Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-17T07:45:37.712Z Has data issue: false hasContentIssue false
  • English
  • Français

Durum wheat in-field monitoring and early-yield prediction: assessment of potential use of high resolution satellite imagery in a hilly area of Tuscany, Central Italy

Published online by Cambridge University Press:  16 December 2013

A. DALLA MARTA ,
D. GRIFONI ,
M. MANCINI ,
F. ORLANDO ,
F. GUASCONI  and
S. ORLANDINI
A. DALLA MARTA*
Affiliation:
Department of Agrifood Production and Environmental Sciences, University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy
D. GRIFONI
Affiliation:
Institute of Biometeorology (CNR-IBIMET)/LaMMA, Via Madonna del Piano 10, Sesto Fiorentino, Italy
M. MANCINI
Affiliation:
Interdepartmental Center of Bioclimatology, University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy
F. ORLANDO
Affiliation:
Department of Agrifood Production and Environmental Sciences, University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy
F. GUASCONI
Affiliation:
Department of Agrifood Production and Environmental Sciences, University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy
S. ORLANDINI
Affiliation:
Department of Agrifood Production and Environmental Sciences, University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy Interdepartmental Center of Bioclimatology, University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy
*
*To whom all correspondence should be addressed. Email: anna.dallamarta@unifi.it
Get access Rights & Permissions [Opens in a new window]

