Issue
Predicting some quality factors of annual ryegrass (Lolium multiflorum lam.) by means of spectral reflectance values
Corresponding Author(s) : Yaşar í–zyiğit
Cellular and Molecular Biology,
Vol. 64 No. 13: Issue 13
Abstract
This study was conducted to predict some quality factors of the annual ryegrass (Lolium multiflorum Lam.) with spectral reflectance values in the Forage Crops Laboratory in the Department of Field Crops at the Agriculture Faculty, Akdeniz University, Turkey. In the study, variations resulting from different implementations (moisture level, bale density, propionic acid application and storage period) made during haymaking were determined with reflectance values. For reflectance measurements, a portable spectroradiometer and a contact probe (plant probe) were used and predicting models were created. Results of this study showed that quality factors of dried Annual ryegrass could be predicted with reflectance values, and that reflectances had higher efficacy in the red region for, the red and green, and the NIR region for crude protein, crude ash and crude cellulose, respectively. The results reveal that in dried annual ryegrass, there are significant relationships between feed quality factors such as crude protein, crude ash and crude cellulose, and reflectance values, and that especially crude protein levels can be rapidly and cheaply predicted by using reflectance values.
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- Ilavenil S, Arasu MV, Lee JC, Kim DH, Vijayakumar M, Lee KD, Choi KC. Positive regulations of adipogenesis by Italian ryegrass [Lolium multiflorum] in 3T3-L1 cells. BMC Biotechnol 2014; 14(1): 54 (p. 11).
- Pavinato PS, Restelatto R, Sartor LR, Paris W. Production and nutritive value of ryegrass (cv. Barjumbo) under nitrogen fertilization. Ciíªnc Agron 2014; 45(2): 230-237.
- Yasuda M, Takenouchi Y, Nitta Y, Ishii Y, Ohta K. Italian ryegrass (Lolium multiflorum Lam) as a high-potential bio-ethanol resource. Bioenergy Res 2015; 8(3): 1303-1309.
- Rechițean D, Dragoș M, Dragomir N, Horablaga M, Sauer M, Camen D, Toth I, Sala A. Associated culture of Italian ryegrass (Lolium multiflorum) and crimson clover (Trifolium incarnatum) under nitrogen fertilization. Scientific Papers: Animal Sci Biotechnol, 2018; 51(1): 129-132.
- Minson DJ. Forage in Ruminant Nutrition. Academic Press, Madison, WI. 1990; Pp. 463.
- Mohajer S, Ghods H, Taha RM, Talati A. Effect of different harvest time on yield and forage quality of three varieties of common millet (Panicum miliaceum). Sci. Res. Essays 2012; 7(34): 3020-3025.
- Karthikeyan BJ, Babu C, Amalraj JJ. Nutritive Value and Fodder Potential of Different Sorghum (Sorghum bicolor L. Moench) Cultivars. Int. J. Curr. Microbiol. App. Sci 2017; 6(8): 898-911.
- Tariq AS, Akram Z, Shabbir G, Khan KS, Mahmood T, Iqbal MS. Heterosis and combining ability evaluation for quality traits in forage sorghum (Sorghum bicolor L.). SABRAO J. Breed. Gen, 2014; 46(2).174-182.
- Zhang R, Zhu W, Zhu W, Liu J, Mao S. Effect of dietary forage sources on rumen microbiota, rumen fermentation and biogenic amines in dairy cows. J Sci Food Agric 2014; 94(9): 1886-1895.
- Mohajer S, Taha RM, Khorasani A, Mubarak EE. Comparative studies of forage yield and quality traits among proso millet, foxtail millet and sainfoin varieties. Int J of Environ Sci Dev 2013; 4(5): 465-469
- Reddersen B, Fricke T, Wachendorf M. Effects of sample preparation and measurement standardization on the NIRS calibration quality of nitrogen, ash and NDFom content in extensive experimental grassland biomass. Anim Feed Sci Technol 2013; 183(3-4): 77-85.
- Monrroy M, Gutiérrez D, Miranda M, Hernández K, Renán García, JOSí‰. Determination of brachiaria spp. forage quality by near-infrared spectroscopy and partial least squares regression. J Chil Chem Soc 2017; 62(2); 3472-3477.
