The European Union (EU) has designated areas in which agricultural production is more challenging due to challenging biophysical conditions, the Less Favoured Areas (LFAs). To mitigate production risks, ensure continuous use of agricultural land, maintenance of the countryside and to enhance the future sustainability of farming systems, the EU has historically provided both financial and structural support to hill farmers (European Commission, 2005). Agricultural production in these landscapes is restricted by poor climate, lower soil productivity, steep slopes and harsh conditions (European Commission, 2006; Harvey and Scott, 2015). These natural handicaps associated with the spatial characteristics of the production system can decrease productivity within the LFAs and consequently, result in decrease or cessation of farming activities hence in land abandonment and a series of environmental and social risks related to loss of biodiversity, depopulation or loss of agricultural land (European Commission, 2005, 2010). Therefore, an emerging need for the design of strategies and policies to enhance productivity while simultaneously reducing the environmental pressures generated by the production system is realised. A sustainable development paradigm which will also account for the balance between sustainability and enhanced productivity will enable the Sustainable Intensification (SI) (Gadanakis et al., 2015) of hill farming systems.

Grazing livestock production is the predominant agricultural activity within the LFAs of England (DEFRA, 2010). On average, crop production accounts for ~5% of the total output of farm businesses in the LFAs (Harvey and Scott, 2015). Specialisation of livestock production has emerged in areas dominated by natural handicaps with smaller farm holdings, where animal production has long been established as the predominant type of agricultural activity (Peyraud et al., 2014; Martin et al., 2016). However, specialisation of production on hill farms has led to environmental degradation and reduction of biodiversity (Tichit et al., 2011; Bonaudo et al., 2014; Peyraud et al., 2014).

Integration of crop and livestock production systems (ICLS) has gained momentum as a strategic management approach in the sustainable planning of agricultural systems (Hendrickson et al., 2008; Sanderson et al., 2013; Duru and Therond, 2015). From the economic perspective, diversification of agricultural production enables benefits from economies of scope (Sanderson et al., 2013) which result in lower costs of production in integrated production systems when compared to specialised ones (Panzar and Willig, 1981). ICLS allow the farm businesses to be less dependent on external inputs by utilising home grown crops as feeding stuffs as well as, by using animal manure as natural fertilisation for crop production (Soussana and Lemaire, 2014). Furthermore, utilisation of crops as forage at the farm level has been described as a process that enhances productivity within integrated agricultural systems (Bell et al., 2014). ICLS is therefore promoting a production system based in the circular economy concept as the system is closing the loop between materials and substances (Van Zanten et al., 2019).

From the environmental perspective, integration of agricultural production systems provides the option of reusing resources at the farm level (Schiere et al., 2002) which leads to nutrient cycle improvement (Reganold et al., 2010). In addition, ICLS provide environmental benefits through management of intractable weeds and diseases, enhanced water consumption efficiency (production of dual-purpose crops, that are being foraged during vegetative phase while harvesting for grain, are sown earlier and have longer vegetative phase and thus, higher water use efficiency due to deeper rooting) and improvement of soil quality (Bell et al., 2014).

While ICLS create synergies that increase productivity and resource use efficiency (Herrero et al., 2010), a series of parameters discourage farmers from implementing it. Within the economic and political context, historically, mass-production objectives promoted the development of economies of scale which resulted in specialisation of production and larger farm sizes (Ryschawy et al., 2013). Additionally, various farm structures require the relevant knowledge of the dynamics between crop and livestock production systems which emerges as another barrier as many farmers may lack the technical knowledge (Martin et al., 2016). Furthermore, the implementation of ICLS depends on the suitability of the spatial features of the landscape and climate which implies that in areas with more harsh conditions it may be more difficult to adopt such organisation plans. More specifically, in areas with more favourable conditions, production of cash crops is preferred because of the high-yield potentials (Gil et al., 2015), while in areas with more unfavourable environmental and physical conditions the cultivation of land is not feasible due to restrictions on mechanisation (e.g., steep slopes). The latter provides an interpretation of why ICLS is to be implemented in intermediate areas, in which crop production is feasible but on the other hand does not attain high yield productivity (Bonaudo et al., 2014). Furthermore, relevant studies have suggested that the adoption of ICLS depends on labour availability (as greater workload may be required) (Lemaire et al., 2014; Poffenbarger et al., 2017), local supply chain infrastructure (storage plants, production-input suppliers) and financial capital to invest in the new production systems and technology (Garrett et al., 2017).

