Nitrogen fertilization and greenhouse gas emissions: exploring the potential of mitigation through precision agriculture

Murgia, Teresa (a.a. 2018/2019) Nitrogen fertilization and greenhouse gas emissions: exploring the potential of mitigation through precision agriculture. Tesi di Laurea in Sistemi agrari (LM-69), Università degli studi di Sassari, relatore Roggero Pier Paolo, pp. 41. [Tesi di Laurea magistrale]

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Abstract

It is estimated that in 2050 the world population will be 9 billion, hence it is of primary importance to ensure food security for the growing population on the same agricultural land through sustainable intensification, i.e. increasing food production without increasing GHG emissions. This study is focused on the application of the DSSAT crop model on spring barley in Scotland to support the design of nitrogen fertilization based on precision farming principles. The modelling exercise started from the identification of homogeneous management zones based on multi-year field observations and the quantification of N2O emissions and NO3 leaching for each yield zone, with the aim of optimizing fertilization to achieve high yields with substantially reduced emissions at field scale. The simulations proved that 80 N treatment was very effective vs. the business as usual fertilization rate (120 N) by maitaining crop yield reduction within -5% but reducing nitrate leaching by between -41% and -51% and N2O gas emissions by between -48% and -50% in relation to the management zone. However, given the relatively homogeneous field conditions, the model output showed negligible advantages from a precision fertilization approach vs. the homogeneous distribution of the optimal N fertilization rate.

Tipologia di tesi: Tesi di Laurea magistrale
Autore: Murgia, Teresa
Relatore: Roggero, Pier Paolo
Correlatore: Cammarano, Davide; Mula, Laura
Disciplina MIUR: Area 07 - Scienze agrarie e veterinarie > AGR/02 AGRONOMIA E COLTIVAZIONI ERBACEE
Struttura: Dipartimento di Agraria
Corsi di Laurea: Sistemi agrari (LM-69)
Anno Accademico: 2018/2019
Sessione: Autunnale
Parole Chiave: Digital agriculture, spring barley, crop models
Codice ID dell'EPrint: 1253
Data di Deposito: 06 Nov 2019 11:00
Tipo di tesi: Sperimentale
URI: http://unisslaurea.uniss.it/id/eprint/1253

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