- Zhang J, Li S, Lin M, Yang E, Chen X. A near-infrared reflectance spectroscopic method for the direct analysis of several fodder-related chemical components in drumstick (Moringa oleifera Lam.) leaves. Biosci Biotechnol Biochem 2018; 82(5): 768-774
- Schröder I, Huang DYQ, Ellis O, Gibson JH, Wayne NL. Laboratory safety attitudes and practices: A comparison of academic, government, and industry researchers. Chem Health Saf 2016; 23(1): 12-23.
- Zhao D, Starks PJ, Brown MA, Phillips WA, Coleman SW. Assessment of forage biomass and quality parameters of bermudagrass using proximal sensing of pasture canopy reflectance. Grassl Sci 2007; 53: 39-49.
- Zarco-Tejada PJ, Mateos L, Fereres E, Villalobos FJ. New Tools and Methods in Agronomy. In Principles of Agronomy for Sustainable Agriculture 2016; (pp. 503-514). Springer, Cham.
- Rani DS, Venkatesh MN, Sri CNS, Kumar KA. Remote Sensing as Pest Forecasting Model in Agriculture. Int J Curr Microbiol Appl Sci 2018; 7(3): 2680-2689.
- De Jong SM, Van der Meer FD, Clevers JGPW. Basic of remote sensing. De Jong SM & Van der Meer FD. (Eds.) Remote sensing image analysis: including the spatial domain 2004; pp. 1-16, Springer Science & Business Media.
- Coops NC, Tooke TR. Introduction to Remote Sensing. In Learning Landscape Ecology 2017; (pp. 3-19). Springer, New York, NY.
- Calví£o T, Pessoa MF. Remote sensing in food production–a review. Emir J Food Agric 2015; 27(2): 138-151.
- Ray AS. Remote Sensing in Agriculture. Int J Environ Agric Biotech 2016; 1(3): 362-367.
- Atzberger C. Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sens 2013; 5(2): 949-981.
- Lamb DW, Brown RB. Pa-precision agriculture: Remote-sensing and mapping of weeds in crops. J Plant Biochem Biotechnol 2001; 78(2): 117-125.
- Tripathi R, Sahoo RN, Gupta VK, Sehgal VK, Sahoo PM. Developing Vegetation Health Index from biophysical variables derivedusing MODIS satellite data in the Trans-Gangetic plains of India. Emir J Food Agric 2013; 25(5): 376-384.
- Verger A, Vigneau N, Chéron C, Gilliot J, Comar A, Baret F. Green area index from an unmanned aerial system over wheat and rapeseed crops. Remote Sens Environ 2014; 152: 654–664.
- Jin X, Liu S, Baret F, Hemerlé M, Comar A. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sens Environ, 2017; 198: 105-114.
- Mahajan GR, Pandey RN, Sahoo RN, Gupta VK, Datta SC, Kumar D. Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing. Precis Agric 2017; 18(5): 736-761.
- Noland RL, Wells MS, Coulter JA, Tiede T, Baker JM, Martinson KL, Sheaffer CC. Estimating alfalfa yield and nutritive value using remote sensing and air temperature. Field Crops Res 2018; 222: 189-196.
- Arzani H, Sanaei A, Barker AV, Ghafari S, Motamedi J. Estimating nitrogen and acid detergent fiber contents of grass species using near infrared reflectance spectroscopy (NIRS). J of Range Sci 2015; 5(4): 260-268.
- Asekova S, Han SI, Choi HJ, Park SJ, Shin DH, Kwon CH, et al. Determination of forage quality by near-infrared reflectance spectroscopy in soybean. Turk J Agric For 2016; 40(1): 45-52.
- Vinutha KS, Kumar GA, Blümmel M, Rao PS. Evaluation of yield and forage quality in main and ratoon crops of different sorghum lines. Trop. Grassl Forrajes Trop 2017; 5(1): 40-49.
- Yang Z, Nie G, Pan L, Zhang Y, Huang L, Ma X, Zhang X. Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum. PeerJ, 2017; 5(3867): 1-20.
- Saha U, Vann RA, Chris Reberg"Horton S, Castillo MS, Mirsky SB, McGee RJ, Sonon L. Near"infrared spectroscopic models for analysis of winter pea (Pisum sativum L.) quality constituents. J Sci Food Agric. 2018; 98: 4253–4267.
- Carlier L, Vlahova M. Improvement and evaluation of the quality of forage crops. Biotechnol Biotechnol Equip, 1995; 9(2-3): 3-14.
