Mepiquat Chloride Effects on Irrigated Cotton in Arizona

Transcription

1 Mepiquat Chloride Effects on Irrigated Cotton in Arizona E.J. Norton and J.C. Silvertooth Abstract A series of experiments have been conducted from 1988 to 1999 at various locations across the cotton producing regions of Arizona to evaluate mepiquat chloride (MC) applications in terms of plant growth and yield. These experiments were designed to evaluate MC under three application regimes. These regimes included low rate multiple applications, late season applications, and a feedback vs. scheduled management of MC and nitrogen (N) applications. The objective of this summary (including a total of 31 site-years) is to determine which of these three application regimes offer the greatest opportunity for a positive lint yield response to MC. Stability analysis was conducted by regressing the treatment mean lint yield against the environmental mean for each application regime. Results from the stability analyses revealed that the most viable method of application is a feedback approach for both MC and fertilizer N. The most reliable technique associated with plant assessment in a feedback approach was the height to node ratio (HNR) to indicate vegetative tendencies for determining the appropriate rate and timing of MC applications. Introduction The growth and development of crop plants can usually be characterized by a period of vegetative growth followed by a reproductive growth phase. During these periods of growth, photosynthetic products are partitioned into various plant parts. The degree to which plants allocate photosynthates between vegetative and reproductive structures determines in large part the growth rate and yield of many economically important crops (Brown, 1984). The cotton (Gossypium spp.) plant is somewhat unique by having a perennial nature and indeterminate growth pattern in comparison to many other more determinate crop plants. Ramifications of this growth habit are that vegetative and reproductive growth occur simultaneously on the cotton plant and thus compete for photosynthetic products (Brown, 1984). This presents specific challenges when managing the cotton plant as an annual in a crop production system. Excessive vegetative tendencies in cotton have been shown to lead to losses in reproductive structures (squares, flowers, and bolls) (Gausman, et al., 1979; York, 1983b; Fletcher et al., 1994). The loss of reproductive structures (carbohydrate sinks) can shift energy from reproductive to vegetative portions of the plant, resulting in a rapid proliferation of main stem growth (Mauney, 1986). Self-shading may also contribute to the loss of reproductive structures in some cases due to the fact that the major portion of the assimilate supply for these structures is obtained from the subtending leaf to the reproductive structure (Ashley, 1972; Benedict and Kohel, 1975). When these leaves are shaded by excess vegetative growth, assimilate supply is depleted due to decreased photosynthetic ability and the abortion of fruiting structures can result (Mauney, 1986; York, 1983b). The most likely cause of this reproductive structure loss is not one single event, however, but combinations of several factors and processes working in concert. Collectively, these physiological processes can serve to initiate a process of reproductive structure abortion and a decline in overall fruit retention (FR) on the plant (Guinn, 1982). Maintaining an optimum fruit load in relation to vegetative structure is often difficult. Research efforts have been directed to developing methods and indices to monitor cotton plants on an in-season basis. Indices that have been This is part of the 2000 Arizona Cotton Report, The University of Arizona College of Agriculture, index at

