Farm Typology Research Articles

Identification of beef production farms in the Pampas and Campos area that stand out in economic and environmental performance

Worldwide, native grasslands are being converted to non-native pastures and cropland. This process threatens local grassland biomes as well as the livelihoods of farm families that utilize these grasslands.In the Río de la Plata grasslands region meat production and multispecies native grasslands have coexisted for more than 400 years. Low levels of meat productivity and farm income, however, trigger replacement of native grasslands by crops and leys and threaten the survival of local beef farming systems. We studied the economic and environmental performance of beef farming in the region based on interviews and field measurements on 280 case study farms with the following aims: (a) to identify the multi-functional economic and environmental performance of beef farms across the Rio de la Plata grasslands biome; (b) to identify farms with ‘outstanding’ multi-functional performance; (c) to compare performance levels with those found in other regions; and (d) to discuss the implications of the outstanding farms for the development of new systems of meat production. The representativeness of the case study farms was ascertained by comparing them with a farm typology constructed from survey data of 15,448 beef farms situated predominantly on native grasslands in Argentina, Brazil and Uruguay. We identified seven farm types on the basis of farm size, labour, farm specialization, land use and stocking rate.We identified positive deviant farms based on Pareto-ranking and compared these with a classification based on threshold values provided by experts. Out of the 280 farms, 41 were ranked as Pareto-optimal, i.e. outperformed other farms in one or more indicators without being outperformed in other indicators. Out of these, 5 were positive deviants, achieving on average 192 kg LW ha−1 yr−1 of livestock productivity and 201 US$ ha−1 year−1 farm income, having most favourable values for fossil energy consumption, phosphorus balance, carbon footprint and having over 95% of their land under native grassland as a proxy for biodiversity conservation value. Four of these farms belonged to farm types that together represented 55% of the population, suggesting scope for widescale improvement.Compared to the values reported for the OECD countries the beef farming systems of the Río de la Plata grasslands region consume less energy and positive deviant farms demonstrated approximately average livestock productivity and carbon footprint. Increasing livestock productivity in the Rio de la Plata grasslands region resulted in a stronger decline of the carbon footprint without compromising the current negligible levels of fossil fuel energy use. Further elucidation of management practices that lead to positive deviant performance will require modelling of the interaction of pasture and herd dynamics at farm level and is needed to support targeted policy support for sustainable natural grassland-based beef production in the region.

Typology of cattle farms in the northern regions of Cameroon

The aim of this study was to establish the typology of cattle farms in the northern regions of Cameroon in order to reveal structural and functional characteristics that could be selected for an artificial insemination program. A survey was carried out among 54 breeders. The results showed that the farmers were predominantly of the Fulani ethnic group (81.5%) with men prevailing (92.6%). Their levels of education were primary (31.5%), secondary (18.5%), and higher (25.9%). They were involved in full-time agricultural production activity (42.6%), cattle breeding and agropastoralism. Civil servants represented more than a third of the sample. The genetic structure of the herds was dominated by local breeds: 53.7% Gudali zebus, 44.4% Djafoun and 5.6% Akou. In addition, 44.4% of them bred crossbreeds and 16.7% Holsteins. A multiple correspondence analysis was performed to determine the typology of the farms studied. Based on the structural (herd size, forage area, dairy productivity per cow per day, and related infrastructure) and functional (feeding, herd management, technological choice) characteristics of the farms, three types were identified. Type I breeders (37%) practiced the extensive system, and type II breeders (18.5%) the semi-intensive system. They were present in the Adamawa region and preferred natural mating (56%) and artificial insemination (100%), respectively, as a reproductive means. Type III breeders (44.5%) practiced the semi-extensive system. They were only present in the North and Far North regions and preferred (98%) natural mating as a reproductive means. Type II farmers were located in Vina Division (Adamawa) and they offered the best potentialities with the practice of artificial insemination. They may be considered as pilot for the revival of the dairy sector in this part of Cameroon.

