Improvement in regression of corn yield with plant height using relative data
BACKGROUND: The traditional approach of analyzing absolute variable data across multiple locations and/or years has drawbacks in precision agriculture. This study was conducted to evaluate the impacts of using relative yield and plant height data of corn (Zea mays L.) on the regression of yield with...
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| Published in | Journal of the science of food and agriculture Vol. 91; no. 14; pp. 2606 - 2612 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Ltd
01.11.2011
Wiley John Wiley and Sons, Limited |
| Subjects | |
| Online Access | Get full text |
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| Abstract | BACKGROUND: The traditional approach of analyzing absolute variable data across multiple locations and/or years has drawbacks in precision agriculture. This study was conducted to evaluate the impacts of using relative yield and plant height data of corn (Zea mays L.) on the regression of yield with plant height using linear and exponential models in a nitrogen (N) rate field trial under four cropping systems.
RESULTS: The use of relative yield to replace absolute yield frequently increased the determination coefficient (R2) values in the regression of yield with plant height on datasets combined across cropping systems or/and years. Relative yield and relative plant height sometimes further enhanced the R2 values compared with relative yield and absolute plant height. All these improvements mostly occurred when the fit of the model was not strong with absolute yield and absolute plant height or relative yield and absolute plant height. The advantages of using relative data of yield or/and plant height were similar for the two regression models.
CONCLUSION: The use of relative yield or relative data of both yield and plant height may be effective in improving the regression of corn yield with plant height across multiple cropping systems/locations and years in precision agriculture. Copyright © 2011 Society of Chemical Industry |
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| AbstractList | The traditional approach of analyzing absolute variable data across multiple locations and/or years has drawbacks in precision agriculture. This study was conducted to evaluate the impacts of using relative yield and plant height data of corn (Zea mays L.) on the regression of yield with plant height using linear and exponential models in a nitrogen (N) rate field trial under four cropping systems. The use of relative yield to replace absolute yield frequently increased the determination coefficient (R...) values in the regression of yield with plant height on datasets combined across cropping systems or/and years. Relative yield and relative plant height sometimes further enhanced the R... values compared with relative yield and absolute plant height. All these improvements mostly occurred when the fit of the model was not strong with absolute yield and absolute plant height or relative yield and absolute plant height. The advantages of using relative data of yield or/and plant height were similar for the two regression models. The use of relative yield or relative data of both yield and plant height may be effective in improving the regression of corn yield with plant height across multiple cropping systems/locations and years in precision agriculture. (ProQuest: ... denotes formulae/symbols omitted.) The traditional approach of analyzing absolute variable data across multiple locations and/or years has drawbacks in precision agriculture. This study was conducted to evaluate the impacts of using relative yield and plant height data of corn (Zea mays L.) on the regression of yield with plant height using linear and exponential models in a nitrogen (N) rate field trial under four cropping systems. The use of relative yield to replace absolute yield frequently increased the determination coefficient (R(2) ) values in the regression of yield with plant height on datasets combined across cropping systems or/and years. Relative yield and relative plant height sometimes further enhanced the R(2) values compared with relative yield and absolute plant height. All these improvements mostly