- Albayrak S. Use of reflectance measurements for the detection of N, P, K, ADF and NDF contents in sainfoin pasture. Sensors 2008; 8: 7275-7286.
- Huang Y, Lee MA, Thomson SJ, Reddy KN.. Ground-based hyperspectral remote sensing for weed management in crop production. Int J Agric Bio Engineer 2016. 9(2): 98-109
- Martínez-Martínez V, Gomez-Gil J, Machado ML, Pinto FA. Leaf and canopy reflectance spectrometry applied to the estimation of angular leaf spot disease severity of common bean crops. PloS one, 2018; 13(4): e0196072.
- Genc H, Genc L, Turhan H, Smith SE, Nation JL. Vegetation indices as indicators of damage by the sunn pest (Hemiptera: Scutelleridae) to field grown wheat. Afr J Biotechnol 2008; 7(2): 173-180.
- Thito K, Wolski P, Murray-Hudson M. Spectral reflectance of floodplain vegetation communities of the Okavango Delta. Wetl Ecol Manag, 2015; 23(4): 637-648.
- Ming Y, Cheng L, Yu H, Wang C. Quantitative Inversion of Vegetation Biochemical Components Based on HJ1-A HSI in Coal Mining Area. J Indian Soc Remote Sens 2018; 46(1): 69-79.
- Peñuelas J, Filella I. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends Plant Sci 1998; 3(4): 151-156.
- Xue L, Yang L. Deriving leaf chlorophyll content of green-leafy vegetables from hyperspectral reflectance. ISPRS J Photogramm Remote Sens 2009; 64(1): 97–106.
- Beamish AL, Coops NC, Hermosilla T, Chabrillat S, Heim B. Monitoring pigment"driven vegetation changes in a low"Arctic tundra ecosystem using digital cameras. Ecosphere 2018; 9(2): 1-14.
- Gao BC. NDWI”A normalized difference water index for remote sensing of vegetation liquid water from space. Remote sens environ 1996; 58(3): 257-266.
- Romero AP, Alarcón A, Valbuena RI, Galeano CH. Physiological Assessment of Water Stress in Potato Using Spectral Information. Front Plant Sci 2017; 8(article 1608): 1-13.
- Ulrich M, Grosse G, Chabrillat S, Schirrmeister L. Spectral characterization of periglacial surfaces and geomorphological units in the Arctic Lena Delta using field spectrometry and remote sensing. Remote Sens Environ 2009; 113(6): 1220-1235.
- Kokaly RF, Despain DG, Clark RN, Livo KE. Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data. Remote Sens Environ 2003; 84(3): 437-456.
- Elvidge CD. Visible and near infrared reflectance characteristics of dry plant materials. Remote Sens 1990; 11(10): 1775-1795.
- Patel NK, Saxena RK, Shiwalkar AJAY. Study of fractional vegetation cover using high spectral resolution data. J Indian Soc Remote Sens 2007; 35(1): 73-79.
- Beeri O, Phillips R, Hendrickson J, Frank AB, Kronberg S. Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sens Environ 2007; 110(2): 216-225.
- Guo X, Wilmshurst JF, Li Z. Comparison of laboratory and field remote sensing methods to measure forage quality. Int J Environ Res 2010; 7(9): 3513-3530.
- Konca Y, Beyzi SB, Ayaşan T, Kaliber M, Kiraz AB. The effects of freezing and supplementation of molasses and inoculants on chemical and nutritional composition of sunflower silage. Asian-Australas J Anim Sci 2016; 29(7): 965-970.
- Hoffman PC. Ash content of forages. Focus on Forage, Wisconsin team forage. University of Wisconsin Board of Regents Wisconsin , USA 2005; 7(1): 1-2.
- Ayala-Silva T, Beyl CA. Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency. Adv Space Res 2005; 35(2): 305-317.
- Ferwerda JG, Skidmore AK. Can nutrient status of four woody plant species be predicted using field spectrometry?. ISPRS J Photogramm Remote Sens 2007; 62(6): 406-414.
- Neto AJS, Toledo JV, Zolnier S, Lopes DDC, Pires CV, Da Silva TGF. Prediction of mineral contents in sugarcane cultivated under saline conditions based on stalk scanning by Vis/NIR spectral reflectance. Biosyst Eng 2017; 156: 17-26.
- Starks PJ, Coleman SW, Phillips WA. Determination of forage chemical composition using remote sensing. J Range Manage 2004; 57: 635-640.
