Quantification of the yield and protein response to N and water availability by two wheat classes in the semiarid prairies

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1 Quantification of the yield and protein response to N and water availability by two wheat classes in the semiarid prairies F. Selles 1, J. M. Clarke 1, and R. M. DePauw 2 1 Agriculture and Agri-Food Canada. Brandon Research Centre, PO Box 1000A RR#3, Brandon, Manitoba, Canada R7A 5Y3 (sellesf@agr.gc.ca); and 2 Agriculture and Agri-Food Canada, Semiarid Prairie Agricultural Research Centre, Swift Current, Saskatchewan, Canada S9H 3X2. Received 14 January 2005, accepted 13 April Selles, F., Clarke, J. M. and DePauw, R. M Quantification of the yield and protein response to N and water availability by two wheat classes in the semiarid prairies. Can. J. Plant Sci. 86: Genetic improvements have increased grain yield of newer wheat (Triticum spp.) cultivars relative to older benchmark cultivars. However, the improvements have been larger in the Canada Western Amber Durum wheat class (CWAD) [Triticum turgidum L. var. durum (T. durum)] than the Canada Western Red Spring class (CWRS) (Triticum aestivum L.). Thus, it is necessary to determine if N fertilizer recommendations for these two wheat classes need to be segregated. We conducted fertility trials for four CWAD and seven CWRS cultivars during 4 yr, in two soils in southwestern Saskatchewan, under fallow- and stubble-cropping to determine if there were differences in the N response of the two wheat classes under various water availability conditions. Grain yields of CWAD were consistently higher than those of CWRS, while protein concentrations were higher for CWRS than for CWAD. A regression model consisting of available water (W) plus the interactions of W with fertilizer N (N F ), N F 2, soil NO 3 -N in the 0- to 60-cm depth (N S ), N S 2, and with the N F N S interaction explained 76% of the yield variability of all cultivars and site years. Inclusion of wheat class as an indicator variable increased the proportion of the explained variability to 80%, and determined that both classes of wheat had similar response to N availability, and that CWAD had a larger response to available water than CWRS. A regression model consisting of a quadratic function of N and a linear term for W with wheat class as an indicator variable explained 58% of the variability in grain protein of all varieties, and indicated that both classes had the same protein response to N availability, but that the protein of CWAD decreased faster than that of CWRS as water availability increased. Inclusion of wheat cultivars as indicator variable, instead of wheat class, did not increase the resolution of the regression model for either yield or protein responses. Results of these analyses indicate that the amount of N required for maximum yield is the same for both wheat classes. Key words: Durum wheat, hard red spring wheat, grain yield, grain protein, fertilizer N, soil N, available water Selles, F,. Clarke, J. M. et DePauw, R. M Quantification du rendement de deux classes de blés et concentration de protéines en fonction de l azote et de l eau disponibles dans les régions mi-arides des Prairies. Can. J. Plant Sci. 86: La génétique a permis d accroître le rendement grainier des nouveaux cultivars de blé (Triticum sp.) par rapport aux variétés plus anciennes utilisées comme point de comparaison. Ces améliorations concernent cependant davantage la catégorie de blé dur ambré de l Ouest canadien (BDAOC) [Triticum turgidum L. var. durum (T. durum)] que celle du blé roux de printemps de l Ouest canadien (BRPOC) (Triticum aestivum L.). Il convient donc d établir s il faut formuler des recommandations distinctes pour la fertilisation azotée de chaque type de blé. Pendant quatre ans, les auteurs ont procédé à des essais de fertilisation sur quatre cultivars BDAOC et sept cultivars BRPOC. Les essais se sont déroulés sur deux sols du sud-ouest de la Saskatchewan, sur jachère ou sur chaume, dans des conditions variables quant à la quantité d eau disponible. Le BDAOC donne toujours un rendement grainier supérieur à celui du BRPOC, mais le grain du BRPOC est constamment plus riche en protéines que celui du BDAOC. Un modèle de régression intégrant la quantité d eau disponible (W) plus les interactions de W avec l engrais N (N F ), N F 2, la concentration de N-NO 3 dans la couche de 0 à 60 cm du sol (N S ), N S 2 et l interaction N F N S explique 76 % de la variabilité du rendement pour les cultivars et les années-site. Quand on inclut la catégorie de blé comme variable indicatrice au modèle, cette proportion passe à 80 % et on constate que les deux types de blé réagissent de la même manière à la quantité d azote disponible, mais aussi que le BDAOC réagit plus que le BRPOC à l eau disponible. Un modèle de régression comprenant une fonction quadratique de N et un terme linéaire de W, et incluant la catégorie de blé comme variable indicatrice, explique 58 % de la variabilité de la teneur en protéines du grain pour toutes les variétés et révèle que la disponibilité de l azote a la même incidence sur la concentration de protéines chez les deux types de blé, mais que cette dernière diminue plus vite chez le BDAOC que chez le BRPOC quand la quantité d eau disponible augmente. L inclusion du cultivar au lieu de la catégorie comme variable indicatrice n améliore pas la résolution du modèle de régression au niveau du rendement ou de la variation de la teneur en protéines. Les résultats de ces analyses indiquent que les deux types de blé ont les mêmes besoins azotés pour atteindre leur rendement maximal. Mots clés: Blé dur, blé roux vitreux de printemps, rendement grainier, teneur en protéines du grain, engrais N, N du sol, eau disponible 981 Abbreviations: CWAD, Canada Western Amber Durum; CWRS, Canada Western Red Spring; GPC, grain protein concentration; GSP, growing season precipitation; SSM, spring soil moisture

