The thesis assesses impacts of climate change and variability on regional and global crop yields using econometric approaches to analyze global gridded data. Using a large dimension panel data of six Global Gridded Crop Models (GGCMs) for four rainfed crops (maize, rice, soybeans and wheat) an emulator suitable/amenable of being integrated into Integrated Assessment Models (IAMs) is built. The performance of the emulator is evaluated against observational-based, empirical models at regional scale by building a statistical model calibrated on historical observed crop yields data for United States (U.S.) counties. Chapter 1 provides the background of existing research methodologies in agronomic literature. The gaps in existing research and scope for research are laid down as motivation and objectives of the research that follows in the subsequent chapters. Chapter 2 discusses the data, methodology and framework used in the construction of a simple statistical emulator of the response of crops to weather shocks simulated by crop models. To facilitate the integration of the emulator into IAMs, the simplest model using a base specification of linear fixed effect with time trend interactions is developed. Chapter 3 investigates modifications to the base specification with a series of robustness checks exploring the suitability of an additional predictor variable, the stratification of coefficients geographically by groups of Agro-Ecological Zones (AEZs); and most importantly, the role of spatial dependence in variables by applying a spatial model. Chapter 4 compares the performance of the statistical emulator calibrated on crop model results, with an empirical models of crop responses based on historical data. The comparison focuses on U.S. counties. The base specification from Chapter 2 together with historical observed data from the U.S. Department of Agriculture (USDA), are utilized in an inter-comparison exercise for divergence in results and subsequent implications. Collectively, the three chapters (2-4) address several important questions: (1) what do reduced-form statistical response surfaces trained on crop model outputs from various simulation specifications look like; (2) do model-based crop response functions vary systematically over space (e.g., crop suitability zones) and across crop models?, (3) how do model-based crop response functions compare to crop responses estimated using historical observations? and (4) what are the implications for the characterization of future climate risks? Chapter 5 concludes the thesis providing a summary of key contributions and suggestions for future work.

La tesi valuta gli impatti dei cambiamenti climatici e della variabilità climatica sulla produttività agricola a scala regionale e globale analizzando dati ad alta risoluzione spaziale con metodi econometrici. La tesi utilizza dati provenienti da sei modelli globali delle rese agricole per quattro coltivazioni non irrigate (mais, riso, soia, e grano) per costruire un emulatore da integrare in modelli di valutazione integrata (IAMs). La prestazione dell’emulatore statistico è valutata su scala regionale utilizzando modelli empirici basati su osservazioni storiche per gli Stati Uniti. Il Capitolo 1 fornisce il contesto della ricerca esistente e descrive le metodologie disponibili nell’ambito dell’agronomia. Introduce la motivazione e gli obiettivi della ricerca sviluppata nei capitoli successivi. Il Capitolo 2 discute i dati, la metodologia usata per sviluppare un semplice emulatore statistico della funzione di risposta delle rese agricole a shock meteorologici simulati da modelli di processo. Per facilitare l’integrazione dell’emulatore in modelli IAMs, questo capitolo testa un modello semplice ad effetti fissi con l’interazione con trend temporali. Il Capitolo 3 esplora delle varianti del modello base che esplorano 1) altre variabili esplicative 2) variazioni geografiche in base a diverse aree agronomiche (Agro-Ecological Zones, AEZs), 3) il ruolo della dipendenza spaziale nei dati. Il Capitolo 4 confronta la performance dell’emulatore statistico calibrato sui dati dei modelli di processo con dei modelli empirici basati su dati storici. Il confronto analizza i dati per gli Stati Uniti. Si basa sul modello base sviluppato nel Capitolo 2 e dati storici per gli Stati Uniti dal Dipartimento dell’Agricoltura (USDA). Nel loro insieme i tre capitoli 2-4 affrontano diverse importanti domande: 1) come si caratterizzano le funzioni di risposta in forma ridotta stimate a partire da dati generati da modelli di processo 2) come queste variano geograficamente e in base al modello che genera i dati 3) come queste differiscono rispetto a funzioni di risposta stimate a partire dai dati osservati storicamente e 4) quali sono le implicazioni per l’analisi del rischio climatico. Il Capitolo 5 conclude la tesi con un riassunto dei contributi chiave e suggerimenti per lavori futuri.

Impacts of climate change and variability on crop yields using emulators and empirical models

MALCOLM MISTRY
2017-01-01

Abstract

The thesis assesses impacts of climate change and variability on regional and global crop yields using econometric approaches to analyze global gridded data. Using a large dimension panel data of six Global Gridded Crop Models (GGCMs) for four rainfed crops (maize, rice, soybeans and wheat) an emulator suitable/amenable of being integrated into Integrated Assessment Models (IAMs) is built. The performance of the emulator is evaluated against observational-based, empirical models at regional scale by building a statistical model calibrated on historical observed crop yields data for United States (U.S.) counties. Chapter 1 provides the background of existing research methodologies in agronomic literature. The gaps in existing research and scope for research are laid down as motivation and objectives of the research that follows in the subsequent chapters. Chapter 2 discusses the data, methodology and framework used in the construction of a simple statistical emulator of the response of crops to weather shocks simulated by crop models. To facilitate the integration of the emulator into IAMs, the simplest model using a base specification of linear fixed effect with time trend interactions is developed. Chapter 3 investigates modifications to the base specification with a series of robustness checks exploring the suitability of an additional predictor variable, the stratification of coefficients geographically by groups of Agro-Ecological Zones (AEZs); and most importantly, the role of spatial dependence in variables by applying a spatial model. Chapter 4 compares the performance of the statistical emulator calibrated on crop model results, with an empirical models of crop responses based on historical data. The comparison focuses on U.S. counties. The base specification from Chapter 2 together with historical observed data from the U.S. Department of Agriculture (USDA), are utilized in an inter-comparison exercise for divergence in results and subsequent implications. Collectively, the three chapters (2-4) address several important questions: (1) what do reduced-form statistical response surfaces trained on crop model outputs from various simulation specifications look like; (2) do model-based crop response functions vary systematically over space (e.g., crop suitability zones) and across crop models?, (3) how do model-based crop response functions compare to crop responses estimated using historical observations? and (4) what are the implications for the characterization of future climate risks? Chapter 5 concludes the thesis providing a summary of key contributions and suggestions for future work.
2017
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3716714
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