Nitrogen fertilization plays a key role in rice productivity and environmental impact of rice-based cropping systems, as well as on farmers’ income, representing one of the main cost items of rice farming. Average nitrogen use efficiency in rice paddies is often very low (about 30%), leading to groundwater contamination, greenhouse gases emission, and economic losses for farmers. The resulting pressure on many actors in the rice production chain has generated a need for operational tools and techniques able to increase nitrogen use efficiency. We present an operational workflow for producing nitrogen nutritional index (NNI) maps at sub-field scale based on the combined use of high-resolution satellite images and ground-based estimates of Leaf Area Index (LAI) and plant nitrogen concentration (PNC, %) data collected using smart apps. The workflow was tested in northern Italy. The analysis reveals that vegetation indices are satisfactorily correlated with LAI (r2 > 0.77, p < 0.01) and PNC (r2 > 0.55, p < 0.01); whereas most patterns of NNI maps are coherent with the available information on soil texture and performed agro-practices. Key features of the proposed approach are (i) the time- and cost-effectiveness for producing NNI maps even in operational contexts and (ii) the full exploitation of smart scouting techniques to drive field data acquisitions using smartphones as sensors. The use of operational, free-of-charge products from Sentinel-2 for real-time field monitoring to potentially support variable rate fertilizations is also discussed.
An operational workflow to assess rice nutritional status based on satellite imagery and smartphone apps / F. Nutini, R. Confalonieri, A. Crema, E. Movedi, L. Paleari, D. Stavrakoudis, M. Boschetti. - In: COMPUTERS AND ELECTRONICS IN AGRICULTURE. - ISSN 0168-1699. - 154(2018), pp. 80-92.
|Titolo:||An operational workflow to assess rice nutritional status based on satellite imagery and smartphone apps|
NUTINI, FRANCESCO (Corresponding)
|Parole Chiave:||Plant nitrogen status; Precision agriculture; Satellite monitoring; Smart apps|
|Settore Scientifico Disciplinare:||Settore AGR/02 - Agronomia e Coltivazioni Erbacee|
|Progetto:||An Earth obseRvation Model based RicE|
Satelliti e altre tecnologie innovative a supporto di tecniche di fertilizzazione a rateo variabile in risicoltura
|Data di pubblicazione:||nov-2018|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.compag.2018.08.008|
|Appare nelle tipologie:||01 - Articolo su periodico|
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