Results from application of the methodology Driver – dimension relationship

Changes in farm type dimensions were attributed to technological progress, as well as policy and market developments. Through the indicator assigned to each driver and structural dimension the historical impact of each driver on structural change was assessed.

As an outcome of the historical trend analysis we obtained the historical relationships between a driver and a dimension (Table 3.2). We used them to project the future impact of each driver on the structural dimension per scenario.

Integrated-assessment-Flevoland-AgriAdapt-project.doc 44 Table 3.2 Contribution of drivers to farm structural change

dimension (indic.)

0 no significant impact on structural change + impact on structural change

++ strong impact on structural change

Regarding orientation, policy incentives largely stimulated adoption of non-agricultural activities. The impact from market was indirect: the farmers looked for alternative sources of income due to decrease in prices for major crops over time.

Farm size was influenced by technology and market. Increase in crop productivity was mainly caused by technological advances (input intensity, efficient machinery, new crop varieties with higher yields and pest/disease resistance, new management techniques). The output prices define to a large extent farm gross income and therefore they influence farm economic size. While prices for major crops in Flevoland decreased over time, farmers took advantage of economy of scales to increase farm size and compensate for low prices. Intensity was not influenced by the drivers directly. Although productivity increased, and also the types of crops became more intensive, farm area also increased, and the NGE unit is adapted over time to reflect developments. As to specialization, specific crop subsidies or quotas influenced crop choice on farms. Crops with high gross margins like root- and tuber crops, vegetables and flower bulbs increased their share in a typical rotation in Flevoland.

So far, in Flevoland there is no strong evidence of climate change impact on crop choice or any of the other dimensions of the farm typology. However, Olesen and Bindi (2002) and Reidsma et al. (2007) observed that elsewhere in Europe there is impact of climate change through spatial variability in yields and crop choice. Thus we assume a future relationship between climate change and specialization.

Applying the scenario assumptions on changes in technology, policy, markets, and climate (presented earlier in Table 3.1) we projected the impact of drivers per dimension in two scenarios (Table 3.3).

Table 3.3 Impact of drivers on farm structural change in future scenarios

Change in drivers ++ Technology

(input intensity) ^{0 ++ 0 ++}

Change in drivers + Technology

(input intensity) ^{0 + 0 +} technology driver. In B2-G scenario the technology changes will be in the direction of energy-saving and environmentally friendly, which will have less influence on farm structure than in A1-W scenario. For orientation, policy is the major driver that has a different focus per scenario with respect to stimuli for adoption of particular non-agricultural activities on the farm. For example, in B2-G scenario policy will largely stimulate alternative functions that agriculture can provide to the society, especially nature conservation. The smaller influence of drivers in B2-G scenario compared to A1-W scenario suggests that farm structural change in B2-G scenario will be less significant than in A1-W scenario.

Regional farm structural change

The regional level results on farm structural change in two scenarios are presented in Table 3.4. In A1-W scenario the average farm size will increase from 95 to 118 NGE due to increase in crop productivity, and shift to more profitable crops and an average farm area increase. Since area is a limited factor in the province, and there have been increases in farm size in NGE, we observe further intensification. In specialization there is a shift towards crops with high standard gross margin (flower bulbs and vegetables) and energy crops (these crops are part of diverse arable specialization). In terms of orientation there is projected to be a larger share of entrepreneurial farms (around 15% of total farm population). Increase in share of entrepreneurial, or multifunctional farming happens, since farmers seek alternative

Integrated-assessment-Flevoland-AgriAdapt-project.doc 46

sources of income (e.g. recreation, processing and selling own products) due to changes in the agricultural policy paradigm (abolishment of payments and little alternative subsidies).

Table 3.4 Farm structural characteristics at regional level

Structural characteristics 2008 A1-W scenario B2-G scenario

Arable UAA, ha 78118¹ 67785² 72149

Arable UAA under arable farms, ha 50775 40921 38280

% arable UAA under arable farms 64.5 60.4 53.0

In B2-G scenario there is a larger diversity in farming the landscape. We estimate that average farm size (economic and area) only slightly increases and remains close to the current level. No major changes are expected in the specialization of the farms either.

Regarding orientation, a large share of nature conservation farms will be notable for the B2-G scenario (around 30% of the farms will do nature and landscape conservation). This comes when subsidies exceed gross margin of crops and the activity is more profitable, as the level of payment for social and environmental services will be increased in B2-G scenario.

Farm level structural change

Using the example of ‘production oriented-medium size-medium intensive-diverse mainly root crops and specialized root crops’ farm type, we demonstrate the application of rules that have been developed to translate the regional level results to changes in the distribution of farm types in A1-W scenario (Figure 3.3). From the results of historical trend analysis we know that medium size production oriented farms will disappear, as the only options for them in A1-W scenario are either size enlargement or quitting. The other options (i.e. change in intensity, specialization or orientation) do not apply for medium size farms in A1-W scenario. According to the results at regional level (Table 3.1), there will be 25% enlargement for both area and economic size for arable farms in Flevoland. This implies that 25% of farm population from the farm type medium will move to large and 75% of farms from this farm type will quit. This percentage comes from subtraction from a total farm population (100%) the percentage of farms undergoing structural change (25% for size enlargement). This is the result of application of the transition rules we developed (see methodology section Dimensions at farm level, where we indicate

1 CBS, 2009

2 Riedijk et al., 2007

that percentage of change in dimension at regional level will correspond directly to percentage of farms from certain farm type shifting to another farm type. Note, that the high percentage of quitting farms is typical for medium sized farms, as the total decrease in number of farms is 45 % (Table 3.4).

Nature 0 %

Figure 3.3 Transition rules for a production-oriented-medium size-medium intensity-diverse mainly root crops, intensity-diverse arable, specialized root crops farm type in the A1-W scenario

For other farm types there could be other options available (e.g. also change in orientation), depending on the current farm type. For example, 30% of farms from the farm type ‘production oriented-very large-medium intensive-diverse mainly root crops’ will change to entrepreneur farms.

The most important farm type in A1-W scenario is production oriented-very large-medium intensive-diverse mainly root crops. In B2-G scenario it is entrepreneur oriented-large-medium intensive-diverse mainly root crops and specialized root crops (Table 3.5).