A participatory approach for comparing stakeholders’ evaluation of P loss mitigation options in a high ecological status river catchment

Phosphorus (P) transfer from land to water is a source of diffuse pollution that contributes to the decline in ecological status of river bodies in the European Union. The Water Framework Directive (2000/60/EC) provides for the protection of water bodies that represent pristine or near-pristine condition, classified as high ecological status through the adoption of an agri-environmental decision making process that promotes stakeholder participation. However, successful implementation of agri-environmental policies can prove challenging when faced with uncertainties and diverging opinions due to the variety of actors involved. This study adopted a participatory approach including stakeholders with conflicting interests in the selection of P transfer mitigation policies. Fifteen P transfer mitigation options were shortlisted based on agronomic and environmental data from a case-study agricultural catchment and presented to a group of experts and farmers. Results showed significant disparities between perceived effectiveness by farmers and experts groups, with experts prioritizing problems related to connectivity issues, while farmers to soil compaction and erosion. In addition, measured agronomic and environmental variables were used to model effectiveness from a decision support tool (FARMSCOPER) and compared with stakeholder groups’ perceived effectiveness. This approach combined the scientific research with the empirical knowledge of farmers and the modelling of quantified field and farm data. This study showed that stakeholders are diverse, and perceive effectiveness based on group-specific operational and social factors. Experts identified effectiveness at catchment scale, whilst farmers identified field scale effectiveness. For decision support tools and simulation models to be beneficial for policy makers, they need to be calibrated to local conditions and farm typologies to select the right measure at farm scale. The study recommends improved knowledge transfer between interested actors and the need for integration of conflicting opinions in policy design. A bottom-up approach to decision making is suggested, to assist in the decentralization of the procedures towards more effectively implemented P transfer mitigation policies.

Analysis of Economic Efficiency in Some Irish Farms Using the DEA Approach

Irish farms are predominately and highly specialized in crops as cereals, protein crops and in dairy productions. The aim of this research was to estimate the economic efficiency in Irish farms part of the Farm Accountancy Data Network (FADN) dataset stratified in function of their own typology of productive specialization since 2004 to 2015 by a quantitative approach such as the Data Envelopment Analysis (DEA). Positive has been the role of inputs as financial subsidies allocated by the Common Agricultural Policy, the first and second pillar, in increasing the economic efficiency of Irish farms. Field crops farms have not had the best results in terms of the economic efficiency even if over the time, in particular during the economic crises 2008-2009, findings have not been stable with significant fluctuations and a sharply decrease of efficiency as a consequence of economic turbulences. Focusing the attention on the research outcomes in all years of investigation comparing also the different typology of farming, mixed farms and farms with animals, such as specialist cattle, sheep, goats and other grazing livestock, have had the highest levels of economic efficiency equal to 100%; by contrast Irish dairy farms have had the modest levels of economic efficiency close to 77%.

A Non-Parametric Analysis of Technical Efficiency in Bulgarian Farms Using the Fadn Dataset

Abstract The Bulgarian countryside has suffered a significant phenomenon of rural emigration since the early 1970s. The main consequence of rural depopulation has been a decline of investments in Bulgarian farms and in their own level of technical and economic efficiency. The aim of this research was to assess afterwards the enlargement of the European Union in 2007, the technical efficiency by a non-parametric approach such as the Data Envelopment Analysis (DEA), using some findings and variables investigated in the Farm Accountancy Data Network annual survey from 2007 till 2015. Farms have been stratified into functions of their typology of farming and their geographical localization. Research findings have pointed out that specialized farms as dairy farms and granivores ones have had the highest levels of technical efficiency compared to mixed farms and wine farms. To sum up, financial subsidies allocated by the Common Agricultural Policy have had a positive impact towards farmers, both increasing the technical efficiency and also in reducing the socio-economic marginalization of Bulgarian rural areas.

