Remote Sensing and Site Specific Crop Management in Precision Agriculture

Ali, Abid (2020) Remote Sensing and Site Specific Crop Management in Precision Agriculture, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Scienze e tecnologie agrarie, ambientali e alimentari, 32 Ciclo.
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Abstract

Application of variable crop inputs in the right quantity and place is very important for optimizing plant growth and final yield through efficient use of finite resources and minimum environmental impacts. In this framework, actions were carried out to support the adoption of PA: In Chapter 1 several remotely sensed vegetation indices (VIs) were used to estimate the spatial crop yields of winter cereals (durum and bread wheat) and spring dicots (sunflower and coriander) through simple correlation over five years. Pixel level study was also investigated between original VIs data and kriged crop yield data. Results showed that spatial variability of crops can be effectively assessed through Landsat imagery with 30 m resolution even on a relatively small area (11.07 ha). Simple ratio and normalized difference vegetation index were shown slightly better indices during vegetative to reproductive stages as compared to enhanced vegetation index, soil adjusted vegetation index, green normalized difference vegetation index and green chlorophyll index. Pixel level study also demonstrated a good agreement between five classes of VIs and grain yield. In Chapter 2, three yield stability classes (YSCs) were developed using spatio-temporal yield maps over five years: high yielding and stable (HYS), low yielding and stable (LYS), and unstable class. Thereafter, we evaluated the YSCs through simple correlations and statistical differences of soil data with spatiotemporal yield within YSCs. Results showed that spatial maps were more consistent with the YSCs map than the temporal stability map. Yield classes were found considerably consistent with soil properties. Lower values of soil apparent electrical conductivity (ECa), in the average, were consistent with HYS class featuring maximum crop yield (122 %), compared to LYS and unstable class. In addition, the balance between precipitation and evapo-transpiration support the fluctuations of yield across years in the unstable area.

Abstract
Tipologia del documento
Tesi di dottorato
Autore
Ali, Abid
Supervisore
Co-supervisore
Dottorato di ricerca
Ciclo
32
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Landsat imagery; spectral vegetation indices; geostatistics; field spatial variability; grain yield prediction; crop rotation; spatiotemporal variability; yield stability classes; GIS; ECa directed to soil sampling; weather data; precision agriculture.
URN:NBN
Data di discussione
1 Aprile 2020
URI

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