Anna Gloria Billè, Marco Rogna
arXiv 10 Feb 2020 · Econometrics · publishedJournal of the Royal Statistical Society Series A (Statistics in Society) (2020) · 2 citations (OpenAlex)
arXiv:2002.03922 · PDF · DOI · OpenAlex · Extracted main text
Given the extreme dependence of agriculture on weather conditions, this paper analyses the effect of climatic variations on this economic sector, by considering both a huge dataset and a flexible spatio-temporal model specification. In particular, we study the response of N-fertilizer application to abnormal weather conditions, while accounting for other relevant control variables. The dataset consists of gridded data spanning over 21 years (1993-2013), while the methodological strategy makes use of a spatial dynamic panel data (SDPD) model that accounts for both space and time fixed effects, besides dealing with both space and time dependences. Time-invariant short and long term effects, as well as time-varying marginal effects are also properly defined, revealing interesting results on the impact of both GDP and weather conditions on fertilizer utilizations. The analysis considers four macro-regions -- Europe, South America, South-East Asia and Africa -- to allow for comparisons among different socio-economic societies. In addition to finding both spatial (in the form of knowledge spillover effects) and temporal dependences as well as a good support for the existence of an environmental Kuznets curve for fertilizer application, the paper shows peculiar responses of N-fertilization to deviations from normal weather conditions of moisture for each selected region, calling for ad hoc policy interventions.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Purcell, L. C., Serraj, R., Sinclair, T. R., and De, A (2004) https://dl.sciencesocieties.org/publications/cs/abstracts/44/2/484Soybean N 2 fixation estimates, ureide concentration, and yiel… | 0.843 | 3 | 3 | 100% |
| 2 | Elhorst, J. P (2014) Spatial Econometrics: From Cross-sectional Data to Spatial Panels, volume 479 | 0.811 | 4 | 2 | 100% |
| 3 | Vicente-Serrano, S. M., Beguerá, S., and López-Moreno, J. I (2010) https://journals.ametsoc.org/doi/abs/10.1175/2009JCLI2909.1A multiscalar drought index sensitive to global warming: the standard… | 0.644 | 4 | 1 | 100% |
| 4 | Barrett, C. B (2007) Displaced distortions: Financial market failures and seemingly inefficient resource allocation in low-income rural communities | 0.644 | 2 | 2 | 100% |
| 5 | Lu, C. and Tian, H (2017) https://www.earth-syst-sci-data.net/9/181/2017/Global nitrogen and phosphorus fertilizer use for agriculture production in the p… | 0.644 | 2 | 2 | 100% |
| 6 | Vlek, P. L. and Byrnes, B. H (1986) https://link.springer.com/chapter/10.1007/978-94-009-4428-2_7The efficacy and loss of fertilizer N in lowland rice | 0.644 | 2 | 2 | 100% |
| 7 | The World Bank (2018) https://data.worldbank.org/indicator/SL.AGR.EMPL.ZSEmployment in agriculture (% of total employment) | 0.511 | 2 | 1 | 100% |
| 8 | Ding, Y., Schoengold, K., and Tadesse, T (2009) https://www.jstor.org/stable/41548424?seq=1#metadata_info_tab_contentsThe impact of weather extremes on agricultural production… | 0.511 | 2 | 1 | 100% |
| 9 | Lee, L.-f. and Yu, J (2010) Some recent developments in spatial panel data models | 0.511 | 2 | 1 | 100% |
| 10 | Lee, L.-F. and Yu, J (2010) A spatial dynamic panel data model with both time and individual fixed effects | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 56 scored citations.