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Globally, 38% of the land area is agricultural land, of which 45% are located on drylands, mainly in Africa and Asia, constituting the basis for about 60% of the world’s food production. Of all farms worldwide, 83% are smallholder farm systems, whose livelihoods depend on effective land management and allocation. While land is often cultivated by the various members of a farm household, land allocation decisions depend on the approval, the ambition and the abilities of influential household members, likely affecting all other household members, too. While intra-household decision-making processes have been described to depend on the interplay of prevailing interests and power positions, so far knowledge on interests and power positions is based on individual reports rather than actual observations. With the aim to explore the process of land allocation in a socially complex smallholder farm system, we invited members of a smallholder community in Northern Ghana to join a closed, experimental serious game, simulating a negotiation process between a male household head (HHH), a wife and the eldest son of a hypothetical local farm household. We observed an integrative negotiation style, resulting into high levels of satisfaction with the negotiation process and outcome by all parties, who reported a high level of similarity between simulated and real-life negotiations. Power was observed to be actively deployed, withheld or passively overruled depending on decision domains and process dynamics. While the HHH was the key decision maker acting as a strategic gatekeeper in a funnel-like process, the wife and the son had a significant influence on ‘his decision’ i.e. the household-level negotiation outcome. Model-based analysis also showed that the household-level outcome was more profitable as well as agro-biologically and nutritionally more diverse and productive as compared to the HHHs’ suggestion. The proposed game proved to be a culturally adequate, simple, cost and time effective tool to capture how household-level land use decisions may come about and whose interests they represent. Our study provides a powerful framework for further research and for policies to foster more equitable land use decisions and therewith more sustainable socio-ecological systems.

The most common three-dimensional (3D) printing method is material extrusion, where a pre-made filament is deposited layer-by-layer. In recent years, low-cost polycaprolactone (PCL) material has increasingly been used in 3D printing, exhibiting a sufficiently high quality for consideration in cranio-maxillofacial reconstructions. To increase osteoconductivity, prefabricated filaments for bone repair based on PCL can be supplemented with hydroxyapatite (HA). However, few reports on PCL/HA composite filaments for material extrusion applications have been documented. In this study, solvent-free fabrication for PCL/HA composite filaments (HA 0%, 5%, 10%, 15%, 20%, and 25% weight/weight PCL) was addressed, and parameters for scaffold fabrication in a desktop 3D printer were confirmed. Filaments and scaffold fabrication temperatures rose with increased HA content. The pore size and porosity of the six groups’ scaffolds were similar to each other, and all had highly interconnected structures. Six groups’ scaffolds were evaluated by measuring the compressive strength, elastic modulus, water contact angle, and morphology. A higher amount of HA increased surface roughness and hydrophilicity compared to PCL scaffolds. The increase in HA content improved the compressive strength and elastic modulus. The obtained data provide the basis for the biological evaluation and future clinical applications of PCL/HA material.

Typologies may be used as tools for dealing with farming system heterogeneity. This is achieved by classifying farms into groups that have common characteristics, i.e. farm types, which can support the implementation of a more tailored approach to agricultural development. This article explored patterns of farming system diversity through the classification of 70 smallholder farm households in two districts (Savelugu-Nanton and Tolon-Kumbungu) of Ghana⿿s Northern Region. Based on 2013 survey data, the typology was constructed using the multivariate statistical techniques of principal component analysis and cluster analysis. Results proposed six farm types, stratified on the basis of household, labour, land use, livestock and income variables, explaining the structural and functional differences between farming systems. Types 1 and 2 were characterized by relatively high levels of resource endowment and oriented towards non-farm activities and crop sales respectively. Types 3 and 4 were moderately resource-endowed with income derived primarily from on-farm activities. Types 5 and 6 were resource constrained, with production oriented towards subsistence. The most salient differences among farm types concerned herd size (largest for Type 1), degree of legume integration (largest for Types 2⿿4), household size and hired labour (smallest household size for Types 4 and 6, and largest proportion of hired labour for Type 4), degree of diversification into off/non-farm activities (highest for Type 1 and lowest for Type 5) and severity of resource constraints (Type 6 was most constrained with a small farm area and herd comprised mainly of poultry). It was found that livelihood strategies reflected the distinctive characteristics of farm households; with poorly-endowed types restricted to a ⿿survival strategy⿿ and more affluent types free to pursue a ⿿development strategy⿿. This study clearly demonstrates that using the established typology as a practical framework allows identification of type-specific farm household opportunities and constraints for the targeting of agricultural interventions and innovations, which will be further analysed in the research-for-development project. We conclude that a more flexible approach to typology construction, for example through the incorporation of farmer perspectives, might provide further context and insight into the causes, consequences and negotiation of farm diversity.

Somalia has frequently been affected by droughts, famines and water-related humanitarian crises. Water is scarce and the only perennial streams, the Juba and Shabelle rivers, are trans-boundary wit...