2 developed for cotton include a height to node ratio (HNR), fruit retention (FR), main stem node numbers (MSN), growth rate (GR), and nodes above top white flower (NAWF) (Bourland et al., 1992; Kerby et al., 1997; Kerby et al., 1998). These indices are more effective in terms of management when they are made region- and area-specific. A set of indices have been developed for Arizona (Silvertooth et al., 1993b; Silvertooth, 1994; Silvertooth and Norton, 1998c). The use of these indices to monitor the plant in-season has many practical implications. Foremost among these is the fact that it can alert the grower to any change in the reproductive to vegetative balance in the plant that may prove detrimental to yield. It may then be decided if corrective measures are needed. Mepiquat chloride (MC) is a plant growth regulator that has been used in cotton production for several decades as a management tool in controlling vegetative growth. Mepiquat chloride is a gibberellic acid suppressant that is absorbed by the green portions of the plant and serves to reduce cell elongation, thus offering the potential of decreasing leaf area and restricting additional plant height increases (York, 1983a and Kerby, 1985). Mepiquat chloride has also been associated with enhancing earliness with regards to fruiting development (Walter, et al., 1980; York, 1983b; Kerby, 1985). Much research has been devoted to determining optimum rates and MC application regimes for irrigated cotton production (Kerby, 1985; McConnell, et al., 1992; Boman and Westerman, 1994). However, application strategies that result in consistently significant increases in lint yield from MC have yet to be identified or demonstrated. These inconsistencies in yield response have been attributed to various factors, including length of season, lack of excess vegetative growth, and stresses (including drought, fertility, insect, and disease) (Briggs, 1980; Crawford, 1981). A series of MC related studies have been conducted in recent years in Arizona. Low rate multiple (LRM) application studies were conducted across the cotton producing regions of Arizona (Silvertooth et al., 1989, 1990, 1991a, 1993a, 1995b, 1997b; Hood and Silvertooth, 1993; Husman and Silvertooth, 1994). The objectives of the studies were twofold. First, to measure any response to MC such as plant height (PH), MSN, HNR, NAWF, and percent canopy closure (PCC). Secondly, to determine yield responses with multiple applications of MC. From these results a final objective was to determine optimal rates and application regimes of multiple MC applications for irrigated Upland (G. hirsutum L.) and American Pima (G. barbadense L.) cotton. Field studies were conducted at various locations across Arizona to evaluate the effects of late-season (LS) applications of MC on Upland cotton (Silvertooth et al., 1993a; Silvertooth and Norton, 1995b, 1997b). These studies were designed to examine the effects of the late-season applications not only on yield but on plant characteristics associated with earliness as indicated by NAWF, HNR, and FR. In addition, a series of feedback versus scheduled applications of N and MC interaction studies were conducted at various locations throughout Arizona (Fletcher et al., 1994; Silvertooth and Norton, 1995a, 1996a, 1997a, 1998a; Norton et al., 1999). The objectives of these studies were to use plant growth parameters such as measured inseason HNR and FR in a feedback approach to N and MC applications and to evaluate this approach versus a scheduled approach to N and MC applications. A feedback approach to crop input management, such as with MC, is conducted in reference to established criteria for optimum or excessive vegetative growth patterns. Accordingly, guidelines relative to FR levels and HNR have been developed for this purpose (Silvertooth et al., 1991b, 1992a; Fletcher et al., 1994; Silvertooth et al., 1995a; Silvertooth and Norton, 1996a, 1997a, 1998a, 1998c; Norton et al., 1999). Management guidelines for fertilizer N inputs to cotton have also developed along a similar line of rationale regarding feedback versus scheduled approaches (Silvertooth et al., 1991c, 1992b, 1993c, 1994, 1995c; and Silvertooth and Norton, 1996b, 1997c, 1998b, 1998c, 1999). One of the challenges in the assessment and analysis of long term studies such as these is to provide a measure of the effect of environment over time. Assessing environment by treatment interaction by use of conventional analysis of variance becomes difficult as site-years increase and also due to the multiple factors influencing environment by treatment interaction (Raun et al., 1993). Yates and Cochran (1938) first proposed stability analysis for the interpretation of genotype by environment interaction. Their methodology held for the linear regression of variety yield on experimental mean yield in order to observe varietal stability across varying environments (Finlay and Wilkinson, 1963; Eberhart and Russell, 1966). More recently, stability analysis has been adapted for use in comparing agronomic treatments across differing environments consisting of the linear regression of treatment mean yield on the environmental mean (Raun et al., 1993; Boman et al., 1995; Unruh and Silvertooth, 1997).

