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RESEARCH ARTICLE

Use of normalised difference vegetation index, nitrogen concentration, and total nitrogen content of whole maize plant and plant fractions to estimate yield and nutritive value of hybrid forage maize

M. R. Islam A C , S. C. (Yani) Garcia A and D. Henry B
+ Author Affiliations
- Author Affiliations

A Dairy Science Group, Faculty of Veterinary Science, The University of Sydney, Camden, NSW 2570, Australia.

B Dairy Australia, Southbank, Vic. 3006, Australia.

C Corresponding author. Email: md.islam@sydney.edu.au

Crop and Pasture Science 62(5) 374-382 https://doi.org/10.1071/CP10244
Submitted: 20 July 2010  Accepted: 17 April 2011   Published: 1 June 2011

Abstract

This study was conducted to investigate the potentials of normalised difference vegetation index (NDVI), nitrogen (N) concentration (%), and N content (g/plant) of whole maize plant to estimate yield and nutritive value of hybrid forage maize. Hybrid forage maize was grown with two rates of pre-sowing fertiliser N (0, 135 kg/ha) and three rates of post-sowing fertiliser N (0, 79, 158 kg N/ha) applied at the six-leaf stage. Data on the NDVI and N (% and g/plant) of maize were collected at 2-, 3-, 6-, 8-, 12-, 16-, 18-leaf stages and at harvest. Metabolisable energy (ME) content of the whole maize plant at harvest was estimated from in vitro digestibility. Simple, polynomial, and multiple regression analyses were conducted and only the best-fit models were selected. The 8-leaf stage was found to be the most effective stage for use of the NDVI in predicting biomass yield (R2 = 0.81), grain yield (R2 = 0.72), and N (%) (R2 = 0.92) of forage maize. Nitrogen (%) at the 8-leaf stage was also best related to biomass yield (R2 = 0.88). Multiple regressions at the 3-leaf stage increased the coefficient of determination for both biomass yield and grain yield (R2 = 0.77) over the relationships obtained from N (%) of the whole plant at 2- or 3-leaf stage. The NDVI and N (%) of the whole plant at 8-leaf stage were the best predictors of yield, but failed to predict ME content of the hybrid forage maize. Multiple regression models at the 3-leaf stage were almost as effective as the NDVI and N (%) of whole maize plant at the 8-leaf stage in predicting biomass and grain yield of forage maize.

Additional keywords: forage maize, N content, NDVI, quality, yield.


References

Armstrong RD, Fitzpatrick J, Rab MA, Abuzar M, Fisher PD, O’Leary G (2009) Advances in precision agriculture in south-eastern Australia. III. Interactions between soil properties and water use help explain spatial variability of crop production in the Victorian Mallee. Crop & Pasture Science 60, 870–884.
Advances in precision agriculture in south-eastern Australia. III. Interactions between soil properties and water use help explain spatial variability of crop production in the Victorian Mallee.Crossref | GoogleScholarGoogle Scholar |

Birch CJ, McLean G, Sawers A (2008) Analysis of high yielding maize production – a study based on a commercial crop. Australian Journal of Experimental Agriculture 48, 296–303.
Analysis of high yielding maize production – a study based on a commercial crop.Crossref | GoogleScholarGoogle Scholar |

Clay DE, Kim K, Chang J, Clay SA, Dalsted K (2006) Characterizing water and nitrogen stress in corn using remote sensing. Agronomy Journal 98, 579–587.
Characterizing water and nitrogen stress in corn using remote sensing.Crossref | GoogleScholarGoogle Scholar |

Dellinger AE, Schmidt JP, Beegle DB (2008) Developing nitrogen fertiliser recommendations for corn using an active sensor. Agronomy Journal 100, 1546–1552.
Developing nitrogen fertiliser recommendations for corn using an active sensor.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXivFGgug%3D%3D&md5=9b38cf01e3f7c002611a79785b46f51cCAS |

Dickson T, Aitken RL, Dwyer JC (1993) Prediction of nitrogen fertiliser requirements of maize in subtropical Queensland. Australian Journal of Experimental Agriculture 33, 53–58.
Prediction of nitrogen fertiliser requirements of maize in subtropical Queensland.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK3sXmtVams7k%3D&md5=c284ceecd3db09cf0142dc047145601dCAS |

Diker K, Bausch WC (2003) Potential use of nitrogen reflectance index to estimate plant parameters and yield of maize. Biosystems Engineering 85, 437–447.
Potential use of nitrogen reflectance index to estimate plant parameters and yield of maize.Crossref | GoogleScholarGoogle Scholar |