2 982 CANADIAN JOURNAL OF PLANT SCIENCE Wheat production is highly dependent on an adequate supply of N to ensure the crop achieves its highest yield and quality under the environmental conditions of the growing season (Selles and Zentner 2001). Typically, the yield response of wheat to N fertilization is largely dependent on growing season water availability (Davidson and Campbell 1984; Selles et al. 1992). Past studies comparing the responses of various wheat cultivars have indicated that N cultivar interactions are seldom seen under dryland farming in subhumid or semiarid environments (McNeal et al. 1971; Knapp and Harms1988; Selles et al. 1991). In an attempt to improve grain yield, wheat breeding programs in western Canada have introduced a number of new cultivars within the Canada Western Red Spring (CWRS) and Canada Western Amber Durum (CWAD) wheat classes. Earlier studies indicated that genetic yield gains in these new varieties were due mostly to increases in the number of kernels rather than in kernel size (Hucl and Baker 1987; McCaig and DePauw 1995; McCaig and Clarke 1995). In general, genetic yield improvement within the CWAD class between 1947and 1992 has been in the order of 23 kg ha 1 yr 1 (McCaig and Clarke 1995), while for the CWRS class it has been in the order of 6 to 9 kg ha 1 yr 1 (McCaig and DePauw 1995). These rates of gain increased in the later 1990s in the moister areas of the prairies, but in the Brown soil zone the gains have been negligible due to the water stress imposed by the semiarid environment (Clarke et al. 1997; Clarke et al. 1999; DePauw et al. 1999; Graf et al. 2003) For these genetic yield improvements, or any other improvements in production technologies, to be expressed as actual yield increases in commercial crop production, other factors limiting crop yield should be minimized. Selles and Zentner (1993) demonstrated that for wheat production in southwest Saskatchewan, the yield benefits of improved production technologies and new cultivars were highly dependent on levels of N available to the crop. Thus, an adequate fertilization program that takes into account crop needs and N supply from the soil is fundamental for producers to capitalize on the benefits of improved wheat cultivars. Given that CWAD has a higher yield potential than CWRS (Selles et al. 1991), it is logical to question whether the current practice of making common fertilizer N recommendations for both wheat classes (Troy McInnis, Enviro- Test Laboratories, personal communication) is still justified after the genetic yield improvements in current wheat cultivars grown in western Canada. Furthermore, possible deleterious effects of fertilizer nutrients on the environment (Chambers et al. 2001), the increasing need for food production to feed a burgeoning world population, and decreasing margins of production at the farm level dictate that nutrient requirement recommendations for crops must be revised to meet environmental, food production, and farm profitability objectives. The objective of this study was to determine if there were significant differences between the quantitative grain yield and grain protein responses of the CWAD and CWRS wheat classes to N fertilization under the available water and environmental conditions prevalent in southwest Saskatchewan using a regression response model, in order (i) to decide whether to segregate fertilizer N recommendations for these two wheat classes, and (ii) to generate actualized response functions that could be used to estimate N requirements under various water availability conditions. MATERIALS AND METHODS Four CWAD cultivars: AC Avonlea (Clarke et al. 1999), AC Morse, AC Navigator (Clarke et al. 2001), and Kyle (Townley-Smith et al. 1987); and seven CWRS cultivars: AC Barrie (McCaig et al. 1996), AC Cadillac (DePauw et al. 1998), AC Elsa (Clarke et al. 1997), AC Intrepid (DePauw et al. 1999), AC Majestic, McKenzie (Graf et al. 2003), and Neepawa (Campbell 1970) were grown from 1998 to 2001 under different levels of N fertilization near Stewart Valley, SK (50 36 N, W), and near Swift Current, SK (50 17 N, W). At Stewart Valley the soil was a Sceptre Heavy Clay, a Rego Brown Chernozem derived from clayey weakly calcareous glacio-lacustrine deposits, and at Swift Current the soil was a Swinton Silt Loam, and Orthic Brown Chernozem derived from eolian deposits overlaying glacial till (Ayres et al. 1985). The field experiment was conducted under fallow- and stubble-cropping conditions to obtain a wider range of available water and residual soil nitrogen, and thus obtain a high- and a low-productivity test in each site-year. Fertilizer nitrogen was applied as urea (46-0-0) at rates of 0, 25, 50, 75, 100, and 125 kg N ha 1. All treatments received a blanket application of 10 kg P ha 1 as triple superphosphate (0 46 0) and, at the Swinton soil only, 10 kg S ha 1 as potassium sulphate ( ) to ensure that no nutrient other than N limited crop yields. All fertilizers were