3 The aggregate results of all studies involved (31 site-years) are summarized in this paper. Thus, the objectives of this paper are to analyze the three MC application regimes and determine which regime offers the greatest probability for a positive lint yield response. Materials and Methods Field studies were conducted across the cotton producing regions of Arizona from 1988 through 1999 (study locations summarized in Table 1). Management of all studies with respect to irrigation, general fertility, and pest control was carried out in a uniform and optimal manner across all treatments at each site. All data from these studies were analyzed (e.g. analysis of variance) in accordance to procedures outlined by Steel and Torrie (1980) and the SAS Institute (1997) to determine treatment differences for each site-year (Silvertooth et al., 1989, 1990, 1991a, 1993a, 1995b, 1997b; Hood and Silvertooth, 1993; Husman and Silvertooth, 1994). The LRM MC application studies utilized a randomized complete block design (RCBD) with three or four replications at each location. Plots at each location were a minimum of 8 rows wide and extended the full length of the irrigation run (a minimum of 600 ft.). Applications of MC were made via ground rig with a total application volume of 20 gallons/acre. Plant growth and development parameters were measured on 14 day intervals and included PH, MSN, HNR, NAWF, and PCC. Treatments were initiated at or about pinhead to first matchhead square formation. Individual applications ranged from to 1.0 pt. MC /ac (Silvertooth et al., 1993a; Silvertooth and Norton, 1995b, 1997b). The LS application studies utilized a RCBD with four replications at each location. Plots at each location extended the full length of the irrigation run (a minimum of 600 ft.). Treatments of MC were made with ground rig applicators. Plant growth and development parameters were measured on 14 day intervals and included NAWF, HNR, and FR. Treatments were initiated at or about cutout. Individual treatments consisted of a single application ranging from 0.5 to 1.5 pt. MC/ac. The feedback vs. scheduled approach to N and MC applications studies utilized a RCBD with four replications at each location. Plots at each location were eight rows wide and extended the full length of the irrigation run (600 ft.). Treatments of MC were made with ground rig applicators and all treatments of N were made with urea or ammonium sulfate fertilizers with side dress applicators. Plant growth and development parameters were measured on 14 day intervals and included PH, MSN, HNR, FR, NAWF, and PCC. Treatments consisted of all combinations of N and MC in feedback and scheduled applications. The feedback MC treatments generally consisted of one or two applications of MC ranging from 0.75 to 1.0 pt/ac per application as needed based upon measured growth parameters. The scheduled MC treatments generally consisted of two applications of MC also ranging from 0.75 to 1.0 pt MC/ac per application based upon stage of growth. Both the feedback and the scheduled N treatments consisted of split applications ranging from 40 to 50 lbs. N/ac per application. The scheduled treatments were a more aggressive approach to N fertility and generally received a higher rate and one extra application (Fletcher et al., 1994; Silvertooth and Norton, 1995a, 1996a, 1997a, 1998a; Norton et al., 1999). Stability analysis was conducted utilizing the corresponding yield data from each experimental application regime. Treatment mean lint yields were regressed on the environmental mean. Environmental mean (EM) was calculated for each site-year by taking the mean lint yield across all treatments and replicates for each site-year (Raun, et al., 1993). Relative stability analysis was conducted on selected treatment pairs from the feedback vs. scheduled application regime. Relative stability is assessed by studying the joint distribution of data pairs (i.e. mean for treatments A and B in a given year). Slopes of the regression line are compared when the average yield ((A+B)/2) is regressed on the yield difference (A B) between the two treatments. A slope close to zero would indicate that the treatments change similarly across environments and that they are equally stable. A positive slope indicates that B is more stable than A since there is more variability in A. A negative slope would indicate that A is more stable than B. A

