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2.
J Environ Manage ; 156: 276-89, 2015 Jun 01.
Article in English | MEDLINE | ID: mdl-25900091

ABSTRACT

Using a novel approach that links geospatial land resource information with individual farm-scale simulation, we conducted a regional assessment of nitrogen (N) and phosphorous (P) losses to water and greenhouse gas (GHG) emissions to air from the predominant mix of pastoral industries in Southland, New Zealand. An evaluation of the cost-effectiveness of several nutrient loss mitigation strategies applied at the farm-scale, set primarily for reducing N and P losses and grouped by capital cost and potential ease of adoption, followed an initial baseline assessment. Grouped nutrient loss mitigation strategies were applied on an additive basis on the assumption of full adoption, and were broadly identified as 'improved nutrient management' (M1), 'improved animal productivity' (M2), and 'restricted grazing' (M3). Estimated annual nitrate-N leaching losses occurring under representative baseline sheep and beef (cattle) farms, and representative baseline dairy farms for the region were 10 ± 2 and 32 ± 6 kg N/ha (mean ± standard deviation), respectively. Both sheep and beef and dairy farms were responsive to N leaching loss mitigation strategies in M1, at a low cost per kg N-loss mitigated. Only dairy farms were responsive to N leaching loss abatement from adopting M2, at no additional cost per kg N-loss mitigated. Dairy farms were also responsive to N leaching loss abatement from adopting M3, but this reduction came at a greater cost per kg N-loss mitigated. Only dairy farms were responsive to P-loss mitigation strategies, in particular by adopting M1. Only dairy farms were responsive to GHG abatement; greater abatement was achieved by the most intensified dairy farm system simulated. Overall, M1 provided for high levels of regional scale N- and P-loss abatement at a low cost per farm without affecting overall farm production, M2 provided additional N-loss abatement but only marginal P-loss abatement, whereas M3 provided the greatest N-loss abatement, but delivered no additional P abatement, and came at a large financial cost to farmers, sheep and beef farmers in particular. The modelling approach provides a farm-scale framework that can be extended to other regions to accommodate different farm production systems and performances, capturing the interactions between farm types, land use capabilities and production levels, as these influence nutrient losses and GHG emissions, and the effectiveness of mitigation strategies.


Subject(s)
Agriculture , Carbon Dioxide/analysis , Conservation of Natural Resources/economics , Greenhouse Effect/prevention & control , Nitrogen/analysis , Phosphorus/analysis , Waste Management , Water/chemistry , Agriculture/economics , Agriculture/methods , Animals , Cattle , Conservation of Natural Resources/methods , Cost-Benefit Analysis , New Zealand , Nitrates/analysis , Sheep , Waste Management/economics , Waste Management/methods
3.
PLoS One ; 8(10): e75934, 2013.
Article in English | MEDLINE | ID: mdl-24098408

ABSTRACT

BACKGROUND: Targeted food pricing policies may improve population diets. To assess their effects on inequalities, it is important to determine responsiveness to price changes across income levels and ethnic groups. OBJECTIVE: Our goal was to estimate price elasticity (PE) values for major commonly consumed food groups in New Zealand, by income and ethnicity. PE values represent percentage change in demand associated with 1% change in price of that good (own-PE) or another good (cross-PE). DESIGN: We used food expenditure data from national household economic surveys in 2007/08 and 2009/10 and Food Price Index data from 2007 and 2010. Adopting an Almost Ideal Demand System approach, own-PE and cross-PE estimates were derived for 24 food categories, household income quintiles, and two ethnic groups (Maori and non-Maori). RESULTS: Own-PE estimates (with two exceptions) ranged from -0.44 to -1.78. Cross-PE estimates were generally small; only 31% of absolute values were greater than 0.10. Excluding the outlier 'energy drinks', nine of 23 food groups had significantly stronger own-PEs for the lowest versus highest income quintiles (average regression-based difference across food groups -0.30 (95% CI -0.62 to 0.02)). Six own-PEs were significantly stronger among Maori; the average difference for Maori: non-Maori across food groups was -0.26 (95% CI -0.52 to 0.00). CONCLUSIONS: Food pricing policies have potential to improve population diets. The greater sensitivity of low-income households and Maori to price changes suggests the beneficial effects of such policies on health would be greatest for these groups.


Subject(s)
Commerce/statistics & numerical data , Ethnicity/statistics & numerical data , Food/economics , Income/statistics & numerical data , Family Characteristics/ethnology , Humans
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