Farmers perceive diminishing ecosystem services, but overlook dis-services in intensively used agricultural landscapes in the North China Plain.

The UN sustainable development goals ask countries to advance sustainable production methods in agriculture. While the need for a transition to sustainable agricultural production is widely felt, there is little insight into local stakeholders' perceptions regarding agroecosystem (dis)services in areas with intensive production methods. The North China Plain is an agricultural production area with intensive production systems and simplified agricultural landscapes. We conducted a survey with 267 farmers in Quzhou county in the North China Plain in 2020 to measure the perceived level of agroecosystem (dis)services supply and the changes therein between 2015 and 2020. We analyzed which explanatory factors were associated with farmers' perceptions. Provisioning services were at a high level, while the regulating and supporting ecosystem services were considered to be in low supply, as evidenced by low scores for the presence of natural enemies and earthworms, and for natural habitats such as hedgerows and windbreaks. Most of the participants did not perceive dis-services from agriculture. Differences in perception between villages with contrasting biophysical and socio-economic conditions highlight the relevance of contextualized policy development for agricultural landscape composition and configuration to manage ecosystem (dis)services.

A multi-method approach for the integrative assessment of soil functions: Application on a coastal mountainous site of the Philippines.

The projected increase of the world's population and the sustainability challenges the agricultural sector is facing, call for the enhancement of multi-functionality in agriculture in order to simultaneously provide food while meeting environmental targets. Here, we use the Functional Land Management (FLM) framework to assess the supply of and the demand for soil functions to inform agri-environmental policy for Udalo, a mountainous site in the Philippines. As many emerging communities in developing nations, Udalo is on the cusp of rapid development due to the construction of a major road increasing its accessibility and attractiveness for land investment. We assessed the supply of four soil functions in relation to six land-use types and four slope categories. The function "productivity" was assessed by interviews with 128 farmers, "habitat for biodiversity" by a vegetation survey, and "soil conservation" and "water conservation" via a literature review. The demand for functions was first assessed from the "top-down" policy perspective via interviews and reviews of policy targets, then complemented by integrating the local "bottom-up" demands for functions. These were assessed by applying a Q methodology, providing insights in the prioritisation of functions from the perspective of 22 local actors. Maps of supply and demands were generated for each function: supply maps by overlaying land use and slope category, top-down demand maps from administrative zoning/land-use plans, and bottom-up demand maps from local actors designation of geomorphological areas. Our results revealed contrasting demands for functions, as well as a heterogeneous spatial distribution of supply and demands. Discrepancies emerged (i) between supply and demand, (ii) between bottom-up (local) demands and the top-down (policy driven) demand, and (iii) among local actors perspectives. Our study indicates that discrepancies are not necessarily conflicting, but can uncover pathways for defining compromises, representing attainable policy entry points. Not one single development model can meet the needs of every stakeholder; however, a combination of land uses and management strategies can meet divergent interests and allow for optimisation of functions. This integrative approach of FLM provides a socially embedded biophysical analysis and is a valuable tool for the design of customized land-use and agri-environmental policies.

Capturing farm diversity with hypothesis-based typologies: An innovative methodological framework for farming system typology development.

Creating typologies is a way to summarize the large heterogeneity of smallholder farming systems into a few farm types. Various methods exist, commonly using statistical analysis, to create these typologies. We demonstrate that the methodological decisions on data collection, variable selection, data-reduction and clustering techniques can bear a large impact on the typology results. We illustrate the effects of analysing the diversity from different angles, using different typology objectives and different hypotheses, on typology creation by using an example from Zambia's Eastern Province. Five separate typologies were created with principal component analysis (PCA) and hierarchical clustering analysis (HCA), based on three different expert-informed hypotheses. The greatest overlap between typologies was observed for the larger, wealthier farm types but for the remainder of the farms there were no clear overlaps between typologies. Based on these results, we argue that the typology development should be guided by a hypothesis on the local agriculture features and the drivers and mechanisms of differentiation among farming systems, such as biophysical and socio-economic conditions. That hypothesis is based both on the typology objective and on prior expert knowledge and theories of the farm diversity in the study area. We present a methodological framework that aims to integrate participatory and statistical methods for hypothesis-based typology construction. This is an iterative process whereby the results of the statistical analysis are compared with the reality of the target population as hypothesized by the local experts. Using a well-defined hypothesis and the presented methodological framework, which consolidates the hypothesis through local expert knowledge for the creation of typologies, warrants development of less subjective and more contextualized quantitative farm typologies.

