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Precision Nutrient Management and Crop Sensing

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Phenomics in Crop Plants: Trends, Options and Limitations

Abstract

Sensing of nutrient status in crop plants is achievable with remote sensing techniques because the nutrient concentration affects the reflectance spectrum. Techniques have been developed with both active and passive sensors engineered to detect the reflectance in specific wavebands and applied mainly to nitrogen status in maize and wheat canopies based on the observation that changes in spectral indices are correlated with plant biomass in the early stages of plant development, and if these deficiencies were due to nitrogen, then additions of nitrogen would allow the plant to achieve potential yield, if there is no other limitation to production, e.g., water or pests. There has been extensive research on the use of techniques which mainly use the normalized difference vegetative index (NDVI); however, the management tools rely on the use of a nitrogen-rich strip in the field. The positive aspects of improving nutrient management are the potential for improved precision management both spatially and temporally. Although, the current approaches have been evaluated for a number of crops in addition to maize and wheat, there remain some challenges in application of the methods which may potentially be overcome by evaluating other spectral methods which are more sensitive to canopy chlorophyll content and less sensitive to biomass. Application of technologies to improve nitrogen management has been shown to have a positive impact on reducing nitrogen application, improving yield of grain and sugar in sugar beets and sugarcane, increasing profitability, and decreasing the negative effect from excess nitrogen in the environment.

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References

  • Adami M, Rudorff BFT, Breunig FM, Ponzoni FJ, Galvão LS, Moreira MA, Freitas JG, Marino Rodrigues Sala V (2010) Effect of nitrogen and endophytic bacteria on biophysical and spectral parameters of wheat canopy. Agron J 102:544–552

    Article  Google Scholar 

  • Adams ML, Norwell WA, Philpot WD, Peverly JH (2000) Spectral detection of micronutrient deficiency in ‘Bragg’ soybean. Agron J 92:261–268

    Article  CAS  Google Scholar 

  • Al-Abbas AH, Barr R, Hall JD, Crane FL, Baumgardner MF (1974) Spectral of normal and nutrient-deficient maize leaves. Agron J 66:16–20

    Article  CAS  Google Scholar 

  • Arnall DB, Mallarino AP, Ruark MD, Varvel GE, Solie JB, Stone ML, Mullock JL, Taylor RK, Raun WR (2013) Relationship between grain crop yield potential and nitrogen response. Agron J 105:1335–1344

    Article  CAS  Google Scholar 

  • Baret F, GuyotG, Major DJ (1989) TSAVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation. In: Proceedings of the IGARRS “0/ 12th Canadian Symposium on Remote Sensing, vol 3. Vancouver, British Columbia, Canada, pp 1355–1358

    Google Scholar 

  • Barker DW, Sawyer JE (2010) Using active canopy sensors to quantify corn nitrogen stress and nitrogen application rate. Agron J 102:964–971

    Article  CAS  Google Scholar 

  • Bélanger M-C, Viau AA, Samson G, Chamberland M (2005) Determination of a multivariate indicator of nitrogen imbalance (MINI) in potato using reflectance and fluorescence spectroscopy. Agron J 97:1515–1523

    Article  Google Scholar 

  • Birth GS, McVey G (1968) Measuring the color of growing turf with a reflectance spectrophotometer. Agron J 60:640–643

    Article  Google Scholar 

  • Blackburn GA (1998) Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sens Environ 66:273–285

    Article  Google Scholar 

  • Blackmer TM, Schepers JS (1995) Use of a chlorophyll meter to monitor N status and schedule fertigation of corn. J Prod Agric 8:56–60

    Article  Google Scholar 

  • Blackmer TM, Schepers JS (1996) Aerial photography to detect nitrogen stress in corn. J Plant Physiol 148:440–444

    Article  CAS  Google Scholar 

  • Blackmer TM, Schepers JS, Vigil MF (1993) Chlorophyll meter readings in corn as affected by plant spacing. Commun Soil Sci Plant Anal 24:2507–2516

    Article  CAS  Google Scholar 

  • Blackmer TM, Schepers JS, Varvel GE (1994) Light reflectance compared with other nitrogen stress measurements in corn leaves. Agron J 86:934–938