- Wu L, Li M, Huang J, Zhang H, Zou W, Hu S, et al. A near infrared spectroscopic assay for stalk soluble sugars, bagasse enzymatic saccharification and wall polymers in sweet sorghum. Bioresour Technol 2015; 177:118-124.
- Pu R, Ge S, Kelly NM, Gong P. Spectral absorption features as indicators of water status in coast live oak (Quercus agrifolia) leaves. Int J Remote Sens 2003; 24(9): 1799-1810.
- Ustin SL, Gamon JA. Remote sensing of plant functional types. New Phytol 2010; 186(4): 795-816.
References
Ilavenil S, Arasu MV, Lee JC, Kim DH, Vijayakumar M, Lee KD, Choi KC. Positive regulations of adipogenesis by Italian ryegrass [Lolium multiflorum] in 3T3-L1 cells. BMC Biotechnol 2014; 14(1): 54 (p. 11).
Pavinato PS, Restelatto R, Sartor LR, Paris W. Production and nutritive value of ryegrass (cv. Barjumbo) under nitrogen fertilization. Ciíªnc Agron 2014; 45(2): 230-237.
Yasuda M, Takenouchi Y, Nitta Y, Ishii Y, Ohta K. Italian ryegrass (Lolium multiflorum Lam) as a high-potential bio-ethanol resource. Bioenergy Res 2015; 8(3): 1303-1309.
Rechițean D, Dragoș M, Dragomir N, Horablaga M, Sauer M, Camen D, Toth I, Sala A. Associated culture of Italian ryegrass (Lolium multiflorum) and crimson clover (Trifolium incarnatum) under nitrogen fertilization. Scientific Papers: Animal Sci Biotechnol, 2018; 51(1): 129-132.
Minson DJ. Forage in Ruminant Nutrition. Academic Press, Madison, WI. 1990; Pp. 463.
Mohajer S, Ghods H, Taha RM, Talati A. Effect of different harvest time on yield and forage quality of three varieties of common millet (Panicum miliaceum). Sci. Res. Essays 2012; 7(34): 3020-3025.
Karthikeyan BJ, Babu C, Amalraj JJ. Nutritive Value and Fodder Potential of Different Sorghum (Sorghum bicolor L. Moench) Cultivars. Int. J. Curr. Microbiol. App. Sci 2017; 6(8): 898-911.
Tariq AS, Akram Z, Shabbir G, Khan KS, Mahmood T, Iqbal MS. Heterosis and combining ability evaluation for quality traits in forage sorghum (Sorghum bicolor L.). SABRAO J. Breed. Gen, 2014; 46(2).174-182.
Zhang R, Zhu W, Zhu W, Liu J, Mao S. Effect of dietary forage sources on rumen microbiota, rumen fermentation and biogenic amines in dairy cows. J Sci Food Agric 2014; 94(9): 1886-1895.
Mohajer S, Taha RM, Khorasani A, Mubarak EE. Comparative studies of forage yield and quality traits among proso millet, foxtail millet and sainfoin varieties. Int J of Environ Sci Dev 2013; 4(5): 465-469
Reddersen B, Fricke T, Wachendorf M. Effects of sample preparation and measurement standardization on the NIRS calibration quality of nitrogen, ash and NDFom content in extensive experimental grassland biomass. Anim Feed Sci Technol 2013; 183(3-4): 77-85.
Monrroy M, Gutiérrez D, Miranda M, Hernández K, Renán García, JOSí‰. Determination of brachiaria spp. forage quality by near-infrared spectroscopy and partial least squares regression. J Chil Chem Soc 2017; 62(2); 3472-3477.
Zhang J, Li S, Lin M, Yang E, Chen X. A near-infrared reflectance spectroscopic method for the direct analysis of several fodder-related chemical components in drumstick (Moringa oleifera Lam.) leaves. Biosci Biotechnol Biochem 2018; 82(5): 768-774
Schröder I, Huang DYQ, Ellis O, Gibson JH, Wayne NL. Laboratory safety attitudes and practices: A comparison of academic, government, and industry researchers. Chem Health Saf 2016; 23(1): 12-23.
Zhao D, Starks PJ, Brown MA, Phillips WA, Coleman SW. Assessment of forage biomass and quality parameters of bermudagrass using proximal sensing of pasture canopy reflectance. Grassl Sci 2007; 53: 39-49.