Summary

Modern agriculture is based on the control of in-field variability, which is determined by the interactions of numerous factors such as soil, climate and crop. For this reason, the use of remote sensing is becoming increasingly important, thanks to the technological development of satellites able to supply information with high spatial resolution and revisit frequency. Despite the large number of studies on the use of remote sensing for crop monitoring, very few have addressed the problem of spatial variability at field scale or the early prediction of crop yield and grain quality. The aim of the current research was to assess the potential use of high resolution satellite imagery for monitoring durum wheat growth and development, addressing forecast grain yield and protein content, through vegetation indices at two stages of crop development. To best represent the natural variability of agricultural production, the study was conducted in wheat fields managed by local farmers. As regards dry weight, leaf area index and nitrogen (N) content, the possibility of describing the crop state is evident at stem elongation, while at anthesis this potential is completely lost. However, satellites seem to be unable to estimate the N concentration. Aboveground biomass accumulated from emergence to stem elongation is strictly related to the final yield, while it has been confirmed that the crop parameters observed at anthesis are less informative, despite approaching harvesting time.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 153 , Issue 1 , January 2015 , pp. 68 - 77
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Altenbach, S. B. (2012). New insights into the effects of high temperature, drought and post-anthesis fertilizer on wheat grain development. Journal of Cereal Science 56, 39–50.Google Scholar
Aparicio, N., Villegas, D., Casadesus, J., Araus, J. L. & Royo, C. (2000). Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal 92, 83–91.CrossRefGoogle Scholar
Aparicio, N., Villegas, D., Araus, J. L., Casadesus, J. & Royo, C. (2002). Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Science 42, 1547–1555.Google Scholar
Basnet, B., Apan, A., Kelly, R., Jensen, T., Strong, W. & Butler, D. (2003). Relating satellite imagery with grain protein content of grain crops. In Spatial Knowledge without Boundaries. Proceedings of the Inaugural Conference of the Spatial Sciences Institute, 22–26 September 2003 (Ed. Lees, B.), pp. 22–27. Canberra: Australasian Urban & Regional Information Systems Association.Google Scholar
Basso, B., Fiorentino, C., Cammarano, D., Cafiero, G. & Dardanelli, J. (2012). Analysis of rainfall distribution on spatial and temporal patterns of wheat yield in Mediterranean environment. European Journal of Agronomy 41, 52–65.Google Scholar
Beeri, O. & Peled, A. (2009). Geographical model for precise agriculture monitoring with real-time remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 64, 47–54.CrossRefGoogle Scholar
Blondlot, A., Gate, P. & Poilvé, H. (2005). Providing operational nitrogen recommendations to farmers using satellite imagery. In Precision Agriculture ‘05 (Ed. Stafford, J. V.), pp. 345–352. Wageningen, The Netherlands: Wageningen Academic Publishers.Google Scholar
Broge, N. H. & Leblanc, E. (2001). 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.Google Scholar
Cammarano, D., Fitzgerald, G. J., Basso, B., Chen, D., Grace, P. & O'Leary, G. J. (2011). Remote estimation of chlorophyll on two wheat cultivars in two rainfed environments. Crop & Pasture Science 62, 269–275.Google Scholar
Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S. & Grégoire, J. M. (2001). Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment 77, 22–23.CrossRefGoogle Scholar
Christensen, L. K., Rodriquez, D., Belford, R., Sadras, V., Rampant, P. & Fisher, P. (2005). Temporal prediction of nitrogen status in winter wheat under the influence of water deficiency using spectral and thermal information. In Precision Agriculture ‘05 (Ed. Stafford, J. V.), pp. 209–216. Wageningen, The Netherlands: Wageningen Academic Publishers.Google Scholar
Coste, S., Baraloto, C., Leroy, C., Marcon, E., Renaud, A., Richardson, A. D., Roggy, J. C., Schimann, H., Uddling, J. & Herault, B. (2010). Assessing foliar chlorophyll contents with the SPAD-502 chlorophyll meter: a calibration test with thirteen tree species of tropical rainforest in French Guiana. Annals of Forest Science 67, 607.CrossRefGoogle Scholar
Dalla Marta, A., Grifoni, D., Mancini, M., Zipoli, G. & Orlandini, S. (2011). The influence of climate on durum wheat quality in Tuscany, Central Italy. International Journal of Biometeorology 55, 87–96.CrossRefGoogle ScholarPubMed
Dalling, M. J. (1985). The physiological basis of nitrogen redistribution during grain filling in cereals. In Exploitation of Physiological and Genetic Variability to Enhance Crop Productivity (Eds Harper, J. E., Schrader, L. E. & Howell, R. W.), pp. 55–71. Rockville, MD, USA: American Society of Plant Physiologists.Google Scholar
Dang, Y. P., Pringle, M. J., Schmidt, M., Dalal, R. C. & Apan, A. (2011). Identifying the spatial variability of soil constraints using multi-year remote sensing. Field Crop Research 123, 248–258.Google Scholar
Delegido, J., Verrelst, J., Meza, C. M., Rivera, J. P., Alonso, L. & Moreno, J. (2013). A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. European Journal of Agronomy 46, 42–52.Google Scholar
Diacono, M., Castrignanò, A., Troccoli, A., De Benedetto, D., Basso, B. & Rubino, P. (2012). Spatial and temporal variability of wheat grain yield and quality in a Mediterranean environment: a multivariate geostatistical approach. Field Crops Research 131, 49–62.Google Scholar