Linear Programming (LP) modelling can be employed to examine how various levels of integration between crop and livestock production affect farm level sustainability (Sneessens et al., 2016). Furthermore, LP can optimise agricultural land use allocation by proposing minimisation of costs as well as maximisation of spatial compactness at the field scale (Aerts et al., 2003; Memmah et al., 2015). Additionally, approaches have attempted to optimise the integration between the various enterprises within the farm business, aiming towards profit maximisation (Glen, 1986; Annetts and Audsley, 2002; Veysset et al., 2005; Gameiro et al., 2016).

Here we quantified the potential transition of the English LFA livestock systems through integrated crop and livestock production strategies and optimal spatial allocation of 11 major crops and two livestock species to estimate a higher level of attainable farm-level profitability. We analysed differences in profitability (here Annual Gross Margin¹ and Net Farm Income, AGM and NFI respectively) emerging from the optimised crop-livestock integration and utilisation of available resources. In addition, structural changes in farm businesses from optimised farm organisation were examined, along with resulting land use changes.

To explore the different strategies promoting the future model of the SI of LFA farms, a LP model was constructed integrating the parameters of feed requirements, housing capacity, stocking densities, labour and land use. The LP model distinguishes several agricultural production land adjustments scenarios that resemble a gradual set of production systems ranging from systems that utilise poor permanent pasture only, to farms that cultivate arable land and integrate crop and livestock production. This allowed the investigation of various levels of integration between crop and livestock production systems on economic as well as environmental performance.

Results indicate that there is a potential for increasing the AGM in LFA farm businesses through integration of crop and livestock production systems, however the four optimisation scenarios are suggesting different pathways. The profitability of hill farming systems is greatly affected by the quality of forage land. In particular, higher quality pasture land (here arable land used as temporary grassland) allows higher effective stocking densities due to higher forage yield, which in turn triggers higher AGM per hectare. This finding is in line with Willems et al. (2013), who found that good quality forage is a positive determinant of production performance in alpine sheep farming systems.

Moreover, our economic and spatial estimations point out that different levels of crop-livestock integration are related to financial performance on a per hectare basis. Studies have examined the direct effects of crop-livestock integration, highlighting that farm income increases when the level of crop production system increases within the farm business (Sneessens et al., 2016). On the other hand, Ripoll-Bosch et al. (2012), suggest that the economic performance of sheep farms does not depend on the diversity of production itself, rather it is the lower dependency on external inputs and thus improved feed self-sufficiency that drives performance. All scenarios examined by the optimisation model estimate the production of crops utilising as much of the land suitable for cultivation as is available hence, home produced feeding stuffs and feed self-sufficiency are promoted, contributing to improvements in financial performances (Bonaudo et al., 2014; Peyraud et al., 2014) via the pathway of circular economy (Van Zanten et al., 2019). Thus, it concludes towards a two-fold strategy where (a). land use intensity increases in the parts of land where optimally reallocated agricultural systems can return profits to the farmer and (b). land is being released when costs over exceed market returns and thus production is not economically sustainable. The optimisation results indicate a mix of inputs and the spatial allocation of natural resources such as land which improve farm level production efficiency. The suggested solutions by the LP in the four scenarios examined could serve as a framework to review by farm managers to review their current production structure and update their long-term goals towards a more sustainable and resilient farm business model.

