Survey data on livelihoods and inputs and outputs of crop production in Quzhou county on the North China Plain.

Farmers' decisions on crop choice, management practices, and livelihood strategies are essential to agricultural sustainability. This data article describes three datasets on crop production in Quzhou, a county in the central part of North China Plain. The three datasets cover different scales. The village dataset assembles basic data on all 342 villages of Quzhou county, including information on population, land area, crop grown, labour, irrigation and markets. Data was sourced from the yearbook data of 2017 and a village cadres survey in 2018. The village dataset was used to create a village typology from which 35 villages belonging to seven village types (five for each type) were selected for stratified random sampling to collect information on farm characteristics and cropping practices. We surveyed these 35 villages, interviewing fifteen farmer households per village (525 in total) in 2020. The interviewees represented two farm management models: smallholder farms and business farms. The resulting household dataset provides farm-level data, including demographic data of farming decision-makers and the number of household members, land use and machinery resources, crop production management practices, and government subsidies. The crop-level dataset was derived from the household survey and included input-output inventories for each crop grown during one year on each field greater than 1/30th ha (1/2 mu) on the 525 surveyed farms within a year. This dataset comprises information on cropping practices in 1352 fields. The three datasets provide a basis for analyses on cropping practices and sustainability attributes of farms and crops in a typical agricultural county on the North China Plain.

A systematic review of the methodology of trade-off analysis in agriculture.

Trade-off analysis (TOA) is central to policy and decision-making aimed at promoting sustainable agricultural landscapes. Yet, a generic methodological framework to assess trade-offs in agriculture is absent, largely due to the wide range of research disciplines and objectives for which TOA is used. In this study, we systematically reviewed 119 studies that have implemented TOAs in landscapes and regions dominated by agricultural systems around the world. Our results highlight that TOAs tend to be unbalanced, with a strong emphasis on productivity rather than environmental and socio-cultural services. TOAs have mostly been performed at farm or regional scales, rarely considering multiple spatial scales simultaneously. Mostly, TOAs fail to include stakeholders at study development stage, disregard recommendation uncertainty due to outcome variability and overlook risks associated with the TOA outcomes. Increased attention to these aspects is critical for TOAs to guide agricultural landscapes towards sustainability.

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.

Spatial farming systems diversity and micronutrient intakes of rural children in Ethiopia.

Own production contributes much of the food supply in smallholder production systems in low- and middle-income countries like Ethiopia. Understanding the potential as well as constraints of these production systems in terms of nutrient supplies is thus a critical step to design interventions to improve nutrient intakes. The objectives of this study were (1) to assess the usual total intakes of vitamin A, iron and zinc among rural children and (2) to investigate whether the intakes these nutrients are associated with differences in the dominant farming systems between spatial clusters. Using nationally representative intake data of 4,902 children 6-35 months of age, usual intake and the proportion of inadequate intakes of vitamin A, iron and zinc were calculated. A multi-level model was used to examine the association between individual-level and cluster-level variables with the usual total dietary intakes of these nutrients. The diet was dominated by starchy foods. Consumption of animal source foods, vitamin A-rich fruits and vegetables was low. We found a high prevalence of inadequate intake of vitamin A and zinc (85.4% and 49.5%, respectively). Relatively, low prevalence of inadequate intake of iron (8.4%) was reported. The spatial farming systems diversity across the rural clusters explained 48.2%, 57.2% and 26.7% of the observed variation in the usual total dietary intakes of vitamin A, iron and zinc, respectively. Our findings indicated the importance of farming system diversity at the landscape level as one of the determinant factors for individual usual total dietary intakes of vitamin A, iron and zinc.

Socio-cognitive constraints and opportunities for sustainable intensification in South Asia: insights from fuzzy cognitive mapping in coastal Bangladesh.

