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1.
Environ Manage ; 74(4): 757-774, 2024 Oct.
Article in English | MEDLINE | ID: mdl-39090440

ABSTRACT

Semi-natural grasslands (SNGLs) in Estonia are threatened by abandonment. This threat is leading to concerns about the degradation of biodiversity within grassland communities. Despite the high relevance of economic incentives in this context, how such incentives influence land managers' decision-making regarding the agricultural use of SNGLs has not been investigated. To obtain its socio-ecological implications for policy-making, we developed regionally specific agricultural scenarios (compensation payments, livestock capacity, hey export, and bioenergy production) and an interdisciplinary modelling approach that made it possible to simulate agricultural land use changes through land managers' responses to varied economic conditions. Through this approach, we found that some economic factors hampered the use of SNGLs: the moderate profitability of beef production, labour shortages, and the relatively high profitability of mulching. We observed a positive relationship between SNGLs and habitat suitability for breeding and feeding birds. However, due to the high maintenance costs of SNGLs, the modelling results indicated that increasing the use of SNGLs through public budgets caused crowding-out effects, i.e., the deteriorating market integration of regional agriculture. This study emphasises the need for policy measures aimed at cost-effective, labour-efficient management practices for SNGLs.


Subject(s)
Agriculture , Conservation of Natural Resources , Grassland , Estonia , Conservation of Natural Resources/methods , Agriculture/economics , Animals , Models, Economic , Biodiversity
2.
Reg Environ Change ; 23(3): 97, 2023.
Article in English | MEDLINE | ID: mdl-37489177

ABSTRACT

Diverse agricultural land uses are a typical feature of multifunctional landscapes. The uncertain change in the drivers of global land use, such as climate, market and policy technology and demography, challenges the long-term management of agricultural diversification. As these global drivers also affect smaller scales, it is important to capture the traits of regionally specific farm activities to facilitate adaptation to change. By downscaling European shared socioeconomic pathways (SSPs) for agricultural and food systems, combined with representative concentration pathways (RCP) to regionally specific, alternative socioeconomic and climate scenarios, the present study explores the major impacts of the drivers of global land use on regional agriculture by simulating farm-level decisions and identifies the socio-ecological implications for promoting diverse agricultural landscapes in 2050. A hilly orchard region in northern Switzerland was chosen as a case study to represent the multifunctional nature of Swiss agriculture. Results show that the different regionalised pathways lead to contrasting impacts on orchard meadows, production levels and biodiversity. Increased financial support for ecological measures, adequate farm labour supplies for more labour-intensive farming and consumer preferences that favour local farm produce can offset the negative impacts of climate change and commodity prices and contribute to agricultural diversification and farmland biodiversity. However, these conditions also caused a significant decline in farm production levels. This study suggests that considering a broader set of land use drivers beyond direct payments, while acknowledging potential trade-offs and diverse impacts across different farm types, is required to effectively manage and sustain diversified agricultural landscapes in the long run. Supplementary information: The online version contains supplementary material available at 10.1007/s10113-023-02092-5.

3.
J Environ Manage ; 232: 310-320, 2019 Feb 15.
Article in English | MEDLINE | ID: mdl-30496960

ABSTRACT

Mountain regions cover one quarter of the Earth's terrestrial surface, and are both valuable and vulnerable areas with complex human-environmental interrelationships. In this coupled system, land-use changes induced by political or socio-economic transformations generate consequences for ecological landscape functions like soil productivity and species richness, and integrative land-use concepts provide the potential of a sustainable land development. In the Kazbegi region in the central Greater Caucasus of Georgia, these transformations further lead to landscape-structure change and population marginalization. Hence, we developed three agricultural land-use scenarios that meet Agenda 2030 Sustainable Development Goals to ensure a sustainable rural land development and the conservation of mountain ecosystems. Our normative scenario approach integrates quantitative and qualitative findings of empirical research in landscape ecology, soil science, vegetation ecology as well as agronomics and socio-economics. According to the examined environmental and socio-economic resources, we defined various scenario logics and normative assumptions that combine optimized livestock production (in dairy cow keeping and cattle fattening) with ecological limitations to maintain the functioning of mountain ecosystems. The rule-based scenarios achieved measurably increased outputs in biomass yields, livestock production and related revenues at the regional scale. Further, GIS generated scenario maps demonstrate the related land-use patterns spatially explicit and in high resolution, and visualize the alternative future from local to the regional scale. In conclusion, scenario development helps to determine region-specific and integrated land-use options to provide a sound base for land users and decision makers. Based on research on multiple landscape functions, this approach can assist sustainable land development in a mountain region.


Subject(s)
Conservation of Natural Resources , Ecosystem , Agriculture , Animals , Cattle , Ecology , Female , Humans , Socioeconomic Factors
4.
PLoS One ; 12(8): e0181954, 2017.
Article in English | MEDLINE | ID: mdl-28763471

ABSTRACT

In agricultural production, land-use decisions are components of economic planning that result in the strategic allocation of fields. Climate variability represents an uncertainty factor in crop production. Considering yield impact, climatic influence is perceived during and evaluated at the end of crop production cycles. In practice, this information is then incorporated into planning for the upcoming season. This process contributes to attitudes toward climate-induced risk in crop production. In the literature, however, the subjective valuation of risk is modeled as a risk attitude toward variations in (monetary) outcomes. Consequently, climatic influence may be obscured by political and market influences so that risk perceptions during the production process are neglected. We present a utility concept that allows the inclusion of annual risk scores based on mid-season risk perceptions that are incorporated into field-planning decisions. This approach is exemplified and implemented for winter wheat production in the Kraichgau, a region in Southwest Germany, using the integrated bio-economic simulation model FarmActor and empirical data from the region. Survey results indicate that a profitability threshold for this crop, the level of "still-good yield" (sgy), is 69 dt ha-1 (regional mean Kraichgau sample) for a given season. This threshold governs the monitoring process and risk estimators. We tested the modeled estimators against simulation results using ten projected future weather time series for winter wheat production. The mid-season estimators generally proved to be effective. This approach can be used to improve the modeling of planning decisions by providing a more comprehensive evaluation of field-crop response to climatic changes from an economic risk point of view. The methodology further provides economic insight in an agrometeorological context where prices for crops or inputs are lacking, but farmer attitudes toward risk should still be included in the analysis.


Subject(s)
Agriculture/methods , Climate , Crops, Agricultural/physiology , Algorithms , Climate Change , Computer Simulation , Decision Making , Germany , Models, Economic , Models, Statistical , Risk , Seasons , Weather
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