    Article  Google Scholar 

  • Blackmer TM, Schepers JS, Varvel GE, Meyer GE (1996a) Analysis of aerial photography for nitrogen stress within corn fields. Agron J 88:729–733

    Article  Google Scholar 

  • Blackmer TM, Schepers JS, Varvel GE, Walter-Shea EA (1996b) Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopies. Agron J 88:1–5

    Article  CAS  Google Scholar 

  • Bronson KF, Booker JD, Keeling JW, Boman RK, Wheeler TA, Lascano RJ, Nichols RL (2005) Cotton canopy reflectance at landscape scale as affected by nitrogen fertilization. Agron J 97:654–660

    Article  Google Scholar 

  • Bronson KF, Malapati A, Scharf PC, Nichols RL (2011) Canopy reflectance0-based nitrogen management strategies for subsurface drip irrigated cotton in the Texas High Plains. Agron J 103:422–430

    Article  Google Scholar 

  • Buschmann C, Nagel E (1993) In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. Intl J Remote Sens 14:711–722

    Article  Google Scholar 

  • Cammarano D, Fitzferald G, Basso B, O’Leary G, Chen D, Grace P, Fiorentino C (2011) Use of the canopy chlorophyll content index (CCCI) for remote estimation of wheat nitrogen content in rainfed environments. Agron J 103:1597–1603

    Article  CAS  Google Scholar 

  • Carter GA (1994) Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Intl J Remote Sens 15:697–703

    Article  Google Scholar 

  • Chappelle EW, Kim MS, McMurtrey JE III (1992) Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sens Environ 39:239–247

    Article  Google Scholar 

  • Clay DE, Kim K, Chang J, Clay SA, Dalsted K (2006) Characterizing water and nitrogen stress in corn using remote sensing. Agron J 98:579–587

    Article  Google Scholar 

  • Clay DE, Kharel TP, Reese C, Beck D, Carlson CG, Clay SA, Reicks G (2012) Winter wheat crop reflectance and nitrogen sufficiency index values are influenced by nitrogen and water stress. Agron J 104:1612–1617

    Article  Google Scholar 

  • Deering DW (1978) Rangeland reflectance characteristics measured by aircraft and spacecraft sensors. Ph.D. Dissertation, Texas A & M University, College Station, TX 338 pp

    Google Scholar 

  • Delegido J, Vergara C, Verrelst J, Gandia S, Moreno J (2011) Remote estimation of crop chlorophyll content by means of high-spectral-resolution reflectance techniques. Agron J 103:1834–1842

    Article  CAS  Google Scholar 

  • Eitel JUH, Long DS, Gessler PE, Smith AMS (2007) Using in-situ measurement to evaluate new RapidEyeâ„¢ satellite series for prediction of wheat nitrogen status. Int J Remote Sens 28:4183–4190

    Article  Google Scholar 

  • Eitel JUH, Long DS, Gessler PE, Hunt ER (2008) Combined spectral index to improved ground-based estimates of nitrogen status in dry land wheat. Agron J 100:1694–1702

    Article  CAS  Google Scholar 

  • Ferguson RB, Hergert GW, Schepers JS, Gotway CA, Cahoon JE, Peterson TA (2002) Site-specific nitrogen management of irrigated maize: yield and soil residual nitrate effects. Soil Sci Soc Am J 66:544–553

    Article  CAS  Google Scholar 

  • Filella I, Serrano I, Serra I, Peñuelas J (1995) Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Sci 35:1400–1405

    Article  Google Scholar 

  • Flowers M, Weisz R, Heiniger R (2001) Remote sensing of winter wheat tiller density for early nitrogen application decisions. Agron J 93:783–789

    Article  Google Scholar 

  • Flowers M, Weisz R, Heiniger R, Tarleton B, Meijer A (2003) Field validation of a remote sensing technique for early nitrogen application decisions in wheat. Agron J 95:167–176

    Article  Google Scholar 

  • Flowers MD, Hart JM, Young WC III, Mellbye ME, Garbacik CJ (2010) Using remote sensing to assess the in-season nitrogen status of perennial ryegrass for seed production. Agron J 102:1441–1447

    Article  Google Scholar 

  • Freeman KW, Girma 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. Agron J 99:530–536