Ding L, Wang KJ, Jiang GM, Biswas DK, Xu H, Li LF, Li YH (2005) Effects of N deficiency on photosynthetic traits of maize hybrids released in different years. Annals of Botany 96, 925–930.
Effects of N deficiency on photosynthetic traits of maize hybrids released in different years.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXhtFOjsLfO&md5=2c829378a3d22808667893ce76ca155cCAS | 16103036PubMed |

Donald GE, Gherardi SG, Edirisinghe A, Gittins SP, Henry DA, Mata G (2010) Using MODIS imagery, climate and soil data to estimate pasture growth rates on farms in the south-west of Western Australia. Animal Production Science 50, 611–615.
Using MODIS imagery, climate and soil data to estimate pasture growth rates on farms in the south-west of Western Australia.Crossref | GoogleScholarGoogle Scholar |

Durieux RP, Brown HJ, Stewart EJ, Zhao JQ, Jokela WE, Magdoff FR (1995) Implications of nitrogen management strategies for nitrate leaching potential: roles of nitrogen source and fertiliser recommendations system. Agronomy Journal 87, 884–887.
Implications of nitrogen management strategies for nitrate leaching potential: roles of nitrogen source and fertiliser recommendations system.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK28XjvFyksA%3D%3D&md5=5b6dec84c1337d6db7847927bff567d9CAS |

Eghball B, Schepers JS, Negahban M, Schlemmer MR (2003) Spatial and temporal variability of soil nitrate and corn yield: multifractal analysis. Agronomy Journal 95, 339–346.
Spatial and temporal variability of soil nitrate and corn yield: multifractal analysis.Crossref | GoogleScholarGoogle Scholar |

Feil B, Garibay SV, Ammon HU, Stamp P (1997) Maize production in a grass mulch system: seasonal patterns of indicators of the nitrogen status of maize. European Journal of Agronomy 7, 171–179.
Maize production in a grass mulch system: seasonal patterns of indicators of the nitrogen status of maize.Crossref | GoogleScholarGoogle Scholar |

Fisher PD, Abuzar M, Rab MA, Best F, Chandra S (2009) Advances in precision agriculture in south-eastern Australia. I. A regression methodology to simulate spatial variation in cereal yields using farmers historical paddock yield and normalized difference vegetation index. Crop & Pasture Science 60, 844–858.
Advances in precision agriculture in south-eastern Australia. I. A regression methodology to simulate spatial variation in cereal yields using farmers historical paddock yield and normalized difference vegetation index.Crossref | GoogleScholarGoogle Scholar |

Freeman KW, Grima K, Arnall DB, Mullen RW, Martin KL, Teal RK, Raun WR (2007) By-plant prediction of corn forage biomass and nitrogen uptake at various growth stages using remote sensing and plant height. Agronomy Journal 99, 530–536.
By-plant prediction of corn forage biomass and nitrogen uptake at various growth stages using remote sensing and plant height.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXkslymsbo%3D&md5=dfca096c714b1ee38ad7bc58b5683b83CAS |

Garcia SC, Fulkerson WJ, Brookes SU (2008) Dry matter production, nutritive value and efficiency of nutrient utilization of a complementary forage rotation compared to a grass pasture system. Grass and Forage Science 63, 284–300.
Dry matter production, nutritive value and efficiency of nutrient utilization of a complementary forage rotation compared to a grass pasture system.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXht1ait73N&md5=4e0129527c674ad1894ea42ed72e23c1CAS |

Hong XL, Zhang YL, Hui FX (2006) Estimation of nitrogen content and C/N in rice leaves and plant with canopy reflectance spectra. Acta Agronomica 32, 430–435.

Magdoff FR, Ross D, Amadon J (1984) A soil test for nitrogen availability to corn. Soil Science Society of America Journal 48, 1301–1304.
A soil test for nitrogen availability to corn.Crossref | GoogleScholarGoogle Scholar |

Martin KL, Grima K, Freeman KW, Teal RK, Tubana B, Arnall DB, Chung B, Walsh O, Solie JB, Stone ML, Raun WR (2007) Expression of variability in corn as influenced by growth stage using optical sensor measurements. Agronomy Journal 99, 384–389.
Expression of variability in corn as influenced by growth stage using optical sensor measurements.Crossref | GoogleScholarGoogle Scholar |

Perry EM, Roberts DA (2008) Sensitivity of narrow band and broad band indices for assessing nitrogen availability and water stress in an annual crop. Agronomy Journal 100, 1211–1219.
Sensitivity of narrow band and broad band indices for assessing nitrogen availability and water stress in an annual crop.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXpsFOrsLo%3D&md5=ad279f513e436b54fe020c49611a5345CAS |

Peterson TA, Blackmer TM, Francis DD, Scheppers JS (1993) Using a chlorophyll meter to improve N management. NebGuide G93-1171-A. University of Nebraska Cooperative Extension, Lincoln, NE.