Appreciating and dealing with the plurality of farmers' perceptions and their contextual knowledge and perspectives of the functioning and performance of their agroecosystems-in other words, their 'mental models'-is central for appropriate and sustainable agricultural development. In this respect, the sustainable development goals (SDGs) aim to eradicate poverty and food insecurity by 2030 by envisioning social inclusivity that incorporates the preferences and knowledge of key stakeholders, including farmers. Agricultural development interventions and policies directed at sustainable intensification (SI), however, do not sufficiently account for farmers' perceptions, beliefs, priorities, or interests. Considering two contrasting agroecological systems in coastal Bangladesh, we used a fuzzy cognitive mapping (FCM)-based simulation and sensitivity analysis of mental models of respondents of different farm types from 240 farm households. The employed FCM mental models were able to (1) capture farmers' perception of farming system concepts and relationships for each farm type and (2) assess the impact of external interventions (drivers) on cropping intensification and food security. We decomposed the FCM models' variance into the first-order sensitivity index (SVI) and total sensitivity index (TSI) using a winding stairs algorithm. Both within and outside polder areas, the highest TSIs (35-68%) were observed for effects of agricultural extension on changes in other concepts in the map, particularly food security and income (SI indicators), indicating the importance of extension programs for SI. Outside polders, drainage and micro-credit were also influential; within polders, the availability of micro-credit appears to affect farmer perceptions of SI indicators more than drainage. This study demonstrated the importance of reflection on the differing perspectives of farmers both within and outside polders to identify entry points for development interventions. In addition, the study underscores the need for micro-farming systems-level research to assess the context-based feasibility of introduced interventions as perceived by farmers of different farm types. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10668-021-01342-y.

Farm typology for planning targeted farming systems interventions for smallholders in Indo-Gangetic Plains of India.

Due to complexity of smallholder farms, many times technologies with great potential fail to achieve the desired impact in leveraging productivity and profitability of the farming community. In the Indo-Gangetic Plains there is an urgent need to understand the diversity of farm households, identifying the main drivers deciding their system thus, classifying them into homogenous groups. In the present study, the diversity of smallholder farms was assessed using crop, livestock and income related characteristics and associated farm mechanization. Using principal component analysis and cluster analysis for 252 farm households, 4 farm types were identified i.e. Type 1. Small Farm households with cereal-based cropping system and subsistence livestock (39%), Type 2. Small Farm households with diversified cropping system dominated by cereal and fodder crops with only cattle herd (9%), Type 3. Marginal Farm household with diversified cropping system dominated by cash crop and herd comprising of only cattle (39%), Type 4. Marginal Farm household with diversified cropping system dominated by cereal crops and herd dominated by small ruminants (12%). Based on the constraints identified for different components of farming systems, low-cost interventions were planned for each farm type. These interventions have resulted in 84.8-103.2 per cent increase in the income of the farm HH under study suggesting usefulness of typology-based intervention planning in increasing income of small farm holders.

Conceptual Links between Landscape Diversity and Diet Diversity: A Roadmap for Transdisciplinary Research.

Malnutrition linked to poor quality diets affects at least 2 billion people. Forests, as well as agricultural systems linked to trees, are key sources of dietary diversity in rural settings. In the present article, we develop conceptual links between diet diversity and forested landscape mosaics within the rural tropics. First, we summarize the state of knowledge regarding diets obtained from forests, trees, and agroforests. We then hypothesize how disturbed secondary forests, edge habitats, forest access, and landscape diversity can function in bolstering dietary diversity. Taken together, these ideas help us build a framework illuminating four pathways (direct, agroecological, energy, and market pathways) connecting forested landscapes to diet diversity. Finally, we offer recommendations to fill remaining knowledge gaps related to diet and forest cover monitoring. We argue that better evaluation of the role of land cover complexity will help avoid overly simplistic views of food security and, instead, uncover nutritional synergies with forest conservation and restoration.

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.

The risk posed by Xanthomonas wilt disease of banana: Mapping of disease hotspots, fronts and vulnerable landscapes.