Assessing regional farming system diversity using a mixed methods typology: the value of comparative agriculture tested in broadacre Australia

Most farm and farmer typologies focus on specific aspects and use standard structural and socio-economic indicators. Regional assessments of agricultural diversity based on farming systems are rarely done, as detailed and representative information is difficult to collect. The discipline of comparative agriculture addresses these challenges but remains little known, and seldom applied to broadacre situations. This study demonstrates in Western Australia the value of its mixed methods and multi-disciplinary concepts to determine the level and nature of regional farming system heterogeneity.The typology built comprised six farming systems based on 36 farms that represented half the farming population of a 4000 km2 area (broadacre rainfed systems dominated by winter cereals and sheep, Mediterranean climate). The farm groups corresponding to these farming systems differed across 36 variables representing biophysical, technical, and social aspects at varied spatial and temporal scales. Results were compared with five sets of farm clusters produced through multivariate clustering procedures commonly employed to build typologies. These farm clusters differed across fewer variables than the farm groups of the comparative agriculture typology.The analytical, methodological and conceptual tools used in comparative agriculture to solve the challenges associated with the holistic study of farming system heterogeneity are discussed. These included basing data collection and analysis on an empirical approach that assessed groups of farms rather than individuals, solving data scarcity through a range of qualitative techniques, and progressively informing the choice of typology criteria. Comparative agriculture thus provides an alternative to standard typology paradigms that deserves wider application.

Climate change-related risks and adaptation strategies as perceived in dairy cattle farming systems in Tunisia

The perception of risks due to climate change by farmers and the measures they take to address those risks are of paramount importance in policy-making if the implementations of targeted adaptation and mitigation strategies are to be economically and environmentally sustainable. This study focused on Tunisian dairy farmers’ perceptions of the risks and the actions taken to cope with changes attributable to climate change. Using a bottom-up approach, 566 surveys were carried out randomly among dairy farmers throughout Tunisia. A total of 70 diagnostic variables relating to farm characteristics, resources, management, performances and profit, in addition to climate change risk perception and adaptation strategies, were identified and analyzed. Using multivariate statistical analysis, four dairy farming groups were identified. The largest proportions of farmers belonged to the two above-ground dairy systems: without utilized agricultural areas; and with non-dairy utilized agricultural areas (Clusters 1 and 2). A minority of farmers belonged to medium-sized and large farms that specialized in milk production (Clusters 3 and 4) and has access to sufficient land, water and capital resources. In all the clusters, almost all the farmers perceived that the greatest impact of climate change would be on cow performance and forage production. The attitudes of the farmers towards adaptation to climate change are associated with farm typology. They focused mainly on increasing water capacity for livestock and crop production and improving livestock and housing conditions. The knowledge obtained from this study could be helpful for decision-makers and stakeholders in efforts to develop policies for farm management practices that address climate change and can be adapted to the country’s diverse farming systems.

Organic and conventional agriculture in Kenya: A typology of smallholder farms in Kajiado and Murang'a counties

Understanding the diversity of smallholder farms is key for the development of interventions, strategies and policies aimed at addressing the numerous challenges these farmers face as well as for those shaping the future of smallholder farming in Kenya, Africa and beyond. In this study, we developed a typology for smallholder farms in Kenya using survey data from 488 farm households in Kajiado and Murang'a counties. Multivariate statistical techniques (principal component and cluster analyses) were used to group farms into five types differentiated by household characteristics, resource endowment, cropping practices, social networks, access to information, dietary diversity and gender equity. Types 2, 3 and 5 were mostly market oriented, possessed high to medium levels of wealth and had strong social networks. Types 3 and 5, however, mainly practised organic agriculture while Type 2 farms included organic and non-organic cultivated areas. Types 1 and 4 were characterised by low to medium levels of wealth, maintained poor social networks and had low adherence to organic agriculture practices. Yet, while Type 4 farms mainly practised conventional market-oriented agriculture, farms of Type 1 could be defined as organic-by-default and were self-subsistent. The majority of the surveyed farms belonged to Type 2, the wealthiest group of farmers and mostly located in Kajiado county. Murang'a county was dominated by farms of Type 5 practising mainly certified organic agriculture. Overall, the practice of organic agriculture was associated with higher agricultural income, legal ownership of land, older household heads, larger household sizes, stronger social networks, higher access to information, more diverse diets and higher levels of gender equity. In contrast, poorer, younger and less well-connected farmers were less involved in organic agriculture. The results of this study may help to increase efficiency in the implementation of pro-poor and organic agricultural interventions, strategies and policies on the ground and to shape policy instruments accordingly.