    Article  CAS  Google Scholar 

  • Gamon JA, Surfus JS (1999) Assessing leaf pigment content and activity with a reflectometer. New Phytol 143:105–117

    Article  CAS  Google Scholar 

  • Gamon JA, Penuelas J, Field CB (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens Environ 41:35–44

    Article  Google Scholar 

  • Gehl RJ, Boring TJ (2011) In-season prediction of sugarbeet yield, quality, and nitrogen status using an active sensor. Agron J 103:1012–1018

    Article  Google Scholar 

  • Gitelson AA (2004) Wide dynamic range vegetation index for remote quantification of crop biophysical characteristics. J Plant Physiol 161:165–173

    Article  CAS  PubMed  Google Scholar 

  • Gitelson AA, Merzlyak MN (1994) Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. J Photochem Photobiol 22:247–252

    Article  CAS  Google Scholar 

  • Gitelson AA, Kaufman YJ, Merzlyak MN (1996a) Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ 58:289–298

    Article  Google Scholar 

  • Gitelson A, Merzlyak M, LichtenthalerH H (1996b) Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. J Plant Physiol 148:501–508

    Article  CAS  Google Scholar 

  • Gitelson AA, Buschmann C, Lichtenthaler HK (1999) The chlorophyll fluorescence ratio F735/F700 as an accurate measure of the chlorophyll content in plants. Remote Sens Environ 69:296–302

    Article  Google Scholar 

  • Gitelson AA, ZurY COB, Merzlyak MN (2002) Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem Photobiol 75:272–281

    Article  CAS  PubMed  Google Scholar 

  • Gitelson AA, Gritz U, Merzlyak MN (2003) Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol 160:271–282

    Article  CAS  PubMed  Google Scholar 

  • Gitelson AA, Viña A, Rundquist DC, Ciganda V, Arkebauer TJ (2005) Remote estimation of canopy chlorophyll content in crops. Geophys Res Lett 32:l08–l403

    Article  Google Scholar 

  • Haboudane D, Miller DR, Pattey E, Zarco-Tejada PJ, Strachan IB (2004) Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ 90:337–352

    Article  Google Scholar 

  • Hatfield JL, Prueger JH (2010) Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sens 2:562–578

    Article  Google Scholar 

  • Hatfield JL, Gitelson AA, Schepers JS, Walthall CL (2008) Application of spectral remote sensing for agronomic decisions. Agron J 100:S117–S131

    Article  CAS  Google Scholar 

  • Hawkins JA, Sawyer JE, Barker DW, Lundvall JP (2007) Using relative chlorophyll meter values to determine nitrogen application rates for corn. Agron J 99:1034–1040

    Article  CAS  Google Scholar 

  • Hong N, White JG, Weisz R, Crozier CR, Gumpertz ML, Cassel DK (2006) Remote sensing-informed variable-rate nitrogen management of wheat and corn: agronomic and groundwater outcomes. Agron J 98:327–338

    Article  Google Scholar 

  • Huete AR (1988) A soil-adjusted vegetative index (SAVI). Remote Sens Environ 25:295–309

    Article  Google Scholar 

  • Huete A, DidanK MT, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83:195–213

    Article  Google Scholar 

  • Hunt ER Jr, Daughtry CST, Eitel JUH, Long DS (2011) Remote sensing leaf chlorophyll content using a visible band index. Agron J 103:1090–1099

    Article  Google Scholar 

  • Inman D, Khosla R, Reich R, Westfall DG (2008) Normalized difference vegetation index and soil color-based management zones in irrigated maize. Agronomy 100:60–66

    Article  Google Scholar 

  • Jordan CF (1969) Derivation of leaf area index from quality of light on the forest floor. Ecology 50:663–666

    Article  Google Scholar 

  • Kitchen NR, Sudduth KA, Drummond ST, Scharf PC, Palm H, Roberts DF, Vories ED (2010) Ground-based canopy reflectance sensing for variable-rate nitrogen corn fertilization. Agron J 102:71–84

    Article  CAS  Google Scholar 

  • Kyveryga PM, Tao H, Morris TF, Blackmer TM (2010) Identification of nitrogen management categories by corn stalk nitrate sampling guided by aerial imagery. Agron J 102:858–867