occurred when the fit of the model was not strong with absolute yield and absolute plant height or relative yield and absolute plant height. The advantages of using relative data of yield or/and plant height were similar for the two regression models. The use of relative yield or relative data of both yield and plant height may be effective in improving the regression of corn yield with plant height across multiple cropping systems/locations and years in precision agriculture. BACKGROUND: The traditional approach of analyzing absolute variable data across multiple locations and/or years has drawbacks in precision agriculture. This study was conducted to evaluate the impacts of using relative yield and plant height data of corn (Zea mays L.) on the regression of yield with plant height using linear and exponential models in a nitrogen (N) rate field trial under four cropping systems. RESULTS: The use of relative yield to replace absolute yield frequently increased the determination coefficient (R2) values in the regression of yield with plant height on datasets combined across cropping systems or/and years. Relative yield and relative plant height sometimes further enhanced the R2 values compared with relative yield and absolute plant height. All these improvements mostly occurred when the fit of the model was not strong with absolute yield and absolute plant height or relative yield and absolute plant height. The advantages of using relative data of yield or/and plant height were similar for the two regression models. CONCLUSION: The use of relative yield or relative data of both yield and plant height may be effective in improving the regression of corn yield with plant height across multiple cropping systems/locations and years in precision agriculture. Copyright © 2011 Society of Chemical Industry BACKGROUND: The traditional approach of analyzing absolute variable data across multiple locations and/or years has drawbacks in precision agriculture. This study was conducted to evaluate the impacts of using relative yield and plant height data of corn (Zea mays L.) on the regression of yield with plant height using linear and exponential models in a nitrogen (N) rate field trial under four cropping systems. RESULTS: The use of relative yield to replace absolute yield frequently increased the determination coefficient (R2) values in the regression of yield with plant height on datasets combined across cropping systems or/and years. Relative yield and relative plant height sometimes further enhanced the R2 values compared with relative yield and absolute plant height. All these improvements mostly occurred when the fit of the model was not strong with absolute yield and absolute plant height or relative yield and absolute plant height. The advantages of using relative data of yield or/and plant height were similar for the two regression models. CONCLUSION: The use of relative yield or relative data of both yield and plant height may be effective in improving the regression of corn yield with plant height across multiple cropping systems/locations and years in precision agriculture. BACKGROUND: The traditional approach of analyzing absolute variable data across multiple locations and/or years has drawbacks in precision agriculture. This study was conducted to evaluate the impacts of using relative yield and plant height data of corn ( Zea mays L.) on the regression of yield with plant height using linear and exponential models in a nitrogen (N) rate field trial under four cropping systems. RESULTS: The use of relative yield to replace absolute yield frequently increased the determination coefficient ( R 2 ) values in the regression of yield with plant height on datasets combined across cropping systems or/and years. Relative yield and relative plant height sometimes further enhanced the R 2 values compared with relative yield and absolute plant height. All these improvements mostly occurred when the fit of the model was not strong with absolute yield and absolute plant height or relative yield and absolute plant height. The advantages of using relative data of yield or/and plant height were similar for the two regression models. CONCLUSION: The use of relative yield or relative data of both yield and plant height