applied at seeding in a side band approximately 2.5 cm to the side and 2.5 cm below the seed row. At each site, the experiment was set up as a randomized complete block design with a full factorial combination of wheat cultivars and N fertilizer rates, and with two replicates. Experimental plots (1 by 6 m) were seeded with a plot seeder equipped with offset double disk openers (Dyck et al. 1993) and 25-cm row spacing. Typically, plots were seeded around May 14 and were harvested the last week of August or the first week of September. Each experimental site was equipped with a portable meteorological station for measurement of precipitation, air temperature, and soil temperature. Class A pan evaporation was measured at a meteorological station near the Swift Current plots. Every year in early spring, six to eight soil samples were taken from each experimental site to a depth of 1.2 m with a coring device. Samples were separated into 0 to 15, 15 to 30, 30 to 60, 60 to 90, and 90 to 120 cm depth increments and combined by depth increment. Gravimetric soil water content was determined for every depth, and the values were converted to depth of available water using the bulk density of the cores and their moisture content at 1.5 MPa. Representative sub-samples were extracted with 0.5 M NaHCO 3 for determination of NO 3 -N, PO 4 -P, and available K; NO 3 -N was determined by the hydrazine reduction

3 SELLES ET AL. WHEAT RESPONSE TO N FERTILIZATION 983 method, PO 4 -P by the molybdate/ascorbic acid method (Hamm et al. 1970) and K by atomic absorption (McKeague 1978). Sulphate S was determined in CaCl 2 extracts by the methyltymol blue method (Adamski and Willard 1975). Nutrient concentrations were converted to kg ha 1 using the bulk density of each core sample. At harvest, the plots were trimmed to a uniform area and the crop was harvested with a plot combine. Grain yield was determined for each sample. Grain N concentration was determined by colorimetric methods in a Technicom Auto Analyzer after wet oxidation (Starr and Smith 1978). We used least squares regression to obtain a quantitative relationship between grain yield and grain protein concentration with available N and water. We used a model consisting of a second degree polynomial of available water and available N plus an N W interaction term to describe the yield and protein responses (Eq. 1). Yield = a + bn + cn 2 + dw +ew 2 + fnw (1) where N represents available N, calculated as the sum of NO 3 -N in the top 60 cm of the soil profile and N applied as fertilizer; W represents water available to the crop, defined as the sum of available water in the soil profile to 120 cm depth plus precipitation received from seeding to harvest; and a, b,, f are regression coefficients. Although a number of different models have been proposed to describe crop responses (Thornley 1978; Cerrato and Blackmer 1990; Bullock and Bullock 1994; Halvorson et al. 2004), we chose this model because it allows an easy way to incorporate class variables to estimate the effects of wheat class or cultivars on the response function, identify which terms in the model are affected by these variables, and quantify the magnitude of the effect (Freund et al. 2003). Furthermore, by taking the first derivative of this model with respect to N, equating the derivative to zero, and solving for N allows for calculation of the amount of N required to achieve maximum yield ( maxy ) (Selles et al. 1992) (Eq. 2). N maxy = (b + fw)/2c (2) To determine the effects of wheat classes on the regression coefficients of the models, we added wheat classes as a class variable in the regression model. Thus, the model defined in Eq. 1 became: Yield = (a + m a ) + (b + m b )N + (c + m c )N 2 + (d + m d )W + (e + m e )W 2 + (f + m f )NW (3) where m a, m b,, m f are regression coefficients produced by dummy variables, or indicator variables, that take a value of +1 when the wheat class is CWAD or a value of 1 when the wheat class is CWRS. These regression coefficients, sometimes called shifters, measure the amount and direction of shift of the regression coefficients between classes. We used a similar approach to determine the effect of wheat cultivar on the response models. To determine which variables had a significant effect on the response models, we used a stepwise regression procedure with a mixed variable selection option (SAS Institute, Inc. 2002). RESULTS AND DISCUSSION Environmental Conditions The crop was seeded as early as possible, which depended on surface soil moisture and temperature conditions. Typically, the plots in the lighter-textured Swinton soil at Swift Current were seeded during the first or second week of May, a few days ahead of the plots in the heavier- textured Sceptre soil at Stewart Valley (Table 1). The plots were harvested when the crop reached maturity and grain moisture was below 15%. Typically, harvest was done between the third week of August and the third week of September. Growing period length varied among site-years from 93 to 123 d, depending on weather conditions. Because the fallow system had higher water availability than the stubble system, in some years the growing period length for fallow-seeded crop was a few days longer than for crop seed on stubble (Table 1). Weather conditions during the study period varied substantially among soils and years (Table 2). In general, the crop was subjected to lower water stress in 1999 and 2000, and to higher water stress in 1998 