Assessment of winter spelt and wheat growth and yield by ground spectral measurements

• Autorzy: Piekarczyk J. , Sulewska H.

Warianty tytułu

PL

Ocena stanu upraw orkiszu i pszenicy oraz prognozowanie plonow na podstawie polowych pomiarow spektralnych

• Języki publikacji: EN

Abstrakty

EN

Information on the physiological state of plant canopies of crops during the growing season is essential for growers in decision-making such as foliar N and plant growth regulator applications for maximum yield. The aim of the study was to determine optimal vegetation indices for discriminating among four winter spelt and wheat varieties cultivated at six different organic and inorganic N fertilizer treatments. The suitability of the vegetation indices to predict yield was investigated as well. The results showed that the MCARI (Modified Chlorophyll Absorption Reflectance Index) was found to be the best for discriminating among spelt and wheat varieties and the most spectrally distinctive spelt variety was Schwabencorn. Among the tested indices, NDVI (Normalized Difference Vegetation Index) and MCARI could be used to most clearly distinguish the N treatments. A relatively high correlation occurred between the vegetation indices and grain yield of spelt and wheat. This relationship is stronger if the spectral data from different measurement dates are taken into consideration. Therefore, the best yield predictions (R2 = 0.79) were obtained using the cumulative SR index.

PL

Informacje dotyczące kondycji roślin w uprawie, w sezonie wegetacyjnym, są niezbędne przy podejmowaniu decyzji produkcyjnych dotyczących na przykład nawożenia lub stosowania regulatorów wzrostu. Celem badań było stwierdzenie występowania różnic między właściwościami spektralnymi różnych odmian orkiszu uprawianego w odmiennych wariantach nawożenia oraz określenie przydatności wskaźników roślinnych uzyskanych w trakcie naziemnych pomiarów hiperspektralnych do prognozowania wysokości plonów orkiszu. Największe zróżnicowanie spektralne między trzema odmianami orkiszu ozimego i jedną odmianą pszenicy obserwowano w przypadku wskaźnika MCARI. Wskaźniki NDVI i MCARI były najbardziej przydatne do odróżniania poletek uprawianych w odmiennych wariantach nawożenia. Między wartościami wszystkich analizowanych wskaźników roślinnych i wielkością plonu z poletek występowała istotna zależność. Najsilniejsza zależność występowała, gdy do jej określenia wykorzystano dane spektralne zarejestrowane w trzech terminach na początku sezonu wegetacyjnego. Wówczas wartość współczynnika determinacji obliczonego dla zależności między plonem ziarna i wartościami wskaźnika SR wynosiła R2 = 0,79.

Słowa kluczowe

EN

Triticum spelta; ground spectral measurement; hexaploid species; inorganic nitrogen fertilizer; organic nitrogen fertilizer; plant growth; remote sensing; spelt; wheat; winter spelt; winter wheat; yield

• Wydawca: -
• Czasopismo: Acta Agrophysica
• Rocznik: 2010
• Tom: 15
• Numer: 2[178]

Twórcy

autor

Piekarczyk J.

• Institute of Physical Geography and Environmental Planning, Adam Mickiewicz University, Dziegielowa 27, 61-680 Poznan, Poland

autor

Sulewska H.