may be effective in improving the regression of corn yield with plant height across multiple cropping systems/locations and years in precision agriculture. Copyright © 2011 Society of Chemical Industry The traditional approach of analyzing absolute variable data across multiple locations and/or years has drawbacks in precision agriculture. This study was conducted to evaluate the impacts of using relative yield and plant height data of corn (Zea mays L.) on the regression of yield with plant height using linear and exponential models in a nitrogen (N) rate field trial under four cropping systems.BACKGROUNDThe traditional approach of analyzing absolute variable data across multiple locations and/or years has drawbacks in precision agriculture. This study was conducted to evaluate the impacts of using relative yield and plant height data of corn (Zea mays L.) on the regression of yield with plant height using linear and exponential models in a nitrogen (N) rate field trial under four cropping systems.The use of relative yield to replace absolute yield frequently increased the determination coefficient (R(2) ) values in the regression of yield with plant height on datasets combined across cropping systems or/and years. Relative yield and relative plant height sometimes further enhanced the R(2) values compared with relative yield and absolute plant height. All these improvements mostly occurred when the fit of the model was not strong with absolute yield and absolute plant height or relative yield and absolute plant height. The advantages of using relative data of yield or/and plant height were similar for the two regression models.RESULTSThe use of relative yield to replace absolute yield frequently increased the determination coefficient (R(2) ) values in the regression of yield with plant height on datasets combined across cropping systems or/and years. Relative yield and relative plant height sometimes further enhanced the R(2) values compared with relative yield and absolute plant height. All these improvements mostly occurred when the fit of the model was not strong with absolute yield and absolute plant height or relative yield and absolute plant height. The advantages of using relative data of yield or/and plant height were similar for the two regression models.The use of relative yield or relative data of both yield and plant height may be effective in improving the regression of corn yield with plant height across multiple cropping systems/locations and years in precision agriculture.CONCLUSIONThe use of relative yield or relative data of both yield and plant height may be effective in improving the regression of corn yield with plant height across multiple cropping systems/locations and years in precision agriculture. |
| Author | Hayes, Robert M Yin, Xinhua McClure, M Angela |
| Author_xml | – sequence: 1 givenname: Xinhua surname: Yin fullname: Yin, Xinhua email: xyin2@utk.edu organization: Department of Plant Sciences, The University of Tennessee, Jackson, TN 38301, USA – sequence: 2 givenname: M Angela surname: McClure fullname: McClure, M Angela organization: Department of Plant Sciences, The University of Tennessee, Jackson, TN 38301, USA – sequence: 3 givenname: Robert M surname: Hayes fullname: Hayes, Robert M organization: West Tennessee Research and Education Center, The University of Tennessee, Jackson, TN 38301, USA |
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| Cites_doi | 10.1080/03650340701597251 10.2135/cropsci2002.1564 10.2134/agronj2001.931131x 10.1016/S0168-1699(99)00075-7 10.1080/00103628909368178 10.2134/agronj2002.8150 10.2134/agronj2010.0450 10.1017/S0021859607006995 10.2136/sssaj2009.0197 10.2134/agronj2003.1447 10.2134/agronj2000.923395x 10.2134/agronj2007.0145 10.1007/s11119-006-9017-6 10.2134/agronj2006.0135 10.1080/01904160802208261 10.2134/agronj2003.1000 10.2135/cropsci1994.0011183X003400030038x 10.2134/agronj2006.0103 |
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| Keywords | Improvement relative yield regression relative plant height Corn Cereal Height Yield plant height |