Diacono, M., Rubino, P. & Montemurro, F. (2013). Precision nitrogen management of wheat. A review. Agronomy for Sustainable Development 33, 219–241.CrossRefGoogle Scholar
Eitel, J. U. H., Vierling, L. A., Long, D. S. & Hunt, E. R. (2011). Early season remote sensing of wheat nitrogen status using a green scanning laser. Agricultural and Forest Meteorology 151, 1338–1345.Google Scholar
Filella, I. & Peñuelas, J. (1994). The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing 15, 1459–1470.Google Scholar
Fitzgerald, G., Rodriguez, D. & O'Leary, G. (2010). Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index – the canopy chlorophyll content index (CCCI). Field Crops Research 116, 318–324.Google Scholar
Gitelson, A. A. & Merzlyak, M. (1997). Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing 18, 2691–2697.Google Scholar
Guasconi, F., Dalla Marta, A., Grifoni, D., Mancini, M., Orlando, F. & Orlandini, S. (2011). Influence of climate on durum wheat production and use of remote sensing and weather data to predict quality and quantity of harvests. Italian Journal of Agrometeorology 3, 21–28.Google Scholar
Hansen, P. M., Jørgensen, J. R. & Thomsen, A. (2002). Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression. Journal of Agricultural Science, Cambridge 139, 307–318.CrossRefGoogle Scholar
Huete, A. R. (1988). A soil adjusted vegetation index (SAVI). Remote Sensing of Environment 25, 295–309.Google Scholar
Inoue, Y., Peñuelas, J., Miyata, A. & Mano, M. (2008). Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice. Remote Sensing of Environment 112, 156–172.Google Scholar
Labus, M. P., Nielsen, G. A., Lawrence, R. L., Engel, R. & Long, D. S. (2002). Wheat yield estimates using multi-temporal NDVI satellite imagery. International Journal of Remote Sensing 23, 4169–4180.Google Scholar
Li, F., Miao, Y., Hennig, S. D., Gnyp, M. L., Chen, X., Jia, L. & Bareth, G. (2010). Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precision Agriculture 11, 335–357.Google Scholar
Liu, L., Wang, J., Bao, Y., Huang, W., Ma, Z. & Zhao, C. (2006). Predicting winter wheat condition, grain yield and protein content using multi-temporal EnviSat–ASAR and Landsat TM satellite images. International Journal of Remote Sensing 27, 737–753.Google Scholar
Lobell, D. B., Asner, G. P., Ortiz-Monasterio, J. I. & Benning, T. L. (2003). Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties. Agriculture, Ecosystems & Environment 94, 205–220.Google Scholar
Maselli, F. & Rembold, F. (2001). Analysis of GAC NDVI data for cropland identification and yield forecasting in Mediterranean African countries. Photogrammetric Engineering & Remote Sensing 67, 593–602.Google Scholar
Mkhabela, M. S., Bullock, P., Raj, S., Wang, S. & Yang, Y. (2011). Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology 151, 385–393.Google Scholar
Moldestad, A., Fergestad, E. M., Hoel, B., Skjelvag, A. O. & Uhlen, A. K. (2011). Effect of temperature variation during grain filling on wheat gluten resistance. Journal of Cereal Science 53, 347–354.CrossRefGoogle Scholar
Nicolas, H. (2004). Using remote sensing to determine of the date of a fungicide application on winter wheat. Crop Protection 23, 853–863.Google Scholar
Perry, E. M. & Roberts, D. A. (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.Google Scholar
Rodriguez, D., Fitzgerald, G. J., Belford, R. & Christensen, L. K. (2006). Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts. Australian Journal of Agricultural Research 57, 781–789.Google Scholar
Rondeaux, G., Steven, M. & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 55, 95–107.CrossRefGoogle Scholar
Rouse, J. W., Haas, R. H., Schell, J. A. & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In 3rd Earth Resources Technology Satellite-1 Symposium. Vol. 1: Technical Presentations. NASA SP-351 (Eds Freden, S. C., Mercanti, E. P. & Becker, M. A.), pp. 309–317. Washington, DC: NASA.Google Scholar
Sims, D. A. & Gamon, J. A. (2003). Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sensing of Environment 84, 526–537.Google Scholar
Soil Survey Staff (1998). Keys to Soil Taxonomy, 8th edn. Washington, DC: USDA & NRCS.Google Scholar
Suprayogi, Y., Clarke, J. M., Bueckert, R., Clarke, F. R. & Pozniak, C. J. (2011). Nitrogen remobilization and post-anthesis nitrogen uptake in relation to elevated grain protein concentration in durum wheat. Canadian Journal of Plant Science 91, 273–282.Google Scholar
Tilling, A. K., O'Leary, G. J., Ferwerda, J. G., Jones, S. D., Fitzgerald, G. J., Rodriguez, D. & Belford, R. (2007). Remote sensing of nitrogen and water stress in wheat. Field Crops Research 104, 77–85.Google Scholar
Vuolo, F., Atzberger, C., Richter, K., D'Urso, G. & Dash, J. (2010). Retrieval of biophysical vegetation products from rapideye imagery. In ISPRS TC VII Symposium – 100 Years ISPRS, Vienna, Austria, July 5–7, 2010 (Eds Wagner, W. & Székely, B.), pp. 281–286. IAPRS Archives, Vol. XXXVIII, Part 7A. Vienna: IAPRS.Google Scholar
Yan, L., Jones, G., Villette, S., Paoli, J. N. & Gée, C. (2012). Combining spatial and spectral information to improve crop/weed discrimination algorithms. In Image Processing: Machine Vision Applications V. Proceedings of the SPIE, vol. 83000 (Eds Bingham, P. R. & Lam, E. Y.), article ID. 83000E. Burlingame, CA, USA: SPIE.Google Scholar
Zadoks, J. C., Chang, T. T. & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research 14, 415–421.Google Scholar
Zhang, N., Wang, M. & Wang, N. (2002). Precision agriculture: a worldwide overview. Computers and Electronics in Agriculture 36, 113–132.Google Scholar