Evaluation of Pakistani farmers’ willingness to pay for crop insurance using contingent valuation method: The case of Khyber Pakhtunkhwa province

In Pakistan, agriculture is susceptible to numerous risks, particularly in flood prone areas. Crop insurance could act as an effective tool for managing risks in Pakistan. For many years, the country has confronted with the losses from natural disasters without crop insurance. This study aims to examine the willingness of Pakistani farmers to pay for insurance premiums by conducting a household survey. Primary data of 600 farmers were collected in the four flood prone districts namely Charsadda, Peshawar, Mardan and Nowshera of Khyber-Pakhtunkhwa (KP) province of Pakistan. In a rural farm-household survey, residents of the study area were asked about their preferences for a hypothesized crop insurance program using contingent valuation (CV) method by asking their willingness to pay (WTP) for crop insurance to eradicate forthcoming disaster risks. Multiple linear regression model was utilized to check construct validity and reliability of the WTP estimates. The outcomes of the study revealed that about 30% of the participants accepted the concept of crop insurance as the mechanism for disaster risk reduction. Our findings reveal that the severity and frequency of past weather associated extremes, farm typology, socio-economic settings, and the ability of farming communities to pay should be considered while introducing crop insurance programs in the region. Moreover, disseminating awareness in farming communities regarding the futures changes in climate and the related risks of the existence of extreme weather incidences is important. The government support to subsidize insurance schemes might increase the need for crop insurance among subsistence farmers in and cushion them against adverse effects arising from such extreme weather conditions for sustenance of livelihoods.

Farm diversity and resource use efficiency: Targeting agricultural policy interventions in East Africa farming systems

This paper aimed to provide empirical evidence on the links between farm diversity and resource use efficiency. Using farm typology and stochastic production frontier approaches, we grouped households into those pursuing similar livelihood strategies and assessed their resource use efficiency. At 60% coefficient of similarity, we identified three distinct farm types – Farm-specialised, Diversified and Off-farm specialised. Significant (p<0.01) differences across farm types were observed for, the proportion of income from farming, farmed area, and land use patterns, confirming these as good indicators for distinguishing between farm types. Over 50% of surveyed households were categorised as Diversified and Off-farm specialised, and mainly pursued off-farm livelihood strategies. Farm-specialised households pursued mainly farm-based activities and earned higher net incomes compared to other farm types. However, they exhibited technical inefficiency in the use of labour and fertiliser compared to other farm types. Access to extension and commercial orientation showed significant (p<0.01) positive effect on technical efficiency for Farm-specialised households. Results have implications for policies and programmes aimed at improving agricultural productivity. There is need to focus support on interventions that make a significant contribution to farm efficiency, in particular, extension services and market access. Agricultural programmes are likely to be successful if they are targeted to households reliant on agriculture, while, off-farm households could be oriented towards off-farm agri-enterprises such as processing and marketing.

Differences across farm typologies in capital investment during 1996-2013

PurposeThe purpose of this paper is to examine the impact of changes in farm economic conditions and macroeconomic trends on US farm capital expenditures between 1996 and 2013.Design/methodology/approachA synthetic panel is constructed from Agricultural Resource Management Survey (ARMS) data. A dynamic system GMM regression model is estimated for farms as a whole and separately within farm typology categories. The use of farm typologies allows for comparison of the relative magnitudes of these estimates across farms by farm sales level and the operator’s primary occupation.FindingsChanges in gross farm income levels, tax depreciation rates, and interest rates have a significant impact on crop farm investment, while changes in output prices, net cash farm income levels, tax depreciation rates, and farm specialization levels have significant impacts on livestock farm capital investment. The relative significance and magnitudes of these impacts differ within farm typologies. Significant differences include a greater responsiveness to change in tax policy variables for residential crop farms, greater responsiveness to changes in output prices and debt to asset ratios for intermediate livestock farms, and larger changes in commercial crop and livestock farm investment given equivalent changes in farm sales or the returns to investment.Research limitations/implicationsThese findings are of interest to agricultural economists when constructing farm investment models and employing pseudo panel methods, to those in the agricultural equipment and manufacturing sector when constructing models to manage inventories and plan for production needs across regions and over time, to those involved in drafting tax policy and evaluating the potential impacts of tax changes on agricultural investment, and for those in the agricultural lending sector when designing and executing agricultural capital lending programs.Originality/valueThis study uniquely identifies differences in the level of investment and the magnitude of investment responsiveness to changes in farm economic conditions and macroeconomic trends given differences in income levels and primary operator occupation. In addition, this study is one of the few which utilizes ARMS data to study farm capital investment. Utilizing ARMS data provides a rich panel data set, covering producers across many different crop production types and regions. Finally, employing pseudo panel construction methods contributes to efforts to effectively employ cross-sectional data and dynamic models to study farm behavior across time.