Plénet D, Lemaire G (1999) Relationships between dynamics of nitrogen uptake and dry matter accumulation in maize crops: determination of critical N concentration. Plant and Soil 216, 65–82.
Relationships between dynamics of nitrogen uptake and dry matter accumulation in maize crops: determination of critical N concentration.Crossref | GoogleScholarGoogle Scholar |

Raun WR, Solie JB, Stone ML, Zavondi DL, Martin KL, Freeman KW (2005) Automated calibration stamp technology for improved in-season nitrogen fertilisation. Agronomy Journal 97, 338–342.

Raun WR, Solie JB, Taylor RK, Arnall DB, Mack CJ, Edmonds DE (2008) Ramp calibration strip technology for determining midseason nitrogen rates in corn and wheat. Agronomy Journal 100, 1088–1093.
Ramp calibration strip technology for determining midseason nitrogen rates in corn and wheat.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXpsFOrs7c%3D&md5=f3bab4cfe62ce8483fb611a4c2453886CAS |

Rouse JW, Haas RH, Deering DW (1974) Monitoring vegetation systems in the Great Plains with ERTS. In ‘ERTS 3rd Symposium’. Goddard Space Flight Centre, Greenbelt, MD. NASA SP-351. Vol. 1. pp. 309–317.

SCA (1990) ‘Feeding standards for agricultural livestock ruminants.’ (CSIRO: East Melbourne, Vic.)

Schepers JS, Francis DD, Vigil M, Below RE (1992) Comparison of corn leaf nitrogen concentration and chlorophyll meter readings. Communications in Soil Science and Plant Analysis 23, 2173–2187.
Comparison of corn leaf nitrogen concentration and chlorophyll meter readings.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK3sXnvF2qtQ%3D%3D&md5=ec38f38017e2b55ca5359277492694c5CAS |

Schmidt JP, Dellinger A, Beegle DB (2009) Nitrogen recommendations for corn: an on-the-go sensor compared with current recommendation methods. Agronomy Journal 101, 916–924.
Nitrogen recommendations for corn: an on-the-go sensor compared with current recommendation methods.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXps1OmtLY%3D&md5=5000b4e91a03d592de38f1aa9fb8b169CAS |

Schut AGT, Stephens DJ, Stovold RGH, Adams M, Craig RL (2009) Improved wheat yield and production forecasting with a moisture stress index, AVHRR and MODIS data. Crop & Pasture Science 60, 60–70.
Improved wheat yield and production forecasting with a moisture stress index, AVHRR and MODIS data.Crossref | GoogleScholarGoogle Scholar |

Shaver TM, Westfall DG, Khosla R (2007) Comparison of three active hand held NDVI remote sensors for nitrogen management in corn. In ‘Papers presented at the 6th European Conference on Precision Agriculture’. Skiathos, Greece, 3–6 June 2007. pp. 373–379. (Wageningen Academic Publishers: Wageningen, The Netherlands)

Spellman DE, Rongni A, Westfall DG, Waskom RM, Soltanpour PN (1996) Presidedress nitrate soil testing to manage nitrogen fertility in irrigated corn in semi-arid environment. Communications in Soil Science and Plant Analysis 27, 561–574.
Presidedress nitrate soil testing to manage nitrogen fertility in irrigated corn in semi-arid environment.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK28XhsFWqtL4%3D&md5=d77840dfeb3d37f9d78d07f5baa2a84cCAS |

Stamatiadis S, Tsadilas C, Schepers JS (2010) Ground-based canopy sensing for detecting effects of water stress in cotton. Plant and Soil 331, 277–287.
Ground-based canopy sensing for detecting effects of water stress in cotton.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXlvVOhsLk%3D&md5=bcbf6426cdcd0185f29bb5f234b232d8CAS |

