Spatial analysis of dairy yields response to intensive farming in New Zealand

2019 ◽  
Vol 11 (1) ◽  
pp. 79-99
Author(s):  
Wei Yang ◽  
Basil Sharp

Purpose The New Zealand (NZ) dairy industry faces the challenge of increasing productivity and dealing with public concerns over nutrient pollution. The effective policy needs to address regional differences in productivity and fertilizer use. The purpose of this paper is to investigate how spatial effects influence the relationship between dairy yields and intensive farming practices across regions in NZ. Design/methodology/approach This paper employs spatial panel data models to establish whether unobserved spatial effects exist in the relationship between dairy yields and nutrient inputs regionally and nationally using 2002, 2007 and 2012 data from Statistics NZ and DairyNZ. Findings The results show positive spatial spillovers for most intensive inputs. The high level of effluent use and estimated negative yield response to nitrogen suggests that an opportunity exists for greater use of effluent as a substitute for nitrogenous fertilizer. Substitution has the potential to reduce dependence on fertilizer and contribute to a reduction in the nutrient pollution. Originality/value This paper is the first empirical application of spatial econometric methods to examine the spatial relevance of dairy yields and intensive farming in NZ. In particular, the spatial panel data model accounts for cross-sectional dependence and controls for heterogeneity. The results contribute to an understanding of how farmers can improve their management of intensive inputs and contribute to the formation of regional environmental policy that recognizes regional heterogeneity.

Info ◽  
2015 ◽  
Vol 17 (5) ◽  
pp. 46-65 ◽  
Author(s):  
Maria Veronica Alderete

Purpose – This paper aims to determine if there is a spatial dependence in the entrepreneurial activity among countries. The existence of a “digital proximity” could explain the spatial pattern of entrepreneurship. Design/methodology/approach – This question is empirically addressed by using a five-period, 2008-2012, panel data for 35 countries. A spatial fixed effects panel data model is estimated by using the total entrepreneurial activity published by the global entrepreneurship monitor as the dependent variable. Findings – A significant negative influence of the digital proximity on the entrepreneurial activity is observed. Mobile broadband (MB) direct effect is positive while the indirect effect (the spatial spillovers) is negative, leading to a negative total effect on the total entrepreneurial activity. This result is contrary to non-spatial models’ results. Besides, a higher MB penetration in a country would lead to a competitive advantage fostering its opportunities for entrepreneurship, but reducing those of its neighbours’. Originality/value – This paper examines the relationship between information and communication technology (ICT) and entrepreneurship, by introducing the spatial effects is the main contribution. This paper expands the scant literature on the ICT impact on entrepreneurship. Results obtained support policies towards enforcing innovation, education and reducing entry regulations for encouraging entrepreneurship. Meanwhile, MB policies could counteract the entrepreneurial policies’ results due to the spatial dependence.


2016 ◽  
Vol 97 ◽  
pp. S63-S78 ◽  
Author(s):  
Hermann Pythagore Pierre Donfouet ◽  
P. Wilner Jeanty ◽  
Eric Malin

2020 ◽  
Vol 240 (4) ◽  
pp. 387-415
Author(s):  
Philipp Süß

AbstractEconomic theory predicts a positive effect of an increase in income inequality on the prevalence of crime, but the international empirical evidence is mixed. For Germany, research on this topic is virtually non-existent. Therefore, I used fixed effect regressions to estimate the effect of a market income inequality proxy on property damages, thefts from motor vehicles, domestic burglaries and assaults in Germany. The models without spatial lags suggest economically small to moderate own-district elasticities between 0.13 and 0.95. The models with spatial lags generally show insignificant own-district estimates, but significant spatial spillovers.


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