Agricultural land‐use change and its drivers in Terceira Island, Portugal (2008–2013)

AbstractThis work aims at analysing how compensatory direct payments regimes to farmers influence land‐use patterns and changes on agricultural landscape. For this purpose a case‐study examines land‐use changes in Terceira Island (Azores, Portugal) for the period of 2008–2013. It is specifically focused on the land‐use changes associated to maize for silage annually declared on the detailed land parcels by farmers which apply for direct payments under the Common Agricultural Policy (CAP). A total increase of 28 per cent is reported on arable crops area (mostly maize silage) for the period. This process should be related not only to the agricultural coupled support policy for arable crops but also to the relative prices of farm products and raw materials for animal feed. The access facilities to the land parcels appear to be another major farm‐related factor at local context. A logistic regression analysis is performed at the land parcel level and the occurrence of the agricultural land‐use ‘maize for silage’ is explained by a set of explanatory variables. The independent variables comprise biophysical factors and socio‐economic factors. Results indicate biophysical factors (average annual temperature, annual accumulated precipitation, slope and soil agricultural use capability), accessibility, farm typology, previous land‐use, arable crops aid, milk price and bovine meat price as key factors for maize silage land‐use occupancy on Terceira Island. These conclusions seem valuable for decision‐making on the implementation of coupled support schemes under agricultural policies and their impacts on spatial arrangements in land‐use dynamics and landscape change.

To produce or not to produce: an analysis of bioenergy and crop production decisions based on farmer typologies in Brandenburg, Germany

The future course of the political regulation of bioenergy will have a significant sustainability impact on many levels. Understanding the specific effects of different political governance strategies on the agricultural system is essential for developing a stable and economically, ecologically as well as socially sustainable market for bioenergy. This paper contributes to this objective by providing an analysis of different decision patterns of farmers in the production of energy crops. For this purpose, an empirical analysis was conducted among farmers in the federal state of Brandenburg in northern Germany. A cluster analysis of structural factors resulted in a typology of farmers that differ in their energy crop production decisions. Six cluster typologies are identified for each of which a cluster-specific conjoint analysis helped to identify decision preferences in order to understand how and to what degree structural farm characteristics as well as respective production “traditions” influence the willingness to produce crops for energy use.

Peri-urban agriculture: an analysis of farm typologies in Italy

Peri-urban agriculture: an analysis of farm typologies in Italy

Climate mitigation by dairy intensification depends on intensive use of spared grassland.

Milk and beef production cause 9% of global greenhouse gas (GHG) emissions. Previous life cycle assessment (LCA) studies have shown that dairy intensification reduces the carbon footprint of milk by increasing animal productivity and feed conversion efficiency. None of these studies simultaneously evaluated indirect GHG effects incurred via teleconnections with expansion of feed crop production and replacement suckler-beef production. We applied consequential LCA to incorporate these effects into GHG mitigation calculations for intensification scenarios among grazing-based dairy farms in an industrialized country (UK), in which milk production shifts from average to intensive farm typologies, involving higher milk yields per cow and more maize and concentrate feed in cattle diets. Attributional LCA indicated a reduction of up to 0.10kg CO2 e kg-1 milk following intensification, reflecting improved feed conversion efficiency. However, consequential LCA indicated that land use change associated with increased demand for maize and concentrate feed, plus additional suckler-beef production to replace reduced dairy-beef output, significantly increased GHG emissions following intensification. International displacement of replacement suckler-beef production to the "global beef frontier" in Brazil resulted in small GHG savings for the UK GHG inventory, but contributed to a net increase in international GHG emissions equivalent to 0.63kg CO2 e kg-1 milk. Use of spared dairy grassland for intensive beef production can lead to net GHG mitigation by replacing extensive beef production, enabling afforestation on larger areas of lower quality grassland, or by avoiding expansion of international (Brazilian) beef production. We recommend that LCA boundaries are expanded when evaluating livestock intensification pathways, to avoid potentially misleading conclusions being drawn from "snapshot" carbon footprints. We conclude that dairy intensification in industrialized countries can lead to significant international carbon leakage, and only achieves GHG mitigation when spared dairy grassland is used to intensify beef production, freeing up larger areas for afforestation.