4 probability level of P<0.05 indicates that the slope from the regression equation is significantly different from zero (Raun et al., 1993). Results Yield responses to MC treatments among all experiments are summarized in Table 2. We found that 15% of the MC treatments demonstrated a positive yield response as compared to 17% of the MC treatments demonstrating a negative yield response when compared with the control treatments (no MC applied). The HNR and FR of those treatments that had yield responses to MC are summarized in Table 3. All references to established baselines are based upon University of Arizona Extension guidelines (Silvertooth and Norton, 1998c). The HNR and FR measurements provide a reasonable basis for assessing the vegetative to reproductive conditions of the crop. Also, the HNR measurements are clearly the most reliable in this respect. The regression of treatment mean lint yields on the EM for the LRM application regimes, both the Upland and Pima species, are summarized and displayed in Tables 4 and 5 and Figures 1 and 2 respectively. Treatments consisted of differing rates of total MC applied. It is significant to note in these analyses that all treatments are relatively similar with regard to slope and intercept in both low and high yielding environments. Thus, none of the MC treatments were consistently better that the control (treatment 1). The regression of treatment mean lint yields on the EM for the late season application regimes are summarized and displayed in Table 6 and Figure 3. Treatments consisted of differing rates of total MC applied near crop cut-out. It is significant to note in these analyses that as higher yielding, longer season environments are encountered, late season applications of MC are not beneficial in relation to the control. The regression of treatment lint yields on the EM for the feedback vs. scheduled application regimes are summarized and displayed in Table 7 and Figure 4. Treatments consisted of all combinations of N and MC feedback and scheduled applications. Treatment two, which utilized a feedback approach to both N and MC, has the highest elevation (intercept) of all treatments in both low and high yielding environments. Thus, treatment two is the most beneficial and stable treatment across all environments. The relative stability analyses for selected treatment pairs are displayed in Figures 5(a-d). The relative stability diagrams further substantiate the results that were seen in the stability analysis for the feedback vs. scheduled application regimes indicating the better stability and superiority of treatment two in contrast to other N and MC treatments. Summary and Conclusions It was determined that MC produced significant positive yield responses in only 15% and negative yield responses in 17% of cases. Therefore, it is important to understand what crop conditions may have contributed to or predicted these yield responses. An analysis of plant growth parameters at the time of application revealed that all positive yield responses occurred when the HNR was above the UA baseline levels and FR was usually above baselines. Conversely, all negative yield responses occurred when HNR was below baselines and FR was at or above baseline levels. These results indicate that the greatest potential for a positive yield increase from MC applications is present when crop conditions are tending toward vegetative conditions, as indicated by the high HNR parameter relative to baselines. This agrees in principle with Kerby s findings (Kerby, 1985) that the greatest response from MC is achieved when final control plant height is 130 cm which indicates vegetative growth tendencies by use of indices for the San Joaquin Valley of CA. Another issue to consider is the best application regime to maximize the probability for a positive yield response with respect to MC applications. The analysis of the LRM application and the LS application regimes of MC indicated that these methods offered no benefit in terms of consistent yield increases relative to the control plots.

