DETECTING THE EFFECT OF IRRIGATION WATER QUALITIES ON THE HETEROGENEITY OF CLOVER BIOMASS USING GEOGRAPHIC INFORMATION SYSTEMS (GIS) AND PASSIVE REFLECTANCE SENSOR

Document Type : Original Article

Authors

1 Assistant Prof. of Agric. Eng. Dept., Faculty of Agric., Al-Mansoura University, Egypt.

2 Assistant Prof. of Agric. Eng. Evaluation of Natural Resources Department, Environmental Studies and Research Institute, Sadat City University, Egypt.

Abstract

Heterogeneous biomasses stands of cultivars require maximize the efficiency of crop inputs within small areas of the farm field. However, its determination via classical measurements to cover full area of site is tedious and time-consuming. The precision agriculture technologies based on the passive reflectance sensor and GIS tools have the potential for fast and non-destructive measurements for different parameters. In the present study, the performance of GIS maps based on two spectral reflectance indices (R780/R740 and R780/R700) for assessing the heterogeneity in vegetation Clover biomass under fresh, drainage and mixed water was tested. The results showed that there were significant variations in spectral index (R780/R740) values, spectral index (R780/R700) values and Clover biomass weights under fresh water, drainage water and mixed water. There were a more pronounced strong negative relationships between spectral index (R780/R740) and Clover biomass and the coefficients of determination varied between (0.92*** to 0.93***) under three water qualities. As well as there were a more pronounced strong positive relationships between spectral index (R780/R700) and Clover biomass and the coefficients of determination varied between (0.88** to 0.96**) under three water qualities. In conclusion, the GIS maps based on two spectral reflectance indices (R780/R740 and R780/R700) were useful to describe the heterogeneity in vegetation Clover biomass under three water qualities.

Keywords

References

Allam A.E. and Negm, A.M. (2013). Agricultural drainage water quality analysis and its suitability for direct reuse in irrigation: case study: Kafr  El-Sheikh  governorate,  Egypt”,  Seventeenth  International Water Technology Conference, IWTC17 Istanbul, 5-7 November.
Aparicio, N., Villegas, J., Araus, L., Casadesus, L., Royo, C. (2002). Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Sci. 42, 1547- 1555.
Auerswald, K., Sippel, R., Kainz, M., Demmel, M., Scheinost, S., Sinowski, W., Maidl, F. X. (1997). The crop response to soil variability in an agroecosystem. In: Auerswald, K., H. Stanjek, and J. M. Bigham (Ed.), Soil and Environment: Soil Processes from Mineral to Landscape Scale. Catena Verlag, 39-54.
Brus, D.J., Gruijter, J.J., Marsman, B.A., Visschers, R., Bregt, A.K., Breeuwsma, A., (1996). The performance of spatial interpolation methods and choropleth maps to estimate properties at points: a soil survey case study. Environmetrics. 7: 1-16
El-Hendawy, S., Al-Suhaibani, N., Salem, A., Ur Rehman, S., Schmidhalter, U. (2015). Spectral reflectance indices as a rapid nondestructive phenotyping tool for estimating different morphophysiological traits of contrasting spring wheat germplasms under arid conditions. Turk. J. Agric. For. 39: 572-587.
Elsayed, S., Rischbeck, P., Schmidhalter, U. (2015). Comparing the performance of active and passive reflectance sensors to assess the normalized relative canopy temperature and grain yield of drought-stressed barley cultivars. Field Crop Res. 177:148-160.
El-Shikha, D.M., Waller, P., Hunsaker, D., Clarke, T., Barnes E. (2007). Ground-based remote sensing for assessing water and nitrogen status of broccoli. Agr Water Mange. 92:183-193.
Erdle, K., Mistele, B., Schmidhalter, U. (2011). Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Res. 124: 74-84.
Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., Dextraze, L., (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application in precision agriculture. Remote Sens. Environ. 814: 16-426.
Heil, K., Schmidhalter, U. (2015).Comparison of the EM38 and EM 38-MK2 electromagnetic induction-based sensors for spatial soil analysis at field scale. Comput Electron Agric. 11: 264-28.
Jurwakar, A.S. Jurwakar, A., Deshbharatar, P.B., Bal, A.S. (1991). Asian Experiences in Integrated Plant Nutrition, 178-201, RAPA. FAO, Bangkok.
Kiziloglu,  F.M.,  Turan,  M.,  Sahin,  U.,  Kuslu,  Y.,  Dursun, A., (2008). Effects of untreated and treated  wastewater irrigation on some chemical properties  of  cauliflower  (Brassica  olerecea L. var. botrytis) and red cabbage (Brassica olerecea L. var. rubra) grown  on  calcareous  soil  in  Turkey. Agr Water Mange.  95: 716-724.
Mistele, B., Schmidhalter, U. (2008). Spectral measurements of the total aerial N and biomass dry weight in maize using a quadrilateral-view optic. Field Crops Res. 106: 94-103.
Rischbeck, P., Elsayed, S., Mistele, B., Barmeier, G., Heil K., Schmidhalter U. (2016). Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley. Eur. J. Agron. 78: 44-59.
Thoren, D., Schmidhalter, U. (2009). Nitrogen status and biomass determination of oilseed rape by laser-induced chlorophyll fluorescence. Eur. J. Agron. 30: 238-242.
Yadar, R.K., Goyal, R., Sharma, R.K., Dubey, S.K., Minchas, R.S. (2002). Post irrigation impact of domestic sewage effluent on composition of soils, crops and ground water – a case study. J. Environ. Int. 28:481–486.
Woolley, J. T. (1971). Reflectance and transmittance of light by leaves. Plant Physiol. 47: 656- 662.

Files

Share

How to cite

Statistics