Northern Savo

NorthernSavoGeneral information

Name of region Northern Savo (Pohjois-Savo), Finland
Global Environmental Zone(s) (Metzger) G. Cold and mesic
Population density (persons per km2)  14.8
Contact (general) Heikki Lehtonen
Contact (ag. scenarios) Heikki Lehtonen
Location (NUTS code) FI132
Dominant regional farming system(s)
(SEAMLESS nomenclature)
Dairy farms
The three most important
farming systems in region

(SEAMLESS nomenclature)
  • arable-cereal
  • dairy cattle-temporary grass
  • dairy cattle-land independent
Main crop species  
Main livestock species  

[Short paper] [Poster]

Regional development goal in rural spatial planning

Specific issues the region deals with/will deal with

Grassland fodder production dominate land use in Pohjois-Savo region, while cereals, especially barley, oats and mixed cereals for feed have a strong role in land use as well. Livestock production has a strong role in terms of farm income. Feed production re-organisations, connected to both crop yield changes and other operations of a livestock farm, are the most potential options of producing economic value at least for a number of future climate and market scenarios. Due to the situation of high production costs and uncompetitive agriculture that has prevailed now for many years, we are interested in possibilities of increased domestic feed production productivity at low environmental costs, since that could partly solve the inefficiency problems due to high and increasing production costs at livestock farms in particular. However, productivity gains require necessary investments and changes in inputs and variable costs. Hence we need to identify also market and socio-economic conditions where climate change implications of increased plant disease pressure, more frequent adverse weather conditions, leading to increased production and financial risks inhibit farmers from necessary adaptation and lead to reduced productivity and increased production costs. Increased price fluctuations may increase overall risks at a farm and thus the expected benefits from productivity promoting investments and variable inputs are dependent on expected prices and policy incvesntives.More specifically the following themes are under detailed analysis: 1)Drought (or flood) risks for silage grass production, future developments of such risks and their direct and indirect cost implications for farms; we calculate how much the likelihood of severe drought and shortage of silage will increase, and what is the cost of preparing for adverse conditions, necessary to keep the risk at acceptable levels (which depend on the farm orientation and its feeding system). We also evaluate the likely changes in the variability of the quality of silage under different climate conditions since e.g. low protein content implies increased purchased feed costs for a farm (dependent on global cereals and oilseed prices)2)Increased pressure and impact risk of pests and plant diseases, the role of new cultivars in terms of yield potential and tolerance of multiple stresses– the benefits of improved crop protection management versus additional costs (esp. cereals farms), dependent on global input and output prices.3)Economic benefits of higher productivity and resulting production re-organisation, including machinery choices and logistic benefits due to higher yields (especially logistic and roughage storage costs in dairy production).

Regional challenges with regard to climate change

In northern Europe and esp. Finland climate change basically means increased temperature and precipitation (Ruosteenoja et. al. 2011). Increase in temperature is likely to be 1.5-2 times higher in Northern European areas such as Finland than the global average increase in temperature. Winter time precipitation is likely to increase relatively more than then precipitation during the growing season (see, Sloth madsen et al., 2012; Rötter et al., 2013). Taking into account longer growing season and potentially higher biomass production, early summer drought may get even more severe, at least the possibility for drought related problems in crop production, esp. in cereals production may exacerbate. Early summer drought, in combination with intensive daylight in early summer and relatively short yield determination period of seed crops, is traditionally a major yield limiting factor in northern European agriculture. This problem could, at least in principle, be mitigated by plant breeding efforts for seed crops, which may lead to significant increases in potential yields (Peltonen-Sainio et. al. 2009). For grasslands, higher summer time temperatures and possibility for drought may also cause problems, but it is concluded that grass crops may still benefit from longer growing season. However, harvesting of some crops may become more disturbed by heavy rainfalls (which has been a problem also earlier, implying losses in yield quality and volume). Higher climatic variability and more frequent and severe extreme events (e.g. heat waves, dry spells, heavy rains) during the growing season is very likely increase various abiotic and biotic stresses. Such problems have been widely observed in northern Europe already and require more attention to crop protection and crop rotation and other mitigation measures. On the other hand, longer growing season will provide possibilities for higher yields and implied cost savings for skilled farmers who are able to cope with the problems and increased risks. Such benefits are needed due to the fact the production costs are relatively high in northern Europe, especially in least favoured areas where dependence on subsidies is a major limiting factor.Climate change, if resulting in higher volatility of agricultural output and input prices, will negatively affect profitability of investments in agriculture, also of those investments which are necessary in terms of agricultural adaptation and mitigation.