Farm-scale greenhouse gas balances, hotspots and uncertainties in smallholder crop-livestock systems in Central Kenya

Abstract Climate-smart approaches have gained momentum in tropical, agricultural development. However, to date, few studies have examined whole-farm greenhouse gas (GHG) balances in smallholder crop-livestock systems. This study aimed to quantify GHG balances at farm-scale, identify GHG hotspots and assess mitigation options in coffee-dairy farms undergoing agricultural intensification in Central Kenya. In recent decades, decreasing farm size has forced the shift from extensive practices to zero-grazing systems and higher nitrogen (N) inputs. We hypothesised that different farm strategies and intensification levels determine the farm’s GHG balance. A farm typology was constructed through principal component analysis (PCA) and hierarchical clustering from 125 farms surveyed. Four farm types were identified ranging relatively from small to large farms, low to high livestock intensities, and low to high N input rates. Whole-farm GHG balances were estimated using an adapted version of the Cool Farm Tool (CFT). Farms were found to be net sources of GHG, averaging from 4.5 t CO 2 eq ha −1 yr −1 in less intensive farms to 12.5 t CO 2 eq ha −1 yr −1 in high intensive farms. Within the farm GHG hotspots identified, methane (CH 4 ) from enteric fermentation processes accounted for 26–39% of total farm GHG emissions; nitrous oxide (N 2 O) and CH 4 from manure management systems (MMS) for 26–38%; soil background and fertilizer induced N 2 O emissions for 24–29%; off-farm production of feeds and agrochemicals for 10–22%; and crop residue management (CRM) for the remaining 1–3%. Within the mitigation practices assessed, zero-grazing stalls already lowered the livestock maintenance energy requirements, reducing enteric fermentation emissions. Stall-feeding, however, brings the necessity-opportunity to manage the manure and our results showed that MMS can be a determining factor in the GHG balance. Increasing the frequency of manure collection from stalls in favour of solid storage systems can reduce N 2 O emissions by up to 75%. Furthermore, dry manure storage reduced the CH 4 emissions of liquid slurry systems by more than 70%. Further benefits in terms of carbon (C) sequestration were identified along farm types from manure and crop residues applications in soils (with averages of −1.3 to −2.3 t CO 2 eq ha −1 yr −1 ) and biomass growth in agroforestry systems (−1.2 to −2 t CO 2 eq ha −1 yr −1 ). Together, soils and woody biomass offset 25–36% of farm emissions. We conclude that reduced farm size and increased livestock density lead to higher emissions per unit area, though this increase is smoothed by larger negative fluxes in soils (by higher C inputs) and woody biomass (by higher tree densities) until a steady state is reached. Average yield-scaled emissions, or product carbon footprints (CFs), resulted in 1.08 kg CO 2 eq kg coffee berry −1 , 0.64 kg CO 2 eq kg maize −1 and 1.05 kg CO 2 eq kg milk −1 on average. CFs did not always differ between farm types and intensification levels, meaning that increases in productivity were not higher than increases in GHG fluxes from intensification. This may be due to: 1) increases in productivity are the result of more processes other than N inputs; and/or 2) emissions from N inputs are overestimated by EFs and GHG calculators. Smallholders may benefit in the near future from climate initiatives and further field characterisation, models calibration and monitoring are required to overcome critical levels of uncertainty and provide more accurate estimations of GHG balances at farm-scale.

