bu-1) x yield goal. The two approaches are equivalent at locations that fall on the 1:1 line. Data summarize 193 experiments of corn response to fertilizer N in five states where fertilizer N increased corn yield. Legend indicates whether manure was applied in the past 3 yr and the previous crop; alfalfa includes alfalfa 1 and/or 2 yr before corn.

 
The best-fit models for delta yield vs. EONR had an intercept significantly greater than 0 for individual states and the combined five-state data (Fig. 2). Theoretically, the model must pass through the intercept (0 delta yield, 0 optimum N rate). We have few locations with a delta yield below approximately 0.6 Mg ha-1 (10 bu acre-1) because it is difficult to measure significant differences less than this in field fertilizer N response experiments. We can infer that in the range where we have no data, there is a greater requirement for N per unit increase in delta yield. The true model of delta yield predicting EONR is likely to be nonlinear starting at the intercept, with a diminishing slope rapidly approaching the constant positive slope derived in Fig. 2. The lack of locations with small delta yield and variability make it impossible to empirically derive the curvature at the lower end of this relationship.

Theoretically Derived Equation for Estimating Economically Optimum Nitrogen Rate Based on Delta Yield
In the previous section, we derived an empirical equation relating delta yield to EONR through regression analysis (Fig. 2). A theoretical equation predicting EONR from delta yield can be derived by considering pools of N in corn grain. Corn grain yield in fertilized systems can be divided into two components:

[4]

All N in the delta yield component of the grain is assumed to be derived from fertilizer N. Application of fertilizer N also has the potential to increase the grain protein content in the Yg 0N component of yield as the plant moves from a N-deficient to a N-sufficient status with the application of fertilizer N.

Fertilizer N rate can then be estimated as:

[5]
where CNOPTN is the grain N concentration at EONR and {Delta}NC is the potential increase in grain N concentration observed as the plant moves from restricted N status to optimum N status with the application of fertilizer N. Units for Nf are kg N ha-1, {Delta}YgFN and Yg 0N have units of Mg ha-1, CNOPTN and {Delta}NC have units of g N kg-1, and FNUE is unitless. This analysis assumes that application of fertilizer N does not affect the size and utilization of Ns by the plant.

The theoretical equation estimating EONR using delta yield requires estimating N concentration in the grain and the change in N concentration as the plant moves from zero fertilizer N to sufficient N (Eq. [5]). Grain N concentration of the control (zero N) decreased as delta yield increased in the Minnesota subset of data that included grain N concentration and was responsive to applied fertilizer N (Fig. 4) . Grain N concentration at the EONR was predicted to be 14 g kg-1. Stanford and Legg (1984) reported grain N concentration varying from 14 to 16 g kg-1 in an Iowa survey. Our estimate may be in the lower end of the range because it does not include excessive N plots that may have an additional increase in N content. The Nf can be calculated by integrating information from Fig. 4 into Eq. [5]:

[6]



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  		Fig. 4. The correlation of corn grain N concentration of the control with delta yield for 38 fertilizer N response experiments in Minnesota. All locations were responsive to fertilizer N.

 
The five-state database of 193 locations responsive to fertilizer N included estimates of Yg 0N and {Delta}YgFN for each location; we only lacked an estimate of FNUE to use Eq. [6] to estimate Nf. To evaluate the utility of Eq. [6] to predict Nf, we selected the value of FNUE that resulted in the best-fit slope of 1.0 (forced through the intercept) when comparing measured EONR and Nf as predicted by Eq. [6] (Fig. 5) . A value of FNUE = 0.47 (47%) resulted in a slope of 1.0 between measured and predicted EONR (Fig. 5). The implication is that mean FNUE was 47% among these 193 locations, which is within the range of 30 to 70% FNUE typically seen with in-season N applications (Bock, 1984).



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  		Fig. 5. Economically optimum vs. predicted fertilizer N rate. Predicted fertilizer N rate was predicted using a theoretically derived equation based on delta yield (Eq. [6]) and assumes 47% fertilizer N use efficiency. Data summarize 193 experiments of corn response to fertilizer N in five states where fertilizer N increased corn yield.

 
Limits of yield-based estimates of EONR used by most states are evident in Fig. 2 and 5; discrepancies between actual and predicted EONR were more than 50% of the variation in the delta yield relationship (empirical approach, Fig. 2, r2 = 0.47; theoretical approach, Fig. 5, r2 = 0.46). Equation [6] indicates that most of this variation is differences in FNUE. Yield goal–based recommendations will likely have lower ability to estimate EONR because of the added errors from estimating Ns supply. At the other extreme, systems that do not use yield potential, such as Wisconsin and Iowa, will also be limited in their ability to predict EONR.