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| References | Raun WR, Johnson GV, Stone ML, Solie JB, Lukina EV and Thomason WE, In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agron J 93: 131-178 (2001). Yau SK and Hamblin J, Relative yield as a measure of entry performance in variable environments. Crop Sci 34: 813-817 (1994). Biermacher JT, Epplin FM, Brorsen BW, Solie JB and Raun WR, Maximum benefit of a precise nitrogen application system for wheat. Precision Agric 7: 1-12 (2006). Machado S, Bynum ED Jr, Archer TL, Lascano RJ, Wilson LT, Bordovsky J et al, Spatial and temporal variability of corn growth and grain yield: implications for site-specific farming. Crop Sci 42: 1564-1576 (2002). Moges SM, Girma K, Teal RK, Freeman KW, Zhang H, Arnall DB et al, In-season estimation of grain sorghum yield potential using a hand-held optical sensor. Arch Agron Soil Sci 53: 617-628 (2007). Yin X, McClure MA, Jaja N, Tyler DD and Hayes RM, In-season prediction of corn yield using plant height under major corn production systems. Agron J 103: 923-929 (2011). Kucharik CJ, Contribution of planting date trends to increased maize yields in the central United States. Agron J 100: 328-336 (2008). Teal RK, Tubana B, Girma K, Freeman KW, Arnall DB, Walsh O et al, In-season prediction of corn grain yield potential using normalized difference vegetation index. Agron J 98: 1488-1494 (2006). Blackmore S, The interpretation of trends from multiple yield maps. Comput Electron Agric 26: 37-51 (2000). Tubana BS, Arnall DB, Walsh O, Chung B, Solie JB, Girma K et al, Adjusting midseason nitrogen rate using a sensor-based optimization algorithm to increase use efficiency in corn. J Plant Nutr 31: 1393-1419 (2008). Freeman KW, Girma K, Arnall DB, Mullen RW, Martin KL, Teal RK et al, By-plant prediction of corn forage biomass and nitrogen uptake at various growth stages using remote sensing and plant height. Agron J 99: 530-536 (2007). Sims JT, Comparison of Mehlich 1 and Mehlich 3 extractants for P, K, Ca, Mg, Mn, Cu and Zn in Atlantic coastal plain soils. Commun Soil Sci Plant Anal 20: 1707-1726 (1989). Sadler EJ, Bauer PJ and Busscher WJ, Site-specific analysis of a droughted corn crop. I. Growth and grain yield. Agron J 92: 395-402 (2000). Slaton NA, Golden BR, DeLong RE and Mozaffari M, Correlation and calibration of soil potassium availability with soybean yield and trifoliolate potassium. Soil Sci Soc Am J 74: 1642-1651 (2010). Katsvairo TW, Cox WJ and Van Es HM, Spatial growth and nitrogen uptake variability of corn at two nitrogen levels. Agron J 95: 1000-1011 (2003). Chang J, Clay DE, Dalsted K, Clay S and O'Neill M, Corn (Zea mays L.) yield prediction using multispectral and multidate reflectance. Agron J 95: 1447-1453 (2003). Ortiz-Monasterio JI and Raun WR, Reduced nitrogen and improved farm income for irrigated spring wheat in the Yaqui Valley, Mexico, using sensor based nitrogen management. J Agric Sci 145: 1-8 (2007). Raun WR, Solie JB, Johnson GV, Stone ML, Mullen RW, Freeman KW et al, Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agron J 94: 815-820 (2002). 2007; 145 2001; 93 2011; 103 1989; 20 2002; 42 2000; 26 2002; 94 2006; 98 2006; 7 1994; 34 1998 2000; 92 2002 2008; 31 2007; 53 2008; 100 2003; 95 2007; 99 2010; 74 e_1_2_6_8_2 e_1_2_6_7_2 e_1_2_6_18_2 e_1_2_6_9_2 e_1_2_6_19_2 e_1_2_6_4_2 e_1_2_6_3_2 Watson ME (e_1_2_6_20_2) 1998 e_1_2_6_6_2 e_1_2_6_5_2 e_1_2_6_12_2 e_1_2_6_13_2 e_1_2_6_23_2 e_1_2_6_2_2 e_1_2_6_10_2 e_1_2_6_22_2 e_1_2_6_11_2 e_1_2_6_21_2 e_1_2_6_16_2 e_1_2_6_17_2 e_1_2_6_14_2 e_1_2_6_15_2 |
| References_xml | – reference: Machado S, Bynum ED Jr, Archer TL, Lascano RJ, Wilson LT, Bordovsky J et al, Spatial and temporal variability of corn growth and grain yield: implications for site-specific farming. Crop Sci 42: 1564-1576 (2002). – reference: Yin X, McClure MA, Jaja N, Tyler DD and Hayes RM, In-season prediction of corn yield using plant height under major corn production systems. Agron J 103: 923-929 (2011). – reference: Kucharik CJ, Contribution of planting date trends to increased maize yields in the central United States. Agron J 100: 328-336 (2008). – reference: Tubana BS, Arnall DB, Walsh O, Chung B, Solie JB, Girma K et al, Adjusting midseason nitrogen rate using a sensor-based optimization algorithm to increase use efficiency in corn. J Plant Nutr 31: 1393-1419 (2008). – reference: Raun WR, Solie JB, Johnson GV, Stone ML, Mullen RW, Freeman KW et al, Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agron J 94: 815-820 (2002). – reference: Sims JT, Comparison of Mehlich 1 and Mehlich 3 extractants for P, K, Ca, Mg, Mn, Cu and Zn in Atlantic coastal plain soils. Commun Soil Sci Plant Anal 20: 1707-1726 (1989). – reference: Biermacher JT, Epplin FM, Brorsen BW, Solie JB and Raun WR, Maximum benefit of a precise nitrogen application system for wheat. Precision Agric 7: 1-12 (2006). – reference: Moges SM, Girma K, Teal RK, Freeman KW, Zhang H, Arnall DB et al, In-season estimation of grain sorghum yield potential using a hand-held optical sensor. Arch Agron Soil Sci 53: 617-628 (2007). – reference: Sadler EJ, Bauer PJ and Busscher WJ, Site-specific analysis of a droughted corn crop. I. Growth and grain yield. Agron J 92: 395-402 (2000). – reference: Chang J, Clay DE, Dalsted K, Clay S and O'Neill M, Corn (Zea mays L.) yield prediction using multispectral and multidate reflectance. Agron J 95: 1447-1453 (2003). – reference: Katsvairo TW, Cox WJ and Van Es HM, Spatial growth and nitrogen uptake variability of corn at two nitrogen levels. Agron J 95: 1000-1011 (2003). – reference: Teal RK, Tubana B, Girma K, Freeman KW, Arnall DB, Walsh O et al, In-season prediction of corn grain yield potential using normalized difference vegetation index. Agron J 98: 1488-1494 (2006). – reference: Yau SK and Hamblin J, Relative yield as a measure of entry performance in variable environments. Crop Sci 34: 813-817 (1994). – reference: Ortiz-Monasterio JI and Raun WR, Reduced nitrogen and improved farm income for irrigated spring wheat in the Yaqui Valley, Mexico, using sensor based nitrogen management. J Agric Sci 145: 1-8 (2007). – reference: Raun WR, Johnson GV, Stone ML, Solie JB, Lukina EV and Thomason WE, In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agron J 93: 131-178 (2001). – reference: Blackmore S, The interpretation of trends from multiple yield maps. Comput Electron Agric 26: 37-51 (2000). – reference: Freeman KW, Girma K, Arnall DB, Mullen RW, Martin KL, Teal RK et al, By-plant prediction of corn forage biomass and nitrogen uptake at various growth stages using remote sensing and plant height. Agron J 99: 530-536 (2007). – reference: Slaton NA, Golden BR, DeLong RE and Mozaffari M, Correlation and calibration of soil potassium availability with soybean yield and trifoliolate potassium. Soil Sci Soc Am J 74: 1642-1651 (2010). – volume: 103 start-page: 923 year: 2011 end-page: 929 article-title: In‐season prediction of corn yield using plant height under major corn production systems publication-title: Agron J – volume: 34 start-page: 813 year: 1994 end-page: 817 article-title: Relative yield as a measure of entry performance in variable environments publication-title: Crop Sci – volume: 53 start-page: 617 year: 2007 end-page: 628 article-title: In‐season estimation of grain sorghum yield potential using a hand‐held optical sensor publication-title: Arch Agron Soil Sci – volume: 31 start-page: 1393 year: 2008 end-page: 1419 article-title: Adjusting midseason nitrogen rate using a sensor‐based optimization algorithm to increase use efficiency in corn publication-title: J Plant Nutr – volume: 98 start-page: 1488 year: 2006 end-page: 1494 article-title: In‐season prediction of corn grain yield potential using normalized difference vegetation index publication-title: Agron J – volume: 26 start-page: 37 year: 2000 end-page: 51 article-title: The interpretation of trends from multiple yield maps publication-title: Comput Electron Agric – volume: 145 start-page: 1 year: 2007 end-page: 8 article-title: Reduced nitrogen and improved farm income for irrigated spring wheat in the Yaqui Valley, Mexico, using sensor based nitrogen management publication-title: J Agric Sci – volume: 92 start-page: 395 year: 2000 end-page: 402 article-title: Site‐specific analysis of a droughted corn crop. I. Growth and grain yield publication-title: Agron J – year: 2002 – volume: 99 start-page: 530 year: 2007 end-page: 536 article-title: By‐plant prediction of corn forage biomass and nitrogen uptake at various growth stages using remote sensing and plant height publication-title: Agron J – start-page: 13 year: 1998 end-page: 16 – volume: 100 start-page: 328 year: 2008 end-page: 336 article-title: Contribution of planting date trends to increased maize yields in the central United States publication-title: Agron J – volume: 95 start-page: 1000 year: 2003 end-page: 1011 article-title: Spatial growth and nitrogen uptake variability of corn at two nitrogen levels publication-title: Agron J – volume: 42 start-page: 1564 year: 2002 end-page: 1576 article-title: Spatial and temporal variability of corn growth and grain yield: implications for site‐specific farming publication-title: Crop Sci – volume: 93 start-page: 131 year: 2001 end-page: 178 article-title: In‐season prediction of potential grain yield in winter wheat using canopy reflectance publication-title: Agron J – volume: 95 start-page: 1447 year: 2003 end-page: 1453 article-title: Corn ( L.) yield prediction using multispectral and multidate reflectance publication-title: Agron J – volume: 20 start-page: 1707 year: 1989 end-page: 1726 article-title: Comparison of Mehlich 1 and Mehlich 3 extractants for P, K, Ca, Mg, Mn, Cu and Zn in Atlantic coastal plain soils publication-title: Commun Soil Sci Plant Anal – volume: 74 start-page: 1642 year: 2010 end-page: 1651 article-title: Correlation and calibration of soil potassium availability with soybean yield and trifoliolate potassium publication-title: Soil Sci Soc Am J – volume: 7 start-page: 1 year: 2006 end-page: 12 article-title: Maximum benefit of a precise nitrogen application system for wheat publication-title: Precision Agric – volume: 94 start-page: 815 year: 2002 end-page: 820 article-title: Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application publication-title: Agron J – ident: e_1_2_6_17_2 doi: 10.1080/03650340701597251 – ident: e_1_2_6_10_2 doi: 10.2135/cropsci2002.1564 – ident: e_1_2_6_2_2 – ident: e_1_2_6_5_2 doi: 10.2134/agronj2001.931131x – ident: e_1_2_6_18_2 doi: 10.1016/S0168-1699(99)00075-7 – ident: e_1_2_6_21_2 doi: 10.1080/00103628909368178 – ident: e_1_2_6_23_2 – ident: e_1_2_6_6_2 doi: 10.2134/agronj2002.8150 – ident: e_1_2_6_12_2 doi: 10.2134/agronj2010.0450 – ident: e_1_2_6_4_2 doi: 10.1017/S0021859607006995 – ident: e_1_2_6_19_2 doi: 10.2136/sssaj2009.0197 – ident: e_1_2_6_22_2 – ident: e_1_2_6_14_2 doi: 10.2134/agronj2003.1447 – ident: e_1_2_6_11_2 doi: 10.2134/agronj2000.923395x – ident: e_1_2_6_16_2 doi: 10.2134/agronj2007.0145 – ident: e_1_2_6_3_2 doi: 10.1007/s11119-006-9017-6 – ident: e_1_2_6_15_2 doi: 10.2134/agronj2006.0135 – start-page: 13 volume-title: Recommended Chemical Soil Test Procedures for the North Central Region year: 1998 ident: e_1_2_6_20_2 – ident: e_1_2_6_8_2 doi: 10.1080/01904160802208261 – ident: e_1_2_6_9_2 doi: 10.2134/agronj2003.1000 – ident: e_1_2_6_13_2 doi: 10.2135/cropsci1994.0011183X003400030038x – ident: e_1_2_6_7_2 doi: 10.2134/agronj2006.0103 |
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| Snippet | BACKGROUND: The traditional approach of analyzing absolute variable data across multiple locations and/or years has drawbacks in precision agriculture. This... BACKGROUND: The traditional approach of analyzing absolute variable data across multiple locations and/or years has drawbacks in precision agriculture. This... The traditional approach of analyzing absolute variable data across multiple locations and/or years has drawbacks in precision agriculture. This study was... |
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| SubjectTerms | Agricultural production Agriculture Agriculture - methods Biological and medical sciences Cereal and baking product industries Coefficients Corn Crop science Crops, Agricultural Crops, Agricultural - growth & development Effectiveness studies field experimentation Food industries Foods Fundamental and applied biological sciences. Psychology Glycine max - growth & development Gossypium Gossypium - growth & development growth & development linear models methods Models, Biological multiple cropping nitrogen Nitrogen Cycle Plant growth plant height precision agriculture Regression Regression analysis relative plant height relative yield Reproducibility of Results Soybeans Statistics as Topic Tennessee yield Zea mays Zea mays - growth & development |
| Title | Improvement in regression of corn yield with plant height using relative data |
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