Assessing climate change and associated socio-economic scenarios for arable farming in the Netherlands: An application of benchmarking and bio-economic farm modelling
Introduction
Interrelated changes of climate, market and agro-environmental policies affect agricultural production all over the world (O’Brien and Leichenko, 2000, Lobell et al., 2008, Van Ittersum et al., 2008, Giller et al., 2011, Schneider et al., 2011, Ray et al., 2013). Future farming systems are challenged to adapt to the changing socio-economic and bio-physical environment in order to remain competitive and to meet the increasing requirements for food and fibre. To deal with the uncertainty related to how climate, markets and technology will change, research focused on developing integrated scenarios to provide images of the world in the future (Westhoek et al., 2006, Abildtrup et al., 2006, Riedijk et al., 2007, Van Drunen and Berkhout, 2008). Issues such as rise of temperature, changes in precipitation patterns, rise of sea level, the state of international cooperation and the role of public and private sector in future economies have been taken into account. This enables the quantification of important economic and environmental indicators at macro-level and the definition of comprehensive story lines of future development in agriculture and food production (Audsley et al., 2006, Riedijk et al., 2007). The consequences of these global and regional scenarios on farm structure have been assessed based on historical analysis of driver-impact relationships (Mandryk et al., 2012b). However, detailed quantitative, analysis at farm level, which will improve understanding of farm level adaptation, is still a challenge (Reidsma et al., 2010). With regard to climate change adaptation, the focus has mainly been on adapting crop management to increase or maintain yields at field level (Easterling et al., 2007). Nevertheless, at farm level, other options such as adjusting specialization (e.g. land use, livestock types) and changing level of diversification and scale of production might be better adaptation measures (Reidsma et al., 2009). To assess the effectiveness and adoption of such adaptations at farm level, integrated analysis of changes in the climate and the socio-economic context is required.
Bio-economic farm modelling (Janssen and van Ittersum, 2007), can be used to evaluate different adaptation options and to reveal the consequences of climatic, socio-economic, technological and institutional (policy) changes. The available set of production activities is identified and the relationship between agricultural inputs and outputs is quantified. Economic criteria are used to simulate the farmer's behaviour. In many cases, bio-economic studies assume that the relationship between agricultural inputs and outputs is linear and independent of the scale of production (Louhichi et al., 2010). Moreover, spatial and temporal interactions (e.g. rotational effects), between different activities are ignored and complementarity and substitution between different agricultural inputs and outputs are not taken into account. Availability of capital is not taken into account as a constraint. To account for variation between farms, and for temporal and spatial interactions between the outputs of agricultural activities, individual farm data and benchmarking techniques like Data Envelopment Analysis (DEA) (Cooper et al., 2007) has been used in agriculture and agricultural economics. For example, DEA was proposed by De Koeijer et al. (2002) to measure environmental and economic sustainability of Dutch sugar beet farmers. Fraser and Cordina (1999) used DEA to analyze productivity of dairy farms in Australia. Novo et al. (2013) measured productivity of family dairy farmers in Brazil. Piot-Lepetit et al. (1997) used DEA to measure the potential of reducing agricultural inputs in French agriculture while Latruffe et al. (2005) assessed technical and scale efficiency and make comparisons between crop and livestock farms in Poland.
The objective of this article is to assess the impact of climate change and associated socio-economic scenarios on arable farming systems in Flevoland (the Netherlands) and to explore different adaptation strategies at farm level. To this end we developed an integrated method in which we applied DEA (Cooper et al., 2007) using empirical data from individual farms to identify “best” current farm practices and derive the input–output relationships of current farm management. A bio-economic farm model was used to optimize the production plan of individual farmers and explore the impact of scenarios for 2050. By using DEA to quantify the input–output relationship of the bio-economic farm model we account implicitly for existing non-linearities in production and temporal and spatial interactions between crops and managements. Impacts of gradual climate change on crop yields, the effects of technological change (i.e. new crop varieties) but also expected price and policy changes were taken into account. We specifically focus on comparing the impact of different drivers, so we first demonstrate the applicability of the proposed methodology by simulating multiple integrated scenarios. Then we zoom in and discuss in detail the results from one of the evaluated scenarios that assumes strong temperature rise within a globalized economy (Riedijk et al., 2007).
Section snippets
Analysis of farm productivity with DEA
Data Envelopment Analysis (DEA) can be used to rank and benchmark farms according to their capacity to convert multiple inputs (e.g. capital, labour, land, fertilizers, agro-chemicals) into multiple outputs (e.g. potatoes, sugar beet, vegetables). Farms are technically efficient when the use of inputs cannot be decreased or production of outputs cannot be increased without decreasing outputs or increasing inputs respectively (Cooper et al., 2007, p. 3). A production frontier is developed by
Results
In this section, we first present results of DEA where technical efficiency of FADN farms was assessed and the best farm practices were identified. Second we present results from the base year simulation of FSSIM in which we maximized gross margin of each individual farm given current yields and prices. Third, an overview of results of all the evaluated scenarios for 2050 is presented. Finally, we focus on analysing results of A1W scenario and disentangle effects of the various drivers of
Discussion
Assessing the technical efficiency of farmers in Flevoland using DEA resulted in high efficiency scores. This is in line with our expectations since Flevoland is a modern agricultural region in the Netherlands. However, it is important to mention that high efficiency scores are also related to the large number of inputs and outputs that we explicitly included in the DEA analysis. The calculated efficiency scores increase with the number of dimensions (inputs and outputs) included in DEA. On the
Conclusion
The objective of this article was to assess the impact of climate change in the context of other changes in the socio-economic environment of arable farming systems in Flevoland (The Netherlands) and to explore different adaptation strategies at farm level. We showed that using individual farm data, DEA and bio-economic modelling, we can give insights in farm decision-making and assess possible impacts and adaptation in future scenarios. We analyzed the current production activities and we
Acknowledgements
The work presented in this publication was funded by the project for assessing the adaptive capacity of agriculture in the Netherlands to the impacts of climate change under different market and policy scenarios (AgriAdapt-NL), funded by the Dutch research programme Climate Changes Spatial Planning. We would also like to acknowledge Dr. M. Florin for commenting on earlier version of the manuscript.
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2018, Agricultural SystemsCitation Excerpt :This attempt was partly successful, and as mentioned above, the model has been applied in multiple countries, for different farm types and for different aims (e.g., Belhouchette et al., 2011; Mouratiadou et al., 2010). The development of FSSIM has however drifted into two directions, with improvements made by Kanellopoulos et al. (2014) followed by several scientific publications (e.g., Paas et al., 2016; Reidsma et al., 2015a; Wolf et al., 2015) and improvements made by Louhichi et al. (2015) at the Joint Research Centre (JRC), with the direct purpose of policy impact assessment for the EC (see also Langrell et al., 2013). At JRC, the model is re-named as EU-Wide Individual Farm Model for Common Agricultural Policy Analysis (IFM-CAP), and is part of their MIDAS platform (Modelling Inventory Database & Access Services).