5 The analysis of the feedback vs. scheduled application experiments indicated that the feedback approach to management of inputs, in this case combinations of MC and N, resulted in the greatest yield benefits across all environments. Thus indicating that the feedback approach to MC applications is the most viable method. The use of the HNR growth parameter to indicate vegetative tendencies and to indicate the optimal timing of MC applications in a feedback approach to management appears to be the most probable method of realizing a positive lint yield response. The utilization of a feedback approach to any input offers many positive features. Perhaps the most significant of these is of that being able to improve upon the efficiencies associated with crop inputs such as MC. In a crop production system, such as that of irrigated crops in the desert Southwest where inputs are high, it is a good exercise for producers to evaluate inputs in a feedback approach to management in an effort to optimize any benefit from those inputs. Also, the use of a feedback approach allows the grower to respond to in-season changes in crop conditions. Therefore, the use of a feedback approach, with well established, regionally-specific baselines, provides flexibility and improves the ability of the grower to better determine when a positive yield response to MC (or other inputs) can be realized. References Ashley, D.A C-labeled photosynthate translocation and utilization in cotton plants. Crop Sci. 12: Benedict, C.R. and R.J. Kohel Export of 14 C-assimilates in cotton leaves. Crop Sci. 15: Boman, R.K. and R.L. Westerman Nitrogen and mepiquat chloride effects on the production of nonrank, irrigated, short-season cotton. J. Prod. Agric. 7: Boman, R.K., W.R. Raun, R.L. Westerman, and J.C. Banks Nitrogen by environment interactions in longterm cotton production. p In D.A. Richter (ed.) Proc. Beltwide Cotton Prod. Res. Conf., San Antonio, TX. 4-7 Jan National Cotton Council of America, Memphis, TN. Bourland, F.M., D.M. Oosterhuis, and N.P. Tugwell Concept for monitoring the growth and development of cotton plants using mainstem node counts. J. Prod. Agric. 5: Briggs, R.E Effect of the plant regulator Pix on cotton in Arizona. p. 32. In J.M. Brown (ed.) Proc. Beltwide Cotton Prod. Res. Conf., St. Louis, MO Jan National Cotton Council of America, Memphis, TN. Brown, R.H Growth of the green plant. p In M.B. Tesar (ed.) Physiological Basis of Crop Growth and Development. American Society of Agronomy Crop Science Society of America, Madison, WI. Crawford, S.H Effects of mepiquat chloride on cotton in Northeast Louisiana. p In J.M. Brown (ed.) Proc. Beltwide Cotton Prod. Res. Conf., New Orleans, LA. 4-8 Jan National Cotton Council of America, Memphis, TN. Eberthart, S.A. and W.A. Russell Stability parameters for comparing varieties. Crop Sci. 6: Finlay, K.W. and G.N. Wilkinson The analysis of adaptation in a plant-breeding programme. Aust. J. Agric. Res. 14: Fletcher, D.C., J.C. Silvertooth, E.R. Norton, B.L. Unruh, and E.A. Lewis Evaluation of a feedback approach vs. scheduled approach to Pix application. Cotton, A College of Agriculture Report. University of Arizona, Series P-96:

6 Gausman, H.W., L.N. Namken, M.D. Heilman, H. Walter, and F.R. Rittig Physiological effects of a growth regulator (Pix TM ) on the cotton plant. p In J.M. Brown (ed.) Proc. Beltwide Cotton Production Res. Conf., Phoenix, Ariz Jan National Cotton Council of America, Memphis, Tenn. Guinn, G Causes of square and boll shedding in cotton. U.S.D.A. Tech. Bull. No Hood, L.R. and J.C. Silvertooth High rate Pix use on Upland cotton. Cotton, A College of Agriculture Report. University of Arizona. Series P-94: Husman, S.H. and J.C. Silvertooth The effects of Pix application timing of Upland cotton lint yield and growth and development parameters. Cotton, A College of Agriculture Report. University of Arizona, Series P-96: Kerby, T.A Cotton response to mepiquat chloride. Agron. J. 77: Kerby, T.A., R.E. Plant, and R.D. Horrocks Height-to-node ratio as an index of early season cotton growth. J. Prod. Agric. 10: Kerby, T.A., R.E. Plant, S. Johnson-Hake, and R.D. Horrocks Environmental and cultivar effects on heightto-node ratio and growth rate in Acala cotton. J. Prod. Agric. 11: Mauney, J.R Vegetative growth and development of fruiting sites. p In J.R. Mauney and J. Stewart (eds.) Cotton Physiology, Number One, The Cotton Foundation Reference Book Series. The Cotton Foundation, Memphis, Tenn. McConnell, J.S., W.H. Baker, B.S. Frizzell, and J.J. Varvil Response of cotton to nitrogen fertilization and early multiple applications of mepiquat chloride. J. Plnt. Nutr. 15: Norton, E.J., J.C. Silvertooth, and E.R. Norton Evaluation of a feedback approach to nitrogen and Pix applications, Cotton, A College of Agriculture Report. University of Arizona, Series P-119: Raun, W.R., H.J. Barreto, and R.L. Westerman Use of stability analysis for long-term soil fertility experiments. Agron. J. 85: SAS Institute The SAS system for Windows. Release 7.0. SAS Inst., Cary, NC. Silvertooth, J.C., D.R. Howell, C.R. Farr, and J.E. Malcuit Evaluation of Pix multiple application treatments on Upland and Pima cotton in Arizona, Cotton, A College of Agriculture Report. University of Arizona. Series P-77: Silvertooth, J.C., J.E. Malcuit, D.R. Howell, and C.R. Farr Pix multiple application evaluations in Arizona on Upland and Pima cotton, Cotton, A College of Agriculture Report. University of Arizona. Series P-81: Silvertooth, J.C., J.E. Malcuit, S.H. Husman, W.S. Winans, and L. Hood. 1991a. Cotton response to multiple applications of Pix, Cotton, A College of Agriculture Report. University of Arizona. Series P- 87: Silvertooth, J.C., P.W. Brown, and J.E. Malcuit. 1991b. Basic crop development patterns. Cotton, A College of Agriculture Report. University of Arizona. Series P-87: Silvertooth, J.C., L.J. Clark, J.E. Malcuit, E.W. Carpenter, T.A. Doerge, and J.E. Watson. 1991c. Nitrogen management experiments for Upland and Pima cotton, Cotton, a College of Agriculture Report. University of Arizona. Series P-87:

7 Silvertooth, J.C., P.W. Brown, and J.E. Malcuit. 1992a. Cotton crop growth and development patterns. Cotton, A College of Agriculture Report. University of Arizona. Series P-91:9-24. Silvertooth, J.C., L.J. Clark, J.E. Malcuit, E.W. Carpenter, T.A. Doerge, and J.E. Watson. 1992b. Nitrogen management experiments for Upland and Pima cotton, Cotton, a College of Agriculture Report. University of Arizona. Series P-91: Silvertooth, J.C., J.E. Malcuit, L. Hood, and S.H. Husman. 1993a. Cotton response to applications of Pix, Cotton, A College of Agriculture Report. University of Arizona. Series P-94: Silvertooth, J.C., P.W. Brown, and J.E. Malcuit. 1993b. The development and delivery of a crop monitoring program for Upland and Pima cotton. Cotton, A College of Agriculture Report. University of Arizona. Series P-94: Silvertooth, J.C., L.J. Clark, J.E. Malcuit, E.W. Carpenter. 1993c. Nitrogen management experiments for Upland and Pima cotton, Cotton, a College of Agriculture Report. University of Arizona. Series P-94: Silvertooth, J.C Practical uses of crop monitoring for Arizona cotton. Cotton, A College of Agriculture Report. University of Arizona. Series P-96:18-23 Silvertooth, J.C., E.R. Norton, B.L. Unruh, J.A. Navarro, L.J. Clark, and E.W. Carpenter Nitrogen management experiments for Upland and Pima cotton, Cotton, A College of Agriculture Report. University of Arizona. Series P-96: Silvertooth, J.C. and E.R. Norton. 1995a. Evaluation of a feedback approach to nitrogen and Pix application. Cotton, A College of Agriculture Report. University of Arizona. Series P-99: Silvertooth, J.C. and E.R. Norton. 1995b. Evaluation of late-season Pix TM applications. Cotton, A College of Agriculture Report. University of Arizona. Series P-99: Silvertooth, J.C., E.R. Norton, B.L. Unruh, J.A. Navarro, L.J. Clark, and E.W. Carpenter. 1995c. Nitrogen management experiments for Upland and Pima cotton, Cotton, A College of Agriculture Report. University of Arizona. Series P-99: Silvertooth, J.C. and E.R. Norton. 1996a. Evaluation of a feedback approach to nitrogen and Pix applications. Cotton, A College of Agriculture Report. University of Arizona. Series P-103: Silvertooth, J.C., E.R. Norton. 1996b. Nitrogen management experiments for Upland and Pima cotton, Cotton, A College of Agriculture Report. University of Arizona. Series P-103:317:332. Silvertooth, J.C. and E.R. Norton. 1997a. Evaluation of a feedback approach to nitrogen and Pix application. Cotton, A College of Agriculture Report. University of Arizona. Series P-108: Silvertooth, J.C. and E.R. Norton. 1997b. Evaluation of late-season Pix TM applications. Cotton, A College of Agriculture Report. University of Arizona. Series P-108: Silvertooth, J.C., E.R. Norton. 1997c. Nitrogen management experiments for Upland and Pima cotton, Cotton, A College of Agriculture Report. University of Arizona. Series P-108: Silvertooth, J.C. and E.R. Norton. 1998a. Evaluation of a feedback approach to nitrogen and Pix applications, Cotton, A College of Agriculture Report. University of Arizona. Series P-112: Silvertooth, J.C. and E.R. Norton. 1998b. Nitrogen management experiments for Upland and Pima cotton, Cotton, A College of Agriculture Report. University of Arizona. Series P-112:

8 Silvertooth, J.C. and E.R. Norton. 1998c. Cotton monitoring and management system. Publication no. AZ1049, University of Arizona, College of Agriculture, Tucson, AZ. Silvertooth, J.C. and E.R. Norton Nitrogen management experiments for Upland and Pima cotton, Cotton, A College of Agriculture Report. University of Arizona. Series P-116: Steel, R.G.D., and J.H. Torrie Principles and procedures of statistics. McGraw-Hill, New York. Unruh, B.L. and J.C. Silvertooth Planting and irrigation timing effects on the yield of upland and pima cotton. J. Prod. Agric. 10: Walter, H., H.W. Gausman, F.R. Rittig, L.N. Namken, D.E. Escobar, and R.R. Rodriguez Effect of mepiquat chloride on cotton plant leaf and canopy structure and dry weights of its components. p In J.M. Brown (ed.) Proc. Beltwide Cotton Prod. Res. Conf., St. Louis, MO Jan National Cotton Council of America. Memphis, TN. Yates, F., and W.G. Cochran The analysis of a group of experiments. J. Agric. Sci. 28: York, A.C. 1983a. Cotton cultivar response to mepiquat chloride. Agron. J. 75: York, A.C. 1983b. Response of cotton to mepiquat chloride with varying N rates and plant populations. Agron. J. 75:

9 Table 1. Summary of MC related experiments, Year Study Type Location Elevation (ft) Soil Series 1988 LRM Yuma Valley 100 Holtville Clay 1988 LRM Yuma Valley 100 Holtville Clay 1988 LRM Gila Bend 750 Brios Sandy Loam 1988 LRM Gila Bend 750 Brios Sandy Loam 1989 LRM Yuma Valley 100 Gadsden Clay 1989 LRM Yuma Valley 100 Holtville Clay 1989 LRM Gila Bend 750 Brios Sandy Loam 1989 LRM Gila Bend 750 Brios Sandy Loam 1990 LRM Waddell 1350 Avondale Clay Loam 1990 LRM Gila Bend 750 Brios Sandy Loam 1990 LRM Poston 400 Holtville Clay Loam 1991 LRM Waddell 1350 Glenbar Loam 1991 LRM Maricopa 1200 Casa Grande Sandy Loam 1992 LRM Buckeye 1000 Laveen Sandy Loam 1992 LRM Maricopa 1200 Casa Grande Sandy Loam 1992 LRM Mohave 600 Indio Clay 1992 LRM Gila Bend 750 Brios Sandy Loam 1992 LRM Sunsites 4000 Tubac Sandy Clay Loam 1992 LRM Mohave 600 Indio Clay 1992 LS Sunsites 4000 Tubac Sandy Clay Loam 1993 LRM Buckeye 1000 Laveen Sandy Loam 1993 LRM Maricopa 1200 Casa Grande Sandy Loam 1993 FB Maricopa 1200 Casa Grande Sandy Loam 1994 FB Maricopa 1200 Casa Grande Sandy Loam 1994 LS Maricopa 1200 Casa Grande Sandy Loam 1995 FB Maricopa 1200 Casa Grande Sandy Loam 1996 FB Maricopa 1200 Casa Grande Sandy Loam 1996 LS Marana 1950 Pima Clay Loam 1997 FB Marana 1950 Pima Clay Loam 1998 FB Marana 1950 Pima Clay Loam 1999 FB Marana 1950 Pima Clay Loam LRM = Low Rate Multiple Application; LS = Late Season Application; FB = Feedback Approach Table 2. Summary of yield responses to MC experiments, Positive Yield Response Negative Yield Response No Yield Response 15% 17% 68% Responses significant at the P 0.05 level. n=148