    Article  CAS  Google Scholar 

  • Lee Y, Yang C, Chang K, Shen Y (2008) A simple spectral index using reflectance of 735 nm to assess nitrogen status of rice canopy. Agron J 100:205–212

    Article  CAS  Google Scholar 

  • Lichtenthaler HK (1987) Chlorophyll and carotenoids: pigments of photosynthetic biomembranes. Meth Enzym 148:331–382

    Google Scholar 

  • Lofton J, Turbaña BS, Kanke Y, Teboh J, Viator H (2012) Predicting sugarcane response to nitrogen using a canopy reflectance-based response index value. Agron J 104:1067–1112

    Article  Google Scholar 

  • Merzlyak MN, Gitelson AA, Chivkunova OB, Rakitin VY (1999) Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol Plant 106:135–141

    Article  CAS  Google Scholar 

  • Moran MS, Inoue Y, Barnes EM (1997) Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens Environ 61:319–346

    Article  Google Scholar 

  • Mullen RW, Freeman KW, Raun WR, Johnson GV, Stone ML, Solie JB (2003) Identifying an in-season response index and the potential to increase wheat yield with nitrogen. Agron J 95:347–351

    Article  Google Scholar 

  • Peñuelas J, Gamon JA, Fredeen AL, Merino J, Field CB (1994) Reflectance indices associated with physiological changes in nitrogen and water-limited sunflower leaves. Remote Sens Environ 48:135–146

    Article  Google Scholar 

  • Raper TB, Varco JJ, Hubbard KJ (2013) Canopy-based normalized difference vegetation index sensors for monitoring cotton nitrogen status. Agronomy 105:1345–1354

    Article  CAS  Google Scholar 

  • Raun WR, Johnson GV, Stone ML, Solie JB, Lukina EV, Thomason WE, Schepers JS (2001) In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agron J 93:131–138

    Article  Google Scholar 

  • Raun WR, Slie JB, Johnson GV, Stone ML, Mullen RW, Freeman KW, Thomason WE, Lukina EV (2002) Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agron J 94:815–820

    Article  Google Scholar 

  • 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. Agron J 100:1088–1092

    Article  CAS  Google Scholar 

  • Raun WR, Solie JB, Stone ML (2011) Independence of yield potential and crop nitrogen response. PrecAgric 12:508–518

    Google Scholar 

  • Richardson AJ, Wiegand CL (1977) Distinguishing vegetation from soil background information. Photogramm Eng Remote Sens 43:1541–1552

    Google Scholar 

  • Roberts DF, Kitchen NR, Scharf PC, Sudduth KA (2010) Will variable-rate nitrogen fertilization using corn canopy reflectance sensing deliver environmental benefits? Agron J 102:85–95

    Article  CAS  Google Scholar 

  • Scharf PC, Lory JA (2002) Calibrating corn color from aerial photographs to predict side dress nitrogen need. Agron J 94:397–404

    Article  Google Scholar 

  • Scharf PC, Lory JA (2009) Calibrating reflectance measurements to predict optimal sidedress nitrogen rate for corn. Agron J 101:615–625

    Article  CAS  Google Scholar 

  • Scharf PC, Shannon DK, Palm HL, Sudduth KA, Drummond ST, Kitchen NR, Mueller LJ, Hubbard VC, Oliveira LF (2011) Sensor-based nitrogen applications out-perform producer-chosen rates for on corn in on-farm demonstrations. Agron J 103:1683–1691

    Article  Google Scholar 

  • Schepers JS, Francis DD, Vigil M, Below FE (1992) Comparison of corn leaf nitrogen concentration and chlorophyll meter readings. Commun Soil Sci Plant Anal 23:2173–2187

    Article  CAS  Google Scholar 

  • Schepers JS, Blackmer TM, Wilhelm WW, Resende M (1996) Transmittance and reflectance measurements of corn leaves from plants with different nitrogen and water supply. J Plant Physiol 148:523–529

    Article  CAS  Google Scholar 

  • Shanahan JF, Kitchen NR, Raun WR, Schepers JS (2008) Responsive in-season nitrogen management for cereals. Comput Electron Agric 61:51–62

    Article  Google Scholar 

  • Solari F, Shanahan JF, Ferguson RB, Adamchuk VI (2010) An active sensor algorithm for corn N applications based on a chlorophyll meter sufficiency index framework. Agron J 102:1090–1098

