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Eory, V., MacLeod, M., Shrestha, S., & Roberts, D. (2014). Linking an economic and a life-cycle analysis biophysical model to support agricultural greenhouse gas mitigation policy. German Journal of Agricultural Economics, 63, 133–142.
Abstract: Greenhouse gas (GHG) mitigation is one of the main challenges facing agriculture, exacerbated by the increasing demand for food, in particular for livestock products. Production expansion needs to be accompanied by reductions in the GHG emission intensity of agricultural products, if significant increases in emissions are to be avoided. Suggested farm management changes often have systemic effects on farm, therefore their investigation requires a whole farm approach. At the same time, changes in GHG emissions arising offfarm in food supply chains (pre- or post-farm) can also occur as a consequence of these management changes. A modelling framework that quantifies the whole-farm, life-cycle effects of GHG mitigation measures on emissions and farm finances has been developed. It is demonstrated via a case study of sexed semen on Scottish dairy farms. The results show that using sexed semen on dairy farms might be a costeffective way to reduce emissions from cattle production by increasing the amount of lower emission intensity ‘dairy beef’ produced. It is concluded that a modelling framework combining a GHG life cycle analysis model and an economic model is a useful tool to help designing targeted agri-environmental policies at regional and national levels. It has the flexibility to model a wide variety of farm types, locations and management changes, and the LCA-approach adopted helps to ensure that GHG emission leakage does not occur in the supply chain.
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Ghaley, B. B., Vesterdal, L., & Porter, J. R. (2014). Quantification and valuation of ecosystem services in diverse production systems for informed decision-making. Environmental Science & Policy, 39, 139–149.
Abstract: The empirical evidence of decline in ecosystem services (ES) over the last century has reinforced the call for ES quantification, monitoring and valuation. Usually, only provisioning ES are marketable and accounted for, whereas regulating, supporting and cultural ES are typically non-marketable and overlooked in connection with land-use or management decisions. The objective of this study was to quantify and value total ES (marketable and non-marketable) of diverse production systems and management intensities in Denmark to provide a basis for decisions based on economic values. The production systems were conventional wheat (Cwheat), a combined food and energy (CFE) production system and beech forest. Marketable (provisioning ES) and non-marketable ES (supporting, regulating and cultural) ES were quantified by dedicated on-site field measurements supplemented by literature data. The value of total ES was highest in CFE (US$ 3142 ha(-1) yr(-1)) followed by Cwheat (US$ 2767 ha (1) yr(-1)) and beech forest (US$ 2328 ha(-1) yr(-1)). As the production system shifted from Cwheat – CFE-beech, the marketable ES share decreased from 88% to 75% in CFE and 55% in beech whereas the non-marketable ES share increased to 12%, 25% and 45% of total ES in Cwheat, CFE and beech respectively, demonstrating production system and management effects on ES values. Total ES valuation, disintegrated into marketable and non-marketable share is a potential way forward to value ES and `tune’ our production systems for enhanced ES provision. Such monetary valuation can be used by policy makers and land managers as a tool to assess ES value and monitor the sustained flow of ES. The application of ES-based valuation for land management can enhance ES provision for maintaining the productive capacity of the land without depending on the external fossil-based fertilizer and chemical input. (C) 2013 Elsevier Ltd. All rights reserved.
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Shrestha, S., Hennessy, T., Abdalla, M., Forristal, D., & Jones, M. J. (2014). Determining short term responses of Irish dairy farms under climate change. German Journal of Agricultural Economics, 63(3), 143–155.
Abstract: This study aimed to determine short term farm responses of Irish dairy farms under climate change. The Irish National Farm Survey data and Irish weather data were the main datasets used in this study. A set of simulation models were used to determine grass yields and field time under a baseline scenario and a future climate scenario. An optimising farm level model which maximises farm net income under limiting farm resources was then run under these scenarios. Changes in farm net incomes under the climate change scenario compared to the baseline scenario were taken as a measure to determine the effect of climate change on farms. Any changes in farm activities under the climate run compared to the baseline run were considered as farm’s responses to maximise farm profits. The results showed that there was a substantial increase in yields of grass (49% to 56%) in all regions. The impact of climate change on farms was different based on the regions. Dairy farms in the Border, Midlands and South East regions suffered whereas dairy farms in other regions generally fared better under the climate change scenario. For a majority of farms, a substitution of concentrate feed with grass based feeds and increasing stocking rate were identified as the most common farm responses. However, farms replaced concentrate feed at varying degree. Dairy farms in the Mid East showed a move towards beef production system where medium dairy farms in the South East regions shifted entire tillage land to grass land. Farms in the South East region also kept animals on grass longer under the climate change scenario compared to the baseline scenario.
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Asseng, S., Ewert, F., Martre, P., Rötter, R. P., Lobell, D. B., Cammarano, D., et al. (2014). Rising temperatures reduce global wheat production. Nat. Clim. Change, 5(2), 143–147.
Abstract: Crop models are essential tools for assessing the threat of climate change to local and global food production1. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature2. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 °C to 32 °C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each °C of further temperature increase and become more variable over space and time.
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García-López, J., Lorite, I. J., García-Ruiz, R., & Domínguez, J. (2014). Evaluation of three simulation approaches for assessing yield of rainfed sunflower in a Mediterranean environment for climate change impact modelling. Clim. Change, 124(1-2), 147–162.
Abstract: The determination of the impact of climate change on crop yield at a regional scale requires the development of new modelling methodologies able to generate accurate yield estimates with reduced available data. In this study, different simulation approaches for assessing yield have been evaluated. In addition to two well-known models (AquaCrop and Stewart function), a methodological proposal considering a simplified approach using an empirical model (SOM) has been included in the analysis. This empirical model was calibrated using rainfed sunflower experimental field data from three sites located in Andalusia, southern Spain, and validated using two additional locations, providing very satisfactory results compared with the other models with higher data requirements. Thus, only requiring weather data (accumulated rainfall from the beginning of the season fixed on September 1st, and maximum temperature during flowering) the approach accurately described the temporal and spatial yield variability observed (RMSE = 391 kg ha(-1)). The satisfactory results for assessing yield of sunflower under semi-arid conditions obtained in this study demonstrate the utility of empirical approaches with few data requirements, providing an excellent decision tool for climate change impact analyses at a regional scale, where available data is very limited.
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