and In 1998, temperature was above the long-term average, and growing season precipitation (GSP) and potential evaporation were near their long-term means; water stress arose from above-normal temperatures in May, July, and August, and below-normal precipitation in May and July. The driest conditions during the study were experienced in 2001, when average temperatures and potential evaporation were higher than the long-term average, while GSP was below the long-term average. In 1999 mean temperatures were close to the longterm average, potential evaporation and GSP were lower than normal, but spring soil available water was higher than normal because of late rains the previous fall. During 2000, temperatures were close to the long-term average, potential evaporation was low, and GSP was well above the longterm average. In southern Saskatchewan, water availability is one of the main factors limiting grain yields (Selles et al. 1992); thus, the average annual yields (Table 3) reflected the levels of SSM and GSP, together with temperature and potential evaporation. Yields were highest in 1999 and 2000 when the crop was under lower water stress, intermediate in 1998, and lowest in 2001, a year considered as a drought in the region. Yield of stubble-seeded wheat was lower than when seeded on fallowed land (Table 3). In the two favourable growing seasons (1999 and 2000), yield of the crop on stubble was on average 70% of the fallow yields. In the other 2 yr, with more adverse conditions, it was only 40% of fallow yields (data not shown), reflecting the fact that stubble cropping relies more on timely and abundant rains than fallow cropping, which buffers the system with water accumulated during the fallow period (Campbell et al. 1983)

4 984 CANADIAN JOURNAL OF PLANT SCIENCE Table 1. Seeding and harvesting dates, and growing season length (GSL) for each site year Fallow Stubble Soil Year Seeding Harvest GSL(d) Seeding Harvest GSL(d) Sceptre 1998 May 16 Sep May 16 Aug May 27 Sep May 27 Sep May 18 Sep May 18 Sep May 12 Sep May 12 Sep Swinton 1998 May 07 Aug May 07 Aug May 25 Sep May 25 Sep May 09 Sep May 10 Aug May 09 Aug May 10 Aug Table 2. Mean temperature (Mean T), precipitation (GSP), Class A pan evaporation (Evap), degree days (DD), spring soil available water to 120 cm depth (SSM), and total crop available water (AvW) SSM AvW Mean T DD z GSP Evap Fallow Stubble Fallow Stubble Soil Year ( C) (mm) Sceptre Swinton z Degree days calculated with base 0 C Table 3. Soil available water to 120 cm depth (SSM), NO 3 -N to 60 cm (STN), PO 4 -P to 15 cm (STP), available K to 15 cm (STK), and SO 4 -S to 60 cm (STS) sampled in early spring Fallow Stubble Soil Year SSM STN STP STK STS SSM STN STP STK STS (mm) (kg ha 1 ) (kg ha 1 ) Sceptre Swinton Although some authors consider rainfall from May 01 to Jul. 31 as GSP (Bole and Pittman 1980; Campbell et al. 1988), in this study we used precipitation accumulated from seeding to harvest of the crop. Using May 01 to Jul. 31 precipitation produced a model with R 2 = 0.62 and a root mean square of the error (RMSE) of 715 kg ha 1, when using precipitation from seeding to harvest, the model R 2 increased to 0.76 and the RMSE decreased to 575 kg ha 1. Available Nutrients Soil nutrient levels measured in spring exhibited large variation among site-years, and reflected both the effect of cropping system and soil properties, and the effect of weather in the previous growing season. In general, fallow systems had higher levels of spring available water and available N and P than the stubble system (Table 3), reflecting the accumulation of products of mineralization processes and water recharge ongoing during the fallow period. Levels of available N in the soil in spring suggest that, in years with average precipitation, between 40 and 50 kg N ha 1 should be applied to wheat seed on stubble, and between 0 and 45 kg N ha 1 to wheat seeded on fallow to achieve maximum production, according to current recommendations (Saskatchewan Advisory Council in Soils and Agronomy 1988). The levels of available P, especially in stubble fields, indicated that between 7 and 11 kg P ha 1 should be applied

5 SELLES ET AL. WHEAT RESPONSE TO N FERTILIZATION 985 to ensure this nutrient does not limit grain yields (Troy McInnis, Enviro-Test Laboratories, personal communication). Available K in both soils was well above the critical level (157 kg ha 1 ) established for dryland wheat production (Saskatchewan Advisory Council in Soils and Agronomy 1988). Levels of K in the Sceptre soil were substantially higher than in the Swinton soil, reflecting the differences in clay content between the two soils. The SO 4 -S content of the Swinton soil was around the critical level for Saskatchewan soils (Saskatchewan Advisory Council in Soils and Agronomy 1988), and it tended to be similar for fallow and stubble. The Sceptre soil, however, had exceptionally high levels of SO 4 -S; this soil is affected by low level salinity, and in Saskatchewan SO 4 2 is the dominant anion in soil salts (Mermut et al. 1992). The lower levels of SO 4 -S on fallow may reflect the downward movement of salts due to water recharge during the fallow year. Conversely, the higher levels observed in the stubble system reflect the lack of downward movement of water and solutes in the drier stubble system. Grain Yield Response to N and Available Water Grain yields varied substantially among site-years, reflecting the variability in weather conditions