Subedi KD, Ma BL (2005) Nitrogen uptake and partitioning in stay-green and leafy maize hybrids. Crop Science 45, 740–747.
Nitrogen uptake and partitioning in stay-green and leafy maize hybrids.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXjtVGisrs%3D&md5=4acc75cdaa30584a251adc1b6199d33dCAS |

Teal RK, Tubana B, Girma K, Freeman KW, Arnall DB, Walsh O, Raun WR (2006) In-season prediction of corn grain yield potential using normalised difference vegetation index. Agronomy Journal 98, 1488–1494.
In-season prediction of corn grain yield potential using normalised difference vegetation index.Crossref | GoogleScholarGoogle Scholar |

Turner FT, Jund MF (1994) Assessing the nitrogen requirements of rice crops with a chlorophyll meter. Australian Journal of Experimental Agriculture 34, 1001–1005.
Assessing the nitrogen requirements of rice crops with a chlorophyll meter.Crossref | GoogleScholarGoogle Scholar |

Varvel GE, Schepers JS, Francis DD (1997) Ability for in-season correction of nitrogen deficiency in corn using chlorophyll meters. Soil Science Society of America Journal 61, 1233–1239.
Ability for in-season correction of nitrogen deficiency in corn using chlorophyll meters.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK2sXlt1Sht7w%3D&md5=5e628f5e433bd3f28a619d85d564d03dCAS |

Varvel GE, Wilhelm WW, Shanahan JF, Schepers JS (2007) An algorithm for corn nitrogen recommendations using a chlorophyll meter based sufficiency index. Agronomy Journal 99, 701–706.
An algorithm for corn nitrogen recommendations using a chlorophyll meter based sufficiency index.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXms1Okurs%3D&md5=539ddbc54197f083395c705350632964CAS |

Wang J, Xu R, Yang S (2009) Estimation of plant water content by spectral absorption features centered at 1450 nm and 1940 nm regions. Environmental Monitoring and Assessment 157, 459–469.
Estimation of plant water content by spectral absorption features centered at 1450 nm and 1940 nm regions.Crossref | GoogleScholarGoogle Scholar | 18853268PubMed |

Wolfe DW, Henderson DW, Hsiao TC, Alvino A (1988) Interactive water and nitrogen effects on senescence of maize: II. Photosynthetic decline and longevity of individual leaves. Agronomy Journal 80, 865–870.
Interactive water and nitrogen effects on senescence of maize: II. Photosynthetic decline and longevity of individual leaves.Crossref | GoogleScholarGoogle Scholar |

Xiong X, Bell GE, Solie JB, Smith MW, Martin B (2007) Bermudagrass seasonal responses to nitrogen fertilization and irrigation detected using optical sensing. Crop Science 47, 1603–1610.
Bermudagrass seasonal responses to nitrogen fertilization and irrigation detected using optical sensing.Crossref | GoogleScholarGoogle Scholar |

Zhu Y, Zhou D, Yao X, Tian Y, Cao W (2007) Quantitative relationships of leaf nitrogen status to canopy spectral reflectance in rice. Australian Journal of Agricultural Research 58, 1077–1085.
Quantitative relationships of leaf nitrogen status to canopy spectral reflectance in rice.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhtlGrtLvI&md5=455bec9a8892ed5d8c0724e6200da742CAS |

Ziadi N, Brassard M, Belanger G, Cambouris AN, Tremblay N, Nolin MC, Claessens A, Parent LE (2008a) Critical nitrogen curve and nitrogen nutrition index for corn in eastern Canada. Agronomy Journal 100, 271–276.
Critical nitrogen curve and nitrogen nutrition index for corn in eastern Canada.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXkvFykurk%3D&md5=3054649bbbadf2c9c0cdb4a3423613bfCAS |

Ziadi N, Brassard M, Belanger G, Claessens A, Tremblay N, Cambouris AN, Nolin MC, Parent LE (2008b) Chlorophyll measurements and nitrogen nutrition index for the evaluation of corn nitrogen status. Agronomy Journal 100, 1264–1273.
Chlorophyll measurements and nitrogen nutrition index for the evaluation of corn nitrogen status.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXht1aqtb3O&md5=99dadf828fe34c9a629ed57630cae788CAS |

Ziadi N, Belanger G, Gastal F, Claessens A, Lemaire G, Tremblay N (2009) Leaf nitrogen concentration as an indicator of corn nitrogen status. Agronomy Journal 101, 947–957.
Leaf nitrogen concentration as an indicator of corn nitrogen status.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXps1Omtb4%3D&md5=4cfbfe2a5b17a866093bb4d0e797a48fCAS |