Zarco-Tejada PJ, Mateos L, Fereres E, Villalobos FJ. New Tools and Methods in Agronomy. In Principles of Agronomy for Sustainable Agriculture 2016; (pp. 503-514). Springer, Cham.
Rani DS, Venkatesh MN, Sri CNS, Kumar KA. Remote Sensing as Pest Forecasting Model in Agriculture. Int J Curr Microbiol Appl Sci 2018; 7(3): 2680-2689.
De Jong SM, Van der Meer FD, Clevers JGPW. Basic of remote sensing. De Jong SM & Van der Meer FD. (Eds.) Remote sensing image analysis: including the spatial domain 2004; pp. 1-16, Springer Science & Business Media.
Coops NC, Tooke TR. Introduction to Remote Sensing. In Learning Landscape Ecology 2017; (pp. 3-19). Springer, New York, NY.
Calví£o T, Pessoa MF. Remote sensing in food production–a review. Emir J Food Agric 2015; 27(2): 138-151.
Ray AS. Remote Sensing in Agriculture. Int J Environ Agric Biotech 2016; 1(3): 362-367.
Atzberger C. Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sens 2013; 5(2): 949-981.
Lamb DW, Brown RB. Pa-precision agriculture: Remote-sensing and mapping of weeds in crops. J Plant Biochem Biotechnol 2001; 78(2): 117-125.
Tripathi R, Sahoo RN, Gupta VK, Sehgal VK, Sahoo PM. Developing Vegetation Health Index from biophysical variables derivedusing MODIS satellite data in the Trans-Gangetic plains of India. Emir J Food Agric 2013; 25(5): 376-384.
Verger A, Vigneau N, Chéron C, Gilliot J, Comar A, Baret F. Green area index from an unmanned aerial system over wheat and rapeseed crops. Remote Sens Environ 2014; 152: 654–664.
Jin X, Liu S, Baret F, Hemerlé M, Comar A. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sens Environ, 2017; 198: 105-114.
Mahajan GR, Pandey RN, Sahoo RN, Gupta VK, Datta SC, Kumar D. Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing. Precis Agric 2017; 18(5): 736-761.
Noland RL, Wells MS, Coulter JA, Tiede T, Baker JM, Martinson KL, Sheaffer CC. Estimating alfalfa yield and nutritive value using remote sensing and air temperature. Field Crops Res 2018; 222: 189-196.
Arzani H, Sanaei A, Barker AV, Ghafari S, Motamedi J. Estimating nitrogen and acid detergent fiber contents of grass species using near infrared reflectance spectroscopy (NIRS). J of Range Sci 2015; 5(4): 260-268.
Asekova S, Han SI, Choi HJ, Park SJ, Shin DH, Kwon CH, et al. Determination of forage quality by near-infrared reflectance spectroscopy in soybean. Turk J Agric For 2016; 40(1): 45-52.
Vinutha KS, Kumar GA, Blümmel M, Rao PS. Evaluation of yield and forage quality in main and ratoon crops of different sorghum lines. Trop. Grassl Forrajes Trop 2017; 5(1): 40-49.
Yang Z, Nie G, Pan L, Zhang Y, Huang L, Ma X, Zhang X. Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum. PeerJ, 2017; 5(3867): 1-20.
Saha U, Vann RA, Chris Reberg"Horton S, Castillo MS, Mirsky SB, McGee RJ, Sonon L. Near"infrared spectroscopic models for analysis of winter pea (Pisum sativum L.) quality constituents. J Sci Food Agric. 2018; 98: 4253–4267.
Carlier L, Vlahova M. Improvement and evaluation of the quality of forage crops. Biotechnol Biotechnol Equip, 1995; 9(2-3): 3-14.
Albayrak S. Use of reflectance measurements for the detection of N, P, K, ADF and NDF contents in sainfoin pasture. Sensors 2008; 8: 7275-7286.
Huang Y, Lee MA, Thomson SJ, Reddy KN.. Ground-based hyperspectral remote sensing for weed management in crop production. Int J Agric Bio Engineer 2016. 9(2): 98-109
Martínez-Martínez V, Gomez-Gil J, Machado ML, Pinto FA. Leaf and canopy reflectance spectrometry applied to the estimation of angular leaf spot disease severity of common bean crops. PloS one, 2018; 13(4): e0196072.