during the growing season. CWAD cultivars yielded on average 22% more than CWRS cultivars regardless of cropping system (Table 4 and Fig. 1a), but these yield differences were not consistent among years, and ranged from 6 to 44%. Yield response to fertilization was observed every year, although the response magnitude varied among years and soils. On average, yield increases of both wheat classes were higher on stubble than on fallow, mainly because of differences in soil available N and water. Under fallow cropping maximum yields were observed with applications of 75 and 100 kg ha 1, while under stubble maximum yields were obtained when 125 kg N ha 1 were applied (Fig. 2a). Responses to N were generally small under conditions of low water availability, and increased substantially as water availability became higher. Fitting Eq. 1 to the data produced a model with an adjusted R 2 of 0.71, and a root mean square error (RMSE), an estimator of the error variance, of 620 kg ha 1. However, the residuals of this model were not normally distributed according to the Shapiro-Wilk test. To improve the model fit, we separated available N into fertilizer N (N F ) and soil NO 3 -N in the 0- to 60-cm depth (N S ). These terms were combined in a model consisting of available water (W) plus the interactions of W with N F, N F 2, N S, N S 2, and with the N F N S interaction (Eq. 4), in a model similar to one used by Selles et al. (1992). Yield = a + bw + cwn F + dwn F 2 + ewn S + fwn S 2 + gwn F N S (4) where a, b,, g are regression coefficients. This model had an R 2 adj = 0.76 and RMSE = 575. Using wheat class as an indicator variable in the model increased the adjusted R 2 to 0.80 and decreased the RMSE by 15% to 527 kg ha 1. The stepwise regression procedure indicated that the only term affected by wheat class was the response to available water (P < ) (Table 5). The lack of effect of the wheat class in any model term containing a variable representing N availability (N F and N S ) indicated that the yield advantage, commonly observed for CWAD cultivars over those of the CWRS class, resulted from a larger response of CWAD to water availability, rather than to differences in the response to N availability between the wheat classes. The residuals of this model were normally distributed according to the Shapiro-Wilk test (W = 0.998, P = 0.26) with a mean of 0 and a standard deviation of 525. Factoring the model shown in Eq. 4 yields a model with the general form y = a+kw, where k is a measure of the water use efficiency of the crop (Staple and Lehane 1954; Campbell et al. 1988; Karamanos and Henry 1991). In our study, however, k is not a constant, but a variable whose value depends on the level of N supply to the crop (N F and N S ), and on wheat class. The presence of a genotype water interaction, evidenced by the significant wheat class shift of the slope of W (Table 5), indicates that CWAD has a water use efficiency 1.82 kg ha 1 mm 1 higher than CWRS at any combination of N F and N S. The value of k in our study for CWAD varied from a low of 4.8 kg ha 1 mm 1 in low-fertility treatments to a high of 12.8 kg ha 1 mm 1 in high-fertility treatments; corresponding values for CWRS are 3.0 and 11.0 kg ha 1 mm 1, respectively. The values of k obtained in this study are well within the range of values reported in other studies (Staple and Lehane 1954; Campbell et al. 1988; Karamanos and Henry 1991). Similarly, the yield response of the crop to N availability becomes more pronounced as the amounts of water available to the crop increases (Fig. 3a). The absence of a genotype N interaction in our study agrees with the results of other studies that found that the absence of N availability cultivar interaction indicates that N recommendations for wheat can be applied to a large set of cultivars (McNeal et al. 1971; Knapp and Harms 1988). This probably reflects the methodology employed for variety selection. In western Canada, advanced breeding lines are grown in regional trails distributed throughout western Canada, with varying climatic conditions, and where soil fertility has been optimized for each test site. Thus, because variety selection pressure did not include fertility levels as factor, all lines and cultivars selected have been optimized for yield response under sufficient nutrient availability levels. The model structure indicates that available water is the most important factor determining grain yields on soils of the Brown soil zone of Saskatchewan (Selles et al. 1992; Campbell et al. 1993; McCaig and Clarke 1995), and justifies the criteria used by Enviro-Test Laboratories for targeting yields in Saskatchewan for recommending fertilization rates (Karamanos and Henry 1991). Because the achievable yield becomes larger as water availability increases, logically it follows that as yield increases, the amount of fertilizer N required to achieve maximum yield (N F,maxY ) should increase. However, the