10 Table 3. Summary of HNR and FR with respect to baselines in treatments responding to MC. Positive Yield Responses Negative Yield Responses HNR FR HNR FR 100% above 61% above 100% below 100% above 39% below Table 4. Linear regression equations of treatment mean yield on the environmental mean, low rate multiple application regimes (Upland), Treatment Intercept Standard Error Slope Standard Error C.V. r 2 pt. MC/ac % Table 5. Linear regression equations of treatment mean yield on the environmental mean, low rate multiple application regimes (American Pima), Treatment Intercept Standard Error Slope Standard Error C.V. r 2 pt. MC/ac % Table 6. Linear regression equations of treatment mean yield on the environmental mean, late season application regimes (Upland), Treatment Intercept Standard Error Slope Standard Error C.V. r 2 pt. MC/ac %

11 Table 7. Linear regression equations of treatment mean yield on the environmental mean, feedback vs. scheduled application regimes (Upland), Treatment Intercept Standard Error Slope Standard Error C.V. r 2 % 1 NF NF MF NF MS NS MF NS MS NF, NS, MF, and MS refer to feedback and scheduled approaches to applications of Nitrogen fertilizers and Mepiquat Chloride. Table 8. Difference in slopes for selected treatment regression equation comparisons. Comparison t-calc PR > t Feedback Approach Trmt. 2 vs. Trmt Trmt. 2 vs. Trmt Trmt. 2 vs. Trmt Trmt. 2 vs. Trmt Late Season Regime Trmt. 1 vs. Trmt Trmt. 1 vs. Trmt Low-Rate Multiple Regime (Upland) Trmt. 1 vs. Trmt Trmt. 1 vs. Trmt Trmt. 1 vs. Trmt Trmt. 1 vs. Trmt Low-Rate Multiple Regime (Pima) Trmt. 1 vs. Trmt Trmt. 1 vs. Trmt PR> t, probability of a greater absolute value of t. t-calc, t test of differences in slopes.

12 2000 Treatment Mean Lint Yield (lbs. lint/ac) Trmt 1 Trmt 2 Trmt 3 Trmt 4 Trmt Environmental Mean (lbs. lint/ac) Figure 1. Treatment mean vs. environmental mean for low rate multiple application regimes (upland), Treatment Mean Lint Yield (lbs. lint/ac) Trmt 1 Trmt 2 Trmt Environmental Mean (lbs. lint/ac) Figure 2. Treatment mean vs. environmental mean for low rate multiple application regimes (pima),

13 1350 Treatment Mean Lint Yield (lbs.lint/ac) Trmt 1 Trmt 2 Trmt Environmental Mean (lbs. lint/ac) Figure 3. Treatment mean vs. environmental mean for late season application regimes, Treatment Mean Lint Yield (lbs. lint/ac) Trmt Trmt Trmt 3 Trmt Trmt Environmental Mean (lbs. lint/ac) Figure 4. Treatment mean vs. environmental mean for feedback vs. scheduled application regimes,

14 120 (A) y = (x) P = (B) y = (x) P = Yield Difference (lbs. lint/ac) Treatment 2 vs. Treatment 1 Treatment 2 vs. Treatment (C) y = (x) P = (D) y = (x) P = Yield Difference (lbs. lint/ac) Treatment 2 vs. Treatment 4 Treatment 2 vs. Treatment Mean Yield (lbs. lint/ac) Mean Yield (lbs. lint/ac) Figure 5(a-d). Yield difference vs. mean yield for selected treatment pairs, feedback application regime.