Sustainability levels in Irish dairy farming: a farm typology according to sustainable performance indicators

Feeding the world’s population in a sustainable manner is one of the key challenges facing the future of global agriculture. The recent removal of the milk quota regime in the European Union has prompted an expansionary phase in dairy farming, especially in Ireland. Achieving this expansion in a sustainable manner is crucial to the long-term survival and success of the Irish dairy sector. In this paper we examine the sustainability of Irish dairy farming, defining ‘sustainability’ as economically profitable, environmentally friendly and socially efficient. A typology of Irish dairy farms has been created using data on profitability, environmental efficiency and social integration derived from the Teagasc National Farm Survey. Economic, social and environmental performance indicators were determined and aggregated and then used in a multivariate analysis for the identification and classification of farm clusters. The purpose of this study to classify Irish dairy farms using performance indicators, thereby, assisting policy makers in identifying patterns in farm performance with a view to formulating more targeted policies. Two of the three clusters elicited from the analysis were similar in regards to their respective indicator scores. However, the remaining cluster was found to perform poorly in comparison. The results indicate a clear distinction between ‘good’ and ‘weak’ performers, and the positive relationship between the economic, environmental and social performance of Irish dairy farms is evident.

Embedded Competence: A Study of Farmers’ Relation to Competence and Knowledge

The aim of this paper is to explore the diversity of farmers’ relationship to the networks that make up the knowledge and innovation systems. We approach farmer’s knowledge diversity through the development of a typology based on a principal component analysis (PCA) of Norwegian farmers. The analysis indicates a preliminary typology of farmers where the main types are: 1) the self-confident farmers; 2) the knowledge-seeking farmers; 3) the knowledge-purchasing farmers; 4) the experienced farmers; 5) the collective- knowledge farmers; and, 6) the well-educated farmers. We discuss how extension service may improve communication, knowledge services, and knowledge networks.

Creating adaptive farm typologies using Naive Bayesian classification

The applicability of statistical typologies that capture farming systems diversity in innovation and development projects would increase if their adaptability would be enhanced, so that newly encountered farms can be classified and used to update the typology. In this paper we propose Naïve Bayesian (NB) classification as a method to allocate farms to types by using only a few variables, thus allowing the addition of new entries to a typology. We show for two example datasets that the performance of NB classification is already acceptable when 50% of the original survey dataset to construct the typology is used for training the NB classifier. For our datasets, the performance of Naïve Bayesian classification was improved when probabilities for observations to belong to multiple types were used, requiring a sample size of 30% of the survey dataset. Based on the results in this paper, we argue that NB classification is a powerful and promising statistical approach to increase the adaptability and usability of farm typologies.

Effects of European Union agricultural policies on the sustainability of grazingland use in a typical Greek rural area

Recent changes in the Common Agricultural Policy (CAP) introduced different definitions regarding the eligibility of various types of land used by grazing animals. In particular, shrublands and other low-quality areas which have traditionally been used as grazinglands are now not eligible for European Union income support payments. Subject to these changes, a large part of livestock farms will be affected, being partially or fully deprived of EU income support. In addition to these policy-driven changes, pasture-fed livestock farms in Greece are faced with a unique system of grazingland allocation, as all grazing areas in the country are state-owned. These areas are allocated for a short period either directly to farmers who are permanent residents of the Municipalities or to other livestock farmers through an auction system, which does not take into account the grazing capacity thus bringing about environmental degradation. This paper investigates the effects of CAP changes and of this communal land allocation system based on the findings of a typological analysis. The study focuses on a typical Greek territory in terms of land uses, economic activities and sociodemographic developments. Data from a questionnaire survey are used to build a typology of local livestock farms in terms of their nutritional management and land use characteristics and then the profile of each type is investigated through the estimation of a Multinomial Logit Model where the dependent variable is the cluster participation. Three distinct types of farms (clusters) are determined. Cluster 1 includes traditional farms which rear sheep and goats and bovine for meat, which are highly dependent on grazingland uses and EU income support. Cluster 2 farms – mainly sheep and goat – have evolved a ‘double’ dependency on land in the form of grazingland but also of cropland for feedstuff production, which renders them more resilient to policy changes. Finally, Cluster 3 farms are intensive dairy cattle farms and use artificial (cultivated) privately-owned grazinglands and cropland for feedstuff production. Market-oriented measures are proposed for each type and suggestions for an integrated land use plan are made, including long-term leasing of land and the consideration of environmental criteria for land allocation, thus rendering farmers responsible for keeping their land in a good production state.