6 986 CANADIAN JOURNAL OF PLANT SCIENCE Table 4. Grain yields of CWAD and CWRS cultivars by site-year and cropping system, averaged over N fertilizer rates CWAD CWRS Fallow Stubble Fallow Stubble Soil ASSn Year (kg ha 1 ) Sceptre Swinton N F,maxY calculated from the model (Eq. 5) shows that this amount is independent of W and is only a function of N S. N F,maxY = N s (5) Although it appears surprising that N F,maxY may be independent of water availability, one must consider that the mean rate of N application at which maximum yield was observed, was 99 kg ha 1 with a 95% confidence limit of ± 4 kg ha 1, and in 80% of the observations maximum yield was achieved with N rates between 75 and 125 kg N ha 1. Considering that the increment in N application rates is 25 kg ha 1, this is equivalent to ± 1 rate increment. Equation 5 indicates that N S is 44% more efficient than N F. There is no clear agreement about the relative value of N S with respect to N F in dryland farming. In other studies conducted in the area, or similar environments, where N F and N S have been considered separately, some studies have shown that N S is 40% less efficient than N F (Campbell et al. 1997). Other studies, however, indicated that the relative efficiency of both sources of N is the same (Selles et al. 1992; Halvorson et al. 2004). In another study, the relative efficiency of N S was 114% higher than that of N F (Campbell et al. 1993). The approach used by soil testing institutions in Saskatchewan is to consider both sources of N as equally efficient (Saskatchewan Soil Testing Laboratory 1990). Nevertheless, the relative efficiency of N S with respect to N F determined in our model is in agreement with studies that have shown that on average only 60% of N F banded in the spring is available to the crop under Canadian prairie conditions (Soper et al. 1971; Grant et al. 1991). In moister environments, recovery of fertilizer N applied in the spring to winter wheat ranges from 51 to 68%, depending on early spring rainfall (Powlson et al. 1986). Multiplying the first derivative of grain yield with respect to N F by the price of wheat (Pw) and dividing by the cost of N (Cn) yields the marginal cost to marginal return ratio (Mrr) for the investment in N F (Selles et al. 1992). Solving this equality for N F, yields the economic rate of N F application (N F,econ ) for the chosen Mrr and Cn/Pw ratio at various levels of N S and W (Eq. 6). N F,econ = N F,maxY MrrCn/0.0045WPw (6) Equation 6 indicates that N F,econ is obtained by reducing N F,maxY by a factor that is directly proportional to Mrr and Cn, and inversely proportional to W and Pw. Thus, as Cn increases relative to Pw the last term in Eq. 6 becomes larger, resulting in smaller N F,econ values. Conversely, as the price of wheat increases relative to the cost of N, or as water availability increases, the last term becomes smaller, and N F,econ increases asymptotically towards N F,maxY. In this study, it was of interest to elucidate differences among wheat cultivars, so wheat cultivars were included as an indicator variable in Eq. 4 instead of wheat class. Results of this analysis were similar to those obtained for wheat classes as indicator variables, but it did not improve the model fit. In the regression model for grain yield, cultivars had an effect only on the yield response to water, with no effect on any model term containing N. Durum cultivars had significantly larger estimates for the indicator variable, or shifter, to the slope of water (P 0.05) than all cultivars of the CWRS class (Table 6). Within the CWAD class, the newer cultivars AC Avonlea, AC Morse, and AC Navigator had significantly larger water shifter than the older cultivar. The difference in water response for the newer cultivars compared with Kyle are consistent with the yield improvements of 22.6 kg ha 1 yr 1 reported by McCaig and Clarke (1995) for the durum wheat breeding program. Within the CWRS class, all cultivars had water slope shifters significantly smaller than the CWAD class (P 0.05). McKenzie, and AC Cadillac had a shifter not significantly different from zero (P > 0.10), while all other cultivars of this class used in the study had shifters significantly lower than zero (P 0.002). In a study conducted near Swift Current over a 3-yr period, the yield of AC Barrie, AC Cadillac, AC Elsa, and AC Intrepid was only 5.9% higher than that of the older cultivar Neepawa and, furthermore, the yield differences were significant (P = 0.10) in only 1 of the 3 yr (Wang et al. 2002). In this study, the yield differences were small, and ranged from 0.5 to 11% greater than Neepawa. In a larger sample of replicated trials in space and time (Clarke et al 1997; DePauw et al. 1999; Graf et al. 2003), however, it was shown that all of the CWRS cultivars used in this study, except AC Majestic, had significantly higher yield than Neepawa in the moister areas of the prairies. Protein Concentration Response to N and Available Water Grain protein concentration was lower for cultivars of the CWAD than for the CWRS class. On average, CWAD had 8% lower protein than CWRS, regardless of cropping system, but the differences exhibited large variations among site-years (Fig. 1b). Annual variation in protein concentration followed an opposite trend to that of grain yields; in years with low grain yield, protein was high, and in years with low yields, protein was high. A regression between average grain yields and average protein for site-years and cropping systems indicated that, in agreement with previous studies, there was a significant (P < ) inverse relationship between the two variables. This relationship indicated that grain protein decreased by 1.0 and 2.4 mg g 1 per

7 SELLES ET AL. WHEAT RESPONSE TO N FERTILIZATION 987 Fig. 1. Mean performance of CWAD and CWRS wheat on fallow and stubble cropping at each site year. Grain yields (a), grain protein concentration (b). Error bars indicate LSD 0.05 for the year-site crop class interaction.

