Climate change impact and adaptation research requires integrated assessment and farming systems analysis: a case study in the Netherlands

Integrated assessment provides added value compared to disciplinary research, as it allows to better understand the complexity of the system (Rotmans and Van Asselt 1998). At the same time, the complexity causes communication to be an on-going challenge. In this paper we highlight innovative approaches and the main results, and for details we refer to already published papers (see below; appendix 1).

As changes at higher levels influence the local level, assessments were performed at two levels of organization (figure 2). At the EU level and for the year 2050, the crop model LINTUL-FAST was applied to assess changes in crop yields due to climate change, and a statistical model was used to assess changes due to technological development (Angulo et al 2013). Climate change scenarios were based on the SRES storylines and a 15 GCM model ensemble. Next, the partial equilibrium market model CAPRI was used to assess changes in agricultural commodity prices due to changes in supply (based on LINTUL-FAST) and demand (Britz et al 2007, Ewert et al 2011). These EU level price changes were input for the regional level assessment for Flevoland (appendix 2).

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Flevoland (figure 1) is a province with mainly arable agriculture (75% of the area). Most important crops in Flevoland are potato (31% of arable area), sugar beet (17%), winter wheat (17%) and onion (11%) (in 2008–2010; CBS Statline and LEI). The province was reclaimed from the lake IJsselmeer in the 1950s, and has high quality soils, good infrastructure, allotment of land and high water availability, all favourable for agricultural production (Rienks and Meulenkamp 2009).

At the provincial level, four climate change scenarios from the Royal Netherlands Meteorological Institute (KNMI) were used, i.e. the KNMI'06 scenarios (appendix 3; van den Hurk et al 2006). In this paper we focus on results from one scenario (W+) for the year 2050 since it is our aim to show climate change impacts and adaptation in the context of other drivers of change, rather than showing differences between different climate scenarios. The W+ scenario relates to a global 2 °C increase, and assumes changes in air circulation patterns resulting in drier summers in the Netherlands. CO₂ concentrations were assumed to be 567 μmol CO₂ mol⁻¹. The climate change scenario W+ was linked to the global economy scenario similar to the IPCC SRES storyline A1, according to Riedijk et al (2007). Where relevant, for example to show uncertainty, we additionally present results from the B2/G scenario, which is a regional communities scenario, with a global 1 °C increase, no changes in air circulation patterns, slightly increasing annual precipitation, and CO₂ concentrations of 478 μmol CO₂ mol⁻¹. Assessments of impacts of climate change and adaptation are described in the next sections.

Impacts of climate change and adaptation on crops in Flevoland were assessed with the crop growth simulation model WOFOST (appendix 4; Wolf et al 2011, Boogaard et al 2013). Simulated adaptation measures included (i) changing the sowing date (i.e. 15 days earlier except for winter wheat) and (ii) changing the crop varieties (assuming more southern varieties with a 10% increase in temperature requirements for phenological development).

Crop models assess impacts of gradual climate change, but these models are designed to assess potential or water-limited yields rather than actual yield, and hence the influence of (sub-optimal) management is largely neglected (e.g. Reidsma et al 2009). Moreover, when climate change will become apparent, also technological development will have taken place (Ewert et al 2005).

Crop model results therefore need to be put into context. Regarding technological development, a literature review was performed to investigate the possibilities to increase the potential yield level in 2050 due to changes in physiological, phenological and morphological characteristics of crops (Wolf et al 2011). Moreover, data were analysed to assess genetic progress in Dutch potential crop yields over the past 30 years (Rijk et al 2013). Although technological development cannot be completely separated from climate change adaptation, in this paper we assume technological development to be independent of climate change.

Progress in genetic yield potential (technological development) still leads to substantial yield increases. Yield potential (Y_P) can be expressed as a function of light intercepted (LI), radiation use efficiency (RUE), and the partitioning of biomass to yield, or harvest index (HI): Y_P = LI · RUE · HI. LI and HI have been optimized for, in particular, grain crops during the past decades, and future genetic progress in yield of grain (and other main) crops will most likely be achieved by focusing on constraints to RUE (Reynolds et al 2005, Fischer et al 2014). Long et al (2006) describe six potential routes of increasing RUE by improving photosynthetic efficiency, and collectively, these changes could improve RUE and, therefore, Y_p still substantially (Fischer and Edmaedes 2010, Fischer et al 2014).

Regarding management, we assumed that yield gaps (1 minus actual yield/simulated potential yield) can be reduced towards 2050, but to no less than 20%. Due to years with extreme conditions, disease infestations in wet years, economic and environmental efficiency, farmers generally do not realize the full potential and hence the exploitable yield is often assumed 80% of the potential (Cassman 1999, Van Ittersum et al 2013).

