An approach for assessing the effects of site-specific fertilization on crop growth and yield of durum wheat in organic agriculture
- 613 Downloads
Precision agriculture (PA) technologies allow us to assess field variability and support site-specific (SSP) application of inputs. The joint application of PA and organic farming practices might be synergetic. The objective of this 3-year study was to propose a multivariate statistical and geostatistical approach, to evaluate the effects of SSP nitrogen (N) fertilization on durum wheat in transition to organic farming. Soil parameters were measured to assess soil fertility level before the SSP fertilization on wheat, which was carried out by management zones in the third year. Radiometric measurements were performed with a hyperspectral spectroradiometer and N-uptake at anthesis and grain yield were determined. The expected values and 95 % confidence intervals of the soil parameters, N-uptake and yield data were estimated with polygon kriging for each management zone. Reflectance data were reduced through principal component analysis and the retained principal components were submitted to factorial co-kriging analysis to estimate orthogonal scale-dependent factors. Comparisons between N-uptake and yield and between the retained regionalized factors (F1) and yield were performed. The spatial pattern of F1 at shorter scales was mostly reproduced in the N-uptake map, suggesting the predictive capacity of hyperspectral data for crop N-status. Within-cluster variance for yield was reduced, quite probably as a combined effect of meteorological pattern and management. The preliminary results seem to be promising in the perspective of PA. Moreover, an inverse relationship between grain yield and crop N-status was observed.
KeywordsPrecision fertilization Hyperspectral data Plant response Grain yield variability Polygon kriging
The work has been supported by Italian Ministry of Agriculture and Forestry Policies under contract no. 24324/7742/2009 (“Sistemi colturali e interventi agronomici innovativi in agricoltura biologica” BIOINNOVA, Coordinator: Dr. Domenico Ventrella, CRA—SCA, Research Unit for Cropping Systems in Dry Environments, Bari). The authors thank M. Mastrangelo for his skilful technical assistance for chemical analysis. This work was benefited from the fertilizer supplying for free from the ILSA S.p.A. (Dr. Eugenio Babini). The authors wish to thank also the John Deere Company (Dr. Matteo Antonello).
- Barnes, E. M., Clarke, T. R., & Richards, S. E. (2000). Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In P. C. Robert, R. H. Rust, & W. E Larson (Eds.), Proceedings of the 5th international conference on precision agriculture. American Society of Agronomy, Madison.Google Scholar
- Diacono, M., Abd El Rahman, H., Cocozza, C., De Benedetto, D., Troccoli, A., Rubino, P., et al. (2011). Delineation of homogeneous field zones based on soil fertility indices in a durum wheat—Chickpea rotation. In J. V. Stafford (Ed.), Precision agriculture 2011 (pp. 164–179). UK: Ampthill.Google Scholar
- Digital Globe. (2009). White Paper. The Benefits of the 8 Spectral Bands of WorldView-2. (p. 10). Retrieved from www.digitalglobe.com.
- FAO. (1995). Sustainability issues in agricultural and rural development policies, Trainer’s manual, vol 1.Google Scholar
- Geovariances. (2012). Isatis® Technical Ref., 12.04 Geovariances and Ecole Des Mines De Paris: Avon Cedex, France.Google Scholar
- Horneck, D. A., & Miller, R. O. (1998). Determination of total nitrogen in plant tissue. In Y. P. Kalra (Ed.), Handbook of reference methods for plant analysis (pp. 75–83). Boca Raton: CRC Press.Google Scholar
- Lajaunie, C., & Béhaxétéguy, J. P. (1989). Elaboration d’un programme d’ajustement semi-automatique d’un modèle de corégionalisation—Theorie, technical report N21/89/G. Paris: ENSMP.Google Scholar
- Matheron, G. (1982). Pour une analyse krigeante des données régionalisées. Rapport N-732. Centre de Géostatistiques, École des Mines de Paris, Fontainebleau.Google Scholar
- Page, A. L., Miller, R. H., & Keeny, D. R. (1982). Methods of soil analysis, Part II (2nd ed.). Madison, WI: American Society of Agronomy.Google Scholar
- Ray, S. S., Singh, J. P., & Panigraphy, S. (2010). Use of hyperspectral remote sensing data for crop stress detection: Ground-based studies. International Archives of Photogrammetry, Remote Sensing and Spatial Information Science, vol. 38, Part 8. Kyoto, Japan.Google Scholar
- SAS Institute Inc., (2012). SAS/STAT software release 9.3. Cary, NC: SAS Institute.Google Scholar
- Soil Survey Staff. (1999). Soil taxonomy: A basic system of soil classification for making and interpreting soil surveys. Agriculture Handbook No. 436, 2nd edn. Washington, DC: U.S. Gov. Print. Office.Google Scholar
- Stellacci, A. M., Castrignanò, A., Diacono, M., Troccoli, A., Ciccarese, A., Armenise, E., et al. (2012). Combined approach based on principal component analysis and canonical discriminant analysis for investigating hyperspectral plant response. Italian Journal of Agronomy, 7, 247–253.CrossRefGoogle Scholar
- Webster, R., & Oliver, M. (2001). Geostatistics for environmental scientists. Statistics in practice. Chichester: Wiley.Google Scholar