Banana production landscapes in the African Great Lakes Region (AGLR) have been under immense pressure from Xanthomonas wilt (XW) disease over the past two decades. XW, first reported on banana in central Uganda and eastern DR Congo in 2001, has since spread to the entire AGLR. XW is currently spreading westwards from hot spots in eastern DR Congo highlands, putting the plantain (Musa AAB genome) belt of central and west Africa at risk. In-depth understanding of the key variables responsible for disease spread, current hotspots, and vulnerable landscapes is crucial for disease early warning and management. We mapped aggregated disease distribution and hotspots in the AGLR and identified vulnerable landscapes across African banana production zones. Available data on disease prevalence collected over 11 years was regressed against environmental and expert developed covariates to develop the AGLR XW hotspots map. For the Africa-wide risk map, precipitation, distance to hotspots, degree of trade in fresh banana products, production zone interconnectedness and banana genotype composition were used as covariates. In the AGLR, XW was mainly correlated to precipitation and disease/banana management. Altitude and temperature had unexpectedly low effects, possibly due to an overriding impact of tool-mediated spread which is part of the management covariate. In the AGLR, the eastern part of DR Congo was a large hotspot with highest vulnerability. Apart from endemic zones in the AGLR and Ethiopia, northern Mozambique was perceived as a moderate risk zone mainly due to the predominance of 'Bluggoe' (Musa ABB type) which is highly susceptible to insect-vectored transmission. Presence of XW hotspots (e.g. eastern DR Congo) and vulnerable areas with low (e.g. north-western Tanzania) or no disease (e.g. Congo basin, western DR Congo and northern Mozambique) pressure suggest key areas where proactive measures e.g. quarantines and information sharing on XW diagnosis, epidemiology, and control could be beneficial.

Complex rice systems to improve rice yield and yield stability in the face of variable weather conditions.

Extreme weather events and pest outbreaks decrease rice yields and increase their variability, presenting challenges for the agricultural agenda to increase rice productivity and yield stability in Asia. The integration of azolla, fish and ducks has been shown to create robust systems that maintain high yields under heavy rainfall, but no clear evidence exists that rice yields in these systems are stable across locations and throughout time under divergent weather conditions. We show that the introduction of additional elements into the rice cropping system enhanced the adaptive capacity to extreme weather events across four locations and three cropping cycles. The complex system showed both static and dynamic stability, and had the highest reliability index, thereby outperforming the conventional and organic monoculture systems. The complex rice system design provides a promising example for resilience towards the impacts of climate change on rice production and for safeguarding food security in Asia and beyond.

Risks Posed by Intercrops and Weeds as Alternative Hosts to Xanthomonas campestris pv. musacearum in Banana Fields.

Alternative host plants are important in the survival and perpetuation of several crop pathogens and have been suspected to play a role in the survival of Xanthomonas campestris pv. musacearum (Xcm) and perpetuation of Xanthomonas wilt (XW) disease of banana and enset. This study determined the potential risk posed by two weeds (Canna spp. and wild sorghum) and common banana intercrops (maize, millet, sorghum, taro, and sugarcane) as alternative hosts to Xcm. The study employed screenhouse experiments, laboratory procedures and diagnosis of banana fields in XW-affected landscapes. Typical XW symptoms were only observed in artificially inoculated Canna sp., with an incidence of 96%. Leaf lesions characteristic of xanthomonads occurred on millet (50%) and sorghum (35%), though the plants recovered. No symptoms occurred in maize, sugarcane, taro or wild sorghum. However, Xcm was recovered from all these plant species, with higher recoveries in Canna sp. (47%), millet (27%), sugarcane (27%), and wild sorghum (25%). Only isolates recovered from Canna sp., millet, sorghum and wild sorghum caused disease in banana plantlets. The presence and incidence of XW on-farm was positively associated with the presence of susceptible ABB Musa genotypes and negatively with number of banana cultivars on farm and household access to training on XW management. Only 0.02% of field sampled Canna spp. plants had Xcm. Risk posed by Canna spp. on-farm could be limited to tool transmission as it has persistent floral bracts that prevent insect-mediated infections. Given the high susceptibility, perennial nature and propagation through rhizomes of Canna sp., it could pose a moderate-high risk, thus warranting some attention in the management of XW disease. Sugarcane could offer a low-moderate risk due to its perennial nature and propagation through rhizomes while risk from maize, millet, and sorghum was deemed zero-low due to their annual nature, wind-mediated mode of pollination and propagation through seed. Understanding the interactions of a crop pathogen with other plants is thus important when diversifying agroecosystems. The study findings also suggest other factors such as cultivar composition and management of the disease at farm and landscape level to be important in the perpetuation of XW disease.

Integrating local knowledge and remote sensing for eco-type classification map in the Barotse Floodplain, Zambia.