8 988 CANADIAN JOURNAL OF PLANT SCIENCE Fig. 2. Means response of CWAD and CWRS wheat to N fertilization under fallow and stubble cropping. Grain yields (a), grain protein concentration (b). Error bars indicate LSD 0.05 for the site-year crop N interaction. each 100 kg ha 1 increase in grain yield (P < ) under fallow and stubble, respectively. Further, the rate of change in protein concentration was independent of wheat class (P > 0.40). In general, negative yield protein relationships are observed under conditions of relatively constant N supply and where factors other than N supply limit grain yields (Selles and Zentner 2001). Protein concentration exhibited the classic response to N fertilization (Fig. 2b). Under fallow, where available N was relatively high, N fertilization produced protein increases at all rates used. However, under stubble, where soil available N was low, the protein response showed the characteristic lag phase at low rates of N fertilization, when small increments in N availability produce a relatively large yield increase that dilutes protein concentration (Selles and Zentner 1998). Above this lag phase, protein responses to N availability were almost linear, and they were nearly the same for both wheat classes under both cropping systems. A stepwise regression procedure indicated that the most appropriate protein response model to N and water availability was given by a quadratic response to N and a linear response to water, with no interaction terms (Table 7). This model explained 54% of the observed variability in grain protein concentration (GPC) with an error variance of 15.6

9 SELLES ET AL. WHEAT RESPONSE TO N FERTILIZATION 989 Table 5 Regression coefficients and wheat class slope shift for the yield regression model including fertilizer nitrogen (FN), soil NO 3 -N in the 0- to 60-cm depth (SN), and available water (W) Wheat class slope shift Regression CWAD CWRS Model term coefficient Prob > [t] z Shift Prob > t Shift Prob >[t] Intercept NS NS W 2.19 < < < W FN < NS NS W FN < NS NS W SN < NS NS W SN < NS NS W FN SN < NS NS z Probability of obtaining a value different from zero by chance alone. y NS, not significantly different from zero (P > 0.05). Table 6. Effect of cultivars on the magnitude of the water response slope Water slope shift due to wheat cultivar Wheat Grain yield Grain protein class Cultivar Estimate Prob > [t] z Estimate Prob > [t] CWAD AC Avonlea 1.44 < AC Morse 1.25 < < AC Navigator 1.30 < < Kyle < CWRS AC Barrie < AC Cadillac 0.27 NS y NS AC Elsa AC Intrepid < AC Majestic 1.60 < McKenzie 0.01 NS NS Neepawa 0.94 < Lsd z Probability of obtaining a value different from zero by chance alone. y NS, not significantly different from zero (P > 0.05). mg g 1. The model indicated that, on average, GPC decreased by 0.20 mg g 1 for each additional mm of available water, and increased by 0.21 mg g 1 with an increase of 1 kg ha 1 in N availability (Fig. 3b). The positive coefficient for the quadratic N term indicates that the slope of the N response becomes steeper as N availability increases. As N availability increases, marginal yield responses to N availability decreases, while protein increases at a faster rate (Selles et al. 1998). Scaled estimates indicated that the magnitude of the water and N effects on grain protein was nearly the same but of opposite signs. However, the standardized estimates indicated that the effect of water was about 1.5 times more intense than the effect of nitrogen. Similar to the results of the grain yield analysis, including wheat class as an indicator variable to assess differences in responses between CWAD and CWRS indicated that the effect of wheat class was significant (P 0.10) only for the response to water. Inclusion of wheat class increased the fit of the model to an R 2 of 0.58 and reduced the RMSE from 15.6 to 14.9 mg g 1. Further, the new model showed that the GPC of CWAD decreased by 0.22 mg g 1 per mm of available water, while the corresponding GPC decrease of CWRS was significantly smaller (P < ) at only 0.18 mg g 1 mm 1 (Table 7). When using cultivars as an indicator variable instead of wheat class, we obtained no improvement in the model fit, because R 2 and RMS remained unchanged. The cultivar categorical variable produced a significant (P < ) shifter to the water slope, just as observed for wheat class, and had no effect on the slopes of the N terms. The magnitude of the shifters to the water response separated the cultivars into two broad categories that were coincidental with the two wheat classes used in this study (Table 6). Additionally, within each class the shifters showed a separation of cultivars based on their ability to maintain their protein concentration under conditions conducive to high yields. Within the CWAD class, the oldest cultivar, Kyle, had the largest Table 7. Regression coefficients and wheat class shifters for the protein regression model including available nitrogen (N), and available water (W) Wheat class Shifters Regression CWAD CWRS Model term coefficient Prob > [t] z Shifter Prob > [t] Shifter Prob > [t] Intercept 153 < NS y NS N 0.21 < NS NS N NS NS W < < < z Probability of obtaining a value different from zero by chance alone. y NS, not significantly different from zero (P > 0.05).

10 990 CANADIAN JOURNAL OF PLANT SCIENCE Fig. 3. Predicted yield (a) and protein concentration (b) responses to N and water availability.