Bibliografia

• Broge N.H., Leblanc E., 2000. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76, 156-172.
• Calera A., Gonza Lez-Piqueras J., Melia J., 2004. Monitoring barley and corn growth from remote sensing data at field scale. International Journal of Remote Sensing, 25(1), 97-109.
• Capouchova I., 2001. Technological quality of spelt (Triticum spelta L.) from ecological growing system. Scientia Agriculturae Bohemica, 32, 4 307-322.
• Chen L., Gao Y., Cheng Y., Wei Z., Xiao Q., Liu Q., Yu T., Liu Q., Gu X., Tian G., 2005. Biomass estimation and uncertainty analysis based on CBERS-02 CCD camera data and field measurement. Science in China Ser. E Engineering & Materials Science, 48, 116-128.
• Chrenkova M., Ceresnakova Z., Sommer A., Galova Z., Kralova V., 2000. Assessment of nutritional value in spelt (Triticum spelta L.) and winter (Triticum aestivum L.) wheat by chemical and biological methods. Czech Journal of Animal Science, 45, 133-137.
• Daughtry C.S.T., Walthall C.L., Kim M.S., Brown de Colstoun E., McMurtrey J.E., 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 229-239.
• Fernandez C.J., McInnes K.J., Cothren J.T., 1996. Water status and leaf area production in waterand nitrogen-stressed cotton. Crop Science, 36, 1224-1233.
• Gao J., 2006. Quantification of grassland properties: how it can benefit from geoinformatic technologies?. International Journal of Remote Sensing, 27(7), 1351-1365.
• Haboudane D., Miller J.R., Tremblay N., Zarco-Tejada P.J., Dextraze L., 2002. Integration of hyperspectral vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2-3), 416–426.
• Ma B.L., Dwyer L.M., Costa C., Cober E.R., Morrison M.J., 2001. Early prediction of soybean yield from canopy reflectance measurements. Agronomy Journal, 93, 1227-1234.
• Moges S.M., Raun W.R., Mullen R.W., Freeman K.W., Johnson G.V., Solie J.B., 2004. Evaluation of green, red, and near infrared bands for predicting winter wheat biomass, nitrogen uptake, and final grain yield. Journal of Plant Nutrition, 27, 1431-1441.
• Plant R.E., Munk D.S., Roberts B.R., Vargas R.N., Travis R.L., Rains R.W., Hutmacher R.B., 2001. Application of Remote Sensing to Strategic Questions in Cotton Management and Research. The Journal of Cotton Science, 5, 30-41.
• Reddy K.R., Zhao D., Kakani V.G., Read J.J., Koti S., 2003. Estimating cotton growth and development parameters through remote sensing. In: Gao, W., Shaw, D. (Eds.), Proceedings of the SPIE Ecosystems Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture, vol. 5153. SPIEE, Bellingham, WA, pp. 277-288.
• Rouse J.W., Haas R.H., Schell J.A., Deering D.W., Harlan J.C., 1974. Monitoring the vernal advancement and retrogradation greenwave effect of natural vegetation. NASA/GSFC Type II Final Report, Greenbelt, Maryland, 371 p.
• Serrano L., Fillela J., Penuelas J., 2000. Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science, 40, 723-731.
• Serrano L., Penuelas J., Ustin S.L., 2002. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals. Remote Sensing of Environment, 81, 355-364.
• Stauss R., 1994. Compendium of growth stage identification keys for mono- and dicotyledonous plants. Extended BBCH scale. Ciba-Geigy AG, Postfach, Basel . ISBN 3-9520749-0-x.
• Zarco-Tejada P.J., Ustin S.L., Whiting M.L., 2005. Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agronomy Journal, 97(3), 641-653.
• Zhao D., Oosterhuis D.M., 2000. Nitrogen application effect on leaf photosynthesis, nonstructural carbohydrate concentrations and yield of field-grown cotton. In: Oosterhuis, D.M. (Ed.), Proceedings of the 2000 Arkansas Cotton Res., AAES Special Report 198, pp. 69-71.
• Zhao D., Reddy K.R., Kakani V.G., Read J.J., Carter G.A., 2003. Corn (Zea mays L.) growth, leaf pigment concentration, photosynthesis and leaf hyperspectral reflectance properties as affected by nitrogen supply. Plant and Soil, 257, 205-217.
• Zhao D., Reddya K.R., Kakania V.G., Reddyb V.R., 2005. Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum. European Journal of Agronomy, 22, 391-403.
• Zhao D., Reddyb K.R., Kakani V.G., Read J.J., Koti S., 2007. Canopy reflectance in cotton for growth assessment and lint yield prediction. European Journal of Agronomy, 26, 335-344.