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. Estación Experimental Agropecuaria Bariloche Fil: Alvarez, Stephanie. Wageningen University & Research. Farming Systems Ecology; Holanda Fil: Timler, Carl J. Wageningen University & Research. Farming Systems Ecology; Holanda Fil: Michalscheck, Mirja. Wageningen University & Research. Farming Systems Ecology; Holanda Fil: Paas, Wim. Wageningen University & Research. Farming Systems Ecology; Holanda Fil: Descheemaeker, Katrien. Wageningen University & Research. Plant Production Systems; Holanda Fil: Tittonell, Pablo Adrian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Área de Recursos Naturales; Argentina Fil: Andersson, Jens A. International Maize and Wheat Improvement Center (CIMMYT); Zimbawe Fil: Groot, Jeroen C. J. Wageningen University & Research. Farming Systems Ecology Group, Plant Sciences; Holanda

Agricultural production in Northern Ghana is dominated by smallholder farm systems, which are characterized by low inputs and low outputs, declining soil fertility, large yield gaps and limited adoption of agricultural technologies. There is an urgent need for alternative farm designs that are more productive, yet more sustainable. Technology packages for sustainable intensification are promoted by an R4D project in the Upper East, Upper West and Northern Regions of Ghana. In this paper, we analyse differences in perceived suitability, and modelled technical impact per technology package. We used a locally validated framework to categorise farm systems diversity that considers both, the horizontal (between households) and vertical (within households) dimension of diversity. Farm households were classified along a gradient of resource endowment. We selected one representative farm per type and per region to assess and compare their socio-economic and environmental performance (farm profitability, labour and soil organic matter inputs) using the whole-farm model Farm DESIGN. We then used Farm DESIGN to assess the potential impact of five proposed technology packages and to explore promising alternative farm configurations. We discussed model assumptions and results with farmers, including alternative cropping patterns and trade-offs. We evaluated the packages with different household members using a weighted scoring technique, subsequently juxtaposing model results with farmer perceptions. Large differences prevailed among and within farms per type and per region, with low resource endowed farms being projected to benefit most in relative and least in absolute terms from an adoption of the packages. Farmer feedback confirmed the accuracy of alternative farm configurations, as determined by the model. However, the feedback also revealed that the most profitable farm designs would be hard to attain in reality, particularly for members of low and medium resource endowed households, due to high initial investment costs. Within households, women were more positive about the packages than men, since men heavily penalized extra costs and labour, translating into a greater congruence of model results with the male evaluation. We discuss the importance of distinguishing between technical (technology i.e. purchased tools and inputs) and managerial (techniques e.g. row planting) package components. We conclude that operationalizing inter- and intra-household diversity is a fundamental step in identifying sensible solutions for the challenges smallholder farm systems face in Northern Ghana.

In Zambia maize is the main staple food crop and, with a share of 52% in the daily calorie intake of the local population, it is critical for ensuring the national food security (FAOSTAT, 2013). Of the total maize consumed in Zambia, smallholder farmers produce 80% in rain-fed systems under low soil fertility, frequent drought and with a limited use of high yielding varieties or inorganic fertiliser (Sitko et al., 2011). In eastern Zambia, the livelihoods of small-scale farmers depend largely on maize-legume mixed systems characterised by low productivity, extreme poverty and environmental degradation (Sitko et al., 2011). Thus, there seems to be a great need for sustainable intensification of these farming systems, for instance through promoting best practices in maize–legume integration. Maize–legume cropping provides protein-rich food for humans, residues for animal feed, composting and soil amendments and nitrogen inputs through symbiotic fixation by the legume. Sustainable intensification of farming systems can take place through changes in resource use and allocation that increase farm productivity while reducing pressure on local ecosystems and safeguarding social relations. According to Pretty et al. (2011), this entails the efficient use of all inputs to produce more outputs while reducing damage to the environment and building a resilient natural capital from which environmental services can be obtained. Sustainable intensification results from the application of technological and socio-economic approaches that may be categorised into genetic, ecological and socio-economic intensification (The Montpellier Panel, 2013).

This study measures the long-run effect of the Abu Tawfeer media campaign in Jordan. Based on a representative sample (N = 367) conducted five years after the end of the campaign, a multivariate instrumental variable regression analysis shows that the campaign only marginally changed people’s water conservation awareness and behaviour. The rigorous methodological approach allows disentangling the distinct channels through which the effect of the conservation campaign was transmitted. Moreover, this is one of the first studies that comprehensively examines the role of awareness in determining water conservation behaviour.

Typologies are often used to understand and capture smallholder farming system heterogeneity, and may be derived using different approaches and methods. This article aims to compare a quantitative, statistical typology based on a survey dataset and multivariate analysis, with a qualitative participatory typology based on informal group sessions and activities with local stakeholders from three communities in Northern Ghana. The statistical typology resulted in six clusters, with farm households categorized on the basis of their structural (resource endowment)- and functional (production objectives/livelihood strategies) characteristics. The participatory typology identified five farm types, based primarily on endowment (farm size, income investment), gender and age-related criteria. While the entire household was adopted as the unit of analysis of the statistical typology, the participatory typology provided a more nuanced differentiation by grouping individual farmers; with possibly several farmer types per household (e.g. ‘small’ and ‘female farmers’) as well as ‘farm-less’ individuals as a result. Other sources of dissimilarity which contributed to limited overlap between the typologies included changes that occurred in the communities between the two data collection efforts and inaccuracies in the data. The underlying causes of the latter seemed to mainly relate to socio-cultural issues that distorted information collection in both typologies; including power and status differences between both the researchers and farmers, as well as the farmers themselves. We conclude that although statistical techniques warrant objectivity and reproducibility in the analysis, the complexity of data collection and representation of the local reality might limit their effectiveness in selection of farms, innovation targeting and out-scaling in R4D projects. In addition, while participatory typologies offer a more contextualized representation of heterogeneity, their accuracy can still be compromised by socio-cultural constraints. Therefore, we recommend making effective use of the advantages offered by each approach by applying them in a complementary manner.