Other limitations of crop models are that (i) the impact of extreme events including pests and diseases cannot be simulated adequately (e.g. Jamieson et al 1999), (ii) they do not address crop quality, and (iii) only few adaptation options can be simulated. To address these issues, the Agro Climatic Calendar (ACC) was developed, a semi-quantitative and participatory method that specifically focused on the impacts of extreme events and pests and diseases under climate change (Schaap et al 2011, Schaap et al 2013). Current extreme climate factors affecting crop yield and quality were identified, and the impact of these climate factors expressed as the potential economic loss of a single event, were estimated by scientists, experts and stakeholders. Economic losses were defined as percentages of crop output (€ ha⁻¹ yr⁻¹). Low and high impact levels were estimated, as economic losses can differ depending on local conditions and farm management.

The future risks from extreme climate factors were expressed as changed frequencies of their occurrence in the 30-year period around 2050 compared with a 30-year period around 1990. Main climate risks are the climate factors that have high economic impacts (€ ha⁻¹ yr⁻¹), calculated by multiplying potential economic loss as a result of a single event with the change in frequencies. Identifying the main climate risks allows to target adaptation measures. After identifying adaptation measures, a cost-benefit analysis including annual costs, investment costs and effectiveness in damage reduction was performed to estimate most effective adaptation measures (Schaap et al 2013).

Farm performance is not only influenced by crop yields. Farm performance is primarily measured by farm income, which also depends on farm plans, prices of inputs and outputs, and farm size. Farmers can adapt their farm plans and management, and besides climate change and technological development, markets and policies are the main drivers affecting farm performance. Farms are diverse with respect to available resources, constraints and farmer's objectives (figure 3), and therefore they adapt differently to changes in drivers.

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Benchmarking (using Data Envelopment Analysis; DEA; Cooper et al 2007) and bio-economic farm modelling (using the Farming Systems Simulator; FSSIM; Louhichi et al 2010) were used to assess the impact of different drivers at farm level (see Kanellopoulos et al 2014). By using DEA, current 'best' farm activities (i.e. technical efficient) were identified from a survey of 75 farms (from the Farm Accountancy Data Network) fully characterized by their input and output data (averages from 2000 to 2006). FSSIM allowed to assess the impact of farm level adaptation, including changes in cropping patterns, inputs and outputs.

We assessed impacts of management, climate change, farm level climate change adaptation, price and policy changes, and technological development with FSSIM; each in addition to the previous change. First, the impact of management was assessed by comparing the optimal, profit maximizing production plans derived from using FSSIM, with the observed situation. Although farmers have various objectives, profit maximization was found to be the most important one in the region (Mandryk et al 2014). Similar to yield gaps, a large part of this income gap may be closed towards 2050 by increasing efficiency and profitability.

Secondly, the impact of climate change without farm level adaptation in 2050 was assessed with FSSIM keeping the farm activities (from the profit maximizing production plans in the current situation) the same, but allowing changes in yields due to climate change and associated fertilizer input. Climate change impacts on yields were based on WOFOST output (table 1; for A1/W+ including crop level adaptation, for B2/G excluding crop level adaptation). Thirdly, the impact of farm level climate change adaptation was assessed by allowing farms to change their activities to maximize profit under climate change. Fourth, the impacts of changes in prices (based on the market model CAPRI, section 2.1) and policy (in A1/W+ abolishment of sugar beet quota and subsidies) were assessed. Lastly, the impacts of possible technological development (table 1) were added. In these FSSIM simulations effects of extreme events were not included.

The 75 farms assessed using FSSIM (section 2.3) differ in their farm structure, influencing impacts of climate and socio-economic change. In the period 1980–2010 the average farm size increased with 20% and the number of arable farms decreased with 30% (Mandryk et al 2012). As farm structure will change towards 2050, it needs to be considered in climate change impact assessments. Historical analysis and stakeholder workshops were used to derive relationships between drivers (technology, markets, policy, climate change) and farm type dimensions (farm size, intensity, specialization and orientation), and scenario analyses were performed to project farm structural change towards 2050 (Mandryk et al 2012). In this paper we did not use changes in farm structure as a basis for FSSIM simulations (section 2.3), but re-calculated average regional impacts based on farm structural change. This means that farm area did not increase in FSSIM simulations, but for calculating average impacts we considered that there are relatively more large farms, for which impacts differed from medium sized farms.

3.1.1. Crop yield change due to gradual climate change, adaptation, management and technological development

3.1.2. Crop yield change influenced by extreme events, pests and diseases, and adaptation

Results of the ACC show that while gradual climate change as simulated by WOFOST resulted in positive changes for all crops (table 1), extreme events can pose large risks. Currently, the largest risks are wet fields in spring and autumn for potatoes, causing delayed planting and harvesting, and damage to tubers (Schaap et al 2011, Van Oort et al 2012). Towards 2050, in the A1/W+ scenario especially heat waves (−88%) and warm winters (−43%) for potatoes, and warm and wet periods (−36%) for onions were calculated to have large negative impacts if no adaptation takes place (figure 4). Risks for sugar beet and winter wheat were small, so impacts of climate change likely remain positive.