    Article  Google Scholar 

  • Solie JB, Monroe AD, Raun WR, Stone ML (2012) Generalized algorithm for variable-rate nitrogen applications in cereal grains. Agron J 104:378–387

    Article  Google Scholar 

  • Sripada RP, Heiniger RW, White JG, Weisz R (2005) Aerial color infrared photography for determining late-season nitrogen requirements in corn. Agron J 97:1443–1451

    Article  Google Scholar 

  • Sripada RP, Heiniger RW, White JG, Meijer AD (2006) Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agron J 98:968–977

    Article  Google Scholar 

  • Sripada RP, Farrer DC, Weisz R, Heiniger RW, White JG (2007) Aerial color infrared photography to optimize in-season nitrogen fertilizer recommendations in winter wheat. Agron J 99:1424–1435

    Article  CAS  Google Scholar 

  • Sripada RP, Schmidt JP, Dellinger AE, Beegle DB (2008) Evaluating multiple indices from a canopy reflectance sensor to estimate corn N requirements. Agron J 100:1553–1561

    Article  CAS  Google Scholar 

  • Stone ML, Solie JB, Raun WR, Whitney RW, Taylor SL, Ringer JD (1996) Use of spectral radiance for correcting in-season fertilizer nitrogen deficiencies in winter wheat. Trans ASAE 39:1623–1631

    Article  Google Scholar 

  • Thomas JR, Gausman HW (1977) Leaf reflectance vs. leaf chlorophyll and carotenoid concentrations for eight crops. Agron J 69:799–802

    Article  CAS  Google Scholar 

  • Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150

    Article  Google Scholar 

  • Turbaña B, Harrell D, Walker T, Teboh J, Lofton J, Kanke Y, Phillips S (2011) Relationships of spectral vegetation indices with rice biomass and grain yield at different sensor view angles. Agron J 103:1405–1413

    Article  Google Scholar 

  • Turbaña BS, Harrell DL, Walker T, Teboh J, Lofton J, Kanke Y (2012) In-season canopy reflectance-based estimation of rice yield response to nitrogen. Agron J 104:1604–1611

    Article  Google Scholar 

  • Yin X, McClure MA (2013) Relationship of corn yield, biomass, and leaf nitrogen with normalized difference vegetation index and plant height. Agron J 105:1005–1016

    Article  Google Scholar 

  • Yin X, McClure MA, Jaja N, Tyler DD, Hayes RM (2011) In-season prediction of corn yield using plant height under major production systems. Agron J 103:923–931

    Article  Google Scholar 

  • Yoder BJ, Pettigrew-Crosby RE (1995) Predicting nitrogen and chlorophyll concentrations from reflectance spectra (400-2500 nm) at leaf and canopy scales. Remote Sens Environ 53:199–211

    Article  Google Scholar 

  • Zhao D, Reddy KR, Kakani VG, Read JJ, Koti S (2005) Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton. Agron J 97:89–98

    Article  CAS  Google Scholar 

  • Zillman E, Graeff S, Link J, Batchelor WD, Claupin W (2006) Assessment of cereal nitrogen requirements derived by optical on-the-go sensors on heterogeneous soils. Agron J 98:682–690

    Article  Google Scholar 

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  1. National Laboratory for Agriculture and the Environment, Ames, IA, USA

    Jerry L. Hatfield

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  1. Jerry L. Hatfield
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Correspondence to Jerry L. Hatfield .

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Editors and Affiliations

  1. Division of Crop Improvement, ICAR - Indian Institute of Pulses Research, Kalyanpur, Kanpur, Uttar Pradesh, India

    Jitendra Kumar

  2. Division of Crop Improvement, ICAR - Indian Institute of Pulses Research, Kalyanpur, Kanpur, Uttar Pradesh, India

    Aditya Pratap

  3. Rabat - Instituts, International Center for Agricultural Research in the Dry Areas, Rabat, Morocco

    Shiv Kumar

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Hatfield, J.L. (2015). Precision Nutrient Management and Crop Sensing. In: Kumar, J., Pratap, A., Kumar, S. (eds) Phenomics in Crop Plants: Trends, Options and Limitations. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2226-2_14

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