11 SELLES ET AL. WHEAT RESPONSE TO N FERTILIZATION 991 Fig. 4. Magnitude of the yield and protein shifters to the yield and protein response to water in the regression models. Error bars indicate 95% confidence interval for each shifter. negative shifter, which made the negative slope of water even more negative, while AC Avonlea, a recently released cultivar, had the least negative slope of all CWAD cultivars. This indicates the capacity of AC Avonlea to maintain a higher protein content in high yield potential environments. AC Morse and AC Navigator had intermediate negative shifters that were not significantly different (P > 0.05) from either AC Avonlea or Kyle. Within the CWRS class, except for AC Cadillac and McKenzie, which had shifters not significantly different from zero (P > 0.10), all cultivars had significantly larger shifters (P 0.004), showing the capacity of these cultivars to maintain higher protein concentration in high yield potential environments, and the shifters were not significantly different among these cultivars (Table 6). It is interesting to note that, in general, the magnitude of the shifter to the water slope in the protein regression model had a negative association with the same shifters in the yield model. Plotting the magnitude of both shifters and their 95% confidence intervals revealed more clearly the separation of the different cultivars (Fig. 4). All CWAD cultivars appeared grouped in the lower right quadrant of the plot, showing their superior yield response to water availability, and lower protein concentration compared with the CWRS cultivars that were grouped in the upper left quadrant. Within the CWAD class, one can verify that the three newer cultivars have superior yield potential to Kyle, but of these, only AC Avonlea was able to capture improvements both in yield and protein concentration (Fig. 4). Within the CWRS wheat class, in spite of genetic gains in the breeding program (McCaig et al. 1995; Clarke et al. 1997, 1999; DePauw et al. 1999, 1998; Graf et al. 2003), the gains were limited in the Brown soil zone because of the semiarid environment. Thus, in this study, compared with the older cultivar Neepawa, AC Elsa, AC Cadillac, and McKenzie showed substantial improvements in grain yield. Of these cultivars, AC Elsa maintained the same protein level as Neepawa while significantly (P > 0.05) increasing yield levels, despite the protein increase noted in regional variety trials (Saskatchewan Agriculture, Food and Rural Revitalization 2004). The regression technique showed that yield improvements in AC Cadillac and McKenzie appear to have been achieved at the expense of protein concentration. AC Majestic, while maintaining the same protein potential as Neepawa, showed significantly (P 0.05) lower yield. AC Barrie and AC Intrepid, while not significantly different from Neepawa (P > 0.05), showed a displacement toward higher yield and protein levels; this displacement in the yield-protein domain is consistent with yield protein relationships observed for these cultivars in registration variety trails (McCaig et al 1996; DePauw et al. 1999). The relative position of the cultivars within the graph in Fig. 4 agrees closely with the comparative ranking of cultivars for southern Saskatchewan (Saskatchewan Agriculture, Food and Rural Revitalization 2004).

12 992 CANADIAN JOURNAL OF PLANT SCIENCE These results demonstrate the potential of using regressions techniques with indicator variables in fertility response studies to evaluate the effects of cultivars and crop classes on responses to continuous variables. CONCLUSIONS Results of this study demonstrated that under the environmental conditions of southwest Saskatchewan, N fertilization produces substantial yield and protein increases of CWAD and CWRS wheat grown on fallow or stubble. The yield response to increased N availability is dependent on the amount of water available to the crop. Under low available water conditions, yield responses are small, but nonetheless significant. As moisture availability improves, yield responses become larger, and higher amounts of available N are required to achieve maximum yields. Grain protein responded negatively to increases in water availability because of dilution of the protein with larger yields. Unlike grain yield, GPC responded positively to improved N supply, independently of water availability. The use of genotypes as indicator variables in regression analysis of yield and protein was useful in providing insights about the overall success of wheat breeding programs in capturing yield and protein improvements in recently released cultivars. The analysis used in this study showed that there were no genotype N interactions for yield and protein response. Because of this, the amount of N required to reach maximum yield is the same for both wheat classes studied. Furthermore, this analysis demonstrated that the yield advantage of CWAD over CWRS wheat is due to the higher response of the former class to water availability. The difference in protein between the two wheat classes appears to be related to differences in yield response to water; thus, CWRS, which has a lower yield response to water, is more able to maintain higher GPC levels than CWAD under conditions conducive to high yields. Within the CWAD class, the three new cultivars showed superior yield potential compared with Kyle, but of these only AC Avonlea captured improvements both in yield and protein concentration. ACKNOWLEDGEMENTS The authors wish to thank Mr. Dean James and Mr. Dean Klassen for their technical contribution. Funding from the Matching Investment Initiative of Agriculture and Agri- Food Canada is gratefully appreciated. Ayres, K. W., Acton, D. F. and Ellis, J. G The soils of the Swift Current Map Area 72J Saskatchewan. Publ. 481, Extension Div. University of Saskatchewan, Saskatoon, SK. Adamski, J. M and Willard, S. P Application of the methylthymol blue sulfate method to water and waste water analysis. Anal. 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