Proposed solutions to overcome the challenges

Contribution to answering the focus question

Value of investments in productivity promotion are evaluated, esp. long-term investments in soil improvements, drainage and plant / (animal breeding ?) are evaluated. The role of reducing yield gaps (differences between potential yields, attainable yields and actual yields) are evaluated in the context of different CC and market scenarios, i.e. RRAPS (regional representative agricultural pathways) explicitly developed as one of the main outputs in the northern European case studies. The joint evaluation and comparison of the RRAPs require some interaction with the stakeholders as well. In this, an economic dimension is emphasized and some scenario development from RRAPs are still needed to quantify crucial parameters in farm and sector level economic models. For example, evolution of productivity and efficiency parameters in response to market and policy variables includes significant uncertainties. Nevertheless, sensitivity analysis facilitated by the economic models consistently linked to results of agro-ecological models and farm level adaptation analysis provides early understanding on the role and potential of main adaptation measures in creating economic value for farms and farming sector. This is done taking into account the presence of CC mitigation measures that can be reasonably anticipated since several studies and research projects exist on the effectiveness of different CC mitigation measures in agriculture e.g. in Finland and Norway already.Uncertainty analysis includes not only sensitivity analysis in terms of key parameters in specific economic models, but also in terms of explicit silage yield risk analysis (mentioned above) and risk aversion accounting in dynamic economic crop rotation modeling (mentioned above). This means that the significance of extreme events, market and policy drivers, as well as risk aversion behavior of different levels are all included considering the most relevant parts the farm level. At the same time it must be understood that all uncertainty and risk assessments are incomplete given the many changing issues in long-term agricultural developments. Nevertheless from this perspective the treatment of uncertainty, which starts from the work done in CropM and continued here at the farm level and extending to the sector level implications is a good start.

Important adaptation measures that are or will be considered in the study

Water management is important to this region.
Irrigation is important to this region.
Drainage is important to this region.
Species/varietal choice is important to this region AND is/will be included in the modelling exercise.
Plant breeding is important to this region AND is/will be included in the modelling exercise.
Changed planting/sowing days is important to this region AND is/will be included in the modelling exercise.
Crop rotations is important to this region AND is/will be included in the modelling exercise.
Alternative tillage methods  
Pest/weed management is important to this region AND is/will be included in the modelling exercise.
Housing of livestock is important to this region.
Land consolidation is important to this region.
Management of feeding and reproduction of livestock is important to this region AND is/will be included in the modelling exercise.
Structure and scale of production adjustment is important to this region AND is/will be included in the modelling exercise.
Crop insurance is important to this region.
Exit from agriculture is important to this region AND is/will be included in the modelling exercise.
Climate alertness is important to this region.
Political regulations at various administrative levels is important to this region AND is/will be included in the modelling exercise.
Others is important to this region AND is/will be included in the modelling exercise.
  Also overall farm production re-organisation implied by the CC adaptation / mitiogation needs, is taken into account. The work is going on.

Models, stakeholders, advancement of knowledge

Models used in the study
Socio-economyCropsGrasslandLivestock
The following economic models (in MTT) Dremfia (Dynamic multi-regional sector model of Finnish agriculture), dynamic farm level economic model of crop rotation and management; RAPs - European prices neededMore specific issues to be described in regional RAPs under construction for northern European regions the following agro-ecological models: WOFOST (work by Reimund Rötter, Taru Palosuo); and DAISY for grassland modeling (Taru Paloso), and possibly COUP (Tapio Salo) and a few others not yet determined (Fulu Tao)Input - the potential and attainable yields under specific key CC scenarios (developed jointly in MACSUR –consortium, i.e. CropM) + fertilization needs + some specific agricultural practices and inputs needed for certain yield levelsWofost. There is also modeling cooperation between MTT grassland experts and their collegues in Norway and Canada on grass fodder quality changes at northern latitudes under different scenarios of climate change (use of some models is probably possible)All models listed can be run and modified (to certain extent) by MTT researchers. DAISY model under development/testing at MTT, Finland No Finland specific livestock model to be used. However, most likely some cooperation with Norwegian team of HOLOS (farm level animal production calculation model of Canadian origin, adjusted for Norway), in cooperation with the Norwegian case study (contact person of HOLOS/Norway: Helge Bonesmo).
How are results of of crop and livestock models assimilated in socio-economic models?How is technological progress in arable agriculture taken into account?How is technological progress in livestock farming taken into account?
     

Participating stakeholders

Agro-business or agro-food chainAdministrative bodies or regional or national governments

Kari Virranta - Director General of North ELY Centre. Overall view on the needs of the agriculture in the region

Ilpo Lukkarinen - CEO of dairy cooperative “Osuuskunta Itä-Maito”. Actively engaged e.g. in the steering group of a research project “Nurmet Rahaksi” (grasslands for feed in the context of climate change in the North Savo region)

Arja Mustonen - Grassland advisor of ProAgria (extension organization). Understanding of farm level issues related to climate change, grasslands for ruminant feed; engaged in e.g. research projects Nurmet Rahaksi, FACCE MACSUR

Mikko Peltonen,Research Director, Ministry of Agriculture and Forestry, Finland

Approaches for involving stakeholders
 

Improvement of the modelling capability by involving stakeholders

How did the modelling capability improve by involving stakeholders?Effect of the involvement of stakeholders on the questions asked, on the assessment, or on the solutions suggested
   
Points that researchers learned from stakeholdersPoints that stakeholders learned from researchers
   