Concerning environmental performance, declining requirements for N fertiliser use are estimated for all scenarios (median values). The latter is due to reductions in grassland (temporary and permanent) which requires significant amounts of N fertiliser. However, across the models, integration with higher levels of crop production indicate higher levels of N fertiliser application. Nonetheless, this is compensated by an average release of grassland of 22%. This is in accordance with the findings of Perrot et al. (2012) and Sneessens et al. (2016), who report that mixed livestock-crop production systems have worse environmental performances in comparison to specialised livestock production systems.

The effects of structural changes derived from optimal land use allocation suggest reductions in permanent pasture areas while land that is suitable for cultivation is utilised completely for crop production (that is suggested within the limits of land availability at a farm level and farm suitability). Among arable land, cereals (wheat, barley and oats) and peas production dominate while temporary grass land is also included to cover the feeding requirements of livestock within the farm business. This is a plausible result as crop enterprises obtain relatively higher AGM than livestock (Sneessens et al., 2016) thus, they are preferred over grass financially. In an analysis of strategies for integrated crop-sheep production systems Olaizola et al. (2015) show that land use integration provides resilience in prices fluctuation and greater financial stability for the farm business. Furthermore, studies have found that an additional positive effect emerges from land use diversification with grazing livestock, that is to support agri-tourism activities (Martin et al., 2016; Moraine et al., 2017).

In the optimisation scenarios, the total livestock units are reduced. Specifically, the optimisation solution increases sheep numbers and reduces beef cattle stocks as the former systems are more preferable from the financial perspective. On average, through all the scenarios, sheep numbers increase by 92% while beef cattle decrease by 92%. This result is in accordance to Acs et al. (2010) who note a structural change in optimised livestock production systems in which livestock numbers generally decrease while beef cattle production declines the most and sheep production is preferred especially on the areas with semi-natural flora or rock outcrops (e.g., upland moorland). One of the factors that constrained the number of livestock (for beef cattle enterprises) is the livestock housing capacity of farms. Specifically, 50% of the farm businesses that maintained beef cattle production after the optimisation method were constrained by this particular parameter. In other words, land abandonment is partially triggered due to fixed housing capacity.

As with all modelling studies, our findings are subject to some uncertainties and limitations. Limitations in the current approach emerged due to aggregated data in the FBS dataset for beef cattle and sheep enterprises. In particular, the FBS dataset disaggregates the livestock enterprises on the basis of different age groups such as store or fat cattle. However, through this classification, the live weight of the animals remains unknown. Thus, calculation of accurate feeding rations was not achievable. As a result, the study considered the feeding requirements of livestock covering only forage rather than including bought concentrates and homegrown silage cereals. Application of the latter would increase the accuracy of the estimations while assessing the cost effectiveness of crop production that contributes to the feed requirements of livestock (Dillon, 2007; Kilcline et al., 2014). In addition, this methodology studied the maximisation of one parameter only (AGM) without accounting for the multiple objectives that farmers might have relating to maintenance of environmental quality and cultural values of hill farming (DEFRA, 2010). Furthermore, systems transitions often require farmers adaptiveness to new knowledge and technologies while also include a certain degree of risk associated with returns on investment at harvest (Hochman et al., 2013) which can be a very important factor for profit maximisation (Monjardino et al., 2015).

This paper presents a method to investigate optimised integration of crop and livestock production systems (ICLS) for farm businesses in the LFAs of England examining a range of scenarios and integration options. The methodology incorporates the parameters of housing capacity, stocking densities, feed requirements, labour and land use. Through this analysis the farm organisation was reformed to maximise AGM under optimised utilisation of available resources and the spatial features of hill farms. The most significant impacts from structural changes were reflected in livestock numbers, livestock type, land use and fertiliser application.