Genc H, Genc L, Turhan H, Smith SE, Nation JL. Vegetation indices as indicators of damage by the sunn pest (Hemiptera: Scutelleridae) to field grown wheat. Afr J Biotechnol 2008; 7(2): 173-180.
Thito K, Wolski P, Murray-Hudson M. Spectral reflectance of floodplain vegetation communities of the Okavango Delta. Wetl Ecol Manag, 2015; 23(4): 637-648.
Ming Y, Cheng L, Yu H, Wang C. Quantitative Inversion of Vegetation Biochemical Components Based on HJ1-A HSI in Coal Mining Area. J Indian Soc Remote Sens 2018; 46(1): 69-79.
Peñuelas J, Filella I. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends Plant Sci 1998; 3(4): 151-156.
Xue L, Yang L. Deriving leaf chlorophyll content of green-leafy vegetables from hyperspectral reflectance. ISPRS J Photogramm Remote Sens 2009; 64(1): 97–106.
Beamish AL, Coops NC, Hermosilla T, Chabrillat S, Heim B. Monitoring pigment"driven vegetation changes in a low"Arctic tundra ecosystem using digital cameras. Ecosphere 2018; 9(2): 1-14.
Gao BC. NDWI”A normalized difference water index for remote sensing of vegetation liquid water from space. Remote sens environ 1996; 58(3): 257-266.
Romero AP, Alarcón A, Valbuena RI, Galeano CH. Physiological Assessment of Water Stress in Potato Using Spectral Information. Front Plant Sci 2017; 8(article 1608): 1-13.
Ulrich M, Grosse G, Chabrillat S, Schirrmeister L. Spectral characterization of periglacial surfaces and geomorphological units in the Arctic Lena Delta using field spectrometry and remote sensing. Remote Sens Environ 2009; 113(6): 1220-1235.
Kokaly RF, Despain DG, Clark RN, Livo KE. Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data. Remote Sens Environ 2003; 84(3): 437-456.
Elvidge CD. Visible and near infrared reflectance characteristics of dry plant materials. Remote Sens 1990; 11(10): 1775-1795.
Patel NK, Saxena RK, Shiwalkar AJAY. Study of fractional vegetation cover using high spectral resolution data. J Indian Soc Remote Sens 2007; 35(1): 73-79.
Beeri O, Phillips R, Hendrickson J, Frank AB, Kronberg S. Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sens Environ 2007; 110(2): 216-225.
Guo X, Wilmshurst JF, Li Z. Comparison of laboratory and field remote sensing methods to measure forage quality. Int J Environ Res 2010; 7(9): 3513-3530.
Konca Y, Beyzi SB, Ayaşan T, Kaliber M, Kiraz AB. The effects of freezing and supplementation of molasses and inoculants on chemical and nutritional composition of sunflower silage. Asian-Australas J Anim Sci 2016; 29(7): 965-970.
Hoffman PC. Ash content of forages. Focus on Forage, Wisconsin team forage. University of Wisconsin Board of Regents Wisconsin , USA 2005; 7(1): 1-2.
Ayala-Silva T, Beyl CA. Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency. Adv Space Res 2005; 35(2): 305-317.
Ferwerda JG, Skidmore AK. Can nutrient status of four woody plant species be predicted using field spectrometry?. ISPRS J Photogramm Remote Sens 2007; 62(6): 406-414.
Neto AJS, Toledo JV, Zolnier S, Lopes DDC, Pires CV, Da Silva TGF. Prediction of mineral contents in sugarcane cultivated under saline conditions based on stalk scanning by Vis/NIR spectral reflectance. Biosyst Eng 2017; 156: 17-26.
Starks PJ, Coleman SW, Phillips WA. Determination of forage chemical composition using remote sensing. J Range Manage 2004; 57: 635-640.
Wu L, Li M, Huang J, Zhang H, Zou W, Hu S, et al. A near infrared spectroscopic assay for stalk soluble sugars, bagasse enzymatic saccharification and wall polymers in sweet sorghum. Bioresour Technol 2015; 177:118-124.
Pu R, Ge S, Kelly NM, Gong P. Spectral absorption features as indicators of water status in coast live oak (Quercus agrifolia) leaves. Int J Remote Sens 2003; 24(9): 1799-1810.
Ustin SL, Gamon JA. Remote sensing of plant functional types. New Phytol 2010; 186(4): 795-816.