To predict more than 50% of the variation in EONR will require a system that addresses both yield and FNUE. Current fertilizer recommendation systems based on Eq. [2] have no mechanism for incorporating variations in FNUE into fertilizer N recommendations. Equation [6] provides a mechanism to incorporate FNUE into a fertilizer N recommendation system. Using a uniform estimate of FNUE of 47% for all locations explained 46% of the variation in the data (Fig. 5). Successfully adjusting individual locations for having FNUE greater or less than 47% would improve model fit.

Implementing a Delta Yield Fertilization Recommendation System for Corn
The close association of delta yield with EONR suggests it may have potential as an alternative approach for developing fertilizer N recommendations or end-of-season assessment of fertilizer N rate. Farmers should be encouraged to use delta yield rather than yield goal to determine fertilizer N need. Instead of asking the question, what yield do I expect from this field? farmers should be encouraged to ask the questions, what yield do I expect if I don't fertilize? and how much more yield can I expect if I apply fertilizer N? This focuses the farmer's fertilizer assessment on the component of yield most associated with fertilizer N need.

A history of delta yields for a field over time is a rough estimate of the quantity of fertilizer N needed and the variability in need over years. Variation of delta yield within a field is an estimate of the variation in EONR within a field. Delta yield is a relatively simple measurement, and maintaining records of delta yield within a field and over years would provide a more effective predictor of EONR in the field than yield. Determination of delta yield has the potential to be automated for farmers applying fertilizer N with variable rate applicators and using georeferenced yield monitors. Kachanoski et al. (1996) also suggested that delta yield has potential to improve fertilizer N recommendations within a field.

Delta yield can be determined by applying no fertilizer N to patches or strips of a corn field. This can be done by shutting off the fertilizer N applicator at predetermined locations within a field. Location of the zero-N strip should change each year corn is grown. Comparing yield in these zero-N areas with yield in the surrounding fertilized areas is a direct measure of delta yield. Yields can be determined from a yield map generated with a yield monitor or by hand-harvesting a known length of row in the zero-N and fertilized areas of the field. The mean cost from reduced grain yield of such as system among the 193 responsive locations we analyzed would be less than $3.42 ha-1 ($1.38 acre-1) if each patch were 18 by 36 m (60 by 120 feet), there was one patch every 4 ha (10 acres), and corn grain value was $68.90 Mg-1 ($1.75 bu-1).

Delta yield also has potential as a diagnostic tool evaluating fertilizer N management in past years. This is a relatively insensitive tool for evaluating over- or underapplication of fertilizer N because of the relatively large variation about the best-fit line (Fig. 2). For the empirical approach (Fig. 2), the optimum fertilizer N rate can be estimated from delta yield within ±48 kg N ha-1 (54 lb N acre-1) within the range of reported data 95% of the time. However, this could be improved on if postmortem analysis included a refined estimate of FNUE (Eq. [5]).

Developing fertilizer N recommendations based on delta yield will require more information on the annual variability of delta yield. To what degree is annual variation in fertilizer N need due to delta yield, or does annual variation in FNUE play a more prominent role? What is the role of delta yield in hybrid improvement? In a delta yield recommendation system, should unanticipated residual N in the profile be counted as a fertilizer source and subtracted from the predicted EONR?


   CONCLUSIONS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
REFERENCES
 
Among 193 locations in five states, the delta yield component of yield described 47% of the variation in EONR. This implies that fertilizer recommendation procedures that rely solely on yield components or ignore yield components will likely never explain more than 50% of the variation in EONR. A theoretically derived equation based on the delta yield concept provides a procedure to predict EONR that does not require directly estimating Ns supply and provides a procedure to integrate estimates of FNUE into a fertilizer recommendation. Such a system may find first use in diagnostic evaluations of the past year's fertilizer N rates. Greater understanding of the delta yield concept is needed before relying on it to predict fertilizer N requirements.


   ACKNOWLEDGMENTS
 
We gratefully acknowledge Larry Bundy, Bob Hoeft, and Mike Schmitt for sharing data from N response experiments in their states.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Contrib. of the Univ. of Missouri Agric. Exp. Stn. and the Univ. of Missouri Commercial Agric. Progr.


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




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Right arrow Maize
Right arrow Nutrient Management
Right arrow Soil Fertility and Productivity
Right arrow Maize Management

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The SCI Journals Crop Science Vadose Zone Journal
Journal of Natural Resources
and Life Sciences Education		 Soil Science Society of America Journal
Journal of Plant Registrations Journal of
Environmental Quality		 The Plant Genome