Further information

In the Northern Europe regions it will be considered both the productivity potential due to longer growing season due to climate change, and the likely increase in climate and market related risks. Specific themes deserving special attention are drought (or flood) risks for silage grass production, future developments of such risks and their direct and indirect cost implications for farms; similar analysis in the case of pig farms in the context of high cereals and protein feed prices. We see highly relevant to analyse the impacts and necessary actions to cope with increased pressure of pests and plant diseases, the role of new cultivars – i.e. the benefits of improved crop protection management versus additional costs. This task is primarily attacked using farm level dynamic crop rotation models whose applications will be modelled on dynamic farm level management, land use and crop rotation analysis in climate and market scenarios of longer than 20 year span. This analysis, when done in cases of different farms in terms of size, orientation (part-time, full-time, high and low level of specialization), risk aversion, and other key preferences (such as available labour and price of labour), reveals already key insights on the socio-economics of CC adaptation.Also relevant are economic benefits of higher productivity and resulting production re-organisation, including machinery choices and logistic benefits due to higher yields (especially logistic and roughage storage costs in dairy production). Such benefits are less important but probably still significant in the case of cereals-pigmeat production. The GHG mitigation costs include changes in logistic costs of feed and manure, which are conditional on the distance from different field parcels to farm centre, and on the development of feed crop yields. Different adaptations can be taken into account as in the case of manure processing such as mechanical separation of slurry into liquid and solid fractions. More efficient utilization of manure nutrients with related additional costs and cost savings can be analysed from the viewpoint of farm level profitability and reduced need for purchased inorganic fertilizers. In Norway, the crop model LINGRA, among others, the livestock model HOLOS and the sector model Jordmod will be applied to address these questions in an integrated approach. While crop models such as WOFOST are calibrated in Finnish conditions, and results utilised in farm and sector level models and economic analysis of adaptation (utilising multi-regional dynamic sector model DREMFIA), it is aimed that specific problems and solutions of multilevel adaptation analysis and integration are shared between northern European case studies.

References

Bindi M., Palosuo T., Trnka M., Semenov M. 2015. Modelling climate change impacts on crop production for food security INTRODUCTION. Climate Research 65: 3-5

van Bussels L.G.J., Ewert F., Zhao G., Hoffmann H., Enders, A., Wallach, D., Asseng, S., Baigorria, G.A., Basso, B., Biernath, C., Cammarano, D., Chryssanthacopoulos, J., Constantin, J., Elliott, J., Glotter, M., Heinlein, F., Kersebaum, K.C., Klein, C., Nendel, C., Priesack, E., Raynal, H., Romero, C.C., Rötter, R.P., Specka, X., Tao, F. 2016. Spatial sampling of weather data for regional crop yield simulations. Agricultural and Forest Meteorology 220: 101-115.

Ewert F., Rötter R.P., Bindi M., Webber H., Trnka, M., Kersebaum, K.C., Olesen, J.O., van Ittersum, M.K., Janssen, S., Rivington, M., Semenov, M.A., Wallach, D., Porter, J.R., Stewart, D., Verhagen, J., Gaiser, T., Palosuo, T., Tao, F., Nendel, C., Roggero, P.P., Bartošová, L., Asseng, S. 2015 Crop modelling for integrated assessment of risk to food production from climate change. Environmental Modelling & Software, 72: 287-303.

Kässi, P., Känkänen H., Niskanen O., Lehtonen H. & Höglind, M. 2015. Farm level approach to manage grass yield variation under climate change in Finland and north-western Russia. Biosystems Engineering 140: 11-22. doi: 10.1016/j.biosystemseng.2015.08.006.

Lehtonen, H., Liu, X. and Purola, T. 2016. Balancing climate change mitigation and adaptation with socio-economic goals at farms in northern Europe. In: Paloviita, A. and Järvelä, M. (Eds) 2015. Climate Change Adaptation and Food Supply Chain Management (Routledge Advances in Climate Change Research), Routledge, London, 264 pp. ISBN13: 978-1138796669, http://www.tandf.net/books/details/9781317634034/. p. 132-146.

Lehtonen, H. 2015. Evaluating adaptation and the production development of Finnish agriculture in climate and global change. Agricultural and Food Science 24: 219-234. http://ojs.tsv.fi/index.php/AFS/article/view/51080

Lehtonen, H. & Irz, X. 2013. Impacts of reducing red meat consumption on agricultural production in Finland. Agricultural and Food Science 22:356-370. http://ojs.tsv.fi/index.php/AFS/article/view/8007/6412

Lehtonen, H.S., Rötter, R.P., Palosuo, T.I., Salo, T.J., Helin, J.A., Pavlova, Y., Kahiluoto, H.M. 2010. A Modelling Framework for Assessing Adaptive Management Options of Finnish Agrifood Systems to Climate Change. Journal of Agricultural Science, Vol 2, No 2 (2010), p. 3-16. ISSN: 1916-9752. E-ISSN: 1916-9760. http://ccsenet.org/journal/index.php/jas/article/viewFile/4599/4888

Liu, X., Lehtonen, H., Purola, T., Pavlova, Y., Rötter, R. & Palosuo, T. 2016. Dynamic economic modelling of crop rotations with farm management practices under future pest pressure. Agricultural Systems (2016), pp. 65-76 DOI: 10.1016/j.agsy.2015.12.003

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