Findings suggested that there is a potential for improving the input use efficiency therefore, the profitability of farming systems in the study area. Farm businesses with higher percentages of arable land within their farmed area have much higher gross margins than their counterparts with no arable land, as crop production is more profitable than livestock (Sneessens et al., 2016). This triggered increased levels of crop production (when arable land was available) which in terms of land use decreased areas of temporary grass and increased areas for cash crops such as cereals and beans. The latter highlights how the modelling identifies transition pathways towards the intensification of integrated crop and livestock production systems. Concerning livestock production, in all optimisation scenarios sheep enterprises were preferred over beef cattle from the perspective of profitability while livestock units in total remained the same under the PPO and PPM and decreased under PPA and AMP scenario.

A future step may expand this study towards enhancing the spatial analysis of these findings with the use of Geographic Information Systems (GIS). More specifically, this process may investigate the occurrence of spatial patterns or clusters of farm businesses with higher or lower potentials for profit maximisation. Furthermore, this examination may also seek spatial aggregations of the constraining factors (endowments). This will enable the identification of broad geographic blocks in which agricultural performance is more sensitive to existence of particular factors.

The focus of future policies for the development of the LFAs could be on promoting the integration of crop and livestock production systems, providing a range of structural alternatives for farmers to adopt towards future proofing their farm businesses and sustainably intensifying their production capacity. This suggestion however, highlights the importance of knowledge and information and thus, requires assisting farmers in getting access to such information. A potential policy tool to address this could be the establishment of knowledge transfer networks and social networks of farmers that will enable their collaboration with research institutes, scientists and advisory services (Asai et al., 2018). This will inform farmers about the positive effects of ICLS for their production systems and increase their willingness to change and adopt (Chalak et al., 2017). Furthermore training and technical assistance will be provided on agricultural production methods and farming practises that hill farmers may not have experience with (e.g., livestock farmers expanding to crop production) (Gil et al., 2016). The latter may also relate to lack of harvest equipment and high initial transaction costs (Asai et al., 2018) that can be accommodated by the implementation of machinery-sharing arrangements and joint ownership of equipment within established networks or farmer associations (Larsén, 2010). In that way, the principal factors affecting the adoption of integrated production systems will be narrowed down, thus providing solutions to encourage integration.

To ensure the future sustainability of hill farms it is necessary to both increase profitability but also to release natural resources that have not previously been used efficiently by the production systems. The modelling exercise developed herein suggested the release of grassland in favour of profitability of the production systems. This in average allows the restructure of land use for the provision of other land-based services and products. Thus, farming systems would have the opportunity to increase the provision of ecosystem services (carbon storage, biodiversity, habitat creation, water management, pollination, soil health etc.) on the spared land and support policy instruments for the provision of public goods from the agricultural sector.

Towards the direction of designing and evaluating scenarios that analyse the trade-offs between various integration options, simulation models can provide essential insights and point out acceptable interactions between ecosystem services and socioeconomic benefits (Martin et al., 2016; Ryschawy et al., 2017). Finally, in doing so the decision makers will be able to accommodate restrictions on productivity, emerging from natural constraints or availability of resources and thus mitigate the risks resulting from the challenges that agricultural production systems face in the LFAs.

Data preparation, analysis and estimations were carried out in R (R Core Team, 2014) using the package “dplyr” (Wickham et al., 2020), where visualisations and plots were created using the packages “ggplot2” (Wickham, 2016). Geographical visualisations of results and inputs were produced with ESRI ArcGIS 10.6.1 [Environmental Systems Research Institute (ESRI), 2018].

The datasets presented in this article are not readily available because data is private and confidential. Access to data can be requested from the Farm Business Survey (UK). Requests to access the datasets should be directed to http://www.farmbusinesssurvey.co.uk.

YV, YG, and SM contributed to conception and design of the study and wrote the sections of the manuscript. YV organised the datasets, performed the analysis of the model, and wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

This study is part of a PhD project that has been funded partially from the University of Reading and partially from the Edith Mary Gayton bequest managed by the Farm Management Unit of the School of Agriculture, Policy and Development.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.