This eco-type map presents land units with distinct vegetation and exposure to floods (or droughts) in three villages in the Barotseland, Zambia. The knowledge and eco-types descriptions were collected from participatory mapping and focus group discussions with 77 participants from Mapungu, Lealui, and Nalitoya. We used two Landsat 8 Enhanced Thematic Mapper (TM) images taken in March 24th and July 14th, 2014 (path 175, row 71) to calculate water level and vegetation type which are the two main criteria used by Lozi People for differentiating eco-types. We calculated water levels by using the Water Index (WI) and vegetation type by using the Normalized Difference Vegetation Index (NDVI). We also calculated the Normalized Burn Ratio (NBR) index. We excluded burned areas in 2014 and built areas to reduce classification error. Control points include field data from 99 farmers' fields, 91 plots of 100 m2 and 65 waypoints randomly selected in a 6 km radius around each village. We also used Google Earth Pro to create control points in areas flooded year-round (e.g., deep waters and large canals), patches of forest and built areas. The eco-type map has a classification accuracy of 81% and a pixel resolution of 30 m. The eco-type map provides a useful resource for agriculture and conservation planning at the landscape level in the Barotse Floodplain.

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.

Bedding additives reduce ammonia emission and improve crop N uptake after soil application of solid cattle manure.

This study examined the influences of three potential additives, i.e., lava meal, sandy soil top-layer and zeolite (used in animal bedding) amended solid cattle manures on (i) ammonia (NH3), dinitrous oxide (N2O), carbon dioxide (CO2) and methane (CH4) emissions and (ii) maize crop or grassland apparent N recovery (ANR). Diffusion samplers were installed at 20 cm height on grassland surface to measure the concentrations of NH3 from the manures. A photoacoustic gas monitor was used to quantitate the fluxes of N2O, CH4 and CO2 after manures' incorporation into the maize-field. Herbage ANR was calculated from dry matter yield and N uptake of three successive harvests, while maize crop ANR was determined at cusp of juvenile stage, outset of grain filling as well as physiological maturity stages. Use of additives decreased the NH3 emission rates by about two-third from the manures applied on grassland surface than control untreated-manure. Total herbage ANR was more than doubled in treated manures and was 25% from manure amended with farm soil, 26% and 28% from zeolite and lava meal, respectively compared to 11% from control manure. In maize experiment, mean N2O and CO2 emission rates were the highest from the latter treatment but these rates were not differed from zero control in case of manures amended with farm soil or zeolite. However, mean CH4 emissions was not differed among all treatments during the whole measuring period. The highest maize crop ANR was obtained at the beginning of grain filling stage (11-40%), however ample lower crop recoveries (8-14%) were achieved at the final physiological maturity stage. This phenomenon was occurred due to leaf senescence N losses from maize crop during the period of grains filling. The lowest losses were observed from control manure at this stage. Hence, all additives decreased the N losses from animal manure and enhanced crop N uptake thus improved the agro-environmental worth of animal manure.

Simulation of Long-Term Carbon and Nitrogen Dynamics in Grassland-Based Dairy Farming Systems to Evaluate Mitigation Strategies for Nutrient Losses.

Many measures have been proposed to mitigate gaseous emissions and other nutrient losses from agroecosystems, which can have large detrimental effects for the quality of soils, water and air, and contribute to eutrophication and global warming. Due to complexities in farm management, biological interactions and emission measurements, most experiments focus on analysis of short-term effects of isolated mitigation practices. Here we present a model that allows simulating long-term effects at the whole-farm level of combined measures related to grassland management, animal housing and manure handling after excretion, during storage and after field application. The model describes the dynamics of pools of organic carbon and nitrogen (N), and of inorganic N, as affected by farm management in grassland-based dairy systems. We assessed the long-term effects of delayed grass mowing, housing type (cubicle and sloping floor barns, resulting in production of slurry and solid cattle manure, respectively), manure additives, contrasting manure storage methods and irrigation after application of covered manure. Simulations demonstrated that individually applied practices often result in compensatory loss pathways. For instance, methods to reduce ammonia emissions during storage like roofing or covering of manure led to larger losses through ammonia volatilization, nitrate leaching or denitrification after application, unless extra measures like irrigation were used. A strategy of combined management practices of delayed mowing and fertilization with solid cattle manure that is treated with zeolite, stored under an impermeable sheet and irrigated after application was effective to increase soil carbon stocks, increase feed self-sufficiency and reduce losses by ammonia volatilization and soil N losses. Although long-term datasets (>25 years) of farm nutrient dynamics and loss flows are not available to validate the model, the model is firmly based on knowledge of processes and measured effects of individual practices, and allows the integrated exploration of effective emission mitigation strategies.

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.