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In figure 5 adaptation measures are presented for the largest future climate risk, heat waves causing second growth in potatoes (Bodlaender et al 1964). Without adaptation, economic impact was −29 to −88% in the A1/W+ scenario. When applying drip irrigation, this impact could be reduced to −18 to −24%. In addition, other measures can be adopted to further reduce the impact. Relevant for farmers is that while drip irrigation is not cost-effective in the current climate, it was assessed to be the most cost-effective adaptation measure in the A1/W+ scenario (Schaap et al 2013). Also for other main climate risks adaptation measures are available (Schaap et al 2013).

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Similar to the crop level, the impacts of different drivers were assessed at farm level, now investigating changes in 'farm income' and 'crop production'. Table 2 suggests that although observed yields are close to potential yields (table 1), more efficient management regarding inputs (capital, labour, land, crop protection, fertilizers, energy, other inputs) and outputs (yields, prices), and changes in cropping patterns, could still lead to a 89% increase in farm income (i.e., the difference between observed and profit maximizing plans). This especially led to an increase in potato production (see also Kanellopoulos et al 2014).

Climate change impacts on farm income without farm level adaptation were larger in A1/W+ than in B2/G, due to larger crop yield increases (table 1). Increases in farm income were smaller (+7.3% in A1/W+) than the projected yield increases (+6 to +33% depending on the crop). One reason is that in the 'climate change only' simulation, current policies were considered, in which sugar beet production is constrained by sugar beet quota. If cropping area is fixed, it is not interesting to increase sugar beet yields, as the price obtained for production above the quota is relatively low.

When allowing adaptation of cropping patterns, and their inputs and outputs, gross margin increases were almost tripled (table 2; 1.073 × 1.116 = 1.197, a 20% increase compared to 7%). Changes were however still partly related to the sugar beet quota: higher sugar beet yields lead to smaller sugar beet areas, and hence a proportional decrease in farm activities including sugar beet. Other activities with more wheat and other arable output (mainly flower bulbs) became more interesting. On average, the impacts of farm level adaptation (table 2) were in the same range as impacts of crop level adaptation (table 1).

When adding changes in policy and prices, in A1/W+, farm income slightly decreased, but in B2/G it strongly decreased until 40% of the present level (see table 2: 1.89 × 1.048 × 1.09 × (1 − 0.814) = 0.40). In A1/W+ sugar beet production largely increased due to the abolishment of the sugar beet quota, while changes in relative prices caused vegetable production to increase. Although it is clear that impacts of price changes are large, we should note that price changes are highly uncertain. Nelson et al (2014) demonstrated that different models estimate highly different price changes when simulating climate change scenarios.

Finally, impacts of technological development were assessed. In the A1/W+ scenario farm income increased more (64%) than the estimated yield increases (table 1: 30%). In the B2/G scenario the negative impacts of changes in prices could however still not be compensated. For both scenarios, when including the impact of management, the overall change in gross margin compared to the observed situation was +235% for A1/W+ and −40% in B2/G. When excluding management changes, as these may not be possible for all farms and farmers also have other objectives than profit maximization (Mandryk et al 2014), the change was +77% in A1/W+ and −68% in B2/G. Overall, we conclude that climate change affects farm income, but impacts were small in comparison to possible effects of changes in management, technology, markets and policy.

While changes for average farms give a good indication of impacts of different drivers, impacts differed across farms. Figure 6 shows that impacts of climate change were relatively less positive for very large farms than for smaller farms. This is related to the relatively large share of potato area on very large farms. When farm level adaptation was included, impacts were however similar on all farm types, as larger farms have more capacity to increase the area of other arable output (mainly flower bulbs). The difference in impact among farm size classes was larger for price and policy changes. In A1/W+, very large farms were not affected (same level as previous sub-scenario), while the cumulative impact was −32% for medium farms. Also this is related to the capacity to change crops. In B2/G, impacts were very negative for all farm size classes, but also more for medium farms. The impact of technological development was relatively similar for all farm types, but together with other impacts resulted in more positive (A1/W+) or less negative impacts (B2/G) for larger farms.

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Mandryk et al (2012) projected that in 2050 in the A1/W+ scenario, the percentage of medium farms decreases from 22% to 4%, while the percentage of very large farms increases from 32% to 49% (in B2/G the change is small). Instead of averaging farm income changes across 75 farms assuming no change in farm structure (as in table 2), we must take into account that in 2050 there may well be fewer but larger farms. When considering the changes in percentage of medium, large and very large farms, for B2/G resulting average farm income did not change much, but for A1/W+ the positive impact from all changes together increased from +77% to +132% (compared to observed + improved management). Increasing farm size can thus also be considered as an adaptation at regional level.