Landscape character assessment using region growing techniques in geographical information systems.

Landscape character can be defined as the presence, variety and arrangement of landscape features, which give a landscape a specific identity and make it stand out from surrounding landscapes. Landscape character contributes to the esthetical and perceptional value of an area, which is important for the development of non-production functions in the countryside as demanded by society. In this paper we present a new methodology for landscape character assessment using the pattern of landscape features as stored in a GIS to delineate, characterize and evaluate landscapes using a region growing algorithm. We have applied this methodology in a case study area in the north of The Netherlands and compared the results with a series of expert classifications of the study area. The results of the region growing algorithms were good and interpretable in relation to the underlying data. The resemblance between the expert classification and the classification based on the region growing results varied between 34% and 100% for the different landscape types. The differences between the two data sets can be explained in terms of input data and knowledge about the study area. The classification of the region growing algorithm was more consistent than the expert classification throughout the study area. The presented methodology for landscape character assessment is proposed as support for spatial planning processes and policy development for landscape conservation by providing a quantitative tool to analyze landscape patterns, to discriminate between the various landscapes in a study area and by elucidating features that are important for the identity of a region.

Internalization of agri-environmental policies and the role of institutions.

Recent investigations have indicated that environmental and conservation policies frequently fail to reach anticipated aims, which raises concern over the cost-effectiveness of governmental policy-related expenditure. The limited effectiveness of policies is often attributed to methodological aspects of policy implementation. However, an alternative reason can be the limited internalization of policies, so that these are only implemented to a minimum level to attain benefits or avoid penalties. It is postulated that increased internalization of policies can considerably improve their effectiveness and that suitable institutional arrangements exist to support such increases in internalization. In this paper, we review the available literature on internalization and its institutional aspects, and propose a framework, based on self-determination theory, for evaluation of potential internalization by farm managers that are expected to implement policies. This method was applied to a small case study for dairy farmers in the nationally important landscape of the Northern Friesian Woodlands, The Netherlands. The results showed that organic farmers were internally motivated for nature conservation and had strong institutional links. They were more likely to internalize the goals of environmental policy schemes than conventional farmers who focused predominantly on financial rewards. We suggest that policy developers promote internalization of policies by tuning policy instruments to the specificities of farmers' motivations to preserve farmers' nature and to focus on institutional support to help internalization.

On the contribution of modelling to multifunctional agriculture: learning from comparisons.

In this paper a set of criteria is proposed for the evaluation of the potential contribution of modelling tools to strengthening the multifunctionality of agriculture. The four main areas of evaluation are (1) policy relevance, (2) the temporal resolution and scope, (3) the degree to which spatial and socio-institutional scales and heterogeneity are addressed and (4) the level of integration in the assessment of scientific dimensions and of the multiple functions of agriculture. The evaluative criteria are applied to the portfolio of modelling approaches developed and applied in a joint project of the French research institute INRA and the Dutch Wageningen University & Research Centre. The CLUE-S model focuses on prediction of changes in multifunctional land-use at regional scale, given a set of predetermined scenarios or policy variants, e.g. for ex-ante policy assessment and initiation of discussions on regional development. The two other modelling approaches are complementary and aim to address multifunctional farming activities. The Landscape IMAGES framework generates a range of static images of possible but sometimes distant futures for multifunctional farming activities in a small region or landscape. It supports the exploration of trade-offs between financial returns from agriculture, landscape quality, nature conservation and restoration, and environmental quality. Co-Viability Analysis generates trajectories of states and farming decisions fulfilling a given set of ecological and productive constraints representing a desired and sustainable future. The three modelling approaches differ in their policy relevance, in the ways that spatial and socio-institutional scales are addressed and in their degree of explicitation of interaction between the various functions of agriculture, but jointly cover most of the desired capabilities for assessment of multifunctionality. Caveats were particularly identified in the integration of the socio-institutional dimension and the related heterogeneity. Although the model portfolio did not completely satisfy the demands of the set of evaluative criteria, it is concluded that, due to their complementarities, in combination the three models could significantly contribute to further development and strengthening of multifunctionality.